[FIXED] How can I create a label encoder utilizing only numpy (and not sklearn LabelEncoder)?

Issue

I am trying to recreate something similar to the sklearn.preprocessing.LabelEncoder

However I do not want to use sklearn or pandas. I would like to only use numpy and the Python standard library. Here's what I would like to achieve:

import numpy as np
input = np.array([['hi', 'there'],
                     ['scott', 'james'],
                     ['hi', 'scott'],
                     ['please', 'there']])

# Output would look like
np.ndarray([[0, 0],
            [1, 1],
            [0, 2],
            [2, 0]])

It would also be great to be able to map it back as well, so a result would then look exactly like the input again.

If this were in a spreadsheet, the input would look like this:

Solution

Here's a simple comprehension, using the return_inverse result from np.unique

arr = np.array([['hi', 'there'], ['scott', 'james'],
                ['hi', 'scott'], ['please', 'there']])

np.column_stack([np.unique(arr[:, i], return_inverse=True)[1] for i in range(arr.shape[1])])

array([[0, 2],
       [2, 0],
       [0, 1],
       [1, 2]], dtype=int64)

---

Or applying along the axis:

np.column_stack(np.apply_along_axis(np.unique, 0, arr, return_inverse=True)[1])

Answered By - ALollz