Top Python Libraries For Data Science In 2022

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Python is the world’s most popular programming language. Python rarely fails to astound its users when it comes to addressing data science projects and obstacles. The majority of data scientists already use Python programming on a daily basis. Python is a simple, easy-to-debug, extensively used, object-oriented, open-source, high-performance programming language, and it has many more advantages. Python has numerous Python libraries for data science that programmers utilize on a daily basis to solve challenges.

In this blog, we will talk about the top 15 Python Libraries for Data Science in 2021. Let’s get started, shall we?

Also Read: Top 10 Python IDEs For Developers In 2021

1. TensorFlow

TensorFlow is an open-source library for deep learning applications built by the Google Brain Team. Initially conceived for numeric computations, it now provides a rich, flexible and wide range of tools, libraries, and community resources that developers may use to create and deploy machine learning-based applications. TensorFlow 2.5.0, which was first released in 2015, has just been updated by the Google Brain team to include new functionality. 


Features of TensorFlow

  • Works quickly with multi-dimensional arrays and mathematical equations.
  • Deep neural networks and machine learning principles are well supported.
  • Computation on both GPUs and CPUs, where the same code could run on both architectures.
  • High computational scalability between devices and large data sets.

2. NumPy

NumPy, or Numerical Python, was created by Travis Oliphant in 2015 and is a key library for scientific and mathematical computing. The open-source software includes linear algebra, Fourier transform, and matrix calculation functions, and is mostly utilized for applications that require performance and resources. NumPy intends to make array objects 50 times quicker than Python lists. NumPy is the foundation for data science libraries such as SciPy, Matplotlib, Pandas, Scikit-Learn, and Statsmodels.


Features of NumPy

  • NumPy arrays can be either one-dimensional or multidimensional.
  • It includes tools for incorporating C/C++ and Fortran code.
  • It has the ability to perform functions on generic data types.
  • It can execute intricate operations on items such as linear algebra, Fourier transform, and so on.
  • It broadcasts the shape of smaller arrays based on the geometry of larger arrays.

3. SciPy

SciPy, or Scientific Python, is a programming language that is used to solve complicated math, science, and engineering issues. It’s based on the NumPy extension, and it lets programmers modify and visualize data. For linear algebra, statistics, integration, and optimization, SciPy offers user-friendly and efficient numerical procedures. Multidimensional image processing, Fourier transformations, and differential equations are among its uses.


Features of SciPy

  • SciPy has a number of sub-packages that aid with the most prevalent problems in Scientific Computation.
  • The SciPy Python package is by far the most popular scientific library, coming in second only to the GNU Scientific Library for C/C++ and Matlab.
  • It’s simple to use and comprehend, yet it has a lot of computing power.
  • It can work with a NumPy library array.

4. Pandas

Pandas is a data manipulation and analysis tool created by Wes McKinney. It has efficient, versatile, and powerful data structures, as well as functionality like missing data handling, sophisticated indexing, and data alignment. It allows programmers deal with labelled and relational data by providing quick, adaptable, and expressive data structures. It is built on the Series and Frames data structures.


Features of Pandas

  • With default and customizable indexing, the DataFrame object is quick and simple.
  • Utilities for importing data from various file formats into in-memory data objects.
  • Data alignment and handling of missing data in a unified manner.
  • Data sets can be reshaped and pivoted.
  • Slicing, indexing, and subsetting of big data sets depending on labels.
  • A data structure’s columns could be deleted or inserted.

5. Matplotlib

Matplotlib, created by John Hunter, is among the most widely used libraries in the Python world. It’s used to make data visualizations that are static, animated, and interactive. Matplotlib allows for a great deal of customization and charting. It allows programmers to scatter, customize, and modify graphs using histograms. For incorporating plots into applications, the open-source library provides an object-oriented API.


Features of Matplotlib

  • Matplotlib is a cross-platform, data visualization and graphical plotting library.
  • It provides a viable open-source alternative to MATLAB.
  • Programmers use Matplotlib’s APIs (Application Programming Interfaces) to embed plots in GUI applications.
  • Matplotlib utilities lie under the pyplot submodule and are usually imported under the plt alias.

6. Keras

Keras is an open-source TensorFlow library interface that allows for rapid deep neural network testing. Francois Chollet created it, and it was initially launched in 2015. Keras provides tools for constructing models, visualizing graphs, and analyzing datasets. It also includes prelabeled datasets that may be directly imported and loaded. It’s simple to use, adaptable, and well-suited to exploratory study.


Features of Keras

  • It is a high-level interface with a backend based on Theano or Tensorflow.
  • It works without a glitch on both the CPU and GPU.
  • Keras supports nearly all neural network models, including fully connected, convolutional, pooling, recurrent, embedding, and so forth. These models can also be merged to create more sophisticated models.
  • Keras’ modular design makes it very expressive, adaptable, and well-suited to cutting-edge research.
  • Keras is a Python-based framework, making it simple to debug and examine.

7. Plotly

Plotly is web-based, interactive analytics and graphing application. It’s among the most sophisticated libraries for machine learning, data science, and AI. It is a data visualization tool that is both publishable and engaging. It provides the flexibility to import data into charts, enabling developers to quickly create slide presentations and dashboards. It is used to create programs such as Dash and Chart Studio.


Features of Plotly

  • Plotly for R is an interactive, browser-based charting library built on the open-source javascript graphing library, plotly.js.
  • It works entirely locally, through the HTML widgets framework.
  • Plotly.js is “a high-level, declarative charting library.
  • plotly.js ships with 20 chart types, including 3D charts, statistical graphs, and SVG maps.
  • It is built on top of d3.js and

8. Statsmodels

For rigorous statistics, Statsmodels is a fantastic library. This multipurpose library is a mix of multiple Python libraries, drawing on Matplotlib for its graphical functionalities, Pandas for data handling, Pasty for handling R-like calculations, and NumPy and SciPy for its foundation. It’s particularly useful for developing statistical models, such as OLS, as well as running statistical tests.


