OTT_analysis.ipynb

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        "<a href=\"https://colab.research.google.com/github/sunfishes/Data-Analysys-Programming-2/blob/main/OTT_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "import pandas as pd"
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      "outputs": []
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      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/오티티분석/ott_analysis.csv\", encoding=\"euc-kr\")"
      ],
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              "   serial number  year   nation classification division  number_of_cases  \\\n",
              "0              1  2019    India        country      all              322   \n",
              "1              2  2019   Turkey        country      all              321   \n",
              "2              3  2019   Canada        country      all              281   \n",
              "3              4  2019  Hungary        country      all              223   \n",
              "4              5  2019    India            sex     male              159   \n",
              "\n",
              "   drama  variety  music  news  ...  comedy  cultureart  quizeshow  \\\n",
              "0    9.6      2.5    9.3   9.9  ...    10.6         0.0        0.0   \n",
              "1    4.4      3.1    2.8   7.8  ...     9.0         0.0        2.5   \n",
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              "3    5.4      0.9    9.0   2.2  ...    14.3         0.0        0.0   \n",
              "4    7.5      3.1   10.7  10.1  ...    10.1         0.0        0.0   \n",
              "\n",
              "   quizeshow.1  kids  reality  religion  lifestyle  series  etc  \n",
              "0          2.2   1.2      8.4       0.3        0.0     0.0  0.3  \n",
              "1          1.2   5.9      4.0       0.6        0.0     0.9  0.6  \n",
              "2          2.8   3.2      5.0       0.7        0.0     0.4  0.4  \n",
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              "4          2.5   1.9      4.4       0.0        0.0     0.0  0.6  \n",
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          "metadata": {},
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        "df.info()"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 110 entries, 0 to 109\n",
            "Data columns (total 25 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   serial number    110 non-null    int64  \n",
            " 1   year             110 non-null    int64  \n",
            " 2   nation           110 non-null    object \n",
            " 3   classification   110 non-null    object \n",
            " 4   division         110 non-null    object \n",
            " 5   number_of_cases  110 non-null    int64  \n",
            " 6   drama            110 non-null    float64\n",
            " 7   variety          110 non-null    float64\n",
            " 8   music            110 non-null    float64\n",
            " 9   news             110 non-null    float64\n",
            " 10  education        110 non-null    float64\n",
            " 11  movie            110 non-null    float64\n",
            " 12  sports           110 non-null    float64\n",
            " 13  dacumentary      110 non-null    float64\n",
            " 14  animation        110 non-null    float64\n",
            " 15  comedy           110 non-null    float64\n",
            " 16  cultureart       110 non-null    float64\n",
            " 17  quizeshow        110 non-null    float64\n",
            " 18  quizeshow.1      110 non-null    float64\n",
            " 19  kids             110 non-null    float64\n",
            " 20  reality          110 non-null    float64\n",
            " 21  religion         110 non-null    float64\n",
            " 22  lifestyle        110 non-null    float64\n",
            " 23  series           110 non-null    float64\n",
            " 24  etc              110 non-null    float64\n",
            "dtypes: float64(19), int64(3), object(3)\n",
            "memory usage: 21.6+ KB\n"
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    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 국가별로 장르 선호도의 평균 계산\n",
        "genre_columns = ['drama', 'variety', 'music', 'news', 'education', 'movie', 'sports',\n",
        "                 'dacumentary', 'animation', 'comedy', 'cultureart', 'quizeshow',\n",
        "                 'quizeshow.1', 'kids', 'reality', 'religion', 'lifestyle', 'series', 'etc']\n",
        "\n",
        "df['average_genre_preference'] = df[genre_columns].mean(axis=1)\n",
        "\n",
        "# 국가별 평균 장르 선호도 시각화\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.barplot(x='nation', y='average_genre_preference', data=df)\n",
        "plt.title('Average Genre Preference by Nation')\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "O-NJq_V3j0J5",
        "outputId": "0454b21a-a159-469b-ec2f-81266b3c8228"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "수치형 변수(각 ott별 장르)의 통계를 나타내서 확인하였습니다."
