Numpy Concatenate

import numpy as np x = np.array([8,5,3,1,77,1]) y = np.array([6,71,56,11,23]) print(x)print(y) z=np.concatenate([x,y]) print()print(‘Concatenated array: ‘,z) Output [ 8 5 3 1 77 1][ 6 71 56 11 23] Concatenated array: [ 8 5 3 1 77 1 6 71 56 11 23]

Numpy reduce function

Numpy reduce function

Matrix aggregate functions

from numpy import * x = matrix([[11,22,33],[44,55,66],[1,2,3]]) print(‘Matrix: \n’,x) print() print(‘Largest Element: ‘,x.max()) print() print(‘Smallest Element: ‘,x.min()) print() print(‘Sum: ‘,x.sum()) print() print(‘Mean: ‘,x.mean()) Output Matrix: [[11 22 33][44 55 66][ 1 2 3]] Largest Element: 66 Smallest Element: 1 Sum: 237 Mean: 26.333333333333332

Matrix diagonal

Matrix diagonal elements

Matrix transpose

from numpy import * x = matrix([[11,22,33],[44,55,66],[1,2,3]]) print(‘Matrix before transpose:\n’,x) print() print(‘Matrix after transpose:\n’,x.transpose()) Output Matrix before transpose:[[11 22 33][44 55 66][ 1 2 3]] Matrix after transpose:[[11 44 1][22 55 2][33 66 3]]

Matrix arithmetic operations

from numpy import * x = matrix([[11,22,33],[44,55,66],[1,2,3]]) y = matrix([[10,20,30],[40,50,51],[4,5,6]]) print(‘Matrix x:\n’,x)print(‘Matrix y:\n’,y) print() print(‘Addition: \n’,x + y) print() print(‘Subtraction: \n’,x – y) print() print(‘Multiplication: \n’,x * y) print() print(‘Division: \n’,x / y) Output Matrix x:[[11 22 33][44 55 66][ 1 2 3]]Matrix y:[[10 20 30][40 50 51][ 4 5 6]] Addition: [[ 21 42

Numpy array attributes and functions

Numpy reshape, flatten

Function returns multiple functions

function returns multiple functions

Numpy array

Numpy array

MySQL select with where

select with where