01_02_music_chart_analyze.ipynb

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  "metadata": {
    "colab": {
      "name": "01_02 music chart analyze.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jaygo-kr/brunch/blob/master/01_02_music_chart_analyze.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mQ5KF0ZfU8xw",
        "colab_type": "text"
      },
      "source": [
        "# 음악차트 데이터 분석 및 시각화\n",
        "\n",
        "\n",
        "기존에 추출한 네이버뮤직 - 시대별 음악 차트 활용 \n",
        "\n",
        "[네이버 뮤직 - 시대별 음악 크롤링하기](https://github.com/jaygo-kr/brunch/blob/master/01_01_get_naver_best_music.ipynb)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YDqKZIGsS4aq",
        "colab_type": "code",
        "outputId": "b7f547be-06b6-4955-ddc6-d24536404e5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from google.colab import files, drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 1,
      "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"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AHJrs1v4kvc2",
        "colab_type": "code",
        "outputId": "cdad1725-570c-4af6-c466-d7aeed1406ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        }
      },
      "source": [
        "! apt-get install fonts-nanum* # 한글 출력을 위한 글꼴 설치"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "Note, selecting 'fonts-nanum-eco' for glob 'fonts-nanum*'\n",
            "Note, selecting 'fonts-nanum' for glob 'fonts-nanum*'\n",
            "Note, selecting 'fonts-nanum-gothic-light' for glob 'fonts-nanum*'\n",
            "Note, selecting 'fonts-nanum-coding' for glob 'fonts-nanum*'\n",
            "Note, selecting 'fonts-nanum-extra' for glob 'fonts-nanum*'\n",
            "fonts-nanum is already the newest version (20170925-1).\n",
            "fonts-nanum-coding is already the newest version (2.5-1).\n",
            "fonts-nanum-eco is already the newest version (1.000-6).\n",
            "fonts-nanum-extra is already the newest version (20170925-1).\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-410\n",
            "Use 'apt autoremove' to remove it.\n",
            "0 upgraded, 0 newly installed, 0 to remove and 7 not upgraded.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SvAs0thvS4eM",
        "colab_type": "code",
        "outputId": "e0615ed7-f8a9-43de-8036-1d8a818ce549",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        }
      },
      "source": [
        "os.listdir('./gdrive/My Drive/Colab/writing note/01. music chart analyze/data/') # 01_01 get_naver_best_music.ipynb 로 생성한 음악차트 위치 확인"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['full_data_sep.csv',\n",
              " 'full_data_era.csv',\n",
              " 'full_data_genre.csv',\n",
              " 'full_data_all.csv',\n",
              " 'merged_data.csv']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4lG_r9zDS4gy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "music_data = pd.read_csv('./gdrive/My Drive/Colab/writing note/01. music chart analyze/data/merged_data.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zDU8dMc3ZwI3",
        "colab_type": "text"
      },
      "source": [
        "## 1. 시대별 TOP 10"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kTzwGIA1TJjk",
        "colab_type": "code",
        "outputId": "556b52d3-0d24-4c87-c206-4d78df1a98e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "music_data[music_data['시대']==1980].sort_values(by=['순위/시대'])[['곡명','아티스트','앨범','시대','장르']].head(10).reset_index(drop=True)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>곡명</th>\n",
              "      <th>아티스트</th>\n",
              "      <th>앨범</th>\n",
              "      <th>시대</th>\n",
              "      <th>장르</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>가로수 그늘아래 서면</td>\n",
              "      <td>이문세</td>\n",
              "      <td>5집 가로수 그늘아래서면</td>\n",
              "      <td>1980</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>사랑하기 때문에</td>\n",
              "      <td>유재하</td>\n",
              "      <td>1집 사랑하기 때문에</td>\n",
              "      <td>1980</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>너에게로 또 다시</td>\n",
              "      <td>변진섭</td>\n",
              "      <td>2집 너에게로 또다시</td>\n",
              "      <td>1980</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>가리워진 길</td>\n",
              "      <td>유재하</td>\n",
              "      <td>1집 사랑하기 때문에</td>\n",
              "      <td>1980</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>비처럼 음악처럼</td>\n",
              "      <td>김현식</td>\n",
              "      <td>3집 비처럼 음악처럼</td>\n",
              "      <td>1980</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>빗속에서</td>\n",
              "      <td>이문세</td>\n",
              "      <td>3집 난 아직 모르잖아요</td>\n",
              "      <td>1980</td>\n",
              "      <td>록/포크</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>그대 내 품에</td>\n",
              "      <td>유재하</td>\n",
              "      <td>1집 사랑하기 때문에</td>\n",
              "      <td>1980</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>나 항상 그대를</td>\n",
              "      <td>이선희</td>\n",
              "      <td>4집 사랑이 지는 이자리</td>\n",
              "      <td>1980</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>내 마음에 비친 내 모습</td>\n",
              "      <td>유재하</td>\n",
              "      <td>1집 사랑하기 때문에</td>\n",
