I currently have a dataframe like this
| Person | Analysis | Dexterity | Skills |
|---|---|---|---|
| 174 | 3.76 | 4.12 | 1.20 |
| 239 | 4.10 | 3.78 | 3.77 |
| 557 | 5.00 | 2.00 | 4.40 |
| 674 | 2.23 | 2.40 | 2.80 |
| 122 | 3.33 | 4.80 | 4.10 |
I want to add an column to compile all this information like below
| Person | Analysis | Dexterity | Skills | new_column |
|---|---|---|---|---|
| 174 | 3.76 | 4.12 | 1.20 | {"Analysis":"3.76", "Dexterity":"4.12", "Skills":"1.20"} |
| 239 | 4.10 | 3.78 | 3.77 | {"Analysis":"4.10", "Dexterity":"3.78", "Skills":"3.77"} |
| 557 | 5.00 | 2.00 | 4.40 | {"Analysis":"5.00", "Dexterity":"2.00", "Skills":"4.40"} |
| 674 | 2.23 | 2.40 | 2.80 | {"Analysis":"2.23", "Dexterity":"2.40", "Skills":"2.80"} |
| 122 | 3.33 | 4.80 | 4.10 | {"Analysis":"3.33", "Dexterity":"4.80", "Skills":"4.10"} |
>Solution :
You can use to to_dict method like so:
import pandas as pd
rows = [
{'a': 1, 'b': 2},
{'a': 3, 'b': 4},
]
df = pd.DataFrame(rows)
# define new column as the json format of another
# also convert to str as that is what you have in your output
df['c'] = df[['a', 'b']].astype(str).to_dict(orient='records')
to_dict has a really nice interface for transforming to JSON format or to other object types. In this instance you are looking for orient='records' which is a dictionary of keys.
In your case, you will use:
df['new_column'] = df[
['Analysis', 'Dexterity', 'Skills']
].astype(str).to_dict(orient='records')