我做错了什么？无法执行一行代码。Python / Data Science \ Big Mart销售数据集

Question

我一直在努力学习分析大商场的销售数据集。在NaN列中有一些Item_Weight值。因此，我想通过从包含pivot_table的Item_Identifier as Index和Item_Weight中找到值来更新这些值。这是图像

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cdata['Item_Weight'] = cdata[['Item_Weight','Item_Identifier']].apply(lambda x: item_avg_weight.loc[x[1],'Item_Weight'] if pd.isnull(x[0]) else x[1]).astype(float)

但是，当我运行上面的代码行时，我会得到一个错误

我无法理解为什么我会得到这个错误。

指向数据集的链接：https://www.kaggle.com/brijbhushannanda1979/bigmart-sales-data/data

编辑1

错误日志

ValueError                                Traceback (most recent call last)
<ipython-input-158-7ecf8cf7385f> in <module>
----> 1 cdata['Item_Weight'] = cdata[['Item_Weight','Item_Identifier']].apply(lambda x: item_avg_weight.loc[x[1],'Item_Weight'] if pd.isnull(x[0]) else x[1]).astype(float)

~\Anaconda3\lib\site-packages\pandas\core\generic.py in astype(self, dtype, copy, errors, **kwargs)
   5689             # else, only a single dtype is given
   5690             new_data = self._data.astype(dtype=dtype, copy=copy, errors=errors,
-> 5691                                          **kwargs)
   5692             return self._constructor(new_data).__finalize__(self)
   5693 

~\Anaconda3\lib\site-packages\pandas\core\internals\managers.py in astype(self, dtype, **kwargs)
    529 
    530     def astype(self, dtype, **kwargs):
--> 531         return self.apply('astype', dtype=dtype, **kwargs)
    532 
    533     def convert(self, **kwargs):

~\Anaconda3\lib\site-packages\pandas\core\internals\managers.py in apply(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)
    393                                             copy=align_copy)
    394 
--> 395             applied = getattr(b, f)(**kwargs)
    396             result_blocks = _extend_blocks(applied, result_blocks)
    397 

~\Anaconda3\lib\site-packages\pandas\core\internals\blocks.py in astype(self, dtype, copy, errors, values, **kwargs)
    532     def astype(self, dtype, copy=False, errors='raise', values=None, **kwargs):
    533         return self._astype(dtype, copy=copy, errors=errors, values=values,
--> 534                             **kwargs)
    535 
    536     def _astype(self, dtype, copy=False, errors='raise', values=None,

~\Anaconda3\lib\site-packages\pandas\core\internals\blocks.py in _astype(self, dtype, copy, errors, values, **kwargs)
    631 
    632                     # _astype_nansafe works fine with 1-d only
--> 633                     values = astype_nansafe(values.ravel(), dtype, copy=True)
    634 
    635                 # TODO(extension)

~\Anaconda3\lib\site-packages\pandas\core\dtypes\cast.py in astype_nansafe(arr, dtype, copy, skipna)
    700     if copy or is_object_dtype(arr) or is_object_dtype(dtype):
    701         # Explicit copy, or required since NumPy can't view from / to object.
--> 702         return arr.astype(dtype, copy=True)
    703 
    704     return arr.view(dtype)

ValueError: could not convert string to float: 'DRC01'

我需要什么？

我想更新cdata DataFrame，以便Item_Weight列没有任何NaN值。我想借助一个枢轴表，即item_avg_weight，它包含了Item_Identifier的项目权重。

编辑2

有关数据的信息

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 14204 entries, 0 to 14203
Data columns (total 13 columns):
Item_Fat_Content             14204 non-null object
Item_Identifier              14204 non-null object
Item_MRP                     14204 non-null float64
Item_Outlet_Sales            8523 non-null float64
Item_Type                    14204 non-null object
Item_Visibility              14204 non-null float64
Item_Weight                  11765 non-null float64
Outlet_Establishment_Year    14204 non-null int64
Outlet_Identifier            14204 non-null object
Outlet_Location_Type         14204 non-null object
Outlet_Size                  10188 non-null object
Outlet_Type                  14204 non-null object
source                       14204 non-null object
dtypes: float64(4), int64(1), object(8)
memory usage: 1.4+ MB

Answer 1

我的理解是，对于Item_Identifier

• This，您想要fillna，基于follows

• There中每个组的mean可以使用groupby和groupby.apply来完成，因为groupby.apply应该是两个文件，所以为每个文件

• 创建一个数据文件NaN值可以被填充，或者像df_test.dropna(inplace=True)

那样被删除

import pandas as pd

# create two dataframes
df_test = pd.read_csv('datasets_9961_14084_Test.csv')
df_train = pd.read_csv('datasets_9961_14084_Train.csv')

# fill the NaN values in Item_Weight with the mean of their repective Item_Identifier group
df_test.Item_Weight = df_test.groupby('Item_Identifier')['Item_Weight'].apply(lambda x: x.fillna(x.mean()))
df_train.Item_Weight = df_train.groupby('Item_Identifier')['Item_Weight'].apply(lambda x: x.fillna(x.mean()))