Python Libraries for Data Science You Should Know
Tags: python, datascience, machinelearning, computerscience
Published: 2020-07-20 14:43:18

This article was originally published at [CodePerfectPlus.com](https://codeperfectplus.herokuapp.com/python-libraries-for-data-science-you-should-know)

According to Wikipedia —

**Data Science** is an inter-disciplinary field that uses _scientific_ methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured _data_.
Data science is related to [data mining](https://en.wikipedia.org/wiki/Data_mining "Data mining"), [deep learning](https://en.wikipedia.org/wiki/Deep_learning "Deep learning") and [big data](https://en.wikipedia.org/wiki/Big_data "Big data").

In an easy way —

Data Science is the process of extracting useful information from data in order to solve real-world problems.

There are some must-known python libraries for a Data scientist to ease these things.

**Numpy:-** It's is a python **library** used for working with arrays. It also has functions for working in the domain of linear algebra, Fourier transform, and matrices. **NumPy** was created in 2005 by Travis Oliphant. It is an open-source project and you can use it freely.

**Pandas:-** In computer programming, **pandas** is a software **library** written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license.

**Matplotlib:-** It's is a plotting **library** for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+.

**Seaborn:-** It's is a **library** for making statistical graphics in Python. It is built on top of matplotlib and closely integrated with pandas data structures. ... Options for visualizing univariate or bivariate distributions and for comparing them between subsets of data.

**Scikit-learn:-** **Scikit**-**learn** is a library in Python that provides many unsupervised and supervised **learning** algorithms. It's built upon some of the technology you might already be familiar with, like NumPy, pandas, and Matplotlib!

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**Tensorflow:- Created by the Google Brain team,  **TensorFlow**  is an open-source  **library**  for numerical computation and large-scale machine learning.  **TensorFlow**  bundles together a slew of machine learning and deep learning (aka neural networking) models and algorithms and makes them useful by way of a common metaphor.

**Keras:-** **Keras** is an open-source neural-network **library** written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible.

**PyTorch:-**  PyTorch is an open-source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook's AI Research lab. It is free and open-source software released under the Modified BSD license.

**Conclusion**

These are some Python libraries for Data Science, Machine Learning and Artificial Intelligence. As you will learn more you will get familiar with more advanced libraries and tools.

Thanks for reading articles.


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