numpy数组映射并取平均值

Question

I have three arrays

import numpy as np
value = np.array ([1, 3, 3, 5, 5, 7, 3])
index = np.array ([1, 1, 3, 3, 6, 6, 6])
data  = np.array ([1, 2, 3, 4, 5, 6])

Arrays "index" & "value" have same size and I want to group the items in "value" by taking average. For example: For the first two items [1, 3, ... in "value", have the same key 1 in "index", so for the final array the value is the mean of the 1st & 2rd items in value : (1 + 3 )/2 which is equal 2

The final array is:

[2, nan, 4, nan, nan, 5]

first value is the average of 1st and 2nd of "value"
second value is nan because there is not any key in "index" (no "2" in array index)
third value is the average of 3rd and 4th of "value" ...

Thanks for your help!!!

Regards, Roy

Answer 1

>>> [value[index==i].mean() for i in data]
[2.0, nan, 4.0, nan, nan, 5.0]

Answer 2

Maybe you would like to use numpy.bincount() ?

value = np.array([1, 3, 3, 5, 5, 7, 3])
index = np.array([1, 1, 3, 3, 6, 6, 6])
np.bincount(index, value) / np.bincount(index)
# array([ NaN,   2.,  NaN,   4.,  NaN,  NaN,   5.])

Answer 3

Is this the general idea you are looking for?

import numpy as np
value = np.array ([1, 3, 3, 5, 5, 7, 3])
index = np.array ([1, 1, 3, 3, 6, 6, 6])
data  = np.array ([1, 2, 3, 4, 5, 6])

answer = np.array(data, dtype=float)
for i, e in enumerate(data):
    idx = np.where(index==e)[0]
    val = value[idx]
    answer[i] = np.mean(val)

print answer # [  2.  nan   4.  nan  nan   5.]

If your data array is very large, there may be better solutions.

Answer 4

I have searched for use numpy histogram to solve the huge array:

value = np.array ([1, 3, 3, 5, 5, 7, 3], dtype='float')
index = np.array ([1, 1, 3, 3, 6, 6, 6], dtype='float')
data = np.array ([1, 2, 3, 4, 5, 6])

sums = np.histogram(index , bins=np.arange(index.min(), index.max()+2), weights=value)[0]
counter = np.histogram(index , bins=np.arange(index.min(), index.max()+2))[0]

sums / counter

array([ 2., NaN, 4., NaN, NaN, 5.])