{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Data collection through Webscraping\n",
        "\n",
        "Rafiq Islam  \n",
        "2024-08-14\n",
        "\n",
        "## Introduction\n",
        "\n",
        "Collecting data and preparing it for a project is one of the most\n",
        "important tasks in any data science or machine learning project. There\n",
        "are many sources from where we can collect data for a project, such as\n",
        "\n",
        "-   Connecting to a SQL database server  \n",
        "-   Data Source Websites such as\n",
        "    <a href=\"https://www.kaggle.com\" target=\"_blank\">Kaggle</a>,\n",
        "    <a href=\"https://datasetsearch.research.google.com\"\n",
        "    target=\"_blank\">Google Dataset Search</a>,\n",
        "    <a href=\"https://archive.ics.uci.edu/datasets\" target=\"_blank\">UCI\n",
        "    Machine Learning Repo</a> etc  \n",
        "-   Web Scraping with Beautiful Soup\n",
        "-   Using Python API\n",
        "\n",
        "## Data Source Websites\n",
        "\n",
        "Data source websites mainly falls into two categories such as data\n",
        "repositories and data science competitions. There are many such\n",
        "websites.\n",
        "\n",
        "1.  The\n",
        "    <a href=\"https://archive.ics.uci.edu/datasets\" target=\"_blank\">UCI\n",
        "    Machine Learning Repository</a>  \n",
        "2.  The <a href=\"https://dataverse.harvard.edu\" target=\"_blank\">Harvard\n",
        "    Dataverse</a>\n",
        "3.  The\n",
        "    <a href=\"https://data.mendeley.com\" target=\"_blank\">Mendeley Data\n",
        "    Repository</a>\n",
        "4.  The <a href=\"https://github.com/fivethirtyeight/data\"\n",
        "    target=\"_blank\">538</a>\n",
        "5.  The\n",
        "    <a href=\"https://github.com/nytimes\" target=\"_blank\">New Yourk Times</a>  \n",
        "6.  The <a\n",
        "    href=\"https://www.competitionsciences.org/competitions/international-data-analysis-olympiad/\"\n",
        "    target=\"_blank\">International Data Analysis Olympiad</a>\n",
        "7.  <a href=\"https://www.kaggle.com\" target=\"_blank\">Kaggle Competition</a>\n",
        "\n",
        "Example of collecting data from\n",
        "<a href=\"https://archive.ics.uci.edu/datasets\" target=\"_blank\">UCI\n",
        "Machine Learning Repository</a>"
      ],
      "id": "ca293291-0509-4576-b100-316c308b5ec9"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'uci_id': 53, 'name': 'Iris', 'repository_url': 'https://archive.ics.uci.edu/dataset/53/iris', 'data_url': 'https://archive.ics.uci.edu/static/public/53/data.csv', 'abstract': 'A small classic dataset from Fisher, 1936. One of the earliest known datasets used for evaluating classification methods.\\n', 'area': 'Biology', 'tasks': ['Classification'], 'characteristics': ['Tabular'], 'num_instances': 150, 'num_features': 4, 'feature_types': ['Real'], 'demographics': [], 'target_col': ['class'], 'index_col': None, 'has_missing_values': 'no', 'missing_values_symbol': None, 'year_of_dataset_creation': 1936, 'last_updated': 'Tue Sep 12 2023', 'dataset_doi': '10.24432/C56C76', 'creators': ['R. A. Fisher'], 'intro_paper': {'ID': 191, 'type': 'NATIVE', 'title': 'The Iris data set: In search of the source of virginica', 'authors': 'A. Unwin, K. Kleinman', 'venue': 'Significance, 2021', 'year': 2021, 'journal': 'Significance, 2021', 'DOI': '1740-9713.01589', 'URL': 'https://www.semanticscholar.org/paper/4599862ea877863669a6a8e63a3c707a787d5d7e', 'sha': None, 'corpus': None, 'arxiv': None, 'mag': None, 'acl': None, 'pmid': None, 'pmcid': None}, 'additional_info': {'summary': 'This is one of the earliest datasets used in the literature on classification methods and widely used in statistics and machine learning.  The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant.  One class is linearly separable from the other 2; the latter are not linearly separable from each other.\\n\\nPredicted attribute: class of iris plant.\\n\\nThis is an exceedingly simple domain.\\n\\nThis data differs from the data presented in Fishers article (identified by Steve Chadwick,  spchadwick@espeedaz.net ).  The 35th sample should be: 4.9,3.1,1.5,0.2,\"Iris-setosa\" where the error is in the fourth feature. The 38th sample: 4.9,3.6,1.4,0.1,\"Iris-setosa\" where the errors are in the second and third features.  ', 'purpose': 'N/A', 'funded_by': None, 'instances_represent': 'Each instance is a plant', 'recommended_data_splits': None, 'sensitive_data': None, 'preprocessing_description': None, 'variable_info': None, 'citation': None}}\n",
            "           name     role         type demographic  \\\n",
            "0  sepal length  Feature   Continuous        None   \n",
            "1   sepal width  Feature   Continuous        None   \n",
            "2  petal length  Feature   Continuous        None   \n",
            "3   petal width  Feature   Continuous        None   \n",
            "4         class   Target  Categorical        None   \n",
            "\n",
