Cast column values to list based on the vallues of another column-CodePudding

I have the following dataframe

df1 = pd.DataFrame({'Parent': ['Stay home', "Stay home","Stay home", 'Go swimming', "Go swimming","Go swimming"],
                   'Parent1': ['Severe weather', "Severe weather", "Severe weather", 'Not Severe weather', "Not Severe weather", "Not Severe weather"],
                   'Child':   ["Extreme rainy", "Extreme windy",   "Severe snow", "Sunny", "some windy", "No snow"]
                   })

    Parent      Parent1              Child
0   Stay home   Severe weather       Extreme rainy
1   Stay home   Severe weather       Extreme windy
2   Stay home   Severe weather       Severe snow
3   Go swimming Not Severe weather   Sunny
4   Go swimming Not Severe weather   some windy
5   Go swimming Not Severe weather   No snow

I want to cast the Child column values to a list groupby the values of Parent1

Expected outcome:

    Parent      Parent1              Child          List1
0   Stay home   Severe weather       Extreme rainy  [Extreme rainy,Extreme windy,Severe snow]
1   Stay home   Severe weather       Extreme windy   
2   Stay home   Severe weather       Severe snow
3   Go swimming Not Severe weather   Sunny          [Sunny,some windy,No snow]
4   Go swimming Not Severe weather   some windy
5   Go swimming Not Severe weather   No snow

One solution that I tried cannot return me the expected outcome:

df1["list1"] = df1.groupby('Parent1')['Child'].transform(lambda x: x.tolist())

Any ideas?

CodePudding user response：

here is one way to do it.

Groupby Parent1 and create a list using apply. Then map to only the first occurrence by filtering the df with unique values

df1['list']=  df1[~df1.duplicated(subset=['Parent1'], keep='first')
                 ]['Parent1'].map(df1.groupby('Parent1')['Child'].apply(list) )

df1.fillna('', inplace=True)
df1

Parent  Parent1     Child                            list
0   Stay home       Severe weather  Extreme rainy    [Extreme rainy, Extreme windy, Severe snow]
1   Stay home       Severe weather  Extreme windy   
2   Stay home       Severe weather  Severe snow     
3   Go swimming     Not Severe weather  Sunny        [Sunny, some windy, No snow]
4   Go swimming     Not Severe weather  some windy  
5   Go swimming     Not Severe weather  No snow