Part 4-1. Cluster 기반 Mapping_성악.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8deecdad",
   "metadata": {},
   "source": [
    "# PART 4-1 < Cluster 기반 Mapping : 성악 >"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85eff495",
   "metadata": {},
   "source": [
    "# Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ddbacc2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot  as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "82acf569",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_solo = pd.read_csv('data/concert_성악_code3.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94631f21",
   "metadata": {},
   "source": [
    "# 분포 시각화 및 Mapping 을 위한 테이블 분할 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b7c59f5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# little preprocessing \n",
    "\n",
    "data_solo = data_solo.iloc[:,1:]\n",
    "data_solo['열'] = data_solo['열'].str.replace('열', '')\n",
    "data_solo = data_solo[~data_solo['열'].isin(['box'])]\n",
    "data_solo['열'] = data_solo['열'].fillna(0)\n",
    "data_solo['열'] = data_solo['열'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cc5cfe1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 박스 별 구분 \n",
    "\n",
    "solo_C = data_solo[data_solo['블록(박스)'] == 'C']\n",
    "solo_B = data_solo[data_solo['블록(박스)'] == 'B']\n",
    "solo_D = data_solo[data_solo['블록(박스)'] == 'D']\n",
    "solo_A = data_solo[data_solo['블록(박스)'] == 'A']\n",
    "solo_E = data_solo[data_solo['블록(박스)'] == 'E']\n",
    "solo_G = data_solo[data_solo['블록(박스)'] == 'G']\n",
    "solo_M = data_solo[data_solo['블록(박스)'] == 'M']\n",
    "solo_F = data_solo[data_solo['블록(박스)'] == 'F']\n",
    "solo_N = data_solo[data_solo['블록(박스)'] == 'N']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3077cc6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 해당 박스의 층별 구분 \n",
    "solo_C_f1 = solo_C[solo_C['층'] == 1]\n",
    "solo_C_f2 = solo_C[solo_C['층'] == 2]\n",
    "solo_C_f3 = solo_C[solo_C['층'] == 3]\n",
    "\n",
    "solo_B_f1 = solo_B[solo_B['층'] == 1]\n",
    "solo_B_f2 = solo_B[solo_B['층'] == 2]\n",
    "solo_B_f3 = solo_B[solo_B['층'] == 3]\n",
    "\n",
    "solo_D_f1 = solo_D[solo_D['층'] == 1]\n",
    "solo_D_f2 = solo_D[solo_D['층'] == 2]\n",
    "solo_D_f3 = solo_D[solo_D['층'] == 3]\n",
    "\n",
    "solo_A_f1 = solo_A[solo_A['층'] == 1]\n",
    "solo_A_f2 = solo_A[solo_A['층'] == 2]\n",
    "solo_A_f3 = solo_A[solo_A['층'] == 3]\n",
    "\n",
    "solo_E_f1 = solo_E[solo_E['층'] == 1]\n",
    "solo_E_f2 = solo_E[solo_E['층'] == 2]\n",
    "solo_E_f3 = solo_E[solo_E['층'] == 3]\n",
    "\n",
    "solo_G_f1 = solo_G[solo_G['층'] == 1]\n",
    "solo_G_f2 = solo_G[solo_G['층'] == 2]\n",
    "solo_G_f3 = solo_G[solo_G['층'] == 3]\n",
    "\n",
    "solo_M_f1 = solo_M[solo_M['층'] == 1]\n",
    "solo_M_f2 = solo_M[solo_M['층'] == 2]\n",
    "solo_M_f3 = solo_M[solo_M['층'] == 3]\n",
    "\n",
    "solo_F_f1 = solo_F[solo_F['층'] == 1]\n",
    "solo_F_f2 = solo_F[solo_F['층'] == 2]\n",
    "solo_F_f3 = solo_F[solo_F['층'] == 3]\n",
    "\n",
    "solo_N_f1 = solo_N[solo_N['층'] == 1]\n",
    "solo_N_f2 = solo_N[solo_N['층'] == 2]\n",
    "solo_N_f3 = solo_N[solo_N['층'] == 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bae56896",
   "metadata": {},
   "source": [
    "# 분포 시각화"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a03ef7c",
   "metadata": {},
   "source": [
    "**나누어진 테이블 기반 시각화**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f3b4051",
   "metadata": {},
   "source": [
    "- 클러스터별 '열' 선호도 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "835901fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x4200 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing \n",
    "datas = [solo_C_f1,solo_C_f2,solo_C_f3,solo_B_f3,solo_B_f2,solo_B_f1,solo_A_f3,solo_A_f2,solo_A_f1,solo_D_f1,solo_D_f2,solo_D_f3,\n",
    "         solo_E_f1,solo_E_f2,solo_E_f3,solo_F_f1,solo_F_f2,solo_F_f3,solo_G_f1,solo_G_f2,solo_G_f3,solo_M_f1,solo_M_f2,solo_M_f3,\n",
