Lezhin_Data_Challenge_2017_Simple_EDA.ipynb

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    "colab": {
      "name": "Lezhin Data Challenge 2017 - Simple EDA.ipynb",
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    "kernelspec": {
      "name": "python3",
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    "accelerator": "TPU"
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        "# Lezhin Data Challenge 2017 - Simple EDA\n",
        "\n",
        "### **Columns Information**\n",
        "\n",
        "* Feature 수: 167개\n",
        "\n",
        "    * 0 : label 해당 유저가 목록에 진입하고 1시간 이내에 구매했는지 여부\n",
        "    * 1 : 사용 플랫폼 A\n",
        "    * 2 : 사용 플랫폼 B\n",
        "    * 3 : 사용 플랫폼 C\n",
        "    * 4 : 사용 플랫폼 D\n",
        "    * 5 : 목록 진입시점 방문 총 세션 수 (범위별로 부여된 순차 ID)\n",
        "    * 6 : 작품을 나타내는 해쉬\n",
        "    * 7-9 : 개인정보\n",
        "    * 10-109 : 주요 작품 구매 여부\n",
        "    * 110 : 작품 태그 정보\n",
        "    * 111 : 구매할 때 필요한 코인\n",
        "    * 112 : 완결 여부\n",
        "    * 113-122 : 스케쥴 정보\n",
        "    * 123-140 : 장르 정보\n",
        "    * 141 : 해당 작품의 마지막 에피소드 발행 시점 (범위별로 부여된 순차 ID)\n",
        "    * 142 : 단행본 여부\n",
        "    * 143 : 작품 발행 시점 (범위별로 부여된 순차 ID)\n",
        "    * 144 : 총 발행 에피소드 수 (범위별로 부여된 순차 ID)\n",
        "    * 145-150 : 작품 태그 정보\n",
        "    * 151-166 : 유저의 성향 정보 (과거에 구매를 했을 때만 기록)"
      ]
    },
    {
      "metadata": {
        "id": "pE_-D4yMUKEl",
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      "cell_type": "markdown",
      "source": [
        "## 목차\n",
        "\n",
        "* Settings\n",
        "* 사용 플랫폼별 1시간 이내 구매 여부 관계\n",
        "* 목록 진입시점 방문 총 세션 수와 1시간 이내 구매 여부 관계\n",
        "* 작품과 1시간 이내 구매 여부 관계\n",
        "* 주요 작품 구매 여부와 1시간 이내 구매 여부 관계\n",
        "* 작품 태그 정보와 1시간 이내 구매 여부 관계\n",
        "* 구매할 때 필요한 코인과 1시간 이내 구매 여부 관계\n",
        "* 완결여부와 1시간 이내 구매 여부 관계\n",
        "* 스케쥴 정보와 1시간 이내 구매 여부 관계\n",
        "* 장르 정보와 1시간 이내 구매 여부 관계\n",
        "* 유저 성향 정보와 1시간 이내 구매 여부 관계\n",
        "\n",
        "목차 항목은 [전영호님의 Data Exploration](https://github.com/ForwardYH/Lezin-Data-Challenge/blob/master/1.%20Lezin%20Data%20Challenge%20-%20Data%20Exploration.ipynb)을 참고했습니다."
      ]
    },
    {
      "metadata": {
        "id": "MiVoLX-rvGnP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Settings"
      ]
    },
    {
      "metadata": {
        "id": "gSdnyzIvUKD9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.font_manager as fm\n",
        "import plotly as py\n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YIG7uONJhvQ1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# !apt-get install fonts-nanum* -qq"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ANsYNRKZgPsX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "fm._rebuild()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "LHZQk1tBfMif",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "mpl.rc('font', family='nanumgothic')\n",
        "mpl.rc('axes', unicode_minus=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "h_oeUl4gXg0p",
        "colab_type": "code",
        "outputId": "3fc17aa4-12ee-4fab-e484-efd3f62ed46b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "jotq1KYnUKEB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"/content/gdrive/My Drive/Colab Notebooks/lezhin_dataset_v2_training.tsv\", sep=\"\\t\", header=None)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tOZoa0FYUKEF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "7f3f732d-44c3-47bd-b615-426543086b3e"
      },
      "cell_type": "code",
      "source": [
        "data.info(memory_usage='deep')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 650965 entries, 0 to 650964\n",
            "Columns: 167 entries, 0 to 166\n",
            "dtypes: float64(16), int64(148), object(3)\n",
            "memory usage: 919.7 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "V0dCV_HmUKEm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 사용 플랫폼별 1시간 이내 구매 여부 관계"
      ]
    },
    {
      "metadata": {
        "id": "RzVZzSUyJddw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "2c391bae-161f-476f-f28d-e7e1a42f5351"
      },
      "cell_type": "code",
      "source": [
        "data[[1, 2, 3, 4]].head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
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fTErpWWB3RFyq8OWe0GvYu/cRjhx5aaRitpBZvjllC7B9+53PUci1m1u2fV67WWUL+eW7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\nqXD/B+vkvVZW0qKOAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4fe6b26748>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Ga4EBL8bK7Oq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "outputId": "656320a9-7339-4178-81e6-ab82dd74e726"
      },
      "cell_type": "code",
      "source": [
        "platform = pd.DataFrame(index=range(1,5), columns=[\"조회수\", \"구매수\"])\n",
        "platform"
      ],
      "execution_count": 11,
      "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>조회수</th>\n",
              "      <th>구매수</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   조회수  구매수\n",
              "1  NaN  NaN\n",
              "2  NaN  NaN\n",
              "3  NaN  NaN\n",
              "4  NaN  NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "id": "TN6GkyqRKefc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "for i in range(1,5):\n",
        "  platform.loc[i][\"조회수\"] = data[data[i] == 1].shape[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ecZ5RXZHUKEn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "for i in range(1, 5):\n",
        "  platform.loc[i][\"구매수\"] = data[(data[i] == 1) & (data[0] == 1)].shape[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qKg_nOUwMuvR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 605
        },
        "outputId": "ba258d74-9d9a-4210-9277-f2ad7390efc8"
      },
      "cell_type": "code",
      "source": [
        "platform.plot.bar(figsize=(15,10))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4fd3a752e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Whz9lAACAY8tir/idm+SL+ZlQ7L1/YN7HM4PlW5K8r/f+7iQPJ9k24nUAAACWYFHh13v/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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4fe746a7f0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "5tnWBhgLUKEs",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 목록 진입시점 방문 총 세션 수와 1시간 이내 구매 여부 관계"
      ]
    },
    {
      "metadata": {
        "id": "CnHpPh93UKEt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "t_session = pd.DataFrame({\n",
        "    \"ORDERED\":data[0],\n",
        "    \"SessionID\":data[5]\n",
        "})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xQoETk42UKEw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "t_session = t_session.groupby(['SessionID', 'ORDERED']).size().unstack().fillna(0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4W6L26OlUKE0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "ce6c8b44-b670-42be-d4a9-da67c7fdecb9"
      },
      "cell_type": "code",
      "source": [
        "t_session.columns = ['X', 'O']\n",
        "t_session.head()"
      ],
      "execution_count": 17,
      "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>X</th>\n",
              "      <th>O</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SessionID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12927.0</td>\n",
              "      <td>11205.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6114.0</td>\n",
              "      <td>4235.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5242.0</td>\n",
              "      <td>3244.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4720.0</td>\n",
              "      <td>2769.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4690.0</td>\n",
              "      <td>2539.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 X        O\n",
              "SessionID                  \n",
              "1          12927.0  11205.0\n",
              "2           6114.0   4235.0\n",
              "3           5242.0   3244.0\n",
              "4           4720.0   2769.0\n",
              "5           4690.0   2539.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "MvwfHY1-UKE3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "t_session = pd.DataFrame(\n",
        "    {\n",
