Select specific columns from pandas df

Question

Is there an efficient way to drop n number of columns from a pandas df. For example, if you don't want anything past a certain column can you incorporate a function df.drop > n

For the df below I want to drop everything past column 2. I doing this via.

import pandas as pd

d = ({
    '1' : ['X','Y'],  
    '2' : ['A','B'], 
    '3' : ['C','D'],   
    '4' : ['A','B'], 
    '5' : ['C','D'], 
     })

df = pd.DataFrame(data=d)

df = df.drop(['3','4','5'], axis = 1)

But if there are more or less than 5 columns I would have to manually add or subtract the column values. If I'm constantly doing this it becomes cumbersome.

df.iloc[:,:n] will drop everything after n columns. If the number of columns is less than n then no error will occur. — Space Impact
– Space Impact, Commented Aug 22, 2018 at 4:26

Pyd · Accepted Answer · 2018-08-22 04:30:14Z

1

you can use df.iloc,

df=df.iloc[:,:3] #sliced based on indices. will keeps columns 0 to 2 (indices).
(or)
df=df.loc[:,:'3'] #sliced based on column names
print(df)
1   2   3
X   A   C
Y   B   D

answered Aug 22, 2018 at 4:30

Pyd

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