Refactor overview charts with shared helper functions

- Extract common aggregation logic into _aggregate_data helper
- Extract year relabeling logic into _relabel_years helper
- Create _create_year_comparison_bar_chart for segment and category charts
- Refactor overview_by_customer_segment to use px.bar instead of go.Figure
- Refactor overview_by_product_category to use px.bar instead of go.Figure
- Update parameter names from value_col to column for consistency
- Eliminate 76 lines of duplicated code

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

app.py

@@ -114,10 +114,10 @@ overview_page = vm.Page(
             selector=vm.RadioItems(options=["Sales", "Profit", "Order ID", "Customer ID"]),
             targets=[
                 "region_bar_chart.value_col",
-                "category_bar_chart.value_col",
+                "category_bar_chart.column",
                 "order_status_pie_chart.column",
                 "month_line_chart.column",
-                "segment_bar_chart.value_col",
+                "segment_bar_chart.column",
             ],
             visible=False,
         )

charts.py

@@ -169,123 +169,48 @@ def overview_by_region(data_frame, value_col="Sales"):
     return fig
 
 
-@capture("graph")
-def overview_by_customer_segment(data_frame, value_col="Sales"):
-    """Custom bar chart made with Plotly."""
-    data_frame["Order Date"] = pd.to_datetime(data_frame["Order Date"])
-    data_frame["Year"] = data_frame["Order Date"].dt.year
-
-    if value_col == "Order ID":
-        agg_df = (
-            data_frame.groupby(["Segment", "Year"], as_index=False)["Order ID"]
-            .nunique()
-            .rename(columns={"Order ID": "Orders"})
-        )
-        agg_col = "Orders"
-    elif value_col == "Customer ID":
-        agg_df = (
-            data_frame.groupby(["Segment", "Year"], as_index=False)["Customer ID"]
-            .nunique()
-            .rename(columns={"Customer ID": "Customers"})
-        )
-        agg_col = "Customers"
-    else:
-        agg_df = data_frame.groupby(["Segment", "Year"], as_index=False)[value_col].sum()
-        agg_col = value_col
+def _aggregate_data(data_frame, group_columns, column):
+    """Aggregate data using the appropriate function for the column."""
+    return data_frame.groupby(group_columns, as_index=False).agg({column: COLUMN_TO_AGGFUNC[column]})
 
-    pivot_df = agg_df.pivot_table(index="Segment", columns="Year", values=agg_col).reset_index()
 
-    fig = go.Figure()
+# TODO: move to data_processing?
+def _relabel_years(data_frame):
+    """Relabel years for human-readable plotting."""
+    data_frame["Year"] = data_frame["Year"].map({THIS_YEAR: "This year", LAST_YEAR: "Last year"})
+    return data_frame
 
-    if PREVIOUS_YEAR in pivot_df.columns:
-        fig.add_trace(
-            go.Bar(
-                x=pivot_df["Segment"],
-                y=pivot_df[PREVIOUS_YEAR],
-                name="Previous year",
-                marker_color=SECONDARY_COLOR,
-            )
-        )
 
-    if CURRENT_YEAR in pivot_df.columns:
-        fig.add_trace(
-            go.Bar(
-                x=pivot_df["Segment"],
-                y=pivot_df[CURRENT_YEAR],
-                name="Current year",
-                marker_color=PRIMARY_COLOR,
-            )
-        )
+def _create_year_comparison_bar_chart(data_frame, group_column, column, title_suffix):
+    """Helper function to create a grouped bar chart comparing years."""
+    grouped_df = _aggregate_data(data_frame, [group_column, "Year"], column)
+    grouped_df = _relabel_years(grouped_df)
 
-    fig.update_layout(
+    fig = px.bar(
+        grouped_df,
+        x=group_column,
+        y=column,
+        color="Year",
         barmode="group",
-        xaxis_title=None,
-        yaxis_title=None,
-        bargap=0.4,
-        title=f"{agg_col} | By Customer Segment",
-        showlegend=False,
+        title=f"{COLUMN_TO_METRIC[column]} | {title_suffix}",
+        color_discrete_map={"This year": PRIMARY_COLOR, "Last year": SECONDARY_COLOR},
     )
 
