Solved lab

Solved _lab.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ed411b38",
+   "metadata": {},
+   "source": [
+    "## LAB Connecting Python to SQL\n",
+    "### Name: Bryan Calderon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "dbb2bb91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import pymysql\n",
+    "from sqlalchemy import create_engine\n",
+    "import getpass  # To get the password without showing the input\n",
+    "password = getpass.getpass()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a18cbf9",
+   "metadata": {},
+   "source": [
+    "1. Establish a connection between Python and the Sakila database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "cfcb3c2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Engine(mysql+pymysql://root:***@localhost/sakila)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bd = \"sakila\"\n",
+    "connection_string = 'mysql+pymysql://root:' + password + '@localhost/'+bd\n",
+    "engine = create_engine(connection_string)\n",
+    "engine"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c26b19c",
+   "metadata": {},
+   "source": [
+    "2. Write a Python function called rentals_month that retrieves rental data for a given month and year (passed as parameters) from the Sakila database as a Pandas DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "c4101041",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rentals_month(engine, month, year):\n",
+    "    query = f\"\"\"\n",
+    "    SELECT * FROM rental\n",
+    "    WHERE MONTH(rental_date) = {month}\n",
+    "    AND YEAR(rental_date) = {year};\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    df = pd.read_sql(query, engine)\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cd94a97",
+   "metadata": {},
+   "source": [
+    "3. Develop a Python function called rental_count_month that takes the DataFrame provided by rentals_month as input along with the month and year and returns a new DataFrame containing the number of rentals made by each customer_id during the selected month and year."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "a6ed86cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rental_count_month(df, month, year):\n",
+    "    column_name = f\"rentals_{month:02d}_{year}\"\n",
+    "    \n",
+    "    result = (df.groupby(\"customer_id\")\n",
+    "            .size()\n",
+    "            .reset_index(name=column_name)\n",
+    "    )\n",
+    "    \n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "584ddf80",
+   "metadata": {},
+   "source": [
+    "4. Create a Python function called compare_rentals that takes two DataFrames as input containing the number of rentals made by each customer in different months and years. The function should return a combined DataFrame with a new 'difference' column, which is the difference between the number of rentals in the two months."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "ffad14b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compare_rentals(df1, df2):\n",
+    "    merged = pd.merge(df1, df2, on=\"customer_id\", how=\"outer\").fillna(0)\n",
+    "    \n",
+    "    col1 = df1.columns[1]\n",
+    "    col2 = df2.columns[1]\n",
+    "    \n",
+    "    merged[\"difference\"] = merged[col2] - merged[col1]\n",
+    "    \n",
+    "    return merged"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9cda3071",
+   "metadata": {},
+   "source": [
+    "### EXAMPLE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "ed39f866",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Rentals\n",
+    "may_rentals = rentals_month(engine, 5, 2005)\n",
+    "june_rentals = rentals_month(engine, 6, 2005)\n",
+    "# Counts\n",
+    "may_counts = rental_count_month(may_rentals, 5, 2005)\n",
+    "june_counts = rental_count_month(june_rentals, 6, 2005)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "213c59c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>customer_id</th>\n",
+       "      <th>rentals_05_2005</th>\n",
+       "      <th>rentals_06_2005</th>\n",
+       "      <th>difference</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   customer_id  rentals_05_2005  rentals_06_2005  difference\n",
+       "0            1              2.0              7.0         5.0\n",
+       "1            2              1.0              1.0         0.0\n",
+       "2            3              2.0              4.0         2.0\n",
+       "3            4              0.0              6.0         6.0\n",
+       "4            5              3.0              5.0         2.0"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Comparision \n",
+    "compar = compare_rentals(may_counts, june_counts)\n",
+    "compar.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ab886e3",
+   "metadata": {},
+   "source": [
+    "### That is all :)"
+   ]
+  }
+ ],
+ "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
+}