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The Python Tutorial

Python is an easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. Python’s elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms.

The Python interpreter and the extensive standard library are freely available in source or binary form for all major platforms from the Python Web site, https://www.python.org/, and may be freely distributed. The same site also contains distributions of and pointers to many free third party Python modules, programs and tools, and additional documentation.

The Python interpreter is easily extended with new functions and data types implemented in C or C++ (or other languages callable from C). Python is also suitable as an extension language for customizable applications.

This tutorial introduces the reader informally to the basic concepts and features of the Python language and system. It helps to have a Python interpreter handy for hands-on experience, but all examples are self-contained, so the tutorial can be read off-line as well.

For a description of standard objects and modules, see The Python Standard Library. The Python Language Reference gives a more formal definition of the language. To write extensions in C or C++, read Extending and Embedding the Python Interpreter and Python/C API Reference Manual. There are also several books covering Python in depth.

This tutorial does not attempt to be comprehensive and cover every single feature, or even every commonly used feature. Instead, it introduces many of Python’s most noteworthy features, and will give you a good idea of the language’s flavor and style. After reading it, you will be able to read and write Python modules and programs, and you will be ready to learn more about the various Python library modules described in The Python Standard Library.

The Glossary is also worth going through.

General Docs:

https://codesandbox.io/s/ds-algo-forked-lfujh?from-embed

Strings

Strings Continued

Working with Strings

Let’s look at some of the ways Python lets you write, print, and access strings in your code.

String Literals

Typing string values in Python code is fairly straightforward: they begin and end with a single quote. But then how can you use a quote inside a string? Typing 'That is Alice's cat.' won’t work, because Python thinks the string ends after Alice, and the rest (s cat.') is invalid Python code. Fortunately, there are multiple ways to type strings.

Double Quotes

Strings can begin and end with double quotes, just as they do with single quotes. One benefit of using double quotes is that the string can have a single quote character in it. Enter the following into the interactive shell:

>>> spam = "That is Alice's cat."

Since the string begins with a double quote, Python knows that the single quote is part of the string and not marking the end of the string. However, if you need to use both single quotes and double quotes in the string, you’ll need to use escape characters.

Escape Characters

An escape character lets you use characters that are otherwise impossible to put into a string. An escape character consists of a backslash (\) followed by the character you want to add to the string. (Despite consisting of two characters, it is commonly referred to as a singular escape character.) For example, the escape character for a single quote is \'. You can use this inside a string that begins and ends with single quotes. To see how escape characters work, enter the following into the interactive shell:

>>> spam = 'Say hi to Bob\'s mother.'

Python knows that since the single quote in Bob\'s has a backslash, it is not a single quote meant to end the string value. The escape characters \' and \" let you put single quotes and double quotes inside your strings, respectively.

Table 6-1 lists the escape characters you can use.

Table 6-1: Escape Characters

Enter the following into the interactive shell:

>>> print("Hello there!\nHow are you?\nI\'m doing fine.") Hello there! How are you? I'm doing fine.

Raw Strings

You can place an r before the beginning quotation mark of a string to make it a raw string. A raw string completely ignores all escape characters and prints any backslash that appears in the string. For example, enter the following into the interactive shell:

>>> print(r'That is Carol\'s cat.') That is Carol\'s cat.

Because this is a raw string, Python considers the backslash as part of the string and not as the start of an escape character. Raw strings are helpful if you are typing string values that contain many backslashes, such as the strings used for Windows file paths like r'C:\Users\Al\Desktop' or regular expressions described in the next chapter.

Multiline Strings with Triple Quotes

While you can use the \n escape character to put a newline into a string, it is often easier to use multiline strings. A multiline string in Python begins and ends with either three single quotes or three double quotes. Any quotes, tabs, or newlines in between the “triple quotes” are considered part of the string. Python’s indentation rules for blocks do not apply to lines inside a multiline string.

Open the file editor and write the following:

print('''Dear Alice, Eve's cat has been arrested for catnapping, cat burglary, and extortion. Sincerely, Bob''')

Save this program as catnapping.py and run it. The output will look like this:

Dear Alice, Eve's cat has been arrested for catnapping, cat burglary, and extortion. Sincerely, Bob

Notice that the single quote character in Eve's does not need to be escaped. Escaping single and double quotes is optional in multiline strings. The following print() call would print identical text but doesn’t use a multiline string:

print('Dear Alice,\n\nEve\'s cat has been arrested for catnapping, cat burglary, and extortion.\n\nSincerely,\nBob')

Multiline Comments

While the hash character (#) marks the beginning of a comment for the rest of the line, a multiline string is often used for comments that span multiple lines. The following is perfectly valid Python code:

"""This is a test Python program. Written by Al Sweigart al@inventwithpython.com This program was designed for Python 3, not Python 2. """ def spam(): """This is a multiline comment to help explain what the spam() function does.""" print('Hello!')

Indexing and Slicing Strings

Strings use indexes and slices the same way lists do. You can think of the string 'Hello, world!' as a list and each character in the string as an item with a corresponding index.

' H e l l o , w o r l d ! ' 0 1 2 3 4 5 6 7 8 9 10 11 12

The space and exclamation point are included in the character count, so 'Hello, world!' is 13 characters long, from H at index 0 to ! at index 12.

Enter the following into the interactive shell:

>>> spam = 'Hello, world!' >>> spam[0] 'H' >>> spam[4] 'o' >>> spam[-1] '!' >>> spam[0:5] 'Hello' >>> spam[:5] 'Hello' >>> spam[7:] 'world!'

If you specify an index, you’ll get the character at that position in the string. If you specify a range from one index to another, the starting index is included and the ending index is not. That’s why, if spam is 'Hello, world!', spam[0:5] is 'Hello'. The substring you get from spam[0:5] will include everything from spam[0] to spam[4], leaving out the comma at index 5 and the space at index 6. This is similar to how range(5) will cause a for loop to iterate up to, but not including, 5.

Note that slicing a string does not modify the original string. You can capture a slice from one variable in a separate variable. Try entering the following into the interactive shell:

>>> spam = 'Hello, world!' >>> fizz = spam[0:5] >>> fizz 'Hello'

By slicing and storing the resulting substring in another variable, you can have both the whole string and the substring handy for quick, easy access.

The in and not in Operators with Strings

The in and not in operators can be used with strings just like with list values. An expression with two strings joined using in or not in will evaluate to a Boolean True or False. Enter the following into the interactive shell:

>>> 'Hello' in 'Hello, World' True >>> 'Hello' in 'Hello' True >>> 'HELLO' in 'Hello, World' False >>> '' in 'spam' True >>> 'cats' not in 'cats and dogs' False

These expressions test whether the first string (the exact string, case-sensitive) can be found within the second string.

Putting Strings Inside Other Strings

Putting strings inside other strings is a common operation in programming. So far, we’ve been using the + operator and string concatenation to do this:

>>> name = 'Al' >>> age = 4000 >>> 'Hello, my name is ' + name + '. I am ' + str(age) + ' years old.' 'Hello, my name is Al. I am 4000 years old.'

However, this requires a lot of tedious typing. A simpler approach is to use string interpolation, in which the %s operator inside the string acts as a marker to be replaced by values following the string. One benefit of string interpolation is that str() doesn’t have to be called to convert values to strings. Enter the following into the interactive shell:

>>> name = 'Al' >>> age = 4000 >>> 'My name is %s. I am %s years old.' % (name, age) 'My name is Al. I am 4000 years old.'

Python 3.6 introduced f-strings, which is similar to string interpolation except that braces are used instead of %s, with the expressions placed directly inside the braces. Like raw strings, f-strings have an f prefix before the starting quotation mark. Enter the following into the interactive shell:

>>> name = 'Al' >>> age = 4000 >>> f'My name is {name}. Next year I will be {age + 1}.' 'My name is Al. Next year I will be 4001.'

Remember to include the f prefix; otherwise, the braces and their contents will be a part of the string value:

>>> 'My name is {name}. Next year I will be {age + 1}.' 'My name is {name}. Next year I will be {age + 1}.'

Useful String Methods

Several string methods analyze strings or create transformed string values. This section describes the methods you’ll be using most often.

The upper(), lower(), isupper(), and islower() Methods

The upper() and lower() string methods return a new string where all the letters in the original string have been converted to uppercase or lowercase, respectively. Nonletter characters in the string remain unchanged. Enter the following into the interactive shell:

>>> spam = 'Hello, world!' >>> spam = spam.upper() >>> spam 'HELLO, WORLD!' >>> spam = spam.lower() >>> spam 'hello, world!'

Note that these methods do not change the string itself but return new string values. If you want to change the original string, you have to call upper() or lower() on the string and then assign the new string to the variable where the original was stored. This is why you must use spam = spam.upper() to change the string in spam instead of simply spam.upper(). (This is just like if a variable eggs contains the value 10. Writing eggs + 3 does not change the value of eggs, but eggs = eggs + 3 does.)

The upper() and lower() methods are helpful if you need to make a case-insensitive comparison. For example, the strings 'great' and 'GREat' are not equal to each other. But in the following small program, it does not matter whether the user types Great, GREAT, or grEAT, because the string is first converted to lowercase.

print('How are you?') feeling = input() if feeling.lower() == 'great': print('I feel great too.') else: print('I hope the rest of your day is good.')

When you run this program, the question is displayed, and entering a variation on great, such as GREat, will still give the output I feel great too. Adding code to your program to handle variations or mistakes in user input, such as inconsistent capitalization, will make your programs easier to use and less likely to fail.

How are you? GREat I feel great too.

You can view the execution of this program at https://autbor.com/convertlowercase/. The isupper() and islower() methods will return a Boolean True value if the string has at least one letter and all the letters are uppercase or lowercase, respectively. Otherwise, the method returns False. Enter the following into the interactive shell, and notice what each method call returns:

>>> spam = 'Hello, world!' >>> spam.islower() False >>> spam.isupper() False >>> 'HELLO'.isupper() True >>> 'abc12345'.islower() True >>> '12345'.islower() False >>> '12345'.isupper() False

Since the upper() and lower() string methods themselves return strings, you can call string methods on those returned string values as well. Expressions that do this will look like a chain of method calls. Enter the following into the interactive shell:

>>> 'Hello'.upper() 'HELLO' >>> 'Hello'.upper().lower() 'hello' >>> 'Hello'.upper().lower().upper() 'HELLO' >>> 'HELLO'.lower() 'hello' >>> 'HELLO'.lower().islower() True

The isX() Methods

Along with islower() and isupper(), there are several other string methods that have names beginning with the word is. These methods return a Boolean value that describes the nature of the string. Here are some common isX string methods:

isalpha() Returns True if the string consists only of letters and isn’t blank

isalnum() Returns True if the string consists only of letters and numbers and is not blank

isdecimal() Returns True if the string consists only of numeric characters and is not blank

isspace() Returns True if the string consists only of spaces, tabs, and newlines and is not blank

istitle() Returns True if the string consists only of words that begin with an uppercase letter followed by only lowercase letters

Enter the following into the interactive shell:

>>> 'hello'.isalpha() True >>> 'hello123'.isalpha() False >>> 'hello123'.isalnum() True >>> 'hello'.isalnum() True >>> '123'.isdecimal() True >>> ' '.isspace() True >>> 'This Is Title Case'.istitle() True >>> 'This Is Title Case 123'.istitle() True >>> 'This Is not Title Case'.istitle() False >>> 'This Is NOT Title Case Either'.istitle() False

The isX() string methods are helpful when you need to validate user input. For example, the following program repeatedly asks users for their age and a password until they provide valid input. Open a new file editor window and enter this program, saving it as validateInput.py:

while True: print('Enter your age:') age = input() if age.isdecimal(): break print('Please enter a number for your age.') while True: print('Select a new password (letters and numbers only):') password = input() if password.isalnum(): break print('Passwords can only have letters and numbers.')

In the first while loop, we ask the user for their age and store their input in age. If age is a valid (decimal) value, we break out of this first while loop and move on to the second, which asks for a password. Otherwise, we inform the user that they need to enter a number and again ask them to enter their age. In the second while loop, we ask for a password, store the user’s input in password, and break out of the loop if the input was alphanumeric. If it wasn’t, we’re not satisfied, so we tell the user the password needs to be alphanumeric and again ask them to enter a password.

When run, the program’s output looks like this:

Enter your age: forty two Please enter a number for your age. Enter your age: 42 Select a new password (letters and numbers only): secr3t! Passwords can only have letters and numbers. Select a new password (letters and numbers only): secr3t

You can view the execution of this program at https://autbor.com/validateinput/. Calling isdecimal() and isalnum() on variables, we’re able to test whether the values stored in those variables are decimal or not, alphanumeric or not. Here, these tests help us reject the input forty two but accept 42, and reject secr3t! but accept secr3t.

The startswith() and endswith() Methods

The startswith() and endswith() methods return True if the string value they are called on begins or ends (respectively) with the string passed to the method; otherwise, they return False. Enter the following into the interactive shell:

>>> 'Hello, world!'.startswith('Hello') True >>> 'Hello, world!'.endswith('world!') True >>> 'abc123'.startswith('abcdef') False >>> 'abc123'.endswith('12') False >>> 'Hello, world!'.startswith('Hello, world!') True >>> 'Hello, world!'.endswith('Hello, world!') True

These methods are useful alternatives to the == equals operator if you need to check only whether the first or last part of the string, rather than the whole thing, is equal to another string.

The join() and split() Methods

The join() method is useful when you have a list of strings that need to be joined together into a single string value. The join() method is called on a string, gets passed a list of strings, and returns a string. The returned string is the concatenation of each string in the passed-in list. For example, enter the following into the interactive shell:

>>> ', '.join(['cats', 'rats', 'bats']) 'cats, rats, bats' >>> ' '.join(['My', 'name', 'is', 'Simon']) 'My name is Simon' >>> 'ABC'.join(['My', 'name', 'is', 'Simon']) 'MyABCnameABCisABCSimon'

Notice that the string join() calls on is inserted between each string of the list argument. For example, when join(['cats', 'rats', 'bats']) is called on the ', ' string, the returned string is 'cats, rats, bats'.

Remember that join() is called on a string value and is passed a list value. (It’s easy to accidentally call it the other way around.) The split() method does the opposite: It’s called on a string value and returns a list of strings. Enter the following into the interactive shell:

>>> 'My name is Simon'.split() ['My', 'name', 'is', 'Simon']

By default, the string 'My name is Simon' is split wherever whitespace characters such as the space, tab, or newline characters are found. These whitespace characters are not included in the strings in the returned list. You can pass a delimiter string to the split() method to specify a different string to split upon. For example, enter the following into the interactive shell:

>>> 'MyABCnameABCisABCSimon'.split('ABC') ['My', 'name', 'is', 'Simon'] >>> 'My name is Simon'.split('m') ['My na', 'e is Si', 'on']

A common use of split() is to split a multiline string along the newline characters. Enter the following into the interactive shell:

>>> spam = '''Dear Alice, How have you been? I am fine. There is a container in the fridge that is labeled "Milk Experiment." Please do not drink it. Sincerely, Bob''' >>> spam.split('\n') ['Dear Alice,', 'How have you been? I am fine.', 'There is a container in the fridge', 'that is labeled "Milk Experiment."', '', 'Please do not drink it.', 'Sincerely,', 'Bob']

Passing split() the argument '\n' lets us split the multiline string stored in spam along the newlines and return a list in which each item corresponds to one line of the string.

Splitting Strings with the partition() Method

The partition() string method can split a string into the text before and after a separator string. This method searches the string it is called on for the separator string it is passed, and returns a tuple of three substrings for the “before,” “separator,” and “after” substrings. Enter the following into the interactive shell:

>>> 'Hello, world!'.partition('w') ('Hello, ', 'w', 'orld!') >>> 'Hello, world!'.partition('world') ('Hello, ', 'world', '!')

If the separator string you pass to partition() occurs multiple times in the string that partition() calls on, the method splits the string only on the first occurrence:

>>> 'Hello, world!'.partition('o') ('Hell', 'o', ', world!')

If the separator string can’t be found, the first string returned in the tuple will be the entire string, and the other two strings will be empty:

>>> 'Hello, world!'.partition('XYZ') ('Hello, world!', '', '')

You can use the multiple assignment trick to assign the three returned strings to three variables:

>>> before, sep, after = 'Hello, world!'.partition(' ') >>> before 'Hello,' >>> after 'world!'

The partition() method is useful for splitting a string whenever you need the parts before, including, and after a particular separator string.

Justifying Text with the rjust(), ljust(), and center() Methods

The rjust() and ljust() string methods return a padded version of the string they are called on, with spaces inserted to justify the text. The first argument to both methods is an integer length for the justified string. Enter the following into the interactive shell:

>>> 'Hello'.rjust(10) ' Hello' >>> 'Hello'.rjust(20) ' Hello' >>> 'Hello, World'.rjust(20) ' Hello, World' >>> 'Hello'.ljust(10) 'Hello '

'Hello'.rjust(10) says that we want to right-justify 'Hello' in a string of total length 10. 'Hello' is five characters, so five spaces will be added to its left, giving us a string of 10 characters with 'Hello' justified right.

An optional second argument to rjust() and ljust() will specify a fill character other than a space character. Enter the following into the interactive shell:

>>> 'Hello'.rjust(20, '*') '***************Hello' >>> 'Hello'.ljust(20, '-') 'Hello---------------'

The center() string method works like ljust() and rjust() but centers the text rather than justifying it to the left or right. Enter the following into the interactive shell:

>>> 'Hello'.center(20) ' Hello ' >>> 'Hello'.center(20, '=') '=======Hello========'

These methods are especially useful when you need to print tabular data that has correct spacing. Open a new file editor window and enter the following code, saving it as picnicTable.py:

def printPicnic(itemsDict, leftWidth, rightWidth): print('PICNIC ITEMS'.center(leftWidth + rightWidth, '-')) for k, v in itemsDict.items(): print(k.ljust(leftWidth, '.') + str(v).rjust(rightWidth)) picnicItems = {'sandwiches': 4, 'apples': 12, 'cups': 4, 'cookies': 8000} printPicnic(picnicItems, 12, 5) printPicnic(picnicItems, 20, 6)

You can view the execution of this program at https://autbor.com/picnictable/. In this program, we define a printPicnic() method that will take in a dictionary of information and use center(), ljust(), and rjust() to display that information in a neatly aligned table-like format.

The dictionary that we’ll pass to printPicnic() is picnicItems. In picnicItems, we have 4 sandwiches, 12 apples, 4 cups, and 8,000 cookies. We want to organize this information into two columns, with the name of the item on the left and the quantity on the right.

To do this, we decide how wide we want the left and right columns to be. Along with our dictionary, we’ll pass these values to printPicnic().

The printPicnic() function takes in a dictionary, a leftWidth for the left column of a table, and a rightWidth for the right column. It prints a title, PICNIC ITEMS, centered above the table. Then, it loops through the dictionary, printing each key-value pair on a line with the key justified left and padded by periods, and the value justified right and padded by spaces.

After defining printPicnic(), we define the dictionary picnicItems and call printPicnic() twice, passing it different widths for the left and right table columns.

When you run this program, the picnic items are displayed twice. The first time the left column is 12 characters wide, and the right column is 5 characters wide. The second time they are 20 and 6 characters wide, respectively.

---PICNIC ITEMS-- sandwiches.. 4 apples...... 12 cups........ 4 cookies..... 8000 -------PICNIC ITEMS------- sandwiches.......... 4 apples.............. 12 cups................ 4 cookies............. 8000

Using rjust(), ljust(), and center() lets you ensure that strings are neatly aligned, even if you aren’t sure how many characters long your strings are.

Removing Whitespace with the strip(), rstrip(), and lstrip() Methods

Sometimes you may want to strip off whitespace characters (space, tab, and newline) from the left side, right side, or both sides of a string. The strip() string method will return a new string without any whitespace characters at the beginning or end. The lstrip() and rstrip() methods will remove whitespace characters from the left and right ends, respectively. Enter the following into the interactive shell:

>>> spam = ' Hello, World ' >>> spam.strip() 'Hello, World' >>> spam.lstrip() 'Hello, World ' >>> spam.rstrip() ' Hello, World'

Optionally, a string argument will specify which characters on the ends should be stripped. Enter the following into the interactive shell:

>>> spam = 'SpamSpamBaconSpamEggsSpamSpam' >>> spam.strip('ampS') 'BaconSpamEggs'

Passing strip() the argument 'ampS' will tell it to strip occurrences of a, m, p, and capital S from the ends of the string stored in spam. The order of the characters in the string passed to strip() does not matter: strip('ampS') will do the same thing as strip('mapS') or strip('Spam').

Numeric Values of Characters with the ord() and chr() Functions

Computers store information as bytes—strings of binary numbers, which means we need to be able to convert text to numbers. Because of this, every text character has a corresponding numeric value called a Unicode code point. For example, the numeric code point is 65 for 'A', 52 for '4', and 33 for '!'. You can use the ord() function to get the code point of a one-character string, and the chr() function to get the one-character string of an integer code point. Enter the following into the interactive shell:

>>> ord('A') 65 >>> ord('4') 52 >>> ord('!') 33 >>> chr(65) 'A'

These functions are useful when you need to do an ordering or mathematical operation on characters:

>>> ord('B') 66 >>> ord('A') < ord('B') True >>> chr(ord('A')) 'A' >>> chr(ord('A') + 1) 'B'

There is more to Unicode and code points, but those details are beyond the scope of this book. If you’d like to know more, I recommend watching Ned Batchelder’s 2012 PyCon talk, “Pragmatic Unicode, or, How Do I Stop the Pain?” at https://youtu.be/sgHbC6udIqc.

Copying and Pasting Strings with the pyperclip Module

The pyperclip module has copy() and paste() functions that can send text to and receive text from your computer’s clipboard. Sending the output of your program to the clipboard will make it easy to paste it into an email, word processor, or some other software.

RUNNING PYTHON SCRIPTS OUTSIDE OF MU

So far, you’ve been running your Python scripts using the interactive shell and file editor in Mu. However, you won’t want to go through the inconvenience of opening Mu and the Python script each time you want to run a script. Fortunately, there are shortcuts you can set up to make running Python scripts easier. The steps are slightly different for Windows, macOS, and Linux, but each is described in Appendix B. Turn to Appendix B to learn how to run your Python scripts conveniently and be able to pass command line arguments to them. (You will not be able to pass command line arguments to your programs using Mu.)

The pyperclip module does not come with Python. To install it, follow the directions for installing third-party modules in Appendix A. After installing pyperclip, enter the following into the interactive shell:

>>> import pyperclip >>> pyperclip.copy('Hello, world!') >>> pyperclip.paste() 'Hello, world!'

Of course, if something outside of your program changes the clipboard contents, the paste() function will return it. For example, if I copied this sentence to the clipboard and then called paste(), it would look like this:

>>> pyperclip.paste() 'For example, if I copied this sentence to the clipboard and then called paste(), it would look like this:'

Project: Multi-Clipboard Automatic Messages

If you’ve responded to a large number of emails with similar phrasing, you’ve probably had to do a lot of repetitive typing. Maybe you keep a text document with these phrases so you can easily copy and paste them using the clipboard. But your clipboard can only store one message at a time, which isn’t very convenient. Let’s make this process a bit easier with a program that stores multiple phrases.

Step 1: Program Design and Data Structures

You want to be able to run this program with a command line argument that is a short key phrase—for instance, agree or busy. The message associated with that key phrase will be copied to the clipboard so that the user can paste it into an email. This way, the user can have long, detailed messages without having to retype them.

THE CHAPTER PROJECTS

This is the first “chapter project” of the book. From here on, each chapter will have projects that demonstrate the concepts covered in the chapter. The projects are written in a style that takes you from a blank file editor window to a full, working program. Just like with the interactive shell examples, don’t only read the project sections—follow along on your computer!

Open a new file editor window and save the program as mclip.py. You need to start the program with a #! (shebang) line (see Appendix B) and should also write a comment that briefly describes the program. Since you want to associate each piece of text with its key phrase, you can store these as strings in a dictionary. The dictionary will be the data structure that organizes your key phrases and text. Make your program look like the following:

Step 2: Handle Command Line Arguments

The command line arguments will be stored in the variable sys.argv. (See Appendix B for more information on how to use command line arguments in your programs.) The first item in the sys.argv list should always be a string containing the program’s filename ('mclip.py'), and the second item should be the first command line argument. For this program, this argument is the key phrase of the message you want. Since the command line argument is mandatory, you display a usage message to the user if they forget to add it (that is, if the sys.argv list has fewer than two values in it). Make your program look like the following:

#! python3 # mclip.py - A multi-clipboard program. TEXT = {'agree': """Yes, I agree. That sounds fine to me.""", 'busy': """Sorry, can we do this later this week or next week?""", 'upsell': """Would you consider making this a monthly donation?"""} import sys if len(sys.argv) < 2: print('Usage: python mclip.py [keyphrase] - copy phrase text') sys.exit() keyphrase = sys.argv[1] # first command line arg is the keyphrase

Step 3: Copy the Right Phrase

Now that the key phrase is stored as a string in the variable keyphrase, you need to see whether it exists in the TEXT dictionary as a key. If so, you want to copy the key’s value to the clipboard using pyperclip.copy(). (Since you’re using the pyperclip module, you need to import it.) Note that you don’t actually need the keyphrase variable; you could just use sys.argv[1] everywhere keyphrase is used in this program. But a variable named keyphrase is much more readable than something cryptic like sys.argv[1].

Make your program look like the following:

#! python3 # mclip.py - A multi-clipboard program. TEXT = {'agree': """Yes, I agree. That sounds fine to me.""", 'busy': """Sorry, can we do this later this week or next week?""", 'upsell': """Would you consider making this a monthly donation?"""} import sys, pyperclip if len(sys.argv) < 2: print('Usage: py mclip.py [keyphrase] - copy phrase text') sys.exit() keyphrase = sys.argv[1] # first command line arg is the keyphrase if keyphrase in TEXT: pyperclip.copy(TEXT[keyphrase]) print('Text for ' + keyphrase + ' copied to clipboard.') else: print('There is no text for ' + keyphrase)

This new code looks in the TEXT dictionary for the key phrase. If the key phrase is a key in the dictionary, we get the value corresponding to that key, copy it to the clipboard, and print a message saying that we copied the value. Otherwise, we print a message saying there’s no key phrase with that name.

That’s the complete script. Using the instructions in Appendix B for launching command line programs easily, you now have a fast way to copy messages to the clipboard. You will have to modify the TEXT dictionary value in the source whenever you want to update the program with a new message.

On Windows, you can create a batch file to run this program with the WIN-R Run window. (For more about batch files, see Appendix B.) Enter the following into the file editor and save the file as mclip.bat in the C:\Windows folder:

@py.exe C:\path_to_file\mclip.py %* @pause

With this batch file created, running the multi-clipboard program on Windows is just a matter of pressing WIN-R and typing mclip key phrase.

Project: Adding Bullets to Wiki Markup

When editing a Wikipedia article, you can create a bulleted list by putting each list item on its own line and placing a star in front. But say you have a really large list that you want to add bullet points to. You could just type those stars at the beginning of each line, one by one. Or you could automate this task with a short Python script.

The bulletPointAdder.py script will get the text from the clipboard, add a star and space to the beginning of each line, and then paste this new text to the clipboard. For example, if I copied the following text (for the Wikipedia article “List of Lists of Lists”) to the clipboard:

Lists of animals Lists of aquarium life Lists of biologists by author abbreviation Lists of cultivars

and then ran the bulletPointAdder.py program, the clipboard would then contain the following:

* Lists of animals * Lists of aquarium life * Lists of biologists by author abbreviation * Lists of cultivars

This star-prefixed text is ready to be pasted into a Wikipedia article as a bulleted list.

Step 1: Copy and Paste from the Clipboard

You want the bulletPointAdder.py program to do the following:

Paste text from the clipboard.
Do something to it.
Copy the new text to the clipboard.

That second step is a little tricky, but steps 1 and 3 are pretty straightforward: they just involve the pyperclip.copy() and pyperclip.paste() functions. For now, let’s just write the part of the program that covers steps 1 and 3. Enter the following, saving the program as bulletPointAdder.py:

#! python3 # bulletPointAdder.py - Adds Wikipedia bullet points to the start # of each line of text on the clipboard. import pyperclip text = pyperclip.paste() # TODO: Separate lines and add stars. pyperclip.copy(text)

The TODO comment is a reminder that you should complete this part of the program eventually. The next step is to actually implement that piece of the program.

Step 2: Separate the Lines of Text and Add the Star

The call to pyperclip.paste() returns all the text on the clipboard as one big string. If we used the “List of Lists of Lists” example, the string stored in text would look like this:

'Lists of animals\nLists of aquarium life\nLists of biologists by author abbreviation\nLists of cultivars'

The \n newline characters in this string cause it to be displayed with multiple lines when it is printed or pasted from the clipboard. There are many “lines” in this one string value. You want to add a star to the start of each of these lines.

You could write code that searches for each \n newline character in the string and then adds the star just after that. But it would be easier to use the split() method to return a list of strings, one for each line in the original string, and then add the star to the front of each string in the list.

Make your program look like the following:

#! python3 # bulletPointAdder.py - Adds Wikipedia bullet points to the start # of each line of text on the clipboard. import pyperclip text = pyperclip.paste() # Separate lines and add stars. lines = text.split('\n') for i in range(len(lines)): # loop through all indexes in the "lines" list lines[i] = '* ' + lines[i] # add star to each string in "lines" list pyperclip.copy(text)

We split the text along its newlines to get a list in which each item is one line of the text. We store the list in lines and then loop through the items in lines. For each line, we add a star and a space to the start of the line. Now each string in lines begins with a star.

Step 3: Join the Modified Lines

The lines list now contains modified lines that start with stars. But pyperclip.copy() is expecting a single string value, however, not a list of string values. To make this single string value, pass lines into the join() method to get a single string joined from the list’s strings. Make your program look like the following:

#! python3 # bulletPointAdder.py - Adds Wikipedia bullet points to the start # of each line of text on the clipboard. import pyperclip text = pyperclip.paste() # Separate lines and add stars. lines = text.split('\n') for i in range(len(lines)): # loop through all indexes for "lines" list lines[i] = '* ' + lines[i] # add star to each string in "lines" list text = '\n'.join(lines) pyperclip.copy(text)

When this program is run, it replaces the text on the clipboard with text that has stars at the start of each line. Now the program is complete, and you can try running it with text copied to the clipboard.

Even if you don’t need to automate this specific task, you might want to automate some other kind of text manipulation, such as removing trailing spaces from the end of lines or converting text to uppercase or lowercase. Whatever your needs, you can use the clipboard for input and output.

A Short Progam: Pig Latin

Pig Latin is a silly made-up language that alters English words. If a word begins with a vowel, the word yay is added to the end of it. If a word begins with a consonant or consonant cluster (like ch or gr), that consonant or cluster is moved to the end of the word followed by ay.

Let’s write a Pig Latin program that will output something like this:

Enter the English message to translate into Pig Latin: My name is AL SWEIGART and I am 4,000 years old. Ymay amenay isyay ALYAY EIGARTSWAY andyay Iyay amyay 4,000 yearsyay oldyay.

This program works by altering a string using the methods introduced in this chapter. Type the following source code into the file editor, and save the file as pigLat.py:

# English to Pig Latin print('Enter the English message to translate into Pig Latin:') message = input() VOWELS = ('a', 'e', 'i', 'o', 'u', 'y') pigLatin = [] # A list of the words in Pig Latin. for word in message.split(): # Separate the non-letters at the start of this word: prefixNonLetters = '' while len(word) > 0 and not word[0].isalpha(): prefixNonLetters += word[0] word = word[1:] if len(word) == 0: pigLatin.append(prefixNonLetters) continue # Separate the non-letters at the end of this word: suffixNonLetters = '' while not word[-1].isalpha(): suffixNonLetters += word[-1] word = word[:-1] # Remember if the word was in uppercase or title case. wasUpper = word.isupper() wasTitle = word.istitle() word = word.lower() # Make the word lowercase for translation. # Separate the consonants at the start of this word: prefixConsonants = '' while len(word) > 0 and not word[0] in VOWELS: prefixConsonants += word[0] word = word[1:] # Add the Pig Latin ending to the word: if prefixConsonants != '': word += prefixConsonants + 'ay' else: word += 'yay' # Set the word back to uppercase or title case: if wasUpper: word = word.upper() if wasTitle: word = word.title() # Add the non-letters back to the start or end of the word. pigLatin.append(prefixNonLetters + word + suffixNonLetters) # Join all the words back together into a single string: print(' '.join(pigLatin))

Let’s look at this code line by line, starting at the top:

# English to Pig Latin print('Enter the English message to translate into Pig Latin:') message = input() VOWELS = ('a', 'e', 'i', 'o', 'u', 'y')

First, we ask the user to enter the English text to translate into Pig Latin. Also, we create a constant that holds every lowercase vowel letter (and y) as a tuple of strings. This will be used later in our program.

Next, we’re going to create the pigLatin variable to store the words as we translate them into Pig Latin:

pigLatin = [] # A list of the words in Pig Latin. for word in message.split(): # Separate the non-letters at the start of this word: prefixNonLetters = '' while len(word) > 0 and not word[0].isalpha(): prefixNonLetters += word[0] word = word[1:] if len(word) == 0: pigLatin.append(prefixNonLetters) continue

We need each word to be its own string, so we call message.split() to get a list of the words as separate strings. The string 'My name is AL SWEIGART and I am 4,000 years old.' would cause split() to return ['My', 'name', 'is', 'AL', 'SWEIGART', 'and', 'I', 'am', '4,000', 'years', 'old.'].

We need to remove any non-letters from the start and end of each word so that strings like 'old.' translate to 'oldyay.' instead of 'old.yay'. We’ll save these non-letters to a variable named prefixNonLetters.

# Separate the non-letters at the end of this word: suffixNonLetters = '' while not word[-1].isalpha(): suffixNonLetters += word[-1] word = word[:-1]

A loop that calls isalpha() on the first character in the word will determine if we should remove a character from a word and concatenate it to the end of prefixNonLetters. If the entire word is made of non-letter characters, like '4,000', we can simply append it to the pigLatin list and continue to the next word to translate. We also need to save the non-letters at the end of the word string. This code is similar to the previous loop.

Next, we’ll make sure the program remembers if the word was in uppercase or title case so we can restore it after translating the word to Pig Latin:

# Remember if the word was in uppercase or title case. wasUpper = word.isupper() wasTitle = word.istitle() word = word.lower() # Make the word lowercase for translation.

For the rest of the code in the for loop, we’ll work on a lowercase version of word.

To convert a word like sweigart to eigart-sway, we need to remove all of the consonants from the beginning of word:

# Separate the consonants at the start of this word: prefixConsonants = '' while len(word) > 0 and not word[0] in VOWELS: prefixConsonants += word[0] word = word[1:]

We use a loop similar to the loop that removed the non-letters from the start of word, except now we are pulling off consonants and storing them to a variable named prefixConsonants.

If there were any consonants at the start of the word, they are now in prefixConsonants and we should concatenate that variable and the string 'ay' to the end of word. Otherwise, we can assume word begins with a vowel and we only need to concatenate 'yay':

# Add the Pig Latin ending to the word: if prefixConsonants != '': word += prefixConsonants + 'ay' else: word += 'yay'

Recall that we set word to its lowercase version with word = word.lower(). If word was originally in uppercase or title case, this code will convert word back to its original case:

# Set the word back to uppercase or title case: if wasUpper: word = word.upper() if wasTitle: word = word.title()

At the end of the for loop, we append the word, along with any non-letter prefix or suffix it originally had, to the pigLatin list:

# Add the non-letters back to the start or end of the word. pigLatin.append(prefixNonLetters + word + suffixNonLetters) # Join all the words back together into a single string: print(' '.join(pigLatin))

After this loop finishes, we combine the list of strings into a single string by calling the join() method. This single string is passed to print() to display our Pig Latin on the screen.

You can find other short, text-based Python programs like this one at https://github.com/asweigart/pythonstdiogames/.

Summary

Text is a common form of data, and Python comes with many helpful string methods to process the text stored in string values. You will make use of indexing, slicing, and string methods in almost every Python program you write.

The programs you are writing now don’t seem too sophisticated—they don’t have graphical user interfaces with images and colorful text. So far, you’re displaying text with print() and letting the user enter text with input(). However, the user can quickly enter large amounts of text through the clipboard. This ability provides a useful avenue for writing programs that manipulate massive amounts of text. These text-based programs might not have flashy windows or graphics, but they can get a lot of useful work done quickly.

Another way to manipulate large amounts of text is reading and writing files directly off the hard drive. You’ll learn how to do this with Python in Chapter 9.

That just about covers all the basic concepts of Python programming! You’ll continue to learn new concepts throughout the rest of this book, but you now know enough to start writing some useful programs that can automate tasks. If you’d like to see a collection of short, simple Python programs built from the basic concepts you’ve learned so far, check out https://github.com/asweigart/pythonstdiogames/. Try copying the source code for each program by hand, and then make modifications to see how they affect the behavior of the program. Once you have an understanding of how the program works, try re-creating the program yourself from scratch. You don’t need to re-create the source code exactly; just focus on what the program does rather than how it does it.

You might not think you have enough Python knowledge to do things such as download web pages, update spreadsheets, or send text messages, but that’s where Python modules come in! These modules, written by other programmers, provide functions that make it easy for you to do all these things. So let’s learn how to write real programs to do useful automated tasks.

Practice Questions

1. What are escape characters?

2. What do the \n and \t escape characters represent?

3. How can you put a \ backslash character in a string?

4. The string value "Howl's Moving Castle" is a valid string. Why isn’t it a problem that the single quote character in the word Howl's isn’t escaped?

5. If you don’t want to put \n in your string, how can you write a string with newlines in it?

6. What do the following expressions evaluate to?

'Hello, world!'[1]
'Hello, world!'[0:5]
'Hello, world!'[:5]
'Hello, world!'[3:]

7. What do the following expressions evaluate to?

'Hello'.upper()
'Hello'.upper().isupper()
'Hello'.upper().lower()

8. What do the following expressions evaluate to?

'Remember, remember, the fifth of November.'.split()
'-'.join('There can be only one.'.split())

9. What string methods can you use to right-justify, left-justify, and center a string?

10. How can you trim whitespace characters from the beginning or end of a string?

Practice Projects

For practice, write programs that do the following.

Table Printer

Write a function named printTable() that takes a list of lists of strings and displays it in a well-organized table with each column right-justified. Assume that all the inner lists will contain the same number of strings. For example, the value could look like this:

tableData = [['apples', 'oranges', 'cherries', 'banana'], ['Alice', 'Bob', 'Carol', 'David'], ['dogs', 'cats', 'moose', 'goose']]

Your printTable() function would print the following:

apples Alice dogs oranges Bob cats cherries Carol moose banana David goose

Hint: your code will first have to find the longest string in each of the inner lists so that the whole column can be wide enough to fit all the strings. You can store the maximum width of each column as a list of integers. The printTable() function can begin with colWidths = [0] * len(tableData), which will create a list containing the same number of 0 values as the number of inner lists in tableData. That way, colWidths[0] can store the width of the longest string in tableData[0], colWidths[1] can store the width of the longest string in tableData[1], and so on. You can then find the largest value in the colWidths list to find out what integer width to pass to the rjust() string method.

Zombie Dice Bots

Programming games are a game genre where instead of playing a game directly, players write bot programs to play the game autonomously. I’ve created a Zombie Dice simulator, which allows programmers to practice their skills while making game-playing AIs. Zombie Dice bots can be simple or incredibly complex, and are great for a class exercise or an individual programming challenge.

Zombie Dice is a quick, fun dice game from Steve Jackson Games. The players are zombies trying to eat as many human brains as possible without getting shot three times. There is a cup of 13 dice with brains, footsteps, and shotgun icons on their faces. The dice icons are colored, and each color has a different likelihood of each event occurring. Every die has two sides with footsteps, but dice with green icons have more sides with brains, red-icon dice have more shotguns, and yellow-icon dice have an even split of brains and shotguns. Do the following on each player’s turn:

Place all 13 dice in the cup. The player randomly draws three dice from the cup and then rolls them. Players always roll exactly three dice.
They set aside and count up any brains (humans whose brains were eaten) and shotguns (humans who fought back). Accumulating three shotguns automatically ends a player’s turn with zero points (regardless of how many brains they had). If they have between zero and two shotguns, they may continue rolling if they want. They may also choose to end their turn and collect one point per brain.
If the player decides to keep rolling, they must reroll all dice with footsteps. Remember that the player must always roll three dice; they must draw more dice out of the cup if they have fewer than three footsteps to roll. A player may keep rolling dice until either they get three shotguns—losing everything—or all 13 dice have been rolled. A player may not reroll only one or two dice, and may not stop mid-reroll.
When someone reaches 13 brains, the rest of the players finish out the round. The person with the most brains wins. If there’s a tie, the tied players play one last tiebreaker round.

Zombie Dice has a push-your-luck game mechanic: the more you reroll the dice, the more brains you can get, but the more likely you’ll eventually accrue three shotguns and lose everything. Once a player reaches 13 points, the rest of the players get one more turn (to potentially catch up) and the game ends. The player with the most points wins. You can find the complete rules at https://github.com/asweigart/zombiedice/.

Install the zombiedice module with pip by following the instructions in Appendix A. You can run a demo of the simulator with some pre-made bots by running the following in the interactive shell:

>>> import zombiedice >>> zombiedice.demo() Zombie Dice Visualization is running. Open your browser to http:// localhost:51810 to view it. Press Ctrl-C to quit.

Figure 6-1: The web GUI for the Zombie Dice simulator

You’ll create bots by writing a class with a turn() method, which is called by the simulator when it’s your bot’s turn to roll the dice. Classes are beyond the scope of this book, so the class code is already set up for you in the myzombie.py program, which is in the downloadable ZIP file for this book at https://nostarch.com/automatestuff2/. Writing a method is essentially the same as writing a function, and you can use the turn() code in the myZombie.py program as a template. Inside this turn() method, you’ll call the zombiedice.roll() function as often as you want your bot to roll the dice.

import zombiedice class MyZombie: def __init__(self, name): # All zombies must have a name: self.name = name def turn(self, gameState): # gameState is a dict with info about the current state of the game. # You can choose to ignore it in your code. diceRollResults = zombiedice.roll() # first roll # roll() returns a dictionary with keys 'brains', 'shotgun', and # 'footsteps' with how many rolls of each type there were. # The 'rolls' key is a list of (color, icon) tuples with the # exact roll result information. # Example of a roll() return value: # {'brains': 1, 'footsteps': 1, 'shotgun': 1, # 'rolls': [('yellow', 'brains'), ('red', 'footsteps'), # ('green', 'shotgun')]} # REPLACE THIS ZOMBIE CODE WITH YOUR OWN: brains = 0 while diceRollResults is not None: brains += diceRollResults['brains'] if brains < 2: diceRollResults = zombiedice.roll() # roll again else: break zombies = ( zombiedice.examples.RandomCoinFlipZombie(name='Random'), zombiedice.examples.RollsUntilInTheLeadZombie(name='Until Leading'), zombiedice.examples.MinNumShotgunsThenStopsZombie(name='Stop at 2 Shotguns', minShotguns=2), zombiedice.examples.MinNumShotgunsThenStopsZombie(name='Stop at 1 Shotgun', minShotguns=1), MyZombie(name='My Zombie Bot'), # Add any other zombie players here. ) # Uncomment one of the following lines to run in CLI or Web GUI mode: #zombiedice.runTournament(zombies=zombies, numGames=1000) zombiedice.runWebGui(zombies=zombies, numGames=1000)

The turn() method takes two parameters: self and gameState. You can ignore these in your first few zombie bots and consult the online documentation for details later if you want to learn more. The turn() method should call zombiedice.roll() at least once for the initial roll. Then, depending on the strategy the bot uses, it can call zombiedice.roll() again as many times as it wants. In myZombie.py, the turn() method calls zombiedice.roll() twice, which means the zombie bot will always roll its dice two times per turn regardless of the results of the roll.

