wrap_up.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=6> <b> 3주차 강의 Notebook </b> </font>\n",
    "<div class=\"alert alert-block alert-success\">\n",
    " 이번 시간에는, 지난 강의에 이어 Pandas의 아래와 같은 고급 기능에 대해 알아본다. <br>\n",
    " 주요 내용: visualization, profiling, join/merge, pivot/pivot_table, apply/applymap, etc.<br>\n",
    "</div>\n",
    "<p style=\"text-align:right;\"> sumyeon@gmail.com </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 필요한 모듈/패키지를 import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:22.792053Z",
     "iopub.status.busy": "2020-08-04T11:05:22.792053Z",
     "iopub.status.idle": "2020-08-04T11:05:22.800051Z",
     "shell.execute_reply": "2020-08-04T11:05:22.799053Z",
     "shell.execute_reply.started": "2020-08-04T11:05:22.792053Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Appendix의 EDA 자동화 tool용 import. 문제 발생시 무시하고 진행해도 됨 </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:22.802053Z",
     "iopub.status.busy": "2020-08-04T11:05:22.802053Z",
     "iopub.status.idle": "2020-08-04T11:05:24.014050Z",
     "shell.execute_reply": "2020-08-04T11:05:24.013051Z",
     "shell.execute_reply.started": "2020-08-04T11:05:22.802053Z"
    }
   },
   "outputs": [],
   "source": [
    "# pandas-profiling 설치 필요\n",
    "import os\n",
    "import pandas_profiling\n",
    "import webbrowser"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 가끔씩 발생하는 warning message들의 출력을 막는 기능 (Optional) </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-06T11:44:33.714674Z",
     "iopub.status.busy": "2020-08-06T11:44:33.714674Z",
     "iopub.status.idle": "2020-08-06T11:44:33.718675Z",
     "shell.execute_reply": "2020-08-06T11:44:33.717675Z",
     "shell.execute_reply.started": "2020-08-06T11:44:33.714674Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:24.016052Z",
     "iopub.status.busy": "2020-08-04T11:05:24.016052Z",
     "iopub.status.idle": "2020-08-04T11:05:24.021055Z",
     "shell.execute_reply": "2020-08-04T11:05:24.020054Z",
     "shell.execute_reply.started": "2020-08-04T11:05:24.016052Z"
    }
   },
   "outputs": [],
   "source": [
    "os.chdir('d:/jupyternotebook/python_anal_atoz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:24.023053Z",
     "iopub.status.busy": "2020-08-04T11:05:24.023053Z",
     "iopub.status.idle": "2020-08-04T11:05:24.061052Z",
     "shell.execute_reply": "2020-08-04T11:05:24.061052Z",
     "shell.execute_reply.started": "2020-08-04T11:05:24.023053Z"
    }
   },
   "outputs": [],
   "source": [
    "# read csv file into data frame (main memory)\n",
    "airdf = pd.read_csv('seoul_air_polution.csv')\n",
    "airdf.columns = ['date','loc','NO2','O3','CO','SO2','PM10','PM2.5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:24.063052Z",
     "iopub.status.busy": "2020-08-04T11:05:24.062051Z",
     "iopub.status.idle": "2020-08-04T11:05:24.079052Z",
     "shell.execute_reply": "2020-08-04T11:05:24.078052Z",
     "shell.execute_reply.started": "2020-08-04T11:05:24.063052Z"
    }
   },
   "outputs": [],
   "source": [
    "parkdf = pd.read_csv('seoul_greenpark.tsv', sep='\\t')\n",
    "parkdf.columns = ['loc','total_area','park_area','park_ratio']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 필요없는 Index, Column 삭제"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Index 값을 지정해서 해당 row를 삭제 </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:24.082052Z",
     "iopub.status.busy": "2020-08-04T11:05:24.081052Z",
     "iopub.status.idle": "2020-08-04T11:05:24.108052Z",
     "shell.execute_reply": "2020-08-04T11:05:24.107052Z",
     "shell.execute_reply.started": "2020-08-04T11:05:24.082052Z"
    }
   },
   "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>date</th>\n",
       "      <th>loc</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20171009</td>\n",
       "      <td>강남구</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.007</td>\n",
       "      <td>28.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20171009</td>\n",
       "      <td>중구</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.005</td>\n",
       "      <td>25.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date  loc    NO2     O3   CO    SO2  PM10  PM2.5\n",
       "0  20171009  강남구  0.024  0.020  0.4  0.007  28.0   22.0\n",
       "2  20171009   중구  0.031  0.025  0.5  0.005  25.0   18.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airdf.drop(1).head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> index 범위를 지정해서 해당 row를 삭제 </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:24.109052Z",
     "iopub.status.busy": "2020-08-04T11:05:24.109052Z",
     "iopub.status.idle": "2020-08-04T11:05:24.142052Z",
     "shell.execute_reply": "2020-08-04T11:05:24.141053Z",
     "shell.execute_reply.started": "2020-08-04T11:05:24.109052Z"
    }
   },
   "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>date</th>\n",
       "      <th>loc</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>20171007</td>\n",
       "      <td>강남구</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.004</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>20171007</td>\n",
       "      <td>도산대로</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.018</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   loc   NO2     O3   CO    SO2  PM10  PM2.5\n",
       "100  20171007   강남구  0.02  0.018  0.3  0.004   7.0    5.0\n",
       "101  20171007  도산대로  0.03  0.018  1.0  0.004  15.0    6.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airdf.drop(airdf.index[0:100]).head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 컬럼을 지정해서 해당 컬럼을 삭제 (= 해당 컬럼을 제외한 나머지 컬럼을 선택) </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:24.146057Z",
     "iopub.status.busy": "2020-08-04T11:05:24.146057Z",
     "iopub.status.idle": "2020-08-04T11:05:24.165053Z",
     "shell.execute_reply": "2020-08-04T11:05:24.165053Z",
     "shell.execute_reply.started": "2020-08-04T11:05:24.146057Z"
    }
   },
   "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>date</th>\n",
       "      <th>loc</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>SO2</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20171009</td>\n",
       "      <td>강남구</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.007</td>\n",
       "      <td>28.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20171009</td>\n",
       "      <td>한강대로</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.006</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date   loc    NO2     O3    SO2  PM10  PM2.5\n",
       "0  20171009   강남구  0.024  0.020  0.007  28.0   22.0\n",
       "1  20171009  한강대로  0.037  0.019  0.006  32.0   16.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airdf.drop('CO', axis=1).head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b> Note:</b> 위의 모든 operation은 원본 dataframe을 변경하지 않음. 필요한 작업 결과는 저장하거나, \"inplace=True\" 옵션을 사용해야함 </b>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:24.167053Z",
     "iopub.status.busy": "2020-08-04T11:05:24.167053Z",
     "iopub.status.idle": "2020-08-04T11:05:24.190052Z",
     "shell.execute_reply": "2020-08-04T11:05:24.190052Z",
     "shell.execute_reply.started": "2020-08-04T11:05:24.167053Z"
    }
   },
   "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>date</th>\n",
       "      <th>loc</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20171009</td>\n",
       "      <td>강남구</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.007</td>\n",
       "      <td>28.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20171009</td>\n",
       "      <td>한강대로</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date   loc    NO2     O3   CO    SO2  PM10  PM2.5\n",
       "0  20171009   강남구  0.024  0.020  0.4  0.007  28.0   22.0\n",
       "1  20171009  한강대로  0.037  0.019  0.5  0.006  32.0   16.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airdf.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filter를 사용하는 Selection 방법"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:24.193053Z",
     "iopub.status.busy": "2020-08-04T11:05:24.193053Z",
     "iopub.status.idle": "2020-08-04T11:05:24.212051Z",
     "shell.execute_reply": "2020-08-04T11:05:24.211053Z",
     "shell.execute_reply.started": "2020-08-04T11:05:24.193053Z"
    }
   },
   "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>CO</th>\n",
       "      <th>NO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CO    NO2\n",
       "0  0.4  0.024\n",
       "1  0.5  0.037"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select columns using column name\n",
    "airdf.filter(items=['CO','NO2']).head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:24.213053Z",
     "iopub.status.busy": "2020-08-04T11:05:24.213053Z",
     "iopub.status.idle": "2020-08-04T11:05:24.233052Z",
     "shell.execute_reply": "2020-08-04T11:05:24.232052Z",
     "shell.execute_reply.started": "2020-08-04T11:05:24.213053Z"
    }
   },
   "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>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>SO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.024</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.037</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     NO2     O3    SO2\n",
       "0  0.024  0.020  0.007\n",
       "1  0.037  0.019  0.006"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airdf.filter(regex='O.+').head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:24.234052Z",
     "iopub.status.busy": "2020-08-04T11:05:24.234052Z",
     "iopub.status.idle": "2020-08-04T11:05:24.259052Z",
     "shell.execute_reply": "2020-08-04T11:05:24.258052Z",
     "shell.execute_reply.started": "2020-08-04T11:05:24.234052Z"
    }
   },
   "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>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.024</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.037</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     NO2     O3   CO    SO2\n",
       "0  0.024  0.020  0.4  0.007\n",
       "1  0.037  0.019  0.5  0.006"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airdf.filter(like='O').head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b> Tip:</b> filter는 Series의 경우 index에, Dataframe의 경우 columns에 적용됨. 필요한 경우, axis=0/1 으로 지정 가능 </b>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Colum 조건에 따라 새로운 column값 구하는 법"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Where문 사용 : if true first value else second value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:24.261052Z",
     "iopub.status.busy": "2020-08-04T11:05:24.260052Z",
     "iopub.status.idle": "2020-08-04T11:05:24.295052Z",
     "shell.execute_reply": "2020-08-04T11:05:24.294050Z",
     "shell.execute_reply.started": "2020-08-04T11:05:24.261052Z"
    }
   },
   "outputs": [],
   "source": [
    "# select columns using column values\n",
    "airdf['district'] = airdf['loc'].where( airdf['loc'].str.contains('구'), other = '별도측정소')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:24.297054Z",
     "iopub.status.busy": "2020-08-04T11:05:24.296053Z",
     "iopub.status.idle": "2020-08-04T11:05:24.324052Z",
     "shell.execute_reply": "2020-08-04T11:05:24.323053Z",
     "shell.execute_reply.started": "2020-08-04T11:05:24.297054Z"
    }
   },
   "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>date</th>\n",
       "      <th>loc</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>district</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20171009</td>\n",
       "      <td>강남구</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.007</td>\n",
       "      <td>28.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>강남구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20171009</td>\n",
       "      <td>한강대로</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>별도측정소</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20171009</td>\n",
       "      <td>중구</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.005</td>\n",
       "      <td>25.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>중구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20171009</td>\n",
