How about doing it with Pillow:

from PIL import Image
img = Image.open('image.png').convert('L')
img.save('greyscale.png')

If an alpha (transparency) channel is present in the input image and should be preserved, use mode LA:

img = Image.open('image.png').convert('LA')

Using matplotlib and the formula

Y' = 0.2989 R + 0.5870 G + 0.1140 B 

you could do:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

def rgb2gray(rgb):
    return np.dot(rgb[...,:3], [0.2989, 0.5870, 0.1140])

img = mpimg.imread('image.png')     
gray = rgb2gray(img)    
plt.imshow(gray, cmap=plt.get_cmap('gray'), vmin=0, vmax=1)
plt.show()

Here is a working code:

def grayConversion(image):
    grayValue = 0.07 * image[:,:,2] + 0.72 * image[:,:,1] + 0.21 * image[:,:,0]
    gray_img = grayValue.astype(np.uint8)
    return gray_img

orig = cv2.imread(r'C:\Users\Jackson\Desktop\drum.png', 1)
g = grayConversion(orig)

cv2.imshow("Original", orig)
cv2.imshow("GrayScale", g)
cv2.waitKey(0)
cv2.destroyAllWindows()

Two issues. Firstly, you are not converting to greyscale. Secondly, the whole point of numpy is to vectorise and avoid for loops because they are slow.

So starting with this image:

You want something like this:

#!/usr/local/bin/python3
import numpy as np
from PIL import Image

# Load coins and convert to greyscale
grey = np.array(Image.open('coins.png').convert('L'))

# Threshold at 128
thresholded=((grey>128)*255).astype(np.uint8)

# Save
Image.fromarray(thresholded).save('result.png')