The return value of zombiedice.roll() tells your code the results of the dice roll. It is a dictionary with four keys. Three of the keys, 'shotgun', 'brains', and 'footsteps', have integer values of how many dice came up with those icons. The fourth 'rolls' key has a value that is a list of tuples for each die roll. The tuples contain two strings: the color of the die at index 0 and the icon rolled at index 1. Look at the code comments in the turn() method’s definition for an example. If the bot has already rolled three shotguns, then zombiedice.roll() will return None.

Try writing some of your own bots to play Zombie Dice and see how they compare against the other bots. Specifically, try to create the following bots:

A bot that, after the first roll, randomly decides if it will continue or stop
A bot that stops rolling after it has rolled two brains
A bot that stops rolling after it has rolled two shotguns
A bot that initially decides it’ll roll the dice one to four times, but will stop early if it rolls two shotguns
A bot that stops rolling after it has rolled more shotguns than brains

Run these bots through the simulator and see how they compare to each other. You can also examine the code of some premade bots at https://github.com/asweigart/zombiedice/. If you find yourself playing this game in the real world, you’ll have the benefit of thousands of simulated games telling you that one of the best strategies is to simply stop once you’ve rolled two shotguns. But you could always try pressing your luck . . .

Touple

A tuple is an ordered collection of items. An ordered collection keeps the items in the order you insert or initialize them. In other words, the order is preserved. This is in contrast to dictionaries or sets, where the order is not preserved (unordered collections).

Tuples are like lists but vary in the following aspects: They are immutable, (we cannot change them) unlike lists which are mutable (we can change them). Let us learn more about tuples and their related methods. We’ll also learn to effectively use them in Python.

For more background on the different data structures in Python, check out the following articles:

Note: Prerequisites – Make sure you have basic Python knowledge before diving into this article. It also might be a good idea to check out some linear data structures. (links are given above)

Tuples: Let’s Code

As we discussed, a Tuple is a collection of items that are immutable. Let’s start by creating a tuple.

Creating a Tuple

A tuple can be created in multiple ways. The simplest way of creating a tuple is by setting a variable to a pair of empty parantheses.

The code above snippet gives an output of <class: 'tuple'>, which indicates that the tuple has been created successfully. We can also create a tuple by using the in-built tuple() method in Python.

While initializing a tuple, we can also specify what data exists inside it.

Accessing Items in a Tuple

Positive Indexing

For example, let’s create a tuple, tuple1. Tuple elements can be accessed the same way as a list element.

This tuple follows zero indexing.

Negative Indexing

Similar to lists, we can also use negative indexing on a tuple. Therefore, ‘-1’ refers to the Nth element of a tuple, -2 refers to the (N-1)th element, and so on (where N is the length of the tuple).

Tuple Negative Indexing

Slicing

In Python, slicing is used to return a range of values. Like lists, tuples can also be sliced.

Modifying Tuples

Tuples are immutable.

For example:

If we execute the code above, the Python interpreter throws the following error:

This is because a tuple is designed to be immutable. However, we can change a tuple that contains mutable objects.

For example, let us take a tuple of lists.

This works perfectly because we are modifying the list within a tuple (which is mutable). We can also create new tuples from existing ones.

Tuple Methods

Tuples have the following in-built methods that make them extremely powerful:

cmp(tuple1, tuple2)
len(tuple)
min(tuple)
max(tuple)
tuple(list)
t.count(el)
t.index(el)

cmp(tuple1, tuple2)

Note: The cmp() method existed in python2. It wasn’t included in python3. Therefore we define our own compare method.

The compare method analyses two tuples element by element.

It compares them and returns the following:

If tuple1 > tuple2: the method returns 1.
If tuple2 > tuple1: the method returns -1.
If tuple1 == tuple2: the method returns 0.

len(tuple)

The length method returns the length of the tuple.

min(tuple)

The min method returns the smallest element in the tuple.

max(tuple)

The max method returns the largest element in the tuple.

tuple(list)

The tuple method converts the list that is passed as parameter into a tuple.

t.count(el)

The count method returns the count of the element passed as parameter.

t.index(el)

The index method returns the index of the first occurence of the element in a tuple.

You can also return the index of the last occurence of the element by using this method.

It’s also possible to specify a range to search.

Applications of Tuples

Tuples are especially used as protection against modification. Since they are immutable, we can use tuples to write-protect data.
When iterating over a tuple, a considerable performance gain is observed when we compare it to lists. This is more evident when the size of the tuple is large. Using the timeit module in Python, we see that tuples are considerably faster to iterate when compared to lists.

The dictionary data structure has an immutable key. Therefore tuples can be used as a key in a dictionary.
Tuples can be used to group related data. For example, a row in a database table can be grouped together and stored in a tuple.

Values Expressions & Statments

2. Values, expressions, and statements

2.1. Programs and data

We can restate our previous definition of a computer program colloquially:

A computer program is a step-by-step set of instructions to tell a computer to do things to stuff.

We will be spending the rest of this book deepening and refining our understanding of exactly what kinds of things a computer can do. Your ability to program a computer effectively will depend in large part on your ability to understand these things well, so that you can express what you want to accomplish in a language the computer can execute.

Before we get to that, however, we need to talk about the stuff on which computers operate.

Computer programs operate on . A single piece of data can be called a datum, but we will use the related term, .

A value is one of the fundamental things — like a letter or a number — that a program manipulates. The values we have seen so far are 4 (the result when we added 2 + 2), and "Hello, World!".

Values are grouped into different or .

Note

At the level of the hardware of the machine, all values are stored as a sequence of , usually represented by the digits 0 and 1. All computer data types, whether they be numbers, text, images, sounds, or anything else, ultimately reduce to an interpretation of these bit patterns by the computer.

Thankfully, high-level languages like Python give us flexible, high-level data types which abstract away the tedious details of all these bits and better fit our human brains.

4 is an integer, and "Hello, World!" is a string, so-called because it contains a string of letters. You (and the interpreter) can identify strings because they are enclosed in quotation marks.

If you are not sure what class a value falls into, Python has a function called type which can tell you.

What about values like "17" and "3.2"? They look like numbers, but they are in quotation marks like strings.

They are strings!

Don’t use commas in ints

When you type a large integer, you might be tempted to use commas between groups of three digits, as in 42,000. This is not a legal integer in Python, but it does mean something else, which is legal:

Well, that’s not what we expected at all! Because of the comma, Python treats this as a pair of values in a tuple. So, remember not to put commas or spaces in your integers. Also revisit what we said in the previous chapter: formal languages are strict, the notation is concise, and even the smallest change might mean something quite different from what you intended.

2.2. Three ways to write strings

Strings in Python can be enclosed in either single quotes (') or double quotes ("), or three of each (''' or """)

Double quoted strings can contain single quotes inside them, as in "Bruce's beard", and single quoted strings can have double quotes inside them, as in 'The knights who say "Ni!"'.

Strings enclosed with three occurrences of either quote symbol are called triple quoted strings. They can contain either single or double quotes:

Triple quoted strings can even span multiple lines:

Python doesn’t care whether you use single or double quotes or the three-of-a-kind quotes to surround your strings: once it has parsed the text of your program or command, the way it stores the value is identical in all cases, and the surrounding quotes are not part of the value. But when the interpreter wants to display a string, it has to decide which quotes to use to make it look like a string.

So the Python language designers chose to usually surround their strings by single quotes. What do think would happen if the string already contained single quotes? Try it for yourself and see.

2.3. String literals and escape sequences

In string literals, most characters represent themselves, so if we want the literal with letters s-t-r-i-n-g, we simply write 'string'.

There are several of these escape sequences that are helpful to know.

\n is the most frequently used of these. The following example will hopefully make what it does clear.

2.4. Names and assignment statements

In order to write programs that do things to the stuff we now call values, we need a way to store our values in the memory of the computer and to name them for later retrieval.

The example above makes three assignments. The first assigns the string value "What's up, Doc?" to the name message. The second gives the integer 17 the name n, and the third assigns the floating-point number 3.14159 the name pi.

Assignment statements create names and associate these names with values. The values can then be retrieved from the computer’s memory by refering to the name associated with them.

The type of a variable is the type of the value it currently refers to.

2.5. Variables are variable

We use variables in a program to “remember” things, like the current score at the football game. But variables are variable. This means they can change over time, just like the scoreboard at a football game. You can assign a value to a variable, and later assign a different value to the same variable.

Note

This is different from math. In math, if you give x the value 3, it cannot change to link to a different value half-way through your calculations!

You’ll notice we changed the value of day three times, and on the third assignment we even gave it a value that was of a different type.

Note

A great deal of programming is about having the computer remember things, like assigning a variable to the number of missed calls on your phone, and then arranging to update the variable when you miss another call.

In the Python shell, entering a name at the prompt causes the interpreter to look up the value associated with the name (or return an error message if the name is not defined), and to display it. In a script, a defined name not in a print function call does not display at all.

2.6. The assignment operator is not an equal sign!

The semantics of the assignment statement can be confusing to beginning programmers, especially since the assignment token, = can be easily confused with the with equals (Python uses the token == for equals, as we will see soon). It is not!

The middle statement above would be impossible if = meant equals, since n could never be equal to n + 1. This statement is perfectly legal Python, however. The assignment statement links a name, on the left hand side of the operator, with a value, on the right hand side.

The two ns in n = n + 1 have different meanings: the n on the right is a memory look-up that is replaced by a value when the right hand side is evaluated by the Python interpreter. It has to already exist or a name error will result. The right hand side of the assignment statement is evaluated first.

The n on the left is the name given to the new value computed on the right hand side as it is stored in the computer’s memory. It does not have to exist previously, since it will be added to the running program’s available names if it isn’t there already.

Note

The left hand side of the assignment statement does have to be a valid Python variable name. This is why you will get an error if you enter:

Tip

When reading or writing code, say to yourself “n is assigned 17” or “n gets the value 17”. Don’t say “n equals 17”.

Note

2.7. Variable names and keywords

Valid variable names in Python must conform to the following three simple rules:

They are an arbitrarily long sequence of letters and digits.
The sequence must begin with a letter.
In addtion to a..z, and A..Z, the underscore (_) is a letter.

Although it is legal to use uppercase letters, by convention we don’t. If you do, remember that case matters. day and Day would be different variables.

The underscore character ( _) can appear in a name. It is often used in names with multiple words, such as my_name or price_of_tea_in_china.

There are some situations in which names beginning with an underscore have special meaning, so a safe rule for beginners is to start all names with a letter other than the underscore.

If you give a variable an illegal name, you get a syntax error:

76trombones is illegal because it does not begin with a letter. more$ is illegal because it contains an illegal character, the dollar sign. But what’s wrong with class?

It turns out that class is one of the Python keywords. Keywords define the language’s syntax rules and structure, and they cannot be used as variable names.

Python 3 has thirty-three keywords (and every now and again improvements to Python introduce or eliminate one or two):

You might want to keep this list handy. Actually, as will often be the case when learning to program with Python, when you aren’t sure about something, you can ask Python:

The list of keywords, keyword.kwlist, comes to us, appropriately, in a Python list.

If the interpreter complains about one of your variable names and you don’t know why, see if it is on this list.

Programmers generally choose names for their variables that are meaningful to the human readers of the program — they help the programmer document, or remember, what the variable is used for.

Caution

Beginners sometimes confuse meaningful to the human readers with meaningful to the computer. So they’ll wrongly think that because they’ve called some variable average or pi, it will somehow automatically calculate an average, or automatically associate the variable pi with the value 3.14159. No! The computer doesn’t attach semantic meaning to your variable names. It is up to you to do that.

2.8. Statements and expressions

When you type a statement on the command line, Python executes it. The interpreter does not display any results.

In this example len is a built-in Python function that returns the number of characters in a string. We’ve previously seen the print and the type functions, so this is our third example of a function.

The evaluation of an expression produces a value, which is why expressions can appear on the right hand side of assignment statements. A value all by itself is a simple expression, and so is a variable.

2.9. Operators and operands

The following are all legal Python expressions whose meaning is more or less clear:

The tokens + and -, and the use of parenthesis for grouping, mean in Python what they mean in mathematics. The asterisk (*) is the token for multiplication, and ** is the token for exponentiation (raising a number to a power).

When a variable name appears in the place of an operand, it is replaced with its value before the operation is performed.

Addition, subtraction, multiplication, and exponentiation all do what you expect.

Example: so let us convert 645 minutes into hours:

Oops! In Python 3, the division operator / always yields a floating point result. What we might have wanted to know was how many whole hours there are, and how many minutes remain. Python gives us two different flavors of the division operator. The second, called integer division uses the token //. It always truncates its result down to the next smallest integer (to the left on the number line).

Take care that you choose the correct division operator. If you’re working with expressions where you need floating point values, use the division operator that does the division appropriately.

2.10. The modulus operator

The modulus operator works on integers (and integer expressions) and gives the remainder when the first number is divided by the second. In Python, the modulus operator is a percent sign (%). The syntax is the same as for other operators:

So 7 divided by 3 is 2 with a remainder of 1.

The modulus operator turns out to be surprisingly useful. For example, you can check whether one number is divisible by another – if x % y is zero, then x is divisible by y.

Also, you can extract the right-most digit or digits from a number. For example, x % 10 yields the right-most digit of x (in base 10). Similarly x % 100 yields the last two digits.

It is also extremely useful for doing conversions, say from seconds, to hours, minutes and seconds. So let’s write a program to ask the user to enter some seconds, and we’ll convert them into hours, minutes, and remaining seconds.

2.11. Order of operations

When more than one operator appears in an expression, the order of evaluation depends on the rules of precedence. Python follows the same precedence rules for its mathematical operators that mathematics does. The acronym PEMDAS is a useful way to remember the order of operations:

Parentheses have the highest precedence and can be used to force an expression to evaluate in the order you want. Since expressions in parentheses are evaluated first, 2 * (3-1) is 4, and (1+1)**(5-2) is 8. You can also use parentheses to make an expression easier to read, as in (minute * 100) / 60, even though it doesn’t change the result.
Exponentiation has the next highest precedence, so 2**1+1 is 3 and not 4, and 3*1**3 is 3 and not 27.
Multiplication and both Division operators have the same precedence, which is higher than Addition and Subtraction, which also have the same precedence. So 2*3-1 yields 5 rather than 4, and 5-2*2 is 1, not 6. #. Operators with the same precedence are evaluated from left-to-right. In algebra we say they are left-associative. So in the expression 6-3+2, the subtraction happens first, yielding 3. We then add 2 to get the result 5. If the operations had been evaluated from right to left, the result would have been 6-(3+2), which is 1. (The acronym PEDMAS could mislead you to thinking that division has higher precedence than multiplication, and addition is done ahead of subtraction - don’t be misled. Subtraction and addition are at the same precedence, and the left-to-right rule applies.)

Note

Due to some historical quirk, an exception to the left-to-right left-associative rule is the exponentiation operator **, so a useful hint is to always use parentheses to force exactly the order you want when exponentiation is involved:

The immediate mode command prompt of Python is great for exploring and experimenting with expressions like this.

2.12. Operations on strings

In general, you cannot perform mathematical operations on strings, even if the strings look like numbers. The following are illegal (assuming that message has type string):

Interestingly, the + operator does work with strings, but for strings, the + operator represents concatenation, not addition. Concatenation means joining the two operands by linking them end-to-end. For example:

The output of this program is banana nut bread. The space before the word nut is part of the string, and is necessary to produce the space between the concatenated strings.

The * operator also works on strings; it performs repetition. For example, 'Fun' * 3 is 'FunFunFun'. One of the operands has to be a string; the other has to be an integer.

On one hand, this interpretation of + and * makes sense by analogy with addition and multiplication. Just as 4 * 3 is equivalent to 4 + 4 + 4, we expect "Fun" * 3 to be the same as "Fun" + "Fun" + "Fun", and it is. On the other hand, there is a significant way in which string concatenation and repetition are different from integer addition and multiplication. Can you think of a property that addition and multiplication have that string concatenation and repetition do not?

2.13. Type converter functions

Here we’ll look at three more Python functions, int, float and str, which will (attempt to) convert their arguments into types int, float and str respectively. We call these type converter functions.

The int function can take a floating point number or a string, and turn it into an int. For floating point numbers, it discards the fractional portion of the number - a process we call truncation towards zero on the number line. Let us see this in action:

The last case shows that a string has to be a syntactically legal number, otherwise you’ll get one of those pesky runtime errors.

The type converter float can turn an integer, a float, or a syntactically legal string into a float.

The type converter str turns its argument into a string:

2.14. Input

There is a built-in function in Python for getting input from the user:

The user of the program can enter the name and press return. When this happens the text that has been entered is returned from the input function, and in this case assigned to the variable name.

The string value inside the parentheses is called a prompt and contains a message which will be displayed to the user when the statement is executed to prompt their response.

Even if you asked the user to enter their age, you would get back a string like "17". It would be your job, as the programmer, to convert that string into a int or a float, using the int or float converter functions we saw in the previous section, which leads us to …

2.15. Composition

So far, we have looked at the elements of a program — variables, expressions, statements, and function calls — in isolation, without talking about how to combine them.

One of the most useful features of programming languages is their ability to take small building blocks and compose them into larger chunks.

Firstly, we’ll do the four steps one at a time:

Now let’s compose the first two lines into a single line of code, and compose the second two lines into another line of code.

If we really wanted to be tricky, we could write it all in one statement:

Such compact code may not be most understandable for humans, but it does illustrate how we can compose bigger chunks from our building blocks.

If you’re ever in doubt about whether to compose code or fragment it into smaller steps, try to make it as simple as you can for the human reader to follow.

2.16. More about the `print` function

At the end of the previous chapter, you learned that the print function can take a series of arguments, seperated by commas, and that it prints a string with each argument in order seperated by a space.

In the example in the previous section of this chapter, you may have noticed that the arguments don’t have to be strings.

By default, print uses a single space as a seperator and a \n as a terminator (at the end of the string). Both of these defaults can be overridden.

You will explore these new features of the print function in the exercises.

2.17. Glossary

assignment statement

A statement that assigns a value to a name (variable). To the left of the assignment operator, =, is a name. To the right of the assignment token is an expression which is evaluated by the Python interpreter and then assigned to the name. The difference between the left and right hand sides of the assignment statement is often confusing to new programmers. In the following assignment:

n plays a very different role on each side of the =. On the right it is a value and makes up part of the expression which will be evaluated by the Python interpreter before assigning it to the name on the left.assignment token

= is Python’s assignment token, which should not be confused with the mathematical comparison operator using the same symbol.composition

The ability to combine simple expressions and statements into compound statements and expressions in order to represent complex computations concisely.concatenate

To join two strings end-to-end.data type

A set of values. The type of a value determines how it can be used in expressions. So far, the types you have seen are integers (int), floating-point numbers (float), and strings (str).escape sequence

A sequence of characters starting with the escape character (\) used to represent string literals such as linefeeds and tabs.evaluate

To simplify an expression by performing the operations in order to yield a single value.expression

A combination of variables, operators, and values that represents a single result value.float

A Python data type which stores floating-point numbers. Floating-point numbers are stored internally in two parts: a base and an exponent. When printed in the standard format, they look like decimal numbers. Beware of rounding errors when you use floats, and remember that they are only approximate values.int

A Python data type that holds positive and negative whole numbers.integer division

An operation that divides one integer by another and yields an integer. Integer division yields only the whole number of times that the numerator is divisible by the denominator and discards any remainder.keyword

A reserved word that is used by the compiler to parse program; you cannot use keywords like if, def, and while as variable names.literal

A notation for representing for representing a constant value of one of Python’s built-in types. \n, for example, is a literal representing the newline character.modulus operator

An operator, denoted with a percent sign ( %), that works on integers and yields the remainder when one number is divided by another.object diagram

A graphical representation of a set of variables (objects) and the values to which they refer, taken at a particular instant during the program’s execution.operand

One of the values on which an operator operates.operator

A special symbol that represents a simple computation like addition, multiplication, or string concatenation.rules of precedence

The set of rules governing the order in which expressions involving multiple operators and operands are evaluated.statement

An instruction that the Python interpreter can execute. So far we have only seen the assignment statement, but we will soon meet the import statement and the for statement.str

A Python data type that holds a string of characters.tripple quoted strings

A string enclosed by either """ or '''. Tripple quoted strings can span several lines.value

A number, string, or any of the other things that can be stored in a variable or computed in an expression.variable

A name that refers to a value.variable name

A name given to a variable. Variable names in Python consist of a sequence of letters (a..z, A..Z, and _) and digits (0..9) that begins with a letter. In best programming practice, variable names should be chosen so that they describe their use in the program, making the program self documenting.

Dictionaries, sets, files, and modules

6. Dictionaries, sets, files, and modules

Dictionaries

Note

As an example, we will create a dictionary to translate English words into Spanish. For this dictionary, the keys are strings.

One way to create a dictionary is to start with the empty dictionary and add key-value pairs. The empty dictionary is denoted with a pair of curly braces, {}:

The first assignment creates a dictionary named eng2sp; the other assignments add new key-value pairs to the dictionary. We can print the current value of the dictionary in the usual way:

The key-value pairs of the dictionary are seperated by commas. Each pair contains a key and a value separated by a colon.

The order of the pairs may not be what you expected. Python uses complex algorithms to determine where the key-value pairs are stored in a dictionary. For our purposes we can think of this ordering as unpredicatable, so you should not try to rely on it. Instead, look up values by using a known key.

Another way to create a dictionary is to provide a list of key-value pairs using the same syntax as the previous output:

It doesn’t matter what order we write the pairs. The values in a dictionary are accessed with keys, not with indices, so ordering is unimportant.

Here is how we use a key to look up the corresponding value:

The key 'two' yields the value 'dos'.

6.2. Dictionary operations

The del statement removes a key-value pair from a dictionary. For example, the following dictionary contains the names of various fruits and the number of each fruit in stock:

If someone buys all of the pears, we can remove the entry from the dictionary:

Or if we’re expecting more pears soon, we might just change the value associated with pears:

The len function also works on dictionaries; it returns the number of key-value pairs:

The in operator returns True if the key appears in the dictionary and False otherwise:

This operator can be very useful, since looking up a non-existant key in a dictionary causes a runtime error:

To address this problem, the built-in get method provides a default value that is returned when a key is not found:

Python’s built-in sorted function returns a list of a dictionaries keys in sorted order:

6.3. Aliasing and copying

Because dictionaries are mutable, you need to be aware of aliasing. Whenever two variables refer to the same object, changes to one affect the other.

If you want to modify a dictionary and keep a copy of the original, use the copy method. For example, opposites is a dictionary that contains pairs of opposites:

an_alias and opposites refer to the same object; a_copy refers to a fresh copy of the same dictionary. If we modify alias, opposites is also changed:

If we modify a_copy, opposites is unchanged:

6.4. Sets

To create an empty set, you can not use empty curly braces.

Instead, use the set type converter function without an argument.

Sets contain a unique collection of elements of any type. You can add to a set using its add method, and test for membership with the in operator.

Since sets hold unique collections of elements, you can use the set type conversion function to remove duplicates from a list.

6.5. Formatting Strings

6.6. The `format` method for strings

The easiest and most powerful way to format a string in Python 3 is to use the format method.

The key idea is that one provides a formatter string which contains placeholder fields, ... {0} ... {1} ... {2} ... etc. The format method of a string uses the numbers as indexes into its arguments, and substitutes the appropriate argument into each placeholder field.

Each of the placeholders can also contain a format specification — it is always introduced by the : symbol. This can control things like

whether the field is aligned left <, centered ^, or right >
the width allocated to the field within the result string (a number like 10)
the type of conversion (we’ll initially only force conversion to float, f, as we did in line 11 of the code above, or perhaps we’ll ask integer numbers to be converted to hexadecimal using x)
if the type conversion is a float, you can also specify how many decimal places are wanted (typically, .2f is useful for working with currencies to two decimal places.)

You can have multiple placeholders indexing the same argument, or perhaps even have extra arguments that are not referenced at all:

This produces the following:

As you might expect, you’ll get an index error if your placeholders refer to arguments that you do not provide:

In addition to positional arguments in format strings, named arguments are also supported:

Notice that the order of the arguments to the format method example doesn’t correspond with the order they appear in the format string. These are keywords, as with dictionaries, so their order is not relevant.

Old style format strings

Earlier versions of Python used a cryptic way to format strings. It is considered deprecated and will eventually disappear from the language.

While we won’t use it in this book, you will still see it around in lots of existing Python code, so it is good to be aware of it.

The syntax for the old string formatting operation looks like this:

To see how this works, here are a few examples:

6.7. Files

6.7.1. About files

To use a file, you have to open it. When you’re done, you have to close it. When you open the file, you have to decide ahead of time whether you want to read data from the file or write data to it. If you plan to write data to the file you have to choose between starting a new version of the file or writing data at the end of what was already there. This second option for writing to the file is called appending. The first option destroys any previously existing data in the file.

6.7.2. The `open` function

The open function takes two arguments. The first is the name of the file, and the second is the mode. Mode 'w' means that we are opening the file for writing. Mode 'r' means reading, and mode 'a' means appending.

Let’s begin with an example that shows these three modes in operation:

The first line opens the test.txt for writing. If there is no file named test.txt on the disk, it will be created. If there already is one, it will be replaced by the file we are writing and any previous data in it will be lost.

Closing the file handle tells the system that we are done writing and makes the disk file available for reading by other programs (or by our own program).

We finish this example by openning test.txt for reading. We then call the read method, assigning the contents of the file, which is a string, to a variable named contents, and finally print contents to see that it is indeed what we wrote to the file previously.

If we want to add to an already existing file, use the append mode.

6.8. Opening a file that doesn’t exist

If we try to open a file that doesn’t exist, we get an error:

In Python, exceptions are handled with the try ... except statement.

In this example we try to open the data file for reading. If it succeeds, we use the read() method to read the file contents as a string into the variable mydata and close the file. If an IOError exception occurs, we still create mydata as an empty string and continue on with the program.

6.9. Reading data from files

Python file descriptors have three methods for reading in data from a file. We’ve already seen the read() method, which returns the entire contents of the file as a single string. For really big files this may not be what you want.

This is a handy pattern for our toolbox. In bigger programs, we’d squeeze more extensive logic into the body of the loop at line 8 — for example, if each line of the file contained the name and email address of one of our friends, perhaps we’d split the line into some pieces and call a function to send the friend a party invitation.

On line 8 we suppress the newline character that print usually appends to our strings. Why? This is because the string already has its own newline: the readline method in line 3 returns everything up to and including the newline character. This also explains the end-of-file detection logic: when there are no more lines to be read from the file, readline returns an empty string — one that does not even have a newline at the end, hence it’s length is 0.

6.9.1. Turning a file into a list of lines

It is often useful to fetch data from a disk file and turn it into a list of lines. Suppose we have a file containing our friends and their email addresses, one per line in the file. But we’d like the lines sorted into alphabetical order. A good plan is to read everything into a list of lines, then sort the list, and then write the sorted list back to another file:

The readlines method in line 2 reads all the lines and returns a list of the strings.

We could have used the template from the previous section to read each line one-at-a-time, and to build up the list ourselves, but it is a lot easier to use the method that the Python implementors gave us!

6.9.2. An example

Many useful line-processing programs will read a text file line-at-a-time and do some minor processing as they write the lines to an output file. They might number the lines in the output file, or insert extra blank lines after every 60 lines to make it convenient for printing on sheets of paper, or extract some specific columns only from each line in the source file, or only print lines that contain a specific substring. We call this kind of program a filter.

Here is a filter that copies one file to another, omitting any lines that begin with #:

The continue statement at line 9 skips over the remaining lines in the current iteration of the loop, but the loop will still iterate. This style looks a bit contrived here, but it is often useful to say “get the lines we’re not concerned with out of the way early, so that we have cleaner more focussed logic in the meaty part of the loop that might be written around line 11.”

Thus, if text is the empty string, the loop exits. If the first character of text is a hash mark, the flow of execution goes to the top of the loop, ready to start processing the next line. Only if both conditions fail do we fall through to do the processing at line 11, in this example, writing the line into the new file.

Let’s consider one more case: suppose your original file contained empty lines. At line 6 above, would this program find the first empty line in the file, and terminate immediately? No! Recall that readline always includes the newline character in the string it returns. It is only when we try to read beyond the end of the file that we get back the empty string of length 0.

6.10. `repr()` and `eval()` functions

Python has a built-in function named repr that takes a Python object as an argument and returns a string representation of that object. For Python’s built-in types, the string representation of an object can be evaluated using the built-in eval function to recreate the object.

The way this works is easiest to demonstrate by example.

The list object, mylist is converted into a string representation using the repr function, and this string representation is than converted back into a Python list object using the eval function (which evaluates the string representation).

While we will learn much better ways to achieve the goal of storing Python objects into data files later, repr and eval provide us with an easy to understand tool for writing and then reading back Python data to files that we can use now.

6.11. Modules

A module is a file containing Python definitions and statements intended for use in other Python programs. There are many Python modules that come with Python as part of the standard library. We have seen two of these already, the doctest module and the string module.

6.12. Creating modules

All we need to create a module is a text file with a .py extension on the filename:

We can now use our module in both scripts and the Python shell. To do so, we must first import the module. There are two ways to do this:

and:

In the first example, remove_at is called just like the functions we have seen previously. In the second example the name of the module and a dot (.) are written before the function name.

Notice that in either case we do not include the .py file extension when importing. Python expects the file names of Python modules to end in .py, so the file extention is not included in the import statement.

The use of modules makes it possible to break up very large programs into manageable sized parts, and to keep related parts together.

6.13. Namespaces

A namespace is a syntactic container which permits the same name to be used in different modules or functions (and as we will see soon, in classes and methods).

Each module determines its own namespace, so we can use the same name in multiple modules without causing an identification problem.

We can now import both modules and access question and answer in each:

If we had used from module1 import * and from module2 import * instead, we would have a naming collision and would not be able to access question and answer from module1.

Functions also have their own namespace:

Running this program produces the following output:

The three n’s here do not collide since they are each in a different namespace.

Namespaces permit several programmers to work on the same project without having naming collisions.

6.14. Attributes and the dot operator

Variables defined inside a module are called attributes of the module. They are accessed by using the dot operator ( .). The question attribute of module1 and module2 are accessed using module1.question and module2.question.

Modules contain functions as well as attributes, and the dot operator is used to access them in the same way. seqtools.remove_at refers to the remove_at function in the seqtools module.

In Chapter 7 we introduced the find function from the string module. The string module contains many other useful functions:

You should use pydoc to browse the other functions and attributes in the string module.

6.15. Glossary

dictionary

A collection of key-value pairs that maps from keys to values. The keys can be any immutable type, and the values can be any type.file

A named entity, usually stored on a hard drive, floppy disk, or CD-ROM, that contains a stream of characters.file system

A method for naming, accessing, and organizing files and the data they contain.fully qualified name

A name that is prefixed by some namespace identifier and the dot operator, or by an instance object, e.g. math.sqrt or f.open('myfile.txt', 'r').handle

An object in our program that is connected to an underlying resource (e.g. a file). The file handle lets our program manipulate / read/ write / close the actual file that is on our disk.import

A statement which permits functions and variables defined in a Python script to be brought into the environment of another script or a running Python shell.For example, assume the following is in a script named tryme.py:

Now begin a python shell from within the same directory where tryme.py is located:

Three names are defined in tryme.py: print_thrice, n, and s. If we try to access any of these in the shell without first importing, we get an error:

If we import everything from tryme.py, however, we can use everything defined in it:

Note that you do not include the .py from the script name in the import statement.key

A data item that is mapped to a value in a dictionary. Keys are used to look up values in a dictionary.key-value pair

One of the pairs of items in a dictionary. Values are looked up in a dictionary by key.mapping type

A distinct method of operation within a computer program. Files in Python can be openned in one of three modes: read ('r'), write ('w'), and append ('a').non-volatile memory

Memory that can maintain its state without power. Hard drives, flash drives, and rewritable compact disks (CD-RW) are each examples of non-volatile memory.path

A sequence of directory names that specifies the exact location of a file.set

A collection of unique, unordered elements.text file

A file that contains printable characters organized into lines separated by newline characters.volatile memory

Memory which requires an electrical current to maintain state. The main memory or RAM of a computer is volatile. Information stored in RAM is lost when the computer is turned off.

Modules

What is a Module

A module is a file containing a set of codes or a set of functions which can be included to an application. A module could be a file containing a single variable, a function or a big code base.

Creating a Module

To create a module we write our codes in a python script and we save it as a .py file. Create a file named mymodule.py inside your project folder. Let us write some code in this file.

Create main.py file in your project directory and import the mymodule.py file.

Importing a Module

To import the file we use the import keyword and the name of the file only.

Import Functions from a Module

We can have many functions in a file and we can import all the functions differently.

Import Functions from a Module and Renaming

During importing we can rename the name of the module.

Import Built-in Modules

Like other programming languages we can also import modules by importing the file/function using the key word import. Let's import the common module we will use most of the time. Some of the common built-in modules: math, datetime, os,sys, random, statistics, collections, json,re

OS Module

Using python os module it is possible to automatically perform many operating system tasks. The OS module in Python provides functions for creating, changing current working directory, and removing a directory (folder), fetching its contents, changing and identifying the current directory.

Sys Module

The sys module provides functions and variables used to manipulate different parts of the Python runtime environment. Function sys.argv returns a list of command line arguments passed to a Python script. The item at index 0 in this list is always the name of the script, at index 1 is the argument passed from the command line.

Example of a script.py file:

Now to check how this script works I wrote in command line:

The result:

Some useful sys commands:

Statistics Module

The statistics module provides functions for mathematical statistics of numeric data. The popular statistical functions which are defined in this module: mean, median, mode, stdev etc.

Math Module

Module containing many mathematical operations and constants.

Now, we have imported the math module which contains lots of function which can help us to perform mathematical calculations. To check what functions the module has got, we can use help(math), or dir(math). This will display the available functions in the module. If we want to import only a specific function from the module we import it as follows:

It is also possible to import multiple functions at once

But if we want to import all the function in math module we can use * .

When we import we can also rename the name of the function.

String Module

A string module is a useful module for many purposes. The example below shows some use of the string module.

Random Module

By now you are familiar with importing modules. Let us do one more import to get very familiar with it. Let us import random module which gives us a random number between 0 and 0.9999.... The random module has lots of functions but in this section we will only use random and randint.

🌕 You are going far. Keep going! You have just completed day 12 challenges and you are 12 steps a head in to your way to greatness. Now do some exercises for your brain and muscles.

💻 Exercises: Day 12

Exercises: Level 1

Writ a function which generates a six digit/character random_user_id.
Modify the previous task. Declare a function named user_id_gen_by_user. It doesn’t take any parameters but it takes two inputs using input(). One of the inputs is the number of characters and the second input is the number of IDs which are supposed to be generated.

Write a function named rgb_color_gen. It will generate rgb colors (3 values ranging from 0 to 255 each).

Exercises: Level 2

Write a function list_of_hexa_colors which returns any number of hexadecimal colors in an array (six hexadecimal numbers written after #. Hexadecimal numeral system is made out of 16 symbols, 0-9 and first 6 letters of the alphabet, a-f. Check the task 6 for output examples).
Write a function list_of_rgb_colors which returns any number of RGB colors in an array.
Write a function generate_colors which can generate any number of hexa or rgb colors.

Built In Functions

Built-in Functions:

Built-in Functions — Python 3.9.7 documentationdocs.python.org

‌

The Python interpreter has a number of functions and types built into it that are always available. They are listed here in alphabetical order.

Zip Function

Output:

The zipped result is : {('Shambhavi', 3, 60), ('Astha', 2, 70),
('Manjeet', 4, 40), ('Nikhil', 1, 50)}

How to unzip? Unzipping means converting the zipped values back to the individual self as they were. This is done with the help of “*” operator.

# Python code to demonstrate the working of 
# unzip
  
# initializing lists
  
name = [ "Manjeet", "Nikhil", "Shambhavi", "Astha" ]
roll_no = [ 4, 1, 3, 2 ]
marks = [ 40, 50, 60, 70 ]
  
# using zip() to map values
mapped = zip(name, roll_no, marks)
  
# converting values to print as list
mapped = list(mapped)
  
# printing resultant values 
print ("The zipped result is : ",end="")
print (mapped)
  
print("\n")
  
# unzipping values
namz, roll_noz, marksz = zip(*mapped)
  
print ("The unzipped result: \n",end="")
  
# printing initial lists
print ("The name list is : ",end="")
print (namz)
  
print ("The roll_no list is : ",end="")
print (roll_noz)
  
print ("The marks list is : ",end="")
print (marksz)

Functions

5. Functions

Human beings are quite limited in their ability hold distinct pieces of information in their working memories. Research suggests that for most people the number of unrelated chunks is about seven. Computers, by contrast, have no difficulty managing thousands of separate pieces of information without ever forgetting them or getting them confused.

Note

See The Magical Number Seven, Plus or Minus Two for more about this facinating topic.

To make it possible for human beings (programmers) to write complex programs that can span thousands of lines of code, programming languages have features that allow programmers to use the power of abstracton to give names to a sequence of instructions and then to use the new names without having to consider the details of the instructions to which they refer.

This chapter discusses functions, one of Python’s language features that support this kind of abstraction.

5.1. Function definition and use

In the context of programming, a function is a named sequence of statements that performs a desired operation. This operation is specified in a function definition. In Python, the syntax for a function definition is:

def NAME( LIST OF PARAMETERS ):
    STATEMENTS

You can make up any names you want for the functions you create, except that you can’t use a name that is a Python keyword. The list of parameters specifies what information, if any, you have to provide in order to use the new function.

There can be any number of statements inside the function, but they have to be indented from the def.

Function definitions are compound statements, similar to the branching and looping statements we saw in the Conditionals and loops chapter, which means they have the following parts:

A header, which begins with a keyword and ends with a colon.
A body consisting of one or more Python statements, each indented the same amount (4 spaces is the Python standard ) from the header.

In a function definition, the keyword in the header is def, which is followed by the name of the function and a list of parameters enclosed in parentheses. The parameter list may be empty, or it may contain any number of parameters. In either case, the parentheses are required.

5.2. Building on what you learned in high school Algebra

The idea behind this diagram is that a function is like a machine that takes an input, x, and transforms it into an output, f(x). The light yellow box f is an abstraction of the process used to do the transformation from x to f(x).

Functions in Python can be thought of much the same way, and the similarity with functions from Algebra may help you understand them.

The following quadratic function is an example:

f(x) = 3x 2 - 2x + 5

Here is the same function in Python:

def f(x):
    return 3 * x ** 2 - 2 * x + 5

Defining a new function does not make the function run. To do that we need a function call. Function calls contain the name of the function being executed followed by a list of values, called arguments, which are assigned to the parameters in the function definition.

Here is our function f being called with several different arguments:

>>> f(3)
26
>>> f(0)
5
>>> f(1)
6
>>> f(-1)
10
>>> f(5)
70

The function definition must first be entered into the Python shell before it can be called:

>>> def f(x):
...     return 3 * x ** 2 - 2 * x + 5
...
>>>

Function calls involve an implicit assignment of the argument to the parameter

The relationship between the parameter and the argument in the definition and calling of a function is that of an implicit assignment. It is as if we had executed the assignment statements x = 3, x = 0, x = 1, x = -1, and x = 5 respectively before making the function calls to f in the preceding example.

5.3. The `return` statement

The return statement causes a function to immediately stop executing statements in the function body and to send back (or return) the value after the keyword return to the calling statement.

>>> result = f(3)
>>> result
26
>>> result = f(3) + f(-1)
>>> result
36

A return statement with no value after it still returns a value, of a type we haven’t seen before:

>>> def mystery():
...    return
...
>>> what_is_it = mystery()
>>> what_is_it
>>> type(what_is_it)
<class 'NoneType'>
>>> print(what_is_it)
None

None is the sole value of Python’s NoneType. We will use it frequently later on to represent an unknown or unassigned value. For now you need to be aware that it is the value that is returned by a return statement without an argument.

All Python function calls return a value. If a function call finishes executing the statements in its body without hitting a return statement, a None value is returned from the function.

>>> def do_nothing_useful(n, m):
...     x = n + m
...     y = n - m
...
>>> do_nothing_useful(5, 3)
>>> result = do_nothing_useful(5, 3)
>>> result
>>>
>>> print(result)
None

Since do_nothing_useful does not have a return statement with a value, it returns a None value, which is assigned to result. None values don’t display in the Python shell unless they are explicited printed.

Any statements in the body of a function after a return statement is encountered will never be executed and are referred to as dead code.

>>> def try_to_print_dead_code():
...    print("This will print...")
...    print("...and so will this.")
...    return
...    print("But not this...")
...    print("because it's dead code!")
...
>>> try_to_print_dead_code()
This will print...
...and so will this.
>>>

5.4. Flow of execution

In order to ensure that a function is defined before its first use, you have to know the order in which statements are executed, which is called the flow of execution.

Execution always begins at the first statement of the program. Statements are executed one at a time, in order from top to bottom.

Function definitions do not alter the flow of execution of the program, but remember that statements inside the function are not executed until the function is called.

Function calls are like a detour in the flow of execution. Instead of going to the next statement, the flow jumps to the first line of the called function, executes all the statements there, and then comes back to pick up where it left off.

That sounds simple enough, until you remember that one function can call another. While in the middle of one function, the program might have to execute the statements in another function. But while executing that new function, the program might have to execute yet another function!

Fortunately, Python is adept at keeping track of where it is, so each time a function completes, the program picks up where it left off in the function that called it. When it gets to the end of the program, it terminates.

What’s the moral of this sordid tale? When you read a program, don’t just read from top to bottom. Instead, follow the flow of execution. Look at this program:

def f1():
    print("Moe")

def f2():
    f4()
    print("Meeny")

def f3():
    f2()
    print("Miny")
    f1()

def f4():
    print("Eeny")

f3()

The output of this program is:

Eeny
Meeny
Miny
Moe

Follow the flow of execution and see if you can understand why it does that.

5.5. Encapsulation and generalization

Encapsulation is the process of wrapping a piece of code in a function, allowing you to take advantage of all the things functions are good for.

Generalization means taking something specific, such as counting the number of digits in a given positive integer, and making it more general, such as counting the number of digits of any integer.

To see how this process works, let’s start with a program that counts the number of digits in the number 4203:

number = 4203
count = 0

while number != 0:
    count += 1
    number //= 10

print(count)

Apply what you learned in Tracing a program to this until you feel confident you understand how it works. This program demonstrates an important pattern of computation called a counter. The variable count is initialized to 0 and then incremented each time the loop body is executed. When the loop exits, count contains the result — the total number of times the loop body was executed, which is the same as the number of digits.

