TOP 10 Python Libraries for Data Science

In Data Scientist’s daily duties, python programming plays a crucial role in combining statistical and machine learning methods for analyzing and interpreting complicated information. Python can be used for nearly all the steps engaged in data science procedures because of its versatility. It can ingest multiple information formats and can readily import SQL tables into your software and also enables datasets to be created or any sort of information set to be found on Google. In this post, we take look at TOP 10 Python Libraries.


License: BSD License

Fundamental package for scientific computing, it comprises of

  • Powerful N-dimensional array object
  • Sophisticated (broadcasting) functions
  • Tools for integrating C/C++ and Fortran code
  • Useful Linear algebra, Fourier transform, and random number capabilities
  • An efficient multi-dimensional container of generic data
  • Arbitrary data types can also be defined.

Documentation can be found here.


License: BSD License

Image – SciPy Logo

Collection of mathematical algorithms and convenience functions built on the NumPy extension of Python consists of the following projects :

  • NumPy: Base N-dimensional array package
  • SciPy library: Fundamental library for scientific computing
  • Matplotlib : Comprehensive 2D Plotting
  • IPython : Enhanced Interactive Console
  • Sympy : Symbolic mathematics
  • pandas : Data structures & analysis

Documentation can be found on the respective links above.


License: BSD License

Image – StatsModels

It provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration.

Key Features :

  • Support for Linear regression models
  • Mixed Linear Model with mixed effects and variance components
  • GLM: Generalized linear models with support for all of the one-parameter exponential family distributions
  • Bayesian Mixed GLM for Binomial and Poisson
  • GEE: Generalized Estimating Equations for one-way clustered or longitudinal data
  • Support for various Discrete models
  • RLM: Robust linear models with support for several M-estimators.
  • Time Series Analysis: models for time series analysis
  • Survival analysis
  • Multivariate
  • Nonparametric statistics: Univariate and multivariate kernel density estimators
  • Datasets: Datasets used for examples and in testing
  • Statistics: a wide range of statistical tests
  • Imputation with MICE, regression on order statistic, and Gaussian imputation
  • Mediation analysis
  • Graphics includes plot functions for visual analysis of data and model results
  • Miscellaneous models
  • Sandbox: statsmodels contains a sandbox folder with code in various stages of development and testing.

Documentation can be found here.


License: BSD License

Image – Pandas Logo

Provides high-performance, easy-to-use data structures, and data analysis tools. It is used in a wide variety of academic and commercial domains, including Finance, Neuroscience, Economics, Statistics, Advertising, Web Analytics, and more.

Why Pandas?

  • Fast and efficient DataFrame object for data manipulation with integrated indexing
  • Tools for reading and writing data between in-memory data structures and different formats
  • Intelligent data alignment and integrated handling of missing data
  • Flexible reshaping and pivoting of data sets
  • Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;
  • Columns can be inserted and deleted from data structures for size mutability;
  • Aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on data sets;
  • High performance merging and joining of data sets;
  • Hierarchical axis indexing provides an intuitive way of working with high-dimensional data in a lower-dimensional data structure;
  • Time series-functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting, and lagging.
  • Highly optimized for performance, with critical code paths written in Cython or C.

Pandas is well suited for many different kinds of data:

  • Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet
  • Ordered and unordered (not necessarily fixed-frequency) time-series data.
  • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels
  • Any other form of observational/statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure

Documentation can be found here.


License: PSF license

Image – Matplotlib Logo

Python 2D plotting library which produces publication quality figures in a variety of hard copy formats and interactive environments across platforms. It can be used to generate plots, histograms, power spectra, bar charts, error charts, scatterplots, etc., with just a few lines of code.

Some of the notable kits include :

  • Basemap: It is a map plotting toolkit with various map projections, coastlines, and political boundaries.
  • Cartopy: It is a mapping library featuring object-oriented map projection definitions, and arbitrary point, line, polygon, and image transformation capabilities.
  • Excel tools: Matplotlib provides utilities for exchanging data with Microsoft Excel.
  • Mplot3d: It is used for 3-D plots.
  • Natgrid: It is an interface to the natgrid library for irregular gridding of the spaced data.

Documentation can be found here.


Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

Why Seaborn?

  • A dataset-oriented API for examining relationships between multiple variables
  • Specialized support for using categorical variables to show observations or aggregate statistics
  • Options for visualizing univariate or bivariate distributions and for comparing them between subsets of data
  • Automatic estimation and plotting of linear regression models for different kinds of dependent variables
  • Convenient views onto the overall structure of complex datasets
  • High-level abstractions for structuring multi-plot grids that let you easily build complex visualizations
  • Concise control over matplotlib figure styling with several built-in themes
  • Tools for choosing color palettes that faithfully reveal patterns in your data

Documentation can be found here.


