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@@ -0,0 +1,630 @@
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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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+ "# Guided Project Solution: Building Fast Queries on a CSV"
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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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+ "# Reading the Inventory\n",
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+ "\n",
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+ "Use the `csv` module to read the `laptops.csv` file and separate the header from the rows."
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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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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "['Id', 'Company', 'Product', 'TypeName', 'Inches', 'ScreenResolution', 'Cpu', 'Ram', 'Memory', 'Gpu', 'OpSys', 'Weight', 'Price']\n",
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+ "['6571244', 'Apple', 'MacBook Pro', 'Ultrabook', '13.3', 'IPS Panel Retina Display 2560x1600', 'Intel Core i5 2.3GHz', '8GB', '128GB SSD', 'Intel Iris Plus Graphics 640', 'macOS', '1.37kg', '1339']\n",
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+ "['7287764', 'Apple', 'Macbook Air', 'Ultrabook', '13.3', '1440x900', 'Intel Core i5 1.8GHz', '8GB', '128GB Flash Storage', 'Intel HD Graphics 6000', 'macOS', '1.34kg', '898']\n",
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+ "['3362737', 'HP', '250 G6', 'Notebook', '15.6', 'Full HD 1920x1080', 'Intel Core i5 7200U 2.5GHz', '8GB', '256GB SSD', 'Intel HD Graphics 620', 'No OS', '1.86kg', '575']\n",
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+ "['9722156', 'Apple', 'MacBook Pro', 'Ultrabook', '15.4', 'IPS Panel Retina Display 2880x1800', 'Intel Core i7 2.7GHz', '16GB', '512GB SSD', 'AMD Radeon Pro 455', 'macOS', '1.83kg', '2537']\n",
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+ "['8550527', 'Apple', 'MacBook Pro', 'Ultrabook', '13.3', 'IPS Panel Retina Display 2560x1600', 'Intel Core i5 3.1GHz', '8GB', '256GB SSD', 'Intel Iris Plus Graphics 650', 'macOS', '1.37kg', '1803']\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "import csv\n",
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+ "\n",
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+ "with open('laptops.csv') as f:\n",
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+ " reader = csv.reader(f)\n",
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+ " rows = list(reader)\n",
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+ " header = rows[0]\n",
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+ " rows = rows[1:]\n",
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+ " \n",
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+ "print(header)\n",
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+ "for i in range(5):\n",
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+ " print(rows[i])"
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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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+ "# Inventory Class\n",
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+ "\n",
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+ "Start implementing a class to represent the inventory. It get the name of the CSV file as argument and reads it into `self.header` and `self.rows`."
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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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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "['Id', 'Company', 'Product', 'TypeName', 'Inches', 'ScreenResolution', 'Cpu', 'Ram', 'Memory', 'Gpu', 'OpSys', 'Weight', 'Price']\n",
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+ "1303\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "class Inventory(): # step 1\n",
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+ " \n",
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+ " def __init__(self, csv_filename): # step 2\n",
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+ " with open(csv_filename) as f: # step 3\n",
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+ " reader = csv.reader(f)\n",
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+ " rows = list(reader)\n",
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+ " self.header = rows[0] # step 4\n",
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+ " self.rows = rows[1:]\n",
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+ " for row in self.rows: # step 5\n",
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+ " row[-1] = int(row[-1])\n",
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+ "\n",
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+ "inventory = Inventory('laptops.csv') # step 6\n",
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+ "print(inventory.header) # step 7\n",
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+ "print(len(inventory.rows)) # step 8"
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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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+ "# Finding a Laptop From the Id\n",
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+ "\n",
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+ "Implement a `get_laptop_from_id()` function that given a laptop identifier find the row corresponding to that laptop."
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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": 21,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "class Inventory(): \n",
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+ " \n",
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+ " def __init__(self, csv_filename):\n",
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+ " with open(csv_filename) as f: \n",
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+ " reader = csv.reader(f)\n",
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+ " rows = list(reader)\n",
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+ " self.header = rows[0] \n",
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+ " self.rows = rows[1:]\n",
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+ " for row in self.rows: \n",
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+ " row[-1] = int(row[-1])\n",
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+ " \n",
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+ " def get_laptop_from_id(self, laptop_id): # step 1\n",
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+ " for row in self.rows: # step 2\n",
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+ " if row[0] == laptop_id:\n",
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+ " return row\n",
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+ " return None # step 3"
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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": 20,
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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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+ "['3362737', 'HP', '250 G6', 'Notebook', '15.6', 'Full HD 1920x1080', 'Intel Core i5 7200U 2.5GHz', '8GB', '256GB SSD', 'Intel HD Graphics 620', 'No OS', '1.86kg', 575]\n",
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+ "None\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "inventory = Inventory('laptops.csv') # step 4\n",
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+ "print(inventory.get_laptop_from_id('3362737')) # step 5\n",
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+ "print(inventory.get_laptop_from_id('3362736')) # step 6"
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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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+ "# Improving Id Lookups\n",
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+ "\n",
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+ "Improve the time complexity of finding a laptop with a given id by precomputing a dictionary that maps laptop ids to rows."