The first step in encapsulating this logic is to wrap it in a function:

def num_digits():
    number = 4203
    count = 0

    while number != 0:
        count += 1
        number //= 10

    return count

print(num_digits())

Running this program will give us the same result as before, but this time we are calling a function. It may seem like we have gained nothing from doing this, since our program is longer than before and does the same thing, but the next step reveals something powerful:

def num_digits(number):
    count = 0

    while number != 0:
        count += 1
        number //= 10

    return count

print(num_digits(4203))

By parameterizing the value, we can now use our logic to count the digits of any positive integer. A call to print(num_digits(710)) will print 3. A call to print(num_digits(1345109)) will print 7, and so forth.

This function also contains bugs. If we call num_digits(0), it will return a 0, when it should return a 1. If we call num_digits(-23), the program goes into an infinite loop. You will be asked to fix both of these bugs as an exercise.

5.6. Composition

Just as with mathematical functions, Python functions can be composed, meaning that you use the result of one function as the input to another.

>>> def f(x):
...     return 2 * x
...
>>> def g(x):
...     return x + 5
...
>>> def h(x):
...     return x ** 2 - 3
>>> f(3)
6
>>> g(3)
8
>>> h(4)
13
>>> f(g(3))
16
>>> g(f(3))
11
>>> h(f(g(0)))
97
>>>

We can also use a variable as an argument:

>>> # Assume function definitions for f and g as in previous example
>>> val = 10
>>> f(val)
20
>>> f(g(val))
30
>>>

Notice something very important here. The name of the variable we pass as an argument (val) has nothing to do with the name of the parameter (x). Again, it is as if x = val is executed when f(val) is called. It doesn’t matter what the value was named in the caller, inside f and g its name is x.

5.7. Functions are data too

The functions you define in Python are a type of data.

>>> def f():
...     print("Hello from function f!")
...
>>> type(f)
<type 'function'>
>>> f()
Hello, from function f!
>>>

Function values can be elements of a list. Assume f, g, and h have been defined as in the Composition section above.

>>> do_stuff = [f, g, h]
>>> for func in do_stuff:
...     func(10)
...
20
15
97

As usual, you should trace the execution of this example until you feel confident you understand how it works.

5.8. List parameters

Passing a list as an argument actually passes a reference to the list, not a copy of the list. Since lists are mutable changes made to the parameter change the argument as well. For example, the function below takes a list as an argument and multiplies each element in the list by 2:

def double_stuff_v1(a_list):
    index = 0
    for value in a_list:
        a_list[index] = 2 * value
        index += 1

To test this function, we will put it in a file named pure_v_modify.py, and import it into our Python shell, were we can experiment with it:

>>> from pure_v_modify import double_stuff_v1
>>> things = [2, 5, 'Spam', 9.5]
>>> double_stuff_v1(things)
>>> things
[4, 10, 'SpamSpam', 19.0]

Note

The file containing the imported code must have a .py file extention, which is not written in the import statement.

Since the list object is shared by two frames, we drew it between them.

If a function modifies a list parameter, the caller sees the change.

5.9. Pure functions and modifiers

Functions which take lists as arguments and change them during execution are called modifiers and the changes they make are called side effects.

A pure function does not produce side effects. It communicates with the calling program only through parameters, which it does not modify, and a return value. Here is double_stuff_v2 written as a pure function:

def double_stuff_v2(a_list):
    new_list = []
    for value in a_list:
        new_list += [2 * value]
    return new_list

This version of double_stuff does not change its arguments:

>>> from pure_v_modify import double_stuff_v2
>>> things = [2, 5, 'Spam', 9.5]
>>> double_stuff_v2(things)
[4, 10, 'SpamSpam', 19.0]
>>> things
[2, 5, 'Spam', 9.5]
>>>

To use the pure function version of double_stuff to modify things, you would assign the return value back to things:

>>> things = double_stuff(things)
>>> things
[4, 10, 'SpamSpam', 19.0]
>>>

5.10. Which is better?

Anything that can be done with modifiers can also be done with pure functions. In fact, some programming languages only allow pure functions. There is some evidence that programs that use pure functions are faster to develop and less error-prone than programs that use modifiers. Nevertheless, modifiers are convenient at times, and in some cases, functional programs are less efficient.

In general, we recommend that you write pure functions whenever it is reasonable to do so and resort to modifiers only if there is a compelling advantage. This approach might be called a functional programming style.

5.11. Polymorphism and duck typing

The ability to call the same function with different types of data is called polymorphism. In Python, implementing polymorphism is easy, because Python functions handle types through duck typing. Basically, this means that as long as all the operations on a function parameter are valid, the function will handle the function call without complaint. The following simple example illustrates the concept:

>>> def double(thing):
...    return 2 * thing
...
>>> double(5)
10
>>> double('Spam')
'SpamSpam'
>>> double([1, 2])
[1, 2, 1, 2]
>>> double(3.5)
7.0
>>> double(('a', 'b'))
('a', 'b', 'a', 'b')
>>> double(None)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<stdin>", line 2, in double
TypeError: unsupported operand type(s) for *: 'int' and 'NoneType'
>>>

Since * is defined for integers, strings, lists, floats, and tuples, calling our double function with any of these types as an argument is not a problem. * is not defined for the NoneType, however, so sending the double function a None value results in a run time error.

5.12. Two-dimensional tables

A two-dimensional table is a table where you read the value at the intersection of a row and a column. A multiplication table is a good example. Let’s say you want to print a multiplication table for the values from 1 to 6.

A good way to start is to write a loop that prints the multiples of 2, all on one line:

for i in range(1, 7):
    print(2 * i, end="   ")
print()

Here we’ve used the range function, but made it start its sequence at 1. As the loop executes, the value of i changes from 1 to 6. When all the elements of the range have been assigned to i, the loop terminates. Each time through the loop, it displays the value of 2 * i, followed by three spaces.

Again, the extra end=" " argument in the print function suppresses the newline, and uses three spaces instead. After the loop completes, the call to print at line 3 finishes the current line, and starts a new line.

The output of the program is:

2      4      6      8      10     12

So far, so good. The next step is to encapulate and generalize.

5.13. More encapsulation

This function encapsulates the previous loop and generalizes it to print multiples of n:

def print_multiples(n):
    for i in range(1, 7):
        print(n * i, end="   ")
    print()

To encapsulate, all we had to do was add the first line, which declares the name of the function and the parameter list. To generalize, all we had to do was replace the value 2 with the parameter n.

If we call this function with the argument 2, we get the same output as before. With the argument 3, the output is:

3      6      9      12     15     18

With the argument 4, the output is:

4      8      12     16     20     24

By now you can probably guess how to print a multiplication table — by calling print_multiples repeatedly with different arguments. In fact, we can use another loop:

for i in range(1, 7):
    print_multiples(i)

Notice how similar this loop is to the one inside print_multiples. All we did was replace the print function with a function call.

The output of this program is a multiplication table:

1      2      3      4      5      6
2      4      6      8      10     12
3      6      9      12     15     18
4      8      12     16     20     24
5      10     15     20     25     30
6      12     18     24     30     36

5.14. Still more encapsulation

To demonstrate encapsulation again, let’s take the code from the last section and wrap it up in a function:

def print_mult_table():
    for i in range(1, 7):
        print_multiples(i)

This process is a common development plan. We develop code by writing lines of code outside any function, or typing them in to the interpreter. When we get the code working, we extract it and wrap it up in a function.

This development plan is particularly useful if you don’t know how to divide the program into functions when you start writing. This approach lets you design as you go along.

5.15. Local variables

You might be wondering how we can use the same variable, i, in both print_multiples and print_mult_table. Doesn’t it cause problems when one of the functions changes the value of the variable?

The answer is no, because the i in print_multiples and the i in print_mult_table are not the same variable.

Variables created inside a function definition are local; you can’t access a local variable from outside its home function. That means you are free to have multiple variables with the same name as long as they are not in the same function.

Python examines all the statements in a function — if any of them assign a value to a variable, that is the clue that Python uses to make the variable a local variable.

The value of i in print_mult_table goes from 1 to 6. In the diagram it happens to be 3. The next time through the loop it will be 4. Each time through the loop, print_mult_table calls print_multiples with the current value of i as an argument. That value gets assigned to the parameter n.

Inside print_multiples, the value of i goes from 1 to 6. In the diagram, it happens to be 2. Changing this variable has no effect on the value of i in print_mult_table.

It is common and perfectly legal to have different local variables with the same name. In particular, names like i and j are used frequently as loop variables. If you avoid using them in one function just because you used them somewhere else, you will probably make the program harder to read.

5.16. Recursive data structures

All of the Python data types we have seen can be grouped inside lists and tuples in a variety of ways. Lists and tuples can also be nested, providing myriad possibilities for organizing data. The organization of data for the purpose of making it easier to use is called a data structure.

It’s election time and we are helping to compute the votes as they come in. Votes arriving from individual wards, precincts, municipalities, counties, and states are sometimes reported as a sum total of votes and sometimes as a list of subtotals of votes. After considering how best to store the tallies, we decide to use a nested number list, which we define as follows:

A nested number list is a list whose elements are either:

numbers
nested number lists

Notice that the term, nested number list is used in its own definition. Recursive definitions like this are quite common in mathematics and computer science. They provide a concise and powerful way to describe recursive data structures that are partially composed of smaller and simpler instances of themselves. The definition is not circular, since at some point we will reach a list that does not have any lists as elements.

Now suppose our job is to write a function that will sum all of the values in a nested number list. Python has a built-in function which finds the sum of a sequence of numbers:

>>> sum([1, 2, 8])
11
>>> sum((3, 5, 8.5))
16.5
>>>

For our nested number list, however, sum will not work:

>>> sum([1, 2, [11, 13], 8])
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unsupported operand type(s) for +: 'int' and 'list'
>>>

The problem is that the third element of this list, [11, 13], is itself a list, which can not be added to 1, 2, and 8.

5.17. Recursion

To sum all the numbers in our recursive nested number list we need to traverse the list, visiting each of the elements within its nested structure, adding any numeric elements to our sum, and repeating this process with any elements which are lists.

Modern programming languages generally support recursion, which means that functions can call themselves within their definitions. Thanks to recursion, the Python code needed to sum the values of a nested number list is surprisingly short:

def recursive_sum(nested_num_list):
    the_sum = 0
    for element in nested_num_list:
        if type(element) == list:
            the_sum = the_sum + recursive_sum(element)
        else:
            the_sum = the_sum + element
    return the_sum

The body of recursive_sum consists mainly of a for loop that traverses nested_num_list. If element is a numerical value (the else branch), it is simply added to the_sum. If element is a list, then recursive_sum is called again, with the element as an argument. The statement inside the function definition in which the function calls itself is known as the recursive call.

Recursion is truly one of the most beautiful and elegant tools in computer science.

A slightly more complicated problem is finding the largest value in our nested number list:

def recursive_max(nested_num_list):
    """
      >>> recursive_max([2, 9, [1, 13], 8, 6])
      13
      >>> recursive_max([2, [[100, 7], 90], [1, 13], 8, 6])
      100
      >>> recursive_max([2, [[13, 7], 90], [1, 100], 8, 6])
      100
      >>> recursive_max([[[13, 7], 90], 2, [1, 100], 8, 6])
      100
    """
    largest = nested_num_list[0]
    while type(largest) == type([]):
        largest = largest[0]

    for element in nested_num_list:
        if type(element) == type([]):
            max_of_elem = recursive_max(element)
            if largest < max_of_elem:
                largest = max_of_elem
        else:                           # element is not a list
            if largest < element:
                largest = element

    return largest

Doctests are included to provide examples of recursive_max at work.

The added twist to this problem is finding a numerical value for initializing largest. We can’t just use nested_num_list[0], since that my be either a number or a list. To solve this problem we use a while loop that assigns largest to the first numerical value no matter how deeply it is nested.

The two examples above each have a base case which does not lead to a recursive call: the case where the element is a number and not a list. Without a base case, you have infinite recursion, and your program will not work. Python stops after reaching a maximum recursion depth and returns a runtime error.

Write the following in a file named infinite_recursion.py:

#
# infinite_recursion.py
#
def recursion_depth(number):
    print "Recursion depth number %d." % number
    recursion_depth(number + 1)

recursion_depth(0)

At the unix command prompt in the same directory in which you saved your program, type the following:

python infinite_recursion.py

After watching the messages flash by, you will be presented with the end of a long traceback that ends in with the following:

  ...
  File "infinite_recursion.py", line 3, in recursion_depth
    recursion_depth(number + 1)
RuntimeError: maximum recursion depth exceeded

We would certainly never want something like this to happen to a user of one of our programs, so before finishing the recursion discussion, let’s see how errors like this are handled in Python.

5.18. Exceptions

Whenever a runtime error occurs, it creates an exception. The program stops running at this point and Python prints out the traceback, which ends with the exception that occured.

For example, dividing by zero creates an exception:

>>> print 55/0
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ZeroDivisionError: integer division or modulo by zero
>>>

So does accessing a nonexistent list item:

>>> a = []
>>> print a[5]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
IndexError: list index out of range
>>>

Or trying to make an item assignment on a tuple:

>>> tup = ('a', 'b', 'd', 'd')
>>> tup[2] = 'c'
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: 'tuple' object does not support item assignment
>>>

In each case, the error message on the last line has two parts: the type of error before the colon, and specifics about the error after the colon.

Sometimes we want to execute an operation that might cause an exception, but we don’t want the program to stop. We can handle the exception using the try and except statements.

For example, we might prompt the user for the name of a file and then try to open it. If the file doesn’t exist, we don’t want the program to crash; we want to handle the exception:

filename = raw_input('Enter a file name: ')
try:
    f = open (filename, "r")
except:
    print 'There is no file named', filename

The try statement executes the statements in the first block. If no exceptions occur, it ignores the except statement. If any exception occurs, it executes the statements in the except branch and then continues.

We can encapsulate this capability in a function: exists takes a filename and returns true if the file exists, false if it doesn’t:

def exists(filename):
    try:
        f = open(filename)
        f.close()
        return True
    except:
        return False

You can use multiple except blocks to handle different kinds of exceptions (see the Errors and Exceptions lesson from Python creator Guido van Rossum’s Python Tutorial for a more complete discussion of exceptions).

If your program detects an error condition, you can make it raise an exception. Here is an example that gets input from the user and checks that the number is non-negative.

#
# learn_exceptions.py
#
def get_age():
    age = input('Please enter your age: ')
    if age < 0:
        raise ValueError, '%s is not a valid age' % age
    return age

The raise statement takes two arguments: the exception type, and specific information about the error. ValueError is the built-in exception which most closely matches the kind of error we want to raise. The complete listing of built-in exceptions is found in the Built-in Exceptions section of the Python Library Reference, again by Python’s creator, Guido van Rossum.

If the function that called get_age handles the error, then the program can continue; otherwise, Python prints the traceback and exits:

>>> get_age()
Please enter your age: 42
42
>>> get_age()
Please enter your age: -2
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "learn_exceptions.py", line 4, in get_age
    raise ValueError, '%s is not a valid age' % age
ValueError: -2 is not a valid age
>>>

The error message includes the exception type and the additional information you provided.

Using exception handling, we can now modify infinite_recursion.py so that it stops when it reaches the maximum recursion depth allowed:

#
# infinite_recursion.py
#
def recursion_depth(number):
    print "Recursion depth number %d." % number
    try:
        recursion_depth(number + 1)
    except:
        print "Maximum recursion depth exceeded."

recursion_depth(0)

Run this version and observe the results.

5.19. Tail recursion

When the only thing returned from a function is a recursive call, it is refered to as tail recursion.

Here is a version of the countdown function from chapter 6 written using tail recursion:

def countdown(n):
    if n == 0:
        print "Blastoff!"
    else:
        print n
        countdown(n-1)

Any computation that can be made using iteration can also be made using recursion. Here is a version of find_max written using tail recursion:

def find_max(seq, max_so_far):
    if not seq:
        return max_so_far
    if max_so_far < seq[0]:
        return find_max(seq[1:], seq[0])
    else:
        return find_max(seq[1:], max_so_far)

Tail recursion is considered a bad practice in Python, since the Python compiler does not handle optimization for tail recursive calls. The recursive solution in cases like this use more system resources than the equivalent iterative solution.

5.20. Recursive mathematical functions

Several well known mathematical functions are defined recursively. Factorial, for example, is given the special operator, !, and is defined by:

0! = 1
n! = n(n-1)

We can easily code this into Python:

def factorial(n):
    if n == 0:
        return 1
    else:
        return n * factorial(n-1)

Another well know recursive relation in mathematics is the fibonacci sequence, which is defined by:

fibonacci(0) = 1
fibonacci(1) = 1
fibonacci(n) = fibonacci(n-1) + fibonacci(n-2)

This can also be written easily in Python:

def fibonacci (n):
    if n == 0 or n == 1:
        return 1
    else:
        return fibonacci(n-1) + fibonacci(n-2)

Calling factorial(1000) will exceed the maximum recursion depth. And try running fibonacci(35) and see how long it takes to complete (be patient, it will complete).

You will be asked to write an iterative version of factorial as an exercise, and we will see a better way to handle fibonacci in the next chapter.

5.21. Glossary

argument

A value provided to a function when the function is called. This value is assigned to the corresponding parameter in the function.flow of execution

The order in which statements are executed during a program run.frame

A box in a stack diagram that represents a function call. It contains the local variables and parameters of the function.function

A named sequence of statements that performs some useful operation. Functions may or may not take parameters and may or may not produce a result.function call

A statement that executes a function. It consists of the name of the function followed by a list of arguments enclosed in parentheses.function composition

Using the output from one function call as the input to another.function definition

A statement that creates a new function, specifying its name, parameters, and the statements it executes.header

The first part of a compound statement. Headers begin with a keyword and end with a colon (:)local variable

A variable defined inside a function. A local variable can only be used inside its function.None

The sole value of <class ‘NoneType’>. None is often used to represent the absence of a value. It is also returned by a return statement with no argument or a function that reaches the end of its body without hitting a return statement containing a value.parameter

A name used inside a function to refer to the value passed as an argument.stack diagram

A graphical representation of a stack of functions, their variables, and the values to which they refer.traceback

A list of the functions that are executing, printed when a runtime error occurs. A traceback is also commonly refered to as a stack trace, since it lists the functions in the order in which they are stored in the runtime stack.

Inheritance

8. Inheritance

8.1. Composition

By now, you have seen several examples of composition. One of the first examples was using a method invocation as part of an expression. Another example is the nested structure of statements; you can put an if statement within a while loop, within another if statement, and so on.

Having seen this pattern, and having learned about lists and objects, you should not be surprised to learn that you can create lists of objects. You can also create objects that contain lists (as attributes); you can create lists that contain lists; you can create objects that contain objects; and so on.

In this chapter we will look at some examples of these combinations, using Card objects as an example.

8.2. `Card` objects

If you are not familiar with common playing cards, now would be a good time to get a deck, or else this chapter might not make much sense. There are fifty-two cards in a deck, each of which belongs to one of four suits and one of thirteen ranks. The suits are Spades, Hearts, Diamonds, and Clubs (in descending order in bridge). The ranks are Ace, 2, 3, 4, 5, 6, 7, 8, 9, 10, Jack, Queen, and King. Depending on the game that you are playing, the rank of Ace may be higher than King or lower than 2.

If we want to define a new object to represent a playing card, it is obvious what the attributes should be: rank and suit. It is not as obvious what type the attributes should be. One possibility is to use strings containing words like "Spade" for suits and "Queen" for ranks. One problem with this implementation is that it would not be easy to compare cards to see which had a higher rank or suit.

An alternative is to use integers to encode the ranks and suits. By encode, we do not mean what some people think, which is to encrypt or translate into a secret code. What a computer scientist means by encode is to define a mapping between a sequence of numbers and the items I want to represent. For example:

An obvious feature of this mapping is that the suits map to integers in order, so we can compare suits by comparing integers. The mapping for ranks is fairly obvious; each of the numerical ranks maps to the corresponding integer, and for face cards:

The reason we are using mathematical notation for these mappings is that they are not part of the Python program. They are part of the program design, but they never appear explicitly in the code. The class definition for the Card type looks like this:

As usual, we provide an initialization method that takes an optional parameter for each attribute.

To create an object that represents the 3 of Clubs, use this command:

The first argument, 0, represents the suit Clubs.

8.3. Class attributes and the `str` method

In order to print Card objects in a way that people can easily read, we want to map the integer codes onto words. A natural way to do that is with lists of strings. We assign these lists to class attributes at the top of the class definition:

Class attributes like Card.SUITS and Card.RANKS are defined outside of any method, and can be accessed from any of the methods in the class.

Inside __str__, we can use SUITS and RANKS to map the numerical values of suit and rank to strings. For example, the expression Card.SUITS[self.suit] means use the attribute suit from the object self as an index into the class attribute named SUITS, and select the appropriate string.

The reason for the "narf" in the first element in ranks is to act as a place keeper for the zero-eth element of the list, which will never be used. The only valid ranks are 1 to 13. This wasted item is not entirely necessary. We could have started at 0, as usual, but it is less confusing to encode 2 as 2, 3 as 3, and so on.

We have a doctest in the __str__ method to confirm that Card(2, 11) will display as “Queen of Hearts”.

8.4. Comparing cards

For primitive types, there are conditional operators ( <, >, ==, etc.) that compare values and determine when one is greater than, less than, or equal to another. For user-defined types, we can override the behavior of the built-in operators by providing a method named __cmp__. By convention, __cmp__ takes two parameters, self and other, and returns 1 if the first object is greater, -1 if the second object is greater, and 0 if they are equal to each other.

Some types are completely ordered, which means that you can compare any two elements and tell which is bigger. For example, the integers and the floating-point numbers are completely ordered. Some sets are unordered, which means that there is no meaningful way to say that one element is bigger than another. For example, the fruits are unordered, which is why you cannot compare apples and oranges.

The set of playing cards is partially ordered, which means that sometimes you can compare cards and sometimes not. For example, you know that the 3 of Clubs is higher than the 2 of Clubs, and the 3 of Diamonds is higher than the 3 of Clubs. But which is better, the 3 of Clubs or the 2 of Diamonds? One has a higher rank, but the other has a higher suit.

In order to make cards comparable, you have to decide which is more important, rank or suit. To be honest, the choice is arbitrary. For the sake of choosing, we will say that suit is more important, because a new deck of cards comes sorted with all the Clubs together, followed by all the Diamonds, and so on.

With that decided, we can write __cmp__:

In this ordering, Aces appear lower than Deuces (2s).

8.5. Decks

Now that we have objects to represent Cards, the next logical step is to define a class to represent a Deck. Of course, a deck is made up of cards, so each Deck object will contain a list of cards as an attribute.

The following is a class definition for the Deck class. The initialization method creates the attribute cards and generates the standard set of fifty-two cards:

The easiest way to populate the deck is with a nested loop. The outer loop enumerates the suits from 0 to 3. The inner loop enumerates the ranks from 1 to 13. Since the outer loop iterates four times, and the inner loop iterates thirteen times, the total number of times the body is executed is fifty-two (thirteen times four). Each iteration creates a new instance of Card with the current suit and rank, and appends that card to the cards list.

The append method works on lists but not, of course, tuples.

8.6. Printing the deck

As usual, when we define a new type of object we want a method that prints the contents of an object. To print a Deck, we traverse the list and print each Card:

Here, and from now on, the ellipsis ( ...) indicates that we have omitted the other methods in the class.

As an alternative to print_deck, we could write a __str__ method for the Deck class. The advantage of __str__ is that it is more flexible. Rather than just printing the contents of the object, it generates a string representation that other parts of the program can manipulate before printing, or store for later use.

Here is a version of __str__ that returns a string representation of a Deck. To add a bit of pizzazz, it arranges the cards in a cascade where each card is indented one space more than the previous card:

This example demonstrates several features. First, instead of traversing self.cards and assigning each card to a variable, we are using i as a loop variable and an index into the list of cards.

Second, we are using the string multiplication operator to indent each card by one more space than the last. The expression " " * i yields a number of spaces equal to the current value of i.

Third, instead of using the print function to print the cards, we use the str function. Passing an object as an argument to str is equivalent to invoking the __str__ method on the object.

Finally, we are using the variable s as an accumulator. Initially, s is the empty string. Each time through the loop, a new string is generated and concatenated with the old value of s to get the new value. When the loop ends, s contains the complete string representation of the Deck, which looks like this:

And so on. Even though the result appears on 52 lines, it is one long string that contains newlines.

8.7. Shuffling the deck

If a deck is perfectly shuffled, then any card is equally likely to appear anywhere in the deck, and any location in the deck is equally likely to contain any card.

To shuffle the deck, we will use the randrange function from the random module. With two integer arguments, a and b, randrange chooses a random integer in the range a <= x < b. Since the upper bound is strictly less than b, we can use the length of a list as the second parameter, and we are guaranteed to get a legal index. For example, this expression chooses the index of a random card in a deck:

An easy way to shuffle the deck is by traversing the cards and swapping each card with a randomly chosen one. It is possible that the card will be swapped with itself, but that is fine. In fact, if we precluded that possibility, the order of the cards would be less than entirely random:

Rather than assume that there are fifty-two cards in the deck, we get the actual length of the list and store it in num_cards.

For each card in the deck, we choose a random card from among the cards that haven’t been shuffled yet. Then we swap the current card ( i) with the selected card ( j). To swap the cards we use a tuple assignment:

8.8. Removing and dealing cards

Another method that would be useful for the Deck class is remove, which takes a card as a parameter, removes it, and returns True if the card was in the deck and False otherwise:

The in operator returns True if the first operand is in the second, which must be a list or a tuple. If the first operand is an object, Python uses the object’s __cmp__ method to determine equality with items in the list. Since the __cmp__ in the Card class checks for deep equality, the remove method checks for deep equality.

To deal cards, we want to remove and return the top card. The list method pop provides a convenient way to do that:

Actually, pop removes the last card in the list, so we are in effect dealing from the bottom of the deck.

One more operation that we are likely to want is the boolean function is_empty, which returns true if the deck contains no cards:

8.9. Inheritance

The language feature most often associated with object-oriented programming is inheritance. Inheritance is the ability to define a new class that is a modified version of an existing class.

The primary advantage of this feature is that you can add new methods to a class without modifying the existing class. It is called inheritance because the new class inherits all of the methods of the existing class. Extending this metaphor, the existing class is sometimes called the parent class. The new class may be called the child class or sometimes subclass.

Inheritance is a powerful feature. Some programs that would be complicated without inheritance can be written concisely and simply with it. Also, inheritance can facilitate code reuse, since you can customize the behavior of parent classes without having to modify them. In some cases, the inheritance structure reflects the natural structure of the problem, which makes the program easier to understand.

On the other hand, inheritance can make programs difficult to read. When a method is invoked, it is sometimes not clear where to find its definition. The relevant code may be scattered among several modules. Also, many of the things that can be done using inheritance can be done as elegantly (or more so) without it. If the natural structure of the problem does not lend itself to inheritance, this style of programming can do more harm than good.

In this chapter we will demonstrate the use of inheritance as part of a program that plays the card game Old Maid. One of our goals is to write code that could be reused to implement other card games.

8.10. A hand of cards

For almost any card game, we need to represent a hand of cards. A hand is similar to a deck, of course. Both are made up of a set of cards, and both require operations like adding and removing cards. Also, we might like the ability to shuffle both decks and hands.

A hand is also different from a deck. Depending on the game being played, we might want to perform some operations on hands that don’t make sense for a deck. For example, in poker we might classify a hand (straight, flush, etc.) or compare it with another hand. In bridge, we might want to compute a score for a hand in order to make a bid.

This situation suggests the use of inheritance. If Hand is a subclass of Deck, it will have all the methods of Deck, and new methods can be added.

In the class definition, the name of the parent class appears in parentheses:

This statement indicates that the new Hand class inherits from the existing Deck class.

The Hand constructor initializes the attributes for the hand, which are name and cards. The string name identifies this hand, probably by the name of the player that holds it. The name is an optional parameter with the empty string as a default value. cards is the list of cards in the hand, initialized to the empty list:

For just about any card game, it is necessary to add and remove cards from the deck. Removing cards is already taken care of, since Hand inherits remove from Deck. But we have to write add:

Again, the ellipsis indicates that we have omitted other methods. The list append method adds the new card to the end of the list of cards.

8.11. Dealing cards

Now that we have a Hand class, we want to deal cards from the Deck into hands. It is not immediately obvious whether this method should go in the Hand class or in the Deck class, but since it operates on a single deck and (possibly) several hands, it is more natural to put it in Deck.

deal should be fairly general, since different games will have different requirements. We may want to deal out the entire deck at once or add one card to each hand.

deal takes two parameters, a list (or tuple) of hands and the total number of cards to deal. If there are not enough cards in the deck, the method deals out all of the cards and stops:

The second parameter, num_cards, is optional; the default is a large number, which effectively means that all of the cards in the deck will get dealt.

The loop variable i goes from 0 to nCards-1. Each time through the loop, a card is removed from the deck using the list method pop, which removes and returns the last item in the list.

The modulus operator ( %) allows us to deal cards in a round robin (one card at a time to each hand). When i is equal to the number of hands in the list, the expression i % nHands wraps around to the beginning of the list (index 0).

8.12. Printing a Hand

To print the contents of a hand, we can take advantage of the printDeck and __str__ methods inherited from Deck. For example:

It’s not a great hand, but it has the makings of a straight flush.

Although it is convenient to inherit the existing methods, there is additional information in a Hand object we might want to include when we print one. To do that, we can provide a __str__ method in the Hand class that overrides the one in the Deck class:

Initially, s is a string that identifies the hand. If the hand is empty, the program appends the words is empty and returns s.

Otherwise, the program appends the word contains and the string representation of the Deck, computed by invoking the __str__ method in the Deck class on self.

It may seem odd to send self, which refers to the current Hand, to a Deck method, until you remember that a Hand is a kind of Deck. Hand objects can do everything Deck objects can, so it is legal to send a Hand to a Deck method.

In general, it is always legal to use an instance of a subclass in place of an instance of a parent class.

8.13. The `CardGame` class

The CardGame class takes care of some basic chores common to all games, such as creating the deck and shuffling it:

This is the first case we have seen where the initialization method performs a significant computation, beyond initializing attributes.

To implement specific games, we can inherit from CardGame and add features for the new game. As an example, we’ll write a simulation of Old Maid.

The object of Old Maid is to get rid of cards in your hand. You do this by matching cards by rank and color. For example, the 4 of Clubs matches the 4 of Spades since both suits are black. The Jack of Hearts matches the Jack of Diamonds since both are red.

To begin the game, the Queen of Clubs is removed from the deck so that the Queen of Spades has no match. The fifty-one remaining cards are dealt to the players in a round robin. After the deal, all players match and discard as many cards as possible.

When no more matches can be made, play begins. In turn, each player picks a card (without looking) from the closest neighbor to the left who still has cards. If the chosen card matches a card in the player’s hand, the pair is removed. Otherwise, the card is added to the player’s hand. Eventually all possible matches are made, leaving only the Queen of Spades in the loser’s hand.

In our computer simulation of the game, the computer plays all hands. Unfortunately, some nuances of the real game are lost. In a real game, the player with the Old Maid goes to some effort to get their neighbor to pick that card, by displaying it a little more prominently, or perhaps failing to display it more prominently, or even failing to fail to display that card more prominently. The computer simply picks a neighbor’s card at random.

8.14. `OldMaidHand` class

A hand for playing Old Maid requires some abilities beyond the general abilities of a Hand. We will define a new class, OldMaidHand, that inherits from Hand and provides an additional method called remove_matches:

We start by making a copy of the list of cards, so that we can traverse the copy while removing cards from the original. Since self.cards is modified in the loop, we don’t want to use it to control the traversal. Python can get quite confused if it is traversing a list that is changing!

For each card in the hand, we figure out what the matching card is and go looking for it. The match card has the same rank and the other suit of the same color. The expression 3 - card.suit turns a Club (suit 0) into a Spade (suit 3) and a Diamond (suit 1) into a Heart (suit 2). You should satisfy yourself that the opposite operations also work. If the match card is also in the hand, both cards are removed.

The following example demonstrates how to use remove_matches:

Notice that there is no __init__ method for the OldMaidHand class. We inherit it from Hand.

8.15. `OldMaidGame` class

Now we can turn our attention to the game itself. OldMaidGame is a subclass of CardGame with a new method called play that takes a list of players as a parameter.

Since __init__ is inherited from CardGame, a new OldMaidGame object contains a new shuffled deck:

The writing of printHands() is left as an exercise.

Some of the steps of the game have been separated into methods. remove_all_matches traverses the list of hands and invokes remove_matches on each:

count is an accumulator that adds up the number of matches in each hand and returns the total.

When the total number of matches reaches twenty-five, fifty cards have been removed from the hands, which means that only one card is left and the game is over.

The variable turn keeps track of which player’s turn it is. It starts at 0 and increases by one each time; when it reaches numHands, the modulus operator wraps it back around to 0.

The method playOneTurn takes a parameter that indicates whose turn it is. The return value is the number of matches made during this turn:

If a player’s hand is empty, that player is out of the game, so he or she does nothing and returns 0.

Otherwise, a turn consists of finding the first player on the left that has cards, taking one card from the neighbor, and checking for matches. Before returning, the cards in the hand are shuffled so that the next player’s choice is random.

The method find_neighbor starts with the player to the immediate left and continues around the circle until it finds a player that still has cards:

If find_neighbor ever went all the way around the circle without finding cards, it would return None and cause an error elsewhere in the program. Fortunately, we can prove that that will never happen (as long as the end of the game is detected correctly).

We have omitted the print_hands method. You can write that one yourself.

The following output is from a truncated form of the game where only the top fifteen cards (tens and higher) were dealt to three players. With this small deck, play stops after seven matches instead of twenty-five.

So Jeff loses.

8.16. Glossary

encode

To represent one set of values using another set of values by constructing a mapping between them.class attribute

A variable that is defined inside a class definition but outside any method. Class attributes are accessible from any method in the class and are shared by all instances of the class.accumulator

A variable used in a loop to accumulate a series of values, such as by concatenating them onto a string or adding them to a running sum.inheritance

The ability to define a new class that is a modified version of a previously defined class.parent class

The class from which a child class inherits.child class

A new class created by inheriting from an existing class; also called a subclass.

Python Objects & Classes

Creating object and classes #

Python is an object-oriented language. In python everything is object i.e int, str, bool even modules, functions are also objects.

Object oriented programming use objects to create programs, and these objects stores data and behaviours.

Defining class #

Class name in python is preceded with class keyword followed by a colon (:). Classes commonly contains data field to store the data and methods for defining behaviors. Also every class in python contains a special method called initializer (also commonly known as constructors), which get invoked automatically every time new object is created.

Let's see an example.

Here we have created a class called Person which contains one data field called name and method whoami().

What is self? #

All methods in python including some special methods like initializer have first parameter self. This parameter refers to the object which invokes the method. When you create new object the self parameter in the __init__ method is automatically set to reference the object you have just created.

Creating object from class #

Expected Output:

note:

When you call a method you don't need to pass anything to self parameter, python automatically does that for you behind the scenes.

You can also change the name data field.

Expected Output:

Although it is a bad practice to give access to your data fields outside the class. We will discuss how to prevent this next.

Hiding data fields #

To hide data fields you need to define private data fields. In python you can create private data field using two leading underscores. You can also define a private method using two leading underscores.

Let's see an example

Expected Output:

Let's try to access __balance data field outside of class.

Expected Output:

AttributeError: 'BankAccount' object has no attribute '__balance'

As you can see, now the __balance field is not accessible outside the class.

index

Creating object and classes # Python is an object-oriented language. In python everything is object i.e int, str, bool even modules, functions are al…

Python Object and Classes

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Updated on Jan 07, 2020

Creating object and classes

Python is an object-oriented language. In python everything is object i.e int, str, bool even modules, functions are also objects.

Object oriented programming use objects to create programs, and these objects stores data and behaviours.

Defining class

Let's see an example.

Here we have created a class called Person which contains one data field called name and method whoami().

What is self?

Expected Output:

note:

When you call a method you don't need to pass anything to self parameter, python automatically does that for you behind the scenes.

You can also change the name data field.

Expected Output:

Although it is a bad practice to give access to your data fields outside the class. We will discuss how to prevent this next.

Let's see an example

Expected Output:

Let's try to access __balance data field outside of class.

Expected Output:

AttributeError: 'BankAccount' object has no attribute '__balance'

As you can see, now the __balance field is not accessible outside the class.

Dictionaries

A dictionary is a collection of unordered, modifiable(mutable) paired (key: value) data type.

Creating a Dictionary

To create a dictionary we use curly brackets, {} or the dict() built-in function.

Example:

The dictionary above shows that a value could be any data types:string, boolean, list, tuple, set or a dictionary.

Dictionary Length

It checks the number of 'key: value' pairs in the dictionary.

Example:

Accessing Dictionary Items

We can access Dictionary items by referring to its key name.

Example:

Accessing an item by key name raises an error if the key does not exist. To avoid this error first we have to check if a key exist or we can use the get method. The get method returns None, which is a NoneType object data type, if the key does not exist.

Adding Items to a Dictionary

We can add new key and value pairs to a dictionary

Example:

Modifying Items in a Dictionary

We can modify items in a dictionary

Example:

Checking Keys in a Dictionary

We use the in operator to check if a key exist in a dictionary

Removing Key and Value Pairs from a Dictionary

pop(key): removes the item with the specified key name:
popitem(): removes the last item
del: removes an item with specified key name

Example:

Changing Dictionary to a List of Items

The items() method changes dictionary to a list of tuples.

Clearing a Dictionary

If we don't want the items in a dictionary we can clear them using clear() method

Deleting a Dictionary

If we do not use the dictionary we can delete it completely

Copy a Dictionary

We can copy a dictionary using a copy() method. Using copy we can avoid mutation of the original dictionary.

Getting Dictionary Keys as a List

The keys() method gives us all the keys of a a dictionary as a list.

Getting Dictionary Values as a List

The values method gives us all the values of a a dictionary as a list.

Conditionals and loops

Conditional execution

4.1.1. The `if` statement

In order to write useful programs, we almost always need the ability to check conditions and change the behavior of the program accordingly. give us this ability. The simplest form is the if statement, which has the genaral form:

A few important things to note about if statements:

The colon (:) is significant and required. It separates the header of the compound statement from the body.
The line after the colon must be indented. It is standard in Python to use four spaces for indenting.
All lines indented the same amount after the colon will be executed whenever the BOOLEAN_EXPRESSION is true.

Here is an example:

The boolean expression after the if statement is called the condition. If it is true, then all the indented statements get executed. What happens if the condition is false, and food is not equal to 'spam'? In a simple if statement like this, nothing happens, and the program continues on to the next statement.

Run this example code and see what happens. Then change the value of food to something other than 'spam' and run it again, confirming that you don’t get any output.

As with the for statement from the last chapter, the if statement is a compound statement. Compound statements consist of a header line and a body. The header line of the if statement begins with the keyword if followed by a boolean expression and ends with a colon (:).

The indented statements that follow are called a block. The first unindented statement marks the end of the block. Each statement inside the block must have the same indentation.

Indentation and the PEP 8 Python Style Guide

PEP 8 recommends the use of 4 spaces per indentation level. We will follow this (and the other PEP 8 recommendations) in this book.

4.1.2. The `if else` statement

It is frequently the case that you want one thing to happen when a condition it true, and something else to happen when it is false. For that we have the if else statement.

Here, the first print statement will execute if food is equal to 'spam', and the print statement indented under the else clause will get executed when it is not.

The syntax for an if else statement looks like this:

Each statement inside the if block of an if else statement is executed in order if the boolean expression evaluates to True. The entire block of statements is skipped if the boolean expression evaluates to False, and instead all the statements under the else clause are executed.

There is no limit on the number of statements that can appear under the two clauses of an if else statement, but there has to be at least one statement in each block. Occasionally, it is useful to have a section with no statements (usually as a place keeper, or scaffolding, for code you haven’t written yet). In that case, you can use the pass statement, which does nothing except act as a placeholder.

Python terminology

Python documentation sometimes uses the term suite of statements to mean what we have called a block here. They mean the same thing, and since most other languages and computer scientists use the word block, we’ll stick with that.

Notice too that else is not a statement. The if statement has two clauses, one of which is the (optional) else clause. The Python documentation calls both forms, together with the next form we are about to meet, the if statement.

4.2. Chained conditionals

Sometimes there are more than two possibilities and we need more than two branches. One way to express a computation like that is a chained conditional:

elif is an abbreviation of else if. Again, exactly one branch will be executed. There is no limit of the number of elif statements but only a single (and optional) final else statement is allowed and it must be the last branch in the statement:

Each condition is checked in order. If the first is false, the next is checked, and so on. If one of them is true, the corresponding branch executes, and the statement ends. Even if more than one condition is true, only the first true branch executes.

4.3. Nested conditionals

One conditional can also be nested within another. (It is the same theme of composibility, again!) We could have written the previous example as follows:

The outer conditional contains two branches. The second branch contains another if statement, which has two branches of its own. Those two branches could contain conditional statements as well.

Although the indentation of the statements makes the structure apparent, nested conditionals very quickly become difficult to read. In general, it is a good idea to avoid them when you can.

Logical operators often provide a way to simplify nested conditional statements. For example, we can rewrite the following code using a single conditional:

The print function is called only if we make it past both the conditionals, so we can use the and operator:

Note

Python actually allows a short hand form for this, so the following will also work:

4.4. Iteration

Computers are often used to automate repetitive tasks. Repeating identical or similar tasks without making errors is something that computers do well and people do poorly.

Before we look at those, we need to review a few ideas.

4.4.1. Reassignmnent

The output of this program is

because the first time bruce is printed, its value is 5, and the second time, its value is 7.

With reassignment it is especially important to distinguish between an assignment statement and a boolean expression that tests for equality. Because Python uses the equal token (=) for assignment, it is tempting to interpret a statement like a = b as a boolean test. Unlike mathematics, it is not! Remember that the Python token for the equality operator is ==.

Note too that an equality test is symmetric, but assignment is not. For example, if a == 7 then 7 == a. But in Python, the statement a = 7 is legal and 7 = a is not.

Furthermore, in mathematics, a statement of equality is always true. If a == b now, then a will always equal b. In Python, an assignment statement can make two variables equal, but because of the possibility of reassignment, they don’t have to stay that way:

The third line changes the value of a but does not change the value of b, so they are no longer equal.

Note

In some programming languages, a different symbol is used for assignment, such as <- or :=, to avoid confusion. Python chose to use the tokens = for assignment, and == for equality. This is a common choice, also found in languages like C, C++, Java, JavaScript, and PHP, though it does make things a bit confusing for new programmers.

4.4.2. Updating variables

When an assignment statement is executed, the right-hand-side expression (i.e. the expression that comes after the assignment token) is evaluated first. Then the result of that evaluation is written into the variable on the left hand side, thereby changing it.

One of the most common forms of reassignment is an update, where the new value of the variable depends on its old value.

The second line means “get the current value of n, multiply it by three and add one, and put the answer back into n as its new value”. So after executing the two lines above, n will have the value 16.

If you try to get the value of a variable that doesn’t exist yet, you’ll get an error:

Before you can update a variable, you have to initialize it, usually with a simple assignment:

This second statement — updating a variable by adding 1 to it — is very common. It is called an increment of the variable; subtracting 1 is called a decrement.

4.5. The `for` loop

The for loop processes each item in a sequence, so it is used with Python’s sequence data types - strings, lists, and tuples.

Each item in turn is (re-)assigned to the loop variable, and the body of the loop is executed.

