refactor: Add theme and improve plots

app.py

@@ -79,27 +79,26 @@ def _(mo):
 def _():
     # 📌 IMPORT LIBRARIES AND PACKAGES
 
-    from pandas import DataFrame
     from pathlib import Path
+
+    from pandas import DataFrame
     from sqlalchemy import create_engine
 
     from src import config
     from src.extract import extract
     from src.load import load
-    from src.transform import QueryEnum, run_queries
-
     from src.plots import (
-        plot_revenue_by_month_year,
-        plot_real_vs_predicted_delivered_time,
+        plot_freight_value_weight_relationship,
         plot_global_amount_order_status,
+        plot_order_amount_per_day_with_holidays,
+        plot_real_vs_predicted_delivered_time,
+        plot_revenue_by_month_year,
         plot_revenue_per_state,
         plot_top_10_least_revenue_categories,
-        plot_top_10_revenue_categories_amount,
         plot_top_10_revenue_categories,
-        plot_freight_value_weight_relationship,
-        plot_delivery_date_difference,
-        plot_order_amount_per_day_with_holidays,
+        plot_top_10_revenue_categories_amount,
     )
+    from src.transform import QueryEnum, run_queries
     return (
         DataFrame,
         Path,
@@ -311,7 +310,7 @@ def _(mo):
 
 @app.cell
 def _(mo):
-    mo.md(r"""# 📋 Tables""")
+    mo.md(r"""# 📊 Charts""")
     return
 
 
@@ -321,63 +320,86 @@ def _(
     global_amount_order_status,
     mo,
     orders_per_day_and_holidays,
+    plot_freight_value_weight_relationship,
+    plot_global_amount_order_status,
+    plot_order_amount_per_day_with_holidays,
+    plot_real_vs_predicted_delivered_time,
+    plot_revenue_by_month_year,
+    plot_revenue_per_state,
+    plot_top_10_least_revenue_categories,
+    plot_top_10_revenue_categories,
+    plot_top_10_revenue_categories_amount,
     real_vs_estimated_delivery_time,
     revenue_by_month_year,
     revenue_per_state,
     top_10_least_revenue_categories,
     top_10_revenue_categories,
 ):
-    overview_table_tab = mo.vstack(
+    overview_tab = mo.vstack(
         align="center",
         justify="center",
         gap=2,
         items=[
             mo.center(mo.md("## Global Order Status Overview")),
-            global_amount_order_status,
+            plot_global_amount_order_status(df=global_amount_order_status),
         ],
     )
-    revenue_table_tab = mo.vstack(
+
+    revenue_tab = mo.vstack(
         align="center",
         justify="center",
         gap=2,
         items=[
             mo.center(mo.md("## Revenue by Month and Year")),
-            revenue_by_month_year,
+            plot_revenue_by_month_year(df=revenue_by_month_year, year=2017),
             mo.center(mo.md("## Revenue by State")),
-            revenue_per_state,
+            plot_revenue_per_state(revenue_per_state),
         ],
     )
-    categories_table_tab = mo.vstack(
+
+    categories_tab = mo.vstack(
         align="center",
         justify="center",
         gap=2,
         items=[
             mo.center(mo.md("## Top 10 Revenue Categories")),
-            top_10_revenue_categories,
+            plot_top_10_revenue_categories(top_10_revenue_categories),
+            mo.center(mo.md("## Top 10 Revenue Categories by Amount")),
+            plot_top_10_revenue_categories_amount(top_10_revenue_categories),
             mo.center(mo.md("## Bottom 10 Revenue Categories")),
-            top_10_least_revenue_categories,
+            plot_top_10_least_revenue_categories(top_10_least_revenue_categories),
         ],
     )
-    delivery_table_tab = mo.vstack(
-        align="center",
-        justify="center",
+
+    delivery_tab = mo.vstack(
         gap=2,
+        justify="center",
+        align="center",
+        heights="equal",
         items=[
-            mo.center(mo.md("## Freight Value vs Product Weight")),
-            freight_value_weight_relationship,
             mo.center(mo.md("## Real vs Estimated Delivery Time")),
-            real_vs_estimated_delivery_time,
+            plot_real_vs_predicted_delivered_time(
+                df=real_vs_estimated_delivery_time, year=2017
+            ),
+            mo.center(mo.md("## Freight Value vs Product Weight")),
+            plot_freight_value_weight_relationship(
+                freight_value_weight_relationship
+            ),
             mo.center(mo.md("## Orders and Holidays")),
-            orders_per_day_and_holidays,
+            plot_order_amount_per_day_with_holidays(orders_per_day_and_holidays),
         ],
     )
+    return categories_tab, delivery_tab, overview_tab, revenue_tab
+
 
