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Pandas: Devide a DataFrame in a given ratio

Pandas: DataFrame Exercise-38 with Solution

Write a Pandas program to devide a DataFrame in a given ratio.

Sample data:
Original DataFrame:
0 1
0 0.316147 -0.767359
1 -0.813410 -2.522672
2 0.869615 1.194704
3 -0.892915 -0.055133
4 -0.341126 0.518266
5 1.857342 1.361229
6 -0.044353 -1.205002
7 -0.726346 -0.535147
8 -1.350726 0.563117
9 1.051666 -0.441533
70% of the said DataFrame:
0 1
8 -1.350726 0.563117
2 0.869615 1.194704
5 1.857342 1.361229
6 -0.044353 -1.205002
3 -0.892915 -0.055133
1 -0.813410 -2.522672
0 0.316147 -0.767359
30% of the said DataFrame:
0 1
4 -0.341126 0.518266
7 -0.726346 -0.535147
9 1.051666 -0.441533

Sample Solution :

Python Code :

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(10, 2))
print("Original DataFrame:")
print(df)
part_70 = df.sample(frac=0.7,random_state=10)
part_30 = df.drop(part_70.index)
print("\n70% of the said DataFrame:")
print(part_70)
print("\n30% of the said DataFrame:")
print(part_30)

Sample Output:

Original DataFrame:
          0         1
0  0.316147 -0.767359
1 -0.813410 -2.522672
2  0.869615  1.194704
3 -0.892915 -0.055133
4 -0.341126  0.518266
5  1.857342  1.361229
6 -0.044353 -1.205002
7 -0.726346 -0.535147
8 -1.350726  0.563117
9  1.051666 -0.441533

70% of the said DataFrame:
          0         1
8 -1.350726  0.563117
2  0.869615  1.194704
5  1.857342  1.361229
6 -0.044353 -1.205002
3 -0.892915 -0.055133
1 -0.813410 -2.522672
0  0.316147 -0.767359

30% of the said DataFrame:
          0         1
4 -0.341126  0.518266
7 -0.726346 -0.535147
9  1.051666 -0.441533                  

Python Code Editor:


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Python: Tips of the Day

Creates a dictionary with the same keys as the provided dictionary and values generated by running the provided function for each value

Example:

def tips_map_values(obj, fn):
  ret = {}
  for key in obj.keys():
    ret[key] = fn(obj[key])
  return ret
users = {
  'Owen': { 'user': 'Owen', 'age': 29 },
  'Eddie': { 'user': 'Eddie', 'age': 15 }
}

print(tips_map_values(users, lambda u : u['age'])) # {'Owen': 29, 'Eddie': 15}

Output:

{'Owen': 29, 'Eddie': 15}