Numpy Integer to String Based on Lookup Table - numpy

Question

I have a numpy array called "landuse" that's a series of numbers 1-3 representing different landuse categories. I want to convert this to a string based on a lookup table.
ids = [0,1,2,3]
lookup_table = ['None', 'Forest', 'Water', 'Urban']

Answer 1

First let me explain why your loop isn't working, in python an assignment, ie a = 1 takes the object 1 and gives it the name a. When you do name = "Water", name forgets what it was pointing to before and now points to "Water", but that doesn't mean the previous object that was assigned to name gets replaced with "Water".
That's the problem, and now for a fix. If you have your landuse as an array of integer codes you can just use a lookup table. The table should be big enough so you don't get an indexing error when you do lookup_table[landuse.max()]
import numpy as np
landuse = np.array([1,2,3,1,2,4])
lookup_table = np.array(['None', 'Forest', 'Water', 'Urban', 'Other'])
landuse_title = lookup_table[landuse]
And for the final part of your question, the numpy ndarray is a homogenous data structure, meaning everything in the array must have the same data type. With that limitation in mind, it should be clear that you cannot take a row of the integers and replace it with a row of strings. Numpy does have "flexible dtypes" which allow you to do something like:
>>> dt = np.dtype([('name', 'S4'), ('age', 'int'), ('height', 'float')])
>>> array = np.array([('Mark', 25, 70.5),('Ben',40,72.75)], dtype=dt)
>>> array
array([('Mark', 25, 70.5), ('Ben', 40, 72.75)],
dtype=[('name', '|S4'), ('age', '<i4'), ('height', '<f8')])
>>> array.shape
(2,)
>>> array['name']
array(['Mark', 'Ben'],
dtype='|S4')
We've created an array that hold a name, age and height for each person, but notice that the shape of the array is (2,) because we have two "people" in the array. I'm not sure exactly what your needs are, but you could try and use the flexible dtype to hold all the information in one array if that's what you need. Depending on what my end goal, I often find it's easier to just use a few separate arrays, or a list of arrays. Hope that helps.

Answer 2

I am not entirely clear what your question is, but it seems you could use a dictionary for this:
import numpy as np
landuse=np.array([1,2,3,1,2,4],dtype=np.integer)
a={1:'Forest',2:'Water'}
print [a.setdefault(i,'Urban') for i in landuse]
which will emit a list containing the strings you are interested in:
['Forest', 'Water', 'Urban', 'Forest', 'Water', 'Urban']
If you objective is to have the final result in a numpy array of strings, you can do this:
name=np.array([a.setdefault(i,'Urban') for i in landuse],dtype='|S10')