fix: consider all nan series as numerical (#1513)

* fix: consider all nan series as numerical

* fix(linting): code formatting

---------

Co-authored-by: Azory YData Bot <azory@ydata.ai>

Author: aquemy

src/ydata_profiling/model/pandas/describe_numeric_pandas.py

@@ -50,19 +50,30 @@ def numeric_stats_numpy(
     index_values = vc.index.values
 
     # FIXME: can be performance optimized by using weights in std, var, kurt and skew...
-
-    return {
-        "mean": np.average(index_values, weights=vc.values),
-        "std": np.std(present_values, ddof=1),
-        "variance": np.var(present_values, ddof=1),
-        "min": np.min(index_values),
-        "max": np.max(index_values),
-        # Unbiased kurtosis obtained using Fisher's definition (kurtosis of normal == 0.0). Normalized by N-1.
-        "kurtosis": series.kurt(),
-        # Unbiased skew normalized by N-1
-        "skewness": series.skew(),
-        "sum": np.dot(index_values, vc.values),
-    }
+    if len(index_values):
+        return {
+            "mean": np.average(index_values, weights=vc.values),
+            "std": np.std(present_values, ddof=1),
+            "variance": np.var(present_values, ddof=1),
+            "min": np.min(index_values),
+            "max": np.max(index_values),
+            # Unbiased kurtosis obtained using Fisher's definition (kurtosis of normal == 0.0). Normalized by N-1.
+            "kurtosis": series.kurt(),
+            # Unbiased skew normalized by N-1
+            "skewness": series.skew(),
+            "sum": np.dot(index_values, vc.values),
+        }
+    else:  # Empty numerical series
+        return {
+            "mean": np.nan,
+            "std": 0.0,
+            "variance": 0.0,
+            "min": np.nan,
+            "max": np.nan,
+            "kurtosis": 0.0,
+            "skewness": 0.0,
+            "sum": 0,
+        }
 
 
 @describe_numeric_1d.register
@@ -151,13 +162,14 @@ def pandas_describe_numeric_1d(
     else:
         stats["monotonic"] = 0
 
-    stats.update(
-        histogram_compute(
-            config,
-            value_counts[~infinity_index].index.values,
-            summary["n_distinct"],
-            weights=value_counts[~infinity_index].values,
+    if len(value_counts[~infinity_index].index.values) > 0:
+        stats.update(
+            histogram_compute(
+                config,
+                value_counts[~infinity_index].index.values,
+                summary["n_distinct"],
+                weights=value_counts[~infinity_index].values,
+            )
         )
-    )
 
     return config, series, stats

src/ydata_profiling/report/structure/variables/render_real.py

@@ -118,12 +118,12 @@ def render_real(config: Settings, summary: dict) -> dict:
         style=config.html.style,
     )
 
-    if isinstance(summary["histogram"], list):
+    if isinstance(summary.get("histogram", []), list):
         mini_histo = Image(
             mini_histogram(
                 config,
-                [x[0] for x in summary["histogram"]],
-                [x[1] for x in summary["histogram"]],
+                [x[0] for x in summary.get("histogram", [])],
+                [x[1] for x in summary.get("histogram", [])],
             ),
             image_format=image_format,
             alt="Mini histogram",
@@ -243,13 +243,14 @@ def render_real(config: Settings, summary: dict) -> dict:
         sequence_type="grid",
     )
 
-    if isinstance(summary["histogram"], list):
+    if isinstance(summary.get("histogram", []), list):
         hist_data = histogram(
             config,
-            [x[0] for x in summary["histogram"]],
-            [x[1] for x in summary["histogram"]],
+            [x[0] for x in summary.get("histogram", [])],
+            [x[1] for x in summary.get("histogram", [])],
         )
-        hist_caption = f"<strong>Histogram with fixed size bins</strong> (bins={len(summary['histogram'][0][1]) - 1})"
+        bins = len(summary["histogram"][0][1]) - 1 if "histogram" in summary else 0
+        hist_caption = f"<strong>Histogram with fixed size bins</strong> (bins={bins})"
     else:
         hist_data = histogram(config, *summary["histogram"])
         hist_caption = f"<strong>Histogram with fixed size bins</strong> (bins={len(summary['histogram'][1]) - 1})"

src/ydata_profiling/visualisation/plot.py

@@ -73,14 +73,15 @@ def _plot_histogram(
         plot = fig.add_subplot(111)
 
         for idx in reversed(list(range(n_labels))):
-            diff = np.diff(bins[idx])
-            plot.bar(
-                bins[idx][:-1] + diff / 2,  # type: ignore
-                series[idx],
-                diff,
-                facecolor=config.html.style.primary_colors[idx],
-                alpha=0.6,
-            )
+            if len(bins):
+                diff = np.diff(bins[idx])
+                plot.bar(
+                    bins[idx][:-1] + diff / 2,  # type: ignore
+                    series[idx],
+                    diff,
+                    facecolor=config.html.style.primary_colors[idx],
+                    alpha=0.6,
+                )
 
             if date:
                 plot.xaxis.set_major_formatter(FuncFormatter(format_fn))