+@app.cell
+def _(categories_tab, delivery_tab, mo, overview_tab, revenue_tab):
     mo.ui.tabs(
         {
-            "📊 Overview": overview_table_tab,
-            "💰 Revenue": revenue_table_tab,
-            "📦 Categories": categories_table_tab,
-            "🚚 Freight & Delivery": delivery_table_tab,
+            "📊 Overview": overview_tab,
+            "💰 Revenue": revenue_tab,
+            "📦 Categories": categories_tab,
+            "🚚 Freight & Delivery": delivery_tab,
         }
     )
     return
@@ -391,7 +413,17 @@ def _(mo):
 
 @app.cell
 def _(mo):
-    mo.md(r"""# 📊 Charts""")
+    mo.center(
+        mo.md(
+            "**Connect with me:** 💼 [Linkedin](https://www.linkedin.com/in/alex-turpo/) • 🐱 [GitHub](https://github.com/iBrokeTheCode) • 🤗 [Hugging Face](https://huggingface.co/iBrokeTheCode)"
+        )
+    )
+    return
+
+
+@app.cell
+def _(mo):
+    mo.md(r"""# 📋 Tables""")
     return
 
 
@@ -401,106 +433,67 @@ def _(
     global_amount_order_status,
     mo,
     orders_per_day_and_holidays,
-    plot_freight_value_weight_relationship,
-    plot_global_amount_order_status,
-    plot_order_amount_per_day_with_holidays,
-    plot_real_vs_predicted_delivered_time,
-    plot_revenue_by_month_year,
-    plot_revenue_per_state,
-    plot_top_10_least_revenue_categories,
-    plot_top_10_revenue_categories,
-    plot_top_10_revenue_categories_amount,
     real_vs_estimated_delivery_time,
     revenue_by_month_year,
     revenue_per_state,
     top_10_least_revenue_categories,
     top_10_revenue_categories,
 ):
-    overview_tab = mo.vstack(
+    overview_table_tab = mo.vstack(
         align="center",
         justify="center",
         gap=2,
         items=[
             mo.center(mo.md("## Global Order Status Overview")),
-            plot_global_amount_order_status(df=global_amount_order_status),
+            global_amount_order_status,
         ],
     )
-
-    revenue_tab = mo.vstack(
+    revenue_table_tab = mo.vstack(
         align="center",
         justify="center",
         gap=2,
         items=[
             mo.center(mo.md("## Revenue by Month and Year")),
-            plot_revenue_by_month_year(df=revenue_by_month_year, year=2017),
+            revenue_by_month_year,
             mo.center(mo.md("## Revenue by State")),
-            plot_revenue_per_state(revenue_per_state),
+            revenue_per_state,
         ],
     )
-
-    categories_tab = mo.vstack(
+    categories_table_tab = mo.vstack(
         align="center",
         justify="center",
         gap=2,
         items=[
             mo.center(mo.md("## Top 10 Revenue Categories")),
-            plot_top_10_revenue_categories(top_10_revenue_categories),
-            mo.center(mo.md("## Top 10 Revenue Categories by Amount")),
-            plot_top_10_revenue_categories_amount(top_10_revenue_categories),
+            top_10_revenue_categories,
             mo.center(mo.md("## Bottom 10 Revenue Categories")),
-            plot_top_10_least_revenue_categories(top_10_least_revenue_categories),
+            top_10_least_revenue_categories,
         ],
     )
-
-    delivery_tab = mo.vstack(
-        gap=2,
-        justify="center",
+    delivery_table_tab = mo.vstack(
         align="center",
-        heights="equal",
+        justify="center",
+        gap=2,
         items=[
-            mo.center(mo.md("## Freight Value vs Product Weight")),
-            plot_freight_value_weight_relationship(
-                freight_value_weight_relationship
-            ),
             mo.center(mo.md("## Real vs Estimated Delivery Time")),
-            plot_real_vs_predicted_delivered_time(
-                df=real_vs_estimated_delivery_time, year=2017
-            ),
+            real_vs_estimated_delivery_time,
+            mo.center(mo.md("## Freight Value vs Product Weight")),
+            freight_value_weight_relationship,
             mo.center(mo.md("## Orders and Holidays")),
-            plot_order_amount_per_day_with_holidays(orders_per_day_and_holidays),
+            orders_per_day_and_holidays,
         ],
     )
-    return categories_tab, delivery_tab, overview_tab, revenue_tab
-
 
