2. Pandas_and_DataFrame/Most_Popular_Couses_in_coursera.ipynb

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        "<a href=\"https://colab.research.google.com/github/seungwon0601/Tech_Online_Courses_Analysis/blob/master/Most_Popular_Couses_in_coursera.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# What are the most popular courses in Coursera?\n",
        "This is Data Analysis Project with Coursera course dataset(kaggle).\n",
        "\n",
        "- First, Let's look at Coursera courses related to 'Data'\n",
        "- And then Look at the entire courses by programming language(or Frameworks)\n",
        "- Then by type(field or sector)\n"
      ]
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      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
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      "outputs": []
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      "source": [
        "data = pd.read_csv('/content/coursea_data.csv')"
      ],
      "execution_count": null,
      "outputs": []
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        "## Step 1. Data Selection\n",
        "\n",
        "### Coursera Course Dataset | [Kaggle](https://www.kaggle.com/siddharthm1698/coursera-course-dataset#)\n",
        ">This dataset contains mainly 6 columns and 890 course data. The detailed description:\n",
        "1. **course_title** : Contains the course title.\n",
        "2. **course_organization** : It tells which organization is conducting the courses.\n",
        "3. **courseCertificatetype** : It has details about what are the different certifications available in courses.\n",
        "4. **course_rating** : It has the ratings associated with each course.\n",
        "5. **course_difficulty** : It tells about how difficult or what is the level of the course.\n",
        "6. **coursestudentsenrolled** : It has the number of students that are enrolled in the course."
      ]
    },
    {
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      "source": [
        "## Step 2. Data Lookup"
      ]
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      "source": [
        "# columns를 조회합니다.\n",
        "data.head()"
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              "      <td>University of Pennsylvania</td>\n",
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              "      <td>4.8</td>\n",
              "      <td>Mixed</td>\n",
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              "   Unnamed: 0  ... course_students_enrolled\n",
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      "source": [
        "# 분석에 필요한 데이터만 요약합니다.\n",
        "data[['course_title','course_rating','course_difficulty','course_students_enrolled']]"
      ],
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              "      <td>A Crash Course in Data Science</td>\n",
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              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>Психолингвистика (Psycholinguistics)</td>\n",
              "      <td>4.8</td>\n",
              "      <td>Mixed</td>\n",
              "      <td>21k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>Разработка интерфейсов: вёрстка и JavaScript</td>\n",
              "      <td>4.5</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>30k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>Русский как иностранный</td>\n",
              "      <td>4.6</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>9.8k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>Финансовые инструменты для частного инвестора</td>\n",
              "      <td>4.7</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>38k</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>891 rows × 4 columns</p>\n",
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              "                                          course_title  ...  course_students_enrolled\n",
              "0    (ISC)² Systems Security Certified Practitioner...  ...                      5.3k\n",
              "1    A Crash Course in Causality:  Inferring Causal...  ...                       17k\n",
              "2                       A Crash Course in Data Science  ...                      130k\n",
              "3                              A Law Student's Toolkit  ...                       91k\n",
              "4                  A Life of Happiness and Fulfillment  ...                      320k\n",
              "..                                                 ...  ...                       ...\n",
              "886                         Программирование на Python  ...                       52k\n",
              "887               Психолингвистика (Psycholinguistics)  ...                       21k\n",
              "888       Разработка интерфейсов: вёрстка и JavaScript  ...                       30k\n",
              "889                            Русский как иностранный  ...                      9.8k\n",
              "890      Финансовые инструменты для частного инвестора  ...                       38k\n",
              "\n",
              "[891 rows x 4 columns]"
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      "source": [
        "## Step 3. Create DataFrame"
      ]
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    {
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        "outputId": "2e7e7ccb-887f-4f32-d4cd-e18771481bda"
      },
      "source": [
        "# 데이터프레임을 만들어봅니다.\n",
        "df = pd.DataFrame(data[['course_title','course_rating','course_difficulty','course_students_enrolled']])\n",
        "df"
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              "      <th>2</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>Психолингвистика (Psycholinguistics)</td>\n",
              "      <td>4.8</td>\n",
              "      <td>Mixed</td>\n",
              "      <td>21k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>Разработка интерфейсов: вёрстка и JavaScript</td>\n",
              "      <td>4.5</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>30k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>Русский как иностранный</td>\n",
              "      <td>4.6</td>\n",
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              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>Финансовые инструменты для частного инвестора</td>\n",
              "      <td>4.7</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>38k</td>\n",
              "    </tr>\n",
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              "                                          course_title  ...  course_students_enrolled\n",
              "0    (ISC)² Systems Security Certified Practitioner...  ...                      5.3k\n",
              "1    A Crash Course in Causality:  Inferring Causal...  ...                       17k\n",
              "2                       A Crash Course in Data Science  ...                      130k\n",
              "3                              A Law Student's Toolkit  ...                       91k\n",
              "4                  A Life of Happiness and Fulfillment  ...                      320k\n",
              "..                                                 ...  ...                       ...\n",
              "886                         Программирование на Python  ...                       52k\n",
              "887               Психолингвистика (Psycholinguistics)  ...                       21k\n",
              "888       Разработка интерфейсов: вёрстка и JavaScript  ...                       30k\n",
              "889                            Русский как иностранный  ...                      9.8k\n",
              "890      Финансовые инструменты для частного инвестора  ...                       38k\n",
              "\n",
              "[891 rows x 4 columns]"
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        "df.columns=['title', 'rating', 'level', 'enrolled']\n",
        "df"
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A Law Student's Toolkit</td>\n",
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              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>Программирование на Python</td>\n",
              "      <td>4.5</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>52k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>Психолингвистика (Psycholinguistics)</td>\n",
              "      <td>4.8</td>\n",
              "      <td>Mixed</td>\n",
              "      <td>21k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>Разработка интерфейсов: вёрстка и JavaScript</td>\n",
              "      <td>4.5</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>30k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>Русский как иностранный</td>\n",
              "      <td>4.6</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>9.8k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>Финансовые инструменты для частного инвестора</td>\n",
              "      <td>4.7</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>38k</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  ...  enrolled\n",
              "0    (ISC)² Systems Security Certified Practitioner...  ...      5.3k\n",
              "1    A Crash Course in Causality:  Inferring Causal...  ...       17k\n",
              "2                       A Crash Course in Data Science  ...      130k\n",
              "3                              A Law Student's Toolkit  ...       91k\n",
              "4                  A Life of Happiness and Fulfillment  ...      320k\n",
              "..                                                 ...  ...       ...\n",
              "886                         Программирование на Python  ...       52k\n",
              "887               Психолингвистика (Psycholinguistics)  ...       21k\n",
              "888       Разработка интерфейсов: вёрстка и JavaScript  ...       30k\n",
              "889                            Русский как иностранный  ...      9.8k\n",
              "890      Финансовые инструменты для частного инвестора  ...       38k\n",
              "\n",
              "[891 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B2W_Fen8Cap0",
        "colab_type": "text"
      },
      "source": [
        "## Step 4. Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-0sFw0yXD9xp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "59eb61c2-f4cb-4db0-f733-3ae46c16a865"
      },
      "source": [
        "# level column 확인\n",
        "df['level'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Beginner', 'Intermediate', 'Mixed', 'Advanced'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jO2lF1_0eexd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# level column을 숫자형태로 변형합니다.\n",
        "def levels(x):\n",
        "    if x == 'Beginner':\n",
        "        return 1\n",
        "    elif x == 'Mixed':\n",
        "        return 2\n",
        "    elif x == 'Intermediate':\n",
        "        return 3\n",
        "    elif x == 'Advanced':\n",
        "        return 4"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4zEoGxQSewAO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e618197c-46ed-45ec-b805-ada01b6687dc"
      },
      "source": [
        "df['level'] = df['level'].apply(levels)\n",
        "df['level'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 3, 2, 4])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rvxgmxU-QmWO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "02ce9202-eb96-404b-b714-6533226ff048"
      },
      "source": [
        "# enrolled column 확인\n",
        "df['enrolled']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      5.3k\n",
              "1       17k\n",
              "2      130k\n",
              "3       91k\n",
              "4      320k\n",
              "       ... \n",
              "886     52k\n",
              "887     21k\n",
              "888     30k\n",
              "889    9.8k\n",
              "890     38k\n",
              "Name: enrolled, Length: 891, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WHa7EMxbfKno",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 단위(k)를 숫자로 변형합니다.\n",
        "def counts(x):\n",
        "    rx = x.replace('k','000')\n",
        "    if '.' in rx:\n",
        "        rx = rx.replace('.','')\n",
        "        rx = rx[:-1]\n",
        "        return int(rx)\n",
        "    return int(rx)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y4d6jEjYu7BI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "aedb9a2a-f1a8-4015-9bc9-ca7e1b796ac1"
      },
      "source": [
        "# counts()함수가 잘 동작하는지 테스트합니다.\n",
        "df['enrolled'].apply(counts)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        5300\n",
              "1       17000\n",
              "2      130000\n",
              "3       91000\n",
              "4      320000\n",
              "        ...  \n",
              "886     52000\n",
              "887     21000\n",
              "888     30000\n",
              "889      9800\n",
              "890     38000\n",
              "Name: enrolled, Length: 891, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SRmr42z5KtrM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "b586b7bc-c64f-4fea-d8f3-9149756d9c96"
      },
      "source": [
        "# 데이터 프레임에 적용합니다.\n",
        "df['enrolled'] = df['enrolled'].apply(counts)\n",
        "df"
      ],
      "execution_count": null,
      "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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(ISC)² Systems Security Certified Practitioner...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>5300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A Crash Course in Causality:  Inferring Causal...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>17000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A Crash Course in Data Science</td>\n",
              "      <td>4.5</td>\n",
              "      <td>2</td>\n",
              "      <td>130000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A Law Student's Toolkit</td>\n",
              "      <td>4.7</td>\n",
              "      <td>2</td>\n",
              "      <td>91000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A Life of Happiness and Fulfillment</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>320000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>Программирование на Python</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>52000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>Психолингвистика (Psycholinguistics)</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>21000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>Разработка интерфейсов: вёрстка и JavaScript</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>30000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>Русский как иностранный</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3</td>\n",
              "      <td>9800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>Финансовые инструменты для частного инвестора</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>38000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "0    (ISC)² Systems Security Certified Practitioner...     4.7      1      5300\n",
              "1    A Crash Course in Causality:  Inferring Causal...     4.7      3     17000\n",
              "2                       A Crash Course in Data Science     4.5      2    130000\n",
              "3                              A Law Student's Toolkit     4.7      2     91000\n",
              "4                  A Life of Happiness and Fulfillment     4.8      2    320000\n",
              "..                                                 ...     ...    ...       ...\n",
              "886                         Программирование на Python     4.5      3     52000\n",
              "887               Психолингвистика (Psycholinguistics)     4.8      2     21000\n",
              "888       Разработка интерфейсов: вёрстка и JavaScript     4.5      3     30000\n",
              "889                            Русский как иностранный     4.6      3      9800\n",
              "890      Финансовые инструменты для частного инвестора     4.7      1     38000\n",
              "\n",
              "[891 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mOqkL9mmSh-y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "57840139-19a9-4d44-90ce-97692a255389"
      },
      "source": [
        "# 중앙값을 확인해봅니다.\n",
        "df.median()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rating          4.7\n",
              "level           1.0\n",
              "enrolled    42000.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zthGoLxYSlKH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "5ac91d2c-fed7-45f5-b3a4-7d952055ee86"
      },
      "source": [
        "# 평균을 확인해봅니다.\n",
        "df.mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rating          4.677329\n",
              "level           1.718294\n",
              "enrolled    81012.328844\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ds7M64SFSxP3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "b64f802e-9afe-4f82-bfce-281a82da681a"
      },
      "source": [
        "# 통계 요약 정보를 확인해봅니다.\n",
        "df.describe()"
      ],
      "execution_count": null,
      "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>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.677329</td>\n",
              "      <td>1.718294</td>\n",
              "      <td>81012.328844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.162225</td>\n",
              "      <td>0.880687</td>\n",
              "      <td>108555.686774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>3.300000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>13.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4.600000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.700000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>42000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.800000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>97500.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>830000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           rating       level       enrolled\n",
              "count  891.000000  891.000000     891.000000\n",
              "mean     4.677329    1.718294   81012.328844\n",
              "std      0.162225    0.880687  108555.686774\n",
              "min      3.300000    1.000000      13.000000\n",
              "25%      4.600000    1.000000   17000.000000\n",
              "50%      4.700000    1.000000   42000.000000\n",
              "75%      4.800000    2.000000   97500.000000\n",
              "max      5.000000    4.000000  830000.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uw7FA276LcwZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "ddd72c53-2d47-4284-881f-d133bb7a18e8"
      },
      "source": [
        "# 데이터 프레임에서 'Data' 단어를 포함하고 있는 강의를 추출/확인합니다.\n",
        "df[df['title'].str.contains('Data')]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A Crash Course in Causality:  Inferring Causal...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>17000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A Crash Course in Data Science</td>\n",
              "      <td>4.5</td>\n",
              "      <td>2</td>\n",
              "      <td>130000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Advanced Data Science with IBM</td>\n",
              "      <td>4.4</td>\n",
              "      <td>4</td>\n",
