Converting a 3D numpy array to coordinates and values

December 22, 2022

I have a 3D numpy array of shape (7,100,50) that represents a stack of 7 100×50 images.

I want to convert this array to a dataframe containing the position of all pixels x,y,z and the value of the pixel (id)

I have managed to do this for a single image (no z):

import numpy as np
import pandas as pd
img = np.random.randint(0,30,size=(100,50))
cell_id = img.flatten() 
x = [i % img.shape[1] for i in range(len(cell_id))]
y = [y_ for y_ in range(img.shape[1]) for _ in range(img.shape[0])]
df = pd.DataFrame(data={"id":cell_id, "x":x, "y":y, "z":0})
df:
    id  x   y   z
0   29  0   0   0
1   16  1   0   0
2   3   2   0   0
3   15  3   0   0
4   23  4   0   0
...     ...     ...     ...     ...
4995    7   45  49  0
4996    6   46  49  0
4997    1   47  49  0
4998    5   48  49  0
4999    7   49  49  0

5000 rows × 4 columns

How do I adjust this to work for

zimg = np.random.randint(0,30,size=(7,100,50))

>Solution :

I see you mentioned np.ndenumerate in another comment, this should do the trick:

import pandas as pd
import numpy as np

def constructor(array, z=0):
    """Transform an array into df

    Here we assume z=0 as in your example
    """
    for (img_id, y, x), value in np.ndenumerate(array):
            yield (img_id, value, x, y, z)

a = np.random.randint(0,30,size=(7,100,50))  

df = pd.DataFrame(
    constructor(a), 
    columns=('image_id', 'id', 'x', 'y', 'z')
)