              "      <td>1980</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>그 아픔까지 사랑한거야</td>\n",
              "      <td>조정현</td>\n",
              "      <td>1집 그 아픔까지 사랑한거야</td>\n",
              "      <td>1980</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              곡명 아티스트               앨범    시대    장르\n",
              "0    가로수 그늘아래 서면  이문세    5집 가로수 그늘아래서면  1980   발라드\n",
              "1       사랑하기 때문에  유재하      1집 사랑하기 때문에  1980   발라드\n",
              "2      너에게로 또 다시  변진섭      2집 너에게로 또다시  1980   발라드\n",
              "3         가리워진 길  유재하      1집 사랑하기 때문에  1980   발라드\n",
              "4       비처럼 음악처럼  김현식      3집 비처럼 음악처럼  1980   발라드\n",
              "5           빗속에서  이문세    3집 난 아직 모르잖아요  1980  록/포크\n",
              "6        그대 내 품에  유재하      1집 사랑하기 때문에  1980   발라드\n",
              "7       나 항상 그대를  이선희    4집 사랑이 지는 이자리  1980   발라드\n",
              "8  내 마음에 비친 내 모습  유재하      1집 사랑하기 때문에  1980   발라드\n",
              "9   그 아픔까지 사랑한거야  조정현  1집 그 아픔까지 사랑한거야  1980   발라드"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zxxafpPtZQcF",
        "colab_type": "code",
        "outputId": "af17d644-ac72-4721-a62a-b0b18541df46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "music_data[music_data['시대']==1990].sort_values(by=['순위/시대'])[['곡명','아티스트','앨범','시대','장르']].head(10).reset_index(drop=True)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\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>곡명</th>\n",
              "      <th>아티스트</th>\n",
              "      <th>앨범</th>\n",
              "      <th>시대</th>\n",
              "      <th>장르</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>잘못된 만남</td>\n",
              "      <td>김건모</td>\n",
              "      <td>3집 잘못된 만남</td>\n",
              "      <td>1990</td>\n",
              "      <td>댄스</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>커플</td>\n",
              "      <td>젝스키스</td>\n",
              "      <td>3.5집 Special Album</td>\n",
              "      <td>1990</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>애상</td>\n",
              "      <td>쿨(COOL)</td>\n",
              "      <td>4집 애상</td>\n",
              "      <td>1990</td>\n",
              "      <td>댄스</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>여전히 아름다운지</td>\n",
              "      <td>김연우</td>\n",
              "      <td>4집 A Night In Seoul</td>\n",
              "      <td>1990</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>아름다운 이별</td>\n",
              "      <td>김건모</td>\n",
              "      <td>3집 잘못된 만남</td>\n",
              "      <td>1990</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>회상 (December)</td>\n",
              "      <td>터보</td>\n",
              "      <td>3집 Born Again</td>\n",
              "      <td>1990</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>기억의 습작</td>\n",
              "      <td>전람회</td>\n",
              "      <td>1집 Exhibition</td>\n",
              "      <td>1990</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>I'm Your Girl</td>\n",
              "      <td>S.E.S.</td>\n",
              "      <td>1집 S.E.S</td>\n",
              "      <td>1990</td>\n",
              "      <td>댄스</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Love Is... (3+3=0)</td>\n",
              "      <td>터보(Turbo)</td>\n",
              "      <td>2집 New Sensation</td>\n",
              "      <td>1990</td>\n",
              "      <td>댄스</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>그녀와의 이별</td>\n",
              "      <td>김현정</td>\n",
              "      <td>1집 Legend</td>\n",
              "      <td>1990</td>\n",
              "      <td>댄스</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   곡명       아티스트                   앨범    시대   장르\n",
              "0              잘못된 만남        김건모            3집 잘못된 만남  1990   댄스\n",
              "1                  커플       젝스키스   3.5집 Special Album  1990  발라드\n",
              "2                  애상    쿨(COOL)                4집 애상  1990   댄스\n",
              "3           여전히 아름다운지        김연우  4집 A Night In Seoul  1990  발라드\n",
              "4             아름다운 이별        김건모            3집 잘못된 만남  1990  발라드\n",
              "5       회상 (December)         터보        3집 Born Again  1990  발라드\n",
              "6              기억의 습작        전람회        1집 Exhibition  1990  발라드\n",
              "7       I'm Your Girl     S.E.S.             1집 S.E.S  1990   댄스\n",
              "8  Love Is... (3+3=0)  터보(Turbo)     2집 New Sensation  1990   댄스\n",
              "9             그녀와의 이별        김현정            1집 Legend  1990   댄스"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LL3tVQFCZSrX",
        "colab_type": "code",
        "outputId": "47ba4f34-4d40-483c-c1e6-f2d8f353bcda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "music_data[music_data['시대']==2000].sort_values(by=['순위/시대'])[['곡명','아티스트','앨범','시대','장르']].head(10).reset_index(drop=True)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>곡명</th>\n",
              "      <th>아티스트</th>\n",
              "      <th>앨범</th>\n",
              "      <th>시대</th>\n",
              "      <th>장르</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>소주 한 잔</td>\n",
              "      <td>임창정</td>\n",
              "      <td>10집 Bye</td>\n",
              "      <td>2000</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>눈의 꽃</td>\n",
              "      <td>박효신</td>\n",
              "      <td>미안하다 사랑한다 OST (KBS 미니시리즈)</td>\n",