              "      <td>320000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Applied Data Science</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>220000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Applied Data Science Capstone</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>42000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>825</th>\n",
              "      <td>Tools for Data Science</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>841</th>\n",
              "      <td>Understanding and Visualizing Data with Python</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>30000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>849</th>\n",
              "      <td>Using Databases with Python</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>220000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>850</th>\n",
              "      <td>Using Python to Access Web Data</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>310000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>864</th>\n",
              "      <td>What is Data Science?</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>260000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>70 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "1    A Crash Course in Causality:  Inferring Causal...     4.7      3     17000\n",
              "2                       A Crash Course in Data Science     4.5      2    130000\n",
              "27                      Advanced Data Science with IBM     4.4      4    320000\n",
              "54                                Applied Data Science     4.6      1    220000\n",
              "55                       Applied Data Science Capstone     4.7      3     42000\n",
              "..                                                 ...     ...    ...       ...\n",
              "825                             Tools for Data Science     4.6      1    120000\n",
              "841     Understanding and Visualizing Data with Python     4.7      1     30000\n",
              "849                        Using Databases with Python     4.8      2    220000\n",
              "850                    Using Python to Access Web Data     4.8      2    310000\n",
              "864                              What is Data Science?     4.7      1    260000\n",
              "\n",
              "[70 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
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        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "outputId": "7b3c17bf-7712-486c-c145-909b782c6da3"
      },
      "source": [
        "# 데이터 프레임에서 'data' 단어를 포함하고 있는 강의를 추출/확인합니다.\n",
        "df[df['title'].str.contains('data')]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
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            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [title, rating, level, enrolled]\n",
              "Index: []"
            ]
          },
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          "execution_count": 27
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PYdqskprxS1u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "4bcb5a58-178c-4a4e-fb15-c3979a2037a0"
      },
      "source": [
        "# 모두 'D'(대문자) 형태로 시작합니다. 'Data'를 포함하는 강의를 최다 등록 순으로 정렬합니다.\n",
        "df[df['title'].str.contains('Data')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>Data Science</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1</td>\n",
              "      <td>830000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>Data Science: Foundations using R</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>740000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Applied Data Science with Python</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>480000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420</th>\n",
              "      <td>IBM Data Science</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>480000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>800</th>\n",
              "      <td>The Data Scientist’s Toolbox</td>\n",
              "      <td>4.6</td>\n",
              "      <td>2</td>\n",
              "      <td>420000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>612</th>\n",
              "      <td>Modernizing Data Lakes and Data Warehouses wit...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>9100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>Big Data – Introducción al uso práctico de dat...</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1</td>\n",
              "      <td>8800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>Building Batch Data Pipelines on GCP</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>7300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>405</th>\n",
              "      <td>Health Information Literacy for Data Analytics</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>5400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>Data Engineering on Google Cloud Platform 日本語版</td>\n",
              "      <td>4.4</td>\n",
              "      <td>3</td>\n",
              "      <td>3000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>70 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "196                                       Data Science     4.5      1    830000\n",
              "199                  Data Science: Foundations using R     4.6      1    740000\n",
              "56                    Applied Data Science with Python     4.5      3    480000\n",
              "420                                   IBM Data Science     4.6      1    480000\n",
              "800                       The Data Scientist’s Toolbox     4.6      2    420000\n",
              "..                                                 ...     ...    ...       ...\n",
              "612  Modernizing Data Lakes and Data Warehouses wit...     4.7      3      9100\n",
              "89   Big Data – Introducción al uso práctico de dat...     4.5      1      8800\n",
              "104               Building Batch Data Pipelines on GCP     4.5      3      7300\n",
              "405     Health Information Literacy for Data Analytics     4.5      3      5400\n",
              "191     Data Engineering on Google Cloud Platform 日本語版     4.4      3      3000\n",
              "\n",
              "[70 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7AD6t0-5xxV1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c16b9c69-1d7f-4c59-9842-e256d4d53079"
      },
      "source": [
        "# 평점이 너무 낮은 강의는 제외합니다. 4이하의 강의가 있는지 확인합니다.\n",
        "df[df['title'].str.contains('Data')].sort_values(by=['enrolled'], ascending=False)['rating'].min()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nB8I_lJM0oRy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 평점은 모두 좋은 편입니다. 새로운 변수로 저장해둡니다.\n",
        "Courses_data = df[df['title'].str.contains('Data')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rliXctiA05O4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "175ff4c2-c1db-4020-e37d-73b85360cff1"
      },
      "source": [
        "# 잘 저장되었는지 확인합니다.\n",
        "Courses_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "        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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>Data Science</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1</td>\n",
              "      <td>830000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>Data Science: Foundations using R</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>740000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Applied Data Science with Python</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>480000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420</th>\n",
              "      <td>IBM Data Science</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>480000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>800</th>\n",
              "      <td>The Data Scientist’s Toolbox</td>\n",
              "      <td>4.6</td>\n",
              "      <td>2</td>\n",
              "      <td>420000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                 title  rating  level  enrolled\n",
              "196                       Data Science     4.5      1    830000\n",
              "199  Data Science: Foundations using R     4.6      1    740000\n",
              "56    Applied Data Science with Python     4.5      3    480000\n",
              "420                   IBM Data Science     4.6      1    480000\n",
              "800       The Data Scientist’s Toolbox     4.6      2    420000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ZW3MYqvzCtN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4935e8db-41e9-49c5-82ac-2274e7196950"
      },
      "source": [
        "# 수강생이 100,000 명 이상인 강의만 추출합니다.\n",
        "Courses_data[Courses_data['enrolled']>100000]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>Data Science: Foundations using R</td>\n",
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              "      <td>740000</td>\n",
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              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Applied Data Science with Python</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>480000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420</th>\n",
              "      <td>IBM Data Science</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>480000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>800</th>\n",
              "      <td>The Data Scientist’s Toolbox</td>\n",
              "      <td>4.6</td>\n",
              "      <td>2</td>\n",
              "      <td>420000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>684</th>\n",
              "      <td>Python Data Structures</td>\n",
              "      <td>4.9</td>\n",
              "      <td>2</td>\n",
              "      <td>420000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>487</th>\n",
              "      <td>Introduction to Data Science in Python</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>390000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Advanced Data Science with IBM</td>\n",
              "      <td>4.4</td>\n",
              "      <td>4</td>\n",
              "      <td>320000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>850</th>\n",
              "      <td>Using Python to Access Web Data</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>310000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>486</th>\n",
              "      <td>Introduction to Data Science</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>310000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>590</th>\n",
              "      <td>Mastering Data Analysis in Excel</td>\n",
              "      <td>4.2</td>\n",
              "      <td>2</td>\n",
              "      <td>280000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>864</th>\n",
              "      <td>What is Data Science?</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>260000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>632</th>\n",
              "      <td>Object Oriented Java Programming: Data Structu...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>849</th>\n",
              "      <td>Using Databases with Python</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>220000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Applied Data Science</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>220000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187</th>\n",
              "      <td>Data Analysis and Presentation Skills: the PwC...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>220000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>Data Science: Statistics and Machine Learning</td>\n",
              "      <td>4.4</td>\n",
              "      <td>3</td>\n",
              "      <td>210000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>687</th>\n",
              "      <td>Python for Data Science and AI</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>170000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>Executive Data Science</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1</td>\n",
              "      <td>170000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>509</th>\n",
              "      <td>Introduction to Probability and Data</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>160000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>470</th>\n",
              "      <td>Introduction to Big Data</td>\n",
              "      <td>4.6</td>\n",
              "      <td>2</td>\n",
              "      <td>160000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>711</th>\n",
              "      <td>SQL for Data Science</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>160000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>553</th>\n",
              "      <td>Learn SQL Basics for Data Science</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>160000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>Data Science Math Skills</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>Data Mining</td>\n",
              "      <td>4.4</td>\n",
              "      <td>3</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>465</th>\n",
              "      <td>Introducción a Data Science: Programación Esta...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>205</th>\n",
              "      <td>Data Warehousing for Business Intelligence</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A Crash Course in Data Science</td>\n",
              "      <td>4.5</td>\n",
              "      <td>2</td>\n",
              "      <td>130000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>Data Visualization and Communication with Tableau</td>\n",
              "      <td>4.7</td>\n",
              "      <td>2</td>\n",
              "      <td>130000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>Data Engineering, Big Data, and Machine Learni...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3</td>\n",
              "      <td>120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>Data Engineering with Google Cloud</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3</td>\n",
              "      <td>120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>389</th>\n",
              "      <td>Google Cloud Platform Big Data and Machine Lea...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3</td>\n",
              "      <td>120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>207</th>\n",
              "      <td>Data-driven Decision Making</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>825</th>\n",
              "      <td>Tools for Data Science</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188</th>\n",
              "      <td>Data Analysis with Python</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>110000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>208</th>\n",
              "      <td>Databases and SQL for Data Science</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>110000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "196                                       Data Science     4.5      1    830000\n",
              "199                  Data Science: Foundations using R     4.6      1    740000\n",
              "56                    Applied Data Science with Python     4.5      3    480000\n",
              "420                                   IBM Data Science     4.6      1    480000\n",
              "800                       The Data Scientist’s Toolbox     4.6      2    420000\n",
              "684                             Python Data Structures     4.9      2    420000\n",
              "487             Introduction to Data Science in Python     4.5      3    390000\n",
              "27                      Advanced Data Science with IBM     4.4      4    320000\n",
              "850                    Using Python to Access Web Data     4.8      2    310000\n",
              "486                       Introduction to Data Science     4.6      1    310000\n",
              "590                   Mastering Data Analysis in Excel     4.2      2    280000\n",
              "864                              What is Data Science?     4.7      1    260000\n",
              "632  Object Oriented Java Programming: Data Structu...     4.7      3    250000\n",
              "849                        Using Databases with Python     4.8      2    220000\n",
              "54                                Applied Data Science     4.6      1    220000\n",
              "187  Data Analysis and Presentation Skills: the PwC...     4.6      1    220000\n",
              "200      Data Science: Statistics and Machine Learning     4.4      3    210000\n",
              "687                     Python for Data Science and AI     4.6      1    170000\n",
              "299                             Executive Data Science     4.5      1    170000\n",
              "509               Introduction to Probability and Data     4.7      1    160000\n",
              "470                           Introduction to Big Data     4.6      2    160000\n",
              "711                               SQL for Data Science     4.6      1    160000\n",
              "553                  Learn SQL Basics for Data Science     4.6      1    160000\n",
              "197                           Data Science Math Skills     4.5      1    140000\n",
              "195                                        Data Mining     4.4      3    140000\n",
              "465  Introducción a Data Science: Programación Esta...     4.7      1    140000\n",
              "205         Data Warehousing for Business Intelligence     4.5      4    140000\n",
              "2                       A Crash Course in Data Science     4.5      2    130000\n",
              "201  Data Visualization and Communication with Tableau     4.7      2    130000\n",
              "193  Data Engineering, Big Data, and Machine Learni...     4.6      3    120000\n",
              "192                 Data Engineering with Google Cloud     4.6      3    120000\n",
              "389  Google Cloud Platform Big Data and Machine Lea...     4.6      3    120000\n",
              "207                        Data-driven Decision Making     4.6      1    120000\n",
              "825                             Tools for Data Science     4.6      1    120000\n",
              "188                          Data Analysis with Python     4.7      1    110000\n",
              "208                 Databases and SQL for Data Science     4.7      1    110000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1B3_5Lxwzk34",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "105360a8-0873-4c64-bfb2-5a8580f4431b"
      },
      "source": [
        "# 강의 수를 확인합니다.\n",
        "len(Courses_data[Courses_data['enrolled']>100000])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "36"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S3vAnhJb1ZN1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 100000 enrolled 강의를 저장하고, Top 10 강의도 따로 추출해둡니다.\n",
        "AboutData_BestSellers = Courses_data[Courses_data['enrolled']>100000]\n",
        "AboutData_Top_Ten = BestSellers[:10]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5adiGoSB16RU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "9cc59a49-0a15-4794-e68c-36179262a836"
      },
      "source": [
        "AboutData_Top_Ten"
      ],
      "execution_count": null,
