GetStartWith_Numpy

Numpy_Tutorial

Install and import

Numpy is an easy package to install. Open up your terminal program or command line and install it using either of the following commands:

conda install numpy

OR

pip install numpy

The NumPy library is a popular open source Python library used for scientific computing applications, and is an acronym for "Numerical Python". NumPy's operations are divided into three main categories:

1. Fourier Transform and Shape Manipulation
2. Mathematical and Logical Operations, and
3. Linear Algebra and Random Number Generation.

NumPy is an incredible library which works perfectly well for multi-dimensional arrays and matrices multiplication.

NumPy is often used along with packages like SciPy (Scientific Python) and Mat−plotlib (plotting library).

This repository Covered following Numpy Operations..

Have a look on Things_to_Know(Mastering_Numpy).md before diving into the code...

Creating a NumPy Array

1. The array Method
2. The arange Method
3. The zeros Method
4. The ones Method
5. The linspace Method
6. The eye Method
7. The random Method
8. The empty Method
9. The logspace method

Reshaping NumPy Array

Array Indexing in NumPy

1. Indexing with 1-D Arrays
2. Indexing with 2-D Arrays

Arithmetic Operations with NumPy Arrays

1. The log Function
2. The exp Function
3. The sqrt Function
4. The sin Function
5. Finding Max/Min Values

Linear Algebra Operations with NumPy Arrays

1. Finding the Vector Dot Product
2. Matrix Multiplication
3. Finding the Inverse of a Matrix
4. Finding the Determinant of a Matrix
5. Finding the Trace of a Matrix

Plots Using MatPlotLib