              "      <td>2000</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>벌써 일년</td>\n",
              "      <td>브라운 아이즈</td>\n",
              "      <td>1집 Brown Eyes</td>\n",
              "      <td>2000</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>어떻게 사랑이 그래요</td>\n",
              "      <td>이승환</td>\n",
              "      <td>9집 Hwantastic</td>\n",
              "      <td>2000</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>거리에서</td>\n",
              "      <td>성시경</td>\n",
              "      <td>5집 The Ballads</td>\n",
              "      <td>2000</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>우산 (Feat. 윤하)</td>\n",
              "      <td>에픽하이 (EPIK HIGH)</td>\n",
              "      <td>5집 Pieces, Part One</td>\n",
              "      <td>2000</td>\n",
              "      <td>힙합</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>친구라도 될걸 그랬어</td>\n",
              "      <td>거미</td>\n",
              "      <td>1집 Like Them</td>\n",
              "      <td>2000</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>두 사람</td>\n",
              "      <td>성시경</td>\n",
              "      <td>4집 다시 꿈꾸고 싶다</td>\n",
              "      <td>2000</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>다시 사랑한다 말할까</td>\n",
              "      <td>김동률</td>\n",
              "      <td>3집 귀향 (歸鄕)</td>\n",
              "      <td>2000</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>만약에</td>\n",
              "      <td>태연 (TAEYEON)</td>\n",
              "      <td>쾌도홍길동 OST (KBS 드라마)</td>\n",
              "      <td>2000</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              곡명              아티스트                         앨범    시대   장르\n",
              "0         소주 한 잔               임창정                    10집 Bye  2000  발라드\n",
              "1           눈의 꽃               박효신  미안하다 사랑한다 OST (KBS 미니시리즈)  2000  발라드\n",
              "2          벌써 일년           브라운 아이즈              1집 Brown Eyes  2000  발라드\n",
              "3    어떻게 사랑이 그래요               이승환              9집 Hwantastic  2000  발라드\n",
              "4           거리에서               성시경             5집 The Ballads  2000  발라드\n",
              "5  우산 (Feat. 윤하)  에픽하이 (EPIK HIGH)        5집 Pieces, Part One  2000   힙합\n",
              "6    친구라도 될걸 그랬어                거미               1집 Like Them  2000  발라드\n",
              "7           두 사람               성시경               4집 다시 꿈꾸고 싶다  2000  발라드\n",
              "8    다시 사랑한다 말할까               김동률                 3집 귀향 (歸鄕)  2000  발라드\n",
              "9            만약에      태연 (TAEYEON)        쾌도홍길동 OST (KBS 드라마)  2000  발라드"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UHrVjT1xZVZP",
        "colab_type": "code",
        "outputId": "9d473117-fb26-49b1-a103-9f727fe13978",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "music_data[music_data['시대']==2010].sort_values(by=['순위/시대'])[['곡명','아티스트','앨범','시대','장르']].head(10).reset_index(drop=True)"
      ],
      "execution_count": 8,
      "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>곡명</th>\n",
              "      <th>아티스트</th>\n",
              "      <th>앨범</th>\n",
              "      <th>시대</th>\n",
              "      <th>장르</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>그대네요 (With 아이유)</td>\n",
              "      <td>성시경</td>\n",
              "      <td>그대네요</td>\n",
              "      <td>2010</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>좋은 날</td>\n",
              "      <td>아이유(IU)</td>\n",
              "      <td>Real</td>\n",
              "      <td>2010</td>\n",
              "      <td>댄스</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>미리 메리 크리스마스 (Feat. 천둥 of MBLAQ)</td>\n",
              "      <td>아이유(IU)</td>\n",
              "      <td>Real</td>\n",
              "      <td>2010</td>\n",
              "      <td>댄스</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>끝사랑</td>\n",
              "      <td>김범수</td>\n",
              "      <td>7집 SOLISTA Part 2</td>\n",
              "      <td>2010</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>너는 나의 봄이다</td>\n",
              "      <td>성시경</td>\n",
              "      <td>시크릿 가든 OST Part 4</td>\n",
              "      <td>2010</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>너 때문에 미쳐</td>\n",
              "      <td>티아라(T-ara)</td>\n",
              "      <td>Breaking Heart</td>\n",
              "      <td>2010</td>\n",
              "      <td>댄스</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>그 사람</td>\n",
              "      <td>이승철</td>\n",
              "      <td>제빵왕 김탁구 OST Part 3</td>\n",
              "      <td>2010</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>나만 몰랐던 이야기</td>\n",
              "      <td>아이유(IU)</td>\n",
              "      <td>Real +</td>\n",
              "      <td>2010</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Run Devil Run</td>\n",
              "      <td>소녀시대 (GIRLS` GENERATION)</td>\n",
              "      <td>'Run Devil Run' The 2nd Album Repackage</td>\n",
              "      <td>2010</td>\n",
              "      <td>댄스</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>미워요</td>\n",
              "      <td>정인</td>\n",
              "      <td>정인 From Andromeda</td>\n",
              "      <td>2010</td>\n",
              "      <td>발라드</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                곡명                      아티스트  ...    시대   장르\n",
