Built-in Data Types
In programming, data type is an important concept.
Variables can store data of different types, and different types can do different things.
Python has the following data types built-in by default, in these categories:
Text Type: | str
|
Numeric Types: | int , float , complex
|
Sequence Types: | list , tuple , range
|
Mapping Type: | dict
|
Set Types: | set , frozenset
|
Boolean Type: | bool
|
Binary Types: | bytes , bytearray , memoryview
|
None Type: | NoneType
|
Getting the Data Type
You can get the data type of any object by using the type[]
function:
Example
Print the data type of the variable x:
x = 5
print[type[x]]
Try it Yourself »
Setting the Data Type
In Python, the data type is set when you assign a value to a variable:
x = "Hello World" | str | Try it » |
x = 20 | int | Try it » |
x = 20.5 | float | Try it » |
x = 1j | complex | Try it » |
x = ["apple", "banana", "cherry"] | list | Try it » |
x = ["apple", "banana", "cherry"] | tuple | Try it » |
x = range[6] | range | Try it » |
x = {"name" : "John", "age" : 36} | dict | Try it » |
x = {"apple", "banana", "cherry"} | set | Try it » |
x = frozenset[{"apple", "banana", "cherry"}] | frozenset | Try it » |
x = True | bool | Try it » |
x = b"Hello" | bytes | Try it » |
x = bytearray[5] | bytearray | Try it » |
x = memoryview[bytes[5]] | memoryview | Try it » |
x = None | NoneType | Try it » |
Setting the Specific Data Type
If you want to specify the data type, you can use the following constructor functions:
x = str["Hello World"] | str | Try it » |
x = int[20] | int | Try it » |
x = float[20.5] | float | Try it » |
x = complex[1j] | complex | Try it » |
x = list[["apple", "banana", "cherry"]] | list | Try it » |
x = tuple[["apple", "banana", "cherry"]] | tuple | Try it » |
x = range[6] | range | Try it » |
x = dict[name="John", age=36] | dict | Try it » |
x = set[["apple", "banana", "cherry"]] | set | Try it » |
x = frozenset[["apple", "banana", "cherry"]] | frozenset | Try it » |
x = bool[5] | bool | Try it » |
x = bytes[5] | bytes | Try it » |
x = bytearray[5] | bytearray | Try it » |
x = memoryview[bytes[5]] | memoryview | Try it » |
Test Yourself With Exercises
Exercise:
The following code example would print the data type of x, what data type would that be?
Start the Exercise
Data types in Python
Every value in Python has a datatype. Since everything is an object in Python programming, data types are actually classes and variables are instance [object] of these classes.
There are various data types in Python. Some of the important types are listed below.
Python Numbers
Integers, floating point numbers and complex numbers fall under
Python numbers category. They are defined as int
, float
and complex
classes in Python.
We can use the type[]
function to know which class a variable or a value belongs to. Similarly, the isinstance[]
function is used to check if an object belongs to a particular class.
a = 5
print[a, "is of type", type[a]]
a = 2.0
print[a, "is of type", type[a]]
a = 1+2j
print[a, "is complex number?", isinstance[1+2j,complex]]
Output
5 is of type 2.0 is of type [1+2j] is complex number? True
Integers can be of any length, it is only limited by the memory available.
A floating-point number is accurate up to 15 decimal places. Integer and floating points are separated by decimal points. 1 is an integer, 1.0 is a floating-point number.
Complex numbers are written in the form, x + yj
, where x is the real part and y is the imaginary part. Here are some examples.
>>> a = 1234567890123456789
>>> a
1234567890123456789
>>> b = 0.1234567890123456789
>>> b
0.12345678901234568
>>> c = 1+2j
>>> c
[1+2j]
Notice that the float
variable b got truncated.
Python List
List is an ordered sequence of items. It is one of the most used datatype in Python and is very flexible. All the items in a list do not need to be of the same type.
Declaring a list is pretty straight forward. Items separated by commas are enclosed within brackets [ ]
.
a = [1, 2.2, 'python']
We can use the slicing operator [ ]
to extract an item or a range of items from a list. The index starts from 0 in
Python.
a = [5,10,15,20,25,30,35,40]
# a[2] = 15
print["a[2] = ", a[2]]
# a[0:3] = [5, 10, 15]
print["a[0:3] = ", a[0:3]]
# a[5:] = [30, 35, 40]
print["a[5:] = ", a[5:]]
Output
a[2] = 15 a[0:3] = [5, 10, 15] a[5:] = [30, 35, 40]
Lists are mutable, meaning, the value of elements of a list can be altered.
a = [1, 2, 3]
a[2] = 4
print[a]
Output
[1, 2, 4]
Python Tuple
Tuple is an ordered sequence of items same as a list. The only difference is that tuples are immutable. Tuples once created cannot be modified.
