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