NumPy

According to https://numpy.org/doc/stable/user/whatisnumpy.html

NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.

Direction

The following files have the documentation embedded in the code as comments. Please read the files in the sequence given below to better understand the concepts.

1_matrix_create.py

It covers the following concepts

1. creating a matrix
2. Shape attribute
3. Size attribute
4. Accessing a element
5. Accessing a row
6. Accessing a column
7. Slice
8. Zero indexing
9. Access a matrix

2_matrix_addition.py

Concepts covered

1. addition of matrices
2. subtraction of matrices

3_matrix_multiplication.py

concepts covered

1. @ operator for standard matrix multiplcation
2. .dot() method for multiplication

4_matrix_product_specialcase.py

concepts covered

1. • operator for matrix multiplication
2. all() method of array

5_matrix_scalarProduct.py

1. dtype attribute
2. min() method
3. max()
4. scalar product of matrix

6_matrix_exponents.py

concepts covered

1. square of a matrix

7_trigo_ratio.py

concepts covered

1. sin() ratio
2. deg2rad() method
3. round() method

8_trigo_ratio.py

concepts covered

1. sin()

9_trigo_ratio.py

concepts covered

1. tan() ratio
2. arctan() ratio
3. rad2deg() method

10_statistics.py

concepts covered

1. array()
2. mean()
3. median()
4. max()
5. min()
6. ptp()
7. quantile(data, 0)
8. quantile(data, 0.5)
9. quantile(data, 1)
10. std()
11. var()