3_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "********************\n",
    "\n",
    "#### 탐색 결과를 소개하기 앞서 대회에서 제공하는 해당 데이터는 대회 진행을 목적으로 적합한 샘플링 작업을 거친 데이터라는 점을 알려드립니다.\n",
    "\n",
    "#### 소개할 내용들이 <font color = \"red\">실제 멜론 유저의 소비 패턴과는 전혀 무관</font>하며, <font color = \"red\">멜론 DJ 플레이리스트 서비스를 일반화할 수 없음</font>을 참고하여 주시기 바랍니다.\n",
    "\n",
    "********************"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KAKAO Arena 3rd Compitition : Melon Playlist Continuation\n",
    "\n",
    "## 대회 데이터 간단하게 EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install konlpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:00:41.231826Z",
     "start_time": "2020-04-26T23:00:24.271547Z"
    }
   },
   "outputs": [],
   "source": [
    "from datetime import timedelta, datetime\n",
    "import glob\n",
    "from itertools import chain\n",
    "import json\n",
    "import os\n",
    "import re\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from wordcloud import WordCloud\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "#from konlpy.tag import Twitter\n",
    "from collections import Counter\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as fm\n",
    "font_path = '/Users/kywmac/dev/캐글/NanumGothic-Regular.ttf'\n",
    "font_name = fm.FontProperties(fname=font_path, size=10).get_name()\n",
    "plt.rc('font', family=font_name, size=12)\n",
    "plt.rcParams[\"figure.figsize\"] = (20, 10)\n",
    "register_matplotlib_converters()\n",
    "\n",
    "mpl.font_manager._rebuild()\n",
    "mpl.pyplot.rc('font', family='NanumGothic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:00:41.240148Z",
     "start_time": "2020-04-26T23:00:41.237465Z"
    }
   },
   "outputs": [],
   "source": [
    "pd.options.mode.chained_assignment = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br><br><br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 곡 장르 코드 데이터 : `genre_gn_all.json`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 데이터 불러오기 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:00:41.287704Z",
     "start_time": "2020-04-26T23:00:41.241507Z"
    }
   },
   "outputs": [],
   "source": [
    "genre_gn_all = pd.read_json('genre_gn_all.json', typ = 'series')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:00:41.294122Z",
     "start_time": "2020-04-26T23:00:41.289083Z"
    }
   },
   "outputs": [],
   "source": [
    "# 장르코드 : gnr_code, 장르명 : gnr_name\n",
    "genre_gn_all = pd.DataFrame(genre_gn_all, columns = ['gnr_name']).reset_index().rename(columns = {'index' : 'gnr_code'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:00:41.342662Z",
     "start_time": "2020-04-26T23:00:41.295466Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gnr_code</th>\n",
       "      <th>gnr_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GN0100</td>\n",
       "      <td>발라드</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GN0101</td>\n",
       "      <td>세부장르전체</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GN0102</td>\n",
       "      <td>'80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GN0103</td>\n",
       "      <td>'90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GN0104</td>\n",
       "      <td>'00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>GN2900</td>\n",
       "      <td>뮤지컬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>GN2901</td>\n",
       "      <td>세부장르전체</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>GN2902</td>\n",
       "      <td>국내뮤지컬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>GN2903</td>\n",
       "      <td>국외뮤지컬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>GN3000</td>\n",
       "      <td>크리스마스</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>254 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    gnr_code gnr_name\n",
       "0     GN0100      발라드\n",
       "1     GN0101   세부장르전체\n",
       "2     GN0102      '80\n",
       "3     GN0103      '90\n",
       "4     GN0104      '00\n",
       "..       ...      ...\n",
       "249   GN2900      뮤지컬\n",
       "250   GN2901   세부장르전체\n",
       "251   GN2902    국내뮤지컬\n",
       "252   GN2903    국외뮤지컬\n",
       "253   GN3000    크리스마스\n",
       "\n",
       "[254 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre_gn_all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 장르 코드는 **총 254개의 장르코드가 존재**하며 **30개의 대분류 장르코드**와 **224개의 상세 장르코드**로 분류할 수 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 대분류 장르코드 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 대분류 장르코드는 장르코드(`gnr_code`)에서 **숫자 네 자리 중 뒷자리 두 자리가 `00`인 코드로 분류**할 수 있습니다.\n",
    "- 대분류 장르는 총 30개가 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:00:41.357671Z",
     "start_time": "2020-04-26T23:00:41.343953Z"
    }
   },
   "outputs": [],
   "source": [
    "# 장르코드 뒷자리 두 자리가 00인 코드를 필터링\n",
    "gnr_code = genre_gn_all[genre_gn_all['gnr_code'].str[-2:] == '00']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:00:41.371136Z",
     "start_time": "2020-04-26T23:00:41.359662Z"
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   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gnr_code</th>\n",
       "      <th>gnr_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GN0100</td>\n",
       "      <td>발라드</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GN0200</td>\n",
       "      <td>댄스</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GN0300</td>\n",
       "      <td>랩/힙합</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>GN0400</td>\n",
       "      <td>R&amp;B/Soul</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>GN0500</td>\n",
       "      <td>인디음악</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>GN0600</td>\n",
       "      <td>록/메탈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>GN0700</td>\n",
       "      <td>성인가요</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>GN0800</td>\n",
       "      <td>포크/블루스</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>GN0900</td>\n",
       "      <td>POP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>GN1000</td>\n",
       "      <td>록/메탈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>GN1100</td>\n",
       "      <td>일렉트로니카</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>GN1200</td>\n",
       "      <td>랩/힙합</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>GN1300</td>\n",
       "      <td>R&amp;B/Soul</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>GN1400</td>\n",
       "      <td>포크/블루스/컨트리</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>GN1500</td>\n",
       "      <td>OST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>GN1600</td>\n",
       "      <td>클래식</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>GN1700</td>\n",
       "      <td>재즈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>GN1800</td>\n",
       "      <td>뉴에이지</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>GN1900</td>\n",
       "      <td>J-POP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>GN2000</td>\n",
       "      <td>월드뮤직</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>GN2100</td>\n",
       "      <td>CCM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>GN2200</td>\n",
       "      <td>어린이/태교</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>GN2300</td>\n",
       "      <td>종교음악</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>GN2400</td>\n",
       "      <td>국악</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>GN2500</td>\n",
       "      <td>아이돌</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>GN2600</td>\n",
       "      <td>일렉트로니카</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>GN2700</td>\n",
       "      <td>EDM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>GN2800</td>\n",
       "      <td>뮤직테라피</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>GN2900</td>\n",
       "      <td>뮤지컬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>GN3000</td>\n",
       "      <td>크리스마스</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    gnr_code    gnr_name\n",
       "0     GN0100         발라드\n",
       "6     GN0200          댄스\n",
       "12    GN0300        랩/힙합\n",
       "18    GN0400    R&B/Soul\n",
       "22    GN0500        인디음악\n",
       "32    GN0600        록/메탈\n",
       "39    GN0700        성인가요\n",
       "50    GN0800      포크/블루스\n",
       "56    GN0900         POP\n",
       "65    GN1000        록/메탈\n",
       "80    GN1100      일렉트로니카\n",
       "90    GN1200        랩/힙합\n",
       "100   GN1300    R&B/Soul\n",
       "109   GN1400  포크/블루스/컨트리\n",
       "117   GN1500         OST\n",
       "128   GN1600         클래식\n",
       "144   GN1700          재즈\n",
       "155   GN1800        뉴에이지\n",
       "163   GN1900       J-POP\n",
       "176   GN2000        월드뮤직\n",
       "187   GN2100         CCM\n",
       "198   GN2200      어린이/태교\n",
       "215   GN2300        종교음악\n",
       "218   GN2400          국악\n",
       "225   GN2500         아이돌\n",
       "232   GN2600      일렉트로니카\n",
       "237   GN2700         EDM\n",
       "242   GN2800       뮤직테라피\n",
       "249   GN2900         뮤지컬\n",
       "253   GN3000       크리스마스"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnr_code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 상세 장르코드"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 상세 장르코드는 장르코드(`gnr_code`)에서 **숫자 네 자리 중 뒷자리 두 자리가 `00`이 아닌 나머지 코드로 분류**할 수 있습니다.\n",
    "- 상세 장르는 총 224개가 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:00:41.379616Z",
     "start_time": "2020-04-26T23:00:41.372594Z"
    }
   },
   "outputs": [],
   "source": [
    "# 장르코드 뒷자리 두 자리가 00이 아닌 코드를 필터링\n",
    "dtl_gnr_code = genre_gn_all[genre_gn_all['gnr_code'].str[-2:] != '00']\n",
    "dtl_gnr_code.rename(columns = {'gnr_code' : 'dtl_gnr_code', 'gnr_name' : 'dtl_gnr_name'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:00:41.393342Z",
     "start_time": "2020-04-26T23:00:41.380918Z"
    }
   },
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dtl_gnr_code</th>\n",
       "      <th>dtl_gnr_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GN0101</td>\n",
       "      <td>세부장르전체</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GN0102</td>\n",
       "      <td>'80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GN0103</td>\n",
       "      <td>'90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GN0104</td>\n",
       "      <td>'00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GN0105</td>\n",
       "      <td>'10-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>GN2805</td>\n",
       "      <td>숙면유도</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>GN2806</td>\n",
       "      <td>반려동물</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>GN2901</td>\n",
       "      <td>세부장르전체</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>GN2902</td>\n",
       "      <td>국내뮤지컬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>GN2903</td>\n",
       "      <td>국외뮤지컬</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>224 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    dtl_gnr_code dtl_gnr_name\n",
       "1         GN0101       세부장르전체\n",
       "2         GN0102          '80\n",
       "3         GN0103          '90\n",
       "4         GN0104          '00\n",
       "5         GN0105         '10-\n",
       "..           ...          ...\n",
       "247       GN2805         숙면유도\n",
       "248       GN2806         반려동물\n",
       "250       GN2901       세부장르전체\n",
       "251       GN2902        국내뮤지컬\n",
       "252       GN2903        국외뮤지컬\n",
       "\n",
       "[224 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtl_gnr_code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 장르 코드 트리(Tree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 위에서 구분한 **대분류 장르코드와 상세 장르코드 앞자리 네 자리를 활용**하여 장르 코드 트리도 구성해볼 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:00:41.485298Z",
     "start_time": "2020-04-26T23:00:41.394678Z"
    }
   },
   "outputs": [
    {
     "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>gnr_code</th>\n",
       "      <th>gnr_name</th>\n",
       "      <th>dtl_gnr_code</th>\n",
       "      <th>dtl_gnr_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GN0100</td>\n",
       "      <td>발라드</td>\n",
       "      <td>GN0101</td>\n",
       "      <td>세부장르전체</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GN0100</td>\n",
       "      <td>발라드</td>\n",
       "      <td>GN0102</td>\n",
       "      <td>'80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GN0100</td>\n",
       "      <td>발라드</td>\n",
       "      <td>GN0103</td>\n",
       "      <td>'90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GN0100</td>\n",
       "      <td>발라드</td>\n",
       "      <td>GN0104</td>\n",
       "      <td>'00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GN0100</td>\n",
       "      <td>발라드</td>\n",
       "      <td>GN0105</td>\n",
       "      <td>'10-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>GN2800</td>\n",
       "      <td>뮤직테라피</td>\n",
       "      <td>GN2806</td>\n",
       "      <td>반려동물</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>GN2900</td>\n",
       "      <td>뮤지컬</td>\n",
       "      <td>GN2901</td>\n",
       "      <td>세부장르전체</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>GN2900</td>\n",
       "      <td>뮤지컬</td>\n",
       "      <td>GN2902</td>\n",
       "      <td>국내뮤지컬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>GN2900</td>\n",
       "      <td>뮤지컬</td>\n",
       "      <td>GN2903</td>\n",
       "      <td>국외뮤지컬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>GN3000</td>\n",
       "      <td>크리스마스</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>225 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    gnr_code gnr_name dtl_gnr_code dtl_gnr_name\n",
       "0     GN0100      발라드       GN0101       세부장르전체\n",
       "1     GN0100      발라드       GN0102          '80\n",
       "2     GN0100      발라드       GN0103          '90\n",
       "3     GN0100      발라드       GN0104          '00\n",
       "4     GN0100      발라드       GN0105         '10-\n",
       "..       ...      ...          ...          ...\n",
       "220   GN2800    뮤직테라피       GN2806         반려동물\n",
       "221   GN2900      뮤지컬       GN2901       세부장르전체\n",
       "222   GN2900      뮤지컬       GN2902        국내뮤지컬\n",
       "223   GN2900      뮤지컬       GN2903        국외뮤지컬\n",
       "224   GN3000    크리스마스          NaN          NaN\n",
       "\n",
       "[225 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 앞자리 네 자리 공통코드 추출\n",
    "gnr_code = gnr_code.assign(join_code = gnr_code['gnr_code'].str[0:4])\n",
    "dtl_gnr_code = dtl_gnr_code.assign(join_code = dtl_gnr_code['dtl_gnr_code'].str[0:4])\n",
    "\n",
    "# Merge\n",
    "gnr_code_tree = pd.merge(gnr_code, dtl_gnr_code, how = 'left', on = 'join_code')\n",
    "\n",
    "gnr_code_tree[['gnr_code', 'gnr_name', 'dtl_gnr_code', 'dtl_gnr_name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:00:41.496315Z",
     "start_time": "2020-04-26T23:00:41.486600Z"
    }
   },
   "outputs": [
    {
     "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>gnr_code</th>\n",
       "      <th>gnr_name</th>\n",
       "      <th>join_code</th>\n",
       "      <th>dtl_gnr_code</th>\n",
       "      <th>dtl_gnr_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>GN0900</td>\n",
       "      <td>POP</td>\n",
       "      <td>GN09</td>\n",
       "      <td>GN0901</td>\n",
       "      <td>세부장르전체</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>GN0900</td>\n",
       "      <td>POP</td>\n",
       "      <td>GN09</td>\n",
       "      <td>GN0902</td>\n",
       "      <td>얼터너티브팝</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>GN0900</td>\n",
       "      <td>POP</td>\n",
       "      <td>GN09</td>\n",
       "      <td>GN0903</td>\n",
       "      <td>올디스</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>GN0900</td>\n",
       "      <td>POP</td>\n",
       "      <td>GN09</td>\n",
       "      <td>GN0904</td>\n",
       "      <td>월드팝</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>GN0900</td>\n",
       "      <td>POP</td>\n",
       "      <td>GN09</td>\n",
       "      <td>GN0905</td>\n",
       "      <td>'60-'70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>GN0900</td>\n",
       "      <td>POP</td>\n",
       "      <td>GN09</td>\n",
       "      <td>GN0906</td>\n",
       "      <td>'80-'90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>GN0900</td>\n",
       "      <td>POP</td>\n",
       "      <td>GN09</td>\n",
       "      <td>GN0907</td>\n",
       "      <td>'00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>GN0900</td>\n",
       "      <td>POP</td>\n",
       "      <td>GN09</td>\n",
       "      <td>GN0908</td>\n",
       "      <td>'10-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gnr_code gnr_name join_code dtl_gnr_code dtl_gnr_name\n",
       "48   GN0900      POP      GN09       GN0901       세부장르전체\n",
       "49   GN0900      POP      GN09       GN0902       얼터너티브팝\n",
       "50   GN0900      POP      GN09       GN0903          올디스\n",
       "51   GN0900      POP      GN09       GN0904          월드팝\n",
       "52   GN0900      POP      GN09       GN0905      '60-'70\n",
       "53   GN0900      POP      GN09       GN0906      '80-'90\n",
       "54   GN0900      POP      GN09       GN0907          '00\n",
