pythonpandasgroup-byffill

How to forward fill a subset data to have a continuous monthly data

I need to copy data as a forward fill from Jan 2023 to Feb 2023 and March 2023 for SKU1, Location1 since data submissions are missing. For this: 1. need to create a continuous months for each sku, location combination 2. ffill for NA

expected result is shown in below image, the blue highlights are carried forward ensuring there is continuous monthly data

enter image description here

Thanks in Advance!

please refer to the below input data frame,

import pandas as pd

# Sample data
data = {
    'submissionmonth': ['1/1/2023', '1/1/2023', '1/1/2023', '1/1/2023', '1/1/2023', '1/1/2023', '4/1/2023', '4/1/2023', '4/1/2023', '4/1/2023', '4/1/2023', '4/1/2023', '4/1/2023', '4/1/2023', '4/1/2023', '4/1/2023', '4/1/2023', '4/1/2023', '10/1/2023', '10/1/2023', '10/1/2023', '10/1/2023', '10/1/2023', '10/1/2023', '10/1/2023', '10/1/2023'],
    'sku': ['SKU1', 'SKU1', 'SKU1', 'SKU1', 'SKU1', 'SKU1', 'SKU1', 'SKU1', 'SKU1', 'SKU1', 'SKU1', 'SKU1', 'SKU2', 'SKU2', 'SKU2', 'SKU2', 'SKU2', 'SKU2', 'SKU2', 'SKU2', 'SKU2', 'SKU2', 'SKU2', 'SKU2', 'SKU2', 'SKU2'],
    'location': ['Location1', 'Location1', 'Location1', 'Location1', 'Location1', 'Location1', 'Location1', 'Location1', 'Location1', 'Location1', 'Location1', 'Location1', 'Location2', 'Location2', 'Location2', 'Location2', 'Location2', 'Location2', 'Location2', 'Location2', 'Location2', 'Location2', 'Location2', 'Location2', 'Location2', 'Location2'],
    'forecastmonth': ['1/1/2023', '2/1/2023', '3/1/2023', '4/1/2023', '5/1/2023', '6/1/2023', '4/1/2023', '5/1/2023', '6/1/2023', '7/1/2023', '8/1/2023', '9/1/2023', '4/1/2023', '5/1/2023', '6/1/2023', '7/1/2023', '8/1/2023', '9/1/2023', '11/1/2023', '12/1/2023', '1/1/2024', '2/1/2024', '3/1/2024', '4/1/2024', '4/1/2024', '4/1/2024'],
    'cost': [100, 100, 100, 100, 100, 100, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200]
}

# Create DataFrame
df = pd.DataFrame(data)

# Convert to datetime
df['submissionmonth'] = pd.to_datetime(df['submissionmonth'], format='%m/%d/%Y')
df['forecastmonth'] = pd.to_datetime(df['forecastmonth'], format='%m/%d/%Y')

print(df.shape)
print(df)

i tried grouping and ffill, resulted in shape error

Solution

  • Looks like you need to deduplicate the rows with groupby.cumcount, then use groupby.resample:

    out = (df
   # there are several "forecastmonth" per combination of date/sku/location
   # we first need to deduplicate them unambiguously
   .assign(n=lambda x: x.groupby(['submissionmonth', 'sku', 'location'])
                        .cumcount())
   .set_index('submissionmonth')
   .groupby(['n', 'sku', 'location'], group_keys=False)
   .resample('MS').ffill()
   .reset_index()[df.columns]
   # below is optional
   .sort_values(by=['submissionmonth', 'sku', 'location', 'forecastmonth'],
                kind='stable', ignore_index=True)
)

