Python 如何在不明确列出列的情况下从 Pandas DataFrame 中选择具有一个或多个空值的行？
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How to select rows with one or more nulls from a pandas DataFrame without listing columns explicitly?
提问by Lev Selector
I have a dataframe with ~300K rows and ~40 columns. I want to find out if any rows contain null values - and put these 'null'-rows into a separate dataframe so that I could explore them easily.
我有一个约 300K 行和约 40 列的数据框。我想找出是否有任何行包含空值 - 并将这些“空”行放入单独的数据框中，以便我可以轻松地探索它们。
I can create a mask explicitly:
我可以明确地创建一个掩码：
mask = False
for col in df.columns:
mask = mask | df[col].isnull()
dfnulls = df[mask]
Or I can do something like:
或者我可以这样做：
df.ix[df.index[(df.T == np.nan).sum() > 1]]
Is there a more elegant way of doing it (locating rows with nulls in them)?
有没有更优雅的方法（定位包含空值的行）？
回答by DSM
[Updated to adapt to modern pandas, which has isnullas a method of DataFrames..]
[更新以适应现代pandas，它具有isnull作为DataFrames..的方法]
You can use isnulland anyto build a boolean Series and use that to index into your frame:
您可以使用isnull和any构建一个布尔系列并使用它来索引您的框架：
>>> df = pd.DataFrame([range(3), [0, np.NaN, 0], [0, 0, np.NaN], range(3), range(3)])
>>> df.isnull()
0 1 2
0 False False False
1 False True False
2 False False True
3 False False False
4 False False False
>>> df.isnull().any(axis=1)
0 False
1 True
2 True
3 False
4 False
dtype: bool
>>> df[df.isnull().any(axis=1)]
0 1 2
1 0 NaN 0
2 0 0 NaN
[For older pandas:]
[对于老年人pandas：]
You could use the function isnullinstead of the method:
您可以使用函数isnull而不是方法：
In [56]: df = pd.DataFrame([range(3), [0, np.NaN, 0], [0, 0, np.NaN], range(3), range(3)])
In [57]: df
Out[57]:
0 1 2
0 0 1 2
1 0 NaN 0
2 0 0 NaN
3 0 1 2
4 0 1 2
In [58]: pd.isnull(df)
Out[58]:
0 1 2
0 False False False
1 False True False
2 False False True
3 False False False
4 False False False
In [59]: pd.isnull(df).any(axis=1)
Out[59]:
0 False
1 True
2 True
3 False
4 False
leading to the rather compact:
导致相当紧凑：
In [60]: df[pd.isnull(df).any(axis=1)]
Out[60]:
0 1 2
1 0 NaN 0
2 0 0 NaN
回答by Roko Mijic
def nans(df): return df[df.isnull().any(axis=1)]
then when ever you need it you can type:
然后当你需要它时，你可以输入：
nans(your_dataframe)