       "55   GN0900      POP      GN09       GN0908         '10-"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# POP 장르(GN0900)의 상세 장르 살펴보기\n",
    "gnr_code_tree[gnr_code_tree['gnr_code'] == 'GN0900']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:00:41.511410Z",
     "start_time": "2020-04-26T23:00:41.497657Z"
    }
   },
   "outputs": [
    {
     "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>gnr_code</th>\n",
       "      <th>gnr_name</th>\n",
       "      <th>join_code</th>\n",
       "      <th>dtl_gnr_code</th>\n",
       "      <th>dtl_gnr_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>GN2200</td>\n",
       "      <td>어린이/태교</td>\n",
       "      <td>GN22</td>\n",
       "      <td>GN2201</td>\n",
       "      <td>유아동요</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>GN2200</td>\n",
       "      <td>어린이/태교</td>\n",
       "      <td>GN22</td>\n",
       "      <td>GN2202</td>\n",
       "      <td>창작동요</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>GN2200</td>\n",
       "      <td>어린이/태교</td>\n",
       "      <td>GN22</td>\n",
       "      <td>GN2203</td>\n",
       "      <td>영어동요</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>GN2200</td>\n",
       "      <td>어린이/태교</td>\n",
       "      <td>GN22</td>\n",
       "      <td>GN2204</td>\n",
       "      <td>영어동요</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>GN2200</td>\n",
       "      <td>어린이/태교</td>\n",
       "      <td>GN22</td>\n",
       "      <td>GN2205</td>\n",
       "      <td>영어동요</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>GN2200</td>\n",
       "      <td>어린이/태교</td>\n",
       "      <td>GN22</td>\n",
       "      <td>GN2206</td>\n",
       "      <td>영어동화</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>GN2200</td>\n",
       "      <td>어린이/태교</td>\n",
       "      <td>GN22</td>\n",
       "      <td>GN2207</td>\n",
       "      <td>만화</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>GN2200</td>\n",
       "      <td>어린이/태교</td>\n",
       "      <td>GN22</td>\n",
       "      <td>GN2208</td>\n",
       "      <td>자장가</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>GN2200</td>\n",
       "      <td>어린이/태교</td>\n",
       "      <td>GN22</td>\n",
       "      <td>GN2209</td>\n",
       "      <td>태교동화</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>GN2200</td>\n",
       "      <td>어린이/태교</td>\n",
       "      <td>GN22</td>\n",
       "      <td>GN2210</td>\n",
       "      <td>태교</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>GN2200</td>\n",
       "      <td>어린이/태교</td>\n",
       "      <td>GN22</td>\n",
       "      <td>GN2211</td>\n",
       "      <td>릴렉싱&amp;힐링</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>GN2200</td>\n",
       "      <td>어린이/태교</td>\n",
       "      <td>GN22</td>\n",
       "      <td>GN2212</td>\n",
       "      <td>명작동화</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>GN2200</td>\n",
       "      <td>어린이/태교</td>\n",
       "      <td>GN22</td>\n",
       "      <td>GN2213</td>\n",
       "      <td>교과서동요</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>GN2200</td>\n",
       "      <td>어린이/태교</td>\n",
       "      <td>GN22</td>\n",
       "      <td>GN2214</td>\n",
       "      <td>전래동요</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>GN2200</td>\n",
       "      <td>어린이/태교</td>\n",
       "      <td>GN22</td>\n",
       "      <td>GN2215</td>\n",
       "      <td>창작동화</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>GN2200</td>\n",
       "      <td>어린이/태교</td>\n",
       "      <td>GN22</td>\n",
       "      <td>GN2216</td>\n",
       "      <td>어린이클래식</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    gnr_code gnr_name join_code dtl_gnr_code dtl_gnr_name\n",
       "177   GN2200   어린이/태교      GN22       GN2201         유아동요\n",
       "178   GN2200   어린이/태교      GN22       GN2202         창작동요\n",
       "179   GN2200   어린이/태교      GN22       GN2203         영어동요\n",
       "180   GN2200   어린이/태교      GN22       GN2204         영어동요\n",
       "181   GN2200   어린이/태교      GN22       GN2205         영어동요\n",
       "182   GN2200   어린이/태교      GN22       GN2206         영어동화\n",
       "183   GN2200   어린이/태교      GN22       GN2207           만화\n",
       "184   GN2200   어린이/태교      GN22       GN2208          자장가\n",
       "185   GN2200   어린이/태교      GN22       GN2209         태교동화\n",
       "186   GN2200   어린이/태교      GN22       GN2210           태교\n",
       "187   GN2200   어린이/태교      GN22       GN2211       릴렉싱&힐링\n",
       "188   GN2200   어린이/태교      GN22       GN2212         명작동화\n",
       "189   GN2200   어린이/태교      GN22       GN2213        교과서동요\n",
       "190   GN2200   어린이/태교      GN22       GN2214         전래동요\n",
       "191   GN2200   어린이/태교      GN22       GN2215         창작동화\n",
       "192   GN2200   어린이/태교      GN22       GN2216       어린이클래식"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 어린이/테교 장르(GN2200)의 상세 장르 살펴보기\n",
    "gnr_code_tree[gnr_code_tree['gnr_code'] == 'GN2200']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 곡 별 메타 데이터 : `song_meta.json`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 데이터 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:00:56.977184Z",
     "start_time": "2020-04-26T23:00:46.031689Z"
    }
   },
   "outputs": [],
   "source": [
    "song_meta = pd.read_json('song_meta.json', typ = 'frame')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:00:57.011940Z",
     "start_time": "2020-04-26T23:00:56.979715Z"
    }
   },
   "outputs": [
    {
     "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>song_gn_dtl_gnr_basket</th>\n",
       "      <th>issue_date</th>\n",
       "      <th>album_name</th>\n",
       "      <th>album_id</th>\n",
       "      <th>artist_id_basket</th>\n",
       "      <th>song_name</th>\n",
       "      <th>song_gn_gnr_basket</th>\n",
       "      <th>artist_name_basket</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[GN0901]</td>\n",
       "      <td>20140512</td>\n",
       "      <td>불후의 명곡 - 7080 추억의 얄개시대 팝송베스트</td>\n",
       "      <td>2255639</td>\n",
       "      <td>[2727]</td>\n",
       "      <td>Feelings</td>\n",
       "      <td>[GN0900]</td>\n",
       "      <td>[Various Artists]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[GN1601, GN1606]</td>\n",
       "      <td>20080421</td>\n",
       "      <td>Bach : Partitas Nos. 2, 3 &amp; 4</td>\n",
       "      <td>376431</td>\n",
       "      <td>[29966]</td>\n",
       "      <td>Bach : Partita No. 4 In D Major, BWV 828 - II....</td>\n",
       "      <td>[GN1600]</td>\n",
       "      <td>[Murray Perahia]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[GN0901]</td>\n",
       "      <td>20180518</td>\n",
       "      <td>Hit</td>\n",
       "      <td>4698747</td>\n",
       "      <td>[3361]</td>\n",
       "      <td>Solsbury Hill (Remastered 2002)</td>\n",
       "      <td>[GN0900]</td>\n",
       "      <td>[Peter Gabriel]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[GN1102, GN1101]</td>\n",
       "      <td>20151016</td>\n",
       "      <td>Feeling Right (Everything Is Nice) (Feat. Popc...</td>\n",
       "      <td>2644882</td>\n",
       "      <td>[838543]</td>\n",
       "      <td>Feeling Right (Everything Is Nice) (Feat. Popc...</td>\n",
       "      <td>[GN1100]</td>\n",
       "      <td>[Matoma]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[GN1802, GN1801]</td>\n",
       "      <td>20110824</td>\n",
       "      <td>그남자 그여자</td>\n",
       "      <td>2008470</td>\n",
       "      <td>[560160]</td>\n",
       "      <td>그남자 그여자</td>\n",
       "      <td>[GN1800]</td>\n",
       "      <td>[Jude Law]</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707984</th>\n",
       "      <td>[GN2001]</td>\n",
       "      <td>19991219</td>\n",
       "      <td>The Best Best Of The Black President</td>\n",
       "      <td>65254</td>\n",
       "      <td>[166499]</td>\n",
       "      <td>Coffin For Head Of State</td>\n",
       "      <td>[GN2000]</td>\n",
       "      <td>[Fela Kuti]</td>\n",
       "      <td>707984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707985</th>\n",
       "      <td>[GN0901]</td>\n",
       "      <td>19860000</td>\n",
       "      <td>True Colors</td>\n",
       "      <td>44141</td>\n",
       "      <td>[11837]</td>\n",
       "      <td>Change Of Heart</td>\n",
       "      <td>[GN0900]</td>\n",
       "      <td>[Cyndi Lauper]</td>\n",
       "      <td>707985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707986</th>\n",
       "      <td>[GN0105, GN0101]</td>\n",
       "      <td>20160120</td>\n",
       "      <td>행보 2015 윤종신 / 작사가 윤종신 Live Part.1</td>\n",
       "      <td>2662866</td>\n",
       "      <td>[437]</td>\n",
       "      <td>스치듯 안녕</td>\n",
       "      <td>[GN0100]</td>\n",
       "      <td>[윤종신]</td>\n",
       "      <td>707986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707987</th>\n",
       "      <td>[GN1807, GN1801]</td>\n",
       "      <td>20131217</td>\n",
       "      <td>명상의 시간을 위한 뉴에이지 음악</td>\n",
       "      <td>2221722</td>\n",
       "      <td>[729868]</td>\n",
       "      <td>숲의 빛</td>\n",
       "      <td>[GN1800]</td>\n",
       "      <td>[Nature Piano]</td>\n",
       "      <td>707987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707988</th>\n",
       "      <td>[GN0601, GN0604]</td>\n",
       "      <td>19980000</td>\n",
       "      <td>김경호 Live</td>\n",
       "      <td>34663</td>\n",
       "      <td>[895]</td>\n",
       "      <td>Queen 명곡 멜로디</td>\n",
       "      <td>[GN0600]</td>\n",
       "      <td>[김경호]</td>\n",
       "      <td>707988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>707989 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       song_gn_dtl_gnr_basket  issue_date  \\\n",
       "0                    [GN0901]    20140512   \n",
       "1            [GN1601, GN1606]    20080421   \n",
       "2                    [GN0901]    20180518   \n",
       "3            [GN1102, GN1101]    20151016   \n",
       "4            [GN1802, GN1801]    20110824   \n",
       "...                       ...         ...   \n",
       "707984               [GN2001]    19991219   \n",
       "707985               [GN0901]    19860000   \n",
       "707986       [GN0105, GN0101]    20160120   \n",
       "707987       [GN1807, GN1801]    20131217   \n",
       "707988       [GN0601, GN0604]    19980000   \n",
       "\n",
       "                                               album_name  album_id  \\\n",
       "0                            불후의 명곡 - 7080 추억의 얄개시대 팝송베스트   2255639   \n",
       "1                           Bach : Partitas Nos. 2, 3 & 4    376431   \n",
       "2                                                     Hit   4698747   \n",
       "3       Feeling Right (Everything Is Nice) (Feat. Popc...   2644882   \n",
       "4                                                 그남자 그여자   2008470   \n",
       "...                                                   ...       ...   \n",
       "707984               The Best Best Of The Black President     65254   \n",
       "707985                                        True Colors     44141   \n",
       "707986                  행보 2015 윤종신 / 작사가 윤종신 Live Part.1   2662866   \n",
       "707987                                 명상의 시간을 위한 뉴에이지 음악   2221722   \n",
       "707988                                           김경호 Live     34663   \n",
       "\n",
       "       artist_id_basket                                          song_name  \\\n",
       "0                [2727]                                           Feelings   \n",
       "1               [29966]  Bach : Partita No. 4 In D Major, BWV 828 - II....   \n",
       "2                [3361]                    Solsbury Hill (Remastered 2002)   \n",
       "3              [838543]  Feeling Right (Everything Is Nice) (Feat. Popc...   \n",
       "4              [560160]                                            그남자 그여자   \n",
       "...                 ...                                                ...   \n",
       "707984         [166499]                           Coffin For Head Of State   \n",
       "707985          [11837]                                    Change Of Heart   \n",
       "707986            [437]                                             스치듯 안녕   \n",
       "707987         [729868]                                               숲의 빛   \n",
       "707988            [895]                                       Queen 명곡 멜로디   \n",
       "\n",
       "       song_gn_gnr_basket artist_name_basket      id  \n",
       "0                [GN0900]  [Various Artists]       0  \n",
       "1                [GN1600]   [Murray Perahia]       1  \n",
       "2                [GN0900]    [Peter Gabriel]       2  \n",
       "3                [GN1100]           [Matoma]       3  \n",
       "4                [GN1800]         [Jude Law]       4  \n",
       "...                   ...                ...     ...  \n",
       "707984           [GN2000]        [Fela Kuti]  707984  \n",
       "707985           [GN0900]     [Cyndi Lauper]  707985  \n",
       "707986           [GN0100]              [윤종신]  707986  \n",
       "707987           [GN1800]     [Nature Piano]  707987  \n",
       "707988           [GN0600]              [김경호]  707988  \n",
       "\n",
       "[707989 rows x 9 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "song_meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:00:57.647218Z",
     "start_time": "2020-04-26T23:00:57.013747Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 707989 entries, 0 to 707988\n",
      "Data columns (total 9 columns):\n",
      " #   Column                  Non-Null Count   Dtype \n",
      "---  ------                  --------------   ----- \n",
      " 0   song_gn_dtl_gnr_basket  707989 non-null  object\n",
      " 1   issue_date              707989 non-null  int64 \n",
      " 2   album_name              707985 non-null  object\n",
      " 3   album_id                707989 non-null  int64 \n",
      " 4   artist_id_basket        707989 non-null  object\n",
      " 5   song_name               707989 non-null  object\n",
      " 6   song_gn_gnr_basket      707989 non-null  object\n",
      " 7   artist_name_basket      707989 non-null  object\n",
      " 8   id                      707989 non-null  int64 \n",
      "dtypes: int64(3), object(6)\n",
      "memory usage: 48.6+ MB\n"
     ]
    }
   ],
   "source": [
    "song_meta.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **총 707,989곡의 메타 정보가 담겨 있으며** 포함하는 값은 아래와 같습니다.\n",
    "  + `song_gn_dtl_gnr_basket` : 상세 장르 코드\n",
    "  + `issue_date` : 곡 발매 일자 (yyyymmdd)\n",
    "  + `album_name` : 앨범 명\n",
    "  + `album_id` : 앨범 아이디\n",
    "  + `artist_id_basket` : 아티스트 아이디 (복수일 경우 띄어쓰기로 구분)\n",
    "  + `song_name` : 곡 명\n",
    "  + `song_gn_gnr_basket` : 대분류 장르코드\n",
    "  + `artist_name_basket` : 아티스트 명 (복수일 경우 띄어쓰기로 구분)\n",
    "  + `id` : 곡 아이디"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 곡 별 매핑 되는 장르 수는?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:03:51.097055Z",
     "start_time": "2020-04-26T23:03:48.067165Z"
    }
   },
   "outputs": [],
   "source": [
    "# 곡 아이디(id)와 대분류 장르코드 리스트(song_gn_gnr_basket) 추출\n",
    "song_gnr_map = song_meta.loc[:, ['id', 'song_gn_gnr_basket']]\n",
    "\n",
    "# unnest song_gn_gnr_basket\n",
    "song_gnr_map_unnest = np.dstack(\n",
    "    (\n",
    "        np.repeat(song_gnr_map.id.values, list(map(len, song_gnr_map.song_gn_gnr_basket))), \n",
    "        np.concatenate(song_gnr_map.song_gn_gnr_basket.values)\n",
    "    )\n",
    ")\n",
    "\n",
    "# unnested 데이터프레임 생성 : song_gnr_map\n",
    "song_gnr_map = pd.DataFrame(data = song_gnr_map_unnest[0], columns = song_gnr_map.columns)\n",
    "song_gnr_map['id'] = song_gnr_map['id'].astype(str)\n",
    "song_gnr_map.rename(columns = {'id' : 'song_id', 'song_gn_gnr_basket' : 'gnr_code'}, inplace = True)\n",
    "\n",
    "# unnest 객체 제거\n",
    "del song_gnr_map_unnest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:03:52.358995Z",
     "start_time": "2020-04-26T23:03:52.349514Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>GN2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802855</th>\n",
       "      <td>707985</td>\n",
       "      <td>GN0900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802856</th>\n",
       "      <td>707986</td>\n",
       "      <td>GN0100</td>\n",
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       "      <th>802857</th>\n",
       "      <td>707987</td>\n",
       "      <td>GN1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802858</th>\n",
       "      <td>707988</td>\n",
       "      <td>GN0600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>802859 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       song_id gnr_code\n",
       "0            0   GN0900\n",
       "1            1   GN1600\n",
       "2            2   GN0900\n",
       "3            3   GN1100\n",
       "4            4   GN1800\n",
       "...        ...      ...\n",
       "802854  707984   GN2000\n",
       "802855  707985   GN0900\n",
       "802856  707986   GN0100\n",
       "802857  707987   GN1800\n",
       "802858  707988   GN0600\n",
       "\n",
       "[802859 rows x 2 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "song_gnr_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:03:59.204173Z",
     "start_time": "2020-04-26T23:03:57.651570Z"
