Stock_data_analysis.ipynb

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    "colab": {
      "name": "Stock_data_analysis.ipynb",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t243rfF-qfPZ"
      },
      "source": [
        "# **Extracting and Visualizing Stock Data**\n",
        "\n",
        "### **Description**\n",
        "\n",
        "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",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rc94uZ9Dq18k",
        "outputId": "a2f4038c-87f7-4c27-b756-d2daa640cf30"
      },
      "source": [
        "!pip install yfinance\n",
        "#!pip install pandas\n",
        "#!pip install requests\n",
        "!pip install bs4\n",
        "#!pip install plotly"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting yfinance\n",
            "  Downloading yfinance-0.1.63.tar.gz (26 kB)\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.1.5)\n",
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            "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.7/dist-packages (from yfinance) (2.23.0)\n",
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            "Collecting lxml>=4.5.1\n",
            "  Downloading lxml-4.6.3-cp37-cp37m-manylinux2014_x86_64.whl (6.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.3 MB 29.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2.8.2)\n",
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            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (3.0.4)\n",
            "Building wheels for collected packages: yfinance\n",
            "  Building wheel for yfinance (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for yfinance: filename=yfinance-0.1.63-py2.py3-none-any.whl size=23918 sha256=6d9c6d8363dff9333625fd0f6fcdbb63d6c66a2e176e4927297a356cabf97203\n",
            "  Stored in directory: /root/.cache/pip/wheels/fe/87/8b/7ec24486e001d3926537f5f7801f57a74d181be25b11157983\n",
            "Successfully built yfinance\n",
            "Installing collected packages: lxml, yfinance\n",
            "  Attempting uninstall: lxml\n",
            "    Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "Successfully installed lxml-4.6.3 yfinance-0.1.63\n",
            "Requirement already satisfied: bs4 in /usr/local/lib/python3.7/dist-packages (0.0.1)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.7/dist-packages (from bs4) (4.6.3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i3AD4LWOq2_a"
      },
      "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"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q5H7a7oqq7If"
      },
      "source": [
        "# **Define Graphing Function**\n",
        "\n",
        "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."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i6s7yykvrCoM"
      },
      "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()"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4c6aEeXOrFs4"
      },
      "source": [
        "# **Question 1: Use yfinance to Extract Stock Data**\n",
        "\n",
        "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",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oA5BXCDMrLFj"
      },
      "source": [
        "Tesla = yf.Ticker(\"TSLA\")\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4e0hJ_yMrOAr"
      },
      "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."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ajgCbtorQeg"
      },
      "source": [
        "tesla_data = Tesla.history(period=\"max\")"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PZ96OqYqrVBy"
      },
      "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."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "hpHE_KJCrYcp",
        "outputId": "f22cdd4c-9e54-4e27-951f-bec1674339bc"
      },
      "source": [
        "tesla_data.reset_index(inplace=True)\n",
        "\n",
        "tesla_data.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "        vertical-align: middle;\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Dividends</th>\n",
              "      <th>Stock Splits</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010-06-29</td>\n",
              "      <td>3.800</td>\n",
              "      <td>5.000</td>\n",
              "      <td>3.508</td>\n",
              "      <td>4.778</td>\n",
              "      <td>93831500</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010-06-30</td>\n",
              "      <td>5.158</td>\n",
              "      <td>6.084</td>\n",
              "      <td>4.660</td>\n",
              "      <td>4.766</td>\n",
              "      <td>85935500</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2010-07-01</td>\n",
              "      <td>5.000</td>\n",
              "      <td>5.184</td>\n",
              "      <td>4.054</td>\n",
              "      <td>4.392</td>\n",
              "      <td>41094000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2010-07-02</td>\n",
              "      <td>4.600</td>\n",
              "      <td>4.620</td>\n",
              "      <td>3.742</td>\n",
              "      <td>3.840</td>\n",
              "      <td>25699000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2010-07-06</td>\n",
              "      <td>4.000</td>\n",
              "      <td>4.000</td>\n",
              "      <td>3.166</td>\n",
              "      <td>3.222</td>\n",
              "      <td>34334500</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date   Open   High    Low  Close    Volume  Dividends  Stock Splits\n",
