[英]Multidimensional boolean indexing of Pandas DataFrame - remove NaN rows and columns
I have a Pandas DataFrame like 我有一个Pandas DataFrame
df = pd.DataFrame([[1,-2,-3],[4,5,6],[1,3,4]])
which looks like 看起来像
0 1 2
0 1 -2 -3
1 4 5 6
2 1 3 4
I would like to get a subset of this DataFrame with only negative values 我想获得此DataFrame的一个子集,仅带有负值
1 2
0 -2 -3
I would like to try boolean indexing (but I don't see how to use 2 dimensional boolean indexing) 我想尝试布尔索引(但我看不到如何使用二维布尔索引)
In [7]: df_flag = df < 0
In [8]: df_flag
Out[8]:
0 1 2
0 False True True
1 False False False
2 False False False
So I did 所以我做了
In [15]: df[df_flag]
Out[15]:
0 1 2
0 NaN -2 -3
1 NaN NaN NaN
2 NaN NaN NaN
Isn't there a way to (automatically) remove columns and rows full of NaN when using 2 dimensional boolean indexing ? 使用二维布尔索引时,没有办法(自动)删除充满NaN的列和行吗?
You can make 2 calls to dropna
, dropna
accepts a thresh
param which won't drop the entire axis if there are n
non-Na values so the following drops rows then columns: 您可以对
dropna
进行2次调用, dropna
接受thresh
参数,如果存在n
非Na值,则不会丢弃整个轴,因此以下代码将删除行,然后删除列:
In [283]:
df[df<0].dropna(axis=0, thresh=1).dropna(axis=1)
Out[283]:
1 2
0 -2 -3
The result of the first dropna
: 第一个
dropna
的结果:
In [284]:
df[df<0].dropna(axis=0, thresh=1)
Out[284]:
0 1 2
0 NaN -2 -3
UPDATE UPDATE
the axis
param accepts multiple args so in fact you can do it a single call, thanks @scls: axis
参数接受多个参数,因此实际上您可以一次调用,谢谢@scls:
In [285]:
df[df<0].dropna(axis=[0,1], thresh=1)
Out[285]:
1 2
0 -2 -3
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