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Pandas: Split a specified dataframe into groups and calculate quarterly purchase amount

Pandas Grouping and Aggregating: Split-Apply-Combine Exercise-21 with Solution

Write a Pandas program to split the following dataframe into groups and calculate quarterly 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("\nQuartly purchase amount:")
result = df.set_index('ord_date').groupby(pd.Grouper(freq='Q')).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

Quartly purchase amount:
            purch_amt
ord_date             
2012-06-30    3511.81
2012-09-30    2857.04
2012-12-31   11172.33

Python Code Editor:


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Negative Indexing:

In Python you can use negative indexing. While positive index starts with 0, negative index starts with -1.

name="Welcome"
print(name[0])
print(name[-1])
print(name[0:3])
print(name[-1:-4:-1])

Output:

W
e
Wel
emo