如何使用key作为isnan对numpy数组进行排序？

Question

我有一个像numpy数组

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)

现在我想用key作为isnan对值进行排序？我怎样才能做到这一点？所以我最终会在阵列中

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()没用.在pandas中可以实现相同的sorted功能,通过使用带有键的函数的已排序列作为pd.isnull(),但寻找速度的numpy答案.

在熊猫里

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)

Answer 1

方法#1

这里有一个量化的方法借用的概念,masking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

几乎没有修改,简化版本的想法来自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