Python Data Types

Python offers numerous built-in data types, each designed to handle specific forms of data effectively. In this comprehensive guide, we’ll explore Python’s most important data types, with examples and best practices.

In Python, data types define the nature of the value stored in a variable. Each data type specifies the operations that can be performed and how the data is stored in memory.

Python includes various built-in data types, categorized as follows:

• Numeric: Integer, Float, Complex
• Text: String
• Boolean: True or False
• Sequences: List, Tuple, Range
• Mapping: Dictionary
• Sets: Set, Frozenset
• NoneType: Represents the absence of a value (None)

Python provides three main numeric types:

x = 100          # Integer
y = 3.14         # Float
z = 1 + 2j       # Complex number

print(type(x))   # <class 'int'>
print(type(y))   # <class 'float'>
print(type(z))   # <class 'complex'>

Strings in Python represent sequences of characters, defined using single (' ') or double (" ") quotes.

name = "Alice"
message = 'Hello, World!'

print(name)
print(message)

String Operations:

• Concatenation (+): "Hello " + "World" → "Hello World"
• Repetition (*): "Hi" * 3 → "HiHiHi"
• Slicing: "Python"[0:2] → "Py"

Boolean types represent logical values:

• True
• False

is_valid = True
is_admin = False

print(type(is_valid))  # <class 'bool'>

Booleans commonly control flow in conditional statements:

if is_valid:
    print("Valid user")
else:
    print("Invalid user")

a) Lists

Lists are mutable, ordered collections of elements.

numbers = [1, 2, 3, 4, 5]
names = ["Alice", "Bob", "Charlie"]

numbers[0] = 10  # lists are mutable
print(numbers)

b) Tuples

Tuples are immutable, ordered collections.

coordinates = (10.0, 20.0)
colors = ("red", "green", "blue")

# coordinates[0] = 15.0  # Error: tuples are immutable

c) Ranges

Ranges represent sequences of numbers generated on demand.

for i in range(5):  # 0 to 4
    print(i)

Dictionaries store key-value pairs and are mutable.

person = {
    "name": "John",
    "age": 30,
    "is_student": False
}

print(person["name"])  # "John"
person["age"] = 31

Sets are unordered collections of unique elements.

• Set: mutable
• Frozenset: immutable version of a set

unique_numbers = {1, 2, 2, 3, 4}
print(unique_numbers)  # {1, 2, 3, 4}

# frozenset
fs = frozenset([1, 2, 3])

None represents the absence of a value, similar to null in other languages.

result = None
if result is None:
    print("No result yet")

Use the type() function to identify the type of a variable.

x = 10
y = "Hello"
print(type(x))  # <class 'int'>
print(type(y))  # <class 'str'>

Use isinstance() to check if a variable belongs to a certain type or class:

print(isinstance(x, int))      # True
print(isinstance(y, str))      # True
print(isinstance(y, int))      # False

Python allows converting data from one type to another using built-in functions:

• int(): to integer
• float(): to float
• str(): to string
• list(), tuple(), set(): between collection types

# String to integer
num = int("10")

# Float to integer
val = int(5.6)  # val = 5

# Integer to float
f = float(7)  # f = 7.0

# List to tuple
t = tuple([1, 2, 3])  # t = (1, 2, 3)