      ],
      "metadata": {
        "id": "vD6U8098AjfS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# 수치형 변수들의 통계 정보\n",
        "numeric_stats = df[['drama', 'variety', 'music', 'news', 'education', 'movie', 'sports', 'dacumentary', 'animation',\n",
        "                    'comedy', 'cultureart', 'quizeshow', 'quizeshow.1', 'kids', 'reality', 'religion',\n",
        "                    'lifestyle', 'series', 'etc']].describe()\n",
        "\n",
        "print(numeric_stats)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_UnLOqz28exQ",
        "outputId": "60caf59b-e772-405c-f1ff-2b73fd9c8dc4"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            drama     variety       music        news   education       movie  \\\n",
            "count  110.000000  110.000000  110.000000  110.000000  110.000000  110.000000   \n",
            "mean    10.883636    3.843636    5.991818    6.044545    2.290909   26.672727   \n",
            "std      6.313834    4.969439    3.615566    3.419845    1.833458   12.027475   \n",
            "min      0.000000    0.000000    0.000000    0.000000    0.000000    1.000000   \n",
            "25%      5.325000    1.200000    3.425000    4.000000    1.025000   19.550000   \n",
            "50%     10.800000    2.550000    4.900000    5.850000    2.000000   27.050000   \n",
            "75%     15.375000    3.925000    8.075000    7.800000    3.100000   35.600000   \n",
            "max     28.600000   20.800000   19.400000   17.900000    7.100000   57.100000   \n",
            "\n",
            "           sports  dacumentary   animation      comedy  cultureart  \\\n",
            "count  110.000000   110.000000  110.000000  110.000000  110.000000   \n",
            "mean     8.388182     5.028182    2.941818   11.550000    0.644545   \n",
            "std      5.199166     3.252497    2.800504    6.249694    1.299371   \n",
            "min      0.000000     0.000000    0.000000    0.000000    0.000000   \n",
            "25%      4.600000     2.050000    1.200000    7.825000    0.000000   \n",
            "50%      8.400000     5.300000    2.100000   10.750000    0.000000   \n",
            "75%     11.775000     7.375000    4.100000   14.525000    0.700000   \n",
            "max     24.400000    13.800000   16.700000   33.300000    6.300000   \n",
            "\n",
            "        quizeshow  quizeshow.1        kids     reality    religion  \\\n",
            "count  110.000000    110.00000  110.000000  110.000000  110.000000   \n",
            "mean     0.806364      1.96000    2.655455    4.178182    0.634545   \n",
            "std      1.303719      1.64262    2.472253    3.095999    0.968157   \n",
            "min      0.000000      0.00000    0.000000    0.000000    0.000000   \n",
            "25%      0.000000      0.82500    0.900000    1.950000    0.000000   \n",
            "50%      0.000000      1.70000    1.850000    3.900000    0.000000   \n",
            "75%      1.300000      2.47500    3.900000    6.225000    0.975000   \n",
            "max      8.800000      8.00000   14.600000   12.300000    4.000000   \n",
            "\n",
            "        lifestyle      series         etc  \n",
            "count  110.000000  110.000000  110.000000  \n",
            "mean     0.104545    0.551818    0.372727  \n",
            "std      0.314886    1.170958    0.719188  \n",
            "min      0.000000    0.000000    0.000000  \n",
            "25%      0.000000    0.000000    0.000000  \n",
            "50%      0.000000    0.000000    0.000000  \n",
            "75%      0.000000    0.000000    0.475000  \n",
            "max      2.100000    4.200000    3.600000  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "각 국가별 인기가 가장 많은 장르의 빈도수를 계산하여 그래프로 나타내었습니다."