      "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",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>Data Science</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1</td>\n",
              "      <td>830000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>Data Science: Foundations using R</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>740000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Applied Data Science with Python</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>480000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420</th>\n",
              "      <td>IBM Data Science</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>480000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>800</th>\n",
              "      <td>The Data Scientist’s Toolbox</td>\n",
              "      <td>4.6</td>\n",
              "      <td>2</td>\n",
              "      <td>420000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>684</th>\n",
              "      <td>Python Data Structures</td>\n",
              "      <td>4.9</td>\n",
              "      <td>2</td>\n",
              "      <td>420000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>487</th>\n",
              "      <td>Introduction to Data Science in Python</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>390000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Advanced Data Science with IBM</td>\n",
              "      <td>4.4</td>\n",
              "      <td>4</td>\n",
              "      <td>320000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>850</th>\n",
              "      <td>Using Python to Access Web Data</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>310000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>486</th>\n",
              "      <td>Introduction to Data Science</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>310000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                      title  rating  level  enrolled\n",
              "196                            Data Science     4.5      1    830000\n",
              "199       Data Science: Foundations using R     4.6      1    740000\n",
              "56         Applied Data Science with Python     4.5      3    480000\n",
              "420                        IBM Data Science     4.6      1    480000\n",
              "800            The Data Scientist’s Toolbox     4.6      2    420000\n",
              "684                  Python Data Structures     4.9      2    420000\n",
              "487  Introduction to Data Science in Python     4.5      3    390000\n",
              "27           Advanced Data Science with IBM     4.4      4    320000\n",
              "850         Using Python to Access Web Data     4.8      2    310000\n",
              "486            Introduction to Data Science     4.6      1    310000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J6BL1dBc17zm",
        "colab_type": "text"
      },
      "source": [
        "## Step 5. Apply - Language/Framework\n",
        "지금까지 Coursera의 890개 강의에 대해 'Data'를 포함하고 있는 강의들을 누적 수강생 순으로 추출해냈습니다.\n",
        "이와 같은 방식으로 언어별 혹은 프레임워크별 Best Seller를 확인해보겠습니다.\n",
        "\n",
        "1. **Python** Best Sellers\n",
        "2. **R** Best Sellers\n",
        "3. **Java** Best Sellers\n",
        "4. **JavaScrips** Best Sellers\n",
        "5. **Django** Best Sellers\n",
        "6. **Flask** Best Sellers\n",
        "7. **React** Best Sellers\n",
        "8. **Vue** Best Sellers\n",
        "9. **Tensorflow** Best Sellers\n",
        "10. **PyTorch** Best Sellers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Whnmhjz3NkY",
        "colab_type": "text"
      },
      "source": [
        "### 1. Python Best Sellers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LGtfQl8D5LWO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "23c468e0-1296-45c0-d4ef-9025ee2ba366"
      },
      "source": [
        "# 모든 Python 강의 확인\n",
        "df[df['title'].str.contains('Python')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Applied Data Science with Python</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>480000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>684</th>\n",
              "      <td>Python Data Structures</td>\n",
              "      <td>4.9</td>\n",
              "      <td>2</td>\n",
              "      <td>420000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>487</th>\n",
              "      <td>Introduction to Data Science in Python</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>390000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>850</th>\n",
              "      <td>Using Python to Access Web Data</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>310000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>849</th>\n",
              "      <td>Using Databases with Python</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>220000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>687</th>\n",
              "      <td>Python for Data Science and AI</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>170000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Applied Machine Learning in Python</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3</td>\n",
              "      <td>150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>570</th>\n",
              "      <td>Machine Learning with Python</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>530</th>\n",
              "      <td>Introdução à Ciência da Computação com Python ...</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1</td>\n",
              "      <td>120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>682</th>\n",
              "      <td>Python Basics</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1</td>\n",
              "      <td>110000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188</th>\n",
              "      <td>Data Analysis with Python</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>110000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>466</th>\n",
              "      <td>Introducción a la programación en Python I: Ap...</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1</td>\n",
              "      <td>100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>Applied Plotting, Charting &amp; Data Representati...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3</td>\n",
              "      <td>97000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>391</th>\n",
              "      <td>Google IT Automation with Python</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>93000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>883</th>\n",
              "      <td>Основы программирования на Python</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>83000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>Capstone: Retrieving, Processing, and Visualiz...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>2</td>\n",
              "      <td>82000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>Crash Course on Python</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1</td>\n",
              "      <td>81000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>880</th>\n",
              "      <td>Математика и Python для анализа данных</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1</td>\n",
              "      <td>67000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>203</th>\n",
              "      <td>Data Visualization with Python</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3</td>\n",
              "      <td>66000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>Программирование на Python</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>52000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>513</th>\n",
              "      <td>Introduction to Scripting in Python</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>49000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>885</th>\n",
              "      <td>Погружение в Python</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>45000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>686</th>\n",
              "      <td>Python and Statistics for Financial Analysis</td>\n",
              "      <td>4.4</td>\n",
              "      <td>3</td>\n",
              "      <td>41000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>752</th>\n",
              "      <td>Statistics with Python</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>34000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>841</th>\n",
              "      <td>Understanding and Visualizing Data with Python</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>30000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>685</th>\n",
              "      <td>Python Functions, Files, and Dictionaries</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1</td>\n",
              "      <td>26000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>683</th>\n",
              "      <td>Python Classes and Inheritance</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>21000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>851</th>\n",
              "      <td>Using Python to Interact with the Operating Sy...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>19000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>190</th>\n",
              "      <td>Data Collection and Processing with Python</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>19000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>532</th>\n",
              "      <td>Investment Management with Python and Machine ...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>14000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>508</th>\n",
              "      <td>Introduction to Portfolio Construction and Ana...</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>11000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>Aprende a programar con Python</td>\n",
              "      <td>4.2</td>\n",
              "      <td>1</td>\n",
              "      <td>6600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>688</th>\n",
              "      <td>Python for Everybody</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>674</th>\n",
              "      <td>Programming for Everybody (Getting Started wit...</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "56                    Applied Data Science with Python     4.5      3    480000\n",
              "684                             Python Data Structures     4.9      2    420000\n",
              "487             Introduction to Data Science in Python     4.5      3    390000\n",
              "850                    Using Python to Access Web Data     4.8      2    310000\n",
              "849                        Using Databases with Python     4.8      2    220000\n",
              "687                     Python for Data Science and AI     4.6      1    170000\n",
              "57                  Applied Machine Learning in Python     4.6      3    150000\n",
              "570                       Machine Learning with Python     4.7      3    120000\n",
              "530  Introdução à Ciência da Computação com Python ...     4.9      1    120000\n",
              "682                                      Python Basics     4.8      1    110000\n",
              "188                          Data Analysis with Python     4.7      1    110000\n",
              "466  Introducción a la programación en Python I: Ap...     4.4      1    100000\n",
              "58   Applied Plotting, Charting & Data Representati...     4.6      3     97000\n",
              "391                   Google IT Automation with Python     4.7      1     93000\n",
              "883                  Основы программирования на Python     4.6      1     83000\n",
              "128  Capstone: Retrieving, Processing, and Visualiz...     4.6      2     82000\n",
              "171                             Crash Course on Python     4.8      1     81000\n",
              "880             Математика и Python для анализа данных     4.8      1     67000\n",
              "203                     Data Visualization with Python     4.6      3     66000\n",
              "886                         Программирование на Python     4.5      3     52000\n",
              "513                Introduction to Scripting in Python     4.7      1     49000\n",
              "885                                Погружение в Python     4.7      3     45000\n",
              "686       Python and Statistics for Financial Analysis     4.4      3     41000\n",
              "752                             Statistics with Python     4.6      1     34000\n",
              "841     Understanding and Visualizing Data with Python     4.7      1     30000\n",
              "685          Python Functions, Files, and Dictionaries     4.8      1     26000\n",
              "683                     Python Classes and Inheritance     4.7      3     21000\n",
              "851  Using Python to Interact with the Operating Sy...     4.7      1     19000\n",
              "190         Data Collection and Processing with Python     4.7      3     19000\n",
              "532  Investment Management with Python and Machine ...     4.6      1     14000\n",
              "508  Introduction to Portfolio Construction and Ana...     4.8      2     11000\n",
              "59                      Aprende a programar con Python     4.2      1      6600\n",
              "688                               Python for Everybody     4.8      1        15\n",
              "674  Programming for Everybody (Getting Started wit...     4.8      2        13"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gBSs12vc5QXZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "outputId": "f01e1b3c-4219-414b-99e1-816e9984b271"
      },
      "source": [
        "# 누적 수강생 10만건 이상 Python 강의\n",
        "Python_Courses = df[df['title'].str.contains('Python')].sort_values(by=['enrolled'], ascending=False)\n",
        "Python_Courses[Python_Courses['enrolled']>100000]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Applied Data Science with Python</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>480000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>684</th>\n",
              "      <td>Python Data Structures</td>\n",
              "      <td>4.9</td>\n",
              "      <td>2</td>\n",
              "      <td>420000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>487</th>\n",
              "      <td>Introduction to Data Science in Python</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>390000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>850</th>\n",
              "      <td>Using Python to Access Web Data</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>310000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>849</th>\n",
              "      <td>Using Databases with Python</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>220000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>687</th>\n",
              "      <td>Python for Data Science and AI</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>170000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Applied Machine Learning in Python</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3</td>\n",
              "      <td>150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>570</th>\n",
              "      <td>Machine Learning with Python</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>530</th>\n",
              "      <td>Introdução à Ciência da Computação com Python ...</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1</td>\n",
              "      <td>120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>682</th>\n",
              "      <td>Python Basics</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1</td>\n",
              "      <td>110000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188</th>\n",
              "      <td>Data Analysis with Python</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>110000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "56                    Applied Data Science with Python     4.5      3    480000\n",
              "684                             Python Data Structures     4.9      2    420000\n",
              "487             Introduction to Data Science in Python     4.5      3    390000\n",
              "850                    Using Python to Access Web Data     4.8      2    310000\n",
              "849                        Using Databases with Python     4.8      2    220000\n",
              "687                     Python for Data Science and AI     4.6      1    170000\n",
              "57                  Applied Machine Learning in Python     4.6      3    150000\n",
              "570                       Machine Learning with Python     4.7      3    120000\n",
              "530  Introdução à Ciência da Computação com Python ...     4.9      1    120000\n",
              "682                                      Python Basics     4.8      1    110000\n",
              "188                          Data Analysis with Python     4.7      1    110000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PxVSmmdK50Mo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 데이터 저장\n",
        "Python_BestSellers = Python_Courses[Python_Courses['enrolled']>100000]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eRfujIZW6Bmo",
        "colab_type": "text"
      },
      "source": [
        "### 2. R Best Sellers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5O9zsnA06G7I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "e1480522-f1fb-48e8-b8d7-95db18e9a737"
      },
      "source": [
        "# R 강의 확인(1)\n",
        "df[df['title'].str.contains('R ')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>691</th>\n",
              "      <td>R Programming</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3</td>\n",
              "      <td>480000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>Human Resource Management: HR for People Managers</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>98000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>751</th>\n",
              "      <td>Statistical Analysis with R for Public Health</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>16000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>844</th>\n",
              "      <td>Unity XR: How to Build AR and VR Apps</td>\n",
              "      <td>4.2</td>\n",
              "      <td>1</td>\n",
              "      <td>12000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "691                                      R Programming     4.6      3    480000\n",
              "415  Human Resource Management: HR for People Managers     4.7      1     98000\n",
              "751      Statistical Analysis with R for Public Health     4.7      1     16000\n",
              "844              Unity XR: How to Build AR and VR Apps     4.2      1     12000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wmdVT95T68EM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "736cea0c-897e-4e51-db4d-81d05c4bae88"
      },
      "source": [
        "# R 강의 확인(1)\n",
        "# 691행만 추출합니다.\n",
        "df[df['title'].str.contains('R ')].sort_values(by=['enrolled'], ascending=False).iloc[0,:]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "title       R Programming\n",
              "rating                4.6\n",
              "level                   3\n",
              "enrolled           480000\n",
