refactor: Return figure instead of using plt.plot()

app.py

@@ -20,31 +20,9 @@ def _(mo):
 def _(mo):
     mo.md(
         r"""
-    📘 Want a step-by-step walkthrough instead?
+    💡 Want a step-by-step walkthrough instead?
 
-    You can check the tutorial version of this app here: 👉 [Tutorial app](https://huggingface.co/spaces/iBrokeTheCode/E-Commerce_ELT/blob/main/tutorial_app.py)
-
-    Or the Jupyter notebook version here: 👉 [Jupyter version](https://huggingface.co/spaces/iBrokeTheCode/E-Commerce_ELT/blob/main/tutorial_app.ipynb)
-    """
-    )
-    return
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""## Table of Contents""")
-    return
-
-
-@app.cell
-def _(mo):
-    mo.md(
-        r"""
-    - [Description](#1-description)
-    - [ETL](#2-etl)
-        - [Extract & Load](#21-extract-and-load)
-        - [Transform](#22-transform)
-    - [Plots](#3-plots)
+    You can check the Jupyter notebook version here: 👉 [Jupyter version](https://huggingface.co/spaces/iBrokeTheCode/E-Commerce_ELT/blob/main/tutorial_app.ipynb)
     """
     )
     return

src/plots.py

@@ -2,29 +2,37 @@ import matplotlib.pyplot as plt
 import plotly.express as px
 import seaborn as sns
 from matplotlib import rc_file_defaults
+from matplotlib.figure import Figure
 from pandas import DataFrame, to_datetime
 
 
-def plot_revenue_by_month_year(df: DataFrame, year: int) -> None:
+def plot_revenue_by_month_year(df: DataFrame, year: int) -> Figure:
     """
-    Plot the revenue by month and year
+    Generate and return a matplotlib figure showing monthly revenue for a given year.
+
+    Designed to be used in interactive environments like Marimo, where the figure
+    will be rendered automatically when returned from a code cell.
 
     Args:
-        df (DataFrame): The dataframe
-        year (int): The year
+        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="darkgrid", rc=None)
 
-    _, ax1 = plt.subplots(figsize=(12, 6))
+    fig, ax1 = plt.subplots(figsize=(12, 6))
 
     sns.lineplot(data=df[f"Year{year}"], marker="o", sort=False, ax=ax1)
     ax2 = ax1.twinx()
-
     sns.barplot(data=df, x="month", y=f"Year{year}", alpha=0.5, ax=ax2)
+
     ax1.set_title(f"Revenue by month in {year}")
 
-    plt.show()
+    return fig
 
 
 def plot_real_vs_predicted_delivered_time(df: DataFrame, year: int) -> None:

