CODEWITHSUNDEEP
pythonpandasnumpydataframegroup-by
doroz
doroz

Reputation: 13

How can I add a weight column conditional on several other columns?

I have the following pandas dataframe called 'production' and would like to obtain the weight of every plant's produce conditional on its species, greenhouse and year_month

plant  species  greenhouse  produce  year_month
0001     S1         GH1       200      2020-05
0002     S1         GH1       200      2020-05
0003     S2         GH1       100      2020-05
0004     S2         GH1        50      2020-05
0005     S1         GH2        90      2020-05
0006     S2         GH2        60      2020-05
0007     S1         GH1       150      2020-04
0008     S1         GH2       250      2020-04
0009     S1         GH2       100      2020-04
0010     S2         GH2       150      2020-04
0011     S2         GH2       190      2020-04
0012     S2         GH2        10      2020-04

For example, for '2020-05' we have GH1 and GH2. In GH1, S1 (plant 0001 and 0002) had total produce of 400 (200 + 200), so plant 0001 was responsible for a weight of 0.50 and plant 0002 for 0.50. S2 had total produce of 150 (100 + 50); so to plant 0003 we attribute a weight of 0.66 (100 / 150), and to plant 0004 a weight of 0.33 (50 / 150). And so on.

I would like to generate these 'conditional group-by' weights and assign them as a new column to the dataframe, such that the result looks like so:

plant  species  greenhouse  produce  year_month  contribution_weight
0001     S1         GH1       200      2020-05        0.50
0002     S1         GH1       200      2020-05        0.50
0003     S2         GH1       100      2020-05        0.66
0004     S2         GH1        50      2020-05        0.33
0005     S1         GH2        90      2020-05        1.00
0006     S2         GH2        60      2020-05        1.00
0007     S1         GH1       150      2020-04        1.00
0008     S1         GH2       250      2020-04        0.71
0009     S1         GH2       100      2020-04        0.29
0010     S2         GH2       150      2020-04        0.42
0011     S2         GH2       190      2020-04        0.54
0012     S2         GH2        10      2020-04        0.02

How can I add the contribution_weight column programmatically?

I have tried using pandas to manually compute each weight, but this is very tedious, prone to error and generates a lot of sub datasets. This is live data, so next month there would be another batch with year_month '2020-06' and there could also be additional GH and species, so I'm looking for a solution that is general and abstract enough to work even with additional labels in each of the columns. Perhaps some function would work?

Upvotes: 1

Views: 72

Answers (1)

BENY
BENY

Reputation: 323226

We can do transform

s=df.groupby(['year_month','greenhouse','species']).produce.transform('sum')
df['New']=df.produce/s
df
    plant species greenhouse  produce year_month       New
0       1      S1        GH1      200    2020-05  0.500000
1       2      S1        GH1      200    2020-05  0.500000
2       3      S2        GH1      100    2020-05  0.666667
3       4      S2        GH1       50    2020-05  0.333333
4       5      S1        GH2       90    2020-05  1.000000
5       6      S2        GH2       60    2020-05  1.000000
6       7      S1        GH1      150    2020-04  1.000000
7       8      S1        GH2      250    2020-04  0.714286
8       9      S1        GH2      100    2020-04  0.285714
9      10      S2        GH2      150    2020-04  0.428571
10     11      S2        GH2      190    2020-04  0.542857
11     12      S2        GH2       10    2020-04  0.028571

Upvotes: 1

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