Fillna在Python Pandas中的多列中

问题描述

我有一个混合类型的pandas dataFrame，有些是字符串，有些是数字.我想用'.'替换字符串列中的NAN值，并用0替换浮点列中的NAN值.

I have a pandas dataFrame of mixed types, some are strings and some are numbers. I would like to replace the NAN values in string columns by '.', and the NAN values in float columns by 0.

考虑这个小的虚拟示例:

Consider this small fictitious example:

df = pd.DataFrame({'Name':['Jack','Sue',pd.np.nan,'Bob','Alice','John'],
    'A': [1, 2.1, pd.np.nan, 4.7, 5.6, 6.8],
    'B': [.25, pd.np.nan, pd.np.nan, 4, 12.2, 14.4],
    'City':['Seattle','SF','LA','OC',pd.np.nan,pd.np.nan]})

现在，我可以分为3行:

Now, I can do it in 3 lines:

df['Name'].fillna('.',inplace=True)
df['City'].fillna('.',inplace=True)
df.fillna(0,inplace=True)

由于这是一个很小的数据帧，所以3行可能就可以了.在我的实际示例(由于数据机密性的原因，在此无法共享)中，我还有更多的字符串列和数字列.所以我最终只为fillna写了很多行.有一个简洁的方法吗?

Since this is a small dataframe, 3 lines is probably ok. In my real example (which I cannot share here due to data confidentiality reasons), I have many more string columns and numeric columns. SO I end up writing many lines just for fillna. Is there a concise way of doing this?

推荐答案

您可以使用dtype是否为numeric进行检查，并检查 dtype.kind :

You could use apply for your columns with checking dtype whether it's numeric or not by checking dtype.kind:

res = df.apply(lambda x: x.fillna(0) if x.dtype.kind in 'biufc' else x.fillna('.'))

print(res)
     A      B     City   Name
0  1.0   0.25  Seattle   Jack
1  2.1   0.00       SF    Sue
2  0.0   0.00       LA      .
3  4.7   4.00       OC    Bob
4  5.6  12.20        .  Alice
5  6.8  14.40        .   John

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