tutorial_app.ipynb

@@ -13,44 +13,22 @@
    "id": "vblA",
    "metadata": {},
    "source": [
-    "📘 Want a step-by-step walkthrough instead?\n",
+    "💡 Want a step-by-step walkthrough instead?\n",
     "\n",
-    "You can check the tutorial version of this app here: 👉 [Tutorial app](https://huggingface.co/spaces/iBrokeTheCode/E-Commerce_ELT/blob/main/tutorial_app.py)\n",
-    "\n",
-    "Or the Jupyter notebook version here: 👉 [Jupyter version](https://huggingface.co/spaces/iBrokeTheCode/E-Commerce_ELT/blob/main/tutorial_app.ipynb)\n"
+    "You can check the Jupyter notebook version here: 👉 [Jupyter version](https://huggingface.co/spaces/iBrokeTheCode/E-Commerce_ELT/blob/main/tutorial_app.ipynb)\n"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "bkHC",
    "metadata": {},
-   "source": [
-    "## Table of Contents\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "lEQa",
-   "metadata": {},
-   "source": [
-    "- [Description](#1-description)\n",
-    "- [ETL](#2-etl)\n",
-    "  - [Extract & Load](#21-extract-and-load)\n",
-    "  - [Transform](#22-transform)\n",
-    "- [Plots](#3-plots)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "PKri",
-   "metadata": {},
    "source": [
     "## 1. Description\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "Xref",
+   "id": "lEQa",
    "metadata": {},
    "source": [
     "This project analyzes e-commerce data from a Brazilian marketplace to explore key business metrics related to **revenue** and **delivery performance**. Using an interactive Marimo application, the analysis provides insights into:\n",
@@ -63,7 +41,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "SFPL",
+   "id": "PKri",
    "metadata": {},
    "source": [
     "## 2. ETL\n"
@@ -71,8 +49,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "BYtC",
+   "execution_count": 26,
+   "id": "Xref",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,7 +66,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "RGSE",
+   "id": "SFPL",
    "metadata": {},
    "source": [
     "### 2.1 Extract and Load\n"
@@ -96,8 +74,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "Kclp",
+   "execution_count": 27,
+   "id": "BYtC",
    "metadata": {},
    "outputs": [
     {
@@ -130,7 +108,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "emfo",
+   "id": "RGSE",
    "metadata": {},
    "source": [
     "### 2.2 Transform\n"
@@ -138,8 +116,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "Hstk",
+   "execution_count": 28,
+   "id": "Kclp",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -148,7 +126,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "nWHF",
+   "id": "emfo",
    "metadata": {},
    "source": [
     "**A. Revenue by Month and Year**\n"
@@ -156,8 +134,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "iLit",
+   "execution_count": 29,
+   "id": "Hstk",
    "metadata": {},
    "outputs": [
     {
@@ -305,7 +283,7 @@
        "11       12   Dec    960.85  1082600.69        0.00"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -317,7 +295,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ZHCJ",
+   "id": "nWHF",
    "metadata": {},
    "source": [
     "**B. Top 10 Revenue by categories**\n"
@@ -326,7 +304,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ROlb",
+   "id": "iLit",
    "metadata": {},
    "outputs": [
     {
@@ -434,7 +412,7 @@
        "9             cool_stuff       3559   744649.32"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -446,7 +424,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "qnkX",
+   "id": "ZHCJ",
    "metadata": {},
    "source": [
     "**C. Top 10 Least Revenue by Categories**\n"
@@ -454,8 +432,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "TqIu",
+   "execution_count": 31,
+   "id": "ROlb",
    "metadata": {},
    "outputs": [
     {
@@ -563,7 +541,7 @@
        "9     fashio_female_clothing         36  4902.67"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -577,7 +555,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "Vxnm",
+   "id": "qnkX",
    "metadata": {},
    "source": [
     "**D. Revenue per State**\n"
@@ -585,8 +563,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "DnEU",
+   "execution_count": 32,
+   "id": "TqIu",
    "metadata": {},
    "outputs": [
     {
@@ -683,7 +661,7 @@
        "9             ES   317682.65"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -695,7 +673,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ulZA",
+   "id": "Vxnm",
    "metadata": {},
    "source": [
     "**E. Delivery Date Difference**\n"
@@ -704,7 +682,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ecfG",
+   "id": "DnEU",
    "metadata": {},
    "outputs": [
     {
@@ -903,7 +881,7 @@
        "26    RO                   20"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -915,7 +893,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "Pvdt",
+   "id": "ulZA",
    "metadata": {},
    "source": [
     "**F. Real vs. Predicted Delivered Time**\n"
@@ -923,8 +901,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "ZBYS",
+   "execution_count": 34,
+   "id": "ecfG",
    "metadata": {},
    "outputs": [
     {
@@ -1125,7 +1103,7 @@
        "11                26.030012                27.681340                      NaN  "
       ]
      },
-     "execution_count": 11,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1139,7 +1117,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aLJB",
