pandas function any() 不返回我想要的结果

Question

I have the following DataFrame

df = pd.DataFrame(
    {
        'class': ['0','0','0','0','0','0','0','0','0','0','0','0','0','0','0','0','0','0','0'],
        'item':  ['1','1','2','2','2','3','3','3','3','3','4','4','5','5','5','5','5','5','5'],
        'last_PO_code': ['103','103','103','104','103','103','104','105','106','103','103','104','103','103','104','105','105','106','1046'],
        'qty': [3,4,3,3,2,4,4,3,3,3,5,5,2,6,8,2,6,2,6],

    }
)

I apply the following rules for each unique item in the item column to this DataFrame :

1. last_PO_code has '103' only.

2. last_PO_code has ( '103' & '104' ) and ( qty column of '103' > qty column of '104' )

3. last_PO_code has ( '103' & '104' & '105' & '106' ) and ( qty column of '105' == qty column of '106' ) and ( qty column of '103' > qty column of '104' )

4. last_PO_code don't have '103'

5. last_PO_code has ( '103' & '104' ) and ( qty column of '103' == qty column of '104' )

6. last_PO_code has ( '103' & '104' & '105' & '106' ) and ( qty column of '105' == qty column of '106' ) and ( qty column of '103' == qty column of '104' )

I wrote the following code, but the result is not what I want.

regle1 = lambda x: True if x['last_PO_code'].eq('103').all() else False
regle2 = lambda x: True if x['last_PO_code'].eq('103').any() \
    and x['last_PO_code'].eq('104').any() \
    and x['last_PO_code'].eq('103').sum() > x['last_PO_code'].eq('104').sum() \
    else False
regle3 = lambda x: True if x['last_PO_code'].eq('103').any() \
    and x['last_PO_code'].eq('104').any() \
    and x['last_PO_code'].eq('105').any() \
    and x['last_PO_code'].eq('106').any() \
    and x['last_PO_code'].eq('103').sum() > x['last_PO_code'].eq('104').sum() \
    and x['last_PO_code'].eq('105').sum() == x['last_PO_code'].eq('106').sum() \
    else False
regle4 = lambda x: False if x['last_PO_code'].eq('103').any() else True

regle5 = lambda x: True if (x['last_PO_code'].eq('103').any() \
    and x['last_PO_code'].eq('104').any()) \
    and x['last_PO_code'].eq('103').sum() == x['last_PO_code'].eq('104').sum() \
    else False
regle6 = lambda x: True if x['last_PO_code'].eq('103').any() \
    and x['last_PO_code'].eq('104').any() \
    and x['last_PO_code'].eq('105').any() \
    and x['last_PO_code'].eq('106').any() \
    and x['last_PO_code'].eq('103').sum() == x['last_PO_code'].eq('104').sum() \
    and x['last_PO_code'].eq('105').sum() == x['last_PO_code'].eq('106').sum() \
    else False

df2 = df.groupby(['class','item']).apply(lambda x: pd.Series({'regle1' : regle1(x),
                                  'regle2': regle2(x),
                                  'regle3' : regle3(x)
                                  }))

Only regle1 does what I want for all items. For me the problem comes from the any() function. Either I use it badly or I don't understand it well.

What I have:

           regle1   regle2  regle3  regle4  regle5  regle6
class   item                        
0       1   True    False   False   False   False   False
        2   False   True    False   False   False   False
        3   False   True    True    False   False   False
        4   False   False   False   False   True    False
        5   False   True    True    False   False   False

What I want:

           regle1   regle2  regle3  regle4  regle5  regle6
class   item                        
0       1   True    False   False   False   False   False
        2   False   True    False   False   False   False
        3   False   True    True    False   False   False
        4   False   False   False   False   True    False
        5   False   False   False   False   True    True

All the mistakes I noticed were on item 5, but I don't understand why

Answer 1

The problem is, that you are summing the number of 'last_PO_code' instead of ' qty '. In each lambda, you must have:

(x['last_PO_code'].eq('103')*x['qty']).sum()

or as mozway suggested, even better:

x.loc[x['last_PO_code'].eq('103'), 'qty'].sum()

instead of:

x['last_PO_code'].eq('103').sum()

The whole code:

egle1 = lambda x: True if x['last_PO_code'].eq('103').all() else False
regle2 = lambda x: True if x['last_PO_code'].eq('103').any() \
    and x['last_PO_code'].eq('104').any() \
    and (x['last_PO_code'].eq('103') * x['qty']).sum()  > (x['last_PO_code'].eq('104') * x['qty']).sum()  \
    else False
regle3 = lambda x: True if x['last_PO_code'].eq('103').any() \
    and x['last_PO_code'].eq('104').any() \
    and x['last_PO_code'].eq('105').any() \
    and x['last_PO_code'].eq('106').any() \
    and (x['last_PO_code'].eq('103')*x['qty']).sum() > (x['last_PO_code'].eq('104')*x['qty']).sum() \
    and (x['last_PO_code'].eq('105')*x['qty']).sum() == (x['last_PO_code'].eq('106')*x['qty']).sum() \
    else False
regle4 = lambda x: False if x['last_PO_code'].eq('103').any() else True

regle5 = lambda x: True if (x['last_PO_code'].eq('103').any() \
    and x['last_PO_code'].eq('104').any()) \
    and (x['last_PO_code'].eq('103')*x['qty']).sum() == (x['last_PO_code'].eq('104')*x['qty']).sum() \
    else False
regle6 = lambda x: True if x['last_PO_code'].eq('103').any() \
    and x['last_PO_code'].eq('104').any() \
    and x['last_PO_code'].eq('105').any() \
    and x['last_PO_code'].eq('106').any() \
    and (x['last_PO_code'].eq('103')*x['qty']).sum() == (x['last_PO_code'].eq('104')*x['qty']).sum() \
    and (x['last_PO_code'].eq('105')*x['qty']).sum() == (x['last_PO_code'].eq('106')*x['qty']).sum() \
    else False

df2 = df.groupby(['class','item']).apply(lambda x: pd.Series({'regle1' : regle1(x),
                                  'regle2' : regle2(x),
                                  'regle3' : regle3(x),
                                  'regle4' : regle4(x),
                                  'regle5' : regle5(x),
                                  'regle6' : regle6(x),
                                  }))

#               regle1  regle2  regle3  regle4  regle5  regle6
#class  item                        
#0      1       True    False   False   False   False   False
#       2       False   True    False   False   False   False
#       3       False   True    True    False   False   False
#       4       False   False   False   False   True    False
#       5       False   False   False   False   True    True

PS. At this moment maybe it's time to use normal functions instead of lambdas, to have cleaner code:D. You also have repeatable chunks of code in your lambda, which could be easily automated.

PS2. I assumed, that in your example data, you have a typo (there shuld be 106 instead of 1046