+
+
Retail Sales Historical Data Analysis
+
Interactive visualization of sales patterns across stores and products.
+
+ """)
+
+ # Create overview metrics
+ total_sales = sales_data["sales"].sum()
+ avg_daily_sales = sales_data.groupby("date")["sales"].sum().mean()
+ num_stores = sales_data["store"].nunique()
+ num_items = sales_data["item"].nunique()
+ min_date = sales_data["date"].min().strftime("%Y-%m-%d")
+ max_date = sales_data["date"].max().strftime("%Y-%m-%d")
+ date_range = f"{min_date} to {max_date}"
+
+ html_parts.append(f"""
+
+
Dataset Overview
+
+
+
Total Sales
+
{total_sales:,.0f} units
+
+
+
Avg. Daily Sales
+
{avg_daily_sales:,.1f} units
+
+
+
Stores × Items
+
{num_stores} × {num_items}
+
+
+
Date Range
+
{date_range}
+
+
+
+ """)
+
+ # 1. Time Series - Overall Sales Trend
+ df_daily = sales_data.groupby("date")["sales"].sum().reset_index()
+ fig_trend = px.line(
+ df_daily,
+ x="date",
+ y="sales",
+ title="Daily Total Sales Across All Stores and Products",
+ template="plotly_white",
+ )
+ fig_trend.update_traces(line=dict(width=2))
+ fig_trend.update_layout(
+ xaxis_title="Date", yaxis_title="Total Sales (units)", height=500
+ )
+ trend_html = fig_trend.to_html(full_html=False, include_plotlyjs="cdn")
+ html_parts.append(f"""
+
+
Overall Sales Trend
+ {trend_html}
+
+
Insights: Observe weekly patterns and special events that impact overall sales volume.
+
+
+ """)
+
+ # 2. Store Comparison
+ store_sales = (
+ sales_data.groupby(["date", "store"])["sales"].sum().reset_index()
+ )
+ fig_stores = px.line(
+ store_sales,
+ x="date",
+ y="sales",
+ color="store",
+ title="Sales Comparison by Store",
+ template="plotly_white",
+ )
+ fig_stores.update_layout(
+ xaxis_title="Date", yaxis_title="Total Sales (units)", height=500
+ )
+ stores_html = fig_stores.to_html(full_html=False, include_plotlyjs="cdn")
+ html_parts.append(f"""
+
+
Store Comparison
+ {stores_html}
+
+
Insights: Compare performance across different stores to identify top performers and potential issues.
+
+
+ """)
+
+ # 3. Product Performance
+ item_sales = (
+ sales_data.groupby(["date", "item"])["sales"].sum().reset_index()
+ )
+ fig_items = px.line(
+ item_sales,
+ x="date",
+ y="sales",
+ color="item",
+ title="Sales Comparison by Product",
+ template="plotly_white",
+ )
+ fig_items.update_layout(
+ xaxis_title="Date", yaxis_title="Total Sales (units)", height=500
+ )
+ items_html = fig_items.to_html(full_html=False, include_plotlyjs="cdn")
+ html_parts.append(f"""
+
+
Product Performance
+ {items_html}
+
+
Insights: Identify best-selling products and those with unique seasonal patterns.
+
+
+ """)
+
+ # 4. Weekly Patterns
+ sales_data["day_of_week"] = sales_data["date"].dt.day_name()
+ day_order = [
+ "Monday",
+ "Tuesday",
+ "Wednesday",
+ "Thursday",
+ "Friday",
+ "Saturday",
+ "Sunday",
+ ]
+ weekly_pattern = (
+ sales_data.groupby("day_of_week")["sales"]
+ .mean()
+ .reindex(day_order)
+ .reset_index()
+ )
+
+ fig_weekly = px.bar(
+ weekly_pattern,
+ x="day_of_week",
+ y="sales",
+ title="Average Sales by Day of Week",
+ template="plotly_white",
+ color="sales",
+ color_continuous_scale="Blues",
+ )
+ fig_weekly.update_layout(
+ xaxis_title="", yaxis_title="Average Sales (units)", height=500
+ )
+ weekly_html = fig_weekly.to_html(full_html=False, include_plotlyjs="cdn")
+ html_parts.append(f"""
+
+
Weekly Patterns
+ {weekly_html}
+
+
Insights: Identify peak sales days to optimize inventory and staffing.
+
+
+ """)
+
+ # 5. Sample Store-Item Combinations
+ # Select 3 random series to display
+ sample_series = np.random.choice(
+ series_ids, size=min(3, len(series_ids)), replace=False
+ )
+
+ # Create subplots for train/test visualization
+ fig_samples = make_subplots(
+ rows=len(sample_series),
+ cols=1,
+ subplot_titles=[f"Series: {series_id}" for series_id in sample_series],
+ shared_xaxes=True,
+ vertical_spacing=0.1,
+ )
+
+ for i, series_id in enumerate(sample_series):
+ train_data = train_data_dict[series_id]
+ test_data = test_data_dict[series_id]
+
+ # Add train data
+ fig_samples.add_trace(
+ go.Scatter(
+ x=train_data["ds"],
+ y=train_data["y"],
+ mode="lines+markers",
+ name=f"{series_id} (Training)",
+ line=dict(color="blue"),
+ legendgroup=series_id,
+ showlegend=(i == 0),
+ ),
+ row=i + 1,
+ col=1,
+ )
+
+ # Add test data
+ fig_samples.add_trace(
+ go.Scatter(
+ x=test_data["ds"],
+ y=test_data["y"],
+ mode="lines+markers",
+ name=f"{series_id} (Test)",
+ line=dict(color="green"),
+ legendgroup=series_id,
+ showlegend=(i == 0),
+ ),
+ row=i + 1,
+ col=1,
+ )
+
+ fig_samples.update_layout(
+ height=300 * len(sample_series),
+ title_text="Train/Test Split for Sample Series",
+ template="plotly_white",
+ )
+
+ samples_html = fig_samples.to_html(full_html=False, include_plotlyjs="cdn")
+ html_parts.append(f"""
+
+
Sample Series with Train/Test Split
+ {samples_html}
+
+
Insights: Visualize how historical data is split into training and testing sets for model evaluation.
+
+
+ """)
+
+ # Close HTML document
+ html_parts.append("""
+
+ 
+ 
+ 
+ 
+ 
+