+   "id": "Pvdt",
    "metadata": {},
    "source": [
     "**G. Global Amount of Order Status**\n"
@@ -1148,7 +1126,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "nHfw",
+   "id": "ZBYS",
    "metadata": {},
    "outputs": [
     {
@@ -1233,7 +1211,7 @@
        "7  unavailable     609"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1245,7 +1223,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "xXTn",
+   "id": "aLJB",
    "metadata": {},
    "source": [
     "**H. Orders per Day and Holidays in 2017**\n"
@@ -1253,8 +1231,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "AjVT",
+   "execution_count": 36,
+   "id": "nHfw",
    "metadata": {},
    "outputs": [
     {
@@ -1372,7 +1350,7 @@
        "[361 rows x 3 columns]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1386,7 +1364,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "pHFh",
+   "id": "xXTn",
    "metadata": {},
    "source": [
     "**I. Freight Value Weight Relationship**\n"
@@ -1394,8 +1372,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "NCOB",
+   "execution_count": 37,
+   "id": "AjVT",
    "metadata": {},
    "outputs": [
     {
@@ -1513,7 +1491,7 @@
        "[96478 rows x 3 columns]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1527,7 +1505,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aqbW",
+   "id": "pHFh",
    "metadata": {},
    "source": [
     "## 3. Plots\n"
@@ -1535,8 +1513,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "TRpd",
+   "execution_count": 38,
+   "id": "NCOB",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1556,7 +1534,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "TXez",
+   "id": "aqbW",
    "metadata": {},
    "source": [
     "**A. Revenue by Month in 2017**\n"
@@ -1564,8 +1542,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "dNNg",
+   "execution_count": 39,
+   "id": "TRpd",
    "metadata": {},
    "outputs": [
     {
@@ -1585,7 +1563,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "yCnT",
+   "id": "TXez",
    "metadata": {},
    "source": [
     "**B. Real vs. Predicted Delivered Time**\n"
@@ -1594,7 +1572,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "wlCL",
+   "id": "dNNg",
    "metadata": {},
    "outputs": [
     {
@@ -1614,7 +1592,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "kqZH",
+   "id": "yCnT",
    "metadata": {},
    "source": [
     "**C. Global Amount of Order Status**\n"
@@ -1622,8 +1600,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "wAgl",
+   "execution_count": 41,
+   "id": "wlCL",
    "metadata": {},
    "outputs": [
     {
@@ -1643,7 +1621,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "rEll",
+   "id": "kqZH",
    "metadata": {},
    "source": [
     "**D. Revenue per State**\n"
@@ -1651,8 +1629,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "dGlV",
+   "execution_count": 42,
+   "id": "wAgl",
    "metadata": {},
    "outputs": [
     {
@@ -2520,7 +2498,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "SdmI",
+   "id": "rEll",
    "metadata": {},
    "source": [
     "**E. Top 10 Least Revenue by Categories**\n"
@@ -2528,8 +2506,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "lgWD",
+   "execution_count": 43,
+   "id": "dGlV",
    "metadata": {},
    "outputs": [
     {
@@ -2549,7 +2527,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "yOPj",
+   "id": "SdmI",
    "metadata": {},
    "source": [
     "**F. Top 10 Revenue Categories Amount**\n"
@@ -2557,8 +2535,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "fwwy",
+   "execution_count": 44,
+   "id": "lgWD",
    "metadata": {},
    "outputs": [
     {
@@ -2578,7 +2556,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "LJZf",
+   "id": "yOPj",
    "metadata": {},
    "source": [
     "**G. Top 10 Revenue by Categories**\n"
@@ -2586,8 +2564,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "urSm",
+   "execution_count": 45,
+   "id": "fwwy",
    "metadata": {},
    "outputs": [
     {
@@ -3455,7 +3433,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "jxvo",
+   "id": "LJZf",
    "metadata": {},
    "source": [
     "**H. Freight Value vs. Product Weight**\n"
@@ -3463,8 +3441,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "mWxS",
+   "execution_count": 46,
+   "id": "urSm",
    "metadata": {},
    "outputs": [
     {
@@ -3484,7 +3462,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "CcZR",
+   "id": "jxvo",
    "metadata": {},
    "source": [
     "**I. Diffrence Between Deliver Estimated Date and Delivery Date**\n"
@@ -3492,8 +3470,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "YWSi",
+   "execution_count": 47,
+   "id": "mWxS",
    "metadata": {},
    "outputs": [
     {
@@ -3513,7 +3491,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "zlud",
+   "id": "CcZR",
    "metadata": {},
    "source": [
     "**J. Order Amount per Day with Holidays**\n"
@@ -3521,8 +3499,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "tZnO",
+   "execution_count": 48,
+   "id": "YWSi",
    "metadata": {},
    "outputs": [
     {
@@ -3542,22 +3520,8 @@
   }
  ],
  "metadata": {
-  "kernelspec": {
-   "display_name": "E-Commerce_ELT",
-   "language": "python",
-   "name": "python3"
-  },
   "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "name": "python"
   }
  },
  "nbformat": 4,

