Working with data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a histogram of 1D data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import List, Dict\n",
    "from collections import Counter\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def bucketize(point: float, bucket_size: float) -> float:\n",
    "    #Floor the point to the next lower multiple of bucket size\n",
    "    return bucket_size*math.floor(point/bucket_size)\n",
    "\n",
    "def make_histogram(points:List[float], bucket_size: float) -> Dict[float,int]:\n",
    "    #Buckets the points and counts how many in each bucket\n",
    "    return Counter(bucketize(point, bucket_size) for point in points)\n",
    "\n",
    "def plot_histogram (points:List[float], bucket_size: float, title: str = \"\"):\n",
    "    histogram = make_histogram(points, bucket_size)\n",
    "    plt.bar(histogram.keys(), histogram.values(), width = bucket_size)\n",
    "    plt.title(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data\n",
    "import random\n",
    "from Probability import inverse_normal_cdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Histogram plotting with data of different distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(0)\n",
    "\n",
    "# Data 1\n",
    "#uniform between -100 and 100\n",
    "uniform = [200*random.random() -100 for _ in range(10000)]\n",
    "\n",
    "#Data 2\n",
    "#normal distrinution with mean 0, standard deviation 57\n",
    "normal = [57*inverse_normal_cdf(random.random())\n",
    "         for _ in range(10000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Data 1\n",
    "plot_histogram(uniform, 10, \"Uniform Histogram\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Data 2\n",
    "plot_histogram(normal, 10, \"Normal Histogram\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2 Dimensional data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def random_normal() -> float:\n",
    "    # Returns a random draw from a standard normal distribution\n",
    "    return inverse_normal_cdf(random.random())\n",
    "\n",
    "xs = [random_normal() for _ in range(1000)]\n",
    "ys1 = [ x+random_normal()/2 for x in xs]\n",
    "ys2 = [-x+random_normal()/2 for x in xs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Data 1\n",
    "plot_histogram(ys1, 10, \"Normal Histogram\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Data 2\n",
    "plot_histogram(ys2, 10, \"Normal Histogram\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(xs,ys1, marker = '.', color = 'black', label ='ys1')\n",
    "plt.scatter(xs,ys2, marker = '.', color = 'red', label ='ys2')\n",
    "plt.xlabel('xs')\n",
    "plt.ylabel('ys')\n",
    "plt.legend(loc = 9)\n",
    "plt.title('Very different joint distributions')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9010493686379609\n",
      "-0.8920981526880033\n"
     ]
    }
   ],
   "source": [
    "# Difference between ys1 and ys2 would be apparent through correlations\n",
    "from Statistics import correlation\n",
    "print(correlation(xs,ys1)) # about 0.9\n",
    "print(correlation(xs,ys2)) # about -0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# corr_data is a list of four 100-d vectors\n",
    "# corr_data = [[random.random() for _ in range(100)] for _ in range(4)]\n",
    "num_points = 100\n",
    "\n",
    "def random_row() -> List[float]:\n",
    "    row = [0.0, 0, 0, 0]\n",
    "    row[0] = random_normal()\n",
    "    row[1] = -5 * row[0] + random_normal()\n",
    "    row[2] = row[0] + row[1] + 5 * random_normal()\n",
    "    row[3] = 6 if row[2] > -2 else 0\n",
    "    return row\n",
    "\n",
    "random.seed(0)\n",
    "# each row has 4 points, but really we want the columns\n",
    "corr_rows = [random_row() for _ in range(num_points)]\n",
    "\n",
    "corr_data = [list(col) for col in zip(*corr_rows)]\n",
    "\n",
    "num_vectors = len(corr_data)\n",
    "\n",
    "fig, ax = plt.subplots(num_vectors, num_vectors)\n",
    "\n",
    "for i in range(num_vectors):\n",
    "    for j in range(num_vectors):\n",
    "        # Scatter column_j on the x-axis vs. column on the y-axis\n",
    "        if i != j: ax[i][j].scatter(corr_data[j], corr_data[i])\n",
    "        # Unless i == j, in which case show the series name\n",
    "        else: ax[i][j].annotate(\"series\" + str(i), (0.5,0.5),\n",
    "                               xycoords = 'axes fraction',\n",
    "                               ha = \"center\", va = \"center\")\n",
    "            \n",
    "# Fix the bottom right and top left axis labels, which are wrong because\n",
    "    # their charts only have text in them\n",
    "ax[-1][-1].set_xlim(ax[0][-1].get_xlim())\n",
    "ax[0][0].set_ylim(ax[0][1].get_ylim())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'closing_price': 102.06, 'date': datetime.date(2014, 8, 29), 'symbol': 'AAPL'}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create dictionary\n",
    "import datetime\n",
    "stock_price = {'closing_price': 102.06,\n",
    "               'date':datetime.date(2014,8,29),\n",
    "               'symbol':'AAPL'}\n",
    "stock_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StockPrice(symbol='MSFT', date=datetime.date(2018, 12, 14), closing_price=106.03)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To avoid confusions and typos, use namedtuple\n",
    "from collections import namedtuple\n",
    "\n",
    "StockPrice = namedtuple('StockPrice', ['symbol','date','closing_price']) \n",
    "price = StockPrice('MSFT',datetime.date(2018,12,14), 106.03)\n",
