Python Data Science Libraries

Python is today’s most popular programming language. Python never ceases to amaze its users when it comes to solving data science tasks and challenges. Most 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 language, and it has many other advantages. Python has been designed with extraordinary Python libraries for data science that programmers use every day to solve problems.

Python in Data Science:

Because of its statistical analysis, data modeling, and readability, Python is one of the best programming languages for extracting value from this data.

Python is one of the best fits for data science for the following reasons–

  • Built-in libraries to support a variety of data science tasks.
  • Various development modules are available for use.
  • Excellent memory management abilities.
  • Algorithms for complex tasks processing

With the benefits listed above, Python can be used as a powerful tool to handle and solve data science problems.

Python Data Science Libraries

  1. NumPy
  2. TensorFlow
  3. SciPy
  4. Pandas
  5. Matplotlib
  6. Keras
  7. Seaborn
  8. Beautiful Soup
  9. PyTorch

  10. Scrapy


It is a free Python software library that allows you to perform numerical computations on data in the form of large arrays and multi-dimensional matrices. These multidimensional matrices are the main objects in NumPy, where their dimensions are referred to as axes and the number of axes is referred to as a rank. NumPy also includes a variety of tools for working with these arrays, as well as high-level mathematical functions for manipulating this data using linear algebra, Fourier transforms, random number crunching, and so on. Adding, slicing, multiplying, flattening, reshaping, and indexing arrays are some of the basic array operations that NumPy can perform. Stacking arrays, splitting them into sections, broadcasting arrays, and other advanced functions are also available.


TensorFlow is a high-performance numerical computation library with approximately 35,000 comments and a vibrant community of approximately 1,500 contributors. It is used in a variety of scientific fields. TensorFlow is essentially a framework for defining and running computations involving tensors, which are partially defined computational objects that produce a value.

TensorFlow  Features:

  • improved visualization of computational graphs
  • In neural machine learning, it reduces error by 50 to 60%.
  • Parallel computing is used to run complex models.
  • Google-backed seamless library management.
  • Quicker updates and more frequent new releases to keep you up to date on the latest features.


  • Image and speech recognition
  • Text-based applications
  • Analysis of Time-series
  • Video recognition/detection


The Python SciPy library is largely based on the NumPy library. It performs the majority of the advanced computations related to data modeling. The SciPy library enables us to perform statistical data analysis, algebraic computations, algorithm optimization, and other tasks.

We can even perform parallel computations on it using SciPy. It includes functions for data science operations like regression, probability, and so on.

In a nutshell, the SciPy module can easily handle all advanced computations in statistics, modelling, and algebra.


This is a free Python data analysis and manipulation software library. It was developed as a community library project and was first made available in 2008. Pandas offer a variety of high-performance and user-friendly data structures and operations for manipulating data in the form of numerical tables and time series. Pandas also include a number of tools for reading and writing data between in-memory data structures and various file formats.

In a nutshell, it is ideal for quick and easy data manipulation, data aggregation, reading and writing data, and data visualization. Pandas can also read data from files such as CSV, Excel, and others, or from a SQL database, and generate a Python object known as a data frame. A data frame is made up of rows and columns and can be used to manipulate data using operations like join, merge, groupby, concatenate, and so on.


Matplotlib’s visualizations are both powerful and wonderful. It’s a Python plotting library with over 26,000 comments on GitHub and a thriving community of over 700 contributors. It’s widely used for data visualization because of the graphs and plots it generates. It also includes an object-oriented API for embedding those plots into applications.

Matplotlib Features:

  • It can be used as a MATLAB replacement and has the advantage of being free and open source.
  • Supports dozens of backends and output types, so you can use it regardless of your operating system or output format preferences.
  • Pandas can be used as MATLAB API wrappers to drive MATLAB like a cleaner.
  • Low memory consumption and improved runtime performance


  • Visualize the models’ 95 percent confidence intervals.
  • Visualize data distribution to gain instant insights.
  • Outlier detection with a scatter plot.
  • Correlation analysis of variables.


Keras is a Python-based deep learning API that runs on top of the TensorFlow machine learning platform. It was created with the goal of allowing for quick experimentation. “Being able to go from idea to result as quickly as possible is key to doing good research,” says Keras.

Many people prefer Keras over TensorFlow because it provides a much better “user experience.” Keras was developed in Python, making it easier for Python developers to understand. It is an easy-to-use library with a lot of power.


Seaborn is a Python library for data visualization that is based on Matplotlib. Data scientists can use Seaborn to create a variety of statistical models, such as heatmaps. Seaborn offers an impressive array of data visualization options, including time-series visualization, joint plots, violin diagrams, and many more. Seaborn uses semantic mapping and statistical aggregation to generate informative plots with deep insights.

Beautiful Soup

BeautifulSoup is a fantastic Python parsing module that supports web scraping from HTML and XML documents.

BeautifulSoup identifies encodings and handles HTML documents elegantly, even when they contain special characters. We can explore a parsed document and discover what we need, making it quick and easy to extract data from web pages.


PyTorch, is a Python-based scientific computing tool that makes use of the power of graphics processing units, PyTorch is a popular deep learning research platform that is designed to provide maximum flexibility and speed.

  • PyTorch is well-known for giving two of the most high-level features
  • tensor computations with significant GPU acceleration support,
  • construction of deep neural networks on a tape-based autograd system.


Scrapy is a must-have Python module for anyone interested in data scraping (extracting data from the screen). Scrapy allows you to improve the screen-scraping and web crawling processes. Scrapy is used by data scientists for data mining as well as automated testing. Scrapy is an open-source framework that many IT professionals use throughout the world to extract data from websites. Scrapy is developed in Python and is extremely portable, running on Linux, Windows, BSD, and Mac. Because of its great interactivity, many skilled developers favour Python for data analysis and scraping.