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Pandas: Groupby to find first dates for each group

Pandas Grouping and Aggregating: Split-Apply-Combine Exercise-31 with Solution

Write a Pandas program to split the following dataset using group by on 'salesman_id' and find the first order date for each group.

Test Data:

    ord_no  purch_amt    ord_date  customer_id  salesman_id
0    70001     150.50  2012-10-05         3002         5002
1    70009     270.65  2012-09-10         3001         5003
2    70002      65.26  2012-10-05         3001         5001
3    70004     110.50  2012-08-17         3003         5003
4    70007     948.50  2012-09-10         3002         5002
5    70005    2400.60  2012-07-27         3002         5001
6    70008    5760.00  2012-09-10         3001         5001
7    70010    1983.43  2012-10-10         3004         5003
8    70003    2480.40  2012-10-10         3003         5003
9    70012     250.45  2012-06-27         3002         5002
10   70011      75.29  2012-08-17         3003         5003
11   70013    3045.60  2012-04-25         3001         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':[3005,3001,3002,3009,3005,3007,3002,3004,3009,3008,3003,3002],
'salesman_id': [5002,5005,5001,5003,5002,5001,5001,5004,5003,5002,5004,5001]})
print("Original Orders DataFrame:")
print(df)
print("\nGroupby to find first order date for each group(salesman_id):")
result = df.groupby('salesman_id')['ord_date'].min()
print(result)

Sample Output:

Original Orders DataFrame:
    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         5004
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         5004
11   70013    3045.60  2012-04-25         3002         5001

Groupby to find first order date for each group(salesman_id):
salesman_id
5001    2012-04-25
5002    2012-06-27
5003    2012-08-17
5004    2012-08-17
5005    2012-09-10
Name: ord_date, dtype: object

Python Code Editor:


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Previous: Write a Pandas program to split the following dataset using group by on first column and aggregate over multiple lists on second column.
Next: Write a Pandas program to split a given dataset using group by on multiple columns and drop last n rows of from each group.

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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