    }
   },
   "outputs": [
    {
     "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>장르 수</th>\n",
       "      <th>매핑된 곡 수</th>\n",
       "      <th>비율(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>612806</td>\n",
       "      <td>86.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>92378</td>\n",
       "      <td>13.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1694</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>48</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   장르 수  매핑된 곡 수  비율(%)\n",
       "0     1   612806  86.69\n",
       "1     2    92378  13.07\n",
       "2     3     1694   0.24\n",
       "3     4       48   0.01\n",
       "4     5        1   0.00\n",
       "5     6        3   0.00"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. 곡 별 장르 개수 count 테이블 생성 : song_gnr_count\n",
    "song_gnr_count = song_gnr_map.groupby('song_id').gnr_code.nunique().reset_index(name = 'mapping_gnr_cnt')\n",
    "\n",
    "# 2. 1번에서 생성한 테이블을 가지고 매핑된 장르 개수 별 곡 수 count 테이블 생성 : gnr_song_count\n",
    "gnr_song_count = song_gnr_count.groupby('mapping_gnr_cnt').song_id.nunique().reset_index(name = '매핑된 곡 수')\n",
    "\n",
    "# 3. 2번 테이블에 비율 값 추가\n",
    "gnr_song_count.loc[:, '비율(%)'] = round(gnr_song_count['매핑된 곡 수']/sum(gnr_song_count['매핑된 곡 수'])*100, 2)\n",
    "gnr_song_count = gnr_song_count.reset_index().rename(columns = {'mapping_gnr_cnt' : '장르 수'})\n",
    "gnr_song_count[['장르 수', '매핑된 곡 수', '비율(%)']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **대부분의 곡들은 한 개의 대분류 장르와 매핑**되어 있습니다.\n",
    "- 전체 곡의 약 13%는 2개 이상의 대분류 장르를 가지며 아래 임의 예시입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:04:37.486190Z",
     "start_time": "2020-04-26T23:04:37.471486Z"
    }
   },
   "outputs": [
    {
     "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>song_gn_dtl_gnr_basket</th>\n",
       "      <th>issue_date</th>\n",
       "      <th>album_name</th>\n",
       "      <th>album_id</th>\n",
       "      <th>artist_id_basket</th>\n",
       "      <th>song_name</th>\n",
       "      <th>song_gn_gnr_basket</th>\n",
       "      <th>artist_name_basket</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>456506</th>\n",
       "      <td>[GN0403, GN0101, GN1501, GN1504]</td>\n",
       "      <td>20060731</td>\n",
       "      <td>오버 더 레인보우 OST</td>\n",
       "      <td>321962</td>\n",
       "      <td>[1928]</td>\n",
       "      <td>그리운 건 어쩔 수 없어</td>\n",
       "      <td>[GN0400, GN1500, GN0100]</td>\n",
       "      <td>[환희]</td>\n",
       "      <td>456506</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  song_gn_dtl_gnr_basket  issue_date     album_name  album_id  \\\n",
       "456506  [GN0403, GN0101, GN1501, GN1504]    20060731  오버 더 레인보우 OST    321962   \n",
       "\n",
       "       artist_id_basket      song_name        song_gn_gnr_basket  \\\n",
       "456506           [1928]  그리운 건 어쩔 수 없어  [GN0400, GN1500, GN0100]   \n",
       "\n",
       "       artist_name_basket      id  \n",
       "456506               [환희]  456506  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#song_gnr_count[song_gnr_count['mapping_gnr_cnt'] >= 2]\n",
    "song_meta[song_meta['id'] == 456506] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 가장 많이 매핑 되는 장르는?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:04:46.254514Z",
     "start_time": "2020-04-26T23:04:45.071903Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['NanumGothic'] not found. Falling back to DejaVu Sans.\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51109 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47476 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48324 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47588 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 54609 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 46108 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 44257 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49688 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48516 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 54252 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "findfont: Font family ['NanumGothic'] not found. Falling back to DejaVu Sans.\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47197 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47700 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 53448 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51068 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47113 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 53944 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47196 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 45768 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 52852 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 53364 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47000 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49885 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48156 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 46972 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 46300 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51116 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51592 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 45684 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 50640 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51060 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51648 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51064 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 46356 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51020 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 50501 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47017 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 55193 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 54633 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 45828 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49828 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 50612 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47536 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 53468 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 44368 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 53356 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48660 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47336 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 52968 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47532 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 50900 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48036 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51649 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49457 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 44032 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 50836 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 50500 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 46028 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 44397 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 53580 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 54588 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 52972 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51333 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47197 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47700 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 53448 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51068 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47113 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 53944 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47196 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 45768 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 52852 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 53364 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47000 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49885 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48156 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 46972 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 46300 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51116 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51592 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 45684 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 50640 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51060 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51648 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51064 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 46356 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51020 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 50501 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47017 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 55193 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 54633 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 45828 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49828 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 50612 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47536 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 53468 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 44368 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 54252 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 53356 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48660 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47336 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 52968 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47532 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 50900 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48036 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51649 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49457 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 44032 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 50836 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 50500 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 46028 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 44397 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 53580 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 54588 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 52972 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51333 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 45824 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47448 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 53076 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 45824 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48516 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47448 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51109 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47476 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 53076 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 44257 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49688 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48324 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47588 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 54609 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 46108 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. 장르 별 곡 수 count 테이블 생성 : gnr_count\n",
    "gnr_count = song_gnr_map.groupby('gnr_code').song_id.nunique().reset_index(name = 'song_cnt')\n",
    "\n",
    "# 2. 1번 테이블과 장르 meta와 join\n",
    "gnr_count = pd.merge(gnr_count, gnr_code.loc[:, ['gnr_code', 'gnr_name']], how = 'left', on = 'gnr_code')\n",
    "gnr_count['gnr_code_name'] = gnr_count['gnr_code'] + ' (' + gnr_count['gnr_name'] + ')'\n",
    "\n",
    "# 3. 매핑이 되지 않은 일부 곡들은 제거\n",
    "gnr_count = gnr_count[['gnr_code_name', 'song_cnt']].dropna()\n",
    "\n",
    "# 4. 많은 곡이 매핑된 순 기준으로 내림차순 리스트 생성\n",
    "gnr_list_desc = gnr_count.sort_values('song_cnt', ascending = False).gnr_code_name\n",
    "\n",
    "# 5. plotting\n",
    "gnr_code_name_plot = sns.barplot(x = 'gnr_code_name', y = 'song_cnt', data = gnr_count, order = gnr_list_desc)\n",
    "gnr_code_name_plot.set_title('장르 별 매핑된 곡 수 분포')\n",
    "gnr_code_name_plot.set_xlabel('대분류 장르코드')\n",
    "gnr_code_name_plot.set_ylabel('곡 수')\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **POP 장르(`GN0900`)의 곡 비중이 가장 높았고**, 국악, 뮤지컬, 뮤직테라피, 종교음악 등의 장르 비중은 1% 미만의 비중을 보입니다.\n",
    "- 아이돌 아티스트 관련 장르인 아이돌 장르(`GN2500`)와 댄스 장르(`GN0200`) 비중도 비교적 낮게 나타나는 점이 보입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 발매 년도 별 곡 비중은?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:05:27.233402Z",
     "start_time": "2020-04-26T23:05:24.744509Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48156 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47588 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 45380 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 46020 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48324 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 44257 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49688 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 52628 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51060 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48156 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47588 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 45380 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 46020 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 44257 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49688 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48324 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 52628 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51060 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. 곡 아이디(id)와 발매일자(issue_date) 추출\n",
    "song_issue_date = song_meta[['id', 'issue_date']]\n",
    "song_issue_date['issue_date'] = song_issue_date['issue_date'].astype(str)\n",
    "\n",
    "# 2. issue_date의 앞자리 네 자리를 추출하여 발매년도(issue_year) 변수 생성\n",
    "song_issue_date['issue_year'] = song_issue_date['issue_date'].str[0:4]\n",
    "song_issue_date.rename(columns = {'id' : 'song_id'}, inplace = True)\n",
    "song_issue_date['song_id'] = song_issue_date['song_id'].astype(str)\n",
    "\n",
    "# 3. 1990년도~ 필터링\n",
    "song_issue_date_filter = song_issue_date[song_issue_date.issue_year >= '1990']\n",
    "\n",
    "# 4. 발매년도 별 곡 수 count 테이블 생성 : issue_year_song_cnt\n",
    "issue_year_song_cnt = song_issue_date_filter.groupby('issue_year').song_id.nunique().reset_index(name = 'song_cnt')\n",
    "\n",
    "# 5. plotting\n",
    "issue_year_song_cnt_plot = sns.barplot(x = 'issue_year', y = 'song_cnt', data = issue_year_song_cnt, color = 'grey')\n",
    "issue_year_song_cnt_plot.set_title('발매년도 별 곡 수 추이 (1990년~)')\n",
    "issue_year_song_cnt_plot.set_xlabel('발매년도')\n",
    "issue_year_song_cnt_plot.set_ylabel('곡 수')\n",
    "plt.xticks(rotation = 45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 1990년 이후로 각 곡들의 발매년도 별 곡의 비중을 확인해본 결과 **2014~2019년도에 발매된 곡의 비중**이 높았습니다.  \n",
    "  (실제 전체 메타 풀에서 약 50% 정도의 비중을 보임)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 학습 데이터 : `train.json`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 데이터 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:05:44.935013Z",
     "start_time": "2020-04-26T23:05:42.448978Z"
    }
   },
   "outputs": [],
   "source": [
    "train = pd.read_json('train.json', typ = 'frame')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:05:45.717239Z",
     "start_time": "2020-04-26T23:05:45.691335Z"
    }
   },
   "outputs": [
    {
     "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>tags</th>\n",
       "      <th>id</th>\n",
       "      <th>plylst_title</th>\n",
       "      <th>songs</th>\n",
       "      <th>like_cnt</th>\n",
       "      <th>updt_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[락]</td>\n",
       "      <td>61281</td>\n",
       "      <td>여행같은 음악</td>\n",
       "      <td>[525514, 129701, 383374, 562083, 297861, 13954...</td>\n",
       "      <td>71</td>\n",
       "      <td>2013-12-19 18:36:19.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[추억, 회상]</td>\n",
       "      <td>10532</td>\n",
       "      <td>요즘 너 말야</td>\n",
       "      <td>[432406, 675945, 497066, 120377, 389529, 24427...</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-12-02 16:19:42.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[까페, 잔잔한]</td>\n",
       "      <td>76951</td>\n",
       "      <td>편하게, 잔잔하게 들을 수 있는 곡.-</td>\n",
       "      <td>[83116, 276692, 166267, 186301, 354465, 256598...</td>\n",
       "      <td>17</td>\n",
       "      <td>2017-08-28 07:09:34.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[연말, 눈오는날, 캐럴, 분위기, 따듯한, 크리스마스캐럴, 겨울노래, 크리스마스,...</td>\n",
       "      <td>147456</td>\n",
       "      <td>크리스마스 분위기에 흠뻑 취하고 싶을때</td>\n",
       "      <td>[394031, 195524, 540149, 287984, 440773, 10033...</td>\n",
       "      <td>33</td>\n",
       "      <td>2019-12-05 15:15:18.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[댄스]</td>\n",
       "      <td>27616</td>\n",
       "      <td>추억의 노래 ㅋ</td>\n",
       "      <td>[159327, 553610, 5130, 645103, 294435, 100657,...</td>\n",
       "      <td>9</td>\n",
       "      <td>2011-10-25 13:54:56.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115066</th>\n",
