how to Filter by Column Not Equal to Specific Values in pandas?

Method 1: Filter where Column is Not Equal to One Specific Value

#filter rows where team column is not equal to 'Nets'
df_filtered = df[df['team'] != 'Nets']

Method 2: Filter where Column is Not Equal to Several Specific Values

#filter rows where team column is not equal to 'Nets', 'Mavs' or 'Kings'
df_filtered = df[~df['team'].isin(['Nets', 'Mavs', 'Kings'])]

The following examples show how to use each method in practice with the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team': ['Mavs', 'Mavs', 'Nets', 'Nets', 'Heat', 'Heat', 'Kings'],
                   'points': [22, 28, 35, 34, 29, 28, 23]})

#view DataFrame
print(df)

    team  points
0   Mavs      22
1   Mavs      28
2   Nets      35
3   Nets      34
4   Heat      29
5   Heat      28
6  Kings      23

Example 1: Filter where Column is Not Equal to One Specific Value

We can use the following syntax to filter the DataFrame to only contain rows where the team column is not equal to ‘Nets’:

#filter rows where team column is not equal to 'Nets'
df_filtered = df[df['team'] != 'Nets']

#view filtered DataFrame
print(df_filtered)

    team  points
0   Mavs      22
1   Mavs      28
4   Heat      29
5   Heat      28
6  Kings      23

Notice that each row where the team name was ‘Nets’ has been filtered out of the DataFrame.

Note: The symbol != represents “not equal” in pandas.

Example 2: Filter where Column is Not Equal to Several Specific Values

We can use the following syntax to filter the DataFrame to only contain rows where the team column is not equal to ‘Nets’, ‘Mavs’ or ‘Kings’:

#filter rows where team column is not equal to 'Nets', 'Mavs' or 'Kings'
df_filtered = df[~df['team'].isin(['Nets', 'Mavs', 'Kings'])]

#view filtered DataFrame
print(df_filtered)

   team  points
4  Heat      29
5  Heat      28

Notice that each row where the team name was ‘Nets’, ‘Mavs’ or ‘Kings’ has been filtered out of the DataFrame.

Note: The symbol ~ represents “not” in pandas.