pandas 将数据转换为熊猫中的缺失数据
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Converting data to missing in pandas
提问by DanB
I have a DataFramewith a mix of 0's and other numbers. I would like to convert the 0's to missing.
我有一个DataFrame混合了 0 和其他数字的数字。我想将 0 转换为缺失。
For example, I am looking for the command that would convert
例如，我正在寻找可以转换的命令
In [618]: a=DataFrame(data=[[1,2],[0,1],[1,2],[0,0]])
In [619]: a
Out[619]:
0 1
0 1 2
1 0 1
2 1 2
3 0 0
to
到
In [619]: a
Out[619]:
0 1
0 1 2
1 NaN 1
2 1 2
3 NaN NaN
I tried pandas.replace(0, NaN), but I get an error that NaN is not defined. And I don't see anywhere to import NaN from.
我试过 pandas.replace(0, NaN)，但我收到一个错误，指出未定义 NaN。而且我看不到任何地方可以从中导入 NaN。
回答by BrenBarn
Just do from numpy import nan. (You will have to convert your DataTable to float type, because you can't use NaNin integer arrays.)
就做from numpy import nan。（您必须将 DataTable 转换为浮点类型，因为您不能NaN在整数数组中使用。）