              "Name: 691, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O1npjN4d7d5z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 추출한 행 저장\n",
        "R_temp_1 = df[df['title'].str.contains('R ')].sort_values(by=['enrolled'], ascending=False).iloc[0,:]\n",
        "R_temp_1=pd.DataFrame(R_temp_1).T"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c1O2UFae6MQ-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "541c329f-80f5-4f52-e1a8-6b9976a4537d"
      },
      "source": [
        "# R 강의 확인(2)\n",
        "df[df['title'].str[-1] =='R'].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "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",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>Data Science: Foundations using R</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>740000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>753</th>\n",
              "      <td>Statistics with R</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>220000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>652</th>\n",
              "      <td>Photography Basics and Beyond: From Smartphone...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>160000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>465</th>\n",
              "      <td>Introducción a Data Science: Programación Esta...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>592</th>\n",
              "      <td>Mastering Software Development in R</td>\n",
              "      <td>4.3</td>\n",
              "      <td>1</td>\n",
              "      <td>52000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "199                  Data Science: Foundations using R     4.6      1    740000\n",
              "753                                  Statistics with R     4.7      1    220000\n",
              "652  Photography Basics and Beyond: From Smartphone...     4.7      1    160000\n",
              "465  Introducción a Data Science: Programación Esta...     4.7      1    140000\n",
              "592                Mastering Software Development in R     4.3      1     52000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t3wi-mhb6bnf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "acf1d7e6-796c-42c3-ee00-bc075c78dd14"
      },
      "source": [
        "# R 강의 확인(2)\n",
        "# 199, 753행만 추출합니다.\n",
        "df[df['title'].str[-1] =='R'].sort_values(by=['enrolled'], ascending=False).iloc[:2,:]"
      ],
      "execution_count": null,
      "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",
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              "    }\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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>Data Science: Foundations using R</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>740000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>753</th>\n",
              "      <td>Statistics with R</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>220000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                 title  rating  level  enrolled\n",
              "199  Data Science: Foundations using R     4.6      1    740000\n",
              "753                  Statistics with R     4.7      1    220000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WySEV02y7adI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 추출한 행 저장\n",
        "R_temp_2 = df[df['title'].str[-1] =='R'].sort_values(by=['enrolled'], ascending=False).iloc[:2,:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0-WbjhFm71NB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "f8c17648-70b2-49cd-acda-2d66df2bd87a"
      },
      "source": [
        "pd.concat([R_temp_1, R_temp_2])"
      ],
      "execution_count": null,
      "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",
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              "    }\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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>691</th>\n",
              "      <td>R Programming</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3</td>\n",
              "      <td>480000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>Data Science: Foundations using R</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>740000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>753</th>\n",
              "      <td>Statistics with R</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>220000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                 title rating level enrolled\n",
              "691                      R Programming    4.6     3   480000\n",
              "199  Data Science: Foundations using R    4.6     1   740000\n",
              "753                  Statistics with R    4.7     1   220000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G0otrL3q_zfA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 베스트셀러 저장\n",
        "R_BestSellers = pd.concat([R_temp_1, R_temp_2])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YN_qljmk8f6I",
        "colab_type": "text"
      },
      "source": [
        "### 3. Java Best Sellers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sPKBMGZh9End",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "8a72178a-3d7e-49fd-9a50-f72de030ee80"
      },
      "source": [
        "# Java 강의 확인\n",
        "df[df['title'].str.contains('Java')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "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",
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              "    }\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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>535</th>\n",
              "      <td>Java Programming and Software Engineering Fund...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>380000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>633</th>\n",
              "      <td>Object Oriented Programming in Java</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>330000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>632</th>\n",
              "      <td>Object Oriented Java Programming: Data Structu...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>672</th>\n",
              "      <td>Programming Foundations with JavaScript, HTML ...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>HTML, CSS, and Javascript for Web Developers</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>240000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>536</th>\n",
              "      <td>Java Programming: Solving Problems with Software</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>160000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>538</th>\n",
              "      <td>Kotlin for Java Developers</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>37000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>645</th>\n",
              "      <td>Parallel, Concurrent, and Distributed Programm...</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>30000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>Разработка интерфейсов: вёрстка и JavaScript</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>30000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>Building Scalable Java Microservices with Spri...</td>\n",
              "      <td>4.3</td>\n",
              "      <td>3</td>\n",
              "      <td>24000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "535  Java Programming and Software Engineering Fund...     4.6      1    380000\n",
              "633                Object Oriented Programming in Java     4.6      1    330000\n",
              "632  Object Oriented Java Programming: Data Structu...     4.7      3    250000\n",
              "672  Programming Foundations with JavaScript, HTML ...     4.6      1    250000\n",
              "399       HTML, CSS, and Javascript for Web Developers     4.8      2    240000\n",
              "536   Java Programming: Solving Problems with Software     4.6      1    160000\n",
              "538                         Kotlin for Java Developers     4.7      3     37000\n",
              "645  Parallel, Concurrent, and Distributed Programm...     4.5      3     30000\n",
              "888       Разработка интерфейсов: вёрстка и JavaScript     4.5      3     30000\n",
              "107  Building Scalable Java Microservices with Spri...     4.3      3     24000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TuDfsZpQ9RQq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "7c75b73e-6702-49cf-efc2-eceb0ce6a91f"
      },
      "source": [
        "Java_Courses = df[df['title'].str.contains('Java')].sort_values(by=['enrolled'], ascending=False)\n",
        "Java_BestSellers = Java_Courses[Java_Courses['enrolled']>100000]\n",
        "Java_BestSellers"
      ],
      "execution_count": null,
      "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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>535</th>\n",
              "      <td>Java Programming and Software Engineering Fund...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>380000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>633</th>\n",
              "      <td>Object Oriented Programming in Java</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>330000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>632</th>\n",
              "      <td>Object Oriented Java Programming: Data Structu...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>672</th>\n",
              "      <td>Programming Foundations with JavaScript, HTML ...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>HTML, CSS, and Javascript for Web Developers</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>240000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>536</th>\n",
              "      <td>Java Programming: Solving Problems with Software</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>160000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "535  Java Programming and Software Engineering Fund...     4.6      1    380000\n",
              "633                Object Oriented Programming in Java     4.6      1    330000\n",
              "632  Object Oriented Java Programming: Data Structu...     4.7      3    250000\n",
              "672  Programming Foundations with JavaScript, HTML ...     4.6      1    250000\n",
              "399       HTML, CSS, and Javascript for Web Developers     4.8      2    240000\n",
              "536   Java Programming: Solving Problems with Software     4.6      1    160000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Kudpm4K939o",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "5606ea91-6c12-4884-e08a-b47589df79ed"
      },
      "source": [
        "# JavaScript도 포함되어 있습니다. 제외해줍니다.\n",
        "Java_BestSellers = Java_BestSellers[Java_BestSellers['title'].str.contains('JavaScript')==False]\n",
        "Java_BestSellers = Java_BestSellers[Java_BestSellers['title'].str.contains('Javascript')==False]\n",
        "Java_BestSellers"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>535</th>\n",
              "      <td>Java Programming and Software Engineering Fund...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>380000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>633</th>\n",
              "      <td>Object Oriented Programming in Java</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>330000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>632</th>\n",
              "      <td>Object Oriented Java Programming: Data Structu...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>536</th>\n",
              "      <td>Java Programming: Solving Problems with Software</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>160000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "535  Java Programming and Software Engineering Fund...     4.6      1    380000\n",
              "633                Object Oriented Programming in Java     4.6      1    330000\n",
              "632  Object Oriented Java Programming: Data Structu...     4.7      3    250000\n",
              "536   Java Programming: Solving Problems with Software     4.6      1    160000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gusdn3Hx-M76",
        "colab_type": "text"
      },
      "source": [
        "### 4. JavaScript Best Sellers\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U98BXSZ0-mjx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "7a73290d-7609-41b3-c3d1-3f9dd84bb980"
      },
      "source": [
        "# JavaScript 강의 확인\n",
        "df[df['title'].str.contains('JavaScript')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>672</th>\n",
              "      <td>Programming Foundations with JavaScript, HTML ...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>Разработка интерфейсов: вёрстка и JavaScript</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>30000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "672  Programming Foundations with JavaScript, HTML ...     4.6      1    250000\n",
              "888       Разработка интерфейсов: вёрстка и JavaScript     4.5      3     30000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VmKiKkXO_PPq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "JavaScript_temp_1 = df[df['title'].str.contains('JavaScript')].sort_values(by=['enrolled'], ascending=False).iloc[0,:]\n",
        "JavaScript_temp_1 = pd.DataFrame(JavaScript_temp_1).T"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PO9lW0vT_dSG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "e1e09530-9a6f-43bf-a793-9f10e15c3d75"
      },
      "source": [
        "# 병합 전 임시 할당\n",
        "JavaScript_temp_1"
      ],
      "execution_count": null,
      "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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>672</th>\n",
              "      <td>Programming Foundations with JavaScript, HTML ...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>250000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title rating level enrolled\n",
              "672  Programming Foundations with JavaScript, HTML ...    4.6     1   250000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R797L7aP-6BJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "b789f3d4-e7ac-43a4-ade5-3185e30b1389"
      },
      "source": [
        "df[df['title'].str.contains('Javascript')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>HTML, CSS, and Javascript for Web Developers</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>240000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            title  rating  level  enrolled\n",
              "399  HTML, CSS, and Javascript for Web Developers     4.8      2    240000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6dAc0z7f_KCa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 병합 전 임시 할당\n",
        "JavaScript_temp_2 = df[df['title'].str.contains('Javascript')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "59R9O0l9_n0l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "f61de042-a6f5-4beb-9742-87af1bcf34e8"
      },
      "source": [
        "pd.concat([JavaScript_temp_1, JavaScript_temp_2])"
      ],
      "execution_count": null,
      "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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>672</th>\n",
              "      <td>Programming Foundations with JavaScript, HTML ...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>HTML, CSS, and Javascript for Web Developers</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>240000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title rating level enrolled\n",
              "672  Programming Foundations with JavaScript, HTML ...    4.6     1   250000\n",
              "399       HTML, CSS, and Javascript for Web Developers    4.8     2   240000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SIsHnc_5ACZM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 베스트셀러 저장\n",
        "JavaScript_BestSellers = pd.concat([JavaScript_temp_1, JavaScript_temp_2])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fq4_tNEQAF8d",
        "colab_type": "text"
      },
      "source": [
        "### 5. Django Best Sellers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QXjXyA7wAjMY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "outputId": "edb43b65-4bc5-473c-9287-a28a832f782b"
      },
      "source": [
        "# Django 강의 확인\n",
        "df[df['title'].str.contains('Django')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [title, rating, level, enrolled]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "io9z2x6MBiq9",
        "colab_type": "text"
      },
      "source": [
        "### 6. Flask Best Sellers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n0yinYVJAlrg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "outputId": "e494bbfc-f606-4dd4-f02c-e51c8dfec8d2"
      },
      "source": [
        "# Flask 강의 확인\n",
        "df[df['title'].str.contains('Flask')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [title, rating, level, enrolled]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z51iqst4BqiT",
        "colab_type": "text"
      },
      "source": [
        "### 7. React Best Sellers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P58Z1MODBoz9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "a9814fcf-385f-48d3-eeae-b6863275164c"
      },
      "source": [
        "# React 강의 확인\n",
        "df[df['title'].str.contains('React')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>339</th>\n",
              "      <td>Full-Stack Web Development with React</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>476</th>\n",
              "      <td>Introduction to Chemistry:  Reactions and Ratios</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1</td>\n",
              "      <td>46000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>336</th>\n",
              "      <td>Front-End Web Development with React</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>45000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                title  rating  level  enrolled\n",
              "339             Full-Stack Web Development with React     4.7      3    140000\n",
              "476  Introduction to Chemistry:  Reactions and Ratios     4.8      1     46000\n",
              "336              Front-End Web Development with React     4.7      3     45000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EYaDqutQBwB1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "React_Courses = df[df['title'].str.contains('React')].sort_values(by=['enrolled'], ascending=False)\n",
        "React_BestSellers = React_Courses[React_Courses['enrolled']>100000]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4JxaaFe6B8Kc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "89478428-0291-4816-ba6a-d48f39cdd201"
      },
      "source": [
        "# 베스트셀러 저장\n",
        "React_BestSellers"
      ],
      "execution_count": null,
      "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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>339</th>\n",
              "      <td>Full-Stack Web Development with React</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                     title  rating  level  enrolled\n",
              "339  Full-Stack Web Development with React     4.7      3    140000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tBJDhAFBB9NS",
        "colab_type": "text"
      },
      "source": [
        "### 8. Vue Best Sellers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-5dKU6e1CEfB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "outputId": "4c86c410-5d4d-401f-a636-61ff48a3788d"
      },