            "                                         description units missing_values  \n",
            "0                                               None    cm             no  \n",
            "1                                               None    cm             no  \n",
            "2                                               None    cm             no  \n",
            "3                                               None    cm             no  \n",
            "4  class of iris plant: Iris Setosa, Iris Versico...  None             no  "
          ]
        }
      ],
      "source": [
        "from ucimlrepo import fetch_ucirepo \n",
        "  \n",
        "# fetch dataset \n",
        "iris = fetch_ucirepo(id=53) \n",
        "  \n",
        "# data (as pandas dataframes) \n",
        "X = iris.data.features \n",
        "y = iris.data.targets \n",
        "  \n",
        "# metadata \n",
        "print(iris.metadata) \n",
        "  \n",
        "# variable information \n",
        "print(iris.variables) "
      ],
      "id": "e4e39c21"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "you may need to install the\n",
        "<a href=\"https://archive.ics.uci.edu/datasets\" target=\"_blank\">UCI\n",
        "Machine Learning Repository</a> as a package using pip.\n",
        "\n",
        "    pip install ucimlrepo"
      ],
      "id": "bb1d0e6f-40b3-47f9-9361-d5636db65579"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "\n",
              "</div>"
            ]
          }
        }
      ],
      "source": [
        "X.head()"
      ],
      "id": "d22cdb63"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Web Scraping\n",
        "\n",
        "We scrapping is another way of collecting the data for the research if\n",
        "the data is not available in any repositiory. We can collect the data\n",
        "from a website using a library called `BeautifulSoup` if the website has\n",
        "permision for other people to collect data from the website."
      ],
      "id": "3ddcf85c-5639-4494-9957-403c1f610168"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "import bs4                      # library for BeautifulSoup\n",
        "from bs4 import BeautifulSoup   # import the BeautifulSoup object\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from seaborn import set_style\n",
        "set_style(\"whitegrid\")"
      ],
      "id": "cbea31aa"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now let’s make a html object using `BeautifulSoup`. Let’s say we have a\n",
        "html website that looks like below"
      ],
      "id": "3dfb3f6a-0b0f-4ff5-a278-b97a0fa7559d"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "html_doc=\"\"\"\n",
        "<!DOCTYPE html>\n",
        "<html lang=\"en\">\n",
        "<head>\n",
        "    <title>My Dummy HTML Document</title>\n",
        "</head>\n",
        "<body>\n",
        "    <h1>Welcome to My Dummy HTML Document</h1>\n",
        "    <p>This is a paragraph in my dummy HTML document.</p>\n",
        "    <a href=\"https://mrislambd.github.io/blog\" class=\"blog\" id=\"blog\"> Blog </a>\n",
        "    <a href=\"htpps://mrislambd.github.io/research\" class=\"research\" id=\"research\"> Research </a>\n",
        "</body>\n",
        "</html>\n",
        "\"\"\""
      ],
      "id": "a77b005b"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now we want to grab information from the dummy html documnet above."
      ],
      "id": "a23e733d-95af-42f9-b69b-db3e61086f94"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "soup=BeautifulSoup(html_doc, features='html.parser')"
      ],
      "id": "8936be00"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now that we have the object `soup` we can walk through each elements in\n",
        "this object. For example, if we want to grab the title element,"
      ],
      "id": "90cf2ff4-a785-485d-9235-fe8281201490"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/plain": [
              "<title>My Dummy HTML Document</title>"
            ]
          }
        }
      ],
      "source": [
        "soup.html.head.title"
      ],
      "id": "7d6496b7"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Since the html document has only one title, therefore, we can simply use\n",
        "the following command"
      ],
      "id": "6d6f5d56-f736-403e-94ea-e24f12cf4cfb"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/plain": [
              "<title>My Dummy HTML Document</title>"
            ]
          }
        }
      ],
      "source": [
        "soup.title "
      ],
      "id": "d963d69d"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "or this command to get the text only"
      ],
      "id": "1b735eb1-9b8f-453f-86e6-c1f41de204e3"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/plain": [
              "'My Dummy HTML Document'"
            ]
          }
        }
      ],
      "source": [
        "soup.title.text"
      ],
      "id": "f935ba93"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This `soup` object is like a family tree. It has parents, children,\n",
        "greatgrand parents etc."