       "      <td>마포구</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.006</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>마포구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20171009</td>\n",
       "      <td>광진구</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.006</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>광진구</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date   loc    NO2     O3   CO    SO2  PM10  PM2.5 district\n",
       "0  20171009   강남구  0.024  0.020  0.4  0.007  28.0   22.0      강남구\n",
       "1  20171009  한강대로  0.037  0.019  0.5  0.006  32.0   16.0    별도측정소\n",
       "2  20171009    중구  0.031  0.025  0.5  0.005  25.0   18.0       중구\n",
       "3  20171009   마포구  0.021  0.031  0.4  0.006  22.0   19.0      마포구\n",
       "4  20171009   광진구  0.041  0.026  0.8  0.006  25.0   19.0      광진구"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airdf.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b> Tip:</b> Series의 where가 아닌, numpy의 where 도 있음. 전자의 경우 조건이 True인 경우 원래 자신의 값인 반면 np.where는 True/False 모두 값을 지정</b> \n",
    "<pre> np.where(condition, A, B) = A.where(condition, B) </pre>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply/ApplyMap : 함수를 사용해서 조건에 맞는 값을 선택"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 단순한 True/False를 넘어서 복잡한 로직에 따른 값을 선택하고 싶다 </b> </br>\n",
    "<b> 예) PM2.5 값에 따라 4가지 레벨 ('좋음','보통','나쁨','아주나쁨')을 값을 dataframe에 추가 하고 싶다! </b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PM2.5값에 따라 4가지 레벨을 리턴하는 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:24.326054Z",
     "iopub.status.busy": "2020-08-04T11:05:24.325051Z",
     "iopub.status.idle": "2020-08-04T11:05:24.335052Z",
     "shell.execute_reply": "2020-08-04T11:05:24.334053Z",
     "shell.execute_reply.started": "2020-08-04T11:05:24.326054Z"
    }
   },
   "outputs": [],
   "source": [
    "def pm25tolevel(value):\n",
    "    \"\"\" PM2.5 값에 따른 4단계 분류 값을 리턴\"\"\"\n",
    "    if value <= 15:\n",
    "        return 'good'\n",
    "    elif value <= 50:\n",
    "        return 'normal'\n",
    "    elif value <= 100:\n",
    "        return 'bad'\n",
    "    else:\n",
    "        return 'verybad'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply 함수 : Series/Dataframe에 적용 가능"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> PM2.5의 값을 레벨로 변환하여 'pm25_level' 컬럼에 저장 </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:24.337052Z",
     "iopub.status.busy": "2020-08-04T11:05:24.337052Z",
     "iopub.status.idle": "2020-08-04T11:05:24.362052Z",
     "shell.execute_reply": "2020-08-04T11:05:24.361052Z",
     "shell.execute_reply.started": "2020-08-04T11:05:24.337052Z"
    }
   },
   "outputs": [],
   "source": [
    "# 'PM2.5'가 lambda 함수의 'value'인자로 전달됨\n",
    "airdf['pm25_level'] = airdf['PM2.5'].apply(lambda value : pm25tolevel(value) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:24.364053Z",
     "iopub.status.busy": "2020-08-04T11:05:24.364053Z",
     "iopub.status.idle": "2020-08-04T11:05:24.394053Z",
     "shell.execute_reply": "2020-08-04T11:05:24.393051Z",
     "shell.execute_reply.started": "2020-08-04T11:05:24.364053Z"
    }
   },
   "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>date</th>\n",
       "      <th>loc</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>district</th>\n",
       "      <th>pm25_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20171009</td>\n",
       "      <td>강남구</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.007</td>\n",
       "      <td>28.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>강남구</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20171009</td>\n",
       "      <td>한강대로</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>별도측정소</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date   loc    NO2     O3   CO    SO2  PM10  PM2.5 district pm25_level\n",
       "0  20171009   강남구  0.024  0.020  0.4  0.007  28.0   22.0      강남구     normal\n",
       "1  20171009  한강대로  0.037  0.019  0.5  0.006  32.0   16.0    별도측정소     normal"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airdf.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> PM10과 PM2.5의 두 가지 모두 verybad 레벨일 경우 'worst'라는 컬럼에 True로 저장 </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:24.395051Z",
     "iopub.status.busy": "2020-08-04T11:05:24.395051Z",
     "iopub.status.idle": "2020-08-04T11:05:24.760052Z",
     "shell.execute_reply": "2020-08-04T11:05:24.759054Z",
     "shell.execute_reply.started": "2020-08-04T11:05:24.395051Z"
    }
   },
   "outputs": [],
   "source": [
    "# 'PM2.5'가 lambda 함수의 'value'인자로 전달됨\n",
    "airdf['worst'] = airdf.apply(lambda row : True if row['PM2.5'] > 100 and row['PM10'] > 150 else False, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:24.762051Z",
     "iopub.status.busy": "2020-08-04T11:05:24.761052Z",
     "iopub.status.idle": "2020-08-04T11:05:24.783051Z",
     "shell.execute_reply": "2020-08-04T11:05:24.782052Z",
     "shell.execute_reply.started": "2020-08-04T11:05:24.762051Z"
    }
   },
   "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>date</th>\n",
       "      <th>loc</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>district</th>\n",
       "      <th>pm25_level</th>\n",
       "      <th>worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20171009</td>\n",
       "      <td>강남구</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.007</td>\n",
       "      <td>28.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>강남구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20171009</td>\n",
       "      <td>한강대로</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>별도측정소</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20171009</td>\n",
       "      <td>중구</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.005</td>\n",
       "      <td>25.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>중구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20171009</td>\n",
       "      <td>마포구</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.006</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>마포구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20171009</td>\n",
       "      <td>광진구</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.006</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>광진구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date   loc    NO2     O3   CO    SO2  PM10  PM2.5 district pm25_level  \\\n",
       "0  20171009   강남구  0.024  0.020  0.4  0.007  28.0   22.0      강남구     normal   \n",
       "1  20171009  한강대로  0.037  0.019  0.5  0.006  32.0   16.0    별도측정소     normal   \n",
       "2  20171009    중구  0.031  0.025  0.5  0.005  25.0   18.0       중구     normal   \n",
       "3  20171009   마포구  0.021  0.031  0.4  0.006  22.0   19.0      마포구     normal   \n",
       "4  20171009   광진구  0.041  0.026  0.8  0.006  25.0   19.0      광진구     normal   \n",
       "\n",
       "   worst  \n",
       "0  False  \n",
       "1  False  \n",
       "2  False  \n",
       "3  False  \n",
       "4  False  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airdf.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b> Tip:</b> Apply 기능은, Series일 경우 하나의 개별 value에, Dataframe의 경우 개별 row에 함수를 적용</b>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:24.784051Z",
     "iopub.status.busy": "2020-08-04T11:05:24.784051Z",
     "iopub.status.idle": "2020-08-04T11:05:24.814052Z",
     "shell.execute_reply": "2020-08-04T11:05:24.813051Z",
     "shell.execute_reply.started": "2020-08-04T11:05:24.784051Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    normal\n",
       "1    normal\n",
       "2    normal\n",
       "3    normal\n",
       "4    normal\n",
       "Name: PM2.5, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airdf['PM2.5'].map(lambda value : pm25tolevel(value) ).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:24.815053Z",
     "iopub.status.busy": "2020-08-04T11:05:24.815053Z",
     "iopub.status.idle": "2020-08-04T11:05:24.891054Z",
     "shell.execute_reply": "2020-08-04T11:05:24.890055Z",
     "shell.execute_reply.started": "2020-08-04T11:05:24.815053Z"
    }
   },
   "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>date</th>\n",
       "      <th>loc</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>district</th>\n",
       "      <th>pm25_level</th>\n",
       "      <th>worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date   loc   NO2    O3    CO   SO2  PM10  PM2.5  district  pm25_level  \\\n",
       "0  True  True  True  True  True  True  True   True      True        True   \n",
       "1  True  True  True  True  True  True  True   True      True        True   \n",
       "2  True  True  True  True  True  True  True   True      True        True   \n",
       "3  True  True  True  True  True  True  True   True      True        True   \n",
       "4  True  True  True  True  True  True  True   True      True        True   \n",
       "\n",
       "   worst  \n",
       "0   True  \n",
       "1   True  \n",
       "2   True  \n",
       "3   True  \n",
       "4   True  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airdf.applymap(lambda value : True).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:24.893056Z",
     "iopub.status.busy": "2020-08-04T11:05:24.892053Z",
     "iopub.status.idle": "2020-08-04T11:05:24.915053Z",
     "shell.execute_reply": "2020-08-04T11:05:24.914054Z",
     "shell.execute_reply.started": "2020-08-04T11:05:24.893056Z"
    }
   },
   "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>date</th>\n",
       "      <th>loc</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>district</th>\n",
       "      <th>pm25_level</th>\n",
       "      <th>worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20171009</td>\n",
       "      <td>강남구</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.007</td>\n",
       "      <td>28.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>강남구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20171009</td>\n",
       "      <td>한강대로</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>별도측정소</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date   loc    NO2     O3   CO    SO2  PM10  PM2.5 district pm25_level  \\\n",
       "0  20171009   강남구  0.024  0.020  0.4  0.007  28.0   22.0      강남구     normal   \n",
       "1  20171009  한강대로  0.037  0.019  0.5  0.006  32.0   16.0    별도측정소     normal   \n",
       "\n",
       "   worst  \n",
       "0  False  \n",
       "1  False  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airdf.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b> Tip:</b> map (for Series), applymap (for Dataframe)은 elementwise로 함수를 적용하고 결과를 전달 </b>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Frame merge, join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 끝 부분에서, 다음 가설을 확인해 보려 합니다. => 녹지가 많은 지역의 대기 오염도가 낮은 걸 아닐까? </b> (DBMS의 Join) <br>\n",
    "<b> 가설 확인을 위해서, 대기오염 정보 dataframe과 공원녹지 정보 dataframe을 연결 해야함! </b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "<table>\n",
    "  <tr>\n",
    "      <th> airdf </th>\n",
    "      <th> -------- </th>\n",
    "      <th> parkdf </th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "      <td> <pre>\n",
    "|    |     date | loc      |   NO2 |    O3 |   CO |   SO2 |   PM10 |   PM2.5 |\n",
    "|---:|---------:|:---------|------:|------:|-----:|------:|-------:|--------:|\n",
    "|  0 | 20171009 | 강남구   | 0.024 | 0.02  |  0.4 | 0.007 |     28 |      22 |\n",
    "|  1 | 20171009 | 한강대로 | 0.037 | 0.019 |  0.5 | 0.006 |     32 |      16 |\n",
    "|  2 | 20171009 | 중구     | 0.031 | 0.025 |  0.5 | 0.005 |     25 |      18 |\n",
    "|  3 | 20171009 | 마포구   | 0.021 | 0.031 |  0.4 | 0.006 |     22 |      19 |\n",
    "|  4 | 20171009 | 광진구   | 0.041 | 0.026 |  0.8 | 0.006 |     25 |      19 |\n",
    "          </pre>\n",
    "      </td> \n",
    "      <td> </td>\n",
    "      <td>\n",
    "      <pre>\n",
    "|    | loc    | total_area   | park_area   |   park_ratio |\n",
    "|---:|:-------|:-------------|:------------|-------------:|\n",
