What are the best Python libraries for Data Science?

Python is one of the most preferred languages for data science. It provides a lot of useful libraries that help you in data manipulating, exploratory data analysis, and building machine learning models.

Some of the import libraries in Python for Data Science are:


Numpy, deal with vector or matric calculation. I often use it with Pandas when cleaning or transforming data. High performance multi-dimensional arrays.



Needed for handling input which comes in different format. It provides expressive, fast and flexible Data structures to easily work with structured and Time Series Data.

  • Easily delete and add columns from DataFrame
  • Convert data structures to DataFrame objects
  • Handle missing data, represents as NaNs
  • Powerful grouping functionality


It helps with Data Analyzing and is a numerical plotting library. The most well-known charts library in Python. It allows you to do all kinds of settings and create charts more flexibly.


Scikit Learn

It is a machine learning library, used to build machine learning models, various classification, regression and clustering algorithms.



Scipy used for optimization, linear algebra, integration, interpolation etc. Apart from this, SciPy also supports multi-dimensional image processing and can solve differential equations.



TensorFlow takes you to the world of machine intelligence and being created by Google. This is an open source python library which comes pretty useful for doing calculations using data flow graphs and tensorflow, happens to be another well-known Python library that supports deep learning and machine learning.



Beautiful Soup is a Python package for parsing HTML and XML documents. Use BeautifulSoup to parse the Html so you end up with the raw text content of each web page you want analysed.


Learn more about Data Science

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