      "source": [
        "# Vue 강의 확인\n",
        "df[df['title'].str.contains('Vue')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [title, rating, level, enrolled]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NpIh1XbpCIuk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "outputId": "553e873c-e514-4518-e5f6-58b7f1f03fb2"
      },
      "source": [
        "# Vue 강의 확인\n",
        "df[df['title'].str.contains('vue')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [title, rating, level, enrolled]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6AttzhNNCN6c",
        "colab_type": "text"
      },
      "source": [
        "### 9. Tensorflow Best Sellers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AlfvoOHfCLHQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "outputId": "a28b5e0d-b4df-46f5-b9fa-7e0e7dc8ac9e"
      },
      "source": [
        "# Tensorflow 강의 확인\n",
        "df[df['title'].str.contains('TensorFlow')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>787</th>\n",
              "      <td>TensorFlow in Practice</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>170000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>520</th>\n",
              "      <td>Introduction to TensorFlow for Artificial Inte...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>571</th>\n",
              "      <td>Machine Learning with TensorFlow on Google Clo...</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>72000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>Convolutional Neural Networks in TensorFlow</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>46000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>621</th>\n",
              "      <td>Natural Language Processing in TensorFlow</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3</td>\n",
              "      <td>40000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Advanced Machine Learning with TensorFlow on G...</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4</td>\n",
              "      <td>35000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>788</th>\n",
              "      <td>TensorFlow: Data and Deployment</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>12000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "787                             TensorFlow in Practice     4.7      3    170000\n",
              "520  Introduction to TensorFlow for Artificial Inte...     4.7      3    150000\n",
              "571  Machine Learning with TensorFlow on Google Clo...     4.5      3     72000\n",
              "163        Convolutional Neural Networks in TensorFlow     4.7      3     46000\n",
              "621          Natural Language Processing in TensorFlow     4.6      3     40000\n",
              "29   Advanced Machine Learning with TensorFlow on G...     4.5      4     35000\n",
              "788                    TensorFlow: Data and Deployment     4.5      3     12000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jQMI9BsODAnh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "fd19a7bc-deb3-44d1-fb50-74ebc4a0e43c"
      },
      "source": [
        "Tensorflow_Courses = df[df['title'].str.contains('TensorFlow')].sort_values(by=['enrolled'], ascending=False)\n",
        "Tensorflow_BestSellers = Tensorflow_Courses[Tensorflow_Courses['enrolled']>100000]\n",
        "# 베스트셀러 저장\n",
        "Tensorflow_BestSellers"
      ],
      "execution_count": null,
      "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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>787</th>\n",
              "      <td>TensorFlow in Practice</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>170000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>520</th>\n",
              "      <td>Introduction to TensorFlow for Artificial Inte...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>150000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "787                             TensorFlow in Practice     4.7      3    170000\n",
              "520  Introduction to TensorFlow for Artificial Inte...     4.7      3    150000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "arwUkX0NCx3-",
        "colab_type": "text"
      },
      "source": [
        "### 10. PyTorch Best Sellers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9MVsZ9ziCVLP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "outputId": "daeb8415-4e07-4769-fbad-971e96dae21f"
      },
      "source": [
        "# Tensorflow 강의 확인\n",
        "df[df['title'].str.contains('PyTorch')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "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",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [title, rating, level, enrolled]\n",
              "Index: []"
            ]
          },
          "metadata": {
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          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ii81uwpCDqvY",
        "colab_type": "text"
      },
      "source": [
        "## Step 6. Apply - Sector\n",
        "다음으로 분야별 인기 강의를 알아봅니다.\n",
        "\n",
        "1. Web\n",
        "2. App\n",
        "3. Algorithm\n",
        "4. Computer\n",
        "5. hacking\n",
        "6. Finance\n",
        "7. Commerce\n",
        "8. neural network\n",
        "9. machine learning \n",
        "10. deep learning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IZ3iZl2vCip4",
        "colab_type": "text"
      },
      "source": [
        "### 1. Web"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7IW6Lwm1Eef8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "outputId": "dccb906a-a2b2-4f58-a739-22f27d13add7"
      },
      "source": [
        "# Web 강의 확인\n",
        "df[df['title'].str.contains('Web')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>850</th>\n",
              "      <td>Using Python to Access Web Data</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>310000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>859</th>\n",
              "      <td>Web Design for Everybody: Basics of Web Develo...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>280000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>HTML, CSS, and Javascript for Web Developers</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>240000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>701</th>\n",
              "      <td>Responsive Website Development and Design</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1</td>\n",
              "      <td>170000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>338</th>\n",
              "      <td>Full Stack Web and Multiplatform Mobile App De...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>339</th>\n",
              "      <td>Full-Stack Web Development with React</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>How To Create a Website in a Weekend! (Project...</td>\n",
              "      <td>3.3</td>\n",
              "      <td>2</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>858</th>\n",
              "      <td>Web Applications for Everybody</td>\n",
              "      <td>4.8</td>\n",
              "      <td>3</td>\n",
              "      <td>120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>337</th>\n",
              "      <td>Front-End Web UI Frameworks and Tools: Bootstr...</td>\n",
              "      <td>4.8</td>\n",
              "      <td>3</td>\n",
              "      <td>89000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>526</th>\n",
              "      <td>Introduction to Web Development</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>76000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>336</th>\n",
              "      <td>Front-End Web Development with React</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>45000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "850                    Using Python to Access Web Data     4.8      2    310000\n",
              "859  Web Design for Everybody: Basics of Web Develo...     4.7      1    280000\n",
              "399       HTML, CSS, and Javascript for Web Developers     4.8      2    240000\n",
              "701          Responsive Website Development and Design     4.5      1    170000\n",
              "338  Full Stack Web and Multiplatform Mobile App De...     4.7      3    150000\n",
              "339              Full-Stack Web Development with React     4.7      3    140000\n",
              "413  How To Create a Website in a Weekend! (Project...     3.3      2    140000\n",
              "858                     Web Applications for Everybody     4.8      3    120000\n",
              "337  Front-End Web UI Frameworks and Tools: Bootstr...     4.8      3     89000\n",
              "526                    Introduction to Web Development     4.7      1     76000\n",
              "336               Front-End Web Development with React     4.7      3     45000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Rj7jcjcEhEC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "48b5a204-6aee-484e-f882-05a0a21c18df"
      },
      "source": [
        "Web_Courses = df[df['title'].str.contains('Web')].sort_values(by=['enrolled'], ascending=False)\n",
        "# 베스트셀러 저장\n",
        "Web_BestSellers = Web_Courses[Web_Courses['enrolled']>100000]\n",
        "Web_BestSellers"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "        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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>850</th>\n",
              "      <td>Using Python to Access Web Data</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>310000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>859</th>\n",
              "      <td>Web Design for Everybody: Basics of Web Develo...</td>\n",
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              "      <td>280000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>HTML, CSS, and Javascript for Web Developers</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>240000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>701</th>\n",
              "      <td>Responsive Website Development and Design</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1</td>\n",
              "      <td>170000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>338</th>\n",
              "      <td>Full Stack Web and Multiplatform Mobile App De...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>339</th>\n",
              "      <td>Full-Stack Web Development with React</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>How To Create a Website in a Weekend! (Project...</td>\n",
              "      <td>3.3</td>\n",
              "      <td>2</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>858</th>\n",
              "      <td>Web Applications for Everybody</td>\n",
              "      <td>4.8</td>\n",
              "      <td>3</td>\n",
              "      <td>120000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "850                    Using Python to Access Web Data     4.8      2    310000\n",
              "859  Web Design for Everybody: Basics of Web Develo...     4.7      1    280000\n",
              "399       HTML, CSS, and Javascript for Web Developers     4.8      2    240000\n",
              "701          Responsive Website Development and Design     4.5      1    170000\n",
              "338  Full Stack Web and Multiplatform Mobile App De...     4.7      3    150000\n",
              "339              Full-Stack Web Development with React     4.7      3    140000\n",
              "413  How To Create a Website in a Weekend! (Project...     3.3      2    140000\n",
              "858                     Web Applications for Everybody     4.8      3    120000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HtUbj5i7FGIl",
        "colab_type": "text"
      },
      "source": [
        "### 2. App"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZT9Wm7iiEsAr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 762
        },
        "outputId": "56d25058-34e7-41e1-d5fb-1ac218ab0bba"
      },
      "source": [
        "# App 강의 확인\n",
        "df[df['title'].str.contains('App')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "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",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Applied Data Science with Python</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>480000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>221</th>\n",
              "      <td>Developing Applications with Google Cloud Plat...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>Machine Learning Foundations: A Case Study App...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>2</td>\n",
              "      <td>240000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Applied Data Science</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>220000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187</th>\n",
              "      <td>Data Analysis and Presentation Skills: the PwC...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>220000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>418</th>\n",
              "      <td>IBM Applied AI</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>220000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Applied Machine Learning in Python</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3</td>\n",
              "      <td>150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>338</th>\n",
              "      <td>Full Stack Web and Multiplatform Mobile App De...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>858</th>\n",
              "      <td>Web Applications for Everybody</td>\n",
              "      <td>4.8</td>\n",
              "      <td>3</td>\n",
              "      <td>120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Android App Development</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1</td>\n",
              "      <td>120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>Econometrics: Methods and Applications</td>\n",
              "      <td>4.7</td>\n",
              "      <td>2</td>\n",
              "      <td>110000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>Applied Plotting, Charting &amp; Data Representati...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3</td>\n",
              "      <td>97000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>873</th>\n",
              "      <td>iOS App Development with Swift</td>\n",
              "      <td>3.9</td>\n",
              "      <td>3</td>\n",
              "      <td>76000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Applied Data Science Capstone</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>42000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>AWS Fundamentals: Building Serverless Applicat...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>27000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>657</th>\n",
              "      <td>Positive Psychology: Applications and Interven...</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1</td>\n",
              "      <td>23000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>321</th>\n",
              "      <td>Fintech: Foundations &amp; Applications of Financi...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>20000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>872</th>\n",
              "      <td>e-Learning Ecologies: Innovative Approaches to...</td>\n",
              "      <td>4.5</td>\n",
              "      <td>2</td>\n",
              "      <td>17000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>445</th>\n",
              "      <td>Instructional Design Foundations and Applications</td>\n",
              "      <td>4.2</td>\n",
              "      <td>2</td>\n",
              "      <td>12000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>628</th>\n",
              "      <td>New Approaches to Countering Terror: Counterin...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>12000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>844</th>\n",
              "      <td>Unity XR: How to Build AR and VR Apps</td>\n",
              "      <td>4.2</td>\n",
              "      <td>1</td>\n",
              "      <td>12000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>222</th>\n",
              "      <td>Developing Applications with Google Cloud Plat...</td>\n",
              "      <td>4.8</td>\n",
              "      <td>3</td>\n",
              "      <td>4000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>776</th>\n",
              "      <td>Swift 5 iOS Application Developer</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1</td>\n",
              "      <td>3900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "56                    Applied Data Science with Python     4.5      3    480000\n",
              "221  Developing Applications with Google Cloud Plat...     4.7      3    300000\n",
              "565  Machine Learning Foundations: A Case Study App...     4.6      2    240000\n",
              "54                                Applied Data Science     4.6      1    220000\n",
              "187  Data Analysis and Presentation Skills: the PwC...     4.6      1    220000\n",
              "418                                     IBM Applied AI     4.6      1    220000\n",
              "57                  Applied Machine Learning in Python     4.6      3    150000\n",
              "338  Full Stack Web and Multiplatform Mobile App De...     4.7      3    150000\n",
              "858                     Web Applications for Everybody     4.8      3    120000\n",
              "49                             Android App Development     4.5      1    120000\n",
              "246             Econometrics: Methods and Applications     4.7      2    110000\n",
              "58   Applied Plotting, Charting & Data Representati...     4.6      3     97000\n",
              "873                     iOS App Development with Swift     3.9      3     76000\n",
              "55                       Applied Data Science Capstone     4.7      3     42000\n",
              "14   AWS Fundamentals: Building Serverless Applicat...     4.7      1     27000\n",
              "657  Positive Psychology: Applications and Interven...     4.8      1     23000\n",
              "321  Fintech: Foundations & Applications of Financi...     4.6      1     20000\n",
              "872  e-Learning Ecologies: Innovative Approaches to...     4.5      2     17000\n",
              "445  Instructional Design Foundations and Applications     4.2      2     12000\n",
              "628  New Approaches to Countering Terror: Counterin...     4.7      1     12000\n",
              "844              Unity XR: How to Build AR and VR Apps     4.2      1     12000\n",
              "222  Developing Applications with Google Cloud Plat...     4.8      3      4000\n",
              "776                  Swift 5 iOS Application Developer     4.5      1      3900"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TGdZ_DF7E0M_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "outputId": "e4e2fb3f-e088-4a49-ee4a-73db778cf6fa"
      },
      "source": [
        "App_Courses = df[df['title'].str.contains('App')].sort_values(by=['enrolled'], ascending=False)\n",
        "# 베스트셀러 저장\n",
        "App_BestSellers = App_Courses[App_Courses['enrolled']>100000]\n",
        "App_BestSellers"
      ],
      "execution_count": null,