              "0                  그대네요 (With 아이유)                       성시경  ...  2010  발라드\n",
              "1                             좋은 날                   아이유(IU)  ...  2010   댄스\n",
              "2  미리 메리 크리스마스 (Feat. 천둥 of MBLAQ)                   아이유(IU)  ...  2010   댄스\n",
              "3                              끝사랑                       김범수  ...  2010  발라드\n",
              "4                        너는 나의 봄이다                       성시경  ...  2010  발라드\n",
              "5                         너 때문에 미쳐                티아라(T-ara)  ...  2010   댄스\n",
              "6                             그 사람                       이승철  ...  2010  발라드\n",
              "7                       나만 몰랐던 이야기                   아이유(IU)  ...  2010  발라드\n",
              "8                    Run Devil Run  소녀시대 (GIRLS` GENERATION)  ...  2010   댄스\n",
              "9                              미워요                        정인  ...  2010  발라드\n",
              "\n",
              "[10 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VhFkVodMTJmP",
        "colab_type": "code",
        "outputId": "714808fb-3606-4dea-e966-dbf5c94d2428",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "music_data.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(17575, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XqEqnvtWWYJ4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 루프에 활용할 변수 설정\n",
        "ERA = music_data['시대'].unique()\n",
        "GENRE = music_data['장르'].unique()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SdFbOxbkRARx",
        "colab_type": "text"
      },
      "source": [
        "## 2. 아티스트 다양성"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kMsIQVnMRJId",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "*   차트에 존재하는 아티스트의 수\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1MJqXlc9TJou",
        "colab_type": "code",
        "outputId": "fbb57d4f-dbe3-44e9-88fa-5319a90b407f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "len(music_data['아티스트'].unique())"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2339"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b7320KYnRQPY",
        "colab_type": "text"
      },
      "source": [
        "* 시대별 아티스트의 다양성"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvvlTl3YTJq_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "era_static = pd.DataFrame(columns=['ERA','music_num','artist_num'])\n",
        "for era in ERA:\n",
        "  music_num = len(music_data[music_data['시대']==era])\n",
        "  artist_num = len(music_data[music_data['시대']==era]['아티스트'].unique())\n",
        "  era_static = era_static.append({'ERA':era, 'music_num':music_num ,'artist_num':artist_num}, ignore_index=True)\n",
        "era_static['ratio'] = era_static['artist_num']/era_static['music_num'] # for문에 포함하면 전체 값이 float이 되어서 별도 수행"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2isyiWXRWj85",
        "colab_type": "code",
        "outputId": "f53abb7b-0a93-4e7d-e4f4-a046d41ba169",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "era_static"
      ],
      "execution_count": 13,
      "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>ERA</th>\n",
              "      <th>music_num</th>\n",
              "      <th>artist_num</th>\n",
              "      <th>ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1980</td>\n",
              "      <td>3094</td>\n",
              "      <td>337</td>\n",
              "      <td>0.10892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1990</td>\n",
              "      <td>4486</td>\n",
              "      <td>702</td>\n",
              "      <td>0.156487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2000</td>\n",
              "      <td>4996</td>\n",
              "      <td>1096</td>\n",
              "      <td>0.219376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2010</td>\n",
              "      <td>4999</td>\n",
              "      <td>1115</td>\n",
              "      <td>0.223045</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    ERA music_num artist_num     ratio\n",
              "0  1980      3094        337   0.10892\n",
              "1  1990      4486        702  0.156487\n",
              "2  2000      4996       1096  0.219376\n",
              "3  2010      4999       1115  0.223045"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oGOWcXuaYOgo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.font_manager as fm\n",
        "fm._rebuild()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-7nAMKBzS4jd",
        "colab_type": "code",
        "outputId": "ac0215ab-e8a3-4b28-89a0-c267b4348f21",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        }
      },
      "source": [
        "sns.set(style=\"whitegrid\",font=\"nanumgothic\")\n",
        "f, ax = plt.subplots(figsize=(6, 6))\n",
        "sns.set_color_codes(\"pastel\")\n",
        "sns.despine()\n",
        "sns.barplot(x=\"ERA\", y=\"ratio\", data=era_static, palette=\"rocket\")\n",
        "ax.set_title(\"시대별 아티스트의 다양성\")\n",
        "plt.xlabel('시대')\n",
        "plt.ylabel('다양성')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, '다양성')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FziahTV0Azhyh4S7Nix/V1GU0C+NLljR1CcBpw4wEAGCEIAEAGCFIAABGCBIAgBGCBABg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\nYII=\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5uMPhS9AU2ex",
