Final Assignment (1).ipynb

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    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0220ENSkillsNetwork900-2022-01-01\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\">\n",
    "    </a>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Extracting and Visualizing Stock Data</h1>\n",
    "<h2>Description</h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extracting essential data from a dataset and displaying it is a necessary part of data science; therefore individuals can make correct decisions based on the data. In this assignment, you will extract some stock data, you will then display this data in a graph.\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>Define a Function that Makes a Graph</li>\n",
    "        <li>Question 1: Use yfinance to Extract Stock Data</li>\n",
    "        <li>Question 2: Use Webscraping to Extract Tesla Revenue Data</li>\n",
    "        <li>Question 3: Use yfinance to Extract Stock Data</li>\n",
    "        <li>Question 4: Use Webscraping to Extract GME Revenue Data</li>\n",
    "        <li>Question 5: Plot Tesla Stock Graph</li>\n",
    "        <li>Question 6: Plot GameStop Stock Graph</li>\n",
    "    </ul>\n",
    "<p>\n",
    "    Estimated Time Needed: <strong>30 min</strong></p>\n",
    "</div>\n",
    "\n",
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Note***:- If you are working in IBM Cloud Watson Studio, please replace the command for installing nbformat from `!pip install nbformat==4.2.0` to simply `!pip install nbformat`\n"
   ]
  },
  {
   "cell_type": "code",
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     "text": [
      "Collecting yfinance==0.1.67\n",
      "  Downloading yfinance-0.1.67-py2.py3-none-any.whl (25 kB)\n",
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      "Installing collected packages: multitasking, yfinance\n",
      "Successfully installed multitasking-0.0.11 yfinance-0.1.67\n",
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      "\n",
      "        mamba (1.4.2) supported by @QuantStack\n",
      "\n",
      "        GitHub:  https://github.com/mamba-org/mamba\n",
      "        Twitter: https://twitter.com/QuantStack\n",
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      "Installing collected packages: nbformat\n",
      "  Attempting uninstall: nbformat\n",
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      "    Uninstalling nbformat-5.8.0:\n",
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      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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      "\u001b[0mSuccessfully installed nbformat-4.2.0\n"
     ]
    }
   ],
   "source": [
    "!pip install yfinance==0.1.67\n",
    "!mamba install bs4==4.10.0 -y\n",
    "!pip install nbformat==4.2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yfinance as yf\n",
    "import pandas as pd\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Python, you can ignore warnings using the warnings module. You can use the filterwarnings function to filter or ignore specific warning messages or categories.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "# Ignore all warnings\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Graphing Function\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we define the function `make_graph`. You don't have to know how the function works, you should only care about the inputs. It takes a dataframe with stock data (dataframe must contain Date and Close columns), a dataframe with revenue data (dataframe must contain Date and Revenue columns), and the name of the stock.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def make_graph(stock_data, revenue_data, stock):\n",
    "    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=(\"Historical Share Price\", \"Historical Revenue\"), vertical_spacing = .3)\n",
    "    stock_data_specific = stock_data[stock_data.Date <= '2021--06-14']\n",
    "    revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']\n",
    "    fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype(\"float\"), name=\"Share Price\"), row=1, col=1)\n",
    "    fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype(\"float\"), name=\"Revenue\"), row=2, col=1)\n",
    "    fig.update_xaxes(title_text=\"Date\", row=1, col=1)\n",
    "    fig.update_xaxes(title_text=\"Date\", row=2, col=1)\n",
    "    fig.update_yaxes(title_text=\"Price ($US)\", row=1, col=1)\n",
    "    fig.update_yaxes(title_text=\"Revenue ($US Millions)\", row=2, col=1)\n",
    "    fig.update_layout(showlegend=False,\n",
    "    height=900,\n",
    "    title=stock,\n",
    "    xaxis_rangeslider_visible=True)\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1: Use yfinance to Extract Stock Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `Ticker` function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is Tesla and its ticker symbol is `TSLA`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import yfinance as yf\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "tesla = yf.Ticker(\"TSLA\")\n",
    "#print(tesla)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the ticker object and the function `history` extract stock information and save it in a dataframe named `tesla_data`. Set the `period` parameter to `max` so we get information for the maximum amount of time.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "tesla_price_data = tesla.history(period='max')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "tags": []
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   "outputs": [
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       "      <th>2010-06-29</th>\n",
       "      <td>1.266667</td>\n",
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       "      <th>2010-07-01</th>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.728000</td>\n",
       "      <td>1.351333</td>\n",
       "      <td>1.464000</td>\n",
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       "      <th>2010-07-02</th>\n",
       "      <td>1.533333</td>\n",
       "      <td>1.540000</td>\n",
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       "      <td>1.280000</td>\n",