    "         solo_N_f1,solo_N_f2,solo_N_f3]\n",
    "datas_title = ['solo_C_f1','solo_C_f2','solo_C_f3','solo_B_f3','solo_B_f2','solo_B_f1','solo_A_f3','solo_A_f2','solo_A_f1','solo_D_f1','solo_D_f2','solo_D_f3',\n",
    "         'solo_E_f1','solo_E_f2','solo_E_f3','solo_F_f1','solo_F_f2','solo_F_f3','solo_G_f1','solo_G_f2','solo_G_f3','solo_M_f1','solo_M_f2','solo_M_f3',\n",
    "         'solo_N_f1','solo_N_f2','solo_N_f3']\n",
    "\n",
    "col_n = 5\n",
    "row_n = 6\n",
    "\n",
    "fig, ax = plt.subplots(ncols=col_n, nrows=row_n, figsize=(20,row_n*7))\n",
    "\n",
    "n = 0\n",
    "for i in  range(len(datas)):\n",
    "    if len(datas[i]) != 0:\n",
    "        p = sns.boxplot(y='열',x='cluster',data=datas[i], ax=ax[int(n/col_n),int(n%col_n)])\n",
    "        p.set_title(datas_title[i])\n",
    "    n+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "924a5d66",
   "metadata": {},
   "source": [
    "- 클러스터별 '행'(자리번호) 선호도 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ea60d6eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x4200 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_n = 5\n",
    "row_n = 6\n",
    "\n",
    "fig, ax = plt.subplots(ncols=col_n, nrows=row_n, figsize=(20,row_n*7))\n",
    "n = 0\n",
    "for i in  range(len(datas)):\n",
    "    if len(datas[i]) != 0:\n",
    "        p = sns.countplot(x='자리번호',data=datas[i], ax=ax[int(n/col_n),int(n%col_n)],palette='pastel')\n",
    "        p.set_title(datas_title[i])\n",
    "    n+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b209ce86",
   "metadata": {},
   "source": [
    "# Mapping \n",
    "- 위 분포 시각화를 통해 콘서트홀 좌석배치표에 맵핑\n",
    "- 해당 맵핑 결과를 기반으로 데이터 라벨링"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a381eed7",
   "metadata": {},
   "source": [
    "## 1 층"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "940bfd29",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_C_f1['excel'] = solo_C_f1['열'].apply(lambda x: 1 if x < 8 else (2 if 8<=x<=14 else (3 if x < 19 else 4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2267c77a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "solo_D_f1['excel'] =  solo_D_f1['열'].apply(lambda x: 1 if x < 8 else (2 if 8<=x<=14 else (3 if x < 19 else 4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cff96c7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = solo_D_f1['열'] <= 14\n",
    "b = 11<=solo_D_f1['열']\n",
    "c = solo_D_f1['자리번호'] <=12\n",
    "d = 11 <= solo_D_f1['자리번호'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8b8bea8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_D_f1.loc[a&b&c&d, 'excel'] = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1bc736c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_E_f1['excel'] = solo_E_f1['자리번호'].apply(lambda x: 3 if x <=6 else 4 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c469698d",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = solo_E_f1['열'] <= 6\n",
    "b = solo_E_f1['자리번호'] <=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f67d9a49",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_E_f1.loc[a&b,'excel'] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "db088ef1",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_B_f1['excel'] = solo_B_f1['열'].apply(lambda x: 1 if x < 8 else (2 if 8<=x<=14 else (3 if x < 19 else 4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ec0f46c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_A_f1['excel'] = solo_A_f1['자리번호'].apply(lambda x: 4 if x <=3 else 3 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "06c201ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = solo_A_f1['열'] <= 6\n",
    "b = 1<=solo_A_f1['열'] \n",
    "c = solo_A_f1['자리번호'] <=9\n",
    "d = 8 <= solo_A_f1['자리번호'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "18ba6d73",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_A_f1.loc[a&b&c&d,'excel']= 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db4a025a",
   "metadata": {},
   "source": [
