用熊猫中B列中的前一行值填充A列中的行值

Question

我有这样的数据，我想用前一行 groupby reg 值填充 Na。例如。第二行可以是SHE，第四行是XMN，由于最后两行的schedule_from相同，所以我选择不使用SHA填充第五行的Na

reg	schedule_from	schedule_to
X-346	CAN	SHE
X-346	NaN	ZUH
A-583	CTU	NaN
A-583	XMN	SZX
T-777	SHA	NaN
T-777	SHA	CVG

我累了一些编码，里面也显示了错误

    KeyError                                  Traceback (most recent call last)
<ipython-input-20-19798ba7d29d> in <module>
      4         if asub['schedule_from'][ind] != asub['schedule_from'][ind-1]:
      5             if (pd.isnull(asub['schedule_from'][ind])== True)&(pd.isnull(asub['schedule_to'][ind-1])==False):
----> 6                 asub.groupby(['aircraft_registration_no']).apply(lambda x: x['schedule_from'][ind] == x['schedule_to'][ind-1])
      7            #     asub.groupby(['aircraft_registration_no']).apply(lambda x: x['schedule_to'][ind] == x['schedule_from'][ind+1])

~\anaconda3\lib\site-packages\pandas\core\groupby\groupby.py in apply(self, func, *args, **kwargs)
    857         with option_context("mode.chained_assignment", None):
    858             try:
--> 859                 result = self._python_apply_general(f, self._selected_obj)
    860             except TypeError:
    861                 # gh-20949

~\anaconda3\lib\site-packages\pandas\core\groupby\groupby.py in _python_apply_general(self, f, data)
    890             data after applying f
    891         """
--> 892         keys, values, mutated = self.grouper.apply(f, data, self.axis)
    893 
    894         return self._wrap_applied_output(

~\anaconda3\lib\site-packages\pandas\core\groupby\ops.py in apply(self, f, data, axis)
    211             # group might be modified
    212             group_axes = group.axes
--> 213             res = f(group)
    214             if not _is_indexed_like(res, group_axes):
    215                 mutated = True

<ipython-input-20-19798ba7d29d> in <lambda>(x)
      4         if asub['schedule_from'][ind] != asub['schedule_from'][ind-1]:
      5             if (pd.isnull(asub['schedule_from'][ind])== True)&(pd.isnull(asub['schedule_to'][ind-1])==False):
----> 6                 asub.groupby(['aircraft_registration_no']).apply(lambda x: x['schedule_from'][ind] == x['schedule_to'][ind-1])
      7            #     asub.groupby(['aircraft_registration_no']).apply(lambda x: x['schedule_to'][ind] == x['schedule_from'][ind+1])

~\anaconda3\lib\site-packages\pandas\core\series.py in __getitem__(self, key)
    880 
    881         elif key_is_scalar:
--> 882             return self._get_value(key)
    883 
    884         if is_hashable(key):

~\anaconda3\lib\site-packages\pandas\core\series.py in _get_value(self, label, takeable)
    987 
    988         # Similar to Index.get_value, but we do not fall back to positional
--> 989         loc = self.index.get_loc(label)
    990         return self.index._get_values_for_loc(self, loc, label)
    991 

~\anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
   2895                 return self._engine.get_loc(casted_key)
   2896             except KeyError as err:
-> 2897                 raise KeyError(key) from err
   2898 
   2899         if tolerance is not None:

KeyError: 7

Answer 1

import numpy as np
def handle_group_reg(group):
    # find the column is null to handle schedule_from and schedule_to is same
    cond_is_from_null = group['schedule_from'].isnull()
    cond_is_to_null   = group['schedule_to'  ].isnull()
    
    # fill schedule_from with previous schedule_to
    group['schedule_from'] = group['schedule_from'].combine_first(group['schedule_to'].shift(1))
    
    # fill schedule_to with next schedule_from
    group['schedule_to'] = group['schedule_to'].combine_first(group['schedule_from'].shift(-1))
    
    # handle schedule_from and schedule_to is same
    cond_is_from_to_same = group['schedule_from'] == group['schedule_to']
    group.loc[(cond_is_from_null & cond_is_from_to_same), 'schedule_from'] = np.nan
    group.loc[(cond_is_to_null   & cond_is_from_to_same), 'schedule_to'  ] = np.nan    
    
    return group


nan = np.nan
df = pd.DataFrame([{'reg': 'X-346', 'schedule_from': 'CAN', 'schedule_to': 'SHE'},
 {'reg': 'X-346', 'schedule_from': nan, 'schedule_to': 'ZUH'},
 {'reg': 'X-346', 'schedule_from': nan, 'schedule_to': 'SHA'},
 {'reg': 'X-346', 'schedule_from': 'SHA', 'schedule_to': 'PEK'},
 {'reg': 'X-346', 'schedule_from': 'PEK', 'schedule_to': nan},
 {'reg': 'X-346', 'schedule_from': 'XMN', 'schedule_to': 'SHA'},
 {'reg': 'A-583', 'schedule_from': 'CTU', 'schedule_to': nan},
 {'reg': 'A-583', 'schedule_from': 'XMN', 'schedule_to': 'SZX'},
 {'reg': 'T-777', 'schedule_from': 'SHA', 'schedule_to': nan},
 {'reg': 'T-777', 'schedule_from': 'SHA', 'schedule_to': 'CVG'}])
dfn = df.groupby('reg').apply(handle_group_reg)
print(dfn.fillna(0))

    #      reg schedule_from schedule_to
    # 0  X-346           CAN         SHE
    # 1  X-346           SHE         ZUH
    # 2  X-346           ZUH         SHA
    # 3  X-346           SHA         PEK
    # 4  X-346           PEK         XMN
    # 5  X-346           XMN         SHA
    # 6  A-583           CTU         XMN
    # 7  A-583           XMN         SZX
    # 8  T-777           SHA           0
    # 9  T-777           SHA         CVG