      "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",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Applied Data Science with Python</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>480000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>221</th>\n",
              "      <td>Developing Applications with Google Cloud Plat...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>Machine Learning Foundations: A Case Study App...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>2</td>\n",
              "      <td>240000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Applied Data Science</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>220000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187</th>\n",
              "      <td>Data Analysis and Presentation Skills: the PwC...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>220000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>418</th>\n",
              "      <td>IBM Applied AI</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>220000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Applied Machine Learning in Python</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3</td>\n",
              "      <td>150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>338</th>\n",
              "      <td>Full Stack Web and Multiplatform Mobile App De...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>858</th>\n",
              "      <td>Web Applications for Everybody</td>\n",
              "      <td>4.8</td>\n",
              "      <td>3</td>\n",
              "      <td>120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Android App Development</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1</td>\n",
              "      <td>120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>Econometrics: Methods and Applications</td>\n",
              "      <td>4.7</td>\n",
              "      <td>2</td>\n",
              "      <td>110000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "56                    Applied Data Science with Python     4.5      3    480000\n",
              "221  Developing Applications with Google Cloud Plat...     4.7      3    300000\n",
              "565  Machine Learning Foundations: A Case Study App...     4.6      2    240000\n",
              "54                                Applied Data Science     4.6      1    220000\n",
              "187  Data Analysis and Presentation Skills: the PwC...     4.6      1    220000\n",
              "418                                     IBM Applied AI     4.6      1    220000\n",
              "57                  Applied Machine Learning in Python     4.6      3    150000\n",
              "338  Full Stack Web and Multiplatform Mobile App De...     4.7      3    150000\n",
              "858                     Web Applications for Everybody     4.8      3    120000\n",
              "49                             Android App Development     4.5      1    120000\n",
              "246             Econometrics: Methods and Applications     4.7      2    110000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SAUgZueNFIMs",
        "colab_type": "text"
      },
      "source": [
        "### 3. Algorithm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n9V3jwviE-1d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "4ff1df3e-fc6d-4997-829f-7188eb06cee4"
      },
      "source": [
        "# Algorithm 데이터 확인\n",
        "df[df['title'].str.contains('Algorithm')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "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",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Algorithmic Toolbox</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>220000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Algorithms</td>\n",
              "      <td>4.8</td>\n",
              "      <td>3</td>\n",
              "      <td>150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>238</th>\n",
              "      <td>Divide and Conquer, Sorting and Searching, and...</td>\n",
              "      <td>4.8</td>\n",
              "      <td>3</td>\n",
              "      <td>130000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Algorithms for Battery Management Systems</td>\n",
              "      <td>4.8</td>\n",
              "      <td>3</td>\n",
              "      <td>14000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "37                                 Algorithmic Toolbox     4.7      3    220000\n",
              "38                                          Algorithms     4.8      3    150000\n",
              "238  Divide and Conquer, Sorting and Searching, and...     4.8      3    130000\n",
              "39           Algorithms for Battery Management Systems     4.8      3     14000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H4u24kmLFNmA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "821ddd68-ebdb-4487-853c-b409173f61ae"
      },
      "source": [
        "Algorithm_Courses = df[df['title'].str.contains('Algorithm')].sort_values(by=['enrolled'], ascending=False)\n",
        "# 베스트셀러 저장\n",
        "Algorithm_BestSellers = Algorithm_Courses[Algorithm_Courses['enrolled']>100000]\n",
        "Algorithm_BestSellers"
      ],
      "execution_count": null,
      "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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Algorithmic Toolbox</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>220000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Algorithms</td>\n",
              "      <td>4.8</td>\n",
              "      <td>3</td>\n",
              "      <td>150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>238</th>\n",
              "      <td>Divide and Conquer, Sorting and Searching, and...</td>\n",
              "      <td>4.8</td>\n",
              "      <td>3</td>\n",
              "      <td>130000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "37                                 Algorithmic Toolbox     4.7      3    220000\n",
              "38                                          Algorithms     4.8      3    150000\n",
              "238  Divide and Conquer, Sorting and Searching, and...     4.8      3    130000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jg5tF1q-FtT0",
        "colab_type": "text"
      },
      "source": [
        "### 4. Computer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uBz8u4OtF6QJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "outputId": "32aa001a-d84c-4be4-91b4-b7bd3ee4dfe3"
      },
      "source": [
        "# Computer 데이터 확인\n",
        "df[df['title'].str.contains('Computer')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>794</th>\n",
              "      <td>The Bits and Bytes of Computer Networking</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>130000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>Build a Modern Computer from First Principles:...</td>\n",
              "      <td>4.9</td>\n",
              "      <td>2</td>\n",
              "      <td>95000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>489</th>\n",
              "      <td>Introduction to Discrete Mathematics for Compu...</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1</td>\n",
              "      <td>75000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>480</th>\n",
              "      <td>Introduction to Computer Science and Programming</td>\n",
              "      <td>4.3</td>\n",
              "      <td>1</td>\n",
              "      <td>32000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>Computer Security and Systems Management</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>27000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Accelerated Computer Science Fundamentals</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>22000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>479</th>\n",
              "      <td>Introduction to Computer Programming</td>\n",
              "      <td>4.3</td>\n",
              "      <td>1</td>\n",
              "      <td>20000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "794          The Bits and Bytes of Computer Networking     4.7      1    130000\n",
              "103  Build a Modern Computer from First Principles:...     4.9      2     95000\n",
              "489  Introduction to Discrete Mathematics for Compu...     4.4      1     75000\n",
              "480   Introduction to Computer Science and Programming     4.3      1     32000\n",
              "152           Computer Security and Systems Management     4.6      1     27000\n",
              "19           Accelerated Computer Science Fundamentals     4.7      3     22000\n",
              "479               Introduction to Computer Programming     4.3      1     20000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GvKMAcDxF-wF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "438066de-3004-45b0-cf26-5013ae9f2f71"
      },
      "source": [
        "Computer_Courses = df[df['title'].str.contains('Computer')].sort_values(by=['enrolled'], ascending=False)\n",
        "# 베스트셀러 저장\n",
        "Computer_BestSellers = Computer_Courses[Computer_Courses['enrolled']>100000]\n",
        "Computer_BestSellers"
      ],
      "execution_count": null,
      "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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>794</th>\n",
              "      <td>The Bits and Bytes of Computer Networking</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>130000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         title  rating  level  enrolled\n",
              "794  The Bits and Bytes of Computer Networking     4.7      1    130000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E4I622CIGfNz",
        "colab_type": "text"
      },
      "source": [
        "### 5. Hacking"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qpEMVY2wGkCy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "outputId": "96e22521-55bc-4a30-907c-1f829dfff0b3"
      },
      "source": [
        "# Hacking 데이터 확인\n",
        "df[df['title'].str.contains('Hacking')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [title, rating, level, enrolled]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PbvBsgRUGpGE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "outputId": "588c5704-93f1-4e63-ed82-03ae6902bd22"
      },
      "source": [
        "# 유관 데이터 확인\n",
        "df[df['title'].str.contains('Security')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "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",
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              "\n",
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              "        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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>721</th>\n",
              "      <td>Security in Google Cloud Platform</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>422</th>\n",
              "      <td>IT Security: Defense against the digital dark ...</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1</td>\n",
              "      <td>62000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>435</th>\n",
              "      <td>Information Security: Context and Introduction</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>32000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>483</th>\n",
              "      <td>Introduction to Cyber Security</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>32000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>Computer Security and Systems Management</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>27000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>624</th>\n",
              "      <td>Networking and Security Architecture with VMwa...</td>\n",
              "      <td>4.8</td>\n",
              "      <td>3</td>\n",
              "      <td>17000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>720</th>\n",
              "      <td>Security &amp; Safety Challenges in a Globalized W...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>14000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>AWS Fundamentals: Addressing Security Risk</td>\n",
              "      <td>4.3</td>\n",
              "      <td>1</td>\n",
              "      <td>11000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>410</th>\n",
              "      <td>Homeland Security and Cybersecurity</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>5500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(ISC)² Systems Security Certified Practitioner...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>5300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>459</th>\n",
              "      <td>International Security Management</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1</td>\n",
              "      <td>2200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "721                  Security in Google Cloud Platform     4.7      3    300000\n",
              "422  IT Security: Defense against the digital dark ...     4.8      1     62000\n",
              "435     Information Security: Context and Introduction     4.6      1     32000\n",
              "483                     Introduction to Cyber Security     4.7      1     32000\n",
              "152           Computer Security and Systems Management     4.6      1     27000\n",
              "624  Networking and Security Architecture with VMwa...     4.8      3     17000\n",
              "720  Security & Safety Challenges in a Globalized W...     4.7      1     14000\n",
              "13          AWS Fundamentals: Addressing Security Risk     4.3      1     11000\n",
              "410                Homeland Security and Cybersecurity     4.7      1      5500\n",
              "0    (ISC)² Systems Security Certified Practitioner...     4.7      1      5300\n",
              "459                  International Security Management     4.5      1      2200"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yDk1aE0uGvfn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "7b909b4c-7d4c-4e9f-cac9-00c8b6cfc5da"
      },
      "source": [
        "Security_Courses = df[df['title'].str.contains('Security')].sort_values(by=['enrolled'], ascending=False)\n",
        "# 베스트셀러 저장\n",
        "Security_BestSellers = Security_Courses[Security_Courses['enrolled']>100000]\n",
        "Security_BestSellers"
      ],
      "execution_count": null,
      "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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>721</th>\n",
              "      <td>Security in Google Cloud Platform</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>300000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                 title  rating  level  enrolled\n",
              "721  Security in Google Cloud Platform     4.7      3    300000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uv70tkirHPhF",
        "colab_type": "text"
      },
      "source": [
        "### 6. Finance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DXPfJmAdHc3l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "outputId": "4b007201-e94e-4dac-ddf8-bb7ffb891f94"
      },
      "source": [
        "# Finance 데이터 확인\n",
        "df[df['title'].str.contains('Finance')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>306</th>\n",
              "      <td>Finance &amp; Quantitative Modeling for Analysts</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1</td>\n",
              "      <td>260000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>481</th>\n",
              "      <td>Introduction to Corporate Finance</td>\n",
              "      <td>4.6</td>\n",
              "      <td>2</td>\n",
              "      <td>130000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307</th>\n",
              "      <td>Finance for Non-Finance Professionals</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1</td>\n",
              "      <td>69000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>Corporate Finance Essentials</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>56000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>Behavioral Finance</td>\n",
              "      <td>4.4</td>\n",
              "      <td>2</td>\n",
              "      <td>55000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>285</th>\n",
              "      <td>Essentials of Corporate Finance</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3</td>\n",
              "      <td>54000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>Machine Learning and Reinforcement Learning in...</td>\n",
              "      <td>3.7</td>\n",
              "      <td>3</td>\n",
              "      <td>29000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>326</th>\n",
              "      <td>Foundational Finance for Strategic Decision Ma...</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1</td>\n",
              "      <td>14000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>304</th>\n",
              "      <td>FinTech: Finance Industry Transformation and R...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>13000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>308</th>\n",
              "      <td>Finance for Non-Financial Managers</td>\n",
              "      <td>4.4</td>\n",
              "      <td>3</td>\n",
              "      <td>12000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>838</th>\n",
              "      <td>Understanding Modern Finance</td>\n",
              "      <td>4.4</td>\n",
              "      <td>3</td>\n",
              "      <td>11000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>266</th>\n",
              "      <td>Entrepreneurial Finance: Strategy and Innovation</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>7900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>768</th>\n",
              "      <td>Supply Chain Finance and Blockchain Technology</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3</td>\n",
              "      <td>4900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "306       Finance & Quantitative Modeling for Analysts     4.5      1    260000\n",
              "481                  Introduction to Corporate Finance     4.6      2    130000\n",
              "307              Finance for Non-Finance Professionals     4.8      1     69000\n",
              "165                       Corporate Finance Essentials     4.8      2     56000\n",
              "87                                  Behavioral Finance     4.4      2     55000\n",
              "285                    Essentials of Corporate Finance     4.6      3     54000\n",
              "566  Machine Learning and Reinforcement Learning in...     3.7      3     29000\n",
              "326  Foundational Finance for Strategic Decision Ma...     4.8      1     14000\n",
              "304  FinTech: Finance Industry Transformation and R...     4.6      1     13000\n",
              "308                 Finance for Non-Financial Managers     4.4      3     12000\n",
              "838                       Understanding Modern Finance     4.4      3     11000\n",
              "266   Entrepreneurial Finance: Strategy and Innovation     4.5      3      7900\n",
              "768     Supply Chain Finance and Blockchain Technology     4.6      3      4900"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZEsX6rGaHovc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "fdf053bb-7909-487c-b277-524fd8a07339"
      },
      "source": [
        "Finance_Courses = df[df['title'].str.contains('Finance')].sort_values(by=['enrolled'], ascending=False)\n",