              "0 2010-06-29  3.800  5.000  3.508  4.778  93831500          0           0.0\n",
              "1 2010-06-30  5.158  6.084  4.660  4.766  85935500          0           0.0\n",
              "2 2010-07-01  5.000  5.184  4.054  4.392  41094000          0           0.0\n",
              "3 2010-07-02  4.600  4.620  3.742  3.840  25699000          0           0.0\n",
              "4 2010-07-06  4.000  4.000  3.166  3.222  34334500          0           0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uR6MHQIerf_O"
      },
      "source": [
        "# **Question 2: Use Webscraping to Extract Tesla Revenue Data**\n",
        "\n",
        "Use the requests library to download the webpage https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue. Save the text of the response as a variable named html_data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "31_Nm6gerk55"
      },
      "source": [
        "url = \"https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0220ENSkillsNetwork23455606-2021-01-01\"\n",
        "\n",
        "data = requests.get(url).text"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Tp_Ncf8rniS"
      },
      "source": [
        "Parse the html data using beautiful_soup."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xX0nF2xVrqk3"
      },
      "source": [
        "soup = BeautifulSoup(data, 'html5lib')"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7yHVYNTSrtNM"
      },
      "source": [
        "Using beautiful soup extract the table with Tesla Quarterly Revenue and store it into a dataframe named tesla_revenue. The dataframe should have columns Date and Revenue."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3ob28jlUr4Js",
        "outputId": "ae42f735-0821-4ab4-bc50-3b1b32929a8f"
      },
      "source": [
        "tesla_revenue = pd.DataFrame(columns=[\"Date\", \"Revenue\"])\n",
        "\n",
        "tables = soup.find_all('table') \n",
        "\n",
        "\n",
        "for index,table in enumerate(tables):\n",
        "  \n",
        "    if (\"Tesla Quarterly Revenue\" in str(table)):\n",
        "\n",
        "        table_index = index\n",
        "       \n",
        "\n",
        "        for row in tables[table_index].tbody.find_all(\"tr\"):\n",
        "            col = row.find_all(\"td\")\n",
        "            if (col != []):\n",
        "                Date = col[0].text\n",
        "                Revenue = col[1].text\n",
        "                tesla_revenue = tesla_revenue.append({\"Date\":Date,\"Revenue\":Revenue}, ignore_index=True)\n",
        "                \n",
        "\n",
        "                \n",
        "                \n",
        "tesla_revenue\n",
        "    \n",
        "   "
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>2021-06-30</td>\n",
              "      <td>$11,958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021-03-31</td>\n",
              "      <td>$10,389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>$10,744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-09-30</td>\n",
              "      <td>$8,771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-06-30</td>\n",
              "      <td>$6,036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2020-03-31</td>\n",
              "      <td>$5,985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2019-12-31</td>\n",
              "      <td>$7,384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2019-09-30</td>\n",
              "      <td>$6,303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2019-06-30</td>\n",
              "      <td>$6,350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2019-03-31</td>\n",
              "      <td>$4,541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2018-12-31</td>\n",
              "      <td>$7,226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2018-09-30</td>\n",
              "      <td>$6,824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2018-06-30</td>\n",
              "      <td>$4,002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2018-03-31</td>\n",
              "      <td>$3,409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2017-12-31</td>\n",
              "      <td>$3,288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2017-09-30</td>\n",
              "      <td>$2,985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2017-06-30</td>\n",
              "      <td>$2,790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2017-03-31</td>\n",
              "      <td>$2,696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2016-12-31</td>\n",
              "      <td>$2,285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2016-09-30</td>\n",
              "      <td>$2,298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2016-06-30</td>\n",
              "      <td>$1,270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2016-03-31</td>\n",
              "      <td>$1,147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2015-12-31</td>\n",
              "      <td>$1,214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2015-09-30</td>\n",
              "      <td>$937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2015-06-30</td>\n",
              "      <td>$955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2015-03-31</td>\n",
              "      <td>$940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2014-12-31</td>\n",