License: BSD License

Image – Scikit-learn logo

Scikit-learn is a Python module for machine learning, it provides simple and efficient tools for data mining and data analysis. This library is built upon the SciPy (Scientific Python) that must be installed before you can use sci-kit-learn. This stack includes:

  • NumPy: Base n-dimensional array package
  • SciPy: Fundamental library for scientific computing
  • Matplotlib: Comprehensive 2D/3D plotting
  • IPython: Enhanced interactive console
  • Sympy: Symbolic mathematics
  • Pandas: Data structures and analysis

Why Scikit-Learn ?

Some popular groups of models provided by scikit-learn include:

  • Classification – Identifying to which category an object belongs.
  • Regression – Predicting a continuous-valued attribute associated with an object.
  • Clustering – Automatic grouping of similar objects into sets.
  • Dimensionality reduction – Reducing the number of random variables to consider.
  • Model selection – Comparing, validating, and choosing parameters and models.
  • Preprocessing – Feature extraction and normalization.

Documentation can be found here.


Image – XGBoost Logo

It is an optimized distributed gradient boosting library designed to be highly efficientflexible, and portable. It implements machine learning algorithms under the Gradient Boosting framework.

XGBoost software library can be downloaded and install on your machine, then access from a variety of interfaces.

  • Command Line Interface (CLI).
  • C++ (the language in which the library is written).
  • Python interface as well as a model in scikit-learn.
  • R interface as well as a model in the caret package.
  • Julia.
  • Java and JVM languages like Scala and platforms like Hadoop.

XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.

XGBoost library provides a system for use in a range of computing environments :

  • Parallelization of tree construction using all of your CPU cores during training.
  • Distributed Computing for training very large models using a cluster of machines.
  • Out-of-Core Computing for very large datasets that don’t fit into memory.
  • Cache Optimization of data structures and algorithm to make the best use of hardware.

Why XGBoost ?

  • Fast when compared to other implementations of gradient boosting.
  • Dominates structured or tabular datasets on classification and regression predictive modeling problems.

Documentation can be found here.


License: Apache License

Image – TensorFlow

TensorFlow is an end-to-end platform that makes it easy for you to build and deploy ML models. It is an open-source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture enables you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard, a data visualization toolkit.

Why TensorFlow?

  • Build and train models by using the high-level Keras API
  • TensorFlow lets you train and deploy your model easily, no matter what language or platform you use.
  • Flexibility and control with features like the Keras Functional API and Model Subclassing API for the creation of complex topologies.
  • Supports an ecosystem of powerful add-on libraries and models to experiment with, including Ragged Tensors, TensorFlow Probability, Tensor2Tensor, and BERT.

Documentation can be found here.


License: BSD License

PyTorch enables fast, flexible experimentation and efficient production through a hybrid front-end, distributed training, and ecosystem of tools and libraries.

Why PyTorch?

  • Hybrid front-end provides ease-of-use and flexibility
  • Optimize performance in both research and production by taking advantage of native support for asynchronous execution
  • Deeply integrated into Python so it can be used with popular libraries and packages such as Cython and Numba.
  • An active community of researchers and developers have built a rich ecosystem of tools and libraries
  • Supported on major cloud platforms, providing frictionless development and easy scaling through prebuilt images, large scale training on GPUs, ability to run models in a production scale environment

Documentation can be found here.

The core libraries are NumPy and SciPy. For Statistics, Statsmodels is whereas Pandas are important for data loading and processing. Matplotlib and Seaborn are categorized as the most common Python packages, Scikit-Learn and XgBoost are for machine learning architecture.TensorFlow and PyTorch as the most popular packages of Python are peculiar for Deep Learning.

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Additional Resources

  • Curated Python Course Collection
  • Python programming courses from Coursera
    • Data Analysis with PythonThis course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more! Topics covered: 1) Importing Datasets 2) Cleaning the Data 3) Data frame manipulation 4) Summarizing the Data 5) Building machine learning Regression models 6) Building data pipelines Data Analysis with Python will be delivered through lecture, lab, and assignments.
    • Data Processing Using PythonThis course is mainly for non-computer majors. It starts with the basic syntax of Python, to how to acquire data in Python locally and from network, to how to present data, then to how to conduct basic and advanced statistic analysis and visualization of data, and finally to how to design a simple GUI to present and process data, advancing level by level. 
    • Data Visualization with PythonThis course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we will be using several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium. 
    • Python Data AnalysisThis course will continue the introduction to Python programming that started with Python Programming Essentials and Python Data Representations. We’ll learn about reading, storing, and processing tabular data, which are common tasks. We will also teach you about CSV files and Python’s support for reading and writing them. 
    • Python Data VisualizationThis if the final course in the specialization which builds upon the knowledge learned in Python Programming Essentials, Python Data Representations, and Python Data Analysis. We will learn how to install external packages for use within Python, acquire data from sources on the Web, and then we will clean, process, analyze, and visualize that data. This course will combine the skills learned throughout the specialization to enable you to write interesting, practical, and useful programs. By the end of the course, you will be comfortable installing Python packages, analyzing existing data, and generating visualizations of that data.
    • ULTIMATE GUIDE to Coursera Specializations That Will Make Your Career Better (Over 100+ Specializations covered)
TOP 10 Python Libraries for Data Science
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