Top 20 python libraries you must know

There are so many python libraries, but sometimes we don’t know which one can be used to solve a certain problem. In this post, I will describe some of the most popular python libraries for different tasks. 

  1. Django: It is most famous framework to develop web applications.  It is a high-level Python Web framework that encourages rapid development and clean, pragmatic design.

  2. IPython.  IPython is a command line shell for interactive computing with many useful enhancements over the “default” Python interpreter.

     IPython Notebooks are a great environment for scientific computing: Not only to execute code, but also to add informative documentation via Markdown, HTML, LaTeX, embedded images, and inline data plots via e.g., matplotlib. It also provides high performance tools for parallel computing

  3. Requests.  Requests is an elegant and simple HTTP library for Python, built for human beings.

  4. NumPy. NumPy is the fundamental package for scientific computing with Python. It provides some advance math functionalities to python.

  5. SciPy. SciPy (pronounced “Sigh Pie”) is a Python-based ecosystem of open-source software for mathematics, science, and engineering. It provides a wide range of algorithms and mathematical tools for data scientist.

  6. Scrapy. An open source and collaborative framework for extracting the data you need from websites. In a fast, simple, yet extensible way.

  7. wxPython. wxPython is a GUI toolkit for the Python programming language. It allows Python programmers to create programs with a robust, highly functional graphical user interface, simply and easily.

  8. Pillow. Pillow is the friendly PIL fork by Alex Clark and Contributors. PIL is the Python Imaging Library by Fredrik Lundh and Contributors. It is highly recommend  for those who works with images.

  9. SQLAlchemy. SQLAlchemy is the Python SQL toolkit and Object Relational Mapper that gives application developers the full power and flexibility of SQL.

  10. BeautifulSoup. You’re just trying to get some data out of it. Beautiful Soup is here to help. Since 2004, it’s been saving programmers hours or days of work on quick-turnaround screen scraping projects.

  11. Twisted. Twisted makes it easy to implement custom network applications. It is one of the most important tools for developing network applications. 

  12. matplotlib. matplotlib is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.  It is a must have for any data scientist or any data analyst.

  13.  Pygame. Pygame is a set of Python modules designed for writing games. Pygame adds functionality on top of the excellent SDL library. This allows you to create fully featured games and multimedia programs in the python language.

  14.  Pygletpyglet provides an object-oriented programming interface for developing games and other visually-rich applications for Windows, Mac OS X and Linux.

  15.  nltk.  Natural Language Toolkit (NLTK) is a leading platform for building Python programs to work with human language data. 

  16. gensim: gensim is one of the most robust, efficient and hassle-free piece of software to realize unsupervised semantic modelling from plain text. It can be easily used to train topic models. 

  17. pandas: Pandas is a library for operating with table-like structures. It comes with a powerful DataFrame object, which is a multi-dimensional array object for efficient numerical operations similar to NumPy’s ndarray with additional functionalities.

  18. SymPy: SymPy is a Python library for symbolic mathematical computations. It has a broad range of features, ranging from calculus, algebra, geometry, discrete mathematics, and even quantum physics. It also includes basic plotting functionality and print functions with LaTeX support.

  19. Scikit-learn: Scikit-learn is  the most famous machine library for Python. It includes a broad range of different classifiers, cross-validation and other model selection methods, dimensionality reduction techniques, modules for regression and clustering analysis, and a useful data-preprocessing module.

  20. PyMC: The focus of PyMC is Bayesian statistics and comes with a broad range of algorithms (including Markov Chain Monte Carlo, MCMC) for model fitting.