The general form of a for loop is:

This is another example of a compound statement in Python, and like the branching statements, it has a header terminated by a colon (:) and a body consisting of a sequence of one or more statements indented the same amount from the header.

The loop variable is created when the for statement runs, so you do not need to create the variable before then. Each iteration assigns the the loop variable to the next element in the sequence, and then executes the statements in the body. The statement finishes when the last element in the sequence is reached.

This type of flow is called a loop because it loops back around to the top after each iteration.

Running through all the items in a sequence is called traversing the sequence, or traversal.

You should run this example to see what it does.

Tip

As with all the examples you see in this book, you should try this code out yourself and see what it does. You should also try to anticipate the results before you do, and create your own related examples and try them out as well.

If you get the results you expected, pat yourself on the back and move on. If you don’t, try to figure out why. This is the essence of the scientific method, and is essential if you want to think like a computer programmer.

Often times you will want a loop that iterates a given number of times, or that iterates over a given sequence of numbers. The range function come in handy for that.

4.6. Tables

One of the things loops are good for is generating tables. Before computers were readily available, people had to calculate logarithms, sines and cosines, and other mathematical functions by hand. To make that easier, mathematics books contained long tables listing the values of these functions. Creating the tables was slow and boring, and they tended to be full of errors.

When computers appeared on the scene, one of the initial reactions was, “This is great! We can use the computers to generate the tables, so there will be no errors.” That turned out to be true (mostly) but shortsighted. Soon thereafter, computers and calculators were so pervasive that the tables became obsolete.

Well, almost. For some operations, computers use tables of values to get an approximate answer and then perform computations to improve the approximation. In some cases, there have been errors in the underlying tables, most famously in the table the Intel Pentium processor chip used to perform floating-point division.

Although a log table is not as useful as it once was, it still makes a good example. The following program outputs a sequence of values in the left column and 2 raised to the power of that value in the right column:

Using the tab character ('\t') makes the output align nicely.

4.7. The `while` statement

The general syntax for the while statement looks like this:

Like the branching statements and the for loop, the while statement is a compound statement consisting of a header and a body. A while loop executes an unknown number of times, as long at the BOOLEAN EXPRESSION is true.

Here is a simple example:

Notice that if number is set to 42 on the first line, the body of the while statement will not execute at all.

Here is a more elaborate example program demonstrating the use of the while statement

The flow of execution for a while statement works like this:

Evaluate the condition (BOOLEAN EXPRESSION), yielding False or True.
If the condition is false, exit the while statement and continue execution at the next statement.
If the condition is true, execute each of the STATEMENTS in the body and then go back to step 1.

The body consists of all of the statements below the header with the same indentation.

The body of the loop should change the value of one or more variables so that eventually the condition becomes false and the loop terminates. Otherwise the loop will repeat forever, which is called an infinite loop.

An endless source of amusement for computer programmers is the observation that the directions on shampoo, lather, rinse, repeat, are an infinite loop.

In the case here, we can prove that the loop terminates because we know that the value of len(name) is finite, and we can see that the value of pos increments each time through the loop, so eventually it will have to equal len(name). In other cases, it is not so easy to tell.

What you will notice here is that the while loop is more work for you — the programmer — than the equivalent for loop. When using a while loop one has to control the loop variable yourself: give it an initial value, test for completion, and then make sure you change something in the body so that the loop terminates.

4.8. Choosing between `for` and `while`

So why have two kinds of loop if for looks easier? This next example shows a case where we need the extra power that we get from the while loop.

Use a for loop if you know, before you start looping, the maximum number of times that you’ll need to execute the body. For example, if you’re traversing a list of elements, you know that the maximum number of loop iterations you can possibly need is “all the elements in the list”. Or if you need to print the 12 times table, we know right away how many times the loop will need to run.

So any problem like “iterate this weather model for 1000 cycles”, or “search this list of words”, “find all prime numbers up to 10000” suggest that a for loop is best.

By contrast, if you are required to repeat some computation until some condition is met, and you cannot calculate in advance when this will happen, as we did in the “greatest name” program, you’ll need a while loop.

We call the first case definite iteration — we have some definite bounds for what is needed. The latter case is called indefinite iteration — we’re not sure how many iterations we’ll need — we cannot even establish an upper bound!

4.9. Tracing a program

To write effective computer programs a programmer needs to develop the ability to trace the execution of a computer program. Tracing involves “becoming the computer” and following the flow of execution through a sample program run, recording the state of all variables and any output the program generates after each instruction is executed.

At the start of the trace, we have a local variable, name with an initial value of 'Harrison'. The user will enter a string that is stored in the variable, guess. Let’s assume they enter 'Maribel'. The next line creates a variable named pos and gives it an intial value of 0.

To keep track of all this as you hand trace a program, make a column heading on a piece of paper for each variable created as the program runs and another one for output. Our trace so far would look something like this:

Since guess != name and pos < len(name) evaluates to True (take a minute to convince yourself of this), the loop body is executed.

The user will now see

Assuming the user enters Karen this time, pos will be incremented, guess != name and pos < len(name) again evaluates to True, and our trace will now look like this:

A full trace of the program might produce something like this:

Tracing can be a bit tedious and error prone (that’s why we get computers to do this stuff in the first place!), but it is an essential skill for a programmer to have. From a trace we can learn a lot about the way our code works.

4.10. Abbreviated assignment

Incrementing a variable is so common that Python provides an abbreviated syntax for it:

count += 1 is an abreviation for count = count + 1 . We pronouce the operator as “plus-equals”. The increment value does not have to be 1:

There are similar abbreviations for -=, *=, /=, //= and %=:

4.11. Another `while` example: Guessing game

The following program implements a simple guessing game:

This program makes use of the mathematical law of trichotomy (given real numbers a and b, exactly one of these three must be true: a > b, a < b, or a == b).

4.12. The `break` statement

The break statement is used to immediately leave the body of its loop. The next statement to be executed is the first one after the body:

This prints:

4.13. The `continue` statement

This is a control flow statement that causes the program to immediately skip the processing of the rest of the body of the loop, for the current iteration. But the loop still carries on running for its remaining iterations:

This prints:

4.14. Another `for` example

Here is an example that combines several of the things we have learned:

Trace this program and make sure you feel confident you understand how it works.

4.15. Nested Loops for Nested Data

Now we’ll come up with an even more adventurous list of structured data. In this case, we have a list of students. Each student has a name which is paired up with another list of subjects that they are enrolled for:

Here we’ve assigned a list of five elements to the variable students. Let’s print out each student name, and the number of subjects they are enrolled for:

Python agreeably responds with the following output:

Now we’d like to ask how many students are taking CompSci. This needs a counter, and for each student we need a second loop that tests each of the subjects in turn:

You should set up a list of your own data that interests you — perhaps a list of your CDs, each containing a list of song titles on the CD, or a list of movie titles, each with a list of movie stars who acted in the movie. You could then ask questions like “Which movies starred Angelina Jolie?”

4.16. List comprehensions

A list comprehension is a syntactic construct that enables lists to be created from other lists using a compact, mathematical syntax:

The general syntax for a list comprehension expression is:

This list expression has the same effect as:

As you can see, the list comprehension is much more compact.

4.17. Glossary

append

To add new data to the end of a file or other data object.block

A group of consecutive statements with the same indentation.body

The block of statements in a compound statement that follows the header.branch

One of the possible paths of the flow of execution determined by conditional execution.chained conditional

A conditional branch with more than two possible flows of execution. In Python chained conditionals are written with if ... elif ... else statements.compound statement

A Python statement that has two parts: a header and a body. The header begins with a keyword and ends with a colon (:). The body contains a series of other Python statements, all indented the same amount.

Note

We will use the Python standard of 4 spaces for each level of indentation.condition

The boolean expression in a conditional statement that determines which branch is executed.conditional statement

A statement that controls the flow of execution depending on some condition. In Python the keywords if, elif, and else are used for conditional statements.counter

A variable used to count something, usually initialized to zero and incremented in the body of a loop.cursor

An invisible marker that keeps track of where the next character will be printed.decrement

Decrease by 1.definite iteration

A loop where we have an upper bound on the number of times the body will be executed. Definite iteration is usually best coded as a for loop.delimiter

A sequence of one or more characters used to specify the boundary between separate parts of text.increment

Both as a noun and as a verb, increment means to increase by 1.infinite loop

A loop in which the terminating condition is never satisfied.indefinite iteration

A loop where we just need to keep going until some condition is met. A while statement is used for this case.initialization (of a variable)

To initialize a variable is to give it an initial value. Since in Python variables don’t exist until they are assigned values, they are initialized when they are created. In other programming languages this is not the case, and variables can be created without being initialized, in which case they have either default or garbage values.iteration

Repeated execution of a set of programming statements.loop

A statement or group of statements that execute repeatedly until a terminating condition is satisfied.loop variable

A variable used as part of the terminating condition of a loop.nested loop

A loop inside the body of another loop.nesting

One program structure within another, such as a conditional statement inside a branch of another conditional statement.newline

A special character that causes the cursor to move to the beginning of the next line.prompt

A visual cue that tells the user to input data.reassignment

Making more than one assignment to the same variable during the execution of a program.tab

A special character that causes the cursor to move to the next tab stop on the current line.trichotomy

Given any real numbers a and b, exactly one of the following relations holds: a < b, a > b, or a == b. Thus when you can establish that two of the relations are false, you can assume the remaining one is true.trace

To follow the flow of execution of a program by hand, recording the change of state of the variables and any output produced.

Lists

List type is another sequence type defined by the list class of python. List allows you add, delete or process elements in very simple ways. List is very similar to arrays.

You can create list using the following syntax.

here each elements in the list is separated by comma and enclosed by a pair of square brackets ([]). Elements in the list can be of same type or different type. For e.g:

Other ways of creating list.

You can use index operator ([]) to access individual elements in the list. List index starts from 0.

Slice operator ([start:end]) allows to fetch sublist from the list. It works similar to string.

Similar to string start index is optional, if omitted it will be 0.

The end index is also optional, if omitted it will be set to the last index of the list.

note:

If start >= end, list[start : end] will return an empty list. If end specifies a position which is beyond the end of the list, Python will use the length of the list for end instead.

The + operator joins the two list.

The * operator replicates the elements in the list.

The in operator is used to determine whether the elements exists in the list. On success it returns True on failure it returns False.

Similarly not in is the opposite of in operator.

As already discussed list is a sequence and also iterable. Means you can use for loop to loop through all the elements of the list.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

>>> list1 \= [2, 3, 4, 1, 32, 4] >>> list1.append(19) >>> list1 [2, 3, 4, 1, 32, 4, 19] >>> list1.count(4) # Return the count for number 4 2 >>> list2 \= [99, 54] >>> list1.extend(list2) >>> list1 [2, 3, 4, 1, 32, 4, 19, 99, 54] >>> list1.index(4) # Return the index of number 4 2 >>> list1.insert(1, 25) # Insert 25 at position index 1 >>> list1 [2, 25, 3, 4, 1, 32, 4, 19, 99, 54] >>> >>> list1 \= [2, 25, 3, 4, 1, 32, 4, 19, 99, 54] >>> list1.pop(2) 3 >>> list1 [2, 25, 4, 1, 32, 4, 19, 99, 54] >>> list1.pop() 54 >>> list1 [2, 25, 4, 1, 32, 4, 19, 99] >>> list1.remove(32) # Remove number 32 >>> list1 [2, 25, 4, 1, 4, 19, 99] >>> list1.reverse() # Reverse the list >>> list1 [99, 19, 4, 1, 4, 25, 2] >>> list1.sort() # Sort the list >>> list1 [1, 2, 4, 4, 19, 25, 99] >>>

List comprehension provides a concise way to create list. It consists of square brackets containing expression followed by for clause then zero or more for or if clauses.

Reverse A List

Introduction

Python is a flexible language, and there's typically several ways to perform the same, menial task. Choosing an approach can depend on the time or space complexity, or simply on your personal preference.

Python's data structures are quite handy and intuitive, and their built-in functionalities are easy to work with. In this article, we'll be looking at how to reverse a list in Python. A Python List is a heterogenous (can contain differing types) array-like structure that stores references to objects in memory.

When manipulating a list, we can either create a new, changed list, or change the original list in-place. We'll see the differences in these as we proceed through the article.

Reverse a List Using the reverse() Method

Python has a powerful built-in library of methods when it comes to manipulating data in data structures. For the purposes of reversing a list, we can utilize the built-in reverse() method.

Note: The reverse() method reverses the list in-place. Reversing a list in-place means that the original list is changed, instead of creating a new, reversed list.

Due to this, we can't assign the resulting object to a new variable, and if you want to keep the original list in memory, you'll have to copy it before reversing:

There's no return value - the list is reversed in-place. However, we can copy() it before reversing:

This results in

Reverse a List Using Slice Notation

When you slice a list, a portion is returned from that list, and every stepth element is included:

This results in:

By omitting the start and end arguments, you can include the entire collection. And by setting the step to a negative number, you iterate through the collection in reverse. Naturally, if you pair these together:

This results in:

The Slice Notation doesn't affect the original list at all, so the original_list stays the same even after the operation.

Reverse a List Using slice() Method

The slice() method accepts the very same parameters - start, end and step, and performs much the same operation as the Slice Notation. Though, instead of omitting the start and end arguments, you can pass in None.

Its return type is a Slice object, which can be then be used to slice a collection according to its contents. It's not called on the collection you're slicing - you're passing in the Slice object after creation, allowing you to create a single reusable and callable object for many different collections.

It's internally transpiled into Slice Notation, so the end result is the same:

This results in:

Reverse a List Using a For Loop

Depending on whether we want to keep the original list intact or not, we can pop() elements from the original list and add them to a new one, or we can just append them in reverse order.

pop() removes the last element from a collection and returns it. We can combine the append() method with this to directly append the removed element to a new list, effectively resulting in a reversed list:

Alternatively, we can iterate through the list backwards, until the -1th index (non-inclusive) and add each element we see along that list. The range() method accepts 3 arguments - start, end and step, which can again be used in much the same way as before:

Reversing a List Using the reversed() Method

Since iterating with a negative step and then accessing each element in the original list is a bit verbose, the reversed() method was added, which makes it much easier to manually implement the reversal logic, in case you want to add your own twist on it.

The reversed() method returns an iterator, iterating over the collection in a reversed order - and we can easily add these elements into a new list:

Conclusion

Depending on whether you need a new reversed list, an in-place reversed list, as well as whether you want the logic to be taken care of for you, or if you'd like to have the flexibility of adding additional operations or twists during the reversal - there are several ways to reverse a list in Python.

In this tutorial, we've gone over these scenarios, highlighting the difference between each.

Classes and objects

Classes and Objects

Python is an object oriented programming language. Everything in Python is an object, with its properties and methods. A number, string, list, dictionary, tuple, set etc. used in a program is an object of a corresponding built-in class. We create class to create an object. A class is like an object constructor, or a "blueprint" for creating objects. We instantiate a class to create an object. The class defines attributes and the behavior of the object, while the object, on the other hand, represents the class.

We have been working with classes and objects right from the beginning of this challenge unknowingly. Every element in a Python program is an object of a class. Let us check if everything in python is a class:

asabeneh@Asabeneh:~$ python
Python 3.9.6 (default, Jun 28 2021, 15:26:21)
[Clang 11.0.0 (clang-1100.0.33.8)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> num = 10
>>> type(num)
<class 'int'>
>>> string = 'string'
>>> type(string)
<class 'str'>
>>> boolean = True
>>> type(boolean)
<class 'bool'>
>>> lst = []
>>> type(lst)
<class 'list'>
>>> tpl = ()
>>> type(tpl)
<class 'tuple'>
>>> set1 = set()
>>> type(set1)
<class 'set'>
>>> dct = {}
>>> type(dct)
<class 'dict'>

Creating a Class

To create a class we need the key word class followed by the name and colon. Class name should be CamelCase.

# syntax
class ClassName:
  code goes here

Example:

class Person:
  pass
print(Person)

<__main__.Person object at 0x10804e510>

Creating an Object

We can create an object by calling the class.

p = Person()
print(p)

Class Constructor

In the examples above, we have created an object from the Person class. However, a class without a constructor is not really useful in real applications. Let us use constructor function to make our class more useful. Like the constructor function in Java or JavaScript, Python has also a built-in init() constructor function. The init constructor function has self parameter which is a reference to the current instance of the class Examples:

class Person:
      def __init__ (self, name):
        # self allows to attach parameter to the class
          self.name =name

p = Person('Asabeneh')
print(p.name)
print(p)

# output
Asabeneh
<__main__.Person object at 0x2abf46907e80>

Let us add more parameters to the constructor function.

class Person:
      def __init__(self, firstname, lastname, age, country, city):
          self.firstname = firstname
          self.lastname = lastname
          self.age = age
          self.country = country
          self.city = city


p = Person('Asabeneh', 'Yetayeh', 250, 'Finland', 'Helsinki')
print(p.firstname)
print(p.lastname)
print(p.age)
print(p.country)
print(p.city)

# output
Asabeneh
Yetayeh
250
Finland
Helsinki

Object Methods

Objects can have methods. The methods are functions which belong to the object.

Example:

class Person:
      def __init__(self, firstname, lastname, age, country, city):
          self.firstname = firstname
          self.lastname = lastname
          self.age = age
          self.country = country
          self.city = city
      def person_info(self):
        return f'{self.firstname} {self.lastname} is {self.age} years old. He lives in {self.city}, {self.country}'

p = Person('Asabeneh', 'Yetayeh', 250, 'Finland', 'Helsinki')
print(p.person_info())

# output
Asabeneh Yetayeh is 250 years old. He lives in Helsinki, Finland

Object Default Methods

Sometimes, you may want to have a default values for your object methods. If we give default values for the parameters in the constructor, we can avoid errors when we call or instantiate our class without parameters. Let's see how it looks:

Example:

class Person:
      def __init__(self, firstname='Asabeneh', lastname='Yetayeh', age=250, country='Finland', city='Helsinki'):
          self.firstname = firstname
          self.lastname = lastname
          self.age = age
          self.country = country
          self.city = city

      def person_info(self):
        return f'{self.firstname} {self.lastname} is {self.age} years old. He lives in {self.city}, {self.country}.'

p1 = Person()
print(p1.person_info())
p2 = Person('John', 'Doe', 30, 'Nomanland', 'Noman city')
print(p2.person_info())

# output
Asabeneh Yetayeh is 250 years old. He lives in Helsinki, Finland.
John Doe is 30 years old. He lives in Noman city, Nomanland.

Method to Modify Class Default Values

In the example below, the person class, all the constructor parameters have default values. In addition to that, we have skills parameter, which we can access using a method. Let us create add_skill method to add skills to the skills list.

class Person:
      def __init__(self, firstname='Asabeneh', lastname='Yetayeh', age=250, country='Finland', city='Helsinki'):
          self.firstname = firstname
          self.lastname = lastname
          self.age = age
          self.country = country
          self.city = city
          self.skills = []

      def person_info(self):
        return f'{self.firstname} {self.lastname} is {self.age} years old. He lives in {self.city}, {self.country}.'
      def add_skill(self, skill):
          self.skills.append(skill)

p1 = Person()
print(p1.person_info())
p1.add_skill('HTML')
p1.add_skill('CSS')
p1.add_skill('JavaScript')
p2 = Person('John', 'Doe', 30, 'Nomanland', 'Noman city')
print(p2.person_info())
print(p1.skills)
print(p2.skills)

# output
Asabeneh Yetayeh is 250 years old. He lives in Helsinki, Finland.
John Doe is 30 years old. He lives in Noman city, Nomanland.
['HTML', 'CSS', 'JavaScript']
[]

Inheritance

Using inheritance we can reuse parent class code. Inheritance allows us to define a class that inherits all the methods and properties from parent class. The parent class or super or base class is the class which gives all the methods and properties. Child class is the class that inherits from another or parent class. Let us create a student class by inheriting from person class.

class Student(Person):
    pass


s1 = Student('Eyob', 'Yetayeh', 30, 'Finland', 'Helsinki')
s2 = Student('Lidiya', 'Teklemariam', 28, 'Finland', 'Espoo')
print(s1.person_info())
s1.add_skill('JavaScript')
s1.add_skill('React')
s1.add_skill('Python')
print(s1.skills)

print(s2.person_info())
s2.add_skill('Organizing')
s2.add_skill('Marketing')
s2.add_skill('Digital Marketing')
print(s2.skills)

output
Eyob Yetayeh is 30 years old. He lives in Helsinki, Finland.
['JavaScript', 'React', 'Python']
Lidiya Teklemariam is 28 years old. He lives in Espoo, Finland.
['Organizing', 'Marketing', 'Digital Marketing']

We did not call the init() constructor in the child class. If we didn't call it then we can still access all the properties from the parent. But if we do call the constructor we can access the parent properties by calling super. We can add a new method to the child or we can override the parent class methods by creating the same method name in the child class. When we add the init() function, the child class will no longer inherit the parent's init() function.

Overriding parent method

class Student(Person):
    def __init__ (self, firstname='Asabeneh', lastname='Yetayeh',age=250, country='Finland', city='Helsinki', gender='male'):
        self.gender = gender
        super().__init__(firstname, lastname,age, country, city)
    def person_info(self):
        gender = 'He' if self.gender =='male' else 'She'
        return f'{self.firstname} {self.lastname} is {self.age} years old. {gender} lives in {self.city}, {self.country}.'

s1 = Student('Eyob', 'Yetayeh', 30, 'Finland', 'Helsinki','male')
s2 = Student('Lidiya', 'Teklemariam', 28, 'Finland', 'Espoo', 'female')
print(s1.person_info())
s1.add_skill('JavaScript')
s1.add_skill('React')
s1.add_skill('Python')
print(s1.skills)

print(s2.person_info())
s2.add_skill('Organizing')
s2.add_skill('Marketing')
s2.add_skill('Digital Marketing')
print(s2.skills)

Eyob Yetayeh is 30 years old. He lives in Helsinki, Finland.
['JavaScript', 'React', 'Python']
Lidiya Teklemariam is 28 years old. She lives in Espoo, Finland.
['Organizing', 'Marketing', 'Digital Marketing']

We can use super() built-in function or the parent name Person to automatically inherit the methods and properties from its parent. In the example above we override the parent method. The child method has a different feature, it can identify, if the gender is male or female and assign the proper pronoun(He/She).

🌕 Now, you are fully charged with a super power of programming. Now do some exercises for your brain and muscles.

💻 Exercises: Day 21

Exercises: Level 1

Python has the module called statistics and we can use this module to do all the statistical calculations. However, to learn how to make function and reuse function let us try to develop a program, which calculates the measure of central tendency of a sample (mean, median, mode) and measure of variability (range, variance, standard deviation). In addition to those measures, find the min, max, count, percentile, and frequency distribution of the sample. You can create a class called Statistics and create all the functions that do statistical calculations as methods for the Statistics class. Check the output below.

ages = [31, 26, 34, 37, 27, 26, 32, 32, 26, 27, 27, 24, 32, 33, 27, 25, 26, 38, 37, 31, 34, 24, 33, 29, 26]

print('Count:', data.count()) # 25
print('Sum: ', data.sum()) # 744
print('Min: ', data.min()) # 24
print('Max: ', data.max()) # 38
print('Range: ', data.range() # 14
print('Mean: ', data.mean()) # 30
print('Median: ', data.median()) # 29
print('Mode: ', data.mode()) # {'mode': 26, 'count': 5}
print('Standard Deviation: ', data.std()) # 4.2
print('Variance: ', data.var()) # 17.5
print('Frequency Distribution: ', data.freq_dist()) # [(20.0, 26), (16.0, 27), (12.0, 32), (8.0, 37), (8.0, 34), (8.0, 33), (8.0, 31), (8.0, 24), (4.0, 38), (4.0, 29), (4.0, 25)]

# you output should look like this
print(data.describe())
Count: 25
Sum:  744
Min:  24
Max:  38
Range:  14
Mean:  30
Median:  29
Mode:  (26, 5)
Variance:  17.5
Standard Deviation:  4.2
Frequency Distribution: [(20.0, 26), (16.0, 27), (12.0, 32), (8.0, 37), (8.0, 34), (8.0, 33), (8.0, 31), (8.0, 24), (4.0, 38), (4.0, 29), (4.0, 25)]

"""
Abstract class is an extension of a basic class. Like a basic class, an
abstract class has methods and state. Unlike a basic class, it inherits
the `ABC` class and has at least one `abstractmethod`. That means we
cannot create an instance directly from its constructor. In this module,
we will create an abstract class and two concrete classes.

For more about abstract classes, click the link below:

https://www.python.org/dev/peps/pep-3119/
"""
from abc import ABC, abstractmethod


class Employee(ABC):
    """Abstract definition of an employee.

    Any employee can work and relax. The way that one type of employee
    can work and relax is different from another type of employee.
    """

    def __init__(self, name, title):
        self.name = name
        self.title = title

    def __str__(self):
        return self.name

    @abstractmethod
    def do_work(self):
        """Do something for work."""
        raise NotImplementedError

    @abstractmethod
    def do_relax(self):
        """Do something to relax."""
        raise NotImplementedError


class Engineer(Employee):
    """Concrete definition of an engineer.

    The Engineer class is concrete because it implements every
    `abstractmethod` that was not implemented above.

    Notice that we leverage the parent's constructor when creating
    this object. We also define `do_refactor` for an engineer, which
    is something that a manager prefers not to do.
    """

    def __init__(self, name, title, skill):
        super().__init__(name, title)
        self.skill = skill

    def do_work(self):
        return f"{self} is coding in {self.skill}"

    def do_relax(self):
        return f"{self} is watching YouTube"

    def do_refactor(self):
        """Do the hard work of refactoring code, unlike managers."""
        return f"{self} is refactoring code"


class Manager(Employee):
    """Concrete definition of a manager.

    The Manager class is concrete for the same reasons as the Engineer
    class is concrete. Notice that a manager has direct reports and
    has the responsibility of hiring people on the team, unlike an
    engineer.
    """

    def __init__(self, name, title, direct_reports):
        super().__init__(name, title)
        self.direct_reports = direct_reports

    def do_work(self):
        return f"{self} is meeting up with {len(self.direct_reports)} reports"

    def do_relax(self):
        return f"{self} is taking a trip to the Bahamas"

    def do_hire(self):
        """Do the hard work of hiring employees, unlike engineers."""
        return f"{self} is hiring employees"


def main():
    # Declare two engineers
    engineer_john = Engineer("John Doe", "Software Engineer", "Android")
    engineer_jane = Engineer("Jane Doe", "Software Engineer", "iOS")
    engineers = [engineer_john, engineer_jane]

    # These engineers are employees but not managers
    assert all(isinstance(engineer, Employee) for engineer in engineers)
    assert all(not isinstance(engineer, Manager) for engineer in engineers)

    # Engineers can work, relax and refactor
    assert engineer_john.do_work() == "John Doe is coding in Android"
    assert engineer_john.do_relax() == "John Doe is watching YouTube"
    assert engineer_john.do_refactor() == "John Doe is refactoring code"

    # Declare manager with engineers as direct reports
    manager_max = Manager("Max Doe", "Engineering Manager", engineers)

    # Managers are employees but not engineers
    assert isinstance(manager_max, Employee)
    assert not isinstance(manager_max, Engineer)

    # Managers can work, relax and hire
    assert manager_max.do_work() == "Max Doe is meeting up with 2 reports"
    assert manager_max.do_relax() == "Max Doe is taking a trip to the Bahamas"
    assert manager_max.do_hire() == "Max Doe is hiring employees"


if __name__ == "__main__":
    main()



"""
A class is made up of methods and state. This allows code and data to be
combined as one logical entity. This module defines a basic car class,
creates a car instance and uses it for demonstration purposes.
"""
from inspect import isfunction, ismethod, signature


class Car:
    """Basic definition of a car.

    We begin with a simple mental model of what a car is. That way, we
    can start exploring the core concepts that are associated with a
    class definition.
    """

    def __init__(self, make, model, year, miles):
        """Constructor logic."""
        self.make = make
        self.model = model
        self.year = year
        self.miles = miles

    def __repr__(self):
        """Formal representation for developers."""
        return f"<Car make={self.make} model={self.model} year={self.year}>"

    def __str__(self):
        """Informal representation for users."""
        return f"{self.make} {self.model} ({self.year})"

    def drive(self, rate_in_mph):
        """Drive car at a certain rate in MPH."""
        return f"{self} is driving at {rate_in_mph} MPH"


def main():
    # Create a car with the provided class constructor
    car = Car("Bumble", "Bee", 2000, 200000.0)

    # Formal representation is good for debugging issues
    assert repr(car) == "<Car make=Bumble model=Bee year=2000>"

    # Informal representation is good for user output
    assert str(car) == "Bumble Bee (2000)"

    # Call a method on the class constructor
    assert car.drive(75) == "Bumble Bee (2000) is driving at 75 MPH"

    # As a reminder: everything in Python is an object! And that applies
    # to classes in the most interesting way - because they're not only
    # subclasses of object - they are also instances of object. This
    # means that we can modify the `Car` class at runtime, just like any
    # other piece of data we define in Python
    assert issubclass(Car, object) and isinstance(Car, object)

    # To emphasize the idea that everything is an object, let's look at
    # the `drive` method in more detail
    driving = getattr(car, "drive")

    # The variable method is the same as the instance method
    assert driving == car.drive

    # The variable method is bound to the instance
    assert driving.__self__ == car

    # That is why `driving` is considered a method and not a function
    assert ismethod(driving) and not isfunction(driving)

    # And there is only one parameter for `driving` because `__self__`
    # binding is implicit
    driving_params = signature(driving).parameters
    assert len(driving_params) == 1
    assert "rate_in_mph" in driving_params


if __name__ == "__main__":
    main()

7. Classes and objects

7.1. Object-oriented programming

Python is an object-oriented programming language, which means that it provides features that support object-oriented programming ( OOP).

Object-oriented programming has its roots in the 1960s, but it wasn’t until the mid 1980s that it became the main programming paradigm used in the creation of new software. It was developed as a way to handle the rapidly increasing size and complexity of software systems, and to make it easier to modify these large and complex systems over time.

Up to now we have been writing programs using a procedural programming paradigm. In procedural programming the focus is on writing functions or procedures which operate on data. In object-oriented programming the focus is on the creation of objects which contain both data and functionality together.

7.2. User-defined compound types

We will now introduce a new Python keyword, class, which in essence defines a new data type. We have been using several of Python’s built-in types throughout this book, we are now ready to create our own user-defined type: the Point.

Consider the concept of a mathematical point. In two dimensions, a point is two numbers (coordinates) that are treated collectively as a single object. In mathematical notation, points are often written in parentheses with a comma separating the coordinates. For example, (0, 0) represents the origin, and (x, y) represents the point x units to the right and y units up from the origin.

A natural way to represent a point in Python is with two numeric values. The question, then, is how to group these two values into a compound object. The quick and dirty solution is to use a list or tuple, and for some applications that might be the best choice.

An alternative is to define a new user-defined compound type, called a class. This approach involves a bit more effort, but it has advantages that will be apparent soon.

A class definition looks like this:

class Point:
    pass

Class definitions can appear anywhere in a program, but they are usually near the beginning (after the import statements). The syntax rules for a class definition are the same as for other compound statements. There is a header which begins with the keyword, class, followed by the name of the class, and ending with a colon.

This definition creates a new class called Point. The pass statement has no effect; it is only necessary because a compound statement must have something in its body. A docstring could serve the same purpose:

class Point:
    "Point class for storing mathematical points."

By creating the Point class, we created a new type, also called Point. The members of this type are called instances of the type or objects. Creating a new instance is called instantiation, and is accomplished by calling the class. Classes, like functions, are callable, and we instantiate a Point object by calling the Point class:

>>> type(Point)
<class 'type'>
>>> p = Point()
>>> type(p)
<class '__main__.Point'>

The variable p is assigned a reference to a new Point object.

It may be helpful to think of a class as a factory for making objects, so our Point class is a factory for making points. The class itself isn’t an instance of a point, but it contains the machinary to make point instances.

7.3. Attributes

Like real world objects, object instances have both form and function. The form consists of data elements contained within the instance.

We can add new data elements to an instance using dot notation:

>>> p.x = 3
>>> p.y = 4

This syntax is similar to the syntax for selecting a variable from a module, such as math.pi or string.uppercase. Both modules and instances create their own namespaces, and the syntax for accessing names contained in each, called attributes, is the same. In this case the attribute we are selecting is a data item from an instance.

The following state diagram shows the result of these assignments:

The variable p refers to a Point object, which contains two attributes. Each attribute refers to a number.

We can read the value of an attribute using the same syntax:

>>> print(p.y)
4
>>> x = p.x
>>> print(x)
3

The expression p.x means, “Go to the object p refers to and get the value of x”. In this case, we assign that value to a variable named x. There is no conflict between the variable x and the attribute x. The purpose of dot notation is to identify which variable you are referring to unambiguously.

You can use dot notation as part of any expression, so the following statements are legal:

print('({0}, {1})'.format(p.x, p.y))
distance_squared = p.x * p.x + p.y * p.y

The first line outputs (3, 4); the second line calculates the value 25.

7.4. The initialization method and `self`

Since our Point class is intended to represent two dimensional mathematical points, all point instances ought to have x and y attributes, but that is not yet so with our Point objects.

>>> p2 = Point()
>>> p2.x
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'Point' object has no attribute 'x'
>>>

To solve this problem we add an initialization method to our class.

class Point:
    def __init__(self, x, y):
        self.x = x
        self.y = y

A method behaves like a function but it is part of an object. Like a data attribute it is accessed using dot notation.

The initialization method is a special method that is invoked automatically when an object is created by calling the class. The name of this method is __init__ (two underscore characters, followed by init, and then two more underscores). This name must be used to make a method an initialization method in Python.

There is no conflict between the attribute self.x and the parameter x. Dot notation specifies which variable we are referring to.

Let’s add another method, distance_from_origin, to see better how methods work:

class Point:
    def __init__(self, x, y):
        self.x = x
        self.y = y

    def distance_from_origin(self):
        return ((self.x ** 2) + (self.y ** 2)) ** 0.5

Let’s create a few point instances, look at their attributes, and call our new method on them:

>>> p = Point(3, 4)
>>> p.x
3
>>> p.y
4
>>> p.distance_from_origin()
5.0
>>> q = Point(5, 12)
>>> q.x
5
>>> q.y
12
>>> q.distance_from_origin()
13.0
>>> r = Point(0, 0)
>>> r.x
0
>>> r.y
0
>>> r.distance_from_origin()
0.0

When defining a method, the first parameter refers to the instance being created. It is customary to name this parameter self. In the example session above, the self parameter refers to the instances p, q, and r respectively.

7.5. Instances as parameters

You can pass an instance as a parameter to a function in the usual way. For example:

def print_point(p):
    print('({0}, {1})'.format(p.x, p.y))

print_point takes a point as an argument and displays it in the standard format. If you call print_point(p) with point p as defined previously, the output is (3, 4).

To convert print_point to a method, do the following:

Indent the function definition so that it is inside the class definition.
Rename the parameter to self.

class Point:
    def __init__(self, x=0, y=0):
        self.x = x
        self.y = y

    def distance_from_origin(self):
        return ((self.x ** 2) + (self.y ** 2)) ** 0.5

    def print_point(self):
        print('({0}, {1})'.format(self.x, self.y))

We can now invoke the method using dot notation.

>>> p = Point(3, 4)
>>> p.print_point()
(3, 4)

The object on which the method is invoked is assigned to the first parameter, so in this case p is assigned to the parameter self. By convention, the first parameter of a method is called self. The reason for this is a little convoluted, but it is based on a useful metaphor.

The syntax for a function call, print_point(p), suggests that the function is the active agent. It says something like, Hey print_point! Here’s an object for you to print.

In object-oriented programming, the objects are the active agents. An invocation like p.print_point() says Hey p! Please print yourself!

This change in perspective might be more polite, but it is not obvious that it is useful. In the examples we have seen so far, it may not be. But sometimes shifting responsibility from the functions onto the objects makes it possible to write more versatile functions, and makes it easier to maintain and reuse code.

7.6. Object-oriented features

It is not easy to define object-oriented programming, but we have already seen some of its characteristics:

Programs are made up of class definitions which contain attributes that can be data (instance variables) or behaviors (methods).
Each object definition corresponds to some object or concept in the real world, and the functions that operate on that object correspond to the ways real-world objects interact.
Most of the computation is expressed in terms of operations on objects.

For example, the Point class corresponds to the mathematical concept of a point.

7.7. Time

As another example of a user-defined type, we’ll define a class called Time that records the time of day. Since times will need hours, minutes, and second attributes, we’ll start with an initialization method similar to the one we created for Points.

The class definition looks like this:

class Time:
    def __init__(self, hours, minutes, seconds):
        self.hours = hours
        self.minutes = minutes
        self.seconds = seconds

When we call the Time class, the arguments we provide are passed along to init:

>>> current_time = Time(9, 14, 30)
>>> current_time.hours
9
>>> current_time.minutes
14
>>> current_time.seconds
30

Here is a print_time method for our Time objects that uses string formating to display minutes and seconds with two digits.

To save space, we will leave out the initialization method, but you should include it:

class Time:
    # previous method definitions here...

    def print_time(self):
        s = "{0}:{1:02d}:{2:02d}"
        print(s.format(self.hours, self.minutes, self.seconds)

which we can now invoke on time instances in the usual way:

>>> t1 = Time(9, 14, 30)
>>> t1.print_time()
9:14:30
>>> t2 = Time(7, 4, 0)
>>> t2.print_time()
7:04:00

7.8. Optional arguments

We have seen built-in functions that take a variable number of arguments. For example, string.find can take two, three, or four arguments.

It is possible to write user-defined functions with optional argument lists. For example, we can upgrade our own version of find to do the same thing as string.find.

This is the original version:

def find(str, ch):
    index = 0
    while index < len(str):
        if str[index] == ch:
            return index
        index = index + 1
    return -1

This is the new and improved version:

def find(str, ch, start=0):
    index = start
    while index < len(str):
        if str[index] == ch:
            return index
        index = index + 1
    return -1

The third parameter, start, is optional because a default value, 0, is provided. If we invoke find with only two arguments, we use the default value and start from the beginning of the string:

>>> find("apple", "p")
1

If we provide a third parameter, it overrides the default:

>>> find("apple", "p", 2)
2
>>> find("apple", "p", 3)
-1

We can rewrite our initialization method for the Time class so that hours, minutes, and seconds are each optional arguments.

class Time:
    def __init__(self, hours=0, minutes=0, seconds=0):
        self.hours = hours
        self.minutes = minutes
        self.seconds = seconds

When we instantiate a Time object, we can pass in values for the three parameters, as we did with

>>> current_time = Time(9, 14, 30)

Because the parameters are now optional, however, we can omit them:

>>> current_time = Time()
>>> current_time.print_time()
0:00:00

Or provide only the first parameter:

>>> current_time = Time(9)
>>> current_time.print_time()
9:00:00

Or the first two parameters:

>>> current_time = Time (9, 14)
>>> current_time.print_time()
9:14:00

Finally, we can provide a subset of the parameters by naming them explicitly:

>>> current_time = Time(seconds = 30, hours = 9)
>>> current_time.print_time()
9:00:30

7.9. Another method

Let’s add a method increment, which increments a time instance by a given number of seconds. To save space, we will continue to leave out previously defined methods, but you should always keep them in your version:

class Time:
    # previous method definitions here...

    def increment(self, seconds):
        self.seconds = seconds + self.seconds

        while self.seconds >= 60:
            self.seconds = self.seconds - 60
            self.minutes = self.minutes + 1

        while self.minutes >= 60:
            self.minutes = self.minutes - 60
            self.hours = self.hours + 1

Now we can invoke increment on a time instance.

>>> current_time = Time(9, 14, 30)
>>> current_time.increment(125)
>>> current_time.print_time()
9:16:35

Again, the object on which the method is invoked gets assigned to the first parameter, self. The second parameter, seconds gets the value 125.

7.10. An example with two `Time`s

Let’s add a boolen method, after, that takes two time instances and returns True when the first one is chronologically after the second.

We can only convert one of the parameters to self; the other we will call other, and it will have to be a parameter of the method.

class Time:
    # previous method definitions here...

    def after(self, other):
        if self.hours > other.hours:
            return True
        if self.hours < other.hours:
            return False

        if self.minutes > other.minutes:
            return True
        if self.minutes < other.minutes:
            return False

        if self.seconds > other.seconds:
            return True
        return False

We invoke this method on one object and pass the other as an argument:

if time1.after(time2):
    print("It's later than you think.")

You can almost read the invocation like English: If time1 is after time2, then…

7.10.1. Pure functions and modifiers (again)

In the next few sections, we’ll write two versions of a function called add_time, which calculates the sum of two Times. They will demonstrate two kinds of functions: pure functions and modifiers, which we first encountered in the Functions chapter.

The following is a rough version of add_time:

def add_time(t1, t2):
    sum = Time()
    sum.hours = t1.hours + t2.hours
    sum.minutes = t1.minutes + t2.minutes
    sum.seconds = t1.seconds + t2.seconds
    return sum

The function creates a new Time object, initializes its attributes, and returns a reference to the new object. This is called a pure function because it does not modify any of the objects passed to it as parameters and it has no side effects, such as displaying a value or getting user input.

Here is an example of how to use this function. We’ll create two Time objects: current_time, which contains the current time; and bread_time, which contains the amount of time it takes for a breadmaker to make bread. Then we’ll use add_time to figure out when the bread will be done. If you haven’t finished writing print_time yet, take a look ahead to Section before you try this:

>>> current_time = Time(9, 14, 30)
>>> bread_time = Time(3, 35, 0)
>>> done_time = add_time(current_time, bread_time)
>>> print_time(done_time)
12:49:30

The output of this program is 12:49:30, which is correct. On the other hand, there are cases where the result is not correct. Can you think of one?

The problem is that this function does not deal with cases where the number of seconds or minutes adds up to more than sixty. When that happens, we have to carry the extra seconds into the minutes column or the extra minutes into the hours column.

Here’s a second corrected version of the function:

def add_time(t1, t2):
    sum = Time()
    sum.hours = t1.hours + t2.hours
    sum.minutes = t1.minutes + t2.minutes
    sum.seconds = t1.seconds + t2.seconds

    if sum.seconds >= 60:
        sum.seconds = sum.seconds - 60
        sum.minutes = sum.minutes + 1

    if sum.minutes >= 60:
        sum.minutes = sum.minutes - 60
        sum.hours = sum.hours + 1

    return sum

Although this function is correct, it is starting to get big. Later we will suggest an alternative approach that yields shorter code.

7.10.2. Modifiers

There are times when it is useful for a function to modify one or more of the objects it gets as parameters. Usually, the caller keeps a reference to the objects it passes, so any changes the function makes are visible to the caller. Functions that work this way are called modifiers.

increment, which adds a given number of seconds to a Time object, would be written most naturally as a modifier. A rough draft of the function looks like this:

def increment(time, seconds):
    time.seconds = time.seconds + seconds

    if time.seconds >= 60:
        time.seconds = time.seconds - 60
        time.minutes = time.minutes + 1

    if time.minutes >= 60:
        time.minutes = time.minutes - 60
        time.hours = time.hours + 1

The first line performs the basic operation; the remainder deals with the special cases we saw before.