    "|  0 | 합계   | 605,196,566  | 168,843,299 |        27.9  |\n",
    "|  1 | 종로구 | 23,912,883   | 11,555,792  |        48.32 |\n",
    "|  2 | 중구   | 9,960,930    | 3,131,968   |        31.44 |\n",
    "|  3 | 용산구 | 21,866,844   | 1,795,641   |         8.21 |\n",
    "|  4 | 성동구 | 16,860,100   | 3,102,064   |        18.4  |\n",
    " </pre>\n",
    "      </td>\n",
    "    </tr>\n",
    "</table>\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge 명령어로 column(s)을 공유하는 두 dataframe을 병합!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:24.922052Z",
     "iopub.status.busy": "2020-08-04T11:05:24.921051Z",
     "iopub.status.idle": "2020-08-04T11:05:24.962053Z",
     "shell.execute_reply": "2020-08-04T11:05:24.961054Z",
     "shell.execute_reply.started": "2020-08-04T11:05:24.922052Z"
    }
   },
   "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>date</th>\n",
       "      <th>loc</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>district</th>\n",
       "      <th>pm25_level</th>\n",
       "      <th>worst</th>\n",
       "      <th>total_area</th>\n",
       "      <th>park_area</th>\n",
       "      <th>park_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20171009</td>\n",
       "      <td>강남구</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.007</td>\n",
       "      <td>28.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>강남구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>39,501,308</td>\n",
       "      <td>6,591,655</td>\n",
       "      <td>16.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20171009</td>\n",
       "      <td>한강대로</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>별도측정소</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20171009</td>\n",
       "      <td>중구</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.005</td>\n",
       "      <td>25.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>중구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>9,960,930</td>\n",
       "      <td>3,131,968</td>\n",
       "      <td>31.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20171009</td>\n",
       "      <td>마포구</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.006</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>마포구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>23,843,621</td>\n",
       "      <td>4,439,901</td>\n",
       "      <td>18.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20171009</td>\n",
       "      <td>광진구</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.006</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>광진구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>17,063,230</td>\n",
       "      <td>3,363,048</td>\n",
       "      <td>19.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date   loc    NO2     O3   CO    SO2  PM10  PM2.5 district pm25_level  \\\n",
       "0  20171009   강남구  0.024  0.020  0.4  0.007  28.0   22.0      강남구     normal   \n",
       "1  20171009  한강대로  0.037  0.019  0.5  0.006  32.0   16.0    별도측정소     normal   \n",
       "2  20171009    중구  0.031  0.025  0.5  0.005  25.0   18.0       중구     normal   \n",
       "3  20171009   마포구  0.021  0.031  0.4  0.006  22.0   19.0      마포구     normal   \n",
       "4  20171009   광진구  0.041  0.026  0.8  0.006  25.0   19.0      광진구     normal   \n",
       "\n",
       "   worst  total_area  park_area  park_ratio  \n",
       "0  False  39,501,308  6,591,655       16.69  \n",
       "1  False         NaN        NaN         NaN  \n",
       "2  False   9,960,930  3,131,968       31.44  \n",
       "3  False  23,843,621  4,439,901       18.62  \n",
       "4  False  17,063,230  3,363,048       19.71  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airdf.merge(parkdf, on='loc', how='left').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:24.966053Z",
     "iopub.status.busy": "2020-08-04T11:05:24.965052Z",
     "iopub.status.idle": "2020-08-04T11:05:25.001056Z",
     "shell.execute_reply": "2020-08-04T11:05:25.000053Z",
     "shell.execute_reply.started": "2020-08-04T11:05:24.966053Z"
    }
   },
   "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>date</th>\n",
       "      <th>loc</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>district</th>\n",
       "      <th>pm25_level</th>\n",
       "      <th>worst</th>\n",
       "      <th>total_area</th>\n",
       "      <th>park_area</th>\n",
       "      <th>park_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20171009</td>\n",
       "      <td>강남구</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.007</td>\n",
       "      <td>28.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>강남구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>39,501,308</td>\n",
       "      <td>6,591,655</td>\n",
       "      <td>16.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20171009</td>\n",
       "      <td>한강대로</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>별도측정소</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20171009</td>\n",
       "      <td>중구</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.005</td>\n",
       "      <td>25.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>중구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>9,960,930</td>\n",
       "      <td>3,131,968</td>\n",
       "      <td>31.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20171009</td>\n",
       "      <td>마포구</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.006</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>마포구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>23,843,621</td>\n",
       "      <td>4,439,901</td>\n",
       "      <td>18.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20171009</td>\n",
       "      <td>광진구</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.006</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>광진구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>17,063,230</td>\n",
       "      <td>3,363,048</td>\n",
       "      <td>19.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date   loc    NO2     O3   CO    SO2  PM10  PM2.5 district pm25_level  \\\n",
       "0  20171009   강남구  0.024  0.020  0.4  0.007  28.0   22.0      강남구     normal   \n",
       "1  20171009  한강대로  0.037  0.019  0.5  0.006  32.0   16.0    별도측정소     normal   \n",
       "2  20171009    중구  0.031  0.025  0.5  0.005  25.0   18.0       중구     normal   \n",
       "3  20171009   마포구  0.021  0.031  0.4  0.006  22.0   19.0      마포구     normal   \n",
       "4  20171009   광진구  0.041  0.026  0.8  0.006  25.0   19.0      광진구     normal   \n",
       "\n",
       "   worst  total_area  park_area  park_ratio  \n",
       "0  False  39,501,308  6,591,655       16.69  \n",
       "1  False         NaN        NaN         NaN  \n",
       "2  False   9,960,930  3,131,968       31.44  \n",
       "3  False  23,843,621  4,439,901       18.62  \n",
       "4  False  17,063,230  3,363,048       19.71  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(airdf, parkdf, on='loc', how='left').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b> Tip:</b> merge 함수에서 두 Dataframe의 join column이 다른 경우, left_on='loc', right_on='loc'와 같이 직접 join key를 지정해 주면 된다. </b> <br>\n",
    "<b> Tip:</b> join 시에 left, right, outer 등의 방식 선정은 how='left'와 같은 방식으로 지정해 주면 된다. </b>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join 명령어로 index가 같은 두 dataframe을 병합!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:25.004053Z",
     "iopub.status.busy": "2020-08-04T11:05:25.003055Z",
     "iopub.status.idle": "2020-08-04T11:05:25.042053Z",
     "shell.execute_reply": "2020-08-04T11:05:25.041054Z",
     "shell.execute_reply.started": "2020-08-04T11:05:25.003055Z"
    }
   },
   "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>date</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>district</th>\n",
       "      <th>pm25_level</th>\n",
       "      <th>worst</th>\n",
       "      <th>total_area</th>\n",
       "      <th>park_area</th>\n",
       "      <th>park_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>강남구</th>\n",
       "      <td>20171009</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.007</td>\n",
       "      <td>28.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>강남구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>39,501,308</td>\n",
       "      <td>6,591,655</td>\n",
       "      <td>16.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강남구</th>\n",
       "      <td>20171008</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.005</td>\n",
       "      <td>16.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>강남구</td>\n",
       "      <td>good</td>\n",
       "      <td>False</td>\n",
       "      <td>39,501,308</td>\n",
       "      <td>6,591,655</td>\n",
       "      <td>16.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강남구</th>\n",
       "      <td>20171007</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.004</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>강남구</td>\n",
       "      <td>good</td>\n",
       "      <td>False</td>\n",
       "      <td>39,501,308</td>\n",
       "      <td>6,591,655</td>\n",
       "      <td>16.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강남구</th>\n",
       "      <td>20171006</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.004</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>강남구</td>\n",
       "      <td>good</td>\n",
       "      <td>False</td>\n",
       "      <td>39,501,308</td>\n",
       "      <td>6,591,655</td>\n",
       "      <td>16.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강남구</th>\n",
       "      <td>20171005</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.004</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>강남구</td>\n",
       "      <td>good</td>\n",
       "      <td>False</td>\n",
       "      <td>39,501,308</td>\n",
       "      <td>6,591,655</td>\n",
       "      <td>16.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date    NO2     O3   CO    SO2  PM10  PM2.5 district pm25_level  \\\n",
       "loc                                                                        \n",
       "강남구  20171009  0.024  0.020  0.4  0.007  28.0   22.0      강남구     normal   \n",
       "강남구  20171008  0.022  0.020  0.4  0.005  16.0   12.0      강남구       good   \n",
       "강남구  20171007  0.020  0.018  0.3  0.004   7.0    5.0      강남구       good   \n",
       "강남구  20171006  0.013  0.020  0.3  0.004  10.0    5.0      강남구       good   \n",
       "강남구  20171005  0.013  0.021  0.3  0.004  13.0    6.0      강남구       good   \n",
       "\n",
       "     worst  total_area  park_area  park_ratio  \n",
       "loc                                            \n",
       "강남구  False  39,501,308  6,591,655       16.69  \n",
       "강남구  False  39,501,308  6,591,655       16.69  \n",
       "강남구  False  39,501,308  6,591,655       16.69  \n",
       "강남구  False  39,501,308  6,591,655       16.69  \n",
       "강남구  False  39,501,308  6,591,655       16.69  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airdf.set_index('loc').join(parkdf.set_index('loc'), how='left').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b> Tip:</b> join함수는 index가 같은 두 Dataframe을 병합하는데 사용됨\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:25.044055Z",
     "iopub.status.busy": "2020-08-04T11:05:25.043052Z",
     "iopub.status.idle": "2020-08-04T11:05:25.059053Z",
     "shell.execute_reply": "2020-08-04T11:05:25.058057Z",
     "shell.execute_reply.started": "2020-08-04T11:05:25.044055Z"
    }
   },
   "outputs": [],
   "source": [
    "mergedf = airdf.merge(parkdf, on='loc', how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pivot/Pivot_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-01T07:25:46.524057Z",
     "iopub.status.busy": "2020-08-01T07:25:46.524057Z",
     "iopub.status.idle": "2020-08-01T07:25:46.531056Z",
     "shell.execute_reply": "2020-08-01T07:25:46.530057Z",
     "shell.execute_reply.started": "2020-08-01T07:25:46.524057Z"
    }
   },
   "source": [
    "<b> 엑셀의 Pivot과 유사한 기능을 제공. </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:25.061053Z",