              "      <td>$957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>2014-09-30</td>\n",
              "      <td>$852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>2014-06-30</td>\n",
              "      <td>$769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>2014-03-31</td>\n",
              "      <td>$621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>2013-12-31</td>\n",
              "      <td>$615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>2013-09-30</td>\n",
              "      <td>$431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>2013-06-30</td>\n",
              "      <td>$405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2013-03-31</td>\n",
              "      <td>$562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2012-12-31</td>\n",
              "      <td>$306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2012-09-30</td>\n",
              "      <td>$50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2012-06-30</td>\n",
              "      <td>$27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>2012-03-31</td>\n",
              "      <td>$30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>2011-12-31</td>\n",
              "      <td>$39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>2011-09-30</td>\n",
              "      <td>$58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>2011-06-30</td>\n",
              "      <td>$58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>2011-03-31</td>\n",
              "      <td>$49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>2010-12-31</td>\n",
              "      <td>$36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>2010-09-30</td>\n",
              "      <td>$31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>2010-06-30</td>\n",
              "      <td>$28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>2010-03-31</td>\n",
              "      <td>$21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>2009-12-31</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>2009-09-30</td>\n",
              "      <td>$46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>2009-06-30</td>\n",
              "      <td>$27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>2008-12-31</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date  Revenue\n",
              "0   2021-06-30  $11,958\n",
              "1   2021-03-31  $10,389\n",
              "2   2020-12-31  $10,744\n",
              "3   2020-09-30   $8,771\n",
              "4   2020-06-30   $6,036\n",
              "5   2020-03-31   $5,985\n",
              "6   2019-12-31   $7,384\n",
              "7   2019-09-30   $6,303\n",
              "8   2019-06-30   $6,350\n",
              "9   2019-03-31   $4,541\n",
              "10  2018-12-31   $7,226\n",
              "11  2018-09-30   $6,824\n",
              "12  2018-06-30   $4,002\n",
              "13  2018-03-31   $3,409\n",
              "14  2017-12-31   $3,288\n",
              "15  2017-09-30   $2,985\n",
              "16  2017-06-30   $2,790\n",
              "17  2017-03-31   $2,696\n",
              "18  2016-12-31   $2,285\n",
              "19  2016-09-30   $2,298\n",
              "20  2016-06-30   $1,270\n",
              "21  2016-03-31   $1,147\n",
              "22  2015-12-31   $1,214\n",
              "23  2015-09-30     $937\n",
              "24  2015-06-30     $955\n",
              "25  2015-03-31     $940\n",
              "26  2014-12-31     $957\n",
              "27  2014-09-30     $852\n",
              "28  2014-06-30     $769\n",
              "29  2014-03-31     $621\n",
              "30  2013-12-31     $615\n",
              "31  2013-09-30     $431\n",
              "32  2013-06-30     $405\n",
              "33  2013-03-31     $562\n",
              "34  2012-12-31     $306\n",
              "35  2012-09-30      $50\n",
              "36  2012-06-30      $27\n",
              "37  2012-03-31      $30\n",
              "38  2011-12-31      $39\n",
              "39  2011-09-30      $58\n",
              "40  2011-06-30      $58\n",
              "41  2011-03-31      $49\n",
              "42  2010-12-31      $36\n",
              "43  2010-09-30      $31\n",
              "44  2010-06-30      $28\n",
              "45  2010-03-31      $21\n",
              "46  2009-12-31         \n",
              "47  2009-09-30      $46\n",
              "48  2009-06-30      $27\n",
              "49  2008-12-31         "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fXzmT5-r7uT"
      },
      "source": [
        "Execute the following line to remove the comma and dollar sign from the Revenue column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1pfQPoEur-c0",
        "outputId": "af4524a3-5bc1-4d84-91e3-63ffbf34f160"
      },
      "source": [
        "tesla_revenue['Revenue'] = tesla_revenue['Revenue'].replace({'\\$': '', ',': ''}, regex=True)\n",
        "tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != '']\n",
        "\n",
        "\n",
        "\n",
        "tesla_revenue"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "        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>2021-06-30</td>\n",
              "      <td>11958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021-03-31</td>\n",
              "      <td>10389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>10744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-09-30</td>\n",
