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Pandas: Split the specified dataframe into groups based on all columns and calculate Groupby value counts on the dataframe

Pandas Grouping and Aggregating: Split-Apply-Combine Exercise-14 with Solution

Write a Pandas program to split the following dataframe into groups based on all columns and calculate GroupBy value counts on the dataframe.

Test Data:

   id  type     book
0   1    10     Math
1   2    15  English
2   1    11  Physics
3   1    20     Math
4   2    21  English
5   1    12  Physics
6   2    14  English

Sample Solution:

Python Code :

import pandas as pd
df = pd.DataFrame( {'id' : [1, 2, 1, 1, 2, 1, 2], 
                    'type' : [10, 15, 11, 20, 21, 12, 14], 
                    'book' : ['Math','English','Physics','Math','English','Physics','English']})

print("Original DataFrame:")
print(df)
result = df.groupby(['id', 'type', 'book']).size().unstack(fill_value=0)
print("\nResult:")
print(result)

Sample Output:

Original DataFrame:
   id  type     book
0   1    10     Math
1   2    15  English
2   1    11  Physics
3   1    20     Math
4   2    21  English
5   1    12  Physics
6   2    14  English

Result:
book     English  Math  Physics
id type                        
1  10          0     1        0
   11          0     0        1
   12          0     0        1
   20          0     1        0
2  14          1     0        0
   15          1     0        0
   21          1     0        0

Python Code Editor:


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Next: Write a Pandas program to split the following dataframe into groups and count unique values of ‘value’ column.

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Python: Tips of the Day

Returns True if there are duplicate values in a flat list, False otherwise

Example:

def tips_duplicates(lst):
  return len(lst) != len(set(lst))

x = [2, 4, 6, 8, 4, 2]
y = [1, 3, 5, 7, 9]
print(tips_duplicates(x))
print(tips_duplicates(y))

Output:

True
False