Numpy is the main library for numerical and scientific computing in Python. Numpy basically stands for Numerical python. It is used to perform the numerical operation on arrays. It provides a rich set of methods and features for performing operations on arrays and matrices. Numpy is better than Python list in terms of size, functionality, and performances.

There are lots of data science and machine learning libraries like Scipy, Matplotlib, Scikit Learn, Tensorflow depends upon Numpy.

This course is basically covers basic of Numpy for the data science beginner. In this course you will learn about how to create numpy array, matrix and their basic operations.

#import numpy library

import numpy as np

#### Create a vector or array

row_vect=np.array([1,2,3,4,5,6])

print("Row_vector =",row_vect)

col_vect=np.array([[1],[2],[3],[4],[5],[6]])

print("Col_vector = ")

print(col_vect)

#### View Number of dimension

row_vect.ndim

col_vect.ndim

#### Select and Slice the elements from the array

#select 2nd element from row_vect

#since array indexing start from zero therefore second element would be

print(row_vect[1])

#same concept here also apply

print(col_vect[1])

#Slice element from 2nd to 5th in row_vect

print(row_vect[2:5])

#same concept here also apply

print(col_vect[2:5])

#Select all element from position 2

print(row_vect[2:])

#### Transpose of the matrix

m#Transpose of a matrix

new_row_vect=row_vect.T

print(new_row_vect)

new_col_vect=col_vect.T

print(new_col_vect)

#### Create a matrix

#Create a matrix

mat=np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]])

print(mat)

array([[ 1, 2, 3, 4],

[ 5, 6, 7, 8],

[ 9, 10, 11, 12],

[13, 14, 15, 16]])

#### Shape of matrix

#view shape

mat.shape

#### View total elements in the matrix

#view total element

mat.size

#### Dimension of matrix

#view dimension of matrix

mat.ndim

#### Maximum/Minimum element in the matrix

#Maximum element in the Matrix

mat.max()

#Minimum element in the Matrix

mat.max()

#Maximum element by the column of the matrix

np.max(mat,axis=0)

#Maximum element by the row of the matrix

np.min(mat,axis=1)

#Minimum element by the column of the matrix

np.min(mat,axis=0)

#Minimum element by the row of the matrix

np.min(mat,axis=1)

#### Diagonal of the matrix

mat.diagonal()

#### Sum of diagonal of the matrix

mat.diagonal().sum()

#### Rank of the matrix

np.linalg.matrix_rank(mat)

#### Flatten the matrix

mat.flatten()

#### Calculate the determinant of the matrix

np.linalg.det(mat)

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