Python Project.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"
   ]
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  {
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   "id": "a3297b8f-d940-495e-b617-311976f3374a",
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   "source": [
    "<h1>Extracting and Visualizing Stock Data</h1>\n",
    "<h2>Description</h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a63757ca-0a1f-4a61-bd6c-3c25f234e7fb",
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   "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",
   "id": "11a783a7-56d2-4e99-97cf-ea53bfc8aa81",
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   "source": [
    "<h2>Table of Contents</h2>\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "    <ul>\n",
    "        <li>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"
   ]
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      "\n",
      "        mamba (0.15.3) supported by @QuantStack\n",
      "\n",
      "        GitHub:  https://github.com/mamba-org/mamba\n",
      "        Twitter: https://twitter.com/QuantStack\n",
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      "Looking for: ['bs4==4.10.0']\n",
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      "\n",
      "Pinned packages:\n",
      "  - python 3.7.*\n",
      "\n",
      "\n",
      "Transaction\n",
      "\n",
      "  Prefix: /home/jupyterlab/conda/envs/python\n",
      "\n",
      "  All requested packages already installed\n",
      "\n",
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   "source": [
    "!pip install yfinance==0.1.67\n",
    "!mamba install bs4==4.10.0 -y\n",
    "!pip install nbformat==5.2.0"
   ]
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  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9860fee0-de38-4cd2-936b-7479745b0f4e",
   "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",
   "id": "c2d6a13c-efae-4024-85e5-4edda1895c92",
   "metadata": {},
   "source": [
    "## Define Graphing Function\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c0e1b5f-6137-4d71-b130-5aabd2cfb7f5",
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   "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": 60,
   "id": "0dcaaa5e-e5ed-4337-beb4-134b5da043d6",
   "metadata": {},
   "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",
   "id": "fbaa3d01-1420-4087-9d79-3a56522c1610",
   "metadata": {},
   "source": [
    "## Question 1: Use yfinance to Extract Stock Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d574fd10-1aab-4e46-a924-53d028709d24",
   "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": 61,
   "id": "408e0d71-3e76-4731-8cb0-60e7efd7c9ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "tesla = yf.Ticker(\"TSLA\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13c78686-b22b-490c-acac-b66d49024e46",
   "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": 62,
   "id": "7f9763e4-9122-41ec-9443-a53343c59b60",
   "metadata": {},
   "outputs": [],
   "source": [
    "tesla_data = tesla.history(period=\"max\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15032e00-90d7-455e-adcd-a1eeec54d6e0",
   "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": "code",
   "execution_count": 63,
   "id": "0123c47b-8378-4c7f-99be-80e571416ebd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Dividends</th>\n",
       "      <th>Stock Splits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-06-29</td>\n",
       "      <td>1.266667</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.169333</td>\n",
       "      <td>1.592667</td>\n",
       "      <td>281494500</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>1.719333</td>\n",
       "      <td>2.028000</td>\n",
       "      <td>1.553333</td>\n",
       "      <td>1.588667</td>\n",
       "      <td>257806500</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>1.666667</td>\n",
       "      <td>1.728000</td>\n",
       "      <td>1.351333</td>\n",
       "      <td>1.464000</td>\n",
       "      <td>123282000</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>1.533333</td>\n",
       "      <td>1.540000</td>\n",
       "      <td>1.247333</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>77097000</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>1.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.055333</td>\n",
       "      <td>1.074000</td>\n",
       "      <td>103003500</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  \\\n",
       "0 2010-06-29  1.266667  1.666667  1.169333  1.592667  281494500          0   \n",
       "1 2010-06-30  1.719333  2.028000  1.553333  1.588667  257806500          0   \n",
       "2 2010-07-01  1.666667  1.728000  1.351333  1.464000  123282000          0   \n",
       "3 2010-07-02  1.533333  1.540000  1.247333  1.280000   77097000          0   \n",
       "4 2010-07-06  1.333333  1.333333  1.055333  1.074000  103003500          0   \n",
       "\n",
       "   Stock Splits  \n",
       "0           0.0  \n",
       "1           0.0  \n",
       "2           0.0  \n",
       "3           0.0  \n",
       "4           0.0  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tesla_data.reset_index(inplace=True)\n",
    "tesla_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b94b5dab-a6f1-40eb-b836-0062988464db",
   "metadata": {},
   "source": [