    "## 2 층"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b55de876",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_C_f2['excel'] =  solo_C_f2['열'].apply(lambda x: 2 if x <=2 else(3 if 2<= x <=5 else 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "efbc89e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_D_f2['excel'] =  solo_D_f2['열'].apply(lambda x: 2 if x <=2 else(3 if 2<= x <=5 else 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8c2c8a47",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_B_f2['excel'] =  solo_B_f2['열'].apply(lambda x: 2 if x <=2 else(3 if 2<= x <=5 else 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4893a259",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_E_f2['excel'] = solo_E_f2['자리번호'].apply(lambda x: 4 if x <= 11 else 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3d8ffc8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_E_f2.loc[6<=solo_E_f2['열'],'excel' ] = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bb5d78d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_A_f2['excel'] =solo_A_f2['자리번호'].apply(lambda x: 5 if x <= 6 else 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f11d6ea6",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_A_f2.loc[6<=solo_A_f2['열'],'excel' ] = 6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ca80726",
   "metadata": {},
   "source": [
    "## 3 층"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8e81bfe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_A_f3['excel'] = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "53e11908",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_B_f3['excel'] = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7487d2f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_C_f3['excel'] = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "38282e83",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_D_f3['excel'] = solo_D_f3['열'].apply(lambda x: 4 if x <= 5 else 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "db730ed2",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_E_f3['excel'] = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "de8f4b61",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_F_f3['excel'] = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "701203e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_G_f3['excel'] = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a79b5183",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_M_f3['excel'] = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "212ab952",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_N_f3['excel'] = 6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abe53ae5",
   "metadata": {},
   "source": [
    "# 데이터 Concat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "40be36d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "ddff = pd.concat([solo_A_f1,solo_A_f2])\n",
    "ddff = pd.concat([ddff,solo_A_f3])\n",
    "ddff = pd.concat([ddff,solo_B_f1])\n",
    "ddff = pd.concat([ddff,solo_B_f2])\n",
    "ddff = pd.concat([ddff,solo_B_f3])\n",
    "ddff = pd.concat([ddff,solo_C_f1])\n",
    "ddff = pd.concat([ddff,solo_C_f2])\n",
    "ddff = pd.concat([ddff,solo_C_f3])\n",
    "ddff = pd.concat([ddff,solo_D_f1])\n",
    "ddff = pd.concat([ddff,solo_D_f2])\n",
    "ddff = pd.concat([ddff,solo_D_f3])\n",
    "ddff = pd.concat([ddff,solo_E_f1])\n",
    "ddff = pd.concat([ddff,solo_E_f2])\n",
    "ddff = pd.concat([ddff,solo_E_f3])\n",
    "ddff = pd.concat([ddff,solo_F_f3])\n",
    "ddff = pd.concat([ddff,solo_G_f3])\n",
    "ddff = pd.concat([ddff,solo_M_f3])\n",
    "ddff = pd.concat([ddff,solo_N_f3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "51bbf6ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "ddff = ddff.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "443077b8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ddff.to_csv('data/성악_라벨_code4.csv', index = False, encoding='utf-8-sig')"
   ]
  }
 ],
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