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Pandas: Split the specified dataframe into groups and calculate monthly purchase amount

Pandas Grouping and Aggregating: Split-Apply-Combine Exercise-11 with Solution

Write a Pandas program to split the following dataframe into groups and calculate monthly purchase amount.

Test Data:

    ord_no  purch_amt    ord_date  customer_id  salesman_id
0    70001     150.50  05-10-2012         3001         5002
1    70009     270.65  09-10-2012         3001         5005
2    70002      65.26  05-10-2012         3005         5001
3    70004     110.50  08-17-2012         3001         5003
4    70007     948.50  10-09-2012         3005         5002
5    70005    2400.60  07-27-2012         3001         5001
6    70008    5760.00  10-09-2012         3005         5001
7    70010    1983.43  10-10-2012         3001         5006
8    70003    2480.40  10-10-2012         3005         5003
9    70012     250.45  06-17-2012         3001         5002
10   70011      75.29  07-08-2012         3005         5007
11   70013    3045.60  04-25-2012         3005         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': ['05-10-2012','09-10-2012','05-10-2012','08-17-2012','10-09-2012','07-27-2012','10-09-2012','10-10-2012','10-10-2012','06-17-2012','07-08-2012','04-25-2012'],
'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)
df['ord_date']= pd.to_datetime(df['ord_date']) 
print("\nMonth wise purchase amount:")
result = df.set_index('ord_date').groupby(pd.Grouper(freq='M')).agg({'purch_amt':sum})
print(result)

Sample Output:

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

Month wise purchase amount:
            purch_amt
ord_date             
2012-04-30    3045.60
2012-05-31     215.76
2012-06-30     250.45
2012-07-31    2475.89
2012-08-31     110.50
2012-09-30     270.65
2012-10-31   11172.33

Python Code Editor:


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Previous: 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.
Next: 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.

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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