    Output:

       submissionmonth   sku   location forecastmonth  cost
0       2023-01-01  SKU1  Location1    2023-01-01   100
1       2023-01-01  SKU1  Location1    2023-02-01   100
2       2023-01-01  SKU1  Location1    2023-03-01   100
3       2023-01-01  SKU1  Location1    2023-04-01   100
4       2023-01-01  SKU1  Location1    2023-05-01   100
5       2023-01-01  SKU1  Location1    2023-06-01   100
6       2023-02-01  SKU1  Location1    2023-01-01   100
7       2023-02-01  SKU1  Location1    2023-02-01   100
8       2023-02-01  SKU1  Location1    2023-03-01   100
9       2023-02-01  SKU1  Location1    2023-04-01   100
10      2023-02-01  SKU1  Location1    2023-05-01   100
11      2023-02-01  SKU1  Location1    2023-06-01   100
12      2023-03-01  SKU1  Location1    2023-01-01   100
13      2023-03-01  SKU1  Location1    2023-02-01   100
14      2023-03-01  SKU1  Location1    2023-03-01   100
15      2023-03-01  SKU1  Location1    2023-04-01   100
16      2023-03-01  SKU1  Location1    2023-05-01   100
17      2023-03-01  SKU1  Location1    2023-06-01   100
18      2023-04-01  SKU1  Location1    2023-04-01   200
19      2023-04-01  SKU1  Location1    2023-05-01   200
20      2023-04-01  SKU1  Location1    2023-06-01   200
21      2023-04-01  SKU1  Location1    2023-07-01   200
22      2023-04-01  SKU1  Location1    2023-08-01   200
23      2023-04-01  SKU1  Location1    2023-09-01   200
24      2023-04-01  SKU2  Location2    2023-04-01   200
25      2023-04-01  SKU2  Location2    2023-05-01   200
26      2023-04-01  SKU2  Location2    2023-06-01   200
27      2023-04-01  SKU2  Location2    2023-07-01   200
28      2023-04-01  SKU2  Location2    2023-08-01   200
29      2023-04-01  SKU2  Location2    2023-09-01   200
30      2023-05-01  SKU2  Location2    2023-04-01   200
31      2023-05-01  SKU2  Location2    2023-05-01   200
32      2023-05-01  SKU2  Location2    2023-06-01   200
33      2023-05-01  SKU2  Location2    2023-07-01   200
34      2023-05-01  SKU2  Location2    2023-08-01   200
35      2023-05-01  SKU2  Location2    2023-09-01   200
36      2023-06-01  SKU2  Location2    2023-04-01   200
37      2023-06-01  SKU2  Location2    2023-05-01   200
38      2023-06-01  SKU2  Location2    2023-06-01   200
39      2023-06-01  SKU2  Location2    2023-07-01   200
40      2023-06-01  SKU2  Location2    2023-08-01   200
41      2023-06-01  SKU2  Location2    2023-09-01   200
42      2023-07-01  SKU2  Location2    2023-04-01   200
43      2023-07-01  SKU2  Location2    2023-05-01   200
44      2023-07-01  SKU2  Location2    2023-06-01   200
45      2023-07-01  SKU2  Location2    2023-07-01   200
46      2023-07-01  SKU2  Location2    2023-08-01   200
47      2023-07-01  SKU2  Location2    2023-09-01   200
48      2023-08-01  SKU2  Location2    2023-04-01   200
49      2023-08-01  SKU2  Location2    2023-05-01   200
50      2023-08-01  SKU2  Location2    2023-06-01   200
51      2023-08-01  SKU2  Location2    2023-07-01   200
52      2023-08-01  SKU2  Location2    2023-08-01   200
53      2023-08-01  SKU2  Location2    2023-09-01   200
54      2023-09-01  SKU2  Location2    2023-04-01   200
55      2023-09-01  SKU2  Location2    2023-05-01   200
56      2023-09-01  SKU2  Location2    2023-06-01   200
57      2023-09-01  SKU2  Location2    2023-07-01   200
58      2023-09-01  SKU2  Location2    2023-08-01   200
59      2023-09-01  SKU2  Location2    2023-09-01   200
60      2023-10-01  SKU2  Location2    2023-11-01   200
61      2023-10-01  SKU2  Location2    2023-12-01   200
62      2023-10-01  SKU2  Location2    2024-01-01   200
63      2023-10-01  SKU2  Location2    2024-02-01   200
64      2023-10-01  SKU2  Location2    2024-03-01   200
65      2023-10-01  SKU2  Location2    2024-04-01   200
66      2023-10-01  SKU2  Location2    2024-04-01   200
67      2023-10-01  SKU2  Location2    2024-04-01   200