How to take all columns except one column in Data Frame Python?

Question

I have DataFrame with for example 24 columns and my question is how can I take all columns except for instance column number 17 ?

data[:,:] using this kind of syntax.

You can just use the drop() function to drop the 17th column, like so: df.drop('17_column', 1) — techytushar
– techytushar, Commented Sep 25, 2020 at 15:23
Manakin, but using your code I only take start col for example 1 and end col for example 16, but I want all columns except number 17 — dingaro
– dingaro, Commented Sep 25, 2020 at 15:28

boozy · Accepted Answer · 2020-09-25 16:03:27Z

3

you can do this:

data.drop(columns=['a']) # for column name
data.drop(columns=data.columns[17]) # for column index

It returns a dataframe that you can use.

For more information: pandas drop

edited Sep 25, 2020 at 16:03

answered Sep 25, 2020 at 15:28

boozy

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2 Comments

anky Over a year ago

Thanks, its way better than the previous edit which didnt work, I cant delete my answer now but this method is more readable.

boozy Over a year ago

I updated the response. drop([17]) was just deleting rows.

Giorgos Myrianthous · Accepted Answer · 2021-10-20 16:21:24Z

2

I think the easiest way would be:

data.loc[:, data.columns != 'col_name']

or

data.loc[:, ~data.columns.isin(['col_name'])]

edited Oct 20, 2021 at 16:21

answered Sep 25, 2020 at 15:43

Giorgos Myrianthous

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Comments

anky · Accepted Answer · 2020-09-25 15:55:31Z

You can use np.r_ with df.iloc like below for position based indexing with slicing:

pos_of_col= 17
df.iloc[:,np.r_[range(pos_of_col-1),range(pos_of_col,len(df.columns))]]

Demo , dropping column at position 4 (column 3 since python indexing starts at 0)

np.random.seed(0)
df = pd.DataFrame(np.random.randint(0,20,(5,10))).add_prefix("col_")
print(df,'\n')

pos_of_col= 4
print(df.iloc[:,np.r_[range(pos_of_col-1),range(pos_of_col,len(df.columns))]])



   col_0  col_1  col_2  col_3  col_4  col_5  col_6  col_7  col_8  col_9
0     12     15      0      3      3      7      9     19     18      4
1      6     12      1      6      7     14     17      5     13      8
2      9     19     16     19      5     15     15      0     18      3
3     17     19     19     19     14      7      0      1      9      0
4     10      3     11     18      2      0      0      4      5      6 

   col_0  col_1  col_2  col_4  col_5  col_6  col_7  col_8  col_9
0     12     15      0      3      7      9     19     18      4
1      6     12      1      7     14     17      5     13      8
2      9     19     16      5     15     15      0     18      3
3     17     19     19     14      7      0      1      9      0
4     10      3     11      2      0      0      4      5      6

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