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       "      <th>2010-07-06</th>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.055333</td>\n",
       "      <td>1.074000</td>\n",
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       "                Open      High       Low     Close     Volume  Dividends  \\\n",
       "Date                                                                       \n",
       "2010-06-29  1.266667  1.666667  1.169333  1.592667  281494500          0   \n",
       "2010-06-30  1.719333  2.028000  1.553333  1.588667  257806500          0   \n",
       "2010-07-01  1.666667  1.728000  1.351333  1.464000  123282000          0   \n",
       "2010-07-02  1.533333  1.540000  1.247333  1.280000   77097000          0   \n",
       "2010-07-06  1.333333  1.333333  1.055333  1.074000  103003500          0   \n",
       "\n",
       "            Stock Splits  \n",
       "Date                      \n",
       "2010-06-29           0.0  \n",
       "2010-06-30           0.0  \n",
       "2010-07-01           0.0  \n",
       "2010-07-02           0.0  \n",
       "2010-07-06           0.0  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tesla_price_data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "\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>2023-12-27</th>\n",
       "      <td>258.350006</td>\n",
       "      <td>263.339996</td>\n",
       "      <td>257.519989</td>\n",
       "      <td>261.440002</td>\n",
       "      <td>106494400</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-28</th>\n",
       "      <td>263.660004</td>\n",
       "      <td>265.130005</td>\n",
       "      <td>252.710007</td>\n",
       "      <td>253.179993</td>\n",
       "      <td>113619900</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-29</th>\n",
       "      <td>255.100006</td>\n",
       "      <td>255.190002</td>\n",
       "      <td>247.429993</td>\n",
       "      <td>248.479996</td>\n",
       "      <td>100615300</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-02</th>\n",
       "      <td>250.080002</td>\n",
       "      <td>251.250000</td>\n",
       "      <td>244.410004</td>\n",
       "      <td>248.419998</td>\n",
       "      <td>104437200</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-03</th>\n",
       "      <td>244.979996</td>\n",
       "      <td>245.679993</td>\n",
       "      <td>236.320007</td>\n",
       "      <td>240.139999</td>\n",
       "      <td>72595610</td>\n",
       "      <td>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",
       "2023-12-27  258.350006  263.339996  257.519989  261.440002  106494400   \n",
       "2023-12-28  263.660004  265.130005  252.710007  253.179993  113619900   \n",
       "2023-12-29  255.100006  255.190002  247.429993  248.479996  100615300   \n",
       "2024-01-02  250.080002  251.250000  244.410004  248.419998  104437200   \n",
       "2024-01-03  244.979996  245.679993  236.320007  240.139999   72595610   \n",
       "\n",
       "            Dividends  Stock Splits  \n",
       "Date                                 \n",
       "2023-12-27          0           0.0  \n",
       "2023-12-28          0           0.0  \n",
       "2023-12-29          0           0.0  \n",
       "2024-01-02          0           0.0  \n",
       "2024-01-03          0           0.0  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tesla_price_data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Reset the index** using the `reset_index(inplace=True)` function on the tesla_data DataFrame and display the first five rows of the `tesla_data` dataframe using the `head` function. Take a screenshot of the results and code from the beginning of Question 1 to the results below.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2: Use Webscraping to Extract Tesla Revenue Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the `requests` library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm Save the text of the response as a variable named `html_data`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "url = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm'\n",
    "\n",
    "tesla_revenue = requests.get(url).text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parse the html data using `beautiful_soup`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "soup = BeautifulSoup(tesla_revenue, 'html5lib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using `BeautifulSoup` or the `read_html` function extract the table with `Tesla Revenue` and store it into a dataframe named `tesla_revenue`. The dataframe should have columns `Date` and `Revenue`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here if you need help locating the table</summary>\n",
    "\n",
    "```\n",
    "    \n",
    "Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab\n",
    "    \n",
    "soup.find_all(\"tbody\")[1]\n",
    "    \n",
    "If you want to use the read_html function the table is located at index 1\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": []
   },
   "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>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Date, Revenue]\n",
       "Index: []"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tesla_rev_data = pd.DataFrame(columns=[\"Date\", \"Revenue\"])\n",
    "\n",
    "tesla_rev_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": []
   },
   "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>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021</td>\n",
       "      <td>$53,823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>$31,536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>$24,578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>$21,461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>$11,759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016</td>\n",
       "      <td>$7,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015</td>\n",
       "      <td>$4,046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014</td>\n",
       "      <td>$3,198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013</td>\n",