        "        \"총 방문수\": t_session[\"X\"] + t_session[\"O\"],\n",
        "        \"구매\": t_session[\"O\"]\n",
        "    },\n",
        "    index=t_session.index,\n",
        "    columns=[\"총 방문수\", \"구매\"]\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2u-BTKtvUKE7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "e238b20d-559f-4239-f6f1-2d71ba91112b"
      },
      "cell_type": "code",
      "source": [
        "t_session.head()"
      ],
      "execution_count": 19,
      "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>총 방문수</th>\n",
              "      <th>구매</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SessionID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>24132.0</td>\n",
              "      <td>11205.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10349.0</td>\n",
              "      <td>4235.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8486.0</td>\n",
              "      <td>3244.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7489.0</td>\n",
              "      <td>2769.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>7229.0</td>\n",
              "      <td>2539.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             총 방문수       구매\n",
              "SessionID                  \n",
              "1          24132.0  11205.0\n",
              "2          10349.0   4235.0\n",
              "3           8486.0   3244.0\n",
              "4           7489.0   2769.0\n",
              "5           7229.0   2539.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {
        "scrolled": false,
        "id": "xVrTYo6gUKE-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "outputId": "e5404622-c8c1-4e03-ee77-d80cb0113ac1"
      },
      "cell_type": "code",
      "source": [
        "t_session.plot.bar(figsize=(15, 10))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4fd2c66e80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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J3py5gDiO4481/26bBv98kqvHcXx6kvcn+eGTHA4AAMApWFPgG8fxTeM4fmhu8L5hGH5u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\nSneP2mm166veJWstWr5bpv9vbZavnlD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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4fd358fba8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "69CtTsGVUKFB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 작품과 1시간 이내 구매 여부 관계"
      ]
    },
    {
      "metadata": {
        "id": "gS970KYlUKFB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "title_df = pd.DataFrame(\n",
        "    {\n",
        "        \"작품명\": data[6],\n",
        "        \"구매여부\": data[0]\n",
        "    }\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WFVyVHMBUKFD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "title_df = title_df.groupby([\"작품명\", \"구매여부\"]).size().unstack().fillna(0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BsyBzrcoUKFG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1937
        },
        "outputId": "2b87f6f0-f58b-4b9e-91d9-bef265e7cbfb"
      },
      "cell_type": "code",
      "source": [
        "title_df = pd.DataFrame(\n",
        "    {\n",
        "        \"조회수\": title_df[0] + title_df[1],\n",
        "        \"구매\": title_df[1]\n",
        "    },\n",
        "    index=title_df.index,\n",
        "    columns=['조회수', \"구매\"]\n",
        ").sort_values(by=\"조회수\", ascending=False)\n",
        "title_df"
      ],
      "execution_count": 23,
      "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>조회수</th>\n",
              "      <th>구매</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>작품명</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6983846D</th>\n",
              "      <td>9690.0</td>\n",
              "      <td>3035.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>00A49090</th>\n",
              "      <td>8782.0</td>\n",
              "      <td>5298.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0365FD34</th>\n",
              "      <td>8443.0</td>\n",
              "      <td>2029.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>621BF66D</th>\n",
              "      <td>7665.0</td>\n",
              "      <td>3318.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D619AC7D</th>\n",
              "      <td>7380.0</td>\n",
              "      <td>5684.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0757A410</th>\n",
              "      <td>6644.0</td>\n",
              "      <td>289.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37C9A863</th>\n",
              "      <td>6487.0</td>\n",
              "      <td>1117.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85803B60</th>\n",
              "      <td>6083.0</td>\n",
              "      <td>3894.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AC627AB1</th>\n",
              "      <td>5667.0</td>\n",
              "      <td>903.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D8CD8806</th>\n",
              "      <td>5468.0</td>\n",
              "      <td>1276.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B74E1909</th>\n",
              "      <td>5334.0</td>\n",
              "      <td>1091.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A7D44D4B</th>\n",
              "      <td>5022.0</td>\n",
              "      <td>897.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E1AE71FB</th>\n",
              "      <td>4839.0</td>\n",
              "      <td>445.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EC612BAF</th>\n",
              "      <td>4743.0</td>\n",
              "      <td>3660.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46F57FFA</th>\n",
              "      <td>4727.0</td>\n",
              "      <td>3103.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F0A27F22</th>\n",
              "      <td>4677.0</td>\n",
              "      <td>3933.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4847D13D</th>\n",
              "      <td>4673.0</td>\n",
              "      <td>826.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BEE0E409</th>\n",
              "      <td>4519.0</td>\n",
              "      <td>3298.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>330C9DF7</th>\n",
              "      <td>4515.0</td>\n",
              "      <td>874.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>707F7107</th>\n",
              "      <td>4487.0</td>\n",
              "      <td>2623.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CFDECE02</th>\n",
              "      <td>4459.0</td>\n",
              "      <td>567.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69F29D71</th>\n",
              "      <td>4424.0</td>\n",
              "      <td>1161.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5977BD95</th>\n",
              "      <td>4392.0</td>\n",
              "      <td>2450.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B3298042</th>\n",
              "      <td>4115.0</td>\n",
              "      <td>138.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B0A70AC1</th>\n",
              "      <td>4006.0</td>\n",
              "      <td>1187.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A01A0380</th>\n",
              "      <td>3979.0</td>\n",
              "      <td>1063.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7394AF9E</th>\n",
              "      <td>3950.0</td>\n",
              "      <td>437.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6E6A8431</th>\n",
              "      <td>3921.0</td>\n",
              "      <td>531.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>607C8CCE</th>\n",
              "      <td>3716.0</td>\n",
              "      <td>259.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81FD3004</th>\n",
              "      <td>3699.0</td>\n",