    "price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-28-f4159df35007>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Immutable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0mprice\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msymbol\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'MSFT'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0mprice\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclosing_price\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m10000\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mprice\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Immutable\n",
    "assert price.symbol == 'MSFT'\n",
    "assert price.closing_price == 10000\n",
    "price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataclasses are a mutable version of NamedTuple\n",
    "from typing import NamedTuple\n",
    "class StockPrice(NamedTuple):\n",
    "    symbol: str\n",
    "    date: datetime.date\n",
    "    closing_price: float\n",
    "        \n",
    "    def is_high_tech(self) -> bool:\n",
    "        return self.symbol in ['MSFT','GOOG','FB','AMZN','AAPL']\n",
    "    \n",
    "price = StockPrice('MSFT', datetime.date(2018,12, 14), 106.3)\n",
    "\n",
    "assert price.symbol == 'MSFT'\n",
    "assert price.closing_price == 106.3\n",
    "assert price.is_high_tech()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StockPrice(symbol='MSFT', date=datetime.date(2018, 12, 14), closing_price=106.3)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dateutil.parser import parse\n",
    "\n",
    "def parse_row(row:List[str])-> StockPrice:\n",
    "    symbol,date,closing_price = row\n",
    "    return StockPrice(symbol = symbol,\n",
    "                      date = parse(date).date(),\n",
    "                      closing_price = float(closing_price))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now test the function\n",
    "stock = parse_row(['MSFT',\"2018-12-14\", \"106.03\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If there's a bad data. For example: a float value that doesn't actually represent a number.\n",
    "# It may return None and crash the program\n",
    "\n",
    "from typing import Optional\n",
    "import re\n",
    "\n",
    "def try_parse_row(row: List[str]) -> Optional[StockPrice]:\n",
    "    symbol, date_, closing_price_ = row\n",
    "\n",
    "    # Stock symbol should be all capital letters\n",
    "    if not re.match(r\"^[A-Z]+$\", symbol):\n",
    "        return None\n",
    "\n",
    "    try:\n",
    "        date = parse(date_).date()\n",
    "    except ValueError:\n",
    "        return None\n",
    "\n",
    "    try:\n",
    "        closing_price = float(closing_price_)\n",
    "    except ValueError:\n",
    "        return None\n",
    "\n",
    "    return StockPrice(symbol, date, closing_price)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert try_parse_row(['MSFT0',\"2018-12-14\", \"106.03\"]) is None\n",
    "assert try_parse_row(['MSFT',\"2018-12--14\", \"106.03\"]) is None\n",
    "assert try_parse_row(['MSFT',\"2018-12-14\", \"x\"]) is None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[StockPrice(symbol='MSFT', date=datetime.date(2000, 1, 1), closing_price=39.81),\n",
       " StockPrice(symbol='MSFT', date=datetime.date(2000, 2, 1), closing_price=36.35),\n",
       " StockPrice(symbol='MSFT', date=datetime.date(2000, 3, 1), closing_price=43.22),\n",
       " StockPrice(symbol='MSFT', date=datetime.date(2000, 4, 1), closing_price=28.37),\n",
       " ..........................................,\n",
       " ..........................................,\n",
       " ..........................................,\n",
       " ..........................................,\n",
       " StockPrice(symbol='AAPL', date=datetime.date(2010, 2, 1), closing_price=204.62),\n",
       " StockPrice(symbol='AAPL', date=datetime.date(2010, 3, 1), closing_price=223.02)]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dateutil.parser import parse\n",
    "import csv\n",
    "\n",
    "with open(\"stocks.csv\", \"r\") as f:\n",
    "    reader = csv.DictReader(f)\n",
    "    rows = [[row['symbol'], row['date'], row['price']]\n",
    "            for row in reader]\n",
    "\n",
    "# skip header\n",
    "maybe_data = [try_parse_row(row) for row in rows]\n",
    "\n",
    "# Make sure they all loaded successfully:\n",
    "assert maybe_data\n",
    "assert all(sp is not None for sp in maybe_data)\n",
    "\n",
    "# This is just to make mypy happy\n",
    "data = [sp for sp in maybe_data if sp is not None]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "223.02\n"
     ]
    }
   ],
   "source": [
    "max_aapl_price = max(stock_price.closing_price \n",
    "                     for stock_price in data \n",
    "                     if stock_price.symbol == 'AAPL')\n",
    "print(max_aapl_price)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(<function __main__.<lambda>()>,\n",
       "            {'MSFT': 43.22,\n",
       "             'AMZN': 135.91,\n",
       "             'IBM': 130.32,\n",
       "             'GOOG': 707.0,\n",
       "             'AAPL': 223.02})"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "max_prices: Dict[str, float] = defaultdict(lambda: float('-inf'))\n",
    "    \n",
    "for sp in data:\n",
    "    symbol, closing_price = sp.symbol, sp.closing_price\n",
    "    \n",
    "    if closing_price > max_prices[symbol]:\n",
    "        max_prices[symbol] = closing_price\n",
