将 2 个 pandas 数据帧组合成 1 个多维一

Question

Dataframe 1 looks like this:

df1 = pd.DataFrame(
    {
        "Farm ID": ["1", "2", "2", "3", "3"],
        "Crop": ["Type A", "Type A", "Type B", "Type B", "Type B"],
        "Area": [8, 4, 2, 3, 5],
        "Diesel": [101, 215, 3, 0.6, 42],
    }
)

df1 = df1.set_index(['Farm ID', 'Crop'])
df1

Dataframe 2 looks like this:

df2 = pd.DataFrame(
    {
        "Name": ["Area", "Diesel"],
        "GHG": [690, 8.5],
        "LU": [2.2, 0.3],
    }
)

df2 = df2.set_index('Name')
df2

I now need to combine both such that I receive the following information:

                       GHG     LU
Farm ID Crop    Name    
1       Type A  Area   8*690    8*2.2
                Diesel 101*690  101*2.2
2       Type A  Area   4*690    4*2.2
                Diesel 215*690  215*2.2 
        Type B  Area   ....

Any suggestions welcome as I am completely clueless. I also take ideas if there are better ways to structure this. I will have to do further analysis (eg aggregation by crop type or name, and similar) on the resulting dataframe and might think too complicated... Thanks a lot!

Answer 1

We can do stack

s = df1.stack()
out = df2.reindex(s.index.get_level_values(2)).mul(s.values,axis=0)
out.index = s.index
out
                          GHG     LU
Farm ID Crop                        
1       Type A Area    5520.0  17.60
               Diesel   858.5  30.30
2       Type A Area    2760.0   8.80
               Diesel  1827.5  64.50
        Type B Area    1380.0   4.40
               Diesel    25.5   0.90
3       Type B Area    2070.0   6.60
               Diesel     5.1   0.18
               Area    3450.0  11.00
               Diesel   357.0  12.60

Answer 2

You can stack the dataframe and let pandas broadcast on the common index:

df1.rename_axis('Name', axis=1).stack().mul(df2.T).T

Output:

                          GHG     LU
Farm ID Crop   Name                 
1       Type A Area    5520.0  17.60
               Diesel   858.5  30.30
2       Type A Area    2760.0   8.80
               Diesel  1827.5  64.50
        Type B Area    1380.0   4.40
               Diesel    25.5   0.90
3       Type B Area    2070.0   6.60
               Diesel     5.1   0.18
               Area    3450.0  11.00
               Diesel   357.0  12.60