4

col_exclusions = ['numerator','Numerator' 'Denominator', "denominator"]

dataframe

id prim_numerator sec_Numerator tern_Numerator tern_Denominator final_denominator Result

1       12                23           45          54                      56         Fail

Final output is id and Result

Sociopath
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rakesh
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    Does this answer your question? [Delete column from pandas DataFrame](https://stackoverflow.com/questions/13411544/delete-column-from-pandas-dataframe) – Sociopath Feb 18 '20 at 09:58
  • possible duplicate: https://stackoverflow.com/questions/51167612/what-is-the-best-way-to-remove-columns-in-pandas – 404pio Feb 18 '20 at 09:58
  • @AkshayNevrekar This is not my question – rakesh Feb 18 '20 at 10:02
  • Im having column names combined with word numerator etc so if numerator comes in any place means i have to remove that – rakesh Feb 18 '20 at 10:03

3 Answers3

5

using regex

import re

pat = re.compile('|'.join(col_exclusions),flags=re.IGNORECASE)

final_cols = [c for c in df.columns if not re.search(pat,c)]

#out:

['id', 'Result']

print(df[final_cols])

   id Result
0   1   Fail

if you want to drop 

df = df.drop([c for c in df.columns if re.search(pat,c)],axis=1)

or the pure pandas approach thanks to @Anky_91

df.loc[:,~df.columns.str.contains('|'.join(col_exclusions),case=False)]
Umar.H
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0

You can be explicit and use del for columns that contain the suffixes in your input list:

for column in df.columns:
    if any([column.endswith(suffix) for suffix in col_exclusions]):
        del df[column]
arnaud
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0

You can also use the following approach where the column names are splitted then matched with col_exclusions

df.drop(columns=[i for i in df.columns if i.split("_")[-1] in col_exclusions], inplace=True)
print(df.head())
Abercrombie
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