appending indices to numpy array

Question

I make a variable corr_matrix by iterating over rows and columns and correlating values.

import numpy as np
import random

enc_dict = {k: int(random.uniform(1,24)) for k in range(24)}
ret_dict = {k: int(random.uniform(1,24)) for k in range(24)}

corr_matrix=np.zeros((24,24))
ind_matrix = np.zeros((24,24))

data = np.random.rand(24,24)
for enc_row in range(0,24):
            for ret_col in range(0,24):
                corr_matrix[enc_row, ret_col] = np.corrcoef(data[enc_row,:], data[ret_col,:])[0,1]
                if enc_dict[enc_row] == ret_dict[ret_col]:
                    ind_matrix = np.append(ind_matrix, [[enc_row, ret_col]])

I want to store the indices in the matrix where enc_dict[enc_row] == ret_dict[ret_col] as a variable to use for indexing corr_matrix. I can print the values, but I can't figure out how to store them in a variable in a way that allows me to use them for indexing later.

I want to:

make a variable, ind_matrix that is the indices where the above statement is true.
I want to use ind_matrix to index within my correlation matrix. I want to be able to index the whole row as well as the exact value where the above statement is true (enc_dict[enc_row] == ret_dict[ret_col])

I tried ind_matrix = np.append(ind_matrix, [[enc_row, ret_col]]) which gives me the correct values but it has a lot of 0s before the #s for some reason. Also it doesn't allow me to call each pair of points together to use for indexing. I want to be able to do something like corr_matrix[ind_matrix[1]]

Give a Minimal, Complete, Verifiable Example in your question. — Alex
– Alex, Commented Mar 5, 2018 at 18:05
@Maria, I've found that just creating a plain old python list then converting it to an array with vstack, hstack, or array tends to work best in terms of readability and performance. — user2699
– user2699, Commented Mar 5, 2018 at 21:19

Paul Panzer · Accepted Answer · 2018-03-05 20:18:15Z

Here is a modified version of your code containing a couple of suggestions and comments:

import numpy as np

# when indices are 0, 1, 2, ... don't use dictionary
# also for integer values use randint
enc_ = np.random.randint(1, 24, (24,))
ret_ = np.random.randint(1, 24, (24,))

data = np.random.rand(24,24)
# np.corrcoef is vectorized, no need to loop:
corr_matrix = np.corrcoef(data)
# the following is the clearest, but maybe not the fastest way of generating
# your index array:
ind_matrix = np.argwhere(np.equal.outer(enc_, ret_))

# this can't be used for indexing directly, you'll have to choose
# one of the following idioms

# EITHER spread to two index arrays
I, J = ind_matrix.T
# or directly I, J = np.where(np.equal.outer(enc_, ret_))
# single index
print(corr_matrix[I[1], J[1]])
# multiple indices
print(corr_matrix[I[[1,2,0]], J[[1,2,0]]])
# whole row
print(corr_matrix[I[1]])

# OR use tuple conversion
ind_matrix = np.array(ind_matrix)
# single index
print(corr_matrix[(*ind_matrix[1],)])
# multiple indices
print(corr_matrix[(*zip(*ind_matrix[[1,2,0]],),)])
# whole row
print(corr_matrix[ind_matrix[1, 0]])

# OR if you do not plan to use multiple indices
as_tuple = list(map(tuple, ind_matrix))
# single index
print(corr_matrix[as_tuple[1]])
# whole row
print(corr_matrix[as_tuple[1][0]])

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