Top 10 Python Libraries for Data Science 2020

on November 22, 2019

Considering how data science world has created more job opportunities for young professionals, we decided to dedicate this space for learning more about the essence of data science projects: noteworthy Python libraries.

Building Python-powered data science models demand dipping a little deep into Python libraries for data science. At its core, any Python library hosts numerous functions, tools and methods that can help in designing a building block or accomplishing a task associated to specific types of datasets. In doing so, the use of powerful Python libraries accelerates the speed of developer’s job.


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Each Python library has unique capabilities and tools to address various goals such as image/text processing, neural networks, data visualization and more. They are all used for building diverse applications like scientific research, prototyping, deep learning models, algorithms and training tools. 

Read Also: Python Tutorials for Beginners

We have narrated here top 10 handpicked data science Python libraries that matter in 2020 and beyond.

1. TensorFlow


tensorflow


Used mainly in scientific fields for performing accurate numerical operations, Tensor basically provides a framework that defines numerical computations. These computing have tensors that are computational objects meant to create certain value. TensorFlow is a crucial library that has an active community of 1500 contributors and nearly 35,000 comments.

Features:

  • Clear computational graph
  • Neural machine learning network with up to 60% less error
  • Supports complex data models with Parallel computing
  • Well-maintained and updated library
  • Google-backed management
  • Frequent updates and releases to implement modern features

Use:

  • Visual and text recognition
  • Speech recognition
  • Building applications with text analysis
  • Video detection
  • Time-series analysis

2. NumPy


numpy


It is the blend of Numerical and Python that supports numerical computation for scientific applications. Numpy is a general-purpose array-processing package that offers high-performance multidimensional objects called arrays and tools. It eliminates the slowness problem partly by providing these multidimensional arrays as well as functions and operators that operate efficiently on these arrays. The python library NumPy has around 18,000 comments on GitHub and has glorious community of 700 contributors. For full-stack developers working on machine learning, it is mandatory to know NumPy libraries.

Features:

  • Easy to use library
  • Generous contribution of open-source files
  • Huge community support
  • Object-oriented computational approach
  • Vectorization enables lean and fast computation 
  • Tools for C/C++ and Fortran code
  • Simplified implementation of complex mathematical functions
  • Noticeable efficiency from array form of computation 

Use

  • Machine learning as primary function 
  • Extensively used in data analysis
  • Presenting images, binary streams and sound in N-dimensional array 
  • Other Python libraries like SciPy and scikit-learn work with NumPy
  • Removes the need for MATLAB when used with SciPy and matplotlib libraries

3. Pandas


pandas


Like NumPy and TensorFlow, Pandas is one of the majorly preferred Python libraries for data science & analysis, most essential for the data science life cycle. The library has received around 17,000 comments on GitHub and an active community of 1,200 contributors. Its primary support is for heavy operations of intense data analysis and cleaning. It provides, fast and flexible data structures that works with structured data very quickly and intuitively.

Features:

  • Clear and fluent code syntax with handy functionalities for dealing with missing data
  • High-level abstraction
  • Create, define and run your function across a series of data structures
  • Offers high-level data structures and high-performance tools
  • Performs custom operations
  • Supports aggregations, re-indexing, iteration and visualization
  • Data manipulation tools enable easy and clear data manipulation
  • Flexible and highly compatible with other Python libraries, packages and tools
  • Remarkable speed indicators
  • Automatically selects the suitable output for applying functions

Use:

  • General data wrangling and cleaning
  • Heavy and complex data analysis
  • Excellent ETL data transformation and storage jobs
  • Transforms and loads CSV files into its desired data viewing format
  • Applied across academic and commercial applications for statistics and scientific functions
  • Supports Time-series functionality 
  • Helps with date range generation, date shifting and linear regression

4. SciPy


scipy


While NumPy stands for numerical computations, SciPy is an open-source Python library for data science used heavily for high-level scientific computations. It shines with around 19,000 comments on GitHub and a reliable community of nearly 600 contributors. Extending what NumPy does, SciPy library is applied for performing various scientific calculations and technical computations.

Features:

  • A set of computational algorithms and functions built using Python-based NumPy extension
  • Open-source tools and functions
  • Excellent execution of clear data manipulation and visualization
  • SciPy.ndimage submodule enables multidimensional image processing
  • Contains certain built-in functions intended for differential equations

Applications:

  • Multidimensional image operations
  • High-performance, effortless scientific computations
  • Applied for solving differential equations and the Fourier transform
  • Data Optimization and computational algorithms
  • Ideal for Linear algebra applications

5. Matplotlib


matplotlib


While many Python libraries explode with great tools, functions and packages, Matplotlib provides powerful data visualizations. At its core, Matplotlib as the name implies a Python plotting library with an active community of about 700 contributors and 26,000 comments on GitHub. It is massively used for data visualizations made effective with the help of the graphs and plots it produces. Python data scientists can also rely on its object-oriented API to embed those plots into data analytics applications.

