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Pandas: Split a given dataframe into groups with bin counts

Pandas Grouping and Aggregating: Split-Apply-Combine Exercise-18 with Solution

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

Python Code Editor:


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Previous: Write a Pandas program to split a given dataframe into groups and create a new column with count from GroupBy.

Next: Write a Pandas program to split a given dataframe into groups with multiple aggregations.

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Python: Tips of the Day

Python: Time library

Time library provides lots of time related functions and methods and is good to know whether you're developing a website or apps and games or working with data science or trading financial markets. Time is essential in most development pursuits and Python's standard time library comes very handy for that.

Let's check out a few simple examples:

moment=time.strftime("%Y-%b-%d__%H_%M_%S",time.localtime())

import time
time_now=time.strftime("%H:%M:%S",time.localtime())
print(time_now)
date_now=time.strftime("%Y-%b-%d",time.localtime())
print(date_now)

Output:

11:36:34
2020-Nov-30