fix: add validation to avoid zero division for cat colums with only m…

…issing data

src/ydata_profiling/model/pandas/describe_categorical_pandas.py

@@ -195,10 +195,12 @@ def length_summary_vc(vc: pd.Series) -> dict:
 
     summary = {
         "max_length": np.max(length_counts.index),
-        "mean_length": np.average(length_counts.index, weights=length_counts.values),
+        "mean_length": np.average(
+            length_counts.index, weights=length_counts.values
+            )  if not length_counts.empty else np.nan,
         "median_length": weighted_median(
             length_counts.index.values, weights=length_counts.values
-        ),
+        ) if not length_counts.empty else np.nan,
         "min_length": np.min(length_counts.index),
         "length_histogram": length_counts,
     }

src/ydata_profiling/model/summary_algorithms.py

@@ -34,6 +34,8 @@ def histogram_compute(
     weights: Optional[np.ndarray] = None,
 ) -> dict:
     stats = {}
+    if len(finite_values) == 0:
+        return {name: []}
     hist_config = config.plot.histogram
     bins_arg = "auto" if hist_config.bins == 0 else min(hist_config.bins, n_unique)
     bins = np.histogram_bin_edges(finite_values, bins=bins_arg)
@@ -54,6 +56,8 @@ def chi_square(
     if histogram is None:
         bins = np.histogram_bin_edges(values, bins="auto")
         histogram, _ = np.histogram(values, bins=bins)
+    if len(histogram) == 0 or np.sum(histogram) == 0:
+        return {"statistic": np.nan, "pvalue": np.nan}
     return dict(chisquare(histogram)._asdict())