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Difficulty level: ♦♦♢♢♢
❝ Wonder is the foundation of all philosophy, inquiry its progress, ignorance its end. ❞
— Michel de Montaigne
Datatypes. Set aside your first Python program for just a minute, and let’s talk about datatypes. In Python, every value has a datatype, but you don’t need to declare the datatype of variables. How does that work? Based on each variable’s original assignment, Python figures out what type it is and keeps tracks of that internally.
Python has many native datatypes. Here are the important ones:
True or False.
1 and 2), floats (1.1 and 1.2), fractions (1/2 and 2/3), or even complex numbers.
Of course, there are more types than these. Everything is an object in Python, so there are types like module, function, class, method, file, and even compiled code. You’ve already seen some of these: modules have names, functions have docstrings, &c. You’ll learn about classes in Classes & Iterators, and about files in Files.
Strings and bytes are important enough — and complicated enough — that they get their own chapter. Let’s look at the others first.
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Booleans are either true or false. Python has two constants, cleverly named True and False, which can be used to assign boolean values directly. Expressions can also evaluate to a boolean value. In certain places (like if statements), Python expects an expression to evaluate to a boolean value. These places are called boolean contexts. You can use virtually any expression in a boolean context, and Python will try to determine its truth value. Different datatypes have different rules about which values are true or false in a boolean context. (This will make more sense once you see some concrete examples later in this chapter.)
For example, take this snippet from humansize.py:
if size < 0:
raise ValueError('number must be non-negative')size is an integer, 0 is an integer, and < is a numerical operator. The result of the expression size < 0 is always a boolean. You can test this yourself in the Python interactive shell:
>>> size = 1 >>> size < 0 False >>> size = 0 >>> size < 0 False >>> size = -1 >>> size < 0 True
Due to some legacy issues left over from Python 2, booleans can be treated as numbers. True is 1; False is 0.
>>> True + True 2 >>> True - False 1 >>> True * False 0 >>> True / False Traceback (most recent call last): File "<stdin>", line 1, in <module> ZeroDivisionError: int division or modulo by zero
Ew, ew, ew! Don’t do that. Forget I even mentioned it.
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Numbers are awesome. There are so many to choose from. Python supports both integers and floating point numbers. There’s no type declaration to distinguish them; Python tells them apart by the presence or absence of a decimal point.
>>> type(1) ① <class 'int'> >>> isinstance(1, int) ② True >>> 1 + 1 ③ 2 >>> 1 + 1.0 ④ 2.0 >>> type(2.0) <class 'float'>
type() function to check the type of any value or variable. As you might expect, 1 is an int.
isinstance() function to check whether a value or variable is of a given type.
int to an int yields an int.
int to a float yields a float. Python coerces the int into a float to perform the addition, then returns a float as the result.
As you just saw, some operators (like addition) will coerce integers to floating point numbers as needed. You can also coerce them by yourself.
>>> float(2) ① 2.0 >>> int(2.0) ② 2 >>> int(2.5) ③ 2 >>> int(-2.5) ④ -2 >>> 1.12345678901234567890 ⑤ 1.1234567890123457 >>> type(1000000000000000) ⑥ <class 'int'>
int to a float by calling the float() function.
float to an int by calling int().
int() function will truncate, not round.
int() function truncates negative numbers towards 0. It’s a true truncate function, not a floor function.
☞Python 2 had separate types for
intandlong. Theintdatatype was limited bysys.maxint, which varied by platform but was usually232-1. Python 3 has just one integer type, which behaves mostly like the oldlongtype from Python 2. See PEP 237 for details.
You can do all kinds of things with numbers.
>>> 11 / 2 ① 5.5 >>> 11 // 2 ② 5 >>> −11 // 2 ③ −6 >>> 11.0 // 2 ④ 5.0 >>> 11 ** 2 ⑤ 121 >>> 11 % 2 ⑥ 1
/ operator performs floating point division. It returns a float even if both the numerator and denominator are ints.
