Final_Assignment_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"
   ]
  },
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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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   "execution_count": 1,
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     "text": [
      "Collecting yfinance==0.2.4\n",
      "  Downloading yfinance-0.2.4-py2.py3-none-any.whl (51 kB)\n",
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      "\u001b[?25hCollecting 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==0.2.4) (4.9.1)\n",
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      "Requirement already satisfied: pycparser in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from cffi>=1.12->cryptography>=3.3.2->yfinance==0.2.4) (2.21)\n",
      "Installing collected packages: multitasking, appdirs, html5lib, frozendict, yfinance\n",
      "Successfully installed appdirs-1.4.4 frozendict-2.3.7 html5lib-1.1 multitasking-0.0.11 yfinance-0.2.4\n"
     ]
    }
   ],
   "source": [
    "!pip install yfinance==0.2.4\n",
    "#!pip install pandas==1.3.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "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": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-04-26 04:58:21--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/data/apple.json\n",
      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104\n",
      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 5699 (5.6K) [application/json]\n",
      "Saving to: ‘apple.json’\n",
      "\n",
      "apple.json          100%[===================>]   5.57K  --.-KB/s    in 0s      \n",
      "\n",
      "2023-04-26 04:58:21 (44.7 MB/s) - ‘apple.json’ saved [5699/5699]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/data/apple.json"
   ]
  },
  {
   "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': 100000,\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; AirPods Max, an over-ear wireless headphone; and wearables, home, and accessories comprising AirPods, Apple TV, Apple Watch, Beats products, HomePod, and iPod touch. Further, it provides AppleCare support services; 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 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': 0.33890998,\n",
       " 'profitMargins': 0.26579002,\n",
       " 'grossMargins': 0.43019,\n",
       " 'operatingCashflow': 112241000448,\n",
       " 'revenueGrowth': 0.112,\n",
       " 'operatingMargins': 0.309,\n",
       " 'ebitda': 128217997312,\n",
       " 'targetLowPrice': 160,\n",
       " 'recommendationKey': 'buy',\n",
       " 'grossProfits': 152836000000,\n",
       " 'freeCashflow': 80153247744,\n",
       " 'targetMedianPrice': 199.5,\n",
       " 'currentPrice': 177.77,\n",
       " 'earningsGrowth': 0.25,\n",
       " 'currentRatio': 1.038,\n",
       " 'returnOnAssets': 0.19875,\n",
       " 'numberOfAnalystOpinions': 44,\n",
       " 'targetMeanPrice': 193.53,\n",
       " 'debtToEquity': 170.714,\n",
       " 'returnOnEquity': 1.45567,\n",
       " 'targetHighPrice': 215,\n",
       " 'totalCash': 63913000960,\n",
       " 'totalDebt': 122797998080,\n",
       " 'totalRevenue': 378323009536,\n",
       " 'totalCashPerShare': 3.916,\n",
       " 'financialCurrency': 'USD',\n",
       " 'revenuePerShare': 22.838,\n",
       " 'quickRatio': 0.875,\n",
       " 'recommendationMean': 1.8,\n",
       " 'exchange': 'NMS',\n",
       " 'shortName': 'Apple Inc.',\n",
       " 'longName': 'Apple Inc.',\n",
       " 'exchangeTimezoneName': 'America/New_York',\n",
       " 'exchangeTimezoneShortName': 'EDT',\n",
       " 'isEsgPopulated': False,\n",
       " 'gmtOffSetMilliseconds': '-14400000',\n",
       " 'quoteType': 'EQUITY',\n",
       " 'symbol': 'AAPL',\n",
       " 'messageBoardId': 'finmb_24937',\n",
       " 'market': 'us_market',\n",
       " 'annualHoldingsTurnover': None,\n",
       " 'enterpriseToRevenue': 7.824,\n",
       " 'beta3Year': None,\n",
       " 'enterpriseToEbitda': 23.086,\n",
       " '52WeekChange': 0.4549594,\n",
       " 'morningStarRiskRating': None,\n",
       " 'forwardEps': 6.56,\n",
       " 'revenueQuarterlyGrowth': None,\n",
       " 'sharesOutstanding': 16319399936,\n",
       " 'fundInceptionDate': None,\n",
       " 'annualReportExpenseRatio': None,\n",
       " 'totalAssets': None,\n",
       " 'bookValue': 4.402,\n",
       " 'sharesShort': 111286790,\n",
       " 'sharesPercentSharesOut': 0.0068,\n",
       " 'fundFamily': None,\n",
       " 'lastFiscalYearEnd': 1632528000,\n",
       " 'heldPercentInstitutions': 0.59397,\n",
       " 'netIncomeToCommon': 100554997760,\n",
       " 'trailingEps': 6.015,\n",
       " 'lastDividendValue': 0.22,\n",
       " 'SandP52WeekChange': 0.15217662,\n",
       " 'priceToBook': 40.38392,\n",
       " 'heldPercentInsiders': 0.0007,\n",
       " 'nextFiscalYearEnd': 1695600000,\n",
       " 'yield': None,\n",
       " 'mostRecentQuarter': 1640390400,\n",
       " 'shortRatio': 1.21,\n",
       " 'sharesShortPreviousMonthDate': 1644883200,\n",
       " 'floatShares': 16302795170,\n",
       " 'beta': 1.185531,\n",
       " 'enterpriseValue': 2959991898112,\n",
       " 'priceHint': 2,\n",
       " 'threeYearAverageReturn': None,\n",
       " 'lastSplitDate': 1598832000,\n",
       " 'lastSplitFactor': '4:1',\n",
       " 'legalType': None,\n",
       " 'lastDividendDate': 1643932800,\n",
       " 'morningStarOverallRating': None,\n",
       " 'earningsQuarterlyGrowth': 0.204,\n",
       " 'priceToSalesTrailing12Months': 7.668314,\n",
       " 'dateShortInterest': 1647302400,\n",
       " 'pegRatio': 1.94,\n",
       " 'ytdReturn': None,\n",
       " 'forwardPE': 27.099087,\n",
       " 'lastCapGain': None,\n",
       " 'shortPercentOfFloat': 0.0068,\n",
       " 'sharesShortPriorMonth': 108944701,\n",
       " 'impliedSharesOutstanding': 0,\n",
       " 'category': None,\n",
       " 'fiveYearAverageReturn': None,\n",
       " 'previousClose': 178.96,\n",
       " 'regularMarketOpen': 178.55,\n",
       " 'twoHundredDayAverage': 156.03505,\n",
       " 'trailingAnnualDividendYield': 0.004833482,\n",
       " 'payoutRatio': 0.1434,\n",
       " 'volume24Hr': None,\n",
       " 'regularMarketDayHigh': 179.61,\n",
       " 'navPrice': None,\n",
       " 'averageDailyVolume10Day': 93823630,\n",
       " 'regularMarketPreviousClose': 178.96,\n",
       " 'fiftyDayAverage': 166.498,\n",
       " 'trailingAnnualDividendRate': 0.865,\n",
       " 'open': 178.55,\n",
       " 'toCurrency': None,\n",
       " 'averageVolume10days': 93823630,\n",
       " 'expireDate': None,\n",
       " 'algorithm': None,\n",
       " 'dividendRate': 0.88,\n",
       " 'exDividendDate': 1643932800,\n",
       " 'circulatingSupply': None,\n",
       " 'startDate': None,\n",
       " 'regularMarketDayLow': 176.7,\n",
       " 'currency': 'USD',\n",
       " 'trailingPE': 29.55445,\n",
       " 'regularMarketVolume': 92633154,\n",
       " 'lastMarket': None,\n",
       " 'maxSupply': None,\n",
       " 'openInterest': None,\n",
       " 'marketCap': 2901099675648,\n",
       " 'volumeAllCurrencies': None,\n",
       " 'strikePrice': None,\n",
       " 'averageVolume': 95342043,\n",
       " 'dayLow': 176.7,\n",
       " 'ask': 178.53,\n",
       " 'askSize': 800,\n",
       " 'volume': 92633154,\n",
       " 'fiftyTwoWeekHigh': 182.94,\n",
       " 'fromCurrency': None,\n",
       " 'fiveYearAvgDividendYield': 1.13,\n",
       " 'fiftyTwoWeekLow': 122.25,\n",
       " 'bid': 178.4,\n",
       " 'tradeable': False,\n",
       " 'dividendYield': 0.005,\n",
       " 'bidSize': 3200,\n",
       " 'dayHigh': 179.61,\n",
       " 'regularMarketPrice': 177.77,\n",
       " 'preMarketPrice': 178.38,\n",
       " 'logo_url': 'https://logo.clearbit.com/apple.com'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "with open('apple.json') as json_file:\n",
    "    apple_info = json.load(json_file)\n",
    "    # Print the type of data variable    \n",
    "    #print(\"Type:\", type(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>Dividends</th>\n",
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       "      <th>1980-12-12 00:00:00-05:00</th>\n",
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       "      <td>469033600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-15 00:00:00-05:00</th>\n",
       "      <td>0.094953</td>\n",
       "      <td>0.094953</td>\n",
       "      <td>0.094519</td>\n",
       "      <td>0.094519</td>\n",
       "      <td>175884800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-16 00:00:00-05:00</th>\n",
       "      <td>0.088015</td>\n",
       "      <td>0.088015</td>\n",
       "      <td>0.087582</td>\n",
       "      <td>0.087582</td>\n",
       "      <td>105728000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-17 00:00:00-05:00</th>\n",
       "      <td>0.089749</td>\n",
       "      <td>0.090183</td>\n",
       "      <td>0.089749</td>\n",
       "      <td>0.089749</td>\n",
       "      <td>86441600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-18 00:00:00-05:00</th>\n",
       "      <td>0.092351</td>\n",
       "      <td>0.092785</td>\n",
       "      <td>0.092351</td>\n",
       "      <td>0.092351</td>\n",
       "      <td>73449600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Open      High       Low     Close     Volume  \\\n",
       "Date                                                                           \n",
       "1980-12-12 00:00:00-05:00  0.099722  0.100155  0.099722  0.099722  469033600   \n",
       "1980-12-15 00:00:00-05:00  0.094953  0.094953  0.094519  0.094519  175884800   \n",
       "1980-12-16 00:00:00-05:00  0.088015  0.088015  0.087582  0.087582  105728000   \n",
       "1980-12-17 00:00:00-05:00  0.089749  0.090183  0.089749  0.089749   86441600   \n",
       "1980-12-18 00:00:00-05:00  0.092351  0.092785  0.092351  0.092351   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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\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",
       "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",
       "2023-02-10 00:00:00-05:00    0.230000\n",
       "Name: Dividends, Length: 78, 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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\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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "amd = yf.Ticker(\"AMD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-04-26 05:00:49--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/data/amd.json\n",
      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104\n",
      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 5838 (5.7K) [application/json]\n",
      "Saving to: ‘amd.json.1’\n",
      "\n",
      "amd.json.1          100%[===================>]   5.70K  --.-KB/s    in 0s      \n",
      "\n",
      "2023-04-26 05:00:49 (37.5 MB/s) - ‘amd.json.1’ saved [5838/5838]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/data/amd.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'zip': '95054',\n",
       " 'sector': 'Technology',\n",
       " 'fullTimeEmployees': 15500,\n",
       " 'longBusinessSummary': 'Advanced Micro Devices, Inc. operates as a semiconductor company worldwide. The company operates in two segments, Computing and Graphics; and Enterprise, Embedded and Semi-Custom. Its products include x86 microprocessors as an accelerated processing unit, chipsets, discrete and integrated graphics processing units (GPUs), data center and professional GPUs, and development services; and server and embedded processors, and semi-custom System-on-Chip (SoC) products, development services, and technology for game consoles. The company provides processors for desktop and notebook personal computers under the AMD Ryzen, AMD Ryzen PRO, Ryzen Threadripper, Ryzen Threadripper PRO, AMD Athlon, AMD Athlon PRO, AMD FX, AMD A-Series, and AMD PRO A-Series processors brands; discrete GPUs for desktop and notebook PCs under the AMD Radeon graphics, AMD Embedded Radeon graphics brands; and professional graphics products under the AMD Radeon Pro and AMD FirePro