      ],
      "metadata": {
        "id": "uQQRLgDiAtFw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 각 국가에서 선호하는 장르의 빈도수 계산\n",
        "most_watched_genre_counts = df.groupby('nation').apply(lambda x: x[['drama', 'variety', 'music', 'news', 'education', 'movie', 'sports',\n",
        "                                                                    'dacumentary', 'animation', 'comedy', 'cultureart', 'quizeshow',\n",
        "                                                                    'quizeshow.1', 'kids', 'reality', 'religion', 'lifestyle', 'series',\n",
        "                                                                    'etc']].idxmax(axis=1).value_counts().idxmax())\n",
        "\n",
        "# 그래프로 나타내기\n",
        "plt.figure(figsize=(12, 4))\n",
        "most_watched_genre_counts.value_counts().plot(kind='bar', color='skyblue')\n",
        "plt.title('Most Preferred Genre by Nation')\n",
        "plt.xlabel('Genre')\n",
        "plt.ylabel('Count')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "WGCx0GqW_SgK",
        "outputId": "ec65c869-2d35-4223-f5f4-96e3bb297efb"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "가장 인기가 많은 장르가 영화와 버라이어티인것으로 나타났는데, 그 때 각 국가별 영화와 버라이어티 선호도를 그래프로 나타내었습니다."
      ],
      "metadata": {
        "id": "2UZLydT1A1D1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 각 국가에서 영화 선호도\n",
        "movie_preferences = df.groupby('nation')['movie'].mean()\n",
        "\n",
        "# 각 국가에서 버라이어티 선호도\n",
        "variety_preferences = df.groupby('nation')['variety'].mean()\n",
        "\n",
        "# 그래프로 나타내기\n",
        "fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(12, 10))\n",
        "\n",
        "# 영화 선호도 그래프\n",
        "axes[0].bar(movie_preferences.index, movie_preferences, color='skyblue')\n",
        "axes[0].set_title('Movie Preference by Nation')\n",
        "axes[0].set_xlabel('Nation')\n",
        "axes[0].set_ylabel('Preference')\n",
        "\n",
        "# 버라이어티 선호도 그래프\n",
        "axes[1].bar(variety_preferences.index, variety_preferences, color='lightcoral')\n",
        "axes[1].set_title('Variety Preference by Nation')\n",
        "axes[1].set_xlabel('Nation')\n",
        "axes[1].set_ylabel('Preference')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "P5S0c08I_eNE",
        "outputId": "8126f3af-022c-4d09-824e-98d0fef3393c"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "연도별(2019년) 선호장르를 볼 수 있는 그래프를 나타내었습니다."
      ],
      "metadata": {
        "id": "gqfm1HVaBn2B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 2019년 데이터 필터링\n",
        "df_2019 = df[df['year'] == 2019]\n",
        "\n",
        "# 각 장르의 선호도 평균 계산\n",
        "genre_preferences_2019 = df_2019[['drama', 'variety', 'music', 'news', 'education', 'movie', 'sports',\n",
        "                                    'dacumentary', 'animation', 'comedy', 'cultureart', 'quizeshow',\n",
        "                                    'quizeshow.1', 'kids', 'reality', 'religion', 'lifestyle', 'series',\n",
        "                                    'etc']].mean()\n",
        "\n",
        "# 그래프로 나타내기\n",
        "plt.figure(figsize=(12, 8))\n",
        "genre_preferences_2019.plot(kind='bar', color='mediumseagreen')\n",
        "plt.title('Genre Preferences in 2019')\n",
        "plt.xlabel('Genre')\n",
        "plt.ylabel('Preference')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 797
        },
        "id": "DVN4U1ElAHpi",
        "outputId": "b4310362-b1d6-4120-ce51-cab4037611f2"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 2020년 데이터 필터링\n",
        "df_2020 = df[df['year'] == 2020]\n",
        "\n",
        "# 각 장르의 선호도 평균 계산\n",
        "genre_preferences_2020 = df_2020[['drama', 'variety', 'music', 'news', 'education', 'movie', 'sports',\n",
        "                                    'dacumentary', 'animation', 'comedy', 'cultureart', 'quizeshow',\n",
        "                                    'quizeshow.1', 'kids', 'reality', 'religion', 'lifestyle', 'series',\n",
        "                                    'etc']].mean()\n",
        "\n",
        "# 그래프로 나타내기\n",
        "plt.figure(figsize=(12, 8))\n",
        "genre_preferences_2020.plot(kind='bar', color='orchid')\n",
        "plt.title('Genre Preferences in 2020')\n",
        "plt.xlabel('Genre')\n",
        "plt.ylabel('Preference')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 797
        },
        "id": "ZAwIEXm_B2ao",
        "outputId": "36cb9ee5-d00b-4ada-e127-cf52e65dbf16"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "역시나 영화가 압도적인 선호도를 나타내는것으로 확인했습니다."