        "colab_type": "text"
      },
      "source": [
        "## 3. 아티스트 순위(곡 수 기준)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dll8Gu_SVQMX",
        "colab_type": "text"
      },
      "source": [
        "* 시대별 아티스트 순위"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3XKAGFEzUobl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "artist_rank_era = pd.DataFrame()\n",
        "\n",
        "for era in ERA:\n",
        "  artist_rank_era = pd.concat([artist_rank_era, music_data[music_data['시대']==era]['아티스트'].value_counts()[:10].reset_index()], axis=1)\n",
        "  \n",
        "artist_rank_era.columns = ['아티스트(1980)','곡 수','아티스트(1990)','곡 수','아티스트(2000)','곡 수','아티스트(2010)','곡 수']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hU42gzG-fLsK",
        "colab_type": "code",
        "outputId": "2890991e-8dd1-4bd8-af3c-c2fd1ed0964a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "artist_rank_era"
      ],
      "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>아티스트(1980)</th>\n",
              "      <th>곡 수</th>\n",
              "      <th>아티스트(1990)</th>\n",
              "      <th>곡 수</th>\n",
              "      <th>아티스트(2000)</th>\n",
              "      <th>곡 수</th>\n",
              "      <th>아티스트(2010)</th>\n",
              "      <th>곡 수</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>조용필</td>\n",
              "      <td>146</td>\n",
              "      <td>김건모</td>\n",
              "      <td>73</td>\n",
              "      <td>에픽하이 (EPIK HIGH)</td>\n",
              "      <td>96</td>\n",
              "      <td>나훈아</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>김연자</td>\n",
              "      <td>115</td>\n",
              "      <td>서태지와 아이들</td>\n",
              "      <td>69</td>\n",
              "      <td>장윤정</td>\n",
              "      <td>81</td>\n",
              "      <td>소녀시대 (GIRLS` GENERATION)</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>나훈아</td>\n",
              "      <td>106</td>\n",
              "      <td>듀스</td>\n",
              "      <td>67</td>\n",
              "      <td>BIGBANG</td>\n",
              "      <td>75</td>\n",
              "      <td>남진</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>이문세</td>\n",
              "      <td>66</td>\n",
              "      <td>DJ DOC</td>\n",
              "      <td>64</td>\n",
              "      <td>다이나믹 듀오(Dynamic Duo)</td>\n",
              "      <td>72</td>\n",
              "      <td>도끼(DOK2)</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>김현식</td>\n",
              "      <td>64</td>\n",
              "      <td>조용필</td>\n",
              "      <td>59</td>\n",
              "      <td>리쌍(LeeSSang)</td>\n",
              "      <td>65</td>\n",
              "      <td>강진</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>김수희</td>\n",
              "      <td>62</td>\n",
              "      <td>업타운(UpTown)</td>\n",
              "      <td>55</td>\n",
              "      <td>Various Artists</td>\n",
              "      <td>57</td>\n",
              "      <td>유지나</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>이미자</td>\n",
              "      <td>62</td>\n",
              "      <td>Various Artists</td>\n",
              "      <td>53</td>\n",
              "      <td>MC몽</td>\n",
              "      <td>54</td>\n",
              "      <td>티아라(T-ara)</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>이선희</td>\n",
              "      <td>53</td>\n",
              "      <td>쿨(COOL)</td>\n",
              "      <td>52</td>\n",
              "      <td>심수봉</td>\n",
              "      <td>48</td>\n",
              "      <td>포맨</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>송창식</td>\n",
              "      <td>50</td>\n",
              "      <td>이문세</td>\n",
              "      <td>52</td>\n",
              "      <td>나훈아</td>\n",
              "      <td>48</td>\n",
              "      <td>자우림</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>들국화</td>\n",
              "      <td>49</td>\n",
              "      <td>박강성</td>\n",
              "      <td>50</td>\n",
              "      <td>이미자</td>\n",
              "      <td>46</td>\n",
              "      <td>2PM</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  아티스트(1980)  곡 수       아티스트(1990)  ...  곡 수                아티스트(2010)  곡 수\n",
              "0        조용필  146              김건모  ...   96                       나훈아   92\n",
              "1        김연자  115         서태지와 아이들  ...   81  소녀시대 (GIRLS` GENERATION)   61\n",
              "2        나훈아  106               듀스  ...   75                        남진   44\n",
              "3        이문세   66           DJ DOC  ...   72                  도끼(DOK2)   41\n",
              "4        김현식   64              조용필  ...   65                        강진   37\n",
              "5        김수희   62      업타운(UpTown)  ...   57                       유지나   33\n",
              "6        이미자   62  Various Artists  ...   54                티아라(T-ara)   31\n",
              "7        이선희   53          쿨(COOL)  ...   48                        포맨   31\n",
              "8        송창식   50              이문세  ...   48                       자우림   31\n",
              "9        들국화   49              박강성  ...   46                       2PM   31\n",
              "\n",
              "[10 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5FcJPZnCVSwN",