-@app.cell
-def _(categories_tab, delivery_tab, mo, overview_tab, revenue_tab):
     mo.ui.tabs(
         {
-            "📊 Overview": overview_tab,
-            "💰 Revenue": revenue_tab,
-            "📦 Categories": categories_tab,
-            "🚚 Freight & Delivery": delivery_tab,
+            "📊 Overview": overview_table_tab,
+            "💰 Revenue": revenue_table_tab,
+            "📦 Categories": categories_table_tab,
+            "🚚 Freight & Delivery": delivery_table_tab,
         }
     )
     return
 
 
-@app.cell
-def _(mo):
-    mo.Html("<br><hr><br>")
-    return
-
-
-@app.cell
-def _(mo):
-    mo.center(
-        mo.md(
-            "**Connect with me:** 💼 [Linkedin](https://www.linkedin.com/in/alex-turpo/) • 🐱 [GitHub](https://github.com/iBrokeTheCode) • 🤗 [Hugging Face](https://huggingface.co/iBrokeTheCode)"
-        )
-    )
-    return
-
-
 if __name__ == "__main__":
     app.run()

src/plots.py

@@ -1,3 +1,4 @@
+import matplotlib.dates as mdates
 import matplotlib.pyplot as plt
 import plotly.express as px
 import plotly.graph_objects as go
@@ -6,135 +7,190 @@ from matplotlib import rc_file_defaults
 from matplotlib.figure import Figure
 from pandas import DataFrame, to_datetime
 
+from src.utils.theme import apply_custom_palette, custom_palette
+
 
 def plot_revenue_by_month_year(df: DataFrame, year: int) -> Figure:
     """
-    Generate and return a matplotlib figure showing monthly revenue for a given year.
+    Generate a matplotlib figure showing monthly revenue for a given year,
+    using consistent color styling.
+    """
 
-    Designed to be used in interactive environments like Marimo, where the figure
-    will be rendered automatically when returned from a code cell.
+    # Set the theme
+    apply_custom_palette()
 
-    Args:
-        df (DataFrame): DataFrame containing revenue data, with a column 'month'
-                        and a column named 'Year{year}' for the selected year.
-        year (int): The year to visualize (e.g., 2018).
-
-    Returns:
-        Figure: A matplotlib figure object with a line and bar chart overlay.
-    """
-    rc_file_defaults()
-    sns.set_style(style=None, rc=None)
+    # Clear any previous settings and set seaborn style
+    sns.set_style("whitegrid")
 
     fig, ax1 = plt.subplots(figsize=(12, 4))
 
-    sns.lineplot(data=df[f"Year{year}"], marker="o", sort=False, ax=ax1)
+    # Line plot for revenue trend
+    sns.lineplot(
+        data=df[f"Year{year}"],
+        marker="o",
+        sort=False,
+        linewidth=2,
+        ax=ax1,
+        label=f"Line: Revenue {year}",
+    )
+
+    # Bar plot with light transparency
     ax2 = ax1.twinx()
-    sns.barplot(data=df, x="month", y=f"Year{year}", alpha=0.5, ax=ax2)
+    sns.barplot(
+        data=df,
+        x="month",
+        y=f"Year{year}",
+        alpha=0.4,
+        ax=ax2,
+        label=f"Bar: Revenue {year}",
+    )
 
