What are Python Packages for Data Science?

What are Python Packages for Data Science?

A python library is a collection of functions and methods that allow you to perform lots of actions without writing any code. The libraries usually consist of built-in models providing different functionalities, which you can use directly. There are a lot of libraries offering a broad range of facilities. Below are some important python libraries used for data science. It is divided into three group i.e. Scientific Computing libraries, Visualization libraries, and Algorithmic libraries.

Scientific Computing Libraries

Pandas (Data Structure and Tools)
Pandas offer data structure and tools for effective data manipulation and analysis. It provides fast access to structure data. The primary instruments of Pandas is a two-dimensional table consists of column and row labels, which are called dataFrame. It is designed to provide easy indexing functionality.

Numpy (Array and matrices)
The numpy library uses the array for its inputs and outputs. It is most important libraries in data science in which vector and matrix operations are easily computed. There are a lot of benefits of using numpy to know more please explore this official site.

It includes the function for some advanced math problems like Integrals, Solving differential equations, optimizations and data visualizations. Visit this site for more details.

Visualization libraries

Visualization is the best way to communicate with other showing the meaningful results of the analysis. Libraries listed here helps to create graphs, charts, and maps.

Mathplotlib is the most popular data visualization libraries. It is great for making graphs and plots. Visit this site to learn more.

This is based on mathplotlib. It is very easy to generate various plots like heat maps, time series, and violin plots.

Algorithmic libraries

With machine learning algorithms, we’re able to develop a model with our dataset and obtain some predictions. The algorithmic libraries tackle some machine learning tasks from basic to complex.

It contains tools for statistical modeling, including regression, classification, clustering and so on. This library is built on numpy, scipy and mathplotlib.

It allows users to explore data, estimate statistical models and perform statistical tests.

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