用相关列的平均值替换数据框中的NaN值的函数 [英] Function to replace NaN values in a dataframe with mean of the related column

问题描述

编辑:该问题不是

This question is not a clone of pandas dataframe replace nan values with average of columns because I want to replace the value of each column with the mean of the column and not with the mean of the dataframe values.

问题

我有一个熊猫数据框( train )，其中有一百列，我必须在其中应用机器学习技术.

I have a pandas dataframe (train) with a hundred columns to which I have to apply Machine Learning techniques.

通常，我是手工进行要素工程的，但是在这种情况下，我要处理很多专栏文章.

Usually I made feature engineering by hand but in this case I have a lot of columns to deal with.

我想构建一个Python函数，

I would like to build a Python function that:

1)在每一列中找到 NaN 值(我想过 df.isnull().any())

1) Find the NaN values in each column (I have thought to df.isnull().any() )

2)对于每个 NaN 值，将其替换为找到NaN值的列的平均值.

2) For each NaN value, replace it with the mean of the column in which the NaN value has been found.

我的想法是这样的:

def replace(value):
    for value in train:
        if train['value'].isnull():
           train['value'] = train['value'].fillna(train['value'].mean())

train = train.apply(replace,axis=1)

但是我收到以下错误

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   3063             try:
-> 3064                 return self._engine.get_loc(key)
   3065             except KeyError:

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 'value'

During handling of the above exception, another exception occurred:

KeyError                                  Traceback (most recent call last)
<ipython-input-25-003b3eb2463c> in <module>()
----> 1 train = train.apply(replace,axis=1)

/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py in apply(self, func, axis, broadcast, raw, reduce, result_type, args, **kwds)
   6012                          args=args,
   6013                          kwds=kwds)
-> 6014         return op.get_result()
   6015 
   6016     def applymap(self, func):

/opt/conda/lib/python3.6/site-packages/pandas/core/apply.py in get_result(self)
    140             return self.apply_raw()
    141 
--> 142         return self.apply_standard()
    143 
    144     def apply_empty_result(self):

/opt/conda/lib/python3.6/site-packages/pandas/core/apply.py in apply_standard(self)
    246 
    247         # compute the result using the series generator
--> 248         self.apply_series_generator()
    249 
    250         # wrap results

/opt/conda/lib/python3.6/site-packages/pandas/core/apply.py in apply_series_generator(self)
    275             try:
    276                 for i, v in enumerate(series_gen):
--> 277                     results[i] = self.f(v)
    278                     keys.append(v.name)
    279             except Exception as e:

<ipython-input-22-2e7fa654e765> in replace(value)
      1 def replace(value):
      2     for value in train:
----> 3         if train['value'].isnull():
      4            train['value'] = train['value'].fillna(df['value'].mean())

/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py in __getitem__(self, key)
   2686             return self._getitem_multilevel(key)
   2687         else:
-> 2688             return self._getitem_column(key)
   2689 
   2690     def _getitem_column(self, key):

/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py in _getitem_column(self, key)
   2693         # get column
   2694         if self.columns.is_unique:
-> 2695             return self._get_item_cache(key)
   2696 
   2697         # duplicate columns & possible reduce dimensionality

/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py in _get_item_cache(self, item)
   2484         res = cache.get(item)
   2485         if res is None:
-> 2486             values = self._data.get(item)
   2487             res = self._box_item_values(item, values)
   2488             cache[item] = res

/opt/conda/lib/python3.6/site-packages/pandas/core/internals.py in get(self, item, fastpath)
   4113 
   4114             if not isna(item):
-> 4115                 loc = self.items.get_loc(item)
   4116             else:
   4117                 indexer = np.arange(len(self.items))[isna(self.items)]

/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   3064                 return self._engine.get_loc(key)
   3065             except KeyError:
-> 3066                 return self._engine.get_loc(self._maybe_cast_indexer(key))
   3067 
   3068         indexer = self.get_indexer([key], method=method, tolerance=tolerance)

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: ('value', 'occurred at index 0')

在寻找解决方案时，我发现:

While searching for solutions, I found:

• 此，但它适用于txt文件(不适用于熊猫数据框)

• This but it works with a txt file (not a pandas dataframe)

此有关 df.isnull().any()方法的问题.

推荐答案

使用各自的平均值填充每列的 NaN :

To fill NaN of each column with its respective mean use:

df.apply(lambda x: x.fillna(x.mean())) 

这篇关于用相关列的平均值替换数据框中的NaN值的函数的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！