Python Built-in Modules Explained

Introduction to Python Built in Modules

There are several built in modules in Python that provide different functionalities. These modules are libraries of pre-written code that can be imported and used in Python programs. 

Some commonly used built-in modules include 

  • OS module for interacting with the operating system
  • Datetime module for working with dates and times
  • Math module for mathematical operations
  • Csv module for reading and writing CSV files
  • JSON module for working with JSON data
  • Urllib module for working with URLs. 

Let’s dive in to further understand built in modules in Python.

What are Python Built in Modules?

Built in modules in Python are pre-written libraries of code that come bundled with the Python programming language. These modules provide various functionalities commonly required in programming, such as interacting with the operating system, working with dates and times, performing mathematical operations, generating random numbers, manipulating text, working with data in various formats, and more.

Python’s built-in modules are a core part of the language, so they don’t need to be installed separately. They are available for use in any Python program by simply importing them. 

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Advantages of Using Built-in Modules

Built in modules in Python is an approach to software design that involves breaking down a program into smaller, self-contained units or modules. This has several advantages:

Facilitates Collaborative Development

By dividing the program into smaller modules, developers can work on different program parts simultaneously. This allows for faster development and testing and easier collaboration among team members.

Improves Readability and Manageability

Modular programming allows developers to organise their code into smaller, more manageable units. This makes the code easier to read, understand, and maintain. Each module can be designed, implemented, and tested independently of the others, making it easier to manage the overall program.

Enhances Reusability of Code

Modular programming makes it easier to reuse code. Modules can be designed for multiple programs, reducing the need to write new code from scratch. This saves time and effort and helps to minimize errors.

Detects Programming Errors More Easily

Since each module is self-contained, errors can be traced back to a specific module or function, making identifying and correcting them easier.

Allows for Better Program Design

Modular programming allows for better program design by breaking down complex programs into smaller, more manageable pieces. 

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Understanding Python’s Standard Library

Python libraries list includes over 200 modules written in C that provide core functionality such as input/output operations and other essential modules that make Python a powerful language.

While the Standard Library is included within Python, developers can access a vast collection of several thousand components from the Python Package Index (PyPI) separately. 

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Here is a detailed Python modules list – 

Working with Built-in Modules

Python’s Standard Library includes many inbuilt modules in Python that provide useful functionality to developers. Working with these modules is straightforward and can be done by importing the module at the beginning of your Python program using the import keyword. Here’s an example:


import math


result = math.sqrt(9)




In this example, the math module is imported and uses its sqrt() function to calculate the square root of 9. The result is then printed to the console.

Some built-in modules require additional configuration or initialisation before they can be used. For example, the datetime module requires a call to its datetime() constructor to create a new datetime object. Here’s an example:


import datetime


now =




In this example, we import the datetime module and use its datetime() constructor to create a new datetime object representing the current date and time. The resulting object is then printed to the console.

File Input and Output Modules

You can use Python inbuilt modules to read and write files in different formats in Python. There are four common file input and output modules: 

  • Os

This module provides functions for working with the operating system, including file input and output. It includes functions for creating, deleting, and renaming files and checking whether a file exists.

  • open()

The open() function opens a file and allows you to read or write data. For instance, if you want to read a text file called “example.txt”, you can use the code with open(‘example.txt’, ‘r’) as file: data = This code reads the file’s entire contents and stores it in the data variable. These modules are useful for working with file-based data in Python.

  • Csv

This module provides functions for working with CSV files, including reading and writing CSV data. It includes functions for parsing CSV data into Python objects and for converting Python objects to CSV format.

  • pickle 

This module provides functions for serialising and deserialising Python objects, allowing you to store them in a file and then load them later. This can be useful for saving and loading program states or sharing data between programs.

Math and Statistical Modules

The math module in Python is a standard module that provides access to various mathematical functions and constants. We can import the module using the ‘import math’ statement. There are different constants in this module. They include – 

  1. Euler’s number (math.e): The mathematical constant e, known as Euler’s number, is approximately 2.71828. It serves as the base of the natural logarithm and plays a significant role in calculus and various other mathematical fields.
  2. Tau (math.tau): Tau, a mathematical constant of about 6.28318, is defined as the ratio of a circle’s circumference to its radius. This makes it a significant constant in trigonometry and geometry.
  3. Infinity (math.inf): Infinity is a mathematical concept that refers to an infinitely large value. It is represented in Python by the math.inf constant, which is used to represent positive infinity. The constant -math.inf is used to represent negative infinity.
  4. Pi (math.pi): Pi, a constant in maths, represents the ratio of the circumference of a circle to its diameter. It is approximately equal to 3.14159 and is used extensively in geometry, trigonometry, and other areas of mathematics.
  5. Not a Number (NaN) (math.nan): NaN is a special value in Python that represents a “not a number” value. It represents the result of certain mathematical operations that do not have a valid numeric value, such as taking the square root of a negative number.

Time and Date Modules

Python does not have a specific data type for dates and times. However, it provides built-in time and date modules or the ” datetime ” module that can be imported into your Python code. This module contains several classes, offering various functions that can be used to manipulate dates, times, and time intervals. The six main classes in the Python datetime module:

  • “date” class – This represents a naive date, assuming that the current Gregorian calendar has always been and will always be in effect. It has three attributes: year, month, and day.
  • “time” class – Time is a theoretical depiction of a day’s time, without reference to a specific day, with the assumption that each day has 246060 seconds. It encompasses attributes like hour, minute, second, microsecond, and tzinfo.
  • “datetime” class – This represents a combination of both date and time, with attributes for year, month, day, hour, minute, second, microsecond, and tzinfo.
  • “timedelta” class – This represents a duration that differentiates between two date, time, or datetime instances to microsecond resolution.
  • “tzinfo” class – The datetime module in Python includes the “tzinfo” class that allows working with time zone information.
  • “timezone” class – This class provides a fixed offset from UTC by implementing the tzinfo abstract base class.

Regular Expression Modules

In Python, Regular Expressions are implemented using the “re” module. This module provides a set of functions that allow us to work with regular expressions. Regular expressions, also called regex, are patterns used to match character combinations in strings.

The “re” module has several functions, including:

  • re.match() – This function checks for a pattern match at the start of the string. It returns a match object if the pattern matches and None if it doesn’t.
  • – This function searches the entire string for the first occurrence of the pattern. If the pattern matches, it returns a match object; otherwise, it returns None.
  • re.findall() – This function returns a list containing all matches of the pattern in the string.
  • re.sub() – This function replaces all occurrences of the pattern in the string with the specified string.

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Whether you are a beginner or an experienced developer, built in packages in Python can provide you with powerful functionalities to create efficient and effective applications. By learning and understanding these modules, you can unlock the full potential of the Python language and elevate your programming skills to new heights.

If you’re looking to strengthen your Python skills further, Python Programming Bootcamp from upGrad is a comprehensive online course covering Python programming fundamentals. This course covers a wide range of topics taught by industry experts with great experience, hoping to create future industry leaders!

What is Python used for?

Python is a general-purpose programming language used for various tasks, including web development, data analysis, scientific computing, artificial intelligence, and automation.

What are the advantages of learning Python?

Python is easy to learn, has a large community and a vast array of libraries, is versatile and can be used for multiple applications, has a high demand for skilled developers, and has a growing job market.

What are some popular Python libraries?

Popular Python libraries include NumPy for numerical computing, pandas for data analysis, Matplotlib for data visualisation, TensorFlow for machine learning, and Django for web development.

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