使用applymap替换Pandas Dataframe中的空值 [英] replacing null values in a Pandas Dataframe using applymap

问题描述

我有一个年龄"(Age)列，但有时会显示NaN值. 我知道我可以为此目的使用"fillna"，但是我尝试定义了自己的函数(并学习了这种方法)并将applymap应用于数据框

I've got an "Age" column, but sometimes NaN values are displayed. I know I can use "fillna" for this purposes but I've tried to define my own function (and learning to do this way) and use applymap to dataframe

到目前为止没有成功.

Age
69
49
NaN
54
NaN

我尝试过

   def get_rid_of_nulls(value):
     if value == np.nan:
        return 'Is Null value'
     else:
        return value

与此都不起作用

 if value == None
   if value isnull
   if value == np.na
   if value ==''
   if value == NaN
   if value == 'NaN'

似乎没有一个比较可行.我肯定是错的，但是我被卡住了，我非常固执地使用fillna

None of the comparisons seems to work. I'm wrong for sure but I'm stuck and I'm very stubborn to use fillna

谢谢

推荐答案

由于标题中存在替换"，并且您提到了fillna而不是replace()方法，因此您也可以通过执行某些操作来获得相同的结果像这样:

As there is "replacing" in your title, and you mentioned fillna but not the replace() method, you can also obtain the same result doing something like that :

df.Age.replace(np.NaN, 'Is Null value', inplace=True)

# Or, depending on your needs:
df['Age'] = df.Age.replace(np.NaN, 'Is Null value')

# Or without `replace` :
df['Age'] = df.Age.apply(lambda x: x if not pd.isnull(x) else 'Is Null value')

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