     "iopub.status.busy": "2020-08-04T11:05:25.060053Z",
     "iopub.status.idle": "2020-08-04T11:05:25.117053Z",
     "shell.execute_reply": "2020-08-04T11:05:25.116056Z",
     "shell.execute_reply.started": "2020-08-04T11:05:25.061053Z"
    }
   },
   "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>loc</th>\n",
       "      <th>강남구</th>\n",
       "      <th>강남대로</th>\n",
       "      <th>강동구</th>\n",
       "      <th>강변북로</th>\n",
       "      <th>강북구</th>\n",
       "      <th>강서구</th>\n",
       "      <th>공항대로</th>\n",
       "      <th>관악구</th>\n",
       "      <th>광진구</th>\n",
       "      <th>구로구</th>\n",
       "      <th>...</th>\n",
       "      <th>정릉로</th>\n",
       "      <th>종로</th>\n",
       "      <th>종로구</th>\n",
       "      <th>중구</th>\n",
       "      <th>중랑구</th>\n",
       "      <th>천호대로</th>\n",
       "      <th>청계천로</th>\n",
       "      <th>한강대로</th>\n",
       "      <th>홍릉로</th>\n",
       "      <th>화랑로</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20161009</th>\n",
       "      <td>19.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20161010</th>\n",
       "      <td>29.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20161011</th>\n",
       "      <td>48.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20161012</th>\n",
       "      <td>60.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>60.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20161013</th>\n",
       "      <td>45.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "loc        강남구  강남대로   강동구  강변북로   강북구   강서구  공항대로   관악구   광진구   구로구  ...  \\\n",
       "date                                                                  ...   \n",
       "20161009  19.0  25.0  20.0  21.0  12.0  19.0  23.0  23.0  17.0  19.0  ...   \n",
       "20161010  29.0  38.0  32.0  35.0  20.0  31.0  56.0  30.0  24.0  30.0  ...   \n",
       "20161011  48.0  56.0  48.0  52.0  36.0  42.0  70.0  45.0  42.0  48.0  ...   \n",
       "20161012  60.0  68.0  63.0  64.0  49.0  57.0  75.0  54.0  55.0  57.0  ...   \n",
       "20161013  45.0  55.0  54.0  52.0  34.0  52.0  71.0  43.0  41.0  46.0  ...   \n",
       "\n",
       "loc        정릉로    종로   종로구  중구   중랑구  천호대로  청계천로  한강대로   홍릉로   화랑로  \n",
       "date                                                                \n",
       "20161009  22.0  20.0  15.0 NaN  19.0  22.0  16.0  28.0  19.0  21.0  \n",
       "20161010  31.0  25.0  25.0 NaN  29.0  24.0  25.0  40.0  28.0  35.0  \n",
       "20161011  45.0  24.0  44.0 NaN  46.0  33.0  41.0  59.0  41.0  50.0  \n",
       "20161012  60.0  33.0  50.0 NaN  59.0  48.0  48.0  69.0  51.0  63.0  \n",
       "20161013  44.0  31.0  37.0 NaN  46.0  39.0  36.0  60.0  39.0  54.0  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot(airdf, index='date', columns='loc', values='PM10').head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Pivot table은 pivot의 indx/columns 가 하나 이상의 column일때도 가능. 추가로, pivot은 중복되는 item 항목이 있을 경우 에러 발생 </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:25.119056Z",
     "iopub.status.busy": "2020-08-04T11:05:25.119056Z",
     "iopub.status.idle": "2020-08-04T11:05:25.165052Z",
     "shell.execute_reply": "2020-08-04T11:05:25.164055Z",
     "shell.execute_reply.started": "2020-08-04T11:05:25.119056Z"
    }
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pm25_level</th>\n",
       "      <th>bad</th>\n",
       "      <th>good</th>\n",
       "      <th>normal</th>\n",
       "      <th>verybad</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>강남구</th>\n",
       "      <td>60.833333</td>\n",
       "      <td>10.494949</td>\n",
       "      <td>28.173554</td>\n",
       "      <td>107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강남대로</th>\n",
       "      <td>63.473684</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>28.391061</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강동구</th>\n",
       "      <td>60.875000</td>\n",
       "      <td>10.572727</td>\n",
       "      <td>28.379310</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강변북로</th>\n",
       "      <td>62.560000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>28.217391</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강북구</th>\n",
       "      <td>60.437500</td>\n",
       "      <td>9.669355</td>\n",
       "      <td>27.659193</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mean                              \n",
       "pm25_level        bad       good     normal verybad\n",
       "loc                                                \n",
       "강남구         60.833333  10.494949  28.173554   107.0\n",
       "강남대로        63.473684  10.800000  28.391061     NaN\n",
       "강동구         60.875000  10.572727  28.379310     NaN\n",
       "강변북로        62.560000  10.500000  28.217391     NaN\n",
       "강북구         60.437500   9.669355  27.659193     NaN"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(airdf, index='loc', columns='pm25_level', values='PM2.5', aggfunc=[np.mean]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-level indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:25.167054Z",
     "iopub.status.busy": "2020-08-04T11:05:25.166052Z",
     "iopub.status.idle": "2020-08-04T11:05:25.181051Z",
     "shell.execute_reply": "2020-08-04T11:05:25.180051Z",
     "shell.execute_reply.started": "2020-08-04T11:05:25.167054Z"
    }
   },
   "outputs": [],
   "source": [
    "mdf = airdf.reset_index().set_index(['date','loc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:25.182053Z",
     "iopub.status.busy": "2020-08-04T11:05:25.182053Z",
     "iopub.status.idle": "2020-08-04T11:05:25.204052Z",
     "shell.execute_reply": "2020-08-04T11:05:25.203051Z",
     "shell.execute_reply.started": "2020-08-04T11:05:25.182053Z"
    }
   },
   "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></th>\n",
       "      <th>index</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>district</th>\n",
       "      <th>pm25_level</th>\n",
       "      <th>worst</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>loc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">20171009</th>\n",
       "      <th>강남구</th>\n",
       "      <td>0</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.007</td>\n",
       "      <td>28.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>강남구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>한강대로</th>\n",
       "      <td>1</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>별도측정소</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>중구</th>\n",
       "      <td>2</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.005</td>\n",
       "      <td>25.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>중구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>마포구</th>\n",
       "      <td>3</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.006</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>마포구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>광진구</th>\n",
       "      <td>4</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.006</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>광진구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               index    NO2     O3   CO    SO2  PM10  PM2.5 district  \\\n",
       "date     loc                                                           \n",
       "20171009 강남구       0  0.024  0.020  0.4  0.007  28.0   22.0      강남구   \n",
       "         한강대로      1  0.037  0.019  0.5  0.006  32.0   16.0    별도측정소   \n",
       "         중구        2  0.031  0.025  0.5  0.005  25.0   18.0       중구   \n",
       "         마포구       3  0.021  0.031  0.4  0.006  22.0   19.0      마포구   \n",
       "         광진구       4  0.041  0.026  0.8  0.006  25.0   19.0      광진구   \n",
       "\n",
       "              pm25_level  worst  \n",
       "date     loc                     \n",
       "20171009 강남구      normal  False  \n",
       "         한강대로     normal  False  \n",
       "         중구       normal  False  \n",
       "         마포구      normal  False  \n",
       "         광진구      normal  False  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:25.206053Z",
     "iopub.status.busy": "2020-08-04T11:05:25.205055Z",
     "iopub.status.idle": "2020-08-04T11:05:25.222051Z",
     "shell.execute_reply": "2020-08-04T11:05:25.222051Z",
     "shell.execute_reply.started": "2020-08-04T11:05:25.206053Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([(20171009,  '강남구'),\n",
       "            (20171009, '한강대로'),\n",
       "            (20171009,   '중구'),\n",
       "            (20171009,  '마포구'),\n",
       "            (20171009,  '광진구')],\n",
       "           names=['date', 'loc'])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf.index[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-level Index <-> Multi-level Column을 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:25.224051Z",
     "iopub.status.busy": "2020-08-04T11:05:25.224051Z",
     "iopub.status.idle": "2020-08-04T11:05:25.289051Z",
     "shell.execute_reply": "2020-08-04T11:05:25.288051Z",
     "shell.execute_reply.started": "2020-08-04T11:05:25.224051Z"
    }
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"10\" halign=\"left\">index</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">worst</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc</th>\n",
       "      <th>강남구</th>\n",
       "      <th>강남대로</th>\n",
       "      <th>강동구</th>\n",
       "      <th>강변북로</th>\n",
       "      <th>강북구</th>\n",
       "      <th>강서구</th>\n",
       "      <th>공항대로</th>\n",
       "      <th>관악구</th>\n",
       "      <th>광진구</th>\n",
       "      <th>구로구</th>\n",
       "      <th>...</th>\n",
       "      <th>정릉로</th>\n",
       "      <th>종로</th>\n",
       "      <th>종로구</th>\n",
       "      <th>중구</th>\n",
       "      <th>중랑구</th>\n",
       "      <th>천호대로</th>\n",
       "      <th>청계천로</th>\n",
       "      <th>한강대로</th>\n",
       "      <th>홍릉로</th>\n",
       "      <th>화랑로</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20161009</th>\n",
       "      <td>14264</td>\n",
       "      <td>14250</td>\n",
       "      <td>14245</td>\n",
       "      <td>14237</td>\n",
       "      <td>14268</td>\n",
       "      <td>14267</td>\n",
       "      <td>14248</td>\n",
       "      <td>14270</td>\n",
       "      <td>14247</td>\n",
       "      <td>14269</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20161010</th>\n",
       "      <td>14202</td>\n",
       "      <td>14225</td>\n",
       "      <td>14219</td>\n",
       "      <td>14232</td>\n",
       "      <td>14217</td>\n",
       "      <td>14222</td>\n",
       "      <td>14206</td>\n",
       "      <td>14203</td>\n",
       "      <td>14229</td>\n",
       "      <td>14218</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20161011</th>\n",
       "      <td>14190</td>\n",
       "      <td>14159</td>\n",
       "      <td>14182</td>\n",
       "      <td>14169</td>\n",
       "      <td>14187</td>\n",
       "      <td>14176</td>\n",
       "      <td>14172</td>\n",
       "      <td>14189</td>\n",
       "      <td>14174</td>\n",
       "      <td>14164</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20161012</th>\n",
       "      <td>14147</td>\n",
       "      <td>14134</td>\n",
       "      <td>14137</td>\n",
       "      <td>14140</td>\n",
       "      <td>14121</td>\n",
       "      <td>14118</td>\n",
       "      <td>14138</td>\n",
       "      <td>14145</td>\n",
       "      <td>14127</td>\n",
       "      <td>14136</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20161013</th>\n",
       "      <td>14088</td>\n",
       "      <td>14081</td>\n",
       "      <td>14113</td>\n",
       "      <td>14091</td>\n",
       "      <td>14108</td>\n",
       "      <td>14105</td>\n",
       "      <td>14115</td>\n",
       "      <td>14110</td>\n",
       "      <td>14098</td>\n",
       "      <td>14086</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 390 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          index                                                          \\\n",
