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
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)]
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]
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())
