2- Use Webscraping to Extract Tesla Revenue Data.ipynb

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   "source": [
    "## Question 2: Use Webscraping to Extract Tesla Revenue Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the `requests` library to download the webpage [https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue](https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork-23455606&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork-23455606&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork-23455606&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork-23455606&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ). Save the text of the response as a variable named `html_data`.\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "url= \"https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue\"\n",
    "html_data=requests.get(url).text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parse the html data using `beautiful_soup`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "soup = BeautifulSoup(html_data,\"html5lib\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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`. Make sure the comma and dollar sign is removed from the `Revenue` column. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tesla_revenue= pd.read_html(url, match=\"Tesla Quarterly Revenue\", flavor='bs4')[0]\n",
    "tesla_revenue=tesla_revenue.rename(columns = {'Tesla Quarterly Revenue(Millions of US $)': 'Date', 'Tesla Quarterly Revenue(Millions of US $).1': 'Revenue'}, inplace = False)\n",
    "tesla_revenue[\"Revenue\"] = tesla_revenue[\"Revenue\"].str.replace(\",\",\"\").str.replace(\"$\",\"\")\n",
    "tesla_revenue.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here if you need help removing the dollar sign and comma</summary>\n",
    "\n",
    "```\n",
    "    \n",
    "If you parsed the HTML table by row and column you can use the replace function on the string\n",
    "    \n",
    "    revenue = col[1].text.replace(\"$\", \"\").replace(\",\", \"\")\n",
    "    \n",
    "If you use the read_html function you can use the replace function on the string representation of the column\n",
    "\n",
    "    tesla_revenue[\"Revenue\"] = tesla_revenue[\"Revenue\"].str.replace(\"$\", \"\").str.replace(\",\", \"\")\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove the rows in the dataframe that are empty strings or are NaN in the Revenue column. Print the entire `tesla_revenue` DataFrame to see if you have any.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tesla_revenue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here if you need help removing the Nan or empty strings</summary>\n",
    "\n",
    "```\n",
    "    \n",
    "If you have NaN in the Revenue column\n",
    "    \n",
    "    tesla_revenue.dropna(inplace=True)\n",
    "    \n",
    "If you have emtpty string in the Revenue column\n",
    "\n",
    "    tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != \"\"]\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the last 5 row of the `tesla_revenue` dataframe using the `tail` function. Take a screenshot of the results.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tesla_revenue.dropna(inplace=True)\n",
    "tesla_revenue.tail()"
   ]
  }
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