如何将两个不同形状的数据框相乘

Question

I have two dataframes:

the first datframe df1 looks like this:

    variable            value
0   plastic             5774
2   glass               42
4   ferrous metal       642
6   non-ferrous metal   14000
8   paper               4000

Here is the head of the second dataframe df2 :

waste_type           total_waste_recycled_tonne   year   energy_saved
non-ferrous metal    160400.0                     2015    NaN
glass                14600.0                      2015    NaN
ferrous metal        15200                        2015    NaN
plastic              766800                       2015    NaN

I want to update the energy_saved in the second dataframe df2 such that I multiply the total_waste_recycled_tonne variable in df2 by the variable in df1 into the energy_saved column in df2.

For example:

For plastic: 5774 will be multipled with every waste_type platic with the total_waste_recycled_tonne variable in df2

ie: energy_saved = 5774 * 766800

Here is what I tried:

df2["energy_saved"] = df1[df1["variable"]=="plastic"]["value"].values[0] * df2["total_waste_recycled_tonne"][df2["waste_type"]=="plastic"]

However the problem was that when I do others, the rest changes back to NaN . I need a better approach to handle this?

Answer 1

Use map :

df2['energy_saved'] = (df2['waste_type'].map(df1.set_index('variable')['value'])
                          .mul(df2['total_waste_recycled_tonne']
                      )

Answer 2

Try via merge() and pass how='right' :

df=df1[['variable','value']].merge(df2[['waste_type','total_waste_recycled_tonne']],left_on='variable',right_on='waste_type',how='right')

Finally:

df2["energy_saved"]=df['value'].mul(df['total_waste_recycled_tonne'])

Output of df2 :

    waste_type          total_waste_recycled_tonne  year    energy_saved
0   non-ferrous metal   160400.0                    2015    2.245600e+09
1   glass               14600.0                     2015    6.132000e+05
2   ferrous metal       15200.0                     2015    9.758400e+06
3   plastic             766800.0                    2015    4.427503e+09
4   plastic             762700.0                    2015    4.403830e+09

Answer 3

A set_index + reindex option:

df2['energy_saved'] = (
        df1.set_index('variable').reindex(df2['waste_type'])['value'] *
        df2.set_index('waste_type')['total_waste_recycled_tonne']
).values

df2 :

          waste_type  total_waste_recycled_tonne  year  energy_saved
0  non-ferrous metal                    160400.0  2015  2.245600e+09
1              glass                     14600.0  2015  6.132000e+05
2      ferrous metal                     15200.0  2015  9.758400e+06
3            plastic                    766800.0  2015  4.427503e+09
4            plastic                    762700.0  2015  4.403830e+09