我正在尝试使用具有一些NaN值的Pandas DataFrame。当我尝试
时df.fillna(df.mean())
我收到以下错误,无法找到解决方案或原因: 错误:
TypeError: cannot label index with a null key
所有列都是int或float。我甚至可以将单个列提取到数组中,在此数组上执行fillna()并重新集成到DataFrame中。
任何想法或提示?非常感谢你!
我的代码:
test=pd.read_csv("../input/test.csv")
test.fillna(test.mean(),inplace=True)
我正在处理的文件来自Kaggle test或train.csv。我对这两个数据都有相同的错误: https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data
错误代码如下:
TypeError Traceback (most recent call last)
<ipython-input-29-ab3e419316e1> in <module>()
14
15 #Also test has NaN's
---> 16 test.fillna(test.mean(),inplace=True)
/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py in fillna(self, value, method, axis, inplace, limit, downcast, **kwargs)
2752 self).fillna(value=value, method=method, axis=axis,
2753 inplace=inplace, limit=limit,
-> 2754 downcast=downcast, **kwargs)
2755
2756 @Appender(_shared_docs['shift'] % _shared_doc_kwargs)
/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py in fillna(self, value, method, axis, inplace, limit, downcast)
3645 if k not in result:
3646 continue
-> 3647 obj = result[k]
3648 obj.fillna(v, limit=limit, inplace=True, downcast=downcast)
3649 return result
/opt/conda/lib/python3.6/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):
/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py in _getitem_column(self, key)
1972
1973 # duplicate columns & possible reduce dimensionality
-> 1974 result = self._constructor(self._data.get(key))
1975 if result.columns.is_unique:
1976 result = result[key]
/opt/conda/lib/python3.6/site-packages/pandas/core/internals.py in get(self, item, fastpath)
3603
3604 if isnull(item):
-> 3605 raise TypeError("cannot label index with a null key")
3606
3607 indexer = self.items.get_indexer_for([item])
TypeError: cannot label index with a null key
The error message is as follows:
答案 0 :(得分:1)
以下示例似乎很有效:
x_1 x_2 x_3 x_4
0 0.000000 0.000000 0.000000 0.000000
1 1.000000 1.000000 1.000000 1.000000
2 2.000000 1.166667 2.000000 2.000000
3 3.000000 3.000000 3.000000 3.000000
4 0.000000 0.000000 0.000000 0.000000
5 1.000000 1.000000 1.000000 1.333333
6 2.000000 2.000000 2.000000 2.000000
7 1.285714 1.166667 1.285714 1.333333
制造
{{1}}
深入了解您的输入数据。
答案 1 :(得分:0)
您可以尝试:
df['your_column'] = df['your_column'].fillna((df['your_column'].mean()))
通过这种方式,您可以使用其自己的列的平均值填充NaN值。