You can use the following syntax to find the first row in a pandas DataFrame that meets specific criteria:
#get first row where value in 'team' column is equal to 'B' df[df.team == 'B'].iloc[0] #get index of first row where value in 'team' column is equal to 'B' df[df.team == 'B'].index[0]
The following examples show how to use this syntax in practice with the following pandas DataFrame:
import pandas as pd #create DataFrame df = pd.DataFrame({'team': ['A', 'A', 'A', 'B', 'B', 'C', 'C', 'C'], 'points': [18, 13, 19, 14, 24, 21, 20, 28], 'assists': [5, 7, 17, 9, 12, 9, 5, 12]}) #view DataFrame print(df) team points assists 0 A 18 5 1 A 13 7 2 A 19 17 3 B 14 9 4 B 24 12 5 C 21 9 6 C 20 5 7 C 28 12
Example 1: Find First Row that Meets One Criteria
We can use the following syntax to find the first row where the value in the team column is equal to ‘B’:
#find first row where team is equal to 'B' df[df.team == 'B'].iloc[0] team B points 14 assists 9 Name: 3, dtype: object #find index of first row where team is equal to 'B' df[df.team == 'B'].index[0] 3
We can see that the first row where the value in the team column is equal to ‘B’ is in index position 3.
Example 2: Find First Row that Meets Multiple Criteria
We can use the following syntax to find the first row where the value in the points column is greater than 15 and the value in the assists column is greater than 10:
#find first row where points > 15 and assists > 10 df[(df.points > 15) & (df.assists > 10)].iloc[0] team A points 19 assists 17 Name: 2, dtype: object #find index of first row where points > 15 and assists > 10 df[(df.points > 15) & (df.assists > 10)].index[0] 2
We can see that the first row where the value in the points column is greater than 15 and the value in the assists column is greater than 10 is in index position 2.
Example 3: Find First Row that Meets One of Several Criteria
We can use the following syntax to find the first row where the value in the points column is greater than 15 or the value in the assists column is greater than 10:
#find first row where points > 15 or assists > 10 df[(df.points > 15) | (df.assists > 10)].iloc[0] team A points 18 assists 5 Name: 0, dtype: object #find index of first row where points > 15 or assists > 10 df[(df.points > 15) | (df.assists > 10)].index[0] 0
We can see that the first row where the value in the points column is greater than 15 or the value in the assists column is greater than 10 is in index position 0.
Additional Resources
The following tutorials explain how to perform other common tasks in pandas:
How to Select Rows without NaN Values in Pandas
How to Select Rows Based on Column Values in Pandas
How to Select Unique Rows in Pandas