              "      <td>2039.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A727AC08</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>342D8597</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A70054CE</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A6C47F4F</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3455F3CB</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>347923B0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A64F7C1F</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A64C94BA</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A82CD007</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33F3F5B9</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33DE2C41</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A8B1AAB5</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AAC1AE43</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31ECB66D</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>322E15CA</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AA8CE56B</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AA81B257</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AA51DE73</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3285668A</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A9F192C3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32BB90E8</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A9903485</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32C39B6E</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A944E8B0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A9393B1E</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A91CDA20</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3364BD82</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>337D99E4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A8B9789F</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FFF1FD1F</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3559 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             조회수      구매\n",
              "작품명                     \n",
              "6983846D  9690.0  3035.0\n",
              "00A49090  8782.0  5298.0\n",
              "0365FD34  8443.0  2029.0\n",
              "621BF66D  7665.0  3318.0\n",
              "D619AC7D  7380.0  5684.0\n",
              "0757A410  6644.0   289.0\n",
              "37C9A863  6487.0  1117.0\n",
              "85803B60  6083.0  3894.0\n",
              "AC627AB1  5667.0   903.0\n",
              "D8CD8806  5468.0  1276.0\n",
              "B74E1909  5334.0  1091.0\n",
              "A7D44D4B  5022.0   897.0\n",
              "E1AE71FB  4839.0   445.0\n",
              "EC612BAF  4743.0  3660.0\n",
              "46F57FFA  4727.0  3103.0\n",
              "F0A27F22  4677.0  3933.0\n",
              "4847D13D  4673.0   826.0\n",
              "BEE0E409  4519.0  3298.0\n",
              "330C9DF7  4515.0   874.0\n",
              "707F7107  4487.0  2623.0\n",
              "CFDECE02  4459.0   567.0\n",
              "69F29D71  4424.0  1161.0\n",
              "5977BD95  4392.0  2450.0\n",
              "B3298042  4115.0   138.0\n",
              "B0A70AC1  4006.0  1187.0\n",
              "A01A0380  3979.0  1063.0\n",
              "7394AF9E  3950.0   437.0\n",
              "6E6A8431  3921.0   531.0\n",
              "607C8CCE  3716.0   259.0\n",
              "81FD3004  3699.0  2039.0\n",
              "...          ...     ...\n",
              "A727AC08     1.0     0.0\n",
              "342D8597     1.0     0.0\n",
              "A70054CE     1.0     0.0\n",
              "A6C47F4F     1.0     0.0\n",
              "3455F3CB     1.0     0.0\n",
              "347923B0     1.0     0.0\n",
              "A64F7C1F     1.0     0.0\n",
              "A64C94BA     1.0     0.0\n",
              "A82CD007     1.0     0.0\n",
              "33F3F5B9     1.0     0.0\n",
              "33DE2C41     1.0     0.0\n",
              "A8B1AAB5     1.0     0.0\n",
              "AAC1AE43     1.0     0.0\n",
              "31ECB66D     1.0     0.0\n",
              "322E15CA     1.0     0.0\n",
              "AA8CE56B     1.0     0.0\n",
              "AA81B257     1.0     0.0\n",
              "AA51DE73     1.0     0.0\n",
              "3285668A     1.0     0.0\n",
              "A9F192C3     1.0     0.0\n",
              "32BB90E8     1.0     0.0\n",
              "A9903485     1.0     0.0\n",
              "32C39B6E     1.0     0.0\n",
              "A944E8B0     1.0     0.0\n",
              "A9393B1E     1.0     0.0\n",
              "A91CDA20     1.0     0.0\n",
              "3364BD82     1.0     0.0\n",
              "337D99E4     1.0     0.0\n",
              "A8B9789F     1.0     0.0\n",
              "FFF1FD1F     1.0     0.0\n",
              "\n",
              "[3559 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "metadata": {
        "id": "cxOOPUqoUKFI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 670
        },
        "outputId": "fdf732a0-12dd-4c31-b731-bbf9d20ce8da"
      },
      "cell_type": "code",
      "source": [
        "title_df.plot.bar(figsize=(15, 10))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4fcf879f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
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EfAAAAI0S8AEAADRKwAcAANAoAR8AAECjBHwAAACNEvABAAA0SsAHAADQKAEfAABAowR8\nAAAAjRLwAQAANErABwAA0CgBHwAAQKMEfAAAAI0S8AEAADRKwAcAANAoAR8AAECjBHwAAACNEvAB\nAAA0SsAHAADQKAEfAABAowR8AAAAjRLwAQAANErABwAA0CgBHwAAQKMEfAAAAI0S8AEAADRKwAcA\nANAoAR8AAECjBHwAAACNEvABAAA0SsAHAADQKAEfAABAowR8AAAAjRLwAQAANErABwAA0CgBHwAA\nQKMEfAAAAI0S8AEAADRKwAcAANAoAR8AAECjBHwAAACNEvABAAA0SsAHAADQKAEfAABAowR8AAAA\njRLwAQAANErABwAA0CgBHwAAQKMEfAAAAI0S8AEAADRKwAcAANAoAR8AAECjBHwAAACNEvABAAA0\nSsAHAADQKAEfAABAowR8AAAAjRLwAQAANErABwAA0CgBHwAAQKMEfAAAAI269TgLd113a5K3JHli\nNpt9Xdd1X5Lk9UmeSvLB2Wz2TcN8G00HAADg+I77Dd+3Jfn+JOe6rjtI8q1JvmI2m70myfmu6750\n0+nHrA8AAACDIwd8Xdd9VZL3JPntYdJnJfmN2Wx2cXj9tiSvOMJ0AAAAtuBIAV/XdX88ySfNZrN/\nW01+XpKHq9cPD9M2nQ4AAMAWHPUZvv85yXO7rvvnSe5K8ieS/FqSe6p57k3yseHfJtMBAADYgiN9\nwzebzf7mbDb7utls9vVJ/laSdyb5p0le1nXd7cNsr07yjiTv3XD6kbzqDQ8cdVEAAIAmbeNnGa4m\nuTKbza4m+btJfqjruu9P8swkP7Xp9C3UBwAAgCQHh4eHZ12HTU1W+FVveCD/5k2vPu26AAAAnLaD\ndWf0w+sAAACNEvABAAA0SsAHAADQKAEfAABAowR8AAAAjRLwAQAANErABwAA0CgBHwAAQKMEfAAA\nAI0S8AEAADRKwAcAANAoAR8AAECjBHwAAACN2uuA7zVvfe1ZVwEAAGBn7XXABwAAwGICPgAAgEYJ\n+AAAABol4AMAAGiUgA8AAKBRAj4AAIBGCfgAAAAaJeADAABolIAPAACgUQI+AACARgn4AAAAGiXg\nAwAAaJSADwAAoFECPgAAgEYJ+AAAABol4AMAAGiUgA8AAKBRAj4AAIBGCfgAAAAaJeADAABolIAP\nAACgUQI+AACARgn4AAAAGiXgAwAAaJSADwAAoFECPgAAgEYJ+AAAABol4AMAAGiUgA8AAKBRAj4A\nAIBGCfgAAAAaJeADAABo1N4FfK9562vPugoAAAB7Ye8CPgAAANYj4AMAAGiUgA8AAKBRzQZ8r3rD\nA2ddBQAAgDPVbMAHAABwsxPwAQAANErABwAA0CgBHwAAQKMEfAAAAI0S8AEAADRKwAcAANAoAR8A\nAECjBHwAAACNEvABAAA0SsAHAADQKAEfAABAowR8AAAAjRLwAQAANErABwAA0CgBHwAAQKMEfAAA\nAI0S8AEAADRKwAcAANAoAR8AAECjBHwAAACNEvABAAA0SsAHAADQqJsm4HvVGx446yoAAACcqpsm\n4AMAALjZCPgAAAAaJeADAABolIAPAACgUQI+AACARgn4AAAAGiXgAwAAaJSADwAAoFECPgAAgEYJ\n+AAAABol4AMAAGiUgA8AAKBRAj4AAIBGCfgAAAAaJeADAABolIAPAACgUTdtwPeqNzxw1lUAAAA4\nUTdtwAcAANA6AR8AAECjBHwAAACNEvABAAA0SsAHAADQKAEfAABAo249zsJd1/2zJNeS3Jvk381m\nsx/suu5Lkrw+yVNJPjibzb5pmHej6QAAABzPsb7hm81mr53NZvcn+aokX9d13UGSb03yFbPZ7DVJ\nzndd96WbTj/WFgEAAJBke7d03p7k4SSfleQ3ZrPZxWH625K84gjTAQAAOKZtBXzfkeQ7kzwvfeBX\nPDxM23Q6AAAAx3TsgK/rutcn+eXZbPbOJB9Lck/19r3DtE2nAwAAcEzHCvi6rntdkqdms9kPDZPe\nm+RlXdfdPrx+dZJ3HGE6AAAAx3TkLJ1d1/2ZJP9nkp/suu6fD5O/PcnfTfJDXdc9meSjSX5qNpsd\ndl239vRjbA8AAACDIwd8s9ns55O8aOKttw//xvNvNB0AAIDj8cPrg1e94YGzrgIAAMBWCfgAAAAa\nJeADAABolIAPAACgUQI+AACARgn4AAAAGiXgAwAAaJSADwAAoFE3VcD3mre+9qyrAAAAcGpuqoAP\nAADgZiLgAwAAaJSADwAAoFECvgVe9YYHzroKAAAAxyLgAwAAaJSADwAAoFECPgAAgEYJ+AAAABol\n4AMAAGiUgA8AAKBRAj4AAIBGCfjW5Hf5AACAfSPgAwAAaJSADwAAoFECPgAAgEYJ+AAAABol4AMA\nAGiUgO+IZO0EAAB2nYAPAACgUQI+AACARgn4AAAAGiXgAwAAaJSADwAAoFECPgAAgEYJ+AAAABol\n4AMAAGiUgA8AAKBRAj4AAIBG3dQB32ve+tqtlfWqNzywtbIAAAC24aYO+AAAAFom4AMAAGiUgA8A\nAKBRAr4T4pk+AADgrAn4AAAAGiXgAwAAaJSADwAAoFECPgAAgEYJ+AAAABol4DslsnYCAACnTcAH\nAADQKAEfAABAowR8AAAAjRLwAQAANErABwAA0KimAr7XvPW1Z10FAACAndFUwLdP/EwDAABw0gR8\nAAAAjRLwAQAANErABwAA0CgB347wTB8AALBtAj4AAIBGCfgAAAAaJeADAABolIBvR3mmDwAAOC4B\nHwAAQKMEfEu85q2vPesqAAAAHJmAb0+4xRMAANiUgA8AAKBRAj4AAIBGCfj2lFs8AQCAVQR8AAAA\njRLwAQAANErAdwx+tgEAANhlAr5GeKYPAAAYE/ABAAA0SsC3RW7xBAAAdomAr1Fu8QQAAAR8AAAA\njRLwAQAANErAd5NwiycAANx8BHwAAACNajrgO+usmWe9/mV84wcAAO1rOuDbtl0O4AAAAMYEfHts\nmwGob/wAAKA9Aj4mCQABAGD/Cfgqu37L5q7XDwAA2C0CPtbiGz8AANg/Ar5T5Bs6AADgNAn4dsg+\nBYS+8QMAgN0n4AMAAGiUgI+t8I0fAADsHgHfGdqnWzgBAID9I+BjoeMEpL7xAwCAsyfgY20CQAAA\n2C8Cvh3W8i2fAkAAADh5Ar6byC4HkAJAAADYPgFfQzYN6E46AByXv8n6BIAAAHB8Aj4+7rgB40kF\nkII/AAA4GgEfe2ccAAoIAQBgmoCPM3Na3wgKEAEAuFntRMDXdd1f77ruJ7qu+/Gu677lrOvDzUWA\nCABAq8484Ou67q4k9yV59Ww2+/Ikn9t13UvOuFrsmE2fFzzJhDSbBojHfQ0AAEd15gFfkj+T5Kdn\ns9nh8PqBJK84w/o0Y5d/huG07fO+OI0As94/pxnQCm4BAE7WweHh4eq5TlDXdV+V5PbZbPYvh9d/\nPsmfns1m/+BMKwYAALDnduEbvo8luad6fe8wDQAAgGPYhYDv3Um+pOu6g+H1X0rys2dYHwAAgCac\n+S2dSdJ13V9L8uokV5L8ymw2++4zrhIAAMDe24mADwAAgO3bhVs6AQAAOAECPgAAgEYJ+AAAABol\n4AMAAGjU3gR8Xdfd1nXdi7uu+7yu624fvfeiBcvcWf3/hV3XfdER130wXueK+W8f/t7Rdd3tXdd9\nwnj68PcVU3Udr7Prupd2XffCer5hf7yg67o/Vy3zour/t1flfEI9fZj2rK7rPqPrultH6/30qfIm\ntrGe73kT799d1eFFZd8P67+9PhZDfe4qZQ7H6q6JMr9wvP6u6z59WPburuueP5q/7KNbFx2/JdNL\nfe8e/f30arvuquYvx/ruMu+y9XRd9+eGupXjeXu1jnrf3F62b1yfZW2yHLuu6+6tpt09muegHI8F\nZbxmyX65oW2M9vfdVZ8dt7G7q//fOS5nap1d193Zdd2Xj6bfOazjM8oxHqZ90Wi+Rdv3rFF9V/bz\nZe1o1fKl31evP77/6366aJ/Uy4/LGqaVY1OPHaV9lfZQjtE94+UXbVf1/7u7+ThUjy+3L5j/hdV8\n4/b/gqn9NRy/z1xQl3q7Pn04fn+y67qX1G2s67ov7/ox54Z+OFHmi4b29cJxPar/l332zKGt1e+N\nt//Pr1pnNe/nVf+/Ybyr61it6zOHsWZqjJk8v43Gk9u6rvsj3RHGxAXzljZct817S7+q6z9a7rbR\nPLdXY9uLuq576fD/53bV+WU47vW5Z+o8MXncu/l59+PrGdXn04b2Nx4n194f1TJLrzVG2/C8oW/d\nUW333V3X/eVN6rCqvQ/9sb7m+Mxufh79hAX7spynbrhOqMau+jz4RV11zTNMu2GsGpVVt88yvryw\n7pMT49et42VHZa68dhn28V3DMa+Px6eM61fVa9UYX9rW5Di7SZ+baIdlH9w57P8ytt+1YP4yVn7R\n0KduuG4ZzVePY+NxrWx/OcdMjtHV/F8xel3v38+tyvnSZeVMlHv3sP0vHNpZ2c/1mFxPv7ubn+Nv\n67rua6u287nVMndtcmyG6S+c+v/wuoyL141VU2PP8Pq2UT3L/2+4Fui67rOG95891WeX2fksnV3X\nfX6SH0jyx9IHqAfV24fVvyT5/SSfmuTaMO1ckluHZa5Vy11McinJsyfKrF1N8uQw38UkH03y/CTP\nrNaZYflSxuGwrjLtcFjHpeFv+ZdhvmtDPctyqV5nNO/5Yd1Xktw+WueVYVsPsziQP0xyOckzqtdl\nO28Z/l4Y/j1vKP8jwzZfG6aXBljvs7Kd5e/lzPd7ef/aUP65YV88s5r33LD+C8P/rw7bd8swz2NJ\n7qreeyjJJwz7485qvktJnkryrCTvT1IGpatJbhvePxjqVvb9bcO0x9If60/Ojcenfn2uKrP8v+zz\nsg/qZVJNn1K3gam2Pd7PRTlmZb66zYzLmVp+XLdrmbfR23Jje74lfRt7OvM2cDH9cSr74en0x7Xu\nD/W2lPqWupR1P5S+bz0v/XEt5ZY6HQ7rvJbk8aoeL5rY5tKm6m1I+rbyjOH9q0M9xy4P75f+cSnz\nNl/275OZ98EPJXnh8N5tub59lG27lPmxfXRY9vbMj1k9Nh0M5d8x7Ks/HPZH0rfZWzIfW5J5G8jw\n94kkv5nk5Znv3+TG/nkwWvZqVUbpuw+m73PPGra11LXep08M85TtHfeD0nauDmVerpYt/f2Ooex6\nHfX+K/tnPMYu6k9T713JfAwodZxavh4Hz1Wvp8bTut+WsfH2zNvQHaP9Ucbn+hgk8z5X9k85xmWd\nZf66vZTtXLb9dZ8u21LaUD0u1+fQc9X/L2d+rril2s5bquXq/TMerx5Lcvfwuj629bZdrco7yPXn\nglLGc4Zyy5hQ6r1o25P5efJq+nHirtzYfkr96/5XnxfrdZRj/bYkr6rqcSXJI5mfK8uydXu5Okyr\nzzllX5djMt6ei1VdPprkuenb7qPp29kLcmP/fnL4e1vmx/lwWObDSV6a+dhUO0x/zizj9rLroeJC\n5uNr2aZH0u+XW9NfLxXXhvU/N32f+Fj6Yzq+Vll1TOuxpLSlso2Xh+nfl+R/S7/v6nnrc1gZS343\nyafn+vP55STvTfKSzM8R4/Z+MCrz0ST3pG8L703y2RPbUl+jFk8P+6Osu7SRMv9T6ftP7VrmfXI8\nJtZ968owrS5v3KZ/L/3Yfi3zPnZH+nPxren7TN03yzmiPr/V6y1jearp43VezfzaNcO2XBzqUcqc\nuo4qx/j8UNcPJPm0TF+nLRqry/Rxna6MtvNa+vPas4d1fizJp+TG/nYx8z78gSSfUZUxvuYq9Stt\nvh53x/t26rxdjvX4/J/04/t9Sf7jbDb72MS2X2cfAr5fTvL5WT0IAQBw81j2ARC0qgRvl5P8m9ls\n9pWrFtiHWzo/J/NPCXc7OgUA4LQI9rhZlW+cP7vruj+9auZ9CPguZPltOAA3Ex98AXAcV1fPwo4r\nsdHdSb5k1cz7EPB9KMlvxUUOm9FeTo592zur/eCDLwCOY+qZTvZH/dzhrbPZ7O+tWmAfAr570jfM\nt6R/QPFS+geUpxyO/pYHpM/nxouz8jBkmac8lLtM/eBwXd7VXJ8Upsx7YXjvUq5f37XRfOWh4/HD\nvfW6ro6mj9V1KuU8Xq23LFeXX+arb5ct68zEsov2T5lnXPbV0euLmb5IXnRsnh5ePzWq73g9T+VG\npSOM551a3/h4lGPycPpjd3lnORJoAAAgAElEQVRBWeNyrub69lTawP+d1bckl4QWl6qyrlTv1+ur\nj8ui9+ttK9vzVDVfaS/18Z1qV1PrGCeOKOWU98+nT7jwSLU9U2UlN7bbqe2pp5W+Ui83PjZTdSvz\nXRj+fyHL93VZR1nuocwTctR9tRzza+nbyx9k8bFYZqptl3VfyvS+Luu9kD55QNnOJ9MnfLmS1cZ9\ndzwerKPsj6eHvx8Z/l4a1fWJoex6nYe5/hjV085nPmb8+rCd9fLluC2qa5mvJCApZS3a12X9pcz3\n5/p2Vu42+fCwrR/OfHxfpZRxZSjnF4cySplPD+VcHc2/jlLnqfWN51lVbv1cyCOZ7ltTSsKvi9W0\nZe3vSpI3D38vpk+iMXV+WVZGOadcyPXtpz5vrqp/3UaeTN9Gp64LHqnKr/teXZdx3UqyoKczP4c8\nWi1frktKX7k8UcaisWTRttXTnxq2qbT/8biwrB8sss4y1zJva+X/df3H568yxj1SzXshm9Vrqg5l\n3Cx9tCRmu1DNdzhM/2jm48Oq82BZbtH4UY7nhfTH+EL6/X45/TXZ29K3g6czv8YZn5eLB4Zl6muQ\nsj2X0p9v6uVLG34i831Y96unh7rU18T18Xp6KKP059KXxn3zcvrELx8ZyhqPW2X5q8P7jyf5nfRJ\nUKb2b91v62n1v6uZjy/l2qrsx7LfrgzTzmfenw5zY99Krh+363nKdXM513ww8/P7vx+24acyH7N/\nJ/31wW/kxjYxfl36Yhnrr6bvp+Xv+fTnld9P8q+GupRyzif5lWH7Shk/O5vNbk1yXVbURfYhacu7\nkvzxzLPUjD+VKBeg5YCX98eZ6T6QPpvWM9M3mtuqMkqGoVszb3C/nf4gf1KSktq+zvRTsvuU9dUZ\nlKbqVLL0lOVLHYuSyay4lsVZtZZ9wl+XXQ+wmwb39b4bZ6qsy3o688yO5bbbkkno8fQD1pelz660\nbhawOjvTuP6lPlP7pl5unOFonKGtbh+r9k3JPlnqMb5QvW1imdJGyj3WdX3qbHhlYHxW5llG70k/\ngHxi5oN8nc2sDPh3pB90Pzl9u6u3c7zdZXAp7bMus7Tdg8yzxtX7p84yVZT2/5Fh+5+X/oT9gfRJ\nlkogVDKKlaxkJXPaoqxZyTwD1sGwP0qWvXq/j9tDyWD12LAvS8bEkknrXLXcLaPlDqvlS9a1Ohtc\nOY6XM8/uWbehVO+X9l3KvZr5Se7e9EHE84ay78iNGVGvZN6eykXhs9K3h5IR847q/StVGeN9WQdb\nt2Z+gnvGsJ3PGdXzXOYXp+fSZyets7SNx6cnMs/wVvZhyWQ3dXzH+2zVuDQeB+qMZmU/Xxi27fHM\ns+XdkhvHmfriZpxJddX611H2czlBPzvXt/Nxf5xa/nBY/u70/frT0t/hcnf6bZvKLldn+xtnh6zH\nwnodU9tf2ko5N5aMpqWsZH5+vJh5FtJyDMfb9WD6DLMH6dtdOT8k/TGrs+SeH61z3F5qdTbqUre6\nXZ4f/pbMf/VF5Lj8eh118FRnSl12vMoF4TMyb/cZLVeOe3n/MP14cFfm2Y7H7ezxzDNdXks/Bj47\n/dh2T1Xnq+n3892ZZ0su2WJLxt3x+FKWHWdPPDd6fUtuPA5lvClZQGvlvYvDvOeHetw9LP/mJF8+\nvC6Zef8w/VhYMjJP9bX6eNXjeP1+GQN/M8kfzTzjaOmTJZj+9iRfn3km9+fkxrYw3hfJ9WPWojGl\nHqeWZUmvyyhtroxj5RheGf7V55rHMs/eXDJCj49jvQ+n+nY9Ll/JPAv0OOP7eDvLBwX3Zp4hvVwT\n19fST6YPfl5UvT8+L5VxK5lnhC4Zm5+Red+4Y5j3Uub9pGzLlcyz2C4ztY/qYzzu22X/1UHnsiyy\nU/uquFq9N87eOe574/nGbbDsv9L36n54kD44/N+TPDibzd49UZeP24eArwQM61yYs9qqixx222kc\nv00udGv72LZOqs7HLXcf9+WmboZtbNEuHrddrNM+OurYf9IcX7he+TDrIH3g/Q9W3da5DwFf+U2M\nXR2IAAAATku5bfz8bDa7d9XM+xBAPSPz2wYBAAB2wWl+c1ZuVU6mf4x9oX0IouqEJuyOw9FfTtYu\n7eddqgvt0b5uPo45i2gb7LrTvN24frztlsyf5VtpHwK+d6VPDPHW9Jna3pg+oUqdNKP2dDWtHijq\nTFF1RqHyr85gNs4UtEqdRa8ud1xG/ZDvYeZJFC5PLFObKmuduo0zRpYMR+OscnU5JXvQokxKxZXq\n79T+emJF3ep567/1OkrWrMez3r5ZZ12XkvzXzPf5puVsw1S7/f1cnzhgvE+m2tf5LM4QOHX8Nv3d\nndJGp7KYHqWtjpeps46tU5exkrSgvF9/MFRnFiv7+0NL6j21rnpb60xgJ2XVhyjbXne9fXXWwWXr\nL5nnxstssq7apfSZx8bzPp75mFz/K5nnxmPulDqbZ1GPtYvqXo/TT4zWUxL71PM9ltUZUUtm6bLM\nVNa4o4xHizL5FqWPPTy8HrfhRWPv2LrHeZW6T74//Rh/OfO+Oa7DVJ3WHTOWlVO2p+y/cSbCVft/\n3C7LNcR4DF/Wl6auN2rjrKplvpJRclFm1vE1TZ219OmsbmfLso9u4ijj+tQYeNxnocevyz5Z1ebX\nHYenxsI6Y3e9rpJdcVn5U+uqM/iua9w+p+o8NR6vyuY71Sbr9S2bf50yx9PqcscZdJ/K9LnrKL8x\nuKrupR9dzOrxcFEbq887U5n5l62/JB8qyWweTX+e/GCSv7KiPkn24xm+FyX5pfRZf+rsWUm/4z+Q\n5DMznQXnMNdnGawz89RfiZbOWTLg3JL+BPncal0lBXjJNlbKHmczPMw8w9VUVqnx/IuyFdYNqg7M\nxwdsnF2p1PXWLFYa2W+lz6j0rKGcJ4f/13W5lnnmrcPMM0mN61NnUayz9dXTL2aecanOLle+kl6V\nmfQw/QXYndV845NBPe/Ue3UGpKmMW/VxrLMtLjpu4/WW7anLHx/Xer5xuyz7b6o9lOkli2KSvDd9\nprNnZ56pc5wVb7x9U0pdyjaXTG2Lsn7VZdcZZseZBMeZacv0ktmrrGecSTO5/lgtOp5l2uXMM53V\nWSvLvyfSZ/8qGXnrWyGeGpY5V9Vh6thNqdtG2cZSdin/SuYZPm/J9W1p3G+uVNOTvk/elXl698eT\nfEq17rK+ciKpyy/7/0r6tPd/NNdnlF20T+vsaVPtZmq58b4oF79l/ecyPSbVJ/VFWfrKvizt/vbq\nvVL25WF6XYe6jFUfbtb1qLetlLVqTE2mM5SW+tfj2tQYUo8Ly9pcnX0zWXz+KHW5mj5A+KRh3jI2\nTK0/w/slA965XH+eTPr9/VT6bIvL6rqojWwyTzm24/F86v9lvxSr1l2Pp+V4HCSZJXlh+ra0zifn\nU+ebqeuQRWNK/d6ibfovSf5Yrt8XqzJBFlPrWqcvLMpkuGj+Ze2g3KV1Jf15qpyjSsbo0lbHY+8i\ndWbDx9OPj9fSfzHw/PQ/VfCp6dv5k5lfs5TtX3XtUCzqk/WHRufS96/nZzoz8KJzZj1PKb++5itj\nRn09kCw+fmWcKdku/zB99u5yLirj10E1rWTDLte9dfbb8TaXdYzb+/i6u/zswJ25cX8u6sPJfFy6\nnORXk/yJYZ4n01+Hj68zNx13ys9YPKuaNp5vPCasupaslf6a9Pv0YubnqfF1TJm/ZNEdX1fW8UiZ\n96lR3ZP5OfZj6fvB/zSbzX5tvOHXVXIPAr4Hkrwqi4OB07TOYLQr6zqNui7r0KfhOOvbRl3XSSVd\nTHXY41j35L1Np318V61zKoX2rlgVYJ+1sziWu2Aftvusx9V1Lep/qz4QmLKtbdzVfbXL6n32gfQf\nAu9CXWrji+Y6LT3rt/tlQVcrxh8Ej382a53t3qckkVeT/NskH5nNZl+3bMZ92KDPzvSntmfhNDvH\ncdd1GnUdr+O0B4/jrG8bdV3Uf6bK3mawV9axD/03OV6/XXacdjHYqwO9XT6Z7nLdTtI+bPdZj6vr\nWtT/FtV32XYcdxt3/QOW2llfx4zV++y0g71l30jWxt+Q37Zk3pOya8ettu6+GN+90KJ6u6Z+I3md\n7Z66g2sXPZz+w48/mTWuB/fhgrHcrrnrF1DQuqP2v3Vui2qFMYpd1FIfm7JP/W6f6nrSdmVfrNM/\ndqWunL5dPfa3pv8W8+6s8Zz1PgR8f5D+vuRd3eHbto0Tc+sn92V2fdt3uX5TdTuN+t4sfRvOij4G\ni+kf7KO70989di79t3xL7UPA95EkP5P+IcgLmWfLW6ZcpC7Lwlb/y6jMTS9yyz3l9XLj7GnrWvTs\nwyole89UGScZRF7LZlmdppTlnz5GGZvOty3j5BDFqgyA5WH1ZeWdhmUPaS+adpi+LxabZms8bYej\nv4uUzInj+cYZ/FatpyxTpm0rw+G66173vfIw/yJTmSSPYmr/P515xs1N+vayfbnttlWykW5bOV88\nNbyus+WtypA4njY+j62z7vJ3VSbmYlEm4E2Ny74wOdd65RxmO/Wqs5aOs1pPrXcqs+F4nto4G/Q6\nFi1T9tdx2uSiZZfVc+octyor7a6b6kebLr+s323SJ6eW3aZ1zj/HXeeijJOPrSh7k2uH5Prxa9F7\ni94/igu5Plv0JuWvM/8m9Sx1KMlarqZP2HJ/kl+YzWZfsKqAfQj4nkjy36XPePPM9Bn2prJE1spF\nap3BrBy4ZJ4176lqmfE94rVVF+8lA1I5IVypXk91gqk6n8/85wfGF1pTAdz5zH8S4TD9vcqLssgt\ne1B+6sS/anuT/t7hwyz+HZC6jGXrT+bZwJ6RxRc89TOcy9Lj18e8PoHXaXvLfruaPptVuXj4SJJH\nknxvrj9240GpLmvRPfEl29PF9IPeuH6LnsFbFayPX5fBqM4cVoL/VRcnmyplHaTvi0WdzXNZXcuy\nx1n/eMAfb3v9XklBPs68tqieJWNWyZJVZ90t48Oyk1P9MPyq4zyuT30RW/5ffnZiqg08meuPcZ0N\nbjz/on1eb1e9/8r4M/X8w5S6j5wfli/j4LUkD1Xvl+29I/NskOOyFp0ol+3L0qfLOpP5h4NHDbjv\nzHzcWXQBNx4bxu2wHkPq/nNrpjPG1Rn5ShlXJ+arX68bCI/LWTb+1EHQo5mfb+oPXEu/G7e5p3P9\ncVhU93L86w8Ml2177cHcmNlw2VhZXyyV1OqHmZ97xlmly7XB1D4r650ac67l+p8RqseUMl9d5wu5\nsd7lfHpttFzJ+rfqmC/aD4+m329TP80wzmxcmzrHrcpYW/uR9OfWen2LPkSr+2593lz14cCiMePx\n9NeQV9K34WKTpEiXM/9Jq7r8p6v3H831xo8glVT64+M9dQzH1zrj9S4zNU99TTS+tqzHpPK6ZIRe\nts6p6eWnI+o29Jzc2P6TeebWqfGuuJI+oElV7jiXx1QfX3Q81/kpn7FnZt4fF12bXU1/XC9V9T1M\nf947zOJjXjKablKncu44SL9/Hk3yt5L8p7UW3oMsnW9O8gXpf4Pvfzlmcas6NpvbhX1aBo5t1KP+\nKYFiG9s4HliPWv4u7O/jWDf7VT24n/T2nkZGrvHJ6qS3aVv77UoWB/S7ap8yrB3FOAhuzXHb7jba\n7FmMs+ViddFPbWxi6jw2Xlf9IdGiD+wOF7y3qsyTUj5Iekb1ulyMT2Vk3NRZHfdN1zn1QeY22s1U\necexjb5c2vFx6rTovLvv11OH6X+Hr5vNZsvukEuyHwHfw+k/Jdi1E/i+N5TT0PqFF7B7jM3HZx8C\n7LYSwD0wm82+fNXM+3Axftfw9zSeg9mEk+Fq+9C+YB/s9idzu8XYfHz2IcBuK3cN/cWu6/7dqpk3\nuQ/7rJRn7+5Ifw/7Jj+2vIufUu5inYDdZszYT+5y4KS5puAsaHdnp+z7Eh+9fzabfdmqhfbhRPTM\n9Ld0loekz/qbvmWftC96b9GDmfUD/eNpydlv67qO++3DNr+9WPRA71HWv2r58YO4JSPVcTIJjh9u\nXpaI4CytevD8qGXtql2u42nXbdP1LZp/naQT6yam2HSeoyx/lP287Bw7lSDjKKbGvG2VPbWObZVX\n/ztqxs6xRZllV7WFddroOpkpt3U8N8mQO05kUTvra4ht7I+z3oajKNerizI/n9Q6x9ZZ78XVs0w6\nynOHY8c9tnXypHUyaS9SjwGbXPedpWvpk8T8dvrn+Fbah4DvC5L8cvqMN8nyb/fGA3edeWgqqJo6\nwJcyzzKXiffrRj5uYCVL3bhOdRbLceKGkvlrPC2ZPj4lA9phrs+Y9lRuzBQ1rn/Z5idG7x2lAdfL\nlSxn9bpqj1V1LoNLnT2vPk5HDb6KRVkzx2VdSvK7VT2eGv5NrWfRQDLOynn3ivXW743bYMlCdW40\n31RWvinj9lpnmSzvH+enR8ZW7edyolt2kVTquWx/TWWxW9elbO9iYVzHq+kzZa5y3P08lc3uMPP9\nuqzfLbr4H49Ri5abMj5e9cm2LFcf88dH89ftsk4aURv3q1JmnRGzzFP6b50d7unMM6BtalyXRWP/\nNizL/LZpOUW9f5YFAlNtY5OLnqQfy5f1ryuZztZZ16/8u31ininjcTO5vs63ZfE4vugaYJyB+TDX\nj51FfUfUov2zKCnXlEUf7h7kxuQj43PFuNxNkn5t6jhj2FTmz3L+XXS+H08bXwfV1xCLTLXjTX+e\noFyzLFquXCeOP7xI5u163SQy9di2bAxapNRz/BNZ44yyU2Wt2/dq4/4x3m9P5MYMyVNttBzbkjV3\nneu/et0lWV+ddXhcxrJjWNdt0TXX1LzjdVzLjVlAt/XhT72O8bbfmeTFWTPg24ekLf85yWelb0TP\nWXOxTTMBrpqnzFd+fuE0rFO3RfNdyPVp88cuZXFK9G1kozyY+H+y3u24i7JkHmZxJrGpOtfZu8rr\no37AcS39xX0J6DbdT9vYr1NlXs7ilMGbuJJ5evJV69z1WzhOso6r2tAm664vDA6rv1cyfZGwquxx\nv3s0yT3D65O4Df6JJM9ec95ds4vt+LTrdJSsfiWYvnODZTapx9T6jrKOum+tO7adtEXn3NO2bJ8e\npv9ZonuH1+Xisv7plvGHPeu+Lse5Huu2kYX5qGPucdax6vU61xpHrcuy8rZ57XYW6z/J8paVedx1\nlTEmxyyn2PRa9Vr6Ly9mq27r3IeA78Ekz8v1F7W7dsLetfrAzeA0Am/PYLXtNC8sOB2rfpKAzZzk\n/tRXYH31ByXlm/ILSd4xm83+8qqF92FQLJ+KrXOr3lkxaMHp27TPHaWPCvbadhLjtnPB2dqH65p9\ncpL7U1+B9dW3nR6kv5PvIMkXr7PwPlzMHKa/ne4oz+Kc1teX+7AfYZXd/rqf06Y9sGu0SXaFtnj6\n7PO58tzghSQ/t84C+xCo3JH+2amSvGCTh/HH91nvu33MVrWOTY7NcY7jLrWBqYd+679nYdGnrav6\nW0kCdFr7d1vrWZSYabyuXWo3tZOu27JnfYoL6U86x3HW49qivrgoocQ2El+U108foayjrnPd907K\neJ0l0c6qJEK1ozx/tc77Z30OGveBqcQd6/aTkzy2uzoWnoXjPO/G0ZzGN8IncXy2XebF9M/bfiTJ\nryf5wDoL7UPA981J/lXmWX/OZf3MR7V1g79FJ/l1pi2yTsrpdcorx+ta+lSs9cnqQ0kezHTmqmUX\nMGX61EXP4YLyppZbp/6LTlibdOLjdPhlGdSm9kHZ/stZLyX3IlP7ZtFD3nUihUUXiYvKXLbuoyxb\n5l3V3+4a/h7l+bijWGc965Rdj391mVdG06fWdzXHaxPbcJRkPZse+1Vt9/b0t93X801l7KzHkvHF\n66J+uc74M1XX455cS7tYlEHzKPv8UvX/un53bFjWVNl/kOkPZcbnvDqD36ptqI/PcdKdL6pPMk+m\ncueSZZads1bNs8x4P2zzHLRsXJhqU4fps+yVc+7F3HhttiwL4bpW9adFy9T9dd3kPpuUPaXOWjku\n87jH/iRsUqfjXMMsOn5T6z+pwOW448HhxL/k+iyXy67LaouyWI9fP1b9f9WH6+u28amf3KjPe/U2\n1PlHtvEB563pk7J9avpfMvjsdRbah4DvPyX579Of1J6Ro9e5PigHE9OKqXTMUw1gk8xQt49eT1mV\nga92S/rMpfXJ6lOTPD/T2fjKPiu/BzjVicbrL+lul2X3m3quctnAt27m1HWmbcuiE37ppOfS17vO\nVLaqXuMT/lQa33XqtaxNLPsAY6qtr/MM7GFu/MZhnW95juo4WVM3LXuTb2ZuXWMd5zJ/tqUud9ly\nqz54mXLU/rAobXbdT+sT0lTdlo2TYwdVGXVWxFur9+tMZouC7dph+p/jGZ8kp4KWur+Mt3H8cyTL\nxr5l2zruk+teWB1kfg44yOq+Xcpc9+LgU7Leh6D1zwMVUxdMyfXHZ90Mr8lm/WyVkml51YcOiwLz\nRf8vY/txAqdlNnnmrbSFF2V+zq2zeY7XtepnbEqZi/ZZyZS5qu3WH9qUOk4FsutcSyyr59S8ZRvH\nY8RUEDBVr2W/3bmNYHHRz4xM/f84ptazqC9OrX/dNl4ConXH+UV1WHd/Hkz8S/p2Px7DV42V5ybq\nM57/IPMM/3V5x4l/DjIfc8fnzMNcfw4cn4+WfcC5alpRyj9If+zW+oZvH7J0viPJn83idOVHsWin\nryJjH7WjtqNtlHeSbbHU4yjr2PY+gW3bxza6zToftawnMv82/2ayj+0FzsJJ9pXT6IcnvY6jlr8s\nULyY5P1J3j6bzV67rJB9CF7+7PB3m3XdZIfXn7Lsw/7i9Gx7YNikvJNsi8f5BMyFEbtuH9voNut8\n1LJuxmAv2c/2AmfhJPvKafTDbaxj1V0iR7FoufNJHk//7ejfW1XIPgQwD6b/dm98K8JpWfajsHAz\nWff2kF23D3UEbi7Gpe2xLzkJ69xif5ofED0r/e2dF2az2QdXzbwPAd87s95966dh6vmWfXZW2fGO\nktVv3QF8l3++Y9et8yzSKkfpFxePsMxxnEbf1aZYZB/bxknWeeq5+aPaRjmLytgkm+hRbPLM7CLj\n89+VbC/pzknbZhs76hh/1hmD18kcXduHsWRcx/Ez1VOO8rz7adhGDpFtuZrkqfQJn+5dZ4F9CPh+\nbPg7lYVs3Yfmi/pB5KlyVlk3e8+40/636vWyDF6r6lGSapQOc2lFeYvWV6aVb02P4mJVzpVsNlA+\nY/S6JApZVka97+vB4LdzfQB53NsQp+qw6IJkm514WeKV+vWydR63PpucJDdd11Ta8dKOb892TKUy\nX8c6dw6s2v/rJm45KePkJOs4jSyjq352YNsnwnWP40mta93EBzeDdff1srtoNr3wW/ccfZR2sCyb\n6LoOM8/gt6gOx2kf4315a+ZJLdYdH87qIvsoCcKmxuXj/EzMSd3Rtej8MX69TjKr2kkmiNmWcR3P\nLXmvWJQpeV1TyY6Ostyi95clBtrEcdrqufRZnl+a5FvWWWDnk7YkSdd1v5s+C2XJMlZ29jYD1lYf\nzF62XZtsc50Va1k55cJzk0xlmxqvb1G9ppRvi+vlD5I8meTZC5Z5On2Qei79ybrOwDSuzyamltt0\ne45bh7ErmR+78b7adN2bJn1ZdxvKfMseZD4Y/X1vkpeM6vVw+tTG+9bvx/t13GbqY1hen0v/Ic1t\n2SzrYil/2T4ar3/cP9fpK1czz1JYPgy4LYvbYH3BXKern2oXq9pxnaToMEfbP/WYVz5YvCXrtf/j\n9N1lfSBrlrvNc9+uJTa7nHk7SrZXt7Pazvq4btK3zi2Zb/x6PH+tHlvG/bzs60V9f8qieZ5O8sxc\nn2nyuOfKo55bW7Wtfr/pdeRB+rZSZ3Dedp2WOem+u6qd1dt4Zfj/U+l/b3zTfXmY5D1JPjHJf1iV\ntGXnA76u6/5Lks/N2XbWchAupP824qQyM7YadK5ys243p2fXLkRriwJ/fWIz40C32Pcxdh/rvK5d\n3bZdHi9O2zb7z6bLH+W67ywCmeOUV4LlcufT5cx/DmpZWYfVPPUHZrVl7bgO7Lexras+gB1Py4br\nPI2xYhfHo0X777H0X1KcT/ITSb511XN8+xDwXUx/Er+c7d36xfqmvk3YtQ4Bx3WUdq0vAACnrXzA\neTXJH85msxeuWmAfPsEqX+lv6zf4tmW3I+VpR6nzuI24wG3XvjzcfxKO0q639XzBcezjOARHpb1v\nxv6CNtWPEKz1CNU+BHyXMv/Ke9NsXic52O1j4LOPdW7FPpx4N312aR37sN2nbZv9UJ/Wxk7Druxj\n7X0zLe+vw5x+hmdOx8384fM66ljoGUnu7brun61aaB8Cvu/L/Bu+co/yqkxHRX3f+VRWnV05idG+\nlk+8y9ys292qkqBkl5xUGztc8P+b0ab7+KzT29O+g3jMZx+t81MhJ/Hh876aynBeP6/5SJKPpJEf\nXv8rWX1b4aqT0cFEGessN7Yr3y7eTM4qxf1ZlbPu9rZ4QbVsf23jmOiTx3eQ/T8Zr9sODhb8f9fs\n4m+P7sLvVdWZXPfZWW/DWa+/GAcJrRzf07Ir+6r+qZAWr2OOYtmxGY+l9euLSX4ryV9u5YfXX5A+\nZf4uNIx1U1yvOy+rncWFw7aO3Ul+oLAPfXdTy/bXNo7JafTJXTmpslhrY/Nxf3v0LI0v4rd9u/Ou\nbOdxnPU2nOT6y3i5zl0DdUbJpJ3je1p2cV+Nx6517947SWexzqMem1uT/MkkX73OzPtw0XiQ+W9H\nrXsr0VnecnTanWrXbq/aFbs4uK2y6JvoXdRaYLPo9r1Nt/M0bi+ERfatnez7t8UcTxkvN2kH+3hu\nr+1bHz1Nm969dxp12EWlDV1J8uEkX7bOQvtwcfm+JHdms1uJdu0ksqqDH2cAOKttPY1Ba2od6653\nnwfVuu7lofRdC+w3/bHbXbfo9r3xj8KelX04CRW7cDfGzWrZj/2epF0+5vsyBm3D5dHrXTwuV866\nAmegPg6L+uiFI5R7WHrONIIAABRNSURBVP19ODce/5N2WP1bx1lfx2ya+HGXXJv4/5X0v8O3Vp/a\nh4DvLyT5UPV6nQHsMMmjmV8sH6b/Jfvy/1WOM0hOlX/ci7WpTvx0+ltdp74CL9Oupe9gf3DM9U/Z\n5gXo1aw/aKx7W+1Ucp9VymBwZfS6/L00TL+c6WOyrYGk3sbyY6zntlj+cW1SjzpxUu3hJB84QnlT\nrqY/RpdG07e5vy5ns1u6VxnX9Sjqk/1RtvUwyUM5+ng3/ia0vtVqnYuXkn15m+s+C/X+q0+89cXN\novod94L8cPR30/c3XU+9fUe5eJpqs9tIBLSsHnW/rff3cS+Ox8eu7JuyLZczP3+cX1HWog83r6W/\njlmnjOTGn6/a5BrvtPrQWinkj2HRdpTjsqrP1e8/VP2/XFiP225pv+V6q27Pjw2vP5Tk1zPvM4e5\nsd0/o/r/eBvG2UjrsbZc7yTJv83JBfnjoKJsw8GwzvF4MDU+PHZCdRvb5JxQXy/X06aCqHL8n5xY\nZt39ftTcDuUYX0mfpKVc8/xukk9cp5CdD/hms9n7krw9fUP+p0l+KfMB8HL6DS4H5nzmO/2O9Dsl\nw/zfm+TB9B2s7jyLBtryacn5JB8blvtw5kHW5dwYRE4FGqVT13+fHi1XDuTVocyyTR9OP0i8M8mv\nVGVeTfLMYRuLx9If+I8l+f2h7Ivpj/EnjepSjBvoYTXt4WHbL6cPGH95qNdXDvvgD4f3Pprkh5P8\nv6Pln8y0qf19Ljfej3+5+jfV8epy/n7mxzpVOasu0Md1eWKY9uokj6ffxou5/vgm/X6+kPkFw4ND\nHcu2P5X+GK97P3q9364l+c/pj+fVJL8wTP9/0u/ri9X8v1PVuaz/0WG+pzNvb2Ol3vXJJ5kPZsvq\n9/4k/zHJ9wzzPzmsp6zrwkSZyTwAKOXck+STq+0p/1al2b6W5CeT3Je+PV7KfB/clnn7rk+Cq1zJ\nvJ09Mfz9WJLyEHT9OzePpG8bZRxZdNG47JP2sp3lb9l/Zb7xsuX9hzK/sMjw+qEkP5q+rf5S+v1R\n973xCfjpof7/Kn27/feZt5cnhvqUf+NjVtrHeFvqbS6vn5nVxrcwlzZQ74NHqzIXfUpenwyfrqY9\nOtR53BfLfijbV/rOI7l+rHli+Fu26zB9m/hI5mPfk+kv5j4wLHtL5uPsucwvhMr2lQx1/z7z4/rR\noS6Pjda/qC9eyrzvLhrrxuegTT6gW/Yh4q3V/8fllv3060neNky7mvk5c3wRVtd9nTtVrqRvl08N\nf8uxLWNxuSD/YK4Pjur61n3r1tH7Gcq6MKzjvenPwY8O21T6ftKPveVYluXPZb4PygdE9Rhb9+GL\nQ9n/NX37+YX055UnhnWVa5yDap23DOt8alhuNpRbb2vdR84Pdc9Qn49lsfpYTvW38ro+hmVbl327\nUOa9umS+dc+Ty9R1fWRBWaWNrbrurdv08zI/dh9KnyDjsSQ/l77tPZX++PxG+mP3O+n7ZxmnyzXM\nQ+nH6F9Of9faf8v8J8fqvlzmr68DL6dv01eT/Gbm59hS/tUkP5M+S+OfGrbv2lCverwu59byuj5X\njvdVqmmXhrIupj9nfDjza+Krw3s/OmzjR6ttr7Pq1+f9ZPr66LiuZj4evCn98boyvC7ntKuZ/8Rb\n/UH+eGw4qF7XH15eGMr87czPkRfTt4lyHr1UrWNqG+tzwqLr5PE5towF5QOgD6Xv/z+d5FeTvHZB\nOdfZ+YBvcD7Jh2ez2TfMZrM/leRL0+/UW9IfwPKpwR3pd8ql9BfNv51+Bz0jydckef6wTP0pWN0J\n6s53W/qD9liS/yF9o/7V9B31JUn+Q5KfHdb//yV5R5IXpu/Ub8u8MVzOPAArA96tmZ+gHs/84vz/\nb+/co/yqqjv+mbxIQiR0gPJwSSLtyg4EU6DKAiQIKioP6aoPCBYcoBQpLArtanksLBUqoLWrVaCU\nEA3BxNYUEDTVYgiUCilSG7FBhe+iIBJChjQhJmYSksmjf+x9uDe/zGR+M5P37M9aWfP73ce5+5yz\nn+fe/G49QKwA3gYcDvwurkwXSWoB7pQ0CPgy7tyeAJ4FHseT4bOB7+BKcA5wVbRXEpiiwMUYSyGx\nnCpp76T6sZx/lXQM8KKkB2J8h4Ts90j6FDCWytEVYy7XWxzt/x9eMLbH/pdjf90hvEKVSL8dmF6T\nqW6wxZAWRV/PjH1FBmJ8l8d34U7zNeCruOP+Z3ye50Sb/wU8GuP1NO7ElwCnAPcCM4FvAz+M8T4z\nxuDTMRelML+dykm+GeOxMfo7N2TbiOvnMnz+3qj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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4fcf9b7cf8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "1UBpe4CcUKFM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 주요 작품 구매 여부와 1시간 이내 구매 여부 관계\n",
        "\n",
        "* 각 컬럼의 값을 작품 구매 횟수(회차)로 보기로 했다."