// operator performs a quirky kind of integer division. When the result is positive, you can think of it as truncating (not rounding) to 0 decimal places, but be careful with that.
// operator rounds “up” to the nearest integer. Mathematically speaking, it’s rounding “down” since −6 is less than −5, but it could trip you up if you were expecting it to truncate to −5.
// operator doesn’t always return an integer. If either the numerator or denominator is a float, it will still round to the nearest integer, but the actual return value will be a float.
** operator means “raised to the power of.” 112 is 121.
% operator gives the remainder after performing integer division. 11 divided by 2 is 5 with a remainder of 1, so the result here is 1.
☞In Python 2, the
/operator usually meant integer division, but you could make it behave like floating point division by including a special directive in your code. In Python 3, the/operator always means floating point division. See PEP 238 for details.
Python isn’t limited to integers and floating point numbers. It can also do all the fancy math you learned in high school and promptly forgot about.
>>> import fractions ① >>> x = fractions.Fraction(1, 3) ② >>> x Fraction(1, 3) >>> x * 2 ③ Fraction(2, 3) >>> fractions.Fraction(6, 4) ④ Fraction(3, 2) >>> fractions.Fraction(0, 0) ⑤ Traceback (most recent call last): File "<stdin>", line 1, in <module> File "fractions.py", line 96, in __new__ raise ZeroDivisionError('Fraction(%s, 0)' % numerator) ZeroDivisionError: Fraction(0, 0)
fractions module.
Fraction object and pass in the numerator and denominator.
Fraction object. 2 * (1/3) = (2/3)
Fraction object will automatically reduce fractions. (6/4) = (3/2)
You can also do basic trigonometry in Python.
>>> import math >>> math.pi ① 3.1415926535897931 >>> math.sin(math.pi / 2) ② 1.0 >>> math.tan(math.pi / 4) ③ 0.99999999999999989
math module has a constant for π, the ratio of a circle’s circumference to its diameter.
math module has all the basic trigonometric functions, including sin(), cos(), tan(), and variants like asin().
tan(π / 4) should return 1.0, not 0.99999999999999989.
You can use numbers in a boolean context, such as an if statement. Zero values are false, and non-zero values are true.
>>> def is_it_true(anything): ① ... if anything: ... print("yes, it's true") ... else: ... print("no, it's false") ... >>> is_it_true(1) ② yes, it's true >>> is_it_true(-1) yes, it's true >>> is_it_true(0) no, it's false >>> is_it_true(0.1) ③ yes, it's true >>> is_it_true(0.0) no, it's false >>> import fractions >>> is_it_true(fractions.Fraction(1, 2)) ④ yes, it's true >>> is_it_true(fractions.Fraction(0, 1)) no, it's false
0.0 is false. Be careful with this one! If there’s the slightest rounding error (not impossible, as you saw in the previous section) then Python will be testing 0.0000000000001 instead of 0 and will return True.
Fraction(0, n) is false for all values of n. All other fractions are true.
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Lists are Python’s workhorse datatype. When I say “list,” you might be thinking “array whose size I have to declare in advance, that can only contain items of the same type, &c.” Don’t think that. Lists are much cooler than that.
☞A list in Python is like an array in Perl 5. In Perl 5, variables that store arrays always start with the
@character; in Python, variables can be named anything, and Python keeps track of the datatype internally.
☞A list in Python is much more than an array in Java (although it can be used as one if that’s really all you want out of life). A better analogy would be to the
ArrayListclass, which can hold arbitrary objects and can expand dynamically as new items are added.
Creating a list is easy: use square brackets to wrap a comma-separated list of values.
>>> a_list = ['a', 'b', 'mpilgrim', 'z', 'example'] ① >>> a_list ['a', 'b', 'mpilgrim', 'z', 'example'] >>> a_list[0] ② 'a' >>> a_list[4] ③ 'example' >>> a_list[-1] ④ 'example' >>> a_list[-3] ⑤ 'mpilgrim'
a_list[0].
a_list[4], because lists are always zero-based.
a_list[-1].
a_list[-n] == a_list[len(a_list) - n]. So in this list, a_list[-3] == a_list[5 - 3] == a_list[2].
