Pandas: Split a given dataframe into groups with bin counts

Last update on April 04 2025 13:44:52 (UTC/GMT +8 hours)

18. Grouping with Bin Counts

Write a Pandas program to split a given dataframe into groups with bin counts.

Test Data:

    ord_no  purch_amt  customer_id  sales_id
0    70001     150.50         3005      5002
1    70009     270.65         3001      5003
2    70002      65.26         3002      5004
3    70004     110.50         3009      5003
4    70007     948.50         3005      5002
5    70005    2400.60         3007      5001
6    70008    5760.00         3002      5005
7    70010    1983.43         3004      5007
8    70003    2480.40         3009      5008
9    70012     250.45         3008      5004
10   70011      75.29         3003      5005
11   70013    3045.60         3002      5001

Sample Solution:

Python Code :

import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
df = pd.DataFrame({
'ord_no':[70001,70009,70002,70004,70007,70005,70008,70010,70003,70012,70011,70013],
'purch_amt':[150.5,270.65,65.26,110.5,948.5,2400.6,5760,1983.43,2480.4,250.45, 75.29,3045.6],
'customer_id':[3005,3001,3002,3009,3005,3007,3002,3004,3009,3008,3003,3002],
'sales_id':[5002,5003,5004,5003,5002,5001,5005,5007,5008,5004,5005,5001]})
print("Original DataFrame:")
print(df)
groups = df.groupby(['customer_id', pd.cut(df.sales_id, 3)])
result = groups.size().unstack()
print(result)

Sample Output:

Original DataFrame:
    ord_no  purch_amt  customer_id  sales_id
0    70001     150.50         3005      5002
1    70009     270.65         3001      5003
2    70002      65.26         3002      5004
3    70004     110.50         3009      5003
4    70007     948.50         3005      5002
5    70005    2400.60         3007      5001
6    70008    5760.00         3002      5005
7    70010    1983.43         3004      5007
8    70003    2480.40         3009      5008
9    70012     250.45         3008      5004
10   70011      75.29         3003      5005
11   70013    3045.60         3002      5001
sales_id     (5000.993, 5003.333]  (5003.333, 5005.667]  (5005.667, 5008.0]
customer_id                                                                
3001                          1.0                   NaN                 NaN
3002                          1.0                   2.0                 NaN
3003                          NaN                   1.0                 NaN
3004                          NaN                   NaN                 1.0
3005                          2.0                   NaN                 NaN
3007                          1.0                   NaN                 NaN
3008                          NaN                   1.0                 NaN
3009                          1.0                   NaN

For more Practice: Solve these Related Problems:

• Write a Pandas program to group a sales dataset and then create bins for purchase amounts, counting the number of orders in each bin.
• Write a Pandas program to split a dataframe into groups using custom bin ranges and then count the frequency of records in each bin.
• Write a Pandas program to group the dataset by binned purchase amounts and then display the count per bin.
• Write a Pandas program to apply pd.cut on a numeric column, group by the resulting bins, and then count the records in each bin.

Python Code Editor:

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