Pandas: Split a dataset to group by two columns and then sort the aggregated results within the groups
Pandas Grouping and Aggregating: Split-Apply-Combine Exercise-9 with Solution
Write a Pandas program to split a dataset to group by two columns and then sort the aggregated results within the groups.
In the following dataset group on 'customer_id', 'salesman_id' and then sort sum of purch_amt within the groups
Test Data:
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 5006 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 5007 11 70013 3045.60 2012-04-25 3002 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':[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_agg = df.groupby(['customer_id','salesman_id']).agg({'purch_amt':sum})
result = df_agg['purch_amt'].groupby(level=0, group_keys=False)
print("\nGroup on 'customer_id', 'salesman_id' and then sort sum of purch_amt within the groups:")
print(result.nlargest())
Sample Output:
Original Orders DataFrame: ord_no purch_amt ord_date customer_id salesman_id 0 70001 150.50 2012-10-05 3001 5002 1 70009 270.65 2012-09-10 3001 5005 2 70002 65.26 2012-10-05 3005 5001 3 70004 110.50 2012-08-17 3001 5003 4 70007 948.50 2012-09-10 3005 5002 5 70005 2400.60 2012-07-27 3001 5001 6 70008 5760.00 2012-09-10 3005 5001 7 70010 1983.43 2012-10-10 3001 5006 8 70003 2480.40 2012-10-10 3005 5003 9 70012 250.45 2012-06-27 3001 5002 10 70011 75.29 2012-08-17 3005 5007 11 70013 3045.60 2012-04-25 3005 5001 Group on 'customer_id', 'salesman_id' and then sort sum of purch_amt within the groups: customer_id salesman_id 3001 5001 2400.60 5006 1983.43 5002 400.95 5005 270.65 5003 110.50 3005 5001 8870.86 5003 2480.40 5002 948.50 5007 75.29 Name: purch_amt, dtype: float64
Python Code Editor:
Have another way to solve this solution? Contribute your code (and comments) through Disqus.
Previous: Write a Pandas program to split a dataset to group by two columns and count by each row.
Next: 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.
What is the difficulty level of this exercise?
Test your Python skills with w3resource's quiz
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
- New Content published on w3resource:
- Scala Programming Exercises, Practice, Solution
- Python Itertools exercises
- Python Numpy exercises
- Python GeoPy Package exercises
- Python Pandas exercises
- Python nltk exercises
- Python BeautifulSoup exercises
- Form Template
- Composer - PHP Package Manager
- PHPUnit - PHP Testing
- Laravel - PHP Framework
- Angular - JavaScript Framework
- React - JavaScript Library
- Vue - JavaScript Framework
- Jest - JavaScript Testing Framework