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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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+ "source": [
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+ "class Inventory(): \n",
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+ " \n",
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+ " def __init__(self, csv_filename):\n",
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+ " with open(csv_filename) as f: \n",
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+ " reader = csv.reader(f)\n",
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+ " rows = list(reader)\n",
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+ " self.header = rows[0] \n",
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+ " self.rows = rows[1:]\n",
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+ " for row in self.rows: \n",
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+ " row[-1] = int(row[-1])\n",
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+ " self.id_to_row = {} # step 1\n",
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+ " for row in self.rows: # step 2\n",
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+ " self.id_to_row[row[0]] = row \n",
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+ " \n",
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+ " def get_laptop_from_id(self, laptop_id):\n",
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+ " for row in self.rows: \n",
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+ " if row[0] == laptop_id:\n",
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+ " return row\n",
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+ " return None \n",
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+ " \n",
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+ " def get_laptop_from_id_fast(self, laptop_id): # step 3\n",
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+ " if laptop_id in self.id_to_row: # step 4\n",
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+ " return self.id_to_row[laptop_id]\n",
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+ " return None"
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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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+ "## Test the code:"
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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": 27,
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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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+ "['3362737', 'HP', '250 G6', 'Notebook', '15.6', 'Full HD 1920x1080', 'Intel Core i5 7200U 2.5GHz', '8GB', '256GB SSD', 'Intel HD Graphics 620', 'No OS', '1.86kg', 575]\n",
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+ "None\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "inventory = Inventory('laptops.csv') # step 5\n",
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+ "print(inventory.get_laptop_from_id_fast('3362737')) # step 6\n",
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+ "print(inventory.get_laptop_from_id_fast('3362736')) # step 7"
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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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+ "# Comparing Performance\n",
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+ "\n",
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+ "Compare the performance of both function for id lookup."
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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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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "0.5494911670684814\n",
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+ "0.002789735794067383\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "import time # step 1\n",
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+ "import random # step 2\n",
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+ "\n",
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+ "ids = [str(random.randint(1000000, 9999999)) for _ in range(10000)] # step 3\n",
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+ "\n",
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+ "inventory = Inventory('laptops.csv') # step 4\n",
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+ "\n",
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+ "total_time_no_dict = 0 # step 5\n",
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+ "for identifier in ids: # step 6\n",
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+ " start = time.time() # step 6.1\n",
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+ " inventory.get_laptop_from_id(identifier) # step 6.2\n",
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+ " end = time.time() # step 6.3\n",
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+ " total_time_no_dict += end - start # step 6.4\n",
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+ " \n",
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+ "total_time_dict = 0 # step 7\n",
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+ "for identifier in ids: # step 8\n",
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+ " start = time.time() # step 8.1\n",
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+ " inventory.get_laptop_from_id_fast(identifier) # step 8.2\n",
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+ " end = time.time() # step 8.3\n",
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+ " total_time_dict += end - start # step 8.4\n",
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+ " \n",
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+ "print(total_time_no_dict) # step 9\n",
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+ "print(total_time_dict)"
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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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+ "## Analysis\n",
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+ "\n",
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+ "We got:\n",
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+ "\n",
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+ "```text\n",
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+ "0.5884554386138916\n",
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+ "0.0024595260620117188\n",
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+ "```\n",
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+ "\n",
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+ "We can see a significant improve in performance. If we divide _0.588_ by _0.002_ we see that the new method is about _294_ times faster for this input size."
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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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+ "# Two Laptop Promotion\n",
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+ "\n",
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+ "Write a method that finds whether we can spend a given amount of money by purchasing either one or two laptops."
