Web Scraping Financial Data 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from bs4 import BeautifulSoup \n",
    "import urllib.request as ur\n",
    "import pandas as pd\n",
    "import matplotlib as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set(style=\"white\", rc={\"axes.facecolor\": (0,0,0,0)})\n",
    "import numpy as np\n",
    "import time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [],
   "source": [
    "from urllib.error import HTTPError"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fetch Symbols and Sectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Security</th>\n",
       "      <th>SEC filings</th>\n",
       "      <th>sector</th>\n",
       "      <th>GICS Sub Industry</th>\n",
       "      <th>CIK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ATVI</td>\n",
       "      <td>Activision Blizzard</td>\n",
       "      <td>reports</td>\n",
       "      <td>Communication Services</td>\n",
       "      <td>Interactive Home Entertainment</td>\n",
       "      <td>718877.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GOOGL</td>\n",
       "      <td>Alphabet Inc Class A</td>\n",
       "      <td>reports</td>\n",
       "      <td>Communication Services</td>\n",
       "      <td>Interactive Media &amp; Services</td>\n",
       "      <td>1652044.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>Alphabet Inc Class C</td>\n",
       "      <td>reports</td>\n",
       "      <td>Communication Services</td>\n",
       "      <td>Interactive Media &amp; Services</td>\n",
       "      <td>1652044.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>T</td>\n",
       "      <td>AT&amp;T Inc.</td>\n",
       "      <td>reports</td>\n",
       "      <td>Communication Services</td>\n",
       "      <td>Integrated Telecommunication Services</td>\n",
       "      <td>732717.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CTL</td>\n",
       "      <td>CenturyLink Inc</td>\n",
       "      <td>reports</td>\n",
       "      <td>Communication Services</td>\n",
       "      <td>Alternative Carriers</td>\n",
       "      <td>18926.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Symbol              Security SEC filings                  sector  \\\n",
       "0   ATVI   Activision Blizzard     reports  Communication Services   \n",
       "1  GOOGL  Alphabet Inc Class A     reports  Communication Services   \n",
       "2   GOOG  Alphabet Inc Class C     reports  Communication Services   \n",
       "3      T             AT&T Inc.     reports  Communication Services   \n",
       "4    CTL       CenturyLink Inc     reports  Communication Services   \n",
       "\n",
       "                       GICS Sub Industry        CIK  \n",
       "0         Interactive Home Entertainment   718877.0  \n",
       "1           Interactive Media & Services  1652044.0  \n",
       "2           Interactive Media & Services  1652044.0  \n",
       "3  Integrated Telecommunication Services   732717.0  \n",
       "4                   Alternative Carriers    18926.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#grab stocks and sector from https://en.wikipedia.org/wiki/List_of_S%26P_500_companies\n",
    "stocks = pd.read_csv('stocks.csv')\n",
    "stocks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Symbol', 'Security', 'SEC filings', 'sector', 'GICS Sub Industry',\n",
       "       'CIK'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Industrials               72\n",
       "Information Technology    71\n",
       "Financials                66\n",
       "Consumer Discretionary    63\n",
       "Health Care               60\n",
       "Consumer Staples          33\n",
       "Real Estate               31\n",
       "Utilities                 28\n",
       "Materials                 28\n",
       "Energy                    27\n",
       "Communication Services    26\n",
       "Name: sector, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sectors = stocks['sector'].value_counts().head(11)\n",
    "sectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Industrials', 'Information Technology', 'Financials',\n",
       "       'Consumer Discretionary', 'Health Care', 'Consumer Staples',\n",
       "       'Real Estate', 'Utilities', 'Materials', 'Energy',\n",
       "       'Communication Services'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sectors = sectors.index\n",
    "sectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "stocks = stocks[stocks['sector'].isin(sectors)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sectorList = list(stocks['sector'])\n",
    "symbolList = list(stocks['Symbol'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scrape PE Ratio for Every Stock Symbol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "def outputPE(symbol):\n",
    "    #create url \n",
    "    myurl = 'https://finance.yahoo.com/quote/'+symbol +'/key-statistics'\n",
    "    #read in hml \n",
    "    read_data = ur.urlopen(myurl).read()\n",
    "    soup = BeautifulSoup(read_data, 'lxml')\n",
    "    #find table node element with financial information \n",
    "    data=[]\n",
    "    table = soup.find('table')\n",
    "    table_body = table.find(\"tbody\")\n",
    "    rows = table_body.find_all('tr')\n",
    "    for row in rows:\n",
    "        cols = row.find_all('td')\n",
    "        cols = [ele.text.strip() for ele in cols]\n",
    "        data.append([ele for ele in cols if ele])\n",
    "        #return table element containing PE ratio \n",
    "    return data[2][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [],
   "source": [
    "from urllib.error import HTTPError"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [],
   "source": [
    "PEList = []\n",
