Pandas: Split the specified dataframe into groups, group by month and year based on order date and find the total purchase amount year wise, month wise
12. Grouping by Month and Year on Order Date
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.
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','05-10-2013','08-17-2013','10-09-2013','07-27-2014','10-09-2012','10-10-2012','10-10-2012','06-17-2014','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("\nYear wise Month wise purchase amount:")
result = df.groupby([df['ord_date'].dt.year, df['ord_date'].dt.month]).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 05-10-2013 3005 5001
3 70004 110.50 08-17-2013 3001 5003
4 70007 948.50 10-09-2013 3005 5002
5 70005 2400.60 07-27-2014 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 06-17-2014 3001 5002
10 70011 75.29 07-08-2012 3005 5007
11 70013 3045.60 04-25-2012 3005 5001
Year wise Month wise purchase amount:
purch_amt
ord_date ord_date
2012 4 3045.60
5 150.50
7 75.29
9 270.65
10 10223.83
2013 5 65.26
8 110.50
10 948.50
2014 6 250.45
7 2400.60
For more Practice: Solve these Related Problems:
- Write a Pandas program to extract month and year from order dates and then group the sales data accordingly to compute total purchase amount.
- Write a Pandas program to group a dataset by both month and year and then display the aggregated purchase amounts in a pivot table.
- Write a Pandas program to split the dataset by year and month, and then calculate the monthly percentage change in purchase amount.
- Write a Pandas program to group the dataset by order date's month and year, and then sort the groups by total purchase amount.
Go to:
Previous: Write a Pandas program to split the following dataframe into groups and calculate monthly purchase amount.
Next: Write a Pandas program to split the following dataframe into groups based on first column and set other column values into a list of values.
Python Code Editor:
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