    "## Question 2: Use Webscraping to Extract Tesla Revenue Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10c4befd-c47e-4592-a72e-7c0b5c678068",
   "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": 64,
   "id": "f65ee884-d9a1-424a-bcb2-bab7e3fbc53f",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm\"\n",
    "\n",
    "html_data  = requests.get(url).text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e08fb22f-128a-4f99-8344-dd18d6279ab6",
   "metadata": {},
   "source": [
    "Parse the html data using `beautiful_soup`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "208b38b4-260c-4098-9b1e-9e88baed91c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<title>Tesla Revenue 2010-2022 | TSLA | MacroTrends</title>]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beautiful_soup = BeautifulSoup(html_data, \"html.parser\")\n",
    "beautiful_soup.find_all('title')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2beb668c-ce60-4121-96fc-0aa47465f941",
   "metadata": {},
   "source": [
    "Using `BeautifulSoup` or the `read_html` function extract the table with `Tesla Quarterly Revenue` and store it into a dataframe named `tesla_revenue`. The dataframe should have columns `Date` and `Revenue`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6d73b96-e834-49d0-a325-4341ab772ad8",
   "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": 66,
   "id": "cd2a6e7c-8f63-48d6-b335-533af47912b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "tesla_revenue = pd.DataFrame(columns = ['Date', 'Revenue'])\n",
    "\n",
    "for row in beautiful_soup.find_all(\"tbody\")[1].find_all(\"tr\"):\n",
    "    col = row.find_all(\"td\")\n",
    "    date = col[0].text\n",
    "    revenue = col[1].text.replace(\"$\", \"\").replace(\",\", \"\")\n",
    "    \n",
    "    tesla_revenue = tesla_revenue.append({\"Date\": date, \"Revenue\": revenue}, ignore_index = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "621993b9-01da-48e3-a3e3-d65364683bae",
   "metadata": {},
   "source": [
    "Execute the following line to remove the comma and dollar sign from the `Revenue` column. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a96aff4d-1999-4f8b-a2e2-a0a9d15f900f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning:\n",
      "\n",
      "The default value of regex will change from True to False in a future version.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tesla_revenue[\"Revenue\"] = tesla_revenue['Revenue'].str.replace(',|\\$',\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92e23a47-3512-489a-b2da-7d596cee444d",
   "metadata": {},
   "source": [
    "Execute the following lines to remove an null or empty strings in the Revenue column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3f14fcdc-450c-4fba-82da-a56ec62ac81d",
   "metadata": {},
   "outputs": [],
   "source": [
    "tesla_revenue.dropna(inplace=True)\n",
    "\n",
    "tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != \"\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e64f6be4-8b4f-4077-b7dc-03ce2c2df456",
   "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": 68,
   "id": "3676dbd9-97ce-4fb8-945c-ca96db5d28d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2010-09-30</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2010-06-30</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2010-03-31</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2009-09-30</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2009-06-30</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date Revenue\n",
       "48  2010-09-30      31\n",
       "49  2010-06-30      28\n",
       "50  2010-03-31      21\n",
       "52  2009-09-30      46\n",
       "53  2009-06-30      27"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tesla_revenue.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ef322a4-e492-4e0e-a514-5122180f8654",
   "metadata": {},
   "source": [
    "## Question 3: Use yfinance to Extract Stock Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "662635b8-6df0-4db5-a536-a82e6dff05dc",
   "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": 69,
   "id": "f45e0206-d410-44f7-869f-f5e8d6985de0",
   "metadata": {},
   "outputs": [],
   "source": [
    "GameStop = yf.Ticker(\"GME\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f9983cb-a323-4f8e-8ff6-e5af19e10286",
   "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": 70,
   "id": "5da839c4-31ae-49aa-ace9-fdebade2eb3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "gme_data = GameStop.history(period = 'max')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1620f225-e2d6-4ba8-ac99-9e701fdcdea0",
   "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": 71,
   "id": "e5dbf4a7-d32e-412b-b6c5-1b22fd5944ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Dividends</th>\n",
       "      <th>Stock Splits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2002-02-13</td>\n",
       "      <td>1.620128</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>2002-02-14</td>\n",
       "      <td>1.712707</td>\n",
       "      <td>1.716074</td>\n",
       "      <td>1.670626</td>\n",
       "      <td>1.683251</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>2002-02-15</td>\n",
       "      <td>1.683251</td>\n",
       "      <td>1.687459</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>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>2002-02-20</td>\n",