Is this function correct? What happens if the parameter seconds is much greater than sixty? In that case, it is not enough to carry once; we have to keep doing it until seconds is less than sixty. One solution is to replace the if statements with while statements:

def increment(time, seconds):
    time.seconds = time.seconds + seconds

    while time.seconds >= 60:
        time.seconds = time.seconds - 60
        time.minutes = time.minutes + 1

    while time.minutes >= 60:
        time.minutes = time.minutes - 60
        time.hours = time.hours + 1

This function is now correct, but it is not the most efficient solution.

7.11. Prototype development versus planning

So far in this chapter, we’ve used an approach to program development that we’ll call prototype development. We wrote a rough draft (or prototype) that performed the basic calculation and then tested it on a few cases, correcting flaws as we found them.

Although this approach can be effective, it can lead to code that is unnecessarily complicated – since it deals with many special cases – and unreliable – since it is hard to know if we’ve found all the errors.

An alternative is planned development, in which high-level insight into the problem can make the programming much easier. In this case, the insight is that a Time object is really a three-digit number in base 60! The second component is the ones column, the minute component is the sixties column, and the hour component is the thirty-six hundreds column.

When we wrote add_time and increment, we were effectively doing addition in base 60, which is why we had to carry from one column to the next.

This observation suggests another approach to the whole problem – we can convert a Time object into a single number and take advantage of the fact that the computer knows how to do arithmetic with numbers. The following function converts a Time object into an integer:

def convert_to_seconds(time):
    minutes = time.hours * 60 + time.minutes
    seconds = minutes * 60 + time.seconds
    return seconds

Now, all we need is a way to convert from an integer to a Time object:

def make_time(seconds):
    time = Time()
    time.hours = seconds // 3600
    seconds = seconds - time.hours * 3600
    time.minutes = seconds // 60
    seconds = seconds - time.minutes * 60
    time.seconds = seconds
    return time

You might have to think a bit to convince yourself that this technique to convert from one base to another is correct. Assuming you are convinced, you can use these functions to rewrite add_time:

def add_time(t1, t2):
    seconds = convert_to_seconds(t1) + convert_to_seconds(t2)
    return make_time(seconds)

This version is much shorter than the original, and it is much easier to demonstrate that it is correct (assuming, as usual, that the functions it calls are correct).

7.12. Generalization

In some ways, converting from base 60 to base 10 and back is harder than just dealing with times. Base conversion is more abstract; our intuition for dealing with times is better.

But if we have the insight to treat times as base 60 numbers and make the investment of writing the conversion functions (convert_to_seconds and make_time), we get a program that is shorter, easier to read and debug, and more reliable.

It is also easier to add features later. For example, imagine subtracting two Times to find the duration between them. The naive approach would be to implement subtraction with borrowing. Using the conversion functions would be easier and more likely to be correct.

Ironically, sometimes making a problem harder (or more general) makes it easier (because there are fewer special cases and fewer opportunities for error).

7.13. Algorithms

When you write a general solution for a class of problems, as opposed to a specific solution to a single problem, you have written an algorithm. We mentioned this word before but did not define it carefully. It is not easy to define, so we will try a couple of approaches.

First, consider something that is not an algorithm. When you learned to multiply single-digit numbers, you probably memorized the multiplication table. In effect, you memorized 100 specific solutions. That kind of knowledge is not algorithmic.

But if you were lazy, you probably cheated by learning a few tricks. For example, to find the product of n and 9, you can write n-1 as the first digit and 10-n as the second digit. This trick is a general solution for multiplying any single-digit number by 9. That’s an algorithm!

Similarly, the techniques you learned for addition with carrying, subtraction with borrowing, and long division are all algorithms. One of the characteristics of algorithms is that they do not require any intelligence to carry out. They are mechanical processes in which each step follows from the last according to a simple set of rules.

In my opinion, it is embarrassing that humans spend so much time in school learning to execute algorithms that, quite literally, require no intelligence.

On the other hand, the process of designing algorithms is interesting, intellectually challenging, and a central part of what we call programming.

Some of the things that people do naturally, without difficulty or conscious thought, are the hardest to express algorithmically. Understanding natural language is a good example. We all do it, but so far no one has been able to explain how we do it, at least not in the form of an algorithm.

7.14. Points revisited

Let’s rewrite the Point class in a more object- oriented style:

class Point:
    def __init__(self, x=0, y=0):
        self.x = x
        self.y = y

    def __str__(self):
        return '({0}, {1})'.format(self.x, self.y)

The next method, __str__, returns a string representation of a Point object. If a class provides a method named __str__, it overrides the default behavior of the Python built-in str function.

>>> p = Point(3, 4)
>>> str(p)
'(3, 4)'

Printing a Point object implicitly invokes __str__ on the object, so defining __str__ also changes the behavior of print:

>>> p = Point(3, 4)
>>> print(p)
(3, 4)

When we write a new class, we almost always start by writing __init__, which makes it easier to instantiate objects, and __str__, which is almost always useful for debugging.

7.15. Operator overloading

Some languages make it possible to change the definition of the built-in operators when they are applied to user-defined types. This feature is called operator overloading. It is especially useful when defining new mathematical types.

For example, to override the addition operator +, we provide a method named __add__:

class Point:
    # previously defined methods here...

    def __add__(self, other):
        return Point(self.x + other.x, self.y + other.y)

As usual, the first parameter is the object on which the method is invoked. The second parameter is conveniently named other to distinguish it from self. To add two Points, we create and return a new Point that contains the sum of the x coordinates and the sum of the y coordinates.

Now, when we apply the + operator to Point objects, Python invokes __add__:

>>>  p1 = Point(3, 4)
>>>  p2 = Point(5, 7)
>>>  p3 = p1 + p2
>>>  print(p3)
(8, 11)

The expression p1 + p2 is equivalent to p1.__add__(p2), but obviously more elegant. As an exercise, add a method __sub__(self, other) that overloads the subtraction operator, and try it out. There are several ways to override the behavior of the multiplication operator: by defining a method named __mul__, or __rmul__, or both.

If the left operand of * is a Point, Python invokes __mul__, which assumes that the other operand is also a Point. It computes the dot product of the two points, defined according to the rules of linear algebra:

def __mul__(self, other):
    return self.x * other.x + self.y * other.y

If the left operand of * is a primitive type and the right operand is a Point, Python invokes __rmul__, which performs scalar multiplication:

def __rmul__(self, other):
    return Point(other * self.x,  other * self.y)

The result is a new Point whose coordinates are a multiple of the original coordinates. If other is a type that cannot be multiplied by a floating-point number, then __rmul__ will yield an error.

This example demonstrates both kinds of multiplication:

>>> p1 = Point(3, 4)
>>> p2 = Point(5, 7)
>>> print(p1 * p2)
43
>>> print(2 * p2)
(10, 14)

What happens if we try to evaluate p2 * 2? Since the first parameter is a Point, Python invokes __mul__ with 2 as the second argument. Inside __mul__, the program tries to access the x coordinate of other, which fails because an integer has no attributes:

>>> print(p2 * 2)
AttributeError: 'int' object has no attribute 'x'

Unfortunately, the error message is a bit opaque. This example demonstrates some of the difficulties of object-oriented programming. Sometimes it is hard enough just to figure out what code is running.

For a more complete example of operator overloading, see Appendix (reference overloading).

7.16. Polymorphism

Most of the methods we have written only work for a specific type. When you create a new object, you write methods that operate on that type.

But there are certain operations that you will want to apply to many types, such as the arithmetic operations in the previous sections. If many types support the same set of operations, you can write functions that work on any of those types.

For example, the multadd operation (which is common in linear algebra) takes three parameters; it multiplies the first two and then adds the third. We can write it in Python like this:

def multadd(x, y, z):
    return x * y + z

This method will work for any values of x and y that can be multiplied and for any value of z that can be added to the product.

We can invoke it with numeric values:

>>> multadd(3, 2, 1)
7

Or with Points:

>>> p1 = Point(3, 4)
>>> p2 = Point(5, 7)
>>> print(multadd(2, p1, p2))
(11, 15)
>>> print(multadd(p1, p2, 1))
44

In the first case, the Point is multiplied by a scalar and then added to another Point. In the second case, the dot product yields a numeric value, so the third parameter also has to be a numeric value.

A function like this that can take parameters with different types is called polymorphic.

As another example, consider the method front_and_back, which prints a list twice, forward and backward:

def front_and_back(front):
    import copy
    back = copy.copy(front)
    back.reverse()
    print(str(front) + str(back))

Because the reverse method is a modifier, we make a copy of the list before reversing it. That way, this method doesn’t modify the list it gets as a parameter.

Here’s an example that applies front_and_back to a list:

>>>   myList = [1, 2, 3, 4]
>>>   front_and_back(myList)
[1, 2, 3, 4][4, 3, 2, 1]

Of course, we intended to apply this function to lists, so it is not surprising that it works. What would be surprising is if we could apply it to a Point.

To determine whether a function can be applied to a new type, we apply the fundamental rule of polymorphism: If all of the operations inside the function can be applied to the type, the function can be applied to the type. The operations in the method include copy, reverse, and print.

copy works on any object, and we have already written a __str__ method for Points, so all we need is a reverse method in the Point class:

def reverse(self):
    self.x , self.y = self.y, self.x

Then we can pass Points to front_and_back:

>>>   p = Point(3, 4)
>>>   front_and_back(p)
(3, 4)(4, 3)

The best kind of polymorphism is the unintentional kind, where you discover that a function you have already written can be applied to a type for which you never planned.

7.17. Glossary

class

A user-defined compound type. A class can also be thought of as a template for the objects that are instances of it.instantiate

To create an instance of a class.instance

An object that belongs to a class.object

A compound data type that is often used to model a thing or concept in the real world.attribute

One of the named data items that makes up an instance.pure function

A function that does not modify any of the objects it receives as parameters. Most pure functions are fruitful.modifier

A function that changes one or more of the objects it receives as parameters. Most modifiers are void.functional programming style

A style of program design in which the majority of functions are pure.prototype development

A way of developing programs starting with a prototype and gradually testing and improving it.planned development

A way of developing programs that involves high-level insight into the problem and more planning than incremental development or prototype development.object-oriented language

A language that provides features, such as user-defined classes and inheritance, that facilitate object-oriented programming.object-oriented programming

A style of programming in which data and the operations that manipulate it are organized into classes and methods.method

A function that is defined inside a class definition and is invoked on instances of that class. :override:: To replace a default. Examples include replacing a default parameter with a particular argument and replacing a default method by providing a new method with the same name.initialization method

A special method that is invoked automatically when a new object is created and that initializes the object’s attributes.operator overloading

Extending built-in operators ( +, -, *, >, <, etc.) so that they work with user-defined types.dot product

An operation defined in linear algebra that multiplies two Points and yields a numeric value.scalar multiplication

An operation defined in linear algebra that multiplies each of the coordinates of a Point by a numeric value.polymorphic

A function that can operate on more than one type. If all the operations in a function can be applied to a type, then the function can be applied to a type.

Built-in Types

The following sections describe the standard types that are built into the interpreter.

The principal built-in types are numerics, sequences, mappings, classes, instances and exceptions.

Some collection classes are mutable. The methods that add, subtract, or rearrange their members in place, and don’t return a specific item, never return the collection instance itself but None.

Some operations are supported by several object types; in particular, practically all objects can be compared for equality, tested for truth value, and converted to a string (with the repr() function or the slightly different str() function). The latter function is implicitly used when an object is written by the print() function.

Truth Value Testing

Any object can be tested for truth value, for use in an if or while condition or as operand of the Boolean operations below.

By default, an object is considered true unless its class defines either a __bool__() method that returns False or a __len__() method that returns zero, when called with the object. 1 Here are most of the built-in objects considered false:

constants defined to be false: None and False.
zero of any numeric type: 0, 0.0, 0j, Decimal(0), Fraction(0, 1)
empty sequences and collections: '', (), [], {}, set(), range(0)

Operations and built-in functions that have a Boolean result always return 0 or False for false and 1 or True for true, unless otherwise stated. (Important exception: the Boolean operations or and and always return one of their operands.)

Boolean Operations — `and`, `or`, `not`

These are the Boolean operations, ordered by ascending priority:

Notes:

This is a short-circuit operator, so it only evaluates the second argument if the first one is false.
This is a short-circuit operator, so it only evaluates the second argument if the first one is true.
not has a lower priority than non-Boolean operators, so not a == b is interpreted as not (a == b), and a == not b is a syntax error.

Comparisons

There are eight comparison operations in Python. They all have the same priority (which is higher than that of the Boolean operations). Comparisons can be chained arbitrarily; for example, x < y <= z is equivalent to x < y and y <= z, except that y is evaluated only once (but in both cases z is not evaluated at all when x < y is found to be false).

This table summarizes the comparison operations:

Objects of different types, except different numeric types, never compare equal. The == operator is always defined but for some object types (for example, class objects) is equivalent to is. The <, <=, > and >= operators are only defined where they make sense; for example, they raise a TypeError exception when one of the arguments is a complex number.

Non-identical instances of a class normally compare as non-equal unless the class defines the __eq__() method.

Instances of a class cannot be ordered with respect to other instances of the same class, or other types of object, unless the class defines enough of the methods __lt__(), __le__(), __gt__(), and __ge__() (in general, __lt__() and __eq__() are sufficient, if you want the conventional meanings of the comparison operators).

The behavior of the is and is not operators cannot be customized; also they can be applied to any two objects and never raise an exception.

Two more operations with the same syntactic priority, in and not in, are supported by types that are iterable or implement the __contains__() method.

Numeric Types — `int`, `float`, `complex`

There are three distinct numeric types: integers, floating point numbers, and complex numbers. In addition, Booleans are a subtype of integers. Integers have unlimited precision. Floating point numbers are usually implemented using double in C; information about the precision and internal representation of floating point numbers for the machine on which your program is running is available in sys.float_info. Complex numbers have a real and imaginary part, which are each a floating point number. To extract these parts from a complex number z, use z.real and z.imag. (The standard library includes the additional numeric types fractions.Fraction, for rationals, and decimal.Decimal, for floating-point numbers with user-definable precision.)

Numbers are created by numeric literals or as the result of built-in functions and operators. Unadorned integer literals (including hex, octal and binary numbers) yield integers. Numeric literals containing a decimal point or an exponent sign yield floating point numbers. Appending 'j' or 'J' to a numeric literal yields an imaginary number (a complex number with a zero real part) which you can add to an integer or float to get a complex number with real and imaginary parts.

Python fully supports mixed arithmetic: when a binary arithmetic operator has operands of different numeric types, the operand with the “narrower” type is widened to that of the other, where integer is narrower than floating point, which is narrower than complex. A comparison between numbers of different types behaves as though the exact values of those numbers were being compared. 2

The constructors int(), float(), and complex() can be used to produce numbers of a specific type.

All numeric types (except complex) support the following operations (for priorities of the operations, see Operator precedence):

Notes:

Also referred to as integer division. The resultant value is a whole integer, though the result’s type is not necessarily int. The result is always rounded towards minus infinity: 1//2 is 0, (-1)//2 is -1, 1//(-2) is -1, and (-1)//(-2) is 0.
Not for complex numbers. Instead convert to floats using abs() if appropriate.
Conversion from floating point to integer may round or truncate as in C; see functions math.floor() and math.ceil() for well-defined conversions.
float also accepts the strings “nan” and “inf” with an optional prefix “+” or “-” for Not a Number (NaN) and positive or negative infinity.
Python defines pow(0, 0) and 0 ** 0 to be 1, as is common for programming languages.
The numeric literals accepted include the digits 0 to 9 or any Unicode equivalent (code points with the Nd property).
See https://www.unicode.org/Public/13.0.0/ucd/extracted/DerivedNumericType.txt for a complete list of code points with the Nd property.

All numbers.Real types (int and float) also include the following operations:

For additional numeric operations see the math and cmath modules.

Bitwise Operations on Integer Types

Bitwise operations only make sense for integers. The result of bitwise operations is calculated as though carried out in two’s complement with an infinite number of sign bits.

The priorities of the binary bitwise operations are all lower than the numeric operations and higher than the comparisons; the unary operation ~ has the same priority as the other unary numeric operations (+ and -).

This table lists the bitwise operations sorted in ascending priority:

Notes:

Negative shift counts are illegal and cause a ValueError to be raised.
A left shift by n bits is equivalent to multiplication by pow(2, n).
A right shift by n bits is equivalent to floor division by pow(2, n).
Performing these calculations with at least one extra sign extension bit in a finite two’s complement representation (a working bit-width of 1 + max(x.bit_length(), y.bit_length()) or more) is sufficient to get the same result as if there were an infinite number of sign bits.

Additional Methods on Integer Types

The int type implements the numbers.Integral abstract base class. In addition, it provides a few more methods:int.bit_length()

Return the number of bits necessary to represent an integer in binary, excluding the sign and leading zeros:>>>

>>> n = -37
>>> bin(n)
'-0b100101'
>>> n.bit_length()
6

More precisely, if x is nonzero, then x.bit_length() is the unique positive integer k such that 2**(k-1) <= abs(x) < 2**k. Equivalently, when abs(x) is small enough to have a correctly rounded logarithm, then k = 1 + int(log(abs(x), 2)). If x is zero, then x.bit_length() returns 0.

Equivalent to:

def bit_length(self):
    s = bin(self)       # binary representation:  bin(-37) --> '-0b100101'
    s = s.lstrip('-0b') # remove leading zeros and minus sign
    return len(s)       # len('100101') --> 6

New in version 3.1.int.to_bytes(length, byteorder, *, signed=False)

Return an array of bytes representing an integer.>>>

>>> (1024).to_bytes(2, byteorder='big')
b'\x04\x00'
>>> (1024).to_bytes(10, byteorder='big')
b'\x00\x00\x00\x00\x00\x00\x00\x00\x04\x00'
>>> (-1024).to_bytes(10, byteorder='big', signed=True)
b'\xff\xff\xff\xff\xff\xff\xff\xff\xfc\x00'
>>> x = 1000
>>> x.to_bytes((x.bit_length() + 7) // 8, byteorder='little')
b'\xe8\x03'

The integer is represented using length bytes. An OverflowError is raised if the integer is not representable with the given number of bytes.

The byteorder argument determines the byte order used to represent the integer. If byteorder is "big", the most significant byte is at the beginning of the byte array. If byteorder is "little", the most significant byte is at the end of the byte array. To request the native byte order of the host system, use sys.byteorder as the byte order value.

The signed argument determines whether two’s complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised. The default value for signed is False.

New in version 3.2.classmethod int.from_bytes(bytes, byteorder, *, signed=False)

Return the integer represented by the given array of bytes.>>>

>>> int.from_bytes(b'\x00\x10', byteorder='big')
16
>>> int.from_bytes(b'\x00\x10', byteorder='little')
4096
>>> int.from_bytes(b'\xfc\x00', byteorder='big', signed=True)
-1024
>>> int.from_bytes(b'\xfc\x00', byteorder='big', signed=False)
64512
>>> int.from_bytes([255, 0, 0], byteorder='big')
16711680

The argument bytes must either be a bytes-like object or an iterable producing bytes.

The signed argument indicates whether two’s complement is used to represent the integer.

New in version 3.2.int.as_integer_ratio()

Return a pair of integers whose ratio is exactly equal to the original integer and with a positive denominator. The integer ratio of integers (whole numbers) is always the integer as the numerator and 1 as the denominator.

New in version 3.8.

Additional Methods on Float

The float type implements the numbers.Real abstract base class. float also has the following additional methods.float.as_integer_ratio()

Return a pair of integers whose ratio is exactly equal to the original float and with a positive denominator. Raises OverflowError on infinities and a ValueError on NaNs.float.is_integer()

Return True if the float instance is finite with integral value, and False otherwise:>>>

>>> (-2.0).is_integer()
True
>>> (3.2).is_integer()
False

Two methods support conversion to and from hexadecimal strings. Since Python’s floats are stored internally as binary numbers, converting a float to or from a decimal string usually involves a small rounding error. In contrast, hexadecimal strings allow exact representation and specification of floating-point numbers. This can be useful when debugging, and in numerical work.float.hex()

Return a representation of a floating-point number as a hexadecimal string. For finite floating-point numbers, this representation will always include a leading 0x and a trailing p and exponent.classmethod float.fromhex(s)

Class method to return the float represented by a hexadecimal string s. The string s may have leading and trailing whitespace.

Note that float.hex() is an instance method, while float.fromhex() is a class method.

A hexadecimal string takes the form:

[sign] ['0x'] integer ['.' fraction] ['p' exponent]

where the optional sign may by either + or -, integer and fraction are strings of hexadecimal digits, and exponent is a decimal integer with an optional leading sign. Case is not significant, and there must be at least one hexadecimal digit in either the integer or the fraction. This syntax is similar to the syntax specified in section 6.4.4.2 of the C99 standard, and also to the syntax used in Java 1.5 onwards. In particular, the output of float.hex() is usable as a hexadecimal floating-point literal in C or Java code, and hexadecimal strings produced by C’s %a format character or Java’s Double.toHexString are accepted by float.fromhex().

Note that the exponent is written in decimal rather than hexadecimal, and that it gives the power of 2 by which to multiply the coefficient. For example, the hexadecimal string 0x3.a7p10 represents the floating-point number (3 + 10./16 + 7./16**2) * 2.0**10, or 3740.0:>>>

>>> float.fromhex('0x3.a7p10')
3740.0

Applying the reverse conversion to 3740.0 gives a different hexadecimal string representing the same number:>>>

>>> float.hex(3740.0)
'0x1.d380000000000p+11'

Hashing of numeric types

For numbers x and y, possibly of different types, it’s a requirement that hash(x) == hash(y) whenever x == y (see the __hash__() method documentation for more details). For ease of implementation and efficiency across a variety of numeric types (including int, float, decimal.Decimal and fractions.Fraction) Python’s hash for numeric types is based on a single mathematical function that’s defined for any rational number, and hence applies to all instances of int and fractions.Fraction, and all finite instances of float and decimal.Decimal. Essentially, this function is given by reduction modulo P for a fixed prime P. The value of P is made available to Python as the modulus attribute of sys.hash_info.

CPython implementation detail: Currently, the prime used is P = 2**31 - 1 on machines with 32-bit C longs and P = 2**61 - 1 on machines with 64-bit C longs.

Here are the rules in detail:

If x = m / n is a nonnegative rational number and n is not divisible by P, define hash(x) as m * invmod(n, P) % P, where invmod(n, P) gives the inverse of n modulo P.
If x = m / n is a nonnegative rational number and n is divisible by P (but m is not) then n has no inverse modulo P and the rule above doesn’t apply; in this case define hash(x) to be the constant value sys.hash_info.inf.
If x = m / n is a negative rational number define hash(x) as -hash(-x). If the resulting hash is -1, replace it with -2.
The particular values sys.hash_info.inf, -sys.hash_info.inf and sys.hash_info.nan are used as hash values for positive infinity, negative infinity, or nans (respectively). (All hashable nans have the same hash value.)
For a complex number z, the hash values of the real and imaginary parts are combined by computing hash(z.real) + sys.hash_info.imag * hash(z.imag), reduced modulo 2**sys.hash_info.width so that it lies in range(-2**(sys.hash_info.width - 1), 2**(sys.hash_info.width - 1)). Again, if the result is -1, it’s replaced with -2.

To clarify the above rules, here’s some example Python code, equivalent to the built-in hash, for computing the hash of a rational number, float, or complex:

import sys, math

def hash_fraction(m, n):
    """Compute the hash of a rational number m / n.

    Assumes m and n are integers, with n positive.
    Equivalent to hash(fractions.Fraction(m, n)).

    """
    P = sys.hash_info.modulus
    # Remove common factors of P.  (Unnecessary if m and n already coprime.)
    while m % P == n % P == 0:
        m, n = m // P, n // P

    if n % P == 0:
        hash_value = sys.hash_info.inf
    else:
        # Fermat's Little Theorem: pow(n, P-1, P) is 1, so
        # pow(n, P-2, P) gives the inverse of n modulo P.
        hash_value = (abs(m) % P) * pow(n, P - 2, P) % P
    if m < 0:
        hash_value = -hash_value
    if hash_value == -1:
        hash_value = -2
    return hash_value

def hash_float(x):
    """Compute the hash of a float x."""

    if math.isnan(x):
        return sys.hash_info.nan
    elif math.isinf(x):
        return sys.hash_info.inf if x > 0 else -sys.hash_info.inf
    else:
        return hash_fraction(*x.as_integer_ratio())

def hash_complex(z):
    """Compute the hash of a complex number z."""

    hash_value = hash_float(z.real) + sys.hash_info.imag * hash_float(z.imag)
    # do a signed reduction modulo 2**sys.hash_info.width
    M = 2**(sys.hash_info.width - 1)
    hash_value = (hash_value & (M - 1)) - (hash_value & M)
    if hash_value == -1:
        hash_value = -2
    return hash_value

Iterator Types

Python supports a concept of iteration over containers. This is implemented using two distinct methods; these are used to allow user-defined classes to support iteration. Sequences, described below in more detail, always support the iteration methods.

One method needs to be defined for container objects to provide iteration support:container.__iter__()

Return an iterator object. The object is required to support the iterator protocol described below. If a container supports different types of iteration, additional methods can be provided to specifically request iterators for those iteration types. (An example of an object supporting multiple forms of iteration would be a tree structure which supports both breadth-first and depth-first traversal.) This method corresponds to the tp_iter slot of the type structure for Python objects in the Python/C API.

The iterator objects themselves are required to support the following two methods, which together form the iterator protocol:iterator.__iter__()

Return the iterator object itself. This is required to allow both containers and iterators to be used with the for and in statements. This method corresponds to the tp_iter slot of the type structure for Python objects in the Python/C API.iterator.__next__()

Return the next item from the container. If there are no further items, raise the StopIteration exception. This method corresponds to the tp_iternext slot of the type structure for Python objects in the Python/C API.

Python defines several iterator objects to support iteration over general and specific sequence types, dictionaries, and other more specialized forms. The specific types are not important beyond their implementation of the iterator protocol.

Once an iterator’s __next__() method raises StopIteration, it must continue to do so on subsequent calls. Implementations that do not obey this property are deemed broken.

Generator Types

Python’s generators provide a convenient way to implement the iterator protocol. If a container object’s __iter__() method is implemented as a generator, it will automatically return an iterator object (technically, a generator object) supplying the __iter__() and __next__() methods. More information about generators can be found in the documentation for the yield expression.

Sequence Types — `list`, `tuple`, `range`

There are three basic sequence types: lists, tuples, and range objects. Additional sequence types tailored for processing of binary data and text strings are described in dedicated sections.

Common Sequence Operations

The operations in the following table are supported by most sequence types, both mutable and immutable. The collections.abc.Sequence ABC is provided to make it easier to correctly implement these operations on custom sequence types.

This table lists the sequence operations sorted in ascending priority. In the table, s and t are sequences of the same type, n, i, j and k are integers and x is an arbitrary object that meets any type and value restrictions imposed by s.

The in and not in operations have the same priorities as the comparison operations. The + (concatenation) and * (repetition) operations have the same priority as the corresponding numeric operations. 3

Sequences of the same type also support comparisons. In particular, tuples and lists are compared lexicographically by comparing corresponding elements. This means that to compare equal, every element must compare equal and the two sequences must be of the same type and have the same length. (For full details see Comparisons in the language reference.)

Notes:

While the in and not in operations are used only for simple containment testing in the general case, some specialised sequences (such as str, bytes and bytearray) also use them for subsequence testing:>>>
```
>>> "gg" in "eggs"
True
```
Values of n less than 0 are treated as 0 (which yields an empty sequence of the same type as s). Note that items in the sequence s are not copied; they are referenced multiple times. This often haunts new Python programmers; consider:>>>
```
>>> lists = [[]] * 3
>>> lists
[[], [], []]
>>> lists[0].append(3)
>>> lists
[[3], [3], [3]]
```
What has happened is that [[]] is a one-element list containing an empty list, so all three elements of [[]] * 3 are references to this single empty list. Modifying any of the elements of lists modifies this single list. You can create a list of different lists this way:>>>
```
>>> lists = [[] for i in range(3)]
>>> lists[0].append(3)
>>> lists[1].append(5)
>>> lists[2].append(7)
>>> lists
[[3], [5], [7]]
```
Further explanation is available in the FAQ entry How do I create a multidimensional list?.
If i or j is negative, the index is relative to the end of sequence s: len(s) + i or len(s) + j is substituted. But note that -0 is still 0.
The slice of s from i to j is defined as the sequence of items with index k such that i <= k < j. If i or j is greater than len(s), use len(s). If i is omitted or None, use 0. If j is omitted or None, use len(s). If i is greater than or equal to j, the slice is empty.
The slice of s from i to j with step k is defined as the sequence of items with index x = i + n*k such that 0 <= n < (j-i)/k. In other words, the indices are i, i+k, i+2*k, i+3*k and so on, stopping when j is reached (but never including j). When k is positive, i and j are reduced to len(s) if they are greater. When k is negative, i and j are reduced to len(s) - 1 if they are greater. If i or j are omitted or None, they become “end” values (which end depends on the sign of k). Note, k cannot be zero. If k is None, it is treated like 1.
Concatenating immutable sequences always results in a new object. This means that building up a sequence by repeated concatenation will have a quadratic runtime cost in the total sequence length. To get a linear runtime cost, you must switch to one of the alternatives below:
- if concatenating str objects, you can build a list and use str.join() at the end or else write to an io.StringIO instance and retrieve its value when complete
- if concatenating bytes objects, you can similarly use bytes.join() or io.BytesIO, or you can do in-place concatenation with a bytearray object. bytearray objects are mutable and have an efficient overallocation mechanism
- if concatenating tuple objects, extend a list instead
- for other types, investigate the relevant class documentation
Some sequence types (such as range) only support item sequences that follow specific patterns, and hence don’t support sequence concatenation or repetition.
index raises ValueError when x is not found in s. Not all implementations support passing the additional arguments i and j. These arguments allow efficient searching of subsections of the sequence. Passing the extra arguments is roughly equivalent to using s[i:j].index(x), only without copying any data and with the returned index being relative to the start of the sequence rather than the start of the slice.

Immutable Sequence Types

The only operation that immutable sequence types generally implement that is not also implemented by mutable sequence types is support for the hash() built-in.

This support allows immutable sequences, such as tuple instances, to be used as dict keys and stored in set and frozenset instances.

Attempting to hash an immutable sequence that contains unhashable values will result in TypeError.

Mutable Sequence Types

The operations in the following table are defined on mutable sequence types. The collections.abc.MutableSequence ABC is provided to make it easier to correctly implement these operations on custom sequence types.

In the table s is an instance of a mutable sequence type, t is any iterable object and x is an arbitrary object that meets any type and value restrictions imposed by s (for example, bytearray only accepts integers that meet the value restriction 0 <= x <= 255).

Notes:

t must have the same length as the slice it is replacing.
The optional argument i defaults to -1, so that by default the last item is removed and returned.
remove() raises ValueError when x is not found in s.
The reverse() method modifies the sequence in place for economy of space when reversing a large sequence. To remind users that it operates by side effect, it does not return the reversed sequence.
clear() and copy() are included for consistency with the interfaces of mutable containers that don’t support slicing operations (such as dict and set). copy() is not part of the collections.abc.MutableSequence ABC, but most concrete mutable sequence classes provide it.
New in version 3.3: clear() and copy() methods.
The value n is an integer, or an object implementing __index__(). Zero and negative values of n clear the sequence. Items in the sequence are not copied; they are referenced multiple times, as explained for s * n under Common Sequence Operations.

Lists

Lists are mutable sequences, typically used to store collections of homogeneous items (where the precise degree of similarity will vary by application).class list([iterable])

Lists may be constructed in several ways:

Using a pair of square brackets to denote the empty list: []
Using square brackets, separating items with commas: [a], [a, b, c]
Using a list comprehension: [x for x in iterable]
Using the type constructor: list() or list(iterable)

The constructor builds a list whose items are the same and in the same order as iterable’s items. iterable may be either a sequence, a container that supports iteration, or an iterator object. If iterable is already a list, a copy is made and returned, similar to iterable[:]. For example, list('abc') returns ['a', 'b', 'c'] and list( (1, 2, 3) ) returns [1, 2, 3]. If no argument is given, the constructor creates a new empty list, [].

Many other operations also produce lists, including the sorted() built-in.

Lists implement all of the common and mutable sequence operations. Lists also provide the following additional method:sort(*, key=None, reverse=False)

This method sorts the list in place, using only < comparisons between items. Exceptions are not suppressed - if any comparison operations fail, the entire sort operation will fail (and the list will likely be left in a partially modified state).

sort() accepts two arguments that can only be passed by keyword (keyword-only arguments):

key specifies a function of one argument that is used to extract a comparison key from each list element (for example, key=str.lower). The key corresponding to each item in the list is calculated once and then used for the entire sorting process. The default value of None means that list items are sorted directly without calculating a separate key value.

The functools.cmp_to_key() utility is available to convert a 2.x style cmp function to a key function.

reverse is a boolean value. If set to True, then the list elements are sorted as if each comparison were reversed.

This method modifies the sequence in place for economy of space when sorting a large sequence. To remind users that it operates by side effect, it does not return the sorted sequence (use sorted() to explicitly request a new sorted list instance).

The sort() method is guaranteed to be stable. A sort is stable if it guarantees not to change the relative order of elements that compare equal — this is helpful for sorting in multiple passes (for example, sort by department, then by salary grade).

For sorting examples and a brief sorting tutorial, see Sorting HOW TO.

CPython implementation detail: While a list is being sorted, the effect of attempting to mutate, or even inspect, the list is undefined. The C implementation of Python makes the list appear empty for the duration, and raises ValueError if it can detect that the list has been mutated during a sort.

Tuples

Tuples are immutable sequences, typically used to store collections of heterogeneous data (such as the 2-tuples produced by the enumerate() built-in). Tuples are also used for cases where an immutable sequence of homogeneous data is needed (such as allowing storage in a set or dict instance).class tuple([iterable])

Tuples may be constructed in a number of ways:

Using a pair of parentheses to denote the empty tuple: ()
Using a trailing comma for a singleton tuple: a, or (a,)
Separating items with commas: a, b, c or (a, b, c)
Using the tuple() built-in: tuple() or tuple(iterable)

The constructor builds a tuple whose items are the same and in the same order as iterable’s items. iterable may be either a sequence, a container that supports iteration, or an iterator object. If iterable is already a tuple, it is returned unchanged. For example, tuple('abc') returns ('a', 'b', 'c') and tuple( [1, 2, 3] ) returns (1, 2, 3). If no argument is given, the constructor creates a new empty tuple, ().

Note that it is actually the comma which makes a tuple, not the parentheses. The parentheses are optional, except in the empty tuple case, or when they are needed to avoid syntactic ambiguity. For example, f(a, b, c) is a function call with three arguments, while f((a, b, c)) is a function call with a 3-tuple as the sole argument.

Tuples implement all of the common sequence operations.

For heterogeneous collections of data where access by name is clearer than access by index, collections.namedtuple() may be a more appropriate choice than a simple tuple object.

Ranges

The range type represents an immutable sequence of numbers and is commonly used for looping a specific number of times in for loops.class range(stop)class range(start, stop[, step])

The arguments to the range constructor must be integers (either built-in int or any object that implements the __index__ special method). If the step argument is omitted, it defaults to 1. If the start argument is omitted, it defaults to 0. If step is zero, ValueError is raised.

For a positive step, the contents of a range r are determined by the formula r[i] = start + step*i where i >= 0 and r[i] < stop.

For a negative step, the contents of the range are still determined by the formula r[i] = start + step*i, but the constraints are i >= 0 and r[i] > stop.

A range object will be empty if r[0] does not meet the value constraint. Ranges do support negative indices, but these are interpreted as indexing from the end of the sequence determined by the positive indices.

Ranges containing absolute values larger than sys.maxsize are permitted but some features (such as len()) may raise OverflowError.

Range examples:>>>

>>> list(range(10))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> list(range(1, 11))
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
>>> list(range(0, 30, 5))
[0, 5, 10, 15, 20, 25]
>>> list(range(0, 10, 3))
[0, 3, 6, 9]
>>> list(range(0, -10, -1))
[0, -1, -2, -3, -4, -5, -6, -7, -8, -9]
>>> list(range(0))
[]
>>> list(range(1, 0))
[]

Ranges implement all of the common sequence operations except concatenation and repetition (due to the fact that range objects can only represent sequences that follow a strict pattern and repetition and concatenation will usually violate that pattern).start

The value of the start parameter (or 0 if the parameter was not supplied)stop

The value of the stop parameterstep

The value of the step parameter (or 1 if the parameter was not supplied)

The advantage of the range type over a regular list or tuple is that a range object will always take the same (small) amount of memory, no matter the size of the range it represents (as it only stores the start, stop and step values, calculating individual items and subranges as needed).

Range objects implement the collections.abc.Sequence ABC, and provide features such as containment tests, element index lookup, slicing and support for negative indices (see Sequence Types — list, tuple, range):>>>

>>> r = range(0, 20, 2)
>>> r
range(0, 20, 2)
>>> 11 in r
False
>>> 10 in r
True
>>> r.index(10)
5
>>> r[5]
10
>>> r[:5]
range(0, 10, 2)
>>> r[-1]
18

Testing range objects for equality with == and != compares them as sequences. That is, two range objects are considered equal if they represent the same sequence of values. (Note that two range objects that compare equal might have different start, stop and step attributes, for example range(0) == range(2, 1, 3) or range(0, 3, 2) == range(0, 4, 2).)

Changed in version 3.2: Implement the Sequence ABC. Support slicing and negative indices. Test int objects for membership in constant time instead of iterating through all items.

Changed in version 3.3: Define ‘==’ and ‘!=’ to compare range objects based on the sequence of values they define (instead of comparing based on object identity).

New in version 3.3: The start, stop and step attributes.

See also

The linspace recipe shows how to implement a lazy version of range suitable for floating point applications.

Text Sequence Type — `str`

Textual data in Python is handled with str objects, or strings. Strings are immutable sequences of Unicode code points. String literals are written in a variety of ways:

Single quotes: 'allows embedded "double" quotes'
Double quotes: "allows embedded 'single' quotes".
Triple quoted: '''Three single quotes''', """Three double quotes"""

Triple quoted strings may span multiple lines - all associated whitespace will be included in the string literal.

String literals that are part of a single expression and have only whitespace between them will be implicitly converted to a single string literal. That is, ("spam " "eggs") == "spam eggs".

See String and Bytes literals for more about the various forms of string literal, including supported escape sequences, and the r (“raw”) prefix that disables most escape sequence processing.

Strings may also be created from other objects using the str constructor.

Since there is no separate “character” type, indexing a string produces strings of length 1. That is, for a non-empty string s, s[0] == s[0:1].

There is also no mutable string type, but str.join() or io.StringIO can be used to efficiently construct strings from multiple fragments.

Changed in version 3.3: For backwards compatibility with the Python 2 series, the u prefix is once again permitted on string literals. It has no effect on the meaning of string literals and cannot be combined with the r prefix.class str(object='')class str(object=b'', encoding='utf-8', errors='strict')

Return a string version of object. If object is not provided, returns the empty string. Otherwise, the behavior of str() depends on whether encoding or errors is given, as follows.

If neither encoding nor errors is given, str(object) returns object.__str__(), which is the “informal” or nicely printable string representation of object. For string objects, this is the string itself. If object does not have a __str__() method, then str() falls back to returning repr(object).

If at least one of encoding or errors is given, object should be a bytes-like object (e.g. bytes or bytearray). In this case, if object is a bytes (or bytearray) object, then str(bytes, encoding, errors) is equivalent to bytes.decode(encoding, errors). Otherwise, the bytes object underlying the buffer object is obtained before calling bytes.decode(). See Binary Sequence Types — bytes, bytearray, memoryview and Buffer Protocol for information on buffer objects.

Passing a bytes object to str() without the encoding or errors arguments falls under the first case of returning the informal string representation (see also the -b command-line option to Python). For example:>>>

>>> str(b'Zoot!')
"b'Zoot!'"

For more information on the str class and its methods, see Text Sequence Type — str and the String Methods section below. To output formatted strings, see the Formatted string literals and Format String Syntax sections. In addition, see the Text Processing Services section.

String Methods

Strings implement all of the common sequence operations, along with the additional methods described below.

Strings also support two styles of string formatting, one providing a large degree of flexibility and customization (see str.format(), Format String Syntax and Custom String Formatting) and the other based on C printf style formatting that handles a narrower range of types and is slightly harder to use correctly, but is often faster for the cases it can handle (printf-style String Formatting).

The Text Processing Services section of the standard library covers a number of other modules that provide various text related utilities (including regular expression support in the re module).str.capitalize()

Return a copy of the string with its first character capitalized and the rest lowercased.

Changed in version 3.8: The first character is now put into titlecase rather than uppercase. This means that characters like digraphs will only have their first letter capitalized, instead of the full character.str.casefold()

Return a casefolded copy of the string. Casefolded strings may be used for caseless matching.

Casefolding is similar to lowercasing but more aggressive because it is intended to remove all case distinctions in a string. For example, the German lowercase letter 'ß' is equivalent to "ss". Since it is already lowercase, lower() would do nothing to 'ß'; casefold() converts it to "ss".

The casefolding algorithm is described in section 3.13 of the Unicode Standard.

New in version 3.3.str.center(width[, fillchar])

Return centered in a string of length width. Padding is done using the specified fillchar (default is an ASCII space). The original string is returned if width is less than or equal to len(s).str.count(sub[, start[, end]])

Return the number of non-overlapping occurrences of substring sub in the range [start, end]. Optional arguments start and end are interpreted as in slice notation.str.encode(encoding="utf-8", errors="strict")

Return an encoded version of the string as a bytes object. Default encoding is 'utf-8'. errors may be given to set a different error handling scheme. The default for errors is 'strict', meaning that encoding errors raise a UnicodeError. Other possible values are 'ignore', 'replace', 'xmlcharrefreplace', 'backslashreplace' and any other name registered via codecs.register_error(), see section Error Handlers. For a list of possible encodings, see section Standard Encodings.

By default, the errors argument is not checked for best performances, but only used at the first encoding error. Enable the Python Development Mode, or use a debug build to check errors.

Changed in version 3.1: Support for keyword arguments added.

Changed in version 3.9: The errors is now checked in development mode and in debug mode.str.endswith(suffix[, start[, end]])

Return True if the string ends with the specified suffix, otherwise return False. suffix can also be a tuple of suffixes to look for. With optional start, test beginning at that position. With optional end, stop comparing at that position.str.expandtabs(tabsize=8)

Return a copy of the string where all tab characters are replaced by one or more spaces, depending on the current column and the given tab size. Tab positions occur every tabsize characters (default is 8, giving tab positions at columns 0, 8, 16 and so on). To expand the string, the current column is set to zero and the string is examined character by character. If the character is a tab (\t), one or more space characters are inserted in the result until the current column is equal to the next tab position. (The tab character itself is not copied.) If the character is a newline (\n) or return (\r), it is copied and the current column is reset to zero. Any other character is copied unchanged and the current column is incremented by one regardless of how the character is represented when printed.>>>

>>> '01\t012\t0123\t01234'.expandtabs()
'01      012     0123    01234'
>>> '01\t012\t0123\t01234'.expandtabs(4)
'01  012 0123    01234'

str.find(sub[, start[, end]])

Return the lowest index in the string where substring sub is found within the slice s[start:end]. Optional arguments start and end are interpreted as in slice notation. Return -1 if sub is not found.