        "colab_type": "text"
      },
      "source": [
        "* 전체 기준 아티스트 순위"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MDcfTSCPL04a",
        "colab_type": "code",
        "outputId": "7396dca2-18e5-4b9f-fe28-989ec60c05a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "artist_rank = music_data['아티스트'].value_counts()[:10].reset_index()\n",
        "artist_rank.columns=['아티스트','곡 수']\n",
        "artist_rank"
      ],
      "execution_count": 18,
      "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>아티스트</th>\n",
              "      <th>곡 수</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>나훈아</td>\n",
              "      <td>282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>조용필</td>\n",
              "      <td>219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>이미자</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>심수봉</td>\n",
              "      <td>127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>이문세</td>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>김연자</td>\n",
              "      <td>123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Various Artists</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>에픽하이 (EPIK HIGH)</td>\n",
              "      <td>106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>이승철</td>\n",
              "      <td>103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>부활</td>\n",
              "      <td>102</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               아티스트  곡 수\n",
              "0               나훈아  282\n",
              "1               조용필  219\n",
              "2               이미자  150\n",
              "3               심수봉  127\n",
              "4               이문세  125\n",
              "5               김연자  123\n",
              "6   Various Artists  121\n",
              "7  에픽하이 (EPIK HIGH)  106\n",
              "8               이승철  103\n",
              "9                부활  102"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rCr6KlMEVaEM",
        "colab_type": "text"
      },
      "source": [
        "* 순위권 아티스트의 시대별 곡 분포"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SZGWLz8UR0DZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "artist_rank_data = pd.DataFrame()\n",
        "for artist in artist_rank['아티스트']:\n",
        "  artist_rank_data = pd.concat([artist_rank_data, music_data[music_data['아티스트']==artist]])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J25vG2YANhaY",
        "colab_type": "code",
        "outputId": "6952b1c0-22cd-4df8-d68d-89b3b8c80308",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        }
      },
      "source": [
        "sns.set(style=\"whitegrid\",font=\"nanumgothic\")\n",
        "f, ax = plt.subplots(figsize=(16, 6))\n",
        "sns.countplot(x=\"아티스트\",hue=\"시대\",data=artist_rank_data)\n",
        "ax.set_title(\"시대별 곡 분포\")\n",
        "plt.ylabel('곡 수')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, '곡 수')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NZszbvXu3evbsKUmy2Wzq0aOH0tLSdOrUKYWFhSkiIkKSFB4ersLCQp0/f15paWl69NFH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\n",
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pNx_w-afDK_v",
        "colab_type": "text"
      },
      "source": [
        "## 4. 시대별 선호 장르"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "leNC5J0nDFQt",
        "colab": {}
      },
      "source": [
        "music_data['시대'] = music_data['시대'].astype('category') # stripplot y축을 카테고리로 제시하기 위하여 변환"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "9526af48-fe89-467a-b26d-54a1d31464d2",
        "id": "nG4TsjyBDFQx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        }
      },
      "source": [
        "sns.set(style=\"whitegrid\", font=\"nanumgothic\")\n",
        "f, ax = plt.subplots(figsize=(12, 6))\n",
        "\n",
        "sns.stripplot(x=\"순위/시대\", y=\"시대\", hue=\"장르\",\n",
        "              data=music_data, dodge=True, jitter=True,\n",
        "              size=3, alpha=.2, zorder=1)\n",
        "\n",
        "\n",
        "sns.pointplot(x=\"순위/시대\", y=\"시대\", hue=\"장르\",\n",
        "              data=music_data, dodge=.65, join=False, palette=\"dark\",\n",
        "              markers=\"d\", scale=.8, ci=None)\n",
        "\n",
        "\n",
        "ax.set_title(\"시대별 장르 분포\")\n",
        "\n",
        "handles, labels = ax.get_legend_handles_labels()\n",
        "ax.legend(handles[5:], labels[5:], title=\"장르\")"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fda8288b048>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+HxfDeAyJ45jrrruOPXv28NWvfpX3vve9POc5z+E1r3kNAFdddRXnnXceAI1G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\nj451dXnRTp1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zLALXFAhnkZ0hBBH69RcJh9vIzpDi9RcoT255YZ100TvXNisJDr2c0awm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zikaUl7ta40tebsScpgEhDHPnXVEhpDFAXsHyZsNxV1WdM2FoQgDD2MNWDNVaDB0rZc2Vxc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qSezdFF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\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8WlCWDVWluPPmOarR7O73+dxP7C2tz8cHU4q8tuS+0ehGc/ftEWdHCcONlrVmGs3FWYaU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\nCQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YdYc8U3dVhLv",
        "colab_type": "text"
      },
      "source": [
        "## 5. 곡 제목 분석"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cYj5-euifkZX",
