Differences between `numpy ndarray` and `xarray`

We know that in PyImageJ:
image with java data type RAI would be converted to ndarray
image with java data type IJ2 Dataset would be converted to xarray

what's the difference of ndarray and xarray?
And why a xarray image has dims information while ndarray image not?
xarray:
ndarray:

The differences between numpy ndarray and xarray are:

while ndarray is the fundamental package that supports multi-dimensional arrays
Xarray attaches labels such dimension names to the arrays, allowing for easier data analysis:
Here are two simple examples of ndarray and Xarray:

# NumPy ndarray
import numpy as np

# Creating a 2D NumPy array
arr = np.array([[1, 2, 3], [4, 5, 6]])

# Accessing elements using positional indices
print(arr[0, 1])  # Output: 2

# Xarray DataArray
import xarray as xr

# Creating a 2D DataArray with labeled dimensions and coordinates
data_arr = xr.DataArray(arr, dims=("x", "y"), coords={"x": [0, 1], "y": [0, 1, 2]})

# Accessing elements using dimension names and coordinates
print(data_arr.sel(x=0, y=1).values)  # Output: 2

Instead of accessing data by positional indices, with xarray we can access data with dimension names and coordinates, providing a more intuitive way to work with data

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Differences between `numpy ndarray` and `xarray` #63