Top 10 Python Libraries You Should Know

Top 10 Python Libraries You Should Know

Python’s rich ecosystem of libraries and frameworks makes it an incredibly versatile and popular programming language. Whether you’re working in data analytics, web scraping, or machine learning, Python has a library for you.

In this blog post, we’ll take a look at the top 10 Python libraries you should know, focusing on why they’re essential for different tasks. The libraries are presented in a particular order, based on their unique features and applications.

1. Yellowbrick

Why You Should Know It

Yellowbrick extends the Scikit-learn API to offer visual diagnostics throughout the machine learning workflow. It helps you understand your models better and assists in fine-tuning them. Yellowbrick is invaluable when you’re looking to visualize the performance of different algorithms quickly.

2. Pyjanitor

Maintainer: Eric Ma

Why You Should Know It

Pyjanitor makes data cleaning more straightforward by extending Pandas with a clean, readable API for data cleaning. It helps simplify many routine data cleaning tasks, easing your data preparation process.

3. Statsmodels

Why You Should Know It

Statsmodels is excellent for estimating and interpreting models for various types of statistical data. It integrates seamlessly with Pandas, providing functions for conducting hypothesis tests, specifying linear regression models, and much more.

4. GeoPandas

Why You Should Know It

Geopandas makes working with geospatial data in Python a breeze. It extends Pandas to enable spatial operations, letting you manipulate geometric shapes, perform spatial joins, and even plot data on maps.

5. Scrapy

Why You Should Know It

Scrapy is an open-source web crawling framework that provides all the tools you need for collecting structured data from the internet. It’s highly customizable and perfect for scraping websites and processing the data.

6. Requests

Why You Should Know It

Requests simplify the process of sending HTTP requests and receiving responses, making it easier to fetch data from the web for subsequent analysis. If you’re dealing with any web scraping or API interaction, this library is a must-know.

7. PyTorch

Why You Should Know It

PyTorch is a dynamic library for machine learning and deep learning tasks. Its dynamic computation graph and efficient memory usage make it popular among researchers and developers working in AI. It’s also backed by a vibrant community and constant updates.

8. Numpy

Why You Should Know It

NumPy is the cornerstone of scientific computing in Python. It provides support for arrays and matrices, as well as numerous mathematical functions to operate on these data structures. You’ll find it almost impossible to do data analysis or machine learning without it.

9. Pandas

Why You Should Know It

Built on top of NumPy, Pandas provides high-level data structures and methods aimed at making data analysis fast and easy in Python. It’s perfect for data manipulation, analysis, and visualization.

10. Matplotlib

Why You Should Know It

Matplotlib is the go-to library for data visualization in Python. It provides both low-level and high-level APIs, enabling you to create a wide variety of plots and charts.

🐍 Do you want to learn the basics so you can use these amazing libraries? 
👉 Check out my “Easy Python Programming for Absolute Beginners” on Udemy! 💻🌟