Features of Statsmodels

  • It includes descriptive statistics and estimation and inference for statistical models.
  • It also offers classes & functions for conducting statistical tests and statistical data exploration.

9. Seaborn

Seaborn, which is built on Matplotlib, is a useful library for developing various visualizations. The ability to create magnified data visuals is one of Seaborn’s most crucial characteristics. Some of the associations that aren’t immediately visible can be represented in a visual context, which helps Data Scientists better comprehend the models. It offers well-designed and remarkable data visualizations, therefore making the plots more appealing, which can subsequently be exhibited to stakeholders, thanks to its adjustable themes and high-level interfaces.


Features of Seaborn

  • Matplotlib graphics can be styled with built-in themes.
  • Data visualization (univariate and bivariate)
  • Fitting linear regression models in and showing them
  • Plotting data from a statistical time series
  • Data structures in NumPy and Pandas function well with Seaborn.
  • It has pre-installed styling themes. Graphics created with Matplotlib.

10. SciKit-Learn

DBSCAN, gradient boosting, support vector machines, and random forests are among the classification, regression, and clustering methods included in SciKit-Learn. For conventional ML and data mining applications, David Cournapeau designed the library on top of SciPy, NumPy, and Matplotlib.


Features of SciKit-Learn

  • Clustering is used for organizing unlabeled data, such as KMeans.
  • Cross-Validation is used for measuring the performance of supervised models using data that hasn’t been seen before.
  • Datasets: for testing and producing datasets with specific attributes in order to investigate model behavior.
  • Principal component analysis, for example, uses dimensionality reduction to reduce the number of attributes in data for summarization, visualization, and feature selection.
  • For merging the predictions of many supervised models, ensemble approaches are used.
  • Feature extraction is a technique for extracting properties from picture and text data.

11. BeautifulSoup

BeautifulSoup is one of the greatest and most often used web crawling and data scraping libraries. This method can be used to extract information from HTML and XML files. It integrates with your preferred parser to offer intuitive navigation, search, and modification of the parse tree. It is normal for programmers to save hours or even days of effort.

Features of BeautifulSoup

  • It helps to get data out of HTML, XML, and other markup languages.
  • Beautiful Soup allows you to extract specific content from a webpage, completely remove the HTML markup, and save the data.
  • It’s a web scraping tool that helps you start cleaning up and parse the content you’ve downloaded from the internet.

12. PyTorch

PyTorch is a Facebook AI researcher’s open-source ML and deep learning framework. Many data scientists use PyTorch for natural language processing and computer vision problems all over the world. It also provides a function for deploying mobile and embedded frameworks.


Features of PyTorch

  • PyTorch is a Python-based library designed to provide flexibility as a deep learning development environment.
  • The user is completely oblivious that the CPU is at work. PyTorch, on the other hand, allows you to access any level of computation. 
  • In terms of training speed, PyTorch is similar to TensorFlow and PyTorch.
  • For data scientists and programmers, dynamic graphics brought clarity. TensorFlow is more difficult to use than PyTorch. 
  • PyTorch has a lot of cool features. One such feature is the ability to quickly bind any module utilizing this capability.

13. XGBoost

XGBoost is a distributed gradient boosting library that is optimized for efficiency, flexibility, and portability. It uses the Gradient Boosting framework to create ML algorithms. XGBoost is a parallel tree boosting algorithm that addresses a variety of data science issues quickly and accurately. The same algorithm may tackle problems with thousands of instances in a distributed environment (Hadoop, SGE, MPI).

Features of XGBoost

  • XGBoost is robust in both distributed and memory-limited environments.
  • XGBoost is an ML library written in Python, R, Julia, Java, C++, and Scala that is open-source.
  • It includes a parallel boosting trees approach for solving Machine Learning problems.

14. PyCaret

PyCaret is a low-code machine learning library written in Python that seeks to streamline machine learning workflows. It is an ML project management solution that shortens the lifecycle of machine learning projects. It enables data scientists to complete end-to-end machine learning jobs fast and easily.


Features of PyCaret

  • It is an open-source ML library developed to make performing standard activities in an ML project simpler.
  • It is a Python version of the Caret machine learning package in R, famous since it enables models to be tested, compared, and tuned on a specific dataset with just a few lines of code.
  • The PyCaret library offers these functionalities, enabling the ML professional in Python to spot check a suite of standard ML algos on a classification or regression dataset with a single function call.

15. Scrapy

Scrapy is among the most widely used Python frameworks for web data extraction. It aids in the effective retrieval of data from websites. Scraping allows us to obtain structured data from the internet that we can then use in our ML model. In terms of interface design, this framework adheres to the Don’t Repeat Yourself Principle. Most data scientists utilize it to acquire data from APIs all over the world.


Features of Scrapy

  • Scrapy is a web crawling platform that is open source and free to use.
  • Scrapy creates feed exports in JSON, CSV, and XML formats.
  • Scrapy features built-in functionality for using XPath or CSS expressions to pick and extract data from sources.

In A Nutshell

Data analytics encompasses a number of activities, including data processing, classification, and visualization. There are many Python libraries for Data Science, and which one the user chooses is mostly based on the kind of project they are engaged in. With the assistance of Python libraries, data scientists may simply execute data analytics. Freshers might also stand out from the crowd if they are familiar with Python libraries for Data Science.

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