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Pandas: GroupBy with condition of two labels and ranges

Pandas Grouping and Aggregating: Split-Apply-Combine Exercise-29 with Solution

Write a Pandas program to split a given dataset using group by on specified column into two labels and ranges.

Split the group on 'salesman_id',
Ranges:
1) (5001...5006)
2) (5007..5012)

Test Data:

    salesman_id  sale_jan
0          5001    150.50
1          5002    270.65
2          5003     65.26
3          5004    110.50
4          5005    948.50
5          5006   2400.60
6          5007   1760.00
7          5008   2983.43
8          5009    480.40
9          5010   1250.45
10         5011     75.29
11         5012   1045.60   

Sample Solution:

Python Code :

import pandas as pd
import numpy as np
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
df = pd.DataFrame({
'salesman_id': [5001,5002,5003,5004,5005,5006,5007,5008,5009,5010,5011,5012],
'sale_jan':[150.5, 270.65, 65.26, 110.5, 948.5, 2400.6, 1760, 2983.43, 480.4,  1250.45, 75.29,1045.6]})
print("Original Orders DataFrame:")
print(df)
result = df.groupby(pd.cut(df['salesman_id'], 
                  bins=[0,5006,np.inf],  
                  labels=['S1', 'S2']))['sale_jan'].sum().reset_index()
print("\nGroupBy with condition of  two labels and ranges:")
print(result)

Sample Output:

Original Orders DataFrame:
    salesman_id  sale_jan
0          5001    150.50
1          5002    270.65
2          5003     65.26
3          5004    110.50
4          5005    948.50
5          5006   2400.60
6          5007   1760.00
7          5008   2983.43
8          5009    480.40
9          5010   1250.45
10         5011     75.29
11         5012   1045.60

GroupBy with condition of  two labels and ranges:
  salesman_id  sale_jan
0          S1   3946.01
1          S2   7595.17

Python Code Editor:


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Previous: Write a Pandas program to split a given dataset, group by one column and remove those groups if all the values of a specific columns are not available.
Next: Write a Pandas program to split the following dataset using group by on first column and aggregate over multiple lists on second column.

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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