Once you’ve defined a list, you can get any part of it as a new list. This is called slicing the list.
>>> a_list ['a', 'b', 'mpilgrim', 'z', 'example'] >>> a_list[1:3] ① ['b', 'mpilgrim'] >>> a_list[1:-1] ② ['b', 'mpilgrim', 'z'] >>> a_list[0:3] ③ ['a', 'b', 'mpilgrim'] >>> a_list[:3] ④ ['a', 'b', 'mpilgrim'] >>> a_list[3:] ⑤ ['z', 'example'] >>> a_list[:] ⑥ ['a', 'b', 'mpilgrim', 'z', 'example']
a_list[1]), up to but not including the second slice index (in this case a_list[3]).
a_list[0:3] returns the first three items of the list, starting at a_list[0], up to but not including a_list[3].
a_list[:3] is the same as a_list[0:3], because the starting 0 is implied.
a_list[3:] is the same as a_list[3:5], because this list has five items. There is a pleasing symmetry here. In this five-item list, a_list[:3] returns the first 3 items, and a_list[3:] returns the last two items. In fact, a_list[:n] will always return the first n items, and a_list[n:] will return the rest, regardless of the length of the list.
a_list[:] is shorthand for making a complete copy of a list.
There are four ways to add items to a list.
>>> a_list = ['a'] >>> a_list = a_list + [2.0, 3] ① >>> a_list ② ['a', 2.0, 3] >>> a_list.append(True) ③ >>> a_list ['a', 2.0, 3, True] >>> a_list.extend(['four', 'Ω']) ④ >>> a_list ['a', 2.0, 3, True, 'four', 'Ω'] >>> a_list.insert(0, 'Ω') ⑤ >>> a_list ['Ω', 'a', 2.0, 3, True, 'four', 'Ω']
+ operator concatenates lists to create a new list. A list can contain any number of items; there is no size limit (other than available memory). However, if memory is a concern, you should be aware that list concatenation creates a second list in memory. In this case, that new list is immediately assigned to the existing variable a_list. So this line of code is really a two-step process — concatenation then assignment — which can (temporarily) consume a lot of memory when you’re dealing with large lists.
append() method adds a single item to the end of the list. (Now we have four different datatypes in the list!)
extend() method takes one argument, a list, and appends each of the items of the argument to the original list.
insert() method inserts a single item into a list. The first argument is the index of the first item in the list that will get bumped out of position. List items do not need to be unique; for example, there are now two separate items with the value 'Ω': the first item, a_list[0], and the last item, a_list[6].
☞
a_list.insert(0, value)is like theunshift()function in Perl. It adds an item to the beginning of the list, and all the other items have their positional index bumped up to make room.
Let’s look closer at the difference between append() and extend().
>>> a_list = ['a', 'b', 'c'] >>> a_list.extend(['d', 'e', 'f']) ① >>> a_list ['a', 'b', 'c', 'd', 'e', 'f'] >>> len(a_list) ② 6 >>> a_list[-1] 'f' >>> a_list.append(['g', 'h', 'i']) ③ >>> a_list ['a', 'b', 'c', 'd', 'e', 'f', ['g', 'h', 'i']] >>> len(a_list) ④ 7 >>> a_list[-1] ['g', 'h', 'i']
extend() method takes a single argument, which is always a list, and adds each of the items of that list to a_list.
append() method takes a single argument, which can be any datatype. Here, you’re calling the append() method with a list of three items.
>>> a_list = ['a', 'b', 'new', 'mpilgrim', 'new']
>>> a_list.count('new') ①
2
>>> 'new' in a_list ②
True
>>> 'c' in a_list
False
>>> a_list.index('mpilgrim') ③
3
>>> a_list.index('new') ④
2
>>> a_list.index('c') ⑤
Traceback (innermost last):
File "<interactive input>", line 1, in ?
