Python numpy.nanargmin()
Python numpy.nanargmin()函数返回数组中最小元素在特定axis上的下标,忽略NaNs。
如果一个切片只包含nan和info,则结果是不可信的。
语法:
numpy.nanargmin(array, axis = None)
参数 :
array : 输入数组来工作。
axis : [int, optional]沿着一个指定的轴,如0或1。
返回 :
与array.shape.相同形状的数组中的索引,去掉了沿轴的维度。
代码 1 :
# Python Program illustrating
# working of nanargmin()
import numpy as geek
# Working on 1D array
array = [geek.nan, 4, 2, 3, 1]
print("INPUT ARRAY 1 : \n", array)
array2 = geek.array([[geek.nan, 4], [1, 3]])
# returning Indices of the min element
# as per the indices ingnoring NaN
print("\nIndices of min in array1 : ",
geek.nanargmin(array))
# Working on 2D array
print("\nINPUT ARRAY 2 : \n", array2)
print("\nIndices of min in array2 : ",
geek.nanargmin(array2))
print("\nIndices at axis 1 of array2 : ",
geek.nanargmin(array2, axis = 1))
输出 :
INPUT ARRAY 1 :
[nan, 4, 2, 3, 1]
Indices of min in array1 : 4
INPUT ARRAY 2 :
[[ nan 4.]
[ 1. 3.]]
Indices of min in array2 : 2
Indices at axis 1 of array2 : [1 0]
代码2:argmin和nanargmin的工作比较
# Python Program illustrating
# working of nanargmin()
import numpy as geek
# Working on 2D array
array = ( [[ 8, 13, 5, 0],
[ geek.nan, geek.nan, 5, 3],
[10, 7, 15, 15],
[3, 11, 4, 12]])
print("INPUT ARRAY : \n", array)
# returning Indices of the min element
# as per the indices
'''
[[ 8 13 5 0]
[ 0 2 5 3]
[10 7 15 15]
[ 3 11 4 12]]
^ ^ ^ ^
0 2 4 0 - element
1 1 3 0 - indices
'''
print("\nIndices of min using argmin : ",
geek.argmin(array, axis = 0))
print("\nIndices of min using nanargmin : : ",
geek.nanargmin(array, axis = 0))
输出 :
INPUT ARRAY :
[[ 8 13 5 0]
[ 0 2 5 3]
[10 7 15 15]
[ 3 11 4 12]]
Indices of min element : [1 1 3 0]