       "      <td>$2,013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2012</td>\n",
       "      <td>$413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2011</td>\n",
       "      <td>$204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2010</td>\n",
       "      <td>$117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2009</td>\n",
       "      <td>$112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2021</td>\n",
       "      <td>$53,823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020</td>\n",
       "      <td>$31,536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2019</td>\n",
       "      <td>$24,578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018</td>\n",
       "      <td>$21,461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017</td>\n",
       "      <td>$11,759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2016</td>\n",
       "      <td>$7,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2015</td>\n",
       "      <td>$4,046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2014</td>\n",
       "      <td>$3,198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2013</td>\n",
       "      <td>$2,013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2012</td>\n",
       "      <td>$413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2011</td>\n",
       "      <td>$204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2010</td>\n",
       "      <td>$117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2009</td>\n",
       "      <td>$112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Date  Revenue\n",
       "0   2021  $53,823\n",
       "1   2020  $31,536\n",
       "2   2019  $24,578\n",
       "3   2018  $21,461\n",
       "4   2017  $11,759\n",
       "5   2016   $7,000\n",
       "6   2015   $4,046\n",
       "7   2014   $3,198\n",
       "8   2013   $2,013\n",
       "9   2012     $413\n",
       "10  2011     $204\n",
       "11  2010     $117\n",
       "12  2009     $112\n",
       "13  2021  $53,823\n",
       "14  2020  $31,536\n",
       "15  2019  $24,578\n",
       "16  2018  $21,461\n",
       "17  2017  $11,759\n",
       "18  2016   $7,000\n",
       "19  2015   $4,046\n",
       "20  2014   $3,198\n",
       "21  2013   $2,013\n",
       "22  2012     $413\n",
       "23  2011     $204\n",
       "24  2010     $117\n",
       "25  2009     $112"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# First we isolate the body of the table which contains all the information\n",
    "# Then we loop through each row and find all the column values for each row\n",
    "for row in soup.find(\"tbody\").find_all('tr'):\n",
    "    col = row.find_all(\"td\")\n",
    "    date = col[0].text\n",
    "    revenue = col[1].text\n",
    "    \n",
    "    # Finally we append the data of each row to the table\n",
    "\n",
    "    tesla_rev_data = tesla_rev_data.append({\"Date\":date, \"Revenue\":revenue}, ignore_index=True)    \n",
    "\n",
    "tesla_rev_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Execute the following line to remove the comma and dollar sign from the `Revenue` column. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": []
   },
   "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>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021</td>\n",
       "      <td>53823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>31536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>24578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>21461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>11759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015</td>\n",
       "      <td>4046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014</td>\n",
       "      <td>3198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2012</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2011</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2010</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2009</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2021</td>\n",
       "      <td>53823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020</td>\n",
       "      <td>31536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2019</td>\n",
       "      <td>24578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018</td>\n",
       "      <td>21461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017</td>\n",
       "      <td>11759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2016</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2015</td>\n",
       "      <td>4046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2014</td>\n",
       "      <td>3198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2012</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2011</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2010</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2009</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Date Revenue\n",
       "0   2021   53823\n",
       "1   2020   31536\n",
       "2   2019   24578\n",
       "3   2018   21461\n",
       "4   2017   11759\n",
       "5   2016    7000\n",
       "6   2015    4046\n",
       "7   2014    3198\n",
       "8   2013    2013\n",
       "9   2012     413\n",
       "10  2011     204\n",
       "11  2010     117\n",
       "12  2009     112\n",
       "13  2021   53823\n",
       "14  2020   31536\n",
       "15  2019   24578\n",
       "16  2018   21461\n",
       "17  2017   11759\n",
       "18  2016    7000\n",
       "19  2015    4046\n",
       "20  2014    3198\n",
       "21  2013    2013\n",
       "22  2012     413\n",
       "23  2011     204\n",
       "24  2010     117\n",
       "25  2009     112"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tesla_rev_data[\"Revenue\"] = tesla_rev_data['Revenue'].str.replace(',|\\$',\"\")\n",
    "tesla_rev_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Execute the following lines to remove an null or empty strings in the Revenue column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "tags": []
   },
   "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>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021</td>\n",