ValueError: list.index(x): x not in list
count() method returns the number of occurrences of a specific value in a list.
in operator is slightly faster than using the count() method. The in operator always returns True or False; it will not tell you how many times the value appears in the list.
in operator nor the count() method will tell you where in the list a value appears. If you need to know where in the list a value is, call the index() method. By default it will search the entire list, although you can specify an optional second argument of the (0-based) index to start from, and even an optional third argument of the (0-based) index to stop searching.
index() method finds the first occurrence of a value in the list. In this case, 'new' occurs twice in the list, in a_list[2] and a_list[4], but the index() method will return only the index of the first occurrence.
index() method will raise an exception.
Wait, what? That’s right: the index() method raises an exception if it doesn’t find the value in the list. This is notably different from most languages, which will return some invalid index (like -1). While this may seem annoying at first, I think you will come to appreciate it. It means your program will crash at the source of the problem instead of failing strangely and silently later. Remember, -1 is a valid list index. If the index() method returned -1, that could lead to some not-so-fun debugging sessions!
Lists can expand and contract automatically. You’ve seen the expansion part. There are several different ways to remove items from a list as well.
>>> a_list = ['a', 'b', 'new', 'mpilgrim', 'new'] >>> a_list[1] 'b' >>> del a_list[1] ① >>> a_list ['a', 'new', 'mpilgrim', 'new'] >>> a_list[1] ② 'new'
del statement to delete a specific item from a list.
1 after deleting index 1 does not result in an error. All items after the deleted item shift their positional index to “fill the gap” created by deleting the item.
Don’t know the positional index? Not a problem; you can remove items by value instead.
>>> a_list.remove('new') ①
>>> a_list
['a', 'mpilgrim', 'new']
>>> a_list.remove('new') ②
>>> a_list
['a', 'mpilgrim']
>>> a_list.remove('new')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: list.remove(x): x not in list
remove() method. The remove() method takes a value and removes the first occurrence of that value from the list. Again, all items after the deleted item will have their positional indices bumped down to “fill the gap.” Lists never have gaps.
remove() method as often as you like, but it will raise an exception if you try to remove a value that isn’t in the list.
Another interesting list method is pop(). The pop() method is yet another way to remove items from a list, but with a twist.
>>> a_list = ['a', 'b', 'new', 'mpilgrim'] >>> a_list.pop() ① 'mpilgrim' >>> a_list ['a', 'b', 'new'] >>> a_list.pop(1) ② 'b' >>> a_list ['a', 'new'] >>> a_list.pop() 'new' >>> a_list.pop() 'a' >>> a_list.pop() ③ Traceback (most recent call last): File "<stdin>", line 1, in <module> IndexError: pop from empty list
pop() list method removes the last item in the list and returns the value it removed.
pop() method. It will remove that item, shift all the items after it to “fill the gap,” and return the value it removed.
pop() on an empty list raises an exception.
☞Calling the
pop()list method without an argument is like thepop()function in Perl. It removes the last item from the list and returns the value of the removed item. Perl has another function,shift(), which removes the first item and returns its value; in Python, this is equivalent toa_list.pop(0).
You can also use a list in a boolean context, such as an if statement.
>>> def is_it_true(anything):
... if anything:
... print("yes, it's true")
... else:
... print("no, it's false")
...
>>> is_it_true([]) ①
no, it's false
>>> is_it_true(['a']) ②
yes, it's true
>>> is_it_true([False]) ③
yes, it's true
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A tuple is an immutable list. A tuple can not be changed in any way once it is created.
>>> a_tuple = ("a", "b", "mpilgrim", "z", "example") ①
>>> a_tuple
('a', 'b', 'mpilgrim', 'z', 'example')
>>> a_tuple[0] ②
'a'
>>> a_tuple[-1] ③
'example'
>>> a_tuple[1:3] ④
('b', 'mpilgrim')
a_tuple[0].
