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Pandas: Split the specified dataframe into groups based on first column and set other column values into a list of values

Pandas Grouping and Aggregating: Split-Apply-Combine Exercise-13 with Solution

Write a Pandas program to split the following dataframe into groups based on first column and set other column values into a list of values.

Test Data:

    X   Y   Z
0  10  10  22
1  10  15  20
2  10  11  18
3  20  20  20
4  30  21  13
5  30  12  10
6  10  14   0

Sample Solution:

Python Code :

import pandas as pd
df = pd.DataFrame( {'X' : [10, 10, 10, 20, 30, 30, 10], 
                    'Y' : [10, 15, 11, 20, 21, 12, 14], 
                    'Z' : [22, 20, 18, 20, 13, 10, 0]})
print("Original DataFrame:")
print(df)
result= df.groupby('X').aggregate(lambda tdf: tdf.unique().tolist())
print(result)

Sample Output:

Original DataFrame:
    X   Y   Z
0  10  10  22
1  10  15  20
2  10  11  18
3  20  20  20
4  30  21  13
5  30  12  10
6  10  14   0
                   Y                Z
X                                    
10  [10, 15, 11, 14]  [22, 20, 18, 0]
20              [20]             [20]
30          [21, 12]         [13, 10]

Python Code Editor:


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Previous: Write a Pandas program to split the following dataframe into groups, group by month and year based on order date and find the total purchase amount year wise, month wise.
Next: Write a Pandas program to split the following dataframe into groups based on all columns and calculate Groupby value counts on the dataframe.

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Python: Tips of the Day

Negative Indexing:

In Python you can use negative indexing. While positive index starts with 0, negative index starts with -1.

name="Welcome"
print(name[0])
print(name[-1])
print(name[0:3])
print(name[-1:-4:-1])

Output:

W
e
Wel
emo