test data


data=[
{"date":"1/2" , "Full name":"Yamada" ,"dessert" : "Apple"  ,"Side dishes"  :"Gyoza" },
{"date":"1/3" , "Full name":"Yamada" ,"dessert" : "Apple"  ,"Side dishes"  :"Gyoza"   },
{"date":"1/4" , "Full name":"Yamada" ,"dessert" : "Banana"  ,"Side dishes"  :"Boiled fish"   },
{"date":"1/5" , "Full name":"Yamada" ,"dessert" : "Apple"   ,"Side dishes"  :"Gyoza"  },

{"date":"1/2" , "Full name":"Sasaki" ,"dessert" : "Orange"   ,"Side dishes"  :"Gyoza"  },
{"date":"1/3" , "Full name":"Sasaki" ,"dessert" : "Apple"   ,"Side dishes"  :"Gyoza"  },
{"date":"1/4" , "Full name":"Sasaki" ,"dessert" : "Apple"   ,"Side dishes"  :"Gyoza"  },
{"date":"1/5" , "Full name":"Sasaki" ,"dessert" : "Apple"   ,"Side dishes"  :"stew"  },

]

import pandas as pd
df=pd.DataFrame(data)
df

Date Name Dessert Side dish
0  1/2 Yamada apple dumplings
1  1/3 Yamada apple dumplings
2  1/4 Yamada Banana Boiled fish
3  1/5 Yamada apple dumplings
4  1/2 Sasaki orange dumplings
5  1/3 Sasaki apple dumplings
6  1/4 Sasaki apple dumplings
7  1/5 Sasaki apple stew

Count the "desserts" for each "name".

df.pivot_table(values= 'Side dishes', index='Full name', columns='dessert' , aggfunc="count" , fill_value=0)

values = If you do not enter'side dishes', it will be aggregated for each remaining column Number of cases with aggfunc = "count" If fill_value = 0 is not entered, 0 will be NaN.

result

Dessert Banana Apple Mandarin
Full name
Sasaki 0 3 1
Yamada 1 3 0

reference note.nkmk.me pandas.pivot_table

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