tutorial_app.py

@@ -1,419 +0,0 @@
-import marimo
-
-__generated_with = "0.14.16"
-app = marimo.App(width="medium")
-
-
-@app.cell
-def _():
-    import marimo as mo
-    return (mo,)
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""# E-Commerce ELT Pipeline""")
-    return
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""## Table of Contents""")
-    return
-
-
-@app.cell
-def _(mo):
-    mo.md(
-        r"""
-    - [Description](#1-description)
-    - [ETL](#2-etl)
-        - [Extract & Load](#21-extract-and-load)
-        - [Transform](#22-transform)
-    - [Plots](#3-plots)
-    """
-    )
-    return
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""## 1. Description""")
-    return
-
-
-@app.cell
-def _(mo):
-    mo.md(
-        r"""
-    This project analyzes e-commerce data from a Brazilian marketplace to explore key business metrics related to **revenue** and **delivery performance**. Using an interactive Marimo application, the analysis provides insights into:
-
-    * **Revenue:** Annual revenue, popular product categories, and sales by state.
-    * **Delivery:** Delivery performance, including time-to-delivery and its correlation with public holidays.
-
-    The data pipeline processes information from multiple CSV files and a public API, storing and analyzing the results using Python. The final interactive report is presented as a Hugging Face Space built with Marimo.
-    """
-    )
-    return
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""## 2. ETL""")
-    return
-
-
-@app.cell
-def _():
-    from pandas import DataFrame
-    from pathlib import Path
-    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
-    return (
-        DataFrame,
-        Path,
-        QueryEnum,
-        config,
-        create_engine,
-        extract,
-        load,
-        run_queries,
-    )
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""### 2.1 Extract and Load""")
-    return
-
-
-@app.cell
-def _(Path, config, create_engine, extract, load):
-    DB_PATH = Path(config.SQLITE_DB_ABSOLUTE_PATH)
-
-    if DB_PATH.exists() and DB_PATH.stat().st_size > 0:
-        print("Database found. Skipping ETL process.")
-        ENGINE = create_engine(f"sqlite:///{DB_PATH}", echo=False)
-    else:
-        print("Database not found or empty. Starting ETL process...")
-        ENGINE = create_engine(f"sqlite:///{DB_PATH}", echo=False)
-
-        csv_dataframes = extract(
-            csv_folder=config.DATASET_ROOT_PATH,
-            csv_table_mapping=config.get_csv_to_table_mapping(),
-            public_holidays_url=config.PUBLIC_HOLIDAYS_URL,
-        )
-
-        load(dataframes=csv_dataframes, database=ENGINE)
-        print("ETL process complete.")
-    return (ENGINE,)
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""### 2.2 Transform""")
-    return
-
-
-@app.cell
-def _(DataFrame, ENGINE, run_queries):
-    query_results: dict[str, DataFrame] = run_queries(database=ENGINE)
-    return (query_results,)
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""**A. Revenue by Month and Year**""")
-    return
-
-
-@app.cell
-def _(QueryEnum, query_results: "dict[str, DataFrame]"):
-    revenue_by_month_year = query_results[QueryEnum.REVENUE_BY_MONTH_YEAR.value]
-    revenue_by_month_year
-    return (revenue_by_month_year,)
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""**B. Top 10 Revenue by categories**""")
-    return
-
-
-@app.cell
-def _(QueryEnum, query_results: "dict[str, DataFrame]"):
-    top_10_revenue_categories = query_results[
-        QueryEnum.TOP_10_REVENUE_CATEGORIES.value
-    ]
-    top_10_revenue_categories
-    return (top_10_revenue_categories,)
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""**C. Top 10 Least Revenue by Categories**""")
-    return
-
-
-@app.cell
-def _(QueryEnum, query_results: "dict[str, DataFrame]"):
-    top_10_least_revenue_categories = query_results[
-        QueryEnum.TOP_10_LEAST_REVENUE_CATEGORIES.value
-    ]
-    top_10_least_revenue_categories
-    return (top_10_least_revenue_categories,)
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""**D. Revenue per State**""")
-    return
-
-
-@app.cell
-def _(QueryEnum, query_results: "dict[str, DataFrame]"):
-    revenue_per_state = query_results[QueryEnum.REVENUE_PER_STATE.value]
-    revenue_per_state
-    return (revenue_per_state,)
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""**E. Delivery Date Difference**""")
-    return
-
-
-@app.cell
-def _(QueryEnum, query_results: "dict[str, DataFrame]"):
-    delivery_date_difference = query_results[
-        QueryEnum.DELIVERY_DATE_DIFFERENCE.value
-    ]
-    delivery_date_difference
-    return (delivery_date_difference,)
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""**F. Real vs. Predicted Delivered Time**""")
