Simple question here about indexing an array to get a subset of its values. Say I have a recarray
which holds ages in one space, and corresponding values in another. I also have an array which is my desired subset of ages. Here is what I mean:
ages = np.arange(100)
values = np.random.uniform(low=0, high= 1, size = ages.shape)
data = np.core.rec.fromarrays([ages, values], names='ages,values')
desired_ages = np.array([1,4, 16, 29, 80])
What I'm trying to do is something like this:
data.values[data.ages==desired_ages]
But, it's not working.
You want to create an subarray containing only the values whose indexes are in desired_ages
.
Python doesn't have any syntax that directly corresponds to this, but list comprehensions can do a pretty nice job:
result = [value for index, value in enumerate(data.values) if index in desired_ages]
However, doing it this way results in Python scanning through desired_ages
for each element in data.values
, which is slow. If you could insert
desired_ages = set(desired_ages)
on the line before, this would improve performance. (You can determine if a value in is a set in constant time, regardless of the set's size.)
import numpy as np
ages = np.arange(100)
values = np.random.uniform(low=0, high= 1, size = ages.shape)
data = np.core.rec.fromarrays([ages, values], names='ages,values')
desired_ages = np.array([1,4, 16, 29, 80])
result = [value for index, value in enumerate(data.values) if index in desired_ages]
print result
Output
[0.45852624094611272, 0.0099713014816563694, 0.26695859251958864, 0.10143425810157047, 0.93647796171383935]
I changed your example a little, shuffle the order of ages:
import numpy as np
np.random.seed(0)
ages = np.arange(3,103)
np.random.shuffle(ages)
values = np.random.uniform(low=0, high= 1, size = ages.shape)
data = np.core.rec.fromarrays([ages, values], names='ages,values')
desired_ages = np.array([4, 16, 29, 80])
If all the elements of desired_ages are in data.ages, you can sort data by age field first, and then use searchsorted() to find all the index quickly:
data.sort(order="ages") # sort by ages
print data.values[np.searchsorted(data.ages, desired_ages)]
or you can use np.in1d the get a bool array and use it as index:
print data.values[np.in1d(data.ages, desired_ages)]
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