Article On Python Libraries For Data Science

1. Pandas

Pandas is a free Python software library for data analysis and data handling. It was created as a community library project and initially released around 2008. Pandas provides various high-performance and easy-to-use data structures and operations for manipulating data in the form of numerical tables and time series. Pandas also has multiple tools for reading and writing data between in-memory data structures and different file formats.

2. NumPy

NumPy is a free Python software library for numerical computing on data that can be in the form of large arrays and multi-dimensional matrices. These multidimensional matrices are the main objects in NumPy where their dimensions are called axes and the number of axes is called a rank. NumPy also provides various tools to work with these arrays and high-level mathematical functions to manipulate this data with linear algebra, Fourier transforms, random number crunchings, etc.

3. SciPy

SciPy is a free software library for scientific computing and technical computing on the data. It was created as a community library project and initially released around 2001. SciPy library is built on the NumPy array object and it is part of the NumPy stack which also includes other scientific computing libraries and tools such as Matplotlib, SymPy, pandas etc. 

4. Scikit-learn

Scikit-learn is a free software library for Machine Learning coding primarily in the Python programming language. It was initially developed as a Google Summer of Code project by David Cournapeau and originally released in June 2007. Scikit-learn is built on top of other Python libraries like NumPy, SciPy, Matplotlib, Pandas, etc. and so it provides full interoperability with these libraries.  

5. TensorFlow

TensorFlow is a free end-to-end open-source platform that has a wide variety of tools, libraries, and resources for Artificial Intelligence. It was developed by the Google Brain team and initially released on November 9, 2015. You can easily build and train Machine Learning models with high-level API’s such as Keras using TensorFlow. It also provides multiple levels of abstraction so you can choose the option you need for your model. TensorFlow also allows you to deploy Machine Learning models anywhere such as the cloud, browser, or your own device.

6. Keras

Keras is a free and open-source neural-network library written in Python. It was primarily created by François Chollet, a Google engineer, and initially released on 27 March 2015. Keras was created to be user friendly, extensible, and modular while being supportive of experimentation in deep neural networks. Hence, it can be run on top of other libraries and languages like TensorFlow, Theano, Microsoft Cognitive Toolkit, R, etc. Keras has multiple tools that make it easier to work with different types of image and textual data for coding in deep neural networks.

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