From 27893978e3319a2c6e1d28ad31148b36304812ec Mon Sep 17 00:00:00 2001
From: Desmond Ugboaja <udesmond33@gmail.com>
Date: Wed, 29 Apr 2026 00:56:11 +0200
Subject: [PATCH] Solved lab

---
 SQL to Python Completed.ipynb | 123 ++++++++++++++++++++++++++++++++++
 1 file changed, 123 insertions(+)
 create mode 100644 SQL to Python Completed.ipynb

diff --git a/SQL to Python Completed.ipynb b/SQL to Python Completed.ipynb
new file mode 100644
index 0000000..7251401
--- /dev/null
+++ b/SQL to Python Completed.ipynb	
@@ -0,0 +1,123 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "63e401db",
+   "metadata": {},
+   "source": [
+    "Importing from SQL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bbab3725",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sqlalchemy import create_engine\n",
+    "import pandas as pd\n",
+    "\n",
+    "engine = create_engine(\"mysql+pymysql://username:Desmond123.@localhost/sakila\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83d70774",
+   "metadata": {},
+   "source": [
+    "query rental rows "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1c0f78d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rentals_month(engine, month, year):\n",
+    "    query = f\"\"\"\n",
+    "        SELECT rental_id, rental_date, inventory_id, customer_id, staff_id\n",
+    "        FROM rental\n",
+    "        WHERE MONTH(rental_date) = {month}\n",
+    "          AND YEAR(rental_date) = {year}\n",
+    "    \"\"\"\n",
+    "    df = pd.read_sql(query, engine)\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee97fed0",
+   "metadata": {},
+   "source": [
+    "counting rentals per customer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1cc1f59d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rental_count_month(df, month, year):\n",
+    "    column_name = f\"rentals_{month:02d}_{year}\"\n",
+    "    \n",
+    "    result = (\n",
+    "        df.groupby(\"customer_id\")\n",
+    "          .size()\n",
+    "          .reset_index(name=column_name)\n",
+    "    )\n",
+    "    \n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ddc8784",
+   "metadata": {},
+   "source": [
+    "merging the monthly summaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f22bf80c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compare_rentals(df1, df2):\n",
+    "    merged = pd.merge(df1, df2, on=\"customer_id\", how=\"inner\")\n",
+    "    \n",
+    "    rental_cols = [col for col in merged.columns if col != \"customer_id\"]\n",
+    "    merged[\"difference\"] = merged[rental_cols[1]] - merged[rental_cols[0]]\n",
+    "    \n",
+    "    return merged"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.13.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}