       "      <td>[록메탈, 밴드사운드, 록, 락메탈, 메탈, 락, extreme]</td>\n",
       "      <td>120325</td>\n",
       "      <td>METAL E'SM #2</td>\n",
       "      <td>[429629, 441511, 612106, 516359, 691768, 38714...</td>\n",
       "      <td>3</td>\n",
       "      <td>2020-04-17 04:31:11.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115067</th>\n",
       "      <td>[일렉]</td>\n",
       "      <td>106976</td>\n",
       "      <td>빠른 리스너를 위한 따끈따끈한 최신 인기 EDM 모음!</td>\n",
       "      <td>[321330, 216057, 534472, 240306, 331098, 23288...</td>\n",
       "      <td>13</td>\n",
       "      <td>2015-12-24 17:23:19.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115068</th>\n",
       "      <td>[담시, 가족, 눈물, 그리움, 주인공, 나의_이야기, 사랑, 친구]</td>\n",
       "      <td>11343</td>\n",
       "      <td>#1. 눈물이 앞을 가리는 나의_이야기</td>\n",
       "      <td>[50512, 249024, 250608, 371171, 229942, 694943...</td>\n",
       "      <td>4</td>\n",
       "      <td>2019-08-16 20:59:22.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115069</th>\n",
       "      <td>[잔잔한, 버스, 퇴근버스, Pop, 풍경, 퇴근길]</td>\n",
       "      <td>131982</td>\n",
       "      <td>퇴근 버스에서 편히 들으면서 하루를 마무리하기에 좋은 POP</td>\n",
       "      <td>[533534, 608114, 343608, 417140, 609009, 30217...</td>\n",
       "      <td>4</td>\n",
       "      <td>2019-10-25 23:40:42.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115070</th>\n",
       "      <td>[노래추천, 팝송추천, 팝송, 팝송모음]</td>\n",
       "      <td>100389</td>\n",
       "      <td>FAVORITE POPSONG!!!</td>\n",
       "      <td>[26008, 456354, 324105, 89871, 135272, 143548,...</td>\n",
       "      <td>17</td>\n",
       "      <td>2020-04-18 20:35:06.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>115071 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     tags      id  \\\n",
       "0                                                     [락]   61281   \n",
       "1                                                [추억, 회상]   10532   \n",
       "2                                               [까페, 잔잔한]   76951   \n",
       "3       [연말, 눈오는날, 캐럴, 분위기, 따듯한, 크리스마스캐럴, 겨울노래, 크리스마스,...  147456   \n",
       "4                                                    [댄스]   27616   \n",
       "...                                                   ...     ...   \n",
       "115066               [록메탈, 밴드사운드, 록, 락메탈, 메탈, 락, extreme]  120325   \n",
       "115067                                               [일렉]  106976   \n",
       "115068             [담시, 가족, 눈물, 그리움, 주인공, 나의_이야기, 사랑, 친구]   11343   \n",
       "115069                      [잔잔한, 버스, 퇴근버스, Pop, 풍경, 퇴근길]  131982   \n",
       "115070                             [노래추천, 팝송추천, 팝송, 팝송모음]  100389   \n",
       "\n",
       "                             plylst_title  \\\n",
       "0                                 여행같은 음악   \n",
       "1                                 요즘 너 말야   \n",
       "2                   편하게, 잔잔하게 들을 수 있는 곡.-   \n",
       "3                   크리스마스 분위기에 흠뻑 취하고 싶을때   \n",
       "4                                추억의 노래 ㅋ   \n",
       "...                                   ...   \n",
       "115066                      METAL E'SM #2   \n",
       "115067     빠른 리스너를 위한 따끈따끈한 최신 인기 EDM 모음!   \n",
       "115068              #1. 눈물이 앞을 가리는 나의_이야기   \n",
       "115069  퇴근 버스에서 편히 들으면서 하루를 마무리하기에 좋은 POP   \n",
       "115070                FAVORITE POPSONG!!!   \n",
       "\n",
       "                                                    songs  like_cnt  \\\n",
       "0       [525514, 129701, 383374, 562083, 297861, 13954...        71   \n",
       "1       [432406, 675945, 497066, 120377, 389529, 24427...         1   \n",
       "2       [83116, 276692, 166267, 186301, 354465, 256598...        17   \n",
       "3       [394031, 195524, 540149, 287984, 440773, 10033...        33   \n",
       "4       [159327, 553610, 5130, 645103, 294435, 100657,...         9   \n",
       "...                                                   ...       ...   \n",
       "115066  [429629, 441511, 612106, 516359, 691768, 38714...         3   \n",
       "115067  [321330, 216057, 534472, 240306, 331098, 23288...        13   \n",
       "115068  [50512, 249024, 250608, 371171, 229942, 694943...         4   \n",
       "115069  [533534, 608114, 343608, 417140, 609009, 30217...         4   \n",
       "115070  [26008, 456354, 324105, 89871, 135272, 143548,...        17   \n",
       "\n",
       "                      updt_date  \n",
       "0       2013-12-19 18:36:19.000  \n",
       "1       2014-12-02 16:19:42.000  \n",
       "2       2017-08-28 07:09:34.000  \n",
       "3       2019-12-05 15:15:18.000  \n",
       "4       2011-10-25 13:54:56.000  \n",
       "...                         ...  \n",
       "115066  2020-04-17 04:31:11.000  \n",
       "115067  2015-12-24 17:23:19.000  \n",
       "115068  2019-08-16 20:59:22.000  \n",
       "115069  2019-10-25 23:40:42.000  \n",
       "115070  2020-04-18 20:35:06.000  \n",
       "\n",
       "[115071 rows x 6 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T10:46:36.429262Z",
     "start_time": "2020-04-26T10:46:36.361687Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 115071 entries, 0 to 115070\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count   Dtype \n",
      "---  ------        --------------   ----- \n",
      " 0   tags          115071 non-null  object\n",
      " 1   id            115071 non-null  int64 \n",
      " 2   plylst_title  115071 non-null  object\n",
      " 3   songs         115071 non-null  object\n",
      " 4   like_cnt      115071 non-null  int64 \n",
      " 5   updt_date     115071 non-null  object\n",
      "dtypes: int64(2), object(4)\n",
      "memory usage: 5.3+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 학습 데이터에는 **총 115,071개 플레이리스트 정보가 담겨져 있으며** 포함하는 값은 아래와 같습니다.\n",
    "  + `tags` : 플레이리스트에 매핑된 태그\n",
    "  + `id` : 플레이리스트 아이디\n",
    "  + `plylst_title` : 플레이리스트 명\n",
    "  + `songs` : 플레이리스트 내 수록된 곡 아이디\n",
    "  + `like_cnt` : 플레이리스트 좋아요 횟수\n",
    "  + `updt_date` : 플레이리스트 업데이트 일시"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:06:03.467113Z",
     "start_time": "2020-04-26T23:05:53.911081Z"
    }
   },
   "outputs": [],
   "source": [
    "# 플레이리스트 아이디(id)와 수록곡(songs) 추출\n",
    "plylst_song_map = train[['id', 'songs']]\n",
    "\n",
    "# unnest songs\n",
    "plylst_song_map_unnest = np.dstack(\n",
    "    (\n",
    "        np.repeat(plylst_song_map.id.values, list(map(len, plylst_song_map.songs))), \n",
    "        np.concatenate(plylst_song_map.songs.values)\n",
    "    )\n",
    ")\n",
    "\n",
    "# unnested 데이터프레임 생성 : plylst_song_map\n",
    "plylst_song_map = pd.DataFrame(data = plylst_song_map_unnest[0], columns = plylst_song_map.columns)\n",
    "plylst_song_map['id'] = plylst_song_map['id'].astype(str)\n",
    "plylst_song_map['songs'] = plylst_song_map['songs'].astype(str)\n",
    "\n",
    "# unnest 객체 제거\n",
    "del plylst_song_map_unnest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:06:03.481135Z",
     "start_time": "2020-04-26T23:06:03.469401Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>songs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>61281</td>\n",
       "      <td>525514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61281</td>\n",
       "      <td>129701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61281</td>\n",
       "      <td>383374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61281</td>\n",
       "      <td>562083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61281</td>\n",
       "      <td>297861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5285866</th>\n",
       "      <td>100389</td>\n",
       "      <td>111365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5285867</th>\n",
       "      <td>100389</td>\n",
       "      <td>51373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5285868</th>\n",
       "      <td>100389</td>\n",
       "      <td>640239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5285869</th>\n",
       "      <td>100389</td>\n",
       "      <td>13759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5285870</th>\n",
       "      <td>100389</td>\n",
       "      <td>154078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5285871 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id   songs\n",
       "0         61281  525514\n",
       "1         61281  129701\n",
       "2         61281  383374\n",
       "3         61281  562083\n",
       "4         61281  297861\n",
       "...         ...     ...\n",
       "5285866  100389  111365\n",
       "5285867  100389   51373\n",
       "5285868  100389  640239\n",
       "5285869  100389   13759\n",
       "5285870  100389  154078\n",
       "\n",
       "[5285871 rows x 2 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plylst_song_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:06:04.915352Z",
     "start_time": "2020-04-26T23:06:03.863235Z"
    }
   },
   "outputs": [],
   "source": [
    "# 플레이리스트 아이디(id)와 매핑된 태그(tags) 추출\n",
    "plylst_tag_map = train[['id', 'tags']]\n",
    "\n",
    "# unnest tags\n",
    "plylst_tag_map_unnest = np.dstack(\n",
    "    (\n",
    "        np.repeat(plylst_tag_map.id.values, list(map(len, plylst_tag_map.tags))), \n",
    "        np.concatenate(plylst_tag_map.tags.values)\n",
    "    )\n",
    ")\n",
    "\n",
    "# unnested 데이터프레임 생성 : plylst_tag_map\n",
    "plylst_tag_map = pd.DataFrame(data = plylst_tag_map_unnest[0], columns = plylst_tag_map.columns)\n",
    "plylst_tag_map['id'] = plylst_tag_map['id'].astype(str)\n",
    "\n",
    "# unnest 객체 제거\n",
    "del plylst_tag_map_unnest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:06:06.397077Z",
     "start_time": "2020-04-26T23:06:06.387772Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>61281</td>\n",
       "      <td>락</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10532</td>\n",
       "      <td>추억</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10532</td>\n",
       "      <td>회상</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>76951</td>\n",
       "      <td>까페</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>76951</td>\n",
       "      <td>잔잔한</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476326</th>\n",
       "      <td>131982</td>\n",
       "      <td>퇴근길</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476327</th>\n",
       "      <td>100389</td>\n",
       "      <td>노래추천</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476328</th>\n",
       "      <td>100389</td>\n",
       "      <td>팝송추천</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476329</th>\n",
       "      <td>100389</td>\n",
       "      <td>팝송</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476330</th>\n",
       "      <td>100389</td>\n",
       "      <td>팝송모음</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>476331 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  tags\n",
       "0        61281     락\n",
       "1        10532    추억\n",
       "2        10532    회상\n",
       "3        76951    까페\n",
       "4        76951   잔잔한\n",
       "...        ...   ...\n",
       "476326  131982   퇴근길\n",
       "476327  100389  노래추천\n",
       "476328  100389  팝송추천\n",
       "476329  100389    팝송\n",
       "476330  100389  팝송모음\n",
       "\n",
       "[476331 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plylst_tag_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:06:07.167691Z",
     "start_time": "2020-04-26T23:06:06.398573Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "곡 수 : 615142\n",
      "태그 수 : 29160\n"
     ]
    }
   ],
   "source": [
    "train_uniq_song_cnt = plylst_song_map.songs.nunique() # 유니크 곡 수\n",
    "train_uniq_tag_cnt = plylst_tag_map.tags.nunique() # 유니크 태그 수\n",
    "\n",
    "print('곡 수 : %s' %train_uniq_song_cnt)\n",
    "print('태그 수 : %s' %train_uniq_tag_cnt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 플레이리스트 별 곡/태그/장르 수 분포는?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 수록곡 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:06:28.550307Z",
     "start_time": "2020-04-26T23:06:23.962698Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. 플레이리스트 별 수록곡 수 count 테이블 생성 : plylst_song_cnt\n",
    "plylst_song_cnt = pd.DataFrame(plylst_song_map.groupby('id').songs.nunique())\n",
    "\n",
    "# 2-1. grid setting\n",
    "grid_list = [i*2 for i in range(1, 101)]\n",
    "\n",
    "# 2-2. plotting\n",
    "plt.hist(plylst_song_cnt['songs'], grid_list, color = \"lightgrey\", edgecolor = \"black\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:06:28.574444Z",
     "start_time": "2020-04-26T23:06:28.551910Z"
    }
   },
   "outputs": [
    {
     "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>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>mean</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>songs</th>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>45.94</td>\n",
       "      <td>54.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>43.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       min   25%   50%   mean   75%    max    std\n",
       "songs  1.0  19.0  30.0  45.94  54.0  200.0  43.95"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(plylst_song_cnt.describe().loc[['min', '25%', '50%', 'mean', '75%', 'max', 'std']].transpose(), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 플레이리스트 별 수록된 곡 수의 분포를 확인해본 결과 **평균 약 46개**(중앙값 30개)**의 곡이 수록**되어 있으며 최대 200곡의 수록곡을 포함한 플레이리스트가 존재합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 태그"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:06:29.200943Z",
     "start_time": "2020-04-26T23:06:28.576167Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. 플레이리스트 별 매핑 태그 수 count 테이블 생성 : plylst_tag_cnt\n",
    "plylst_tag_cnt = pd.DataFrame(plylst_tag_map.groupby('id').tags.nunique())\n",
    "\n",
    "# 2. plotting\n",
    "plt.hist(plylst_tag_cnt['tags'], range(1, 12), color = \"lightgrey\", edgecolor = \"black\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:06:29.223585Z",
     "start_time": "2020-04-26T23:06:29.202500Z"
    }
   },
   "outputs": [
    {
     "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>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>mean</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tags</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.14</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      min  25%  50%  mean  75%   max   std\n",
       "tags  1.0  2.0  3.0  4.14  6.0  11.0  3.07"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(plylst_tag_cnt.describe().loc[['min', '25%', '50%', 'mean', '75%', 'max', 'std']].transpose(), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 플레이리스트 당 **태그는 평균 약 4.1개**(중앙값 3개)**가 포함**되어 있으며 가장 많은 태그 수는 11개 입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 장르"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:06:41.097155Z",
     "start_time": "2020-04-26T23:06:29.224981Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1-1. join 목적으로 곡 아이디 integer to string 변환\n",
    "plylst_song_map['songs'] = plylst_song_map['songs'].astype(str)\n",
    "\n",
    "# 1-2. 플레이리스트-곡 테이블 plylst_song_map + 곡-장르 테이블 song_gnr_map join\n",
    "plylst_song_gnr_map = pd.merge(plylst_song_map, song_gnr_map, how = 'left', left_on = 'songs', right_on = 'song_id')\n",
    "\n",
    "# 2. 플레이리스트 별 곡 장르 수 count 테이블 생성 : plylst_song_gnr_cnt\n",
    "plylst_song_gnr_cnt = pd.DataFrame(plylst_song_gnr_map.groupby('id').gnr_code.nunique())\n",
    "\n",
    "# 3. plotting\n",
    "plt.hist(plylst_song_gnr_cnt['gnr_code'], range(0, 31), color = \"lightgrey\", edgecolor = \"black\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:06:41.121171Z",
     "start_time": "2020-04-26T23:06:41.098915Z"
    }
   },
   "outputs": [
    {
     "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>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>mean</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gnr_code</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.55</td>\n",
       "      <td>9.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          min  25%  50%  mean  75%   max   std\n",
       "gnr_code  0.0  4.0  6.0  6.55  9.0  26.0  3.62"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(plylst_song_gnr_cnt.describe().loc[['min', '25%', '50%', 'mean', '75%', 'max', 'std']].transpose(), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 플레이리스트에 수록된 곡에서 대분류 장르 수 분포를 확인해본 결과 플레이리스트 당 **평균 6.6개**(중앙값 6개)**의 장르를 포함**하고 있습니다. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 곡/태그/플레이리스트명 중복 비율은?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 중복 수록 곡 비중"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:07:20.692703Z",
     "start_time": "2020-04-26T23:07:16.526694Z"
    }
   },
   "outputs": [
    {
     "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>중복 수록 여부</th>\n",
       "      <th>곡 수</th>\n",
       "      <th>비율(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>단일 수록</td>\n",
       "      <td>299028</td>\n",
       "      <td>48.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>중복 수록</td>\n",
       "      <td>316114</td>\n",
       "      <td>51.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  중복 수록 여부     곡 수  비율(%)\n",
       "0    단일 수록  299028  48.61\n",
       "1    중복 수록  316114  51.39"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. 곡 별 수록된 플레이리스트 개수 count 테이블 생성 : song_plylst_cnt\n",