Note

The find() method should be used only if you need to know the position of sub. To check if sub is a substring or not, use the in operator:>>>

>>> 'Py' in 'Python'
True

str.format(*args, **kwargs)

Perform a string formatting operation. The string on which this method is called can contain literal text or replacement fields delimited by braces {}. Each replacement field contains either the numeric index of a positional argument, or the name of a keyword argument. Returns a copy of the string where each replacement field is replaced with the string value of the corresponding argument.>>>

>>> "The sum of 1 + 2 is {0}".format(1+2)
'The sum of 1 + 2 is 3'

See Format String Syntax for a description of the various formatting options that can be specified in format strings.

Note

When formatting a number (int, float, complex, decimal.Decimal and subclasses) with the n type (ex: '{:n}'.format(1234)), the function temporarily sets the LC_CTYPE locale to the LC_NUMERIC locale to decode decimal_point and thousands_sep fields of localeconv() if they are non-ASCII or longer than 1 byte, and the LC_NUMERIC locale is different than the LC_CTYPE locale. This temporary change affects other threads.

Changed in version 3.7: When formatting a number with the n type, the function sets temporarily the LC_CTYPE locale to the LC_NUMERIC locale in some cases.str.format_map(mapping)

Similar to str.format(**mapping), except that mapping is used directly and not copied to a dict. This is useful if for example mapping is a dict subclass:>>>

>>> class Default(dict):
...     def __missing__(self, key):
...         return key
...
>>> '{name} was born in {country}'.format_map(Default(name='Guido'))
'Guido was born in country'

New in version 3.2.str.index(sub[, start[, end]])

Like find(), but raise ValueError when the substring is not found.str.isalnum()

Return True if all characters in the string are alphanumeric and there is at least one character, False otherwise. A character c is alphanumeric if one of the following returns True: c.isalpha(), c.isdecimal(), c.isdigit(), or c.isnumeric().str.isalpha()

Return True if all characters in the string are alphabetic and there is at least one character, False otherwise. Alphabetic characters are those characters defined in the Unicode character database as “Letter”, i.e., those with general category property being one of “Lm”, “Lt”, “Lu”, “Ll”, or “Lo”. Note that this is different from the “Alphabetic” property defined in the Unicode Standard.str.isascii()

Return True if the string is empty or all characters in the string are ASCII, False otherwise. ASCII characters have code points in the range U+0000-U+007F.

New in version 3.7.str.isdecimal()

Return True if all characters in the string are decimal characters and there is at least one character, False otherwise. Decimal characters are those that can be used to form numbers in base 10, e.g. U+0660, ARABIC-INDIC DIGIT ZERO. Formally a decimal character is a character in the Unicode General Category “Nd”.str.isdigit()

Return True if all characters in the string are digits and there is at least one character, False otherwise. Digits include decimal characters and digits that need special handling, such as the compatibility superscript digits. This covers digits which cannot be used to form numbers in base 10, like the Kharosthi numbers. Formally, a digit is a character that has the property value Numeric_Type=Digit or Numeric_Type=Decimal.str.isidentifier()

Return True if the string is a valid identifier according to the language definition, section Identifiers and keywords.

Call keyword.iskeyword() to test whether string s is a reserved identifier, such as def and class.

Example:>>>

>>> from keyword import iskeyword

>>> 'hello'.isidentifier(), iskeyword('hello')
True, False
>>> 'def'.isidentifier(), iskeyword('def')
True, True

str.islower()

Return True if all cased characters 4 in the string are lowercase and there is at least one cased character, False otherwise.str.isnumeric()

Return True if all characters in the string are numeric characters, and there is at least one character, False otherwise. Numeric characters include digit characters, and all characters that have the Unicode numeric value property, e.g. U+2155, VULGAR FRACTION ONE FIFTH. Formally, numeric characters are those with the property value Numeric_Type=Digit, Numeric_Type=Decimal or Numeric_Type=Numeric.str.isprintable()

Return True if all characters in the string are printable or the string is empty, False otherwise. Nonprintable characters are those characters defined in the Unicode character database as “Other” or “Separator”, excepting the ASCII space (0x20) which is considered printable. (Note that printable characters in this context are those which should not be escaped when repr() is invoked on a string. It has no bearing on the handling of strings written to sys.stdout or sys.stderr.)str.isspace()

Return True if there are only whitespace characters in the string and there is at least one character, False otherwise.

A character is whitespace if in the Unicode character database (see unicodedata), either its general category is Zs (“Separator, space”), or its bidirectional class is one of WS, B, or S.str.istitle()

Return True if the string is a titlecased string and there is at least one character, for example uppercase characters may only follow uncased characters and lowercase characters only cased ones. Return False otherwise.str.isupper()

Return True if all cased characters 4 in the string are uppercase and there is at least one cased character, False otherwise.>>>

>>> 'BANANA'.isupper()
True
>>> 'banana'.isupper()
False
>>> 'baNana'.isupper()
False
>>> ' '.isupper()
False

str.join(iterable)

Return a string which is the concatenation of the strings in iterable. A TypeError will be raised if there are any non-string values in iterable, including bytes objects. The separator between elements is the string providing this method.str.ljust(width[, fillchar])

Return the string left justified in a string of length width. Padding is done using the specified fillchar (default is an ASCII space). The original string is returned if width is less than or equal to len(s).str.lower()

Return a copy of the string with all the cased characters 4 converted to lowercase.

The lowercasing algorithm used is described in section 3.13 of the Unicode Standard.str.lstrip([chars])

Return a copy of the string with leading characters removed. The chars argument is a string specifying the set of characters to be removed. If omitted or None, the chars argument defaults to removing whitespace. The chars argument is not a prefix; rather, all combinations of its values are stripped:>>>

>>> '   spacious   '.lstrip()
'spacious   '
>>> 'www.example.com'.lstrip('cmowz.')
'example.com'

See str.removeprefix() for a method that will remove a single prefix string rather than all of a set of characters. For example:>>>

>>> 'Arthur: three!'.lstrip('Arthur: ')
'ee!'
>>> 'Arthur: three!'.removeprefix('Arthur: ')
'three!'

static str.maketrans(x[, y[, z]])

This static method returns a translation table usable for str.translate().

If there is only one argument, it must be a dictionary mapping Unicode ordinals (integers) or characters (strings of length 1) to Unicode ordinals, strings (of arbitrary lengths) or None. Character keys will then be converted to ordinals.

If there are two arguments, they must be strings of equal length, and in the resulting dictionary, each character in x will be mapped to the character at the same position in y. If there is a third argument, it must be a string, whose characters will be mapped to None in the result.str.partition(sep)

Split the string at the first occurrence of sep, and return a 3-tuple containing the part before the separator, the separator itself, and the part after the separator. If the separator is not found, return a 3-tuple containing the string itself, followed by two empty strings.str.removeprefix(prefix, /)

If the string starts with the prefix string, return string[len(prefix):]. Otherwise, return a copy of the original string:>>>

>>> 'TestHook'.removeprefix('Test')
'Hook'
>>> 'BaseTestCase'.removeprefix('Test')
'BaseTestCase'

New in version 3.9.str.removesuffix(suffix, /)

If the string ends with the suffix string and that suffix is not empty, return string[:-len(suffix)]. Otherwise, return a copy of the original string:>>>

>>> 'MiscTests'.removesuffix('Tests')
'Misc'
>>> 'TmpDirMixin'.removesuffix('Tests')
'TmpDirMixin'

New in version 3.9.str.replace(old, new[, count])

Return a copy of the string with all occurrences of substring old replaced by new. If the optional argument count is given, only the first count occurrences are replaced.str.rfind(sub[, start[, end]])

Return the highest index in the string where substring sub is found, such that sub is contained within s[start:end]. Optional arguments start and end are interpreted as in slice notation. Return -1 on failure.str.rindex(sub[, start[, end]])

Like rfind() but raises ValueError when the substring sub is not found.str.rjust(width[, fillchar])

Return the string right justified in a string of length width. Padding is done using the specified fillchar (default is an ASCII space). The original string is returned if width is less than or equal to len(s).str.rpartition(sep)

Split the string at the last occurrence of sep, and return a 3-tuple containing the part before the separator, the separator itself, and the part after the separator. If the separator is not found, return a 3-tuple containing two empty strings, followed by the string itself.str.rsplit(sep=None, maxsplit=-1)

Return a list of the words in the string, using sep as the delimiter string. If maxsplit is given, at most maxsplit splits are done, the rightmost ones. If sep is not specified or None, any whitespace string is a separator. Except for splitting from the right, rsplit() behaves like split() which is described in detail below.str.rstrip([chars])

Return a copy of the string with trailing characters removed. The chars argument is a string specifying the set of characters to be removed. If omitted or None, the chars argument defaults to removing whitespace. The chars argument is not a suffix; rather, all combinations of its values are stripped:>>>

>>> '   spacious   '.rstrip()
'   spacious'
>>> 'mississippi'.rstrip('ipz')
'mississ'

See str.removesuffix() for a method that will remove a single suffix string rather than all of a set of characters. For example:>>>

>>> 'Monty Python'.rstrip(' Python')
'M'
>>> 'Monty Python'.removesuffix(' Python')
'Monty'

str.split(sep=None, maxsplit=-1)

Return a list of the words in the string, using sep as the delimiter string. If maxsplit is given, at most maxsplit splits are done (thus, the list will have at most maxsplit+1 elements). If maxsplit is not specified or -1, then there is no limit on the number of splits (all possible splits are made).

If sep is given, consecutive delimiters are not grouped together and are deemed to delimit empty strings (for example, '1,,2'.split(',') returns ['1', '', '2']). The sep argument may consist of multiple characters (for example, '1<>2<>3'.split('<>') returns ['1', '2', '3']). Splitting an empty string with a specified separator returns [''].

For example:>>>

>>> '1,2,3'.split(',')
['1', '2', '3']
>>> '1,2,3'.split(',', maxsplit=1)
['1', '2,3']
>>> '1,2,,3,'.split(',')
['1', '2', '', '3', '']

If sep is not specified or is None, a different splitting algorithm is applied: runs of consecutive whitespace are regarded as a single separator, and the result will contain no empty strings at the start or end if the string has leading or trailing whitespace. Consequently, splitting an empty string or a string consisting of just whitespace with a None separator returns [].

For example:>>>

>>> '1 2 3'.split()
['1', '2', '3']
>>> '1 2 3'.split(maxsplit=1)
['1', '2 3']
>>> '   1   2   3   '.split()
['1', '2', '3']

str.splitlines([keepends])

Return a list of the lines in the string, breaking at line boundaries. Line breaks are not included in the resulting list unless keepends is given and true.

This method splits on the following line boundaries. In particular, the boundaries are a superset of universal newlines.

Changed in version 3.2: \v and \f added to list of line boundaries.

For example:>>>

>>> 'ab c\n\nde fg\rkl\r\n'.splitlines()
['ab c', '', 'de fg', 'kl']
>>> 'ab c\n\nde fg\rkl\r\n'.splitlines(keepends=True)
['ab c\n', '\n', 'de fg\r', 'kl\r\n']

Unlike split() when a delimiter string sep is given, this method returns an empty list for the empty string, and a terminal line break does not result in an extra line:>>>

>>> "".splitlines()
[]
>>> "One line\n".splitlines()
['One line']

For comparison, split('\n') gives:>>>

>>> ''.split('\n')
['']
>>> 'Two lines\n'.split('\n')
['Two lines', '']

str.startswith(prefix[, start[, end]])

Return True if string starts with the prefix, otherwise return False. prefix can also be a tuple of prefixes to look for. With optional start, test string beginning at that position. With optional end, stop comparing string at that position.str.strip([chars])

Return a copy of the string with the leading and trailing characters removed. The chars argument is a string specifying the set of characters to be removed. If omitted or None, the chars argument defaults to removing whitespace. The chars argument is not a prefix or suffix; rather, all combinations of its values are stripped:>>>

>>> '   spacious   '.strip()
'spacious'
>>> 'www.example.com'.strip('cmowz.')
'example'

The outermost leading and trailing chars argument values are stripped from the string. Characters are removed from the leading end until reaching a string character that is not contained in the set of characters in chars. A similar action takes place on the trailing end. For example:>>>

>>> comment_string = '#....... Section 3.2.1 Issue #32 .......'
>>> comment_string.strip('.#! ')
'Section 3.2.1 Issue #32'

str.swapcase()

Return a copy of the string with uppercase characters converted to lowercase and vice versa. Note that it is not necessarily true that s.swapcase().swapcase() == s.str.title()

Return a titlecased version of the string where words start with an uppercase character and the remaining characters are lowercase.

For example:>>>

>>> 'Hello world'.title()
'Hello World'

The algorithm uses a simple language-independent definition of a word as groups of consecutive letters. The definition works in many contexts but it means that apostrophes in contractions and possessives form word boundaries, which may not be the desired result:>>>

>>> "they're bill's friends from the UK".title()
"They'Re Bill'S Friends From The Uk"

A workaround for apostrophes can be constructed using regular expressions:>>>

>>> import re
>>> def titlecase(s):
...     return re.sub(r"[A-Za-z]+('[A-Za-z]+)?",
...                   lambda mo: mo.group(0).capitalize(),
...                   s)
...
>>> titlecase("they're bill's friends.")
"They're Bill's Friends."

str.translate(table)

Return a copy of the string in which each character has been mapped through the given translation table. The table must be an object that implements indexing via __getitem__(), typically a mapping or sequence. When indexed by a Unicode ordinal (an integer), the table object can do any of the following: return a Unicode ordinal or a string, to map the character to one or more other characters; return None, to delete the character from the return string; or raise a LookupError exception, to map the character to itself.

You can use str.maketrans() to create a translation map from character-to-character mappings in different formats.

See also the codecs module for a more flexible approach to custom character mappings.str.upper()

Return a copy of the string with all the cased characters 4 converted to uppercase. Note that s.upper().isupper() might be False if s contains uncased characters or if the Unicode category of the resulting character(s) is not “Lu” (Letter, uppercase), but e.g. “Lt” (Letter, titlecase).

The uppercasing algorithm used is described in section 3.13 of the Unicode Standard.str.zfill(width)

Return a copy of the string left filled with ASCII '0' digits to make a string of length width. A leading sign prefix ('+'/'-') is handled by inserting the padding after the sign character rather than before. The original string is returned if width is less than or equal to len(s).

For example:>>>

>>> "42".zfill(5)
'00042'
>>> "-42".zfill(5)
'-0042'

`printf`-style String Formatting

Note

The formatting operations described here exhibit a variety of quirks that lead to a number of common errors (such as failing to display tuples and dictionaries correctly). Using the newer formatted string literals, the str.format() interface, or template strings may help avoid these errors. Each of these alternatives provides their own trade-offs and benefits of simplicity, flexibility, and/or extensibility.

String objects have one unique built-in operation: the % operator (modulo). This is also known as the string formatting or interpolation operator. Given format % values (where format is a string), % conversion specifications in format are replaced with zero or more elements of values. The effect is similar to using the sprintf() in the C language.

If format requires a single argument, values may be a single non-tuple object. 5 Otherwise, values must be a tuple with exactly the number of items specified by the format string, or a single mapping object (for example, a dictionary).

A conversion specifier contains two or more characters and has the following components, which must occur in this order:

The '%' character, which marks the start of the specifier.
Mapping key (optional), consisting of a parenthesised sequence of characters (for example, (somename)).
Conversion flags (optional), which affect the result of some conversion types.
Minimum field width (optional). If specified as an '*' (asterisk), the actual width is read from the next element of the tuple in values, and the object to convert comes after the minimum field width and optional precision.
Precision (optional), given as a '.' (dot) followed by the precision. If specified as '*' (an asterisk), the actual precision is read from the next element of the tuple in values, and the value to convert comes after the precision.
Length modifier (optional).
Conversion type.

When the right argument is a dictionary (or other mapping type), then the formats in the string must include a parenthesised mapping key into that dictionary inserted immediately after the '%' character. The mapping key selects the value to be formatted from the mapping. For example:>>>

>>> print('%(language)s has %(number)03d quote types.' %
...       {'language': "Python", "number": 2})
Python has 002 quote types.

In this case no * specifiers may occur in a format (since they require a sequential parameter list).

The conversion flag characters are:

A length modifier (h, l, or L) may be present, but is ignored as it is not necessary for Python – so e.g. %ld is identical to %d.

The conversion types are:

Notes:

The alternate form causes a leading octal specifier ('0o') to be inserted before the first digit.
The alternate form causes a leading '0x' or '0X' (depending on whether the 'x' or 'X' format was used) to be inserted before the first digit.
The alternate form causes the result to always contain a decimal point, even if no digits follow it.
The precision determines the number of digits after the decimal point and defaults to 6.
The alternate form causes the result to always contain a decimal point, and trailing zeroes are not removed as they would otherwise be.
The precision determines the number of significant digits before and after the decimal point and defaults to 6.
If precision is N, the output is truncated to N characters.
See PEP 237.

Since Python strings have an explicit length, %s conversions do not assume that '\0' is the end of the string.

Changed in version 3.1: %f conversions for numbers whose absolute value is over 1e50 are no longer replaced by %g conversions.

Binary Sequence Types — `bytes`, `bytearray`, `memoryview`

The core built-in types for manipulating binary data are bytes and bytearray. They are supported by memoryview which uses the buffer protocol to access the memory of other binary objects without needing to make a copy.

The array module supports efficient storage of basic data types like 32-bit integers and IEEE754 double-precision floating values.

Bytes Objects

Bytes objects are immutable sequences of single bytes. Since many major binary protocols are based on the ASCII text encoding, bytes objects offer several methods that are only valid when working with ASCII compatible data and are closely related to string objects in a variety of other ways.class bytes([source[, encoding[, errors]]])

Firstly, the syntax for bytes literals is largely the same as that for string literals, except that a b prefix is added:

Single quotes: b'still allows embedded "double" quotes'
Double quotes: b"still allows embedded 'single' quotes".
Triple quoted: b'''3 single quotes''', b"""3 double quotes"""

Only ASCII characters are permitted in bytes literals (regardless of the declared source code encoding). Any binary values over 127 must be entered into bytes literals using the appropriate escape sequence.

As with string literals, bytes literals may also use a r prefix to disable processing of escape sequences. See String and Bytes literals for more about the various forms of bytes literal, including supported escape sequences.

While bytes literals and representations are based on ASCII text, bytes objects actually behave like immutable sequences of integers, with each value in the sequence restricted such that 0 <= x < 256 (attempts to violate this restriction will trigger ValueError). This is done deliberately to emphasise that while many binary formats include ASCII based elements and can be usefully manipulated with some text-oriented algorithms, this is not generally the case for arbitrary binary data (blindly applying text processing algorithms to binary data formats that are not ASCII compatible will usually lead to data corruption).

In addition to the literal forms, bytes objects can be created in a number of other ways:

A zero-filled bytes object of a specified length: bytes(10)
From an iterable of integers: bytes(range(20))
Copying existing binary data via the buffer protocol: bytes(obj)

Also see the bytes built-in.

Since 2 hexadecimal digits correspond precisely to a single byte, hexadecimal numbers are a commonly used format for describing binary data. Accordingly, the bytes type has an additional class method to read data in that format:classmethod fromhex(string)

This bytes class method returns a bytes object, decoding the given string object. The string must contain two hexadecimal digits per byte, with ASCII whitespace being ignored.>>>

>>> bytes.fromhex('2Ef0 F1f2  ')
b'.\xf0\xf1\xf2'

Changed in version 3.7: bytes.fromhex() now skips all ASCII whitespace in the string, not just spaces.

A reverse conversion function exists to transform a bytes object into its hexadecimal representation.hex([sep[, bytes_per_sep]])

Return a string object containing two hexadecimal digits for each byte in the instance.>>>

>>> b'\xf0\xf1\xf2'.hex()
'f0f1f2'

If you want to make the hex string easier to read, you can specify a single character separator sep parameter to include in the output. By default between each byte. A second optional bytes_per_sep parameter controls the spacing. Positive values calculate the separator position from the right, negative values from the left.>>>

>>> value = b'\xf0\xf1\xf2'
>>> value.hex('-')
'f0-f1-f2'
>>> value.hex('_', 2)
'f0_f1f2'
>>> b'UUDDLRLRAB'.hex(' ', -4)
'55554444 4c524c52 4142'

New in version 3.5.

Changed in version 3.8: bytes.hex() now supports optional sep and bytes_per_sep parameters to insert separators between bytes in the hex output.

Since bytes objects are sequences of integers (akin to a tuple), for a bytes object b, b[0] will be an integer, while b[0:1] will be a bytes object of length 1. (This contrasts with text strings, where both indexing and slicing will produce a string of length 1)

The representation of bytes objects uses the literal format (b'...') since it is often more useful than e.g. bytes([46, 46, 46]). You can always convert a bytes object into a list of integers using list(b).

Note

For Python 2.x users: In the Python 2.x series, a variety of implicit conversions between 8-bit strings (the closest thing 2.x offers to a built-in binary data type) and Unicode strings were permitted. This was a backwards compatibility workaround to account for the fact that Python originally only supported 8-bit text, and Unicode text was a later addition. In Python 3.x, those implicit conversions are gone - conversions between 8-bit binary data and Unicode text must be explicit, and bytes and string objects will always compare unequal.

Bytearray Objects

bytearray objects are a mutable counterpart to bytes objects.class bytearray([source[, encoding[, errors]]])

There is no dedicated literal syntax for bytearray objects, instead they are always created by calling the constructor:

Creating an empty instance: bytearray()
Creating a zero-filled instance with a given length: bytearray(10)
From an iterable of integers: bytearray(range(20))
Copying existing binary data via the buffer protocol: bytearray(b'Hi!')

As bytearray objects are mutable, they support the mutable sequence operations in addition to the common bytes and bytearray operations described in Bytes and Bytearray Operations.

Also see the bytearray built-in.

Since 2 hexadecimal digits correspond precisely to a single byte, hexadecimal numbers are a commonly used format for describing binary data. Accordingly, the bytearray type has an additional class method to read data in that format:classmethod fromhex(string)

This bytearray class method returns bytearray object, decoding the given string object. The string must contain two hexadecimal digits per byte, with ASCII whitespace being ignored.>>>

>>> bytearray.fromhex('2Ef0 F1f2  ')
bytearray(b'.\xf0\xf1\xf2')

Changed in version 3.7: bytearray.fromhex() now skips all ASCII whitespace in the string, not just spaces.

A reverse conversion function exists to transform a bytearray object into its hexadecimal representation.hex([sep[, bytes_per_sep]])

Return a string object containing two hexadecimal digits for each byte in the instance.>>>

>>> bytearray(b'\xf0\xf1\xf2').hex()
'f0f1f2'

New in version 3.5.

Changed in version 3.8: Similar to bytes.hex(), bytearray.hex() now supports optional sep and bytes_per_sep parameters to insert separators between bytes in the hex output.

Since bytearray objects are sequences of integers (akin to a list), for a bytearray object b, b[0] will be an integer, while b[0:1] will be a bytearray object of length 1. (This contrasts with text strings, where both indexing and slicing will produce a string of length 1)

The representation of bytearray objects uses the bytes literal format (bytearray(b'...')) since it is often more useful than e.g. bytearray([46, 46, 46]). You can always convert a bytearray object into a list of integers using list(b).

Bytes and Bytearray Operations

Both bytes and bytearray objects support the common sequence operations. They interoperate not just with operands of the same type, but with any bytes-like object. Due to this flexibility, they can be freely mixed in operations without causing errors. However, the return type of the result may depend on the order of operands.

Note

The methods on bytes and bytearray objects don’t accept strings as their arguments, just as the methods on strings don’t accept bytes as their arguments. For example, you have to write:

a = "abc"
b = a.replace("a", "f")

and:

a = b"abc"
b = a.replace(b"a", b"f")

Some bytes and bytearray operations assume the use of ASCII compatible binary formats, and hence should be avoided when working with arbitrary binary data. These restrictions are covered below.

Note

Using these ASCII based operations to manipulate binary data that is not stored in an ASCII based format may lead to data corruption.

The following methods on bytes and bytearray objects can be used with arbitrary binary data.bytes.count(sub[, start[, end]])bytearray.count(sub[, start[, end]])

Return the number of non-overlapping occurrences of subsequence sub in the range [start, end]. Optional arguments start and end are interpreted as in slice notation.

The subsequence to search for may be any bytes-like object or an integer in the range 0 to 255.

Changed in version 3.3: Also accept an integer in the range 0 to 255 as the subsequence.bytes.removeprefix(prefix, /)bytearray.removeprefix(prefix, /)

If the binary data starts with the prefix string, return bytes[len(prefix):]. Otherwise, return a copy of the original binary data:>>>

>>> b'TestHook'.removeprefix(b'Test')
b'Hook'
>>> b'BaseTestCase'.removeprefix(b'Test')
b'BaseTestCase'

The prefix may be any bytes-like object.

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.

New in version 3.9.bytes.removesuffix(suffix, /)bytearray.removesuffix(suffix, /)

If the binary data ends with the suffix string and that suffix is not empty, return bytes[:-len(suffix)]. Otherwise, return a copy of the original binary data:>>>

>>> b'MiscTests'.removesuffix(b'Tests')
b'Misc'
>>> b'TmpDirMixin'.removesuffix(b'Tests')
b'TmpDirMixin'

The suffix may be any bytes-like object.

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.

New in version 3.9.bytes.decode(encoding="utf-8", errors="strict")bytearray.decode(encoding="utf-8", errors="strict")

Return a string decoded from the given bytes. Default encoding is 'utf-8'. errors may be given to set a different error handling scheme. The default for errors is 'strict', meaning that encoding errors raise a UnicodeError. Other possible values are 'ignore', 'replace' and any other name registered via codecs.register_error(), see section Error Handlers. For a list of possible encodings, see section Standard Encodings.

By default, the errors argument is not checked for best performances, but only used at the first decoding error. Enable the Python Development Mode, or use a debug build to check errors.

Note

Passing the encoding argument to str allows decoding any bytes-like object directly, without needing to make a temporary bytes or bytearray object.

Changed in version 3.1: Added support for keyword arguments.

Changed in version 3.9: The errors is now checked in development mode and in debug mode.bytes.endswith(suffix[, start[, end]])bytearray.endswith(suffix[, start[, end]])

Return True if the binary data ends with the specified suffix, otherwise return False. suffix can also be a tuple of suffixes to look for. With optional start, test beginning at that position. With optional end, stop comparing at that position.

The suffix(es) to search for may be any bytes-like object.bytes.find(sub[, start[, end]])bytearray.find(sub[, start[, end]])

Return the lowest index in the data where the subsequence sub is found, such that sub is contained in the slice s[start:end]. Optional arguments start and end are interpreted as in slice notation. Return -1 if sub is not found.

The subsequence to search for may be any bytes-like object or an integer in the range 0 to 255.

Note

The find() method should be used only if you need to know the position of sub. To check if sub is a substring or not, use the in operator:>>>

>>> b'Py' in b'Python'
True

Changed in version 3.3: Also accept an integer in the range 0 to 255 as the subsequence.bytes.index(sub[, start[, end]])bytearray.index(sub[, start[, end]])

Like find(), but raise ValueError when the subsequence is not found.

The subsequence to search for may be any bytes-like object or an integer in the range 0 to 255.

Changed in version 3.3: Also accept an integer in the range 0 to 255 as the subsequence.bytes.join(iterable)bytearray.join(iterable)

Return a bytes or bytearray object which is the concatenation of the binary data sequences in iterable. A TypeError will be raised if there are any values in iterable that are not bytes-like objects, including str objects. The separator between elements is the contents of the bytes or bytearray object providing this method.static bytes.maketrans(from, to)static bytearray.maketrans(from, to)

This static method returns a translation table usable for bytes.translate() that will map each character in from into the character at the same position in to; from and to must both be bytes-like objects and have the same length.

New in version 3.1.bytes.partition(sep)bytearray.partition(sep)

Split the sequence at the first occurrence of sep, and return a 3-tuple containing the part before the separator, the separator itself or its bytearray copy, and the part after the separator. If the separator is not found, return a 3-tuple containing a copy of the original sequence, followed by two empty bytes or bytearray objects.

The separator to search for may be any bytes-like object.bytes.replace(old, new[, count])bytearray.replace(old, new[, count])

Return a copy of the sequence with all occurrences of subsequence old replaced by new. If the optional argument count is given, only the first count occurrences are replaced.

The subsequence to search for and its replacement may be any bytes-like object.

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.bytes.rfind(sub[, start[, end]])bytearray.rfind(sub[, start[, end]])

Return the highest index in the sequence where the subsequence sub is found, such that sub is contained within s[start:end]. Optional arguments start and end are interpreted as in slice notation. Return -1 on failure.

The subsequence to search for may be any bytes-like object or an integer in the range 0 to 255.

Changed in version 3.3: Also accept an integer in the range 0 to 255 as the subsequence.bytes.rindex(sub[, start[, end]])bytearray.rindex(sub[, start[, end]])

Like rfind() but raises ValueError when the subsequence sub is not found.

The subsequence to search for may be any bytes-like object or an integer in the range 0 to 255.

Changed in version 3.3: Also accept an integer in the range 0 to 255 as the subsequence.bytes.rpartition(sep)bytearray.rpartition(sep)

Split the sequence at the last occurrence of sep, and return a 3-tuple containing the part before the separator, the separator itself or its bytearray copy, and the part after the separator. If the separator is not found, return a 3-tuple containing two empty bytes or bytearray objects, followed by a copy of the original sequence.

The separator to search for may be any bytes-like object.bytes.startswith(prefix[, start[, end]])bytearray.startswith(prefix[, start[, end]])

Return True if the binary data starts with the specified prefix, otherwise return False. prefix can also be a tuple of prefixes to look for. With optional start, test beginning at that position. With optional end, stop comparing at that position.

The prefix(es) to search for may be any bytes-like object.bytes.translate(table, /, delete=b'')bytearray.translate(table, /, delete=b'')

Return a copy of the bytes or bytearray object where all bytes occurring in the optional argument delete are removed, and the remaining bytes have been mapped through the given translation table, which must be a bytes object of length 256.

You can use the bytes.maketrans() method to create a translation table.

Set the table argument to None for translations that only delete characters:>>>

>>> b'read this short text'.translate(None, b'aeiou')
b'rd ths shrt txt'

Changed in version 3.6: delete is now supported as a keyword argument.

The following methods on bytes and bytearray objects have default behaviours that assume the use of ASCII compatible binary formats, but can still be used with arbitrary binary data by passing appropriate arguments. Note that all of the bytearray methods in this section do not operate in place, and instead produce new objects.bytes.center(width[, fillbyte])bytearray.center(width[, fillbyte])

Return a copy of the object centered in a sequence of length width. Padding is done using the specified fillbyte (default is an ASCII space). For bytes objects, the original sequence is returned if width is less than or equal to len(s).

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.bytes.ljust(width[, fillbyte])bytearray.ljust(width[, fillbyte])

Return a copy of the object left justified in a sequence of length width. Padding is done using the specified fillbyte (default is an ASCII space). For bytes objects, the original sequence is returned if width is less than or equal to len(s).

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.bytes.lstrip([chars])bytearray.lstrip([chars])

Return a copy of the sequence with specified leading bytes removed. The chars argument is a binary sequence specifying the set of byte values to be removed - the name refers to the fact this method is usually used with ASCII characters. If omitted or None, the chars argument defaults to removing ASCII whitespace. The chars argument is not a prefix; rather, all combinations of its values are stripped:>>>

>>> b'   spacious   '.lstrip()
b'spacious   '
>>> b'www.example.com'.lstrip(b'cmowz.')
b'example.com'

The binary sequence of byte values to remove may be any bytes-like object. See removeprefix() for a method that will remove a single prefix string rather than all of a set of characters. For example:>>>

>>> b'Arthur: three!'.lstrip(b'Arthur: ')
b'ee!'
>>> b'Arthur: three!'.removeprefix(b'Arthur: ')
b'three!'

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.bytes.rjust(width[, fillbyte])bytearray.rjust(width[, fillbyte])

Return a copy of the object right justified in a sequence of length width. Padding is done using the specified fillbyte (default is an ASCII space). For bytes objects, the original sequence is returned if width is less than or equal to len(s).

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.bytes.rsplit(sep=None, maxsplit=-1)bytearray.rsplit(sep=None, maxsplit=-1)

Split the binary sequence into subsequences of the same type, using sep as the delimiter string. If maxsplit is given, at most maxsplit splits are done, the rightmost ones. If sep is not specified or None, any subsequence consisting solely of ASCII whitespace is a separator. Except for splitting from the right, rsplit() behaves like split() which is described in detail below.bytes.rstrip([chars])bytearray.rstrip([chars])

Return a copy of the sequence with specified trailing bytes removed. The chars argument is a binary sequence specifying the set of byte values to be removed - the name refers to the fact this method is usually used with ASCII characters. If omitted or None, the chars argument defaults to removing ASCII whitespace. The chars argument is not a suffix; rather, all combinations of its values are stripped:>>>

>>> b'   spacious   '.rstrip()
b'   spacious'
>>> b'mississippi'.rstrip(b'ipz')
b'mississ'

The binary sequence of byte values to remove may be any bytes-like object. See removesuffix() for a method that will remove a single suffix string rather than all of a set of characters. For example:>>>

>>> b'Monty Python'.rstrip(b' Python')
b'M'
>>> b'Monty Python'.removesuffix(b' Python')
b'Monty'

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.bytes.split(sep=None, maxsplit=-1)bytearray.split(sep=None, maxsplit=-1)

Split the binary sequence into subsequences of the same type, using sep as the delimiter string. If maxsplit is given and non-negative, at most maxsplit splits are done (thus, the list will have at most maxsplit+1 elements). If maxsplit is not specified or is -1, then there is no limit on the number of splits (all possible splits are made).

If sep is given, consecutive delimiters are not grouped together and are deemed to delimit empty subsequences (for example, b'1,,2'.split(b',') returns [b'1', b'', b'2']). The sep argument may consist of a multibyte sequence (for example, b'1<>2<>3'.split(b'<>') returns [b'1', b'2', b'3']). Splitting an empty sequence with a specified separator returns [b''] or [bytearray(b'')] depending on the type of object being split. The sep argument may be any bytes-like object.

For example:>>>

>>> b'1,2,3'.split(b',')
[b'1', b'2', b'3']
>>> b'1,2,3'.split(b',', maxsplit=1)
[b'1', b'2,3']
>>> b'1,2,,3,'.split(b',')
[b'1', b'2', b'', b'3', b'']

If sep is not specified or is None, a different splitting algorithm is applied: runs of consecutive ASCII whitespace are regarded as a single separator, and the result will contain no empty strings at the start or end if the sequence has leading or trailing whitespace. Consequently, splitting an empty sequence or a sequence consisting solely of ASCII whitespace without a specified separator returns [].

For example:>>>

>>> b'1 2 3'.split()
[b'1', b'2', b'3']
>>> b'1 2 3'.split(maxsplit=1)
[b'1', b'2 3']
>>> b'   1   2   3   '.split()
[b'1', b'2', b'3']

bytes.strip([chars])bytearray.strip([chars])

Return a copy of the sequence with specified leading and trailing bytes removed. The chars argument is a binary sequence specifying the set of byte values to be removed - the name refers to the fact this method is usually used with ASCII characters. If omitted or None, the chars argument defaults to removing ASCII whitespace. The chars argument is not a prefix or suffix; rather, all combinations of its values are stripped:>>>

>>> b'   spacious   '.strip()
b'spacious'
>>> b'www.example.com'.strip(b'cmowz.')
b'example'

The binary sequence of byte values to remove may be any bytes-like object.

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.

The following methods on bytes and bytearray objects assume the use of ASCII compatible binary formats and should not be applied to arbitrary binary data. Note that all of the bytearray methods in this section do not operate in place, and instead produce new objects.bytes.capitalize()bytearray.capitalize()

Return a copy of the sequence with each byte interpreted as an ASCII character, and the first byte capitalized and the rest lowercased. Non-ASCII byte values are passed through unchanged.

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.bytes.expandtabs(tabsize=8)bytearray.expandtabs(tabsize=8)

Return a copy of the sequence where all ASCII tab characters are replaced by one or more ASCII spaces, depending on the current column and the given tab size. Tab positions occur every tabsize bytes (default is 8, giving tab positions at columns 0, 8, 16 and so on). To expand the sequence, the current column is set to zero and the sequence is examined byte by byte. If the byte is an ASCII tab character (b'\t'), one or more space characters are inserted in the result until the current column is equal to the next tab position. (The tab character itself is not copied.) If the current byte is an ASCII newline (b'\n') or carriage return (b'\r'), it is copied and the current column is reset to zero. Any other byte value is copied unchanged and the current column is incremented by one regardless of how the byte value is represented when printed:>>>

>>> b'01\t012\t0123\t01234'.expandtabs()
b'01      012     0123    01234'
>>> b'01\t012\t0123\t01234'.expandtabs(4)
b'01  012 0123    01234'

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.bytes.isalnum()bytearray.isalnum()

Return True if all bytes in the sequence are alphabetical ASCII characters or ASCII decimal digits and the sequence is not empty, False otherwise. Alphabetic ASCII characters are those byte values in the sequence b'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'. ASCII decimal digits are those byte values in the sequence b'0123456789'.

For example:>>>

>>> b'ABCabc1'.isalnum()
True
>>> b'ABC abc1'.isalnum()
False

bytes.isalpha()bytearray.isalpha()

Return True if all bytes in the sequence are alphabetic ASCII characters and the sequence is not empty, False otherwise. Alphabetic ASCII characters are those byte values in the sequence b'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'.

For example:>>>

>>> b'ABCabc'.isalpha()
True
>>> b'ABCabc1'.isalpha()
False

bytes.isascii()bytearray.isascii()

Return True if the sequence is empty or all bytes in the sequence are ASCII, False otherwise. ASCII bytes are in the range 0-0x7F.

New in version 3.7.bytes.isdigit()bytearray.isdigit()

Return True if all bytes in the sequence are ASCII decimal digits and the sequence is not empty, False otherwise. ASCII decimal digits are those byte values in the sequence b'0123456789'.

For example:>>>

>>> b'1234'.isdigit()
True
>>> b'1.23'.isdigit()
False

bytes.islower()bytearray.islower()

Return True if there is at least one lowercase ASCII character in the sequence and no uppercase ASCII characters, False otherwise.

For example:>>>

>>> b'hello world'.islower()
True
>>> b'Hello world'.islower()
False

Lowercase ASCII characters are those byte values in the sequence b'abcdefghijklmnopqrstuvwxyz'. Uppercase ASCII characters are those byte values in the sequence b'ABCDEFGHIJKLMNOPQRSTUVWXYZ'.bytes.isspace()bytearray.isspace()

Return True if all bytes in the sequence are ASCII whitespace and the sequence is not empty, False otherwise. ASCII whitespace characters are those byte values in the sequence b' \t\n\r\x0b\f' (space, tab, newline, carriage return, vertical tab, form feed).bytes.istitle()bytearray.istitle()

Return True if the sequence is ASCII titlecase and the sequence is not empty, False otherwise. See bytes.title() for more details on the definition of “titlecase”.

For example:>>>

>>> b'Hello World'.istitle()
True
>>> b'Hello world'.istitle()
False

bytes.isupper()bytearray.isupper()

Return True if there is at least one uppercase alphabetic ASCII character in the sequence and no lowercase ASCII characters, False otherwise.

For example:>>>

>>> b'HELLO WORLD'.isupper()
True
>>> b'Hello world'.isupper()
False

Return a copy of the sequence with all the uppercase ASCII characters converted to their corresponding lowercase counterpart.

For example:>>>

>>> b'Hello World'.lower()
b'hello world'

Lowercase ASCII characters are those byte values in the sequence b'abcdefghijklmnopqrstuvwxyz'. Uppercase ASCII characters are those byte values in the sequence b'ABCDEFGHIJKLMNOPQRSTUVWXYZ'.

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.bytes.splitlines(keepends=False)bytearray.splitlines(keepends=False)

Return a list of the lines in the binary sequence, breaking at ASCII line boundaries. This method uses the universal newlines approach to splitting lines. Line breaks are not included in the resulting list unless keepends is given and true.

For example:>>>

>>> b'ab c\n\nde fg\rkl\r\n'.splitlines()
[b'ab c', b'', b'de fg', b'kl']
>>> b'ab c\n\nde fg\rkl\r\n'.splitlines(keepends=True)
[b'ab c\n', b'\n', b'de fg\r', b'kl\r\n']

Unlike split() when a delimiter string sep is given, this method returns an empty list for the empty string, and a terminal line break does not result in an extra line:>>>

>>> b"".split(b'\n'), b"Two lines\n".split(b'\n')
([b''], [b'Two lines', b''])
>>> b"".splitlines(), b"One line\n".splitlines()
([], [b'One line'])

bytes.swapcase()bytearray.swapcase()

Return a copy of the sequence with all the lowercase ASCII characters converted to their corresponding uppercase counterpart and vice-versa.

For example:>>>

>>> b'Hello World'.swapcase()
b'hELLO wORLD'

Lowercase ASCII characters are those byte values in the sequence b'abcdefghijklmnopqrstuvwxyz'. Uppercase ASCII characters are those byte values in the sequence b'ABCDEFGHIJKLMNOPQRSTUVWXYZ'.

Unlike str.swapcase(), it is always the case that bin.swapcase().swapcase() == bin for the binary versions. Case conversions are symmetrical in ASCII, even though that is not generally true for arbitrary Unicode code points.

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.bytes.title()bytearray.title()

Return a titlecased version of the binary sequence where words start with an uppercase ASCII character and the remaining characters are lowercase. Uncased byte values are left unmodified.

For example:>>>

>>> b'Hello world'.title()
b'Hello World'

>>> b"they're bill's friends from the UK".title()
b"They'Re Bill'S Friends From The Uk"

A workaround for apostrophes can be constructed using regular expressions:>>>

>>> import re
>>> def titlecase(s):
...     return re.sub(rb"[A-Za-z]+('[A-Za-z]+)?",
...                   lambda mo: mo.group(0)[0:1].upper() +
...                              mo.group(0)[1:].lower(),
...                   s)
...
>>> titlecase(b"they're bill's friends.")
b"They're Bill's Friends."

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.bytes.upper()bytearray.upper()

Return a copy of the sequence with all the lowercase ASCII characters converted to their corresponding uppercase counterpart.

For example:>>>

>>> b'Hello World'.upper()
b'HELLO WORLD'

Lowercase ASCII characters are those byte values in the sequence b'abcdefghijklmnopqrstuvwxyz'. Uppercase ASCII characters are those byte values in the sequence b'ABCDEFGHIJKLMNOPQRSTUVWXYZ'.

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.bytes.zfill(width)bytearray.zfill(width)

Return a copy of the sequence left filled with ASCII b'0' digits to make a sequence of length width. A leading sign prefix (b'+'/ b'-') is handled by inserting the padding after the sign character rather than before. For bytes objects, the original sequence is returned if width is less than or equal to len(seq).

For example:>>>

>>> b"42".zfill(5)
b'00042'
>>> b"-42".zfill(5)
b'-0042'

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.

`printf`-style Bytes Formatting

Note

The formatting operations described here exhibit a variety of quirks that lead to a number of common errors (such as failing to display tuples and dictionaries correctly). If the value being printed may be a tuple or dictionary, wrap it in a tuple.

Bytes objects (bytes/bytearray) have one unique built-in operation: the % operator (modulo). This is also known as the bytes formatting or interpolation operator. Given format % values (where format is a bytes object), % conversion specifications in format are replaced with zero or more elements of values. The effect is similar to using the sprintf() in the C language.

