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Useful NumPy functions for plotting and linear algebra

From finding relevant plotting points to checking matrix diagonlization

NumPy's main object is the array, which can be used on one to two axes for plotting functions of y with respect to x.

However, NumPy's powerful linalg package allows for further insight into matrix manipulation for solving nxn systems of linear equations.

Both of these topics will be covered through explanation and examples of the following functions:

  • np.where()
  • np.linspace()
  • np.linalg.det()
  • np.linalg.inv()
  • np.linalg.matrix_power()
!pip install jovian --upgrade -q
import jovian
jovian.commit(project='numpy-array-operations')
[jovian] Attempting to save notebook.. [jovian] Please enter your API key ( from https://jovian.ml/ ): API KEY: ········ [jovian] Updating notebook "janrhaverkamp/numpy-array-operations" on https://jovian.ml/ [jovian] Uploading notebook.. [jovian] Capturing environment.. [jovian] Committed successfully! https://jovian.ml/janrhaverkamp/numpy-array-operations

Let's begin by importing Numpy and listing out the functions covered in this notebook.