Finished lab

python_sql_connection.ipynb

@@ -0,0 +1,155 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Engine(mysql+pymysql://root:***@localhost/Sakila)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import pymysql\n",
+    "from sqlalchemy import create_engine\n",
+    "import getpass\n",
+    "password = getpass.getpass()\n",
+    "bd = \"Sakila\"\n",
+    "connection_string = 'mysql+pymysql://root:' + password + '@localhost/'+bd\n",
+    "engine = create_engine(connection_string)\n",
+    "engine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "acc452c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rentals_month(engine, month, year):\n",
+    "    query = \"\"\"\n",
+    "        SELECT *\n",
+    "        FROM rental\n",
+    "        WHERE MONTH(rental_date) = %(month)s\n",
+    "          AND YEAR(rental_date)  = %(year)s\n",
+    "    \"\"\"\n",
+    "    with engine.connect() as conn:\n",
+    "        df = pd.read_sql(query, conn, params={\"month\": month, \"year\": year})\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c87b9113",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   rental_id         rental_date  inventory_id  customer_id  \\\n",
+      "0          1 2005-05-24 22:53:30           367          130   \n",
+      "1          2 2005-05-24 22:54:33          1525          459   \n",
+      "2          3 2005-05-24 23:03:39          1711          408   \n",
+      "3          4 2005-05-24 23:04:41          2452          333   \n",
+      "4          5 2005-05-24 23:05:21          2079          222   \n",
+      "\n",
+      "          return_date  staff_id         last_update  \n",
+      "0 2005-05-26 22:04:30         1 2006-02-15 21:30:53  \n",
+      "1 2005-05-28 19:40:33         1 2006-02-15 21:30:53  \n",
+      "2 2005-06-01 22:12:39         1 2006-02-15 21:30:53  \n",
+      "3 2005-06-03 01:43:41         2 2006-02-15 21:30:53  \n",
+      "4 2005-06-02 04:33:21         1 2006-02-15 21:30:53  \n"
+     ]
+    }
+   ],
+   "source": [
+    "may_rentals = rentals_month(engine, 5, 2005)\n",
+    "print(may_rentals.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19ca0f14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rental_count_month(df, month, year):\n",
+    "    col_name = f\"rentals_{month:02d}_{year}\"\n",
+    "    result = (df.groupby(\"customer_id\")[\"rental_id\"].count().reset_index().rename(columns={\"rental_id\": col_name}))\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b186891c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   customer_id  rentals_05_2005\n",
+      "0            1                2\n",
+      "1            2                1\n",
+      "2            3                2\n",
+      "3            5                3\n",
+      "4            6                3\n"
+     ]
+    }
+   ],
+   "source": [
+    "may_count = rental_count_month(may_rentals, 5, 2005)\n",
+    "print(may_count.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1baf2437",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compare_rentals(df1, df2):\n",
+    "    merged = pd.merge(df1, df2, on=\"customer_id\", how=\"inner\")\n",
+    "    col1, col2 = [c for c in merged.columns if c != \"customer_id\"]\n",
+    "    merged[\"difference\"] = merged[col1] - merged[col2]\n",
+    "    return merged"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.13.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}