              "      <td>8771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-06-30</td>\n",
              "      <td>6036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2020-03-31</td>\n",
              "      <td>5985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2019-12-31</td>\n",
              "      <td>7384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2019-09-30</td>\n",
              "      <td>6303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2019-06-30</td>\n",
              "      <td>6350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2019-03-31</td>\n",
              "      <td>4541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2018-12-31</td>\n",
              "      <td>7226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2018-09-30</td>\n",
              "      <td>6824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2018-06-30</td>\n",
              "      <td>4002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2018-03-31</td>\n",
              "      <td>3409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2017-12-31</td>\n",
              "      <td>3288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2017-09-30</td>\n",
              "      <td>2985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2017-06-30</td>\n",
              "      <td>2790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2017-03-31</td>\n",
              "      <td>2696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2016-12-31</td>\n",
              "      <td>2285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2016-09-30</td>\n",
              "      <td>2298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2016-06-30</td>\n",
              "      <td>1270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2016-03-31</td>\n",
              "      <td>1147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2015-12-31</td>\n",
              "      <td>1214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2015-09-30</td>\n",
              "      <td>937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2015-06-30</td>\n",
              "      <td>955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2015-03-31</td>\n",
              "      <td>940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2014-12-31</td>\n",
              "      <td>957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>2014-09-30</td>\n",
              "      <td>852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>2014-06-30</td>\n",
              "      <td>769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>2014-03-31</td>\n",
              "      <td>621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>2013-12-31</td>\n",
              "      <td>615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>2013-09-30</td>\n",
              "      <td>431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>2013-06-30</td>\n",
              "      <td>405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2013-03-31</td>\n",
              "      <td>562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2012-12-31</td>\n",
              "      <td>306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2012-09-30</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2012-06-30</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>2012-03-31</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>2011-12-31</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>2011-09-30</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>2011-06-30</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>2011-03-31</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>2010-12-31</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>2010-09-30</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>2010-06-30</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>2010-03-31</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>2009-09-30</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>2009-06-30</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date Revenue\n",
              "0   2021-06-30   11958\n",
              "1   2021-03-31   10389\n",
              "2   2020-12-31   10744\n",
              "3   2020-09-30    8771\n",
              "4   2020-06-30    6036\n",
              "5   2020-03-31    5985\n",
              "6   2019-12-31    7384\n",
              "7   2019-09-30    6303\n",
              "8   2019-06-30    6350\n",
              "9   2019-03-31    4541\n",
              "10  2018-12-31    7226\n",
              "11  2018-09-30    6824\n",
              "12  2018-06-30    4002\n",
              "13  2018-03-31    3409\n",
              "14  2017-12-31    3288\n",
              "15  2017-09-30    2985\n",
              "16  2017-06-30    2790\n",
              "17  2017-03-31    2696\n",
              "18  2016-12-31    2285\n",
              "19  2016-09-30    2298\n",
              "20  2016-06-30    1270\n",
              "21  2016-03-31    1147\n",
              "22  2015-12-31    1214\n",
              "23  2015-09-30     937\n",
              "24  2015-06-30     955\n",
              "25  2015-03-31     940\n",
              "26  2014-12-31     957\n",
              "27  2014-09-30     852\n",
              "28  2014-06-30     769\n",
              "29  2014-03-31     621\n",
              "30  2013-12-31     615\n",
              "31  2013-09-30     431\n",
              "32  2013-06-30     405\n",
              "33  2013-03-31     562\n",