      ]
    },
    {
      "metadata": {
        "id": "E_2y4NYeuVlO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "outputId": "62b2f383-5d30-4be8-8cb7-a6eea1ee2b57"
      },
      "cell_type": "code",
      "source": [
        "main_product = data[[0] + list(range(10,110))]\n",
        "main_product.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>...</th>\n",
              "      <th>100</th>\n",
              "      <th>101</th>\n",
              "      <th>102</th>\n",
              "      <th>103</th>\n",
              "      <th>104</th>\n",
              "      <th>105</th>\n",
              "      <th>106</th>\n",
              "      <th>107</th>\n",
              "      <th>108</th>\n",
              "      <th>109</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 101 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   0    10   11   12   13   14   15   16   17   18  ...   100  101  102  103  \\\n",
              "0    0    0   11    0    0    1   12    0    0    0 ...     0    0    0    0   \n",
              "1    0    0    0    0    0    0    0    0    0    0 ...     0    0    0    0   \n",
              "2    0    0    1    0    0    0   25    6    0    0 ...     0    0    0    0   \n",
              "3    0    0   34    0    0    0    0    0    0    0 ...     0    0    0    0   \n",
              "4    0    0    0    0    0    6    0    0    0    0 ...     0    0    0    0   \n",
              "\n",
              "   104  105  106  107  108  109  \n",
              "0    0    0    0    0    0    0  \n",
              "1    0    0    0    0    0    0  \n",
              "2    0    0    0    0    0    0  \n",
              "3    0    0    0    0    0    0  \n",
              "4    0    6    0    0    0    0  \n",
              "\n",
              "[5 rows x 101 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "metadata": {
        "id": "ZNBvAH7U_mhC",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 주요 작품별 구매 여부와 1시간 이내 구매 여부 관계"
      ]
    },
    {
      "metadata": {
        "id": "F3Baok3vx52T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2031
        },
        "outputId": "2808c50f-8b97-4a66-8fb1-21258d276063"
      },
      "cell_type": "code",
      "source": [
        "mp_purchased = pd.DataFrame(index=range(10,110), columns=['총 조회수', '구매'])\n",
        "mp_purchased"
      ],
      "execution_count": 26,
      "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>총 조회수</th>\n",
              "      <th>구매</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>12</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    총 조회수   구매\n",
              "10    NaN  NaN\n",
              "11    NaN  NaN\n",
              "12    NaN  NaN\n",
              "13    NaN  NaN\n",
              "14    NaN  NaN\n",
              "15    NaN  NaN\n",
              "16    NaN  NaN\n",
              "17    NaN  NaN\n",
              "18    NaN  NaN\n",
              "19    NaN  NaN\n",
              "20    NaN  NaN\n",
              "21    NaN  NaN\n",
              "22    NaN  NaN\n",
              "23    NaN  NaN\n",
              "24    NaN  NaN\n",
              "25    NaN  NaN\n",
              "26    NaN  NaN\n",
              "27    NaN  NaN\n",
              "28    NaN  NaN\n",
              "29    NaN  NaN\n",
              "30    NaN  NaN\n",
              "31    NaN  NaN\n",
              "32    NaN  NaN\n",
              "33    NaN  NaN\n",
              "34    NaN  NaN\n",
              "35    NaN  NaN\n",
              "36    NaN  NaN\n",
              "37    NaN  NaN\n",
              "38    NaN  NaN\n",
              "39    NaN  NaN\n",
              "..    ...  ...\n",
              "80    NaN  NaN\n",
              "81    NaN  NaN\n",
              "82    NaN  NaN\n",
              "83    NaN  NaN\n",
              "84    NaN  NaN\n",
              "85    NaN  NaN\n",
              "86    NaN  NaN\n",
              "87    NaN  NaN\n",
              "88    NaN  NaN\n",
              "89    NaN  NaN\n",
              "90    NaN  NaN\n",
              "91    NaN  NaN\n",
              "92    NaN  NaN\n",
              "93    NaN  NaN\n",
              "94    NaN  NaN\n",
              "95    NaN  NaN\n",
              "96    NaN  NaN\n",
              "97    NaN  NaN\n",
              "98    NaN  NaN\n",
              "99    NaN  NaN\n",
              "100   NaN  NaN\n",
              "101   NaN  NaN\n",
              "102   NaN  NaN\n",
              "103   NaN  NaN\n",
              "104   NaN  NaN\n",
              "105   NaN  NaN\n",
              "106   NaN  NaN\n",
              "107   NaN  NaN\n",
              "108   NaN  NaN\n",
              "109   NaN  NaN\n",
              "\n",
              "[100 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {
        "id": "EIzr2kyfyA5c",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "for i in range(10, 110):\n",
        "  mp_purchased.loc[i]['총 조회수'] = main_product[main_product[i] > 0].shape[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1PwCs_hQyBz5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "for i in range(10, 110):\n",
        "  mp_purchased.loc[i]['구매'] = main_product[((main_product[0] == 1) & (main_product[i] > 0))].shape[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "IJ1eQXrV0rcm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 653
        },
        "outputId": "1e16f9b7-8d74-46f5-dbc8-c58a1e87e093"
      },
      "cell_type": "code",
      "source": [
        "mp_purchased.plot.bar(figsize=(15,10),\n",
        "                     title=\"주요 작품별 구매 여부와 1시간 이내 구매 여부 관계\")"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4fce449ef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VAzDAEaduzhGnbh53NwCACbXoQz1rrTtKKW9PcnSSK5J8Mr3DPa/sW+3KJAd0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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4fd5151320>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "i1AqpYMb_vY4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### data[0] == 1일 때, 주요 작품별 구매 정보\n",
        "\n",
        "한번 추가해봤다"
      ]
    },
    {
      "metadata": {
        "id": "CL1G8bxYBEIk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "2a45037d-ed6b-43bf-c4eb-b105625c7fd2"
      },
      "cell_type": "code",
      "source": [
        "main_product[11].nlargest().values[0]"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "metadata": {
        "id": "QfWuo3Lb1Wdi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "mp_info = pd.DataFrame(index=range(10,110), columns=['최댓값', '평균값'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QSdb_566A7J0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "for i in range(10, 110):\n",
        "  mp_info.loc[i]['최댓값'] = main_product[i].nlargest().values[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "LttRRNEYBpX_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "for i in range(10, 110):\n",
        "  mp_info.loc[i]['평균값'] = main_product[i].mean()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "eUM0XuPjBsjn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 653
        },
        "outputId": "4843a3d0-e1f9-429a-ff29-4cc6bf2a4c65"
      },
      "cell_type": "code",
      "source": [
        "mp_info.plot.bar(figsize=(15,10),\n",
        "                title=\"data[0] == 1일 때, 주요 작품별 구매 정보\")"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4fc9c9ccf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AnRmFtlrr47v/f5Tk1yao89YBjAsAAID4cW0AAICmCW0AAAANE9oAAAAaJrQB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\nvA+y2qOzdQ6w1QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4fc91e45f8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "6A3kB3R1DEfS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 작품 태그 정보와 1시간 이내 구매 여부 관계"
      ]
    },
    {
      "metadata": {
        "id": "mv8wnBosFwAR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "2b8e51e5-fc77-4713-ee45-542c5df9e6e5"
      },
      "cell_type": "code",
      "source": [
        "data[110].value_counts()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    405882\n",
              "1    245083\n",
              "Name: 110, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "metadata": {
        "id": "w7rZ_Nv_DCLg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 851
        },
        "outputId": "bfcb930b-aba8-4ad7-f789-4b1ef37cb70e"
      },
      "cell_type": "code",
      "source": [
        "sns.factorplot(x=110, y=0, data=data, kind=\"bar\", size=10)\n",
        "plt.ylim(0,1)\n",
        "plt.title(\"작품 태그 정보와 1시간 이내 구매 여부 관계\")\n",
        "plt.xlabel(\"작품 태그 여부\")\n",
        "plt.ylabel(\"구매율\")"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1428: FutureWarning:\n",
            "\n",
