Prep video4

sales/app.py

@@ -0,0 +1,245 @@
+from pathlib import Path
+import pandas as pd
+import numpy as np
+import calendar
+
+import matplotlib.pyplot as plt
+
+import altair as alt
+
+import folium
+from folium.plugins import HeatMap
+
+import seaborn as sns
+
+import plotly.express as px
+from shiny import reactive
+from shiny.express import render, input, ui
+from shinywidgets import render_plotly, render_altair, render_widget
+
+ui.page_opts(title="Sales Dashboard - Video 4 of 5", fillable=False)
+
+def apply_common_styles(fig):
+    # Set the background to transparent
+    fig.update_layout(
+        {
+            "plot_bgcolor": "rgba(0,0,0,0)",  # Transparent plot background
+            "paper_bgcolor": "rgba(0,0,0,0)",  # Transparent figure background
+            "font": {"family": "Arial, sans-serif", "size": 12, "color": "darkblue"},
+        }
+    )
+    return fig
+
+@reactive.calc
+def dat():
+    infile = Path(__file__).parent / "data/sales.csv"
+    df = pd.read_csv(infile)
+    df["order_date"] = pd.to_datetime(df["order_date"])
+    df["month"] = df["order_date"].dt.month_name()
+    df["hour"] = df["order_date"].dt.hour
+    df['value'] = df['quantity_ordered'] * df['price_each']
+    return df
+
+
+with ui.card(full_screen=True):
+    ui.card_header("Sales by City in 2023")
+    with ui.layout_sidebar():
+        with ui.sidebar(open="open", bg="#f8f8f8"):
+            ui.input_select(
+                "select",
+                "Select an option below:",
+                [
+                    "Dallas (TX)",
+                    "Boston (MA)",
+                    "Los Angeles (CA)",
+                    "San Francisco (CA)",
+                    "Seattle (WA)",
+                    "Atlanta (GA)",
+                    "New York City (NY)",
+                    "Portland (OR)",
+                    "Austin (TX)",
+                    "Portland (ME)",
+                ],
+            )
+
+        @render_altair
+        def sales_over_time():
+            df = dat()
+
+            month_order = calendar.month_name[1:]
+
+            # Creating a month order for sorting in Altair
+            df["month"] = pd.Categorical(
+                df["month"], categories=month_order, ordered=True
+            )
+            counts = (
+                df.groupby(["month", "city"], observed=True)["quantity_ordered"]
+                .sum()
+                .reset_index()
+            )
+            city_counts = counts[counts["city"] == input.select()]
+            # Create the Altair chart
+            chart = (
+                alt.Chart(city_counts)
+                .mark_bar()
+                .encode(
+                    x="month",
+                    y="quantity_ordered",
+                    tooltip=["city", "month", "quantity_ordered"],
+                )
+                .properties(title=f"Sales Over Time in {input.select()}")
+                .configure_axis(labelAngle=0)  # Adjust label angle if needed
+            )
+            return chart
+
+
+with ui.layout_columns(widths=1 / 2):
+    with ui.navset_card_underline():
+        with ui.nav_panel("Top Sellers"):
+            ui.input_numeric(
+                "items", "Number of Items", 5, min=1, max=10, step=None, width=0.5
+            )
+
+            @render_plotly
+            def top_sellers():
+                df = dat()
+                top_5_products = (
+                    df.groupby("product")["quantity_ordered"]
+                    .sum()
+                    .nlargest(input.items())
+                    .reset_index()
+                )
+
+                fig = px.bar(top_5_products, x="product", y="quantity_ordered")
+
+                fig.update_traces(
+                    marker_color=top_5_products["quantity_ordered"],
+                    marker_colorscale="Blues",
+                )
+                apply_common_styles(fig)
+                return fig
+
+        with ui.nav_panel("Top Sellers Value ($)"):
+
+            @render_plotly
+            def top_sellers_value():
+                df = dat()
+                top_5_products = (
+                    df.groupby("product")["value"]
