Python data analysis and machine learning -NumPy_5

import numpy as np
data = np.sin(np.arange(20)).reshape(5,4)
print(data)
ind = data.argmax(axis=0)
print(ind)
data_max = data[ind,range(data.shape[1])]
print(data_max)
all(data_max == data.max(axis=0))

[[ 0.          0.84147098  0.90929743  0.14112001]
 [-0.7568025  -0.95892427 -0.2794155   0.6569866 ]
 [ 0.98935825  0.41211849 -0.54402111 -0.99999021]
 [-0.53657292  0.42016704  0.99060736  0.65028784]
 [-0.28790332 -0.96139749 -0.75098725  0.14987721]]
[2 0 3 1]
[0.98935825 0.84147098 0.99060736 0.6569866 ]





True

a = np.arange(0,40,10)
b = np.tile(a,(3,5))
print(a)
print(b)

[ 0 10 20 30]
[[ 0 10 20 30  0 10 20 30  0 10 20 30  0 10 20 30  0 10 20 30]
 [ 0 10 20 30  0 10 20 30  0 10 20 30  0 10 20 30  0 10 20 30]
 [ 0 10 20 30  0 10 20 30  0 10 20 30  0 10 20 30  0 10 20 30]]

a = np.array([[4,3,5],[1,2,1]])
print(a)
b = np.sort(a,axis=1)
print(b)
a.sort(axis=1)
print(a)
print("---")
a = np.array([4,3,1,2])
j = np.argsort(a)
print(j)
print(a[j])

[[4 3 5]
 [1 2 1]]
[[3 4 5]
 [1 1 2]]
[[3 4 5]
 [1 1 2]]
---
[2 3 1 0]
[1 2 3 4]