       "loc         강남구   강남대로    강동구   강변북로    강북구    강서구   공항대로    관악구    광진구   \n",
       "date                                                                      \n",
       "20161009  14264  14250  14245  14237  14268  14267  14248  14270  14247   \n",
       "20161010  14202  14225  14219  14232  14217  14222  14206  14203  14229   \n",
       "20161011  14190  14159  14182  14169  14187  14176  14172  14189  14174   \n",
       "20161012  14147  14134  14137  14140  14121  14118  14138  14145  14127   \n",
       "20161013  14088  14081  14113  14091  14108  14105  14115  14110  14098   \n",
       "\n",
       "                 ...  worst                                                   \\\n",
       "loc         구로구  ...    정릉로     종로    종로구     중구    중랑구   천호대로   청계천로   한강대로   \n",
       "date             ...                                                           \n",
       "20161009  14269  ...  False  False  False  False  False  False  False  False   \n",
       "20161010  14218  ...  False  False  False  False  False  False  False  False   \n",
       "20161011  14164  ...  False  False  False  False  False  False  False  False   \n",
       "20161012  14136  ...  False  False  False  False  False  False  False  False   \n",
       "20161013  14086  ...  False  False  False  False  False  False  False  False   \n",
       "\n",
       "                        \n",
       "loc         홍릉로    화랑로  \n",
       "date                    \n",
       "20161009  False  False  \n",
       "20161010  False  False  \n",
       "20161011  False  False  \n",
       "20161012  False  False  \n",
       "20161013  False  False  \n",
       "\n",
       "[5 rows x 390 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf.unstack().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:25.291052Z",
     "iopub.status.busy": "2020-08-04T11:05:25.290051Z",
     "iopub.status.idle": "2020-08-04T11:05:25.392054Z",
     "shell.execute_reply": "2020-08-04T11:05:25.391053Z",
     "shell.execute_reply.started": "2020-08-04T11:05:25.291052Z"
    }
   },
   "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></th>\n",
       "      <th>index</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>district</th>\n",
       "      <th>pm25_level</th>\n",
       "      <th>worst</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>loc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">20161009</th>\n",
       "      <th>강남구</th>\n",
       "      <td>14264</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.004</td>\n",
       "      <td>19.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>강남구</td>\n",
       "      <td>good</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강남대로</th>\n",
       "      <td>14250</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.005</td>\n",
       "      <td>25.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>별도측정소</td>\n",
       "      <td>good</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강동구</th>\n",
       "      <td>14245</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.003</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>강동구</td>\n",
       "      <td>good</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강변북로</th>\n",
       "      <td>14237</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.003</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>별도측정소</td>\n",
       "      <td>good</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강북구</th>\n",
       "      <td>14268</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.002</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>강북구</td>\n",
       "      <td>good</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">20171009</th>\n",
       "      <th>천호대로</th>\n",
       "      <td>14</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.005</td>\n",
       "      <td>35.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>별도측정소</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>청계천로</th>\n",
       "      <td>17</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.007</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>별도측정소</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>한강대로</th>\n",
       "      <td>1</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>별도측정소</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>홍릉로</th>\n",
       "      <td>27</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>33.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>별도측정소</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>화랑로</th>\n",
       "      <td>38</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.004</td>\n",
       "      <td>32.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>별도측정소</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14274 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               index    NO2     O3   CO    SO2  PM10  PM2.5 district  \\\n",
       "date     loc                                                           \n",
       "20161009 강남구   14264  0.013  0.026  0.3  0.004  19.0   10.0      강남구   \n",
       "         강남대로  14250  0.044  0.017  0.5  0.005  25.0   11.0    별도측정소   \n",
       "         강동구   14245  0.012  0.028  0.3  0.003  20.0    9.0      강동구   \n",
       "         강변북로  14237  0.021  0.022  0.3  0.003  21.0    9.0    별도측정소   \n",
       "         강북구   14268  0.013  0.028  0.3  0.002  12.0    6.0      강북구   \n",
       "...              ...    ...    ...  ...    ...   ...    ...      ...   \n",
       "20171009 천호대로     14  0.037  0.012  0.6  0.005  35.0   20.0    별도측정소   \n",
       "         청계천로     17  0.036  0.018  0.5  0.007  30.0   20.0    별도측정소   \n",
       "         한강대로      1  0.037  0.019  0.5  0.006  32.0   16.0    별도측정소   \n",
       "         홍릉로      27  0.041  0.016  0.5  0.006  33.0   22.0    별도측정소   \n",
       "         화랑로      38  0.034  0.009  0.6  0.004  32.0   17.0    별도측정소   \n",
       "\n",
       "              pm25_level  worst  \n",
       "date     loc                     \n",
       "20161009 강남구        good  False  \n",
       "         강남대로       good  False  \n",
       "         강동구        good  False  \n",
       "         강변북로       good  False  \n",
       "         강북구        good  False  \n",
       "...                  ...    ...  \n",
       "20171009 천호대로     normal  False  \n",
       "         청계천로     normal  False  \n",
       "         한강대로     normal  False  \n",
       "         홍릉로      normal  False  \n",
       "         화랑로      normal  False  \n",
       "\n",
       "[14274 rows x 10 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf.unstack().stack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 각 행정구역의 PM10, PM2.5 트렌드를 그래프로 확인! </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:25.395053Z",
     "iopub.status.busy": "2020-08-04T11:05:25.394053Z",
     "iopub.status.idle": "2020-08-04T11:05:25.415053Z",
     "shell.execute_reply": "2020-08-04T11:05:25.414052Z",
     "shell.execute_reply.started": "2020-08-04T11:05:25.395053Z"
    }
   },
   "outputs": [],
   "source": [
    "graphdf = airdf.groupby('loc').agg(pm25=('PM2.5','mean'), pm10=('PM10','mean'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataframe에 plot 기능이 추가되어, 간단한 그래프는 직접 Dataframe에서 수행 가능"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-01T09:59:35.155865Z",
     "iopub.status.busy": "2020-08-01T09:59:35.154862Z",
     "iopub.status.idle": "2020-08-01T09:59:35.161867Z",
     "shell.execute_reply": "2020-08-01T09:59:35.160873Z",
     "shell.execute_reply.started": "2020-08-01T09:59:35.155865Z"
    }
   },
   "source": [
    "<b> Dataframe이나 Series에서 plot 함수를 호출 </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:09:05.547052Z",
     "iopub.status.busy": "2020-08-04T11:09:05.546052Z",
     "iopub.status.idle": "2020-08-04T11:09:05.725052Z",
     "shell.execute_reply": "2020-08-04T11:09:05.724053Z",
     "shell.execute_reply.started": "2020-08-04T11:09:05.546052Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23a4fedef88>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphdf.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplot의 함수를 사용해서도 그래프 출력 가능"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-01T09:59:35.155865Z",
     "iopub.status.busy": "2020-08-01T09:59:35.154862Z",
     "iopub.status.idle": "2020-08-01T09:59:35.161867Z",
     "shell.execute_reply": "2020-08-01T09:59:35.160873Z",
     "shell.execute_reply.started": "2020-08-01T09:59:35.155865Z"
    }
   },
   "source": [
    "<b> plt.subplots 사용해서 fig,ax를 만들고, 이 바탕에 그래프를 출력 </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:09:01.040052Z",
     "iopub.status.busy": "2020-08-04T11:09:01.040052Z",
     "iopub.status.idle": "2020-08-04T11:09:01.209052Z",
     "shell.execute_reply": "2020-08-04T11:09:01.208052Z",
     "shell.execute_reply.started": "2020-08-04T11:09:01.040052Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23a3b583208>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# without Hangul font-family settingkkk\n",
    "fig, ax = plt.subplots()\n",
    "graphdf.plot(ax=ax)  # default: kind='line'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:08:56.523052Z",
     "iopub.status.busy": "2020-08-04T11:08:56.523052Z",
     "iopub.status.idle": "2020-08-04T11:08:57.063053Z",
     "shell.execute_reply": "2020-08-04T11:08:57.062053Z",
     "shell.execute_reply.started": "2020-08-04T11:08:56.523052Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23a4c3d8b08>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# without Hangul font-family settingkkk\n",
    "fig, ax = plt.subplots()\n",
    "graphdf.plot(kind='bar', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:26.819053Z",
     "iopub.status.busy": "2020-08-04T11:05:26.819053Z",
     "iopub.status.idle": "2020-08-04T11:05:27.567054Z",
     "shell.execute_reply": "2020-08-04T11:05:27.566054Z",
     "shell.execute_reply.started": "2020-08-04T11:05:26.819053Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23a43ef61c8>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# without Hangul font-family settingkkk\n",
    "fig, ax = plt.subplots()\n",
    "graphdf.plot(kind='bar', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:27.569051Z",
     "iopub.status.busy": "2020-08-04T11:05:27.568055Z",
     "iopub.status.idle": "2020-08-04T11:05:27.948054Z",
     "shell.execute_reply": "2020-08-04T11:05:27.947053Z",
     "shell.execute_reply.started": "2020-08-04T11:05:27.569051Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23a44083588>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# without Hangul font-family settingkkk\n",
    "fig, ax = plt.subplots()\n",
    "graphdf['pm25'].plot(kind='hist', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:27.950053Z",
     "iopub.status.busy": "2020-08-04T11:05:27.950053Z",
     "iopub.status.idle": "2020-08-04T11:05:28.338053Z",
     "shell.execute_reply": "2020-08-04T11:05:28.336052Z",
     "shell.execute_reply.started": "2020-08-04T11:05:27.950053Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23a456e5388>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# without Hangul font-family settingkkk\n",
    "fig, ax = plt.subplots()\n",
    "graphdf.plot(kind='scatter', x='pm25', y='pm10', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:28.340053Z",
     "iopub.status.busy": "2020-08-04T11:05:28.339052Z",
     "iopub.status.idle": "2020-08-04T11:05:28.592055Z",
     "shell.execute_reply": "2020-08-04T11:05:28.590052Z",
     "shell.execute_reply.started": "2020-08-04T11:05:28.340053Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23a4573c408>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# without Hangul font-family settingkkk\n",
    "fig, ax = plt.subplots()\n",
    "graphdf['pm25'].plot(kind='pie', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:08:04.101051Z",
     "iopub.status.busy": "2020-08-04T11:08:04.101051Z",
     "iopub.status.idle": "2020-08-04T11:08:04.477052Z",
     "shell.execute_reply": "2020-08-04T11:08:04.476052Z",