      ],
      "metadata": {
        "id": "fbBNZw2mCB3y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "다음은 연도별 클러스터링 군집화 kmeans를 나타낸것입니다."
      ],
      "metadata": {
        "id": "qmT6x0cQCYzh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 데이터 불러오기\n",
        "df = pd.read_csv(\"/content/drive/MyDrive/오티티분석/ott_analysis.csv\", encoding=\"euc-kr\")\n",
        "\n",
        "# 'year' 열을 기준으로 데이터 선택\n",
        "data = df[['year']]\n",
        "\n",
        "# K-평균 군집화를 위한 데이터 정규화\n",
        "scaler = StandardScaler()\n",
        "data_scaled = scaler.fit_transform(data)\n",
        "\n",
        "# K-평균 군집화\n",
        "kmeans = KMeans(n_clusters=3, random_state=42)\n",
        "df['cluster'] = kmeans.fit_predict(data_scaled)\n",
        "\n",
        "# 군집별 통계량 확인\n",
        "cluster_statistics = df.groupby('cluster').mean()\n",
        "\n",
        "# 군집별 통계량 출력\n",
        "print(cluster_statistics)\n",
        "\n",
        "# 군집별 데이터 시각화 (2D 그래프)\n",
        "plt.scatter(df['year'], df['movie'], c=df['cluster'], cmap='viridis', edgecolors='k', alpha=0.7)\n",
        "plt.title('K-Means Clustering based on Year')\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Movie Preference')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 982
        },
        "id": "e7h-zJ7nBwki",
        "outputId": "cf967ad6-2b4f-4fbf-8b03-2c48a3d76816"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1033: ConvergenceWarning: Number of distinct clusters (2) found smaller than n_clusters (3). Possibly due to duplicate points in X.\n",
            "  return self.fit(X, sample_weight=sample_weight).labels_\n",
            "<ipython-input-30-928c3b7824e9>:21: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  cluster_statistics = df.groupby('cluster').mean()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         serial number    year  number_of_cases      drama   variety  \\\n",
            "cluster                                                                \n",
            "0                 85.5  2020.0           140.74  13.412000  5.914000   \n",
            "1                 30.5  2019.0           114.70   8.776667  2.118333   \n",
            "\n",
            "            music      news  education   movie    sports  ...     comedy  \\\n",
            "cluster                                                   ...              \n",
            "0        5.158000  5.838000   2.462000  21.618  7.976000  ...   9.182000   \n",
            "1        6.686667  6.216667   2.148333  30.885  8.731667  ...  13.523333   \n",
            "\n",
            "         cultureart  quizeshow  quizeshow.1      kids  reality  religion  \\\n",
            "cluster                                                                    \n",
            "0             1.418   1.064000     1.680000  2.484000    2.916  0.930000   \n",
            "1             0.000   0.591667     2.193333  2.798333    5.230  0.388333   \n",
            "\n",
            "         lifestyle    series       etc  \n",
            "cluster                                 \n",
            "0             0.23  0.000000  0.000000  \n",
            "1             0.00  1.011667  0.683333  \n",
            "\n",
            "[2 rows x 22 columns]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "axTCE-E6CHcE"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}