{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Guided Project Solution: Building Fast Queries on a CSV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading the Inventory\n",
    "\n",
    "Use the `csv` module to read the `laptops.csv` file and separate the header from the rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Id', 'Company', 'Product', 'TypeName', 'Inches', 'ScreenResolution', 'Cpu', 'Ram', 'Memory', 'Gpu', 'OpSys', 'Weight', 'Price']\n",
      "['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",
      "['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",
      "['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",
      "['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",
      "['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"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "\n",
    "with open('laptops.csv') as f:\n",
    "    reader = csv.reader(f)\n",
    "    rows = list(reader)\n",
    "    header = rows[0]\n",
    "    rows = rows[1:]\n",
    "    \n",
    "print(header)\n",
    "for i in range(5):\n",
    "    print(rows[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inventory Class\n",
    "\n",
    "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`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Id', 'Company', 'Product', 'TypeName', 'Inches', 'ScreenResolution', 'Cpu', 'Ram', 'Memory', 'Gpu', 'OpSys', 'Weight', 'Price']\n",
      "1303\n"
     ]
    }
   ],
   "source": [
    "class Inventory():                    # step 1\n",
    "    \n",
    "    def __init__(self, csv_filename): # step 2\n",
    "        with open(csv_filename) as f: # step 3\n",
    "            reader = csv.reader(f)\n",
    "            rows = list(reader)\n",
    "        self.header = rows[0]         # step 4\n",
    "        self.rows = rows[1:]\n",
    "        for row in self.rows:         # step 5\n",
    "            row[-1] = int(row[-1])\n",
    "\n",
    "inventory = Inventory('laptops.csv')  # step 6\n",
    "print(inventory.header)               # step 7\n",
    "print(len(inventory.rows))            # step 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding a Laptop From the Id\n",
    "\n",
    "Implement a `get_laptop_from_id()` function that given a laptop identifier find the row corresponding to that laptop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv                            \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",
    "            \n",
    "    def get_laptop_from_id(self, laptop_id):   # step 1\n",
    "        for row in self.rows:                  # step 2\n",
    "            if row[0] == laptop_id:\n",
    "                return row\n",
    "        return None                            # step 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['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",
      "None\n"
     ]
    }
   ],
   "source": [
    "inventory = Inventory('laptops.csv')           # step 4\n",
    "print(inventory.get_laptop_from_id('3362737')) # step 5\n",
    "print(inventory.get_laptop_from_id('3362736')) # step 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Improving Id Lookups\n",
    "\n",
    "Improve the time complexity of finding a laptop with a given id by precomputing a dictionary that maps laptop ids to rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv                            \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 = {}                         # step 1\n",
    "        for row in self.rows:                       # step 2\n",
    "            self.id_to_row[row[0]] = row \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):   # step 3\n",
    "        if laptop_id in self.id_to_row:             # step 4\n",
    "            return self.id_to_row[laptop_id]\n",
    "        return None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test the code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['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",
      "None\n"
     ]
    }
   ],
   "source": [
    "inventory = Inventory('laptops.csv')                # step 5\n",
    "print(inventory.get_laptop_from_id_fast('3362737')) # step 6\n",
    "print(inventory.get_laptop_from_id_fast('3362736')) # step 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing Performance\n",
    "\n",
    "Compare the performance of both function for id lookup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5494911670684814\n",
      "0.002789735794067383\n"
     ]
    }
   ],
   "source": [
    "import time                                                         # step 1\n",
    "import random                                                       # step 2\n",
    "\n",
    "ids = [str(random.randint(1000000, 9999999)) for _ in range(10000)] # step 3\n",
    "\n",
    "inventory = Inventory('laptops.csv')                                # step 4\n",
    "\n",
    "total_time_no_dict = 0                                              # step 5\n",
    "for identifier in ids:                                              # step 6\n",
    "    start = time.time()                                             # step 6.1\n",
    "    inventory.get_laptop_from_id(identifier)                        # step 6.2\n",
    "    end = time.time()                                               # step 6.3\n",
    "    total_time_no_dict += end - start                               # step 6.4\n",
    "    \n",
    "total_time_dict = 0                                                 # step 7\n",
    "for identifier in ids:                                              # step 8\n",
    "    start = time.time()                                             # step 8.1\n",
    "    inventory.get_laptop_from_id_fast(identifier)                   # step 8.2\n",
    "    end = time.time()                                               # step 8.3\n",
    "    total_time_dict += end - start                                  # step 8.4\n",
    "    \n",
    "print(total_time_no_dict)                                           # step 9\n",
    "print(total_time_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis\n",
    "\n",
    "We got:\n",
    "\n",
    "```text\n",
    "0.5884554386138916\n",
    "0.0024595260620117188\n",
    "```\n",
    "\n",
    "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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Two Laptop Promotion\n",
    "\n",
    "Write a method that finds whether we can spend a given amount of money by purchasing either one or two laptops."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv                            \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",
    "    \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):    # step 1\n",
    "        for row in self.rows:                      # step 2\n",
    "            if row[-1] == dollars:\n",
    "                return True\n",
    "        for row1 in self.rows:                     # step 3\n",
    "            for row2 in self.rows:\n",
    "                if row1[-1] + row2[-1] == dollars:\n",
    "                    return True\n",
    "        return False                               # step 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "inventory = Inventory('laptops.csv')               # step 5\n",
    "print(inventory.check_promotion_dollars(1000))     # step 6\n",
    "print(inventory.check_promotion_dollars(442))      # step 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optimizing Laptop Promotion\n",
    "\n",
    "Create a faster version of the promotion method by using the techniques we've learned in the course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv                            \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()                          # step 1\n",
    "        for row in self.rows:                        # step 2\n",
    "            self.prices.add(row[-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): # 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": 6,
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7781209945678711\n",
      "0.0003719329833984375\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_dict = 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_dict += end - start                    # step 4.4\n",
    "    \n",
    "total_time_dict = 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_dict += end - start                       # step 6.4\n",
    "    \n",
    "print(total_time_no_dict)                                # step 7\n",
    "print(total_time_dict)"
   ]
  },
  {
   "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": [
    "import csv                            \n",
    "\n",
    "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"
   ]
  }
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