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Python, as a versatile and dynamically typed programming language, provides a rich set of built-in data types that enable you to handle various kinds of data efficiently and effectively. These built-in types are the fundamental building blocks of Python programs and are used to represent everything from numbers to sequences, containers, and more. In this tutorial, we’ll explore the most important built-in types in Python, along with examples that illustrate their usage.

Table of Contents

  1. Numeric Types
  2. Integers
  3. Floating-Point Numbers
  4. Complex Numbers
  5. Text Type
  6. Sequence Types
  7. Lists
  8. Tuples
  9. Strings
  10. Mapping Type
  11. Dictionaries
  12. Set Types
  13. Sets
  14. Frozen Sets
  15. Boolean Type
  16. None Type

1. Numeric Types

Python supports various numeric types to work with different kinds of numbers.

1.1 Integers

Integers, often referred to as “ints,” represent whole numbers. They can be positive, negative, or zero.


x = 42
y = -10
z = 0

1.2 Floating-Point Numbers

Floating-point numbers, or “floats,” are used to represent real numbers. They include a decimal point and can also be written using scientific notation.


a = 3.14
b = -0.01
c = 2.5e3  # Scientific notation: 2.5 * 10^3 = 2500.0

1.3 Complex Numbers

Complex numbers consist of a real part and an imaginary part. They are written in the form a + bj, where a is the real part and b is the imaginary part.


comp1 = 2 + 3j
comp2 = -1j  # Real part is 0

2. Text Type

The text type in Python is represented using strings. Strings are sequences of characters and can be enclosed in single (”) or double (“”) quotes.


text1 = 'Hello, World!'
text2 = "Python"
text3 = "It's a beautiful day."

3. Sequence Types

Sequence types are collections of items ordered by their positions.

3.1 Lists

Lists are mutable sequences that can contain elements of different types. They are created using square brackets [].


my_list = [1, 2, 3, 'a', 'b', 'c']

3.2 Tuples

Tuples are similar to lists but are immutable, meaning their elements cannot be changed after creation. They are created using parentheses ().


my_tuple = (10, 20, 30)

3.3 Strings

Strings are sequences of characters, as mentioned earlier. They are also considered a type of sequence.


name = "Alice"

4. Mapping Type

Mapping types are collections of key-value pairs.

4.1 Dictionaries

Dictionaries are mutable mappings that store items as key-value pairs. Keys are unique, and they are used to access corresponding values quickly. Dictionaries are created using curly braces {}.


person = {'name': 'John', 'age': 30, 'city': 'New York'}

5. Set Types

Set types are collections of unique elements.

5.1 Sets

Sets are mutable and unordered collections of unique elements. They are created using curly braces {}.


my_set = {1, 2, 3, 4, 5}

5.2 Frozen Sets

Frozen sets are immutable sets. Once created, their elements cannot be modified.


frozen = frozenset([1, 2, 3])

6. Boolean Type

The Boolean type has only two values: True and False. Booleans are often used for conditional expressions and logical operations.


is_active = True
is_admin = False

7. None Type

The None type represents the absence of a value. It is often used to indicate the lack of a meaningful result or absence of data.


result = None

These are the essential built-in types in Python. Understanding and effectively using these types will enable you to work with a wide range of data and solve various programming problems. Experiment with these types in your Python code to build a strong foundation in the language.

Remember that Python’s dynamic typing allows you to create variables without explicitly declaring their type, and variables can change types during runtime. This flexibility can be powerful but also requires careful consideration when writing code.

In this tutorial, we covered the most important built-in types in Python, including numeric types, text type, sequence types, mapping type, set types, Boolean type, and the None type. Armed with this knowledge, you can confidently work with diverse data and construct more robust and efficient Python programs.

Happy coding!

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