        "colab_type": "code",
        "outputId": "1a709091-f828-47ca-e17f-2a63f3c7ae7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        }
      },
      "source": [
        "pip install konlpy"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting konlpy\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e5/3d/4e983cd98d87b50b2ab0387d73fa946f745aa8164e8888a714d5129f9765/konlpy-0.5.1-py2.py3-none-any.whl (19.4MB)\n",
            "\u001b[K     |████████████████████████████████| 19.4MB 62.2MB/s \n",
            "\u001b[?25hCollecting JPype1>=0.5.7 (from konlpy)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c4/4b/60a3e63d51714d4d7ef1b1efdf84315d118a0a80a5b085bb52a7e2428cdc/JPype1-0.6.3.tar.gz (168kB)\n",
            "\u001b[K     |████████████████████████████████| 174kB 34.7MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: JPype1\n",
            "  Building wheel for JPype1 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Stored in directory: /root/.cache/pip/wheels/0e/2b/e8/c0b818ac4b3d35104d35e48cdc7afe27fc06ea277feed2831a\n",
            "Successfully built JPype1\n",
            "Installing collected packages: JPype1, konlpy\n",
            "Successfully installed JPype1-0.6.3 konlpy-0.5.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "39JX98hzfpz1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from konlpy.tag import Komoran, Twitter, Hannanum"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "86fCABGbho2q",
        "colab_type": "code",
        "outputId": "0147a947-4957-44e6-f71e-195babaae39a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "Komoran = Komoran()\n",
        "Twitter = Twitter()\n",
        "Hannanum = Hannanum()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/konlpy/tag/_okt.py:16: UserWarning: \"Twitter\" has changed to \"Okt\" since KoNLPy v0.4.5.\n",
            "  warn('\"Twitter\" has changed to \"Okt\" since KoNLPy v0.4.5.')\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dETfWMqEhsDi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "music_string = music_data['곡명'].to_string(index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iDmiH2yrj8HZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# KoNLPy의 Tagger가 여러 버젼이 있음으로 비교\n",
        "music_noun_ko = Komoran.nouns(music_string)\n",
        "music_noun_tw = Twitter.nouns(music_string)\n",
        "music_noun_han = Hannanum.nouns(music_string)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p9MtbSIOr8i-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "music_noun_ko = pd.Series(music_noun_ko)\n",
        "music_noun_tw = pd.Series(music_noun_tw)\n",
        "music_noun_han = pd.Series(music_noun_han)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "THYlWpaI540c",
        "colab_type": "code",
        "outputId": "25349336-31dd-4271-8a17-e193928dd020",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "music_noun_compare = pd.DataFrame()\n",
        "music_noun_compare = pd.concat([music_noun_compare, music_noun_ko.value_counts()[:10].reset_index()], axis=1)\n",
        "music_noun_compare = pd.concat([music_noun_compare, music_noun_tw.value_counts()[:10].reset_index()], axis=1)\n",
        "music_noun_compare = pd.concat([music_noun_compare, music_noun_han.value_counts()[:10].reset_index()], axis=1)\n",
        "music_noun_compare.columns = ['Komoran','count','Twitter','count','Hannanum','count']\n",
        "music_noun_compare"
      ],
      "execution_count": 29,
      "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>Komoran</th>\n",
              "      <th>count</th>\n",
              "      <th>Twitter</th>\n",
              "      <th>count</th>\n",
              "      <th>Hannanum</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>사랑</td>\n",
              "      <td>910</td>\n",
              "      <td>사랑</td>\n",
              "      <td>1344</td>\n",
              "      <td>사랑</td>\n",
              "      <td>1030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>사람</td>\n",
              "      <td>211</td>\n",
              "      <td>그대</td>\n",
              "      <td>350</td>\n",
              "      <td>나</td>\n",
              "      <td>484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>이별</td>\n",
              "      <td>173</td>\n",
              "      <td>너</td>\n",
              "      <td>349</td>\n",
              "      <td>너</td>\n",
              "      <td>362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>말</td>\n",
              "      <td>152</td>\n",
              "      <td>나</td>\n",
              "      <td>326</td>\n",
              "      <td>그대</td>\n",
              "      <td>354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>여자</td>\n",
              "      <td>141</td>\n",
              "      <td>내</td>\n",
              "      <td>280</td>\n",
              "      <td>내</td>\n",
              "      <td>233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>눈물</td>\n",
              "      <td>140</td>\n",
              "      <td>사람</td>\n",
              "      <td>224</td>\n",
              "      <td>이별</td>\n",
              "      <td>168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>날</td>\n",
              "      <td>130</td>\n",
              "      <td>이별</td>\n",
              "      <td>192</td>\n",
              "      <td>당신</td>\n",
              "      <td>167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>밤</td>\n",
