Numpy maskearray . mean()函数| Python
原文:https://www . geeksforgeeks . org/numpy-masked array-mean-function-python/
numpy.MaskedArray.mean() 函数用于返回沿给定轴的掩码数组元素的平均值。这里被屏蔽的条目被忽略，并且非有限的结果元素将被屏蔽。
语法:
numpy.ma.mean(axis=None, dtype=None, out=None)参数:
轴:【int，可选】计算平均值的轴。默认值(无)是计算展平数组的平均值。 数据类型:【数据类型，可选】返回数组的类型，以及元素相乘的累加器的类型。 out:【n 数组，可选】存储结果的位置。 - >如果提供，它必须具有输入广播到的形状。 - >如果未提供或无，则返回新分配的阵列。
Return:【mean _ along _ axis，ndarray】除非指定 out，否则将返回一个保存结果的新数组，在这种情况下，将返回对 out 的引用。
代码#1 :
# Python program explaining
# numpy.MaskedArray.mean() method
# importing numpy as geek
# and numpy.ma module as ma
import numpy as geek
import numpy.ma as ma
# creating input array
in_arr = geek.array([[1, 2], [ 3, -1], [ 5, -3]])
print ("Input array : ", in_arr)
# Now we are creating a masked array.
# by making entry as invalid.
mask_arr = ma.masked_array(in_arr, mask =[[1, 0], [ 1, 0], [ 0, 0]])
print ("Masked array : ", mask_arr)
# applying MaskedArray.mean
# methods to masked array
out_arr = mask_arr.mean()
print ("mean of masked array along default axis : ", out_arr)
Output:
Input array : [[ 1 2]
[ 3 -1]
[ 5 -3]]
Masked array : [[-- 2]
[-- -1]
[5 -3]]
mean of masked array along default axis : 0.75
代码#2 :
# Python program explaining
# numpy.MaskedArray.mean() method
# importing numpy as geek
# and numpy.ma module as ma
import numpy as geek
import numpy.ma as ma
# creating input array
in_arr = geek.array([[1, 0, 3], [ 4, 1, 6]])
print ("Input array : ", in_arr)
# Now we are creating a masked array.
# by making one entry as invalid.
mask_arr = ma.masked_array(in_arr, mask =[[ 0, 0, 0], [ 0, 0, 1]])
print ("Masked array : ", mask_arr)
# applying MaskedArray.mean methods
# to masked array
out_arr1 = mask_arr.mean(axis = 0)
print ("mean of masked array along 0 axis : ", out_arr1)
out_arr2 = mask_arr.mean(axis = 1)
print ("mean of masked array along 1 axis : ", out_arr2)
Output:
Input array : [[1 0 3]
[4 1 6]]
Masked array : [[1 0 3]
[4 1 --]]
mean of masked array along 0 axis : [2.5 0.5 3.0]
mean of masked array along 1 axis : [1.3333333333333333 2.5]