If format requires a single argument, values may be a single non-tuple object. 5 Otherwise, values must be a tuple with exactly the number of items specified by the format bytes object, or a single mapping object (for example, a dictionary).

A conversion specifier contains two or more characters and has the following components, which must occur in this order:

The '%' character, which marks the start of the specifier.
Mapping key (optional), consisting of a parenthesised sequence of characters (for example, (somename)).
Conversion flags (optional), which affect the result of some conversion types.
Minimum field width (optional). If specified as an '*' (asterisk), the actual width is read from the next element of the tuple in values, and the object to convert comes after the minimum field width and optional precision.
Precision (optional), given as a '.' (dot) followed by the precision. If specified as '*' (an asterisk), the actual precision is read from the next element of the tuple in values, and the value to convert comes after the precision.
Length modifier (optional).
Conversion type.

When the right argument is a dictionary (or other mapping type), then the formats in the bytes object must include a parenthesised mapping key into that dictionary inserted immediately after the '%' character. The mapping key selects the value to be formatted from the mapping. For example:>>>

>>> print(b'%(language)s has %(number)03d quote types.' %
...       {b'language': b"Python", b"number": 2})
b'Python has 002 quote types.'

In this case no * specifiers may occur in a format (since they require a sequential parameter list).

The conversion flag characters are:

A length modifier (h, l, or L) may be present, but is ignored as it is not necessary for Python – so e.g. %ld is identical to %d.

The conversion types are:

Notes:

The alternate form causes a leading octal specifier ('0o') to be inserted before the first digit.
The alternate form causes a leading '0x' or '0X' (depending on whether the 'x' or 'X' format was used) to be inserted before the first digit.
The alternate form causes the result to always contain a decimal point, even if no digits follow it.
The precision determines the number of digits after the decimal point and defaults to 6.
The alternate form causes the result to always contain a decimal point, and trailing zeroes are not removed as they would otherwise be.
The precision determines the number of significant digits before and after the decimal point and defaults to 6.
If precision is N, the output is truncated to N characters.
b'%s' is deprecated, but will not be removed during the 3.x series.
b'%r' is deprecated, but will not be removed during the 3.x series.
See PEP 237.

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.

See also

PEP 461 - Adding % formatting to bytes and bytearray

New in version 3.5.

Memory Views

memoryview objects allow Python code to access the internal data of an object that supports the buffer protocol without copying.class memoryview(object)

Create a memoryview that references object. object must support the buffer protocol. Built-in objects that support the buffer protocol include bytes and bytearray.

A memoryview has the notion of an element, which is the atomic memory unit handled by the originating object. For many simple types such as bytes and bytearray, an element is a single byte, but other types such as array.array may have bigger elements.

len(view) is equal to the length of tolist. If view.ndim = 0, the length is 1. If view.ndim = 1, the length is equal to the number of elements in the view. For higher dimensions, the length is equal to the length of the nested list representation of the view. The itemsize attribute will give you the number of bytes in a single element.

A memoryview supports slicing and indexing to expose its data. One-dimensional slicing will result in a subview:>>>

>>> v = memoryview(b'abcefg')
>>> v[1]
98
>>> v[-1]
103
>>> v[1:4]
<memory at 0x7f3ddc9f4350>
>>> bytes(v[1:4])
b'bce'

If format is one of the native format specifiers from the struct module, indexing with an integer or a tuple of integers is also supported and returns a single element with the correct type. One-dimensional memoryviews can be indexed with an integer or a one-integer tuple. Multi-dimensional memoryviews can be indexed with tuples of exactly ndim integers where ndim is the number of dimensions. Zero-dimensional memoryviews can be indexed with the empty tuple.

Here is an example with a non-byte format:>>>

>>> import array
>>> a = array.array('l', [-11111111, 22222222, -33333333, 44444444])
>>> m = memoryview(a)
>>> m[0]
-11111111
>>> m[-1]
44444444
>>> m[::2].tolist()
[-11111111, -33333333]

If the underlying object is writable, the memoryview supports one-dimensional slice assignment. Resizing is not allowed:>>>

>>> data = bytearray(b'abcefg')
>>> v = memoryview(data)
>>> v.readonly
False
>>> v[0] = ord(b'z')
>>> data
bytearray(b'zbcefg')
>>> v[1:4] = b'123'
>>> data
bytearray(b'z123fg')
>>> v[2:3] = b'spam'
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: memoryview assignment: lvalue and rvalue have different structures
>>> v[2:6] = b'spam'
>>> data
bytearray(b'z1spam')

One-dimensional memoryviews of hashable (read-only) types with formats ‘B’, ‘b’ or ‘c’ are also hashable. The hash is defined as hash(m) == hash(m.tobytes()):>>>

>>> v = memoryview(b'abcefg')
>>> hash(v) == hash(b'abcefg')
True
>>> hash(v[2:4]) == hash(b'ce')
True
>>> hash(v[::-2]) == hash(b'abcefg'[::-2])
True

Changed in version 3.3: One-dimensional memoryviews can now be sliced. One-dimensional memoryviews with formats ‘B’, ‘b’ or ‘c’ are now hashable.

Changed in version 3.4: memoryview is now registered automatically with collections.abc.Sequence

Changed in version 3.5: memoryviews can now be indexed with tuple of integers.

memoryview has several methods:__eq__(exporter)

A memoryview and a PEP 3118 exporter are equal if their shapes are equivalent and if all corresponding values are equal when the operands’ respective format codes are interpreted using struct syntax.

For the subset of struct format strings currently supported by tolist(), v and w are equal if v.tolist() == w.tolist():>>>

>>> import array
>>> a = array.array('I', [1, 2, 3, 4, 5])
>>> b = array.array('d', [1.0, 2.0, 3.0, 4.0, 5.0])
>>> c = array.array('b', [5, 3, 1])
>>> x = memoryview(a)
>>> y = memoryview(b)
>>> x == a == y == b
True
>>> x.tolist() == a.tolist() == y.tolist() == b.tolist()
True
>>> z = y[::-2]
>>> z == c
True
>>> z.tolist() == c.tolist()
True

If either format string is not supported by the struct module, then the objects will always compare as unequal (even if the format strings and buffer contents are identical):>>>

>>> from ctypes import BigEndianStructure, c_long
>>> class BEPoint(BigEndianStructure):
...     _fields_ = [("x", c_long), ("y", c_long)]
...
>>> point = BEPoint(100, 200)
>>> a = memoryview(point)
>>> b = memoryview(point)
>>> a == point
False
>>> a == b
False

Note that, as with floating point numbers, v is w does not imply v == w for memoryview objects.

Changed in version 3.3: Previous versions compared the raw memory disregarding the item format and the logical array structure.tobytes(order=None)

Return the data in the buffer as a bytestring. This is equivalent to calling the bytes constructor on the memoryview.>>>

>>> m = memoryview(b"abc")
>>> m.tobytes()
b'abc'
>>> bytes(m)
b'abc'

For non-contiguous arrays the result is equal to the flattened list representation with all elements converted to bytes. tobytes() supports all format strings, including those that are not in struct module syntax.

New in version 3.8: order can be {‘C’, ‘F’, ‘A’}. When order is ‘C’ or ‘F’, the data of the original array is converted to C or Fortran order. For contiguous views, ‘A’ returns an exact copy of the physical memory. In particular, in-memory Fortran order is preserved. For non-contiguous views, the data is converted to C first. order=None is the same as order=’C’.hex([sep[, bytes_per_sep]])

Return a string object containing two hexadecimal digits for each byte in the buffer.>>>

>>> m = memoryview(b"abc")
>>> m.hex()
'616263'

New in version 3.5.

Changed in version 3.8: Similar to bytes.hex(), memoryview.hex() now supports optional sep and bytes_per_sep parameters to insert separators between bytes in the hex output.tolist()

Return the data in the buffer as a list of elements.>>>

>>> memoryview(b'abc').tolist()
[97, 98, 99]
>>> import array
>>> a = array.array('d', [1.1, 2.2, 3.3])
>>> m = memoryview(a)
>>> m.tolist()
[1.1, 2.2, 3.3]

Changed in version 3.3: tolist() now supports all single character native formats in struct module syntax as well as multi-dimensional representations.toreadonly()

Return a readonly version of the memoryview object. The original memoryview object is unchanged.>>>

>>> m = memoryview(bytearray(b'abc'))
>>> mm = m.toreadonly()
>>> mm.tolist()
[89, 98, 99]
>>> mm[0] = 42
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: cannot modify read-only memory
>>> m[0] = 43
>>> mm.tolist()
[43, 98, 99]

New in version 3.8.release()

Release the underlying buffer exposed by the memoryview object. Many objects take special actions when a view is held on them (for example, a bytearray would temporarily forbid resizing); therefore, calling release() is handy to remove these restrictions (and free any dangling resources) as soon as possible.

After this method has been called, any further operation on the view raises a ValueError (except release() itself which can be called multiple times):>>>

>>> m = memoryview(b'abc')
>>> m.release()
>>> m[0]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: operation forbidden on released memoryview object

The context management protocol can be used for a similar effect, using the with statement:>>>

>>> with memoryview(b'abc') as m:
...     m[0]
...
97
>>> m[0]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: operation forbidden on released memoryview object

New in version 3.2.cast(format[, shape])

Cast a memoryview to a new format or shape. shape defaults to [byte_length//new_itemsize], which means that the result view will be one-dimensional. The return value is a new memoryview, but the buffer itself is not copied. Supported casts are 1D -> C-contiguous and C-contiguous -> 1D.

The destination format is restricted to a single element native format in struct syntax. One of the formats must be a byte format (‘B’, ‘b’ or ‘c’). The byte length of the result must be the same as the original length.

Cast 1D/long to 1D/unsigned bytes:>>>

>>> import array
>>> a = array.array('l', [1,2,3])
>>> x = memoryview(a)
>>> x.format
'l'
>>> x.itemsize
8
>>> len(x)
3
>>> x.nbytes
24
>>> y = x.cast('B')
>>> y.format
'B'
>>> y.itemsize
1
>>> len(y)
24
>>> y.nbytes
24

Cast 1D/unsigned bytes to 1D/char:>>>

>>> b = bytearray(b'zyz')
>>> x = memoryview(b)
>>> x[0] = b'a'
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: memoryview: invalid value for format "B"
>>> y = x.cast('c')
>>> y[0] = b'a'
>>> b
bytearray(b'ayz')

Cast 1D/bytes to 3D/ints to 1D/signed char:>>>

>>> import struct
>>> buf = struct.pack("i"*12, *list(range(12)))
>>> x = memoryview(buf)
>>> y = x.cast('i', shape=[2,2,3])
>>> y.tolist()
[[[0, 1, 2], [3, 4, 5]], [[6, 7, 8], [9, 10, 11]]]
>>> y.format
'i'
>>> y.itemsize
4
>>> len(y)
2
>>> y.nbytes
48
>>> z = y.cast('b')
>>> z.format
'b'
>>> z.itemsize
1
>>> len(z)
48
>>> z.nbytes
48

Cast 1D/unsigned long to 2D/unsigned long:>>>

>>> buf = struct.pack("L"*6, *list(range(6)))
>>> x = memoryview(buf)
>>> y = x.cast('L', shape=[2,3])
>>> len(y)
2
>>> y.nbytes
48
>>> y.tolist()
[[0, 1, 2], [3, 4, 5]]

New in version 3.3.

Changed in version 3.5: The source format is no longer restricted when casting to a byte view.

There are also several readonly attributes available:obj

The underlying object of the memoryview:>>>

>>> b  = bytearray(b'xyz')
>>> m = memoryview(b)
>>> m.obj is b
True

New in version 3.3.nbytes

nbytes == product(shape) * itemsize == len(m.tobytes()). This is the amount of space in bytes that the array would use in a contiguous representation. It is not necessarily equal to len(m):>>>

>>> import array
>>> a = array.array('i', [1,2,3,4,5])
>>> m = memoryview(a)
>>> len(m)
5
>>> m.nbytes
20
>>> y = m[::2]
>>> len(y)
3
>>> y.nbytes
12
>>> len(y.tobytes())
12

Multi-dimensional arrays:>>>

>>> import struct
>>> buf = struct.pack("d"*12, *[1.5*x for x in range(12)])
>>> x = memoryview(buf)
>>> y = x.cast('d', shape=[3,4])
>>> y.tolist()
[[0.0, 1.5, 3.0, 4.5], [6.0, 7.5, 9.0, 10.5], [12.0, 13.5, 15.0, 16.5]]
>>> len(y)
3
>>> y.nbytes
96

New in version 3.3.readonly

A bool indicating whether the memory is read only.format

A string containing the format (in struct module style) for each element in the view. A memoryview can be created from exporters with arbitrary format strings, but some methods (e.g. tolist()) are restricted to native single element formats.

Changed in version 3.3: format 'B' is now handled according to the struct module syntax. This means that memoryview(b'abc')[0] == b'abc'[0] == 97.itemsize

The size in bytes of each element of the memoryview:>>>

>>> import array, struct
>>> m = memoryview(array.array('H', [32000, 32001, 32002]))
>>> m.itemsize
2
>>> m[0]
32000
>>> struct.calcsize('H') == m.itemsize
True

ndim

An integer indicating how many dimensions of a multi-dimensional array the memory represents.shape

A tuple of integers the length of ndim giving the shape of the memory as an N-dimensional array.

Changed in version 3.3: An empty tuple instead of None when ndim = 0.strides

A tuple of integers the length of ndim giving the size in bytes to access each element for each dimension of the array.

Changed in version 3.3: An empty tuple instead of None when ndim = 0.suboffsets

Used internally for PIL-style arrays. The value is informational only.c_contiguous

A bool indicating whether the memory is C-contiguous.

New in version 3.3.f_contiguous

A bool indicating whether the memory is Fortran contiguous.

New in version 3.3.contiguous

A bool indicating whether the memory is contiguous.

New in version 3.3.

Set Types — `set`, `frozenset`

A set object is an unordered collection of distinct hashable objects. Common uses include membership testing, removing duplicates from a sequence, and computing mathematical operations such as intersection, union, difference, and symmetric difference. (For other containers see the built-in dict, list, and tuple classes, and the collections module.)

Like other collections, sets support x in set, len(set), and for x in set. Being an unordered collection, sets do not record element position or order of insertion. Accordingly, sets do not support indexing, slicing, or other sequence-like behavior.

There are currently two built-in set types, set and frozenset. The set type is mutable — the contents can be changed using methods like add() and remove(). Since it is mutable, it has no hash value and cannot be used as either a dictionary key or as an element of another set. The frozenset type is immutable and hashable — its contents cannot be altered after it is created; it can therefore be used as a dictionary key or as an element of another set.

Non-empty sets (not frozensets) can be created by placing a comma-separated list of elements within braces, for example: {'jack', 'sjoerd'}, in addition to the set constructor.

The constructors for both classes work the same:class set([iterable])class frozenset([iterable])

Return a new set or frozenset object whose elements are taken from iterable. The elements of a set must be hashable. To represent sets of sets, the inner sets must be frozenset objects. If iterable is not specified, a new empty set is returned.

Sets can be created by several means:

Use a comma-separated list of elements within braces: {'jack', 'sjoerd'}
Use a set comprehension: {c for c in 'abracadabra' if c not in 'abc'}
Use the type constructor: set(), set('foobar'), set(['a', 'b', 'foo'])

Instances of set and frozenset provide the following operations:len(s)

Return the number of elements in set s (cardinality of s).x in s

Test x for membership in s.x not in s

Test x for non-membership in s.isdisjoint(other)

Return True if the set has no elements in common with other. Sets are disjoint if and only if their intersection is the empty set.issubset(other)set <= other

Test whether every element in the set is in other.set < other

Test whether the set is a proper subset of other, that is, set <= other and set != other.issuperset(other)set >= other

Test whether every element in other is in the set.set > other

Test whether the set is a proper superset of other, that is, set >= other and set != other.union(*others)set | other | ...

Return a new set with elements from the set and all others.intersection(*others)set & other & ...

Return a new set with elements common to the set and all others.difference(*others)set - other - ...

Return a new set with elements in the set that are not in the others.symmetric_difference(other)set ^ other

Return a new set with elements in either the set or other but not both.copy()

Return a shallow copy of the set.

Note, the non-operator versions of union(), intersection(), difference(), and symmetric_difference(), issubset(), and issuperset() methods will accept any iterable as an argument. In contrast, their operator based counterparts require their arguments to be sets. This precludes error-prone constructions like set('abc') & 'cbs' in favor of the more readable set('abc').intersection('cbs').

Both set and frozenset support set to set comparisons. Two sets are equal if and only if every element of each set is contained in the other (each is a subset of the other). A set is less than another set if and only if the first set is a proper subset of the second set (is a subset, but is not equal). A set is greater than another set if and only if the first set is a proper superset of the second set (is a superset, but is not equal).

Instances of set are compared to instances of frozenset based on their members. For example, set('abc') == frozenset('abc') returns True and so does set('abc') in set([frozenset('abc')]).

The subset and equality comparisons do not generalize to a total ordering function. For example, any two nonempty disjoint sets are not equal and are not subsets of each other, so all of the following return False: a<b, a==b, or a>b.

Since sets only define partial ordering (subset relationships), the output of the list.sort() method is undefined for lists of sets.

Set elements, like dictionary keys, must be hashable.

Binary operations that mix set instances with frozenset return the type of the first operand. For example: frozenset('ab') | set('bc') returns an instance of frozenset.

The following table lists operations available for set that do not apply to immutable instances of frozenset:update(*others)set |= other | ...

Update the set, adding elements from all others.intersection_update(*others)set &= other & ...

Update the set, keeping only elements found in it and all others.difference_update(*others)set -= other | ...

Update the set, removing elements found in others.symmetric_difference_update(other)set ^= other

Update the set, keeping only elements found in either set, but not in both.add(elem)

Add element elem to the set.remove(elem)

Remove element elem from the set. Raises KeyError if elem is not contained in the set.discard(elem)

Remove element elem from the set if it is present.pop()

Remove and return an arbitrary element from the set. Raises KeyError if the set is empty.clear()

Remove all elements from the set.

Note, the non-operator versions of the update(), intersection_update(), difference_update(), and symmetric_difference_update() methods will accept any iterable as an argument.

Note, the elem argument to the __contains__(), remove(), and discard() methods may be a set. To support searching for an equivalent frozenset, a temporary one is created from elem.

Mapping Types — `dict`

A mapping object maps hashable values to arbitrary objects. Mappings are mutable objects. There is currently only one standard mapping type, the dictionary. (For other containers see the built-in list, set, and tuple classes, and the collections module.)

A dictionary’s keys are almost arbitrary values. Values that are not hashable, that is, values containing lists, dictionaries or other mutable types (that are compared by value rather than by object identity) may not be used as keys. Numeric types used for keys obey the normal rules for numeric comparison: if two numbers compare equal (such as 1 and 1.0) then they can be used interchangeably to index the same dictionary entry. (Note however, that since computers store floating-point numbers as approximations it is usually unwise to use them as dictionary keys.)

Dictionaries can be created by placing a comma-separated list of key: value pairs within braces, for example: {'jack': 4098, 'sjoerd': 4127} or {4098: 'jack', 4127: 'sjoerd'}, or by the dict constructor.class dict(**kwarg)class dict(mapping, **kwarg)class dict(iterable, **kwarg)

Return a new dictionary initialized from an optional positional argument and a possibly empty set of keyword arguments.

Dictionaries can be created by several means:

Use a comma-separated list of key: value pairs within braces: {'jack': 4098, 'sjoerd': 4127} or {4098: 'jack', 4127: 'sjoerd'}
Use a dict comprehension: {}, {x: x ** 2 for x in range(10)}
Use the type constructor: dict(), dict([('foo', 100), ('bar', 200)]), dict(foo=100, bar=200)

If no positional argument is given, an empty dictionary is created. If a positional argument is given and it is a mapping object, a dictionary is created with the same key-value pairs as the mapping object. Otherwise, the positional argument must be an iterable object. Each item in the iterable must itself be an iterable with exactly two objects. The first object of each item becomes a key in the new dictionary, and the second object the corresponding value. If a key occurs more than once, the last value for that key becomes the corresponding value in the new dictionary.

If keyword arguments are given, the keyword arguments and their values are added to the dictionary created from the positional argument. If a key being added is already present, the value from the keyword argument replaces the value from the positional argument.

To illustrate, the following examples all return a dictionary equal to {"one": 1, "two": 2, "three": 3}:>>>

>>> a = dict(one=1, two=2, three=3)
>>> b = {'one': 1, 'two': 2, 'three': 3}
>>> c = dict(zip(['one', 'two', 'three'], [1, 2, 3]))
>>> d = dict([('two', 2), ('one', 1), ('three', 3)])
>>> e = dict({'three': 3, 'one': 1, 'two': 2})
>>> f = dict({'one': 1, 'three': 3}, two=2)
>>> a == b == c == d == e == f
True

Providing keyword arguments as in the first example only works for keys that are valid Python identifiers. Otherwise, any valid keys can be used.

These are the operations that dictionaries support (and therefore, custom mapping types should support too):list(d)

Return a list of all the keys used in the dictionary d.len(d)

Return the number of items in the dictionary d.d[key]

Return the item of d with key key. Raises a KeyError if key is not in the map.

If a subclass of dict defines a method __missing__() and key is not present, the d[key] operation calls that method with the key key as argument. The d[key] operation then returns or raises whatever is returned or raised by the __missing__(key) call. No other operations or methods invoke __missing__(). If __missing__() is not defined, KeyError is raised. __missing__() must be a method; it cannot be an instance variable:>>>

>>> class Counter(dict):
...     def __missing__(self, key):
...         return 0
>>> c = Counter()
>>> c['red']
0
>>> c['red'] += 1
>>> c['red']
1

The example above shows part of the implementation of collections.Counter. A different __missing__ method is used by collections.defaultdict.d[key] = value

Set d[key] to value.del d[key]

Remove d[key] from d. Raises a KeyError if key is not in the map.key in d

Return True if d has a key key, else False.key not in d

Equivalent to not key in d.iter(d)

Return an iterator over the keys of the dictionary. This is a shortcut for iter(d.keys()).clear()

Remove all items from the dictionary.copy()

Return a shallow copy of the dictionary.classmethod fromkeys(iterable[, value])

Create a new dictionary with keys from iterable and values set to value.

fromkeys() is a class method that returns a new dictionary. value defaults to None. All of the values refer to just a single instance, so it generally doesn’t make sense for value to be a mutable object such as an empty list. To get distinct values, use a dict comprehension instead.get(key[, default])

Return the value for key if key is in the dictionary, else default. If default is not given, it defaults to None, so that this method never raises a KeyError.items()

Return a new view of the dictionary’s items ((key, value) pairs). See the documentation of view objects.keys()

Return a new view of the dictionary’s keys. See the documentation of view objects.pop(key[, default])

If key is in the dictionary, remove it and return its value, else return default. If default is not given and key is not in the dictionary, a KeyError is raised.popitem()

Remove and return a (key, value) pair from the dictionary. Pairs are returned in LIFO order.

popitem() is useful to destructively iterate over a dictionary, as often used in set algorithms. If the dictionary is empty, calling popitem() raises a KeyError.

Changed in version 3.7: LIFO order is now guaranteed. In prior versions, popitem() would return an arbitrary key/value pair.reversed(d)

Return a reverse iterator over the keys of the dictionary. This is a shortcut for reversed(d.keys()).

New in version 3.8.setdefault(key[, default])

If key is in the dictionary, return its value. If not, insert key with a value of default and return default. default defaults to None.update([other])

Update the dictionary with the key/value pairs from other, overwriting existing keys. Return None.

update() accepts either another dictionary object or an iterable of key/value pairs (as tuples or other iterables of length two). If keyword arguments are specified, the dictionary is then updated with those key/value pairs: d.update(red=1, blue=2).values()

Return a new view of the dictionary’s values. See the documentation of view objects.

An equality comparison between one dict.values() view and another will always return False. This also applies when comparing dict.values() to itself:>>>

>>> d = {'a': 1}
>>> d.values() == d.values()
False

d | other

Create a new dictionary with the merged keys and values of d and other, which must both be dictionaries. The values of other take priority when d and other share keys.

New in version 3.9.d |= other

Update the dictionary d with keys and values from other, which may be either a mapping or an iterable of key/value pairs. The values of other take priority when d and other share keys.

New in version 3.9.

Dictionaries compare equal if and only if they have the same (key, value) pairs (regardless of ordering). Order comparisons (‘<’, ‘<=’, ‘>=’, ‘>’) raise TypeError.

Dictionaries preserve insertion order. Note that updating a key does not affect the order. Keys added after deletion are inserted at the end.>>>

>>> d = {"one": 1, "two": 2, "three": 3, "four": 4}
>>> d
{'one': 1, 'two': 2, 'three': 3, 'four': 4}
>>> list(d)
['one', 'two', 'three', 'four']
>>> list(d.values())
[1, 2, 3, 4]
>>> d["one"] = 42
>>> d
{'one': 42, 'two': 2, 'three': 3, 'four': 4}
>>> del d["two"]
>>> d["two"] = None
>>> d
{'one': 42, 'three': 3, 'four': 4, 'two': None}

Changed in version 3.7: Dictionary order is guaranteed to be insertion order. This behavior was an implementation detail of CPython from 3.6.

Dictionaries and dictionary views are reversible.>>>

>>> d = {"one": 1, "two": 2, "three": 3, "four": 4}
>>> d
{'one': 1, 'two': 2, 'three': 3, 'four': 4}
>>> list(reversed(d))
['four', 'three', 'two', 'one']
>>> list(reversed(d.values()))
[4, 3, 2, 1]
>>> list(reversed(d.items()))
[('four', 4), ('three', 3), ('two', 2), ('one', 1)]

Changed in version 3.8: Dictionaries are now reversible.

See also

types.MappingProxyType can be used to create a read-only view of a dict.

Dictionary view objects

The objects returned by dict.keys(), dict.values() and dict.items() are view objects. They provide a dynamic view on the dictionary’s entries, which means that when the dictionary changes, the view reflects these changes.

Dictionary views can be iterated over to yield their respective data, and support membership tests:len(dictview)

Return the number of entries in the dictionary.iter(dictview)

Return an iterator over the keys, values or items (represented as tuples of (key, value)) in the dictionary.

Keys and values are iterated over in insertion order. This allows the creation of (value, key) pairs using zip(): pairs = zip(d.values(), d.keys()). Another way to create the same list is pairs = [(v, k) for (k, v) in d.items()].

Iterating views while adding or deleting entries in the dictionary may raise a RuntimeError or fail to iterate over all entries.

Changed in version 3.7: Dictionary order is guaranteed to be insertion order.x in dictview

Return True if x is in the underlying dictionary’s keys, values or items (in the latter case, x should be a (key, value) tuple).reversed(dictview)

Return a reverse iterator over the keys, values or items of the dictionary. The view will be iterated in reverse order of the insertion.

Changed in version 3.8: Dictionary views are now reversible.

Keys views are set-like since their entries are unique and hashable. If all values are hashable, so that (key, value) pairs are unique and hashable, then the items view is also set-like. (Values views are not treated as set-like since the entries are generally not unique.) For set-like views, all of the operations defined for the abstract base class collections.abc.Set are available (for example, ==, <, or ^).

An example of dictionary view usage:>>>

>>> dishes = {'eggs': 2, 'sausage': 1, 'bacon': 1, 'spam': 500}
>>> keys = dishes.keys()
>>> values = dishes.values()

>>> # iteration
>>> n = 0
>>> for val in values:
...     n += val
>>> print(n)
504

>>> # keys and values are iterated over in the same order (insertion order)
>>> list(keys)
['eggs', 'sausage', 'bacon', 'spam']
>>> list(values)
[2, 1, 1, 500]

>>> # view objects are dynamic and reflect dict changes
>>> del dishes['eggs']
>>> del dishes['sausage']
>>> list(keys)
['bacon', 'spam']

>>> # set operations
>>> keys & {'eggs', 'bacon', 'salad'}
{'bacon'}
>>> keys ^ {'sausage', 'juice'}
{'juice', 'sausage', 'bacon', 'spam'}

Context Manager Types

Python’s with statement supports the concept of a runtime context defined by a context manager. This is implemented using a pair of methods that allow user-defined classes to define a runtime context that is entered before the statement body is executed and exited when the statement ends:contextmanager.__enter__()

Enter the runtime context and return either this object or another object related to the runtime context. The value returned by this method is bound to the identifier in the as clause of with statements using this context manager.

An example of a context manager that returns itself is a file object. File objects return themselves from __enter__() to allow open() to be used as the context expression in a with statement.

An example of a context manager that returns a related object is the one returned by decimal.localcontext(). These managers set the active decimal context to a copy of the original decimal context and then return the copy. This allows changes to be made to the current decimal context in the body of the with statement without affecting code outside the with statement.contextmanager.__exit__(exc_type, exc_val, exc_tb)

Exit the runtime context and return a Boolean flag indicating if any exception that occurred should be suppressed. If an exception occurred while executing the body of the with statement, the arguments contain the exception type, value and traceback information. Otherwise, all three arguments are None.

Returning a true value from this method will cause the with statement to suppress the exception and continue execution with the statement immediately following the with statement. Otherwise the exception continues propagating after this method has finished executing. Exceptions that occur during execution of this method will replace any exception that occurred in the body of the with statement.

The exception passed in should never be reraised explicitly - instead, this method should return a false value to indicate that the method completed successfully and does not want to suppress the raised exception. This allows context management code to easily detect whether or not an __exit__() method has actually failed.

Python defines several context managers to support easy thread synchronisation, prompt closure of files or other objects, and simpler manipulation of the active decimal arithmetic context. The specific types are not treated specially beyond their implementation of the context management protocol. See the contextlib module for some examples.

Python’s generators and the contextlib.contextmanager decorator provide a convenient way to implement these protocols. If a generator function is decorated with the contextlib.contextmanager decorator, it will return a context manager implementing the necessary __enter__() and __exit__() methods, rather than the iterator produced by an undecorated generator function.

Note that there is no specific slot for any of these methods in the type structure for Python objects in the Python/C API. Extension types wanting to define these methods must provide them as a normal Python accessible method. Compared to the overhead of setting up the runtime context, the overhead of a single class dictionary lookup is negligible.

Generic Alias Type

GenericAlias objects are created by subscripting a class (usually a container), such as list[int]. They are intended primarily for type annotations.

Usually, the subscription of container objects calls the method __getitem__() of the object. However, the subscription of some containers’ classes may call the classmethod __class_getitem__() of the class instead. The classmethod __class_getitem__() should return a GenericAlias object.

Note

If the __getitem__() of the class’ metaclass is present, it will take precedence over the __class_getitem__() defined in the class (see PEP 560 for more details).

The GenericAlias object acts as a proxy for generic types, implementing parameterized generics - a specific instance of a generic which provides the types for container elements.

The user-exposed type for the GenericAlias object can be accessed from types.GenericAlias and used for isinstance() checks. It can also be used to create GenericAlias objects directly.T[X, Y, ...]

Creates a GenericAlias representing a type T containing elements of types X, Y, and more depending on the T used. For example, a function expecting a list containing float elements:

def average(values: list[float]) -> float:
    return sum(values) / len(values)

Another example for mapping objects, using a dict, which is a generic type expecting two type parameters representing the key type and the value type. In this example, the function expects a dict with keys of type str and values of type int:

def send_post_request(url: str, body: dict[str, int]) -> None:
    ...

The builtin functions isinstance() and issubclass() do not accept GenericAlias types for their second argument:>>>

>>> isinstance([1, 2], list[str])
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: isinstance() argument 2 cannot be a parameterized generic

The Python runtime does not enforce type annotations. This extends to generic types and their type parameters. When creating an object from a GenericAlias, container elements are not checked against their type. For example, the following code is discouraged, but will run without errors:>>>

>>> t = list[str]
>>> t([1, 2, 3])
[1, 2, 3]

Furthermore, parameterized generics erase type parameters during object creation:>>>

>>> t = list[str]
>>> type(t)
<class 'types.GenericAlias'>

>>> l = t()
>>> type(l)
<class 'list'>

Calling repr() or str() on a generic shows the parameterized type:>>>

>>> repr(list[int])
'list[int]'

>>> str(list[int])
'list[int]'

The __getitem__() method of generics will raise an exception to disallow mistakes like dict[str][str]:>>>

>>> dict[str][str]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: There are no type variables left in dict[str]

However, such expressions are valid when type variables are used. The index must have as many elements as there are type variable items in the GenericAlias object’s __args__.>>>

>>> from typing import TypeVar
>>> Y = TypeVar('Y')
>>> dict[str, Y][int]
dict[str, int]

Standard Generic Collections

These standard library collections support parameterized generics.

Special Attributes of Generic Alias

All parameterized generics implement special read-only attributes.genericalias.__origin__

This attribute points at the non-parameterized generic class:>>>

>>> list[int].__origin__
<class 'list'>

genericalias.__args__

This attribute is a tuple (possibly of length 1) of generic types passed to the original __class_getitem__() of the generic container:>>>

>>> dict[str, list[int]].__args__
(<class 'str'>, list[int])

genericalias.__parameters__

This attribute is a lazily computed tuple (possibly empty) of unique type variables found in __args__:>>>

>>> from typing import TypeVar

>>> T = TypeVar('T')
>>> list[T].__parameters__
(~T,)

See also

PEP 585 – “Type Hinting Generics In Standard Collections”
__class_getitem__() – Used to implement parameterized generics.
Generics – Generics in the typing module.

New in version 3.9.

Other Built-in Types

The interpreter supports several other kinds of objects. Most of these support only one or two operations.

Modules

The only special operation on a module is attribute access: m.name, where m is a module and name accesses a name defined in m’s symbol table. Module attributes can be assigned to. (Note that the import statement is not, strictly speaking, an operation on a module object; import foo does not require a module object named foo to exist, rather it requires an (external) definition for a module named foo somewhere.)

A special attribute of every module is __dict__. This is the dictionary containing the module’s symbol table. Modifying this dictionary will actually change the module’s symbol table, but direct assignment to the __dict__ attribute is not possible (you can write m.__dict__['a'] = 1, which defines m.a to be 1, but you can’t write m.__dict__ = {}). Modifying __dict__ directly is not recommended.

Modules built into the interpreter are written like this: <module 'sys' (built-in)>. If loaded from a file, they are written as <module 'os' from '/usr/local/lib/pythonX.Y/os.pyc'>.

Classes and Class Instances

See Objects, values and types and Class definitions for these.

Functions

Function objects are created by function definitions. The only operation on a function object is to call it: func(argument-list).

There are really two flavors of function objects: built-in functions and user-defined functions. Both support the same operation (to call the function), but the implementation is different, hence the different object types.

See Function definitions for more information.

Methods

Methods are functions that are called using the attribute notation. There are two flavors: built-in methods (such as append() on lists) and class instance methods. Built-in methods are described with the types that support them.

If you access a method (a function defined in a class namespace) through an instance, you get a special object: a bound method (also called instance method) object. When called, it will add the self argument to the argument list. Bound methods have two special read-only attributes: m.__self__ is the object on which the method operates, and m.__func__ is the function implementing the method. Calling m(arg-1, arg-2, ..., arg-n) is completely equivalent to calling m.__func__(m.__self__, arg-1, arg-2, ..., arg-n).

Like function objects, bound method objects support getting arbitrary attributes. However, since method attributes are actually stored on the underlying function object (meth.__func__), setting method attributes on bound methods is disallowed. Attempting to set an attribute on a method results in an AttributeError being raised. In order to set a method attribute, you need to explicitly set it on the underlying function object:>>>

>>> class C:
...     def method(self):
...         pass
...
>>> c = C()
>>> c.method.whoami = 'my name is method'  # can't set on the method
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'method' object has no attribute 'whoami'
>>> c.method.__func__.whoami = 'my name is method'
>>> c.method.whoami
'my name is method'

See The standard type hierarchy for more information.

Code Objects

Code objects are used by the implementation to represent “pseudo-compiled” executable Python code such as a function body. They differ from function objects because they don’t contain a reference to their global execution environment. Code objects are returned by the built-in compile() function and can be extracted from function objects through their __code__ attribute. See also the code module.

Accessing __code__ raises an auditing event object.__getattr__ with arguments obj and "__code__".

A code object can be executed or evaluated by passing it (instead of a source string) to the exec() or eval() built-in functions.

See The standard type hierarchy for more information.

Type Objects

Type objects represent the various object types. An object’s type is accessed by the built-in function type(). There are no special operations on types. The standard module types defines names for all standard built-in types.

Types are written like this: <class 'int'>.

The Null Object

This object is returned by functions that don’t explicitly return a value. It supports no special operations. There is exactly one null object, named None (a built-in name). type(None)() produces the same singleton.

It is written as None.

The Ellipsis Object

This object is commonly used by slicing (see Slicings). It supports no special operations. There is exactly one ellipsis object, named Ellipsis (a built-in name). type(Ellipsis)() produces the Ellipsis singleton.

It is written as Ellipsis or ....

The NotImplemented Object

This object is returned from comparisons and binary operations when they are asked to operate on types they don’t support. See Comparisons for more information. There is exactly one NotImplemented object. type(NotImplemented)() produces the singleton instance.

It is written as NotImplemented.

Boolean Values

Boolean values are the two constant objects False and True. They are used to represent truth values (although other values can also be considered false or true). In numeric contexts (for example when used as the argument to an arithmetic operator), they behave like the integers 0 and 1, respectively. The built-in function bool() can be used to convert any value to a Boolean, if the value can be interpreted as a truth value (see section Truth Value Testing above).

They are written as False and True, respectively.

Internal Objects

See The standard type hierarchy for this information. It describes stack frame objects, traceback objects, and slice objects.

Special Attributes

The implementation adds a few special read-only attributes to several object types, where they are relevant. Some of these are not reported by the dir() built-in function.object.__dict__

A dictionary or other mapping object used to store an object’s (writable) attributes.instance.__class__

The class to which a class instance belongs.class.__bases__

The tuple of base classes of a class object.definition.__name__

The name of the class, function, method, descriptor, or generator instance.definition.__qualname__

The qualified name of the class, function, method, descriptor, or generator instance.

New in version 3.3.class.__mro__

This attribute is a tuple of classes that are considered when looking for base classes during method resolution.class.mro()

This method can be overridden by a metaclass to customize the method resolution order for its instances. It is called at class instantiation, and its result is stored in __mro__.class.__subclasses__()

Each class keeps a list of weak references to its immediate subclasses. This method returns a list of all those references still alive. The list is in definition order. Example:>>>

>>> int.__subclasses__()
[<class 'bool'>]

More-Examples

The List Data Type

A list is a value that contains multiple values in an ordered sequence. The term list value refers to the list itself (which is a value that can be stored in a variable or passed to a function like any other value), not the values inside the list value. A list value looks like this: ['cat', 'bat', 'rat', 'elephant']. Just as string values are typed with quote characters to mark where the string begins and ends, a list begins with an opening square bracket and ends with a closing square bracket, []. Values inside the list are also called items. Items are separated with commas (that is, they are comma-delimited). For example, enter the following into the interactive shell:

>>> [1, 2, 3]
   [1, 2, 3]
   >>> ['cat', 'bat', 'rat', 'elephant']
   ['cat', 'bat', 'rat', 'elephant']
   >>> ['hello', 3.1415, True, None, 42]
   ['hello', 3.1415, True, None, 42]
➊ >>> spam = ['cat', 'bat', 'rat', 'elephant']
   >>> spam
   ['cat', 'bat', 'rat', 'elephant']

The spam variable ➊ is still assigned only one value: the list value. But the list value itself contains other values. The value [] is an empty list that contains no values, similar to '', the empty string.

Getting Individual Values in a List with Indexes

Say you have the list ['cat', 'bat', 'rat', 'elephant'] stored in a variable named spam. The Python code spam[0] would evaluate to 'cat', and spam[1] would evaluate to 'bat', and so on. The integer inside the square brackets that follows the list is called an index. The first value in the list is at index 0, the second value is at index 1, the third value is at index 2, and so on. Figure 4-1 shows a list value assigned to spam, along with what the index expressions would evaluate to. Note that because the first index is 0, the last index is one less than the size of the list; a list of four items has 3 as its last index.

Figure 4-1: A list value stored in the variable spam, showing which value each index refers to

For example, enter the following expressions into the interactive shell. Start by assigning a list to the variable spam.


  >>> spam = ['cat', 'bat', 'rat', 'elephant']
   >>> spam[0]
   'cat'
   >>> spam[1]
   'bat'
   >>> spam[2]
   'rat'
   >>> spam[3]
   'elephant'
   >>> ['cat', 'bat', 'rat', 'elephant'][3]
   'elephant'
➊ >>> 'Hello, ' + spam[0]
➋ 'Hello, cat'
   >>> 'The ' + spam[1] + ' ate the ' + spam[0] + '.'
   'The bat ate the cat.'

Notice that the expression 'Hello, ' + spam[0] ➊ evaluates to 'Hello, ' + 'cat' because spam[0] evaluates to the string 'cat'. This expression in turn evaluates to the string value 'Hello, cat' ➋.

Python will give you an IndexError error message if you use an index that exceeds the number of values in your list value.

>>> spam = ['cat', 'bat', 'rat', 'elephant']
>>> spam[10000]
Traceback (most recent call last):
  File "<pyshell#9>", line 1, in <module>
    spam[10000]
IndexError: list index out of range

Indexes can be only integer values, not floats. The following example will cause a TypeError error:

>>> spam = ['cat', 'bat', 'rat', 'elephant'] >>> spam[1] 'bat' >>> spam[1.0] Traceback (most recent call last): File "<pyshell#13>", line 1, in <module> spam[1.0] TypeError: list indices must be integers or slices, not float >>> spam[int(1.0)] 'bat'

Lists can also contain other list values. The values in these lists of lists can be accessed using multiple indexes, like so:


>>> spam = [['cat', 'bat'], [10, 20, 30, 40, 50]]
>>> spam[0]
['cat', 'bat']
>>> spam[0][1]
'bat'
>>> spam[1][4]
50

The first index dictates which list value to use, and the second indicates the value within the list value. For example, spam[0][1] prints 'bat', the second value in the first list. If you only use one index, the program will print the full list value at that index.

Negative Indexes

While indexes start at 0 and go up, you can also use negative integers for the index. The integer value -1 refers to the last index in a list, the value -2 refers to the second-to-last index in a list, and so on. Enter the following into the interactive shell:


>>> spam = ['cat', 'bat', 'rat', 'elephant']
>>> spam[-1]
'elephant'
>>> spam[-3]
'bat'
>>> 'The ' + spam[-1] + ' is afraid of the ' + spam[-3] + '.'
'The elephant is afraid of the bat.'

Getting a List from Another List with Slices

Just as an index can get a single value from a list, a slice can get several values from a list, in the form of a new list. A slice is typed between square brackets, like an index, but it has two integers separated by a colon. Notice the difference between indexes and slices.

spam[2] is a list with an index (one integer).
spam[1:4] is a list with a slice (two integers).

