datacamp_clusters.ipynb

{
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  "metadata": {
    "colab": {
      "name": "datacamp_clusters.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/brl0/datacamp_courses/blob/master/datacamp_clusters.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HSvfx9mytGtN",
        "colab_type": "text"
      },
      "source": [
        "Simple clustering analysis of courses by DataCamp."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1CGfEbgSce8v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import requests\n",
        "from bs4 import BeautifulSoup\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.cluster import AgglomerativeClustering\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "from scipy.cluster.hierarchy import dendrogram, linkage"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RESjEtZGlsvB",
        "colab_type": "code",
        "outputId": "180807ce-d42f-49e6-c120-1b78eba793d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 672
        }
      },
      "source": [
        "!pip install spacy\n",
        "!python -m spacy download en_core_web_lg\n",
        "import spacy\n",
        "nlp = spacy.load('en_core_web_lg')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: spacy in /usr/local/lib/python3.6/dist-packages (2.0.18)\n",
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            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (2.21.0)\n",
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            "Requirement already satisfied: regex==2018.01.10 in /usr/local/lib/python3.6/dist-packages (from spacy) (2018.1.10)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy) (2.0.2)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (1.16.3)\n",
            "Requirement already satisfied: thinc<6.13.0,>=6.12.1 in /usr/local/lib/python3.6/dist-packages (from spacy) (6.12.1)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2.8)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2019.3.9)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (3.0.4)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (1.24.2)\n",
            "Requirement already satisfied: msgpack<0.6.0,>=0.5.6 in /usr/local/lib/python3.6/dist-packages (from thinc<6.13.0,>=6.12.1->spacy) (0.5.6)\n",
            "Requirement already satisfied: cytoolz<0.10,>=0.9.0 in /usr/local/lib/python3.6/dist-packages (from thinc<6.13.0,>=6.12.1->spacy) (0.9.0.1)\n",
            "Requirement already satisfied: msgpack-numpy<0.4.4 in /usr/local/lib/python3.6/dist-packages (from thinc<6.13.0,>=6.12.1->spacy) (0.4.3.2)\n",
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            "Requirement already satisfied: tqdm<5.0.0,>=4.10.0 in /usr/local/lib/python3.6/dist-packages (from thinc<6.13.0,>=6.12.1->spacy) (4.28.1)\n",
            "Requirement already satisfied: six<2.0.0,>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from thinc<6.13.0,>=6.12.1->spacy) (1.12.0)\n",
            "Requirement already satisfied: toolz>=0.8.0 in /usr/local/lib/python3.6/dist-packages (from cytoolz<0.10,>=0.9.0->thinc<6.13.0,>=6.12.1->spacy) (0.9.0)\n",
            "Collecting en_core_web_lg==2.0.0 from https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-2.0.0/en_core_web_lg-2.0.0.tar.gz#egg=en_core_web_lg==2.0.0\n",
            "\u001b[?25l  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-2.0.0/en_core_web_lg-2.0.0.tar.gz (852.3MB)\n",
            "\u001b[K     |████████████████████████████████| 852.3MB 1.2MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: en-core-web-lg\n",
            "  Building wheel for en-core-web-lg (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-mpyck5oj/wheels/0d/bc/67/e6a9108ab86cd076703af19ad4e0f02f57381ac6583df16249\n",
            "Successfully built en-core-web-lg\n",
            "Installing collected packages: en-core-web-lg\n",
            "Successfully installed en-core-web-lg-2.0.0\n",
            "\n",
            "\u001b[93m    Linking successful\u001b[0m\n",
            "    /usr/local/lib/python3.6/dist-packages/en_core_web_lg -->\n",
            "    /usr/local/lib/python3.6/dist-packages/spacy/data/en_core_web_lg\n",
            "\n",
            "    You can now load the model via spacy.load('en_core_web_lg')\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9P3DWYHmiNKd",
        "colab_type": "code",
        "outputId": "52639657-9023-4d74-c953-8cd5380f0611",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "r = requests.get('https://www.datacamp.com/courses/tech:python')\n",
        "data = r.text\n",
        "soup = BeautifulSoup(data)\n",
        "courses = [item for item in soup.select('body > div.site-wrap > section.courses__explore.dc-u-mt-48 > div > div > article')]\n",
        "print(len(courses))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "87\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qvKsfm20j1Ww",