The major difference between tuples and lists is that tuples can not be changed. In technical terms, tuples are immutable. In practical terms, they have no methods that would allow you to change them. Lists have methods like append(), extend(), insert(), remove(), and pop(). Tuples have none of these methods. You can slice a tuple (because that creates a new tuple), and you can check whether a tuple contains a particular value (because that doesn’t change the tuple), and… that’s about it.
# continued from the previous example
>>> a_tuple
('a', 'b', 'mpilgrim', 'z', 'example')
>>> a_tuple.append("new") ①
Traceback (innermost last):
File "<interactive input>", line 1, in ?
AttributeError: 'tuple' object has no attribute 'append'
>>> a_tuple.remove("z") ②
Traceback (innermost last):
File "<interactive input>", line 1, in ?
AttributeError: 'tuple' object has no attribute 'remove'
>>> a_tuple.index("example") ③
4
>>> "z" in a_tuple ④
True
append() or extend() method.
remove() or pop() method.
in operator to check if an element exists in the tuple.
So what are tuples good for?
assert statement that shows this data is constant, and that special thought (and a specific function) is required to override that.
☞Tuples can be converted into lists, and vice-versa. The built-in
tuple()function takes a list and returns a tuple with the same elements, and thelist()function takes a tuple and returns a list. In effect,tuple()freezes a list, andlist()thaws a tuple.
You can use tuples in a boolean context, such as an if statement.
>>> def is_it_true(anything):
... if anything:
... print("yes, it's true")
... else:
... print("no, it's false")
...
>>> is_it_true(()) ①
no, it's false
>>> is_it_true(('a', 'b')) ②
yes, it's true
>>> is_it_true((False,)) ③
yes, it's true
>>> type((False)) ④
<class 'bool'>
>>> type((False,))
<class 'tuple'>
Here’s a cool programming shortcut: in Python, you can use a tuple to assign multiple values at once.
>>> v = ('a', 2, True)
>>> (x, y, z) = v ①
>>> x
'a'
>>> y
2
>>> z
True
(x, y, z) is a tuple of three variables. Assigning one to the other assigns each of the values of v to each of the variables, in order.
This has all kinds of uses. Suppose you want to assign names to a range of values. You can use the built-in range() function with multi-variable assignment to quickly assign consecutive values.
>>> (MONDAY, TUESDAY, WEDNESDAY, THURSDAY, FRIDAY, SATURDAY, SUNDAY) = range(7) ① >>> MONDAY ② 0 >>> TUESDAY 1 >>> SUNDAY 6
range() function constructs a sequence of integers. (Technically, the range() function returns an iterator, not a list or a tuple, but you’ll learn about that distinction later.) MONDAY, TUESDAY, WEDNESDAY, THURSDAY, FRIDAY, SATURDAY, and SUNDAY are the variables you’re defining. (This example came from the calendar module, a fun little module that prints calendars, like the UNIX program cal. The calendar module defines integer constants for days of the week.)
1, and so forth.
You can also use multi-variable assignment to build functions that return multiple values, simply by returning a tuple of all the values. The caller can treat it as a single tuple, or it can assign the values to individual variables. Many standard Python libraries do this, including the os module, which you'll learn about in the next chapter.
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A set is an unordered “bag” of unique values. A single set can contain values of any immutable datatype. Once you have two sets, you can do standard set operations like union, intersection, and set difference.
First things first. Creating a set is easy.
>>> a_set = {1} ①
>>> a_set
{1}
>>> type(a_set) ②
<class 'set'>
>>> a_set = {1, 2} ③
>>> a_set
{1, 2}
{}).
You can also create a set out of a list.