     "shell.execute_reply.started": "2020-08-04T11:08:04.101051Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23a3e9b8f48>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# without Hangul font-family settingkkk\n",
    "fig, ax = plt.subplots()\n",
    "graphdf['pm25'].plot(kind='bar', ax=ax)\n",
    "graphdf['pm10'].plot(kind='line', ax=ax, color='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b> Tip:</b> 그래프이 개별 항목 조절이 가능하다. 아래는 matplotlib 그래프의 주요 항목 이름 <br>\n",
    "    (From Practical Business Python by  Chris Moffitt)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://pbpython.com/images/matplotlib-anatomy.png\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b> Tip: </b> 그래프를 출력하는 방법이 여러가지가 있음. 외부 article을 보아도 조금씩 다른 방식을 사용함. 개인적으로 pandas보다는 matplot 방식을 추천</b>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 그래프에서 한글 깨짐을 방지 위한 한글 font 등록 방법\n",
    "(아래 부분도 일단 외워두고, 한글이 깨지는 상황에는 무조건 적으로 사용!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> step 1</b>: 먼저, 현재 시스템에 등록된 한글 폰트를 확인. <= 사용할 수 있는 폰트 목록 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:29.217052Z",
     "iopub.status.busy": "2020-08-04T11:05:29.216052Z",
     "iopub.status.idle": "2020-08-04T11:05:29.487051Z",
     "shell.execute_reply": "2020-08-04T11:05:29.486054Z",
     "shell.execute_reply.started": "2020-08-04T11:05:29.217052Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C:\\\\WINDOWS\\\\Fonts\\\\ENBODBI.TTF',\n",
       " 'C:\\\\WINDOWS\\\\Fonts\\\\HYWULM.TTF',\n",
       " 'C:\\\\WINDOWS\\\\Fonts\\\\COPRGTB.TTF',\n",
       " 'C:\\\\WINDOWS\\\\Fonts\\\\HYBDAL.TTF',\n",
       " 'C:\\\\WINDOWS\\\\Fonts\\\\SitkaI.ttc',\n",
       " 'C:\\\\Windows\\\\Fonts\\\\cambriai.ttf',\n",
       " 'C:\\\\Windows\\\\Fonts\\\\GOTHICB.TTF',\n",
       " 'C:\\\\Windows\\\\Fonts\\\\LCALLIG.TTF',\n",
       " 'C:\\\\Windows\\\\Fonts\\\\Easop.ttf',\n",
       " 'C:\\\\WINDOWS\\\\Fonts\\\\MOD20.TTF']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.font_manager as fm\n",
    "font_list = fm.findSystemFonts(fontpaths = None, fontext = 'ttf')\n",
    "font_list[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> step 2</b>: 폰트 경로를 통해, font family name을 확보 </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "font_name = fm.FontProperties(fname='C:\\\\WINDOWS\\\\Fonts\\\\HYWULM.TTF').get_name()\n",
    "plt.rc('font', family=font_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<b>step 3 </b>: (방법 1) 현재 노트북 환경에 '나눔고딕' 한글 폰트를 등록. 새로운 노트북에 매번 동일 내용을 추가해야함\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:09:29.885052Z",
     "iopub.status.busy": "2020-08-04T11:09:29.885052Z",
     "iopub.status.idle": "2020-08-04T11:09:29.889052Z",
     "shell.execute_reply": "2020-08-04T11:09:29.888054Z",
     "shell.execute_reply.started": "2020-08-04T11:09:29.885052Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.rc('font', family='NanumGothic')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<b> step 3 </b>: (방법 2) matplotlib의 설정 파일 (matplotlibrc 파일)에 한글 폰트를 등록하는 방법. <br>\n",
    "한번만 등록하면 새로운 노트북에서도 자동으로 설정이 적용됨 <br><br>\n",
    "\n",
    "<pre>\n",
    "matplotlibrc 파일에서 아래와 같이 내용을 변경\n",
    "\n",
    "font.family : sans-serif  => font.family : NanumGothic\n",
    "axes.unicode_minus: True  => axes.unicode_minus: False  \n",
    "</pre>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "한글 설정에 대한 상세/추가적인 내용은 https://m.blog.naver.com/itisik/221789012960 글 참조"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplot의 함수를 사용해서도 여러 개의 그래프(ax)를 하나의 fig에 출력"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> plt 모듀의 subplots을 이용해서 fig, ax를 먼저 선언한 후 그래프 출력 </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:29.495052Z",
     "iopub.status.busy": "2020-08-04T11:05:29.495052Z",
     "iopub.status.idle": "2020-08-04T11:05:30.695052Z",
     "shell.execute_reply": "2020-08-04T11:05:30.694052Z",
     "shell.execute_reply.started": "2020-08-04T11:05:29.495052Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'PM10')]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA14AAAFVCAYAAAD/gD4YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeVRV5f7H8fcBmQRRcQAUImfkOpUaaJNeh0xMb1R6HdOIUkrrpiaiYSWSmlOamnotc8h7u2plaqOaUc6ZpUlF4ICooCKpTMLh/P5wsX+eQAWZ5fNayxVn72c/+9lndfZ3ffd+9nebLBaLBRERERERESk1NuU9ABERERERkdudEi8REREREZFSpsRLRERERESklCnxEhERERERKWVKvEREREREREqZEi8REREREZFSpsRLRKSSs1gsdOvWjZYtW3LixIl86/v27UtoaGih+zt16hQtWrQw/t1///1ERERw8eLFQm0/ceJE/P39Adi3bx8tWrRg27Zthd7/X+3fv5+AgAC2bNlyy33cSFpaGg8++CCRkZG33MexY8cYOXIkHTp0oG3btgwaNKgER3hjhf2OP/30U4YMGUL79u1p1aoV9913H0uXLgUgMjKSLl26kJaWVhZDFhGpkqqV9wBERKR4du3axcmTJwFYv349//rXv6zWz5w5Eycnp0L3l5ubC0BQUBAPPvggCQkJLFiwgIsXLzJv3rybbm+xWDCbzVZ95f23MD7//HOSk5MZNmwYAG3atGHevHm0bt260H0UhbOzM/PmzaNBgwa3tH1aWhpPPfUU1atXZ8KECZhMJs6dO1fCo7y+wnzHEydOZMOGDXTt2pUJEybg4ODAqVOnqFGjBgBPP/00ffr0wdnZGYCdO3dy4MABnn/++dI/ABGRKkKJl4hIJbd+/Xrs7e3x8fHh448/5oUXXsDG5v8nNPj6+t5Svy1atKBXr14AnD17lpUrV5KZmYmjo2OJjPt6tmzZwuHDh43Ey97enoCAgFLd51133XXL2+7fv5/ExERWrFhBp06dSnBUJWPdunVs2LCBl19+meDg4ALbeHh44OHhYXyOjo7m3XffVeIlIlKCNNVQRKQSu3jxIl999RVdu3ZlwIABnDlzhujoaKs2TzzxBE8++STw/9PSnnvuOdq1a8ekSZMKtZ9GjRphsVg4deoUGRkZvPbaa/j7+9O2bVtGjBjBsWPHCtVPeHi4MQ0RICMjA19fXxYvXgxcTRK/+OILEhMTadGiBR9//DFxcXHG33lWrVrF3//+d1q1asUjjzzCjh07jHUJCQm0aNGCf/3rX/Ts2ZO7776bZ599lqSkpOuOq3PnzoSHhwNXE5UWLVrwyiuvcO+999KpUyciIyPJzs4ucFt7e3sA9u7de93+lyxZQqdOnWjbti2jR48mNTXVWHfhwgXGjh1L+/btueuuuxgzZgxnz5411i9cuBBfX18uX75sNd7Jkydfd3/XWrlyJU2aNGH48OHXbfPmm2/SsmVLALp168a7774LXE2+3377bR599FEee+wxo/2mTZto0aIF+/btK9QYREREiZeISKX26aefkpWVxT/+8Q8CAwOxs7Nj/fr1Vm3MZnO+6Wg7d+5k9OjR9O7du1D7OX36NAA1a9YkNDSUdevWMXjwYMaNG0dCQgKPP/64VbJwPbm5ucY0xLzPFouFnJwcAObPn0+7du1wc3Pjrbfewt/fP9+0xUWLFhEZGck999xDREQEtWrV4tlnnzWSL5PJBMDWrVvp168fY8eOZe/evURFRV13XNd+R3l3C6Ojo3n++ed57LHHWLVqFR9++GGB2wYEBHDfffexaNEiJk6cyJ9//mm1fsWKFcydO5d//OMfvPzyy+zbt49x48YBkJOTw6BBg4iOjmbkyJE8//zz7N27lwEDBpCZmWn1HV07ldBsNhvf2Y2kp6fz+++/06lTJ2xtba/bLjc31+j/1VdfpWvXrgC89dZbPPzwwzz88MMcPnyYhIQEADZv3oy7uzvt27e/6RhEROQqJV4iIpXY+vXrcXNz44EHHsDNzY377ruPbdu2kZKScsPthg0bRnBwMPfee+9122RlZXHhwgV27NjBf/7zHx588EGOHTvGzp07GT9+PGPGjGHo0KEsX76ctLQ04y5JcfTs2RN3d3ecnJzo1asXnp6e+ca0bNkyevTowfTp0+nfvz/vvvsu3t7ezJ8/36rtY489xnPPPcfgwYPp3r07e/bsKdJYJkyYwMCBAxk3bhyenp7X3d5kMrF48WIGDx7MRx99RJ8+fTh48KCx/r333qN79+6MGjWKRx55hJCQEKKjo0lOTmbLli3Ex8fzxhtvEBISQnBwMPPmzSMxMTFfAn0rLl26hMVisZpGeDP3338/jRo1AqBXr140adKEwMBATCYTX3zxBZcvX+a7776jT58+VlNaRUTkxnTGFBGppH799Vd++eUXHnjgAc6dO8eZM2e47777yM7OZuPGjTfctjDPfc2ZM4eAgACeeeYZWrRowdSpUzly5AgAf//73412Pj4+NG/enJiYmOIdUCEcO3aM9PR0q/3b2dnRpUsXfvvtN6u7aT4+PsbfdevW5cKFC0Xa17Xbu7m53XB7e3t7IiIiWLVqFTY2NowYMYITJ05w6dIlzpw5w1dffUXHjh3p2LEjM2fOBK5Wjzxy5Ai2trY8+OCDRl8BAQHUqFHD+K6Lw9XVFZPJdNNE/GYaNmxIu3bt+OKLL/j666+5cuUKffv2Lfb4RESqEhXXEBGppNatWwfAxx9/bPX8E1y9E3ajZ3rs7Oxu2v/gwYN5+OGHadiw4U0r/lksFmOK3438tc31npsqqsLuvyhupb+OHTuyZMkS+vXrx8aNG41n6x555BGeeOIJq7ZNmza94X7ylhe0vrDfm5OTk/Hc3L/+9S/jebRb0adPH6ZOnUpOTg7Nmze/5aItIiJVle54iYhUQleuXOHTTz+ldevWvPPOO1b/goKC+P333/n555+LtY877riDjh07WiVdfn5+AGzfvt1YlpCQwB9//GGsuxFXV1fS0tKM55cKuktmMpmwWCwFbn/nnXdSvXp1q/3n5OQQHR2Nr6/vDZ9jKiu1a9cGoHr16tSoUQM3NzeOHTuGv7+/1T8XFxf8/PzIycnh22+/Nbbft28fly5dMr5PV1dXAKNEfUJCQpHetzVs2DASExOZPXv2db/Xv8pL9q5t//DDD2Nra8uRI0d45JFHCr1/ERG5Sne8REQqoa1bt5KamsrEiRONQgh5mjVrxkcffcS6deto06ZNie63ffv2dOrUiTfffJPU1FTc3NxYuXIljo6ORvn3G+nYsSMrVqxgypQptG/f3niB77Xq1q3LuXPnWLFiBe7u7jRp0sRY5+DgQEhICG+99RYTJ07k7rvvZsuWLRw7dsyojFjWYmNjWbduHS1btiQ7O5v//ve/ODs7ExgYCMCQIUOYP38+L774Il26dCE5OZlz584RHh5O7969Wbx4MWFhYTzzzDPY2tqybNkyPDw8CAoKAqBDhw4ATJ8+nZ49e/LBBx8UaXxBQUHs2bOHFStW8Msvv/DII4/g7OzMhQsXcHV1pV+/fvm2qVu3LnC1GmOdOnV44oknqFOnDgEBAezcuZM+ffoU5ysTEamSlHiJiFRCn3zyCa6ursZ7tq7l5eXFvffey5YtW5g8eTK2trZGEYRq1apZ/bcgN2uzePFipk+fzurVq8nIyKBt27bMnj0bd3d3AGxtbY07T3n/zdt/t27deOaZZ9iwYQPR0dH885//5N///rfVnapBgwaxc+dO3nrrLYYOHUrz5s2t+ggNDcXFxYX33nuPTz/9FB8fH2bPnm0895XX17XTKW1sbG54N+za7+iv39Vfj+mvcnJy2LlzJ2vXrsVkMnH33XezevVq4/t45plnSEtLY+PGjXz99dfUqVOHwYMHG/tYs2YNU6dOZdGiRVgsFvz9/QkPDzfel+bn50d4eDjvvfceBw8epHfv3ly8ePG633FBZsyYQefOnfnvf//L9OnTycnJoW7dukaybGNjY7V937592bJlC++88w49e/Y0pklWr149311QEREpHJOlsPMOREREpEr67bffiIuLY/z48URFRRV4l0xERG5Md7xERETkhtasWcOGDRvo1auXphmKiNwi3fESEREREREpZapqKCIiIiIiUsqUeImIiIiIiJQyJV4iIiIiIiKlTImXiIiIiIhIKVPiJSIiIiIiUsqUeImIiIiIiJQyJV4iIiIiIiKlTImXiIiIiIhIKVPiJSIiIiK3bMSIEfTv359hw4YxdOhQIiIiuHLlCm3atCE8PLzAbZYtW0arVq04ffq0scxsNjN79mweeuihfO23bNlCUFAQ/fv356mnniIxMbHUjkektFQr7wGISMFGjBhBWloajo6OWCwWGjVqxOTJk+nQoQN9+vQhKioq3zbLli3jrbfe4quvvsLT05Ndu3axdOlSsrOzSU9P57HHHmPw4MEF7u/jjz9m/vz5NGzY0Fg2ceJE/Pz8Su0YRUSk8svJyWHOnDl4eXkBMGvWLD766CPq1q3LqVOnSE5Opn79+lbtt27dSuvWrTGbzcbyiIgIunfvzubNm636P3nyJEuWLGH16tXUqFGD77//nvDwcN5///2yOUCREqI7XiIVVF4gW7lyJatWrcLV1TVfIPtr+78GMh8fHxYvXszq1atZuXIlK1eu5OTJkwXuLzc3l0cffZRVq1YZ/5R0iYhIUWRnZ5OUlEStWrUAGDZsGKtWrbJq8/nnn9OjRw+qVbO+/h8ZGUnXrl3z9fnll18SFBREjRo1ALj33nu5ePEiKSkppXQUIqVDiZdIJXCrgaxBgwY4OjoC4OLigo+PDxaLpcB9mEwmtm/fzsiRIxk4cCDvv//+dduKiIhc66WXXmLIkCE8/fTTtGvXzpgu2LVrV/bt20daWprR9n//+x9PPPFEvj5MJlOBfZ88eRIfHx+rZd7e3ppuKJWOphqKVGAvvfQS9vb22Nra0rNnTx566CFmzJhB165dWbp0KWlpaTg7OwNXA9mCBQv45ptvCuzr8OHD1KhRA29v7wLX9+nTh379+mFjY8Ply5d5+eWXqV69eoHBUURE5FrXTjW8lslk4vHHH+d///sfw4cP58CBAzRr1gxXV9dC9329hMzGRvcPpHLR/7EiFdicOXNYvXo177//vtWzWdcGMuCmgez06dMsXLiQV1999br7srOzM4KYi4sLI0eOvG4SJyIiUlh9+/bl888/Jycnh1WrVvHkk08WaXtPT0+OHz9utSwhIQFPT8+SHKZIqVPiJVJJFTaQnTp1imnTpjFt2jRjfnxhubi4lMRQRUSkCrO3t+fvf/877777LsB1Z15cz8MPP8z69eu5dOkSAN9//z01a9bEzc2txMcqUpo01VCkkipMIDtx4gRvvvkmkZGRxvNh15ORkYGTkxMA6enpLFq0iEGDBpX8wEVE5LZia2ubr1AGXJ1JkWfgwIH8/e9/Z8mSJVbb2dra3nA7gIYNG/L8888TEhKCra0tLi4uTJ8+vQSPQKRsmCx6el6kQho+fDjTp0/Hw8PDavlDDz3EF198AcClS5eMQHb33Xcb273xxht4enrSu3dv7O3tre50DRw4kN69e+fb3+bNm1m5ciX29vaYzWYGDhzII488UopHKCIiIlJ1KPESqaJyc3OJiIggIiICe3v78h6OiIiIyG1NiZeIiIiIiEgpU3ENERERERGRUqbES0REREREpJRV6sTr2regi4iIVDSKUyIikqdCPON18uRJ3n77bQD+9re/MXToUADGjBnD/Pnz+eSTT9i4cSNw9cWxAwcOpFu3bkycOJHw8HCjYtuyZcvYvXu3Vd8mk4nBgwfTtWvXMjwiERG5nShOiYhIcZX7e7z27dvHd999h7u7OwDnzp1j3rx5PP300+Tk5ADQr18/+vXrB1x9U/mqVavo1q0bFouFa/PGkJAQQkJCrPo/evQoa9euLXJAe+jDSxxNNRfn0ESkimpUy5Yv+hftZdVScSlOicjtSLGq7JV74tWyZUs8PT359ddfOX36NG3btqVOnTo4OzsbbS5cuEBsbCwWi4WkpCRsbEp/huTRVDOxF3JLfT8iIlKxKU6JiEhJKPfEy8XFhWXLluHs7EzTpk1ZsWIFzz77LCaTyWizdOlScnNzqVmzJiaTiUcffdRYN2bMGHr27MmgQYOIiooiNjbWKuDlTeEQERG5FYpTIiJSEso98QKIi4tjwYIFmEwmnJyc2LdvHy1atODIkSOEhYWRlZVF9+7dqV27NmlpaezatYv9+/cDMH/+fFxdXYGr0z8WL16Mo6NjscfUqJZtsfsQkapJ54/bj+KUiNxudA4pexUi8XrssccYN24ctWvX5tKlS4SFhQHg5+fH9OnTSUlJISYmBjs7Ozw9PWndujX16tXj9OnT2Nr+//80999/P88//zwWi4WYmBhatmwJgK+vL+PHjy/SmDTnVURE8ihOiYhIcVWIxKtr16507tyZf/zjH3h6evL8888zZcoUY72bmxubNm1iz549ZGRkAFenZtxxxx3Y29sb7c6dO2c8xJz3X5PJRIcOHcrwaERE5HajOCUiIsVV7uXkN27cyIYNG7h8+TJmsxkHBwecnJzw8PDghx9+4P3338fT05P58+dTr149OnfuDIDZbGbIkCF88MEH3HnnnYwdO5bU1FSj3+TkZOrXr09GRganT5+mU6dOREVFlddhiohIJaU4JSIiJaHc73j17duXvn378vPPP/Pee++RkZGBra0tfn5+/Pnnn3h6egLw+eef4+PjYwS95ORk3NzcjPK+s2fPLrD/vDK94eHhRRqXyvSKSEWk8r9lT3FKREqDzudVT7knXnlmzJjB4sWLjQeQx48fz+OPP26s9/Dw4PTp0yxZsgSz2YyTkxMNGzbk9OnTNG7cGKBEq0WpTK+IiFxLcUpERIqjwiReISEhvPzyy6Snp+Pk5ESjRo144IEHuHLlCikpKTecfpGWloazs3OJVosSERG5luKUiIgUR7k/4/XNN98QFxdnfP7+++8JCAgwqkCZzWYuXLhA9erVr9tHQEAAHTt25KOPPmLz5s0lUi1KUzhEpCLS1JSypzglIqVB5/Oqp9wTrzzff/89n3/+OUlJSTg4OHD33XczaNAgHBwcAEhMTGT16tUcPXoUgPr16/PPf/4TPz+/AvsLDQ1l0aJFZTZ+ERG5vSlOiYhIcVSIxGvv3r3873//Y8KECdStW5crV67w2Wef8c033zB37lwABg8ezOTJk/H19cVkMnHixAkiIiKYM2cObm5u160Wlcfd3V3VokRE5JYoTomISHHZ3LxJ8SQkJBAcHExwcDCRkZHG8rFjxwKwdOlSZs6cya5du3jppZeIjo7GYrGwfv16cnJyAMjNzeXKlSvExsby66+/cunSJTZs2EBmZibLli0jISGB2bNnG30CVsGscePGCmYiIlIgxSkRESkLpXrH648//uCPP/7It7xjx4688sorVlMsdu3axdKlSzl79iyNGzcmMTGRd955h3r16rFjxw4+/PBDsrKySE5Opm7duqSnpzNq1CgOHDhAUFAQPj4+1x1HSEgIy5YtK9LYNXdeRMqa5vuXPcUpEZHSp/h2ValWNXR1daVhw4YkJCTw22+/0aFDB2rVqlVgNad77rmHVatWsXLlStzc3AgLCzPmzWdnZ9OtWzeCgoKM9gsWLMDLy4sDBw7cdBwmk6nIY1eZXhGR25/ilIiIlJVSTbzq169PbGwsMTEx9OjRg9WrVzN+/HicnZ05ePAgwcHBRERE4Onpyeuvv06/fv1wc3Mzth8zZgw9e/a0mo5xrd9++43k5GTj888//0xERAR16tSxapf3/hQREZFrKU6JiEhZKfX3eG3atIkpU6bg6OhInz59+Oabb3jiiSdo164dixYt4rPPPuONN95gxIgR+Pv7W207f/58XF1d+frrrwvsOyEhgQsXLhif//zzT/r378+gQYOKPe5GtWyL3YeISFHovFM+FKdEREqXzldXlXri1aRJE9avX0+/fv3YsmVLvmBTq1Yt3n77bapVsx6Kk5MTNjZXa3+YTCaysrJIT0/n8uXLJCYmYrFY6N69O+np6Vb7Wr16NVu3buX48ePUq1fPeK/KzJkz811hvBHNQxURqRoUp0REpCyUeuL15JNPEhISwp49