      ],
      "id": "687e837c-7729-48cf-8d43-a10e0d66f67f"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/plain": [
              "<head>\n",
              "<title>My Dummy HTML Document</title>\n",
              "</head>"
            ]
          }
        }
      ],
      "source": [
        "soup.title.parent"
      ],
      "id": "b267844e"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now to grab an attribute from the `soup` object we can use"
      ],
      "id": "52707ecc-ad79-4311-af70-170b6bca2889"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/plain": [
              "<a class=\"blog\" href=\"https://mrislambd.github.io/blog\" id=\"blog\"> Blog </a>"
            ]
          }
        }
      ],
      "source": [
        "soup.a"
      ],
      "id": "213d21ed"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "or any particular thing from the attribute"
      ],
      "id": "114c0cd2-407a-4734-a13e-5b63e5057ea7"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/plain": [
              "['blog']"
            ]
          }
        }
      ],
      "source": [
        "soup.a['class']"
      ],
      "id": "1c303bf9"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We can also find multiple attribute of the same kind"
      ],
      "id": "29caa1b7-a663-4c93-84d0-34e68bed3ec8"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/plain": [
              "[<a class=\"blog\" href=\"https://mrislambd.github.io/blog\" id=\"blog\"> Blog </a>,\n",
              " <a class=\"research\" href=\"htpps://mrislambd.github.io/research\" id=\"research\"> Research </a>]"
            ]
          }
        }
      ],
      "source": [
        "soup.findAll('a')"
      ],
      "id": "723e8b21"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Then if we want any particular object from all `a` attribute"
      ],
      "id": "e6182286-3ef9-480a-84ad-2ca8837697bc"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/plain": [
              "'blog'"
            ]
          }
        }
      ],
      "source": [
        "soup.findAll('a')[0]['id']"
      ],
      "id": "726f6dc2"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "For any `p` tag"
      ],
      "id": "f0347b8e-5159-4320-bc61-92e6a0d0095d"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/plain": [
              "'This is a paragraph in my dummy HTML document.'"
            ]
          }
        }
      ],
      "source": [
        "soup.p.text "
      ],
      "id": "436548e6"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Similarly, if we want to grab all the `href`s from the `a` tags"
      ],
      "id": "ae61a2d7-3b5c-4223-8f8f-4414a4b57abf"
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/plain": [
              "['https://mrislambd.github.io/blog', 'htpps://mrislambd.github.io/research']"
            ]
          }
        }
      ],
      "source": [
        "[h['href'] for h in soup.findAll('a')]"
      ],
      "id": "db9786c2"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Example of Webscraping from a real website\n",
        "\n",
        "In this example we want to obtain some information from\n",
        "<a href=\"https://research.nvidia.com/graduate-fellowships/archive\"\n",
        "target=\"_blank\">NVIDIA Graduate Fellowship Program</a>. Before accessing\n",
        "this website we need to know if we have permision to access their data\n",
        "through webscraping."
      ],
      "id": "842dc31b-6ae7-42cf-92ad-266bf6188c0c"
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/plain": [
              "200"
            ]
          }
        }
      ],
      "source": [
        "import requests\n",
        "response = requests.get(url=\"https://research.nvidia.com/graduate-fellowships/archive\")\n",
        "response.status_code"
      ],
      "id": "e0bc5246"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The `status_code` $200$ ensures that we have enough permision to acccess\n",
        "their website data. However, if we obtain `status_code` of $403, 400,$\n",
        "or $500$ then we do not permision or a `bad request`. For more about the\n",
        "status codes\n",
        "<a href=\"https://developer.mozilla.org/en-US/docs/Web/HTTP/Status\"\n",
        "target=\"_blank\">click here</a>."
