feat: enhance data visualization for numeric and categorical y-axis v…

…alues

src/ia_collection_analyzer/streamlit.py

@@ -140,30 +140,62 @@
 if plot_button and x_axis != y_axis:
     st.write("Plotting the data...")
     st.write(f"X-axis: {x_axis}, Y-axis: {y_axis}")
-
-    # Create comprehensive aggregation table
-    all_metrics = (
-        filtered_pd.groupby(x_axis)[y_axis]
-        .agg(
-            [
-                ("Count", "count"),
-                ("Sum", "sum"),
-                ("Mean", "mean"),
-                ("Median", "median"),
-                ("Min", "min"),
-                ("Max", "max"),
-            ]
+
+    # if y_axis is hashable , plot 
+    if isinstance(filtered_pd[y_axis].iloc[0], (int, float, np.int64, np.float64)):
+        # Create comprehensive aggregation table
+        all_metrics = (
+            filtered_pd.groupby(x_axis)[y_axis]
+            .agg(
+                [
+                    ("Count", "count"),
+                    ("Sum", "sum"),
+                    ("Mean", "mean"),
+                    ("Median", "median"),
+                    ("Min", "min"),
+                    ("Max", "max"),
+                ]
+            )
+            .reset_index()
         )
-        .reset_index()
-    )
-
-    # Display complete aggregated data
-    st.write("Complete aggregation metrics:")
-    # Create multi-line chart (excluding Count since it's often on different scale)
-    metrics_for_plot = all_metrics.drop(columns=["Count", "Sum", "Max"])
-    metrics_for_plot = metrics_for_plot.set_index(x_axis)
-
-    st.write("Multi-metric trend lines:")
-    st.line_chart(metrics_for_plot)
 
-    st.write(all_metrics)
+        # Display complete aggregated data
+        st.write("Complete aggregation metrics:")
+        # Create multi-line chart (excluding Count since it's often on different scale)
+        metrics_for_plot = all_metrics.drop(columns=["Count", "Sum", "Max"])
+        metrics_for_plot = metrics_for_plot.set_index(x_axis)
+
+        st.write("Multi-metric trend lines:")
+        st.line_chart(metrics_for_plot)
+
+        st.write(all_metrics)
+
+    # if y_axis is not numeric, count and plot
+    else:
+        st.write("Analyzing distribution across categories...")
+
+        # Create mask for list and non-list values
+        is_list_mask = filtered_pd[y_axis].apply(lambda x: isinstance(x, list))
+
+        # Handle list values
+        list_data = filtered_pd[is_list_mask][[x_axis, y_axis]].copy()
+        exploded_list = list_data.explode(y_axis)
+
+        # Handle non-list values 
+        non_list_data = filtered_pd[~is_list_mask][[x_axis, y_axis]]
+
+        # Combine results efficiently
+        expanded_df = pd.concat([exploded_list, non_list_data])
+
+        # Create pivot table and plot
+        pivot_table = pd.crosstab(
+            expanded_df[x_axis], 
+            expanded_df[y_axis],
+            normalize='index'
+        ) * 100
+
+        st.bar_chart(pivot_table)
+
+        st.write("Distribution counts:")
+        counts_df = pd.crosstab(expanded_df[x_axis], expanded_df[y_axis])
+        st.write(counts_df)