    "song_plylst_cnt = plylst_song_map.groupby('songs').id.nunique().reset_index(name = 'mapping_plylst_cnt')\n",
    "\n",
    "# 2. 단일/중복 수록 구분 : 곡 별 수록된 플레이리스트가 한 개면 '단일 수록', 두 개 이상이면 '중복 수록'\n",
    "song_plylst_cnt = song_plylst_cnt.assign(\n",
    "    mapping_plylst_cnt_category = pd.cut(song_plylst_cnt['mapping_plylst_cnt'], [0, 1, np.inf], labels = ['단일 수록', '중복 수록'])\n",
    ")\n",
    "\n",
    "# 3. 단일/중복 수록 구분 별 곡 수 count 테이블 생성 : song_plylst_mapping_division\n",
    "song_plylst_mapping_division = pd.DataFrame(song_plylst_cnt.groupby('mapping_plylst_cnt_category').count())\n",
    "\n",
    "# 4. 3번 테이블에 비율 값 추가\n",
    "song_plylst_mapping_division.rename(columns = {'songs' : '곡 수'}, inplace = True)\n",
    "song_plylst_mapping_division.loc[:, '비율(%)'] = round(song_plylst_mapping_division['곡 수']/sum(song_plylst_mapping_division['곡 수'])*100, 2)\n",
    "song_plylst_mapping_division = song_plylst_mapping_division.reset_index().rename(columns = {'mapping_plylst_cnt_category' : '중복 수록 여부'})\n",
    "song_plylst_mapping_division[['중복 수록 여부', '곡 수', '비율(%)']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **수록 곡의 약 51%는 두 개 이상의 플레이리스트에 중복 수록**된 곡 입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 아래는 중복 수록 곡 중 많이 수록된 횟수 기준 상위 30곡 입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:07:54.447311Z",
     "start_time": "2020-04-26T23:07:52.738061Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>곡 아이디</th>\n",
       "      <th>곡 명</th>\n",
       "      <th>아티스트 명</th>\n",
       "      <th>수록 횟수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>144663</td>\n",
       "      <td>밤편지</td>\n",
       "      <td>[아이유]</td>\n",
       "      <td>2175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>116573</td>\n",
       "      <td>안아줘</td>\n",
       "      <td>[정준일]</td>\n",
       "      <td>2121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>357367</td>\n",
       "      <td>비</td>\n",
       "      <td>[폴킴]</td>\n",
       "      <td>1981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>366786</td>\n",
       "      <td>가끔 미치도록 네가 안고 싶어질 때가 있어</td>\n",
       "      <td>[가을방학]</td>\n",
       "      <td>1919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>654757</td>\n",
       "      <td>눈의 꽃</td>\n",
       "      <td>[박효신]</td>\n",
       "      <td>1647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>133143</td>\n",
       "      <td>그대와 나, 설레임 (Feat. 소울맨)</td>\n",
       "      <td>[어쿠스틱 콜라보]</td>\n",
       "      <td>1633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>349492</td>\n",
       "      <td>어떤이별</td>\n",
       "      <td>[임승부]</td>\n",
       "      <td>1599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>675115</td>\n",
       "      <td>야생화</td>\n",
       "      <td>[박효신]</td>\n",
       "      <td>1598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>463173</td>\n",
       "      <td>비가 내렸어 (Vocal by 스티브언니)</td>\n",
       "      <td>[업라이트 (Upright)]</td>\n",
       "      <td>1544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42155</td>\n",
       "      <td>벙어리</td>\n",
       "      <td>[홍아]</td>\n",
       "      <td>1540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>396828</td>\n",
       "      <td>쉬운사랑</td>\n",
       "      <td>[중신]</td>\n",
       "      <td>1538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>610933</td>\n",
       "      <td>널 생각해</td>\n",
       "      <td>[원 모어 찬스 (one more chance)]</td>\n",
       "      <td>1538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>461341</td>\n",
       "      <td>어떻게 지내</td>\n",
       "      <td>[Crush]</td>\n",
       "      <td>1526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>174749</td>\n",
       "      <td>비도 오고 그래서 (Feat. 신용재)</td>\n",
       "      <td>[헤이즈 (Heize)]</td>\n",
       "      <td>1512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>520093</td>\n",
       "      <td>고마운 사람 (Vocal by 이소진)</td>\n",
       "      <td>[업라이트 (Upright)]</td>\n",
       "      <td>1508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>701557</td>\n",
       "      <td>우연히 봄</td>\n",
       "      <td>[로꼬, 유주 (여자친구)]</td>\n",
       "      <td>1507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>549178</td>\n",
       "      <td>금요일에 만나요 (Feat. 장이정 Of HISTORY)</td>\n",
       "      <td>[아이유]</td>\n",
       "      <td>1501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>485155</td>\n",
       "      <td>선물</td>\n",
       "      <td>[민우]</td>\n",
       "      <td>1492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>650494</td>\n",
       "      <td>봄 사랑 벚꽃 말고</td>\n",
       "      <td>[HIGH4 (하이포), 아이유]</td>\n",
       "      <td>1479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>523521</td>\n",
       "      <td>사랑을 놓치고</td>\n",
       "      <td>[박은옥]</td>\n",
       "      <td>1476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>13281</td>\n",
       "      <td>넌 이별 난 아직 (With 한소현 Of 3rd Coast)</td>\n",
       "      <td>[스탠딩 에그]</td>\n",
       "      <td>1473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>648628</td>\n",
       "      <td>분홍빛 가득한 날에 (Vocal by 호수)</td>\n",
       "      <td>[아재]</td>\n",
       "      <td>1470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>449244</td>\n",
       "      <td>스토커</td>\n",
       "      <td>[10CM]</td>\n",
       "      <td>1468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>680366</td>\n",
       "      <td>끝내지 못한 이야기 (Feat. 호수)</td>\n",
       "      <td>[어쿠스틱 멜로디 (Acoustic Melody)]</td>\n",
       "      <td>1467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>169984</td>\n",
       "      <td>같은 시간 속의 너</td>\n",
       "      <td>[나얼]</td>\n",
       "      <td>1463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>422915</td>\n",
       "      <td>연인</td>\n",
       "      <td>[중신]</td>\n",
       "      <td>1455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>11657</td>\n",
       "      <td>Lost Stars</td>\n",
       "      <td>[Adam Levine]</td>\n",
       "      <td>1452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>418935</td>\n",
       "      <td>수고했어, 오늘도</td>\n",
       "      <td>[옥상달빛]</td>\n",
       "      <td>1446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>187047</td>\n",
       "      <td>착각</td>\n",
       "      <td>[박은옥]</td>\n",
       "      <td>1437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>547967</td>\n",
       "      <td>불안해</td>\n",
       "      <td>[혜지 (Hyeji)]</td>\n",
       "      <td>1434</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     곡 아이디                                곡 명                        아티스트 명  \\\n",
       "0   144663                                밤편지                         [아이유]   \n",
       "1   116573                                안아줘                         [정준일]   \n",
       "2   357367                                  비                          [폴킴]   \n",
       "3   366786            가끔 미치도록 네가 안고 싶어질 때가 있어                        [가을방학]   \n",
       "4   654757                               눈의 꽃                         [박효신]   \n",
       "5   133143             그대와 나, 설레임 (Feat. 소울맨)                    [어쿠스틱 콜라보]   \n",
       "6   349492                               어떤이별                         [임승부]   \n",
       "7   675115                                야생화                         [박효신]   \n",
       "8   463173            비가 내렸어 (Vocal by 스티브언니)              [업라이트 (Upright)]   \n",
       "9    42155                                벙어리                          [홍아]   \n",
       "10  396828                               쉬운사랑                          [중신]   \n",
       "11  610933                              널 생각해   [원 모어 찬스 (one more chance)]   \n",
       "12  461341                             어떻게 지내                       [Crush]   \n",
       "13  174749              비도 오고 그래서 (Feat. 신용재)                 [헤이즈 (Heize)]   \n",
       "14  520093              고마운 사람 (Vocal by 이소진)              [업라이트 (Upright)]   \n",
       "15  701557                              우연히 봄               [로꼬, 유주 (여자친구)]   \n",
       "16  549178    금요일에 만나요 (Feat. 장이정 Of HISTORY)                         [아이유]   \n",
       "17  485155                                 선물                          [민우]   \n",
       "18  650494                         봄 사랑 벚꽃 말고            [HIGH4 (하이포), 아이유]   \n",
       "19  523521                            사랑을 놓치고                         [박은옥]   \n",
       "20   13281  넌 이별 난 아직 (With 한소현 Of 3rd Coast)                      [스탠딩 에그]   \n",
       "21  648628           분홍빛 가득한 날에 (Vocal by 호수)                          [아재]   \n",
       "22  449244                                스토커                        [10CM]   \n",
       "23  680366              끝내지 못한 이야기 (Feat. 호수)  [어쿠스틱 멜로디 (Acoustic Melody)]   \n",
       "24  169984                         같은 시간 속의 너                          [나얼]   \n",
       "25  422915                                 연인                          [중신]   \n",
       "26   11657                         Lost Stars                 [Adam Levine]   \n",
       "27  418935                          수고했어, 오늘도                        [옥상달빛]   \n",
       "28  187047                                 착각                         [박은옥]   \n",
       "29  547967                                불안해                  [혜지 (Hyeji)]   \n",
       "\n",
       "    수록 횟수  \n",
       "0    2175  \n",
       "1    2121  \n",
       "2    1981  \n",
       "3    1919  \n",
       "4    1647  \n",
       "5    1633  \n",
       "6    1599  \n",
       "7    1598  \n",
       "8    1544  \n",
       "9    1540  \n",
       "10   1538  \n",
       "11   1538  \n",
       "12   1526  \n",
       "13   1512  \n",
       "14   1508  \n",
       "15   1507  \n",
       "16   1501  \n",
       "17   1492  \n",
       "18   1479  \n",
       "19   1476  \n",
       "20   1473  \n",
       "21   1470  \n",
       "22   1468  \n",
       "23   1467  \n",
       "24   1463  \n",
       "25   1455  \n",
       "26   1452  \n",
       "27   1446  \n",
       "28   1437  \n",
       "29   1434  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. song_plylst_cnt 테이블에서 중복 수록 곡 중 수록 횟수 기준 상위 30곡 필터링 테이블 생성 : duplicate_song_top30\n",
    "duplicate_song_top30 = song_plylst_cnt[song_plylst_cnt['mapping_plylst_cnt_category'] == '중복 수록'].nlargest(30, 'mapping_plylst_cnt')\n",
    "\n",
    "# 2-1. join 목적으로 곡 아이디 integer to string 변환\n",
    "duplicate_song_top30['songs'] = duplicate_song_top30['songs'].astype(str)\n",
    "song_meta['id'] = song_meta['id'].astype(str)\n",
    "\n",
    "# 2-2 상위 30곡 테이블 duplicate_song_top30 + 곡 메타 song_meta join\n",
    "(\n",
    "    pd.merge(duplicate_song_top30, song_meta[['id', 'song_name', 'artist_name_basket']],\n",
    "             how = 'left', left_on = 'songs', right_on = 'id')\n",
    "    [['songs', 'song_name', 'artist_name_basket', 'mapping_plylst_cnt']]\n",
    "    .rename(columns = {'songs' : '곡 아이디', 'song_name' : '곡 명', 'artist_name_basket' : '아티스트 명', 'mapping_plylst_cnt' : '수록 횟수'})\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 아래는 단일 수록 곡 중 가장 많은 곡이 수록된 장르 상위 30개 입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:08:07.362242Z",
     "start_time": "2020-04-26T23:08:05.498877Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>대분류 장르코드</th>\n",
       "      <th>장르명</th>\n",
       "      <th>수록 곡 수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>GN1600</td>\n",
       "      <td>클래식</td>\n",
       "      <td>32841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GN0900</td>\n",
       "      <td>POP</td>\n",
       "      <td>30987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GN1000</td>\n",
       "      <td>록/메탈</td>\n",
       "      <td>30028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GN1100</td>\n",
       "      <td>일렉트로니카</td>\n",
       "      <td>25725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>GN1700</td>\n",
       "      <td>재즈</td>\n",
       "      <td>23158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>GN1800</td>\n",
       "      <td>뉴에이지</td>\n",
       "      <td>19370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>GN1500</td>\n",
       "      <td>OST</td>\n",
       "      <td>16855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GN1200</td>\n",
       "      <td>랩/힙합</td>\n",
       "      <td>15054</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GN0100</td>\n",
       "      <td>발라드</td>\n",
       "      <td>14575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GN1300</td>\n",
       "      <td>R&amp;B/Soul</td>\n",
       "      <td>13136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GN0500</td>\n",
       "      <td>인디음악</td>\n",
       "      <td>12560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>GN2100</td>\n",
       "      <td>CCM</td>\n",
       "      <td>10376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>GN1900</td>\n",
       "      <td>J-POP</td>\n",
       "      <td>9871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GN0600</td>\n",
       "      <td>록/메탈</td>\n",
       "      <td>9386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>GN2200</td>\n",
       "      <td>어린이/태교</td>\n",
       "      <td>8665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>GN2000</td>\n",
       "      <td>월드뮤직</td>\n",
       "      <td>8073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GN0300</td>\n",
       "      <td>랩/힙합</td>\n",
       "      <td>7712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GN1400</td>\n",
       "      <td>포크/블루스/컨트리</td>\n",
       "      <td>6901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GN0700</td>\n",
       "      <td>성인가요</td>\n",
       "      <td>6161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GN0200</td>\n",
       "      <td>댄스</td>\n",
       "      <td>4672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GN0800</td>\n",
       "      <td>포크/블루스</td>\n",
       "      <td>4403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>GN2700</td>\n",
       "      <td>EDM</td>\n",
       "      <td>3806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GN0400</td>\n",
       "      <td>R&amp;B/Soul</td>\n",
       "      <td>3125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>GN2600</td>\n",
       "      <td>일렉트로니카</td>\n",
       "      <td>2292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>GN2400</td>\n",
       "      <td>국악</td>\n",
       "      <td>1226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>GN2500</td>\n",
       "      <td>아이돌</td>\n",
       "      <td>1134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>GN9000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>GN2800</td>\n",
       "      <td>뮤직테라피</td>\n",
       "      <td>909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>GN2900</td>\n",
       "      <td>뮤지컬</td>\n",
       "      <td>818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>GN2300</td>\n",
       "      <td>종교음악</td>\n",
       "      <td>528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   대분류 장르코드         장르명  수록 곡 수\n",
       "15   GN1600         클래식   32841\n",
       "8    GN0900         POP   30987\n",
       "9    GN1000        록/메탈   30028\n",
       "10   GN1100      일렉트로니카   25725\n",
       "16   GN1700          재즈   23158\n",
       "17   GN1800        뉴에이지   19370\n",
       "14   GN1500         OST   16855\n",
       "11   GN1200        랩/힙합   15054\n",
       "0    GN0100         발라드   14575\n",
       "12   GN1300    R&B/Soul   13136\n",
       "4    GN0500        인디음악   12560\n",
       "20   GN2100         CCM   10376\n",
       "18   GN1900       J-POP    9871\n",
       "5    GN0600        록/메탈    9386\n",
       "21   GN2200      어린이/태교    8665\n",
       "19   GN2000        월드뮤직    8073\n",
       "2    GN0300        랩/힙합    7712\n",
       "13   GN1400  포크/블루스/컨트리    6901\n",
       "6    GN0700        성인가요    6161\n",
       "1    GN0200          댄스    4672\n",
       "7    GN0800      포크/블루스    4403\n",
       "26   GN2700         EDM    3806\n",
       "3    GN0400    R&B/Soul    3125\n",
       "25   GN2600      일렉트로니카    2292\n",
       "23   GN2400          국악    1226\n",
       "24   GN2500         아이돌    1134\n",
       "29   GN9000         NaN     924\n",
       "27   GN2800       뮤직테라피     909\n",
       "28   GN2900         뮤지컬     818\n",
       "22   GN2300        종교음악     528"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. song_plylst_cnt 테이블에서 단일 수록 곡만 필터링 하여 테이블 생성 : single_song\n",
    "single_song = song_plylst_cnt[song_plylst_cnt['mapping_plylst_cnt_category'] == '단일 수록']\n",
    "\n",
    "# 2-1. join 목적으로 곡 아이디 integer to string 변환\n",
    "single_song['songs'] = single_song['songs'].astype(str)\n",
    "\n",
    "# 2-2. 단일 수록 곡 테이블 single_song + 곡 메타 song_meta join : 아티스트 기준 single_song_artist, 장르 기준 single_song_gnr\n",
    "single_song_artist = pd.merge(single_song, song_meta[['id', 'song_name', 'artist_name_basket']], how = 'left', left_on = 'songs', right_on = 'id')\n",
    "single_song_gnr = pd.merge(single_song, song_gnr_map, how = 'left', left_on = 'songs', right_on = 'song_id')\n",
    "\n",
    "# 3. 수록 곡 개수 기준 상위 30개 장르 : single_song_gnr_cnt\n",
    "single_song_gnr_cnt = single_song_gnr.groupby('gnr_code').songs.nunique().reset_index(name = 'song_cnt')\n",
    "(\n",
    "    pd.merge(single_song_gnr_cnt, genre_gn_all, how = 'left', on = 'gnr_code')[['gnr_code', 'gnr_name', 'song_cnt']]\n",