    "max_prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'MSFT': [StockPrice(symbol='MSFT', date=datetime.date(2000, 1, 1), closing_price=39.81), StockPrice(symbol='MSFT', date=datetime.date(2000, 2, 1), closing_price=36.35), StockPrice(symbol='MSFT', date=datetime.date(2000, 3, 1), closing_price=43.22), StockPrice(symbol='MSFT', date=datetime.date(2000, 4, 1), closing_price=28.37), StockPrice(symbol='MSFT', date=datetime.date(2000, 5, 1), closing_price=25.45), StockPrice(symbol='MSFT', date=datetime.date(2000, 6, 1), closing_price=32.54), StockPrice(symbol='MSFT', date=datetime.date(2000, 7, 1), closing_price=28.4), StockPrice(symbol='MSFT', date=datetime.date(2000, 8, 1), closing_price=28.4), StockPrice(symbol='MSFT', date=datetime.date(2000, 9, 1), closing_price=24.53), StockPrice(symbol='MSFT', date=datetime.date(2000, 10, 1), closing_price=28.02), StockPrice(symbol='MSFT', date=datetime.date(2000, 11, 1), closing_price=23.34), StockPrice(symbol='MSFT', date=datetime.date(2000, 12, 1), closing_price=17.65), StockPrice(symbol='MSFT', date=datetime.date(2001, 1, 1), closing_price=24.84), StockPrice(symbol='MSFT', date=datetime.date(2001, 2, 1), closing_price=24.0), StockPrice(symbol='MSFT', date=datetime.date(2001, 3, 1), closing_price=22.25), StockPrice(symbol='MSFT', date=datetime.date(2001, 4, 1), closing_price=27.56), StockPrice(symbol='MSFT', date=datetime.date(2001, 5, 1), closing_price=28.14), StockPrice(symbol='MSFT', date=datetime.date(2001, 6, 1), closing_price=29.7), StockPrice(symbol='MSFT', date=datetime.date(2001, 7, 1), closing_price=26.93), StockPrice(symbol='MSFT', date=datetime.date(2001, 8, 1), closing_price=23.21), StockPrice(symbol='MSFT', date=datetime.date(2001, 9, 1), closing_price=20.82), StockPrice(symbol='MSFT', date=datetime.date(2001, 10, 1), closing_price=23.65), StockPrice(symbol='MSFT', date=datetime.date(2001, 11, 1), closing_price=26.12), StockPrice(symbol='MSFT', date=datetime.date(2001, 12, 1), closing_price=26.95), StockPrice(symbol='MSFT', date=datetime.date(2002, 1, 1), closing_price=25.92), StockPrice(symbol='MSFT', date=datetime.date(2002, 2, 1), closing_price=23.73), StockPrice(symbol='MSFT', date=datetime.date(2002, 3, 1), closing_price=24.53), StockPrice(symbol='MSFT', date=datetime.date(2002, 4, 1), closing_price=21.26), StockPrice(symbol='MSFT', date=datetime.date(2002, 5, 1), closing_price=20.71), StockPrice(symbol='MSFT', date=datetime.date(2002, 6, 1), closing_price=22.25), StockPrice(symbol='MSFT', date=datetime.date(2002, 7, 1), closing_price=19.52), StockPrice(symbol='MSFT', date=datetime.date(2002, 8, 1), closing_price=19.97), StockPrice(symbol='MSFT', date=datetime.date(2002, 9, 1), closing_price=17.79), StockPrice(symbol='MSFT', date=datetime.date(2002, 10, 1), closing_price=21.75), StockPrice(symbol='MSFT', date=datetime.date(2002, 11, 1), closing_price=23.46), StockPrice(symbol='MSFT', date=datetime.date(2002, 12, 1), closing_price=21.03), StockPrice(symbol='MSFT', date=datetime.date(2003, 1, 1), closing_price=19.31), StockPrice(symbol='MSFT', date=datetime.date(2003, 2, 1), closing_price=19.34), StockPrice(symbol='MSFT', date=datetime.date(2003, 3, 1), closing_price=19.76), StockPrice(symbol='MSFT', date=datetime.date(2003, 4, 1), closing_price=20.87), StockPrice(symbol='MSFT', date=datetime.date(2003, 5, 1), closing_price=20.09), StockPrice(symbol='MSFT', date=datetime.date(2003, 6, 1), closing_price=20.93), StockPrice(symbol='MSFT', date=datetime.date(2003, 7, 1), closing_price=21.56), StockPrice(symbol='MSFT', date=datetime.date(2003, 8, 1), closing_price=21.65), StockPrice(symbol='MSFT', date=datetime.date(2003, 9, 1), closing_price=22.69), StockPrice(symbol='MSFT', date=datetime.date(2003, 10, 1), closing_price=21.45), StockPrice(symbol='MSFT', date=datetime.date(2003, 11, 1), closing_price=21.1), StockPrice(symbol='MSFT', date=datetime.date(2003, 12, 1), closing_price=22.46), StockPrice(symbol='MSFT', date=datetime.date(2004, 1, 1), closing_price=22.69), StockPrice(symbol='MSFT', date=datetime.date(2004, 2, 1), closing_price=21.77), StockPrice(symbol='MSFT', date=datetime.date(2004, 3, 1), closing_price=20.46), StockPrice(symbol='MSFT', date=datetime.date(2004, 4, 1), closing_price=21.45), StockPrice(symbol='MSFT', date=datetime.date(2004, 5, 1), closing_price=21.53), StockPrice(symbol='MSFT', date=datetime.date(2004, 6, 1), closing_price=23.44), StockPrice(symbol='MSFT', date=datetime.date(2004, 7, 1), closing_price=23.38), StockPrice(symbol='MSFT', date=datetime.date(2004, 8, 1), closing_price=22.47), StockPrice(symbol='MSFT', date=datetime.date(2004, 9, 1), closing_price=22.76), StockPrice(symbol='MSFT', date=datetime.date(2004, 10, 1), closing_price=23.02), StockPrice(symbol='MSFT', date=datetime.date(2004, 11, 1), closing_price=24.6), StockPrice(symbol='MSFT', date=datetime.date(2004, 12, 1), closing_price=24.52), StockPrice(symbol='MSFT', date=datetime.date(2005, 1, 1), closing_price=24.11), StockPrice(symbol='MSFT', date=datetime.date(2005, 2, 1), closing_price=23.15), StockPrice(symbol='MSFT', date=datetime.date(2005, 3, 1), closing_price=22.24), StockPrice(symbol='MSFT', date=datetime.date(2005, 4, 1), closing_price=23.28), StockPrice(symbol='MSFT', date=datetime.date(2005, 5, 1), closing_price=23.82), StockPrice(symbol='MSFT', date=datetime.date(2005, 6, 1), closing_price=22.93), StockPrice(symbol='MSFT', date=datetime.date(2005, 7, 1), closing_price=23.64), StockPrice(symbol='MSFT', date=datetime.date(2005, 8, 1), closing_price=25.35), StockPrice(symbol='MSFT', date=datetime.date(2005, 9, 1), closing_price=23.83), StockPrice(symbol='MSFT', date=datetime.date(2005, 10, 1), closing_price=23.8), StockPrice(symbol='MSFT', date=datetime.date(2005, 11, 1), closing_price=25.71), StockPrice(symbol='MSFT', date=datetime.date(2005, 12, 1), closing_price=24.29), StockPrice(symbol='MSFT', date=datetime.date(2006, 1, 1), closing_price=26.14), StockPrice(symbol='MSFT', date=datetime.date(2006, 2, 1), closing_price=25.04), StockPrice(symbol='MSFT', date=datetime.date(2006, 3, 1), closing_price=25.36), StockPrice(symbol='MSFT', date=datetime.date(2006, 4, 1), closing_price=22.5), StockPrice(symbol='MSFT', date=datetime.date(2006, 5, 1), closing_price=21.19), StockPrice(symbol='MSFT', date=datetime.date(2006, 6, 1), closing_price=21.8), StockPrice(symbol='MSFT', date=datetime.date(2006, 7, 1), closing_price=22.51), StockPrice(symbol='MSFT', date=datetime.date(2006, 8, 1), closing_price=24.13), StockPrice(symbol='MSFT', date=datetime.date(2006, 9, 1), closing_price=25.68), StockPrice(symbol='MSFT', date=datetime.date(2006, 10, 1), closing_price=26.96), StockPrice(symbol='MSFT', date=datetime.date(2006, 11, 1), closing_price=27.66), StockPrice(symbol='MSFT', date=datetime.date(2006, 12, 1), closing_price=28.13), StockPrice(symbol='MSFT', date=datetime.date(2007, 1, 1), closing_price=29.07), StockPrice(symbol='MSFT', date=datetime.date(2007, 2, 1), closing_price=26.63), StockPrice(symbol='MSFT', date=datetime.date(2007, 3, 1), closing_price=26.35), StockPrice(symbol='MSFT', date=datetime.date(2007, 4, 1), closing_price=28.3), StockPrice(symbol='MSFT', date=datetime.date(2007, 5, 1), closing_price=29.11), StockPrice(symbol='MSFT', date=datetime.date(2007, 6, 1), closing_price=27.95), StockPrice(symbol='MSFT', date=datetime.date(2007, 7, 1), closing_price=27.5), StockPrice(symbol='MSFT', date=datetime.date(2007, 8, 1), closing_price=27.34), StockPrice(symbol='MSFT', date=datetime.date(2007, 9, 1), closing_price=28.04), StockPrice(symbol='MSFT', date=datetime.date(2007, 10, 1), closing_price=35.03), StockPrice(symbol='MSFT', date=datetime.date(2007, 11, 1), closing_price=32.09), StockPrice(symbol='MSFT', date=datetime.date(2007, 12, 1), closing_price=34.0), StockPrice(symbol='MSFT', date=datetime.date(2008, 1, 1), closing_price=31.13), StockPrice(symbol='MSFT', date=datetime.date(2008, 2, 1), closing_price=26.07), StockPrice(symbol='MSFT', date=datetime.date(2008, 3, 1), closing_price=27.21), StockPrice(symbol='MSFT', date=datetime.date(2008, 4, 1), closing_price=27.34), StockPrice(symbol='MSFT', date=datetime.date(2008, 5, 1), closing_price=27.25), StockPrice(symbol='MSFT', date=datetime.date(2008, 6, 1), closing_price=26.47), StockPrice(symbol='MSFT', date=datetime.date(2008, 7, 1), closing_price=24.75), StockPrice(symbol='MSFT', date=datetime.date(2008, 8, 1), closing_price=26.36), StockPrice(symbol='MSFT', date=datetime.date(2008, 9, 1), closing_price=25.78), StockPrice(symbol='MSFT', date=datetime.date(2008, 10, 1), closing_price=21.57), StockPrice(symbol='MSFT', date=datetime.date(2008, 11, 1), closing_price=19.66), StockPrice(symbol='MSFT', date=datetime.date(2008, 12, 1), closing_price=18.91), StockPrice(symbol='MSFT', date=datetime.date(2009, 1, 1), closing_price=16.63), StockPrice(symbol='MSFT', date=datetime.date(2009, 2, 1), closing_price=15.81), StockPrice(symbol='MSFT', date=datetime.date(2009, 3, 1), closing_price=17.99), StockPrice(symbol='MSFT', date=datetime.date(2009, 4, 1), closing_price=19.84), StockPrice(symbol='MSFT', date=datetime.date(2009, 5, 1), closing_price=20.59), StockPrice(symbol='MSFT', date=datetime.date(2009, 6, 1), closing_price=23.42), StockPrice(symbol='MSFT', date=datetime.date(2009, 7, 1), closing_price=23.18), StockPrice(symbol='MSFT', date=datetime.date(2009, 8, 1), closing_price=24.43), StockPrice(symbol='MSFT', date=datetime.date(2009, 9, 1), closing_price=25.49), StockPrice(symbol='MSFT', date=datetime.date(2009, 10, 1), closing_price=27.48), StockPrice(symbol='MSFT', date=datetime.date(2009, 11, 1), closing_price=29.27), StockPrice(symbol='MSFT', date=datetime.date(2009, 12, 1), closing_price=30.34), StockPrice(symbol='MSFT', date=datetime.date(2010, 1, 1), closing_price=28.05), StockPrice(symbol='MSFT', date=datetime.date(2010, 2, 1), closing_price=28.67), StockPrice(symbol='MSFT', date=datetime.date(2010, 3, 1), closing_price=28.8)], 'AMZN': [StockPrice(symbol='AMZN', date=datetime.date(2000, 1, 1), closing_price=64.56), StockPrice(symbol='AMZN', date=datetime.date(2000, 2, 1), closing_price=68.87), StockPrice(symbol='AMZN', date=datetime.date(2000, 3, 1), closing_price=67.0), StockPrice(symbol='AMZN', date=datetime.date(2000, 4, 1), closing_price=55.19), StockPrice(symbol='AMZN', date=datetime.date(2000, 5, 1), closing_price=48.31), StockPrice(symbol='AMZN', date=datetime.date(2000, 6, 1), closing_price=36.31), StockPrice(symbol='AMZN', date=datetime.date(2000, 7, 1), closing_price=30.12), StockPrice(symbol='AMZN', date=datetime.date(2000, 8, 1), closing_price=41.5), StockPrice(symbol='AMZN', date=datetime.date(2000, 9, 1), closing_price=38.44), StockPrice(symbol='AMZN', date=datetime.date(2000, 10, 1), closing_price=36.62), StockPrice(symbol='AMZN', date=datetime.date(2000, 11, 1), closing_price=24.69), StockPrice(symbol='AMZN', date=datetime.date(2000, 12, 1), closing_price=15.56), StockPrice(symbol='AMZN', date=datetime.date(2001, 1, 1), closing_price=17.31), StockPrice(symbol='AMZN', date=datetime.date(2001, 2, 1), closing_price=10.19), StockPrice(symbol='AMZN', date=datetime.date(2001, 3, 1), closing_price=10.23), StockPrice(symbol='AMZN', date=datetime.date(2001, 4, 1), closing_price=15.78), StockPrice(symbol='AMZN', date=datetime.date(2001, 5, 1), closing_price=16.69), StockPrice(symbol='AMZN', date=datetime.date(2001, 6, 1), closing_price=14.15), StockPrice(symbol='AMZN', date=datetime.date(2001, 7, 1), closing_price=12.49), StockPrice(symbol='AMZN', date=datetime.date(2001, 8, 1), closing_price=8.94), StockPrice(symbol='AMZN', date=datetime.date(2001, 9, 