        "Finance_BestSellers = Finance_Courses[Finance_Courses['enrolled']>100000]\n",
        "Finance_BestSellers"
      ],
      "execution_count": null,
      "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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>306</th>\n",
              "      <td>Finance &amp; Quantitative Modeling for Analysts</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1</td>\n",
              "      <td>260000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>481</th>\n",
              "      <td>Introduction to Corporate Finance</td>\n",
              "      <td>4.6</td>\n",
              "      <td>2</td>\n",
              "      <td>130000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            title  rating  level  enrolled\n",
              "306  Finance & Quantitative Modeling for Analysts     4.5      1    260000\n",
              "481             Introduction to Corporate Finance     4.6      2    130000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YaCgvT1gH54x",
        "colab_type": "text"
      },
      "source": [
        "### 7. Commerce"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fq6BS1xuIEXi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "outputId": "b8116014-4572-4ce9-b0ca-1afbd7c2c135"
      },
      "source": [
        "# Commerce 데이터 확인\n",
        "df[df['title'].str.contains('Commerce')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "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",
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              "\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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [title, rating, level, enrolled]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dqPriCb4IIbt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "outputId": "d15f8249-3579-479b-bf9a-b5a5f60e06c5"
      },
      "source": [
        "# 유관 데이터 확인\n",
        "df[df['title'].str.contains('Shopping')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [title, rating, level, enrolled]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kmIrburpIR91",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "728d604c-b723-4fe1-b7f7-b9359ae75ad9"
      },
      "source": [
        "# 유관 데이터 확인\n",
        "df[df['title'].str.contains('Business')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>Business Foundations</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>510000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>Excel to MySQL: Analytic Techniques for Business</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>490000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>Business Analytics</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>280000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>291</th>\n",
              "      <td>Excel Skills for Business</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1</td>\n",
              "      <td>240000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>260</th>\n",
              "      <td>English for Business and Entrepreneurship</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1</td>\n",
              "      <td>230000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>293</th>\n",
              "      <td>Excel Skills for Business: Essentials</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1</td>\n",
              "      <td>200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>Business and Financial Modeling</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1</td>\n",
              "      <td>160000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>205</th>\n",
              "      <td>Data Warehousing for Business Intelligence</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>Business English Communication Skills</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>Business Statistics and Analysis</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>110000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>Business Strategy</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>89000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>796</th>\n",
              "      <td>The Business of Music Production</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1</td>\n",
              "      <td>84000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Advanced Business Analytics</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>78000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>Business English: Networking</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>77000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>294</th>\n",
              "      <td>Excel Skills for Business: Intermediate I</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3</td>\n",
              "      <td>76000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>272</th>\n",
              "      <td>Entrepreneurship: Launching an Innovative Busi...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>69000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>Business Technology Management</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1</td>\n",
              "      <td>67000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>327</th>\n",
              "      <td>Foundations of Business Strategy</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1</td>\n",
              "      <td>63000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>755</th>\n",
              "      <td>Strategic Business Analytics</td>\n",
              "      <td>4.3</td>\n",
              "      <td>4</td>\n",
              "      <td>60000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>Machine Learning for Business Professionals</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3</td>\n",
              "      <td>58000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>Brand Management: Aligning Business, Brand and...</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1</td>\n",
              "      <td>57000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>576</th>\n",
              "      <td>Managerial Economics and Business Analysis</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>55000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>Business Writing</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1</td>\n",
              "      <td>48000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>617</th>\n",
              "      <td>Music Business</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1</td>\n",
              "      <td>42000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>292</th>\n",
              "      <td>Excel Skills for Business: Advanced</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>39000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>295</th>\n",
              "      <td>Excel Skills for Business: Intermediate II</td>\n",
              "      <td>4.8</td>\n",
              "      <td>3</td>\n",
              "      <td>39000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>Business English</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>38000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>How to Start Your Own Business</td>\n",
              "      <td>4.1</td>\n",
              "      <td>1</td>\n",
              "      <td>34000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>618</th>\n",
              "      <td>Music Business Foundations</td>\n",
              "      <td>4.9</td>\n",
              "      <td>2</td>\n",
              "      <td>34000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>Business Strategies for A Better World</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>24000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>183</th>\n",
              "      <td>Cybersecurity for Business</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>23000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>795</th>\n",
              "      <td>The Business of Health Care</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>22000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>451</th>\n",
              "      <td>International Business Essentials</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>22000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>228</th>\n",
              "      <td>Digital Business Models</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1</td>\n",
              "      <td>21000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>473</th>\n",
              "      <td>Introduction to Business Analytics: Communicat...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>14000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>Business Model Innovation</td>\n",
              "      <td>4.7</td>\n",
              "      <td>2</td>\n",
              "      <td>13000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>Business Transformation with Google Cloud</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>10000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>756</th>\n",
              "      <td>Strategic Business Management - Microeconomics</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>9500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>386</th>\n",
              "      <td>Global Trends for Business and Society</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>7600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>Become a Sustainable Business Change Agent</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>4200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>710</th>\n",
              "      <td>SAS Visual Business Analytics</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>2600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "113                               Business Foundations     4.7      1    510000\n",
              "296   Excel to MySQL: Analytic Techniques for Business     4.6      1    490000\n",
              "109                                 Business Analytics     4.6      1    280000\n",
              "291                          Excel Skills for Business     4.9      1    240000\n",
              "260          English for Business and Entrepreneurship     4.8      1    230000\n",
              "293              Excel Skills for Business: Essentials     4.9      1    200000\n",
              "121                    Business and Financial Modeling     4.5      1    160000\n",
              "205         Data Warehousing for Business Intelligence     4.5      4    140000\n",
              "111              Business English Communication Skills     4.7      3    120000\n",
              "115                   Business Statistics and Analysis     4.7      1    110000\n",
              "117                                  Business Strategy     4.7      1     89000\n",
              "796                   The Business of Music Production     4.8      1     84000\n",
              "26                         Advanced Business Analytics     4.5      3     78000\n",
              "112                       Business English: Networking     4.7      3     77000\n",
              "294          Excel Skills for Business: Intermediate I     4.9      3     76000\n",
              "272  Entrepreneurship: Launching an Innovative Busi...     4.6      1     69000\n",
              "118                     Business Technology Management     4.5      1     67000\n",
              "327                   Foundations of Business Strategy     4.8      1     63000\n",
              "755                       Strategic Business Analytics     4.3      4     60000\n",
              "568        Machine Learning for Business Professionals     4.6      3     58000\n",
              "99   Brand Management: Aligning Business, Brand and...     4.9      1     57000\n",
              "576         Managerial Economics and Business Analysis     4.7      1     55000\n",
              "120                                   Business Writing     4.8      1     48000\n",
              "617                                     Music Business     4.8      1     42000\n",
              "292                Excel Skills for Business: Advanced     4.7      3     39000\n",
              "295         Excel Skills for Business: Intermediate II     4.8      3     39000\n",
              "110                                   Business English     4.7      3     38000\n",
              "414                     How to Start Your Own Business     4.1      1     34000\n",
              "618                         Music Business Foundations     4.9      2     34000\n",
              "116             Business Strategies for A Better World     4.7      1     24000\n",
              "183                         Cybersecurity for Business     4.6      1     23000\n",
              "795                        The Business of Health Care     4.6      1     22000\n",
              "451                  International Business Essentials     4.5      3     22000\n",
              "228                            Digital Business Models     4.5      1     21000\n",
              "473  Introduction to Business Analytics: Communicat...     4.7      3     14000\n",
              "114                          Business Model Innovation     4.7      2     13000\n",
              "119          Business Transformation with Google Cloud     4.7      1     10000\n",
              "756     Strategic Business Management - Microeconomics     4.7      1      9500\n",
              "386             Global Trends for Business and Society     4.8      2      7600\n",
              "84          Become a Sustainable Business Change Agent     4.7      3      4200\n",
              "710                      SAS Visual Business Analytics     4.6      1      2600"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AGs2IU7hIYZv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "361694c5-f9b9-4caf-a9eb-319b53eaea3a"
      },
      "source": [
        "Business_Courses = df[df['title'].str.contains('Business')].sort_values(by=['enrolled'], ascending=False)\n",
        "# 베스트셀러 저장\n",
        "Business_BestSellers = Business_Courses[Business_Courses['enrolled']>100000]\n",
        "Business_BestSellers"
      ],
      "execution_count": null,
      "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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>Business Foundations</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>510000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>Excel to MySQL: Analytic Techniques for Business</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>490000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>Business Analytics</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>280000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>291</th>\n",
              "      <td>Excel Skills for Business</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1</td>\n",
              "      <td>240000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>260</th>\n",
              "      <td>English for Business and Entrepreneurship</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1</td>\n",
              "      <td>230000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>293</th>\n",
              "      <td>Excel Skills for Business: Essentials</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1</td>\n",
              "      <td>200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>Business and Financial Modeling</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1</td>\n",
              "      <td>160000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>205</th>\n",
              "      <td>Data Warehousing for Business Intelligence</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>Business English Communication Skills</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>Business Statistics and Analysis</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>110000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                title  rating  level  enrolled\n",
              "113                              Business Foundations     4.7      1    510000\n",
              "296  Excel to MySQL: Analytic Techniques for Business     4.6      1    490000\n",
              "109                                Business Analytics     4.6      1    280000\n",
              "291                         Excel Skills for Business     4.9      1    240000\n",
              "260         English for Business and Entrepreneurship     4.8      1    230000\n",
              "293             Excel Skills for Business: Essentials     4.9      1    200000\n",
              "121                   Business and Financial Modeling     4.5      1    160000\n",
              "205        Data Warehousing for Business Intelligence     4.5      4    140000\n",
              "111             Business English Communication Skills     4.7      3    120000\n",
              "115                  Business Statistics and Analysis     4.7      1    110000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "giUs4lT3I1qB",
        "colab_type": "text"
      },
      "source": [
        "### 8. Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FtH30Uq0IrqV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "590b0cab-446e-47a2-bd4c-9d51f06150e4"
      },
      "source": [
        "# Neural Network 데이터 확인\n",
        "df[df['title'].str.contains('Neural')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "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",
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              "\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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>626</th>\n",
              "      <td>Neural Networks and Deep Learning</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3</td>\n",
              "      <td>630000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>427</th>\n",
              "      <td>Improving Deep Neural Networks: Hyperparameter...</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1</td>\n",
              "      <td>270000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>Convolutional Neural Networks</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3</td>\n",
              "      <td>240000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>Convolutional Neural Networks in TensorFlow</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>46000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "626                  Neural Networks and Deep Learning     4.9      3    630000\n",
              "427  Improving Deep Neural Networks: Hyperparameter...     4.9      1    270000\n",
              "162                      Convolutional Neural Networks     4.9      3    240000\n",
              "163        Convolutional Neural Networks in TensorFlow     4.7      3     46000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eJ4KPM5VI6zI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "outputId": "17c09889-e27d-4c6c-afcd-352c84b9c66a"
      },
      "source": [
        "# 유관 데이터 확인\n",
        "df[df['title'].str.contains('CNN')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [title, rating, level, enrolled]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "emRHOp98I_3s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "outputId": "c1502347-de44-4047-d1af-949556d3fd0a"
      },
      "source": [
        "# 유관 데이터 확인\n",
        "df[df['title'].str.contains('RNN')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [title, rating, level, enrolled]\n",
              "Index: []"