-    ax1.set_title(f"Revenue by month in {year}")
+    # Beautify axes
+    ax1.set_ylabel("Revenue")
+    ax1.set_xlabel("Month")
+    ax1.grid(True, linestyle="--", alpha=0.5)
+
+    # Optional: display value annotations on bars
+    for i, value in enumerate(df[f"Year{year}"]):
+        ax2.text(
+            i,
+            value + value * 0.02,  # small offset above bar
+            f"{int(value):,}",
+            ha="center",
+            va="bottom",
+            fontsize=8,
+            color="black",
+        )
+
+    # Remove default plot title (you handle titles in Marimo)
+    ax1.set_title("")
+    fig.tight_layout()
 
     return fig
 
 
 def plot_real_vs_predicted_delivered_time(df: DataFrame, year: int) -> Figure:
     """
-    Generate and return a matplotlib figure comparing real vs. estimated delivery time
-    by month for a specific year.
-
-    Intended for interactive environments like Marimo where returning the figure
-    automatically renders the plot.
-
-    Args:
-        df (DataFrame): DataFrame with columns:
-            - 'month': Month names or numbers.
-            - f'Year{year}_real_time': Real average delivery time.
-            - f'Year{year}_estimated_time': Estimated average delivery time.
-        year (int): The year to visualize (e.g., 2018).
-
-    Returns:
-        Figure: A matplotlib figure with two overlaid line plots.
+    Create a line plot comparing real vs. estimated delivery time
+    by month for a given year.
     """
     rc_file_defaults()
-    sns.set_style(style=None, rc=None)
+    sns.set_style("whitegrid")  # Use light grid for clarity
+
+    fig, ax = plt.subplots(figsize=(12, 4))
+
+    # Plot each line with explicit color and label
+    sns.lineplot(
+        x=df["month"],
+        y=df[f"Year{year}_real_time"],
+        marker="o",
+        label="Real Time",
+        color=custom_palette[0],
+        ax=ax,
+    )
+    sns.lineplot(
+        x=df["month"],
+        y=df[f"Year{year}_estimated_time"],
+        marker="s",
+        label="Estimated Time",
+        color=custom_palette[1],
+        ax=ax,
+    )
 
-    fig, ax1 = plt.subplots(figsize=(12, 4))
+    # Axis labeling and ticks
+    ax.set_xlabel("Month")
+    ax.set_ylabel("Average Days to Deliver")
+    ax.set_xticks(range(len(df)))
+    ax.set_xticklabels(df["month"].values, rotation=45)
 
-    sns.lineplot(data=df[f"Year{year}_real_time"], marker="o", sort=False, ax=ax1)
-    sns.lineplot(data=df[f"Year{year}_estimated_time"], marker="o", sort=False, ax=ax1)
+    # Legend configuration
+    ax.legend(title="", loc="upper right")
 
-    ax1.set_xticks(range(len(df)))
-    ax1.set_xticklabels(df["month"].values)
-    ax1.set_xlabel("Month")
-    ax1.set_ylabel("Average Days to Deliver")
-    ax1.set_title(f"Average Delivery Time (Real vs Estimated) in {year}")
-    ax1.legend(["Real Time", "Estimated Time"])
+    # Improve spacing
+    fig.tight_layout()
 
     return fig
 
 
-from matplotlib.figure import Figure
-from pandas import DataFrame
-
-
 def plot_global_amount_order_status(df: DataFrame) -> Figure:
     """
-    Create and return a donut pie chart showing the global amount per order status.
+    Create a horizontal bar chart showing the global amount per order status.
 
     Args:
-        df (DataFrame): DataFrame containing:
-            - 'order_status': Status labels (e.g., 'order delivered').
-            - 'Amount': Corresponding counts or totals per status.
+        df (DataFrame): DataFrame with:
+            - 'order_status': Status labels (e.g., 'order delivered')
+            - 'Amount': Count or value per status
 
     Returns:
-        Figure: A matplotlib figure containing a pie (donut) chart with legend.
+        Figure: A matplotlib bar chart figure.
     """
-    fig, ax = plt.subplots(figsize=(12, 4), subplot_kw=dict(aspect="equal"))
+    rc_file_defaults()
+    fig, ax = plt.subplots(figsize=(10, 5))
 
