Numpy Tutorial

Numpy Tutorial for Beginner

NumPy is the fundamental package needed for scientific computing with Python.

It provides:

• a powerful N-dimensional array object
• sophisticated (broadcasting) functions
• tools for integrating C/C++ and Fortran code
• useful linear algebra, Fourier transform, and random number capabilities

This repogistory is a beginner tutorial for you, who want to quickly learn and start using Numpy for scientific computing. The tutorial is inside the notebook file.

Table of Contents

• 1  NumPy = Numerical Python
• 2  The Numpy ndarray: A Multidimensional Array Object
  • 2.1  NumPy Array's Attributes
  • 2.2  What advantages do NumPy arrays offer over (nested) Python lists?
  • 2.3  Numpy Array (ndarry) can have multi-dimension
• 3  Creating NumPy Arrays
  • 3.1  Make “Empty” NumPy Array
  • 3.2  Load NumPy Arrays From Text
• 4  Save NumPy Arrays
• 5  NumPy Broadcasting
• 6  Array Mathematics
  • 6.1  Mathematics Operation
  • 6.2  Logic Operation
• 7  Manipulate NumPy Array
  • 7.1  Subset, Slice, And Index Arrays
    • 7.1.1  Indexing and Subseting NumPy Array
    • 7.1.2  Slicing NumPy Array
  • 7.2  Common Two-Dimensional Slicing for Machine Learning
    • 7.2.1  Split Input and Output Features
    • 7.2.2  Split Train and Test Rows
  • 7.3  Reshaping And Resizing Arrays
    • 7.3.1  Reshape Array for Machine Learning
      • 7.3.1.1  Reshape 1D to 2D Array
      • 7.3.1.2  Reshape 2D to 3D Array
      • 7.3.1.3  Transpose NumPy Arrays
    • 7.3.2  Additional Shape Method
      • 7.3.2.1  Append Arrays
      • 7.3.2.2  Insert And Delete Array Elements
      • 7.3.2.3  Join And Split Arrays
      • 7.3.2.4  Sorting Arrays
• 8  End of tutorial.

Contributing

Please feels free to contribute to this tutorial. If there are any mistake or typo, simply create an issue and i will fix it as soon as I can.

Authors

• Hoang Tung Lam - lamhoangtung

License

This project is licensed under the MIT License - see the LICENSE.md file for details