meeting2.ipynb

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    "# Practicing Webscraping\n",
    "\n",
    "Last week we got a basic taste of webscraping. This week we will be applying that knowledge to a few more websites.  \n",
    "The data we will collect will be put into a pandas dataframe  \n",
    "\n",
    "Helpful link:  \n",
    "https://medium.com/geekculture/web-scraping-tables-in-python-using-beautiful-soup-8bbc31c5803e"
   ]
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   "source": [
    "## Website 1: Olympic Medal Statistics   "
   ]
  },
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    "Imports and getSoup function:"
   ]
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    "import pandas as pd\n",
    "import numpy as np\n",
    "from bs4 import BeautifulSoup\n",
    "import requests\n",
    "\n",
    "def getSoup(url: str) -> BeautifulSoup:\n",
    "    \n",
    "    # your implementation\n",
    "    \n",
    "    return bs"
   ]
  },
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   "source": [
    "Website URL:\n",
    "(hint: you need to create a soup object)"
   ]
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   "source": [
    "\n",
    "url = \"https://en.wikipedia.org/wiki/All-time_Olympic_Games_medal_table\"\n",
    "\n",
    "# get soup object\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise 1.1: Getting the data\n",
    " Scrape the first table and put it into a pandas df with columns 'Country', 'Gold', 'Silver', 'Bronze', and 'Total'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scrape and put into df here"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise 1.2: Visualization\n",
    "Create a visualization of the number of Gold, Silver, and Bronze medals for the top 5 countries with the most total medals using pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualization here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Website 2: Top 100 ATP Tour tennis rankings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Website URL:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://www.atptour.com/en/rankings/singles\"\n",
    "\n",
    "# get soup object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise 2.1: Getting the data\n",
    "Scrape the table of top 100 tennis rankings put into df with columns of your choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scrape and put into df here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise 2.2: Visualization\n",
    "Create any interesting visualization using the df you made"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualization here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Website 3: Choose your own adventure\n",
    "\n",
    "If you have extra time try to scrape a website of your own choosing!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise 3.1: Getting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scrape and put into df here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise 3.2: Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualization here"
   ]
  }
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