如何对键为isnan的numpy数组进行排序? [英] How to sort a numpy array with key as isnan?
本文介绍了如何对键为isnan的numpy数组进行排序?的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
我有一个像
np.array([[1.0, np.nan, 5.0, 1, True, True, np.nan, True],
[np.nan, 4.0, 7.0, 2, True, np.nan, False, True],
[2.0, 5.0, np.nan, 3, False, False, True, np.nan]], dtype=object)
现在我想使用isnan键将值排序?我怎样才能做到这一点?这样我就可以进入数组
Now I want to sort the values with key as isnan? How can I do that? So that I would end up in the array
np.array([[1.0, 5.0, 1, True, True, True, np.nan, np.nan],
[4.0, 7.0, 2, True, False, True, np.nan, np.nan],
[2.0, 5.0, 3, False, False, True, np.nan, np.nan]], dtype=object)
np.sort()
无效.在熊猫中,可以通过使用sorted
函数将列排序为pd.isnull()来应用已排序的列,但为速度寻找一个小小的答案,也可以实现相同的目的.
np.sort()
didn't work. The same can be achieved in pandas by applying sorted over columns with sorted
function with key as pd.isnull(), but looking for a numpy answer for speed.
在大熊猫中
data = pd.DataFrame({'Key': [1, 2, 3], 'Var': [True, True, False], 'ID_1':[1, np.NaN, 2],
'Var_1': [True, np.NaN, False], 'ID_2': [np.NaN, 4, 5], 'Var_2': [np.NaN, False, True],
'ID_3': [5, 7, np.NaN], 'Var_3': [True, True, np.NaN]})
data.apply(lambda x : sorted(x,key=pd.isnull),1).values
输出:
array([[1.0, 5.0, 1, True, True, True, nan, nan],
[4.0, 7.0, 2, True, False, True, nan, nan],
[2.0, 5.0, 3, False, False, True, nan, nan]], dtype=object)
推荐答案
方法1
这是从 this post
-
def mask_app(a):
out = np.empty_like(a)
mask = np.isnan(a.astype(float))
mask_sorted = np.sort(mask,1)
out[mask_sorted] = a[mask]
out[~mask_sorted] = a[~mask]
return out
样品运行-
# Input dataframe
In [114]: data
Out[114]:
ID_1 ID_2 ID_3 Key Var Var_1 Var_2 Var_3
0 1.0 NaN 5.0 1 True True NaN True
1 NaN 4.0 7.0 2 True NaN False True
2 2.0 5.0 NaN 3 False False True NaN
# Use pandas approach for verification
In [115]: data.apply(lambda x : sorted(x,key=pd.isnull),1).values
Out[115]:
array([[1.0, 5.0, 1, True, True, True, nan, nan],
[4.0, 7.0, 2, True, False, True, nan, nan],
[2.0, 5.0, 3, False, False, True, nan, nan]], dtype=object)
# Use proposed approach and verify
In [116]: mask_app(data.values)
Out[116]:
array([[1.0, 5.0, 1, True, True, True, nan, nan],
[4.0, 7.0, 2, True, False, True, nan, nan],
[2.0, 5.0, 3, False, False, True, nan, nan]], dtype=object)
方法2
经过一些修改,简化了版本,其思想来自 this post
-
With few more modifications, a simplified version with the idea from this post
-
def mask_app2(a):
out = np.full(a.shape,np.nan,dtype=a.dtype)
mask = ~np.isnan(a.astype(float))
out[np.sort(mask,1)[:,::-1]] = a[mask]
return out
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