BeautifulSoup_Web_Scrape_Practice.ipynb

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    "colab": {
      "name": "BeautifulSoup Web Scrape Practice",
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      "authorship_tag": "ABX9TyN+swg7D6hThkptj32fq3Q8"
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      "cell_type": "code",
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      "source": [
        "# Get yourself set up\n",
        "import requests as r\n",
        "from bs4 import BeautifulSoup\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
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      "outputs": []
    },
    {
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      "source": [
        "# Empty list to store scraped attributes\n",
        "names = []\n",
        "numbers = []\n",
        "ages = []\n",
        "positions = []\n",
        "hometowns = []\n",
        "heights = []\n",
        "weights = []"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Yc8vlpE1S7K",
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      "source": [
        "# Scrape html content\n",
        "url = 'https://www.lafc.com/players'\n",
        "response = r.get(url)\n",
        "html = response.text\n",
        "soup = BeautifulSoup(html)\n",
        "lafc_players_rows = soup.find('ul', class_ = 'player_list list-reset')\n",
        "players = soup.find_all('li', class_='row')\n",
        "\n",
        "# Place specific player attributes into corresponding lists\n",
        "for player in players:\n",
        "  if player.find('div', class_='name'):\n",
        "    names.append(player.find('a', class_='name_link').text)         \n",
        "  if player.find('span', class_='jersey'):\n",
        "    numbers.append(player.find('span', class_='jersey').text)\n",
        "  if player.find('div', class_='birthdate'):\n",
        "    ages.append(player.find('span', class_='stat age').text)\n",
        "  if player.find('span', class_='position'):\n",
        "    positions.append(player.find('span', class_='position').text)\n",
        "  if player.find('div', class_='hometown'):\n",
        "    hometowns.append(player.find('div', class_='hometown').text)\n",
        "  if player.find('span', class_='stat height'):\n",
        "    heights.append(player.find('span', class_='stat height').text)\n",
        "  if player.find('span', class_='stat weight'):\n",
        "    weights.append(player.find('span', class_='stat weight').text)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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      "source": [
        "# Spot validate\n",
        "print(len(names))\n",
        "print(len(numbers))\n",
        "print(len(ages))\n",
        "print(len(positions))\n",
        "print(len(hometowns))\n",
        "print(len(heights))\n",
        "print(len(weights))\n",
        "print(names)\n",
        "print(numbers)\n",
        "print(ages)\n",
        "print(positions)\n",
        "print(hometowns)\n",
        "print(heights)\n",
        "print(weights)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "23\n",
            "23\n",
            "23\n",
            "23\n",
            "23\n",
            "23\n",
            "23\n",
            "['Eduard Atuesta', 'Tristan Blackmon', 'Latif Blessing', 'José Cifuentes', 'Adama Diomande', 'Mohamed El-Munir', 'Francisco Ginella', 'Alejandro Guido', 'Jordan Harvey', 'Dejan Jaković', 'Mark-Anthony Kaye', 'Tyler Miller', 'Danny Musovski', 'Diego Palacios', 'Josh Pérez', 'Adrien Perez', 'Brian Rodríguez', 'Diego Rossi', 'Eddie Segura', 'Danilo Silva', 'Pablo Sisniega', 'Carlos Vela', 'Walker Zimmerman']\n",
            "['20', '27', '7', '', '99', '13', '', '15', '2', '5', '14', '1', '', '12', '18', '26', '17', '9', '4', '6', '23', '10', '25']\n",
            "['22', '23', '23', '20', '29', '27', '20', '25', '35', '34', '25', '26', '24', '20', '21', '24', '19', '21', '22', '33', '24', '30', '26']\n",
            "['Midfielder', 'Defender', 'Forward/Midfielder', 'Midfielder', 'Forward', 'Defender', 'Midfielder', 'Midfielder', 'Defender', 'Defender', 'Midfielder', 'Goalkeeper', 'Forward', 'Defender', 'Forward', 'Forward', 'Forward', 'Forward', 'Defender', 'Defender', 'Goalkeeper', 'Forward', 'Defender']\n",
            "['Vélez, Colombia ', 'Las Vegas, NV', 'Accra, Ghana', 'Esmeraldas, Ecuador', 'Oslo, Norway', 'Tripoli, Libya', 'Montevideo, Uruguay', 'San Diego, CA', 'Mission Viejo, CA', 'Karlovac, Yugoslavia', 'Toronto, Canada', 'Woodbury, NJ', 'Henderson, NV', 'Guayaquil, Ecuador', 'Montebello, CA', 'Ontario, CA', 'Tranqueras, Uruguay', 'Montevideo, Uruguay', 'Pereira, Colombia', 'Campinas, Brazil', 'Mexico City, Mexico', 'Cancun, Mexico', 'Lawrenceville, GA']\n",
            "['5\\' 11\"', '6\\' 1\"', '5\\' 3\"', '5\\' 8\"', '5\\' 11\"', '5\\' 9\"', '5\\' 10\"', '5\\' 11\"', '5\\' 11\"', '6\\' 2\"', '6\\' 1\"', '6\\' 4\"', '6\\' 0\"', '5\\' 7\"', '5\\' 5\"', '6\\' 0\"', '5\\' 8\"', '5\\' 7\"', '5\\' 9\"', '6\\' 1\"', '6\\' 3\"', '5\\' 10\"', '6\\' 3\"']\n",
