I have a pandas dataframe and it has some columns. I want to drop columns if they are not presented at a list.
pandas dataframe columns:
list(pandas_df.columns.values)
Result:
['id', 'name' ,'region', 'city']
And my expected column names:
final_table_columns = ['id', 'name', 'year']
After x operations result should be:
list(pandas_df.columns.values)
['id', 'name']
Use Index.intersection to find the intersection of an index and a list of (column) labels:
pandas_df = pandas_df[pandas_df.columns.intersection(final_table_columns)]
Index.intersection(), if the number of columns to keep is small, compared to those to exclude. In the opposite case (exclude only a few of a large number) Index.difference() may be the better choice. White-listing vs. black-listing.You could use a list comprehension creating all column-names to drop()
final_table_columns = ['id', 'name', 'year']
df = df.drop(columns=[col for col in df if col not in final_table_columns])
To do it in-place:
df.drop(columns=[col for col in df if col not in final_table_columns], inplace=True)
To do it in-place, consider Index.difference. This was not documented in any prior answer.
df.drop(columns=df.columns.difference(final_table_columns), inplace=True)
To create a new dataframe, Index.intersection also works.
df_final = df.drop(columns=df.columns.difference(final_table_columns)
df_final = df[df.columns.intersection(final_table_columns)] # credited to unutbu
You could also accomplish this much more simply
df = df[final_table_columns]