1), closing_price=5.97), StockPrice(symbol='AMZN', date=datetime.date(2001, 10, 1), closing_price=6.98), StockPrice(symbol='AMZN', date=datetime.date(2001, 11, 1), closing_price=11.32), StockPrice(symbol='AMZN', date=datetime.date(2001, 12, 1), closing_price=10.82), StockPrice(symbol='AMZN', date=datetime.date(2002, 1, 1), closing_price=14.19), StockPrice(symbol='AMZN', date=datetime.date(2002, 2, 1), closing_price=14.1), StockPrice(symbol='AMZN', date=datetime.date(2002, 3, 1), closing_price=14.3), StockPrice(symbol='AMZN', date=datetime.date(2002, 4, 1), closing_price=16.69), StockPrice(symbol='AMZN', date=datetime.date(2002, 5, 1), closing_price=18.23), StockPrice(symbol='AMZN', date=datetime.date(2002, 6, 1), closing_price=16.25), StockPrice(symbol='AMZN', date=datetime.date(2002, 7, 1), closing_price=14.45), StockPrice(symbol='AMZN', date=datetime.date(2002, 8, 1), closing_price=14.94), StockPrice(symbol='AMZN', date=datetime.date(2002, 9, 1), closing_price=15.93), StockPrice(symbol='AMZN', date=datetime.date(2002, 10, 1), closing_price=19.36), StockPrice(symbol='AMZN', date=datetime.date(2002, 11, 1), closing_price=23.35), StockPrice(symbol='AMZN', date=datetime.date(2002, 12, 1), closing_price=18.89), StockPrice(symbol='AMZN', date=datetime.date(2003, 1, 1), closing_price=21.85), StockPrice(symbol='AMZN', date=datetime.date(2003, 2, 1), closing_price=22.01), StockPrice(symbol='AMZN', date=datetime.date(2003, 3, 1), closing_price=26.03), StockPrice(symbol='AMZN', date=datetime.date(2003, 4, 1), closing_price=28.69), StockPrice(symbol='AMZN', date=datetime.date(2003, 5, 1), closing_price=35.89), StockPrice(symbol='AMZN', date=datetime.date(2003, 6, 1), closing_price=36.32), StockPrice(symbol='AMZN', date=datetime.date(2003, 7, 1), closing_price=41.64), StockPrice(symbol='AMZN', date=datetime.date(2003, 8, 1), closing_price=46.32), StockPrice(symbol='AMZN', date=datetime.date(2003, 9, 1), closing_price=48.43), StockPrice(symbol='AMZN', date=datetime.date(2003, 10, 1), closing_price=54.43), StockPrice(symbol='AMZN', date=datetime.date(2003, 11, 1), closing_price=53.97), StockPrice(symbol='AMZN', date=datetime.date(2003, 12, 1), closing_price=52.62), StockPrice(symbol='AMZN', date=datetime.date(2004, 1, 1), closing_price=50.4), StockPrice(symbol='AMZN', date=datetime.date(2004, 2, 1), closing_price=43.01), StockPrice(symbol='AMZN', date=datetime.date(2004, 3, 1), closing_price=43.28), StockPrice(symbol='AMZN', date=datetime.date(2004, 4, 1), closing_price=43.6), StockPrice(symbol='AMZN', date=datetime.date(2004, 5, 1), closing_price=48.5), StockPrice(symbol='AMZN', date=datetime.date(2004, 6, 1), closing_price=54.4), StockPrice(symbol='AMZN', date=datetime.date(2004, 7, 1), closing_price=38.92), StockPrice(symbol='AMZN', date=datetime.date(2004, 8, 1), closing_price=38.14), StockPrice(symbol='AMZN', date=datetime.date(2004, 9, 1), closing_price=40.86), StockPrice(symbol='AMZN', date=datetime.date(2004, 10, 1), closing_price=34.13), StockPrice(symbol='AMZN', date=datetime.date(2004, 11, 1), closing_price=39.68), StockPrice(symbol='AMZN', date=datetime.date(2004, 12, 1), closing_price=44.29), StockPrice(symbol='AMZN', date=datetime.date(2005, 1, 1), closing_price=43.22), StockPrice(symbol='AMZN', date=datetime.date(2005, 2, 1), closing_price=35.18), StockPrice(symbol='AMZN', date=datetime.date(2005, 3, 1), closing_price=34.27), StockPrice(symbol='AMZN', date=datetime.date(2005, 4, 1), closing_price=32.36), StockPrice(symbol='AMZN', date=datetime.date(2005, 5, 1), closing_price=35.51), StockPrice(symbol='AMZN', date=datetime.date(2005, 6, 1), closing_price=33.09), StockPrice(symbol='AMZN', date=datetime.date(2005, 7, 1), closing_price=45.15), StockPrice(symbol='AMZN', date=datetime.date(2005, 8, 1), closing_price=42.7), StockPrice(symbol='AMZN', date=datetime.date(2005, 9, 1), closing_price=45.3), StockPrice(symbol='AMZN', date=datetime.date(2005, 10, 1), closing_price=39.86), StockPrice(symbol='AMZN', date=datetime.date(2005, 11, 1), closing_price=48.46), StockPrice(symbol='AMZN', date=datetime.date(2005, 12, 1), closing_price=47.15), StockPrice(symbol='AMZN', date=datetime.date(2006, 1, 1), closing_price=44.82), StockPrice(symbol='AMZN', date=datetime.date(2006, 2, 1), closing_price=37.44), 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closing_price=536.12), StockPrice(symbol='GOOG', date=datetime.date(2009, 11, 1), closing_price=583.0), StockPrice(symbol='GOOG', date=datetime.date(2009, 12, 1), closing_price=619.98), StockPrice(symbol='GOOG', date=datetime.date(2010, 1, 1), closing_price=529.94), StockPrice(symbol='GOOG', date=datetime.date(2010, 2, 1), closing_price=526.8), StockPrice(symbol='GOOG', date=datetime.date(2010, 3, 1), closing_price=560.19)], 'AAPL': [StockPrice(symbol='AAPL', date=datetime.date(2000, 1, 1), closing_price=25.94), StockPrice(symbol='AAPL', date=datetime.date(2000, 2, 1), closing_price=28.66), StockPrice(symbol='AAPL', date=datetime.date(2000, 3, 1), closing_price=33.95), StockPrice(symbol='AAPL', date=datetime.date(2000, 4, 1), closing_price=31.01), StockPrice(symbol='AAPL', date=datetime.date(2000, 5, 1), closing_price=21.0), StockPrice(symbol='AAPL', date=datetime.date(2000, 6, 1), closing_price=26.19), StockPrice(symbol='AAPL', date=datetime.date(2000, 7, 1), closing_price=25.41), StockPrice(symbol='AAPL', date=datetime.date(2000, 8, 1), closing_price=30.47), StockPrice(symbol='AAPL', date=datetime.date(2000, 9, 1), closing_price=12.88), StockPrice(symbol='AAPL', date=datetime.date(2000, 10, 1), closing_price=9.78), StockPrice(symbol='AAPL', date=datetime.date(2000, 11, 1), closing_price=8.25), StockPrice(symbol='AAPL', date=datetime.date(2000, 12, 