Question

我试图通过根据以下代码根据不同的“ Item_Types”取变量的平均值，来估算“ Item_Weight”变量中的缺失值。但是，当我运行它时，出现如下所示的Key错误。是熊猫版本不允许这样做还是代码有问题？

Item_Weight_Average = 
  train.dropna(subset['Item_Weight']).pivot_table(values='Item_Weight',index='Item_Type')

missing = train['Item_Weight'].isnull()

train.loc[missing,'Item_Weight']= train.loc[missing,'Item_Type'].apply(lambda x: Item_Weight_Average[x])

KeyError                                  Traceback (most recent call last)
C:\Users\m1013523\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
   2441             try:
-> 2442                 return self._engine.get_loc(key)
   2443             except KeyError:

pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas\_libs\index.c:5280)()

pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas\_libs\index.c:5126)()

pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item (pandas\_libs\hashtable.c:20523)()

pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item (pandas\_libs\hashtable.c:20477)()

KeyError: 'Snack Foods'

During handling of the above exception, another exception occurred:

KeyError                                  Traceback (most recent call last)
<ipython-input-25-c9971d0bdaf7> in <module>()
      1 Item_Weight_Average = train.dropna(subset=['Item_Weight']).pivot_table(values='Item_Weight',index='Item_Type')
      2 missing = train['Item_Weight'].isnull()
----> 3 train.loc[missing,'Item_Weight'] = train.loc[missing,'Item_Type'].apply(lambda x: Item_Weight_Average[x])

C:\Users\m1013523\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\series.py in apply(self, func, convert_dtype, args, **kwds)
   2353             else:
   2354                 values = self.asobject
-> 2355                 mapped = lib.map_infer(values, f, convert=convert_dtype)
   2356 
   2357         if len(mapped) and isinstance(mapped[0], Series):

pandas\_libs\src\inference.pyx in pandas._libs.lib.map_infer (pandas\_libs\lib.c:66645)()

<ipython-input-25-c9971d0bdaf7> in <lambda>(x)
      1 Item_Weight_Average = train.dropna(subset=['Item_Weight']).pivot_table(values='Item_Weight',index='Item_Type')
      2 missing = train['Item_Weight'].isnull()
----> 3 train.loc[missing,'Item_Weight'] = train.loc[missing,'Item_Type'].apply(lambda x: Item_Weight_Average[x])

C:\Users\m1013523\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
   1962             return self._getitem_multilevel(key)
   1963         else:
-> 1964             return self._getitem_column(key)
   1965 
   1966     def _getitem_column(self, key):

C:\Users\m1013523\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\frame.py in _getitem_column(self, key)
   1969         # get column
   1970         if self.columns.is_unique:
-> 1971             return self._get_item_cache(key)
   1972 
   1973         # duplicate columns & possible reduce dimensionality

C:\Users\m1013523\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\generic.py in _get_item_cache(self, item)
   1643         res = cache.get(item)
   1644         if res is None:
-> 1645             values = self._data.get(item)
   1646             res = self._box_item_values(item, values)
   1647             cache[item] = res

C:\Users\m1013523\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\internals.py in get(self, item, fastpath)
   3588 
   3589             if not isnull(item):
-> 3590                 loc = self.items.get_loc(item)
   3591             else:
   3592                 indexer = np.arange(len(self.items))[isnull(self.items)]

C:\Users\m1013523\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
   2442                 return self._engine.get_loc(key)
   2443             except KeyError:
-> 2444                 return self._engine.get_loc(self._maybe_cast_indexer(key))
   2445 
   2446         indexer = self.get_indexer([key], method=method, tolerance=tolerance)

pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas\_libs\index.c:5280)()

pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas\_libs\index.c:5126)()

pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item (pandas\_libs\hashtable.c:20523)()

pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item (pandas\_libs\hashtable.c:20477)()

KeyError: 'Snack Foods'

对此有什么想法或解决方法吗？

Answer 1

如果我了解您要执行的操作，那么有一种更简单的方法来解决您的问题。您可以使用item_weight，item_type和groupby通过transform通过np.mean()计算平均值item_weight，而不用进行一系列新的平均值计算，然后填写缺失的使用fillna()在# Setting up some toy data import pandas as pd import numpy as np df = pd.DataFrame({'item_type': [1,1,1,2,2,2], 'item_weight': [2,4,np.nan,10,np.nan,np.nan]}) # The solution df.item_weight.fillna(df.groupby('item_type').item_weight.transform(np.mean), inplace=True)中定位。

   item_type  item_weight
0          1          2.0
1          1          4.0
2          1          3.0
3          2         10.0
4          2         10.0
5          2         10.0

结果：

{{1}}

使用apply和lambda函数估算缺失值

1 个答案: