diff --git a/Solved _lab.ipynb b/Solved _lab.ipynb new file mode 100644 index 0000000..76ae0cc --- /dev/null +++ b/Solved _lab.ipynb @@ -0,0 +1,283 @@ +{ + "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": [ + "
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" + ], + "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 +}