      ],
      "id": "b6699a0c-661d-4399-9125-b9ae1fd87f66"
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [],
      "source": [
        "soup = BeautifulSoup(response.text, 'html.parser')"
      ],
      "id": "3a96bf11"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We want to make an analysis based on the institution of the past\n",
        "graduate fellows. Insepecting the elements in\n",
        "<a href=\"https://developer.mozilla.org/en-US/docs/Web/HTTP/Status\"\n",
        "target=\"_blank\">this website</a> we see that the `div` those have\n",
        "`class=\"archive-group\"` contains the information of the past graduate\n",
        "fellows."
      ],
      "id": "2485b948-6043-4726-ae37-1f16db13f427"
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [],
      "source": [
        "pf = soup.find_all(\"div\", class_=\"archive-group\")"
      ],
      "id": "7143f805"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "and the first element of this `pf` contains the information of the\n",
        "graduate fellows in the year of 2021."
      ],
      "id": "fd0a7af9-cc13-40cb-98ce-d134a9ef65c9"
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/plain": [
              "<div class=\"archive-group\">\n",
              "<h4 class=\"archive-group__title\">2021 Grad Fellows</h4>\n",
              "<div class=\"views-row\"><div class=\"views-field views-field-title\"><span class=\"field-content\">Alexander Sax</span></div><div class=\"views-field views-field-field-grad-fellow-institution\"><div class=\"field-content\">University of California, Berkeley</div></div></div>\n",
              "<div class=\"views-row\"><div class=\"views-field views-field-title\"><span class=\"field-content\">Hanrui Wang</span></div><div class=\"views-field views-field-field-grad-fellow-institution\"><div class=\"field-content\">Massachusetts Institute of Technology</div></div></div>\n",
              "<div class=\"views-row\"><div class=\"views-field views-field-title\"><span class=\"field-content\">Ji Lin</span></div><div class=\"views-field views-field-field-grad-fellow-institution\"><div class=\"field-content\">Massachusetts Institute of Technology</div></div></div>\n",
              "<div class=\"views-row\"><div class=\"views-field views-field-title\"><span class=\"field-content\">Krishna Murthy Jatavallabhula</span></div><div class=\"views-field views-field-field-grad-fellow-institution\"><div class=\"field-content\">University of Montreal</div></div></div>\n",
              "<div class=\"views-row\"><div class=\"views-field views-field-title\"><span class=\"field-content\">Rohan Sawhney</span></div><div class=\"views-field views-field-field-grad-fellow-institution\"><div class=\"field-content\">Carnegie Mellon University</div></div></div>\n",
              "<div class=\"views-row\"><div class=\"views-field views-field-title\"><span class=\"field-content\">Sana Damani</span></div><div class=\"views-field views-field-field-grad-fellow-institution\"><div class=\"field-content\">Georgia Institute of Technology</div></div></div>\n",
              "<div class=\"views-row\"><div class=\"views-field views-field-title\"><span class=\"field-content\">Thierry Tambe</span></div><div class=\"views-field views-field-field-grad-fellow-institution\"><div class=\"field-content\">Harvard University</div></div></div>\n",
              "<div class=\"views-row\"><div class=\"views-field views-field-title\"><span class=\"field-content\">Ye Yuan</span></div><div class=\"views-field views-field-field-grad-fellow-institution\"><div class=\"field-content\">Carnegie Mellon University</div></div></div>\n",
              "<div class=\"views-row\"><div class=\"views-field views-field-title\"><span class=\"field-content\">Yunzhu Li</span></div><div class=\"views-field views-field-field-grad-fellow-institution\"><div class=\"field-content\">Massachusetts Institute of Technology</div></div></div>\n",
              "<div class=\"views-row\"><div class=\"views-field views-field-title\"><span class=\"field-content\">Zhiqin Chen</span></div><div class=\"views-field views-field-field-grad-fellow-institution\"><div class=\"field-content\">Simon Fraser University</div></div></div>\n",
              "</div>"
            ]
          }
        }
      ],
      "source": [
        "pf[0]"
      ],
      "id": "f31889d2"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now let’s make a `pandas` dataframe using the information in this page.\n",
        "We can make an use of the output from the above chunk. To grab the year,\n",
        "we see that `archive-group__title` class with a `h4` tag contains the\n",
        "year for all years. With `strip=True`, the text is cleaned by removing\n",
        "extra whitespace from the beginning and end. We need the first element\n",
        "so a `split()[0]` will do the job. Then we make another group called\n",
        "`fellows` that contains the fellows in a certian year by using the `div`\n",
        "and `class\"views-row\"`. Once the new group created, we then iterate\n",
        "through this group to extract their names and corresponding\n",
        "institutions."
