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Mastering Numerical Computing with NumPy

You're reading from   Mastering Numerical Computing with NumPy Master scientific computing and perform complex operations with ease

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788993357
Length 248 pages
Edition 1st Edition
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Authors (3):
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 Mert Cakmak Mert Cakmak
Author Profile Icon Mert Cakmak
Mert Cakmak
Tiago Antao Tiago Antao
Author Profile Icon Tiago Antao
Tiago Antao
 Cuhadaroglu Cuhadaroglu
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Cuhadaroglu
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Toc

Table of Contents (16) Chapters Close

Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
1. Working with NumPy Arrays FREE CHAPTER 2. Linear Algebra with NumPy 3. Exploratory Data Analysis of Boston Housing Data with NumPy Statistics 4. Predicting Housing Prices Using Linear Regression 5. Clustering Clients of a Wholesale Distributor Using NumPy 6. NumPy, SciPy, Pandas, and Scikit-Learn 7. Advanced Numpy 8. Overview of High-Performance Numerical Computing Libraries 9. Performance Benchmarks 1. Other Books You May Enjoy Index

Appendix 1. Other Books You May Enjoy

If you enjoyed this book, you may be interested in these other books by Packt:

SciPy Recipes L. Felipe Martins, Ruben Oliva Ramos, V Kishore Ayyadevara

ISBN: 9781788291460

  • Get a solid foundation in scientific computing using Python
  • Master common tasks related to SciPy and associated libraries such as NumPy, pandas, and matplotlib
  • Perform mathematical operations such as linear algebra and work with the statistical and probability functions in SciPy
  • Master advanced computing such as Discrete Fourier Transform and K-means with the SciPy Stack
  • Implement data wrangling tasks efficiently using pandas
  • Visualize your data through various graphs and charts using matplotlib

Python Data Analysis - Second Edition Armando Fandango

ISBN: 9781787127487

  • Install open source Python modules such NumPy, SciPy, Pandas, Statsmodels, scikit-learn, theano, keras, and tensorflow on various platforms
  • Prepare and clean your data, and use it for exploratory analysis
  • Manipulate your data with Pandas
  • Retrieve and store your data from RDBMS, NoSQL, and distributed file systems such as HDFS and HDF5
  • Visualize your data with open source libraries such as matplotlib, bokeh, and plotly
  • Learn about various machine learning methods such as supervised, unsupervised, probabilistic, and Bayesian
  • Understand signal processing and time series data analysis
  • Get to grips with graph processing and social network analysis
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