Important Python Libraries that one should know.

Libraries are a crucial part of the python programming language. It helps in making our work and life easier. As per the latest stats, Python is a widely used programming language and 80% of developers use python as their preference. It is only because of the tremendous amount of benefits and accessibility python gives to their developers.

Source~ Google

There are many libraries available for Python developers, but here are some important Python libraries that every Python developer should know:

1. NumPy: A library used for numerical computing, NumPy provides efficient and powerful tools for working with arrays, linear algebra, and mathematical functions.
2. Pandas: A library used for data manipulation and analysis, Pandas provides easy-to-use data structures for working with tabular and time-series data.
3. Matplotlib: A library used for data visualization, Matplotlib provides tools for creating high-quality charts, graphs, and plots.
4. Scikit-learn: A library used for machine learning, Scikit-learn provides a range of algorithms for classification, regression, and clustering.
5. TensorFlow: A library used for deep learning, TensorFlow provides tools for building and training neural networks.
6. Pytest: A library used for unit testing, Pytest provides a framework for writing and running tests for Python code.
7. Requests: A library used for HTTP requests, Requests provides an easy-to-use interface for making HTTP requests and handling responses.
8. Beautiful Soup: A library used for web scraping, Beautiful Soup provides tools for parsing HTML and XML documents.
9. Flask: A library used for building web applications, Flask provides a lightweight framework for building web applications and APIs.
10. Django: A library used for building web applications, Django provides a full-featured framework for building scalable web applications and APIs.

Overall, these libraries are essential tools for many common Python development tasks, including data manipulation and analysis, machine learning, data visualization, web development, and testing.