In a slice, the first integer is the index where the slice starts. The second integer is the index where the slice ends. A slice goes up to, but will not include, the value at the second index. A slice evaluates to a new list value. Enter the following into the interactive shell:

>>> spam = ['cat', 'bat', 'rat', 'elephant']
>>> spam[0:4]
['cat', 'bat', 'rat', 'elephant']
>>> spam[1:3]
['bat', 'rat']
>>> spam[0:-1]
['cat', 'bat', 'rat']

As a shortcut, you can leave out one or both of the indexes on either side of the colon in the slice. Leaving out the first index is the same as using 0, or the beginning of the list. Leaving out the second index is the same as using the length of the list, which will slice to the end of the list. Enter the following into the interactive shell:

>>> spam = ['cat', 'bat', 'rat', 'elephant']
>>> spam[:2]
['cat', 'bat']
>>> spam[1:]
['bat', 'rat', 'elephant']
>>> spam[:]
['cat', 'bat', 'rat', 'elephant']

Getting a List’s Length with the len() Function

The len() function will return the number of values that are in a list value passed to it, just like it can count the number of characters in a string value. Enter the following into the interactive shell:

>>> spam = ['cat', 'dog', 'moose'] >>> len(spam) 3

Changing Values in a List with Indexes

Normally, a variable name goes on the left side of an assignment statement, like spam = 42. However, you can also use an index of a list to change the value at that index. For example, spam[1] = 'aardvark' means “Assign the value at index 1 in the list spam to the string 'aardvark'.” Enter the following into the interactive shell:


>>> spam = ['cat', 'bat', 'rat', 'elephant']
>>> spam[1] = 'aardvark'
>>> spam
['cat', 'aardvark', 'rat', 'elephant']
>>> spam[2] = spam[1]
>>> spam
['cat', 'aardvark', 'aardvark', 'elephant']
>>> spam[-1] = 12345
>>> spam
['cat', 'aardvark', 'aardvark', 12345]

List Concatenation and List Replication

Lists can be concatenated and replicated just like strings. The + operator combines two lists to create a new list value and the * operator can be used with a list and an integer value to replicate the list. Enter the following into the interactive shell:


>>> [1, 2, 3] + ['A', 'B', 'C']
[1, 2, 3, 'A', 'B', 'C']
>>> ['X', 'Y', 'Z'] * 3
['X', 'Y', 'Z', 'X', 'Y', 'Z', 'X', 'Y', 'Z']
>>> spam = [1, 2, 3]
>>> spam = spam + ['A', 'B', 'C']
>>> spam
[1, 2, 3, 'A', 'B', 'C']

Removing Values from Lists with del Statements

The del statement will delete values at an index in a list. All of the values in the list after the deleted value will be moved up one index. For example, enter the following into the interactive shell:

>>> spam = ['cat', 'bat', 'rat', 'elephant']
>>> del spam[2]
>>> spam
['cat', 'bat', 'elephant']
>>> del spam[2]
>>> spam
['cat', 'bat']

The del statement can also be used on a simple variable to delete it, as if it were an “unassignment” statement. If you try to use the variable after deleting it, you will get a NameError error because the variable no longer exists. In practice, you almost never need to delete simple variables. The del statement is mostly used to delete values from lists.

Working with Lists

When you first begin writing programs, it’s tempting to create many individual variables to store a group of similar values. For example, if I wanted to store the names of my cats, I might be tempted to write code like this:

catName1 = 'Zophie'
catName2 = 'Pooka'
catName3 = 'Simon'
catName4 = 'Lady Macbeth'
catName5 = 'Fat-tail'
catName6 = 'Miss Cleo'

It turns out that this is a bad way to write code. (Also, I don’t actually own this many cats, I swear.) For one thing, if the number of cats changes, your program will never be able to store more cats than you have variables. These types of programs also have a lot of duplicate or nearly identical code in them. Consider how much duplicate code is in the following program, which you should enter into the file editor and save as allMyCats1.py:

print('Enter the name of cat 1:')
catName1 = input()
print('Enter the name of cat 2:')
catName2 = input()
print('Enter the name of cat 3:')
catName3 = input()
print('Enter the name of cat 4:')
catName4 = input()
print('Enter the name of cat 5:')
catName5 = input()
print('Enter the name of cat 6:')
catName6 = input()
print('The cat names are:')
print(catName1 + ' ' + catName2 + ' ' + catName3 + ' ' + catName4 + ' ' +
catName5 + ' ' + catName6)

Instead of using multiple, repetitive variables, you can use a single variable that contains a list value. For example, here’s a new and improved version of the allMyCats1.py program. This new version uses a single list and can store any number of cats that the user types in. In a new file editor window, enter the following source code and save it as allMyCats2.py:

catNames = []
while True:
    print('Enter the name of cat ' + str(len(catNames) + 1) +
      ' (Or enter nothing to stop.):')
    name = input()
    if name == '':
        break
    catNames = catNames + [name]  # list concatenation
print('The cat names are:')
for name in catNames:
    print('  ' + name)

When you run this program, the output will look something like this:

Enter the name of cat 1 (Or enter nothing to stop.): Zophie Enter the name of cat 2 (Or enter nothing to stop.): Pooka Enter the name of cat 3 (Or enter nothing to stop.): Simon Enter the name of cat 4 (Or enter nothing to stop.): Lady Macbeth Enter the name of cat 5 (Or enter nothing to stop.): Fat-tail Enter the name of cat 6 (Or enter nothing to stop.): Miss Cleo Enter the name of cat 7 (Or enter nothing to stop.): The cat names are: Zophie Pooka Simon Lady Macbeth Fat-tail Miss Cleo

You can view the execution of these programs at https://autbor.com/allmycats1/ and https://autbor.com/allmycats2/. The benefit of using a list is that your data is now in a structure, so your program is much more flexible in processing the data than it would be with several repetitive variables.

Using for Loops with Lists

In Chapter 2, you learned about using for loops to execute a block of code a certain number of times. Technically, a for loop repeats the code block once for each item in a list value. For example, if you ran this code:

for i in range(4): print(i)

the output of this program would be as follows:

0 1 2 3

This is because the return value from range(4) is a sequence value that Python considers similar to [0, 1, 2, 3]. (Sequences are described in “Sequence Data Types” on page 93.) The following program has the same output as the previous one:

for i in [0, 1, 2, 3]: print(i)

The previous for loop actually loops through its clause with the variable i set to a successive value in the [0, 1, 2, 3] list in each iteration.

A common Python technique is to use range(len(someList)) with a for loop to iterate over the indexes of a list. For example, enter the following into the interactive shell:

>>> supplies = ['pens', 'staplers', 'flamethrowers', 'binders'] >>> for i in range(len(supplies)): ... print('Index ' + str(i) + ' in supplies is: ' + supplies[i]) Index 0 in supplies is: pens Index 1 in supplies is: staplers Index 2 in supplies is: flamethrowers Index 3 in supplies is: binders

Using range(len(supplies)) in the previously shown for loop is handy because the code in the loop can access the index (as the variable i) and the value at that index (as supplies[i]). Best of all, range(len(supplies)) will iterate through all the indexes of supplies, no matter how many items it contains.

The in and not in Operators

You can determine whether a value is or isn’t in a list with the in and not in operators. Like other operators, in and not in are used in expressions and connect two values: a value to look for in a list and the list where it may be found. These expressions will evaluate to a Boolean value. Enter the following into the interactive shell:


>>> 'howdy' in ['hello', 'hi', 'howdy', 'heyas']
True
>>> spam = ['hello', 'hi', 'howdy', 'heyas']
>>> 'cat' in spam
False
>>> 'howdy' not in spam
False
>>> 'cat' not in spam
True

For example, the following program lets the user type in a pet name and then checks to see whether the name is in a list of pets. Open a new file editor window, enter the following code, and save it as myPets.py:

myPets = ['Zophie', 'Pooka', 'Fat-tail'] print('Enter a pet name:') name = input() if name not in myPets: print('I do not have a pet named ' + name) else: print(name + ' is my pet.')

The output may look something like this:

Enter a pet name: Footfoot I do not have a pet named Footfoot

You can view the execution of this program at https://autbor.com/mypets/.

https://pythontutor.com/visualize.html#

The Multiple Assignment Trick

The multiple assignment trick (technically called tuple unpacking) is a shortcut that lets you assign multiple variables with the values in a list in one line of code. So instead of doing this:

>>> cat = ['fat', 'gray', 'loud'] >>> size = cat[0] >>> color = cat[1] >>> disposition = cat[2]

you could type this line of code:

>>> cat = ['fat', 'gray', 'loud'] >>> size, color, disposition = cat

The number of variables and the length of the list must be exactly equal, or Python will give you a ValueError:

>>> cat = ['fat', 'gray', 'loud'] >>> size, color, disposition, name = cat Traceback (most recent call last): File "<pyshell#84>", line 1, in <module> size, color, disposition, name = cat ValueError: not enough values to unpack (expected 4, got 3)

Using the enumerate() Function with Lists

Instead of using the range(len(someList)) technique with a for loop to obtain the integer index of the items in the list, you can call the enumerate() function instead. On each iteration of the loop, enumerate() will return two values: the index of the item in the list, and the item in the list itself. For example, this code is equivalent to the code in the “Using for Loops with Lists”

>>> supplies = ['pens', 'staplers', 'flamethrowers', 'binders'] >>> for index, item in enumerate(supplies): ... print('Index ' + str(index) + ' in supplies is: ' + item) Index 0 in supplies is: pens Index 1 in supplies is: staplers Index 2 in supplies is: flamethrowers Index 3 in supplies is: binders

The enumerate() function is useful if you need both the item and the item’s index in the loop’s block.

Using the random.choice() and random.shuffle() Functions with Lists

The random module has a couple functions that accept lists for arguments. The random.choice() function will return a randomly selected item from the list. Enter the following into the interactive shell:

>>> import random >>> pets = ['Dog', 'Cat', 'Moose'] >>> random.choice(pets) 'Dog' >>> random.choice(pets) 'Cat' >>> random.choice(pets) 'Cat'

You can consider random.choice(someList) to be a shorter form of someList[random.randint(0, len(someList) – 1].

The random.shuffle() function will reorder the items in a list. This function modifies the list in place, rather than returning a new list. Enter the following into the interactive shell:

>>> import random >>> people = ['Alice', 'Bob', 'Carol', 'David'] >>> random.shuffle(people) >>> people ['Carol', 'David', 'Alice', 'Bob'] >>> random.shuffle(people) >>> people ['Alice', 'David', 'Bob', 'Carol']

Augmented Assignment Operators

When assigning a value to a variable, you will frequently use the variable itself. For example, after assigning 42 to the variable spam, you would increase the value in spam by 1 with the following code:

>>> spam = 42 >>> spam = spam + 1 >>> spam 43

As a shortcut, you can use the augmented assignment operator += to do the same thing:

>>> spam = 42 >>> spam += 1 >>> spam 43

There are augmented assignment operators for the +, -, *, /, and % operators, described in Table 4-1.

Table 4-1: The Augmented Assignment Operators

The += operator can also do string and list concatenation, and the *= operator can do string and list replication. Enter the following into the interactive shell:

>>> spam = 'Hello,' >>> spam += ' world!' >>> spam 'Hello world!' >>> bacon = ['Zophie'] >>> bacon *= 3 >>> bacon ['Zophie', 'Zophie', 'Zophie']

Methods

A method is the same thing as a function, except it is “called on” a value. For example, if a list value were stored in spam, you would call the index() list method (which I’ll explain shortly) on that list like so: spam.index('hello'). The method part comes after the value, separated by a period.

Each data type has its own set of methods. The list data type, for example, has several useful methods for finding, adding, removing, and otherwise manipulating values in a list.

Finding a Value in a List with the index() Method

List values have an index() method that can be passed a value, and if that value exists in the list, the index of the value is returned. If the value isn’t in the list, then Python produces a ValueError error. Enter the following into the interactive shell:

>>> spam = ['hello', 'hi', 'howdy', 'heyas'] >>> spam.index('hello') 0 >>> spam.index('heyas') 3 >>> spam.index('howdy howdy howdy') Traceback (most recent call last): File "<pyshell#31>", line 1, in <module> spam.index('howdy howdy howdy') ValueError: 'howdy howdy howdy' is not in list

When there are duplicates of the value in the list, the index of its first appearance is returned. Enter the following into the interactive shell, and notice that index() returns 1, not 3:

>>> spam = ['Zophie', 'Pooka', 'Fat-tail', 'Pooka'] >>> spam.index('Pooka') 1

Adding Values to Lists with the append() and insert() Methods

To add new values to a list, use the append() and insert() methods. Enter the following into the interactive shell to call the append() method on a list value stored in the variable spam:

>>> spam = ['cat', 'dog', 'bat'] >>> spam.append('moose') >>> spam ['cat', 'dog', 'bat', 'moose']

The previous append() method call adds the argument to the end of the list. The insert() method can insert a value at any index in the list. The first argument to insert() is the index for the new value, and the second argument is the new value to be inserted. Enter the following into the interactive shell:

>>> spam = ['cat', 'dog', 'bat'] >>> spam.insert(1, 'chicken') >>> spam ['cat', 'chicken', 'dog', 'bat']

Notice that the code is spam.append('moose') and spam.insert(1, 'chicken'), not spam = spam.append('moose') and spam = spam.insert(1, 'chicken'). Neither append() nor insert() gives the new value of spam as its return value. (In fact, the return value of append() and insert() is None, so you definitely wouldn’t want to store this as the new variable value.) Rather, the list is modified in place. Modifying a list in place is covered in more detail later in “Mutable and Immutable Data Types” on page 94.

Methods belong to a single data type. The append() and insert() methods are list methods and can be called only on list values, not on other values such as strings or integers. Enter the following into the interactive shell, and note the AttributeError error messages that show up:

>>> eggs = 'hello' >>> eggs.append('world') Traceback (most recent call last): File "<pyshell#19>", line 1, in <module> eggs.append('world') AttributeError: 'str' object has no attribute 'append' >>> bacon = 42 >>> bacon.insert(1, 'world') Traceback (most recent call last): File "<pyshell#22>", line 1, in <module> bacon.insert(1, 'world') AttributeError: 'int' object has no attribute 'insert'

Removing Values from Lists with the remove() Method

The remove() method is passed the value to be removed from the list it is called on. Enter the following into the interactive shell:

>>> spam = ['cat', 'bat', 'rat', 'elephant'] >>> spam.remove('bat') >>> spam ['cat', 'rat', 'elephant']

Attempting to delete a value that does not exist in the list will result in a ValueError error. For example, enter the following into the interactive shell and notice the error that is displayed:

>>> spam = ['cat', 'bat', 'rat', 'elephant'] >>> spam.remove('chicken') Traceback (most recent call last): File "<pyshell#11>", line 1, in <module> spam.remove('chicken') ValueError: list.remove(x): x not in list

If the value appears multiple times in the list, only the first instance of the value will be removed. Enter the following into the interactive shell:

>>> spam = ['cat', 'bat', 'rat', 'cat', 'hat', 'cat'] >>> spam.remove('cat') >>> spam ['bat', 'rat', 'cat', 'hat', 'cat']

The del statement is good to use when you know the index of the value you want to remove from the list. The remove() method is useful when you know the value you want to remove from the list.

Sorting the Values in a List with the sort() Method

Lists of number values or lists of strings can be sorted with the sort() method. For example, enter the following into the interactive shell:

>>> spam = [2, 5, 3.14, 1, -7] >>> spam.sort() >>> spam [-7, 1, 2, 3.14, 5] >>> spam = ['ants', 'cats', 'dogs', 'badgers', 'elephants'] >>> spam.sort() >>> spam ['ants', 'badgers', 'cats', 'dogs', 'elephants']

You can also pass True for the reverse keyword argument to have sort() sort the values in reverse order. Enter the following into the interactive shell:

>>> spam.sort(reverse=True) >>> spam ['elephants', 'dogs', 'cats', 'badgers', 'ants']

There are three things you should note about the sort() method. First, the sort() method sorts the list in place; don’t try to capture the return value by writing code like spam = spam.sort().

Second, you cannot sort lists that have both number values and string values in them, since Python doesn’t know how to compare these values. Enter the following into the interactive shell and notice the TypeError error:

>>> spam = [1, 3, 2, 4, 'Alice', 'Bob'] >>> spam.sort() Traceback (most recent call last): File "<pyshell#70>", line 1, in <module> spam.sort() TypeError: '<' not supported between instances of 'str' and 'int'

Third, sort() uses “ASCIIbetical order” rather than actual alphabetical order for sorting strings. This means uppercase letters come before lowercase letters. Therefore, the lowercase a is sorted so that it comes after the uppercase Z. For an example, enter the following into the interactive shell:

>>> spam = ['Alice', 'ants', 'Bob', 'badgers', 'Carol', 'cats'] >>> spam.sort() >>> spam ['Alice', 'Bob', 'Carol', 'ants', 'badgers', 'cats']

If you need to sort the values in regular alphabetical order, pass str.lower for the key keyword argument in the sort() method call.

>>> spam = ['a', 'z', 'A', 'Z'] >>> spam.sort(key=str.lower) >>> spam ['a', 'A', 'z', 'Z']

This causes the sort() function to treat all the items in the list as if they were lowercase without actually changing the values in the list.

Reversing the Values in a List with the reverse() Method

If you need to quickly reverse the order of the items in a list, you can call the reverse() list method. Enter the following into the interactive shell:

>>> spam = ['cat', 'dog', 'moose'] >>> spam.reverse() >>> spam ['moose', 'dog', 'cat']

EXCEPTIONS TO INDENTATION RULES IN PYTHON

In most cases, the amount of indentation for a line of code tells Python what block it is in. There are some exceptions to this rule, however. For example, lists can actually span several lines in the source code file. The indentation of these lines does not matter; Python knows that the list is not finished until it sees the ending square bracket. For example, you can have code that looks like this:

spam = ['apples', 'oranges', 'bananas', 'cats'] print(spam)

Of course, practically speaking, most people use Python’s behavior to make their lists look pretty and readable, like the messages list in the Magic 8 Ball program.

You can also split up a single instruction across multiple lines using the \ line continuation character at the end. Think of \ as saying, “This instruction continues on the next line.” The indentation on the line after a \ line continuation is not significant. For example, the following is valid Python code:

print('Four score and seven ' + \ 'years ago...')

These tricks are useful when you want to rearrange long lines of Python code to be a bit more readable.

Like the sort() list method, reverse() doesn’t return a list. This is why you write spam.reverse(), instead of spam = spam.reverse().

Example Program: Magic 8 Ball with a List

Using lists, you can write a much more elegant version of the previous chapter’s Magic 8 Ball program. Instead of several lines of nearly identical elif statements, you can create a single list that the code works with. Open a new file editor window and enter the following code. Save it as magic8Ball2.py.

import random messages = ['It is certain', 'It is decidedly so', 'Yes definitely', 'Reply hazy try again', 'Ask again later', 'Concentrate and ask again', 'My reply is no', 'Outlook not so good', 'Very doubtful'] print(messages[random.randint(0, len(messages) - 1)])

You can view the execution of this program at https://autbor.com/magic8ball2/.

When you run this program, you’ll see that it works the same as the previous magic8Ball.py program.

Notice the expression you use as the index for messages: random.randint (0, len(messages) - 1). This produces a random number to use for the index, regardless of the size of messages. That is, you’ll get a random number between 0 and the value of len(messages) - 1. The benefit of this approach is that you can easily add and remove strings to the messages list without changing other lines of code. If you later update your code, there will be fewer lines you have to change and fewer chances for you to introduce bugs.

Sequence Data Types

Lists aren’t the only data types that represent ordered sequences of values. For example, strings and lists are actually similar if you consider a string to be a “list” of single text characters. The Python sequence data types include lists, strings, range objects returned by range(), and tuples (explained in the “The Tuple Data Type” on page 96). Many of the things you can do with lists can also be done with strings and other values of sequence types: indexing; slicing; and using them with for loops, with len(), and with the in and not in operators. To see this, enter the following into the interactive shell:

>>> name = 'Zophie' >>> name[0] 'Z' >>> name[-2] 'i' >>> name[0:4] 'Zoph' >>> 'Zo' in name True >>> 'z' in name False >>> 'p' not in name False >>> for i in name: ... print('* * * ' + i + ' * * *') * * * Z * * * * * * o * * * * * * p * * * * * * h * * * * * * i * * * * * * e * * *

Mutable and Immutable Data Types

But lists and strings are different in an important way. A list value is a mutable data type: it can have values added, removed, or changed. However, a string is immutable: it cannot be changed. Trying to reassign a single character in a string results in a TypeError error, as you can see by entering the following into the interactive shell:

>>> name = 'Zophie a cat' >>> name[7] = 'the' Traceback (most recent call last): File "<pyshell#50>", line 1, in <module> name[7] = 'the' TypeError: 'str' object does not support item assignment

The proper way to “mutate” a string is to use slicing and concatenation to build a new string by copying from parts of the old string. Enter the following into the interactive shell:

>>> name = 'Zophie a cat' >>> newName = name[0:7] + 'the' + name[8:12] >>> name 'Zophie a cat' >>> newName 'Zophie the cat'

We used [0:7] and [8:12] to refer to the characters that we don’t wish to replace. Notice that the original 'Zophie a cat' string is not modified, because strings are immutable.

Although a list value is mutable, the second line in the following code does not modify the list eggs:

>>> eggs = [1, 2, 3] >>> eggs = [4, 5, 6] >>> eggs [4, 5, 6]

The list value in eggs isn’t being changed here; rather, an entirely new and different list value ([4, 5, 6]) is overwriting the old list value ([1, 2, 3]). This is depicted in Figure 4-2.

If you wanted to actually modify the original list in eggs to contain [4, 5, 6], you would have to do something like this:

Figure 4-2: When eggs = [4, 5, 6] is executed, the contents of eggs are replaced with a new list value.

Figure 4-3: The del statement and the append() method modify the same list value in place.

Changing a value of a mutable data type (like what the del statement and append() method do in the previous example) changes the value in place, since the variable’s value is not replaced with a new list value.

Mutable versus immutable types may seem like a meaningless distinction, but “Passing References” on page 100 will explain the different behavior when calling functions with mutable arguments versus immutable arguments. But first, let’s find out about the tuple data type, which is an immutable form of the list data type.

The Tuple Data Type

The tuple data type is almost identical to the list data type, except in two ways. First, tuples are typed with parentheses, ( and ), instead of square brackets, [ and ]. For example, enter the following into the interactive shell:

>>> eggs = ('hello', 42, 0.5) >>> eggs[0] 'hello' >>> eggs[1:3] (42, 0.5) >>> len(eggs) 3

But the main way that tuples are different from lists is that tuples, like strings, are immutable. Tuples cannot have their values modified, appended, or removed. Enter the following into the interactive shell, and look at the TypeError error message:

>>> eggs = ('hello', 42, 0.5) >>> eggs[1] = 99 Traceback (most recent call last): File "<pyshell#5>", line 1, in <module> eggs[1] = 99 TypeError: 'tuple' object does not support item assignment

If you have only one value in your tuple, you can indicate this by placing a trailing comma after the value inside the parentheses. Otherwise, Python will think you’ve just typed a value inside regular parentheses. The comma is what lets Python know this is a tuple value. (Unlike some other programming languages, it’s fine to have a trailing comma after the last item in a list or tuple in Python.) Enter the following type() function calls into the interactive shell to see the distinction:

>>> type(('hello',)) <class 'tuple'> >>> type(('hello')) <class 'str'>

You can use tuples to convey to anyone reading your code that you don’t intend for that sequence of values to change. If you need an ordered sequence of values that never changes, use a tuple. A second benefit of using tuples instead of lists is that, because they are immutable and their contents don’t change, Python can implement some optimizations that make code using tuples slightly faster than code using lists.

Converting Types with the list() and tuple() Functions

Just like how str(42) will return '42', the string representation of the integer 42, the functions list() and tuple() will return list and tuple versions of the values passed to them. Enter the following into the interactive shell, and notice that the return value is of a different data type than the value passed:

>>> tuple(['cat', 'dog', 5]) ('cat', 'dog', 5) >>> list(('cat', 'dog', 5)) ['cat', 'dog', 5] >>> list('hello') ['h', 'e', 'l', 'l', 'o']

Converting a tuple to a list is handy if you need a mutable version of a tuple value.

References

As you’ve seen, variables “store” strings and integer values. However, this explanation is a simplification of what Python is actually doing. Technically, variables are storing references to the computer memory locations where the values are stored. Enter the following into the interactive shell:

>>> spam = 42 >>> cheese = spam >>> spam = 100 >>> spam 100 >>> cheese 42

When you assign 42 to the spam variable, you are actually creating the 42 value in the computer’s memory and storing a reference to it in the spam variable. When you copy the value in spam and assign it to the variable cheese, you are actually copying the reference. Both the spam and cheese variables refer to the 42 value in the computer’s memory. When you later change the value in spam to 100, you’re creating a new 100 value and storing a reference to it in spam. This doesn’t affect the value in cheese. Integers are immutable values that don’t change; changing the spam variable is actually making it refer to a completely different value in memory.

But lists don’t work this way, because list values can change; that is, lists are mutable. Here is some code that will make this distinction easier to understand. Enter this into the interactive shell:

➊ >>> spam = [0, 1, 2, 3, 4, 5] ➋ >>> cheese = spam # The reference is being copied, not the list. ➌ >>> cheese[1] = 'Hello!' # This changes the list value. >>> spam [0, 'Hello!', 2, 3, 4, 5] >>> cheese # The cheese variable refers to the same list. [0, 'Hello!', 2, 3, 4, 5]

This might look odd to you. The code touched only the cheese list, but it seems that both the cheese and spam lists have changed.

When you create the list ➊, you assign a reference to it in the spam variable. But the next line ➋ copies only the list reference in spam to cheese, not the list value itself. This means the values stored in spam and cheese now both refer to the same list. There is only one underlying list because the list itself was never actually copied. So when you modify the first element of cheese ➌, you are modifying the same list that spam refers to.

Figure 4-4: spam = [0, 1, 2, 3, 4, 5] stores a reference to a list, not the actual list.

Figure 4-5: spam = cheese copies the reference, not the list.

Figure 4-6: cheese[1] = 'Hello!' modifies the list that both variables refer to.

Although Python variables technically contain references to values, people often casually say that the variable contains the value.

Identity and the id() Function

You may be wondering why the weird behavior with mutable lists in the previous section doesn’t happen with immutable values like integers or strings. We can use Python’s id() function to understand this. All values in Python have a unique identity that can be obtained with the id() function. Enter the following into the interactive shell:

>>> id('Howdy') # The returned number will be different on your machine. 44491136

When Python runs id('Howdy'), it creates the 'Howdy' string in the computer’s memory. The numeric memory address where the string is stored is returned by the id() function. Python picks this address based on which memory bytes happen to be free on your computer at the time, so it’ll be different each time you run this code.

Like all strings, 'Howdy' is immutable and cannot be changed. If you “change” the string in a variable, a new string object is being made at a different place in memory, and the variable refers to this new string. For example, enter the following into the interactive shell and see how the identity of the string referred to by bacon changes:

>>> bacon = 'Hello' >>> id(bacon) 44491136 >>> bacon += ' world!' # A new string is made from 'Hello' and ' world!'. >>> id(bacon) # bacon now refers to a completely different string. 44609712

However, lists can be modified because they are mutable objects. The append() method doesn’t create a new list object; it changes the existing list object. We call this “modifying the object in-place.”

>>> eggs = ['cat', 'dog'] # This creates a new list. >>> id(eggs) 35152584 >>> eggs.append('moose') # append() modifies the list "in place". >>> id(eggs) # eggs still refers to the same list as before. 35152584 >>> eggs = ['bat', 'rat', 'cow'] # This creates a new list, which has a new identity. >>> id(eggs) # eggs now refers to a completely different list. 44409800

If two variables refer to the same list (like spam and cheese in the previous section) and the list value itself changes, both variables are affected because they both refer to the same list. The append(), extend(), remove(), sort(), reverse(), and other list methods modify their lists in place.

Python’s automatic garbage collector deletes any values not being referred to by any variables to free up memory. You don’t need to worry about how the garbage collector works, which is a good thing: manual memory management in other programming languages is a common source of bugs.

Passing References

References are particularly important for understanding how arguments get passed to functions. When a function is called, the values of the arguments are copied to the parameter variables. For lists (and dictionaries, which I’ll describe in the next chapter), this means a copy of the reference is used for the parameter. To see the consequences of this, open a new file editor window, enter the following code, and save it as passingReference.py:

def eggs(someParameter): someParameter.append('Hello') spam = [1, 2, 3] eggs(spam) print(spam)

Notice that when eggs() is called, a return value is not used to assign a new value to spam. Instead, it modifies the list in place, directly. When run, this program produces the following output:

[1, 2, 3, 'Hello']

Even though spam and someParameter contain separate references, they both refer to the same list. This is why the append('Hello') method call inside the function affects the list even after the function call has returned.

Keep this behavior in mind: forgetting that Python handles list and dictionary variables this way can lead to confusing bugs.

The copy Module’s copy() and deepcopy() Functions

Although passing around references is often the handiest way to deal with lists and dictionaries, if the function modifies the list or dictionary that is passed, you may not want these changes in the original list or dictionary value. For this, Python provides a module named copy that provides both the copy() and deepcopy() functions. The first of these, copy.copy(), can be used to make a duplicate copy of a mutable value like a list or dictionary, not just a copy of a reference. Enter the following into the interactive shell:

>>> import copy >>> spam = ['A', 'B', 'C', 'D'] >>> id(spam) 44684232 >>> cheese = copy.copy(spam) >>> id(cheese) # cheese is a different list with different identity. 44685832 >>> cheese[1] = 42 >>> spam ['A', 'B', 'C', 'D'] >>> cheese ['A', 42, 'C', 'D']

Figure 4-7: cheese = copy.copy(spam) creates a second list that can be modified independently of the first.

If the list you need to copy contains lists, then use the copy.deepcopy() function instead of copy.copy(). The deepcopy() function will copy these inner lists as well.

A Short Program: Conway’s Game of Life

Figure 4-8: Four steps in a Conway’s Game of Life simulation

Even though the rules are simple, there are many surprising behaviors that emerge. Patterns in Conway’s Game of Life can move, self-replicate, or even mimic CPUs. But at the foundation of all of this complex, advanced behavior is a rather simple program.

We can use a list of lists to represent the two-dimensional field. The inner list represents each column of squares and stores a '#' hash string for living squares and a ' ' space string for dead squares. Type the following source code into the file editor, and save the file as conway.py. It’s fine if you don’t quite understand how all of the code works; just enter it and follow along with comments and explanations provided here as close as you can:

# Conway's Game of Life
import random, time, copy
WIDTH = 60
HEIGHT = 20

# Create a list of list for the cells:
nextCells = []
for x in range(WIDTH):
    column = [] # Create a new column.
    for y in range(HEIGHT):
        if random.randint(0, 1) == 0:
            column.append('#') # Add a living cell.
        else:
            column.append(' ') # Add a dead cell.
    nextCells.append(column) # nextCells is a list of column lists.

while True: # Main program loop.
    print('\n\n\n\n\n') # Separate each step with newlines.
    currentCells = copy.deepcopy(nextCells)

    # Print currentCells on the screen:
    for y in range(HEIGHT):
        for x in range(WIDTH):
            print(currentCells[x][y], end='') # Print the # or space.
        print() # Print a newline at the end of the row.

    # Calculate the next step's cells based on current step's cells:
    for x in range(WIDTH):
        for y in range(HEIGHT):
            # Get neighboring coordinates:
            # `% WIDTH` ensures leftCoord is always between 0 and WIDTH - 1
            leftCoord  = (x - 1) % WIDTH
            rightCoord = (x + 1) % WIDTH
            aboveCoord = (y - 1) % HEIGHT
            belowCoord = (y + 1) % HEIGHT

            # Count number of living neighbors:
            numNeighbors = 0
            if currentCells[leftCoord][aboveCoord] == '#':
                numNeighbors += 1 # Top-left neighbor is alive.
            if currentCells[x][aboveCoord] == '#':
                numNeighbors += 1 # Top neighbor is alive.
            if currentCells[rightCoord][aboveCoord] == '#':
                numNeighbors += 1 # Top-right neighbor is alive.
            if currentCells[leftCoord][y] == '#':
                numNeighbors += 1 # Left neighbor is alive.
            if currentCells[rightCoord][y] == '#':
                numNeighbors += 1 # Right neighbor is alive.
            if currentCells[leftCoord][belowCoord] == '#':
                numNeighbors += 1 # Bottom-left neighbor is alive.
            if currentCells[x][belowCoord] == '#':
                numNeighbors += 1 # Bottom neighbor is alive.
            if currentCells[rightCoord][belowCoord] == '#':
                numNeighbors += 1 # Bottom-right neighbor is alive.

            # Set cell based on Conway's Game of Life rules:
            if currentCells[x][y] == '#' and (numNeighbors == 2 or
numNeighbors == 3):
                # Living cells with 2 or 3 neighbors stay alive:
                nextCells[x][y] = '#'
            elif currentCells[x][y] == ' ' and numNeighbors == 3:
                # Dead cells with 3 neighbors become alive:
                nextCells[x][y] = '#'
            else:
                # Everything else dies or stays dead:
                nextCells[x][y] = ' '
    time.sleep(1) # Add a 1-second pause to reduce flickering.
Let’s look at this code line by line, starting at the top.
# Conway's Game of Life
import random, time, copy
WIDTH = 60
HEIGHT = 20
First we import modules that contain functions we’ll need, namely the random.randint(), time.sleep(), and copy.deepcopy() functions.
# Create a list of list for the cells:
nextCells = []
for x in range(WIDTH):
    column = [] # Create a new column.
    for y in range(HEIGHT):
        if random.randint(0, 1) == 0:
            column.append('#') # Add a living cell.
        else:
            column.append(' ') # Add a dead cell.
    nextCells.append(column) # nextCells is a list of column lists.
The very first step of our cellular automata will be completely random. We need to create a list of lists data structure to store the '#' and ' ' strings that represent a living or dead cell, and their place in the list of lists reflects their position on the screen. The inner lists each represent a column of cells. The random.randint(0, 1) call gives an even 50/50 chance between the cell starting off alive or dead.
We put this list of lists in a variable called nextCells, because the first step in our main program loop will be to copy nextCells into currentCells. For our list of lists data structure, the x-coordinates start at 0 on the left and increase going right, while the y-coordinates start at 0 at the top and increase going down. So nextCells[0][0] will represent the cell at the top left of the screen, while nextCells[1][0] represents the cell to the right of that cell and nextCells[0][1] represents the cell beneath it.
while True: # Main program loop.
    print('\n\n\n\n\n') # Separate each step with newlines.
    currentCells = copy.deepcopy(nextCells)
Each iteration of our main program loop will be a single step of our cellular automata. On each step, we’ll copy nextCells to currentCells, print currentCells on the screen, and then use the cells in currentCells to calculate the cells in nextCells.
    # Print currentCells on the screen:
    for y in range(HEIGHT):
        for x in range(WIDTH):
            print(currentCells[x][y], end='') # Print the # or space.
        print() # Print a newline at the end of the row.
These nested for loops ensure that we print a full row of cells to the screen, followed by a newline character at the end of the row. We repeat this for each row in nextCells.
    # Calculate the next step's cells based on current step's cells:
    for x in range(WIDTH):
        for y in range(HEIGHT):
            # Get neighboring coordinates:
            # `% WIDTH` ensures leftCoord is always between 0 and WIDTH - 1
            leftCoord  = (x - 1) % WIDTH
            rightCoord = (x + 1) % WIDTH
            aboveCoord = (y - 1) % HEIGHT
            belowCoord = (y + 1) % HEIGHT

Next, we need to use two nested for loops to calculate each cell for the next step. The living or dead state of the cell depends on the neighbors, so let’s first calculate the index of the cells to the left, right, above, and below the current x- and y-coordinates.

The % mod operator performs a “wraparound.” The left neighbor of a cell in the leftmost column 0 would be 0 - 1 or -1. To wrap this around to the rightmost column’s index, 59, we calculate (0 - 1) % WIDTH. Since WIDTH is 60, this expression evaluates to 59. This mod-wraparound technique works for the right, above, and below neighbors as well.

# Count number of living neighbors: numNeighbors = 0 if currentCells[leftCoord][aboveCoord] == '#': numNeighbors += 1 # Top-left neighbor is alive. if currentCells[x][aboveCoord] == '#': numNeighbors += 1 # Top neighbor is alive. if currentCells[rightCoord][aboveCoord] == '#': numNeighbors += 1 # Top-right neighbor is alive. if currentCells[leftCoord][y] == '#': numNeighbors += 1 # Left neighbor is alive. if currentCells[rightCoord][y] == '#': numNeighbors += 1 # Right neighbor is alive. if currentCells[leftCoord][belowCoord] == '#': numNeighbors += 1 # Bottom-left neighbor is alive. if currentCells[x][belowCoord] == '#': numNeighbors += 1 # Bottom neighbor is alive. if currentCells[rightCoord][belowCoord] == '#': numNeighbors += 1 # Bottom-right neighbor is alive.

To decide if the cell at nextCells[x][y] should be living or dead, we need to count the number of living neighbors currentCells[x][y] has. This series of if statements checks each of the eight neighbors of this cell, and adds 1 to numNeighbors for each living one.

# Set cell based on Conway's Game of Life rules: if currentCells[x][y] == '#' and (numNeighbors == 2 or numNeighbors == 3): # Living cells with 2 or 3 neighbors stay alive: nextCells[x][y] = '#' elif currentCells[x][y] == ' ' and numNeighbors == 3: # Dead cells with 3 neighbors become alive: nextCells[x][y] = '#' else: # Everything else dies or stays dead: nextCells[x][y] = ' ' time.sleep(1) # Add a 1-second pause to reduce flickering.

Now that we know the number of living neighbors for the cell at currentCells[x][y], we can set nextCells[x][y] to either '#' or ' '. After we loop over every possible x- and y-coordinate, the program takes a 1-second pause by calling time.sleep(1). Then the program execution goes back to the start of the main program loop to continue with the next step.

Several patterns have been discovered with names such as “glider,” “propeller,” or “heavyweight spaceship.” The glider pattern, pictured in Figure 4-8, results in a pattern that “moves” diagonally every four steps. You can create a single glider by replacing this line in our conway.py program:

if random.randint(0, 1) == 0:

with this line:

if (x, y) in ((1, 0), (2, 1), (0, 2), (1, 2), (2, 2)):

You can find out more about the intriguing devices made using Conway’s Game of Life by searching the web. And you can find other short, text-based Python programs like this one at https://github.com/asweigart/pythonstdiogames.

Summary

Lists are useful data types since they allow you to write code that works on a modifiable number of values in a single variable. Later in this book, you will see programs using lists to do things that would be difficult or impossible to do without them.

Lists are a sequence data type that is mutable, meaning that their contents can change. Tuples and strings, though also sequence data types, are immutable and cannot be changed. A variable that contains a tuple or string value can be overwritten with a new tuple or string value, but this is not the same thing as modifying the existing value in place—like, say, the append() or remove() methods do on lists.

Variables do not store list values directly; they store references to lists. This is an important distinction when you are copying variables or passing lists as arguments in function calls. Because the value that is being copied is the list reference, be aware that any changes you make to the list might impact another variable in your program. You can use copy() or deepcopy() if you want to make changes to a list in one variable without modifying the original list.

Practice Questions

1. What is []?

2. How would you assign the value 'hello' as the third value in a list stored in a variable named spam? (Assume spam contains [2, 4, 6, 8, 10].)

For the following three questions, let’s say spam contains the list ['a', 'b', 'c', 'd'].

3. What does spam[int(int('3' * 2) // 11)] evaluate to?

4. What does spam[-1] evaluate to?

5. What does spam[:2] evaluate to?

For the following three questions, let’s say bacon contains the list [3.14, 'cat', 11, 'cat', True].

6. What does bacon.index('cat') evaluate to?

7. What does bacon.append(99) make the list value in bacon look like?

8. What does bacon.remove('cat') make the list value in bacon look like?

9. What are the operators for list concatenation and list replication?

10. What is the difference between the append() and insert() list methods?

11. What are two ways to remove values from a list?

12. Name a few ways that list values are similar to string values.

13. What is the difference between lists and tuples?

14. How do you type the tuple value that has just the integer value 42 in it?

15. How can you get the tuple form of a list value? How can you get the list form of a tuple value?

16. Variables that “contain” list values don’t actually contain lists directly. What do they contain instead?

17. What is the difference between copy.copy() and copy.deepcopy()?

Practice Projects

For practice, write programs to do the following tasks.

Comma Code

Say you have a list value like this:

spam = ['apples', 'bananas', 'tofu', 'cats']

Write a function that takes a list value as an argument and returns a string with all the items separated by a comma and a space, with and inserted before the last item. For example, passing the previous spam list to the function would return 'apples, bananas, tofu, and cats'. But your function should be able to work with any list value passed to it. Be sure to test the case where an empty list [] is passed to your function.

Coin Flip Streaks

For this exercise, we’ll try doing an experiment. If you flip a coin 100 times and write down an “H” for each heads and “T” for each tails, you’ll create a list that looks like “T T T T H H H H T T.” If you ask a human to make up 100 random coin flips, you’ll probably end up with alternating head-tail results like “H T H T H H T H T T,” which looks random (to humans), but isn’t mathematically random. A human will almost never write down a streak of six heads or six tails in a row, even though it is highly likely to happen in truly random coin flips. Humans are predictably bad at being random.

Write a program to find out how often a streak of six heads or a streak of six tails comes up in a randomly generated list of heads and tails. Your program breaks up the experiment into two parts: the first part generates a list of randomly selected 'heads' and 'tails' values, and the second part checks if there is a streak in it. Put all of this code in a loop that repeats the experiment 10,000 times so we can find out what percentage of the coin flips contains a streak of six heads or tails in a row. As a hint, the function call random.randint(0, 1) will return a 0 value 50% of the time and a 1 value the other 50% of the time.

You can start with the following template:

import random numberOfStreaks = 0 for experimentNumber in range(10000): # Code that creates a list of 100 'heads' or 'tails' values. # Code that checks if there is a streak of 6 heads or tails in a row. print('Chance of streak: %s%%' % (numberOfStreaks / 100))

Of course, this is only an estimate, but 10,000 is a decent sample size. Some knowledge of mathematics could give you the exact answer and save you the trouble of writing a program, but programmers are notoriously bad at math.

Character Picture Grid

Say you have a list of lists where each value in the inner lists is a one-character string, like this:

grid = [['.', '.', '.', '.', '.', '.'], ['.', 'O', 'O', '.', '.', '.'], ['O', 'O', 'O', 'O', '.', '.'], ['O', 'O', 'O', 'O', 'O', '.'], ['.', 'O', 'O', 'O', 'O', 'O'], ['O', 'O', 'O', 'O', 'O', '.'], ['O', 'O', 'O', 'O', '.', '.'], ['.', 'O', 'O', '.', '.', '.'], ['.', '.', '.', '.', '.', '.']]

Think of grid[x][y] as being the character at the x- and y-coordinates of a “picture” drawn with text characters. The (0, 0) origin is in the upper-left corner, the x-coordinates increase going right, and the y-coordinates increase going down.

Copy the previous grid value, and write code that uses it to print the image.

..OO.OO.. .OOOOOOO. .OOOOOOO. ..OOOOO.. ...OOO... ....O....

Hint: You will need to use a loop in a loop in order to print grid[0][0], then grid[1][0], then grid[2][0], and so on, up to grid[8][0]. This will finish the first row, so then print a newline. Then your program should print grid[0][1], then grid[1][1], then grid[2][1], and so on. The last thing your program will print is grid[8][5].

Also, remember to pass the end keyword argument to print() if you don’t want a newline printed automatically after each print() call.