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Pandas: Split the specified dataframe into groups based on customer id and create a list of order date for each group

Pandas Grouping and Aggregating: Split-Apply-Combine Exercise-10 with Solution

Write a Pandas program to split the following dataframe into groups based on customer id and create a list of order date for each group.

Test Data:

    ord_no  purch_amt    ord_date  customer_id  salesman_id
0    70001     150.50  2012-10-05         3005         5002
1    70009     270.65  2012-09-10         3001         5005
2    70002      65.26  2012-10-05         3002         5001
3    70004     110.50  2012-08-17         3009         5003
4    70007     948.50  2012-09-10         3005         5002
5    70005    2400.60  2012-07-27         3007         5001
6    70008    5760.00  2012-09-10         3002         5001
7    70010    1983.43  2012-10-10         3004         5006
8    70003    2480.40  2012-10-10         3009         5003
9    70012     250.45  2012-06-27         3008         5002
10   70011      75.29  2012-08-17         3003         5007
11   70013    3045.60  2012-04-25         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],
'ord_date': ['2012-10-05','2012-09-10','2012-10-05','2012-08-17','2012-09-10','2012-07-27','2012-09-10','2012-10-10','2012-10-10','2012-06-27','2012-08-17','2012-04-25'],
'customer_id':[3001,3001,3005,3001,3005,3001,3005,3001,3005,3001,3005,3005],
'salesman_id': [5002,5005,5001,5003,5002,5001,5001,5006,5003,5002,5007,5001]})
print("Original Orders DataFrame:")
print(df)
result = df.groupby('customer_id')['ord_date'].apply(list)
print("\nGroup on 'customer_id' and display the list of order dates in group wise:")
print(result)

Sample Output:

Original Orders DataFrame:
    ord_no  purch_amt    ord_date  customer_id  salesman_id
0    70001     150.50  2012-10-05         3001         5002
1    70009     270.65  2012-09-10         3001         5005
2    70002      65.26  2012-10-05         3005         5001
3    70004     110.50  2012-08-17         3001         5003
4    70007     948.50  2012-09-10         3005         5002
5    70005    2400.60  2012-07-27         3001         5001
6    70008    5760.00  2012-09-10         3005         5001
7    70010    1983.43  2012-10-10         3001         5006
8    70003    2480.40  2012-10-10         3005         5003
9    70012     250.45  2012-06-27         3001         5002
10   70011      75.29  2012-08-17         3005         5007
11   70013    3045.60  2012-04-25         3005         5001

Group on 'customer_id' and display the list of order dates in group wise:
customer_id
3001    [2012-10-05, 2012-09-10, 2012-08-17, 2012-07-2...
3005    [2012-10-05, 2012-09-10, 2012-09-10, 2012-10-1...
Name: ord_date, dtype: object

Python Code Editor:


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Previous: Write a Pandas program to split a dataset to group by two columns and then sort the aggregated results within the groups.
Next: Write a Pandas program to split the following dataframe into groups and calculate monthly purchase amount.

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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