+    fig.update_layout(xaxis_title=None, yaxis_title=None, bargap=0.4, showlegend=False)
+
     return fig
 
 
 @capture("graph")
-def overview_by_product_category(data_frame, value_col="Sales"):
-    """Bar chart comparing current year vs previous year by category."""
-    data_frame["Year"] = data_frame["Order Date"].dt.year
+def overview_by_customer_segment(data_frame, column="Sales"):
+    """Bar chart comparing current year vs previous year by customer segment."""
+    return _create_year_comparison_bar_chart(data_frame, "Segment", column, "By Customer Segment")
 
-    if value_col == "Order ID":
-        agg_df = (
-            data_frame.groupby(["Category", "Year"], as_index=False)["Order ID"]
-            .nunique()
-            .rename(columns={"Order ID": "Orders"})
-        )
-        agg_col = "Orders"
-    elif value_col == "Customer ID":
-        agg_df = (
-            data_frame.groupby(["Category", "Year"], as_index=False)["Customer ID"]
-            .nunique()
-            .rename(columns={"Customer ID": "Customers"})
-        )
-        agg_col = "Customers"
-    else:
-        agg_df = data_frame.groupby(["Category", "Year"], as_index=False)[value_col].sum()
-        agg_col = value_col
-
-    pivot_df = agg_df.pivot_table(index="Category", columns="Year", values=agg_col).reset_index()
-
-    fig = go.Figure()
-
-    if PREVIOUS_YEAR in pivot_df.columns:
-        fig.add_trace(
-            go.Bar(
-                x=pivot_df["Category"],
-                y=pivot_df[PREVIOUS_YEAR],
-                name="Previous year",
-                marker_color=SECONDARY_COLOR,
-            )
-        )
-
-    if CURRENT_YEAR in pivot_df.columns:
-        fig.add_trace(
-            go.Bar(
-                x=pivot_df["Category"],
-                y=pivot_df[CURRENT_YEAR],
-                name="Current year",
-                marker_color=PRIMARY_COLOR,
-            )
-        )
-
-    fig.update_layout(
-        barmode="group",
-        xaxis_title=None,
-        yaxis_title=None,
-        bargap=0.4,
-        title=f"{agg_col} | By Product Category",
-        showlegend=False,
-    )
 
-    return fig
+@capture("graph")
+def overview_by_product_category(data_frame, column="Sales"):
+    """Bar chart comparing current year vs previous year by category."""
+    return _create_year_comparison_bar_chart(data_frame, "Category", column, "By Product Category")
 
 
 import calendar
@@ -293,10 +218,10 @@ import calendar
 
 @capture("graph")
 def overview_by_month(data_frame, column="Sales"):
-    grouped_df = data_frame.groupby(["Year", "Month"], as_index=False).agg({column: COLUMN_TO_AGGFUNC[column]})
+    grouped_df = _aggregate_data(data_frame, ["Year", "Month"], column)
     # Relabel for plotting in human-readable way.
     grouped_df["Month"] = grouped_df["Month"].map({i: calendar.month_abbr[i] for i in range(1, 13)})
-    grouped_df["Year"] = grouped_df["Year"].map({THIS_YEAR: "This year", LAST_YEAR: "Last year"})
+    grouped_df = _relabel_years(grouped_df)
 
     fig = px.line(
         grouped_df,
@@ -319,7 +244,7 @@ def overview_by_month(data_frame, column="Sales"):
 
 @capture("graph")
 def overview_by_order_status(data_frame, column="Sales"):
-    grouped_df = data_frame.groupby("Order Status", as_index=False).agg({column: COLUMN_TO_AGGFUNC[column]})
+    grouped_df = _aggregate_data(data_frame, "Order Status", column)
 
     fig = px.pie(
         grouped_df,