Extracting-Stock-Data-Using-Python-Library.ipynb

{
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   "source": [
    "<center>\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/Images/SN_logo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\">\n",
    "</center>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Extracting Stock Data Using a Python Library</h1>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A company's stock share is a piece of the company more precisely:\n",
    "<p><b>A stock (also known as equity) is a security that represents the ownership of a fraction of a corporation. This\n",
    "entitles the owner of the stock to a proportion of the corporation's assets and profits equal to how much stock they own. Units of stock are called \"shares.\" [1]</p></b>\n",
    "\n",
    "An investor can buy a stock and sell it later. If the stock price increases, the investor profits, If it decreases,the investor with incur a loss.  Determining the stock price is complex; it depends on the number of outstanding shares, the size of the company's future profits, and much more. People trade stocks throughout the day the stock ticker is a report of the price of a certain stock, updated continuously throughout the trading session by the various stock market exchanges. \n",
    "<p>You are a data scientist working for a hedge fund; it's your job to determine any suspicious stock activity. In this lab you will extract stock data using a Python library. We will use the <coode>yfinance</code> library, it allows us to extract data for stocks returning data in a pandas dataframe. You will use the lab to extract.</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Table of Contents</h2>\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "    <ul>\n",
    "        <li>Using yfinance to Extract Stock Info</li>\n",
    "        <li>Using yfinance to Extract Historical Share Price Data</li>\n",
    "        <li>Using yfinance to Extract Historical Dividends Data</li>\n",
    "        <li>Exercise</li>\n",
    "    </ul>\n",
    "<p>\n",
    "    Estimated Time Needed: <strong>30 min</strong></p>\n",
    "</div>\n",
    "\n",
    "<hr>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting yfinance==0.1.93\n",
      "  Downloading yfinance-0.1.93-py2.py3-none-any.whl (31 kB)\n",
      "Requirement already satisfied: cryptography>=3.3.2 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.1.93) (38.0.2)\n",
      "Collecting appdirs>=1.4.4\n",
      "  Downloading appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)\n",
      "Collecting multitasking>=0.0.7\n",
      "  Downloading multitasking-0.0.11-py3-none-any.whl (8.5 kB)\n",
      "Requirement already satisfied: lxml>=4.9.1 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.1.93) (4.9.1)\n",
      "Requirement already satisfied: numpy>=1.16.5 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.1.93) (1.21.6)\n",
      "Requirement already satisfied: pandas>=1.3.0 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.1.93) (1.3.5)\n",
      "Requirement already satisfied: requests>=2.26 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.1.93) (2.28.1)\n",
      "Requirement already satisfied: cffi>=1.12 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from cryptography>=3.3.2->yfinance==0.1.93) (1.15.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from pandas>=1.3.0->yfinance==0.1.93) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from pandas>=1.3.0->yfinance==0.1.93) (2022.6)\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from requests>=2.26->yfinance==0.1.93) (2.1.1)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from requests>=2.26->yfinance==0.1.93) (2022.9.24)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from requests>=2.26->yfinance==0.1.93) (1.26.13)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from requests>=2.26->yfinance==0.1.93) (3.4)\n",
      "Requirement already satisfied: pycparser in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from cffi>=1.12->cryptography>=3.3.2->yfinance==0.1.93) (2.21)\n",
      "Requirement already satisfied: six>=1.5 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas>=1.3.0->yfinance==0.1.93) (1.16.0)\n",
      "Installing collected packages: multitasking, appdirs, yfinance\n",
      "Successfully installed appdirs-1.4.4 multitasking-0.0.11 yfinance-0.1.93\n"
     ]
    }
   ],
   "source": [
    "!pip install yfinance==0.1.93\n",
    "#!pip install pandas==1.3.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yfinance as yf\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the yfinance Library to Extract Stock Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `Ticker` module we can create an object that will allow us to access functions to extract data. To do this we need to provide the ticker symbol for the stock, here the company is Apple and the ticker symbol is `AAPL`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "apple = yf.Ticker(\"AAPL\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can access functions and variables to extract the type of data we need. You can view them and what they represent here https://aroussi.com/post/python-yahoo-finance.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stock Info\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the attribute  <code>info</code> we can extract information about the stock as a Python dictionary.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'zip': '95014',\n",
       " 'sector': 'Technology',\n",
       " 'fullTimeEmployees': 164000,\n",
       " 'longBusinessSummary': 'Apple Inc. designs, manufactures, and markets smartphones, personal computers, tablets, wearables, and accessories worldwide. It also sells various related services. In addition, the company offers iPhone, a line of smartphones; Mac, a line of personal computers; iPad, a line of multi-purpose tablets; and wearables, home, and accessories comprising AirPods, Apple TV, Apple Watch, Beats products, and HomePod. Further, it provides AppleCare support and cloud services store services; and operates various platforms, including the App Store that allow customers to discover and download applications and digital content, such as books, music, video, games, and podcasts. Additionally, the company offers various services, such as Apple Arcade, a game subscription service; Apple Fitness+, a personalized fitness service; Apple Music, which offers users a curated listening experience with on-demand radio stations; Apple News+, a subscription news and magazine service; Apple TV+, which offers exclusive original content; Apple Card, a co-branded credit card; and Apple Pay, a cashless payment service, as well as licenses its intellectual property. The company serves consumers, and small and mid-sized businesses; and the education, enterprise, and government markets. It distributes third-party applications for its products through the App Store. The company also sells its products through its retail and online stores, and direct sales force; and third-party cellular network carriers, wholesalers, retailers, and resellers. Apple Inc. was incorporated in 1977 and is headquartered in Cupertino, California.',\n",
       " 'city': 'Cupertino',\n",
       " 'phone': '408 996 1010',\n",
       " 'state': 'CA',\n",
       " 'country': 'United States',\n",
       " 'companyOfficers': [],\n",
       " 'website': 'https://www.apple.com',\n",
       " 'maxAge': 1,\n",
       " 'address1': 'One Apple Park Way',\n",
       " 'industry': 'Consumer Electronics',\n",
       " 'ebitdaMargins': 0.33105,\n",
       " 'profitMargins': 0.2531,\n",
       " 'grossMargins': 0.43310001,\n",
       " 'operatingCashflow': 122151002112,\n",
       " 'revenueGrowth': 0.081,\n",
       " 'operatingMargins': 0.30289,\n",
       " 'ebitda': 130541002752,\n",
       " 'targetLowPrice': 122,\n",
       " 'recommendationKey': 'buy',\n",
       " 'grossProfits': 170782000000,\n",
       " 'freeCashflow': 90215251968,\n",
