How to perform field significance of a test in Python?

Question

I have the following xarray that contains the Spearman's Rank correlation at each grid point over Africa:

<xarray.DataArray (lat: 162, lon: 162)>
array([[       nan,        nan,        nan, ...,        nan,        nan,
               nan],
       [       nan,        nan,        nan, ...,        nan,        nan,
               nan],
       [       nan,        nan,        nan, ...,        nan,        nan,
               nan],
       ...,
       [       nan,        nan,        nan, ..., 0.40315942, 0.37931034,
        0.43181313],
       [       nan,        nan,        nan, ..., 0.37156525, 0.36329255,
        0.38091   ],
       [       nan,        nan,        nan, ..., 0.3579931 , 0.33615128,
        0.34620091]])
Coordinates:
  * lon      (lon) float32 -20.25 -19.75 -19.25 -18.75 ... 59.25 59.75 60.25
  * lat      (lat) float32 -40.25 -39.75 -39.25 -38.75 ... 39.25 39.75 40.25

I would like to calculate the field significance using a bootstrap method to then only plot grid cells where the test is locally significant at the 5% level of field significance. I have the following code, which I think is doing the right thing:

# Number of bootstrap samples
n_bootstrap_samples = 1000

# Define a function to calculate field significance using bootstrap
def calculate_field_significance(data, n_bootstrap_samples):
    # Get the dimensions of the data
    lat, lon = data['lat'], data['lon']
    n_lat, n_lon = len(lat), len(lon)

    # Initialize an array to store bootstrap results
    field_significance = np.empty((n_lat, n_lon))

    for i in range(n_lat):
        for j in range(n_lon):
            # Select the data at the current grid cell
            current_grid_cell_data = data[i, j].values # Access the numpy array
            
            # Initialize an array to store bootstrap sample results
            bootstrap_results = np.empty(n_bootstrap_samples)

            for k in range(n_bootstrap_samples):
                # Resample the data with replacement
                bootstrap_sample = np.random.choice(current_grid_cell_data, len(current_grid_cell_data))

                # Calculate Spearman's rank correlation coefficient for the bootstrap sample
                bootstrap_statistic = xs.spearman_r(bootstrap_sample, current_grid_cell_data) 

                # Store the bootstrap sample statistic
                bootstrap_results[k] = bootstrap_statistic

            # Calculate the Spearman's rank correlation coefficient for the observed data
            observed_statistic = xs.spearman_r(current_grid_cell_data, current_grid_cell_data)

            # Calculate the p-value by comparing the observed statistic with the bootstrap distribution
            p_value = (np.sum(bootstrap_results >= observed_statistic) + 1) / (n_bootstrap_samples + 1)

            # Store the p-value as the field significance at the current grid cell
            field_significance[i, j] = p_value

    return xr.DataArray(field_significance, coords={'lat': lat, 'lon': lon}, dims=['lat', 'lon'])

# Calculate field significance for your dataset
field_significance = calculate_field_significance(cru_djf_cor, n_bootstrap_samples)

However, I get the following error when I run this code:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_16096/3424556375.py in <module>
     43 
     44 # Calculate field significance for your dataset
---> 45 field_significance = calculate_field_significance(cru_djf_cor, n_bootstrap_samples)
     46 
     47 # You can now access field_significance to see the field significance values for each grid cell.

~\AppData\Local\Temp/ipykernel_16096/3424556375.py in calculate_field_significance(data, n_bootstrap_samples)
     23             for k in range(n_bootstrap_samples):
     24                 # Resample the data with replacement
---> 25                 bootstrap_sample = np.random.choice(current_grid_cell_data, len(current_grid_cell_data))
     26 
     27                 # Calculate Spearman's rank correlation coefficient for the bootstrap sample

TypeError: len() of unsized object

Please advise on why I am getting this error? Does the code look right?