    "    .rename(columns = {'gnr_code' : '대분류 장르코드', 'gnr_name' : '장르명', 'song_cnt' : '수록 곡 수'})\n",
    "    .sort_values('수록 곡 수', ascending = False)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 중복 매핑 태그 비중"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:08:38.150136Z",
     "start_time": "2020-04-26T23:08:37.892796Z"
    }
   },
   "outputs": [
    {
     "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>중복 매핑 여부</th>\n",
       "      <th>태그 수</th>\n",
       "      <th>비율(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>단일 매핑</td>\n",
       "      <td>17431</td>\n",
       "      <td>59.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>중복 매핑</td>\n",
       "      <td>11729</td>\n",
       "      <td>40.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  중복 매핑 여부   태그 수  비율(%)\n",
       "0    단일 매핑  17431  59.78\n",
       "1    중복 매핑  11729  40.22"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. 태그 별 수록된 플레이리스트 개수 count 테이블 생성 : tag_plylst_cnt\n",
    "tag_plylst_cnt = plylst_tag_map.groupby('tags').id.nunique().reset_index(name = 'mapping_plylst_cnt')\n",
    "\n",
    "# 2. 단일/중복 매핑 구분 : 태그 별 매핑된 플레이리스트가 한 개면 '단일 매핑', 두 개 이상이면 '중복 매핑'\n",
    "tag_plylst_cnt = tag_plylst_cnt.assign(\n",
    "    mapping_plylst_cnt_category = pd.cut(tag_plylst_cnt['mapping_plylst_cnt'], [0, 1, np.inf], labels = ['단일 매핑', '중복 매핑'])\n",
    ")\n",
    "\n",
    "# 3. 단일/중복 매핑 구분 별 태그 수 count 테이블 생성 : tag_plylst_mapping_division\n",
    "tag_plylst_mapping_division = pd.DataFrame(tag_plylst_cnt.groupby('mapping_plylst_cnt_category').count())\n",
    "\n",
    "# 4. 3번 테이블에 비율 값 추가\n",
    "tag_plylst_mapping_division.rename(columns = {'tags' : '태그 수'}, inplace = True)\n",
    "tag_plylst_mapping_division.loc[:, '비율(%)'] = round(tag_plylst_mapping_division['태그 수']/sum(tag_plylst_mapping_division['태그 수'])*100, 2)\n",
    "tag_plylst_mapping_division = tag_plylst_mapping_division.reset_index().rename(columns = {'mapping_plylst_cnt_category' : '중복 매핑 여부'})\n",
    "tag_plylst_mapping_division[['중복 매핑 여부', '태그 수', '비율(%)']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 태그의 매핑 중복 비율을 확인해본 결과 **전체 대비 약 40.2%는 두 개 이상 플레이리스트에 매핑된 태그**이며  \n",
    "  한 개 플레이리스트에만 매핑된 태그는 과반 이상의 비중을 차지합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 플레이리스트명 중복 비중"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:08:44.559454Z",
     "start_time": "2020-04-26T23:08:44.243693Z"
    }
   },
   "outputs": [
    {
     "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>중복 이름 여부</th>\n",
       "      <th>플레이리스트 아이디 수</th>\n",
       "      <th>비율(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>중복 없음</td>\n",
       "      <td>108214</td>\n",
       "      <td>97.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>중복 있음</td>\n",
       "      <td>2317</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  중복 이름 여부  플레이리스트 아이디 수  비율(%)\n",
       "0    중복 없음        108214   97.9\n",
       "1    중복 있음          2317    2.1"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. 플레이리스트 아이디(id)와 플레이리스트 명(plylst_title) 추출\n",
    "plylst_title = train[['id', 'plylst_title']]\n",
    "\n",
    "# 2. 플레이리스트 이름 별 플레이리스트 ID count 테이블 생성 : plylst_id_cnt\n",
    "plylst_id_cnt = plylst_title.groupby('plylst_title').id.nunique().reset_index(name = 'mapping_plylst_cnt')\n",
    "\n",
    "# 3. 플레이리스트 명 중복 구분 : 서로 다른 플레이리스트 아이디로 겹치는 플레이리스트 이름이 없으면 '중복 없음', 그 외 '중복 있음'\n",
    "plylst_id_cnt = plylst_id_cnt.assign(\n",
    "    mapping_plylst_cnt_category = pd.cut(plylst_id_cnt['mapping_plylst_cnt'], [0, 1, np.inf], labels = ['중복 없음', '중복 있음'])\n",
    ")\n",
    "\n",
    "# 4. 중복 구분 별 플레이리스트 수 count 테이블 생성 : plylst_id_cnt_division\n",
    "plylst_id_cnt_division = pd.DataFrame(plylst_id_cnt.groupby('mapping_plylst_cnt_category').count())\n",
    "\n",
    "# 5. 4번 테이블에 비율 값 추가\n",
    "plylst_id_cnt_division.rename(columns = {'mapping_plylst_cnt' : '플레이리스트 아이디 수'}, inplace = True)\n",
    "plylst_id_cnt_division.loc[:, '비율(%)'] = round(plylst_id_cnt_division['플레이리스트 아이디 수']/sum(plylst_id_cnt_division['플레이리스트 아이디 수'])*100, 2)\n",
    "plylst_id_cnt_division = plylst_id_cnt_division.reset_index().rename(columns = {'mapping_plylst_cnt_category' : '중복 이름 여부'})\n",
    "plylst_id_cnt_division.loc[:, ['중복 이름 여부', '플레이리스트 아이디 수', '비율(%)']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 또한 **플레이리스트 아이디는 다르지만 같은 이름을 갖는 플레이리스트도 전체 대비 약 2% 정도**로 소수 존재 합니다.\n",
    "- 이러한 플레이리스트들은 대부분 곡 수와 태그 수도 비슷한 편이 많았습니다. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 많은 빈도를 보이는 태그는?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:08:49.641702Z",
     "start_time": "2020-04-26T23:08:48.966705Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=450x450 at 0x1A398AF390>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 태그 별 매핑 빈도 수 저장 \n",
    "tag_cnt = plylst_tag_map.groupby('tags').tags.count().reset_index(name = 'mapping_cnt')\n",
    "tag_cnt['tags'] = tag_cnt['tags'].astype(str)\n",
    "tag_cnt['mapping_cnt'] = tag_cnt['mapping_cnt'].astype(int)\n",
    "\n",
    "# 빈도 수가 1000회 이상인 태그만 저장\n",
    "tag_cnt = tag_cnt[tag_cnt['mapping_cnt'] >= 1000]\n",
    "word_count = list(zip(tag_cnt['tags'], tag_cnt['mapping_cnt']))\n",
    "\n",
    "# plotting\n",
    "wc = WordCloud(font_path = font_path, background_color = 'white', max_words = 100, width = 450, height = 450)\n",
    "wc.generate_from_frequencies(dict(word_count)).to_image()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 매핑 기준 상위 1,000번 이상 매핑된 태그를 살펴본 결과 **기분전환 태그명이 가장 높은 비중을 차지**하고 있으며  \n",
    "  여름/가을/겨울 등 계절이나 드라이브, 카페, 매장음악 등 특정 상황과 어울리는 태그 등이 상위권에 분포되어 있습니다.\n",
    "- 발라드, 힙합, 팝, 인디, 락, 댄스, 뉴에이지 등 **장르와 관련된 태그도 일부 상위권**에 속합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:09:06.170054Z",
     "start_time": "2020-04-26T23:08:52.915631Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47588 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 54609 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 46108 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 53468 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 44536 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47532 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49828 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 53944 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49345 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 50948 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 44060 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 54540 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47112 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51060 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49688 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47588 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 54609 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 46108 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 54540 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47112 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51060 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47532 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49828 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 53944 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49688 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48156 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 46972 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 46300 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 54045 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 46973 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47017 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 55193 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 54633 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 45828 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51068 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47113 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 45684 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 50640 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51648 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49548 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 50872 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 50508 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 50532 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48708 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 44592 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48516 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51204 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 54872 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 55092 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49885 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 55184 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47553 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48164 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49352 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48317 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51092 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 54620 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 53364 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47000 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51064 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 46356 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51116 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51592 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49836 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 54548 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48324 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 52628 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 50613 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 54924 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48652 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49324 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 46993 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49444 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47128 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 44620 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 54168 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51109 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51020 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 50501 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48388 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 50724 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 45716 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 45216 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49328 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 52293 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 50668 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 54665 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47492 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48156 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 46972 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 46300 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 54045 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 46973 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47017 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 55193 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 54633 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 45828 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51068 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47113 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 45684 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 50640 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51648 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49548 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 50872 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 50508 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 50532 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48708 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 44592 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48516 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51204 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 54872 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 55092 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49885 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 55184 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47553 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48164 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49352 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48317 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51092 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 54620 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 53364 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47000 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51064 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 46356 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51116 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51592 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49836 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 54548 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48324 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 52628 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 50613 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 54924 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49345 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48652 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49324 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 46993 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49444 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47128 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 44620 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 54168 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51109 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51020 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 50501 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48388 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 50724 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 45716 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 45216 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49328 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 52293 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 50668 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 54665 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47492 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 53468 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 44536 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 50948 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 44060 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. unnest 데이터프레임인 plylst_tag_map 테이블에서 태그 이름 정렬 후 list로 묶기\n",
    "plylst_tag_list_sort = plylst_tag_map.sort_values(by = ['id', 'tags']).groupby('id').tags.apply(list).reset_index(name = 'tag_list')\n",
    "\n",
    "# 2. 집계를 위해 1번 테이블에서 list 타입을 문자열 타입으로 변경\n",
    "plylst_tag_list_sort['tag_list'] = plylst_tag_list_sort['tag_list'].astype(str)\n",
    "\n",
    "# 3. 태그 리스트 별 매핑되는 플레이리스트 수 집계 테이블 생성 : tag_list_plylst_cnt\n",
    "tag_list_plylst_cnt = plylst_tag_list_sort.groupby('tag_list').id.nunique().reset_index(name = 'plylst_cnt')\n",
    "\n",
    "# 4. 매핑 수 기준 상위 50개 필터링\n",
    "tag_list_plylst_cnt = tag_list_plylst_cnt.nlargest(50, 'plylst_cnt')\n",
    "\n",
    "# 5. plotting\n",
    "plt.figure(figsize = (11, 15))\n",
    "tag_list_plylst_cnt_plot = sns.barplot(y = 'tag_list', x = 'plylst_cnt', data = tag_list_plylst_cnt, color = 'grey')\n",
    "tag_list_plylst_cnt_plot.set_title('매핑된 태그 리스트 상위 50개')\n",
    "tag_list_plylst_cnt_plot.set_xlabel('매핑된 플레이리스트 수')\n",
    "tag_list_plylst_cnt_plot.set_ylabel('태그 리스트')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 플레이리스트에 매핑된 태그 리스트를 기준으로 매핑 기준 상위 50개를 확인해본 결과  \n",
    "  **다른 태그와 조합 없는 장르 관련 태그가 상위권을 차지**하고 있으며, 발라드 단일 태그가 가장 많았습니다.\n",
    "- **이 밖에 가장 빈도가 높았던 기분전환 태그명은 휴식, 힐링 뿐만 아니라 스트레스, 드라이브, 사랑, 설렘 등 다양한 태그와 같이 조합을 이루는 케이스가 많았습니다.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 플레이리스트 태그 별 어떤 곡들이 수록되었을까?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:09:37.788760Z",
     "start_time": "2020-04-26T23:09:06.171816Z"
    }
   },
   "outputs": [],
   "source": [
    "# 1. 플레이리스트-곡 테이블 plylst_song_map + 플레이리스트-태그 테이블 plylst_tag_map join 테이블 생성 : plylst_song_tag_map\n",
    "plylst_song_tag_map = pd.merge(plylst_song_map, plylst_tag_map, how = 'left', on = 'id')\n",
    "\n",
    "# 2. 1번 테이블 plylst_song_tag_map + 곡 장르 테이블 song_gnr_map join\n",
    "plylst_song_tag_map = pd.merge(plylst_song_tag_map, song_gnr_map, how = 'left', left_on = 'songs', right_on = 'song_id')\n",
    "\n",
    "# 3. 2번 테이블에서 장르 메타 join\n",
    "gnr_code['gnr_code_name'] = gnr_code['gnr_code'] + ' (' + gnr_code['gnr_name'] + ')'\n",
    "gnr_code = gnr_code[['gnr_code', 'gnr_code_name']]\n",
    "plylst_song_tag_map = pd.merge(plylst_song_tag_map, gnr_code, how = 'left', on = 'gnr_code')\n",
    "\n",
    "# 4. 최종 테이블 필드 선택\n",
