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  1. import pandas as pd
  2. data = {
  3.     'Pregnancies': [6, 1, 8, 1, 0, 5, 3, 10, 2, 8],
  4.     'Glucose': [148, 85, 183, 89, 137, 116, 78, 197, 125, 110],
  5.     'BloodPressure': [72, 66, 64, 66, 40, 74, 50, 70, 96, 92],
  6.     'SkinThickness': [35, 29, 0, 23, 35, 0, 32, 45, 0, 0],
  7.     'Insulin': [0, 0, 0, 94, 168, 0, 88, 543, 0, 0],
  8.     'BMI': [33.6, 26.6, 23.3, 28.1, 43.1, 25.6, 31.0, 30.5, 0.0, 37.6],
  9.     'DiabetesPedigreeFunction': [0.627, 0.351, 0.672, 0.167, 2.288, 0.201, 0.248, 0.158, 0.191, 0.191],
  10.     'Age': [50, 31, 32, 21, 33, 30, 26, 53, 54, 30],
  11.     'Outcome': [1, 0, 1, 0, 1, 0, 1, 1, 1, 0]
  12. }
  13. df = pd.DataFrame(data)
  14. aggregated_data = df.groupby(pd.cut(df['Glucose'], bins=[0, 100, 125, 150, 200])).mean()
  15. print(aggregated_data)
  16. df = pd.DataFrame(data)
  17. df.drop_duplicates(inplace=True)
  18. print(df)
  19. df = pd.DataFrame(data)
  20. filtered_data = df[df['Glucose'] > 120]
  21. print(filtered_data)
  22. df = pd.DataFrame(data)
  23. correlation = df['Age'].corr(df['Glucose'])
  24. print("Correlation between age and glucose level:", correlation)
  25.  
Success #stdin #stdout 0.42s 60680KB
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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]
   Pregnancies  Glucose  BloodPressure  ...  DiabetesPedigreeFunction  Age  Outcome
0            6      148             72  ...                     0.627   50        1
1            1       85             66  ...                     0.351   31        0
2            8      183             64  ...                     0.672   32        1
3            1       89             66  ...                     0.167   21        0
4            0      137             40  ...                     2.288   33        1
5            5      116             74  ...                     0.201   30        0
6            3       78             50  ...                     0.248   26        1
7           10      197             70  ...                     0.158   53        1
8            2      125             96  ...                     0.191   54        1
9            8      110             92  ...                     0.191   30        0

[10 rows x 9 columns]
   Pregnancies  Glucose  BloodPressure  ...  DiabetesPedigreeFunction  Age  Outcome
0            6      148             72  ...                     0.627   50        1
2            8      183             64  ...                     0.672   32        1
4            0      137             40  ...                     2.288   33        1
7           10      197             70  ...                     0.158   53        1
8            2      125             96  ...                     0.191   54        1

[5 rows x 9 columns]
Correlation between age and glucose level: 0.6146396695225879
https://ideone.com/Mk3wag
language:
Python 3 (python  3.9.5)
created:
9 days ago
visibility:
public

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