       " 'targetMedianPrice': 175,\n",
       " 'currentPrice': 129.62,\n",
       " 'earningsGrowth': 0.048,\n",
       " 'currentRatio': 0.879,\n",
       " 'returnOnAssets': 0.21214001,\n",
       " 'numberOfAnalystOpinions': 41,\n",
       " 'targetMeanPrice': 174.62,\n",
       " 'debtToEquity': 261.446,\n",
       " 'returnOnEquity': 1.75459,\n",
       " 'targetHighPrice': 214,\n",
       " 'totalCash': 48304001024,\n",
       " 'totalDebt': 132480000000,\n",
       " 'totalRevenue': 394328014848,\n",
       " 'totalCashPerShare': 3.036,\n",
       " 'financialCurrency': 'USD',\n",
       " 'revenuePerShare': 24.317,\n",
       " 'quickRatio': 0.709,\n",
       " 'recommendationMean': 2,\n",
       " 'exchange': 'NMS',\n",
       " 'shortName': 'Apple Inc.',\n",
       " 'longName': 'Apple Inc.',\n",
       " 'exchangeTimezoneName': 'America/New_York',\n",
       " 'exchangeTimezoneShortName': 'EST',\n",
       " 'isEsgPopulated': False,\n",
       " 'gmtOffSetMilliseconds': '-18000000',\n",
       " 'quoteType': 'EQUITY',\n",
       " 'symbol': 'AAPL',\n",
       " 'messageBoardId': 'finmb_24937',\n",
       " 'market': 'us_market',\n",
       " 'annualHoldingsTurnover': None,\n",
       " 'enterpriseToRevenue': 5.257,\n",
       " 'beta3Year': None,\n",
       " 'enterpriseToEbitda': 15.88,\n",
       " '52WeekChange': -0.2472269,\n",
       " 'morningStarRiskRating': None,\n",
       " 'forwardEps': 6.75,\n",
       " 'revenueQuarterlyGrowth': None,\n",
       " 'sharesOutstanding': 15908100096,\n",
       " 'fundInceptionDate': None,\n",
       " 'annualReportExpenseRatio': None,\n",
       " 'totalAssets': None,\n",
       " 'bookValue': 3.178,\n",
       " 'sharesShort': 121757434,\n",
       " 'sharesPercentSharesOut': 0.0077,\n",
       " 'fundFamily': None,\n",
       " 'lastFiscalYearEnd': 1663977600,\n",
       " 'heldPercentInstitutions': 0.60847,\n",
       " 'netIncomeToCommon': 99802996736,\n",
       " 'trailingEps': 6.11,\n",
       " 'lastDividendValue': 0.23,\n",
       " 'SandP52WeekChange': -0.16598755,\n",
       " 'priceToBook': 40.786655,\n",
       " 'heldPercentInsiders': 0.00071999995,\n",
       " 'nextFiscalYearEnd': 1727136000,\n",
       " 'yield': None,\n",
       " 'mostRecentQuarter': 1663977600,\n",
       " 'shortRatio': 1.67,\n",
       " 'sharesShortPreviousMonthDate': 1668470400,\n",
       " 'floatShares': 15891414476,\n",
       " 'beta': 1.27241,\n",
       " 'enterpriseValue': 2073008930816,\n",
       " 'priceHint': 2,\n",
       " 'threeYearAverageReturn': None,\n",
       " 'lastSplitDate': 1598832000,\n",
       " 'lastSplitFactor': '4:1',\n",
       " 'legalType': None,\n",
       " 'lastDividendDate': 1667520000,\n",
       " 'morningStarOverallRating': None,\n",
       " 'earningsQuarterlyGrowth': 0.008,\n",
       " 'priceToSalesTrailing12Months': 5.229169,\n",
       " 'dateShortInterest': 1671062400,\n",
       " 'pegRatio': 2.31,\n",
       " 'ytdReturn': None,\n",
       " 'forwardPE': 19.202963,\n",
       " 'lastCapGain': None,\n",
       " 'shortPercentOfFloat': 0.0077,\n",
       " 'sharesShortPriorMonth': 114431895,\n",
       " 'impliedSharesOutstanding': 0,\n",
       " 'category': None,\n",
       " 'fiveYearAverageReturn': None,\n",
       " 'previousClose': 125.02,\n",
       " 'regularMarketOpen': 126.01,\n",
       " 'twoHundredDayAverage': 150.84735,\n",
       " 'trailingAnnualDividendYield': 0.0071988483,\n",
       " 'payoutRatio': 0.14729999,\n",
       " 'volume24Hr': None,\n",
       " 'regularMarketDayHigh': 130.29,\n",
       " 'navPrice': None,\n",
       " 'averageDailyVolume10Day': 81865790,\n",
       " 'regularMarketPreviousClose': 125.02,\n",
       " 'fiftyDayAverage': 141.3484,\n",
       " 'trailingAnnualDividendRate': 0.9,\n",
       " 'open': 126.01,\n",
       " 'toCurrency': None,\n",
       " 'averageVolume10days': 81865790,\n",
       " 'expireDate': None,\n",
       " 'algorithm': None,\n",
       " 'dividendRate': 0.92,\n",
       " 'exDividendDate': 1667520000,\n",
       " 'circulatingSupply': None,\n",
       " 'startDate': None,\n",
       " 'regularMarketDayLow': 124.9,\n",
       " 'currency': 'USD',\n",
       " 'trailingPE': 21.214401,\n",
       " 'regularMarketVolume': 87754715,\n",
       " 'lastMarket': None,\n",
       " 'maxSupply': None,\n",
       " 'openInterest': None,\n",
       " 'marketCap': 2062007795712,\n",
       " 'volumeAllCurrencies': None,\n",
       " 'strikePrice': None,\n",
       " 'averageVolume': 83912658,\n",
       " 'dayLow': 124.9,\n",
       " 'ask': 129.35,\n",
       " 'askSize': 1200,\n",
       " 'volume': 87754715,\n",
       " 'fiftyTwoWeekHigh': 179.61,\n",
       " 'fromCurrency': None,\n",
       " 'fiveYearAvgDividendYield': 0.98,\n",
       " 'fiftyTwoWeekLow': 124.17,\n",
       " 'bid': 129.26,\n",
       " 'tradeable': False,\n",
       " 'dividendYield': 0.0074,\n",
       " 'bidSize': 800,\n",
       " 'dayHigh': 130.29,\n",
       " 'coinMarketCapLink': None,\n",
       " 'regularMarketPrice': 129.62,\n",
       " 'preMarketPrice': None,\n",
       " 'logo_url': 'https://logo.clearbit.com/apple.com',\n",
       " 'trailingPegRatio': 2.5328}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple_info=apple.info\n",
    "apple_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get the <code>'country'</code> using the key country\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'United States'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple_info['country']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting Share Price\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A share is the single smallest part of a company's stock  that you can buy, the prices of these shares fluctuate over time. Using the <code>history()</code> method we can get the share price of the stock over a certain period of time. Using the `period` parameter we can set how far back from the present to get data. The options for `period` are 1 day (1d), 5d, 1 month (1mo) , 3mo, 6mo, 1 year (1y), 2y, 5y, 10y, ytd, and max.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "apple_share_price_data = apple.history(period=\"max\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The format that the data is returned in is a Pandas DataFrame. With the `Date` as the index the share `Open`, `High`, `Low`, `Close`, `Volume`, and `Stock Splits` are given for each day.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Dividends</th>\n",
       "      <th>Stock Splits</th>\n",
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       "      <th>1980-12-12 00:00:00-05:00</th>\n",
       "      <td>0.099874</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>1980-12-16 00:00:00-05:00</th>\n",
       "      <td>0.088149</td>\n",
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       "      <td>0.087715</td>\n",
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       "      <th>1980-12-17 00:00:00-05:00</th>\n",
       "      <td>0.089886</td>\n",
       "      <td>0.090320</td>\n",
       "      <td>0.089886</td>\n",
       "      <td>0.089886</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>1980-12-18 00:00:00-05:00</th>\n",
       "      <td>0.092492</td>\n",
       "      <td>0.092927</td>\n",
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      "text/plain": [
       "                               Open      High       Low     Close     Volume  \\\n",
       "Date                                                                           \n",
       "1980-12-12 00:00:00-05:00  0.099874  0.100308  0.099874  0.099874  469033600   \n",
       "1980-12-15 00:00:00-05:00  0.095098  0.095098  0.094663  0.094663  175884800   \n",
       "1980-12-16 00:00:00-05:00  0.088149  0.088149  0.087715  0.087715  105728000   \n",
       "1980-12-17 00:00:00-05:00  0.089886  0.090320  0.089886  0.089886   86441600   \n",
       "1980-12-18 00:00:00-05:00  0.092492  0.092927  0.092492  0.092492   73449600   \n",
       "\n",