      ],
      "id": "b405958f-103a-4129-a53d-19378d5d2216"
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "\n",
              "</div>"
            ]
          }
        }
      ],
      "source": [
        "data=[]\n",
        "\n",
        "for group in pf:\n",
        "    year = group.find(\n",
        "        \"h4\",class_=\"archive-group__title\"\n",
        "        ).get_text(strip=True).split()[0]\n",
        "\n",
        "    fellows = group.find_all(\"div\", class_=\"views-row\")\n",
        "    for fellow in fellows:\n",
        "        name = fellow.find(\n",
        "            \"div\", class_=\"views-field-title\"\n",
        "            ).get_text(strip=True) \n",
        "        institute = fellow.find(\n",
        "            \"div\", class_=\"views-field-field-grad-fellow-institution\"\n",
        "            ).get_text(strip=True)\n",
        "\n",
        "        data.append({\"Name\": name, \"Year\": year, \"Institute\": institute})\n",
        "\n",
        "data=pd.DataFrame(data)\n",
        "data.head()"
      ],
      "id": "e5197ff0"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now let’s perform some Exploratory Data Analysis (EDA). First, we\n",
        "analyze the unique values and distributions."
      ],
      "id": "e63fddd1-e19d-4fa5-ab92-1a6df2c272cd"
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Year   2006  2018  2017  2007  2013  2012  2011  2008  2019  2021  2003  2009  \\\n",
            "Count    12    11    11    11    11    11    11    10    10    10    10    10   \n",
            "\n",
            "Year   2010  2005  2015  2004  2016  2002  2020  2014  \n",
            "Count     9     8     7     7     6     6     5     5  "
          ]
        }
      ],
      "source": [
        "# Count the number of fellows each year\n",
        "year_counts = data['Year'].value_counts().sort_values(ascending=False)\n",
        "# Create a DataFrame where years are columns and counts are values in the next row\n",
        "year_data = {\n",
        "    'Year': year_counts.index,\n",
        "    'Count': year_counts.values\n",
        "}\n",
        "# Create the DataFrame\n",
        "year_data_counts = pd.DataFrame(year_data)\n",
        "\n",
        "# Transpose the DataFrame and reset index to get years as columns\n",
        "year_data_counts = year_data_counts.set_index('Year').T\n",
        "\n",
        "# Display the DataFrame\n",
        "print(year_data_counts)"
      ],
      "id": "915a622a"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Next we see that most represented universities"
      ],
      "id": "ff88ff25-3eef-4162-a53d-b0fc23acfb4e"
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Institute\n",
            "Stanford University                          24\n",
            "Massachusetts Institute of Technology        15\n",
            "University of California, Berkeley           14\n",
            "Carnegie Mellon University                   13\n",
            "University of Utah                           10\n",
            "University of Washington                      9\n",
            "University of Illinois, Urbana-Champaign      9\n",
            "University of California, Davis               8\n",
            "Georgia Institute of Technology               8\n",
            "University of North Carolina, Chapel Hill     6\n",
            "Name: count, dtype: int64"
          ]
        }
      ],
      "source": [
        "university_counts = data['Institute'].value_counts()\n",
        "print(university_counts.head(10))  # Display the top 10 universities"
      ],
      "id": "3cc450ba"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To visualize the award distributions per year,"
      ],
      "id": "ffce1e26-7abe-4897-8110-9e3a36c038fa"
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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MTIyOHTumnTt3yufzGfnDTXfccYeWLVumYcOGqX///rrxxhvl9Xq1f/9+rVu3TpMmTVJk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\n+3+vm46Ce8bITwAAAABJRU5ErkJggg==\n"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(9,5))\n",
        "sns.countplot(x='Year', data=data, order=sorted(data['Year'].unique()))\n",
        "plt.gca().set_facecolor('#f4f4f4') \n",
        "plt.gcf().patch.set_facecolor('#f4f4f4')\n",
        "plt.title('Number of Fellows Per Year')\n",
        "plt.show()"
      ],
      "id": "97943889"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Top 10 universities visualization"
      ],
      "id": "06f59ab1-a2b2-41c1-b60f-563b8bb1c2bd"