              "34  2012-12-31     306\n",
              "35  2012-09-30      50\n",
              "36  2012-06-30      27\n",
              "37  2012-03-31      30\n",
              "38  2011-12-31      39\n",
              "39  2011-09-30      58\n",
              "40  2011-06-30      58\n",
              "41  2011-03-31      49\n",
              "42  2010-12-31      36\n",
              "43  2010-09-30      31\n",
              "44  2010-06-30      28\n",
              "45  2010-03-31      21\n",
              "47  2009-09-30      46\n",
              "48  2009-06-30      27"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h5b_G04WsCtu"
      },
      "source": [
        "Execute the following lines to remove an null or empty strings in the Revenue column.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iyeif48SsEfb",
        "outputId": "e40a89fe-401c-40a0-fe26-99b125287f92"
      },
      "source": [
        "tesla_revenue.dropna(axis=0,inplace=True)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hNPqt7jEsGr3"
      },
      "source": [
        "Display the last 5 row of the tesla_revenue dataframe using the tail function. Take a screenshot of the results."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "BM2JUwFksMUL",
        "outputId": "1a9f6a03-da21-4766-9a98-0517a8250177"
      },
      "source": [
        "tesla_revenue.tail()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <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>43</th>\n",
              "      <td>2010-09-30</td>\n",
              "      <td>31</td>\n",
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              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>2010-06-30</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>2010-03-31</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>2009-09-30</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>2009-06-30</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date Revenue\n",
              "43  2010-09-30      31\n",
              "44  2010-06-30      28\n",
              "45  2010-03-31      21\n",
              "47  2009-09-30      46\n",
              "48  2009-06-30      27"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2mymZQwwsOgH"
      },
      "source": [
        "# **Question 3: Use yfinance to Extract Stock Data**\n",
        "\n",
        "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."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YDui5XKXsSw3"
      },
      "source": [
        "GameStop = yf.Ticker(\"GME\")"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6VXfvtBasVFD"
      },
      "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",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rh1avDv9sX7R"
      },
      "source": [
        "gme_data = GameStop.history(period=\"max\")\n"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cXg1hMTgsas-"
      },
      "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",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "HFLKYUq5sdxl",
        "outputId": "9f5c7aad-c978-4ca0-9df5-4d19e451aac5"
      },
      "source": [
        "gme_data.reset_index(inplace=True)\n",
        "\n",
        "gme_data.head()\n"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Dividends</th>\n",
              "      <th>Stock Splits</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2002-02-13</td>\n",
              "      <td>6.480513</td>\n",
              "      <td>6.773399</td>\n",
              "      <td>6.413183</td>\n",
              "      <td>6.766666</td>\n",
              "      <td>19054000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2002-02-14</td>\n",
              "      <td>6.850831</td>\n",
              "      <td>6.864296</td>\n",
              "      <td>6.682506</td>\n",
              "      <td>6.733003</td>\n",
              "      <td>2755400</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2002-02-15</td>\n",
              "      <td>6.733001</td>\n",
              "      <td>6.749833</td>\n",
              "      <td>6.632006</td>\n",
              "      <td>6.699336</td>\n",
              "      <td>2097400</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2002-02-19</td>\n",
              "      <td>6.665671</td>\n",
              "      <td>6.665671</td>\n",
              "      <td>6.312189</td>\n",
              "      <td>6.430017</td>\n",
              "      <td>1852600</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2002-02-20</td>\n",
              "      <td>6.463681</td>\n",
              "      <td>6.648838</td>\n",
              "      <td>6.413183</td>\n",
              "      <td>6.648838</td>\n",
              "      <td>1723200</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date      Open      High  ...    Volume  Dividends  Stock Splits\n",
              "0 2002-02-13  6.480513  6.773399  ...  19054000        0.0           0.0\n",
              "1 2002-02-14  6.850831  6.864296  ...   2755400        0.0           0.0\n",
              "2 2002-02-15  6.733001  6.749833  ...   2097400        0.0           0.0\n",
              "3 2002-02-19  6.665671  6.665671  ...   1852600        0.0           0.0\n",
              "4 2002-02-20  6.463681  6.648838  ...   1723200        0.0           0.0\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iAyZ52P8shVH"
      },
      "source": [
        "# **Question 4: Use Webscraping to Extract GME Revenue Data**\n",
        "\n",
