在 Pandas 数据框布尔索引中使用“相反布尔值”的正确方法
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Proper way to use "opposite boolean" in Pandas data frame boolean indexing
提问by Mike Williamson
I wanted to use a boolean indexing, checking for rows of my data frame where a particular column does nothave NaNvalues. So, I did the following:
我想用一个布尔值索引，检查我的数据帧的行，其中特定的列并没有有NaN值。所以，我做了以下事情：
import pandas as pd
my_df.loc[pd.isnull(my_df['col_of_interest']) == False].head()
to see a snippet of that data frame, including only the values that are not NaN(most values are NaN).
查看该数据框的片段，仅包括不是的值NaN（大多数值为NaN）。
It worked, but seems less-than-elegant. I'd want to type:
它有效，但似乎不够优雅。我想输入：
my_df.loc[!pd.isnull(my_df['col_of_interest'])].head()
However, that generated an error. I also spend a lot of time in R, so maybe I'm confusing things. In Python, I usually put in the syntax "not" where I can. For instance, if x is not none:, but I couldn't really do it here. Is there a more elegant way? I don't like having to put in a senseless comparison.
但是，这产生了错误。我也花了很多时间在 R 上，所以也许我把事情搞糊涂了。在 Python 中，我通常在可能的地方输入“not”语法。例如，if x is not none:，但我不能在这里真正做到。有没有更优雅的方式？我不喜欢进行毫无意义的比较。
回答by DSM
In general with pandas (and numpy), we use the bitwise NOT ~instead of !or not(whose behaviour can't be overridden by types).
一般来说，对于 Pandas（和 numpy），我们使用按位 NOT~而不是!or not（其行为不能被类型覆盖）。
While in this case we have notnull, ~can come in handy in situations where there's no special opposite method.
虽然在这种情况下，我们有notnull,~可以在没有特殊相反方法的情况下派上用场。
>>> df = pd.DataFrame({"a": [1, 2, np.nan, 3]})
>>> df.a.isnull()
0 False
1 False
2 True
3 False
Name: a, dtype: bool
>>> ~df.a.isnull()
0 True
1 True
2 False
3 True
Name: a, dtype: bool
>>> df.a.notnull()
0 True
1 True
2 False
3 True
Name: a, dtype: bool
(For completeness I'll note that -, the unary negative operator, will also work on a boolean Series but ~is the canonical choice, and -has been deprecated for numpy boolean arrays.)
（为了完整-起见，我会注意到，一元负运算符也适用于布尔系列，但~它是规范选择，并且-已被 numpy 布尔数组弃用。）
回答by Anand S Kumar
Instead of using pandas.isnull(), you should use pandas.notnull()to find the rows where the column has not null values. Example -
pandas.isnull()您应该使用pandas.notnull()来查找列不包含空值的行，而不是使用。例子 -
import pandas as pd
my_df.loc[pd.notnull(my_df['col_of_interest'])].head()
pandas.notnull()is the boolean inverse of pandas.isnull(), as given in the documentation -
pandas.notnull()是 的布尔倒数pandas.isnull()，如文档中所述 -
See also
pandas.notnull
boolean inverse of pandas.isnull
另请参见
pandas.notnull
pandas.isnull 的布尔逆
