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  1. import pandas as pd
  2.  
  3. # Sample data
  4. data = {
  5.     'Pregnancies': [6, 1, 8, 1, 0, 5, 3, 10, 2, 8],
  6.     'Glucose': [148, 85, 183, 89, 137, 116, 78, 197, 125, 110],
  7.     'BloodPressure': [72, 66, 64, 66, 40, 74, 50, 70, 96, 92],
  8.     'SkinThickness': [35, 29, 0, 23, 35, 0, 32, 45, 0, 0],
  9.     'Insulin': [0, 0, 0, 94, 168, 0, 88, 543, 0, 0],
  10.     'BMI': [33.6, 26.6, 23.3, 28.1, 43.1, 25.6, 31.0, 30.5, 0.0, 37.6],
  11.     'DiabetesPedigreeFunction': [0.627, 0.351, 0.672, 0.167, 2.288, 0.201, 0.248, 0.158, 0.191, 0.191],
  12.     'Age': [50, 31, 32, 21, 33, 30, 26, 53, 54, 30],
  13.     'Outcome': [1, 0, 1, 0, 1, 0, 1, 1, 1, 0]
  14. }
  15.  
  16. # Create DataFrame
  17. df = pd.DataFrame(data)
  18.  
  19. # Aggregate data based on Glucose columns
  20. aggregated_data = df.groupby(pd.cut(df['Glucose'], bins=[0, 100, 125, 150, 200])).mean()
  21. print(aggregated_data)
  22.  
Success #stdin #stdout 0.4s 59236KB
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            Pregnancies  Glucose  ...   Age   Outcome
Glucose                           ...                
(0, 100]       1.666667     84.0  ...  26.0  0.333333
(100, 125]     5.000000    117.0  ...  38.0  0.333333
(125, 150]     3.000000    142.5  ...  41.5  1.000000
(150, 200]     9.000000    190.0  ...  42.5  1.000000

[4 rows x 9 columns]
https://ideone.com/nydT5l
language:
Python 3 (python  3.9.5)
created:
9 days ago
visibility:
public

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