Supposing I have 20x100 numpy array. I want to select all columns except say 50th. So I was following this thread Extracting specific columns in numpy array but it didn't help. I tried using
x=Z[:,[:49,51:]]
but was giving error. In R it is easy to do this
x=Z[,c(1:49,51:100)]
But could not figure out in Python. Please help. Thanks
One way to get an R-like syntax here would be to use np.r_:
>>> Z = np.arange(2000).reshape(20, 100)
>>> Z.shape
(20, 100)
>>> x = Z[:,np.r_[:49,50:100]]
>>> x.shape
(20, 99)
>>> x[0,48:52]
array([48, 50, 51, 52])
and we see that the 50th column (with number 49) is missing from x.
This would work:
>>> a = np.arange(2000).reshape(20, 100)
>>> b = a[:, np.arange(a.shape[1]) != 50]
>>> b.shape
(20, 99)
You can simply delete the 50th column using np.delete() :
A = np.delete(A, 50, 1)
Demo:
>>> import numpy as np
>>> A = np.arange(100).reshape(25,4)
>>> A
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23],
[24, 25, 26, 27],
[28, 29, 30, 31],
[32, 33, 34, 35],
[36, 37, 38, 39],
[40, 41, 42, 43],
[44, 45, 46, 47],
[48, 49, 50, 51],
[52, 53, 54, 55],
[56, 57, 58, 59],
[60, 61, 62, 63],
[64, 65, 66, 67],
[68, 69, 70, 71],
[72, 73, 74, 75],
[76, 77, 78, 79],
[80, 81, 82, 83],
[84, 85, 86, 87],
[88, 89, 90, 91],
[92, 93, 94, 95],
[96, 97, 98, 99]])
>>>
>>> A = np.delete(A, 2, 1)
>>> A
array([[ 0, 1, 3],
[ 4, 5, 7],
[ 8, 9, 11],
[12, 13, 15],
[16, 17, 19],
[20, 21, 23],
[24, 25, 27],
[28, 29, 31],
[32, 33, 35],
[36, 37, 39],
[40, 41, 43],
[44, 45, 47],
[48, 49, 51],
[52, 53, 55],
[56, 57, 59],
[60, 61, 63],
[64, 65, 67],
[68, 69, 71],
[72, 73, 75],
[76, 77, 79],
[80, 81, 83],
[84, 85, 87],
[88, 89, 91],
[92, 93, 95],
[96, 97, 99]])
Alternatively, you can for iloc
import numpy as np
import pandas as pd
data = np.random.normal(size=2000).reshape(20, 100)
df = pd.DataFrame(data, columns=list(range(1,101)))
df.iloc[:,list(range(49)) + list(range(50, 100))]
