For over 4 years in a row, data science is ranked as the top paying career in the US by Glassdoor (source). So, what is data science? Simply put, data science deals with the analysis of large volumes of data using various tools and techniques to discover unseen patterns, derive meaningful information and make profitable business decisions. 

Given the huge adoption of data science and machine learning in today’s world, it is essential to talk about the programming languages that are behind the success of these data science and machine learning projects. Python is the most extensively used programming language for data science and machine learning. It provides excellent functionality to deal with mathematics, statistics, and scientific applications. It also has comprehensive libraries to deal with data processing tasks like data cleaning, data manipulation, data exploration, and some fantastic libraries for applying classification and regression-based machine learning models.  

This article will go through the 10 most popular python libraries for data science, breaking down their features in detail.



NumPy, also known as numerical Python, is an ideal tool for performing high-level mathematical functions on all kinds of arrays and matrices, from basic to advanced. It contains a powerful n-dimensional array object. It is used in array processing to store values of the same data type. It also makes performing math operations on arrays and their vectorization much easier. Interestingly, the vectorization of mathematical operations on the NumPy array type increases performance and hastens the execution time.

  • Fast, precompiled functions for numerical problems
  • Array-oriented computing for greater efficiency
  • Supports object-oriented programming
  • Compact and faster computations with vectorization 
  • Additional linear algebra, Fourier transform, and random number capabilities


SciPy  (Abbreviated as Scientific Python and Pronounced as “Sigh Pi”) makes use of NumPy for more mathematical functions. The basic data structure used is a multidimensional array provided by NumPy. The scientific python library consists of modules for statistics, linear algebra, optimization, and other integration tasks. Its applications include multidimensional image operations, solving differential equations, and the Fourier transform.

  • High-level commands for data manipulation and visualization
  • Built-in functions for solving differential equations
  • Multidimensional image processing with ndimage submodule
  • Algorithms and functions based on NumPy 
  • Used for optimization algorithms


This is a popular Python framework for machine learning and deep learning applications. Its applications include object identification and speech recognition. It helps in the working of artificial neural networks that need to handle multiple data sets. TensorFlow is constantly supplemented with new releases that include fixes in potential security vulnerabilities or improvements in TensorFlow and GPUs’ integration. It is also helpful for time-series analysis and video detection.

  • Improved computational graph visualization
  • 50 – 60% error reduction in neural machine learning
  • Parallel computing to execute complex models
  • Smooth library management backed by Google
  • Faster updates and frequent new releases with the latest features


Keras is a deep learning library used extensively for building and modelling neural networks. This library utilizes other packages such as TensorFlow or Theano as its backend. It’s a great choice if you want to experiment quickly using compact systems.

  • Large number of prelabeled datasets 
  • Various implemented layers for the construction, configuration, training, and evaluation of neural networks
  • Multiple methods for data processing
  • Model evaluation
  • Modularity helps you save the model you train and use it later by loading it


This is an excellent framework for data scientists looking to implement deep learning tasks. PyTorch allows performing tensor computations with GPU acceleration. It can be used to create dynamic computational graphs and calculate gradients automatically. PyTorch is based on Torch, an open-source deep-learning library used in C.

  • Deep neural networks on a tape-based autograd system
  • Native ONNX (Open Neural Network Exchange) support
  • C++ front-end
  • Cloud support
  • Distributed training


PyCaret library is designed to make standard task performance in machine learning simple and more accessible. It is inspired by the caret package in R. The goal is to automate major steps for evaluating machine learning algorithms employed in classification and regression. You can achieve a lot with minimum lines of code and little manual configuration.

  • Reduces hypothesis to insights cycle time in machine learning experiments
  • Enables data scientists to perform end-to-end experiments quickly and efficiently
  • Low-code library that can perform complex tasks with few lines of code
  • All operations performed are automatically stored in a custom pipeline fully orchestrated for deployment
  • Works as a Python wrapper around several machine learning libraries such as scikit-learn, XGBoost, Microsoft LightGBM, spaCy, and more


This is a data science library that helps generate data visualizations. These include 2-D diagrams such as histograms, scatterplots, and non-Cartesian coordinate graphs. Matplotlib is widely used as a plotting library in data science projects and brings Python on the same platform as scientific tools such as MATLAB.

  • A free and open-source MATLAB replacement
  • Low memory consumption
  • Better runtime behavior
  • Supports many backends and output types meaning you can use it irrespective of the operating system 
  • Can run infinite lines through two points


Pandas is based on two main data structures: Series and DataFrames. The former is 1-D and can be thought of as a list of items, while the latter is 2-D and is a table with multiple columns. Pandas allows converting data structures to DataFrame objects. It also makes data wrangling, manipulation, and visualization a whole lot easier.

  • Handles missing data
  • Addition/deletion of columns from a DataFrame
  • Plots data with histogram or plot box
  • Helps users manipulate data with a reduced time-complexity
  • Contains high-level data structures and manipulation tools


Seaborn is based on Matplotlib. It is a valuable Python machine learning tool for visualizing statistical models. These include heatmaps and other types of data visualizations. You can benefit from an extensive range of visualizations while using this library, such as time series, joint plots, and violin diagrams.

  • Built-in themes for styling matplotlib graphics
  • Visualizes univariate and bivariate data
  • Works with NumPy and Pandas data structures
  • Plots statistical time series data 
  • Visualizes linear regression models


Plotly is a web-based tool for data visualization that offers many useful graphics. It can help create interactive and publication-quality graphs. It works well in interactive web applications. The library is being expanded with new graphics and features for supporting multiple linked views, animation, and crosstalk integration.

  • Visualization tool for geographic, scientific, statistical, and financial data
  • Minimal code to create aesthetic plots
  • Easy to modify and export your plot
  • Offers a more ornate visualization compared to Matplotlib
  • Can integrate with Pandas to make plotting even more efficient

We hope you enjoyed this in-depth analysis of the best data science libraries in Python. However, this list of data science libraries is not exhaustive. Python offers many other libraries that can be implemented for data science, so keep learning and experimenting with these remarkable tools right at your disposal!

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