       "      <td>1.615921</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": [
       "        Date      Open      High       Low     Close    Volume  Dividends  \\\n",
       "0 2002-02-13  1.620128  1.693350  1.603296  1.691667  76216000        0.0   \n",
       "1 2002-02-14  1.712707  1.716074  1.670626  1.683251  11021600        0.0   \n",
       "2 2002-02-15  1.683251  1.687459  1.658002  1.674834   8389600        0.0   \n",
       "3 2002-02-19  1.666418  1.666418  1.578047  1.607504   7410400        0.0   \n",
       "4 2002-02-20  1.615921  1.662210  1.603296  1.662210   6892800        0.0   \n",
       "\n",
       "   Stock Splits  \n",
       "0           0.0  \n",
       "1           0.0  \n",
       "2           0.0  \n",
       "3           0.0  \n",
       "4           0.0  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gme_data.reset_index(inplace = True)\n",
    "gme_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96df5f96-275e-47fb-9f39-509e09adbab6",
   "metadata": {},
   "source": [
    "## Question 4: Use Webscraping to Extract GME Revenue Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d40ea356-9393-4bf7-a67f-f95741863ad7",
   "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": 72,
   "id": "35ddd411-d1c1-4a06-9f2e-b1a31566ef2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html\"\n",
    "html_data = requests.get(url).text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18de3ace-3412-47aa-ae7f-97d486debff9",
   "metadata": {},
   "source": [
    "Parse the html data using `beautiful_soup`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c932e23d-cd21-4e85-afc8-7e3193134d74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<title>GameStop Revenue 2006-2020 | GME | MacroTrends</title>]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beautiful_soup = BeautifulSoup(html_data, \"html.parser\")\n",
    "beautiful_soup.find_all('title')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b8e8a88-b39e-4ca0-82e7-d833cf090e85",
   "metadata": {},
   "source": [
    "Using `BeautifulSoup` or the `read_html` function 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.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c7f53d6-b641-4f2a-b7c0-09946f07300f",
   "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": 74,
   "id": "0435b643-007e-4713-bf52-f3bc2d3fc611",
   "metadata": {},
   "outputs": [],
   "source": [
    "gme_revenue = pd.DataFrame(columns = ['Date', 'Revenue'])\n",
    "\n",
    "for row in beautiful_soup.find_all(\"tbody\")[1].find_all(\"tr\"):\n",
    "    col = row.find_all(\"td\")\n",
    "    date = col[0].text\n",
    "    revenue = col[1].text.replace(\"S\", \"\").replace(\",\", \"\")\n",
    "    \n",
    "    gme_revenue = gme_revenue.append({\"Date\": date, \"Revenue\": revenue}, ignore_index = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf5f0a15-d2bc-48e0-9899-4020ed99dbd7",
   "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": 75,
   "id": "fce8dbe5-c305-48f4-aecb-487126ac37b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2006-01-31</td>\n",
       "      <td>$1667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2005-10-31</td>\n",
       "      <td>$534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2005-07-31</td>\n",
       "      <td>$416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2005-04-30</td>\n",
       "      <td>$475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2005-01-31</td>\n",
       "      <td>$709</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date Revenue\n",
       "57  2006-01-31   $1667\n",
       "58  2005-10-31    $534\n",
       "59  2005-07-31    $416\n",
       "60  2005-04-30    $475\n",
       "61  2005-01-31    $709"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tesla_revenue.dropna(inplace=True)\n",
    "tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != \"\"]\n",
    "gme_revenue.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f78c13c-8932-446f-bb66-7f278b559d3e",
   "metadata": {},
   "source": [
    "## Question 5: Plot Tesla Stock Graph\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f149575a-faf8-41bc-8c35-7b17b7928d89",
   "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": 76,
   "id": "35bf663e-3531-43b2-9447-69500a4fb38b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "name": "Share Price",
         "type": "scatter",
         "x": [
          "2010-06-29T00:00:00",
          "2010-06-30T00:00:00",
          "2010-07-01T00:00:00",
          "2010-07-02T00:00:00",
          "2010-07-06T00:00:00",
          "2010-07-07T00:00:00",
          "2010-07-08T00:00:00",
          "2010-07-09T00:00:00",
          "2010-07-12T00:00:00",
          "2010-07-13T00:00:00",
          "2010-07-14T00:00:00",
          "2010-07-15T00:00:00",
          "2010-07-16T00:00:00",
          "2010-07-19T00:00:00",
          "2010-07-20T00:00:00",
          "2010-07-21T00:00:00",
          "2010-07-22T00:00:00",
          "2010-07-23T00:00:00",
          "2010-07-26T00:00:00",
          "2010-07-27T00:00:00",
          "2010-07-28T00:00:00",
          "2010-07-29T00:00:00",
          "2010-07-30T00:00:00",
          "2010-08-02T00:00:00",
          "2010-08-03T00:00:00",
          "2010-08-04T00:00:00",
          "2010-08-05T00:00:00",
          "2010-08-06T00:00:00",
          "2010-08-09T00:00:00",
          "2010-08-10T00:00:00",
          "2010-08-11T00:00:00",
          "2010-08-12T00:00:00",
