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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.

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 . 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 . 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 . 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 .

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 . Turn to 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 . 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 ) 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 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 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 .) 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 .

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 .

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 . 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

. What are escape characters?

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

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

. 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?

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

. What do the following expressions evaluate to?

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

. What do the following expressions evaluate to?

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

. What do the following expressions evaluate to?

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

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

. 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.

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 .

Install the zombiedice module with pip by following the instructions in . 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.

The program launches your web browser, which will look like .

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 . 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

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 . 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 . . .

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 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 , one of Python’s language features that support this kind of abstraction.

5.1. Function definition and use

In the context of programming, a 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:

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 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 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

Back in high school Algebera class you were introduced to mathematical functions. Perhaps you were shown a diagram of a “function machine” that looked something like this:

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 is an example:

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

Here is the same function in Python:

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:

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

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 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.

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

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.

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 .

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 .

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:

The output of this program is:

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:

Apply what you learned in 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:

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:

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.

We can also use a variable as an argument:

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.

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

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:

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:

Note

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

The parameter a_list and the variable things are aliases for the same object. The state diagram looks like this:

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:

This version of double_stuff does not change its arguments:

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

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 . In Python, implementing polymorphism is easy, because Python functions handle types through . 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:

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:

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:

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:

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:

With the argument 4, the output is:

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:

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:

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:

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 stack diagram for this program shows that the two variables named i are not the same variable. They can refer to different values, and changing one does not affect the other.

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 .

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. like this are quite common in mathematics and computer science. They provide a concise and powerful way to describe 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:

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

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 , 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:

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 .

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:

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:

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

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

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:

So does accessing a nonexistent list item:

Or trying to make an item assignment on a tuple:

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:

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:

You can use multiple except blocks to handle different kinds of exceptions (see the lesson from Python creator Guido van Rossum’s 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.

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 section of the , 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:

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:

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:

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:

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. , for example, is given the special operator, !, and is defined by:

We can easily code this into Python:

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

This can also be written easily in Python:

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 .

More On Lists

Creation

In Python, lists are represented by square brackets. Therefore, we create a list as follows.

The above list, colors is stored in memory as shown below.

We can also create a list that contains multiple data types, like strings, integers, and floats.

Accessing Various Elements

Python lists follow a zero indexing structure, meaning the list index starts from 0. Nested lists are accessed using nested indexing.

Python has a very handy negative indexing feature as well, which starts from the end of the list:

List Slicing and Reversing

We can reverse and slice lists using list indices, as follows

For more information regarding list slicing, refer to this .

List Methods

list.index()

list.index() returns the index of a specified element in the list. The syntax is: list.index(element, start, end)

list.append()

The list.append() method adds an item at the end of a list.

list.extend()

list.extend() extends the list by appending items.

list.insert()

list.insert() inserts an element into the mentioned index.

list.remove()

list.remove() removes the first element that matches from the specified list.

list.count(x)

list.count() returns the number of times that ‘x’ appears in the list.

list.pop()

The list.pop() method removes and returns the element specified in the parameter. If the parameter is not specified, it removes and returns the last element in the list.

list.reverse()

The list.reverse() method reverses the list, and updates it. It has no return value.

list.sort()

The list.sort() method sorts the elements of the given list using the syntax: list.sort(key= , reverse= )

list.copy()

The list.copy() method copies the list into another list.

list.clear()

The list.clear() method empties the given list.

List Comprehension

List Comprehensions are advanced features in Python that enable you to create a new list from an existing list, and it consists of expressions within a for statement inside square brackets.

For example:

Conclusion

Lists are one of the most commonly used and most powerful data structures in Python. If one manages to master lists, he/she will perform very well in programming interviews. Once you’re done reading and using the list methods, check out the below links and start solving programs based on lists.

Strings, lists, and tuples

3.1. Sequence data types

Last chapter we introduced Python’s built-in types int, float, and str, and we stumbled upon tuple.

Integers and floats are numeric types, which means they hold numbers. We can use the numeric operators we saw last chapter with them to form numeric expressions. The Python interpreter can then evaluate these expressions to produce numeric values, making Python a very powerful .

Strings, lists, and tuples are all sequence types, so called because they behave like a - an ordered collection of objects.

Squence types are qualitatively different from numeric types because they are compound data types - meaning they are made up of smaller pieces. In the case of strings, they’re made up of smaller strings, each containing one . There is also the empty string, containing no characters at all.

In the case of lists or tuples, they are made up of elements, which are values of any Python datatype, including other lists and tuples.

Lists are enclosed in square brackets ([ and ]) and tuples in parentheses (( and )).

A list containing no elements is called an empty list, and a tuple with no elements is an empty tuple.

