Python 3.x: Merge Two Dictionaries With Same Keys And Values Being Array

Python version: 3.x I have two dictionaries with same keys and the values are arrays. Most of the questions I saw here, for the required purpose, have only one value for each key.

Solution 1:

If you always have the same keys in both dicts, this should fit your needs:

d3 = {key:np.hstack([d1[key],d2[key]]) for key in d1.keys()}

Outputs:

In [7]: d3
Out[7]: 
{(1, 'Autumn'): array([ 2.5,  4.5,  7.5,  9.5, 10.2, 13.3, 15.7, 18.8]),
 (1, 'Spring'): array([10.5, 11.7, 12.3, 15. , 15.6, 20. , 23. , 27. ])}

But this relies on the assumption, that for every key there is a value and that every key appears in both dicts.

Solution 2:

Try this:

>>>import numpy as np>>>d1 = {(1, "Autumn"): np.array([2.5, 4.5, 7.5, 9.5]), (1, "Spring"): np.array([10.5, 11.7, 12.3, 15.0])}>>>d2 = {(1, "Autumn"): np.array([10.2, 13.3, 15.7, 18.8]), (1, "Spring"): np.array([15.6, 20, 23, 27])}>>>d3 = {k: np.concatenate((d1.get(k, np.array([])), d2.get(k, np.array([])))) for k inset(d1.keys()).union(set(d2.keys()))}>>>d3
{(1, 'Spring'): array([10.5, 11.7, 12.3, 15. , 15.6, 20. , 23. , 27. ]), (1, 'Autumn'): array([ 2.5,  4.5,  7.5,  9.5, 10.2, 13.3, 15.7, 18.8])}

Notes:

• It's a dict comprehension
• First, a union of the keys in the 2 dicts is computed, to make sure that no key is left aside (for that, the keys in each dict are converted into a set)
• For each element in the above set, get the corresponding array (empty one if the key is not present) from each dict, and concatenate them
• This is the Pythonic (and also general) approach, my numpy knowledge is somewhere close to 0 (I'm sure that it's pretty obvious from the code snippet - it looks awfully complex with all those parentheses), it's extremely likely that numpy has something to make things in a much more elegant manner
• [SO]: How to merge two dictionaries in a single expression? desired output and the current one (considering that the dict values are simply iterables (whether they are Python or numpy or any other kind is irrelevant)) are 2 different (and equally correct) approaches of the merge concept regarding dicts, in case of common keys:
  • One only keeps the value from the last dict
  • The other sums (whatever sum would mean for the operands) all of them

Solution 3:

I believe you need smth like:

{key:np.append(d1[key], d2[key]) for key in d1.keys()}

Not sure about np.append though. And, of course, it will work only if dicts have the same keys.

Solution 4:

import numpy as np

d1 = {(1, "Autumn"): [2.5, 4.5, 7.5, 9.5], (1, "Spring"): [10.5, 11.7, 12.3, 15.0]}
d2 = {(1, "Autumn"): [10.2, 13.3, 15.7, 18.8], (1, "Spring"): [15.6, 20, 23, 27]}
d3 = {(1, "Autumn"): np.array(d1[(1, "Autumn")] + d2[(1, "Autumn")]), (1,"Spring"): np.array(d1[(1, "Spring")] + d2[(1, "Spring")])}

I used the np.array() in the end because there is difference between lists and numpy arrays. When you use the A + B in numpy, each element of the A added to the array other element of the B. On the other hand, when use A+B where A and B are lists, they join each other.