Faster way to iteratively replace values in relatively large NumPy array

Question

I have a relatively large NumPy array (1212,1612) that contains OBJECTID values corresponding to unique segments of an RGB image. Furthermore, I have a different Pandas Dataframe of 107305 rows that contains all OBJECTID values and their corresponding vegetation type class resulting from a Random Forest classification. I want to (iteratively) replace the OBJECTID values in the NumPy array with the vegetation type class corresponding to that specific OBJECTID value.

The NumPy array ('array') looks like this:

[1,1,1,1,2,2,2,2,3,3,...
 1,1,1,1,1,2,2,2,3,3,...
 1,1,2,4,4,4,2,2,3,3,...] # values 1-4 correspond to OBJECTID

Where the pandas dataframe ('vegdata') looks like:

VEG_TYPE    OBJECTID
Shrub (S)   1
Grass (G)   2
Moss  (M)   3
Grass (G)   4
...   ...   ...

What I want the data to eventually look like is as follows:

[S,S,S,S,G,G,G,G,M,M,...
 S,S,S,S,S,G,G,G,M,M,...
 S,S,G,G,G,G,G,G,M,M,...]

What I am currently doing is:

for row in vegdata.itertuples():
    np.where(array[[array == row.OBJECTID]], row.VEG_TYPE,
             array[[array == row.OBJECTID]])

This code snippet works, yet it is very slow and it takes approximately 30 seconds per 1000 rows in the vegdata dataframe, whereas I have 107305 rows and in some images about ten times as much. I am investigating whether there is an alternative way of performing this analysis, but in the meantime I have not been able to find a suitable approach, so I am wondering whether there is anyone who might know how to do such an analysis in a faster way.

I am new to StackOverflow so I tried to make my answer as clear as possible by providing some code snippets, but if anything is unclear please let me know. Thanks so much in advance!

Answer 1

Here you go:

import numpy as np
import pandas as pd

VEG_TYPE = ['Shrub (S)','Grass (G)','Moss  (M)','Grass (G)']
OBJECTID = [1 ,2 ,3 ,4]

mapping= {k:v for k,v in zip(OBJECTID, VEG_TYPE)}

input_array = np.random.randint(1,5, (10,10))

out = np.empty(input_array.shape, dtype=np.dtype('U100'))
for key,val in mapping.items():
    out[input_array==key] = val