1), closing_price=7.44), StockPrice(symbol='AAPL', date=datetime.date(2001, 1, 1), closing_price=10.81), StockPrice(symbol='AAPL', date=datetime.date(2001, 2, 1), closing_price=9.12), StockPrice(symbol='AAPL', date=datetime.date(2001, 3, 1), closing_price=11.03), StockPrice(symbol='AAPL', date=datetime.date(2001, 4, 1), closing_price=12.74), StockPrice(symbol='AAPL', date=datetime.date(2001, 5, 1), closing_price=9.98), StockPrice(symbol='AAPL', date=datetime.date(2001, 6, 1), closing_price=11.62), StockPrice(symbol='AAPL', date=datetime.date(2001, 7, 1), closing_price=9.4), StockPrice(symbol='AAPL', date=datetime.date(2001, 8, 1), closing_price=9.27), StockPrice(symbol='AAPL', date=datetime.date(2001, 9, 1), closing_price=7.76), StockPrice(symbol='AAPL', date=datetime.date(2001, 10, 1), closing_price=8.78), StockPrice(symbol='AAPL', date=datetime.date(2001, 11, 1), closing_price=10.65), StockPrice(symbol='AAPL', date=datetime.date(2001, 12, 1), closing_price=10.95), StockPrice(symbol='AAPL', date=datetime.date(2002, 1, 1), closing_price=12.36), StockPrice(symbol='AAPL', date=datetime.date(2002, 2, 1), closing_price=10.85), StockPrice(symbol='AAPL', date=datetime.date(2002, 3, 1), closing_price=11.84), StockPrice(symbol='AAPL', date=datetime.date(2002, 4, 1), closing_price=12.14), StockPrice(symbol='AAPL', date=datetime.date(2002, 5, 1), closing_price=11.65), StockPrice(symbol='AAPL', date=datetime.date(2002, 6, 1), closing_price=8.86), StockPrice(symbol='AAPL', date=datetime.date(2002, 7, 1), closing_price=7.63), StockPrice(symbol='AAPL', date=datetime.date(2002, 8, 1), closing_price=7.38), StockPrice(symbol='AAPL', date=datetime.date(2002, 9, 1), closing_price=7.25), StockPrice(symbol='AAPL', date=datetime.date(2002, 10, 1), closing_price=8.03), StockPrice(symbol='AAPL', date=datetime.date(2002, 11, 1), closing_price=7.75), StockPrice(symbol='AAPL', date=datetime.date(2002, 12, 1), closing_price=7.16), StockPrice(symbol='AAPL', date=datetime.date(2003, 1, 1), closing_price=7.18), StockPrice(symbol='AAPL', date=datetime.date(2003, 2, 1), closing_price=7.51), StockPrice(symbol='AAPL', date=datetime.date(2003, 3, 1), closing_price=7.07), StockPrice(symbol='AAPL', date=datetime.date(2003, 4, 1), closing_price=7.11), StockPrice(symbol='AAPL', date=datetime.date(2003, 5, 1), closing_price=8.98), StockPrice(symbol='AAPL', date=datetime.date(2003, 6, 1), closing_price=9.53), StockPrice(symbol='AAPL', date=datetime.date(2003, 7, 1), closing_price=10.54), StockPrice(symbol='AAPL', date=datetime.date(2003, 8, 1), closing_price=11.31), StockPrice(symbol='AAPL', date=datetime.date(2003, 9, 1), closing_price=10.36), StockPrice(symbol='AAPL', date=datetime.date(2003, 10, 1), closing_price=11.44), StockPrice(symbol='AAPL', date=datetime.date(2003, 11, 1), closing_price=10.45), StockPrice(symbol='AAPL', date=datetime.date(2003, 12, 1), closing_price=10.69), StockPrice(symbol='AAPL', date=datetime.date(2004, 1, 1), closing_price=11.28), StockPrice(symbol='AAPL', date=datetime.date(2004, 2, 1), closing_price=11.96), StockPrice(symbol='AAPL', date=datetime.date(2004, 3, 1), closing_price=13.52), StockPrice(symbol='AAPL', date=datetime.date(2004, 4, 1), closing_price=12.89), StockPrice(symbol='AAPL', date=datetime.date(2004, 5, 1), closing_price=14.03), StockPrice(symbol='AAPL', date=datetime.date(2004, 6, 1), closing_price=16.27), StockPrice(symbol='AAPL', date=datetime.date(2004, 7, 1), closing_price=16.17), StockPrice(symbol='AAPL', date=datetime.date(2004, 8, 1), closing_price=17.25), StockPrice(symbol='AAPL', date=datetime.date(2004, 9, 1), closing_price=19.38), StockPrice(symbol='AAPL', date=datetime.date(2004, 10, 1), closing_price=26.2), StockPrice(symbol='AAPL', date=datetime.date(2004, 11, 1), closing_price=33.53), StockPrice(symbol='AAPL', date=datetime.date(2004, 12, 1), closing_price=32.2), StockPrice(symbol='AAPL', date=datetime.date(2005, 1, 1), closing_price=38.45), StockPrice(symbol='AAPL', date=datetime.date(2005, 2, 1), closing_price=44.86), StockPrice(symbol='AAPL', date=datetime.date(2005, 3, 1), closing_price=41.67), StockPrice(symbol='AAPL', date=datetime.date(2005, 4, 1), closing_price=36.06), StockPrice(symbol='AAPL', date=datetime.date(2005, 5, 1), closing_price=39.76), StockPrice(symbol='AAPL', date=datetime.date(2005, 6, 1), closing_price=36.81), StockPrice(symbol='AAPL', date=datetime.date(2005, 7, 1), closing_price=42.65), StockPrice(symbol='AAPL', date=datetime.date(2005, 8, 1), closing_price=46.89), StockPrice(symbol='AAPL', date=datetime.date(2005, 9, 1), closing_price=53.61), StockPrice(symbol='AAPL', date=datetime.date(2005, 10, 1), closing_price=57.59), StockPrice(symbol='AAPL', date=datetime.date(2005, 11, 1), closing_price=67.82), StockPrice(symbol='AAPL', date=datetime.date(2005, 12, 1), closing_price=71.89), StockPrice(symbol='AAPL', date=datetime.date(2006, 1, 1), closing_price=75.51), StockPrice(symbol='AAPL', date=datetime.date(2006, 2, 1), closing_price=68.49), StockPrice(symbol='AAPL', date=datetime.date(2006, 3, 1), closing_price=62.72), StockPrice(symbol='AAPL', date=datetime.date(2006, 4, 1), closing_price=70.39), StockPrice(symbol='AAPL', date=datetime.date(2006, 5, 1), closing_price=59.77), StockPrice(symbol='AAPL', date=datetime.date(2006, 6, 1), closing_price=57.27), StockPrice(symbol='AAPL', date=datetime.date(2006, 7, 1), closing_price=67.96), StockPrice(symbol='AAPL', date=datetime.date(2006, 8, 1), closing_price=67.85), StockPrice(symbol='AAPL', date=datetime.date(2006, 9, 1), closing_price=76.98), StockPrice(symbol='AAPL', date=datetime.date(2006, 10, 1), closing_price=81.08), StockPrice(symbol='AAPL', date=datetime.date(2006, 11, 