I have two tables. The first reference table is below:
| Name | Target | Bonus ||------|--------:|------:|| Joe | 40 | 46 || Phil | 38 | 42 || Dean | 65 | 70 |The Python code to generate the table is:
# Data for the tabledata = {'Name': ['Joe', 'Phil', 'Dean'],'Target': [40, 38, 65],'Bonus': [46, 42, 70]}# Creating the DataFrameref = pd.DataFrame(data)My second table is below:
| week | Metrics | Joe | Dean ||------------|---------|----:|-----:|| 11/6/2023 | Target | 40 | 65 || 11/6/2023 | Bonus | 46 | 70 || 11/6/2023 | Score | 33 | 71 || 11/13/2023 | Target | 40 | NaN || 11/13/2023 | Bonus | 46 | NaN || 11/13/2023 | Score | 45 | NaN || 11/20/2023 | Target | 40 | 65 || 11/20/2023 | Bonus | 46 | 70 || 11/20/2023 | Score | 35 | 68 || 11/27/2023 | Target | NaN | 65 || 11/27/2023 | Bonus | NaN | 70 || 11/27/2023 | Score | NaN | 44 || 12/4/2023 | Target | 40 | 65 || 12/4/2023 | Bonus | 46 | 70 || 12/4/2023 | Score | 42 | 66 |The Python code to generate this table is:
# Data for the new tabledata = {'week': ['11/6/2023', '11/6/2023', '11/6/2023', '11/13/2023', '11/13/2023', '11/13/2023','11/20/2023', '11/20/2023', '11/20/2023', '11/27/2023', '11/27/2023', '11/27/2023','12/4/2023', '12/4/2023', '12/4/2023'],'Metrics': ['Target', 'Bonus', 'Score', 'Target', 'Bonus', 'Score','Target', 'Bonus', 'Score', 'Target', 'Bonus', 'Score','Target', 'Bonus', 'Score'],'Joe': [40, 46, 33, 40, 46, 45, 40, 46, 35, None, None, None, 40, 46, 42],'Dean': [65, 70, 71, None, None, None, 65, 70, 68, 65, 70, 44, 65, 70, 66]}# Creating the DataFramedf = pd.DataFrame(data)As you can see Dean has a week where his Target, Bonus, and Score cells are blank. So does Joe in a later week. In these specific instances where the cell is NaN I want to populate them using the following rules:
- Get Target and Bonus cell values for each person from the first reference table and populate the NaN cell accordingly.
- Set the Score cell equal to the Target cell value for the person.
My desired output table would look like this:
| week | Metrics | Joe | Dean ||------------|---------|----:|-----:|| 11/6/2023 | Target | 40 | 65 || 11/6/2023 | Bonus | 46 | 70 || 11/6/2023 | Score | 33 | 71 || 11/13/2023 | Target | 40 | 65 || 11/13/2023 | Bonus | 46 | 70 || 11/13/2023 | Score | 45 | 65 || 11/20/2023 | Target | 40 | 65 || 11/20/2023 | Bonus | 46 | 70 || 11/20/2023 | Score | 35 | 68 || 11/27/2023 | Target | 40 | 65 || 11/27/2023 | Bonus | 46 | 70 || 11/27/2023 | Score | 40 | 44 || 12/4/2023 | Target | 40 | 65 || 12/4/2023 | Bonus | 46 | 70 || 12/4/2023 | Score | 42 | 66 |