Tuples are used to write-protect data and are usually faster than lists as they cannot change dynamically.
It is defined within parentheses []
where items are separated by commas.
t = [5,'program', 1+3j]
We can use the slicing operator []
to extract items but we cannot change its value.
t = [5,'program', 1+3j]
# t[1] = 'program'
print["t[1] = ", t[1]]
# t[0:3] = [5, 'program', [1+3j]]
print["t[0:3] = ", t[0:3]]
# Generates error
# Tuples are immutable
t[0] = 10
Output
t[1] = program t[0:3] = [5, 'program', [1+3j]] Traceback [most recent call last]: File "test.py", line 11, in t[0] = 10 TypeError: 'tuple' object does not support item assignment
Python Strings
String is sequence of Unicode characters. We can use single quotes or double quotes to
represent strings. Multi-line strings can be denoted using triple quotes, '''
or """
.
s = "This is a string"
print[s]
s = '''A multiline
string'''
print[s]
Output
This is a string A multiline string
Just like a list and tuple, the slicing operator [ ]
can be used with strings. Strings, however, are immutable.
s = 'Hello world!'
# s[4] = 'o'
print["s[4] = ", s[4]]
# s[6:11] = 'world'
print["s[6:11] = ", s[6:11]]
# Generates error
# Strings are immutable in Python
s[5] ='d'
Output
s[4] = o s[6:11] = world Traceback [most recent call last]: File "", line 11, in TypeError: 'str' object does not support item assignment
Python Set
Set is an unordered collection of unique items. Set is defined by values
separated by comma inside braces { }
. Items in a set are not ordered.
a = {5,2,3,1,4}
# printing set variable
print["a = ", a]
# data type of variable a
print[type[a]]
Output
a = {1, 2, 3, 4, 5}
We can perform set operations like union, intersection on two sets. Sets have unique values. They eliminate duplicates.
a = {1,2,2,3,3,3}
print[a]
Output
{1, 2, 3}
Since, set are unordered collection, indexing has no meaning. Hence, the slicing operator []
does not work.
>>> a = {1,2,3}
>>> a[1]
Traceback [most recent call last]:
File "", line 301, in runcode
File "", line 1, in
TypeError: 'set' object does not support indexing
Python Dictionary
Dictionary is an unordered collection of key-value pairs.
It is generally used when we have a huge amount of data. Dictionaries are optimized for retrieving data. We must know the key to retrieve the value.
In Python, dictionaries are defined within braces {}
with each item being a pair in the form key:value
. Key and value can be of any type.
>>> d = {1:'value','key':2}
>>> type[d]
We use key to retrieve the respective value. But not the other way around.
d = {1:'value','key':2}
print[type[d]]
print["d[1] = ", d[1]]
print["d['key'] = ", d['key']]
# Generates error
print["d[2] = ", d[2]]
Output
d[1] = value d['key'] = 2 Traceback [most recent call last]: File "", line 9, in KeyError: 2
Conversion between data types
We can convert between different data types by using different type conversion functions like int[]
, float[]
, str[]
, etc.
>>> float[5]
5.0
Conversion from float to int will truncate the value [make it closer to zero].
>>> int[10.6]
10
>>> int[-10.6]
-10
Conversion to and from string must contain compatible values.
>>> float['2.5']
2.5
>>> str[25]
'25'
>>> int['1p']
Traceback [most recent call last]:
File "", line 301, in runcode
File "", line 1, in
ValueError: invalid literal for int[] with base 10: '1p'
We can even convert one sequence to another.
>>> set[[1,2,3]]
{1, 2, 3}
>>> tuple[{5,6,7}]
[5, 6, 7]
>>> list['hello']
['h', 'e', 'l', 'l', 'o']
To convert to dictionary, each element must be a pair:
>>> dict[[[1,2],[3,4]]]
{1: 2, 3: 4}
>>> dict[[[3,26],[4,44]]]
{3: 26, 4: 44}