在 pandas 中用NaN替换空字符串 [英] replacing empty strings with NaN in Pandas

问题描述

我有一个pandas数据框(是通过导入csv文件创建的).我想用NaN替换空白值.这些空白值中有些是空的，有些包含(可变数量)空格''，' '，' '等.

I have a pandas dataframe (that was created by importing a csv file). I want to replace blank values with NaN. Some of these blank values are empty and some contain a (variable number) of spaces '', ' ', ' ', etc.

使用此线程中的建议拥有

df.replace(r'\s+', np.nan, regex=True, inplace = True)

它确实替换了仅包含空格的所有字符串，但也替换了其中所有包含空格的字符串，这不是我想要的.

which does replace all the strings that only contain spaces, but also replaces every string that has a space in it, which is not what I want.

如何仅用 just 空格和空字符串替换字符串?

How do I replace only strings with just spaces and empty strings?

推荐答案

如果您正在读取csv文件，并且想在读取文件时将所有空字符串都转换为nan ，那么您可以使用该选项

If you are reading a csv file and want to convert all empty strings to nan while reading the file itself then you can use the option

skipinitialspace=True

示例代码

pd.read_csv('Sample.csv', skipinitialspace=True)

这将删除定界符后出现的所有空白，因此将所有空字符串都设为nan

This will remove any white spaces that appear after the delimiters, Thus making all the empty strings as nan

从文档 http://pandas.pydata.org/pandas- docs/stable/io.html

注意: :此选项即使从有效数据中也会删除前一个空格，如果出于任何原因要保留前一个空格，则此选项不适合选择.

Note: This option will remove preceding white spaces even from valid data, if for any reason you want to retain the preceding white space then this option is not a good choice.

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