          "2010-08-13T00:00:00",
          "2010-08-16T00:00:00",
          "2010-08-17T00:00:00",
          "2010-08-18T00:00:00",
          "2010-08-19T00:00:00",
          "2010-08-20T00:00:00",
          "2010-08-23T00:00:00",
          "2010-08-24T00:00:00",
          "2010-08-25T00:00:00",
          "2010-08-26T00:00:00",
          "2010-08-27T00:00:00",
          "2010-08-30T00:00:00",
          "2010-08-31T00:00:00",
          "2010-09-01T00:00:00",
          "2010-09-02T00:00:00",
          "2010-09-03T00:00:00",
          "2010-09-07T00:00:00",
          "2010-09-08T00:00:00",
          "2010-09-09T00:00:00",
          "2010-09-10T00:00:00",
          "2010-09-13T00:00:00",
          "2010-09-14T00:00:00",
          "2010-09-15T00:00:00",
          "2010-09-16T00:00:00",
          "2010-09-17T00:00:00",
          "2010-09-20T00:00:00",
          "2010-09-21T00:00:00",
          "2010-09-22T00:00:00",
          "2010-09-23T00:00:00",
          "2010-09-24T00:00:00",
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          "2010-11-09T00:00:00",
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          "2010-11-16T00:00:00",
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          "2010-11-18T00:00:00",
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          "2010-11-23T00:00:00",
          "2010-11-24T00:00:00",
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          "2010-11-29T00:00:00",
          "2010-11-30T00:00:00",
          "2010-12-01T00:00:00",
          "2010-12-02T00:00:00",
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          "2011-01-03T00:00:00",
          "2011-01-04T00:00:00",
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          "2011-05-10T00:00:00",
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          "2011-05-27T00:00:00",
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          "2011-06-02T00:00:00",
          "2011-06-03T00:00:00",
          "2011-06-06T00:00:00",
          "2011-06-07T00:00:00",
          "2011-06-08T00:00:00",
          "2011-06-09T00:00:00",
          "2011-06-10T00:00:00",
          "2011-06-13T00:00:00",
          "2011-06-14T00:00:00",
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          "2011-06-16T00:00:00",
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          "2011-06-24T00:00:00",
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          "2011-06-28T00:00:00",
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          "2011-07-01T00:00:00",
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          "2011-07-06T00:00:00",
          "2011-07-07T00:00:00",
          "2011-07-08T00:00:00",
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          "2011-07-13T00:00:00",
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          "2011-07-25T00:00:00",
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          "2011-08-22T00:00:00",
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          "2011-09-01T00:00:00",
          "2011-09-02T00:00:00",
          "2011-09-06T00:00:00",
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          "2011-09-08T00:00:00",
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          "2011-09-26T00:00:00",
          "2011-09-27T00:00:00",
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          "2011-09-29T00:00:00",
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          "2011-10-03T00:00:00",
          "2011-10-04T00:00:00",
          "2011-10-05T00:00:00",
          "2011-10-06T00:00:00",
          "2011-10-07T00:00:00",
          "2011-10-10T00:00:00",
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          "2011-10-25T00:00:00",
          "2011-10-26T00:00:00",
          "2011-10-27T00:00:00",
          "2011-10-28T00:00:00",
          "2011-10-31T00:00:00",
          "2011-11-01T00:00:00",
          "2011-11-02T00:00:00",
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          "2011-11-04T00:00:00",
          "2011-11-07T00:00:00",
          "2011-11-08T00:00:00",
          "2011-11-09T00:00:00",
          "2011-11-10T00:00:00",
          "2011-11-11T00:00:00",
          "2011-11-14T00:00:00",
          "2011-11-15T00:00:00",
          "2011-11-16T00:00:00",
          "2011-11-17T00:00:00",
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          "2011-11-29T00:00:00",
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          "2011-12-01T00:00:00",
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          "2011-12-07T00:00:00",
          "2011-12-08T00:00:00",
          "2011-12-09T00:00:00",
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          "2011-12-13T00:00:00",
          "2011-12-14T00:00:00",
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          "2011-12-22T00:00:00",
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "make_graph(tesla_data, tesla_revenue, 'Tesla')"
   ]
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   "id": "c9e237cf-6914-43db-b5cf-c0aaafdbe3ec",
   "metadata": {},
   "source": [
    "## Question 6: Plot GameStop Stock Graph\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a92ff53-3182-4a8d-b69b-7289e023014b",
   "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": 77,