1), closing_price=91.66), StockPrice(symbol='AAPL', date=datetime.date(2006, 12, 1), closing_price=84.84), StockPrice(symbol='AAPL', date=datetime.date(2007, 1, 1), closing_price=85.73), StockPrice(symbol='AAPL', date=datetime.date(2007, 2, 1), closing_price=84.61), StockPrice(symbol='AAPL', date=datetime.date(2007, 3, 1), closing_price=92.91), StockPrice(symbol='AAPL', date=datetime.date(2007, 4, 1), closing_price=99.8), StockPrice(symbol='AAPL', date=datetime.date(2007, 5, 1), closing_price=121.19), StockPrice(symbol='AAPL', date=datetime.date(2007, 6, 1), closing_price=122.04), StockPrice(symbol='AAPL', date=datetime.date(2007, 7, 1), closing_price=131.76), StockPrice(symbol='AAPL', date=datetime.date(2007, 8, 1), closing_price=138.48), StockPrice(symbol='AAPL', date=datetime.date(2007, 9, 1), closing_price=153.47), StockPrice(symbol='AAPL', date=datetime.date(2007, 10, 1), closing_price=189.95), StockPrice(symbol='AAPL', date=datetime.date(2007, 11, 1), closing_price=182.22), StockPrice(symbol='AAPL', date=datetime.date(2007, 12, 1), closing_price=198.08), StockPrice(symbol='AAPL', date=datetime.date(2008, 1, 1), closing_price=135.36), StockPrice(symbol='AAPL', date=datetime.date(2008, 2, 1), closing_price=125.02), StockPrice(symbol='AAPL', date=datetime.date(2008, 3, 1), closing_price=143.5), StockPrice(symbol='AAPL', date=datetime.date(2008, 4, 1), closing_price=173.95), StockPrice(symbol='AAPL', date=datetime.date(2008, 5, 1), closing_price=188.75), StockPrice(symbol='AAPL', date=datetime.date(2008, 6, 1), closing_price=167.44), StockPrice(symbol='AAPL', date=datetime.date(2008, 7, 1), closing_price=158.95), StockPrice(symbol='AAPL', date=datetime.date(2008, 8, 1), closing_price=169.53), StockPrice(symbol='AAPL', date=datetime.date(2008, 9, 1), closing_price=113.66), StockPrice(symbol='AAPL', date=datetime.date(2008, 10, 1), closing_price=107.59), StockPrice(symbol='AAPL', date=datetime.date(2008, 11, 1), closing_price=92.67), StockPrice(symbol='AAPL', date=datetime.date(2008, 12, 1), closing_price=85.35), StockPrice(symbol='AAPL', date=datetime.date(2009, 1, 1), closing_price=90.13), StockPrice(symbol='AAPL', date=datetime.date(2009, 2, 1), closing_price=89.31), StockPrice(symbol='AAPL', date=datetime.date(2009, 3, 1), closing_price=105.12), StockPrice(symbol='AAPL', date=datetime.date(2009, 4, 1), closing_price=125.83), StockPrice(symbol='AAPL', date=datetime.date(2009, 5, 1), closing_price=135.81), StockPrice(symbol='AAPL', date=datetime.date(2009, 6, 1), closing_price=142.43), StockPrice(symbol='AAPL', date=datetime.date(2009, 7, 1), closing_price=163.39), StockPrice(symbol='AAPL', date=datetime.date(2009, 8, 1), closing_price=168.21), StockPrice(symbol='AAPL', date=datetime.date(2009, 9, 1), closing_price=185.35), StockPrice(symbol='AAPL', date=datetime.date(2009, 10, 1), closing_price=188.5), StockPrice(symbol='AAPL', date=datetime.date(2009, 11, 1), closing_price=199.91), StockPrice(symbol='AAPL', date=datetime.date(2009, 12, 1), closing_price=210.73), StockPrice(symbol='AAPL', date=datetime.date(2010, 1, 1), closing_price=192.06), StockPrice(symbol='AAPL', date=datetime.date(2010, 2, 1), closing_price=204.62), StockPrice(symbol='AAPL', date=datetime.date(2010, 3, 1), closing_price=223.02)]}\n"
     ]
    }
   ],
   "source": [
    "from typing import List\n",
    "prices: Dict[str, List[float]] = defaultdict(list)\n",
    "\n",
    "for sp in data:\n",
    "    prices[sp.symbol].append(sp)\n",
    "# order (or sort) the prices by date\n",
    "prices = {symbol: sorted(symbol_prices) \n",
    "          for symbol, symbol_prices in prices.items()}\n",
    "print(prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DailyChange(symbol='AMZN', date=datetime.date(2001, 11, 1), pct_change=0.6217765042979941)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def pct_change(yesterday: StockPrice, today: StockPrice) -> float:\n",
    "    return today.closing_price / yesterday.closing_price - 1\n",
    "\n",
    "class DailyChange(NamedTuple):\n",
    "    symbol: str\n",
    "    date: datetime.date\n",
    "    pct_change: float\n",
    "    \n",
    "def day_over_day_changes(prices: List[StockPrice]) -> List[DailyChange]:\n",
    "    \"\"\"Assumes prices are for once stock and are ordered\"\"\"\n",
    "    return [DailyChange(symbol = today.symbol,\n",
    "                           date = today.date,\n",
    "                           pct_change = pct_change(yesterday, today))\n",
    "            for yesterday, today in zip(prices, prices[1:])]\n",
    "\n",
    "all_changes = [change for symbol_prices in prices.values()\n",
    "              for change in day_over_day_changes(symbol_prices)]\n",
    "max_change = max(all_changes, key = lambda change: change.pct_change)\n",
    "max_change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: [],\n",
       " 2: [],\n",
       " 3: [],\n",
       " 4: [],\n",
       " 5: [],\n",
       " 6: [],\n",
       " 7: [],\n",
       " 8: [],\n",
       " 9: [],\n",
       " 10: [],\n",
       " 11: [],\n",
       " 12: []}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "changes_by_month: List[DailyChange] = {month: [] for month in range(1,13)}\n",
    "changes_by_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: [DailyChange(symbol='MSFT', date=datetime.date(2001, 1, 1), pct_change=0.40736543909348444),\n",
       "  DailyChange(symbol='MSFT', date=datetime.date(2002, 1, 1), pct_change=-0.03821892393320958),\n",
       "  DailyChange(symbol='MSFT', date=datetime.date(2003, 1, 1), pct_change=-0.08178792201616747),\n",
       "  DailyChange(symbol='MSFT', date=datetime.date(2004, 1, 1), pct_change=0.010240427426536192),\n",
