Efficiently replace elements in array based on dictionary - NumPy / Python

Question

First, of all, my apologies if this has been answered elsewhere. All I could find were questions about replacing elements of a given value, not elements of multiple values.<

Answer 1

Approach #1 : Loopy one with array data

One approach would be extracting the keys and values in arrays and then use a similar loop -

k = np.array(list(mapping.keys()))
v = np.array(list(mapping.values()))

out = np.zeros_like(input_array)
for key,val in zip(k,v):
    out[input_array==key] = val

Benefit with this one over the original one is the spatial-locality of the array data for efficient data-fetching, which is used in the iterations.

Also, since you mentioned thousand large np.arrays. So, if the mapping dictionary stays the same, that step to get the array versions - k and v would be a one-time setup process.

Approach #2 : Vectorized one with searchsorted

A vectorized one could be suggested using np.searchsorted -

sidx = k.argsort() #k,v from approach #1

k = k[sidx]
v = v[sidx]

idx = np.searchsorted(k,input_array.ravel()).reshape(input_array.shape)
idx[idx==len(k)] = 0
mask = k[idx] == input_array
out = np.where(mask, v[idx], 0)

Approach #3 : Vectorized one with mapping-array for integer keys

A vectorized one could be suggested using a mapping array for integer keys, which when indexed by the input array would lead us directly to the final output -

mapping_ar = np.zeros(k.max()+1,dtype=v.dtype) #k,v from approach #1
mapping_ar[k] = v
out = mapping_ar[input_array]