Features:

  • A powerful replacement of MATLAB
  • Open-source and free functions and plots for data visualization
  • Supports various operating systems, backends and output formats in use
  • Object-oriented MATLAB API to embed plots into data-driven applications
  • Provides data cleaning function
  • Better runtime behavior
  • Low memory consumption

Use:

  • Gaining insights using data distribution visualization 
  • Accurate analysis of variables and their correlation
  • Visualization of model intervals
  • Use of scatter plot for outlier detection

Read Also: Why should you learn Python for Data science

6. Scikit-Learn


scikit-learn


Built after NumPy, SciPy and Matplotlib libraries, Scikit-Learn is a phenomenal Python library extensively used for data analysis and mining of complex data structures. Basically a machine learning library, it comes with handy and efficient tools that can be used to complete data analysis and mining job. Treated with best releases, Scikit-Learn is fast-growing, frequently modified Python library with much improved training methods like logistics regression and nearest neighbors. 

Features

  • Ability to process and extract features from images and text
  • Cross-validation feature for using multiple metric
  • Highly improved training methods
  • Methods for checking accuracy of models on unseen data
  • Ample types of algorithms, including data mining & clustering, factor analysis, unsupervised neural networks, etc

Use

  • Extensively used for complex data analysis and mining
  • Machine learning and data mining algorithms
  • Data classification and clustering models
  • Algorithms for automated operations like model selection, dimension reduction and more

7. Keras


keras


Keras is at the moment seen as the fanciest machine learning library written in Python and offers developers coolest methods for developing neural networks. It provides facility for processing datasets, simplifying models, visualizing graphs, etc. Keras is potentially equipped to run on top of of CNTK, TensorFlow, and Theano. It is developed with a primary focus on allowing fast experimentation. It uses backend infrastructure to create computational graph and perform operations.

Features

  • Easy to debug and explore due to absolute Python-based structure
  • Eloquent and flexible for innovative experiment and research work
  • Modular in nature
  • Computational graph using backend structure
  • Creates more complex models by compiling Neural network models
  • Runs smoothly on both CPU and GPU
  • Supports various neural network models (embedding, fully connected, convolutional, pooling, and recurrent)

Use

  • Building accurate deep machine learning models
  • Deep learning research 
  • Image and text data extraction
  • Data Experimentation and research
  • Expressing error-free neural networks

8. Seaborn


seaborn


Seaborn is primarily a Python data visualization library that is built on top of the Matplotlib library. Seaborn is integrated with Pandas and offers a high-level interface for drawing eye-catching and informative statistical graphs. Seaborn library is focused on making data visualization a vital part of exploring and understanding data. It examines relationships among multiple variables. This library internally performs all the key semantic mapping and statistical aggregation for producing informative plots.

Features

  • Automatic estimation as well as the plotting of linear regression models
  • Plotting functions operate on arrays of datasets
  • Tools to choose color palettes for recognizing specific dataset patterns
  • Clear and optimized viewing of complex data structures
  • High level abstractions to build multi-plot grid and complex visualizations
  • Visualization of bivariate or univariate distributions
  • Supports using categorized set of variables

Use

  • Data visualization applications
  • Graphical demonstration of complex relationship of variables
  • Connected and synchronized data models
  • Statistical graphs and organized information management

9. NLTK


NLTK


Intended to focus on NLP operations, NLTK is a comprehensive suite of Python libraries for Data science developers. It works as an essential natural language Toolkit that one can easily leverage to perform symbolic and statistical NLP. It includes graphical demonstrations as well as sample data. This Python library comes with a book and a cookbook to make it easier to get started with.

NLTK is embraced wholeheartedly in data science world for preparing prototyping, research systems and effective teaching and study tool.

Features

  • Comes with a part-of-speech tagger
  • Effective information retrieval
  • Cookbook to get started quickly
  • Natural Language processing capabilities
  • Functionalities like classification, semantic reasoning, parsing, stemming, etc.
  • Empirical linguistics
  • Named-entity recognition
  • Lexical analysis

Use

  • Automated high-performance ML models
  • Robust linguistics systems
  • Platform for Prototyping systems
  • Great study and training tools
  • Building research systems

10. PyTorch


pytorch


This open-source machine learning library for Python is used for implementing network architectures like RNN, CNN, LSTM, etc. as well as other high-level algorithms. It is mainly used by researchers, business, communities of ML (Machine Learning) & AI (Artificial Intelligence). Based on Torch, it is used for applications such as natural language processing. It is developed by Facebook’s artificial-intelligence research group and “Pyro” Probabilistic programming language software of Uber is built on it.

Features:

  • Provides ease-of-use and flexibility in eager mode
  • Supports an end-to-end workflow from Python to deployment on iOS and Android
  • Uses python integrations coupled with data science stack.
  • Helps in building computational graphs whenever you want and in a simple way
  • Very well supported on chief cloud platforms

Uses:

  • Used for applications such as computer vision and natural language processing
  • Developing and training neural network based deep learning models
  • Appropriate for building various types of applications
  • Handle large datasets and high- performance tasks
  • Applications of research, data science and artificial intelligence (AI)

Read Also: Why Python is considered as a high level language

Conclusion

The above top 10 powerful, highly updated Python libraries for data science developments have topped the chart for 2020 on the strength of extensive capabilities, frequency of improvements and variety of innovative tools. Also, their online comments, communities and overall popularity contribute to forming the list of these coolest Python libraries that deserve to be explored. The rise of data science and machine learning must have intrigued you to learn more about the opportunities this ever evolving and advancing field contains.


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If that is the case, go on and visit some of the top online data science certification courses and training institutes to grab in-depth information. Using a single Python library may enable limited data science projects; however, for projects of enormous scope and research, developers will have to enhance their stack and cultivate broad expertise in multiple Data science libraries.