Popular Python Libraries You Should Know Before 2025

6 min readDec 5, 2024

As we know, Python is capable of building complex and scalable applications.

But, with complexity, the code becomes unmanageable. To solve this, developers create modules to manage their code efficiently.

In this blog, you will learn about Python models, how they help improve Python’s code readability, different types, and some popular Python modules to learn before 2025.

What are Python Modules?

Python provides a large standard library of built-in modules. In Python,

A module is just a single file with Python code.
It may contain functions, classes, or variables that provide specific functionality.
You can import a module into another Python script to reuse a similar code.
You can create your own custom modules
Install third-party modules to extend Python’s capabilities.

But, most of us get confused between Python modules and libraries.

Are these the same thing? Well, no!

Let’s understand the difference.

How Python Modules Differ From Python Libraries

You must understand that both concepts are completely different.

Python module is a single file that contains code including functions, classes, variables, and code. You can export these codes to be reused in other applications.

Example of a Python Module:

Imagine you have a file called math_utils.py:

# math_utils.py

def add(x, y):

return x + y

def subtract(x, y):

return x — y

Here, math_utils.py is a module that defines two functions, add() and subtract().

You can use this module in another Python script:

# main.py

import math_utils

result = math_utils.add(5, 3)

print(result) # Output: 8

2. Python libraries are collections of related modules that offer specific functionalities.

Example of a Python Library:

NumPy- a numerical computing library. It contains multiple modules (such as numpy.random, numpy.linalg, etc.) for array manipulation, linear algebra, statistics, and more.

You can import and use different parts of the NumPy library like this:

import numpy as np

# Create an array using numpy

arr = np.array([1, 2, 3, 4])

print(arr)

The role of Python modules is very important in improving its performance.

Also read- 10+ top Python Libraries for developers- Data Science and Machine Learning

How are Python Modules Important?

The role of Python modules in its performance.

Organization: As your project grows, it becomes difficult to manage. But you can break down your code into logical units. It makes it easier to manage and maintain the code.
Code Reusability: once you create a module, you can import it anywhere across applications, saving significant development time and ensures code reusability.
Namespace Management: Modules have their namespace. It keeps variables, functions, and classes separated from the rest of your program, ensuring the clean code.

Let us explain how you can use these modules with real-life implementation examples.

Real-Life Example: Working with Python Modules

For example, you want a Python script to analyze some data. You have created a function that cleans, processes, and visualizes the data.

However, the code becomes lengthy and is difficult to maintain and analyze.

You can use modules to manage your code and create separate modules for each function.

Here’s how you can break down your code.

Data Cleaning Module: You create a module called data_cleaning.py containing functions that clean the data — like handling missing values, filtering rows, or formatting columns.

# data_cleaning.py

def clean_missing_values(data):

# Code to clean missing data

return cleaned_data

def filter_outliers(data):

# Code to filter out outliers

return filtered_data

Data Processing Module: Another module, data_processing.py, might contain functions to process the data, like feature extraction, transformation, or aggregation.

# data_processing.py

def extract_features(data):

# Code to extract features from data

return features

Visualization Module: A visualization.py module can hold functions to create graphs or charts, allowing you to visualize your results.

# visualization.py

def plot_histogram(data):

# Code to plot a histogram

pass

Now, you can create a main program where you can import all these modules directly and use their functionalities.

# main.py

import data_cleaning

import data_processing

import visualization

# Use functions from the modules

cleaned_data = data_cleaning.clean_missing_values(raw_data)

features = data_processing.extract_features(cleaned_data)

visualization.plot_histogram(features)

You will find different types of Python models available to offer such extensibility to your code.

Types of Modules in Python

Here are different types of Python modules.

Built-in Modules: these are pre-built modules that come with Python. You do not need any installation to use and import them within your code. For example- math, os, sys, etc.
Custom Modules: These are Python custom files that you create for your own project. You can create a module for data analysis, a custom utility, or any specific functionality you need. These modules have the .py extension and you can import them just like built-in modules.
Third-party Modules: These are external libraries that are created by other developers. You can install them using package managers like Pip to provide additional functionalities. For example- web scraping (requests), data analysis (pandas), and machine learning (scikit-learn).

Now, that you have understood the basics of Python modules. Let us learn what popular modules you can use within your Python programming for improved functionalities and to create an advanced application.

Also read- 13 Best Python Frameworks To Explore in 2024