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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "eccf68ee",
+   "metadata": {},
+   "source": [
+    "# lab-sql-python-connection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59e00018",
+   "metadata": {},
+   "source": [
+    "## 1. Import Libraries\n",
+    "\n",
+    "We will use:\n",
+    "\n",
+    "- `pandas` to work with DataFrames.\n",
+    "- `sqlalchemy` to create the connection engine.\n",
+    "- `text` from SQLAlchemy to safely write SQL queries with parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5ad93640",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sqlalchemy import create_engine, text"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c0b813b",
+   "metadata": {},
+   "source": [
+    "## 2. Create the Database Connection\n",
+    "\n",
+    "Here we create the connection between Python and the Sakila database.\n",
+    "\n",
+    "> Replace `your_password` with your own MySQL password."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96da5463",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Database connection settings\n",
+    "password = \"your_password\"  # Replace with your MySQL password\n",
+    "db = \"sakila\"\n",
+    "\n",
+    "# Create the connection string\n",
+    "connection_string = f\"mysql+pymysql://root:{password}@localhost/{db}\"\n",
+    "\n",
+    "# Create the engine\n",
+    "engine = create_engine(connection_string)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81d390f6",
+   "metadata": {},
+   "source": [
+    "### Test the connection\n",
+    "\n",
+    "Before moving forward, it is useful to test if the connection works correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5ab16ebf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Test connection\n",
+    "query = text(\"SELECT * FROM rental LIMIT 5;\")\n",
+    "\n",
+    "sample_rentals = pd.read_sql(query, engine)\n",
+    "sample_rentals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b02b4c0a",
+   "metadata": {},
+   "source": [
+    "## 3. Function 1: Retrieve Rentals by Month\n",
+    "\n",
+    "The function `rentals_month()` retrieves all rental records for a specific month and year.\n",
+    "\n",
+    "It receives three parameters:\n",
+    "\n",
+    "- `engine`: the database connection engine.\n",
+    "- `month`: the month we want to analyze.\n",
+    "- `year`: the year we want to analyze.\n",
+    "\n",
+    "The function returns a Pandas DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4232b247",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rentals_month(engine, month, year):\n",
+    "    \"\"\"\n",
+    "    Retrieves rental data for a specific month and year from the Sakila database.\n",
+    "    \n",
+    "    Parameters:\n",
+    "    engine: SQLAlchemy engine used to connect to the database.\n",
+    "    month: Integer representing the month.\n",
+    "    year: Integer representing the year.\n",
+    "    \n",
+    "    Returns:\n",
+    "    A pandas DataFrame with rental data for the selected month and year.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    query = text(\"\"\"\n",
+    "        SELECT *\n",
+    "        FROM rental\n",
+    "        WHERE MONTH(rental_date) = :month\n",
+    "          AND YEAR(rental_date) = :year;\n",
+    "    \"\"\")\n",
+    "    \n",
+    "    df = pd.read_sql(query, engine, params={\"month\": month, \"year\": year})\n",
+    "    \n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30bc8a27",
+   "metadata": {},
+   "source": [
+    "## 4. Retrieve May and June 2005 Rentals\n",
+    "\n",
+    "According to the challenge, we need to analyze customers who were active in both May and June."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6328647f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Retrieve rental data for May and June 2005\n",
+    "rentals_may = rentals_month(engine, 5, 2005)\n",
+    "rentals_june = rentals_month(engine, 6, 2005)\n",
+    "\n",
+    "print(\"May rentals shape:\", rentals_may.shape)\n",
+    "print(\"June rentals shape:\", rentals_june.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31b3194e",
+   "metadata": {},
+   "source": [
+    "### Preview May rentals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e46dba7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rentals_may.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98a67688",
+   "metadata": {},
+   "source": [
+    "### Preview June rentals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3f09fd2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rentals_june.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f210e167",
+   "metadata": {},
+   "source": [
+    "## 5. Basic Exploration\n",
+    "\n",
+    "Before comparing customers, we can quickly check how many rental transactions happened in each month and how many unique customers were active."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "301d370d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "may_total_rentals = rentals_may.shape[0]\n",
+    "june_total_rentals = rentals_june.shape[0]\n",
+    "\n",
+    "may_unique_customers = rentals_may[\"customer_id\"].nunique()\n",
+    "june_unique_customers = rentals_june[\"customer_id\"].nunique()\n",
+    "\n",
+    "summary = pd.DataFrame({\n",