              "      <td>129</td>\n",
              "      <td>여자</td>\n",
              "      <td>180</td>\n",
              "      <td>여자</td>\n",
              "      <td>158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>노래</td>\n",
              "      <td>129</td>\n",
              "      <td>그</td>\n",
              "      <td>179</td>\n",
              "      <td>사람</td>\n",
              "      <td>152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>남자</td>\n",
              "      <td>127</td>\n",
              "      <td>당신</td>\n",
              "      <td>171</td>\n",
              "      <td>눈물</td>\n",
              "      <td>142</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Komoran  count Twitter  count Hannanum  count\n",
              "0      사랑    910      사랑   1344       사랑   1030\n",
              "1      사람    211      그대    350        나    484\n",
              "2      이별    173       너    349        너    362\n",
              "3       말    152       나    326       그대    354\n",
              "4      여자    141       내    280        내    233\n",
              "5      눈물    140      사람    224       이별    168\n",
              "6       날    130      이별    192       당신    167\n",
              "7       밤    129      여자    180       여자    158\n",
              "8      노래    129       그    179       사람    152\n",
              "9      남자    127      당신    171       눈물    142"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YXpqr76c9h-7",
        "colab_type": "code",
        "outputId": "3810084f-57c4-4c3b-e20b-815abc7aa4da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "# Tagger들의 결론이 다양함으로 이를 합하여 좀 더 안정적인 결과물을 얻자\n",
        "music_noun_mix = pd.concat([music_noun_ko,music_noun_tw,music_noun_han])\n",
        "music_noun_mix_rank = music_noun_mix.value_counts()[:10].reset_index()\n",
        "music_noun_mix_rank.columns = ['Tagger Mix','count']\n",
        "music_noun_mix_rank['count'] = music_noun_mix_rank['count']//3\n",
        "music_noun_mix_rank"
      ],
      "execution_count": 30,
      "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>Tagger Mix</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>사랑</td>\n",
              "      <td>1094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>나</td>\n",
              "      <td>271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>너</td>\n",
              "      <td>237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>그대</td>\n",
              "      <td>234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>사람</td>\n",
              "      <td>195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>내</td>\n",
              "      <td>193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>이별</td>\n",
              "      <td>177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>여자</td>\n",
              "      <td>159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>말</td>\n",
              "      <td>149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>눈물</td>\n",
              "      <td>149</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Tagger Mix  count\n",
              "0         사랑   1094\n",
              "1          나    271\n",
              "2          너    237\n",
              "3         그대    234\n",
              "4         사람    195\n",
              "5          내    193\n",
              "6         이별    177\n",
              "7         여자    159\n",
              "8          말    149\n",
              "9         눈물    149"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mQK_QlKKc5z6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "music_noun_era= pd.DataFrame()\n",
        "for era in ERA:\n",
        "  music_string = music_data[music_data['시대']==era]['곡명'].to_string(index=False)\n",
        "  music_noun_mix_era = pd.concat([pd.Series(Komoran.nouns(music_string)), pd.Series(Twitter.nouns(music_string)), pd.Series(Hannanum.nouns(music_string))]).reset_index(drop=True)\n",
        "  music_noun_mix_era = pd.concat([music_noun_mix_era, pd.Series([era]*music_noun_mix_era.shape[0])], axis=1)\n",
        "  music_noun_era = pd.concat([music_noun_era, music_noun_mix_era])\n",
        "music_noun_era.columns=['단어','시대']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_xYRcjJtjisw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "music_noun_rank_data = pd.DataFrame()\n",
        "for noun in music_noun_mix_rank['Tagger Mix']:\n",
        "  music_noun_rank_data = pd.concat([music_noun_rank_data, music_noun_era[music_noun_era['단어']==noun]])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tkeIH_vfFiLN",
        "colab_type": "code",
        "outputId": "5daba7f6-cc0e-4c77-f2f3-0be48e8548b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        }
      },
      "source": [
        "sns.set(style=\"whitegrid\",font=\"nanumgothic\")\n",
        "f, ax = plt.subplots(figsize=(16, 6))\n",
        "sns.countplot(x=\"단어\",hue=\"시대\",data=music_noun_rank_data)\n",
        "ax.set_title(\"단어별 출현 빈도 수\")\n",
        "plt.ylabel('곡 수(X3)')"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, '곡 수(X3)')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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a2zU1tnq8obFdU2Orx1ua63UB8D5+XwBobsrKyi47AFqVgN5DvGrV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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}