       "                           Dividends  Stock Splits  \n",
       "Date                                                \n",
       "1980-12-12 00:00:00-05:00        0.0           0.0  \n",
       "1980-12-15 00:00:00-05:00        0.0           0.0  \n",
       "1980-12-16 00:00:00-05:00        0.0           0.0  \n",
       "1980-12-17 00:00:00-05:00        0.0           0.0  \n",
       "1980-12-18 00:00:00-05:00        0.0           0.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple_share_price_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can reset the index of the DataFrame with the `reset_index` function. We also set the `inplace` paramter to `True` so the change takes place to the DataFrame itself.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "apple_share_price_data.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the `Open` price against the `Date`:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S7iJPodnzZnJngUJERORDxMHFGX8/2yEUpioG0V45kFuxQCEiIvIhjnpQrOUaq9Nj4qjQqUssUIiIiHyI2WLfO1Jg8rxLOFVhgUJERORDiszVuHxTDZXcD1MnWKAQERF5KRHB44t2YPrXu9W2eVvSqnxfSnzDKvdpGKi/ltCuGQsUIiIiL5WeXYQVe9Lx+e+nUHRlXMm245eqfF/fG2yXlmkU6Ge3j0Hn3hKBBQoREZGXslivXocpHQhrtlR9bab8RG06rX05UH5gbV1jgUJEROQDSouVqBBDjd+r09hPfs8xKERERFQrZWeELe1MGdktEYDjoqMiWgf7WtiDQkRERNfKeqVCKS02brshUt3WrHFQpe91VKBUtqxNXWCBQkRE5KWUMl0opWNGSi/16DRXP+JjQv0rPY6jAsXKSzxERERUG2V7OUovyRSX6UlZMq4r+t0Yhdfva1vpcQLKTYUPXO2RcZcar2ZMREREnqHsVZjS5xZryURtOq2Crs3C0bVZeJXHCdTbFygcg0JERES1MvOHA+pza7kelJoMkg3Q2/dX8C4eIiIiqpYLeUaMmb8Na/efAwCs3JOhbjuYkQsAsFhKL/FU/hH/0j03qc8DHV3iYQ8KERERVcerKw5g3cFMPPLpdrtts1YfxvSvd+PnoxcAVN2D4ldmcrayd/yUsrh5DAoLFCIiIi9xLreowm17zmTj899P4fe0kqnuHd2ZU1bZMSZ3t4u1255vdO8KyCxQiIiIfFBVPSiFpqsFiJ9Gg7kjUnBLcmO17fVVB10WW3WwQCEiIvISNRkWcqnAVOn2+VuPq881GgV3tI7Bpw93VttOXCyoaXhOxQKFiIjIB32940yl28/nGusoktphgUJEROQlnHljzfXRwc47mAuwQCEiIvJBo7snVrp9eKf4ugmklligEBEReQlB9btQOiY2qnR7TSZycwcWKERERF4oq4pBsFUVIO6eiK0qLFCIiIi8RNmaIvMaB7m6e7XiqrBAISIi8kKNAvWVbt9w6Hyl2/veWDJ7bKsmIU6LyZm4mjEREZEXqmo8yj3tmlS6PTLYH3tm9Eegg4UCPYFnRkVERER2pMIX9uLDA6s8XrC/3zXF40q8xENEROQlLuVfHRh79Hxepfv662r3Ef+PAS1r9T5nq3H0mzZtwqBBgxAbGwtFUbB8+XJ1m9lsxj/+8Q+0bt0aQUFBiI2NxciRI3H27FmbY/Tu3RuKotg8hg8ffs3JEBER+bKjmVeLkvUHMyvdN0CvrdXXaN0kFADQ0s0TudW4QMnPz0fbtm0xZ84cu20FBQXYsWMHnnvuOezYsQNff/01Dh8+jLvvvttu37FjxyI9PV19fPjhh7XLgIiIqB6q6i5hf13tCpTSVZCL3XybT43HoAwcOBADBw50uC00NBRr1qyxaXv33XfRuXNnnDx5EvHxV2etCwwMRHR0dE2/PBEREVWDppYTsZUWKFY3FyguH4OSnZ0NRVHQsGFDm/ZFixYhIiICN910E6ZOnYrc3NwKj2E0GpGTk2PzICIiqs9cVT60bhKKdVN64dMxnave2YVcehdPUVERnn76aTzwwAMICbl6n/WIESOQlJSE6Oho7N27F9OnT8euXbvsel9KzZw5Ey+88IIrQyUiIvIqrpoJNkCvRfPGDVxy7JpQRGqfoaIoWLZsGe699167bWazGffffz9OnjyJDRs22BQo5aWmpqJjx45ITU1FSkqK3Xaj0Qij8eqMeTk5OYiLi0N2dnalxyUiIvIliU+vqPa+x1+704WR1E5OTg5CQ0Or9fntkks8ZrMZf/nLX5CWloY1a9ZUGURKSgr8/Pxw5MgRh9sNBgNCQkJsHkRERPWNXlt/Zgdx+iWe0uLkyJEjWL9+PcLDw6t8z759+2A2mxETE+PscIiIiMgL1bhAycvLw9GjR9XXaWlp2LlzJ8LCwhAbG4v77rsPO3bswPfffw+LxYKMjAwAQFhYGPR6PY4dO4ZFixbhjjvuQEREBPbv348pU6agffv26NGjh/MyIyIiqqee6pvs7hCuWY0LlO3bt6NPnz7q68mTJwMARo0ahRkzZuDbb78FALRr187mfevXr0fv3r2h1+uxbt06zJ49G3l5eYiLi8Odd96J559/Hlpt7e7ZJiIiohKT+yXjsd7N3R3GNatxgdK7d29UNq62qjG3cXFx2LhxY02/LBEREVVjapMnbmvh+jjqQP0ZbUNERERegwUKERGRl6jd3LDeiQUKERGRl3D3+jh1iQUKERGRl7A4KFCeHtjSDZG4HgsUIiIiLxYZbFCfj+6e6L5AnMyla/EQERGRa5ktVvzvsW7YdSobD/VIdHc4TsMChYiIyIuZLYIOCWHokBDm7lCcipd4iIiIvNg1rPnr0VigEBEReTGDzjdnYWeBQkRE5MUCDSxQiIiIyMNoFN+cvo0FChERkRfzzfKEBQoREZFX89EOFBYoRERE3qxb8wh3h+ASLFCIiIi8wJmsQrs2nUZBaICfG6JxPRYoREREXiC7wGzX5qf13Y9x382MiIjIh+h19oNNHC0e6CtYoBAREXkBxcFo2GKr1Q2R1A0WKERERF7KhztQWKAQERF5A19dc6ciLFCIiIi8gLHYdy/nOMIChYiIyAuYLfY9KC/cfZMbIqkbLFCIiIi8gLXcJR6dRsGo7onuCaYOsEAhIiLyAuXHoIy7pZmbIqkbLFCIiIi8QPk7dnx9yCwLFCIiIi9Q/iae8pd8fA0LFCIiIi9gV5D4dn3CAoWIiMgb1LP6hAUKERGRNyg/SDY5KthNkdQNFihEREReoGx58vygGzGkfRO3xVIXalygbNq0CYMGDUJsbCwURcHy5ctttosIZsyYgdjYWAQEBKB3797Yt2+fzT5GoxETJ05EREQEgoKCcPfdd+P06dPXlAgREZEvKx2DckNMCB7qkQSNxn7xQF9S4wIlPz8fbdu2xZw5cxxuf/311zFr1izMmTMH27ZtQ3R0NPr164fc3Fx1n0mTJmHZsmVYsmQJNm/ejLy8PNx1112wWCy1z4SIiMiHld5m7NtlyVW6mr5h4MCBGDhwoMNtIoJ33nkHzzzzDIYMGQIAWLBgAaKiorB48WI8+uijyM7Oxscff4zPPvsMffv2BQAsXLgQcXFxWLt2LW6//fZrSIeIiMg3lY5B0dSTwRlOTTMtLQ0ZGRno37+/2mYwGNCrVy9s3boVAJCamgqz2WyzT2xsLFq1aqXuU57RaEROTo7Ng4iIqD4RtQelfvShOLVAycjIAABERUXZtEdFRanbMjIyoNfr0ahRowr3KW/mzJkIDQ1VH3Fxcc4Mm4iIyOPJlWGyPj70ROWSjiJFsf3uiYhdW3mV7TN9+nRkZ2erj1OnTjktViIiIm9gtV55UsXnqa9waoESHR0NAHY9IZmZmWqvSnR0NEwmEy5fvlzhPuUZDAaEhITYPIiIiOqTQ+dKbjbJLTK7OZK64dQCJSkpCdHR0VizZo3aZjKZsHHjRnTv3h0A0KFDB/j5+dnsk56ejr1796r7EBERka03Vh0CAPx5Pt/NkdSNGt/Fk5eXh6NHj6qv09LSsHPnToSFhSE+Ph6TJk3Cq6++ihYtWqBFixZ49dVXERgYiAceeAAAEBoaijFjxmDKlCkIDw9HWFgYpk6ditatW6t39RAREVH9VuMCZfv27ejTp4/6evLkyQCAUaNGYf78+Zg2bRoKCwsxYcIEXL58GV26dMHq1asRHHx1St63334bOp0Of/nLX1BYWIjbbrsN8+fPh1ardUJKRERE5O0UKT+5vxfIyclBaGgosrOzOR6FiIjqhcSnV6jPj792pxsjqb2afH7Xk+leiIiIyJuwQCEiIiKPwwKFiIiIPA4LFCIiIvI4LFCIiIg8nBfez3LNWKAQERF5OLOFBQoRERF5GJPFWvVOPoYFChERkYfLLqwf6++UxQKFiIjIw+05ne3uEOocCxQiIiIP9/aaw+rzpo0C3BhJ3WGBQkRE5OEOnctVny9+pKsbI6k7LFCIiIi8SHx4oLtDqBMsUIiIiMjjsEAhIiIij8MChYiIiDwOCxQiIiLyOCxQiIiIyOOwQCEiIvISGsXdEdQdFihEREReQluPKhQWKERERF5Co7BAISIiIg/DHhQiIiLyOH7a+vOxXX8yJSIi8nJv3t/W3SHUGRYoREREHq700k6bpqFujqTusEAhIiLyQH+ez0O+sRgWq8BiFQD16xKPzt0BEBERka3nlu/FZ7+eAACsfuoWtd1Py0GyRERE5CalxQkAfPxzmvq8PvWg1J9MiYiIvNAX20+pz1mgEBERkVuISIXbOA8KERERucWx83nuDsEjOL1ASUxMhKIodo/HH38cADB69Gi7bV27dnV2GERERF7JWGx1dwgewel38Wzbtg0Wi0V9vXfvXvTr1w/333+/2jZgwADMmzdPfa3X650dBhERkVfKKSx2dwgewekFSuPGjW1ev/baa2jevDl69eqlthkMBkRHR1f7mEajEUajUX2dk5Nz7YESERF5oHwjCxTAxWNQTCYTFi5ciIcffhhKmRUYN2zYgMjISCQnJ2Ps2LHIzMys9DgzZ85EaGio+oiLi3Nl2ERERG5TbOUlHsDFBcry5cuRlZWF0aNHq20DBw7EokWL8NNPP+Gtt97Ctm3bcOutt9r0kJQ3ffp0ZGdnq49Tp05VuC8REZE3K7ZWfBdPfeLSmWQ//vhjDBw4ELGxsWrbsGHD1OetWrVCx44dkZCQgBUrVmDIkCEOj2MwGGAwGFwZKhERkUcotjguUG5tGVnHkbiXywqUEydOYO3atfj6668r3S8mJgYJCQk4cuSIq0IhIiLyGqknLjtsn9wvuY4jcS+XXeKZN28eIiMjceedd1a638WLF3Hq1CnExMS4KhQiIiKvUXaa+7L8/bR1HIl7uaRAsVqtmDdvHkaNGgWd7monTV5eHqZOnYpffvkFx48fx4YNGzBo0CBERERg8ODBrgiFiIjIJwTo61eB4pJLPGvXrsXJkyfx8MMP27RrtVrs2bMHn376KbKyshATE4M+ffrgiy++QHBwsCtCISIi8gkB9awHxSUFSv/+/R2uJRAQEIBVq1a54ksSERH5tPpWoHAtHiIiIg/SKbGRw3aDrn59ZNevbImIiDxcbpHjmWQ19WglY4AFChERkUc5mJHr7hA8AgsUIiIi8jgsUIiIiDyE2cJ1eEqxQCEiIvIQRWaLu0PwGCxQiIiIPMTpy4XuDsFjsEAhIiLyEANn/+ywPSE8sI4jcT8WKERERB7u4R5J7g6hzrFAISIi8nANDC6Z+N2jsUAhIiLyAFar/RIxpZT6NUcbABYoREREHiHtYn6F21igEBERkVvkFJrV519P6I7EMgNj95zOcUdIbsUChYiIyAMUmkrmQGkR2QAp8Y0w5uarA2MPpLNAISIiIjfINZYsEqi5cj2nuMyYFH+/+vdxXf8yJiIi8kBzfjoKADh0rmSxwMW/nVS3tYgKdktM7sQChYiIyAPsOZNt8/pIZp76XK+tfx/X9S9jIiIiL6PX1b+P6/qXMRERkQcaktIEwNVZY3Waq/cWx4dxqnsiIiJyg3M5RQCA+LAAAMBrQ9uo2+5uG+uWmNyJBQoREZEH2HL0IgAg7ULJhG2hAX7qNo2m/s3UxgKFiIjIg+w7WzLnSf0rSWyxQCEiIvIgU2+/HkD9nN6+LBYoRERELrJ6XwbeXHUIIhUvBFjKcOVOnaaNSsagRIf6uzQ2T1f/1m8mIiKqA5/9chzPfbMPANAuriH63hhV4b5fbj8FY7EVABByZezJTbGheGVwK8Q2DHB9sB6IPShEREQuUFqcAFfHlVRk2le71ech/lcHx47okoA+10c6PzgvwAKFiIjIxSxWq7tD8DosUIiIiFzsP1fW2XGkOuNT6iMWKERERE5W3aLDahUMfX+ri6PxTk4vUGbMmAFFUWwe0dHR6nYRwYwZMxAbG4uAgAD07t0b+/btq+SIRERE3mXVvnPV2u/kpQLsOJmlvn7pnptcFJH3cUkPyk033YT09HT1sWfPHnXb66+/jlmzZmHOnDnYtm0boqOj0a9fP+Tm5roiFCIiojp3IN1+UGzZXpWzWYX4/PeTMFlsx6b0Sq6fA2IdccltxjqdzqbXpJSI4J133sEzzzyDIUOGAAAWLFiAqKgoLF68GI8++qgrwiEiIqpTLaIa2LUVmi0I1Jd87N75n59xucCMW5Ib2+xTdnr7+s4lPShHjhxBbGwskpKSMHz4cPz5558AgLS0NGRkZKB///7qvgaDAb169cLWrRVfgzMajcjJybF5EBERearJX+6ya1v46wn1+eUCMwBg0+HzNvsEGrSuDcyLOL1A6dKlCz799FOsWrUK//d//4eMjAx0794dFy9eREZGBgAgKsp2spqoqCh1myMzZ85EaGio+oiLi3N22ERERE5jKra/rfhsVslqxVZrxQNo/bS8d6WU078TAwcOxNChQ9G6dWv07dsXK1asAFByKaeUUm6BARGxaytr+vTpyM7OVh+nTp1ydthEREQulW8sBgBcKjC5ORLv4PJSLSgoCK1bt8aRI0fUcSnle0syMzPtelXKMhgMCAkJsXkQERF5qk6JjQAAL9/bSm3TaUv+EP9w4zGH72nVhJ9tZbm8QDEajThw4ABiYmKQlJSE6OhorFmzRt1uMpmwceNGdO/e3dWhEBER1QmDrmQsSQPD1XtROiSEAQB2ncp2+J7B7Zu6PjAv4vQCZerUqdi4cSPS0tLw22+/4b777kNOTg5GjRoFRVEwadIkvPrqq1i2bBn27t2L0aNHIzAwEA888ICzQyEiIqpzIoKz2YUAAK1GQf8riwQaiy0AgN+PX3L4vvbxDeskPm/h9NuMT58+jb/+9a+4cOECGjdujK5du+LXX39FQkICAGDatGkoLCzEhAkTcPnyZXTp0gWrV69GcHCws0MhIiKqU8fO5+G2tzaqr09fLkSAvqQ3pdBkqfB9n43pjJT4Ri6Pz5s4vUBZsmRJpdsVRcGMGTMwY8YMZ39pIiIit/rbR7/ZvN57JhvB/iUftUVmi8Mp8G+ICUHPFo3t2us73s9ERETkBLlFZqRnF9