[FIXED] append an element to 2d numpy array

Issue

I have a numpy array that has a shape of (500, 151296). Below is the array format

array:

array([[-0.18510018,  0.13180602,  0.32903048, ...,  0.39744213,
        -0.01461623,  0.06420607],
       [-0.14988784,  0.12030973,  0.34801325, ...,  0.36962894,
         0.04133283,  0.04434045],
       [-0.3080041 ,  0.18728344,  0.36068922, ...,  0.09335024,
        -0.11459247,  0.10187756],
       ...,
       [-0.17399777, -0.02492459, -0.07236133, ...,  0.08901921,
        -0.17250113,  0.22222663],
       [-0.17399777, -0.02492459, -0.07236133, ...,  0.08901921,
        -0.17250113,  0.22222663],
       [-0.17399777, -0.02492459, -0.07236133, ...,  0.08901921,
        -0.17250113,  0.22222663]], dtype=float32)

array[0]:

array([-0.18510018,  0.13180602,  0.32903048, ...,  0.39744213,
       -0.01461623,  0.06420607], dtype=float32)

I have another list that has stopwords which are same size of the numpy array shape

stopwords = ['no', 'not', 'in' .........]

I want to add each stopword to the numpy array which has 500 elements. Below is the code that I am using to add

for i in range(len(stopwords)):
  array = np.append(array[i], str(stopwords[i]))

I am getting the below error

IndexError                                Traceback (most recent call last)
<ipython-input-45-361e2cf6519b> in <module>
      1 for i in range(len(stopwords)):
----> 2   array = np.append(array[i], str(stopwords[i]))

IndexError: index 2 is out of bounds for axis 0 with size 2

Desired output:

array[0]:

array([-0.18510018,  0.13180602,  0.32903048, ...,  0.39744213,
       -0.01461623,  0.06420607, 'no'], dtype=float32)

Can anyone tell me where am I doing wrong?

Solution

What you are doing wrong is that you overwrite the variable array inside the for loop:

for i in range(len(stopwords)):
    array = np.append(array[i], str(stopwords[i]))
#   ^^^^^             ^^^^^

But what you are also doing wrong is to use np.append in a for loop, which is almost always a bad idea.

You could rather do something like:

from string import ascii_letters
from random import choices

import numpy as np

N, M = 50, 7
arr = np.random.randn(N, M)
stopwords = np.array(["".join(choices(ascii_letters, k=10)) for _ in range(N)])
result = np.concatenate([arr, stopwords[:, None]], axis=-1)

assert result.shape == (N, M+1)
print(result[0])  # ['0.1' '-1.2' '-0.1' '1.6' '-1.4' '-0.2' '1.7' 'ybWyFlqhcS']

But it is also wrong, mixing data types for no apparent reason.

Imho, you better just keep the two arrays.

Depending on what you are doing you can iterate over them as follows:

for vector, stopword in zip(arr, stopwords):
    print(f"{stopword = }")
    print(f"{vector   = }")

# stopword = 'RgfTVGzPOl'
# vector   = array([-0.9,  1.1,  0.7 , -0.3 , -0.7 , -0.7, -0.6])
# 
# stopword = 'XlJqKdsvCC'
# vector   = array([-0.5,  0.1, -0.7 , -0.6, -1.1, -0.6, -0.6])
# 
#...

Answered By - paime