       "      <td>53823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>31536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>24578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>21461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>11759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015</td>\n",
       "      <td>4046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014</td>\n",
       "      <td>3198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2012</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2011</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2010</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2009</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2021</td>\n",
       "      <td>53823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020</td>\n",
       "      <td>31536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2019</td>\n",
       "      <td>24578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018</td>\n",
       "      <td>21461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017</td>\n",
       "      <td>11759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2016</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2015</td>\n",
       "      <td>4046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2014</td>\n",
       "      <td>3198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2012</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2011</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2010</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2009</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Date Revenue\n",
       "0   2021   53823\n",
       "1   2020   31536\n",
       "2   2019   24578\n",
       "3   2018   21461\n",
       "4   2017   11759\n",
       "5   2016    7000\n",
       "6   2015    4046\n",
       "7   2014    3198\n",
       "8   2013    2013\n",
       "9   2012     413\n",
       "10  2011     204\n",
       "11  2010     117\n",
       "12  2009     112\n",
       "13  2021   53823\n",
       "14  2020   31536\n",
       "15  2019   24578\n",
       "16  2018   21461\n",
       "17  2017   11759\n",
       "18  2016    7000\n",
       "19  2015    4046\n",
       "20  2014    3198\n",
       "21  2013    2013\n",
       "22  2012     413\n",
       "23  2011     204\n",
       "24  2010     117\n",
       "25  2009     112"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tesla_rev_data.dropna(inplace=True)\n",
    "\n",
    "tesla_rev_data = tesla_rev_data[tesla_rev_data['Revenue'] != \"\"]\n",
    "\n",
    "tesla_rev_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the last 5 row of the `tesla_revenue` dataframe using the `tail` function. Take a screenshot of the results.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "tags": []
   },
   "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>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2012</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2011</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2010</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2009</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Date Revenue\n",
       "21  2013    2013\n",
       "22  2012     413\n",
       "23  2011     204\n",
       "24  2010     117\n",
       "25  2009     112"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tesla_rev_data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 3: Use yfinance to Extract Stock Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `Ticker` function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is GameStop and its ticker symbol is `GME`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yfinance.Ticker object <GME>\n"
     ]
    }
   ],
   "source": [
    "import yfinance as yf\n",
    "\n",
    "gamestop = yf.Ticker('GME')\n",
    "print(gamestop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the ticker object and the function `history` extract stock information and save it in a dataframe named `gme_data`. Set the `period` parameter to `max` so we get information for the maximum amount of time.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "gme_data = gamestop.history(period = 'max')\n",
    "#print(gme_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Reset the index** using the `reset_index(inplace=True)` function on the gme_data DataFrame and display the first five rows of the `gme_data` dataframe using the `head` function. Take a screenshot of the results and code from the beginning of Question 3 to the results below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "tags": []
   },
   "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>index</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>0</td>\n",
       "      <td>2002-02-13</td>\n",
       "      <td>1.620129</td>\n",
       "      <td>1.693350</td>\n",
       "      <td>1.603296</td>\n",
       "      <td>1.691667</td>\n",
       "      <td>76216000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2002-02-14</td>\n",
       "      <td>1.712707</td>\n",
       "      <td>1.716074</td>\n",
       "      <td>1.670626</td>\n",
       "      <td>1.683250</td>\n",
       "      <td>11021600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2002-02-15</td>\n",
       "      <td>1.683250</td>\n",
       "      <td>1.687458</td>\n",
       "      <td>1.658002</td>\n",
       "      <td>1.674834</td>\n",
       "      <td>8389600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2002-02-19</td>\n",
       "      <td>1.666418</td>\n",
       "      <td>1.666418</td>\n",
       "      <td>1.578047</td>\n",
       "      <td>1.607504</td>\n",
       "      <td>7410400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2002-02-20</td>\n",
       "      <td>1.615920</td>\n",
       "      <td>1.662210</td>\n",
       "      <td>1.603296</td>\n",
       "      <td>1.662210</td>\n",
       "      <td>6892800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index       Date      Open      High       Low     Close    Volume  \\\n",
       "0      0 2002-02-13  1.620129  1.693350  1.603296  1.691667  76216000   \n",
