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Commit 2a2950f

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author
Sam LaFell
committed
SQLite3_and_Python_Practice
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‎.DS_Store

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Practice with SQLite 3 with a given prompt"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You are given the below tables, showing Store, Product, and Sales information for a chain of grocery stores. The columns are labeled in such a way that you should be able to interpret what each field is showing."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"First up, we need to create the tables..."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sqlite3"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create DB\n",
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"connection = sqlite3.connect(\"test_DB.db\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Define SQL Tables\n",
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"sql_table1 = \"\"\"\n",
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"CREATE TABLE store ( \n",
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"store_number INTEGER PRIMARY KEY, \n",
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"location text);\"\"\"\n",
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"\n",
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"sql_table2 = \"\"\"\n",
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"CREATE TABLE product ( \n",
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"product_id INTEGER PRIMARY KEY, \n",
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"product_name text,\n",
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"price_usd real);\"\"\"\n",
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"\n",
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"sql_table3 = \"\"\"\n",
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"CREATE TABLE sales ( \n",
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"sale_id INTEGER,\n",
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"product_id integer,\n",
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"store_id integer,\n",
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"date DATE);\"\"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Establish a Connection to the Database\n",
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"cursor = connection.cursor()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<sqlite3.Cursor at 0x111cdef10>"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# delete \n",
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"#cursor.execute(\"\"\"DROP TABLE store;\"\"\")\n",
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"#cursor.execute(\"\"\"DROP TABLE product;\"\"\")\n",
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"#cursor.execute(\"\"\"DROP TABLE sales;\"\"\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<sqlite3.Cursor at 0x111cdef10>"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Initialize the tables\n",
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"cursor.execute(sql_table1)\n",
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"cursor.execute(sql_table2)\n",
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"cursor.execute(sql_table3)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Populate the DB's\n",
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"store_data = [ (\"91110\", \"New York\"),\n",
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" (\"99525\", \"Los Angeles\"),\n",
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" (\"37340\", \"Tokyo\"),\n",
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" (\"32016\", \"Detroit\"),\n",
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" (\"57507\", \"London\")]\n",
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"\n",
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"product_data = [ (\"31331\", \"Apples\", \"2\"),\n",
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" (\"34611\", \"Lettuce\", \"3\"),\n",
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" (\"49760\", \"Chicken\", \"5\"),\n",
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" (\"26583\", \"Lemons\", \"1\"),\n",
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" (\"20267\", \"Bread\", \"2\")]\n",
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"\n",
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"\n",
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"sales_data = [ ( \"1\",\"31331\", \"91110\", \"02/20/2020\"),\n",
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" ( \"1\",\"31331\", \"91110\", \"02/20/2020\"),\n",
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" ( \"2\",\"34611\", \"57507\", \"02/20/2020\"),\n",
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" ( \"3\",\"26583\", \"37340\", \"02/20/2020\"),\n",
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" ( \"3\",\"34611\", \"32016\", \"02/20/2020\"),\n",
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" ( \"3\",\"20267\", \"99525\", \"02/21/2020\"),\n",
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" ( \"4\",\"31331\", \"99525\", \"02/21/2020\"),\n",
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" ( \"5\",\"49760\", \"99525\", \"02/21/2020\"),\n",
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" ( \"6\",\"34611\", \"97507\", \"02/21/2020\"),\n",
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" ( \"7\",\"31331\", \"91110\", \"02/21/2020\")]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Insert the data\n",
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"for p in store_data:\n",
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" format_str1 = \"\"\"INSERT INTO store (store_number, location)\n",
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" VALUES (\"{store_number}\", \"{location}\");\"\"\"\n",
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" \n",
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" sql_command1 = format_str1.format(store_number=p[0], location=p[1])\n",
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" cursor.execute(sql_command1)\n",
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" \n",
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"for l in product_data:\n",
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" format_str2 = \"\"\"INSERT INTO product (product_id, product_name, price_usd)\n",
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" VALUES (\"{product_id}\", \"{product_name}\", \"{price_usd}\");\"\"\"\n",
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" \n",
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" sql_command2 = format_str2.format(product_id=l[0], product_name=l[1], price_usd=l[2])\n",
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" cursor.execute(sql_command2)\n",
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" \n",
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"for k in sales_data:\n",
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" format_str3 = \"\"\"INSERT INTO sales (sale_id, product_id, store_id, date)\n",
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" VALUES (\"{sale_id}\", \"{product_id}\", \"{store_id}\", \"{date}\");\"\"\"\n",
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" \n",
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" sql_command3 = format_str3.format(sale_id=k[0], product_id=k[1], store_id=k[2], date = k[3])\n",
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" cursor.execute(sql_command3)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Never forget this, if you want the changes to be saved:\n",
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"connection.commit()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Close the connection\n",
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"connection.close()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Now, query the data to get the desired answer\n",
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"\n",
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"Using the tables above, write a SQL query to return the number of sales as well as the average sale price (in dollars) for a given location.\n",
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"\n",
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"Your output should return the following columns:\n",
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"\n",
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"location\t, number_sales\t, avg_sale_price\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"connection = sqlite3.connect(\"test_DB.db\")\n",
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"\n",
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"cursor = connection.cursor()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"fetchall:\n",
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"(32016, 'Detroit')\n",
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"(37340, 'Tokyo')\n",
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"(57507, 'London')\n",
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"(91110, 'New York')\n",
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"(99525, 'Los Angeles')\n"
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]
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}
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],
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"source": [
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"# What does the data look like? Does our table pull right?\n",
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"\n",
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"# Make the query\n",
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"cursor.execute(\"SELECT * FROM store\") \n",
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"\n",
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"# Now, print the query\n",
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"print(\"fetchall:\")\n",
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"result = cursor.fetchall() \n",
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"for r in result:\n",
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" print(r)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"fetch one:\n",
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"(32016, 'Detroit')\n"
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]
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}
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],
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"source": [
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"## Similar, but instead do it one at a time\n",
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"cursor.execute(\"SELECT * FROM store\") \n",
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"\n",
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"# Print\n",
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"print(\"\\nfetch one:\")\n",
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"res = cursor.fetchone() \n",
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"print(res)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now again, our problem: \n",
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"- Using the tables above, write a SQL query to return the number of sales as well as the average sale price (in dollars) for a given location."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## The Query, which I put into SQLite\n",
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"\n",
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"SELECT \n",
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"\n",
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" location AS Location,\n",
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"\n",
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" count(sale_id) AS num_sales, \n",
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" \n",
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" avg(price_usd) AS avg_price\n",
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"\n",
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"\n",
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"FROM \n",
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" \n",
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" test_db.sales\n",
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" \n",
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" INNER JOIN test_db.store ON sales.store_id = store.store_number\n",
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" \n",
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" INNER JOIN test_db.product ON sales.product_id = product.product_id\n",
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" \n",
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"\n",
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"GROUP BY \n",
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"\n",
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" sales.store_id\n",
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"\n",
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"ORDER BY\n",
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"\n",
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" avg_price DESC;"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Our Results"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"| Location | NUM_SALES | AVG_PRICE |\n",
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"|----------|-----------|-----------|\n",
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"|New York | 3 | 2 |\n",
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"|Los Angeles | 3 | 3 |\n",
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"|Detroit | 1 | 3 |\n",
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"|Tokyo | 1 | 1 |\n",
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"|London | 1 | 3 |"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We see that New York and LA have the highest number of sales but LA seems to be making more money per sale, on average."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This was a fun way to dive a little bit further into sqlite3 on Python and then connect to the DB in SQLite and answer the question."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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