Select multiple ranges of columns in Pandas DataFrame

Question

I have to read several files some in Excel format and some in CSV format. Some of the files have hundreds of columns.

Is there a way to select several ranges of columns without specifying all the column names or positions? For example something like selecting columns 1 -10, 15, 17 and 50-100:

df = df.ix[1:10, 15, 17, 50:100]

I need to know how to do this both when creating dataframe from Excel files and CSV files and after the data framers created.

Asked By: Alex

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Source

Answer 1

use np.r_

np.r_[1:10, 15, 17, 50:100]

array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 15, 17, 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])

so you can do

df.iloc[:, np.r_[1:10, 15, 17, 50:100]]

Answered By: piRSquared

Answer 2

use inner join
like
result = pd.concat([df1, df4], axis=1, join="inner")

Answered By: Mahmoud Gouda

Answer 3

I find @piRSquared ‘s answer straightforward.

You may also use:

Locs = list(range(0,10)) + [14, 16] + list(range(49, 100))
# columns 1 -10, 15, 17 and 50-100
# [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 14, 16, 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]

df = df.iloc[:Locs]

Answered By: Ahmed El-Gabbas

Select multiple ranges of columns in Pandas DataFrame

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