为什么当我在两列上使用 groupby 时结果是 NaN 但是当我在一列上使用它时它可以正常工作

Question

I use the following dataframe

df = pd.DataFrame({'class': 'a a aa aa b b '.split(),
                    'item': [5,5,7,7,7,6],
                   'last_PO_code': ['103','103','103','104','103','104'],
                   'qty': [5,4,7,6,7,6]
                   })

and I apply this rules on it

regle1 = lambda x: True if x['last_PO_code'].all() == "103" else False

like this with one column on grouby

df['regle1'] = df['class'].map(df.groupby(['class']).apply(regle1))

I have the following result for me, it's good

class   item    last_PO_code    qty regle1
0   a   5       103             5   True
1   a   5       103             4   True
2   aa  7       103             7   False
3   aa  7       104             6   False
4   b   7       103             7   False
5   b   6       104             6   False

but when I want to groupby class and item, I have this result

df['regle1'] = df['class'].map(df.groupby(['class','item']).apply(regle1))

class   item    last_PO_code    qty regle1
0   a   5       103             5   NaN
1   a   5       103             4   NaN
2   aa  7       103             7   NaN
3   aa  7       104             6   NaN
4   b   7       103             7   NaN
5   b   6       104             6   NaN

someone can help me to understand please??

Answer 1

it seems like you're looking for groupby+transform, but trying to implement that via pd.Series.map .

df = pd.DataFrame({'class': 'a a aa aa b b '.split(),
                    'item': [5,5,7,7,7,6],
                   'last_PO_code': ['103','103','103','104','103','104'],
                   'qty': [5,4,7,6,7,6]
                   })
def regle1(x):
  return (x == '103').all()

df['regle1'] = df.groupby(['class', 'item']).last_PO_code.transform(regle1)

The final dataframe now looks like this:

  class  item last_PO_code  qty  regle1
0     a     5          103    5    True
1     a     5          103    4    True
2    aa     7          103    7   False
3    aa     7          104    6   False
4     b     7          103    7    True
5     b     6          104    6   False