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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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+ "class Inventory(): \n",
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+ " \n",
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+ " def __init__(self, csv_filename):\n",
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+ " with open(csv_filename) as f: \n",
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+ " reader = csv.reader(f)\n",
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+ " rows = list(reader)\n",
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+ " self.header = rows[0] \n",
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+ " self.rows = rows[1:]\n",
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+ " for row in self.rows: \n",
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+ " row[-1] = int(row[-1])\n",
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+ " self.id_to_row = {} \n",
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+ " for row in self.rows: \n",
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+ " self.id_to_row[row[0]] = row \n",
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+ " \n",
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+ " def get_laptop_from_id(self, laptop_id):\n",
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+ " for row in self.rows: \n",
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+ " if row[0] == laptop_id:\n",
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+ " return row\n",
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+ " return None \n",
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+ " \n",
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+ " def get_laptop_from_id_fast(self, laptop_id): \n",
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+ " if laptop_id in self.id_to_row: \n",
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+ " return self.id_to_row[laptop_id]\n",
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+ " return None\n",
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+ "\n",
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+ " def check_promotion_dollars(self, dollars): # step 1\n",
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+ " for row in self.rows: # step 2\n",
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+ " if row[-1] == dollars:\n",
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+ " return True\n",
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+ " for row1 in self.rows: # step 3\n",
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+ " for row2 in self.rows:\n",
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+ " if row1[-1] + row2[-1] == dollars:\n",
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+ " return True\n",
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+ " return False # step 4"
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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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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "True\n",
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+ "False\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "inventory = Inventory('laptops.csv') # step 5\n",
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+ "print(inventory.check_promotion_dollars(1000)) # step 6\n",
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+ "print(inventory.check_promotion_dollars(442)) # step 7"
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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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+ "# Optimizing Laptop Promotion\n",
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+ "\n",
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+ "Create a faster version of the promotion method by using the techniques we've learned in the course."
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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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+ "class Inventory(): \n",
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+ " \n",
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+ " def __init__(self, csv_filename):\n",
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+ " with open(csv_filename) as f: \n",
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+ " reader = csv.reader(f)\n",
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+ " rows = list(reader)\n",
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+ " self.header = rows[0] \n",
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+ " self.rows = rows[1:]\n",
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+ " for row in self.rows: \n",
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+ " row[-1] = int(row[-1])\n",
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+ " self.id_to_row = {} \n",
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+ " for row in self.rows: \n",
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+ " self.id_to_row[row[0]] = row\n",
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+ " self.prices = set() # step 1\n",
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+ " for row in self.rows: # step 2\n",
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+ " self.prices.add(row[-1])\n",
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+ " \n",
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+ " def get_laptop_from_id(self, laptop_id):\n",
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+ " for row in self.rows: \n",
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+ " if row[0] == laptop_id:\n",
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+ " return row\n",
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+ " return None \n",
|
|
|
+ " \n",
|
|
|
+ " def get_laptop_from_id_fast(self, laptop_id): \n",
|
|
|
+ " if laptop_id in self.id_to_row: \n",
|
|
|
+ " return self.id_to_row[laptop_id]\n",
|
|
|
+ " return None\n",
|
|
|
+ "\n",
|
|
|
+ " def check_promotion_dollars(self, dollars): \n",
|
|
|
+ " for row in self.rows: \n",
|
|
|
+ " if row[-1] == dollars:\n",
|
|
|
+ " return True\n",
|
|
|
+ " for row1 in self.rows: \n",
|
|
|
+ " for row2 in self.rows:\n",
|
|
|
+ " if row1[-1] + row2[-1] == dollars:\n",
|
|
|
+ " return True\n",
|
|
|
+ " return False \n",
|
|
|
+ " \n",
|
|
|
+ " def check_promotion_dollars_fast(self, dollars): # step 3\n",
|
|
|
+ " if dollars in self.prices: # step 4\n",
|
|
|
+ " return True\n",
|
|
|
+ " for price in self.prices: # step 5\n",
|
|
|
+ " if dollars - price in self.prices:\n",
|
|
|
+ " return True\n",
|
|
|
+ " return False # step 6"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "markdown",
|
|
|
+ "metadata": {},
|
|
|
+ "source": [
|
|
|
+ "## Test the code:"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "code",
|
|
|
+ "execution_count": 5,
|
|
|
+ "metadata": {},
|
|
|
+ "outputs": [
|
|
|
+ {
|
|
|
+ "name": "stdout",
|
|
|
+ "output_type": "stream",
|
|
|
+ "text": [
|
|
|
+ "True\n",
|
|
|
+ "False\n"
|
|
|
+ ]
|
|
|
+ }
|
|
|
+ ],
|
|
|
+ "source": [
|
|
|
+ "inventory = Inventory('laptops.csv') # step 7\n",
|
|
|
+ "print(inventory.check_promotion_dollars_fast(1000)) # step 8\n",
|
|
|
+ "print(inventory.check_promotion_dollars_fast(442)) # step 9"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "markdown",
|
|
|
+ "metadata": {},
|
|
|
+ "source": [
|
|
|
+ "# Comparing Promotion Functions\n",
|
|
|
+ "\n",
|
|
|
+ "Compare the performance of both methods for the promotion."
