Create One-hot-encoding in python using values from a different column

Question

I have a dataframe dfon cafeteria food options:

Meal           Food         Cooked      Percentage 

Breakfast      Yes           Attended      1.00
Breakfast      Apple         No            .25
Breakfast      Oatmeal       Yes           .55
Breakfast      Skipped       Not           .20
Lunch          Yes           Attended      1.00
Lunch          Salad         No            .42
Lunch          Pizza         Yes           .48
Lunch          Skipped       Not           .10

I have tried pd.get_dummies() package, but that gives a binary encoding to the Cooked category column. I want to transform my dataset as such:

Meal           Food         Cooked      Percentage   No    Yes    Not 

Breakfast      Yes           Attended      1.00     .25     .55    .20
Breakfast      Apple         No            .25      .25     .55    .20
Breakfast      Oatmeal       Yes           .55      .25     .55    .20
Breakfast      Skipped       Not           .20      .25     .55    .20
Lunch          Yes           Attended      1.00     .42     .48    .10
Lunch          Salad         No            .42      .42     .48    .10
Lunch          Pizza         Yes           .48      .42     .48    .10
Lunch          Skipped       Not           .10      .42     .48    .10

Hence, I am trying to transpose values of one column into new columns, based on values in a second column.

last row lunch or breakfast

BENY
– BENY

2021-04-17 00:32:51 +00:00
Commented Apr 17, 2021 at 0:32 — BENY
– BENY, Commented Apr 17, 2021 at 0:32

BENY · Accepted Answer · 2021-04-17 00:36:34Z

1

Let us try pivot then reindex + join

s = df.pivot('Meal','Cooked','Percentage').reindex(df.Meal)
s.index = df.index
df = df.join(s)
df
Out[124]: 
        Meal     Food    Cooked  Percentage  Attended    No  Not   Yes
0  Breakfast      Yes  Attended        1.00       1.0  0.25  0.2  0.55
1  Breakfast    Apple        No        0.25       1.0  0.25  0.2  0.55
2  Breakfast  Oatmeal       Yes        0.55       1.0  0.25  0.2  0.55
3  Breakfast  Skipped       Not        0.20       1.0  0.25  0.2  0.55
4      Lunch      Yes  Attended        1.00       1.0  0.42  0.1  0.48
5      Lunch    Salad        No        0.42       1.0  0.42  0.1  0.48
6      Lunch    Pizza       Yes        0.48       1.0  0.42  0.1  0.48
7      Lunch  Skipped       Not        0.10       1.0  0.42  0.1  0.48

answered Apr 17, 2021 at 0:36

BENY

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