        "Use the requests library to download the webpage https://www.macrotrends.net/stocks/charts/GME/gamestop/revenue. Save the text of the response as a variable named html_data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PYLtnMYQsljZ"
      },
      "source": [
        "url = \"https://www.macrotrends.net/stocks/charts/GME/gamestop/revenue?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0220ENSkillsNetwork23455606-2021-01-01\"\n",
        "\n",
        "data = requests.get(url).text"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HCAt1Jnvsn_v"
      },
      "source": [
        "Parse the html data using beautiful_soup."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nC5fhDgOsp09"
      },
      "source": [
        "soup = BeautifulSoup(data, 'html5lib')"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kTMo7lHCsr56"
      },
      "source": [
        "Using beautiful soup extract the table with GameStop Quarterly 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."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-22OMm9WsuHn"
      },
      "source": [
        "gme_revenue = pd.DataFrame(columns=[\"Date\",\"Revenue\"])\n",
        "\n",
        "tables = soup.find_all('table') \n",
        "for index,table in enumerate(tables):\n",
        "\n",
        "    if (\"GameStop Quarterly Revenue\" in str(table)):\n",
        "\n",
        "        table_index = index\n",
        "        \n",
        "for row in tables[table_index].tbody.find_all(\"tr\"):\n",
        "             col = row.find_all(\"td\")\n",
        "             if (col != []):\n",
        "                Date = col[0].text\n",
        "                Revenue = col[1].text\n",
        "                gme_revenue = gme_revenue.append({\"Date\":Date,\"Revenue\":Revenue}, ignore_index=True)\n",
        "\n",
        "                gme_revenue"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Vd2H4ZQs3vq"
      },
      "source": [
        "Display the last five rows of the gme_revenue dataframe using the tail function. Take a screenshot of the results."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "0GT92dsbs1b_",
        "outputId": "1f857385-cd75-4d34-9791-f22d89276b5e"
      },
      "source": [
        "gme_revenue['Revenue'] = gme_revenue['Revenue'].replace({'\\$': '', ',': ''}, regex=True)\n",
        "gme_revenue.dropna(axis=0,inplace=True)\n",
        "\n",
        "gme_revenue.tail()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>61</th>\n",
              "      <td>2006-01-31</td>\n",
              "      <td>1667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>2005-10-31</td>\n",
              "      <td>534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>2005-07-31</td>\n",
              "      <td>416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>2005-04-30</td>\n",
              "      <td>475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>2005-01-31</td>\n",
              "      <td>709</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date Revenue\n",
              "61  2006-01-31    1667\n",
              "62  2005-10-31     534\n",
              "63  2005-07-31     416\n",
              "64  2005-04-30     475\n",
              "65  2005-01-31     709"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v6Z8u1qEs6Ex"
      },
      "source": [
        "# **Question 5: Plot Tesla Stock Graph**\n",
        "\n",
        "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",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 917
        },
        "id": "QiOXLgPJtBmx",
        "outputId": "c26a9ef2-aef3-4d91-b5d9-461970713ca8"
      },
      "source": [
        "make_graph(tesla_data, tesla_revenue, 'Tesla')\n"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>\n",
              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"9a416b00-e249-4a19-9f07-8dbbb01e62dc\" class=\"plotly-graph-div\" style=\"height:900px; width:100%;\"></div>\n",
              "            <script type=\"text/javascript\">\n",
              "                \n",
              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
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              "                    Plotly.newPlot(\n",
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      "source": [
        "# **Question 6: Plot GameStop Stock Graph**\n",
        "\n",
        "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."
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        "outputId": "f03f1f21-67c1-4c25-f732-910e10b55355"
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      "source": [
        "make_graph(gme_data, gme_revenue, 'GameStop')\n"
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      "outputs": [
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          "data": {
            "text/plain": [
              "Date            datetime64[ns]\n",
              "Open                   float64\n",
              "High                   float64\n",
              "Low                    float64\n",
              "Close                  float64\n",
              "Volume                   int64\n",
              "Dividends                int64\n",
              "Stock Splits           float64\n",
              "dtype: object"
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          },
          "metadata": {
            "tags": []
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          "execution_count": 29
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}