The first example is a list of five integers, and the next is a list of three strings. The third is a tuple containing four integers, followed by a tuple containing four strings. The last is a list containing three tuples, each of which contains a pair of strings.

Depending on what we are doing, we may want to treat a compound data type as a single thing, or we may want to access its parts. This ambiguity is useful.

Note

It is possible to drop the parentheses when specifiying a tuple, and only use a comma seperated list of values:

Also, it is required to include a comma when specifying a tuple with only one element:

Except for the case of the empty tuple, it is really the commas, not the parentheses, that tell Python it is a tuple.

3.2. Working with the parts of a sequence

The sequence types share a common set of operations.

3.2.1. Indexing

The indexing operator ([ ]) selects a single element from a sequence. The expression inside brackets is called the index, and must be an integer value. The index indicates which element to select, hence its name.

The expression fruit[1] selects the character with index 1 from fruit, and creates a new string containing just this one character, which you may be surprised to see is 'a'.

You probably expected to see 'b', but computer scientists typically start counting from zero, not one. Think of the index as the numbers on a ruler measuring how many elements you have moved into the sequence from the beginning. Both rulers and indices start at 0.

3.2.2. Length

Last chapter you saw the len function used to get the number of characters in a string:

With lists and tuples, len returns the number of elements in the sequence:

3.2.3. Accessing elements at the end of a sequence

It is common in computer programming to need to access elements at the end of a sequence. Now that you have seen the len function, you might be tempted to try something like this:

That won’t work. It causes the runtime error IndexError: list index out of range. The reason is that len(seq) returns the number of elements in the list, 16, but there is no element at index position 16 in seq.

Since we started counting at zero, the sixteen indices are numbered 0 to 15. To get the last element, we have to subtract 1 from the length:

This is such a common in pattern that Python provides a short hand notation for it, negative indexing, which counts backward from the end of the sequence.

The expression seq[-1] yields the last element, seq[-2] yields the second to last, and so on.

3.2.4. Traversal and the `for` loop

A lot of computations involve processing a sequence one element at a time. The most common pattern is to start at the beginning, select each element in turn, do something to it, and continue until the end. This pattern of processing is called a traversal.

Python’s for loop makes traversal easy to express:

Note

We will discuss looping in greater detail in the next chapter. For now just note that the colon (:) at the end of the first line and the indentation on the second line are both required for this statement to be syntactically correct.

3.3. `enumerate`

As the standard for loop traverses a sequence, it assigns each value in the sequence to the loop variable in the order it occurs in the sequence. Sometimes it is helpful to have both the value and the index of each element. The enumerate function gives us this:

3.3.1. Slices

A subsequence of a sequence is called a slice and the operation that extracts a subsequence is called slicing. Like with indexing, we use square brackets ([ ]) as the slice operator, but instead of one integer value inside we have two, seperated by a colon (:):

The operator [n:m] returns the part of the sequence from the n’th element to the m’th element, including the first but excluding the last. This behavior is counter-intuitive; it makes more sense if you imagine the indices pointing between the characters, as in the following diagram:

If you omit the first index (before the colon), the slice starts at the beginning of the string. If you omit the second index, the slice goes to the end of the string. Thus:

What do you think s[:] means? What about classmates[4:]?

Negative indexes are also allowed, so

Tip

Developing a firm understanding of how slicing works is important. Keep creating your own “experiments” with sequences and slices until you can consistently predict the result of a slicing operation before you run it.

When you slice a sequence, the resulting subsequence always has the same type as the sequence from which it was derived. This is not generally true with indexing, except in the case of strings.

While the elements of a list (or tuple) can be of any type, no matter how you slice it, a slice of a list is a list.

3.3.2. The `in` operator

The in operator returns whether a given element is contained in a list or tuple:

in works somewhat differently with strings. It evaluates to True if one string is a substring of another:

Note that a string is a substring of itself, and the empty string is a substring of any other string. (Also note that computer programmers like to think about these edge cases quite carefully!)

3.4. Objects and methods

Strings, lists, and tuples are , which means that they not only hold values, but have built-in behaviors called , that act on the values in the object.

Let’s look at some string methods in action to see how this works.

Now let’s learn to describe what we just saw. Each string in the above examples is followed by a dot operator, a method name, and a parameter list, which may be empty.

In the first example, the string 'apple' is followed by the dot operator and then the upper() method, which has an empty parameter list. We say that the “upper() method is invoked on the string, 'apple'. Invoking the method causes an action to take place using the value on which the method is invoked. The action produces a result, in this case the string value 'Apple'. We say that the upper() method returns the string 'Apple' when it is invoked on (or called on) the string 'apple'.