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "code",
|
|
|
+ "execution_count": 9,
|
|
|
+ "metadata": {},
|
|
|
+ "outputs": [
|
|
|
+ {
|
|
|
+ "name": "stdout",
|
|
|
+ "output_type": "stream",
|
|
|
+ "text": [
|
|
|
+ "0.3767249584197998\n",
|
|
|
+ "0.00017142295837402344\n"
|
|
|
+ ]
|
|
|
+ }
|
|
|
+ ],
|
|
|
+ "source": [
|
|
|
+ "prices = [random.randint(100, 5000) for _ in range(100)] # step 1\n",
|
|
|
+ "\n",
|
|
|
+ "inventory = Inventory('laptops.csv') # step 2\n",
|
|
|
+ "\n",
|
|
|
+ "total_time_no_set = 0 # step 3\n",
|
|
|
+ "for price in prices: # step 4\n",
|
|
|
+ " start = time.time() # step 4.1\n",
|
|
|
+ " inventory.check_promotion_dollars(price) # step 4.2\n",
|
|
|
+ " end = time.time() # step 4.3\n",
|
|
|
+ " total_time_no_set += end - start # step 4.4\n",
|
|
|
+ " \n",
|
|
|
+ "total_time_set = 0 # step 5\n",
|
|
|
+ "for price in prices: # step 6\n",
|
|
|
+ " start = time.time() # step 6.1\n",
|
|
|
+ " inventory.check_promotion_dollars_fast(price) # step 6.2\n",
|
|
|
+ " end = time.time() # step 6.3\n",
|
|
|
+ " total_time_set += end - start # step 6.4\n",
|
|
|
+ " \n",
|
|
|
+ "print(total_time_no_set) # step 7\n",
|
|
|
+ "print(total_time_set)"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "markdown",
|
|
|
+ "metadata": {},
|
|
|
+ "source": [
|
|
|
+ "## Analysis\n",
|
|
|
+ "\n",
|
|
|
+ "We got:\n",
|
|
|
+ "\n",
|
|
|
+ "```text\n",
|
|
|
+ "0.7781209945678711\n",
|
|
|
+ "0.0003719329833984375\n",
|
|
|
+ "```\n",
|
|
|
+ "\n",
|
|
|
+ "We can see a significant improve in performance. If we divide _0.7781_ by _0.0002_ we see that the new method is about _2593_ times faster for this input size."
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "markdown",
|
|
|
+ "metadata": {},
|
|
|
+ "source": [
|
|
|
+ "# Finding Laptops Within a Budget\n",
|
|
|
+ "\n",
|
|
|
+ "Implement a method for finding the range of indexes of laptops that fall within a budget."