-    return
-
-
-@app.cell
-def _(QueryEnum, query_results: "dict[str, DataFrame]"):
-    real_vs_estimated_delivery_time = query_results[
-        QueryEnum.REAL_VS_ESTIMATED_DELIVERED_TIME.value
-    ]
-    real_vs_estimated_delivery_time
-    return (real_vs_estimated_delivery_time,)
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""**G. Global Amount of Order Status**""")
-    return
-
-
-@app.cell
-def _(QueryEnum, query_results: "dict[str, DataFrame]"):
-    global_amount_order_status = query_results[
-        QueryEnum.GLOBAL_AMOUNT_ORDER_STATUS.value
-    ]
-    global_amount_order_status
-    return (global_amount_order_status,)
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""**H. Orders per Day and Holidays in 2017**""")
-    return
-
-
-@app.cell
-def _(QueryEnum, query_results: "dict[str, DataFrame]"):
-    orders_per_day_and_holidays = query_results[
-        QueryEnum.ORDERS_PER_DAY_AND_HOLIDAYS_2017.value
-    ]
-    orders_per_day_and_holidays
-    return (orders_per_day_and_holidays,)
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""**I. Freight Value Weight Relationship**""")
-    return
-
-
-@app.cell
-def _(QueryEnum, query_results: "dict[str, DataFrame]"):
-    freight_value_weight_relationship = query_results[
-        QueryEnum.GET_FREIGHT_VALUE_WEIGHT_RELATIONSHIP.value
-    ]
-    freight_value_weight_relationship
-    return (freight_value_weight_relationship,)
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""## 3. Plots""")
-    return
-
-
-@app.cell
-def _():
-    from src.plots import (
-        plot_revenue_by_month_year,
-        plot_real_vs_predicted_delivered_time,
-        plot_global_amount_order_status,
-        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,
-    )
-    return (
-        plot_delivery_date_difference,
-        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,
-    )
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""**A. Revenue by Month in 2017**""")
-    return
-
-
-@app.cell
-def _(plot_revenue_by_month_year, revenue_by_month_year):
-    plot_revenue_by_month_year(df=revenue_by_month_year, year=2017)
-    return
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""**B. Real vs. Predicted Delivered Time**""")
-    return
-
-
-@app.cell
-def _(plot_real_vs_predicted_delivered_time, real_vs_estimated_delivery_time):
-    plot_real_vs_predicted_delivered_time(
-        df=real_vs_estimated_delivery_time, year=2017
-    )
-    return
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""**C. Global Amount of Order Status**""")
-    return
-
-
-@app.cell
-def _(global_amount_order_status, plot_global_amount_order_status):
-    plot_global_amount_order_status(df=global_amount_order_status)
-    return
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""**D. Revenue per State**""")
-    return
-
-
-@app.cell
-def _(plot_revenue_per_state, revenue_per_state):
-    plot_revenue_per_state(df=revenue_per_state)
-    return
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""**E. Top 10 Least Revenue by Categories**""")
-    return
-
-
-@app.cell
-def _(plot_top_10_least_revenue_categories, top_10_least_revenue_categories):
-    plot_top_10_least_revenue_categories(df=top_10_least_revenue_categories)
-    return
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""**F. Top 10 Revenue Categories Amount**""")
-    return
-
-
-@app.cell
-def _(plot_top_10_revenue_categories_amount, top_10_revenue_categories):
-    plot_top_10_revenue_categories_amount(df=top_10_revenue_categories)
-    return
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""**G. Top 10 Revenue by Categories**""")
-    return
-
-
-@app.cell
-def _(plot_top_10_revenue_categories, top_10_revenue_categories):
-    plot_top_10_revenue_categories(df=top_10_revenue_categories)
-    return
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""**H. Freight Value vs. Product Weight**""")
-    return
-
-
-@app.cell
-def _(
-    freight_value_weight_relationship,
-    plot_freight_value_weight_relationship,
-):
-    plot_freight_value_weight_relationship(df=freight_value_weight_relationship)
-    return
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""**I. Diffrence Between Deliver Estimated Date and Delivery Date**""")
-    return
-
-
-@app.cell
-def _(delivery_date_difference, plot_delivery_date_difference):
-    plot_delivery_date_difference(df=delivery_date_difference)
-    return
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""**J. Order Amount per Day with Holidays**""")
-    return
-
-
-@app.cell
-def _(orders_per_day_and_holidays, plot_order_amount_per_day_with_holidays):
-    plot_order_amount_per_day_with_holidays(df=orders_per_day_and_holidays)
-    return
-
-
-if __name__ == "__main__":
-    app.run()