In the fourth example, the method isdigit() (again with an empty parameter list) is invoked on the string '42'. Since each of the characters in the string represents a digit, the isdigit() method returns the boolean value True. Invoking isdigit() on 'four' produces False.

The strip() removes leading and trailing whitespace.

3.5. The `dir()` function and docstrings

The previous section introduced several of the methods of string objects. To find all the methods that strings have, we can use Python’s built-in dir function:

We will postpone talking about the ones that begin with double underscores (__) until later. You can find out more about each of these methods by printing out their . To find out what the replace method does, for example, we do this:

Using this information, we can try using the replace method to varify that we know how it works.

The first example replaces all occurances of 'i' with 'X'. The second replaces the single character 'p' with the two characters 'MO'. The third example replaces the first two occurances of 'i'' with the empty string.

3.6. `count` and `index` methods

There are two methods that are common to all three sequence types: count and index. Let’s look at their docstrings to see what they do.

We will explore these functions in the exercises.

3.7. Lists are mutable

Unlike strings and tuples, which are , lists are mutable, which means we can change their elements. Using the bracket operator on the left side of an assignment, we can update one of the elements:

The bracket operator applied to a list can appear anywhere in an expression. When it appears on the left side of an assignment, it changes one of the elements in the list, so the first element of fruit has been changed from 'banana' to 'pear', and the last from 'quince' to 'orange'. An assignment to an element of a list is called item assignment. Item assignment does not work for strings:

but it does for lists:

With the slice operator we can update several elements at once:

We can also remove elements from a list by assigning the empty list to them:

And we can add elements to a list by squeezing them into an empty slice at the desired location:

3.8. List deletion

Using slices to delete list elements can be awkward, and therefore error-prone. Python provides an alternative that is more readable.

del removes an element from a list:

As you might expect, del handles negative indices and causes a runtime error if the index is out of range.

You can use a slice as an index for del:

As usual, slices select all the elements up to, but not including, the second index.

3.9. List methods

In addition to count and index, lists have several useful methods. Since lists are mutable, these methods modify the list on which they are invoked, rather than returning a new list.

The sort method is particularly useful, since it makes it easy to use Python to sort data that you have put in a list.

3.10. Names and mutable values

If we execute these assignment statements,

we know that the names a and b will refer to a list with the numbers 1, 2, and 3. But we don’t know yet whether they point to the same list.

There are two possible states:

In one case, a and b refer to two different things that have the same value. In the second case, they refer to the same object.

We can test whether two names have the same value using ==:

We can test whether two names refer to the same object using the is operator:

This tells us that both a and b do not refer to the same object, and that it is the first of the two state diagrams that describes the relationship.

3.11. Aliasing

Since variables refer to objects, if we assign one variable to another, both variables refer to the same object:

In this case, it is the second of the two state diagrams that describes the relationship between the variables.

Because the same list has two different names, a and b, we say that it is aliased. Since lists are mutable, changes made with one alias affect the other:

Although this behavior can be useful, it is sometimes unexpected or undesirable. In general, it is safer to avoid aliasing when you are working with mutable objects. Of course, for immutable objects, there’s no problem, since they can’t be changed after they are created.

3.12. Cloning lists

If we want to modify a list and also keep a copy of the original, we need to be able to make a copy of the list itself, not just the reference. This process is sometimes called cloning, to avoid the ambiguity of the word copy.

The easiest way to clone a list is to use the slice operator:

Taking any slice of a creates a new list. In this case the slice happens to consist of the whole list.

Now we are free to make changes to b without worrying about a:

3.13. Nested lists

A nested list is a list that appears as an element in another list. In this list, the element with index 3 is a nested list:

If we print nested[3], we get [10, 20]. To extract an element from the nested list, we can proceed in two steps:

Or we can combine them:

Bracket operators evaluate from left to right, so this expression gets the three-eth element of nested and extracts the one-eth element from it.

3.14. Strings and lists

Python has several tools which combine lists of strings into strings and separate strings into lists of strings.

The list command takes a sequence type as an argument and creates a list out of its elements. When applied to a string, you get a list of characters.

The split method invoked on a string and separates the string into a list of strings, breaking it apart whenever a substring called the occurs. The default delimiter is whitespace, which includes spaces, tabs, and newlines.

Here we have 'o' as the delimiter.

Notice that the delimiter doesn’t appear in the list.

The join method does approximately the oposite of the split method. It takes a list of strings as an argument and returns a string of all the list elements joined together.

The string value on which the join method is invoked acts as a separator that gets placed between each element in the list in the returned string.

The separator can also be the empty string.

