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df
A B C D
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
df.drop(['B', 'C'], axis=1, inplace=True)
A D
0 0 3
1 4 7
2 8 11
df.drop(columns=['B', 'C'], inplace = True)
A D
0 0 3
1 4 7
2 8 11
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df.drop('column_name', axis=1, inplace=True)
#no need to reasign df
#axis 1 is columns, 0 is rows
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# Let df be a dataframe
# Let new_df be a dataframe after dropping a column
new_df = df.drop(labels='column_name', axis=1)
# Or if you don't want to change the name of the dataframe
df = df.drop(labels='column_name', axis=1)
# Or to remove several columns
df = df.drop(['list_of_column_names'], axis=1)
# axis=0 for 'rows' and axis=1 for columns
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# Dropping a single column
df = pd.DataFrame({"A":[3,4], "B":[5,6], "C":[7,8]})
df_new = df.drop(columns="B")
# Dropping multiple columns
df_new = df.drop(columns=["A","B"])
# Dropping columns in-place
df.drop(columns="B", inplace=True)
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df.drop(['Col_1', 'Col_2'], axis = 1) # to drop full colum more general way can visulize easily
df.drop(['Col_1', 'Col_2'], axis = 1, inplace = True) # advanced : to generate df without making copies inside memory
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df.drop(['column_1', 'Column_2'], axis = 1, inplace = True)
# Remove all columns between column index 1 to 3
df.drop(df.iloc[:, 1:3], inplace = True, axis = 1)