            "remove_na is deprecated and is a private function. Do not use.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(6.8,0.5,'구매율')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MAACDQAYAgEEgAwDAIJABAGAQyAAAMAhkAAAYBDIAAAwCGQAABoEMAACDQAYAgEEgAwDA\nIJABAGAQyAAAMAhkAAAYBDIAAAz7Vn2Dqnp8kjOTHEzy/u4+a8Px5yS5d5Irk+xN8ozuvmLV4wIA\ngM2sdAa5qvYneWKSR3X3o5Pcs6ruOo7fKsmDu/sJ3f3UJB9O8pBVjgkAAA5l1TPI90vyju5eW26/\nJckDk1y03L48ySer6vZJPpPkK5Occ6gLnnbaLbJv394VDRcAgJu6VQfyrZNcOrYvTXLtDHJ3r1XV\na5M8M8klSd7X3Zcc6oKXXWb1BQAAN96BA/s33b/qJ+ldkuS0sX36cl+SpKq+Lskjuvtnu/tlST5X\nVT+44jEBAMCWVh3IFyR5cFXtWW6fkeTd4/jtk+wZ259L8lUrHhMAAGxpz9ra2vZn3QhV9bgkj0py\ndZILu/vscWxPkhcnuWOSLyS5RZIfOdQyi4sv/uxqBwwAwE3CgQP792y2f+WBvJmqenOSx3T3wcN9\nrEAGAOBoOK4C+cYQyAAAHA1bBbJ30gMAgEEgAwDAIJABAGAQyAAAMAhkAAAYBDIAAAwCGQAABoEM\nAACDQAYAgEEgAwDAIJABAGAQyAAAMAhkAAAYBDIAAAwCGQAABoEMAACDQAYAgEEgAwDAIJABAGAQ\nyAAAMAhkAAAYBDIAAAwCGQAABoEMAACDQAYAgEEgAwDAIJABAGAQyAAAMAhkAAAYBDIAAAwCGQAA\nBoEMAACDQAYAgEEgAwDAIJABAGAQyAAAMAhkAAAYBDIAAAwCGQAABoEMAACDQAYAgEEgAwDAIJAB\nAGAQyAAAMAhkAAAYBDIAAAwCGQAABoEMAACDQAYAgEEgAwDAIJABAGAQyAAAMAhkAAAYBDIAAAwC\nGQAABoEMAACDQAYAgEEgAwDAIJABAGAQyAAAMAhkAAAYBDIAAAwCGQAABoEMAACDQAYAgEEgAwDA\nIJABAGAQyAAAMAhkAAAYBDIAAAwCGQAABoEMAACDQAYAgEEgAwDAIJABAGAQyAAAMAhkAAAYBDIA\nAAwCGQAABoEMAACDQAYAgEEgAwDAIJABAGAQyAAAMAhkAAAYBDIAAAwCGQAABoEMAACDQAYAgEEg\nAwDAIJABAGAQyAAAMAhkAAAYBDIAAAwCGQAABoEMAACDQAYAgEEgAwDAIJABAGAQyAAAMAhkAAAY\nBDIAAAwCGQAABoEMAACDQAYAgEEgAwDAIJABAGAQyAAAMAhkAAAYBDIAAAwCGQAABoEMAACDQAYA\ngEEgAwDAIJABAGAQyAAAMAhkAAAYBDIAAAwCGQAABoEMAACDQAYAgEEgAwDAIJABAGAQyAAAMAhk\nAAAY9q36BlX1+CRnJjmY5P3dfdaG43dO8jPLzYNJfr67P7HqcQEAwGb2rK2treziVbU/yb9P8rDu\nXquq1yd5UXdftDy+J8nvJPnh7r50J9e8+uqDa/v27V3ZmAEAuMnYs9nOVc8g3y/JO7p7vcLfkuSB\nSS5abn9zko8neckypt/V3ecc6oKXXXbFqsYKAMBNyIED+zfdv+o1yLdOMmeGL13uW/dVSe6R5Nnd\n/fgk966qb1/xmAAAYEurDuRLkpw2tk9f7lt3RRYzzJ9fbr81yb1XPCYAANjSqgP5giQPXq41TpIz\nkrx7HP+zJPcZ29+S5EMrHhMAAGxppU/SS5KqelySRyW5OsmF3X32huM/lORBSf4xyf/T3S8+1PUu\nvvizqx0wAAA3CQcO7N/0SXorD+TNVNWbkzymuw8e7mMFMgAAR8NxFcg3hkAGAOBo2CqQvZMeAAAM\nAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMA\nwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQy\nAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBB\nIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgOOxArqqzVzEQ\nAAA4Huzb7oSqunOS07r7g8td96qqPUnum+STSe7V3f9hhWMEAIBj5pAzyFX18CSvTfIjVfVLy917\nknx7kqcnef3yPwEA4ISw3RKL5yQ5o7uflOS+VfUlSb4hya8mOTfJ51c8PgAAOKa2C+R93f2Z5cf/\nJck/S3JhkueudFQAALBLtgvka8bHVy3PX1v+AwCAE852gXxSVe1dfnz3JB9Ncrckz1zpqAAAYJds\nF8hvTPKqqnp2kiu7++IsXrni7cvjX5LkwArHBwAAx9QhX+atu8+pqkuS3CbJU5a7/yHJ72ex/OIX\nk5yy0hECAMAxtO3rIHf3f9yw65Lu/tvlx584+kMCAIDds2dt7Yvr+XYXX/zZL64BAwBwXDpwYP+e\nzfYfcga5ql6TZO8hTtmT5KruftqNGBsAABw3tlti8bxcP5DfnOR7sgjjdQeP9qAAAGC3bBfIP5zr\nAnlPkq/IDd9a+uokLznK4wIAgF2xXSD/51x/Bvntm5xjBhkAgBPGtk/Sq6rvSnKvXH9ZxVqSD3T3\nO1Y4tk15kh4AAEfDET1Jb+nHkvxCrv+20/uyWJ98zAMZAABWaSeBvKe737VxZ1U9fwXjAQCAXbXd\nW01vqqpOymKZBQAAnFB2MoP8zqp6T64L4j3Ljzd7wh4AwPWce+6rc/75f5jv/M6H56lP9dYJHP8O\n+530quod3f2QFY1nW56kBwBfPD7/+c/lKU/5l1lbW8uePSflta99Y0455dTdHhYkuRFP0quqJyW5\nVZJPJflQkpOP7tAAgBPVVVddlfXJuLW1a3LVVVcJZI57O1li8ZwkP57kdkmemuQuKx0RACeEn/h9\nz+UmOfiFq6+3/fPnvyR7b76T/OBE99JHvHi3h7ClnXyHfqa7/2j58Rur6ptWOSAA4MSx56TxF+w9\nG7bhOLWTQN645ndfVb1obF/T3S84ekMCAE4UJ528N7e82+n5h7+5NLe86+k56eS92z8IdtmR/I3j\nYBZvQb3umq1OBAA47T5fntPu8+W7PQzYsSMJ5Gu6+31HfSQAAHAc2MkbhVgsBADATcZOAvnnNmwL\nZgAATljbBnJ3v2fDrj/a9EQAADgB7GQG+Xq6+/h90ToAALiRDjuQAQDgRCaQAQBgEMgAADAIZAAA\nGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AG\nAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAI\nZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAA\ng0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAw75V36CqHp/kzCQHk7y/u8/a\n5Jx9SX4ryWe7+4dXPSYAANjKSmeQq2p/kicmeVR3PzrJPavqrpuc+vwkr0uyd5XjAQCA7ax6Bvl+\nSd7R3WvL7bckeWCSi9ZPqKp/meSDSf5mJxc87bRbZN8+HQ0A8MXswIH9uz2ELa06kG+d5NKxfWmS\na2eQq+obk9yuu99UVV+1kwtedtkVR3WAAAAcexdf/NndHsKWkb7qJ+ldkuS0sX36ct+6xyapqnpl\nkl9M8m1V9cwVjwkAALa06hnkC5I8q6p+ZbnM4owkL1k/2N3PXf94OYP8/O5+xYrHBAAAW1rpDHJ3\nfybJ65OcV1VvSPKh7v7rLU4/mOTqVY4HAAC2s/KXeevu85KcN/dV1ZuTPKa7D47zPp7k6aseDwAA\nHMrKA3kz3f29u3FfAADYjnfSAwCAQSADAMAgkAEAYBDIAAAwCGQAABgEMgAADAIZAAAGgQwAAINA\nBgCAQSADAMAgkAEAYBDIAAAwCGQAABgEMgAADAIZAAAGgQwAAINABgCAQSADAMAgkAEAYBDIAAAw\nCGQAABgEMgAADAIZAAAGgQwAAINABgCAQSADAMAgkAEAYBDIAAAwCGQAABgEMgAADAIZAAAGgQwA\nAINABgCAQSADAMAgkAEAYBDIAAAwCGQAABgEMgAADAIZAAAGgQwAAINABgCAQSADAMAgkAEAYBDI\nAAAwCGQAABgEMgAADAIZAAAGgQwAAINABgCAQSADAMAgkAEAYBDIAAAwCGQAABgEMgAADAIZAAAG\ngQwAAINABgCAQSADAMAgkAEAYBDIAAAwCGQAABgEMgAADAIZAAAGgQwAAINABgCAQSADAMAgkAEA\nYBDIAAAwCGQAABgEMgAADAIZAAAGgQwAAINABgCAQSADAMAgkAEAYBDIAAAwCGQAABgEMgAADAIZ\nAAAGgQwAAINABgCAQSADAMAgkAEAYBDIAAAwCGQAABgEMgAADAIZAAAGgQwAAINABgCAQSADAMAg\nkOFGOvfcV+exj/3unHvuq3d7KADAUbBvtwfwxehZL33rbg+B48Q1B6/KxRf+YZLk/PPflr/49K1z\n0t6Td3lU7LaX/8QZuz0EAG4EM8gAADAIZLgRTtp7ck498LVJklMPfI3ZYwA4AVhiATfSl97pW/Ol\nd/rW3R4GAHCUmEEGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMA\nwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQy\nAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAM+1Z9g6p6fJIzkxxM8v7u\nPmvD8d9Ick2S05P8QXe/YdVjAgCAraw0kKtqf5InJnlYd69V1eur6q7dfdH6Od39jOW5e5K8O8kh\nA/m0026Rffv2rnLYADfKgQP7d3sIAMe94/ln5apnkO+X5B3dvbbcfkuSBya5aJNzb57k0u0ueNll\nVxy90QGswMUXf3a3hwBw3DseflZuFemrXoN861w/ei9d7tvMi5OctcUxAAA4JlYdyJckOW1sn77c\ndz1V9Zwkf9Hd71vxeAAA4JBWHcgXJHnwcn1xkpyRxTrja1XVM5P8Y3e/ccVjAQCAba10DXJ3f6aq\nXp/kvKq6OsmF3f3X68er6n5JfirJH1bVK5e7f7a7L17luAAAYCsrf5m37j4vyXlzX1W9OcljuvtP\nk9xp1WMAAICdWnkgb6a7v3c37gsAANvxTnoAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMA\nwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQy\nAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBB\nIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAA\nGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AG\nAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAI\nZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAA\ng0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgA\nADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaB\nDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBg\nEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkA\nAIZ9q75BVT0+yZlJDiZ5f3efdTjHAQDgWFrpDHJV7U/yxCSP6u5HJ7lnVd11p8cBAOBYW/UM8v2S\nvKO715bbb0nywCQX7fD4DRzF5tB9AAAE0ElEQVQ4sH/Pisa6Y2866/G7PQSA497rnvLy3R4CwBFZ\n9RrkWye5dGxfuty30+MAAHBMrTqQL0ly2tg+fblvp8cBAOCYWnUgX5DkwVW1vizijCTvPozjAABw\nTO1ZW1vb/qwboaoel+RRSa5OcmF3n304xwEA4FhaeSBvpqrenOQx3X3wmN8cAAAOYVcCGQAAjlfe\nSQ8AAIaVv5MenMi8EyTA9qpqb5IXJbl3dz90t8cD2zGDDEfIO0EC7Ngjkrw1Jub4IuEbFY7cYb8T\nJMBNUXe/JUmqareHAjtiBhmOnHeCBIATkECGI+edIAHgBCSQ4ch5J0gAOAEJZDhC3f2ZJK9Pcl5V\nvSHJh7r7r3d5WADHs6t2ewCwE94oBAAABjPIAAAwCGQAABgEMgAADAIZAAAGgQwAAINABjhGquoB\nVfX8DfteXVUfq6oPVNU7q+r9y+2XH+a119/K99er6o5Hc9zjHl9VVeeM7TtW1bmbnHfbqnrTKsYA\ncCzs2+0BAJxoquqXknzrcnN/Fq+R/ZQsfuZe7+dudz+tql6W5HXdfWFV3SPJD3b3s7e49lcnOSfJ\n3uWuP+vuH0ty8+X23vVjVXVqkj9MsmfjdZJc0N3P3eIe357k7OXmx5P8q+6+fJPx783mEy0nbbEf\n4IuCQAY4yrr7eesfV9ULk1x4FC9/5yTv7e6f38E4PpfkgZsdq6p3bbH/lCQvT/Lg7r60ql6c5L9U\n1SeTnJrkrzY85Dur6p0b9t0sySe2Gx/A8UogA6xIVX1PkkcmeeHY/eSqekCSH0jyqSRvTfI1Se5b\nVf+Q5EuSfEVVfU2S7+rug5tc+tSqOpDk5CQHu/tTRzC8K7fYf/8kb+vuS5fbL03yHd39gKq6S5Ln\nbzj//O5+8txRVbdL8rIjGBPAcUEgAxxlVXVykuclOT3Jq5O8tarWZ5Vf190vGKd/5xHc4v5ZLGG4\nKsl/S/Lby/v+cZK7JfnlHVzj6i323zbJ369vdPflVbXV/1dcluTrl/edbpbk7TsYA8BxSSADHEVV\n9eVJXpvkNd39u8t970ry7Un+exZRmar65ixmZ6dTk3xuw76XdPf5G/advyGykyTLWd5X7mCMpyW5\ndIvDn0pyzw3n7q+qxyb5sg33uzzJN253P4AvNgIZ4Oj6ZJJHdve1Sxi6u5P0cvP9y33/NckD5gOr\n6o+7+3r7tlNVpye5xWGO8Y5J/m6LY+9NclZVvXS5zOInkvynJJ9P8oVx3x9K8vjxuJOyeELi5WPf\nWpIzu/vThzk+gF0lkAGOou5eS3JlVf1OFssVptOTvLK7X7G+o6peleRrk1yT5HZjucLfd/djN7nF\nR5O8tKoensVs86VJfucwh3nXJP9zi/F/oap+JMnbqupgkguS/Gh3ry3XIN93ed5rkrxmfB53SPLL\n3f2EwxwLwHFHIAOsQHc/ZuO+qnpwFuuHp7t398Z92WRd7/p1P5pNljVU1Q9s2H5kkh/bYninJlmr\nqjOT/Eh3f2jDPd6X5Fu2eCzACU8gA+yuy6vq3VnMIN8Y60s6Dmbxyhb/KYulEUfTwWz95L5rcuM/\nB4Djwp61tbXdHgPATUJVfWOSr+vu39ztsQCwNYEMAACDtwIFAIBBIAMAwCCQAQBgEMgAADAIZAAA\nGP5/6RDBUxao0v8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4fccfde080>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "CgUVh2uNNviS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "작품 태그가 있을 때 구매율이 더 높아"
      ]
    },
    {
      "metadata": {
        "id": "q7II81HPFmLE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 구매할 때 필요한 코인과 1시간 이내 구매 여부 관계"
      ]
    },
    {
      "metadata": {
        "id": "wHtxg1gNDX3x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "8ee55095-3f84-46eb-9b07-6ede2e590a9b"
      },
      "cell_type": "code",
      "source": [
        "data[111].value_counts(sort=False)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      60518\n",
              "1       2242\n",
              "2     116680\n",
              "3     445848\n",
              "4      23421\n",
              "5       1180\n",
              "50      1076\n",
              "Name: 111, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "metadata": {
        "id": "DElX1xf4EHkD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 851
        },
        "outputId": "dd8f60ca-6cc5-40f0-8a61-4808eb02c279"
      },
      "cell_type": "code",
      "source": [
        "sns.factorplot(x=111, y=0, data=data, kind=\"bar\", size=10)\n",
        "plt.title(\"구매할 때 필요한 코인과 1시간 이내 구매 여부 관계\")\n",
        "plt.xlabel(\"필요한 코인 수\")\n",
        "plt.ylabel(\"구매율\")\n",
        "plt.ylim(0,1)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1428: FutureWarning:\n",
            "\n",
            "remove_na is deprecated and is a private function. Do not use.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZ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\nZQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4fc9cc15f8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "aNiVKrMYNmOw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "0, 5, 1, 3, 4, 2, 50 코인 순으로 구매율이 높다"
      ]
    },
    {
      "metadata": {
        "id": "4BE7FgxcHq-a",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 완결여부와 1시간 이내 구매 여부 관계"
      ]
    },
    {
      "metadata": {
        "id": "6kACrMx-Hqrp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "8ca097e0-fe60-47db-a7c8-40fd88bf87ab"
      },
      "cell_type": "code",
      "source": [
        "data[112].value_counts()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    518854\n",
              "1    132111\n",
              "Name: 112, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "metadata": {
        "id": "TSiu3bykGQBr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 851
        },
        "outputId": "03f59540-ceb0-495c-b182-dd4c0ee76d50"
      },
      "cell_type": "code",
      "source": [
        "sns.factorplot(x=112, y=0, data=data, kind=\"bar\", size=10)\n",
        "plt.title(\"완결 여부와 1시간 이내 구매 여부 관계\")\n",
        "plt.xlabel(\"완결 여부\")\n",
        "plt.ylabel(\"구매율\")\n",
        "plt.ylim(0,1)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1428: FutureWarning:\n",
            "\n",
            "remove_na is deprecated and is a private function. Do not use.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7f7i7fyLJtVX1tDWPCQAA1h7C70vysKo6sFw+L8k7t2z/kiQHtixfm+Seax4TAADkwMbG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\n91KRnAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4fca6ce7b8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "XVHY88eYNfy3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "완결된 작품의 구매율이 더 높은 것을 알 수 있다."