m0dW0eDn+/Kz0oZguMDm4/HprSpE7i8zYsUIiIiJzgUIb9ki1/7RSnXuL56Oc0h5d5DDp+FDvC7woREdE1MhZbcDHffn4TnVaDk5cKruxjRYHZvkDRcXI2h1yyFg8REVF9cvvbm3D8YoFN25Arl27KzipbcGWytrIKKhk8W5+xQCEiIqqlzJwivLTigE1xEuyvw0v3tMI97WIB2F7COX250OExyB4LFCIioloQEXR+dZ1de76xGPe2vzrwtXSQLAA8NH+b+rxJwwCcySpE/5sqnkm9PmOBQkREVEM/7s3A+IWpDreVXwtQX8Eg2B8n9cThc3nokMD5TxzhyBwiIqIaqqg4caRrs3CH7cH+fixOKsEChYiIyIUGtYmxa5s3upMbIvEuLFCIiIhcSFEUm9dt4xqiT8tIN0XjPTgGhYiIqJr2n83B7HWHodMoKC4/2KSawoP0To7KN7EHhYiIqJru+M/PWLXvnFqcLH6kS/Xe1zpafa7TKJXsSaVYoBAREVXDhTyjXVsDf/sLEe/+tb1d29r9merzE+UmdCPHeImHiIioEiKCfJMF3Wbaz3lSdo6TG2NCsPLJng6PYbJcnU320Dn7NXvIHgsUIiKiSoz85Hf8fOSCw23BZXpQHu3VrFrHSwwPdEpcvo4FChERUSUqKk4AICY0AKO7J2LX6SwMbGV/O7EjI7okOCs0n8YChYiIqBYm9G4OAJhx901V7ntdZAMczcwDAIzoGu/SuHwFB8kSERHVUFSIAeNuqd4lHQAYmtJUfR6oZ99AdbBAISIiqsBHP/9p17b4kS745enb0DCw+vOZDGgVXfVOZINlHBERkQMX8ox4ecUBu/auzcKhqeFcJkkRQVj5RE80DjY4KzyfxwKFiIjIgY4vr7Vr65IUVuPipNSNsSHXGlK9wgKFiIioHBHbaewn90vGxFuvs1tXh1yHBQoREVE5C7YeV5/f0y4WT9zWwn3B1FMcJEtERFROenaR+nz2cPup68n1WKAQEZHXs1gF+8/m4I1VB3E+14j07EIUmS21Pl6AvmQK+yHtmzgrRKohXuIhIiKvtnJPOqYu3YUCU0lB8t76YwAqXxunKievLOiXFBHknCCpxligEBGRV5uwaIfD9v3pObU+5rnckks8cWFcN8ddeImHiIionC1HLwIA/P34Meku/M4TEZHPEhFMWvIHZq05XO33lB27cvJSgSvComrgJR4iIvJZ87Ycx/KdZwEAjYMNeLBrxSsJJz69wq7tnnYcJOsuTu9BmTlzJjp16oTg4GBERkbi3nvvxaFDh2z2GT16NBRFsXl07drV2aEQEZGPsloF4z7djhnf7rNp3/L0rfj04c7q6xe/368+f2753gqPN2v1IYftUSH+1xgp1ZbTe1A2btyIxx9/HJ06dUJxcTGeeeYZ9O/fH/v370dQ0NXR0AMGDMC8efPU13p99RddIiKi+m3ZH2ewev85m7Y9M/oj2N8PTRoGVPg+U7EVep0G+cZiBOq16syw//npqN2+K5642blBU404vUD58ccfbV7PmzcPkZGRSE1NxS233KK2GwwGREdzdUciIqq55TvP2LUF+/tV+b6M7CLsT8/G+IU78ECXeLw6uDW+23XW4b43xYZec5xUey4fJJudnQ0ACAsLs2nfsGEDIiMjkZycjLFjxyIzM7PCYxiNRuTk5Ng8iIio/iq/Jk51Vwl+Z91hjF9Yclvy4t9OAgAmfv6Hc4Mjp3BpgSIimDx5Mm6++Wa0atVKbR84cCAWLVqEn376CW+99Ra2bduGW2+9FUaj0eFxZs6cidDQUPURFxfnyrCJiMjDnblse3fN+Vzbz4/H+zR3+L6vd9j2vOQWmR3ut/3ZvtcQHTmDS+/i+fvf/47du3dj8+bNNu3Dhg1Tn7dq1QodO3ZEQkICVqxYgSFDhtgdZ/r06Zg8ebL6Oicnh0UKEVE9dux8vs3r+HITqvlpr/79PaJLPBZd6S0pb9vxSzavXxncCp0TwxDRoHo9MuQ6LitQJk6ciG+//RabNm1C06ZNK903JiYGCQkJOHLkiMPtBoMBBgN/WIiI6rtlf5zGU1/ssm+f0N3mtYKrl4BeGdwam49ewImL9nOaPDx/u/o8PiwQQ1Oawt9P68SIqbacXqCICCZOnIhly5Zhw4YNSEpKqvI9Fy9exKlTpxATE+PscIiIyEeIiF1xcvy1O6v13od7JOH5crckl7d+am9oNUql+1DdcfoYlMcffxwLFy7E4sWLERwcjIyMDGRkZKCwsBAAkJeXh6lTp+KXX37B8ePHsWHDBgwaNAgREREYPHiws8MhIiI3sljFaccaPHdrtfctN4YWPa6LqPI9LE48i9MLlPfffx/Z2dno3bs3YmJi1McXX3wBANBqtdizZw/uueceJCcnY9SoUUhOTsYvv/yC4OBgZ4dDRERu8lXqabR6fhXWlJuvpDZeWbEfO09lVXv/sCDbubWaN658VeKvy10iIvdzySWeygQEBGDVqlXO/rJERORBTl4swNSlJZdjxn66vdqXYhw5ci4X//dzmk2bVqNgQm/Hd+oAwF86xmHHicvomVzSc6IoClpENsCRzDy7fW++LgIp8Y1qHR+5BtfiISIip7vljfU2r0XEbu6S6np/wzGb1z882ROJ4UEI0Fc8mFWv02DWsHY2bWsm91Jnkl27/xyeWPIHXr+vDe5qE1uruMi1uJoxERFds/O5Rtz73hasvXI5p0uS7eSc5ecfqYk1B65eIlr0SBfcEBNSaXFSGb2u5GOv741R2DPjdhYnHowFChERXbNOr6zFzlNZeOTT7dh67AJ+S7OdX2TKUvtbg6vj5MUC5BYVAwBGdUuo1mDX6uKgWM/GAoWIiK5J+bGHD/zfbw7323rsQpXHupRvwlurD+HUpZI5S97feHURv343cv22+oQFChERXZNf/rxY4bbbb4pSn1dUuJSV8tIavPvTUfR8fT3e33AMn/9+St12cwvn9Z6Q52OBQkRE16TstPLlPdb7OpvXBzMqXuy1/Ho6//7xoPq8YwLvsqlvWKAQEdE1mbX6sMP2/z3WDf5+th8zA9752eG+Vqug0ytrK/waUaH+tQ+QvBJvMyYiomvi6BJP6bwnxRZrle/v8+YGpF3Ir3SfEZ3jaxcceS32oBARUa1Zy0xlHxPqj9fva4Ml47qqbTqtBjMG3WjznqPlJksrX5z8846W6vNXBrfC4ke6oLsT794h78AeFCIiqrXLBSb1+TvD2qFLs3C7fUb3SMLuM9nqXCh9Z23E6/e1QWxoALo2C7Pbf9wtzREdGoBLeUaM6JLguuDJo7FAISKiWttw6Lz63FFxUmrWX9rZTNY27avdDvebPbwdAODutpxArb7jJR4iIqq12k7AVpG+N0RVvRPVC+xBISKiassuNKPtC6sBXO3tqK6OCY2w/cTlCrfv+ld/BBn4sUQl+JNARERVEhEkTV9p0/bkkp3q8w/+llLlMZaO74ZPthzHS9/vt9u2e0Z/hPj7XXOc5Dt4iYeIiKp06FxuhdsSwwMxoFVMlcdQFAVjbk7C60Pb2LTf2jKSxQnZYQ8KERHZsFgFjy/agR/3Zahtd7auuAB5/b62NTr+4JQmMFms6HdjFCxWQWSwodaxku9igUJERDaa/3OlXduKPek2r8OD9EiKCMLfuiagc5L9rcKV8dNq8LeuvH2YKscChYiIYCq2IvnZH6rcr/f1jfHB3zrA309bB1FRfcYxKEREhKe+3GnXFqi3L0L+PbQNixOqE+xBISKqZ0QEz32zF35aDc5mFWLagJZYsdv2Ek7azDugKIr6+tSlApgsVkSFcNE+qhssUIiI6plnlu/F4t9Oqq9X7TunPn/vgRTc0TrapjgBgLiwwDqLjwhggUJEVK8UmS02xUlZYUF63Nmm6tuFieoCCxQiIh9mtlix+3Q29qfn4Lnleyvdd9rt19dRVERVY4FCROQh5m44itd/PAQA+FvXeLx8b2sAwKQlfyAjpwiLH+kKjUap7BA2CkzFuPFfqyrc/txdN2JYpzgoAE5fLsT10cHXFD+RMykiIu4OoqZycnIQGhqK7OxshISEuDscIqJr9tufFzHsv7/atG1/ti/eWn0Yn/9ue0nm/g5N8cb9lU+OlnriMoa+v7XC7UdfGQidljdyUt2qyec3fzqJiNwop8iMExfz7YoTAJjz01G74gQAlqaexh8nHS+6V2S2YNPh8w6Lk5hQf4QH6fHztD4sTsjjsQeFiMhN9pzOxqA5m2v9/uOv3Wnz+pudZ2wW8KtoPyJ3YQ8KEZGHm/j5Hw6Lk4MvDcD4Xs3t2o+9egfaNA21aVt/MFN9/tL3++2Kk+/+fjOLE/Ja7EEhIroGGdlFCNBrERpQ8Wq8L3+/Hx9tTqvyWL8/cxsig/2Rnl2IbjN/Utv3v3g7AvUl9zRsOXoBIz76rcpjzX+oE3pfH1mNDIjqTk0+v3kXDxFRLWQXmPH3z3fg5yMXbNobGHRIfa4vfvvzEkZ+8nuVx/nfY90QGuCHxg38ERpYUuTEhAbg8MsD8cfJy0hJaAS/MuNFulSxMN+3f++BNk0b1jwhIg/j1ks8c+fORVJSEvz9/dGhQwf8/PPP7gyHiEiVbyzGHycv41xOEUo7mi/lmzB77REkPr0CbV9cbVecAECesRjXP/tjtYqTbc/0RYeEMFwXGawWJ6X0Og26NAu3KU4AQKfV4PE+9peAAGD6wJYsTshnuO0SzxdffIEHH3wQc+fORY8ePfDhhx/io48+wv79+xEfH1/pe3mJh8j9Tl0qwO9pl/DNrrM4ei4XZ7OLAACP3tIMTRoFIDzIgI82/4knbmuBPl5yqSH1xCV8ue00zuUWIfX4ZeQai51y3Dfvb4v+N0VBBJVeCqoNY7EFRWar049L5Ao1+fx2W4HSpUsXpKSk4P3331fbbrjhBtx7772YOXNmpe91VYFiKrbiXE4RLFaBVqNAr9PAKgKLVSACtc1Po0Gx1QqrlCy6ZRXAKgKrlOxnLdNWdnuxRWAstkCk5GtBAQw6DRoY/OCnVeCn1UBRAI2ioNwyGFBg21B+e8k+lTeIAMVWQbHFeuVfQbHVeuXfknaLVWC2CixWK4xmK/JNFpiKrTBbrDAWW3Ahz4TL+SY0DjagSaMAFJmtAICyc0dZpWT2Sp1GgVUEQQYdNIoCo9kCq5Tsq9GUZKRcyVWjlLzWKApQ9rXmShuufl80SslxL+ebcPxiAU5fLkCxRZBdaEZCeBAiQwywWAV5xmIYdBqIAMZiK7IKTMgtKvnACTJoEajXIVCvRQN/HbSKAo2i4FxOEfJNxTCarQgy6KDXaRAWpIdOoyCiQUnOpccs/fAyF1tRYCqGRqPAoNPC36/kL94CowXmK9/f0vNvFYFGUeDvp4HJIjBbrDAXW6EoQIBehwA/LfQ6TclDq4HBTwMFJT8/QXodtBrF5udOIIBAfV66TXDl39JtjtpQ8vXzjRYUmS0w6DTq+biQa0KAXoMAvQ7ZBSYUmCwwW6w4m12EQxm5OJddVKMP7ydva4G4sEBcyDMir6gYBSYLjMUWNAz0w7kcI1pGByNQr0Oh2YLsAhMEJT0RIlfPd4i/Dk0bBcJfr4XVWvIzW/qvRQQWixUWASxWKyzWkp/BQrMFVhFolZL/u8UWQU6RGdmFZhSZLSgwWbDnTDaSI4NxID2nWjmF+OuQc+XnaMdz/RAWpAcA5BaZoSgKTMVWvLn6EJpFBGF090TezktUhscXKCaTCYGBgVi6dCkGDx6stj/55JPYuXMnNm7caLO/0WiE0WhUX+fk5CAuLs7pBcrRzFz0nbXJaccjqs80Skkh5G2C/XW4q00M+t8UjTZNQvFb2iUczcxDs8ZBaBbRADfGsteWqLY8fpDshQsXYLFYEBUVZdMeFRWFjIwMu/1nzpyJF154weVx6TQaBPhpodUosFgFJou15K99RYFWo6DYUtJWqvSveY1S0hOgUV8rNttKXpc8D9BroaDkOrKCkr/sc4vMai+G5UqvS1nlS0hHv/Pt3mO3veQDQ6fVwE+jQKfVQKdRoNMq0Gk00GlLcvTTaEr+vdKjE2jQwXDlr3k/rYKGgXoE++twJqsQuUXFCPDT2nyt0r9WddqS76FGUdS/hA1+GmgUpcxf8WX+BWzbUeYv//L7ClBstSKigQGHz+Uiz2jBU/1aQKsoyMw14nKBCX7aknNpslihAPD30yLYX4dGgXpYRVBgsqDQZEGesRh5xmIUW0p6xKJCDAj294NBp0FOkRlGsxW5xmKYiq04m1WIc7lGFFusale9QOCn1SBIr4NVBMZiK4rMJb0NIQF+0GtLvreKosBPU/KzYRGB0Wwt6Y3TlvSWWK2CAnNJTCaLFabikp4rY7FV7b0r+T6KXS9T6c8acLWXqWyvFEp/RnH151RByX56nQb+flrotRq1F9AqQKBeCwAoNFkQ7O+HYH8d/LQKwoIMiGigR9qFfAzrFIeE8CC7n8VCkwUBV96/72w2vko9jUv5JlzMMyHfVIybYkPQwOAHvU6Di3lGZBWYAZRcqvD306JhoB80igKDTgODTguLCBQAWYUlE5pZrAKdRgONRoFOU/Jzq1UUaLUl/+o0CjSaK//f/LTqqrxmi7Xk50KvRcMAPwTqtTD4aQEBCs0W3BgbgoSwQESG+Nvkc0drLp5H5A5uvYun/HLeImLXBgDTp0/H5MmT1delPSjOlhgRhAMvDah0n9IuZb8rHzpEZKu0OAGAm2JDcVNsaCV7ExE55pYCJSIiAlqt1q63JDMz065XBQAMBgMMBkNdhVcpjUaBvgaLdREREVHNuWX0ll6vR4cOHbBmzRqb9jVr1qB79+7uCImIiIg8iNsu8UyePBkPPvggOnbsiG7duuG///0vTp48ifHjx7srJCIiIvIQbitQhg0bhosXL+LFF19Eeno6WrVqhZUrVyIhIcFdIREREZGH4Fo8REREVCe4mjERERF5NRYoRERE5HFYoBAREZHHYYFCREREHocFChEREXkcFihERETkcVigEBERkcdx62KBtVU6dUtOTo6bIyEiIqLqKv3crs4UbF5ZoOTm5gKAS1Y0JiIiItfKzc1FaGjlK5175UyyVqsVZ8+eRXBwMBSleisL5+TkIC4uDqdOnfKZ2Wd9MSeAeXkTX8wJ8M28fDEnwDfz8sWcgKt57d+/H9dffz00mspHmXhlD4pGo0HTpk1r9d6QkBCfOuGAb+YEMC9v4os5Ab6Zly/mBPhmXr6YEwA0adKkyuIE4CBZIiIi8kAsUIiIiMjj1JsCxWAw4Pnnn4fBYHB3KE7jizkBzMub+GJOgG/m5Ys5Ab6Zly/mBNQ8L68cJEtERES+rd70oBAREZH3YIFCREREHocFChEREXkcFihERETkcVigEFGFOIbee/BceQ+eq+rxuQLFl058eno6Ll265O4wXIbnyrNlZmaq614BvnG+9u3bh2nTpuHw4cPuDsWpfPFcAb55vniuqs+rCxSTyYR///vfmDNnDjZu3AgA1V6bx5OZTCaMGDECPXr0wKFDh9wdjlPwXHmP4uJijBkzBp07d0bfvn0xYsQIXLhwwavPl8lkwkMPPYTWrVujqKgIiYmJ7g7JKXzxXAG+eb54rmpBvNTKlSslPDxcunbtKikpKdKoUSN55plnpLCw0N2hXZPZs2dLQECAdO/eXf744w93h+MUPFfew2w2y4gRI6Rr166yYcMGmTVrlrRq1Up69uwp+/fvd3d4tfLxxx9LcHCwdO/eXXbv3m2zzWq1uimqa+eL50rEN88Xz1XteG2Bcv/998ujjz4qIiKXLl2SpUuXisFgkLffflsKCgrcHF3tPPDAA6Ioirz//vtqW05Ojhsjcg6eK+9x8uRJadGihXz22WdqW3p6ujRp0kQmTpwoGRkZboyudrp37y433HCDXL58WUREUlNTZeXKlXLo0CG1SPbGDz5fPFcivnm+eK5qxysLlGPHjkmTJk1k4cKFNu0TJ06UDh06yOrVq90U2bX55JNPpHnz5rJ582Y5efKkPProo3LffffJI488IkuXLnV3eLXy559/+tS5MpvNIuKb50pE5I8//pCAgAA5cuSIiIgUFRWJiMicOXPk+uuvly+//NKd4dVI6S/GrVu3SrNmzeSFF16Qu+++W5o1ayY33XSTREVFyfDhw90cZe350rkSufp/yxfPl6+dq+LiYhFx/bnyigJl1apVsnPnTvWbYrVaJTIyUubOnSsiov4VfuHCBWnZsqU89dRTkpub67Z4q6t8XiIit956qyQkJEhMTIzcd999Mn36dLnttttEURT59ttv3Rht9Rw9etSmYrZYLD5xrsrnJeL95+qVV16Rf/3rX/L555+rbUVFRZKQkCDPP/+8iIiYTCZ1W8eOHeWhhx5Sf7l6Ikc5iYiMHj1a/P39ZfTo0bJz507ZvXu3fPfdd+Lv7y8vvviim6KtvhUrVoiI7V+jBQUFkpSU5LXnSsQ+r9J/H3roIa89Xx9++KH897//lY0bN6pteXl5Xn+uSvPasGGDTbsrz5VHFyjz5s2T6Ohoad26tQQHB8uECRPkzJkzIiLy6KOPSps2bdR9S0/4a6+9JnFxcWqXkydylNeJEydEROSXX36R9u3by5dffmlTuIwdO1ZatGhh84PtST7++GOJj4+XDh06SJcuXeSzzz5T4x83bpzXnqvyeS1cuFCMRqOIlPz14I3n6rfffpP4+HhJSUmRgQMHSnBwsAwdOlSOHTsmIiJTp06V5ORkOXfunIiI2lW7YMECadiwoUeOHXKU03333ScHDhwQEZGMjAx59tln1d8fpd58802JiIjw2HP1/fffS5MmTURRFNmyZYuIlBT9IiUFyrRp07zuXIk4zstqtar/jzIzM73ufC1evFgiIyOlW7du0q5dO2ncuLG88sorIiKSnZ3ttefKUV6vvvqqut2V58pjC5SPPvpIrrvuOvn888/l/PnzsmjRIgkKCpKdO3eKiMj//vc/admypbzzzjsicrXL7Pz58xIQECA///yz22KvjKO8GjRoYDPIcuvWrXbjGQ4cOCB6vV62bt1axxFX7Z133lFz2rx5s/zrX/8SRVFk7ty5YrVa5bvvvpPk5GSvO1eO8tJoNPLee++pOWzevNmrzpWIyOTJk+XOO+8UkZIPuz179khCQoKMHz9esrKy5Ndff5WUlBSZMGGCiFz9q3b9+vUSGRkpu3btclvsFakop8cee0z9xelojNDnn38ujRo1kj179tRpvNXx888/y4ABA+Tvf/+7DBw4UDp27Gi3z9q1a6VTp05eda6qyqs0h/z8fLv3eur5WrRokbRt21Y++OADERE5c+aMzJkzR4KCgiQ7O1tERNasWeN156qyvMr+f3LVufK4AqW0in7ggQfkwQcftNmWnJwsO3bsEJGSv4ieeOIJiYuLs6ncVq9eLfHx8Woh4ymqyquieEv/Wvroo48kKirK4/5j5ufnS79+/dSuy9L/dD179pSmTZvKjz/+KEVFRTJx4kSvOVcileeVkJAgX3/9td17PP1cWa1WycrKkptvvlmmTp0qIldjnjt3rrRv3179RfT2229LYGCgfP3112qP0csvvyy9e/f2qAGKVeXUoUMHmT17doXvf+yxx2TIkCF1Emt1lX5/Dx8+LLNmzZI///xTtm/fLoGBgfLRRx+JyNXxGoWFhfL2229LUFCQV5wrkcrzKj13FfG081Wa0/z582XcuHE2g/43b94sycnJ8ssvv4iId56ryvL67bffKj2GM86VxxUopdq1ayePPPKIOrp54sSJcv3118uMGTPUv0yPHTumdjstXLhQjhw5IsOHD5e+ffs6rOg8QWV5/fLLLw67+c6cOSNDhw6V8ePHe9QPsYiI0WiUsLAwWbx4sYhc7bYcOnSoxMbGyoMPPii5ubly+PBh6dGjh9ecq6ryGjlypJw/f97ufZ52rlJTUyUrK8umrWPHjupdVaU9QSaTSYYMGSJ33323nDlzRkwmk/y///f/JDg4WHr16iX333+/BAQEyHvvvSci7r2LoqY5DR48WP78809137S0NDl69KiMGTNG4uPjZfny5SLi/jtDHOVVesnDbDbLlClTpHHjxmp+pdtycnJk2rRpHnmuRGqeV3meeL5SU1NtLk1nZWXZXOYVEdm5c6dER0fLpUuX1DZvOFe1yauUs8+V2wuUL7/8Uh555BF55513bO6jXrJkiSQkJEj//v0lPDxcWrZsKS+++KL06dNH2rRpI6+99pqIlPSkDBgwQG644QZp0qSJ9OjRQ9LS0tyUzVW1yatt27bqNcvLly/L559/Lk899ZSEh4fL7bffbneNr65VlNNf//pXadmypZw+fVpERBYuXCh9+vSRRx55RK677jq169LbzlVleZXtzfPEc/XVV19J06ZNpXnz5hIfHy//+te/1Dxmz54tDRo0UAvD0r/k/ve//0nTpk3VMQEiIkuXLpXnn39exo8fr47ncJfa5hQXF6fmdODAAXn88cclMjJSevfuLYcOHXJPMmU4yis9PV1ESn6xl/5y//PPPyUuLk6mTJkiIva9DV9++aXHnCuR2udV9sPs4MGDHnW+yuf03HPP2dwiXPaczJo1S3r06CEiV38eS3nS/yuR2udVdnyJK86V2wqUCxcuyH333SfR0dEyfvx4ufnmmyU2NlbmzZun7pOZmSlvvPGG9OrVy+Z619ixY2Xw4ME2g43S09M9okvdGXllZWXJhQsX1H3cfUeIo5xiYmLk008/FZGSLttmzZpJs2bNJDY2VgIDA+V///ufiIjodDp1pL5IyV+3nnyuapNXenq6vPnmmx5xrkREtm3bpo7P2rVrl8ydO1caN24sjz32mGRlZcmJEyekefPmao9D2V8y4eHh8vHHH7sr9Apda06llxDy8vJkzZo1smnTJrfkUV5leV28eFFExObuxblz54pOp1N7hIxGozrGwZNca15FRUViNBqluLhYVq1a5RHnqzo5WSwW9fLb4MGD5fHHH3dnyNXirLzy8/Nl9erVTj1XbitQli5dKp07d1b/AhIRueeeeyQpKUm9vm82m2X48OHy8ssvi8jVKnTy5MnSvHlzycvLExH3d4uV5Yy8Sq/3ecrttxXllJiYKMuWLRMRkVOnTsmqVatkwYIF6odDZmamNGvWzGPnBbnWvMrOXeAJ56r0/8H7778vTZs2tfngmjNnjnTu3FlmzpwpIiLvvfeeaLVam1shjx07Js2bN1eLME/grJy++uqrug28ClXl1bVrV3nppZfs3nfx4kXp3r273HPPPZKamir9+/eXzz77zGN+Bzorr379+nlMXjXNyWKxiNVqlebNm8v3338vIiKHDh2S4cOHy8mTJ+s2+Ep4Q15uW4tn8eLFaNq0KZo0aYK8vDwAwODBg3H8+HG89957yMzMhE6nw8WLF7F9+3YAgF6vx7lz53D48GEMHz4cQUFBADxrTRdn5BUQEAAAaNCggdvyKKuinE6cOIE5c+bg/PnzaNq0Kfr27YuRI0fCz88PALB+/Xro9XrcfPPN7gy/QteaV8+ePdVjecK5Kv1/kJaWhuTkZOh0OnXb6NGj0alTJ3zzzTc4fPgwHnvsMQwfPhzDhg3Diy++iJ07d+L1119HYGAgunbt6q4U7Dgrp27durkrBYeqyqtDhw744YcfsG/fPgCAxWIBAISFhWHs2LH49ttv0alTJ+j1egwdOtRjfgc6Ky+DwYAhQ4Z4RF41zUmj0WDbtm0IDAxESkoKJk2ahDZt2uDixYuIjIx0Sw6OeENedVKgbNq0CatWrUJxcbHa1qJFCzXx0l/uBw8exK233oqioiIsX74cADB9+nSsWLECPXr0wIQJE9CxY0fk5ORg3LhxdRF6pXwxr2vJSaPR4Pz58zh48CDmzJmDp556CkOGDEFERITbV+z0xbzWrFmDJ554ArNnz8bvv/+utvfo0QNbt25FRkYGgJIPgaCgINxzzz3QaDRYsWIFFEXBwoULcf/992PZsmW4//77sW3bNixatAixsbHuSskncwJql5eiKFi9ejUAQKvVwmQyYe7cuRgzZgxuueUW7N69G9999536B407uDKvwMBAr8wJAFauXIm9e/fi+uuvx5o1a7BlyxasXr0aBoOhzvMp5ZV5uaRf5orz58/LyJEjRVEUadu2rc2AyGPHjknjxo2lV69e8u9//1u6desmSUlJsm7dOmnbtq08++yz6r7Lli2Tf/zjH/LAAw94xJTAvpjXteT03HPPqfumpqbKvffeK0lJSTbrTriLL+Z19uxZueuuuyQyMlJGjBghrVu3ltDQUPW2v8LCQmnZsqWMGzdORGwHuPXs2VMee+wx9bXFYpH8/Hw5ePBg3SZRji/mJHLteZXOmSFSMsj8ySeflAULFtRtEg74Yl7OzOnll1+Wxo0be8TlUm/Oy2UFitlslrlz58rtt98uS5YskcDAQJk5c6bNrWSbN2+WsWPHSkpKivz9739Xb9t88MEHZejQoa4K7Zr4Yl7Ozqn07hZ388W88vPzZdSoUTJs2DCbW2c7deoko0ePFpGSwYeffvqpaDQamztyRERGjBghffr0UV97wjV+X8xJxPl5eQpfzMsZOfXu3Vt9nZmZWTeBV8Hb83JpD8qvv/4q3333nYiIvPDCC9K4cWOHy9KXvQXr3Llz0qpVK3UAaVUT97iDL+bljJxKR3l7El/Ma9y4cfLDDz+IyNXYXnjhBenSpYu6T1FRkQwePFhuuOEG2bBhg1itVklPT5fOnTurd7R4El/MSYR5eVNevpiTiHfn5dICpfxfMrGxsTJu3Dj11tqy2wsLC8VkMqkzWpadj8LT+GJevpiTiG/mVfYW2tL4//a3v8nYsWNt2goLC6V3794SGRkp/fv3l9jYWOnatatH3UlQyhdzEmFe3pSXL+Yk4t151cltxqV/nX755Zei0+lk9erVNttPnz4tc+fOlY4dO9rM3unpfDEvX8xJxHfzKtWzZ091rp2yi65lZGTI6tWr5ZVXXpFFixa5McKa88WcRJiXN/HFnES8J686nwelW7du0rdvX3WStdJrWosXL5Y333yzrsNxGl/MyxdzEvG9vI4dOyZRUVGyfft2ta38zJXexhdzEmFe3sQXcxLxrrzqrEApvfa1d+9e0Wq1Mnv2bHniiSckJSXFI2YVrS1fzMsXcxLxvbxKu2YXLFggzZs3V9tnzJgh48ePVwswb+KLOYkwL2/iizmJeGdebplJtlOnTqIoiiQkJMiPP/7ojhBcwhfz8sWcRHwrr8cff1ymTZsmq1evlsTERImMjJRVq1a5O6xr4os5iTAvb+KLOYl4V151WqAcPXpUWrVqZbO8ti/wxbx8MScR38ursLBQrrvuOlEURQwGg7qIpjfzxZxEmJc38cWcRLwvL13VU7k5j1arxdChQ/GPf/zDrbMfOpsv5uWLOQG+l5e/vz8SExPRr18/zJo1C/7+/u4O6Zr5Yk4A8/ImvpgT4H15KSJunoOciK6JxWKBVqt1dxhO5Ys5AczLm/hiToB35cUChYiIiDyO21YzJiIiIqoICxQiIiLyOCxQiIiIyOOwQCEiIiKPwwKFiIiIPA4LFCIiIvI4LFCIiIjI47BAISIiIo/DAoWIXGL06NFQFAWKosDPzw9RUVHo168fPvnkE1it1mofZ/78+WjYsKHrAiUij8QChYhcZsCAAUhPT8fx48fxww8/oE+fPnjyySdx1113obi42N3hEZEHY4FCRC5jMBgQHR2NJk2aICUlBf/85z/xzTff4IcffsD8+fMBALNmzULr1q0RFBSEuLg4TJgwAXl5eQCADRs24KGHHkJ2drbaGzNjxgwAgMlkwrRp09CkSRMEBQWhS5cu2LBhg3sSJSKnY4FCRHXq1ltvRdu2bfH1118DADQaDf7zn/9g7969WLBgAX766SdMmzYNANC9e3e88847CAkJQXp6OtLT0zF16lQAwEMPPYQtW7ZgyZIl2L17N+6//34MGDAAR44ccVtuROQ8XCyQiFxi9OjRyMrKwvLly+22DR8+HLt378b+/fvtti1duhSPPfYYLly4AKBkDMqkSZOQlZWl7nPs2DG0aNECp0+fRmxsrNret29fdO7cGa+++qrT8yGiuqVzdwBEVP+ICBRFAQCsX78er776Kvbv34+cnBwUFxejqKgI+fn5CAoKcvj+HTt2QESQnJxs0240GhEeHu7y+InI9VigEFGdO3DgAJKSknDixAnccccdGD9+PF566SWEhYVh8+bNGDNmDMxmc4Xvt1qt0Gq1SE1NhVartdnWoEEDV4dPRHWABQoR1amffvoJe/bswVNPPYXt27ejuLgYb731FjSakiFxX375pc3+er0eFovFpq19+/awWCzIzMxEz5496yx2Iqo7LFCIyGWMRiMyMjJgsVhw7tw5/Pjjj5g5cybuuusujBw5Env27EFxcTHeffddDBo0CFu2bMEHH3xgc4zExETk5eVh3bp1aNu2LQIDA5GcnIwRI0Zg5MiReOutt9C+fXtcuHABP/30E1q3bo077rjDTRkTkbPwLh4icpkff/wRMTExSExMxIABA7B+/Xr85z//wTfffAOtVot27dph1qxZ+Pe//41WrVph0aJFmDlzps0xunfvjvHjx2PYsGFo3LgxXn/9dQDAvHnzMHLkSEyZMgXXX3897r77bvz222+Ii4tzR6pE5GS8i4eIiIg8DntQiIiIyOOwQCEiIiKPwwKFiIiIPA4LFCIiIvI4LFCIiIjI47BAISIiIo/DAoWIiIg8DgsUIiIi8jgsUIiIiMjjsEAhIiIij8MChYiIiDzO/wfqLeHcvm6CAwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "apple_share_price_data.plot(x=\"Date\", y=\"Open\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting Dividends\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dividends are the distribution of a companys profits to shareholders. In this case they are defined as an amount of money returned per share an investor owns. Using the variable `dividends` we can get a dataframe of the data. The period of the data is given by the period defined in the 'history` function.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "1987-05-11 00:00:00-04:00    0.000536\n",
       "1987-08-10 00:00:00-04:00    0.000536\n",
       "1987-11-17 00:00:00-05:00    0.000714\n",
       "1988-02-12 00:00:00-05:00    0.000714\n",
       "1988-05-16 00:00:00-04:00    0.000714\n",
       "                               ...   \n",
       "2021-11-05 00:00:00-04:00    0.220000\n",
       "2022-02-04 00:00:00-05:00    0.220000\n",
       "2022-05-06 00:00:00-04:00    0.230000\n",
       "2022-08-05 00:00:00-04:00    0.230000\n",
       "2022-11-04 00:00:00-04:00    0.230000\n",
