Top 10 Python Libraries Data Scientists Should Know in 2022

If you are a data scientist, these Python libraries are for you! Master them to perform well in your job

Python is such a popular and all-purpose programming language that most data scientists from every level tend to use it. There are many instances when they require to consult with Python libraries to find what particular type of coding they are looking for. Here are the top 10 Python libraries that data scientists should know in 2022. 

Pandas

This is one of the open-source Python libraries which is mainly used in Data Science and machine learning subjects. This library mainly provides data manipulation and analysis tools, which are used for analyzing data using its powerful data structures for manipulating numerical tables and time series analysis. 

Numpy

In Python, NumPy is another library that is used for mathematical functions. The NumPy library is popular for array and matrix processing using a set of mathematical functions.  This library is mostly used in machine learning computations. 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.

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. 

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.

Requests

This is another different library module in Python used for sending HTTP requests and supports functionalities like adding headers, the formation of data and accessing responsive data objects, which include content data, encoding data, status, etc. 

Scipy

In Python, the scipy library is one of the open-source libraries mainly used in mathematical and scientific computations, technical and engineering computations. It is mainly built on NumPy.  

Sqllite 3

Python programming language provides a library for database operations. This library is mainly used for database operations using sql queries.

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.

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.

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.