>>> a_list = ['a', 'b', 'mpilgrim', True, False, 42] >>> a_set = set(a_list) ① >>> a_set ② {'a', False, 'b', True, 'mpilgrim', 42} >>> a_list ③ ['a', 'b', 'mpilgrim', True, False, 42]
set() function. (Pedants who know about how sets are implemented will point out that this is not really calling a function, but instantiating a class. I promise you will learn the difference later in this book. For now, just know that set() acts like a function, and it returns a set.)
Don’t have any values yet? Not a problem. You can create an empty set.
>>> a_set = set() ① >>> a_set ② set() >>> type(a_set) ③ <class 'set'> >>> len(a_set) ④ 0 >>> not_sure = {} ⑤ >>> type(not_sure) <class 'dict'>
set() with no arguments.
{}, perhaps? That would denote an empty dictionary, not an empty set. You’ll learn about dictionaries later in this chapter.
There are two different ways to add values to an existing set: the add() method, and the update() method.
>>> a_set = {1, 2}
>>> a_set.add(4) ①
>>> a_set
{1, 2, 4}
>>> len(a_set) ②
3
>>> a_set.add(1) ③
>>> a_set
{1, 2, 4}
>>> len(a_set) ④
3
add() method takes a single argument, which can be any datatype, and adds the given value to the set.
>>> a_set = {1, 2, 3}
>>> a_set
{1, 2, 3}
>>> a_set.update({2, 4, 6}) ①
>>> a_set ②
{1, 2, 3, 4, 6}
>>> a_set.update({3, 6, 9}, {1, 2, 3, 5, 8, 13}) ③
>>> a_set
{1, 2, 3, 4, 5, 6, 8, 9, 13}
>>> a_set.update([10, 20, 30]) ④
>>> a_set
{1, 2, 3, 4, 5, 6, 8, 9, 10, 13, 20, 30}
update() method takes one argument, a set, and adds all its members to the original set. It’s as if you called the add() method with each member of the set.
update() method with any number of arguments. When called with two sets, the update() method adds all the members of each set to the original set (dropping duplicates).
update() method can take objects of a number of different datatypes, including lists. When called with a list, the update() method adds all the items of the list to the original set.
There are three ways to remove individual values from a set. The first two, discard() and remove(), have one subtle difference.
>>> a_set = {1, 3, 6, 10, 15, 21, 28, 36, 45}
>>> a_set
{1, 3, 36, 6, 10, 45, 15, 21, 28}
>>> a_set.discard(10) ①
>>> a_set
{1, 3, 36, 6, 45, 15, 21, 28}
>>> a_set.discard(10) ②
>>> a_set
{1, 3, 36, 6, 45, 15, 21, 28}
>>> a_set.remove(21) ③
>>> a_set
{1, 3, 36, 6, 45, 15, 28}
>>> a_set.remove(21) ④
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
KeyError: 21
discard() method takes a single value as an argument and removes that value from the set.
discard() method with a value that doesn’t exist in the set, it does nothing. No error; it’s just a no-op.
remove() method also takes a single value as an argument, and it also removes that value from the set.
remove() method raises a KeyError exception.
Like lists, sets have a pop() method.
>>> a_set = {1, 3, 6, 10, 15, 21, 28, 36, 45}
>>> a_set.pop() ①
1
>>> a_set.pop()
3
>>> a_set.pop()
36
>>> a_set
{6, 10, 45, 15, 21, 28}
>>> a_set.clear() ②
>>> a_set
set()
>>> a_set.pop() ③
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
KeyError: 'pop from an empty set'
pop() method removes a single value from a set and returns the value. However, since sets are unordered, there is no “last” value in a set, so there is no way to control which value gets removed. It is completely arbitrary.
clear() method removes all values from a set, leaving you with an empty set. This is equivalent to a_set = set(), which would create a new empty set and overwrite the previous value of the a_set variable.
KeyError exception.
Python’s set type supports several common set operations.