       "1      1 2002-02-14  1.712707  1.716074  1.670626  1.683250  11021600   \n",
       "2      2 2002-02-15  1.683250  1.687458  1.658002  1.674834   8389600   \n",
       "3      3 2002-02-19  1.666418  1.666418  1.578047  1.607504   7410400   \n",
       "4      4 2002-02-20  1.615920  1.662210  1.603296  1.662210   6892800   \n",
       "\n",
       "   Dividends  Stock Splits  \n",
       "0        0.0           0.0  \n",
       "1        0.0           0.0  \n",
       "2        0.0           0.0  \n",
       "3        0.0           0.0  \n",
       "4        0.0           0.0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gme_data.reset_index(inplace=True)\n",
    "\n",
    "gme_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 4: Use Webscraping to Extract GME Revenue Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the `requests` library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html. Save the text of the response as a variable named `html_data`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "\n",
    "url = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html'\n",
    "\n",
    "html_data = requests.get(url).text\n",
    "\n",
    "#html_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parse the html data using `beautiful_soup`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "soup = BeautifulSoup(html_data, 'html5lib')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using `BeautifulSoup` or the `read_html` function extract the table with `GameStop Revenue` and store it into a dataframe named `gme_revenue`. The dataframe should have columns `Date` and `Revenue`. Make sure the comma and dollar sign is removed from the `Revenue` column using a method similar to what you did in Question 2.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here if you need help locating the table</summary>\n",
    "\n",
    "```\n",
    "    \n",
    "Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab\n",
    "    \n",
    "soup.find_all(\"tbody\")[1]\n",
    "    \n",
    "If you want to use the read_html function the table is located at index 1\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "tags": []
   },
   "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>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2009</td>\n",
       "      <td>$8,806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2008</td>\n",
       "      <td>$7,094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2007</td>\n",
       "      <td>$5,319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2006</td>\n",
       "      <td>$3,092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2005</td>\n",
       "      <td>$1,843</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Date Revenue\n",
       "11  2009  $8,806\n",
       "12  2008  $7,094\n",
       "13  2007  $5,319\n",
       "14  2006  $3,092\n",
       "15  2005  $1,843"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "gme_revenue = pd.DataFrame(columns=[\"Date\", \"Revenue\"])\n",
    "\n",
    "for row in soup.find(\"tbody\").find_all('tr'):\n",
    "    col = row.find_all(\"td\")\n",
    "    date = col[0].text\n",
    "    revenue = col[1].text\n",
    "    \n",
    "    # Finally we append the data of each row to the table\n",
    "\n",
    "    gme_revenue = gme_revenue.append({\"Date\":date, \"Revenue\":revenue}, ignore_index=True)  \n",
    "    \n",
    "gme_revenue.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the last five rows of the `gme_revenue` dataframe using the `tail` function. Take a screenshot of the results.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "tags": []
   },
   "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>index</th>\n",
       "      <th>Date</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>$6,466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>$8,285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2018</td>\n",
       "      <td>$8,547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>$7,965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>$9,364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>2015</td>\n",
       "      <td>$9,296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>2014</td>\n",
       "      <td>$9,040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>2013</td>\n",
       "      <td>$8,887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>2012</td>\n",
       "      <td>$9,551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>2011</td>\n",
       "      <td>$9,474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>2010</td>\n",
       "      <td>$9,078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>2009</td>\n",
       "      <td>$8,806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>$7,094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>2007</td>\n",
       "      <td>$5,319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>2006</td>\n",
       "      <td>$3,092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>2005</td>\n",
       "      <td>$1,843</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  Date Revenue\n",
       "0       0  2020  $6,466\n",
       "1       1  2019  $8,285\n",
       "2       2  2018  $8,547\n",
       "3       3  2017  $7,965\n",
       "4       4  2016  $9,364\n",
       "5       5  2015  $9,296\n",
       "6       6  2014  $9,040\n",
       "7       7  2013  $8,887\n",
       "8       8  2012  $9,551\n",
       "9       9  2011  $9,474\n",
       "10     10  2010  $9,078\n",
       "11     11  2009  $8,806\n",
       "12     12  2008  $7,094\n",
       "13     13  2007  $5,319\n",
       "14     14  2006  $3,092\n",
       "15     15  2005  $1,843"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gme_revenue.tail()\n",
    "\n",
    "gme_revenue.reset_index(inplace=True)\n",