-    # Extract last word of each status for cleaner labels
-    elements = [x.split()[-1] for x in df["order_status"]]
+    df = df.copy()
+    df["short_status"] = df["order_status"].apply(lambda x: x.split()[-1].capitalize())
+    sorted_df = df.sort_values("Amount", ascending=True)
 
-    wedges, autotexts = ax.pie(df["Amount"], textprops=dict(color="w"))
+    colors = custom_palette[: len(sorted_df)]
 
-    ax.legend(
-        wedges,
-        elements,
-        title="Order Status",
-        loc="center left",
-        bbox_to_anchor=(1, 0, 0.5, 1),
+    bars = ax.barh(
+        sorted_df["short_status"], sorted_df["Amount"], color=colors, edgecolor="black"
     )
 
-    plt.setp(autotexts, size=8, weight="bold")
-    ax.set_title("Order Status Total")
-
-    # Add donut center
-    center_circle = plt.Circle((0, 0), 0.7, color="white")
-    ax.add_artist(center_circle)
-
+    # Add value labels
+    for bar in bars:
+        width = bar.get_width()
+        ax.text(
+            width + 50,
+            bar.get_y() + bar.get_height() / 2,
+            f"{int(width):,}",
+            va="center",
+            fontsize=9,
+            color="black",
+        )
+
+    ax.set_xlabel("Amount")
+    ax.set_ylabel("Order Status")
+    ax.grid(axis="x", linestyle="--", alpha=0.4)
+    fig.tight_layout()
     return fig
 
 
 def plot_revenue_per_state(df: DataFrame) -> go.Figure:
     """
-    Create a Plotly treemap to visualize revenue per customer state.
-
-    Args:
-        df (DataFrame): DataFrame with columns:
-            - 'customer_state': State or region
-            - 'Revenue': Revenue value per state
-
-    Returns:
-        go.Figure: A Plotly treemap figure object.
+    Create a Plotly treemap to visualize revenue per customer state,
+    using a consistent custom color palette.
     """
     fig = px.treemap(
-        df, path=["customer_state"], values="Revenue", width=800, height=300
+        df,
+        path=["customer_state"],
+        values="Revenue",
+        color="customer_state",  # Important to trigger color mapping
+        color_discrete_sequence=custom_palette,
+        width=800,
+        height=300,
     )
-    fig.update_layout(margin=dict(t=50, l=25, r=25, b=25))
+
+    # Add label customization
+    fig.update_traces(
+        textinfo="label+percent entry+value",  # show label, percentage, and raw value
+        textfont_size=14,
+        marker=dict(
+            line=dict(color="#FFFFFF", width=1)
+        ),  # white borders between blocks
+    )
+
+    fig.update_layout(
+        margin=dict(t=20, l=20, r=20, b=20),
+        uniformtext=dict(minsize=12, mode="hide"),
+    )
+
     return fig
 
 
 def plot_top_10_least_revenue_categories(df: DataFrame) -> Figure:
     """
-    Create a donut pie chart showing the top 10 least revenue categories.
+    Create a horizontal bar chart showing the top 10 least revenue categories.
 
     Args:
         df (DataFrame): DataFrame with columns:
@@ -142,35 +198,41 @@ def plot_top_10_least_revenue_categories(df: DataFrame) -> Figure:
             - 'Revenue': Corresponding revenue values
 
     Returns:
-        Figure: A matplotlib figure with a donut chart and legend.
+        Figure: A matplotlib figure with a horizontal bar chart.
     """
-    fig, ax = plt.subplots(figsize=(12, 4), subplot_kw=dict(aspect="equal"))
-
-    elements = [x.split()[-1] for x in df["Category"]]
-    revenue = df["Revenue"]
+    rc_file_defaults()
+    fig, ax = plt.subplots(figsize=(10, 6))
 
-    wedges, autotexts = ax.pie(revenue, textprops=dict(color="w"))
+    # Sort and plot
+    sorted_df = df.sort_values("Revenue", ascending=True)
+    colors = custom_palette[: len(sorted_df)]
 
-    ax.legend(
-        wedges,
-        elements,
-        title="Top 10 Revenue Categories",
-        loc="center left",
-        bbox_to_anchor=(1, 0, 0.5, 1),
+    bars = ax.barh(
+        sorted_df["Category"], sorted_df["Revenue"], color=colors, edgecolor="black"
     )
 