    "plylst_song_tag_map = plylst_song_tag_map[['id', 'songs', 'tags', 'gnr_code_name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:09:45.068942Z",
     "start_time": "2020-04-26T23:09:37.791231Z"
    }
   },
   "outputs": [],
   "source": [
    "# 태그 = ['드라이브', '스트레스', '기분전환']\n",
    "song_tag_map_sample_1 = plylst_song_tag_map.loc[plylst_song_tag_map.tags.isin(['드라이브', '스트레스', '기분전환']), :]\n",
    "# 태그 = ['밤', '새벽', '잔잔한']\n",
    "song_tag_map_sample_2 = plylst_song_tag_map.loc[plylst_song_tag_map.tags.isin(['밤', '새벽', '잔잔한']), :]\n",
    "# 태그 = ['사랑', '설렘']\n",
    "song_tag_map_sample_3 = plylst_song_tag_map.loc[plylst_song_tag_map.tags.isin(['사랑', '설렘']), :]\n",
    "# 태그 = ['추억', '회상']\n",
    "song_tag_map_sample_4 = plylst_song_tag_map.loc[plylst_song_tag_map.tags.isin(['추억', '회상']), :]\n",
    "# 태그 = ['슬픔', '이별']\n",
    "song_tag_map_sample_5 = plylst_song_tag_map.loc[plylst_song_tag_map.tags.isin(['슬픔', '이별']), :]\n",
    "# 태그 = ['휴식', '힐링']\n",
    "song_tag_map_sample_6 = plylst_song_tag_map.loc[plylst_song_tag_map.tags.isin(['휴식', '힐링']), :]\n",
    "# 태그 = ['휴식', '힐링']\n",
    "song_tag_map_sample_7 = plylst_song_tag_map.loc[plylst_song_tag_map.tags.isin(['카페', '매장음악']), :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:09:48.757462Z",
     "start_time": "2020-04-26T23:09:45.070767Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 44033 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 53468 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 44536 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47532 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49828 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 53944 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48324 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49345 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 50948 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51109 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47476 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48708 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 44368 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 52852 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 54168 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47588 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51020 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 50501 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 46300 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 46972 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51060 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48652 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47112 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 44592 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48516 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51204 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 54872 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 55092 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49885 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 55184 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47553 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48164 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49352 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48317 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51092 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 54620 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49836 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 54548 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49324 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 46993 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49444 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47128 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 52628 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 50613 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 54924 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 52852 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 54168 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47588 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51109 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51020 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 50501 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 46300 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 46972 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51060 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48652 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49828 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 53944 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47112 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 44592 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48516 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51204 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 54872 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 55092 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49885 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 55184 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47553 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48164 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49352 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48317 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51092 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 54620 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49836 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 54548 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48324 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49324 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 46993 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49444 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47128 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 52628 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 50613 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 54924 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49345 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 53468 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 44536 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47532 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 44257 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 50984 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 44257 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48708 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 50984 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 44033 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 50948 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47476 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 44368 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48156 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51064 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 46356 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51068 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47113 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47196 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 45768 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 45684 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 50640 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51648 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47197 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47700 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 53448 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51116 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51592 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48156 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51064 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 46356 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51068 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47113 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47196 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 45768 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 45684 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 50640 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51648 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47197 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47700 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 53448 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51116 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51592 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 각 샘플 태그 리스트 별 장르 비율 집계\n",
    "song_tag_map_sample_1_gnr_rank = song_tag_map_sample_1.groupby('gnr_code_name').songs.nunique().reset_index(name = 'song_cnt')\n",
    "song_tag_map_sample_1_gnr_rank.loc[:, 'song_cnt_ratio'] = round(song_tag_map_sample_1_gnr_rank['song_cnt']/sum(song_tag_map_sample_1_gnr_rank['song_cnt'])*100, 2)\n",
    "song_tag_map_sample_1_gnr_rank['gnr_rank'] = song_tag_map_sample_1_gnr_rank['song_cnt_ratio'].rank(ascending = False)\n",
    "song_tag_map_sample_1_gnr_rank['tag_list'] = '드라이브, 스트레스, 기분전환'\n",
    "song_tag_map_sample_1_gnr_rank.rename(columns = {'gnr_code_name' : '장르', 'song_cnt' : '곡 수', 'song_cnt_ratio' : '비율(%)', 'gnr_rank' : '순위', 'tag_list' : '태그 리스트'}, inplace = True)\n",
    "\n",
    "song_tag_map_sample_2_gnr_rank = song_tag_map_sample_2.groupby('gnr_code_name').songs.nunique().reset_index(name = 'song_cnt')\n",
    "song_tag_map_sample_2_gnr_rank.loc[:, 'song_cnt_ratio'] = round(song_tag_map_sample_2_gnr_rank['song_cnt']/sum(song_tag_map_sample_2_gnr_rank['song_cnt'])*100, 2)\n",
    "song_tag_map_sample_2_gnr_rank['gnr_rank'] = song_tag_map_sample_2_gnr_rank['song_cnt_ratio'].rank(ascending = False)\n",
    "song_tag_map_sample_2_gnr_rank['tag_list'] = '밤, 새벽, 잔잔한'\n",
    "song_tag_map_sample_2_gnr_rank.rename(columns = {'gnr_code_name' : '장르', 'song_cnt' : '곡 수', 'song_cnt_ratio' : '비율(%)', 'gnr_rank' : '순위', 'tag_list' : '태그 리스트'}, inplace = True)\n",
    "\n",
    "song_tag_map_sample_3_gnr_rank = song_tag_map_sample_3.groupby('gnr_code_name').songs.nunique().reset_index(name = 'song_cnt')\n",
    "song_tag_map_sample_3_gnr_rank.loc[:, 'song_cnt_ratio'] = round(song_tag_map_sample_3_gnr_rank['song_cnt']/sum(song_tag_map_sample_3_gnr_rank['song_cnt'])*100, 2)\n",
    "song_tag_map_sample_3_gnr_rank['gnr_rank'] = song_tag_map_sample_3_gnr_rank['song_cnt_ratio'].rank(ascending = False)\n",
    "song_tag_map_sample_3_gnr_rank['tag_list'] = '사랑, 설렘'\n",
    "song_tag_map_sample_3_gnr_rank.rename(columns = {'gnr_code_name' : '장르', 'song_cnt' : '곡 수', 'song_cnt_ratio' : '비율(%)', 'gnr_rank' : '순위', 'tag_list' : '태그 리스트'}, inplace = True)\n",
    "\n",
    "song_tag_map_sample_4_gnr_rank = song_tag_map_sample_4.groupby('gnr_code_name').songs.nunique().reset_index(name = 'song_cnt')\n",
    "song_tag_map_sample_4_gnr_rank.loc[:, 'song_cnt_ratio'] = round(song_tag_map_sample_4_gnr_rank['song_cnt']/sum(song_tag_map_sample_4_gnr_rank['song_cnt'])*100, 2)\n",
    "song_tag_map_sample_4_gnr_rank['gnr_rank'] = song_tag_map_sample_4_gnr_rank['song_cnt_ratio'].rank(ascending = False)\n",
    "song_tag_map_sample_4_gnr_rank['tag_list'] = '추억, 회상'\n",
    "song_tag_map_sample_4_gnr_rank.rename(columns = {'gnr_code_name' : '장르', 'song_cnt' : '곡 수', 'song_cnt_ratio' : '비율(%)', 'gnr_rank' : '순위', 'tag_list' : '태그 리스트'}, inplace = True)\n",
    "\n",
    "song_tag_map_sample_5_gnr_rank = song_tag_map_sample_5.groupby('gnr_code_name').songs.nunique().reset_index(name = 'song_cnt')\n",
    "song_tag_map_sample_5_gnr_rank.loc[:, 'song_cnt_ratio'] = round(song_tag_map_sample_5_gnr_rank['song_cnt']/sum(song_tag_map_sample_5_gnr_rank['song_cnt'])*100, 2)\n",
    "song_tag_map_sample_5_gnr_rank['gnr_rank'] = song_tag_map_sample_5_gnr_rank['song_cnt_ratio'].rank(ascending = False)\n",
    "song_tag_map_sample_5_gnr_rank['tag_list'] = '슬픔, 이별'\n",
    "song_tag_map_sample_5_gnr_rank.rename(columns = {'gnr_code_name' : '장르', 'song_cnt' : '곡 수', 'song_cnt_ratio' : '비율(%)', 'gnr_rank' : '순위', 'tag_list' : '태그 리스트'}, inplace = True)\n",
    "\n",
    "song_tag_map_sample_6_gnr_rank = song_tag_map_sample_6.groupby('gnr_code_name').songs.nunique().reset_index(name = 'song_cnt')\n",
    "song_tag_map_sample_6_gnr_rank.loc[:, 'song_cnt_ratio'] = round(song_tag_map_sample_6_gnr_rank['song_cnt']/sum(song_tag_map_sample_6_gnr_rank['song_cnt'])*100, 2)\n",
    "song_tag_map_sample_6_gnr_rank['gnr_rank'] = song_tag_map_sample_6_gnr_rank['song_cnt_ratio'].rank(ascending = False)\n",
    "song_tag_map_sample_6_gnr_rank['tag_list'] = '휴식, 힐링'\n",
    "song_tag_map_sample_6_gnr_rank.rename(columns = {'gnr_code_name' : '장르', 'song_cnt' : '곡 수', 'song_cnt_ratio' : '비율(%)', 'gnr_rank' : '순위', 'tag_list' : '태그 리스트'}, inplace = True)\n",
    "\n",
    "song_tag_map_sample_7_gnr_rank = song_tag_map_sample_7.groupby('gnr_code_name').songs.nunique().reset_index(name = 'song_cnt')\n",
    "song_tag_map_sample_7_gnr_rank.loc[:, 'song_cnt_ratio'] = round(song_tag_map_sample_7_gnr_rank['song_cnt']/sum(song_tag_map_sample_7_gnr_rank['song_cnt'])*100, 2)\n",
    "song_tag_map_sample_7_gnr_rank['gnr_rank'] = song_tag_map_sample_7_gnr_rank['song_cnt_ratio'].rank(ascending = False)\n",
    "song_tag_map_sample_7_gnr_rank['tag_list'] = '카페, 매장음악'\n",
    "song_tag_map_sample_7_gnr_rank.rename(columns = {'gnr_code_name' : '장르', 'song_cnt' : '곡 수', 'song_cnt_ratio' : '비율(%)', 'gnr_rank' : '순위', 'tag_list' : '태그 리스트'}, inplace = True)\n",
    "\n",
    "# delete object\n",
    "del song_tag_map_sample_1, song_tag_map_sample_2, song_tag_map_sample_3, song_tag_map_sample_4, song_tag_map_sample_5, song_tag_map_sample_6, song_tag_map_sample_7\n",
    "\n",
    "# row binding\n",
    "song_tag_map_sample_rbind = pd.concat(\n",
    "    [song_tag_map_sample_1_gnr_rank, song_tag_map_sample_2_gnr_rank, song_tag_map_sample_3_gnr_rank,\n",
    "     song_tag_map_sample_4_gnr_rank, song_tag_map_sample_5_gnr_rank, song_tag_map_sample_6_gnr_rank,\n",
    "     song_tag_map_sample_7_gnr_rank],\n",
    "    axis = 0\n",
    ")\n",
    "\n",
    "# plotting\n",
    "song_tag_map_sample_rbind_plot = sns.barplot(\n",
    "    data = song_tag_map_sample_rbind.loc[song_tag_map_sample_rbind['순위'] <= 5, :].sort_values(by = '순위'),\n",
    "    x = '태그 리스트', y = '비율(%)', hue = '장르', edgecolor = \"black\"\n",
    ")\n",
    "song_tag_map_sample_rbind_plot.set_title('각 태그 리스트 별 상위 장르 TOP 5 비교')\n",
    "song_tag_map_sample_rbind_plot.set_xlabel('태그 리스트')\n",
    "song_tag_map_sample_rbind_plot.set_ylabel('곡 비율 (%)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **드라이브, 스트레스, 기분전환 태그가 매핑된 플레이리스트**와 **카페, 매장음악 태그가 매핑된 플레이리스트**에서는 **팝(GN0900) 장르와 일렉(GN1100)장르가 많았습니다.**\n",
    "- **이 밖에 대부분 태그에서 발라드(GN0100) 곡 비중이 가장 높았으며** 슬픔, 이별 태그가 매핑된 플레이리스트에서는 발라드 비중이 가장 높았습니다.\n",
    "- 밤, 새벽, 잔잔한, 휴식, 힐링 등의 태그가 있는 플레이리스트에서는 발라드 다음으로 팝 장르 비중이 높았습니다.\n",
    "- **추억, 회상 태그가 매핑된 플레이리스트에서는 OST 비중이 발라드 다음으로 높습니다.**\n",
    "- 이와 같이 태그 별 플레이리스트 수록 곡 특징을 탐색하여 플레이리스트의 곡 장르 성격을 추정해볼 수 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:16:03.034763Z",
