Python Libraries are useful functions that eliminate the need to write codes from scratch. Over 137,000 python libraries are present today, and they play a vital role in developing machine learning, data science, data visualization, image and data manipulation applications, and more. Let us briefly introduce Python Programming Language and directly dive into the most popular Python libraries.
What is a Library?
A library is a collection of pre-combined codes that can be used iteratively to reduce the time required to code. They are particularly useful for accessing the pre-written, frequently used codes instead of writing them from scratch every single time. Similar to physical libraries, these are a collection of reusable resources, which means every library has a root source. This is the foundation behind the numerous open-source libraries available in Python.
What is a Python Library?
A Python library is a collection of modules and packages that offer a wide range of functionalities. These libraries enable developers to perform various tasks without writing code from scratch. They contain pre-written code, classes, functions, and routines that can be used to develop applications, automate tasks, manipulate data, perform mathematical computations, and more. Python’s extensive ecosystem of libraries covers diverse areas such as web development (e.g., Django, Flask), data analysis (e.g., pandas, NumPy), machine learning (e.g., TensorFlow , scikit-learn), image processing (e.g., Pillow, OpenCV), scientific computing (e.g., SciPy), and many others. This wealth of libraries significantly contributes to Python’s popularity among developers, researchers, and data scientists, as it simplifies the development process and efficiently implements complex functionality.
Uses of Python Library
- Import Libraries: Begin by importing libraries using the import statement. You can import entire libraries or specific modules within a library.
- Utilize Functions and Classes: Access functions, classes, and other objects provided by the library. Use imported functions and classes in your program as needed.
- Read Documentation: Familiarize yourself with the documentation of the libraries you use. Documentation provides details about available functionalities, parameters, return values, and usage examples.
- Manage Dependencies: Use tools like pip to install required libraries and their dependencies. Consider using virtual environments to isolate dependencies for different projects and prevent version conflicts.
- Optimize Performance: Libraries often contain optimized code for common tasks, leading to better performance. Leveraging libraries can result in more efficient and faster code execution.
- Customize Functionality: Libraries may offer options for customization or extension. Customize functionality by subclassing existing classes, overriding methods, or using configuration options provided by the library.
Top 30 Python Libraries List
| Rank | Library | Primary Use Case |
|---|---|---|
| 1 | NumPy | Scientific Computing |
| 2 | Pandas | Data Analysis |
Each library has its own strengths and is chosen for specific tasks, from web development frameworks like Django and Flask to machine learning libraries like TensorFlow and PyTorch to data analysis and visualization tools like Pandas and Matplotlib.
Scikit-learn
It is a free software machine learning library for the Python programming language. It can be effectively used for a variety of applications which include classification, regression, clustering, model selection, naive Bayes’, grade boosting, K-means, and preprocessing.
NuPIC
The Numenta Platform for Intelligent Computing (NuPIC) is a platform that aims to implement an HTM learning algorithm and make them a public source as well. It is the foundation for future machine learning algorithms based on the biology of the neocortex.
Pandas
It is an open-source, BSD-licensed library. Pandas enable the provision of easy data structure…