graphics brands. It also offers Radeon Instinct, Radeon PRO V-series, and AMD Instinct accelerators for servers; chipsets under the AMD trademark; microprocessors for servers under the AMD EPYC; embedded processor solutions under the AMD Athlon, AMD Geode, AMD Ryzen, AMD EPYC, AMD R-Series, and G-Series processors brands; and customer-specific solutions based on AMD CPU, GPU, and multi-media technologies, as well as semi-custom SoC products. It serves original equipment manufacturers, public cloud service providers, original design manufacturers, system integrators, independent distributors, online retailers, and add-in-board manufacturers through its direct sales force, independent distributors, and sales representatives. The company was incorporated in 1969 and is headquartered in Santa Clara, California.',\n",
       " 'city': 'Santa Clara',\n",
       " 'phone': '408 749 4000',\n",
       " 'state': 'CA',\n",
       " 'country': 'United States',\n",
       " 'companyOfficers': [],\n",
       " 'website': 'https://www.amd.com',\n",
       " 'maxAge': 1,\n",
       " 'address1': '2485 Augustine Drive',\n",
       " 'industry': 'Semiconductors',\n",
       " 'ebitdaMargins': 0.24674,\n",
       " 'profitMargins': 0.19240999,\n",
       " 'grossMargins': 0.48248002,\n",
       " 'operatingCashflow': 3520999936,\n",
       " 'revenueGrowth': 0.488,\n",
       " 'operatingMargins': 0.22198,\n",
       " 'ebitda': 4055000064,\n",
       " 'targetLowPrice': 107,\n",
       " 'recommendationKey': 'buy',\n",
       " 'grossProfits': 7929000000,\n",
       " 'freeCashflow': 3122749952,\n",
       " 'targetMedianPrice': 150,\n",
       " 'currentPrice': 119.22,\n",
       " 'earningsGrowth': -0.454,\n",
       " 'currentRatio': 2.024,\n",
       " 'returnOnAssets': 0.21327,\n",
       " 'numberOfAnalystOpinions': 38,\n",
       " 'targetMeanPrice': 152.02,\n",
       " 'debtToEquity': 9.764,\n",
       " 'returnOnEquity': 0.47428,\n",
       " 'targetHighPrice': 200,\n",
       " 'totalCash': 3608000000,\n",
       " 'totalDebt': 732000000,\n",
       " 'totalRevenue': 16433999872,\n",
       " 'totalCashPerShare': 3.008,\n",
       " 'financialCurrency': 'USD',\n",
       " 'revenuePerShare': 13.548,\n",
       " 'quickRatio': 1.49,\n",
       " 'recommendationMean': 2.2,\n",
       " 'exchange': 'NMS',\n",
       " 'shortName': 'Advanced Micro Devices, Inc.',\n",
       " 'longName': 'Advanced Micro Devices, Inc.',\n",
       " 'exchangeTimezoneName': 'America/New_York',\n",
       " 'exchangeTimezoneShortName': 'EDT',\n",
       " 'isEsgPopulated': False,\n",
       " 'gmtOffSetMilliseconds': '-14400000',\n",
       " 'quoteType': 'EQUITY',\n",
       " 'symbol': 'AMD',\n",
       " 'messageBoardId': 'finmb_168864',\n",
       " 'market': 'us_market',\n",
       " 'annualHoldingsTurnover': None,\n",
       " 'enterpriseToRevenue': 8.525,\n",
       " 'beta3Year': None,\n",
       " 'enterpriseToEbitda': 34.551,\n",
       " '52WeekChange': 0.51966953,\n",
       " 'morningStarRiskRating': None,\n",
       " 'forwardEps': 4.72,\n",
       " 'revenueQuarterlyGrowth': None,\n",
       " 'sharesOutstanding': 1627360000,\n",
       " 'fundInceptionDate': None,\n",
       " 'annualReportExpenseRatio': None,\n",
       " 'totalAssets': None,\n",
       " 'bookValue': 6.211,\n",
       " 'sharesShort': 27776129,\n",
       " 'sharesPercentSharesOut': 0.0171,\n",
       " 'fundFamily': None,\n",
       " 'lastFiscalYearEnd': 1640390400,\n",
       " 'heldPercentInstitutions': 0.52896,\n",
       " 'netIncomeToCommon': 3161999872,\n",
       " 'trailingEps': 2.57,\n",
       " 'lastDividendValue': 0.005,\n",
       " 'SandP52WeekChange': 0.15217662,\n",
       " 'priceToBook': 19.194977,\n",
       " 'heldPercentInsiders': 0.00328,\n",
       " 'nextFiscalYearEnd': 1703462400,\n",
       " 'yield': None,\n",
       " 'mostRecentQuarter': 1640390400,\n",
       " 'shortRatio': 0.24,\n",
       " 'sharesShortPreviousMonthDate': 1644883200,\n",
       " 'floatShares': 1193798619,\n",
       " 'beta': 1.848425,\n",
