Pandas fillna：输出仍然有 NaN 值
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Pandas fillna: Output still has NaN values
提问by Amelio Vazquez-Reina
I am having a strange problem in Pandas. I have a Dataframe with several NaNvalues. I thought I could fill those NaNvalues using column means (that is, fill every NaNvalue with its column mean) but when I try the following
我在 Pandas 中遇到了一个奇怪的问题。我有一个包含多个NaN值的数据框。我以为我可以NaN使用列均值填充这些值（即，NaN用列均值填充每个值）但是当我尝试以下操作时
col_means = mydf.apply(np.mean, 0)
mydf = mydf.fillna(value=col_means)
I still see some NaNvalues. Why?
我仍然看到一些NaN价值。为什么？
Is it because I have more NaNvalues in my original dataframe than entries in col_means? And what exactly is the difference between fill-by-column vs fill-by-row?
是因为NaN我的原始数据框中的值比 中的条目多col_means吗？按列填充与按行填充之间究竟有什么区别？
回答by Andy Hayden
You can just fillnawith the df.mean()Series (which is dict-like):
您可以只fillna使用df.mean()系列（类似于 dict）：
In [11]: df = pd.DataFrame([[1, np.nan], [np.nan, 4], [5, 6]])
In [12]: df
Out[12]:
0 1
0 1 NaN
1 NaN 4
2 5 6
In [13]: df.fillna(df.mean())
Out[13]:
0 1
0 1 5
1 3 4
2 5 6
Note: that df.mean()is the row-wise mean, which gives the fill values:
注意：这df.mean()是按行的平均值，它给出了填充值：
In [14]: df.mean()
Out[14]:
0 3
1 5
dtype: float64
Note: if df.mean()has some NaN values then these will be used in the DataFrame's fillna, perhaps you want to use a fillnaon this Series i.e.
注意：如果df.mean()有一些 NaN 值，那么这些将用于 DataFrame 的 fillna，也许你想fillna在这个系列上使用 a ，即
df.mean().fillna(0)
df.fillna(df.mean().fillna(0))
