<array> = np.concatenate(<list_of_arrays>, axis=0) # Links arrays along first axis (rows).
<array> = np.concatenate(<list_of_arrays>, axis=0) # Links arrays along first axis (rows).
<array> = np.row_stack/column_stack(<list_of_arrays>) # Treats 1d arrays as rows or columns.
<array> = np.row_stack/column_stack(<list_of_arrays>) # Treats 1d arrays as rows or columns.
<array> = np.tile/repeat(<array>, <int/list>) # Tiles array or repeats its elements.
<array> = np.tile/repeat(<array>, <int/list>[,axis]) # Tiles array or repeats its elements.
```
```
* **Shape is a tuple of dimension sizes. A 100x50 RGB image has shape (50, 100, 3).**
* **Shape is a tuple of dimension sizes. A 100x50 RGB image has shape (50, 100, 3).**
* **Axis is an index of the dimension that gets aggregated. Leftmost dimension has index 0. Summing the RGB image along axis 2 will return a greyscale image with shape (50, 100).**
* **Axis is an index of the dimension that gets aggregated. Leftmost dimension has index 0. Summing the RGB image along axis 2 will return a greyscale image with shape (50, 100).**
Jure Šorn
1 year ago