My original dataframe is as below:
List = [['2024-05-25', 'Group 1', 'Year 1', 23466882], ['2024-05-25', 'Group 1', 'Year 2', 458397284], ['2024-05-25', 'Group 1', 'Year 3', 2344545], ['2024-05-25', 'Group 2', 'Year 1', 6662345], ['2024-05-25', 'Group 2', 'Year 2', 46342], ['2024-05-25', 'Group 3', 'Year 1', 34234], ['2024-05-25', 'Group 3', 'Year 2', 45222]]
df = pd.DataFrame(List, columns = ['Report_date', 'Product_group', 'Year', 'Sales'])
For each product group, if "Year 3" does not exist, a new row with sales of 11 000 should be added to the end.
The output should look like this:
My initial idea is to split the dataframe into each product group and add a new row if the sub- dataframe does not have any info for Year 3 but that approach does not seem to be optimal.
Any comment is appreciated. Thank you in advance!
If need only add missing Year Year 3
for each group use pd.concat
with filtered rows with first non exist groups with added new Year
and Sales
values:
Notice: This solution only added new rows for not exist Year 3
, also working if not exist same years for any group. E.g. if remove first row, so Year 1
is missing.
g = df.loc[df['Year'].eq('Year 3'), 'Product_group']
out = (pd.concat([df,
df.loc[~df['Product_group'].isin(g)]
.drop_duplicates('Product_group').assign(Year='Year 3', Sales=11000)])
.sort_values(['Product_group','Year'], ignore_index=True))
print (out)
Report_date Product_group Year Sales
0 2024-05-25 Group 1 Year 1 23466882
1 2024-05-25 Group 1 Year 2 458397284
2 2024-05-25 Group 1 Year 3 2344545
3 2024-05-25 Group 2 Year 1 6662345
4 2024-05-25 Group 2 Year 2 46342
5 2024-05-25 Group 2 Year 3 11000
6 2024-05-25 Group 3 Year 1 34234
7 2024-05-25 Group 3 Year 2 45222
8 2024-05-25 Group 3 Year 3 11000