I have an array my_array containing some tuple data.
I have another array my_array_values of same length containing some integer values.
For each unique value of my_array_values, I want to retrieve a list of values from my_array, that are in the same index as this value in my_array_values. Here is my code and the expected behavior :
my_array = np.array([('AA','11'),('BB','22'),('CC','33'),('DD','44'),('EE','55'),('FF','66')])
my_array_values = np.array([1,2,3,1,3,2])
my_array_values_unique = np.array([1,2,3])
for v in my_array_values_unique:
print(np.take(my_array, np.where(my_array_values == v)))
Expected behavior :
[('AA', '11'), ('DD', '44')]
[('BB', '22'), ('FF', '66')]
[('CC', '33'), ('EE', '55')]
But actually, my code gives me the following output:
[['AA' '22']]
[['11' '33']]
[['BB' 'CC']]
Can someone explain to me how I can get the correct output?
You don't need to use take or where at all. Equality check on an array returns a boolean array which is a valid indexing array:
for v in my_array_values_unique:
print(my_array[my_array_values == v])
And this prints:
[['AA' '11']
['DD' '44']]
[['BB' '22']
['FF' '66']]
[['CC' '33']
['EE' '55']]
If numpy is not specificly required, this can be easily done using lists as well:
lst = [('AA', '11'), ('BB', '22'), ('CC', '33'), ('DD', '44'), ('EE', '55'), ('FF', '66')]
idxs = [1, 2, 3, 1, 3, 2]
for v in set(idxs):
print([tup for idx, tup in zip(idxs, lst) if idx == v])
Gives:
[('AA', '11'), ('DD', '44')]
[('BB', '22'), ('FF', '66')]
[('CC', '33'), ('EE', '55')]
Another, more efficient, way would be to use defaultdict in order to loop the list once, instead of once for every unique value:
import collections
mapping = collections.defaultdict(list)
for tup, idx in zip(lst, idxs):
mapping[idx].append(tup)
for lst in mapping.values():
print(lst)
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