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Ten Things You Most Likely Didn't Know About Python Libraries For Data Science.

Updated: Dec 31, 2022


Python is today's most popular programming language. Python never ceases to amaze its users when addressing data science tasks and obstacles. The majority of data scientists already use Python programming on a daily basis. Python is an easy-to-learn, easy-to-debug, widely 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.


Another free and open-source Python library for data science that is widely used for high-level computations is SciPy (Scientific Python). SciPy has over 19,000 comments and a community of about 600 contributors on GitHub. Because it extends NumPy and provides many user-friendly and efficient routines for scientific calculations, it is widely used for scientific and technical computations.


  • NumPy is a Python extension that contains a collection of algorithms and routines.

  • Data manipulation and visualization commands at a high level

  • The SciPy ndimage submodule is used to process multidimensional images.

  • Functions for solving differential equations are built-in.


  • Using the Fourier transform to solve differential equations

  • Multidimensional image operations

  • Linear algebra

  • Optimization algorithms


In the data science life cycle, Pandas (Python data analysis) is a must. NumPy in matplotlib is the most popular and commonly used Python package for data research. It is frequently used for data analysis and cleansing, with about 17,00 comments on GitHub and a community of 1,200 contributors. Pandas offer quick, versatile data structures, such as data frame CDs, making working with structured data simple and natural.


  • Rich features and eloquent syntax provide you with the freedom to cope with missing data.

  • Allows you to write your function and apply it to a set of data.

  • Abstraction at a high level

  • High-level data structures and manipulation tools are included.


  • Data cleansing and wrangling in general

  • Because it has great support for loading CSV files into its data frame format, it is ideal for ETL (extract, transform, load) processes for data transformation and storage.

  • Statistics, finance, and neuroscience are just a few of the academic and commercial fields where it's used.

  • Date range creation, moving window, linear regression, and date shifting are examples of time-series-specific capabilities.


NumPy (Numerical Python) is the most important Python library for numerical calculation; it includes a powerful N-dimensional array object. GitHub, has over 18,000 comments and a community of 700 contributors. It's a general-purpose array-processing package that includes high-performance multidimensional objects known as arrays and tools for working with them. NumPy tackles the slowness issue in part by providing these multidimensional arrays, as well as methods and operators that efficiently operate on them.


  • For numerical routines, it provides quick, precompiled functions.

  • For increased efficiency, use array-oriented computation.

  • Encourages the use of an object-oriented strategy.

  • Vectorization allows for more compact and faster computations.


  • It's a tool that's utilized a lot in data analysis.

  • This function generates a powerful N-dimensional array.

  • Other libraries, such as SciPy and scikit-learn, are built on top of it.

  • When used with SciPy and Matplotlib, MATLAB can be replaced.


TensorFlow is the first python library for data science on the list. TensorFlow is a high-performance numerical computation framework with over 35,000 comments and a thriving community of over 1,500 contributors. It is employed in a variety of scientific domains. TensorFlow is a framework for building and executing tensor-based calculations. Tensors are partially defined computational objects that finally output a value.


  • Improved representations of computational graphs

  • In neural machine learning, it reduces error by 50 to 60%.

  • To run sophisticated models in parallel, you'll need to use parallel computing.

  • Google-backed seamless library management

  • More frequent updates and new releases to keep you up to date with the latest features

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BeautifulSoup is the next-generation Python data science package. This is another well-known Python package for web crawling and data scraping. Users can scrape data from a website that doesn't have a proper CSV or API, and BeautifulSoup can assist them in organizing it into the needed format.


Matplotlib's visualizations are both powerful and elegant. It's a Python charting package with over 26,000 GitHub comments and a thriving community of roughly 700 developers. It's widely used for data visualization because of the graphs and charts it generates. It also has an object-oriented API for integrating charts into applications.


  • It can be used as a replacement for MATLAB, with the added benefit of being free and open source.

  • It supports a wide range of backends and output formats, allowing you to utilize it independently of your operating system or desired output format.

  • Pandas can be used as a wrapper around the MATLAB API to make MATLAB behave more like a cleaner.

  • Low memory use and improved runtime behaviour


  • Variable correlation analysis

  • Visualize 95% confidence intervals of the models

  • Detecting outliers with a scatter plot, for example.

  • To acquire immediate insights, visualize the distribution of data.


Scikit-learn, a machine learning toolkit that contains practically all of the machine learning algorithms you would require, is next on the list of the best Python libraries for data science. Scikit-learn is written in NumPy and SciPy and can be interpolated.


  • model selection

  • dimensionality reduction

  • clustering

  • regression

  • classification


Keras is a prominent library that is used extensively for deep learning and neural network modules, similar to TensorFlow. Keras offers both TensorFlow and Theano backends, making it an excellent choice for those who don't want to get too deep into TensorFlow.


  • Keras provides many prelabeled datasets that may be immediately imported and loaded.

  • It has several implemented layers and parameters that may be used to build, configure, train, and evaluate neural networks.


Scrapy is the next well-known Python data science library. Scrapy is a Python-based web crawling framework that is one of the most popular and speedy. With XPath selectors, it is often used to extract data from a web page.


  • Scrapy helps improve online crawling algorithms (spider bots) that retrieve structured data from the internet.

  • Scrappy is also used to collect data from APIs. Its interface is designed with the 'Don't Repeat Yourself' idea in mind, encouraging users to write universal code that can be reused to create and grow huge crawlers.


PyTorch, a Python-based scientific computing programme that harnesses the power of graphics processing units, is next on the list of top python libraries for data science. PyTorch is a popular deep learning research platform designed to provide maximum flexibility and speed.


  • PyTorch is well-known for having two of the most advanced features.

  • Tensor computations with strong GPU acceleration support

  • Using a tape-based autograd system to develop deep neural networks


There are many additional useful Python libraries besides these top 10 Python libraries for data science. If you want to learn and master data science with Python as a next step, visit our training page:

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