I've seen several answers on that, but on remained unclear to me. How would you select those columns of interest? The answer to that is that if you have them gathered in a list, you can just reference the columns using the list.

Example

print(extracted_features.shape)
print(extracted_features)

(63,)
['f000004' 'f000005' 'f000006' 'f000014' 'f000039' 'f000040' 'f000043'
 'f000047' 'f000048' 'f000049' 'f000050' 'f000051' 'f000052' 'f000053'
 'f000054' 'f000055' 'f000056' 'f000057' 'f000058' 'f000059' 'f000060'
 'f000061' 'f000062' 'f000063' 'f000064' 'f000065' 'f000066' 'f000067'
 'f000068' 'f000069' 'f000070' 'f000071' 'f000072' 'f000073' 'f000074'
 'f000075' 'f000076' 'f000077' 'f000078' 'f000079' 'f000080' 'f000081'
 'f000082' 'f000083' 'f000084' 'f000085' 'f000086' 'f000087' 'f000088'
 'f000089' 'f000090' 'f000091' 'f000092' 'f000093' 'f000094' 'f000095'
 'f000096' 'f000097' 'f000098' 'f000099' 'f000100' 'f000101' 'f000103']

I have the following list/numpy array extracted_features, specifying 63 columns. The original dataset has 103 columns, and I would like to extract exactly those, then I would use

dataset[extracted_features]

And you will end up with this

This something you would use quite often in Machine Learning (more specifically, in feature selection). I would like to discuss other ways too, but I think that has already been covered by other stackoverflowers. Hope this've been helpful!

I've seen several answers on that, but one remained unclear to me. How would you select those columns of interest? 

The answer to that is that if you have them gathered in a list, you can just reference the columns using the list.

Example

print(extracted_features.shape)
print(extracted_features)

(63,)
['f000004' 'f000005' 'f000006' 'f000014' 'f000039' 'f000040' 'f000043'
 'f000047' 'f000048' 'f000049' 'f000050' 'f000051' 'f000052' 'f000053'
 'f000054' 'f000055' 'f000056' 'f000057' 'f000058' 'f000059' 'f000060'
 'f000061' 'f000062' 'f000063' 'f000064' 'f000065' 'f000066' 'f000067'
 'f000068' 'f000069' 'f000070' 'f000071' 'f000072' 'f000073' 'f000074'
 'f000075' 'f000076' 'f000077' 'f000078' 'f000079' 'f000080' 'f000081'
 'f000082' 'f000083' 'f000084' 'f000085' 'f000086' 'f000087' 'f000088'
 'f000089' 'f000090' 'f000091' 'f000092' 'f000093' 'f000094' 'f000095'
 'f000096' 'f000097' 'f000098' 'f000099' 'f000100' 'f000101' 'f000103']

I have the following list/NumPy array extracted_features, specifying 63 columns. The original dataset has 103 columns, and I would like to extract exactly those, then I would use

dataset[extracted_features]

And you will end up with this

This something you would use quite often in machine learning (more specifically, in feature selection). I would like to discuss other ways too, but I think that has already been covered by other Stack Overflower users.