-    plt.setp(autotexts, size=8, weight="bold")
-    ax.set_title("Top 10 Least Revenue Categories Amount")
-
-    center_circle = plt.Circle((0, 0), 0.7, color="white")
-    ax.add_artist(center_circle)
-
+    # Add value labels
+    for bar in bars:
+        width = bar.get_width()
+        ax.text(
+            width + 100,  # shift label to the right of the bar
+            bar.get_y() + bar.get_height() / 2,
+            f"${int(width):,}",
+            va="center",
+            fontsize=9,
+            color="black",
+        )
+
+    ax.set_xlabel("Revenue")
+    ax.set_ylabel("Category")
+    ax.grid(axis="x", linestyle="--", alpha=0.4)
+    fig.tight_layout()
     return fig
 
 
 def plot_top_10_revenue_categories_amount(df: DataFrame) -> Figure:
     """
-    Create a donut pie chart showing the revenue distribution of the top 10 categories.
+    Create a horizontal bar chart showing the revenue of the top 10 categories.
 
     Args:
         df (DataFrame): DataFrame with columns:
@@ -180,28 +242,32 @@ def plot_top_10_revenue_categories_amount(df: DataFrame) -> Figure:
     Returns:
         Figure: A matplotlib figure object.
     """
-    fig, ax = plt.subplots(figsize=(12, 4), subplot_kw=dict(aspect="equal"))
-
-    elements = [x.split()[-1] for x in df["Category"]]
-    revenue = df["Revenue"]
+    rc_file_defaults()
+    fig, ax = plt.subplots(figsize=(10, 6))
 
-    wedges, autotexts = ax.pie(revenue, textprops=dict(color="w"))
+    sorted_df = df.sort_values("Revenue", ascending=True)
+    colors = custom_palette[: len(sorted_df)]
 
-    ax.legend(
-        wedges,
-        elements,
-        title="Top 10 Revenue Categories",
-        loc="center left",
-        bbox_to_anchor=(1, 0, 0.5, 1),
+    bars = ax.barh(
+        sorted_df["Category"], sorted_df["Revenue"], color=colors, edgecolor="black"
     )
 
-    plt.setp(autotexts, size=8, weight="bold")
-
-    ax.set_title("Top 10 Revenue Categories Amount")
-
-    center_circle = plt.Circle((0, 0), 0.7, color="white")
-    ax.add_artist(center_circle)
-
+    # Add value labels on the right
+    for bar in bars:
+        width = bar.get_width()
+        ax.text(
+            width + 100,
+            bar.get_y() + bar.get_height() / 2,
+            f"${int(width):,}",
+            va="center",
+            fontsize=9,
+            color="black",
+        )
+
+    ax.set_xlabel("Revenue")
+    ax.set_ylabel("Category")
+    ax.grid(axis="x", linestyle="--", alpha=0.4)
+    fig.tight_layout()
     return fig
 
 
@@ -217,8 +283,20 @@ def plot_top_10_revenue_categories(df: DataFrame) -> go.Figure:
     Returns:
         go.Figure: A Plotly treemap figure object.
     """
-    fig = px.treemap(df, path=["Category"], values="Num_order", width=800, height=400)
-    fig.update_layout(margin=dict(t=50, l=25, r=25, b=25))
+    fig = px.treemap(
+        df,
+        path=["Category"],
+        values="Num_order",
+        color="Num_order",
+        color_continuous_scale=custom_palette,  # Optional for consistency
+        hover_data={"Num_order": ":,"},  # Adds commas to values
+        width=800,
+        height=400,
+    )
+    fig.update_layout(
+        margin=dict(t=40, l=30, r=30, b=30),
+        coloraxis_showscale=False,  # Optional: hides legend bar
+    )
     return fig
 
 
@@ -234,17 +312,25 @@ def plot_freight_value_weight_relationship(df: DataFrame) -> Figure:
     Returns:
         Figure: A matplotlib figure object.
     """
-    fig, ax = plt.subplots(figsize=(8, 4))
+    rc_file_defaults()
+    fig, ax = plt.subplots(figsize=(10, 5))
 
     sns.scatterplot(
-        data=df, x="product_weight_g", y="freight_value", edgecolor="white", ax=ax
+        data=df,
+        x="product_weight_g",
+        y="freight_value",
+        color=custom_palette[2],
+        edgecolor="white",
+        alpha=0.7,
+        s=50,
+        ax=ax,
     )
 