        "colab_type": "code",
        "outputId": "2ca3853d-ec4e-4cc8-a1ad-8c9655501190",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 656
        }
      },
      "source": [
        "courses[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<article class=\"course-block-wrapper col-lg-4 col-sm-6 \" data-id=\"735\">\n",
              "<div class=\"course-block \">\n",
              "<a class=\"course-block__link ds-snowplow-link-course-block\" href=\"/courses/intro-to-python-for-data-science\">\n",
              "<div class=\"course-block__technology course-block__technology--python\"></div>\n",
              "<div class=\"course-block__body\">\n",
              "<h4 class=\"course-block__title\">Introduction to Python</h4>\n",
              "<p class=\"course-block__description\">\n",
              "          Master the basics of data analysis in Python. Expand your skill set by learning scientific computing with numpy.\n",
              "        </p>\n",
              "<div class=\"course-block__extra-info dc-u-fx dc-u-fx-aifs dc-u-fx-jcc dc-u-fx-fww\">\n",
              "<span class=\"course-block__length dc-u-mh-12 dc-u-fx-center\">\n",
              "<span class=\" dc-icon dc-icon--size-12 dc-icon--primary dc-icon--flex dc-u-mr-8 \">\n",
              "<svg class=\"dc-icon__svg\">\n",
              "<use xlink:href=\"#clock\"></use>\n",
              "</svg>\n",
              "</span>\n",
              "\n",
              "                4 hours\n",
              "              </span>\n",
              "</div>\n",
              "</div>\n",
              "</a> <div class=\"course-block__footer\">\n",
              "<a class=\"course-block__footer-link link-unstyled\" href=\"/courses/intro-to-python-for-data-science\">\n",
              "<div class=\"course-block__author\">\n",
              "<img alt=\"Filip Schouwenaars\" class=\"course-block__author-image\" height=\"62\" src=\"https://assets.datacamp.com/users/avatars/000/275/302/square/Filipaboutpic.jpg?1493154242\" width=\"62\"/>\n",
              "<div class=\"course-block__author-body\">\n",
              "<p class=\"course-block__author-name\">Filip Schouwenaars</p>\n",
              "<p class=\"course-block__author-occupation\">\n",
              "                  Data Science Instructor at DataCamp\n",
              "                </p>\n",
              "</div>\n",
              "</div>\n",
              "</a> </div>\n",
              "<span class=\"js-mobile-progress-container js-mobile-course-progress\" data-id=\"735\" data-user-id=\"\">\n",
              "</span>\n",
              "</div>\n",
              "</article>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pWmgBIhFiWEH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.DataFrame(\n",
        "    [(\n",
        "        course.select('div > a > div.course-block__body > h4')[0].text.strip(),\n",
        "        course.select('div > a > div.course-block__body > p')[0].text.strip(),\n",
        "        course.select('div > div > a > div > div > p.course-block__author-name')[0].text.strip(),\n",
        "        course.select('div > a > div > div > p.course-block__author-occupation')[0].text.strip()\n",
        "    )\n",
        "        for course in courses], columns=['title', 'text', 'author', 'occupation']\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DTjXu25iDZrt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "docs = [f'{row.title}\\n{row.text}\\n{row.author}\\n{row.occupation}' for index, row in df.iterrows()]\n",
        "titles = np.array([f'{row.title}' for index, row in df.iterrows()])\n",
        "X = np.array([nlp(doc).vector for doc in docs])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Shd_iZXIHF4k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "clustering = AgglomerativeClustering().fit(X)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3yvPWRjNHtIh",
        "colab_type": "code",
        "outputId": "7f7365a8-835f-4d24-e10b-402463d69ca8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 693
        }
      },
      "source": [
        "_ = plt.figure(figsize=(16,9))\n",
        "mergings = linkage(X, method='complete')\n",
        "dendrogram(mergings, labels=titles, leaf_rotation=90, leaf_font_size=6)\n",
        "_ = plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0ksAKAABALxnDepmZxRAAAKCNFtbLbGsWw2mDpaVLZjIcbW5eEmwBAABOEi2s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\n+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LEsRkwNHtiEFWJKlFI2GwqtmY/sotjWXakVwhHzamx/SbxEa1cxtddc52Q9bEGehp8c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\nneSE1czMzMzMzDrJCauZmZmZmZl10v8AjznLHpxTVAYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RjuGCiPwJA0K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dthzSGEZJNiZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}