You can use the following syntax to perform a “NOT IN” filter in a pandas DataFrame:
df[~df['col_name'].isin(values_list)]
Note that the values in values_list can be either numeric values or character values.
The following examples show how to use this syntax in practice.
Example 1: Perform “NOT IN” Filter with One Column
The following code shows how to filter a pandas DataFrame for rows where a team name is not in a list of names:
import pandas as pd #create DataFrame df = pd.DataFrame({'team': ['A', 'A', 'B', 'B', 'B', 'B', 'C', 'C'], 'points': [25, 12, 15, 14, 19, 23, 25, 29], 'assists': [5, 7, 7, 9, 12, 9, 9, 4], 'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]}) #define list of teams we don't want values_list = ['A', 'B'] #filter for rows where team name is not in list df[~df['team'].isin(values_list)] team points assists rebounds 6 C 25 9 9 7 C 29 4 12
And the following code shows how to filter a pandas DataFrame for rows where the ‘points’ column does not contain certain values:
import pandas as pd #create DataFrame df = pd.DataFrame({'team': ['A', 'A', 'B', 'B', 'B', 'B', 'C', 'C'], 'points': [25, 12, 15, 14, 19, 23, 25, 29], 'assists': [5, 7, 7, 9, 12, 9, 9, 4], 'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]}) #define list of values we don't want values_list = [12, 15, 25] #filter for rows where team name is not in list df[~df['team'].isin(values_list)] team points assists rebounds 3 B 14 9 6 4 B 19 12 6 5 B 23 9 5 7 C 29 4 12
Example 2: Perform “NOT IN” Filter with Multiple Columns
The following code shows how to filter a pandas DataFrame for rows where certain team names are not in one of several columns:
import pandas as pd #create DataFrame df = pd.DataFrame({'star_team': ['A', 'A', 'B', 'B', 'B', 'B', 'C', 'C'], 'backup_team': ['B', 'B', 'C', 'C', 'D', 'D', 'D', 'E'], 'points': [25, 12, 15, 14, 19, 23, 25, 29], 'assists': [5, 7, 7, 9, 12, 9, 9, 4], 'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]}) #define list of teams we don't want values_list = ['C', 'E'] #filter for rows where team name is not in one of several columns df[~df[['star_team', 'backup_team']].isin(values_list).any(axis=1)] star_team backup_team points assists rebounds 0 A B 25 5 11 1 A B 12 7 8 4 B D 19 12 6 5 B D 23 9 5
Notice that we filtered out every row where teams ‘C’ or ‘E’ appeared in either the ‘star_team’ column or the ‘backup_team’ column.
Additional Resources
The following tutorials explain how to perform other common filtering operations in pandas:
How to Use “Is Not Null” in Pandas
How to Filter a Pandas DataFrame by Column Values
How to Filter Pandas DataFrame Rows by Date
How to Filter a Pandas DataFrame on Multiple Conditions
