In my program I current create a numpy array full of zeros and then for loop through each element replacing it with the desired value. Is there a more efficient way of doing this?

Below is an example of what I am doing however, instead of a int I have a list of each row which needs put into the numpy array. Is there a way to put replace whole rows and is that more efficient.

```
import numpy as np
from tifffile import imsave
image = np.zeros((5, 2160, 2560), 'uint16')
num =0
for pixel in np.nditer(image, op_flags=['readwrite']):
pixel = num
num += 1
imsave('multipage.tif', image)
```

Just assign to the whole row using slicing

```
import numpy as np
from tifffile import imsave
list_of_rows = ... # all items in list should have same length
image = np.zeros((len(list_of_rows),'uint16')
for row_idx, row in enumerate(list_of_rows):
image[row_idx, :] = row
imsave('multipage.tif', image)
```

Numpy slicing is extremely powerful and nice. I recommend reading through this documentation to get a feeling of what is possible.