+                    .sum()
+                    .nlargest(input.items())
+                    .reset_index()
+                )
+
+                fig = px.bar(top_5_products, x="product", y="value")
+
+                fig.update_traces(
+                    marker_color=top_5_products["value"], marker_colorscale="Blues"
+                )
+                apply_common_styles(fig)
+                return fig
+
+        with ui.nav_panel("Lowest Sellers"):
+
+            @render_plotly
+            def lowest_sellers():
+                df = dat()
+                top_5_products = (
+                    df.groupby("product")["quantity_ordered"]
+                    .sum()
+                    .nsmallest(input.items())
+                    .reset_index()
+                    .sort_values("quantity_ordered", ascending=False)
+                )
+
+                fig = px.bar(top_5_products, x="product", y="quantity_ordered")
+
+                fig.update_traces(
+                    marker_color=top_5_products["quantity_ordered"],
+                    marker_colorscale="Reds",
+                )
+                apply_common_styles(fig)
+                return fig
+
+        with ui.nav_panel("Lowest Sellers Value ($)"):
+
+            @render_plotly
+            def lowest_sellers_value():
+                df = dat()
+                top_5_products = (
+                    df.groupby("product")["value"]
+                    .sum()
+                    .nsmallest(input.items())
+                    .reset_index()
+                    .sort_values("value", ascending=False)
+                )
+
+                fig = px.bar(top_5_products, x="product", y="value")
+
+                fig.update_traces(
+                    marker_color=top_5_products["value"], marker_colorscale="Reds"
+                )
+                apply_common_styles(fig)
+                return fig
+
+    with ui.card():
+
+        @render.plot
+        def time_heatmap():
+            # Step 1: Extract the hour from the order_date
+            df = dat()
+
+            # Step 2: Aggregate data by hour
+            hourly_counts = df["hour"].value_counts().sort_index()
+
+            # Step 3: Prepare data for heatmap (optional: create a dummy dimension if needed)
+            # For simplicity, let's assume you want to view by hours (24 hrs) and just map counts directly.
+            heatmap_data = np.zeros((24, 1))  # 24 hours, 1 dummy column
+            for hour in hourly_counts.index:
+                heatmap_data[hour, 0] = hourly_counts[hour]
+
+            # Convert heatmap data to integer if needed
+            heatmap_data = heatmap_data.astype(int)
+
+            # Step 4: Plot with Seaborn
+            plt.figure(figsize=(10, 8))
+            sns.heatmap(
+                heatmap_data,
+                annot=True,
+                fmt="d",
+                cmap="coolwarm",
+                cbar=False,
+                xticklabels=[],
+                yticklabels=[f"{i}:00" for i in range(24)],
+            )
+            plt.title("Number of Orders by Hour of Day")
+            plt.ylabel("Hour of Day")
+            plt.xlabel("Order Count")
+
+
+with ui.navset_card_underline():
+    with ui.nav_panel("Sales Locations"):
+
+        @render.ui
+        def heatmap():
+            # Create a map centered roughly in the middle of the US
+            df = dat()
+            map = folium.Map(location=[37.0902, -95.7129], zoom_start=4)
+
+            # Prepare the data for the HeatMap; each entry in heatmap_data is (lat, lon, weight)
+            heatmap_data = [
+                (lat, long, qty)
+                for lat, long, qty in zip(df["lat"], df["long"], df["quantity_ordered"])
+            ]
+            # Add HeatMap to the map
+            HeatMap(heatmap_data).add_to(map)
+
+            return map
+
+
+with ui.navset_card_underline():
+    with ui.nav_panel("Dataframe Sample"):
+
+        @render.data_frame
+        def frame():
+            return dat().head(1000)

requirements.txt → sales/requirements.txt

@@ -6,4 +6,4 @@ shiny
 shinywidgets
 seaborn
 altair
-anywidget
+anywidget