Python Data Types

In Python, data types define the type of data that a variable can hold. They determine how the data is stored in memory, the operations that can be performed on the data, and the constraints associated with each type. Python has several built-in data types, each serving different purposes. Let's explore some of the most commonly used data types with examples:

Numeric Types

  1. int: Represents integers (whole numbers) and can be positive or negative without any decimal point. For example: x = 10
  2. float: Represents floating-point numbers, which include numbers with decimal points. For example: y = 3.14
  3. complex: Represents complex numbers with both a real and imaginary part. For example: z = 2 + 3j

Boolean Type

  1. bool: Represents Boolean values, which can only be either True or False. For example: is_raining = True

Text Type

  1. str: Represents strings of characters, enclosed in single or double quotes. For example: name = "Smith"

Sequence Types

  1. list: Represents ordered, mutable collections of elements. Lists are defined using square brackets. For example: numbers = [1, 2, 3, 4]
  2. tuple: Represents ordered, immutable collections of elements. Tuples are defined using parentheses. For example: coordinates = (10, 20)
  3. range: Represents a sequence of numbers, often used for iterations. For example: range(1, 5) produces the sequence 1, 2, 3, 4.

Set Types

  1. set: Represents an unordered collection of unique elements. Sets are defined using curly braces. For example: colors = {"red", "green", "blue"}

Mapping Type

  1. dict: Represents key-value pairs, where each key maps to a value. Dictionaries are defined using curly braces with key-value pairs separated by a colon. For example: person = {"name": "Smith", "age": 30, "city": "New York"}

None Type

  1. None: Represents a special value, often used to indicate the absence of a value. For example: result = None

Python Data Types: Examples

Here are some examples of how Python data types are used:

# Numeric data types x = 10 # This is an integer y = 1.5 # This is a float z = 1 + 2j # This is a complex number # String data types s = "Hello, world!" # This is a string t = 'This is also a string' # This is also a string # Sequence data types l = [1, 2, 3, 4, 5] # This is a list m = (1, 2, 3, 4, 5) # This is a tuple n = range(10) # This is a range object # Boolean data types b = True # This is a Boolean value c = False # This is a Boolean value # Dictionary data types d = {'key1': 1, 'key2': 2} # This is a dictionary

Dynamically-typed language

Python is a dynamically-typed language, which means you don't need to declare the data type explicitly while creating a variable. The data type is inferred automatically based on the value assigned to the variable.

x = 10 # int y = 3.14 # float z = "Hello" # str is_valid = True # bool

Built-in functions

You can use built-in functions like type() to determine the data type of a variable:

print(type(x)) # <class 'int'> print(type(y)) # <class 'float'> print(type(z)) # <class 'str'> print(type(is_valid)) # <class 'bool'>

Conclusion

Understanding data types is crucial as it helps ensure that operations are performed correctly and efficiently on variables, and it allows you to use the appropriate data structures for different tasks in your Python programs.






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