numpy masked array

import numpy as npa = np.arange(5)print(a)x = np.ma.array(a,mask=[[9,9,9,9,9]])print(x)y = np.ma.array(a,mask=[[0,1,0,1,0]])print(y) Output[0 1 2 3 4][– — — — –][0 — 2 — 4]

Threading demo

from threading import *from time import * class Calc: def __init__(self,n1,n2):        self.num1 = n1       self.num2 = n2 def add(self):        print(self.num1 + self.num2) def subtract(self):        print(self.num1 – self.num2) obj1 = Calc(8,2)obj2 = Calc(10,3) ThreadAdd1 = Thread(target=obj1.add)ThreadAdd2 = Thread(target=obj2.add) ThreadAdd1.start()print()ThreadAdd2.start() ThreadSub1 = Thread(target=obj1.subtract)ThreadSub2 = Thread(target=obj2.subtract) ThreadSub1.start()print()ThreadSub2.start()

Threading

Python threading

Numpy T attribute

from numpy import * arr = array([[10,20,30,40,50],[11,22,33,4,5]])print(‘Original array\n’,arr) print(‘\nTransposed array: ‘) print(arr.T) Output Original array[[10 20 30 40 50][11 22 33 4 5]] Transposed array: [[10 11][20 22][30 33][40 4][50 5]]

Numpy amin

from numpy import * arr = array([[10,20,30,40,50],[11,22,33,4,5]])print(arr) print() print(‘Smallest values across columns: ‘,amin(arr,axis=0)) print() print(‘Smallest values across rows: ‘,amin(arr,axis=1)) Output [[10 20 30 40 50][11 22 33 4 5]] Smallest values across columns: [10 20 30 4 5] Smallest values across rows: [10 4]

Numpy amax

from numpy import * arr = array([[10,20,30,40,50],[11,22,33,4,5]])print(arr) print() print(‘Max values across columns: ‘,amax(arr,axis=0)) print() print(‘Max values across rows: ‘,amax(arr,axis=1)) Output [[10 20 30 40 50][11 22 33 4 5]] Max values across columns: [11 22 33 40 50] Max values across rows: [50 33]

Numpy argmax

Numpy argmax function

Numpy extract

import numpy as np x = np.array([[2,3,4,5],[12,23,44,25],[12,33,64,85]]) print(x) print() evencondition = np.mod(x,2)==0 print(np.extract(x,evencondition)) print() print(x[evencondition]) Output [[ 2 3 4 5][12 23 44 25][12 33 64 85]] [ True False True False True False True False True False True False] [ 2 4 12 44 12 64]

Numpy cumulative product

import numpy as np arr = np.array([[6, 1, 3], [8, 2, 1]]) print(‘2D Array\n’,arr) b = np.cumprod(arr, dtype=int) print(‘Cumulative product of all elements\n’,b) c = np.cumprod(arr,axis=0) print(‘Cumulative product of elements axis=0\n’,c) x = np.array([6, 1, 3]) print(‘Single dimension array ‘,x) y = np.cumprod(x, dtype=int) print(‘Single Dimension array Cumulative product of all elements\n’,y) Output 2D Array[[6

Numpy accumulate

import numpy as np x = np.arange(5,11) print(x) y = np.add.accumulate(x) print(‘Addition: ‘,y) print() x1 = np.arange(1,7) print(x1) y1=np.multiply.accumulate(x1) print(‘Multiplication: ‘,y1) Output [ 5 6 7 8 9 10]Addition: [ 5 11 18 26 35 45] [1 2 3 4 5 6]Multiplication: [ 1 2 6 24 120 720]