    "gme_revenue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 5: Plot Tesla Stock Graph\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the `make_graph` function to graph the Tesla Stock Data, also provide a title for the graph. The structure to call the `make_graph` function is `make_graph(tesla_data, tesla_revenue, 'Tesla')`. Note the graph will only show data upto June 2021.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (3.5.3)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib) (4.38.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: numpy>=1.17 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib) (1.21.6)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib) (23.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib) (8.1.0)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: typing-extensions in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib) (4.5.0)\n",
      "Requirement already satisfied: six>=1.5 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'make_subplots' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1851/1076974388.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmake_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtesla_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtesla_rev_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Tesla'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/tmp/ipykernel_1851/2068038883.py\u001b[0m in \u001b[0;36mmake_graph\u001b[0;34m(stock_data, revenue_data, stock)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmake_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstock_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrevenue_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstock\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshared_xaxes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplot_titles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Historical Share Price\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Historical Revenue\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvertical_spacing\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m.3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mstock_data_specific\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstock_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstock_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDate\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;34m'2021--06-14'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mrevenue_data_specific\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrevenue_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrevenue_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDate\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;34m'2021-04-30'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mScatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstock_data_specific\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfer_datetime_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstock_data_specific\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mClose\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"float\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Share Price\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'make_subplots' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "make_graph(tesla_data, tesla_rev_data, 'Tesla')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 6: Plot GameStop Stock Graph\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the `make_graph` function to graph the GameStop Stock Data, also provide a title for the graph. The structure to call the `make_graph` function is `make_graph(gme_data, gme_revenue, 'GameStop')`. Note the graph will only show data upto June 2021.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'make_subplots' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1851/1051334688.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmake_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgme_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgme_revenue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'GameStop'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/tmp/ipykernel_1851/2068038883.py\u001b[0m in \u001b[0;36mmake_graph\u001b[0;34m(stock_data, revenue_data, stock)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmake_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstock_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrevenue_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstock\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshared_xaxes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplot_titles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Historical Share Price\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Historical Revenue\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvertical_spacing\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m.3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mstock_data_specific\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstock_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstock_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDate\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;34m'2021--06-14'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mrevenue_data_specific\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrevenue_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrevenue_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDate\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;34m'2021-04-30'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mScatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstock_data_specific\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfer_datetime_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstock_data_specific\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mClose\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"float\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Share Price\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'make_subplots' is not defined"
     ]
    }
   ],
   "source": [
    "make_graph(gme_data, gme_revenue, 'GameStop')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>About the Authors:</h2> \n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\">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",
    "| 2022-02-28        | 1.2     | Lakshmi Holla | Changed the URL of GameStop |\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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