       "  DailyChange(symbol='MSFT', date=datetime.date(2005, 1, 1), pct_change=-0.01672104404567698),\n",       
       " ..........................................,\n",
       " ..........................................,\n",
       " ..........................................,\n",
       " ..........................................,\n",
       "  DailyChange(symbol='AAPL', date=datetime.date(2006, 12, 1), pct_change=-0.07440541130264011),\n",
       "  DailyChange(symbol='AAPL', date=datetime.date(2007, 12, 1), pct_change=0.08703764680057091),\n",
       "  DailyChange(symbol='AAPL', date=datetime.date(2008, 12, 1), pct_change=-0.07898996438977024),\n",
       "  DailyChange(symbol='AAPL', date=datetime.date(2009, 12, 1), pct_change=0.05412435596018206)]}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for change in all_changes:\n",
    "    changes_by_month[change.date.month].append(change)\n",
    "changes_by_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_daily_change = {month: sum(change.pct_change for change in changes)/ len(changes)\n",
    "                   for month, changes in changes_by_month.items()}\n",
    "\n",
    "assert avg_daily_change[10] == max(avg_daily_change.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[70, 170, 40]\n",
      "[95, 80, 75, 62]\n",
      "[5, 7, 9]\n",
      "[1, 2, 3]\n",
      "[16, 20]\n",
      "[2, 4, 6]\n",
      "[3.0, 4.0]\n",
      "32\n",
      "14\n",
      "3.7416573867739413\n",
      "27\n",
      "5.196152422706632\n"
     ]
    }
   ],
   "source": [
    "# When data is not of same scale then we may need to rescale the data to have mean 0 and std 1\n",
    "from typing import Tuple\n",
    "from Vector_operations_on_data import vector_mean\n",
    "from Statistics import standard_deviation\n",
    "Vector = List[float]\n",
    "\n",
    "def scale(data: List[Vector]) -> Tuple[Vector, Vector]:\n",
    "    \"\"\"Returns mean and standard deviation of each feature\"\"\"\n",
    "    dim = len(data[0])\n",
    "    means = vector_mean(data)\n",
    "    stdevs = [standard_deviation([vector[i] for vector in data])\n",
    "             for i in range(dim)]\n",
    "    return means, stdevs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([-0.25, 1.0, 2.0], [2.217355782608345, 2.160246899469287, 2.0])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectors = [[-3, -1, 1],[-1, 0, 1], [1, 1, 1], [2, 4, 5]]\n",
    "means, stdevs = scale(vectors)\n",
    "means, stdevs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rescale(data:List[Vector]) -> List[Vector]:\n",
    "    #Rescale the input data\n",
    "    dim = len(data[0])\n",
    "    \n",
    "    means, stdevs = scale(data)\n",
    "    \n",
    "    # Make a copy of each vector\n",
    "    rescaled = [v[:] for v in data]\n",
    "\n",
    "    for v in rescaled:\n",
    "        for i in range(dim):\n",
    "            if stdevs[i]>0:\n",
    "                v[i] = (v[i] - means[i])/stdevs[i]\n",
    "                \n",
    "    return rescaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([-0.13725309579957568, 0.5370899501137243, 1.0],\n",
       " [2.217355782608345, 2.160246899469287, 2.0])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "means,stdevs = scale(rescale(vectors))\n",
    "means,stdevs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 103.37it/s]\n"
     ]
    }
   ],
   "source": [
    "#Produce a progress bar\n",
    "import tqdm\n",
    "\n",
    "for i in tqdm.tqdm(range(100)):\n",
    "    #do something slow\n",
    "    _ = [random.random() for _ in range(100000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "168 primes: 100%|███████████████████████████████████████████████████████████████████| 997/997 [00:01<00:00, 856.60it/s]\n"
     ]
    }
   ],
   "source": [
    "from typing import List\n",
    "\n",
    "def primes_up_to (n: int) -> List[int]:\n",
    "    primes = [2]\n",
    "    \n",
    "    with tqdm.trange(3,n) as t:\n",
    "        for i in t:\n",
    "            # i is prime if no smaller prime divides it\n",
    "            i_is_prime = not any(i % p == 0 for p in primes)\n",
    "            if i_is_prime:\n",
    "                primes.append(i)\n",
    "                \n",
    "            t.set_description(f\"{len(primes)} primes\")\n",
    "    return primes\n",
    "\n",
    "my_primes = primes_up_to(1000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  2,   3,   5,   7,  11,  13,  17,  19,  23,  29,  31,  37,  41,\n",
       "        43,  47,  53,  59,  61,  67,  71,  73,  79,  83,  89,  97, 101,\n",
       "       103, 107, 109, 113, 127, 131, 137, 139, 149, 151, 157, 163, 167,\n",
       "       173, 179, 181, 191, 193, 197, 199, 211, 223, 227, 229, 233, 239,\n",
       "       241, 251, 257, 263, 269, 271, 277, 281, 283, 293, 307, 311, 313,\n",
       "       317, 331, 337, 347, 349, 353, 359, 367, 373, 379, 383, 389, 397,\n",
       "       401, 409, 419, 421, 431, 433, 439, 443, 449, 457, 461, 463, 467,\n",
       "       479, 487, 491, 499, 503, 509, 521, 523, 541, 547, 557, 563, 569,\n",
       "       571, 577, 587, 593, 599, 601, 607, 613, 617, 619, 631, 641, 643,\n",
       "       647, 653, 659, 661, 673, 677, 683, 691, 701, 709, 719, 727, 733,\n",
       "       739, 743, 751, 757, 761, 769, 773, 787, 797, 809, 811, 821, 823,\n",
       "       827, 829, 839, 853, 857, 859, 863, 877, 881, 883, 887, 907, 911,\n",
       "       919, 929, 937, 941, 947, 953, 967, 971, 977, 983, 991, 997])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.transpose(my_primes)"
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
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