>>> a_set = {2, 4, 5, 9, 12, 21, 30, 51, 76, 127, 195}
>>> 30 in a_set ①
True
>>> 31 in a_set
False
>>> b_set = {1, 2, 3, 5, 6, 8, 9, 12, 15, 17, 18, 21}
>>> a_set.union(b_set) ②
{1, 2, 195, 4, 5, 6, 8, 12, 76, 15, 17, 18, 3, 21, 30, 51, 9, 127}
>>> a_set.intersection(b_set) ③
{9, 2, 12, 5, 21}
>>> a_set.difference(b_set) ④
{195, 4, 76, 51, 30, 127}
>>> a_set.symmetric_difference(b_set) ⑤
{1, 3, 4, 6, 8, 76, 15, 17, 18, 195, 127, 30, 51}
in operator. This works the same as lists.
union() method returns a new set containing all the elements that are in either set.
intersection() method returns a new set containing all the elements that are in both sets.
difference() method returns a new set containing all the elements that are in a_set but not b_set.
symmetric_difference() method returns a new set containing all the elements that are in exactly one of the sets.
Three of these methods are symmetric.
# continued from the previous example >>> b_set.symmetric_difference(a_set) ① {3, 1, 195, 4, 6, 8, 76, 15, 17, 18, 51, 30, 127} >>> b_set.symmetric_difference(a_set) == a_set.symmetric_difference(b_set) ② True >>> b_set.union(a_set) == a_set.union(b_set) ③ True >>> b_set.intersection(a_set) == a_set.intersection(b_set) ④ True >>> b_set.difference(a_set) == a_set.difference(b_set) ⑤ False
Finally, there are a few questions you can ask of sets.
>>> a_set = {1, 2, 3}
>>> b_set = {1, 2, 3, 4}
>>> a_set.issubset(b_set) ①
True
>>> b_set.issuperset(a_set) ②
True
>>> a_set.add(5) ③
>>> a_set.issubset(b_set)
False
>>> b_set.issuperset(a_set)
False
False.
You can use sets in a boolean context, such as an if statement.
>>> def is_it_true(anything):
... if anything:
... print("yes, it's true")
... else:
... print("no, it's false")
...
>>> is_it_true(set()) ①
no, it's false
>>> is_it_true({'a'}) ②
yes, it's true
>>> is_it_true({False}) ③
yes, it's true
⁂
A dictionary is an unordered set of key-value pairs. When you add a key to a dictionary, you must also add a value for that key. (You can always change the value later.) Python dictionaries are optimized for retrieving the value when you know the key, but not the other way around.
☞A dictionary in Python is like a hash in Perl 5. In Perl 5, variables that store hashes always start with a
%character. In Python, variables can be named anything, and Python keeps track of the datatype internally.
Creating a dictionary is easy. The syntax is similar to sets, but instead of values, you have key-value pairs. Once you have a dictionary, you can look up values by their key.
>>> a_dict = {'server': 'db.diveintopython3.org', 'database': 'mysql'} ①
>>> a_dict
{'server': 'db.diveintopython3.org', 'database': 'mysql'}
>>> a_dict['server'] ②
'db.diveintopython3.org'
>>> a_dict['database'] ③
'mysql'
>>> a_dict['db.diveintopython3.org'] ④
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
KeyError: 'db.diveintopython3.org'
'server' is a key, and its associated value, referenced by a_dict['server'], is 'db.diveintopython3.org'.
'database' is a key, and its associated value, referenced by a_dict['database'], is 'mysql'.
a_dict['server'] is 'db.diveintopython3.org', but a_dict['db.diveintopython3.org'] raises an exception, because 'db.diveintopython3.org' is not a key.
