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" + ], + "text/plain": [ + " customer_id rentals_05_2005 rentals_06_2005 difference\n", + "0 1 2 7 5\n", + "1 2 1 1 0\n", + "2 3 2 4 2\n", + "3 5 3 5 2\n", + "4 6 3 4 1" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "from sqlalchemy import create_engine\n", + "from urllib.parse import quote_plus\n", + "\n", + "password = quote_plus(\"Gazeb@85\")\n", + "\n", + "engine = create_engine(\n", + " f\"mysql+pymysql://root:{password}@localhost:3306/sakila\"\n", + ")\n", + "\n", + "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", + " \n", + " return pd.read_sql(\n", + " query,\n", + " engine,\n", + " params={\"month\": month, \"year\": year}\n", + " )\n", + "\n", + "\n", + "def rental_count_month(rentals_df, month, year):\n", + " column_name = f\"rentals_{month:02d}_{year}\"\n", + " \n", + " rental_count = (\n", + " rentals_df\n", + " .groupby(\"customer_id\")\n", + " .size()\n", + " .reset_index(name=column_name)\n", + " )\n", + " \n", + " return rental_count\n", + "\n", + "\n", + "def compare_rentals(df_month_1, df_month_2):\n", + " comparison = pd.merge(\n", + " df_month_1,\n", + " df_month_2,\n", + " on=\"customer_id\",\n", + " how=\"inner\"\n", + " )\n", + " \n", + " rental_columns = [\n", + " col for col in comparison.columns \n", + " if col.startswith(\"rentals_\")\n", + " ]\n", + " \n", + " comparison[\"difference\"] = (\n", + " comparison[rental_columns[1]] - comparison[rental_columns[0]]\n", + " )\n", + " \n", + " return comparison\n", + "\n", + "\n", + "rentals_may = rentals_month(engine, 5, 2005)\n", + "rentals_june = rentals_month(engine, 6, 2005)\n", + "\n", + "rentals_may_count = rental_count_month(rentals_may, 5, 2005)\n", + "rentals_june_count = rental_count_month(rentals_june, 6, 2005)\n", + "\n", + "comparison_df = compare_rentals(rentals_may_count, rentals_june_count)\n", + "\n", + "comparison_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1929046c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}