Python Pandas Dataframe，删除“无"是任何列中的值的所有行 [英] Python Pandas Dataframe, remove all rows where 'None' is the value in any column

问题描述

我有一个大数据框.创建时使用'None'作为无法计算数字的值(而不是'nan')

I have a large dataframe. When it was created 'None' was used as the value where a number could not be calculated (instead of 'nan')

如何删除任何列中包含无"的所有行?我虽然可以使用 df.dropna 并设置 na 的值，但我似乎无法做到.

How can I delete all rows that have 'None' in any of it's columns? I though I could use df.dropna and set the value of na, but I can't seem to be able to.

谢谢

我认为这是数据框的一个很好的表示:

I think this is a good representation of the dataframe:

temp = pd.DataFrame(data=[['str1','str2',2,3,5,6,76,8],['str3','str4',2,3,'None',6,76,8]])

推荐答案

设置
借用@MaxU的df

df = pd.DataFrame([
    [1, 2, 3],
    [4, None, 6],
    [None, 7, 8],
    [9, 10, 11]
], dtype=object)

解决方案
你可以只使用 pd.DataFrame.dropna 原样

df.dropna()

   0   1   2
0  1   2   3
3  9  10  11

<小时>

假设你有 None 这样的字符串 df

df = pd.DataFrame([
    [1, 2, 3],
    [4, 'None', 6],
    ['None', 7, 8],
    [9, 10, 11]
], dtype=object)

然后结合 dropna 和 mask

df.mask(df.eq('None')).dropna()

   0   1   2
0  1   2   3
3  9  10  11

当你比较时，你可以确保整个数据帧都是object.

You can ensure that the entire dataframe is object when you compare with.

df.mask(df.astype(object).eq('None')).dropna()

   0   1   2
0  1   2   3
3  9  10  11

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