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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hefPmvPLKKxXeB2trazp37kxYWBgnT57Ez89Pu65nz5789ddffPvtt+Tm5qLR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zly5AhbtmwhJCSEb7/99onbfVF/do96VuMXQgghhHhYnUgArl+/jpeXF9u3b+fNN9/Ex8eHkSNH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\nQmCC\n"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(6,4))\n",
        "top_universities = data['Institute'].value_counts().head(10)\n",
        "sns.barplot(y=top_universities.index, x=top_universities.values)\n",
        "plt.gca().set_facecolor('#f4f4f4') \n",
        "plt.gcf().patch.set_facecolor('#f4f4f4')\n",
        "plt.title('Top 10 Universities by Number of Fellows')\n",
        "plt.xlabel('Number of Fellows')\n",
        "plt.ylabel('University')\n",
        "plt.show()"
      ],
      "id": "ca68b39f"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Trend over time"
      ],
      "id": "b6751365-d188-41c3-a948-c5c5ce0281c0"
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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6/ivB9fpTwsiBzifEVc0WOHi6GFJK69oAAKMz4lRx/WckROGOM3IBAH/dXISq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          }
        }
      ],
      "source": [
        "plt.figure(figsize=(9,5))\n",
        "data['Year'] = data['Year'].astype(int)  \n",
        "yearly_trend = data.groupby('Year').size()\n",
        "yearly_trend.plot(kind='line', marker='o')\n",
        "plt.gca().set_facecolor('#f4f4f4') \n",
        "plt.gcf().patch.set_facecolor('#f4f4f4')\n",
        "plt.title('Trend of Fellows Over Time')\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Number of Fellows')\n",
        "plt.show()"
      ],
      "id": "32e4f250"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This is just a simple example of collecting data through webscraping.\n",
        "This `BeautifulSoup` has endless potentials to use in many projects to\n",
        "collect the data that are not publicly available in cleaned or organized\n",
        "form. Thank you for reading.\n",
        "\n",
        "## References\n",
        "\n",
        "-   <a href=\"https://doi.org/10.24432/C56C76.\" target=\"_blank\">Fisher,R. A..\n",
        "    (1988). Iris. UCI Machine Learning Repository.</a>\n",
        "\n",
        "**Share on**\n",
        "\n",
        "<a href=\"https://www.facebook.com/sharer.php?u=https://mrislambd.github.io/dsandml/posts/datacollection/\" target=\"_blank\" style=\"color:#1877F2; text-decoration: none;\">\n",
        "\n",
        "</a>\n",
        "\n",
        "<a href=\"https://www.linkedin.com/sharing/share-offsite/?url=https://mrislambd.github.io/dsandml/posts/datacollection/\" target=\"_blank\" style=\"color:#0077B5; text-decoration: none;\">\n",
        "\n",
        "</a>\n",
        "\n",
        "<a href=\"https://www.twitter.com/intent/tweet?url=https://mrislambd.github.io/dsandml/posts/datacollection/\" target=\"_blank\" style=\"color:#1DA1F2; text-decoration: none;\">\n",
        "\n",
        "</a>"
      ],
      "id": "bbb5a863-a2b5-4dad-abd4-24de2865642f"
    },
    {
      "cell_type": "raw",
      "metadata": {
        "raw_mimetype": "text/html"
      },
      "source": [
        "<script src=\"https://giscus.app/client.js\"\n",
        "        data-repo=\"mrislambd/mrislambd.github.io\" \n",
        "        data-repo-id=\"R_kgDOMV8crA\"\n",
        "        data-category=\"Announcements\"\n",
        "        data-category-id=\"DIC_kwDOMV8crM4CjbQW\"\n",
        "        data-mapping=\"pathname\"\n",
        "        data-strict=\"0\"\n",
        "        data-reactions-enabled=\"1\"\n",
        "        data-emit-metadata=\"0\"\n",
        "        data-input-position=\"bottom\"\n",
        "        data-theme=\"light\"\n",
        "        data-lang=\"en\"\n",
        "        crossorigin=\"anonymous\"\n",
        "        async>\n",
        "</script>"
      ],
      "id": "3341da5d-2a88-4cf3-a392-5c2f51d6faee"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [],
      "id": "f8bb1228-72f9-4414-8e31-aa1b6477deb7"
    },
    {
      "cell_type": "raw",
      "metadata": {
        "raw_mimetype": "text/html"
      },
      "source": [
        "<script async defer crossorigin=\"anonymous\"\n",
        " src=\"https://connect.facebook.net/en_US/sdk.js#xfbml=1&version=v20.0\"></script>"
      ],
      "id": "3f4144a3-37db-4276-a72a-66cbc0888791"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**You may also like**"
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      "id": "78f713b0-481d-426b-8528-d6f143001edd"
    }
  ],
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