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Pandas Example – Write a Pandas program to split the following dataframe into groups and calculate quarterly purchase amount

By on Tuesday, April 13, 2021

(Python Example for Beginners)

 

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

 

Pandas Example – Write a Pandas program to split the following dataframe into groups and calculate quarterly purchase amount

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There are two sides to machine learning:

  • Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
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