       " 'enterpriseValue': 140104957952,\n",
       " 'priceHint': 2,\n",
       " 'threeYearAverageReturn': None,\n",
       " 'lastSplitDate': 966902400,\n",
       " 'lastSplitFactor': '2:1',\n",
       " 'legalType': None,\n",
       " 'lastDividendDate': 798940800,\n",
       " 'morningStarOverallRating': None,\n",
       " 'earningsQuarterlyGrowth': -0.453,\n",
       " 'priceToSalesTrailing12Months': 11.805638,\n",
       " 'dateShortInterest': 1647302400,\n",
       " 'pegRatio': 0.99,\n",
       " 'ytdReturn': None,\n",
       " 'forwardPE': 25.258476,\n",
       " 'lastCapGain': None,\n",
       " 'shortPercentOfFloat': 0.0171,\n",
       " 'sharesShortPriorMonth': 88709340,\n",
       " 'impliedSharesOutstanding': 0,\n",
       " 'category': None,\n",
       " 'fiveYearAverageReturn': None,\n",
       " 'previousClose': 123.23,\n",
       " 'regularMarketOpen': 123.04,\n",
       " 'twoHundredDayAverage': 116.6998,\n",
       " 'trailingAnnualDividendYield': 0,\n",
       " 'payoutRatio': 0,\n",
       " 'volume24Hr': None,\n",
       " 'regularMarketDayHigh': 125.66,\n",
       " 'navPrice': None,\n",
       " 'averageDailyVolume10Day': 102167370,\n",
       " 'regularMarketPreviousClose': 123.23,\n",
       " 'fiftyDayAverage': 115.95,\n",
       " 'trailingAnnualDividendRate': 0,\n",
       " 'open': 123.04,\n",
       " 'toCurrency': None,\n",
       " 'averageVolume10days': 102167370,\n",
       " 'expireDate': None,\n",
       " 'algorithm': None,\n",
       " 'dividendRate': None,\n",
       " 'exDividendDate': 798940800,\n",
       " 'circulatingSupply': None,\n",
       " 'startDate': None,\n",
       " 'regularMarketDayLow': 118.59,\n",
       " 'currency': 'USD',\n",
       " 'trailingPE': 46.389107,\n",
       " 'regularMarketVolume': 99476946,\n",
       " 'lastMarket': None,\n",
       " 'maxSupply': None,\n",
       " 'openInterest': None,\n",
       " 'marketCap': 194013855744,\n",
       " 'volumeAllCurrencies': None,\n",
       " 'strikePrice': None,\n",
       " 'averageVolume': 102428813,\n",
       " 'dayLow': 118.59,\n",
       " 'ask': 117.24,\n",
       " 'askSize': 1100,\n",
       " 'volume': 99476946,\n",
       " 'fiftyTwoWeekHigh': 164.46,\n",
       " 'fromCurrency': None,\n",
       " 'fiveYearAvgDividendYield': None,\n",
       " 'fiftyTwoWeekLow': 72.5,\n",
       " 'bid': 117.24,\n",
       " 'tradeable': False,\n",
       " 'dividendYield': None,\n",
       " 'bidSize': 900,\n",
       " 'dayHigh': 125.66,\n",
       " 'regularMarketPrice': 119.22,\n",
       " 'preMarketPrice': 116.98,\n",
       " 'logo_url': 'https://logo.clearbit.com/amd.com'}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "with open('amd.json') as json_file:\n",
    "    amd_info = json.load(json_file)\n",
    "    # Print the type of data variable    \n",
    "    #print(\"Type:\", type(apple_info))\n",
    "amd_info"
   ]
  },
  {
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'United States'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Technology'"
      ]
     },
     "execution_count": 21,
     "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": 23,
   "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>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",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-03-17 00:00:00-05:00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.302083</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>3.145833</td>\n",
       "      <td>219600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-03-18 00:00:00-05:00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>2.937500</td>\n",
       "      <td>3.031250</td>\n",
       "      <td>727200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-03-19 00:00:00-05:00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.083333</td>\n",
       "      <td>3.020833</td>\n",
       "      <td>3.041667</td>\n",
       "      <td>295200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-03-20 00:00:00-05:00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.062500</td>\n",