      ]
    },
    {
      "metadata": {
        "id": "Mv2JLX4UNTPU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 스케쥴 정보와 1시간 이내 구매 여부 관계"
      ]
    },
    {
      "metadata": {
        "id": "-1EMXZowIF_E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "a3389119-2b4d-46cf-cdb9-7a6298a61b69"
      },
      "cell_type": "code",
      "source": [
        "# 113-122\n",
        "data[list(range(113,123))].head()"
      ],
      "execution_count": 41,
      "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>113</th>\n",
              "      <th>114</th>\n",
              "      <th>115</th>\n",
              "      <th>116</th>\n",
              "      <th>117</th>\n",
              "      <th>118</th>\n",
              "      <th>119</th>\n",
              "      <th>120</th>\n",
              "      <th>121</th>\n",
              "      <th>122</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   113  114  115  116  117  118  119  120  121  122\n",
              "0    0    0    0    0    0    0    0    0    0    0\n",
              "1    0    0    0    0    0    0    0    0    0    0\n",
              "2    0    0    0    0    0    0    0    0    0    0\n",
              "3    0    0    0    0    0    0    0    0    0    0\n",
              "4    0    0    0    1    0    0    0    0    0    0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "metadata": {
        "id": "byFk3xHOOH-K",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "schedule = pd.DataFrame(index=range(113,123), columns=[\"조회수\", \"구매수\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3BWW2T0GOc4R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "outputId": "edc71228-498a-4fdd-d14f-dc35fd27f09b"
      },
      "cell_type": "code",
      "source": [
        "for i in range(113, 123):\n",
        "  schedule.loc[i][\"조회수\"] = data[data[i] == 1].shape[0]\n",
        "schedule"
      ],
      "execution_count": 43,
      "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>조회수</th>\n",
              "      <th>구매수</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>67120</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>62017</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>87714</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>67548</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>66626</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>58734</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>79912</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>93935</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>1012</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>269</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       조회수  구매수\n",
              "113  67120  NaN\n",
              "114  62017  NaN\n",
              "115  87714  NaN\n",
              "116  67548  NaN\n",
              "117  66626  NaN\n",
              "118  58734  NaN\n",
              "119  79912  NaN\n",
              "120  93935  NaN\n",
              "121   1012  NaN\n",
              "122    269  NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "metadata": {
        "id": "yC9X-HVhOnd-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "for i in range(113, 123):\n",
        "  schedule.loc[i][\"구매수\"] = data[(data[i] == 1) & (data[0] == 1)].shape[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "eL3XjRmvPBrs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        },
        "outputId": "72d1aaea-8945-428d-f7c4-5318b92ab1ed"
      },
      "cell_type": "code",
      "source": [
        "schedule.plot.bar(figsize=(15,10))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4fc817c320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IPAAAgCIEHgAAQBECDwAAoAiBBwAAUITAAwAAKELgAQAAFCHwAAAAihB4AAAARQg8AACA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\n4AEAABQh8AAAAIoQeAAAAEX8f0A9nu7R1cEGAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4fc80617f0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "yYQtpZyKPOOg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1714
        },
        "outputId": "aa674f51-3674-4208-bd8d-acbb36896562"
      },
      "cell_type": "code",
      "source": [
        "_ = schedule.T.plot.barh(figsize=(15,30), subplots=True, sharex=False)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9n3+3s6Wqnpjkzd19nrHd/iY3Zvp2kkcmubq7/2hSN7YzoKpeleT5Wbjb6i3dffFyn4M3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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4fc81656d8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ZfV5h8IlRPCT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EDZbWJrpRcat",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 장르 정보와 1시간 이내 구매 여부 관계"
      ]
    },
    {
      "metadata": {
        "id": "Wkxf_D93RdYs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "01693111-7740-4232-beb6-29bc1a836828"
      },
      "cell_type": "code",
      "source": [
        "# 123-140\n",
        "data[list(range(123,141))].head()"
      ],
      "execution_count": 47,
      "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>123</th>\n",
              "      <th>124</th>\n",
              "      <th>125</th>\n",
              "      <th>126</th>\n",
              "      <th>127</th>\n",
              "      <th>128</th>\n",
              "      <th>129</th>\n",
              "      <th>130</th>\n",
              "      <th>131</th>\n",
              "      <th>132</th>\n",
              "      <th>133</th>\n",
              "      <th>134</th>\n",
              "      <th>135</th>\n",
              "      <th>136</th>\n",
              "      <th>137</th>\n",
              "      <th>138</th>\n",
              "      <th>139</th>\n",
              "      <th>140</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   123  124  125  126  127  128  129  130  131  132  133  134  135  136  137  \\\n",
              "0    0    0    1    0    0    0    0    0    0    0    0    0    0    0    0   \n",
              "1    0    0    1    0    0    0    0    0    0    0    0    0    0    0    0   \n",
              "2    0    0    1    0    0    0    0    0    0    0    0    0    0    0    0   \n",
              "3    0    0    1    0    0    0    0    0    0    0    0    0    0    0    0   \n",
              "4    0    0    1    0    0    0    0    0    0    0    0    0    0    0    0   \n",
              "\n",
              "   138  139  140  \n",
              "0    0    0    0  \n",
              "1    0    0    0  \n",
              "2    0    0    0  \n",
              "3    0    0    0  \n",
              "4    0    0    0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "metadata": {
        "id": "1C7gXqOlRlFX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "outputId": "cd9ba6d2-be1a-46c6-e080-8c946c5d4698"
      },
      "cell_type": "code",
      "source": [
        "genre = pd.DataFrame(index=range(123,141), columns=[\"조회수\", \"구매수\"])\n",
        "genre"
      ],
      "execution_count": 48,
      "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>조회수</th>\n",
              "      <th>구매수</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     조회수  구매수\n",
              "123  NaN  NaN\n",
              "124  NaN  NaN\n",
              "125  NaN  NaN\n",
              "126  NaN  NaN\n",
              "127  NaN  NaN\n",
              "128  NaN  NaN\n",
              "129  NaN  NaN\n",
              "130  NaN  NaN\n",
              "131  NaN  NaN\n",
              "132  NaN  NaN\n",
              "133  NaN  NaN\n",
              "134  NaN  NaN\n",
              "135  NaN  NaN\n",
              "136  NaN  NaN\n",
              "137  NaN  NaN\n",
              "138  NaN  NaN\n",
              "139  NaN  NaN\n",
              "140  NaN  NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "metadata": {
        "id": "AOnnSCuvR2JV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "outputId": "8d90959d-d420-4e5f-9f82-f915320d4c23"
      },
      "cell_type": "code",
      "source": [
        "for i in range(123, 141):\n",
        "  genre.loc[i][\"조회수\"] = data[data[i] == 1].shape[0]\n",
        "genre"
      ],
      "execution_count": 49,
      "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>조회수</th>\n",
              "      <th>구매수</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>41903</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>208487</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>166800</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>60745</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>129414</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>93641</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>2848</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>100788</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>24137</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>8101</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>15455</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>3030</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>1452</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>25426</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>176602</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>94950</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>17543</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>2061</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        조회수  구매수\n",
              "123   41903  NaN\n",
              "124  208487  NaN\n",
              "125  166800  NaN\n",
              "126   60745  NaN\n",
              "127  129414  NaN\n",
              "128   93641  NaN\n",
              "129    2848  NaN\n",
              "130  100788  NaN\n",
              "131   24137  NaN\n",
              "132    8101  NaN\n",
              "133   15455  NaN\n",
              "134    3030  NaN\n",
              "135    1452  NaN\n",
              "136   25426  NaN\n",
              "137  176602  NaN\n",
              "138   94950  NaN\n",
              "139   17543  NaN\n",
              "140    2061  NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "metadata": {
        "id": "3ckcS2IlSAvU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "outputId": "2bed8ea5-8037-4212-8c19-b0f442010cc0"
      },
      "cell_type": "code",
      "source": [
        "for i in range(123, 141):\n",
        "  genre.loc[i][\"구매수\"] = data[(data[i] == 1) & (data[0] == 1)].shape[0]\n",
        "genre"
      ],
      "execution_count": 50,
      "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>조회수</th>\n",
              "      <th>구매수</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>41903</td>\n",
              "      <td>13139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>208487</td>\n",
              "      <td>103035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>166800</td>\n",
              "      <td>73249</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>60745</td>\n",
              "      <td>15655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>129414</td>\n",
              "      <td>32207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>93641</td>\n",
              "      <td>29962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>2848</td>\n",
              "      <td>1198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>100788</td>\n",
              "      <td>26606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>24137</td>\n",
              "      <td>7445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>8101</td>\n",
              "      <td>2231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>15455</td>\n",
              "      <td>3348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>3030</td>\n",
              "      <td>1362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>1452</td>\n",
              "      <td>818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>25426</td>\n",
              "      <td>8593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>176602</td>\n",
              "      <td>54380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>94950</td>\n",
              "      <td>20361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>17543</td>\n",
              "      <td>4155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>2061</td>\n",
              "      <td>519</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        조회수     구매수\n",
              "123   41903   13139\n",
              "124  208487  103035\n",
              "125  166800   73249\n",
              "126   60745   15655\n",
              "127  129414   32207\n",
              "128   93641   29962\n",
              "129    2848    1198\n",
              "130  100788   26606\n",
              "131   24137    7445\n",
              "132    8101    2231\n",
              "133   15455    3348\n",
              "134    3030    1362\n",
              "135    1452     818\n",
              "136   25426    8593\n",
              "137  176602   54380\n",
              "138   94950   20361\n",
              "139   17543    4155\n",
              "140    2061     519"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "metadata": {
        "id": "VJpoCZHMTmvh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "outputId": "3fad68ad-8f3c-4765-824b-aab2a2dde59f"
      },
      "cell_type": "code",
      "source": [
        "_ = genre.plot.bar(figsize=(15, 10))"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cay5+B7XW9lfVtUmekgOnZG5f8fSOHChtt2zQ/JD27r1zDVswneXlbdm9+/a5vf489Zo7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\ngMH9fxnHUZek6igeAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4fcddc71d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "afJsNGMgTTY1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2256
        },
        "outputId": "6f2926f5-5752-4f9d-e47e-4340066711e6"
      },
      "cell_type": "code",
      "source": [
        "_ = genre.T.plot.barh(figsize=(15,40), subplots=True, sharex=False)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PiHk/M+sh6F2b5PDxX2CS5MVJPjPD8QzRP0nyVxl97uRHqmr/cf3IJJ/ehTrLM61+ex52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HMR8RpgQ8PhzMuAA9otn7IXUqBmbVMdZr4hM//fhjLbZpqNbpuIeEP14+kR8c2I+FS1PC3j2kwI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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4fc87a9048>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "c9m187QIT7rq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 유저 성향 정보와 1시간 이내 구매 여부 관계"
      ]
    },
    {
      "metadata": {
        "id": "CB0-UHJXTeBs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "8c3eec72-3692-4cfc-dce4-8b00c8b125cd"
      },
      "cell_type": "code",
      "source": [
        "# 151-166\n",
        "user_data = data[[0] + list(range(151, 167))].fillna(0)\n",
        "user_data.head()"
      ],
      "execution_count": 53,
      "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>0</th>\n",
              "      <th>151</th>\n",
              "      <th>152</th>\n",
              "      <th>153</th>\n",
              "      <th>154</th>\n",
              "      <th>155</th>\n",
              "      <th>156</th>\n",
              "      <th>157</th>\n",
              "      <th>158</th>\n",
              "      <th>159</th>\n",
              "      <th>160</th>\n",
              "      <th>161</th>\n",
              "      <th>162</th>\n",
              "      <th>163</th>\n",
              "      <th>164</th>\n",
              "      <th>165</th>\n",
              "      <th>166</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1952</td>\n",
              "      <td>0.3310</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1490</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2580</td>\n",
              "      <td>0.0030</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4615</td>\n",
              "      <td>0.4596</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0142</td>\n",
              "      <td>0.0072</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0187</td>\n",
              "      <td>0.0047</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0287</td>\n",
              "      <td>0.0055</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.8390</td>\n",
              "      <td>0.0813</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0611</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0186</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1571</td>\n",
              "      <td>0.4217</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0368</td>\n",
              "      <td>0.0140</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0207</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2805</td>\n",
              "      <td>0.0692</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   0    151     152     153  154     155     156  157     158     159  160  \\\n",
              "0    0  0.0  0.1952  0.3310  0.0  0.1490  0.0000  0.0  0.0000  0.0000  0.0   \n",
              "1    0  0.0  0.0000  0.0000  0.0  0.0000  0.0000  0.0  0.5000  0.0000  0.0   \n",
              "2    0  0.0  0.4615  0.4596  0.0  0.0142  0.0072  0.0  0.0187  0.0047  0.0   \n",
              "3    0  0.0  0.8390  0.0813  0.0  0.0611  0.0000  0.0  0.0000  0.0000  0.0   \n",
              "4    0  0.0  0.1571  0.4217  0.0  0.0368  0.0140  0.0  0.0207  0.0000  0.0   \n",
              "\n",
              "   161  162     163     164  165  166  \n",
              "0  0.0  0.0  0.2580  0.0030  0.0  0.0  \n",
              "1  0.0  0.0  0.5000  0.0000  0.0  0.0  \n",
              "2  0.0  0.0  0.0287  0.0055  0.0  0.0  \n",
              "3  0.0  0.0  0.0186  0.0000  0.0  0.0  \n",
              "4  0.0  0.0  0.2805  0.0692  0.0  0.0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "metadata": {
        "id": "eUnrZCdFT611",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "60efd957-c226-4a5a-e2f5-90036e1b8639"
      },
      "cell_type": "code",
      "source": [
        "sns.heatmap(user_data.corr())"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4fc553c978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9qvidwB1t0yMz3ohhip/dSuCZku6YLnZyfEQspDWHfb6krwNXAZ8q27ak70v6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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4fc986c0b8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "5AmXO1tfUoYA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}