       "Name: Dividends, Length: 77, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple.dividends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the dividends overtime:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Yer0eCxYswP79+7Fo0aJqaJGIiOjWRAQneNE41bM6sMTFxeHKlSuYOXMm0tPT0bp1a2zcuBEBAQEAzBeKu/maLO3atbP8d1JSElavXo2AgACcPXsWAJCdnY0xY8YgIyMDnp6eaNeuHbZu3YqOHTveQWtERER/LSuvGDkFBmg0QJAXA4taaURElC6iOuj1enh6eiInJwceHh5Kl0NERDXEzlNZePy/uxHQ0BW/vNBT6XJqncq+f/PcLSIiqtV4wbiawepDQkRERDVB9rViLPzpJHILS26734Hz2QCAIAYWVWNgISIiu7Ty13P4cPuZSu/f2u/Wt48h5TGwEBGRXTpwPgcAEBvhg0j/erfdt4GbM/q35j3s1IyBhYiI7NKRC+bAMuqeQHRq0VDhauhOceiWiIjszpW8IlzMKQQARPjxzFF7wMBCRER258hFPQAgsJEb3F2cFK6GqgMDCxER2Z1D1w8HteLqit1gYCEiIrtz5KI5sLRuwjN/7AUDCxER2Z3DF8yHhHiqsv1gYCEiIruSc82A1D+uAQBaN+EhIXvBwEJERHblSLr5cFDT+nVQz9VZ4WqoujCwEBGRXTnCw0F2iYGFiIjsymHLwC0PB9kTBhYiIrIrh0tPaeYZQnaFgYWIiOxGXlEJTmflA+AhIXvDwEJERHbjaLoeIoCPhw5e7jqly6FqxJsfEhFRjbA9JQvHMvS33WdfWjYArq7YIwYWIiJSvfScAoz8eDdMUrn92zRlYLE3DCxERKR6B9KyYRKgUV1n3NOy0W33dXdxwhOdA2xUGdkKAwsREale6aX27wvzwRsP/03hakgJHLolIiLV47VViIGFiIhUTUR4bRViYCEiInXLzC1CVl4xtBog3JcrLLUVAwsREala6epKS++6qOPsoHA1pBQGFiIiUrXDvJkhgYGFiIhUrnTglvMrtRsDCxERqdqR64eEWvtxfqU2Y2AhIiLVupJXhIs5hQC4wlLbMbAQEZFqHblonl9p0cgNdXW81mltxsBCRESqxfkVKsXAQkREqnXEcoYQ51dqOwYWIiJSrUOlA7dcYan1eECQiIhsLq+oBL+fuwqTyC33KS4xIfWPawCAVlxhqfUYWIiIyOaeX/07fj5+uVL7Nq1fB/Vcne9yRaR2DCxERGRT+kIDtqZkAQAiGntAo7n1vlqNBk91aW6bwkjVGFiIiMimdqRkwWgStGjkho3juyldDtUQHLolIiKb2nL9UFD3UC+FK6GahIGFiIhsRkTwywlzYOkR6q1wNVSTMLAQEZHNHMvIRYa+EC5OWnQKbKB0OVSDMLAQEZHNlB4OimnREC5ODgpXQzUJAwsREdnMluOZAHg4iKzHwEJERDahLzQg6dxVAEAPDtySlRhYiIjIJnakZKHk+unMAQ3dlC6Hahheh4WIiO5Y6pVr0BcabrvPhoPpAHg6M1UNAwsREd2R749k4NlPkiq9P+dXqCoYWIiI6I7sPGm+zL67zhF1XW7/thLm646YFg1tURbZGQYWIiK6I8cv5QIAXn2wFYZGN1W4GrJXHLolIqIqExEczzAHllBfd4WrIXvGwEJERFWWlVeMq9cM0GiAlt51lS6H7BgDCxERVVnK9cNBzRu68cq1dFcxsBARUZWVzq+E+HB1he4uBhYiIqqyE9cDS6gP51fo7qpSYElISEBgYCBcXFwQHR2Nbdu23XLf9PR0PP744wgNDYVWq8WECRMq3O+LL75AREQEdDodIiIisH79+qqURkRENlQ6cBvMwEJ3mdWBZd26dZgwYQJefvll7Nu3D926dUP//v2Rmppa4f5FRUXw8vLCyy+/jMjIyAr32bVrF+Li4jBixAgcOHAAI0aMwKOPPordu3dbWx4REdmIiODEpTwAPEOI7j6NiIg1D+jUqROioqKwePFiy7bw8HAMHjwYc+fOve1je/TogbZt22L+/PlltsfFxUGv12PTpk2Wbf369UP9+vWxZs2aStWl1+vh6emJnJwceHh4VL4hIiKqkgvZBej6+k9wctDgyIx+cHbklAFZr7Lv31b9dBUXFyMpKQmxsbFltsfGxmLnzp1VqxTmFZabn7Nv3763fc6ioiLo9foyH0REZDul8ystGtVlWKG7zqqfsKysLBiNRvj4+JTZ7uPjg4yMjCoXkZGRYfVzzp07F56enpYPf3//Kv/7RERkvRPX51dCeDiIbKBKkVij0ZT5XETKbbvbzzllyhTk5ORYPtLS0u7o3yciIusct5whxFOa6e6z6l5CjRo1goODQ7mVj8zMzHIrJNbw9fW1+jl1Oh10Ol2V/00iIrozpYeEeIYQ2YJVKyzOzs6Ijo5GYmJime2JiYno0qVLlYuIiYkp95ybN2++o+ckIqK7x2gSpJSeIcTAQjZg9d2aJ02ahBEjRqB9+/aIiYnBkiVLkJqaivj4eADmQzUXLlzAihUrLI/Zv38/ACAvLw+XL1/G/v374ezsjIiICADA+PHjce+99+KNN97AoEGD8PXXX+OHH37A9u3bq6FFIiKqbql/XENRiQkuTlr4N3BVuhyqBawOLHFxcbhy5QpmzpyJ9PR0tG7dGhs3bkRAQAAA84Xibr4mS7t27Sz/nZSUhNWrVyMgIABnz54FAHTp0gVr167FK6+8gqlTpyIoKAjr1q1Dp06d7qA1IiK6WywXjPN2h4P2zmYYiSrD6uuwqBWvw0JEVL30hQYs+eU0DpzPLve1i9kFOHU5H0OjmuLtRyu+KChRZVT2/dvqFRYiIrJvJUYT1u1Nw7zNJ3Alv/i2+7ZrVs82RVGtx8BCREQW21OyMPvbZBy7fsgnyMsNf78nEK7ODuX2ddc5oXuol61LpFqKgYWIiHD6ch7mbDyKH45mAgA86zhhYu9gDO8cACcHXsWWlMfAQkRUi+VcM2DBTylYvvMsSkwCR60GI2ICMP6+YNRzdVa6PCILBhYiolqoxGjC6j2peCfxBK5eMwAAeoV546UB4WjpzSvXkvowsBAR1TK/nLiM2RuSkZJpvvBbsHddvDIwAt1DOI9C6sXAQkRUS5zMzMNr3ybj5+OXAQD1XZ0wKTYUwzr4w5FzKqRyDCxERHYu+1ox5v+Qgk9+PQfj9TmVp7o0x/P3BcOzjpPS5RFVCgMLEZGdMhhNWPnrOcz/IQU5BeY5ld7hPnhpQBhaeHFOhWoWBhYiIjsjIthy/DJmfZuM05fzAQBhvu6YOjACXVs2Urg6oqphYCEisiMnLuVi9rdHsfWEeU6loZsz/hkbirgO/rznD9VoDCxERHbgj/xivJN4Aqv3pMJoEjg5aPD3roEY16slPFw4p0I1HwMLEVENVlxiwopdZ/HujynILSwBAPRr5YspA8IQ0NBN4eqIqg8DCxFRDSQi+PFoJl7beBRnssxzKuGNPTBtYARighoqXB1R9WNgISKqYY5l6DFrQzJ2nLwCAGhUV4cX+obg4WjOqZD9YmAhIqohsvKKMC/xBNbuSYVJAGcHLUZ1C8TYHkFw55wK2TkGFiIilSsqMWL5zrN478eTyC0yz6nc36Yx/t0/DP4NXBWujsg2GFiIiFRKRLA5+RLmbDyKc1euAQBaN/HAtIGt0DGwgcLVEdkWAwsRkQoduZiDWRuS8evpPwAA3u46vNA3FEOjmkLLORWqhRhYiIhU5HJuEd7efBzr9qZBBNA5ajHm3haI7x4ENx1/ZVPtxZ9+IiIVKDQY8fGOM0j4+RTyrs+pPBDphxf7haJpfc6pEDGwEBEpSESw6XAG5mw8ivNXCwAAkU09MXVgBNo355wKUSkGFiIihRw6b55T2XPWPKfi6+GCF/uHYlBkE86pEN2EgYWIyMYy9YV46/vj+Pz38xABXJy0ePbeIDzbvQVcnflrmagifGUQEdlIocGID7edRsKWU7hWbAQADG7rh8n9wuBXr47C1RGpGwMLEdFdJiLYcDAdr286hgvZ5jmVtv71MO2BCEQ1q69wdUQ1AwMLEdFddCAtGzM3JCPp3FUAgJ+nC17sH4YHI/2g0XBOhaiyGFiIiO6CjJxCvPndMXy57wIAoI6TA57rEYTR3VqgjrODwtUR1TwMLERE1aig2IglW0/j/V9OocBgnlN5KKoJJvcNg6+ni8LVEdVcDCxERNXAZBL878BFvPHdMaTnFAIA2gfUx9SBEYj0r6dscUR2gIGFiOgO/Z56FTO/Scb+tGwAQJN6dTBlQBjub9OYcypE1YSBhYioii5mF+CN747h6/0XAQCuzg4Y17MlRt0TCBcnzqkQVScGFiIiK10rLsH7v5zGkq2nUGgwQaMBHoluin/FhsLbg3MqRHcDAwsRUSWZTIKv9l/AG98dwyV9EQCgY2ADTBsYgdZNPBWujsi+MbAQEVXC3rN/YOaGZBw8nwMA8G9QBy/1D0e/1r6cUyGyAQYWIqLbOH/1Gl7fdAwbDqYDAOrqHDGuZ0s83bU551SIbIiBhYioAnlFJVi85ST+u+0MikvMcyqPdfDHpD6h8HLXKV0eUa3DwEJEdAOTSfD57+fx1vfHcTnXPKfSuUUDTB0YgVZ+nFMhUgoDCxHRdbtPX8Gsb5Nx+IIeABDQ0BUvDQhHbIQP51SIFMbAQkS1Xtof1zB301FsPJQBAHDXOeIf9wVjZJcA6Bw5p0KkBgwsRFRr5RYasOjnU/h4+xkUG03QaoBhHZthYp8QNKrLORUiNWFgIaJax2gSfLY3Df/ZfBxZecUAgHtaNsIrA8MR5uuhcHVEVBEGFiKqVXaeysKsDUdxNN08p9KikRtevj8cvcK8OadCpGIMLERUK5zNysecjUexOfkSAMDDxRHje4dgROcAODtqFa6OiP4KAwsR2TV9oQELfzqJpTvOwGAUOGg1GN6pGSb0DkEDN2elyyOiSmJgISK7VGI0Yd3eNMzbfAJX8s1zKveGeGHq/eEI9nFXuDoishYDCxHZne0pWZj9bTKOZeQCAIK83PDKwAj0DPVWuDIiqioGFiKyG6cv52HOxqP44WgmAMCzjhMm9g7G8M4BcHLgnApRTcbAQkQ1Xs41Axb8lILlO8+ixCRw1GowIiYA4+8LRj1XzqkQ2QMGFiKqsUqMJqzek4p3Ek/g6jUDAKBXmDdeGhCOlt51Fa6OiKoTAwsR1Ui/nLiM2RuSkZKZBwAI9q6LVwZGoHuIl8KVEdHdwMBCRDXKycw8vPZtMn4+fhkAUN/VCZP6hGBYx2Zw5JwKkd1iYCGiGiH7WjHm/5CCT349B+P1OZWnujTH872C4enqpHR5RHSXMbAQkaoZjCas/PUc5v+QgpwC85xK73AfvDQgDC28OKdCVFtUaf00ISEBgYGBcHFxQXR0NLZt23bb/X/55RdER0fDxcUFLVq0wPvvv1/m68uWLYNGoyn3UVhYWJXyiMgOiAh+PpaJvvO3YsY3ycgpMCDM1x2rnumED59sz7BCVMtYvcKybt06TJgwAQkJCejatSs++OAD9O/fH8nJyWjWrFm5/c+cOYMBAwZg9OjRWLlyJXbs2IGxY8fCy8sLQ4cOtezn4eGB48ePl3msi4tLFVoiopruxKVczP72KLaeMM+pNHRzxj9jQxHXwR8OWt6gkKg20oiIWPOATp06ISoqCosXL7ZsCw8Px+DBgzF37txy+7/44ov43//+h6NHj1q2xcfH48CBA9i1axcA8wrLhAkTkJ2dXcU2AL1eD09PT+Tk5MDDg7eHJ6qJ/sgvxjuJJ7B6TyqMJoGTgwZ/7xqIcb1awsOFcypE9qiy799WHRIqLi5GUlISYmNjy2yPjY3Fzp07K3zMrl27yu3ft29f7N27FwaDwbItLy8PAQEBaNq0KQYOHIh9+/bdtpaioiLo9foyH0RUMxWXmPDhttPo/tbPlqHavq18kDixO6YMCGdYISLrDgllZWXBaDTCx8enzHYfHx9kZGRU+JiMjIwK9y8pKUFWVhYaN26MsLAwLFu2DG3atIFer8e7776Lrl274sCBAwgODq7weefOnYsZM2ZYUz4RqYyI4MejmXht41GcycoHAIQ39sC0gRGICWqocHVEpCZVOktIoyl7DFlEym37q/1v3N65c2d07tzZ8vWuXbsiKioK7733HhYsWFDhc06ZMgWTJk2yfK7X6+Hv729dI0SkmGMZeszakIwdJ68AABrV1eGFviF4OJpzKkRUnlWBpVGjRnBwcCi3mpKZmVluFaWUr69vhfs7OjqiYcOK/4LSarXo0KEDUlJSblmLTqeDTqezpnwiUoGsvCLMSzyBtXtSYRLA2UGLUd0CMbZHENx56IeIbsGqwOLs7Izo6GgkJiZiyJAhlu2JiYkYNGhQhY+JiYnBN998U2bb5s2b0b59ezg5VfzLSUSwf/9+tGnTxpryiEjFikqMWL7zLN778SRyi0oAAPe3aYx/9w+DfwNXhasjIrWz+pDQpEmTMGLECLRv3x4xMTFYsmQJUlNTER8fD8B8qObChQtYsWIFAPMZQQsXLsSkSZMwevRo7Nq1Cx999BHWrFljec4ZM2agc+fOCA4Ohl6vx4IFC7B//34sWrSomtokIqWICL4/cglzNx3FuSvXAACtm3hg2sBW6BjYQOHqiKimsDqwxMXF4cqVK5g5cybS09PRunVrbNy4EQEBAQCA9PR0pKamWvYPDAzExo0bMXHiRCxatAh+fn5YsGBBmWuwZGdnY8yYMcjIyICnpyfatWuHrVu3omPHjtXQIhEp5cjFHMzakIxfT/8BAPBy12Fy31AMjWoKLedUiMgKVl+HRa14HRYi9bicW4S3Nx/Hur1pEAF0jlqM7tYCz/UIgpuOdwQhoj9V9v2bvzmIqNoUGoz4eMcZJPx8CnnX51QeiPTDi/1C0bQ+51SIqOoYWIjojokINh3OwJyNR3H+agEAILKpJ6YOjED75pxTIaI7x8BCRHfk0HnznMqes+Y5FV8PF7zYPxSDIptwToWIqg0DCxFVSaa+EG99fxyf/34eIoCLkxbP3huEZ7u3gKszf7UQUfXibxUiskqhwYgPt51GwpZTuFZsBAAMbuuHyf3C4FevjsLVEZG9YmAhokoREWw4mI7XNx3DhWzznEpb/3qY9kAEoprVV7g6IrJ3DCxE9JcOpGVj5oZkJJ27CgDw83TBi/3D8GCk323vI0ZEVF0YWIjoljJyCvHmd8fw5b4LAIA6Tg54rkcQRndrgTrODgpXR0S1CQMLEZVTUGzEkq2n8f4vp1BgMM+pPBTVBJP7hsHX00Xh6oioNmJgISILk0nwvwMX8cZ3x5CeUwgAaB9QH1MHRiDSv56yxRFRrcbAQkQAgN9Tr2LmN8nYn5YNAGhSrw6mDAjD/W0ac06FiBTHwEJUy13MLsAb3x3D1/svAgBcnR0wrmdLjLonEC5OnFMhInVgYCGqpa4Vl+D9X05jydZTKDSYoNEAj0Q3xb9iQ+HtwTkVIlIXBhaiWsZkEny1/wLe+O4YLumLAAAdAxtg2sAItG7iqXB1REQVY2AhqkX2nv0DMzck4+D5HACAf4M6eKl/OPq19uWcChGpGgMLUS1w/uo1vL7pGDYcTAcA1NU5YlzPlni6a3POqRBRjcDAQmTH8opKsHjLSfx32xkUl5jnVB7r4I9JfULh5a5TujwiokpjYCGyQyaT4PPfz+Ot74/jcq55TqVziwaYOjACrfw4p0JENQ8DC5Gd2X36CmZ9m4zDF/QAgICGrnhpQDhiI3w4p0JENRYDC5GdSL1yDXM3HcWmwxkAAHedI/5xXzBGdgmAzpFzKkRUszGwENVwuYUGLPr5FD7efgbFRhO0GmBYx2aY2CcEjepyToWI7AMDC1ENZTQJPtubhv9sPo6svGIAwD0tG+GVgeEI8/VQuDoiourFwEJUA+08lYVZG47iaLp5TiWwkRteuT8cvcK8OadCRHaJgYWoBjmblY85G49ic/IlAICHiyPG9w7BiM4BcHbUKlwdEdHdw8BCVAPoCw1Y+NNJLN1xBgajwEGrwfBOzTChdwgauDkrXR4R0V3HwEKkYiVGE9b+loZ3Ek/gSr55TuXeEC9MvT8cwT7uCldHRGQ7DCxEKrU9JQuzNiTj+KVcAECQlxteGRiBnqHeCldGRGR7DCxEKnP6ch7mbDyKH45mAgA86zhhYu9gDO8cACcHzqkQUe3EwEKkEjnXDFjwUwqW7zyLEpPAUavBiJgAjL8vGPVcOadCRLUbAwuRwkqMJqzek4p3Ek/g6jUDAKBXmDdeGhCOlt51Fa6OiEgdGFiIFPTLicuYvSEZKZl5AIBg77p4ZWAEuod4KVwZEZG6MLAQKeBkZh5e+zYZPx+/DACo7+qESX1CMKxjMzhyToWIqBwGFiIbuppfjHd/TMEnv56D8fqcylNdmuP5XsHwdHVSujwiItViYCGyAYPRhJW/nsP8H1KQU2CeU+kd7oOXBoShhRfnVIiI/goDC9FdJCLYcvwyZn2bjNOX8wEAoT7umDowAvcEN1K4OiKimoOBheguOXEpF7O/PYqtJ8xzKg3dnDEpNgRx7f05p0JEZCUGFqJq9kd+Md5JPIHVe1JhNAmcHDT4e9dAjOvVEh4unFMhIqoKBhaialJcYsKKXWfx7o8pyC0sAQD0beWDKf3D0byRm8LVERHVbAwsRHdIRPDj0Uy8tvEozmSZ51TCG3tg2sAIxAQ1VLg6IiL7wMBCdAeOZegxa0Mydpy8AgBoVFeHF/qG4OFofzhoNQpXR0RkPxhYiKogK68I8xJPYO2eVJgEcHbQYlS3QIztEQR3zqkQEVU7BhYiKxSVGLF851m89+NJ5BaZ51QGtPHFlP7h8G/gqnB1RET2i4GFqBJEBN8fuYS5m47i3JVrAIDWTTww9f4IdGrBORUioruNgYXoLxy5mINZG5Lx6+k/AABe7jpM7huKoVFNoeWcChGRTTCwEN1CZm4h5m0+gXV70yAC6By1GN2tBZ7rEQQ3HV86RES2xN+6RDcpNBjx8Y4zWPTTSeQXGwEAD0T64cV+oWhan3MqRERKYGAhuk5EsOlwBuZsPIrzVwsAAJFNPTF1YATaN2+gcHVERLUbAwsRgEPnzXMqe86a51R8PHR4sV8YBrdtwjkVIiIVYGChWi1TX4i3vj+Oz38/DxHAxUmLZ+8NwrPdW8DVmS8PIiK14G9kqpUKDUZ8uO00EracwrXrcyqD2/phcr8w+NWro3B1RER0MwYWqlVEBBsOpuP1TcdwIds8p9LWvx6mPRCBqGb1Fa6OiIhuhYGFao39admYtSEZSeeuAgD8PF3wYv8wPBjpB42GcypERGrGwEJ2LyOnEG9+dwxf7rsAAKjj5IDnegRhdLcWqOPsoHB1RERUGQwsZLcKio1YsvU03v/lFAoM5jmVh6KaYHLfMPh6uihcHRERWUNblQclJCQgMDAQLi4uiI6OxrZt2267/y+//ILo6Gi4uLigRYsWeP/998vt88UXXyAiIgI6nQ4RERFYv359VUojgskk+GrfBfR6ewve+eEECgxGRAfUx9fjumLeo20ZVoiIaiCrV1jWrVuHCRMmICEhAV27dsUHH3yA/v37Izk5Gc2aNSu3/5kzZzBgwACMHj0aK1euxI4dOzB27Fh4eXlh6NChAIBdu3YhLi4Os2bNwpAhQ7B+/Xo8+uij2L59Ozp16nTnXd6BohIjRP783GgS6AsNyCkwIOeaASUmufWDrzMYTcgpMEBfYIC+sAQlxr9+jNFkQmGJCYUGI4pLTH+5v4j53yksMaKg2FipuuxVpr4Ixy/lAgCa1KuDKQPCcH+bxpxTISKqwTQiYtU7W6dOnRAVFYXFixdbtoWHh2Pw4MGYO3duuf1ffPFF/O9//8PRo0ct2+Lj43HgwAHs2rULABAXFwe9Xo9NmzZZ9unXrx/q16+PNWvWVKouvV4PT09P5OTkwMPDw5qWbuuhhB34PTW72p6PbMPV2QHjerbEqHsC4eLEORUiIrWq7Pu3VSssxcXFSEpKwr///e8y22NjY7Fz584KH7Nr1y7ExsaW2da3b1989NFHMBgMcHJywq5duzBx4sRy+8yfP/+WtRQVFaGoqMjyuV6vt6aVO+Kg1cCzjhM86zjB2eGvj6pptRp41nFEvTrO8KjjCKfKPEajQR1nB7g4auHsqK3U6oCTgwYuTg5wcXSAo0PtXU1w0GoQE9QQ3u489ENEZC+sCixZWVkwGo3w8fEps93HxwcZGRkVPiYjI6PC/UtKSpCVlYXGjRvfcp9bPScAzJ07FzNmzLCm/CpZ+UwnGG84vKLVaODq7MDDC0RERDZUpaHbm9+sReS2b+AV7X/zdmufc8qUKcjJybF8pKWlVbp+a7g6O8Ldxcny4aZzZFghIiKyMatWWBo1agQHB4dyKx+ZmZnlVkhK+fr6Vri/o6MjGjZseNt9bvWcAKDT6aDT6awpn4iIiGooq1ZYnJ2dER0djcTExDLbExMT0aVLlwofExMTU27/zZs3o3379nBycrrtPrd6TiIiIqpdrD6tedKkSRgxYgTat2+PmJgYLFmyBKmpqYiPjwdgPlRz4cIFrFixAoD5jKCFCxdi0qRJGD16NHbt2oWPPvqozNk/48ePx7333os33ngDgwYNwtdff40ffvgB27dvr6Y2iYiIqCazOrDExcXhypUrmDlzJtLT09G6dWts3LgRAQEBAID09HSkpqZa9g8MDMTGjRsxceJELFq0CH5+fliwYIHlGiwA0KVLF6xduxavvPIKpk6diqCgIKxbt07xa7AQERGROlh9HRa1ulvXYSEiIqK7p7Lv31U6S4iIiIjIlhhYiIiISPUYWIiIiEj1GFiIiIhI9RhYiIiISPUYWIiIiEj1rL4Oi1qVnp1ty7s2ExER0Z0pfd/+q6us2E1gyc3NBQD4+/srXAkRERFZKzc3F56enrf8ut1cOM5kMuHixYtwd3e36d2U9Xo9/P39kZaWVuMvWMde1Im9qJO99GIvfQDsRa3+qhcRQW5uLvz8/KDV3npSxW5WWLRaLZo2barYv+/h4VHjf6hKsRd1Yi/qZC+92EsfAHtRq9v1cruVlVIcuiUiIiLVY2AhIiIi1WNguUM6nQ7Tp0+HTqdTupQ7xl7Uib2ok730Yi99AOxFraqrF7sZuiUiIiL7xRUWIiIiUj0GFiIiIlI9BhYiIiJSPQYWIiIiUj0GFiKFce6d7ib+fKkTvy/WY2D5CyUlJZb/5g+YOpw/fx7p6ekAav73JDMz03IfLKBm93Py5EkkJiYqXcYdS0tLQ1JSEi5evKh0KXcsJycHRqPR8nlN/vk6ceIE4uPjsW3bNqVLuWN83VcNA8stFBcX49///jfGjh2L6dOno6CgwKb3KKpOBoMBS5cuxfr163Hs2DGly6kyg8GAZ599Fl26dMEnn3wCADX2e1JSUoJRo0ahY8eO6N27N4YPH46srKwa28/BgwcREhKCYcOG4dy5c0qXUyWlP19RUVH4+9//jsjISOzYsUPpsqrEYDBg3LhxGDBgAAYMGIBZs2bBaDTWyJ8vk8mEiRMnom3btsjPzy/zRl/T8HV/ZxhYKvDVV18hICAAe/bsgYuLC9566y2MGTMGIlLjkvAHH3wAHx8ffPzxx5gwYQKGDh2KTz/9FID5F0FNkZaWhq5du+LQoUP47LPPMGzYsBr5/QDMv7SeeuopJCcnY/ny5Rg2bBgOHjyIhx56CEePHlW6vCopLi5G37594eTkhDfffFPpcqyWl5eHhx9+GCkpKdi8eTM+/fRTREVFYerUqQBq1l/AiYmJiIiIwJEjR/DCCy/A398fq1atwquvvgqgZvUCAJs2bcJvv/2GTZs24ZNPPsGAAQMsX6tJvfB1Xw2EyigsLJT+/fvLSy+9ZNn21VdfiaurqxQUFChYmXUMBoO888470qZNG1m1apWIiBw4cECef/55iY6OFqPRqHCF1vnwww+ld+/eYjKZREQkLS1NiouLFa6qalJTUyU4OFg++eQTy7b09HRp0qSJPP/885KRkaFgdVXzwQcfyLBhw+THH38UR0dH2b17t9IlWWX37t0SHBwsP/30k2Xbf//7X3nwwQdr1GslJydHnnnmGRk3bpzl9VFUVCTTp0+Xvn37Sn5+vsIVWm/w4MEybtw4ERHZsmWLvPLKK7J06VI5d+6cwpVZh6/7O8cVlpscPHgQW7ZswX333WfZlpGRgTFjxtSYFQkRgcFgsPzV+NhjjwEA/va3v6FVq1ZwdHTE5cuXFa7yr8kNKyh79+5FZGQksrOz8eijj6JPnz7o2LEjxowZg4yMDIUrtc6VK1dw/vx5dO7cGQBQVFQEX19fTJkyBZs3b8bWrVsVrrBybnw96HQ6BAQEoFevXujQoQNmzJgBwHxb+ZqguLgYJ0+etFw6PCsrC4sWLYKfnx8+/vhjFBQUKFxh5YgI7rnnHjzzzDNwcnKCiMDZ2RmFhYUoKCiAq6trjVqVyM3NRVZWFu677z7Mnj0bjz32GA4dOoRp06ahV69e+Oabb5QusdLs5XV/48+PrV/3tT6wbN68GQcOHLAMpnXo0AENGjTAwoULsWnTJrzwwgsYO3YsfvrpJwQHB2Px4sWWN3u1vfBPnToFk8kEjUYDFxcXDB8+HNOmTYNWq7XUWr9+feTl5cHb21vham/t1KlTEBFoNBrLsd3Dhw8DAObPnw8AWLhwIeLj4/HNN99g+vTpuHDhAgD1fU/mzJmD6dOnY+3atZZt4eHh8Pb2xsqVKwEAWq35ZThu3Di4u7tj06ZNKCoqUqTe27m5l9K6AeD3339HXl4eAGDVqlX47rvv0L9/f/Tt21d1c1MVfU/uuecedO/eHU8//TT69+8PHx8f+Pr6wtnZGVOmTMGTTz6JQ4cOKVh1xTZu3Ajgz/Do6emJJ598Em3bti2zPScnBy1atACg3rmv0l5ufA27u7vDYDDgww8/xIkTJ/Dll1/i888/x7lz5xAUFISPP/5YdT9fALBkyRL897//LRNCgoOD4evrW+Ne96W9/PLLLwDMPz+lP1c2f93f1fUbFVu6dKn4+vpKmzZtxN3dXcaOHStpaWkiYl52HDt2rHTs2FFatmwpP/74oxw/flxmz54twcHBsnz5coWrL+ujjz6SZs2aSXR0tHTq1ElWrFhhOXQiImWWtJ9++ml54oknRERUd0jl5j5WrlwpRUVFIiLyn//8RxwcHCQkJER+++03y2OWLl0qrVq1km+++Uapsiu0e/duadasmURFRUn//v3F3d1dhg4dKqdOnRIRkX/9618SEhIily5dEhGxHG5cvny51KtXT1WHHyvq5eGHH5aUlBTLPo899pj88MMPImI+lFKnTh1xcnKSzz//XKmyy7lVH8eOHRMREb1eLykpKdKlSxf5z3/+Y3ncvn37pEWLFvLpp58qVXo5GzZskCZNmohGo5EdO3aIiFR46Kr090CnTp3kww8/LLNNLSrqxWQyWer86KOPRKPRSEhIiGRmZloet3XrVmncuLHs3LlTkborsnr1avH29paYmBhp27ateHl5yWuvvSYi5sN1kydPrjGv+4p6mTNnjoiI5feyrV/3tTKwfPjhh9KyZUtZs2aNXL58WVatWiVubm6yf/9+yz4Gg0FiY2PLhZNWrVqVmW9R2vz58y29bN++XaZNmyZarVYWLVpkCSQmk0lKSkrEYDBIVFSUfPDBB+WeR+nj9BX1odFoZNGiRVJSUiJHjhyRyMhIad68uVy4cKHMY5s0aSKLFy9WqPKKTZo0Se6//34RMf9/e+jQIQkICJD4+HjJzs6WX3/9VaKiomTs2LEi8uebyM8//yze3t5y4MABxWq/2a16ee655+T8+fMiIvLEE0/IiBEjpEOHDuLl5SWzZs2S+vXrl3njV9rt+rh48aKIiPz2228SGhoqmZmZlu9JSUmJqnrZtm2b9OvXT/7v//5P+vfvL+3bt7/t/mfOnBEvLy9LMBMRS3BW+nVfmV6Sk5OlR48eEhERIenp6ZbtBQUFUrduXfnss89sWfItrVq1SiIjI+X9998XEZELFy7IwoULxc3NTXJyckREJDExUTp06KD61/3tetHr9Zb9nnzySZu+7mtVYCl943788cdlxIgRZb4WEhJSJrBcvHhR6tevbxnsKikpkezsbGnfvr0lMSstPz9f+vTpI9OnTxeRP3/4u3XrJgEBAfLVV1+V2Z6eni5Nmza1/OLat2+fPPnkkzav+2a368Pf3182bNggIiJvvvmmODg4lPlLNzMzU9q0aSMrV660ed0VMZlMkp2dLffcc4/861//EpE/3xQSEhKkXbt2ll8C77zzjri6usqXX35p+Ytl9uzZ0qNHD1X8FfxXvURHR8t7770nIiJDhgyRBg0ayLhx4ywh5vXXXxeNRiNnzpxRpP5Slelj/vz5IiJy7Ngx0Wg0kpSUZHn8+vXrJSoqSn7//XfbF3+D0p+JEydOyLx58+T06dOyd+9ecXV1tayeVBRAFi9eLFFRUSIi8vvvv0vHjh3Fy8tLDAaD7Yq/SWV6KSkpsfzvV199JTqdTqZPn275+Vq3bp3ExMRYViuUUtrLsmXLZMyYMXLt2jXL17Zv3y4hISGya9cuETGHrHfeeUfc3NxU+bqvTC+lg7XXrl2TIUOGSMOGDW32uq9VgaVU27Zt5ZlnnrFMZT///PMSGhoqr776quzatUvy8/OlqKhI/va3v0n//v3lwIEDcvbsWRk1apSEh4fL4cOHFe7ArKioSBo0aCCrV68WkT+XF4cOHSp+fn4ycuTIMkuon3zyiXTr1k30er38/e9/FycnJxk0aJAYjUZFXyh/1ceIESPk6tWrkpeXJ0OGDBF/f3+ZPn267Nu3T0aNGiXt2rWz/IWshKSkJMnOzi6zrX379vLss8+KiPnMMxHzIbiHHnpIHnzwQblw4YIUFxfLCy+8IO7u7tK9e3d55JFHpE6dOrJo0SIRUWbpviq9XL16VQ4ePCiHDh0q87jCwkJ58803Ffkr3to+Bg8eLOfOnZP8/HyJi4sTV1dXiY+Pl5EjR4q7u7tMmzZNsddIRb2UvpkbDAb55z//KV5eXpaeSpXW+/zzz8vDDz8sEydOFK1WK6NGjSq3r61Y28uNPzsLFiwQPz8/CQ0NlSFDhoibm5uifzwmJSXJ1atXLZ9nZ2dbeim1f/9+8fX1lT/++MOyTa/Xy+TJk1X3uq9KL3v27JEjR46U2e9uvu7tOrB8+umn8swzz8j8+fPl4MGDlu1r166VgIAAiY2NlYYNG0pYWJjMnDlTevbsKZGRkfL666+LiPkYqZeXl4SEhEjTpk2lZ8+eZY7bq6GXYcOGSVhYmCXdrly5Unr27CnPPPOMhISEyL59+yz7PvbYY+Lg4CDu7u7Svn17OXr0qK3bqFIfwcHBlj6Ki4vlH//4h0RHR0toaKh0795dTp48afM+REQ+//xzadq0qQQFBUmzZs1k2rRplvrfffddqVu3ruU00tK/pL744gtp2rSp5Vi9iMhnn30m06dPl/j4eEW+JyJV76VJkyaqmiG4k+9JaR/5+fkyefJkeeqpp2TkyJFy/Phx1fRSekjkxhmP06dPi7+/v/zzn/+0fK2U0WiUgIAA0Wg00qNHj3JvLrZS1V5uftP79ddfJSEhQaZMmaKa78vUqVPLnJJ8Y83z5s2Trl27isifP2+l1Pi6r2wvSgVeuwwsWVlZ8vDDD4uvr6/Ex8fLPffcI35+frJ06VLLPpmZmfLWW29J9+7dyxyTGz16tAwePFiysrJEROTcuXOyZ88e2bNnj63bEJGKe2ncuLGsWLFCRMxLqi1atJAWLVqIn5+fuLq6yhdffCEiIo6OjvLtt9+KiPmXwrBhw6R58+aWbTWxj1J5eXmKBRUR86xDWFiYzJ8/Xw4cOCAJCQni5eUlzz33nGRnZ8u5c+ckKCjI8hf9jQPODRs2lI8++kip0suxl17utI/SQxGllDxkcrterly5IiJ/rkyYTCZJSEgQR0dHOX36tIiY3xzz8/OloKBA5syZI99//32N7uXG39FKqkwvRqPR8rMzZMgQyzVk1KYm9mKXgeWzzz6Tjh07Wv6yEhEZNGiQBAYGypdffiki5l9Gjz32mMyePVtE/ky/kyZNkqCgIMnLy7N94RW4VS/NmzeX9evXi4j5Imrff/+9LF++3PJLODMzs9yZDSdOnLBp7Te60z7UMlhX+pfg4sWLpWnTppZhOhGRhQsXSseOHWXu3LkiIrJo0SJxcHCQX375xbLPqVOnJCgoyBLGlGQvvdhLHyJ/3Uvnzp1l1qxZ5R535coV6dKliwwaNEiSkpKkT58+ZS5QpoTq6iU2NlY++eQTRQ9bW9tL6WH2oKAgywze8ePH5bHHHpPU1FTbFn+TmtyLXQaWIUOGyEMPPSQiIrm5uSJiHiLSaDRy3333WYa0+vTpI4MHD7Y8LiMjQwYOHCgvv/yy7Yu+hb/qpXRG5eal03Xr1klYWFiZqXol2UsfpSZPniy9evUqc+XQvLw8GTdunHTu3FmOHz8uJpNJhg8fLr6+vjJjxgzZt2+fPPvss9KmTZtyZzopyV56sZc+RG7fS5cuXSxzdDfOGSxdulQ0Go1otVoZOHCgaq5qWx293Dj8qaTK9iJiPo2+TZs2cvHiRRk/frzodDrp06ePYodTblYTe6nxF47bunUrvv/++zJ3VQ4ODsaRI0cAAHXr1gUAHDt2DL169UJhYSG++uorAMCUKVPw7bffomvXrhg7dizat28PvV6PMWPG2LwP4M560Wq1uHz5Mo4dO4aFCxdi4sSJeOihh9CoUSObX0zNXvoAzPdl+cc//oF3330Xe/bssWzv2rUrdu7cabnKrtFohJubGwYNGgStVotvv/0WGo0GK1euxCOPPIL169fjkUcewW+//YZVq1bBz8+PvdTyPqrai0ajwebNmwEADg4OKC4uRkJCAkaNGoV7770XBw8exDfffANXV1e76aVOnTo1qhfAfCG8w4cPIzQ0FImJidixYwc2b95suZoye6kCm8ajanT58mUZOXKkaDQaiYyMLHMK1alTp8TLy0u6d+8ub7zxhsTExEhgYKD8+OOPEhkZKa+88opl3/Xr18uLL74ojz/+uGIXhrqTXqZOnWrZNykpSQYPHiyBgYGKLAfbSx8i5tPaBw4cKN7e3jJ8+HBp06aNeHp6Wk7pKygokLCwMBkzZoyIlF0Z6tatmzz33HOWz41Go+Tn55e5DoYt2Usv9tKHyJ33UnodDxHzyvD48eMVu6AlezG7uZfZs2eLl5eXYoca7amXUjUysBgMBklISJC+ffvK2rVrxdXVVebOnVtmeWr79u0yevRoiYqKkv/7v/+Ty5cvi4jIiBEjZOjQoUqVXk5196LUtSLspQ8R81kiTz75pMTFxVkG/0REOnToIE899ZSImJevV6xYIVqttswZPyIiw4cPl549e1o+V/LYu730Yi99iFR/L0piL38aPny49OjRw/L5jZeUsDV76uVGNTKwiJhPbyu9HPuMGTPEy8urzCm8pW48lezSpUvSunVry6Ct0ld5LFUdvSh5RkMpe+lDRGTMmDGyadMmEfmzphkzZkinTp0s+xQWFsqQIUMkPDxctmzZIiaTSdLT06Vjx47lzjhRkr30Yi99iLAX9nL32VMvpWpsYLn5LyQ/Pz8ZM2aM5fS3G79eUFAgxcXFliuN3nj9DzWwl17spQ+Rsqe8ltb9xBNPyOjRo8tsKygokB49eoi3t7fExsaKn5+fdO7cWfEzAW5kL73YSx8i7IW93H321EupGhtYSpX+tf7pp5+Ko6OjbN68uczXz58/LwkJCdK+ffsyV1NVI3vpxV76uFm3bt0s1/Ipvc2DiPm4++bNm+W1116TVatWKVhh5dlLL/bShwh7USv2oh41PrDcKCYmRnr37m05bbn0uNvq1atVc+OyyrKXXuylj1OnTomPj4/s3bvXsu3mK1fWFPbSi730IcJe1Iq9qItdBJbS43OHDx8WBwcHeffdd+Uf//iHREVFlbu/idrZSy/20kfpsuny5cslKCjIsv3VV1+V+Ph4xW+8Zg176cVe+hBhL2rFXtTJLgLLjTp06CAajUYCAgLku+++U7qcO2IvvdhDH+PGjZPJkyfL5s2bpXnz5uLt7a3o5c7vhL30Yi99iLAXtWIv6mI3geXkyZPSunXrMrcnr6nspRd76aOgoEBatmwpGo1GdDqd5eaYNZG99GIvfYiwF7ViL+rjaNvL1N09Dg4OGDp0KF588UWbXxWxutlLL/bSh4uLC5o3b44+ffpg3rx5cHFxUbqkKrOXXuylD4C9qBV7UR+NiALXOyeqYYxGIxwcHJQuo1rYSy/20gfAXtSKvagLAwsRERGpXo2/+SERERHZPwYWIiIiUj0GFiIiIlI9BhYiIiJSPQYWIiIiUj0GFiIiIlI9BhYiIiJSPQYWIiIiUj0GFiKyiaeeegoajQYajQZOTk7w8fFBnz598PHHH8NkMlX6eZYtW4Z69erdvUKJSJUYWIjIZvr164f09HScPXsWmzZtQs+ePTF+/HgMHDgQJSUlSpdHRCrGwEJENqPT6eDr64smTZogKioKL730Er7++mts2rQJy5YtAwDMmzcPbdq0gZubG/z9/TF27Fjk5eUBALZs2YKnn34aOTk5ltWaV199FQBQXFyMyZMno0mTJnBzc0OnTp2wZcsWZRolomrHwEJEiurVqxciIyPx5ZdfAgC0Wi0WLFiAw4cPY/ny5fjpp58wefJkAECXLl0wf/58eHh4ID09Henp6fjXv/4FAHj66aexY8cOrF27FgcPHsQjjzyCfv36ISUlRbHeiKj68OaHRGQTTz31FLKzs/HVV1+V+9pjjz2GgwcPIjk5udzXPvvsMzz33HPIysoCYJ5hmTBhArKzsy37nDp1CsHBwTh//jz8/Pws23v37o2OHTtizpw51d4PEdmWo9IFEBGJCDQaDQDg559/xpw5c5CcnAy9Xo+SkhIUFhYiPz8fbm5uFT7+999/h4ggJCSkzPaioiI0bNjwrtdPRHcfAwsRKe7o0aMIDAzEuXPnMGDAAMTHx2PWrFlo0KABtm/fjlGjRsFgMNzy8SaTCQ4ODkhKSoKDg0OZr9WtW/dul09ENsDAQkSK+umnn3Do0CFMnDgRe/fuRUlJCd5++21oteYRu08//bTM/s7OzjAajWW2tWvXDkajEZmZmejWrZvNaici22FgISKbKSoqQkZGBoxGIy5duoTvvvsOc+fOxcCBAzFy5EgcOnQIJSUleO+99/DAAw9gx44deP/998s8R/PmzZGXl4cff/wRkZGRcHV1RUhICIYPH46RI0fi7bffRrt27ZCVlYWffvoJbdq0wYABAxTqmIiqC88SIiKb+e6779C4cWM0b94c/fr1w88//4wFCxbg66+/hoODA9q2bYt58+bhjTfeQOvWrbFq1SrMnTu3zHN06dIF8fHxiIuLg5eXF958800AwNKlSzFy5Ej885//RGhoKB588EHs3r0b/v7+SrRKRNWMZwkRERGR6nGFhYiIiFSPgYWIiIhUj4GFiIiIVI+BhYiIiFSPgYWIiIhUj4GFiIiIVI+BhYiIiFSPgYWIiIhUj4GFiIiIVI+BhYiIiFSPgYWIiIhU7/8BPI2JshTo7fQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "apple.dividends.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now using the `Ticker` module create an object for AMD (Advanced Micro Devices) with the ticker symbol is `AMD` called; name the object <code>amd</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "amd = yf.Ticker(\"AMD\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Question 1</b> Use the key  <code>'country'</code> to find the country the stock belongs to, remember it as it will be a quiz question.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'United States'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amd_info = amd.info\n",
    "amd_info['country']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Question 2</b> Use the key  <code>'sector'</code> to find the sector the stock belongs to, remember it as it will be a quiz question.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Technology'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amd_info['sector']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Question 3</b> Obtain stock data for AMD using the `history` function, set the `period` to max. Find the `Volume` traded on the first day (first row).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "amd_stock_data = amd.history(period=\"max\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "amd_stock_data.reset_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Dividends</th>\n",
       "      <th>Stock Splits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980-03-17 00:00:00-05:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.302083</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>3.145833</td>\n",
       "      <td>219600</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1980-03-18 00:00:00-05:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>2.937500</td>\n",
       "      <td>3.031250</td>\n",
       "      <td>727200</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980-03-19 00:00:00-05:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.083333</td>\n",
       "      <td>3.020833</td>\n",
       "      <td>3.041667</td>\n",
       "      <td>295200</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1980-03-20 00:00:00-05:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.062500</td>\n",
       "      <td>3.010417</td>\n",
       "      <td>3.010417</td>\n",
       "      <td>159600</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1980-03-21 00:00:00-05:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.020833</td>\n",
       "      <td>2.906250</td>\n",
       "      <td>2.916667</td>\n",
       "      <td>130800</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Date  Open      High       Low     Close  Volume  \\\n",
       "0 1980-03-17 00:00:00-05:00   0.0  3.302083  3.125000  3.145833  219600   \n",
       "1 1980-03-18 00:00:00-05:00   0.0  3.125000  2.937500  3.031250  727200   \n",
       "2 1980-03-19 00:00:00-05:00   0.0  3.083333  3.020833  3.041667  295200   \n",
       "3 1980-03-20 00:00:00-05:00   0.0  3.062500  3.010417  3.010417  159600   \n",
       "4 1980-03-21 00:00:00-05:00   0.0  3.020833  2.906250  2.916667  130800   \n",
       "\n",
       "   Dividends  Stock Splits  \n",
       "0          0           0.0  \n",
       "1          0           0.0  \n",
       "2          0           0.0  \n",
       "3          0           0.0  \n",
       "4          0           0.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amd_stock_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "219600\n"
     ]
    }
   ],
   "source": [
    "volume = amd_stock_data.loc[0,'Volume']\n",
    "print(volume)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>About the Authors:</h2> \n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0220ENSkillsNetwork900-2022-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n",
    "\n",
    "Azim Hirjani\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By    | Change Description        |\n",
    "| ----------------- | ------- | ------------- | ------------------------- |\n",
    "| 2020-11-10        | 1.1     | Malika Singla | Deleted the Optional part |\n",
    "| 2020-08-27        | 1.0     | Malika Singla | Added lab to GitLab       |\n",
    "\n",
    "<hr>\n",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
    "\n",
    "<p>\n"
   ]
  }
 ],
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