Dictionaries do not have any predefined size limit. You can add new key-value pairs to a dictionary at any time, or you can modify the value of an existing key. Continuing from the previous example:
>>> a_dict
{'server': 'db.diveintopython3.org', 'database': 'mysql'}
>>> a_dict['database'] = 'blog' ①
>>> a_dict
{'server': 'db.diveintopython3.org', 'database': 'blog'}
>>> a_dict['user'] = 'mark' ②
>>> a_dict ③
{'server': 'db.diveintopython3.org', 'user': 'mark', 'database': 'blog'}
>>> a_dict['user'] = 'dora' ④
>>> a_dict
{'server': 'db.diveintopython3.org', 'user': 'dora', 'database': 'blog'}
>>> a_dict['User'] = 'mark' ⑤
>>> a_dict
{'User': 'mark', 'server': 'db.diveintopython3.org', 'user': 'dora', 'database': 'blog'}
'user', value 'mark') appears to be in the middle. In fact, it was just a coincidence that the items appeared to be in order in the first example; it is just as much a coincidence that they appear to be out of order now.
user key back to "mark"? No! Look at the key closely — that’s a capital U in "User". Dictionary keys are case-sensitive, so this statement is creating a new key-value pair, not overwriting an existing one. It may look similar to you, but as far as Python is concerned, it’s completely different.
Dictionaries aren’t just for strings. Dictionary values can be any datatype, including integers, booleans, arbitrary objects, or even other dictionaries. And within a single dictionary, the values don’t all need to be the same type; you can mix and match as needed. Dictionary keys are more restricted, but they can be strings, integers, and a few other types. You can also mix and match key datatypes within a dictionary.
In fact, you’ve already seen a dictionary with non-string keys and values, in your first Python program.
SUFFIXES = {1000: ['KB', 'MB', 'GB', 'TB', 'PB', 'EB', 'ZB', 'YB'],
1024: ['KiB', 'MiB', 'GiB', 'TiB', 'PiB', 'EiB', 'ZiB', 'YiB']}Let's tear that apart in the interactive shell.
>>> SUFFIXES = {1000: ['KB', 'MB', 'GB', 'TB', 'PB', 'EB', 'ZB', 'YB'],
... 1024: ['KiB', 'MiB', 'GiB', 'TiB', 'PiB', 'EiB', 'ZiB', 'YiB']}
>>> len(SUFFIXES) ①
2
>>> 1000 in SUFFIXES ②
True
>>> SUFFIXES[1000] ③
['KB', 'MB', 'GB', 'TB', 'PB', 'EB', 'ZB', 'YB']
>>> SUFFIXES[1024] ④
['KiB', 'MiB', 'GiB', 'TiB', 'PiB', 'EiB', 'ZiB', 'YiB']
>>> SUFFIXES[1000][3] ⑤
'TB'
len() function gives you the number of keys in a dictionary.
in operator to test whether a specific key is defined in a dictionary.
1000 is a key in the SUFFIXES dictionary; its value is a list of eight items (eight strings, to be precise).
1024 is a key in the SUFFIXES dictionary; its value is also a list of eight items.
SUFFIXES[1000] is a list, you can address individual items in the list by their 0-based index.
You can also use a dictionary in a boolean context, such as an if statement.
>>> def is_it_true(anything):
... if anything:
... print("yes, it's true")
... else:
... print("no, it's false")
...
>>> is_it_true({}) ①
no, it's false
>>> is_it_true({'a': 1}) ②
yes, it's true
⁂
NoneNone is a special constant in Python. It is a null value. None is not the same as False. None is not 0. None is not an empty string. Comparing None to anything other than None will always return False.
None is the only null value. It has its own datatype (NoneType). You can assign None to any variable, but you can not create other NoneType objects. All variables whose value is None are equal to each other.
>>> type(None) <class 'NoneType'> >>> None == False False >>> None == 0 False >>> None == '' False >>> None == None True >>> x = None >>> x == None True >>> y = None >>> x == y True
None In A Boolean ContextIn a boolean context, None is false and not None is true.
>>> def is_it_true(anything):
... if anything:
... print("yes, it's true")
... else:
... print("no, it's false")
...
>>> is_it_true(None)
no, it's false
>>> is_it_true(not None)
yes, it's true
⁂
fractions module
math module
© 2001–11 Mark Pilgrim