如何在 pandas 数据框中将单元格设置为NaN [英] How to set a cell to NaN in a pandas dataframe

问题描述

我想用NaN替换数据框中某列中的错误值.

I'd like to replace bad values in a column of a dataframe by NaN's.

mydata = {'x' : [10, 50, 18, 32, 47, 20], 'y' : ['12', '11', 'N/A', '13', '15', 'N/A']}
df = pd.DataFrame(mydata)

df[df.y == 'N/A']['y'] = np.nan

但是，由于最后一行在df副本上起作用，因此最后一行失败并引发警告.那么，处理此问题的正确方法是什么?我已经见过许多使用iloc或ix的解决方案，但是在这里，我需要使用布尔条件.

Though, the last line fails and throws a warning because it's working on a copy of df. So, what's the correct way to handle this? I've seen many solutions with iloc or ix but here, I need to use a boolean condition.

推荐答案

只需使用replace:

In [106]:
df.replace('N/A',np.NaN)

Out[106]:
    x    y
0  10   12
1  50   11
2  18  NaN
3  32   13
4  47   15
5  20  NaN

您正在尝试的方法称为链索引:

What you're trying is called chain indexing: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

您可以使用loc来确保对原始dF进行操作:

You can use loc to ensure you operate on the original dF:

In [108]:
df.loc[df['y'] == 'N/A','y'] = np.nan
df

Out[108]:
    x    y
0  10   12
1  50   11
2  18  NaN
3  32   13
4  47   15
5  20  NaN

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