You can use the following methods to select rows of a pandas DataFrame based on multiple conditions:
Method 1: Select Rows that Meet Multiple Conditions
df.loc[((df['col1'] == 'A') & (df['col2'] == 'G'))]
Method 2: Select Rows that Meet One of Multiple Conditions
df.loc[((df['col1'] > 10) | (df['col2']
The following examples show how to use each of these methods in practice with the following pandas DataFrame:
import pandas as pd #create DataFrame df = pd.DataFrame({'team': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'], 'position': ['G', 'G', 'F', 'F', 'G', 'G', 'F', 'F'], 'assists': [5, 7, 7, 9, 12, 9, 9, 4], 'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]}) #view DataFrame df team position assists rebounds 0 A G 5 11 1 A G 7 8 2 A F 7 10 3 A F 9 6 4 B G 12 6 5 B G 9 5 6 B F 9 9 7 B F 4 12
Method 1: Select Rows that Meet Multiple Conditions
The following code shows how to only select rows in the DataFrame where the team is equal to ‘A’ and the position is equal to ‘G’:
#select rows where team is equal to 'A' and position is equal to 'G'
df.loc[((df['team'] == 'A') & (df['position'] == 'G'))]
team position assists rebounds
0 A G 5 11
1 A G 7 8
There were only two rows in the DataFrame that met both of these conditions.
Method 2: Select Rows that Meet One of Multiple Conditions
The following code shows how to only select rows in the DataFrame where the assists is greater than 10 or where the rebounds is less than 8:
#select rows where assists is greater than 10 or rebounds is less than 8
df.loc[((df['assists'] > 10) | (df['rebounds']
There were only three rows in the DataFrame that met both of these conditions.
Note: In these two examples we filtered rows based on two conditions but using the & and | operators, we can filter on as many conditions as we’d like.
Additional Resources
The following tutorials explain how to perform other common operations in pandas:
How to Create a New Column Based on a Condition in Pandas
How to Drop Rows that Contain a Specific Value in Pandas
How to Drop Duplicate Rows in Pandas