+    "    \"month\": [\"May 2005\", \"June 2005\"],\n",
+    "    \"total_rentals\": [may_total_rentals, june_total_rentals],\n",
+    "    \"unique_customers\": [may_unique_customers, june_unique_customers]\n",
+    "})\n",
+    "\n",
+    "summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3327298",
+   "metadata": {},
+   "source": [
+    "### Initial Insight\n",
+    "\n",
+    "This summary helps us understand the overall activity in each month before going into the customer-level comparison.\n",
+    "\n",
+    "If June has more rentals or more active customers than May, it may suggest an increase in customer activity."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3cc96811",
+   "metadata": {},
+   "source": [
+    "## 6. Function 2: Count Rentals by Customer and Month\n",
+    "\n",
+    "The function `rental_count_month()` receives the rental DataFrame for one month and counts how many rentals each customer made.\n",
+    "\n",
+    "The new column name is created dynamically using the month and year.\n",
+    "\n",
+    "For example:\n",
+    "\n",
+    "- May 2005 → `rentals_05_2005`\n",
+    "- June 2005 → `rentals_06_2005`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7aaa8e7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rental_count_month(df, month, year):\n",
+    "    \"\"\"\n",
+    "    Counts the number of rentals made by each customer during a selected month and year.\n",
+    "    \n",
+    "    Parameters:\n",
+    "    df: DataFrame returned by rentals_month().\n",
+    "    month: Integer representing the month.\n",
+    "    year: Integer representing the year.\n",
+    "    \n",
+    "    Returns:\n",
+    "    A DataFrame with customer_id and the number of rentals for that month.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    column_name = f\"rentals_{month:02d}_{year}\"\n",
+    "    \n",
+    "    rental_count = (\n",
+    "        df.groupby(\"customer_id\")\n",
+    "          .size()\n",
+    "          .reset_index(name=column_name)\n",
+    "    )\n",
+    "    \n",
+    "    return rental_count"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b266b031",
+   "metadata": {},
+   "source": [
+    "## 7. Count Rentals for May and June"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee18607e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rentals_may_count = rental_count_month(rentals_may, 5, 2005)\n",
+    "rentals_june_count = rental_count_month(rentals_june, 6, 2005)\n",
+    "\n",
+    "rentals_may_count.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b1baf9c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rentals_june_count.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e46ea476",
+   "metadata": {},
+   "source": [
+    "## 8. Function 3: Compare Rentals Between Two Months\n",
+    "\n",
+    "The function `compare_rentals()` combines both monthly rental count DataFrames.\n",
+    "\n",
+    "We use an `inner` merge because the challenge asks for customers who were active in both months.\n",
+    "\n",
+    "Then we calculate:\n",
+    "\n",
+    "```text\n",
+    "difference = rentals_06_2005 - rentals_05_2005\n",
+    "```\n",
+    "\n",
+    "This means:\n",
+    "\n",
+    "- Positive difference → the customer rented more in June.\n",
+    "- Negative difference → the customer rented more in May.\n",
+    "- Zero → the customer had the same number of rentals in both months."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c14d1813",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compare_rentals(df1, df2):\n",
+    "    \"\"\"\n",
+    "    Combines two monthly rental count DataFrames and calculates the difference\n",
+    "    between the number of rentals in the second month and the first month.\n",
+    "    \n",
+    "    Parameters:\n",
+    "    df1: Rental count DataFrame for the first month.\n",
+    "    df2: Rental count DataFrame for the second month.\n",
+    "    \n",
+    "    Returns:\n",
+    "    A combined DataFrame with a difference column.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    comparison = pd.merge(df1, df2, on=\"customer_id\", how=\"inner\")\n",
+    "    \n",
+    "    first_month_col = comparison.columns[1]\n",
+    "    second_month_col = comparison.columns[2]\n",
+    "    \n",
+    "    comparison[\"difference\"] = comparison[second_month_col] - comparison[first_month_col]\n",
+    "    \n",
+    "    return comparison"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "744192fb",
+   "metadata": {},
+   "source": [
+    "## 9. Compare May vs June Activity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b46bde7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comparison_df = compare_rentals(rentals_may_count, rentals_june_count)\n",
+    "\n",
+    "comparison_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b796c63",
+   "metadata": {},
+   "source": [
+    "## 10. Analyze the Results\n",
+    "\n",
+    "Now that we have the comparison DataFrame, we can extract useful insights."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8c118436",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Number of customers active in both months\n",
+    "active_both_months = comparison_df[\"customer_id\"].nunique()\n",
+    "\n",
+    "# Customers who increased, decreased, or maintained activity\n",
