Python is one of the most used programming languages. It is so because of its efficiency and power, which no other programming language cannot offer. Well, Python has been used in various fields and is better than various other programming languages.

This article will be listing the 15 best python libraries for data science that one can prefer using. These python libraries for data science and machine learning will help one solve various problems easily. Even complex problems can be easy with using these libraries. The python library for data science can be used for data processing and visualization of the data. Given below is the list of best python packages for data science that data scientists prefer using.

  1. Python Libraries For Data Science

1. Python Libraries For Data Science

A) Scrapy

Scrapy is one of the most preferred python libraries for data science, which can be used for data mining. One can easily use this python library for data science to get structured data from the data. This can be done with the help of crawling programs, which can be created using Scrapy. Data such as USLs and contact information can be retrieved from the web using this library.

B) BeautifulSoup

This is yet another library for Python which can be used to make a web crawler. Scraping and mining data using this library is easy. You can not only retrieve the data but also arrange it in the format you need. 

C) NumPy

NumPy is the package with numerical computation in Python. There are around 700 contributors for the package. With various features, including fast and precompiled functions, array-oriented computers, which work on the OOPs concept, it is one of the best python libraries for data science. This can be used for data processing and modeling. This can be used to perform functions, including n-arrays and matrices. NumPy array increases the performance and also accelerates the time of the execution of the program. 

D) Pandas

Using Pandas, users can work on labeled and relational data—this process data in one dimensional and a two-dimensional array. One can easily convert data structures with Pandas to DataFrame objects and also to handle the missing data.

E) Matplotlib

Developers prefer using python modules for data science as a visualization tool. This library package of Python can help create graphs and plots to arrange the data. The plots can be easily embedded into the application using the object-oriented API that it comes with. Moreover, the library being open-source is free to use.

F) Keras

Developers can use Keras for modeling as well as building neural networks. Keras also uses other packages to increase their functionality. It follows a minimalist design, which makes it look good.

G) Scikit-Learn

This is the industry-standard package for data science projects. This library is meant for only specific functionalities, which include image processing. There are math operations available that can solve various problems with a learning machine algorithm. 

H) SciPy

The package was created with its main functionality upon NumPy. It can be used for various kinds of scientific programming projects, including science, engineering, and mathematics. The package offers various numerical routines, which includes optimization, integration, and various other submodules. 

I) PyTorch

For data scientists looking to perform deep learning tasks, they can prefer using this package. With this module, one can perform tensor computations with GPU acceleration. One can also create graphs and also calculate gradients automatically.

J) TensorFlow

TensorFlow is another one of the most common python libraries for data science one can prefer for data processing and data modeling. This module was ideally developed at Google Brain to accompany machine learning and deep learning as well. Developers prefer using this tool for practices like speech recognition and object identification. The best part about this package is that new updates are released frequently, which expands the features that it already offers to the users. 

K) XGBoost

This is another library package that can be used in Python to implement the machine learning algorithm. This helps resolve various data science problems with parallel tree boosting. 

L) Seaborn

If a developer is looking for a tool to help with data visualization, they can prefer Seaborn. This package or tool is based on Matplotlib. This can be used for creating visualizing statistical models, including heatmaps and various others. One can use it to prepare the output of the data in the firm in an instant. There is an extensive gallery of visualizations, including time series, violin diagrams, and join plots. 

M) Bokeh

If you are a developer looking to create various interactive visuals for your data, then you can use this too. This helps create scalable visualizations. This can be done inside browsers using Javascript widgets. The main task of this tool is to create visualizations through browsers. The package offers graphs, styling, and interaction abilities.

N) Plotly

Plotly is another web-based tool that one can use for data visualization in Python. There are various graphics which one can find on the official website of Plotly. Some new features and graphics keep on releasing to expand the library. The Plotly works to make it one of the top Python libraries for data science, with various features offered, including crosstalk integration, animation, and multiple linked views. When it comes to data visualization packages in Python, Plotly is what many developers prefer using because of its features and extensive library. 

O) Pydot

Using Pydot, developers can make oriented as well as non-oriented graphs with Python. Using Pydot, one can easily show the structure of graphs with the help of the library. 


Various python libraries for data science can come in handy when solving complex problems. Many developers prefer bringing into use these libraries in Python for data science. These can help solve problems such as retrieving data, arranging it, and developing visual graphics of the data as per your desired patterns. Python is an extensive language that is used in various fields all around the world. Using these important python libraries for data science can help with solving even complex programs in seconds.

The ecosystem of Python is vast and offers tools that one can use for data science work. If you are learning data science in Python, then these python libraries are what you should prefer to use. These libraries can help developers get to know machine learning and understand the concept without spending your time.

For efficient working with data sciences and machine learning, which includes scrawling data, processing it, and visualizing it with graphics, one should prefer using the python libraries for data science mentioned in the above list. There are not only these libraries, but there are many more that you can prefer using Python. 

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional. 



Are you ready to build your own career?