From 97cd548713a6ff8d31d3a4aec98982eb3e5c59bd Mon Sep 17 00:00:00 2001 From: Supriya Giri Date: Tue, 28 Apr 2026 21:13:11 +0200 Subject: [PATCH] Add files via upload --- sql_python.ipynb | 134 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 134 insertions(+) create mode 100644 sql_python.ipynb diff --git a/sql_python.ipynb b/sql_python.ipynb new file mode 100644 index 0000000..4c9f259 --- /dev/null +++ b/sql_python.ipynb @@ -0,0 +1,134 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "1136bca2", + "metadata": {}, + "outputs": [], + "source": [ + "from sqlalchemy import create_engine\n", + "\n", + "# Example connection string (adjust username/password/host)\n", + "engine = create_engine(\"mysql+pymysql://root:Satvik2020@localhost:3306/sakila\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "af9a5baa", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "def rentals_month(engine, month, year):\n", + " query = f\"\"\"\n", + " SELECT \n", + " rental_id,\n", + " customer_id,\n", + " rental_date\n", + " 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": "code", + "execution_count": 4, + "id": "545d62d9", + "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": "code", + "execution_count": 6, + "id": "027b1906", + "metadata": {}, + "outputs": [], + "source": [ + "def compare_rentals(df1, df2):\n", + " merged = pd.merge(df1, df2, on=\"customer_id\", how=\"outer\").fillna(0)\n", + " \n", + " # Get rental column names dynamically\n", + " col1 = df1.columns[1]\n", + " col2 = df2.columns[1]\n", + " \n", + " merged[\"difference\"] = merged[col2] - merged[col1]\n", + " \n", + " return merged" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "20bb4152", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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\n" + ] + } + ], + "source": [ + "# Step 1: Get raw data\n", + "may_data = rentals_month(engine, 5, 2005)\n", + "june_data = rentals_month(engine, 6, 2005)\n", + "\n", + "# Step 2: Aggregate counts\n", + "may_counts = rental_count_month(may_data, 5, 2005)\n", + "june_counts = rental_count_month(june_data, 6, 2005)\n", + "\n", + "# Step 3: Compare\n", + "comparison = compare_rentals(may_counts, june_counts)\n", + "\n", + "print(comparison.head())" + ] + } + ], + "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": "3.13.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}