Drop all duplicate rows across multiple columns in Python Pandas

use groupby and filter

import pandas as pd
df = pd.DataFrame({"A":["foo", "foo", "foo", "bar"], "B":[0,1,1,1], "C":["A","A","B","A"]})
df.groupby(["A", "C"]).filter(lambda df:df.shape[0] == 1)

If you want result to be stored in another dataset:

df.drop_duplicates(keep=False)

or

df.drop_duplicates(keep=False, inplace=False)

If same dataset needs to be updated:

df.drop_duplicates(keep=False, inplace=True)

Above examples will remove all duplicates and keep one, similar to DISTINCT * in SQL

Just want to add to Ben's answer on drop_duplicates:

keep : {‘first’, ‘last’, False}, default ‘first’

• first : Drop duplicates except for the first occurrence.

• last : Drop duplicates except for the last occurrence.

• False : Drop all duplicates.

So setting keep to False will give you desired answer.

DataFrame.drop_duplicates(*args, **kwargs) Return DataFrame with duplicate rows removed, optionally only considering certain columns

Parameters: subset : column label or sequence of labels, optional Only consider certain columns for identifying duplicates, by default use all of the columns keep : {‘first’, ‘last’, False}, default ‘first’ first : Drop duplicates except for the first occurrence. last : Drop duplicates except for the last occurrence. False : Drop all duplicates. take_last : deprecated inplace : boolean, default False Whether to drop duplicates in place or to return a copy cols : kwargs only argument of subset [deprecated] Returns: deduplicated : DataFrame

This is much easier in pandas now with drop_duplicates and the keep parameter.

import pandas as pd
df = pd.DataFrame({"A":["foo", "foo", "foo", "bar"], "B":[0,1,1,1], "C":["A","A","B","A"]})
df.drop_duplicates(subset=['A', 'C'], keep=False)