       "      <td>3.010417</td>\n",
       "      <td>3.010417</td>\n",
       "      <td>159600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-03-21 00:00:00-05:00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.020833</td>\n",
       "      <td>2.906250</td>\n",
       "      <td>2.916667</td>\n",
       "      <td>130800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-19 00:00:00-04:00</th>\n",
       "      <td>88.510002</td>\n",
       "      <td>90.540001</td>\n",
       "      <td>88.220001</td>\n",
       "      <td>89.940002</td>\n",
       "      <td>37344500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-20 00:00:00-04:00</th>\n",
       "      <td>88.830002</td>\n",
       "      <td>91.580002</td>\n",
       "      <td>88.730003</td>\n",
       "      <td>90.110001</td>\n",
       "      <td>47082700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-21 00:00:00-04:00</th>\n",
       "      <td>89.800003</td>\n",
       "      <td>89.800003</td>\n",
       "      <td>88.059998</td>\n",
       "      <td>88.430000</td>\n",
       "      <td>41067500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-24 00:00:00-04:00</th>\n",
       "      <td>88.110001</td>\n",
       "      <td>88.639999</td>\n",
       "      <td>86.339996</td>\n",
       "      <td>87.570000</td>\n",
       "      <td>40423800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-25 00:00:00-04:00</th>\n",
       "      <td>86.900002</td>\n",
       "      <td>87.059998</td>\n",
       "      <td>83.760002</td>\n",
       "      <td>83.800003</td>\n",
       "      <td>52467900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10869 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Open       High        Low      Close  \\\n",
       "Date                                                                    \n",
       "1980-03-17 00:00:00-05:00   0.000000   3.302083   3.125000   3.145833   \n",
       "1980-03-18 00:00:00-05:00   0.000000   3.125000   2.937500   3.031250   \n",
       "1980-03-19 00:00:00-05:00   0.000000   3.083333   3.020833   3.041667   \n",
       "1980-03-20 00:00:00-05:00   0.000000   3.062500   3.010417   3.010417   \n",
       "1980-03-21 00:00:00-05:00   0.000000   3.020833   2.906250   2.916667   \n",
       "...                              ...        ...        ...        ...   \n",
       "2023-04-19 00:00:00-04:00  88.510002  90.540001  88.220001  89.940002   \n",
       "2023-04-20 00:00:00-04:00  88.830002  91.580002  88.730003  90.110001   \n",
       "2023-04-21 00:00:00-04:00  89.800003  89.800003  88.059998  88.430000   \n",
       "2023-04-24 00:00:00-04:00  88.110001  88.639999  86.339996  87.570000   \n",
       "2023-04-25 00:00:00-04:00  86.900002  87.059998  83.760002  83.800003   \n",
       "\n",
       "                             Volume  Dividends  Stock Splits  \n",
       "Date                                                          \n",
       "1980-03-17 00:00:00-05:00    219600        0.0           0.0  \n",
       "1980-03-18 00:00:00-05:00    727200        0.0           0.0  \n",
       "1980-03-19 00:00:00-05:00    295200        0.0           0.0  \n",
       "1980-03-20 00:00:00-05:00    159600        0.0           0.0  \n",
       "1980-03-21 00:00:00-05:00    130800        0.0           0.0  \n",
       "...                             ...        ...           ...  \n",
       "2023-04-19 00:00:00-04:00  37344500        0.0           0.0  \n",
       "2023-04-20 00:00:00-04:00  47082700        0.0           0.0  \n",
       "2023-04-21 00:00:00-04:00  41067500        0.0           0.0  \n",
       "2023-04-24 00:00:00-04:00  40423800        0.0           0.0  \n",
       "2023-04-25 00:00:00-04:00  52467900        0.0           0.0  \n",
       "\n",
       "[10869 rows x 7 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amd_share_price_data = amd.history(period=\"max\")\n",
    "amd_share_price_data"
   ]
  },
  {
   "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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