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Pandas: Split a specified datasets into groups on customer id and calculate the number of customers starting with 'C', the list of all products

Pandas Grouping and Aggregating: Split-Apply-Combine Exercise-23 with Solution

Write a Pandas program to split the following datasets into groups on customer id and calculate the number of customers starting with 'C', the list of all products and the difference of maximum purchase amount and minimum purchase amount.

Test Data:

    ord_no  purch_amt    ord_date customer_id  salesman_id
0    70001     150.50  05-10-2012       C3001         5002
1    70009     270.65  09-10-2012       C3001         5005
2    70002      65.26  05-10-2012       D3005         5001
3    70004     110.50  08-17-2012       D3001         5003
4    70007     948.50  10-09-2012       C3005         5002
5    70005    2400.60  07-27-2012       D3001         5001
6    70008    5760.00  10-09-2012       C3005         5001
7    70010    1983.43  10-10-2012       D3001         5006
8    70003    2480.40  10-10-2012       D3005         5003
9    70012     250.45  06-17-2012       C3001         5002
10   70011      75.29  07-08-2012       D3005         5007
11   70013    3045.60  04-25-2012       D3005         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':['C3001','C3001','D3005','D3001','C3005','D3001','C3005','D3001','D3005','C3001','D3005','D3005'],
'salesman_id': [5002,5005,5001,5003,5002,5001,5001,5006,5003,5002,5007,5001]})
print("Original Orders DataFrame:")
print(df)
def customer_id_C(x):
    return (x.str[0] == 'C').sum()
result = df.groupby(['salesman_id'])\
  .agg(customer_id_start_C = ('customer_id', customer_id_C),
       customer_id_list = ('customer_id', lambda x: ', '.join(x)),
       purchase_amt_gap   = ('purch_amt', lambda x: x.max()-x.min())
      )
print("\nNumber of customers  starting with ‘C’, the list of all products and the difference of maximum purchase amount and minimum purchase amount:")
print(result)

Sample Output:

Original Orders DataFrame:
    ord_no  purch_amt    ord_date customer_id  salesman_id
0    70001     150.50  05-10-2012       C3001         5002
1    70009     270.65  09-10-2012       C3001         5005
2    70002      65.26  05-10-2012       D3005         5001
3    70004     110.50  08-17-2012       D3001         5003
4    70007     948.50  10-09-2012       C3005         5002
5    70005    2400.60  07-27-2012       D3001         5001
6    70008    5760.00  10-09-2012       C3005         5001
7    70010    1983.43  10-10-2012       D3001         5006
8    70003    2480.40  10-10-2012       D3005         5003
9    70012     250.45  06-17-2012       C3001         5002
10   70011      75.29  07-08-2012       D3005         5007
11   70013    3045.60  04-25-2012       D3005         5001

Number of customers  starting with ‘C’, the list of all products and the difference of maximum purchase amount and minimum purchase amount:
             customer_id_start_C            customer_id_list  purchase_amt_gap
salesman_id                                                                   
5001                           1  D3005, D3001, C3005, D3005           5694.74
5002                           3         C3001, C3005, C3001            798.00
5003                           0                D3001, D3005           2369.90
5005                           1                       C3001              0.00
5006                           0                       D3001              0.00
5007                           0                       D3005              0.00

Note: Run on Spyder Python 3.7.1

Python Code Editor:


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Previous: Write a Pandas program to split the following dataframe into groups by school code and get mean, min, and max value of age with customized column name for each school.

Next: Write a Pandas program to split the following datasets into groups on customer_id to summarize purch_amt and calculate percentage of purch_amt in each group.

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Python: Tips of the Day

Understanding slice notation:

It's pretty simple really:

a[start:stop]  # items start through stop-1
a[start:]      # items start through the rest of the array
a[:stop]       # items from the beginning through stop-1
a[:]           # a copy of the whole array

There is also the step value, which can be used with any of the above:

a[start:stop:step] # start through not past stop, by step

The key point to remember is that the :stop value represents the first value that is not in the selected slice. So, the difference between stop and start is the number of elements selected (if step is 1, the default).

The other feature is that start or stop may be a negative number, which means it counts from the end of the array instead of the beginning. So:

a[-1]    # last item in the array
a[-2:]   # last two items in the array
a[:-2]   # everything except the last two items

Similarly, step may be a negative number:

a[::-1]    # all items in the array, reversed
a[1::-1]   # the first two items, reversed
a[:-3:-1]  # the last two items, reversed
a[-3::-1]  # everything except the last two items, reversed

Python is kind to the programmer if there are fewer items than you ask for. For example, if you ask for a[:-2] and a only contains one element, you get an empty list instead of an error. Sometimes you would prefer the error, so you have to be aware that this may happen.

Relation to slice() object

The slicing operator [] is actually being used in the above code with a slice() object using the : notation (which is only valid within []), i.e.:

a[start:stop:step]

is equivalent to:

a[slice(start, stop, step)]

Slice objects also behave slightly differently depending on the number of arguments, similarly to range(), i.e. both slice(stop) and slice(start, stop[, step]) are supported. To skip specifying a given argument, one might use None, so that e.g. a[start:] is equivalent to a[slice(start, None)] or a[::-1] is equivalent to a[slice(None, None, -1)].

While the : -based notation is very helpful for simple slicing, the explicit use of slice() objects simplifies the programmatic generation of slicing.

Ref: https://bit.ly/2MHaTp7