-    ax.set_title("Freight Value vs Product Weight")
-    ax.set_xlabel("Product Weight (g)")
+    ax.set_xlabel("Product Weight (grams)")
     ax.set_ylabel("Freight Value ($)")
-    fig.tight_layout()
+    ax.grid(True, linestyle="--", alpha=0.5)
 
+    fig.tight_layout()
     return fig
 
 
@@ -260,12 +346,21 @@ def plot_delivery_date_difference(df: DataFrame) -> Figure:
     Returns:
         Figure: A matplotlib figure object.
     """
+    rc_file_defaults()
     fig, ax = plt.subplots(figsize=(10, 6))
 
-    sns.barplot(data=df, x="Delivery_Difference", y="State", ax=ax)
-    ax.set_title("Difference Between Delivery Estimate Date and Delivery Date")
-    ax.set_xlabel("Delivery Difference (days)")
+    sns.barplot(
+        data=df, x="Delivery_Difference", y="State", color=custom_palette[0], ax=ax
+    )
+
+    ax.set_title(
+        "Difference Between Estimated and Actual Delivery Dates by State",
+        fontsize=12,
+        weight="bold",
+    )
+    ax.set_xlabel("Delivery Difference (Days)")
     ax.set_ylabel("State")
+    ax.grid(True, linestyle="--", alpha=0.4, axis="x")
 
     fig.tight_layout()
     return fig
@@ -284,19 +379,34 @@ def plot_order_amount_per_day_with_holidays(df: DataFrame) -> Figure:
     Returns:
         Figure: A matplotlib figure object.
     """
+    rc_file_defaults()
     df = df.copy()
     df["date"] = to_datetime(df["date"], unit="ms")
     df = df.sort_values("date")
 
-    fig, ax = plt.subplots(figsize=(9, 4))
-    ax.plot(df["date"], df["order_count"], color="green")
+    fig, ax = plt.subplots(figsize=(12, 4))
+    ax.plot(df["date"], df["order_count"], color=custom_palette[2], label="Order Count")
 
     for holiday_date in df[df["holiday"]]["date"]:
-        ax.axvline(holiday_date, color="blue", linestyle="dotted", alpha=0.6)
+        ax.axvline(
+            holiday_date,
+            color=custom_palette[3],
+            linestyle="--",
+            alpha=0.4,
+            label="Holiday",
+        )
 
-    ax.set_title("Order Amount per Day with Holidays")
     ax.set_xlabel("Date")
     ax.set_ylabel("Order Count")
-    fig.tight_layout()
 
+    ax.xaxis.set_major_locator(mdates.MonthLocator())
+    ax.xaxis.set_major_formatter(mdates.DateFormatter("%b %Y"))
+    ax.tick_params(axis="x", rotation=45)
+
+    ax.grid(True, linestyle="--", alpha=0.5)
+    handles, labels = ax.get_legend_handles_labels()
+    by_label = dict(zip(labels, handles))  # avoid duplicate "Holiday" entries
+    ax.legend(by_label.values(), by_label.keys(), loc="upper left")
+
+    fig.tight_layout()
     return fig

src/utils/theme.py

@@ -0,0 +1,21 @@
+custom_palette = [
+    "#636EFA",  # Blue
+    "#EF553B",  # Red
+    "#00CC96",  # Green
+    "#AB63FA",  # Purple
+    "#FFA15A",  # Orange
+    "#19D3F3",  # Cyan
+    "#FF6692",  # Pink
+    "#B6E880",  # Light Green
+    "#FF97FF",  # Magenta
+    "#FECB52",  # Yellow
+]
+
+
+def apply_custom_palette():
+    import matplotlib.pyplot as plt
+    import seaborn as sns
+
+    # Set globally for matplotlib and seaborn
+    plt.rcParams["axes.prop_cycle"] = plt.cycler(color=custom_palette)
+    sns.set_palette(custom_palette)