# Math for Data Science — Lecture 01 Basic Matrix Operations via Python

In this lecture, we will study Matrix Operations (Initialization, Vector, Multiplication, Transpose, Special Matrix) via Python.

*Matrix Definition*

*Matrix Notation*

*Matrix Initialization*

`import numpy as np`

print(np.zeros((2,3)))

print(np.random.randint(low = 5,high=12, size=(2, 3)))

A = np.matrix([[1, 2], [3, 4], [5,6]])

print("Type of Matrix A:{}, having {} rows and {} columns".format(type(A),A.shape[0],A.shape[1]))

*Vectors*

**Row vector**is having 1 row and N columns**Column vector**is having N rows and 1 column

*Matrix Equality*

*Matrix Multiplication*

In below Image 09, matrix multiplication is shown both via python code and hand as well.

- Matrix multiplication isn’t commutative (AB != BA)

*Matrix Transpose*

*Rules of Matrix Transpose*

*Special Matrix*

*Reduce size of Sparse Matrix using CSR (Compressed Sparse Row)*

*Properties of Determinants*

WORK IN PROGRESS !! Jupyter Notebook/.py will be uploaded soon..

Our Next lecture is about using Elementary row operations to find **Row Echelon Form (REF), Reduced Row Echelon Form (RREF) and Rank of Matrix** (https://medium.com/math-for-data-science/math-for-data-science-lecture-02-elementary-row-operations-via-python-46885a33a84c).

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