3.15. Tuple assignment

Once in a while, it is useful to swap the values of two variables. With conventional assignment statements, we have to use a temporary variable. For example, to swap a and b:

If we have to do this often, this approach becomes cumbersome. Python provides a form of tuple assignment that solves this problem neatly:

The left side is a tuple of variables; the right side is a tuple of values. Each value is assigned to its respective variable. All the expressions on the right side are evaluated before any of the assignments. This feature makes tuple assignment quite versatile.

Naturally, the number of variables on the left and the number of values on the right have to be the same:

3.16. Boolean values

We will now look at a new type of value - - named after the British mathematician, . He created the mathematics we call , which is the basis of all modern computer arithmetic.

Note

It is a computer’s ability to alter its flow of execution depending on whether a boolean value is true or false that makes a general purpose computer more than just a calculator.

There are only two boolean values, True and False.

Capitalization is important, since true and false are not boolean values in Python.:

3.17. Boolean expressions

A boolean expression is an expression that evaluates to a boolean value.

The operator == compares two values and produces a boolean value:

In the first statement, the two operands are equal, so the expression evaluates to True; in the second statement, 5 is not equal to 6, so we get False.

The == operator is one of six common comparison operators; the others are:

Although these operations are probably familiar to you, the Python symbols are different from the mathematical symbols. A common error is to use a single equal sign (=) instead of a double equal sign (==). Remember that = is an assignment operator and == is a comparison operator. Also, there is no such thing as =< or =>.

3.18. Logical operators

There are three logical operators: and, or, and not. The semantics (meaning) of these operators is similar to their meaning in English. For example, x > 0 and x < 10 is true only if x is greater than 0 and at the same time, x is less than 10.

n % 2 == 0 or n % 3 == 0 is true if either of the conditions is true, that is, if the number is divisible by 2 or divisible by 3.

Finally, the not operator negates a boolean expression, so not (x > y) is true if (x > y) is false, that is, if x is less than or equal to y.

3.19. Short-circuit evaluation

Boolean expressions in Python use short-circuit evaluation, which means only the first argument of an and or or expression is evaluated when its value is suffient to determine the value of the entire expression.

This can be quite useful in preventing runtime errors. Imagine you want check if the fifth number in a tuple of integers named numbers is even.

The following expression will work:

unless of course there are not 5 elements in numbers, in which case you will get:

Short-circuit evaluation makes it possible to avoid this problem.

Since the left hand side of this and expression is false, Python does not need to evaluate the right hand side to determine that the whole expression is false. Since it uses short-circuit evaluation, it does not, and the runtime error is avoided.

3.20. “Truthiness”

All Python values have a “truthiness” or “falsiness” which means they can be used in places requiring a boolean. For the numeric and sequence types we have seen thus far, truthiness is defined as follows:numberic types

Values equal to 0 are false, all others are true.sequence types

Empty sequences are false, non-empty sequences are true.

Combining this notion of truthiness with an understanding of short-circuit evaluation makes it possible to understand what Python is doing in the following expressions:

3.21. Glossary

aliases

Multiple variables that contain references to the same object.boolean value

There are exactly two boolean values: True and False. Boolean values result when a boolean expression is evaluated by the Python interepreter. They have type bool.boolean expression

An expression that is either true or false.clone

To create a new object that has the same value as an existing object. Copying a reference to an object creates an alias but doesn’t clone the object.comparison operator

One of the operators that compares two values: ==, !=, >, <, >=, and <=.compound data type

A data type in which the values are made up of components, or elements, that are themselves values.element

One of the parts that make up a sequence type (string, list, or tuple). Elements have a value and an index. The value is accessed by using the index operator ([*index*]) on the sequence.immutable data type

A data type which cannot be modified. Assignments to elements or slices of immutable types cause a runtime error.index

A variable or value used to select a member of an ordered collection, such as a character from a string, or an element from a list or tuple.logical operator

One of the operators that combines boolean expressions: and, or, and not.mutable data type

A data type which can be modified. All mutable types are compound types. Lists and dictionaries are mutable data types; strings and tuples are not.nested list

A list that is an element of another list.slice

A part of a string (substring) specified by a range of indices. More generally, a subsequence of any sequence type in Python can be created using the slice operator (sequence[start:stop]).step size

The interval between successive elements of a linear sequence. The third (and optional argument) to the range function is called the step size. If not specified, it defaults to 1.traverse

To iterate through the elements of a collection, performing a similar operation on each.tuple

A data type that contains a sequence of elements of any type, like a list, but is immutable. Tuples can be used wherever an immutable type is required, such as a key in a dictionary (see next chapter).tuple assignment

An assignment to all of the elements in a tuple using a single assignment statement. Tuple assignment occurs in parallel rather than in sequence, making it useful for swapping values.