Here’s a helpful guide on the most used Python libraries worldwide, why they are popular, and estimated usage based on developer surveys and real-world application.

These libraries are used by millions of developers across industries like data science, web development, automation, machine learning, and more.

🔢 Percentages are based on data from Stack Overflow Developer Survey, GitHub stars, and industry trends.

📊 1. NumPy

• Used for: Numerical computing, arrays, matrix operations
• Usage: ~65–70%
• Why Popular: Foundation for data science & machine learning libraries (like pandas, scikit-learn)

import numpy as np
a = np.array([1, 2, 3])

🧮 2. Pandas

• Used for: Data analysis, data frames, table operations
• Usage: ~60–65%
• Why Popular: Makes data handling like Excel but in Python; easy and powerful

import pandas as pd
df = pd.DataFrame({"name": ["Alice", "Bob"], "age": [25, 30]})

📈 3. Matplotlib / Seaborn

• Used for: Data visualization, charts, graphs
• Usage: ~55–60%
• Why Popular: Easy to create line plots, bar charts, and complex visuals

import matplotlib.pyplot as plt
plt.plot([1, 2, 3], [4, 5, 6])

🤖 4. Scikit-learn

• Used for: Machine learning (regression, classification, clustering)
• Usage: ~50–55%
• Why Popular: Beginner-friendly ML with ready-to-use models

from sklearn.linear_model import LinearRegression

🌐 5. Requests

• Used for: Web APIs, sending HTTP requests
• Usage: ~40–50%
• Why Popular: Extremely simple to make GET/POST requests

import requests
r = requests.get("https://api.example.com")

🛠️ 6. Flask

• Used for: Web development (micro web apps)
• Usage: ~35–40%
• Why Popular: Very lightweight and easy to learn for web development

from flask import Flask
app = Flask(__name__)

🔒 7. Django

• Used for: Full-featured web applications
• Usage: ~25–30%
• Why Popular: Built-in admin panel, ORM, user auth — perfect for startups

django-admin startproject mysite

🧪 8. Pytest / Unittest

• Used for: Testing Python code
• Usage: ~25–30%
• Why Popular: Easy to write and manage test cases for automation

🧹 9. BeautifulSoup

• Used for: Web scraping
• Usage: ~20–25%
• Why Popular: Clean and simple way to parse HTML and extract data

🎲 10. OpenCV

• Used for: Image processing, computer vision
• Usage: ~20%
• Why Popular: Used in AI projects, face detection, camera filters

📦 11. TensorFlow / Keras / PyTorch

• Used for: Deep learning
• Usage: ~30–35% among ML developers
• Why Popular: Used in AI apps, self-driving tech, advanced models

🖼️ 12. Pillow

• Used for: Image editing
• Usage: ~15%
• Why Popular: Resize, convert, and edit images easily in Python

📂 13. os / sys / datetime / json

• Used for: System operations, file handling, date/time
• Usage: ~90% (core Python modules)
• Why Popular: Built-in — no install needed, used in almost every project

📊 Summary Table of Most Popular Python Libraries

🧠 Final Tip:

You don’t need to learn all libraries.
Start with the ones you need for your current project or career path (like web dev, data science, etc.)