I have a dataframe :
data = {'label': ['cat','dog','dog','cat','cat'],
'breeds': [ 'bengal','shar pei','pug','maine coon','maine coon'],
'nicknames':[['Loki','Loki' ],['Max'],['Toby','Zeus ','Toby'],['Marty'],['Erin ','Erin']],
'eye color':[['blue','green'],['green'],['brown','brown','brown'],['blue'],['green','brown']]
Output:
label breeds nicknames eye color
0 cat bengal [Loki,Loki] [blue, green]
1 dog shar pei [Max] [green]
2 dog pug [Toby,Zeus,Toby] [brown, brown, brown]
3 cat maine coon [Marty] [blue]
4 cat maine coon [Erin,Erin] [green, brown]
I want to apply the groupby :frame['label', 'breeds'], and calculate value_counts(unique value ) of nicknames and eye color,but output them in different columns: 'nickname_count','eye_count' This code outputs only in one column, how do I output separately?
frame2=frame.groupby(['label','breeds'])['nicknames','eye color'].apply(lambda x: x.astype('str').value_counts().to_dict())
CodePudding user response:
First, we use a groupby
with sum
on the lists as sum
concatenates the lists together :
>>> df_grouped = df.groupby(['label', 'breeds']).agg({'nicknames': sum, 'eye color': sum}).reset_index()
>>> df_grouped
label breeds nicknames eye color
0 cat bengal [Loki, Loki] [blue, green]
1 cat maine coon [Marty, Erin , Erin] [blue, green, brown]
2 dog pug [Toby, Zeus , Toby] [brown, brown, brown]
3 dog shar pei [Max] [green]
Then, we can count the number of unique values in list by converting it to set, using len
and save the output in two new columns to get the expected result :
>>> df_grouped['nickname_count'] = df_grouped['nicknames'].apply(lambda x: list(set(x))).str.len()
>>> df_grouped['eye_count'] = df_grouped['eye color'].apply(lambda x: list(set(x))).str.len()
>>> df_grouped
label breeds nicknames eye color nickname_count eye_count
0 cat bengal [Loki, Loki] [blue, green] 1 2
1 cat maine coon [Marty, Erin , Erin] [blue, green, brown] 3 3
2 dog pug [Toby, Zeus , Toby] [brown, brown, brown] 2 1
3 dog shar pei [Max] [green] 1 1