Python Built-In Types
Simple Values
Type | Example | Description |
---|---|---|
int |
x = 1 |
integers |
float |
x = 1.0 |
floating point numbers |
complex |
x = 1 + 2j |
complex numbers |
bool |
x = True |
boolean: True/False values |
str |
x = ’abc’ |
string: characters or text |
NoneType |
x = None |
null value |
Integers
Integer values are numbers without decimal points.
>>> x = 1 >>> type(x) int
Python integers are variable precision; computations do not overflow
Floating-Point Numbers
Floating-point values can store fractional numbers
Floating-point values can be defined in standard or exponential notation
x = 0.000005 y = 5e-6
An integer can be converted to a float with the
float
constructorfloat(1)
Complex Numbers
Complex numbers have real and imaginary parts (both floating point values).
Complex numbers can be created with the
complex
constuctor:>>> complex(1, 2) (1+2j)
Or alternatively with the “j” suffix
>>> 1 + 2j (1+2j)
String Type
Strings in Python can be created with single or double quotes
message = "what do you like?" response = 'spam'
Python strings have useful functions and methods
Examples:
>>> len(response) 4 >>> response.upper() 'SPAM' >>> message[0] # zero-based indexing 'w'
Boolean Type
The Boolean type has two possible values:
True
andFalse
.Values of any other type can be converted into boolean values with the
bool
constructor.Examples:
>>> bool(123) True >>> bool(0) False >>> bool('') False
None Type
The
NoneType
has only a single possible value:None
>>> type(None) NoneType
A Python function that does not return a value returns
None
Built-In Data Structures
Type | Example | Description |
---|---|---|
list |
[1, 2, 3] |
ordered collection |
tuple |
(1, 2, 3) |
immutable ordered collection |
dict |
{’a’: 1, ’b’: 2} |
unordered (key,value) mapping |
set |
{1, 2, 3} |
unordered collection |
Lists
Lists are the basic ordered and mutable data collection
Lists can be defined comma-separated values between square brackets
>>> L = [2, 3, 5, 7]
Lists have many useful methods
Examples:
>>> len(L) 4 >>> L.append(11) [2, 3, 5, 7, 11]
List Indexing
Elements of a list can be indexed for single values.
Lists use zero based indexing
>>> L = [2, 3, 5, 7, 11] >>> L[0] 2
Lists can be indexed from the end with negative integers
>>> L[-1] 11 >>> L[-2] 7
List Slicing
Elements of a list can be sliced for multiple values.
List slicing syntax uses a colon to indicate the (inclusive) start point and the (exclusive) end point.
>>> L = [2, 3, 5, 7, 11] >>> L[0:3] [2, 3, 5]
An optional third integer can be used to represent a step size
>>> L[::2] [2, 5, 11]
Tuples
Tuples are an immutable, ordered collection
Immutable means that once a tuple is created it cannot be changed
Tuple are defined with parentheses or using commas
>>> t1 = (1, 2, 3) >>> t2 = 1, 2, 3
Tuples can be indexed and sliced like lists
Dictionaries
Dictionaries map keys to values
Dictionaries are created by a comma separated list of
key:value
pairs between curly braces>>> numbers = {'one': 1, 'two': 2}
Items are accessed using the key
>>> numbers['two'] 2
Sets
Sets are unordered collections of unique items
Sets are defined by a comma separated list of values between curly braces
>>> primes = {2, 3, 5, 7} >>> odds = {1, 3, 5, 7, 9}
Sets support mathematical set operations
Example:
>>> primes | odds {1, 2, 3, 5, 7, 9} >>> primes.union(odds) {1, 2, 3, 5, 7, 9}