Understanding Python Data Types with Examples
Python is a dynamically
typed language, meaning you don't need to declare the type of a variable when
you create it. The type is automatically assigned based on the value you
provide. However, understanding data types is crucial for efficient coding and avoiding
bugs in your programs. In this blog, we’ll explore the various data types in
Python with examples.
1. Numeric Data Types
Python supports several
numeric types, including integers, floating-point numbers, and complex numbers.
- Integers (int): These are whole numbers without a
fractional part.
age = 25
print(type(age)) # Output: <class 'int'>
- Floating-point (float): These are numbers with a fractional
part.
height = 5.9
print(type(height)) # Output: <class 'float'>
- Complex numbers (complex): These consist of a real part and an
imaginary part.
complex_number = 3 + 4j
print(type(complex_number)) # Output: <class 'complex'>
2. Sequence Data Types
Python provides several
types to represent sequences of data.
- Strings (str): Strings are immutable sequences of
characters.
name = "Deepak"
print(type(name)) # Output: <class 'str'>
- Lists (list): Lists are mutable sequences,
typically used to store collections of homogeneous items.
fruits = ["apple",
"banana", "cherry"]
print(type(fruits)) # Output: <class 'list'>
- Tuples (tuple): Tuples are immutable sequences,
typically used to store collections of heterogeneous items.
coordinates = (10, 20)
print(type(coordinates)) # Output: <class 'tuple'>
3. Mapping Data Type
- Dictionaries (dict): Dictionaries are unordered
collections of key-value pairs. Keys must be unique and immutable.
person = {"name":
"Deepak", "age": 41}
print(type(person)) # Output: <class 'dict'>
4. Set Data Types
Sets are unordered
collections of unique elements.
- Set (set): A set is mutable and does not allow
duplicate elements.
unique_numbers = {1, 2, 3,
4, 4, 5}
print(unique_numbers) # Output: {1, 2, 3, 4, 5}
print(type(unique_numbers)) # Output: <class 'set'>
- Frozenset (frozenset): An immutable version of a set.
frozen_numbers = frozenset([1,
2, 3, 4, 4, 5])
print(frozen_numbers) # Output: frozenset({1, 2, 3, 4, 5})
print(type(frozen_numbers)) # Output: <class 'frozenset'>
5. Boolean Data Type
- Boolean (bool): Represents one of two values: True
or False.
is_adult = True
print(type(is_adult)) # Output: <class 'bool'>
6. Binary Data Types
Python provides types to
handle binary data.
- Bytes (bytes): Immutable sequences of bytes.
byte_data = b"Hello"
print(type(byte_data)) # Output: <class 'bytes'>
- Bytearray (bytearray): Mutable sequences of bytes.
mutable_byte_data = bytearray(b"Hello")
print(type(mutable_byte_data)) # Output: <class 'bytearray'>
- Memoryview (memoryview): Allows you to access the internal
data of an object that supports the buffer protocol without copying.
mv = memoryview(byte_data)
print(type(mv)) # Output: <class 'memoryview'>
7. None Type
- NoneType (None): Represents the absence of a value.
result = None
print(type(result)) # Output: <class 'NoneType'>
Final Remarks
Understanding Python data types is essential for writing effective code. Each type serves a specific purpose, and knowing when to use which type can significantly improve the performance and readability of your programs. This is just fundamental information about the data types. In next blogs you will study every data type in detail.
Happy coding!