   "id": "d0c80aa1-5117-4ec7-9e0d-e860b7914c14",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: '$1021'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_671/1051334688.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_671/2068038883.py\u001b[0m in \u001b[0;36mmake_graph\u001b[0;34m(stock_data, revenue_data, stock)\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;32m----> 6\u001b[0;31m     \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[0mrevenue_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[0mrevenue_data_specific\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRevenue\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\"Revenue\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\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[0m\u001b[1;32m      7\u001b[0m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_xaxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitle_text\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Date\"\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[1;32m      8\u001b[0m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_xaxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitle_text\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Date\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\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;32m~/conda/envs/python/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   5813\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5814\u001b[0m             \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5815\u001b[0;31m             \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   5816\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5817\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.7/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    416\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 418\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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    419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    420\u001b[0m     def convert(\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.7/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, ignore_failures, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m                     \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 327\u001b[0;31m                     \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    328\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNotImplementedError\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[1;32m    329\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mignore_failures\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.7/site-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    589\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    590\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 591\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_array_safe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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    592\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    593\u001b[0m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_coerce_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.7/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_array_safe\u001b[0;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   1307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1308\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1309\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\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   1310\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\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[1;32m   1311\u001b[0m         \u001b[0;31m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.7/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_array\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m   1255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1256\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1257\u001b[0;31m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\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   1258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1259\u001b[0m     \u001b[0;31m# in pandas we don't store numpy str dtypes, so convert to object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.7/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m   1199\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\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[1;32m   1200\u001b[0m         \u001b[0;31m# Explicit copy, or required since NumPy can't view from / to object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1201\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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   1202\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1203\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: '$1021'"
     ]
    }
   ],
   "source": [
    "make_graph(gme_data, gme_revenue, 'GameStop')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a3dfd40-cc64-45eb-b227-f5fac413302b",
   "metadata": {},
   "source": [
    "<h2>About the Authors:</h2> \n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0220ENSkillsNetwork900-2022-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n",
    "\n",
    "Azim Hirjani\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0241bee-799c-4d33-8a95-0d5572066f14",
   "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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