Final-Assignment-Library.ipynb

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    "<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"
   ]
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  {
   "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"
   ]
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  {
   "cell_type": "markdown",
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   "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"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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    {
     "name": "stdout",
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     "text": [
      "Collecting yfinance==0.1.93\n",
      "  Using cached 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",
      "Requirement already satisfied: appdirs>=1.4.4 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.1.93) (1.4.4)\n",
      "Requirement already satisfied: multitasking>=0.0.7 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.1.93) (0.0.11)\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) (2022.12.7)\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: yfinance\n",
      "  Attempting uninstall: yfinance\n",
      "    Found existing installation: yfinance 0.1.67\n",
      "    Uninstalling yfinance-0.1.67:\n",
      "      Successfully uninstalled yfinance-0.1.67\n",
      "Successfully installed yfinance-0.1.93\n"
     ]
    }
   ],
   "source": [
    "!pip install yfinance==0.1.93\n",
    "#!pip install pandas==1.3.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "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": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "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": 37,
   "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": 37,
     "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": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'United States'"
      ]
     },
     "execution_count": 38,
     "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": 39,
   "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": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
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       "      <th>1980-12-12 00:00:00-05:00</th>\n",
       "      <td>0.099874</td>\n",
       "      <td>0.100308</td>\n",
       "      <td>0.099874</td>\n",
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       "      <th>1980-12-15 00:00:00-05:00</th>\n",
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       "      <td>0.094663</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",
       "      <td>0.088149</td>\n",
       "      <td>0.087715</td>\n",
       "      <td>0.087715</td>\n",
       "      <td>105728000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <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",
       "      <td>86441600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1980-12-18 00:00:00-05:00</th>\n",
       "      <td>0.092492</td>\n",
       "      <td>0.092927</td>\n",
       "      <td>0.092492</td>\n",
       "      <td>0.092492</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": 40,
     "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": 41,
   "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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\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": 43,
   "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": 43,
     "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": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\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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