如何从DataFrame中选择一个或多个零的行，而不显式列出列?

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.

我有一个有~300K行和~40列的dataframe。我想知道是否有任何行包含空值，并将这些'null'行放入一个单独的dataframe中，以便我可以轻松地研究它们。

I can create a mask explicitly:

我可以创建一个明确的面具:

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)?

是否有一种更优雅的方法(查找包含null的行)?

2 个解决方案

#1

196

[Updated to adapt to modern pandas, which has isnull as a method of DataFrames..]

[更新以适应现代熊猫，它有isnull作为数据加密的方法。]

You can use isnull and any to build a boolean Series and use that to index into your frame:

您可以使用isnull和any构建一个布尔序列，并使用它将其索引到您的框架中:

>>> 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:]

(老熊猫:)

You could use the function isnull instead of the method:

你可以用函数isnull代替方法:

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:

导致相当紧凑的:

In [60]: df[pd.isnull(df).any(axis=1)]
Out[60]: 
   0   1   2
1  0 NaN   0
2  0   0 NaN

#2

nans = lambda df: df[df.isnull().any(axis=1)]

then when ever you need it you can type:

当你需要的时候，你可以输入:

nans(your_dataframe)

#1

196