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          "metadata": {
            "tags": []
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          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QMHZiHJCJB0x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "78ddfff8-7092-4151-8d0d-8c9f7705c371"
      },
      "source": [
        "# 유관 데이터 확인\n",
        "df[df['title'].str.contains('Reinforcement')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>Machine Learning and Reinforcement Learning in...</td>\n",
              "      <td>3.7</td>\n",
              "      <td>3</td>\n",
              "      <td>29000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>695</th>\n",
              "      <td>Reinforcement Learning</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>23000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>353</th>\n",
              "      <td>Fundamentals of Reinforcement Learning</td>\n",
              "      <td>4.8</td>\n",
              "      <td>3</td>\n",
              "      <td>22000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "566  Machine Learning and Reinforcement Learning in...     3.7      3     29000\n",
              "695                             Reinforcement Learning     4.7      3     23000\n",
              "353             Fundamentals of Reinforcement Learning     4.8      3     22000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qdfvZS5JJDux",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "d058b50b-d4a0-494a-e668-57cffb25a018"
      },
      "source": [
        "# Neural Network 데이터 확인\n",
        "Neural_Courses = df[df['title'].str.contains('Neural')].sort_values(by=['enrolled'], ascending=False)\n",
        "# 베스트셀러 저장\n",
        "Neural_BestSellers = Neural_Courses[Neural_Courses['enrolled']>100000]\n",
        "Neural_BestSellers"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>626</th>\n",
              "      <td>Neural Networks and Deep Learning</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3</td>\n",
              "      <td>630000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>427</th>\n",
              "      <td>Improving Deep Neural Networks: Hyperparameter...</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1</td>\n",
              "      <td>270000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>Convolutional Neural Networks</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3</td>\n",
              "      <td>240000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "626                  Neural Networks and Deep Learning     4.9      3    630000\n",
              "427  Improving Deep Neural Networks: Hyperparameter...     4.9      1    270000\n",
              "162                      Convolutional Neural Networks     4.9      3    240000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z0vcwjXQJH1M",
        "colab_type": "text"
      },
      "source": [
        "### 9. Machine Learning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AAIeGQRKJY-J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 793
        },
        "outputId": "4d72dbdf-ff98-4cce-aba8-854df8c1ba94"
      },
      "source": [
        "# Machine Learning 데이터 확인\n",
        "df[df['title'].str.contains('Machine')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>563</th>\n",
              "      <td>Machine Learning</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3</td>\n",
              "      <td>290000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>Machine Learning Foundations: A Case Study App...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>2</td>\n",
              "      <td>240000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>763</th>\n",
              "      <td>Structuring Machine Learning Projects</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1</td>\n",
              "      <td>220000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>Data Science: Statistics and Machine Learning</td>\n",
              "      <td>4.4</td>\n",
              "      <td>3</td>\n",
              "      <td>210000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Advanced Machine Learning</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4</td>\n",
              "      <td>190000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Applied Machine Learning in Python</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3</td>\n",
              "      <td>150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>Mathematics for Machine Learning</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>520</th>\n",
              "      <td>Introduction to TensorFlow for Artificial Inte...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>Mathematics for Machine Learning: Linear Algebra</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>570</th>\n",
              "      <td>Machine Learning with Python</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>Data Engineering, Big Data, and Machine Learni...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3</td>\n",
              "      <td>120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>389</th>\n",
              "      <td>Google Cloud Platform Big Data and Machine Lea...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3</td>\n",
              "      <td>120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>375</th>\n",
              "      <td>Getting Started with AWS Machine Learning</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>73000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>571</th>\n",
              "      <td>Machine Learning with TensorFlow on Google Clo...</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>72000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>Machine Learning for Business Professionals</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3</td>\n",
              "      <td>58000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>Mathematics for Machine Learning: Multivariate...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>51000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Advanced Machine Learning with TensorFlow on G...</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4</td>\n",
              "      <td>35000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>Mathematics for Machine Learning: PCA</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3</td>\n",
              "      <td>33000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>Machine Learning and Reinforcement Learning in...</td>\n",
              "      <td>3.7</td>\n",
              "      <td>3</td>\n",
              "      <td>29000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>Machine Learning for All</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>19000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>569</th>\n",
              "      <td>Machine Learning for Trading</td>\n",
              "      <td>3.9</td>\n",
              "      <td>3</td>\n",
              "      <td>15000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>532</th>\n",
              "      <td>Investment Management with Python and Machine ...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>14000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>522</th>\n",
              "      <td>Introduction to Trading, Machine Learning &amp; GCP</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3</td>\n",
              "      <td>13000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>Machine Learning</td>\n",
              "      <td>4.9</td>\n",
              "      <td>2</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "563                                   Machine Learning     4.6      3    290000\n",
              "565  Machine Learning Foundations: A Case Study App...     4.6      2    240000\n",
              "763              Structuring Machine Learning Projects     4.8      1    220000\n",
              "200      Data Science: Statistics and Machine Learning     4.4      3    210000\n",
              "28                           Advanced Machine Learning     4.5      4    190000\n",
              "57                  Applied Machine Learning in Python     4.6      3    150000\n",
              "595                   Mathematics for Machine Learning     4.6      1    150000\n",
              "520  Introduction to TensorFlow for Artificial Inte...     4.7      3    150000\n",
              "596   Mathematics for Machine Learning: Linear Algebra     4.7      1    140000\n",
              "570                       Machine Learning with Python     4.7      3    120000\n",
              "193  Data Engineering, Big Data, and Machine Learni...     4.6      3    120000\n",
              "389  Google Cloud Platform Big Data and Machine Lea...     4.6      3    120000\n",
              "375          Getting Started with AWS Machine Learning     4.5      3     73000\n",
              "571  Machine Learning with TensorFlow on Google Clo...     4.5      3     72000\n",
              "568        Machine Learning for Business Professionals     4.6      3     58000\n",
              "597  Mathematics for Machine Learning: Multivariate...     4.7      1     51000\n",
              "29   Advanced Machine Learning with TensorFlow on G...     4.5      4     35000\n",
              "598              Mathematics for Machine Learning: PCA     4.0      3     33000\n",
              "566  Machine Learning and Reinforcement Learning in...     3.7      3     29000\n",
              "567                           Machine Learning for All     4.6      1     19000\n",
              "569                       Machine Learning for Trading     3.9      3     15000\n",
              "532  Investment Management with Python and Machine ...     4.6      1     14000\n",
              "522    Introduction to Trading, Machine Learning & GCP     4.0      3     13000\n",
              "564                                   Machine Learning     4.9      2        32"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rzmUNgG6JcU4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "outputId": "653e4a9f-1bb7-4a0f-973a-70e330755186"
      },
      "source": [
        "Machine_Courses = df[df['title'].str.contains('Machine')].sort_values(by=['enrolled'], ascending=False)\n",
        "Machine_BestSellers = Machine_Courses[Machine_Courses['enrolled']>100000]\n",
        "Machine_BestSellers"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "      <th>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>563</th>\n",
              "      <td>Machine Learning</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3</td>\n",
              "      <td>290000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>Machine Learning Foundations: A Case Study App...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>2</td>\n",
              "      <td>240000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>763</th>\n",
              "      <td>Structuring Machine Learning Projects</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1</td>\n",
              "      <td>220000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>Data Science: Statistics and Machine Learning</td>\n",
              "      <td>4.4</td>\n",
              "      <td>3</td>\n",
              "      <td>210000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Advanced Machine Learning</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4</td>\n",
              "      <td>190000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Applied Machine Learning in Python</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3</td>\n",
              "      <td>150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>Mathematics for Machine Learning</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>520</th>\n",
              "      <td>Introduction to TensorFlow for Artificial Inte...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>Mathematics for Machine Learning: Linear Algebra</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>570</th>\n",
              "      <td>Machine Learning with Python</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>Data Engineering, Big Data, and Machine Learni...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3</td>\n",
              "      <td>120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>389</th>\n",
              "      <td>Google Cloud Platform Big Data and Machine Lea...</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3</td>\n",
              "      <td>120000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "563                                   Machine Learning     4.6      3    290000\n",
              "565  Machine Learning Foundations: A Case Study App...     4.6      2    240000\n",
              "763              Structuring Machine Learning Projects     4.8      1    220000\n",
              "200      Data Science: Statistics and Machine Learning     4.4      3    210000\n",
              "28                           Advanced Machine Learning     4.5      4    190000\n",
              "57                  Applied Machine Learning in Python     4.6      3    150000\n",
              "595                   Mathematics for Machine Learning     4.6      1    150000\n",
              "520  Introduction to TensorFlow for Artificial Inte...     4.7      3    150000\n",
              "596   Mathematics for Machine Learning: Linear Algebra     4.7      1    140000\n",
              "570                       Machine Learning with Python     4.7      3    120000\n",
              "193  Data Engineering, Big Data, and Machine Learni...     4.6      3    120000\n",
              "389  Google Cloud Platform Big Data and Machine Lea...     4.6      3    120000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4KU1prqJJn5H",
        "colab_type": "text"
      },
      "source": [
        "### 10. Deep Learning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y7MSsyHwJrb2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "906b9fed-9863-49e3-c9be-83d8341388dc"
      },
      "source": [
        "# Deep Learning 데이터 확인\n",
        "df[df['title'].str.contains('Deep')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "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>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>211</th>\n",
              "      <td>Deep Learning</td>\n",
              "      <td>4.8</td>\n",
              "      <td>3</td>\n",
              "      <td>690000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>626</th>\n",
              "      <td>Neural Networks and Deep Learning</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3</td>\n",
              "      <td>630000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>427</th>\n",
              "      <td>Improving Deep Neural Networks: Hyperparameter...</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1</td>\n",
              "      <td>270000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>520</th>\n",
              "      <td>Introduction to TensorFlow for Artificial Inte...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>150000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "211                                      Deep Learning     4.8      3    690000\n",
              "626                  Neural Networks and Deep Learning     4.9      3    630000\n",
              "427  Improving Deep Neural Networks: Hyperparameter...     4.9      1    270000\n",
              "520  Introduction to TensorFlow for Artificial Inte...     4.7      3    150000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1tXvLDskJuWT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 모두 10만이 넘는 수강생이므로 BestSellers로 저장\n",
        "Deep_BestSellers = df[df['title'].str.contains('Deep')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VY5FBZ2hiR0F",
        "colab_type": "text"
      },
      "source": [
        "# 업데이트 중"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z0k7f9wdh3X0",
        "colab_type": "text"
      },
      "source": [
        "### 11. AWS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "erWBkEEphuA5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "outputId": "bf990e2b-6865-4f00-b329-9d4ee8ddcacb"
      },
      "source": [
        "df[df['title'].str.contains('AWS')].sort_values(by=['enrolled'], ascending=False)"
      ],
      "execution_count": null,
      "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",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>AWS Fundamentals</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>130000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>AWS Fundamentals: Going Cloud-Native</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>110000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>375</th>\n",
              "      <td>Getting Started with AWS Machine Learning</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>73000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>390</th>\n",
              "      <td>Google Cloud Platform Fundamentals for AWS Pro...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>36000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>AWS Fundamentals: Building Serverless Applicat...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>27000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>AWS Fundamentals: Migrating to the Cloud</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3</td>\n",
              "      <td>13000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>AWS Fundamentals: Addressing Security Risk</td>\n",
              "      <td>4.3</td>\n",
              "      <td>1</td>\n",
              "      <td>11000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  rating  level  enrolled\n",
              "12                                    AWS Fundamentals     4.6      1    130000\n",
              "15                AWS Fundamentals: Going Cloud-Native     4.7      1    110000\n",
              "375          Getting Started with AWS Machine Learning     4.5      3     73000\n",
              "390  Google Cloud Platform Fundamentals for AWS Pro...     4.7      3     36000\n",
              "14   AWS Fundamentals: Building Serverless Applicat...     4.7      1     27000\n",
              "16            AWS Fundamentals: Migrating to the Cloud     4.5      3     13000\n",
              "13          AWS Fundamentals: Addressing Security Risk     4.3      1     11000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ngkavs2rh1UY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "a31d0a04-42c7-4c8f-cfe8-79411442706c"
      },
      "source": [
        "AWS_Courses = df[df['title'].str.contains('AWS')].sort_values(by=['enrolled'], ascending=False)\n",
        "AWS_BestSellers = AWS_Courses[AWS_Courses['enrolled']>100000]\n",
        "AWS_BestSellers"
      ],
      "execution_count": null,
      "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",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>enrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>AWS Fundamentals</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>130000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>AWS Fundamentals: Going Cloud-Native</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1</td>\n",
              "      <td>110000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                   title  rating  level  enrolled\n",
              "12                      AWS Fundamentals     4.6      1    130000\n",
              "15  AWS Fundamentals: Going Cloud-Native     4.7      1    110000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    }
  ]
}