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Python Notes [N°2] - Datatypes

Python Notes [N°2] - Datatypes

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Davide Camera
·Nov 3, 2020·

6 min read

Hello Data Lovers👋

Another article for Pythonic, Pythonist, Pythoneer and Pythonista. Python is a widely used high-level programming language for general-purpose programming, created by Guido van Rossum and first released in 1991.

Hope you find them educational!

Article outline

  • Booleans

  • Numbers

  • Strings

  • Sequences and collections

  • Built-in constants

  • Testing the type of variables

  • Converting between datatypes

  • Mutable and Immutable Data Types



Bool: a boolean value of either True or False. Logical operations like and, or, not can be performed on booleans.

x or y    #  if x is False then y otherwise  x
x and y    #  if x is False then x otherwise  y
not x    # if x is True then False, otherwise True

In Python 2.x and in Python 3.x, a boolean is also an int. The bool type is a subclass of the int type and True and False are its only instances:

issubclass(bool, int) # True

isinstance(True, bool) # True
isinstance(False, bool) # True

If boolean values are used in arithmetic operations, their integer values (1 and 0 for True and False) will be used to return an integer result:

True + False == 1 #  1  +  0  ==  1
True * True    == 1 #  1  *  1  == 1


  • int: Integer number
a = 2
b = 100
c = 123456789
d = 38563846326424324

Integers in Python are of arbitrary sizes.

Note: in older versions of Python, a long type was available and this was distinct from int. The two have been unified.

  • float: Floating point number; precision depends on the implementation and system architecture, for CPython the float datatype corresponds to a C double.
a = 2.0
b = 100.e0
c = 123456789.e1
  • complex: Complex numbers
a = 2 + 1j
b = 100 + 10j

The <, <=, > and >= operators will raise a TypeError exception when any operand is a complex number.


Python 3.x Version ≥ 3.0

  • str: a unicode string. The type of 'hello'

  • bytes: a byte string. The type of b'hello'

Python 2.x Version ≤ 2.7

  • str: a byte string. The type of 'hello'

  • bytes: synonym for str

  • unicode: a unicode string. The type of u'hello'

Sequences and collections

Python differentiates between ordered sequences and unordered collections (such as set and dict).

  • strings (str, bytes, unicode) are sequences
  • reversed: A reversed order of str with reversed function
a = reversed('hello')
  • tuple: An ordered collection of n values of any type (n >= 0).
a = (1, 2, 3)
b = ('a', 1, 'python', (1, 2))
b[2] = 'something else' # returns a TypeError

Supports indexing; immutable; hashable if all its members are hashable.

  • list: An ordered collection of n values (n >= 0)
a = [1, 2, 3]
b = ['a', 1, 'python', (1, 2), [1, 2]]
b[2] = 'something else' # allowed

Not hashable; mutable.

  • set: An unordered collection of unique values. Items must be hashable.
a = {1, 2, 'a'}
  • dict: An unordered collection of unique key-value pairs; keys must be hashable.
a = {1: 'one',
2: 'two'}

b = {'a': [1, 2, 3],
'b': 'a string'}

Built-in constants

In conjunction with the built-in datatypes there are a small number of built-in constants in the built-in namespace:

  • True: The true value of the built-in type bool

  • False: The false value of the built-in type bool

  • None: A singleton object used to signal that a value is absent.

  • Ellipsis or ...: used in core Python3+ anywhere and limited usage in Python2.7+ as part of array notation. NumPy and related packages use this as a 'include everything' reference in arrays.

  • NotImplemented: a singleton used to indicate to Python that a special method doesn't support the specific arguments, and Python will try alternatives if available.

a = None # No value will be assigned. Any valid datatype can be assigned later

Testing the type of variables

In python, we can check the datatype of an object using the built-in function type.

a = '123'
# Out: <class 'str'>
b = 123
# Out: <class 'int'>

In conditional statements, it is possible to test the datatype with isinstance. However, it is usually not encouraged to rely on the type of the variable.

i = 7
if isinstance(i, int): i += 1
elif isinstance(i, str): i = int(i)
i += 1

To test if something is of NoneType:

x = None
if x is None:
print('Not a surprise, I just defined x as None.')

Converting between datatypes

You can perform explicit datatype conversion.

For example, '123' is of str type and it can be converted to integer using int function.

a = '123'
b = int(a)

Converting from a float string such as '123.456' can be done using float function.

a = '123.456'
b = float(a)
c = int(a)    # ValueError: invalid literal for int() with base 10: '123.456'
d = int(b)    # 123

You can also convert sequence or collection types:

a = 'hello'
list(a)    # ['h', 'e', 'l', 'l', 'o']
set(a)    # {'o', 'e', 'l', 'h'}
tuple(a) # ('h', 'e', 'l', 'l', 'o')

Mutable and Immutable Data Types

An object is called mutable if it can be changed. For example, when you pass a list to some function, the list can be changed:

def f(m):
m.append(3)    # adds a number to the list. This is a mutation.

x = [1, 2]
x == [1, 2]    # False now, since an item was added to the list

An object is called immutable if it cannot be changed in any way. For example, integers are immutable, since there's no way to change them:

def bar():
x = (1, 2)
x == (1, 2)    # Will always be True, since no function can change the object (1, 2)

Note that variables themselves are mutable, so we can reassign the variable x, but this does not change the object that x had previously pointed to. It only made x point to a new object.

Data types whose instances are mutable are called mutable data types, and similarly for immutable objects and datatypes.

Examples of immutable Data Types:

  • int, long, float, complex

  • str

  • bytes

  • tuple

  • frozenset

Examples of mutable Data Types:

  • bytearray

  • list

  • set

  • dict

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