            "['157', '178', '140', '155', '180', '154', '160', '170', '176', '176', '170', '205', '160', '143', '140', '170', '150', '143', '155', '154', '175', '162', '196']\n"
          ],
          "name": "stdout"
        }
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        "outputId": "4eb8308c-51bc-41c6-f9a9-148f2eda5753"
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      "source": [
        "# Turn Weights into Int\n",
        "weight_int = [int(x) for x in weights]\n",
        "weight_int"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[157,\n",
              " 178,\n",
              " 140,\n",
              " 155,\n",
              " 180,\n",
              " 154,\n",
              " 160,\n",
              " 170,\n",
              " 176,\n",
              " 176,\n",
              " 170,\n",
              " 205,\n",
              " 160,\n",
              " 143,\n",
              " 140,\n",
              " 170,\n",
              " 150,\n",
              " 143,\n",
              " 155,\n",
              " 154,\n",
              " 175,\n",
              " 162,\n",
              " 196]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "SFFVFDGl7q4M",
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        "outputId": "3289dd71-f904-460d-c58e-60159a183516",
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          "height": 228
        }
      },
      "source": [
        "# Turn lists into DataFrame\n",
        "zipped_lists = list(zip(names, numbers, ages, positions, hometowns, heights, weights))\n",
        "data = pd.DataFrame(zipped_lists, columns=['Names', 'Numbers', 'Ages', 'Positions', 'Hometowns', 'Heights', 'Weights'])\n",
        "data.head()\n",
        "data = data.set_index('Names')\n",
        "data.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Numbers</th>\n",
              "      <th>Ages</th>\n",
              "      <th>Positions</th>\n",
              "      <th>Hometowns</th>\n",
              "      <th>Heights</th>\n",
              "      <th>Weights</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Names</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Eduard Atuesta</th>\n",
              "      <td>20</td>\n",
              "      <td>22</td>\n",
              "      <td>Midfielder</td>\n",
              "      <td>Vélez, Colombia</td>\n",
              "      <td>5' 11\"</td>\n",
              "      <td>157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tristan Blackmon</th>\n",
              "      <td>27</td>\n",
              "      <td>23</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Las Vegas, NV</td>\n",
              "      <td>6' 1\"</td>\n",
              "      <td>178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Latif Blessing</th>\n",
              "      <td>7</td>\n",
              "      <td>23</td>\n",
              "      <td>Forward/Midfielder</td>\n",
              "      <td>Accra, Ghana</td>\n",
              "      <td>5' 3\"</td>\n",
              "      <td>140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>José Cifuentes</th>\n",
              "      <td></td>\n",
              "      <td>20</td>\n",
              "      <td>Midfielder</td>\n",
              "      <td>Esmeraldas, Ecuador</td>\n",
              "      <td>5' 8\"</td>\n",
              "      <td>155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Adama Diomande</th>\n",
              "      <td>99</td>\n",
              "      <td>29</td>\n",
              "      <td>Forward</td>\n",
              "      <td>Oslo, Norway</td>\n",
              "      <td>5' 11\"</td>\n",
              "      <td>180</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Numbers Ages  ... Heights Weights\n",
              "Names                          ...                \n",
              "Eduard Atuesta        20   22  ...  5' 11\"     157\n",
              "Tristan Blackmon      27   23  ...   6' 1\"     178\n",
              "Latif Blessing         7   23  ...   5' 3\"     140\n",
              "José Cifuentes             20  ...   5' 8\"     155\n",
              "Adama Diomande        99   29  ...  5' 11\"     180\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WGmV3fCiBIsR",
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        "outputId": "471638c9-b95d-45c0-c9c7-5f586bbf733b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        }
      },
      "source": [
        "# Explore Player Weights by Position\n",
        "plt.figure(figsize=(8,8))\n",
        "sns.swarmplot(x='Positions', y='Weights', data=data)\n",
        "plt.title('LAFC Player Weight by Position')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1243: RuntimeWarning: invalid value encountered in sqrt\n",
            "  dx = np.sqrt(d ** 2 - dy ** 2) * 1.05\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'LAFC Player Weight by Position')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
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J0jpY0wfLHNr83Hp180mSpP4a7UVxJDk0yaua4SlJ1vlT5CRJ0vo1qkBPciqd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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}