     "start_time": "2020-04-26T23:12:27.797243Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49688 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47197 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 44257 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48156 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47588 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 45380 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 46020 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48324 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48156 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47588 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 45380 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 46020 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49688 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47197 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 44257 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48324 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "findfont: Font family ['NanumGothic'] not found. Falling back to DejaVu Sans.\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 52628 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 50613 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 54924 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49345 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 52852 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 54168 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51109 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51020 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 50501 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49836 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 54548 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51060 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 55092 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49885 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 55184 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47553 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49324 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 46993 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49444 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47128 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 46300 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 46972 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48652 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49828 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 53944 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 47112 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 44592 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48516 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51204 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 54872 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48164 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 49352 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48317 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 51092 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 54620 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 52628 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 50613 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 54924 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49345 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 52852 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 54168 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51109 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51020 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 50501 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49836 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 54548 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51060 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 55092 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49885 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 55184 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47553 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49324 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 46993 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49444 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47128 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 46300 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 46972 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48652 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49828 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 53944 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 47112 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 44592 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48516 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51204 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 54872 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48164 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 49352 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48317 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 51092 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 54620 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 위에서 생성한 테이블에서 곡 별 발매년도 join\n",
    "plylst_song_tag_map_issue_year = pd.merge(plylst_song_tag_map, song_issue_date, how = 'left', left_on = 'songs', right_on = 'song_id')\n",
    "\n",
    "# 샘플 태그들이 포함되어 있는 플레이리스트만 필터링\n",
    "plylst_song_tag_map_issue_year_filter = plylst_song_tag_map_issue_year.loc[plylst_song_tag_map_issue_year.tags.isin(['드라이브', '스트레스', '기분전환', '밤', '새벽', '잔잔한', '사랑', '설렘', '추억', '회상', '슬픔', '이별', '휴식', '힐링', '카페', '매장음악']), :]\n",
    "plylst_song_tag_map_issue_year_filter['issue_year'] = plylst_song_tag_map_issue_year_filter['issue_year'].apply(pd.to_numeric)\n",
    "plylst_song_tag_map_issue_year_filter = plylst_song_tag_map_issue_year_filter[plylst_song_tag_map_issue_year_filter['issue_year'] >= 1980]\n",
    "\n",
    "del plylst_song_tag_map_issue_year\n",
    "\n",
    "# plotting\n",
    "temp_df = plylst_song_tag_map_issue_year_filter.loc[plylst_song_tag_map_issue_year_filter.tags.isin(['추억', '회상']), :]\n",
    "sns.distplot(temp_df['issue_year'], hist = False, kde = True, label = '추억, 회상')\n",
    "temp_df = plylst_song_tag_map_issue_year_filter.loc[plylst_song_tag_map_issue_year_filter.tags.isin(['카페', '매장음악']), :]\n",
    "sns.distplot(temp_df['issue_year'], hist = False, kde = True, label = '카페, 매장음악')\n",
    "temp_df = plylst_song_tag_map_issue_year_filter.loc[plylst_song_tag_map_issue_year_filter.tags.isin(['슬픔', '이별']), :]\n",
    "sns.distplot(temp_df['issue_year'], hist = False, kde = True, label = '슬픔, 이별')\n",
    "temp_df = plylst_song_tag_map_issue_year_filter.loc[plylst_song_tag_map_issue_year_filter.tags.isin(['휴식', '힐링']), :]\n",
    "sns.distplot(temp_df['issue_year'], hist = False, kde = True, label = '휴식, 힐링')\n",
    "temp_df = plylst_song_tag_map_issue_year_filter.loc[plylst_song_tag_map_issue_year_filter.tags.isin(['사랑', '설렘']), :]\n",
    "sns.distplot(temp_df['issue_year'], hist = False, kde = True, label = '사랑, 설렘')\n",
    "temp_df = plylst_song_tag_map_issue_year_filter.loc[plylst_song_tag_map_issue_year_filter.tags.isin(['드라이브', '스트레스', '기분전환']), :]\n",
    "sns.distplot(temp_df['issue_year'], hist = False, kde = True, label = '드라이브, 스트레스, 기분전환')\n",
    "temp_df = plylst_song_tag_map_issue_year_filter.loc[plylst_song_tag_map_issue_year_filter.tags.isin(['밤', '새벽', '잔잔한']), :]\n",
    "sns.distplot(temp_df['issue_year'], hist = False, kde = True, label = '밤, 새벽, 잔잔한')\n",
    "plt.legend(prop = {'size' : 18})\n",
    "plt.title('수록곡 발매 년도 별 density plot')\n",
    "plt.xlabel('발매년도')\n",
    "plt.ylabel('Density')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 이 밖에 발매년도 기준으로 살펴본 결과  \n",
    "  **추억, 회상이 매핑된 플레이리스트들은 태그명처럼 다른 플레이리스트에 비해 비교적 과거 발매 곡 비중이 조금 더 높은 편**을 확인할 수 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 역으로 곡 별 태그를 매핑해본다면?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 각 플레이리스트에 한 개 이상의 태그들이 매핑되어 있고, 한 곡 이상의 곡들이 수록되어 있습니다.\n",
    "- 이 부분을 고려하여 **곡 별 수록된 플레이리스트의 태그 값을 매핑**해본다면 **곡의 메타 정보를 하나 더 파생**시켜서 볼 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:16:09.113923Z",
     "start_time": "2020-04-26T23:16:07.239478Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>songs</th>\n",
       "      <th>tags</th>\n",
       "      <th>gnr_code_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36032</th>\n",
       "      <td>7984</td>\n",
       "      <td>144663</td>\n",
       "      <td>슬픔</td>\n",
       "      <td>GN0100 (발라드)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36033</th>\n",
       "      <td>7984</td>\n",
       "      <td>144663</td>\n",
       "      <td>거짓말</td>\n",
       "      <td>GN0100 (발라드)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36034</th>\n",
       "      <td>7984</td>\n",
       "      <td>144663</td>\n",
       "      <td>이별</td>\n",
       "      <td>GN0100 (발라드)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36035</th>\n",
       "      <td>7984</td>\n",
       "      <td>144663</td>\n",
       "      <td>아픈</td>\n",
       "      <td>GN0100 (발라드)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115027</th>\n",
       "      <td>106802</td>\n",
       "      <td>144663</td>\n",
       "      <td>힐링</td>\n",
       "      <td>GN0100 (발라드)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29580419</th>\n",
       "      <td>13178</td>\n",
       "      <td>144663</td>\n",
       "      <td>새벽</td>\n",
       "      <td>GN0100 (발라드)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29580420</th>\n",
       "      <td>13178</td>\n",
       "      <td>144663</td>\n",
       "      <td>새벽아침</td>\n",
       "      <td>GN0100 (발라드)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29580421</th>\n",
       "      <td>13178</td>\n",
       "      <td>144663</td>\n",
       "      <td>활기찬</td>\n",
       "      <td>GN0100 (발라드)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29580422</th>\n",
       "      <td>13178</td>\n",
       "      <td>144663</td>\n",
       "      <td>산책</td>\n",
       "      <td>GN0100 (발라드)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29580423</th>\n",
       "      <td>13178</td>\n",
       "      <td>144663</td>\n",
       "      <td>힐링</td>\n",
       "      <td>GN0100 (발라드)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12791 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id   songs  tags gnr_code_name\n",
       "36032       7984  144663    슬픔  GN0100 (발라드)\n",
       "36033       7984  144663   거짓말  GN0100 (발라드)\n",
       "36034       7984  144663    이별  GN0100 (발라드)\n",
       "36035       7984  144663    아픈  GN0100 (발라드)\n",
       "115027    106802  144663    힐링  GN0100 (발라드)\n",
       "...          ...     ...   ...           ...\n",
       "29580419   13178  144663    새벽  GN0100 (발라드)\n",
       "29580420   13178  144663  새벽아침  GN0100 (발라드)\n",
       "29580421   13178  144663   활기찬  GN0100 (발라드)\n",
       "29580422   13178  144663    산책  GN0100 (발라드)\n",
       "29580423   13178  144663    힐링  GN0100 (발라드)\n",
       "\n",
       "[12791 rows x 4 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 학습 데이터 내에서 가장 많은 태그가 매핑된 아이유 - 밤편지 (144663) 예시\n",
    "example_song = plylst_song_tag_map[plylst_song_tag_map['songs'] == '144663']\n",
    "example_song"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:16:26.382874Z",
     "start_time": "2020-04-26T23:16:09.115580Z"
    }
   },
   "outputs": [
    {
     "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>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>mean</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tags_cnt</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.86</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1928.0</td>\n",
       "      <td>44.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          min  25%  50%   mean   75%     max    std\n",
       "tags_cnt  1.0  2.0  7.0  17.86  15.0  1928.0  44.95"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 곡 별 매핑되는 태그 수 집계 : song_tag_map_cnt\n",
    "song_tag_map_cnt = plylst_song_tag_map.groupby('songs').tags.nunique().reset_index(name = 'tags_cnt')\n",
    "round(song_tag_map_cnt.describe().loc[['min', '25%', '50%', 'mean', '75%', 'max', 'std']].transpose(), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 곡 별로 매핑된 태그 값은 **평균 18개**(중앙값 7개)이고 예시로 들었던 아이유 - 밤편지 곡은 1,928개의 태그가 매핑됩니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 태그 기준으로 유사 느낌의 곡 찾기 (아이유 - 밤편지 곡을 예시로 살펴보기)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:16:26.416404Z",
     "start_time": "2020-04-26T23:16:26.385084Z"
    }
   },
   "outputs": [
    {
     "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>songs</th>\n",
       "      <th>tags</th>\n",
       "      <th>mapping_plylst_cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>144663</td>\n",
       "      <td>감성</td>\n",
       "      <td>690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>144663</td>\n",
       "      <td>발라드</td>\n",
       "      <td>543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1462</th>\n",
       "      <td>144663</td>\n",
       "      <td>잔잔한</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>144663</td>\n",
       "      <td>카페</td>\n",
       "      <td>395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>144663</td>\n",
       "      <td>새벽</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>782</th>\n",
       "      <td>144663</td>\n",
       "      <td>밤공기</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>144663</td>\n",
       "      <td>밤감성</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>144663</td>\n",
       "      <td>발랄한노래</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>144663</td>\n",
       "      <td>발라드핫트랙</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927</th>\n",
       "      <td>144663</td>\n",
       "      <td>힙합알앤비</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1928 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       songs    tags  mapping_plylst_cnt\n",
       "241   144663      감성                 690\n",
       "770   144663     발라드                 543\n",
       "1462  144663     잔잔한                 413\n",
       "1699  144663      카페                 395\n",
       "940   144663      새벽                 339\n",
       "...      ...     ...                 ...\n",
       "782   144663     밤공기                   1\n",
       "781   144663     밤감성                   1\n",
       "779   144663   발랄한노래                   1\n",
       "778   144663  발라드핫트랙                   1\n",
       "1927  144663   힙합알앤비                   1\n",
       "\n",
       "[1928 rows x 3 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_song.groupby(['songs', 'tags']).id.nunique().reset_index(name = 'mapping_plylst_cnt').sort_values(by = 'mapping_plylst_cnt', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 아이유 - 밤편지 곡은 **`감성` 태그를 갖는 690개의 플레이리스트에 수록**되어 있습니다.  \n",
    "  두 번째로 높은 태그는 `발라드`, 세 번째는 `잔잔한` 태그 입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T23:16:41.528170Z",
     "start_time": "2020-04-26T23:16:38.265013Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>songs</th>\n",
       "      <th>mapping_plylst_cnt</th>\n",
       "      <th>id</th>\n",
       "      <th>song_name</th>\n",
       "      <th>artist_name_basket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>357367</td>\n",
       "      <td>735</td>\n",
       "      <td>357367</td>\n",
       "      <td>비</td>\n",
       "      <td>[폴킴]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>144663</td>\n",
       "      <td>690</td>\n",
       "      <td>144663</td>\n",
       "      <td>밤편지</td>\n",
       "      <td>[아이유]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>519391</td>\n",
       "      <td>678</td>\n",
       "      <td>519391</td>\n",
       "      <td>Love Again</td>\n",
       "      <td>[김제훈]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>640657</td>\n",
       "      <td>667</td>\n",
       "      <td>640657</td>\n",
       "      <td>내가 말하고 싶은건</td>\n",
       "      <td>[김제훈]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>463173</td>\n",
       "      <td>662</td>\n",
       "      <td>463173</td>\n",
       "      <td>비가 내렸어 (Vocal by 스티브언니)</td>\n",
       "      <td>[업라이트 (Upright)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>349492</td>\n",
       "      <td>659</td>\n",
       "      <td>349492</td>\n",
       "      <td>어떤이별</td>\n",
       "      <td>[임승부]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>427724</td>\n",
       "      <td>653</td>\n",
       "      <td>427724</td>\n",
       "      <td>지워줄게 (Vocal by 스티브언니)</td>\n",
       "      <td>[아재]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>680366</td>\n",
       "      <td>650</td>\n",
       "      <td>680366</td>\n",
       "      <td>끝내지 못한 이야기 (Feat. 호수)</td>\n",
       "      <td>[어쿠스틱 멜로디 (Acoustic Melody)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>520093</td>\n",
       "      <td>641</td>\n",
       "      <td>520093</td>\n",
       "      <td>고마운 사람 (Vocal by 이소진)</td>\n",
       "      <td>[업라이트 (Upright)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>215411</td>\n",
       "      <td>639</td>\n",
       "      <td>215411</td>\n",
       "      <td>지금보다 조금 (Feat. 이원)</td>\n",
       "      <td>[어쿠스틱 멜로디 (Acoustic Melody)]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    songs  mapping_plylst_cnt      id                song_name  \\\n",
       "0  357367                 735  357367                        비   \n",
       "1  144663                 690  144663                      밤편지   \n",
       "2  519391                 678  519391               Love Again   \n",
       "3  640657                 667  640657               내가 말하고 싶은건   \n",
       "4  463173                 662  463173  비가 내렸어 (Vocal by 스티브언니)   \n",
       "5  349492                 659  349492                     어떤이별   \n",
       "6  427724                 653  427724    지워줄게 (Vocal by 스티브언니)   \n",
       "7  680366                 650  680366    끝내지 못한 이야기 (Feat. 호수)   \n",
       "8  520093                 641  520093    고마운 사람 (Vocal by 이소진)   \n",
       "9  215411                 639  215411       지금보다 조금 (Feat. 이원)   \n",
       "\n",
       "             artist_name_basket  \n",
       "0                          [폴킴]  \n",
       "1                         [아이유]  \n",
       "2                         [김제훈]  \n",
       "3                         [김제훈]  \n",
       "4              [업라이트 (Upright)]  \n",
       "5                         [임승부]  \n",
       "6                          [아재]  \n",
       "7  [어쿠스틱 멜로디 (Acoustic Melody)]  \n",
       "8              [업라이트 (Upright)]  \n",
       "9  [어쿠스틱 멜로디 (Acoustic Melody)]  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_temp = plylst_song_tag_map.loc[plylst_song_tag_map['tags'] == '감성', :].groupby('songs').id.nunique().reset_index(name = 'mapping_plylst_cnt').sort_values(by = 'mapping_plylst_cnt', ascending = False)\n",
    "pd.merge(example_temp, song_meta[['id', 'song_name', 'artist_name_basket']], how = 'left', left_on = 'songs', right_on = 'id').head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 감성이라는 태그에 매핑되는 곡은 대략 10만곡으로 매핑 수 기준 상위 10곡 입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.kaggle.com/hongrokchoi/data-introduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "mel = np.load(\"/Users/kywmac/dev/캐글/arena_mel/0/0.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1a3f2a9090>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(mel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}