What are the Python libraries that are used by data scientists?

The most popular Python libraries in use by data scientists are covered in this article.


NumPy is one of the most widely used open-source Python libraries for scientific computation. Its built-in mathematical functions allow for lightning-fast computation and support for multidimensional data and massive matrices. Linear algebra also makes use of it. NumPy Array is frequently preferred over lists because it consumes less memory and is more convenient and efficient.

NumPy is an open-source project that aims to facilitate numerical computing with Python, according to its website. It was designed in 2005 and is based on the Numeric and Numarray libraries' early work. One of NumPy's main advantages is that it was released under a modified BSD license, thus it will always be free to use.


In the field of data science, Pandas is a widely used open-source library. It is mostly used for data analysis, manipulation, and cleansing. Pandas enable simple data modeling and data analysis activities without the need for extensive coding. Pandas, according to their website, is a quick, powerful, versatile, and simple open-source data analysis and manipulation tool.


Matplotlib is a massive visualization toolkit written in Python that can be used to make both static and dynamic visualizations. A significant number of third-party programs, including various higher-level plotting interfaces(Seaborn, HoloViews, ggplot, etc.), enhance and build on Matplotlib's functionality

Matplotlib is intended to be as functional as MATLAB, with the added benefit of being Python-compatible. It also has the advantage of being open-source and free. It allows the user to visualize data using a number of plot types, such as scatterplots, histograms, bar charts, error charts, and boxplots. Furthermore, all visualizations may be created with only a few lines of code.


Seaborn is a powerful interface for building stunningly attractive and insightful statistical visualizations, which are crucial for gaining insight from and studying data. It is another well-liked Python data visualization toolkit built on Matplotlib. This Python module has close ties to both the NumPy and pandas data structures. Seaborn's core principle is to normalize visualisation as a part of data exploration and analysis. hence, its charting algorithms make use of data frames that include detailed data sets.


Create interactive graphs and charts using the popular open-source program Plotly. Data visualizations made with Plotly may be exported to HTML files, viewed in Jupyter notebooks and web applications using Dash, or saved to the cloud. Based on the Plotly JavaScript library (plotly.js).

Included are more than 40 different kinds of graphs, such as scatter plots, histograms, line graphs, bar graphs, pie charts, error bars, box plots, multiple axes, sparklines, dendrograms, and three-dimensional charts. In addition to the standard tools for data visualization, Plotly also offers more specialized options, such as contour charts.

When it comes to interactive visualizations or dashboard-like displays, Plotly is a respectable substitute for Matplotlib and Seaborn. It is now available for usage under the MIT license.


Scikit-learn is crucial for machine learning. As a Python machine-learning library, scikit-learn is extensively utilized. Distributed under the BSD license, this open-source Python library combines features from NumPy, SciPy, and Matplotlib and is suitable for use in commercial environments. The process of analyzing data for future predictions is reduced and accelerated.

While scikit-learn was initially launched in 2007 as a Google Summer of Code project, it has since been maintained through institutional and private funds.

The best part about scikit-learn is really very easy to use.

Python Libraries for Machine Learning


LightGBM is a well-known open-source gradient boosting library that makes use of tree-based algorithms. It has the following benefits −

  • The effectiveness and speed of training have been improved.

  • Reduce memory usage

  • higher accuracy

  • Support for parallel, distributed, and GPU learning

  • Capable of dealing with enormous amounts of data

It can perform supervised classification as well as regression problems. To learn more about this fantastic framework, visit their official documentation or GitHub.


XGBoost is another widely used distributed gradient boosting toolkit with the goals of portability, adaptability, and performance. It enables the use of machine learning techniques inside the gradient boosting framework. In the form of gradient-boosted decision trees (GBDT), XGBoost offers a parallel tree-boosting technique that can rapidly and accurately resolve a wide variety of data science problems. The same code can tackle an infinite number of problems in major distributed settings (Hadoop, SGE, MPI).

The fact that XGBoost can help individuals and teams win practically every Kaggle structured data competition has contributed to its rapid rise in popularity in recent years.

Other machine-learning libraries in Python include CatBoost, Statsmodels, and RAPIDS. AI cuDF and cuML, Optuna, etc.

Python Libraries for Deep Learning


Google's Brain team created TensorFlow, a popular open-source toolkit for high-performance numerical computation that is essential to deep learning studies.

TensorFlow is an open-source, comprehensive machine learning framework, as stated on the project's website. For those working in the field of machine learning, it provides a variety of resources in the form of tools, frameworks, and communities.


PyTorch is a machine learning framework that speeds the transition from research prototyping to production deployment. It is a tensor library intended for deep learning on GPUs and CPUs that is considered an alternative to TensorFlow. PyTorch's popularity has expanded to the point where it has beaten TensorFlow in Google trends.

It was created and maintained by Facebook, and it is currently licensed under BSD.


Keras is an application programming interface for deep learning that was developed with humans in mind, not robots. Keras is built with the user's experience in mind, providing uniform and straightforward APIs, decreasing the number of clicks required for typical use cases, and providing clear and responsive error signals. TensorFlow's TF 2.0 release makes Keras the default API because of how easy it is to work with.

Keras provides a more easy mechanism for expressing neural networks, as well as some of the greatest tools for building models, data set processing, graph visualization, and other tasks.

Other Deep-learning libraries in Python include FastAI, PyTorch Lightning, and so on.

Python Libraries for Natural Language Processing

  • NLTK

  • spaCy

  • Gensim

  • Hugging Face Transformers


We gained an understanding of some of the most well-known Python libraries among data scientists through the reading of this article.