+    "increased_activity = comparison_df[comparison_df[\"difference\"] > 0].shape[0]\n",
+    "decreased_activity = comparison_df[comparison_df[\"difference\"] < 0].shape[0]\n",
+    "same_activity = comparison_df[comparison_df[\"difference\"] == 0].shape[0]\n",
+    "\n",
+    "activity_summary = pd.DataFrame({\n",
+    "    \"activity_change\": [\"Increased in June\", \"Decreased in June\", \"Same activity\"],\n",
+    "    \"number_of_customers\": [increased_activity, decreased_activity, same_activity]\n",
+    "})\n",
+    "\n",
+    "activity_summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36a1523b",
+   "metadata": {},
+   "source": [
+    "### Insight\n",
+    "\n",
+    "This table shows how customer behavior changed between May and June.\n",
+    "\n",
+    "It helps us identify whether customer engagement increased, decreased, or stayed stable among customers who were active in both months."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3113f318",
+   "metadata": {},
+   "source": [
+    "## 11. Top Customers with the Biggest Increase\n",
+    "\n",
+    "These are the customers whose rental activity increased the most from May to June."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11179e6e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "top_increase = comparison_df.sort_values(by=\"difference\", ascending=False).head(10)\n",
+    "top_increase"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ef1bede",
+   "metadata": {},
+   "source": [
+    "## 12. Customers with the Biggest Decrease\n",
+    "\n",
+    "These are the customers whose rental activity decreased the most from May to June."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25a22125",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "top_decrease = comparison_df.sort_values(by=\"difference\", ascending=True).head(10)\n",
+    "top_decrease"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16008747",
+   "metadata": {},
+   "source": [
+    "## 13. Average Rental Activity\n",
+    "\n",
+    "We can also compare the average number of rentals per customer in both months."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b9f2531f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "may_col = \"rentals_05_2005\"\n",
+    "june_col = \"rentals_06_2005\"\n",
+    "\n",
+    "average_activity = pd.DataFrame({\n",
+    "    \"month\": [\"May 2005\", \"June 2005\"],\n",
+    "    \"average_rentals_per_customer\": [comparison_df[may_col].mean(), comparison_df[june_col].mean()]\n",
+    "})\n",
+    "\n",
+    "average_activity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "192b59a9",
+   "metadata": {},
+   "source": [
+    "### Insight\n",
+    "\n",
+    "This helps us understand whether the same group of customers became more or less active on average in June compared to May."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ea4d7e8",
+   "metadata": {},
+   "source": [
+    "## 14. Optional Visualization\n",
+    "\n",
+    "A simple bar chart can help visualize how many customers increased, decreased, or maintained their rental activity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6aa720bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "plt.bar(activity_summary[\"activity_change\"], activity_summary[\"number_of_customers\"])\n",
+    "plt.title(\"Customer Rental Activity Change: May vs June 2005\")\n",
+    "plt.xlabel(\"Activity Change\")\n",
+    "plt.ylabel(\"Number of Customers\")\n",
+    "plt.xticks(rotation=20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60e8bb9f",
+   "metadata": {},
+   "source": [
+    "## 15. Final Conclusions\n",
+    "\n",
+    "Based on the comparison between May and June 2005:\n",
+    "\n",
+    "- We identified customers who were active in both months using an inner merge.\n",
+    "- We calculated how many rentals each customer made in May and June.\n",
+    "- We created a `difference` column to measure how customer activity changed.\n",
+    "- Customers with a positive difference were more active in June.\n",
+    "- Customers with a negative difference were more active in May.\n",
+    "- Customers with a difference of zero maintained the same rental behavior.\n",
+    "\n",
+    "## Business Interpretation\n",
+    "\n",
+    "This type of analysis is useful because it helps a company understand customer engagement over time.\n",
+    "\n",
+    "For a movie rental business like Sakila, this could help identify:\n",
+    "\n",
+    "- Customers becoming more engaged.\n",
+    "- Customers whose activity is declining.\n",
+    "- Opportunities for retention campaigns.\n",
+    "- Customers who may respond well to loyalty programs or personalized recommendations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "871c0369",
+   "metadata": {},
+   "source": [
+    "## 16. Key Takeaway\n",
+    "\n",
+    "Connecting Python to SQL allows us to combine the strengths of both tools:\n",
+    "\n",
+    "- SQL is useful for retrieving structured data from the database.\n",
+    "- Python and Pandas are useful for transforming, analyzing, and visualizing that data.\n",
+    "\n",
+    "This workflow is very common in data analytics projects."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.14.3"
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+ },
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+ "nbformat_minor": 5
+}