    "while (len(PEList)<len(symbolList)):\n",
    "    #webscrape every stock symbol\n",
    "    #if the server kicks you off, wait 5 seconds, then pickup where you left off\n",
    "    for stock in symbolList[len(PEList)]:\n",
    "        try:\n",
    "            PEList.append(outputPE(stock))\n",
    "        except HTTPError as err:\n",
    "            if err.code == 503:\n",
    "                time.sleep(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for ind, value in enumerate(PEList):\n",
    "#     print(ind, value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "looks like the server refuses to load the specific section I'm scraping after 105 iterations. remove the rest of the list and start over. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [],
   "source": [
    "def findFirstInstance(array):\n",
    "    for ind, val in enumerate(array):\n",
    "        if (val=='-1.96%'):\n",
    "            return ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "106"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "findFirstInstance(PEList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [],
   "source": [
    "while (len(PEList)<len(symbolList)):\n",
    "    #webscrape every stock symbol\n",
    "    #if the server kicks you off, wait 5 seconds, then pickup where you left off\n",
    "    for stock in symbolList[len(PEList)]:\n",
    "        try:\n",
    "            PEList.append(outputPE(stock))\n",
    "        except HTTPError as err:\n",
    "            if err.code == 503:\n",
    "                time.sleep(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for ind, value in enumerate(PEList):\n",
    "#     print(ind, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "getting further along. I realize this process could be put into a larger loop, but I would like to track progress with every iteration. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [],
   "source": [
    "PEList = PEList[:findFirstInstance(PEList)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [],
   "source": [
    "while (len(PEList)<len(symbolList)):\n",
    "    #webscrape every stock symbol\n",
    "    #if the server kicks you off, wait 5 seconds, then pickup where you left off\n",
    "    for stock in symbolList[len(PEList)]:\n",
    "        try:\n",
    "            PEList.append(outputPE(stock))\n",
    "        except HTTPError as err:\n",
    "            if err.code == 503:\n",
    "                time.sleep(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [],
   "source": [
    "PEList = PEList[:findFirstInstance(PEList)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "retrieved full list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "505"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(PEList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "505"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(symbolList)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create DataFrame "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sector</th>\n",
       "      <th>Ratio</th>\n",
       "      <th>Symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Communication Services</td>\n",
       "      <td>36.19</td>\n",
       "      <td>ATVI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Communication Services</td>\n",
       "      <td>28.34</td>\n",
       "      <td>GOOGL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Communication Services</td>\n",
       "      <td>28.33</td>\n",
       "      <td>GOOG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Communication Services</td>\n",
       "      <td>14.99</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Communication Services</td>\n",
       "      <td>8.35</td>\n",
       "      <td>CTL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Communication Services</td>\n",
       "      <td>62.84</td>\n",
       "      <td>CHTR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Communication Services</td>\n",
       "      <td>14.50</td>\n",
       "      <td>CMCSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Communication Services</td>\n",
       "      <td>7.08</td>\n",
       "      <td>DISCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Communication Services</td>\n",
       "      <td>6.51</td>\n",
       "      <td>DISCK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Communication Services</td>\n",
       "      <td>12.37</td>\n",
       "      <td>DISH</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Sector  Ratio Symbol\n",
       "0  Communication Services  36.19   ATVI\n",
       "1  Communication Services  28.34  GOOGL\n",
       "2  Communication Services  28.33   GOOG\n",
       "3  Communication Services  14.99      T\n",
       "4  Communication Services   8.35    CTL\n",
       "5  Communication Services  62.84   CHTR\n",
       "6  Communication Services  14.50  CMCSA\n",
       "7  Communication Services   7.08  DISCA\n",
       "8  Communication Services   6.51  DISCK\n",
       "9  Communication Services  12.37   DISH"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.arange(len(symbolList)))\n",
    "df.columns = [\"Sector\"]\n",
    "df[\"Sector\"] = sectorList\n",
    "df[\"Ratio\"] = PEList\n",
    "df[\"Symbol\"] = symbolList \n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Remove Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.833333333333332"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)/len(df[df['Ratio']=='N/A'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['Ratio']!='N/A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "380 1.13k\n"
     ]
    }
   ],
   "source": [
    "for ind, val in enumerate(list(df['Ratio'])):\n",
    "    g= val\n",
    "    if(\"k\" in g):\n",
