Pandas: Groupby and aggregate over multiple lists
Pandas Grouping and Aggregating: Split-Apply-Combine Exercise-30 with Solution
Write a Pandas program to split the following dataset using group by on first column and aggregate over multiple lists on second column.
Test Data:
student_id marks 0 S001 [88, 89, 90] 1 S001 [78, 81, 60] 2 S002 [84, 83, 91] 3 S002 [84, 88, 91] 4 S003 [90, 89, 92] 5 S003 [88, 59, 90]
Sample Solution:
Python Code :
import pandas as pd
import numpy as np
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
df = pd.DataFrame({
'student_id': ['S001','S001','S002','S002','S003','S003'],
'marks': [[88,89,90],[78,81,60],[84,83,91],[84,88,91],[90,89,92],[88,59,90]]})
print("Original DataFrame:")
print(df)
print("\nGroupby and aggregate over multiple lists:")
result = df.set_index('student_id')['marks'].groupby('student_id').apply(list).apply(lambda x: np.mean(x,0))
print(result)
Sample Output:
Original DataFrame: student_id marks 0 S001 [88, 89, 90] 1 S001 [78, 81, 60] 2 S002 [84, 83, 91] 3 S002 [84, 88, 91] 4 S003 [90, 89, 92] 5 S003 [88, 59, 90] Groupby and aggregate over multiple lists: student_id S001 [83.0, 85.0, 75.0] S002 [84.0, 85.5, 91.0] S003 [89.0, 74.0, 91.0] Name: marks, dtype: object
Python Code Editor:
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