data processing

 

Today, we are here with some highly used Data Science and Data Processing libraries in Python including Pandas, NumPy, TensorFlow etc. You will learn some vital features of libraries including their domain of use.

Top Libraries for Machine Learning, NLP, and Data Processing

a) NumPy: “Numerical Python”- NumPy is a core library. 

  • Numpy module gives high-speed precompiled functions for numerical routines.
  • It appends support to Python for extensive multi-dimensions arrays and matrics.
  • It serves in executing the high-level mathematical function to operate on the arrays.

Github Link: NumPy 

b) Pandas:  The name is derived from “Panel Data”.

  • Pandas library is used for data manipulation and analysis.
  • Works with labeled and relational data.
  • Pandas work well even with rough and unlabeled data.
  • Pandas library is a free software, released under the three-clause BSD licence.

Github Link: Pandas

c) TensorFlow: It was developed by Google for the Machine Learning purpose as a successor of DistBelief.

  • TensorFlow helps in quick setup, training, Scand deployment of ANN with a large amount of data.
  • Easy and well understood.

Github Link: TensorFlow

d) Matplotlib: It is used for visualization of data. 

  • Matplotlib is used for visualization of datasets.
  • It is used for creating Graphs, pie charts, and line plots using dataset.
  • The library is low-level so we need to write more codes to get advance visuals.

Github Link: Matplotlib

e) Theano: It is used for Deep Learning.

  • Theno provides high speed for the processing.
  • Theno uses NumPy like syntax to optimize and estimate mathematical expression.

Github Link: Theano

f) SciPy: SciPy is used for software, engineering, and science.

  • SciPy contains contents for optimization, linear algebra, integration, special functions etc.
  • Fundamental library for scientific computing.
READ  Evolution and History Of Machine Learning

Github Link: SciPy

g) Scikit-Learn: Mainly for Machine Learning.

  • Simple for data-mining and data analysis.
  • Built on NumPy, SciPy, and Matplotlib.

Github Link: Scikit-Learn

 

Refrences:

  1. https://www.datasciencecentral.com/profiles/blogs/9-python-analytics-libraries-1
  1. https://www.upwork.com/hiring/data/15-python-libraries-data-science/
  2. https://medium.com /activewizards-machine-learning-company/top-15-python-libraries-for-data-science-in-in-2017-ab61b4f9b4a7

 

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