    "        print(ind, val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[380,1] = 1130"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [],
   "source": [
    "nums =[]\n",
    "for i in list(df['Ratio']):\n",
    "    nums.append(float(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ratio'] = nums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24aef569400>"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(df['Ratio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Log_PE_Ratio'] = np.log(df['Ratio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x24aee03e860>"
      ]
     },
     "execution_count": 419,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 583.2x712.8 with 11 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the FacetGrid object\n",
    "pal = sns.cubehelix_palette(9, rot=-.60, light=.7)\n",
    "g = sns.FacetGrid(df, row=\"Sector\", hue=\"Sector\", aspect=9, height=0.9, palette=pal)\n",
    "\n",
    "# Draw the densities in a few steps\n",
    "g.map(sns.kdeplot, 'Log_PE_Ratio', clip_on=False, shade=True, alpha=1, lw=1.5, bw=.2)\n",
    "g.map(sns.kdeplot, 'Log_PE_Ratio', clip_on=False, color=\"w\", lw=2, bw=.2)\n",
    "\n",
    "def label(Ratio, color, label):\n",
    "    ax = plt.pyplot.gca()\n",
    "    ax.text(0, .2, label, fontweight=\"bold\", color=color,\n",
    "            ha=\"left\", va=\"center\", transform=ax.transAxes)\n",
    "\n",
    "g.map(label, 'Log_PE_Ratio')\n",
    "\n",
    "# Set the subplots to nearly overlap\n",
    "g.fig.subplots_adjust(hspace=0)\n",
    "\n",
    "# Remove axes details that don't play well with overlap\n",
    "g.set_titles(\"\")\n",
    "g.set(yticks=[])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Its easy to see which sectors are comparitively undervalued. We can also see which sectors have a flatter distribution of valuations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's grab the 5 companies with the lowest PE ratios from every sector "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Industrials               70\n",
       "Information Technology    69\n",
       "Financials                65\n",
       "Consumer Discretionary    63\n",
       "Health Care               57\n",
       "Real Estate               31\n",
       "Consumer Staples          30\n",
       "Utilities                 28\n",
       "Communication Services    23\n",
       "Materials                 23\n",
       "Energy                    16\n",
       "Name: Sector, dtype: int64"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Sector'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Industrials', 'Information Technology', 'Financials',\n",
       "       'Consumer Discretionary', 'Health Care', 'Consumer Staples',\n",
       "       'Real Estate', 'Utilities', 'Materials', 'Energy',\n",
       "       'Communication Services'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sector</th>\n",
       "      <th>Symbols</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Sector, Symbols]\n",
       "Index: []"
      ]
     },
     "execution_count": 411,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.DataFrame(columns = ['Sector','Symbols'])\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [],
   "source": [
    "for sector in sectors:\n",
    "    temp = df[df['Sector']==sector].sort_values(by='Ratio').head(5)\n",
    "    bestPerforming = list(temp['Symbol'])\n",
    "    result = result.append({'Sector': sector, 'Symbols':bestPerforming}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sector</th>\n",
       "      <th>Symbols</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Industrials</td>\n",
       "      <td>[DAL, AAL, UAL, ALK, URI]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Information Technology</td>\n",
       "      <td>[ADS, XRX, HPQ, STX, WU]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Financials</td>\n",
       "      <td>[UNM, MET, SYF, AIG, C]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>[NCLH, MGM, KSS, RCL, CCL]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Health Care</td>\n",
       "      <td>[ALGN, ALXN, BIIB, CVS, HSIC]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>[WBA, TSN, ADM, PM, KR]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Real Estate</td>\n",
       "      <td>[VNO, SPG, HST, CBRE, SLG]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Utilities</td>\n",
       "      <td>[NRG, PPL, EXC, CNP, ETR]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Materials</td>\n",
       "      <td>[LYB, WRK, EMN, CF, ALB]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Energy</td>\n",
       "      <td>[MPC, HFC, MRO, COP, EOG]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Communication Services</td>\n",
       "      <td>[VIAC, DISCK, DISCA, CTL, OMC]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Sector                         Symbols\n",
       "0              Industrials       [DAL, AAL, UAL, ALK, URI]\n",
       "1   Information Technology        [ADS, XRX, HPQ, STX, WU]\n",
       "2               Financials         [UNM, MET, SYF, AIG, C]\n",
       "3   Consumer Discretionary      [NCLH, MGM, KSS, RCL, CCL]\n",
       "4              Health Care   [ALGN, ALXN, BIIB, CVS, HSIC]\n",
       "5         Consumer Staples         [WBA, TSN, ADM, PM, KR]\n",
       "6              Real Estate      [VNO, SPG, HST, CBRE, SLG]\n",
       "7                Utilities       [NRG, PPL, EXC, CNP, ETR]\n",
       "8                Materials        [LYB, WRK, EMN, CF, ALB]\n",
       "9                   Energy       [MPC, HFC, MRO, COP, EOG]\n",
       "10  Communication Services  [VIAC, DISCK, DISCA, CTL, OMC]"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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