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "code",
|
|
|
+ "execution_count": 10,
|
|
|
+ "metadata": {},
|
|
|
+ "outputs": [
|
|
|
+ {
|
|
|
+ "name": "stdout",
|
|
|
+ "output_type": "stream",
|
|
|
+ "text": [
|
|
|
+ "683\n",
|
|
|
+ "-1\n"
|
|
|
+ ]
|
|
|
+ }
|
|
|
+ ],
|
|
|
+ "source": [
|
|
|
+ "def row_price(row):\n",
|
|
|
+ " return row[-1]\n",
|
|
|
+ "\n",
|
|
|
+ "class Inventory(): \n",
|
|
|
+ " \n",
|
|
|
+ " def __init__(self, csv_filename):\n",
|
|
|
+ " with open(csv_filename) as f: \n",
|
|
|
+ " reader = csv.reader(f)\n",
|
|
|
+ " rows = list(reader)\n",
|
|
|
+ " self.header = rows[0] \n",
|
|
|
+ " self.rows = rows[1:]\n",
|
|
|
+ " for row in self.rows: \n",
|
|
|
+ " row[-1] = int(row[-1])\n",
|
|
|
+ " self.id_to_row = {} \n",
|
|
|
+ " for row in self.rows: \n",
|
|
|
+ " self.id_to_row[row[0]] = row\n",
|
|
|
+ " self.prices = set() \n",
|
|
|
+ " for row in self.rows: \n",
|
|
|
+ " self.prices.add(row[-1])\n",
|
|
|
+ " self.rows_by_price = sorted(self.rows, key=row_price) # Step 1\n",
|
|
|
+ " \n",
|
|
|
+ " def get_laptop_from_id(self, laptop_id):\n",
|
|
|
+ " for row in self.rows: \n",
|
|
|
+ " if row[0] == laptop_id:\n",
|
|
|
+ " return row\n",
|
|
|
+ " return None \n",
|
|
|
+ " \n",
|
|
|
+ " def get_laptop_from_id_fast(self, laptop_id): \n",
|
|
|
+ " if laptop_id in self.id_to_row: \n",
|
|
|
+ " return self.id_to_row[laptop_id]\n",
|
|
|
+ " return None\n",
|
|
|
+ "\n",
|
|
|
+ " def check_promotion_dollars(self, dollars): \n",
|
|
|
+ " for row in self.rows: \n",
|
|
|
+ " if row[-1] == dollars:\n",
|
|
|
+ " return True\n",
|
|
|
+ " for row1 in self.rows: \n",
|
|
|
+ " for row2 in self.rows:\n",
|
|
|
+ " if row1[-1] + row2[-1] == dollars:\n",
|
|
|
+ " return True\n",
|
|
|
+ " return False \n",
|
|
|
+ " \n",
|
|
|
+ " def check_promotion_dollars_fast(self, dollars):\n",
|
|
|
+ " if dollars in self.prices: \n",
|
|
|
+ " return True\n",
|
|
|
+ " for price in self.prices: \n",
|
|
|
+ " if dollars - price in self.prices:\n",
|
|
|
+ " return True\n",
|
|
|
+ " return False \n",
|
|
|
+ " \n",
|
|
|
+ " def find_laptop_with_price(self, target_price):\n",
|
|
|
+ " range_start = 0 \n",
|
|
|
+ " range_end = len(self.rows_by_price) - 1 \n",
|
|
|
+ " while range_start < range_end:\n",
|
|
|
+ " range_middle = (range_end + range_start) // 2 \n",
|
|
|
+ " value = self.rows_by_price[range_middle][-1]\n",
|
|
|
+ " if value == target_price: \n",
|
|
|
+ " return range_middle \n",
|
|
|
+ " elif value < target_price: \n",
|
|
|
+ " range_start = range_middle + 1 \n",
|
|
|
+ " else: \n",
|
|
|
+ " range_end = range_middle - 1 \n",
|
|
|
+ " if self.rows_by_price[range_start][-1] != target_price: \n",
|
|
|
+ " return -1 \n",
|
|
|
+ " return range_start\n",
|
|
|
+ " \n",
|
|
|
+ " def find_first_laptop_more_expensive(self, target_price): # Step 2\n",
|
|
|
+ " range_start = 0 \n",
|
|
|
+ " range_end = len(self.rows_by_price) - 1 \n",
|
|
|
+ " while range_start < range_end:\n",
|
|
|
+ " range_middle = (range_end + range_start) // 2 \n",
|
|
|
+ " price = self.rows_by_price[range_middle][-1]\n",
|
|
|
+ " if price > target_price:\n",
|
|
|
+ " range_end = range_middle\n",
|
|
|
+ " else:\n",
|
|
|
+ " range_start = range_middle + 1\n",
|
|
|
+ " if self.rows_by_price[range_start][-1] <= target_price: \n",
|
|
|
+ " return -1 \n",
|
|
|
+ " return range_start\n",
|
|
|
+ "\n",
|
|
|
+ "inventory = Inventory('laptops.csv') # Step 3 \n",
|
|
|
+ "print(inventory.find_first_laptop_more_expensive(1000)) # Step 4\n",
|
|
|
+ "print(inventory.find_first_laptop_more_expensive(10000)) # Step 5\n"
|
|
|
+ ]
|
|
|
+ }
|
|
|
+ ],
|
|
|
+ "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": "3.7.4"
|
|
|
+ }
|
|
|
+ },
|
|
|
+ "nbformat": 4,
|
|
|
+ "nbformat_minor": 2
|
|
|
+}
|
Christian Pascual