e+jVqxdt2rQBrgYygE6dOpGens7rr79OYmIiNjY25ObmMmDAAFxcXABo164dixcvZs6cObi4uHDnnXdSUE2QBg0asGTJEgDmzp1LYGAgzZs3L+1DFBGRSkxxSkREykKpl5O3s7MjNjaWtLQ0MjMzjeV5V/iSkpKYNWsWjRs3JjIykmXLluHn58e6desYM2YMAHXq1GHEiBGYTCbS0tJo3bq1cZVxz549AGzYsIF//vOf+Pv74+/vz+rVqxkyZAj+/v507dqV4ODgAitXiYhI1aY4JSIiZaHU73jt2LEDZ2dnkpOTWbFiBZ988gnVqlUjJiaGUaNGkZCQQHp6OtHR0WzYsIH169fzyy+/kJ2dbVSVMpvNHDp0iHbt2gFw4sQJ3NzccHR0NObOBwUFWVWTupbK9IpIVaGSvUWnOCUipUHnY/mrUk+8vLy8mD17NnC1tO6YMWNo1aoVoaGh1KlThzp16tChQwe2bNnC5MmT6dOnD6mpqaxfv97YzmQyUa9ePSwWCzk5OcycOZMPPvgAR0dHvLy8aNCgwQ3HoDK9IiJyPYpTIiJSFsqkuAbAmjVruOuuuzh58iRz584lJibGql29evUYNmwYw4YNIyIiAi8vL2OdjY0N7du3JycnhylTpjBlyhQ2btxIbGwscHWaCKhMr4iIFJ3ilIiIlIVST7wSExNZsGABnp6exMXFcdddd7F8+XJCQ0ONNllZWcayEydOkJaWlq+fn376iXfeeYeGDRvy1VdfMWrUKJydna36UZleEanqdO4qOsUpESkN+o3KX5Vq4pWZmcn777/P6NGjadiwIZmZmXz00Ufcc889Rlne9PR0Jk+eTEhICOnp6Zw6dYrLly9b9XP48GG2bt3KG2+8Qa1atfjpp5/YsGEDQ4cOtWqnMr0iIlIUilMiIlJWSjXxcnR0JDw8nGnTpnHgwAFsbW2pU6cOBw8epEaNqwHjzTffZPjw4aSnpxsPFv/1XSl+fn64uLjw9ttvM3DgQFq0aEFMTAynTp3i2LFjJCQk4O3tTWpqKjk5OcZ2eaV8GzduXKRgJiIiVYPilIiIlJVSn2q4a9cujh8/TkBAAG3atCEgIICsrCweeeQRpk6dSp8+fdi9ezcA/v7+AGzatImlS5eSlJQEQHR0NJ988gm///47Xl5ePPTQQ5w9e5aMjAx69OhBbm4uGzZsYPPmzcZ+XV1dsbe3B+DTTz8lPj6eiRMn0rRp09I+ZBERqUQUp0REpCyUeuJVu3Zt6tevz5UrV1i7di3u7u7k5ORgY2NjPIzcvn17Nm7cyI4dOzCbzdSpU4fg4GDjwebs7Gzuu+8+zp49S3R0NNHR0Zw4cYJdu3aRnJxMnz59blim95lnnmHp0qVFGrfK9IpIWVPp4fKhOCUiZUHneCn1xMvX15epU6cSFhbG5cuXmTVrltW7TwA+/vhjjhw5QkREBHZ2dqxdu5ZFixYxbtw4q75WrVpl/L1gwQKaNGlCdHS0ccWwJKtFqUyviEjVoDglIiJlodQTr6SkJObNm4erqys+Pj6MHTuWBg0aWFV5SkxMpG3bttSsWROA9u3bs3btWho2bHjDvhMSEowXU0LJVosSEZGqQXFKRETKQqkmXhkZGSxcuJCnn36aJk2acObMGdasWcPYsWOt2g0bNoxXX32V9evXYzKZsLOzY8qUKcZ6k8lEVlYW6enpXL58mcTERCwWC927dyc9Pd1oV5LVolQCVETKms47ZU9xSkTKin6zYrLklVQqRUuXLuWpp56yqgL1wgsv8NZbb1m1+/bbb0lMTGTgwIFWy8+fP8/ixYsBcHFx4c477+T48eP06dOHjRs3EhQUhI+Pj9U2c+fOJTAwkObNm5fSUYmIyO1CcUpEREpbqU81BIiJicFsNlsFtLfeeotffvmFOXPmGMsyMjLIycnh66+/NpYFBQURGBjI5MmTrfpcsGABGRkZxue/Vou6cOECP/74I3Z2dsYyVYsSEZGCKE6JiEhpK5PEq379+jz77LPY2lrfYn3hhRdYvnw5CQkJvPrqqzg5ORnr7OzsCA0NpU2bNnzyySds3LgRuDqdY+DAgdSpU4dly5bRoEED4GrgS0xM5ODBg8DVKlUANjY2BAcHExAQUBaHKiIilZDilIiIlLYymWpYFBaLha1bt/LZZ5/x2muv4eLiYrU+ISGBVatWER4eTlhYGCEhIdxxxx1WVwyvdejQIfbu3UtwcHCRxqEyvSJyIyoLXHUpTonIrVLsqNrK5I7XzeTk5HDkyBG2b9/OsWPHOH78OL169eL333+nVatWpKWlERsbi8ViISkpCRsbG2PbevXqXTeYAVy8eBFXV9cij0llekVEJI/ilIiIFFeFSLwWLlyIh4cHQUFBeHt7A1evGO7du5fq1avzySefkJubS82aNTGZTDz66KPGtmPGjKFnz57XLc0bFxdH69aty+Q4RETk9qQ4JSIixVXuUw2XLl3Knj17rrveZDLh4eFBp06dqF27NmlpaSQmJmJnZ8ehQ4cIDw/H1dWVsWPHkpqamm/7+Ph4vLy88Pb2JioqqtDj0hQOEbkRTRepOhSnRKSkKHZUbeWeeP3VuHHjiIiIsJp2kZKSQkxMDPb29jg5OVG3bl3q1avH3LlzGTVqFM7Ozvn6CQ0NZdGiRWU5dBERqQIUp0RE5FZUmMTryJEjfPvtt+zbt4+//e1vNG3alN69exulfWNjYwkJCSEzMxO4eoWxYcOGRERE0KZNGy5fvsyLL75I3uEcOXIEPz8/XFxcePPNN7G3ty+3YxMRkcpPcUpERIqjQiRe+/fv57PPPmPChAlG4Pnxxx/58MMPeeONN4CrUz0aN25M9+7dsVgsfPnll0RGRvLBBx8Y8+2vlXcl8fXXX+f555/Hzc2tTI9JRERuHz/rlt0AACAASURBVIpTIiJSXBWiuEZ2djZJSUmcPn0aLy8v0tLSOH78OJcvXzba1K5dm23btvHDDz9w6tQpYmNjcXBw4OTJk7i7u+e7UmgymQC4dOkSNWoUfS6t5s6LCGg+vlylOCVye9I5XspShUi8OnXqRFZWFu+88w7JyclUr16dtm3bMnPmTKPNqVOnMJlMHD9+HIAHH3yQLl26cPLkSWrXro2vr69Vn1OnTgXAbDbfsIzv9ahMr4iI5FGcEhGR4qoQUw1vpjAVpcxms9V7U/L8+uuv+Pr6EhQURGBgYKH32XxpqgKaiNCstg2/P1OrvIchFZzilEjlpHO8lKVKkXj9VUEVpa61c+dO4uPjGTJkyC3vQ1M4RAQ0DUVujeKUSOWgc7yUpQox1bCw8ipKXbhwgX//+9/5KkqVJP0IRUSkqBSnRETkeirEHa+EhAReffVVq2V2dnaEhobSpk0bcnJyGDRoEMnJyTRq1MiYqpGZmYmnpyezZs0CYMGCBRw8eJDU1FTS09Np0KABNjY2BAcHExAQUNaHJSIitwnFKRERKa4KkXj91Y4dO9iwYQORkZFGpaddu3axZs0axo8fb1SUevnll8nMzGTFihVW2+/atYv4+HgGDx7MoUOH2Lt3L8HBweVwJCIicjtSnBIRkaKqMFMNjx07xtdff83BgweJjY1l2LBhJCUl4ezsjI2NTYEVpdLT0xk7dmy+vu6++25at24NwMWLF687x/5GNHdeREDz/+X/KU6J3D50bpfyUCESrzlz5lCjRg06d+5MnTp1ePPNN4mPj+e7774jIyMDPz8/+vXrh7u7u7FNeno6NWrUoHHjxvn6c3BwwMHBAYC4uDgjuBWFyvSKiEgexSkRESmuck+8zp8/z2OPPWZ8/uqrr2jRogUuLi507drVKMHbtGlT5s2bd91+xo4dS2pqar7l8fHxeHl54e3tTVRUVKkcg4iI3L4Up0REpCSU+zNe27dvJz4+/rrrTSYT/fv357XXXiMlJYXU1FQyMjLw9PQEIDg4mM6dO+fbLjQ0lEWLFt3yuDSFQ0RA01FEcUrkdqRzu5SHck+88mzevJlPP/2UrKwscnNz8fX1ZdSoUdSqZf1Su8K++6S4AU1ERORailMiIlIcFSLx2r59O9u2bSMsLAxnZ2csFgv79u1jxYoVPPfcc8yZM4fMzEz++OMPcnNzsVgs2NraYmNjg7e3N08++SQPPfQQo0aNIjf36nz3I0eO4Ofnh6OjI9OnTzeqTomIiBSV4pSIiBRXuT/jBVCjRg3+/PNPzp07h6OjI2lpaZw5cwYnJyf+9re/sXz58nzbFFTKd9myZcb65557joULFzJnzhwSExPx9fUts+MREZHbi+KUiIgUV4VIvDp06MCVK1dYsmQJSUlJVK9enbvvvjvfQ8Y3K+V7ralTpxZrTJo7LyIVlZ5NKHuKUyLyVzoXS1FViMQLoHPnzgU+fJznVkr5Ajg6OtKwYcMij0dlekVE5FqKUyIiUhwVJvG6kZIq5VsUjWrZlkg/IiIlTeenikdxSqTq0W9QiqpCFNe4mdIq5SsiIlISFKdERORmKkXilaekS/mKiIiUJMUpERG5ngqdeJ0+fRoPDw9MJlOhSvnmyczMJDs726o0b1BQEIGBgeVxGCIicptSnBIRkcKqEIlXZGQkR48eBcDOzo6IiAgaNGhAWFgY4eHhuLq6MnLkSA4fPkyjRo1wcnIiJyeHixcvcscddxjBbNmyZezevduqb5PJxODBg+natWuZH5eIiNweFKdERKS4KkRxjcmTJxt/jxw5kurVq+drM2PGDLZt28b27ds5f/48jo6OZGZmMmHCBKNNSEgIISEhVtsdPXqUtWvXFjmgqUyviNyMSglXHYpTIlJVKLaVngqReOXZsmULfn5+bN68mW3btvH7778b61544QX69u1Lly5djGUuLi75yvKWFJXpFRGRv1KcEhGRW1VhEq/PP/+cjz76CCcnJ4YMGcLgwYMJCwsz1tvY2PDFF1/k265p06Y0btwYgKioKGJjY61eUpk3haOoVCJURG5G54mqRXFKRKoCnVtKT7knXikpKcyaNQsvLy+WLl3K77//TkREBFFRUQBkZ2dz5swZ43NB0tLScHZ25ty5cyxevBhHR8dij0u3WEVEBBSnRESkZJR7cY3Lly+TlpZW4FSM6dOnExQUVOAVxGsFBATQsWNHPvroIzZv3ozFYiEmJoaWLVsC4Ovry/jx40tl/CIicntTnBIRkZJQ7olXnpSUFKKiokhNTcVkMgEwYsQI44WSiYmJrF692qgqVb9+fR5//HGaNm1a4EPOoaGhLFq0qOwOQEREbmuKUyIiUhwVJvEaNWoUtra2ZGRk4OzsTPv27fnqq69Yvnw5s2bNYtOmTfj7++Pg4IC9vT2PPvooU6dOpUePHoSGhjJ27Fh+/vlnLl68CMCVK1ewt7fHZDLh5eVF8+bNbzgNRERE5EYUp0REpDjK/RkvgAMHDhAXF0fv3r0JDg4mOzubFStWEB8fj729PZMmTWLjxo0MHTqU/fv307ZtW5YuXcrx48eNq46zZ88usO9Dhw6xd+9egoODizQmlekVub2oPK4Uh+KUiNyIYowURoVIvC5duoS/vz+enp68+uqrZGVl4ebmRtOmTY2A1apVK3bt2sUXX3zBjh07aNeuHb169SI1NfWGfV+8eBFXV9cij0llekVEJI/ilIiIFFeFSLwefPBBkpOT+fLLLzGbr169q1mzJnPmzDHaODg48Pzzz+Pk5ETbtm3Zvn07W7ZsMebWX09cXBytW7cu8phUSlPk9qLftBSH4pSI3Ih+j1IYFeYZr+vJyMggIiKCvXv3ctdddxEXF4eLiwsPPfQQLVu2JCYmhuHDhzN27NgCryrGx8fj5eWFt7e35s6LiEiJU5wSEZHCKPfEa+nSpXz//ffk5uZib2+fb73JZOK1114DwNHRkRo1ahjtfv75Z/744w+CgoLybadqUSIiUhIUp0REpCSUe+IF8M033/D999+TkpJCamoqtra23HXXXTz55JNWJXgHDRqEo6Mjtra2mM1mbGxsmDdvHi4uLgAsWLCAgwcPAnDkyBH8/PywsbEhODiYgICAcjk2ERGp/BSnRESkuCrEM14xMTEcPHiQf//739SsWROz2czu3buZMGECCxYsAOCPP/7g+PHjfP/998Z2AwYMYN26dQwfPhyA0aNHG+vyriTmVYtSQBMRkVulOCUiIsVVIRIvDw8P4uPjCQ0NxdHREbPZbFxVzHvPSWJiIqmpqezZs4f27dtz/vx5Lly4QHZ2NqdPn8bT09Oqz7wqU7daLUplekXkWioVXLUpTolUPjpvS0VTIRKvRx99FAcHB1asWIGLiwu1atUiKyuLjz/+GBsbG8xmM//973+ZPXs2kyZNonHjxtSrVw8vLy8cHR0LDGhTp04Fbr1alMr0iohIHsUpEREprnJPvNLS0sjKysLe3p6uXbsyYMAAAMaPH09qairZ2dnMmTOHgQMHcv/993PnnXcyZ84cpkyZQkREBP369cPV1bXEq0WpLKiIXEvnhKpLcUqkctJvRCqaci+u8dVXXxEbG3vd9bm5uXTv3h1fX19jmdlsxtbWlrCwMMLDwwucoqFqUSIiUhIUp0REpCSU+x2vHj160KNHDzZv3synn35KVlYWubm5+Pr6MnLkSGrXrg3AjBkzePLJJ/Hw8MDW9uoVDFtbW2xsbMpz+CIicptTnBIRkZJQbolXWloazs7OAGzfvp3du3cze/ZsnJ2dsVgs7Nu3j4kTJ/LOO+8AkJOTQ05OjlUf06ZNK/Nxi4hI1aA4JSIiJanUE6+zZ8+yfPlyzp49S69evejRowcAL7/8MgsXLgRg48aN7Nmzh7i4OKNa1IULF2jUqJHRT/369ZkwYQKOjo5W/ffr14++ffsCV9+JMnv2bOPv4OBgABo3bsykSZNK+1BFRKQSUpwSEZGyUOqJ15QpU5g8eTLu7u7MnTuXVatWYWdnR0xMjNHm0qVLzJo1i02bNpGUlISrqytdunRh0KBBRpuzZ89y5swZPDw8cHR0JDU1lYyMDFJSUow2fn5+LF++HICUlBTc3NwACAkJKfK4VaZXRG5nKrP8/xSnRERKh2KNtVJNvFJTU6lZsyYNGjQArr5IMiMjg1deeYXQ0FCrtp07d6Zz587X7Ss8PBx7e3ueeOIJfHx82LlzJ/Hx8QwZMqTA9nnBDP7/XSlFoTK9IiK3P8UpEREpK6WaeLm6unLs2DEyMjJwcnLi22+/JTo6muDgYKsric2aNTOmW1xr2rRpeHh4FHp/P//8MxEREdSpU8dqeePGjYs8dpUgFZHbmc5xVylOiYiUHp2nrJV6Oflvv/2Wt956CxsbG+666y4mTpyIyWQiNDSUyMhIDh8+fMPtGzVqhLe3N88++yx//PFHvikc/v7+TJkyBYDo6GgSEhKspn6IiIjciOKUiIiUhVJ/xuuBBx7ggQcewGKxWE2lyHv42Gy+8fz03Nyr0yiWLFliLAsNDWX9+vX52jZp0oTVq1ezdetWjh8/Tr169ahevToAM2fOzHeFUURERHFKRETKQpmUkz9//jxRUVFcvHjRWDZkyBDc3Nzo2rUrBw4cYMmSJWRnZwNXg9iAAQN4+OGHi7SfBg0aGIFv7ty5BAYG0rx585I7EBERuS0pTomISGkr9amGZ8+e5amnnsLDw4P+/fvTo0cPMjMz6dKlC9999x22trY88MADNGrUCDs7O+BqQPv1119Zs2YNjRs3ZuzYsaSmphp9JicnU79+feOzu7s7HTp0YO3atZw4cQK4+j4VW1tbTCYTNWrUwMfHh4kTJ9K0adPSPFwREalkFKdERKQslHriFRoayh133EGDBg04efIkR44cITMzk19//dWYN+/v78/06dO55557qFatGocOHWLevHm88847uLi4ABAVFcXWrVvzzZ3v378/w4cPv+EYXnzxRebNm1ekcatMr8jtSaVt5a8Up0SkMlD8qvzKpJz8+PHjWbduHQkJCZw5c4Z+/fpRq1Yto52vry+//fYb69evx2w207RpU2bMmGEEMyh6md5rqUyviIgURHFKRETKSpmUk79y5QoDBgwgJyeHuLg4Dh48aFWm18/Pj3379hmfjxw5QkRERJHL9Bbk0qVLuLq6Fnk7lb8UuT3pty3XUpwSkcpCv/nK77YpJ79hwwY2b96cb/u0tDQuXLiAl5eX5s6LiEg+ilMiIlIWSj3xyvPXMr0vvfQSkydP5qeffrrhdo0bN8bHx8dqWWhoKIsWLbrhdqoWJSIiRaE4JSIipancysk//fTTRpneH374gTfeeIOsrCxq166NjY0NAwYMIC4ujq5duwLw2WefceDAAav59Pv37yctLY0HH3wQgAULFnDw4EEAjh8/zp49e6hRowbBwcEEBASUxaGKiEglpDglIiKlrUzueL3yyisMHjwYX19fADIzMwkJCeG9997D1taWTp06MXLkSLy9vfnggw+IiooiIiKC7Oxs3n33XUaNGsWpU6eMq5EZGRl4e3tz/vx5cnJyaNOmDVFRUVb7zLuSmJWVxd69ewkODi7twxQRkUpKcUpEREpbmdzx8vDwYP/+/Xh5eWFvb8/hw4exs7OjWrVqnDp1Cnt7e3x8fLjnnnu4cuUKK1asIC0tjbi4OIKDgxk1apTR16FDh0hKSiIsLIyvv/6aixcvEhQUlG+fJpMJk8nExYsXb+mhZZXpFZHSopLAFY/ilIhcS+dpKQ1lkniNHDmSdevWMWXKFLKysmjRogVvvvkmADY2Ntx3331Gmd4zZ85Qq1YtZsyYwbRp06zmyB87dowff/yR1157jaioKE6cOEHPnj0L3OeIESNwdnZm165dtG7dushjVpleEZGqQ3FKRERKW5kkXra2tgwYMIABAwbkW5dX/cnT05NWrVqxevVqZs2ahYuLCz/99BPPPvsskyZNYseOHfz444/Y2try448/Eh4eblxJBBg7diypqan5+o+Pj8fLywtvb+980zxuRCU7RaS06PxS8ShOici19PuS0lBmVQ1vJu8B5HvuuQcnJycApkyZwmuvvca7776Lh4cHvXv3xmw2s3btWtq2bUtSUtJ1p3AUpqKUiIhIYSlOiYhIcZTJHa/CWrNmDWvWrCE3NxcXFxfCwsIAeOqpp/jll1/YsWMHDz74IEOGDAHg7Nmz2NjYlOeQRUSkClGcEhGRW1Vh7ngNHDgQX19fpkyZAlwts/viiy/yn//8BwcHB7799lt27dplTNno2bOnUZ43z7Jly9i9ezcAR44cwc/PD5PJxODBg41yvyIiIrdCcUpERIqjwtzxMplMJCcnc+XKFa5cucK+ffvIysoiKysLBwcHvvzySzZv3oy9vT0AW7dupW7duvTp04eRI0cCEBISQkhICADPPfccCxcu5OjRo6xdu1YBTUREikVxSkREiqPCJF533XUXmzZt4qWXXsLe3p4OHTrQqlUrY31kZCSRkZGkpaWxadMmdu/ezejRo2ncuHGB/U2dOrVY41GZXhEpbSpXXLkoTolIZaDYUnFViMTr1KlTJCYm8vrrr7N7924mTJgAwM8//wzA+fPn+fzzzzl06BAmkwmLxYKLiwtffPEF3bp1o3nz5gBERUURGxtrNZ8+bwpHUalMr4iI5FGcEhGR4ir3xOvEiRPMmDGDV155BQ8PDy5cuMD06dONB5bhalBq3bo1/fv358iRIyQnJ9OjRw+SkpKs+jp37hyLFy/G0dGx2ONSGVERKW06z1QOilMiUpno3FBxlXtxjVOnTuHs7EzNmjWNZWazGVtbW8LCwhg6dChz5swx1l24cIGsrCw8PDyMZUFBQQQGBvLRRx+xefNmLBYLMTExtGzZEgBfX1/Gjx9fdgclIiK3DcUpEREpCeWeeOWZMWMGTz75pFWgmjRpEhMnTsTFxYXNmzcTGBhY6P70fhQRESlJilMiIlIc5T7VME9OTg45OTlWy6ZNm2b8fbOANnbsWFJTU43PycnJBAcHG5/d3d2JiooqwRGLiEhVojglIiLFUWHueC1btoxvvvmG06dP4+PjYzx4/NRTT2FnZ8e0adOws7Mz2ru4uBAfH09UVBT33XcfAAsWLODgwYNW/drY2BAcHExAQEDZHYyIiNx2FKdERKQ4Kswdr5CQENq1a8fChQvx9fU1KkZlZmaybds2qlWrxogRIzh8+DBZWVmMHj2aGTNm0KZNG6OP0aNH5+v30KFD7N27t8gBTWV6RSo/ldSVkqQ4JSI3o7gjN1IhEq+UlBT++9//kpCQwNKlS/nmm2948cUX+ec//0nHjh3p3bs3mzZtIjAwEAcHB9LS0qhdu3ah+r548SKurq5FHpPK9IqISB7FKRERKa4KkXitW7eO9u3bM2rUKBYtWkRISAgBAQF8+umneHt707Bhw+tue/jwYXx8fK7bJi4ujtatWxd5TCrFKVL56XcsJUVxSkQKQ79LuZFyT7zOnz/PQw89BMDx48f56aefOH78OHZ2dtx3332YzWbM5utPpTh69CjOzs7MmTPH6qHlPPHx8Xh5eeHt7V2kh5Z1m1hEREBxSkRESka5J16HDx/m6NGjxudOnTrx3XffGZ9NJhOPP/44cPW9KZmZmZw9e5aYmBgAHnnkEVxdXZk9e7ZVvyrTKyIiJUFxSkRESkKFqWq4efNmFi5ciLu7O7m5ufj6+pKTk8PQoUNZvXo1Bw4coF27dlSvXp369evj5+fHtGnTWLp0KfXq1QOsq0UdOXIEPz8/VYsSEZESoTglIiLFUSESr+3bt/PVV19x+fJl5s+fj8ViYd++fbz88sts3LgRV1dXHnvsMWrVqmW13f79+/niiy+sXmaZJ+9KYl61qGvflSIiIlIUilMiIlJc5T7VEKBGjRrEx8dz7tw5srOziYqKYv/+/aSkpBhtPDw8mDNnjtV2vXv3xmQyFdhn3vJbrRalMr0iUhCVCq6aFKdEpLQorlQdFSLxateuHQDu7u706tULPz8/goKCOHLkiNEmMDCQ5cuXW233j3/8g/r16xfY59SpU4FbrxalMr0iIpJHcUpERIqr3BOvjIwMXnvtNcaMGUPnzp1Zv349sbGxjBgxgrCwMBISEvJdQbzW008/TVBQENu2bSvRalEqByoiBdG5oepRnBKR0qTfctVR7s94HT16lEuXLtGmTRtjmdlsxtbWlsjISF588UVcXFyMdTt37uT3339n+PDhN+xX1aJERKQkKE6JiEhJKPc7Xo0aNQLg8uXLREVFkZSUhI2NDbm5uQwdOhQXFxfS09MZM2YMFouF1NRU0tPTiY6OxtnZmblz52JrqysFIiJSOhSnRESkJJR74pVn6dKl2NjYGPPjr1y5Qq9evfjb3/5GvXr1aNasGb///ju1atUyqkb9/PPPHD9+nMaNGwP5y/QGBwerTK+IiJQIxSkRESmOCpN41a1bl23btvHnn39SvXp1fv31V7KysqhW7eoQY2Nj8z20/Prrr1O9enXj8+jRo42/n3vuORYuXGiU6VVAExGR4lCcEhGR4qgwiVfDhg1JTU1l6tSp/PTTT+Tm5mKxWG44PaNz585W8+qvlVctSmV6RaQ4VOZX8ihOiUhpUry5/VWIxCsnJ4cNGzbw0ksvER8fz6xZswAICwsz2jRr1ozg4GBj7nyDBg0AaN26dYFBzc3NDVCZXhERKT7FKRERKa5yT7zyyvQOHjzYKNM7ffp0I5jl5OSQkpJCSEgIAPv37ycuLo4BAwYYfWRlZREeHq4yvSJS4nQuEMUpESkL+k3f/gpdTj42NpZmzZpZLfvtt99o0aJFsQZwszK9jz/+ONu2bbthH/fccw8dOnSwWqYyvSIiVYvilIiIVGSFvuM1depUVq5cabXs9ddfZ82aNcUawM3K9Pr6+uLr68vRo0dZtmwZp06dwmQy4erqytChQ/MFMhERqZoUp0REpCIrdOJV0I2x3NySm1u+cOFCPD09jWkWV65coVu3bqxfv5569erx8ssv07dvX5KSkgAICAhgwYIFzJw5E3d3d0BlekVEqjLFKRERqcgKnXh5eHiwd+9e7rnnHgC+/fZb6tSpU2IDqV69Ojt27GDYsGFGmd7c3Fzs7OwwmUw4ODjQtGlTnnjiCWxtbfntt9/YtGkTdnZ2Rh8q0ysiUnUpTomISEVW6MRr4sSJhIeHs2jRIsxmM9WqVWPGjBklNpBOnToRFxdHZGQk6enpNGnShPbt2xtlet3d3Zk8eTLp6elYLBZMJhN33323URXqr1SmV6RqUjneqktxSkQqC8WqqqnQiZebmxvvvPMO6enpAFYvhCwJP/zwA56enkyYMIEZM2aQmprKb7/9ZqyfPXs2cHVqx8cff8xPP/3EuHHjbjheUJleEZGqQnFKREQqsiKVk//444/Zvn07NjY2dOnShX79+pXIIHJzczlw4ABOTk6cP3+eCRMmAP//fpSUlBT27dvHDz/8gNlsxmw2Y2Njw2effYa/vz9NmjRh7NixKtMrIvrtVnGKUyJSGej3WzUVupz8woULSU1N5cknnwRgxYoV1K5dm+eee67Yg/jPf/6Dra0tAQEBzJs3j5kzZ2Jra0tYWBjh4eEcO3aM9PR02rVrx/Hjx0lJSSEgIIBff/2V1NRUOnXqlK9PlekVEalaFKdERKQisylsw927dzNp0iS8vLzw8vJi0qRJ7N69u9gD+Omnnzh8+DBPPPEE3t7eBAUF8Z///MeqTZs2bQgICMDR0ZGkpCSOHTuGyWSiZcuWBQYzERGpehSnRESkIivSVMNrmUymAkv3FiQjI4OdO3fSrVs3Y1l4eDjh4eHUr1+foUOHEhcXh729Pffeey979uwBYP/+/SQkJDBnzhwuXLjAqVOnMJvNWCwW5s2bh8lkwsvLixEjRhAYGMgPP/xgXD3MK9ML0KpVK/71r3/d6qGKiEglpDglIiIVSaETr2bNmvHuu+8ybNgwTCYT7777Li1atCjUtklJSXz//fdWAS03N5fc3FxmzJhBu3btAHB2dsbb25uEhASjjbe3N8uXL+ftt9/m/vvvp23btkYfu3btIiYmhsDAQADat2/P8uXLgf8v0wvw4osvFvYwRUSkklKcEhGRiqzQiVdYWBiLFy9m8ODBmEwmOnfubDxcfDPnzp0zXh75V1euXGH48OEFrmvevDk1alwttWmxWLCxsZ4ZaWNjc92rmXlleuHqVc+iUplekduPyvfe3hSnRKSyUVyqWgqdeNnb2/PCCy/wwgsvFHknP/zwg1HJacaMGaSlpfHjjz9et316ejoHDx6kWrVqRjBq2rQpb775ptWLKLOzs3n66acL7COvTO+lS5du6f0oKtMrIlK5KE6JiEhFdsPEa+DAgWRnZxe4zmKx4ODgwAcffHDDHdysBG9BMjMziY+PJycnx1j28MMP8/DDD193mw0bNrB58+Z8y9PS0rhw4QLBwcFMnDiRpk2b3nC8eVTmU+T2o9/17UdxSkQqM/2Oq5YbJl5r164t9g4+/PBDunfvTkBAAFFRUUYJ3jw2NjakpaVhsVg4d+4cZrMZNzc3goKCiI6OBmDs2LGcO3eOatWuDjcmJoaWLVsafbi7uxMVFUVQUJCxbO7cuQQGBtK8efNbGrdu+4qIVHyKUyIiUlncclXDwsgrwRsZGQlglOAdPHiw0ebxxx9n1qxZ2NnZUbduXfz9/fP1M3v2bCZOnMikSZNwcXHRu09ERKREKE6JiEhZKdXEq3HjxkRERBif7733Xu69916rNl26dKFLly4cOnSI9PR0MjMzSUlJISMjA4AxY8Ywf/58LBYLublX57LXrFnzuvtcsGABBw8e5Pjx4+zZswdnZ2dsbGwIDg4mICCgFI5SREQqK8UpEREpK6WaeOVVerqZtLQ0oqKiGDJkCHD1QeMvv/wSwJg/X6NGmQP1vQAAIABJREFUDbp37056ejrOzs74+/uTlpZG3bp1WbZsGc2aNQNg9OjRAMybN4/AwECaNWvGoUOH2Lt3rwKaiIhYUZwSEZGyUqqJ142MHj3aCHhmsxkfHx/jPScODg5cvHiRcePGMW3aNAAmTZrEpEmTjCuLAOPGjeO1117D2dk5X/8jRowwll+8eLHIFaNUpldEypJKClc8ilMiVdv/tXfvYVVV+R/H34eb3ESF8AakIioymY4ymU0XZ8b0lzpl/ErNxrLw8oSJNaR5pbxkjqQUXij5OVONVjOVY1pNM4rGY1p4QyHxCgWaioJcAozr+f3hw86DaCIc4MDn9Rdnn3P2WevZnPV91tlrf7bGZalvjTbx8vHxuaHXFBQUcPDgQXr16oWLi8sN7//KZR5paWn06dOnVu1TTK+ISMumOiUiIvWp0SZeN6qoqIj09HSysrL45z//ydGjRwkNDQXg2LFjPPvsszz++OMMGTKEiIgI4z4sV0pPT8fX1xc/Pz+WLFlyQ5+reE8RaUgac2yX6pRI86TvmNS3JjHxMplMVFT8vFyivLzcuCFlp06djPjdYcOGAbB7927S09NZt26dxX6WL19u8bguqVI6tSwiIlVUp0REpK6axMTLzc0NLy8vI1nKzs6OJ554opFbJSIicpnqlIiI1FWTmHjZ2dkxa9Ysi21nz57FbDYDcPjwYVasWGE899NPP1FWVsaOHTuMbSEhIcZFzyIiIvVJdUpEROrK6hOvc+fO8fLLL1NWVmaxvW/fvoSHh/Ppp5/yr3/9C7i8lOPRRx9l2LBhvPHGG8yZMweA3r17M3/+fGOZh4+PD5s2beLee+/lww8/JCQkBD8/P1JTUy2WcaSmphIaGoq/vz9z5861dldFRMQGqU6JiEhDsPrEKzMzk/79+zN58uQanx85ciQjR44EYO3atVdF7s6ePZudO3fyySefcPvttwOX19BfuHCBS5cuUVlZadywMigoyGI9/cWLF/H09GTSpEm1brdiekVEbp4txTCrTomI1C9bqgENyeoTr6plGL8kJSWFAwcOWBS+8PBwhg4dSvv27bn77ruNi5dvlKenJ4BxAXRtKKZXRKRlUJ0SEZGGYPWJl5+fH3/7298IDQ3l2LFjBAQEYG9vT48ePYz18tu2bSM+Pp5OnToRExPDxIkTAYiJicHDw4Nt27bVuO9jx45x/vx543FycjKRkZF4eXlZvM7f37/W7VaEqIjIzbOlMVR1SkSkfml8qplVJ15FRUU4Ozsb9yR5+eWXmTFjhrFMIzc3l+XLl9OrVy+WLFmCyWQiISGBgwcP3tD+T506RW5urvE4Pz+f0aNHM27cuDq3XadHRUSaP9UpERFpKFadeO3evZsTJ04YjwMDA9myZYvx2GQyMWvWLNzd3Y1t9913HwD29va4uroaryspKaG4uJjCwkJ++OEHzGYzQ4YMobi42Hhv9+7dWb9+PfHx8WRkZODt7W3sY9myZVf9wigiIi2b6pSIiDQUk/lGF7fXwWeffcaWLVsoKSmhsrKSwMBAnnnmGdq2bQtAVFQUR48etXjP+fPn+fjjj3FyciInJ4fY2FgA3N3d6dq1KxkZGQwcOJCvv/6akJAQunTpYvH+6OhoRowYQc+ePa3dPRERsXGqUyIiYm1Wn3jt2LGD7du3M2vWLNzc3DCbzezdu5e3336bNWvWADVH+R4/fpwHH3yQGTNm1Bjle/z4cY4ePcqtt97K2LFj2b9/P7GxsRQUFABQXl6Ovb09dnZ2+Pr60qZNG2bPnk1AQIA1uysiIjZGdUpERBqC1cM1WrduTX5+PtnZ2Tg7O1NUVMS5c+dwcXExXlNTlO+sWbOYMmUKUHOUr5eXF/b2P1+4FxISUmOaVEpKCnv27CE0NLRW7VZMr0jLoMhbUZ0SaXk09ktjsPrEKzg4mNLSUt566y2ysrJwdXWlf//+xoXMUHOU77Bhw3B2drbYVj3KNzk5mUOHDtG5c2fGjx9f4+cXFBTg4eFR63YrpldEpGVQnRIRkYZg9YkXwF133cVdd911zeevFeWbmJj4i1G+b7755nULVlpaGn369Kl1mxWDKdIy6LsuoDol0tLo+yONoUHCNa6nqKiIkpIS43H1KF+TyWRE+f7pT38yonwdHR3ZvHkzc+bMwcPDg4iICPLy8q7af3p6Or6+vvj5+Vn8eikiInIjVKdERKQ+NPrEa+vWrRZRvtWZTCbGjx9vEeVbJTExkQEDBuDgcPWJu7CwMOOiaBERkZulOiUiIvWhQZYaXs/999/P/fffX6coXxEREWtRnRIRkfpg9YlXTRG8AH379iU8PJzCwkK2bNnCN998w5///Gd69uzJ8uXLueeee3jooYdISEgA4M477+T48eNUVv58IXF+fj67du3id7/7HQD79+83fj1MTU01EqJuu+02nn/+eWt3VUREbJDqlIiINASrT7xqiuC90rx58/Dy8uL7779n+/btdO/ene+++44ePXpgZ2dnvO7QoUM8++yz9O3b19j29NNPc+TIEaOgDRgwgHXr1gEwdepUVq9eDcBzzz1X63YrplfE9igeWG6G6pSI3AzVHKktq0+8fukSstLSUubPn8/u3bv59NNPmTx5MidOnGDAgAEWKU9ms9miwAHccccdV22rsmjRIuNvk8lU63YrpldEpGVQnRIRkYZg9YnXtSJ4e/ToYUTwgmWU75QpU/j1r3/N/v37jecDAgKIiorC0dHR2FZWVmbE9Vbn6ekJwI8//nhT90dRzKiI7dH3Vm6G6pSI3Ax9B6W2rDrxKioqwtnZ2YjHrR7Bm5ubW+MvjT/99BPp6emUl5cb2x544AEeeOCBa37Wxo0b+eyzz2psQ25uLqGhocyePZuAgIAbartOHYuINH+qUyIi0lCsOvHavXu3RQRvYGAgW7ZsMR6bTCbs7e0pKirCbDaTnZ1NRUUFnp6ehISEsHPnTgAiIiLIzs424niPHDlC7969jf106NCBJUuWEBISYmyLjo5mxIgR9OzZ05pdFBERG6Y6JSIiDaVB7uN1vQjeL7/8koSEBPbt28cf//hHBg4cyNtvv82dd97Jrl27iImJobKykunTpzN58mRcXV2Jiopi8ODB3HvvvXz44YeEhITg5+dHamoqy5cvByAjI4NbbrkFNzc3/P39mTt3rrW7KSIiNkp1SkRErM3q13hFRUXx8ccfExgYiL29PSaTif379/PII4+wbds2Bg8ezODBg3niiScsEqWSkpKMSN6dO3eSmprKrl27cHZ2xtnZmQsXLnDp0iUqKyuN1wUFBRlpUVf+kjhp0qRat1tpUSJSRclVzZvqlIg0B6pVTZ/VJ14VFRX4+/uzYMECfH19KSoqMn49rHLo0CGOHj3K6dOn8fX1xcfHh+zsbAoKCjh79ixlZWX06NGD3bt3Gxctb968ma+//prS0lKLpRtVTCaTkRKltCgREbkW1SkREWkIVp94denShbZt2/LWW2+RlZWFq6sr/fv3Ny5krqio4K233uLvf/87r776KsuWLeOFF15g1qxZ/OY3v+Hs2bMADB061KJwrVy5ku7duxvr6wGSk5OJjIzEy8uL8vJykpOTMZlM+Pv7W7ubIiJio1SnRESkIVh94tWmTRs+/PBD2rVrB0BxcTFfffUVx48f5+WXXyYyMpLHHnuMXr16MW3aNJ5//nlWrVoFwJNPPomHhwfbtm2rcd+nTp0iNzfXeJyfn8/o0aMZN25cndutiFARqaLxoHlTnRKR5kBjQtNn9YnX8OHDGT58OAAvvPACCxYsMGJ6y8rKmDRpkhGdGxgYSGxsLPb2lv84JpOJkpISiouLKSws5IcffsBsNjNkyBCKi4uN13Xv3p3169cTHx9PRkYG3t7euLq6ArBs2TK8vLxuuN1aIysi0jKoTomISEOw+sTrehwdHQkICOAvf/kLTz75JB07djSKmb29PXZ2dgD069ePiRMnkpaWhru7O127dsVsNvPuu+/SuvXPhefjjz+2uKeK2WzGzs6O0NDQWhUzERERUJ0SEZH6Y9U4+bVr15KYmGg8vnDhAt7e3j9/uMlEVFQUM2fO5OzZs3To0MHi/VfG6/7v//4vbdu2NZ7LyMigqKiIhx9+mDFjxrB//36LG1Pm5ubi7u5OSUkJ+fn5+Pj41OrGlCIi0vypTomISEOx6hmvyZMnW0TvXouTk1ON2/v06WP87ePjQ0xMjPH4vffe45tvvjEeh4SE1JgatWvXLs6cOcOjjz5am6YrplekGVHErlyL6pSI3CzVFqmtRl1qWOXuu+9mzJgx3Hvvvdd8zaVLlwgNDcVsNhtLM86fP8+sWbMsfp2sLi0tzaIw3ijF9IqISBXVKRERqasmMfFq06YNr7/+Ou+8847F9vbt27Nw4ULy8vKMWN99+/aRnp7O6NGjjdeVl5cza9Ys8vLyrtp3eno6vr6++Pn5GfsQERGpDdUpERGpK6te43UzqidKnTp1in//+9/XfU9wcDD9+/e32BYWFsaaNWtuuh1awiHSfGg5iNQn1SkRAdUWqb0mccbrevz8/Jg8eTLfffcdcXFxnDlzBpPJhIeHB+PHjyc4ONgqn6svkoiI3AjVKRERuRGNfsZr7dq17Nq1i8rKSpycnK6ZKPXII4/QtWtXunXrxsSJE7Gzs2PSpEmMGTPGuBHlypUrOXjwIACpqakEBQUZMb133nlno/RPRERsm+qUiIjUh0Y/4zV58mQCAwP54YcfeOyxx2p8zdatW6msrGT8+PG0b9+eyMhIwsPDKSsro6Li52UW06ZNM/6eOnUqq1evJiUlhT179qigiYjITVGdEhGR+tDoE68bkZOTQ3h4OPv37+fEiROkpKSwefNmxo0bh6ura43vWbRoEQAFBQV4eHjU+jO1dl6k5dK6fakt1SkR26OxXhqaTUy8Hn74YSIjI8nNzaWsrIylS5dy3333sW3bNgoKCmp8j6enJ6CYXhERsT7VKRER+SVNYuLVuXNnYmJi2LZtG8XFxcavgw4ODqxatYoLFy4QFhZGUVEROTk5ZGVl8dprr9GxY0fjtREREYrpFRERq1CdEhGRumr0cI3qqsf0ZmVlERcXh6OjI/v27eO+++4jMDCQgIAATpw4wY8//khISMhV+1FMr4jcLC0/ketRnRJpHjTWS0NrEme8rqdDhw7MmzeP48ePU1FRQUhICMeOHWPRokVcvHiR8ePHW+Vz9UUUEZEboTolIiI3otHPeK1du5Zt27aRkZEBQGlpKU5OTphMJtq2bYuvry9RUVFMnDiRPn360K5dO6ZPnw7Ali1bKCgo4PHHHwcU0ysiIvVPdUpEROpDo0+8rsVsNjNt2jRWrVpFZmYmL774IkVFRZhMJlxcXHBzcyMnJ4eAgABee+21q95fPaY3NDS0EXohIiLNleqUiIjURpNdamg2m3F2dgbAxcWFiIgIAFJSUsjKymLWrFnXTYtSTK+I1BddByA1UZ0SsW0a26WhNdmJV1ZWFh07dgTA29sbb29vvv/+e5KSkliwYAFLliwhMzOToUOH1vh+xfSKiIg1qU6JiEhtNPrEa+3atSQmJl7z+YkTJxIVFcWnn35KUlIS9vb2JCUlMWfOHItfEhXTKyIi1qA6JSIi9aHJXuN1pb/+9a907NiR4cOHU1FRwfvvv0/fvn3JysqioKBAMb0iYlVajiK/RHVKxPZobJeG1uhnvACSkpKYP38+HTp0sNju6elJVFQUTz/9NIcPHyYhIYH77ruPP/3pTwBcuHABOzs7q7RJX0QREamiOiUiInXVJCZeO3bsoPqJN5PJxNGjRwEoLy8nMjKSS5cu8e677xqvcXZ2ZunSpcbj6jG9oaGhiukVEZE6U50SEZG6ahJLDb/88kvOnTvH2LFjjW1ms5l7772XnTt3ApcL1Isvvkj79u2N13z//fcMHjyY+fPnX7VPxfSKiEh9UZ0SEZG6ahJnvLp06cKGDRvYunWrsc1sNlss6QgKCmLLli0W71uxYgXDhw+vcZ+K6RWR2tJ6f7kW1SkRuRmqK3KlJjHx6tatG3FxcRbbjh49SkJCAgAVFRU89NBDV62td3Z2xsfHp8Z9KqZXRETqi+qUiIjUVaNPvK4V05uTk0NlZSV79+5l6dKlBAQE8Prrr19zP4rpFRERa1CdEhGR+tAkrvG60gsvvMCCBQtwc3Oz2D5jxgwuXrxIXl4ely5dolOnTgCEhoZy1113XbUfxfSKSG1pSYjcCNUpEblRqitypUY/43WjoqKiANi9ezfp6elGVK+16EsiIiK1oTolIiLX06QnXjk5OZw7d44VK1YY23766SfKysrYsWOHsS0kJIQRI0Y0RhNFRKQFU50SEZEb1SSWGs6fP58zZ84AcPz4ccLCwnjssceMZRgVFRX89NNPwOX7pri4uGAymUhLS+P06dPcd999wNX3RwkKCtL9UUREpM5Up0REpK6axBmvqkhdgAsXLrB48WIee+wxY9vx48fZtm0bAOfOncPFxYV58+aRnZ3Nd999ZxS0adOmGe+5ePEinp6exv1RalvQtHZeRK6kdfotm+qUSPOncV6srUlMvABOnz7NxYsX6dq1K23btrV4rnfv3vTu3Ru4nAr11FNP/eL+qmJ6b/b+KIrpFRGRK6lOiYhIXdg1dgOqZGZmkpycTHZ2NgMHDgTg2LFjhIaGcvToUSorK1m6dClZWVm0a9fuhveblpZGQECAtZotIiIthOqUiIjURaOf8UpOTubw4cOkp6eTk5OD2WymsLCQDz/8kF69erFmzRrS0tKIiIjgkUce4amnnmLevHlMmDDBYj/1fX+Ubm3t69o1EWlGNCa0XKpTIi2DvlNibY0+8erQoQMmk4k+ffrg4OCAk5MTbm5ueHl5ceLECQCOHDnCwoULad368rrbN954gzNnzpCVlWXsZ/ny5Rb7rev9UbTGV0REQHVKRETqR5OYeHXo0IHt27ezYcMGY3vr1q2JiIgAYOTIkaSlpbFx40aefPJJnJyc6Nq1K6dOncLeXr9OiIiI9ahOiYhIfWj0iRdAYmIiMTExzJgxA0dHR7p160ZlZSVjxozhyy+/pLKyko0bNxIfH8+PP/7IxIkTcXZ2ZufOnTz55JPGfqrH9IaGhiqmV0RE6kx1SkRE6qpJTLzy8/MpKSkhKSmJW265hffff5/7778fk8kEwHvvvUdZWRlms5n//ve/fPjhhwQEBJCenk55eTmRkZGAZUzv1KlTWb16tWJ6RaRJUmyxbVGdEpHqNI5LbTWJidfQoUMJDAzkgw8+YPv27Zw8eZLg4GB69eoFwNdff010dDRz5swBYPLkyaxdu5ZXXnnF4pfEK1Xdc0UxvSIiUleqUyIiUldNJk4+PT2dW2+9lfDwcMaOHcvjjz/O8ePHiYyMxMPDg8LCQgAqKipwcPh5vjhr1izWrVt31f6q7o+imF4REakPqlMiIlIXTeKMF4DZbMZsNtO7d2+6detGYWEhgYGBLFy4kIyMDJYuXcpvf/tb9u3bx/jx4433LV26FF9fX8X0iohN0Rhje1SnRORK+g5KbZnMZrO5MRvw9ddfk5SURHl5OWazmVatWuHg4EBQUBDr1683onZLS0vJyMjAx8cHV1dXABISEhgwYADu7u5X7beuMb0iIiKgOiUiIvWj0c94DRo0iEGDBhETE8P06dMtnvvHP/5h/P3mm29y6NAhi+dPnz7NRx991CDtFBGRlkl1SkRE6oPVz3glJSWxatWqq7bfc889TJgwAYAFCxbwxRdfEBQUxMKFC/Hx8WHFihVMmTIFNzc3KisryczMpKLicnqTj48PmzZtIjk5GRcXFyZMmICfnx+pqanGDSpTU1MJCgoCwN/fn7lz51qzmyIiYqNUp0REpCE02lLDadOmsXTpUjZv3gxAbGyssRTDxcWF8+fP0717d5YvX863337LJ598wu233w7AsGHD+Oijjzh16hRubm5MmDCBLl26WOy/KqYXYNKkScTFxdWqfYrpFRGpveYUr6w6JSLSfDVGvWrQpYabNm1i1KhRlJWV4eTkRKtWrRgwYABnz57lwQcf5JZbbmHQoEG0bdvWiOX18PCgrKyMu+++m5CQEIv9/eY3vyE9Pb3Gz6qK6QWM+6zUhmJ6RURaHtUpERGxlgadeG3fvp1Ro0aRnZ1Nv379jLjdzZs388QTT5CZmck//vEP40aT4eHhDB06lPbt29e4Pzc3N4uEqOTkZCIjI/Hy8rJ4nb+/v5V6JCIizYnqlIiIWItVJ15r164lMTHReHz06FFCQ0ONxwkJCTz66KMMGjSIvn370rt3b7Zu3Wo8HxMTg4eHB9u2batx/6dOnSI3N9d4nJ+fz+jRoxk3blyd266IUBGR2rO1sVN1SkSkZWqMMdSqE6/JkyczefLk677GbDYTFxdHVFQUpaWlPPvss8DlZRdVSy9MJhMlJSUUFxdTWFjIDz/8gNlsZsiQIRQXFxv76t69O+vXryc+Pp6MjAy8vb2NSN9ly5Zd9Qvj9TSXaxREROTaVKdERKShNEi4xoYNG9i6dSt2dnZUVFTQvn17ZsyYYbE04z//+Q8JCQkUFhbSrVs3/vjHP9KuXTu8vLzIyckhNjYWAHd3d7p27UpGRgYjR45k8+bNhISEXHXRcnR0NCNGjKBnz57W7p6IiNg41SkREbE2q0+84uPj+eabb5gzZ47xy+DRo0d57bXX+L//+z/KysoYNWoUJSUldOrUCXd3dyZOnEh0dDSurq6sXbvW2Ndnn33GiBEjAFi5ciVdunRh06ZNvPTSS+zfv5/Y2FgKCgoAKC8vx97eHjs7O3x9fWnTpg2zZ88mICDAmt0VEREbozolIiINwerhGl5eXmRmZvLdd99x6623UlhYyIEDB/D29gbA0dGR8ePHU1BQwJgxY5g7dy729va0atXqqn1t3LjRKGheXl7Y29tTNW8MCQm5Kk0KICUlhT179lis2b8RiukVad6aU+y51I3qlIg0BapLzZ/VJ179+vVj6tSpvPvuu5w5cwZXV1cGDhxoEaM7duxY/vOf/zB37lxSU1PZsWMHixYtYvHixRb7srf/+SK4cePGkZiYSP/+/encufM1P7+goAAPD49at1sxvSIiLYPqlIiINIQGiZO//fbbjZtKXkv37t25dOkSH330EZ6engAcPHiQ0NBQIiMjcXFxITU1lfLycuLj49m1axfnz5/nzjvvxNHR8Zr7TUtLo0+fPvXaHxERaV5Up0RExNoa9D5eNSksLOTNN98kOzubFStW0KZNG+O5fv36sWbNGgDi4uL4/e9/z0cffcTYsWMZNmwYiYmJHDlyBICIiAiLe6VUSU9Px9fXFz8/P5YsWXLD7VJMr0jzpu+43CjVKRFpCPpON3+NPvEqKyvjgQce4Fe/+tVVz7m7uwNw/vx59u/fz5tvvsmMGTMYOHAg3bp1s3jt8uXLLR6HhYUZxfBmaI2tiIiA6pSIiNSPRp94tWvXjnbt2nHo0CFWr14NQGVlJe7u7kyZMoXy8nJeeuklXnrpJQAiIyNZsmQJr776amM2W0REWgjVKRERqQ8Nch+v6ykrKyMsLIxDhw7Ru3dv3N3dee2118jOzuaxxx7jq6++Iicnhw0bNhAeHm7x3nnz5tG1a1cmTpwIXI7uPXjwIACpqakEBQVhZ2dHaGgod955Z4P3TUREbJ/qlIiI1IdGP+Pl6OhIXFwczz77LI8//jgbNmygoKCAvXv3GlG9Xl5eJCQkcOjQIYv3fvvttxbxu9OmTTP+njp1KqtXrzZiemtb0BTTK9J0KXJXGpLqlEjj07gvzUGjT7yqLF68mJUrV3L48GH+8pe/EBwcTI8ePYznO3bsyIoVKyzeM3PmTIYPH17j/qpigBXTKyIi9UF1SkRE6qLJTLyys7P5/vvv+fjjj42Y3lWrVhkxvSNGjGDdunUW7wkODsbX17fG/VXtQzG9IiJSH1SnRESkLhp94vVLMb1Tp05l4cKF13z/l19+SUhICNu3b1dMr0gLoe+nNCTVKZHGp/93aQ4aPVwjNzeXM2fO1BjTO3PmTJYtW2axbffu3Rw/fpwJEyZcd791jekVEREB1SkREakfjXbGKycnBy8vr+vG9M6aNQuA4uJiwsPDMZvN5OXlUVxczM6dO3FzcyM6Ohp7e/0KIiIi9Ut1SkRE6pNVz3hVRfBWVl6++NfZ2ZnXXnsNFxcX45e+AwcO4O/vz3PPPceqVavYt28fgwcP5quvvmL16tW8//77AGzZsoXbbrvN4oaU06dP5+677+bRRx8FLkfzVt2gsiqmF8Df35+5c+daq5siImKjVKdERKShWPWMV1UEb5Xw8HAqKiyjbxMSEnB3d8fd3Z2UlBTeeecdWrVqxSeffIKdnZ3xurNnz7Jhwwbc3NyMbampqfTq1ct4HBQUZFzYXBXTCzBp0qRat10xvSJSG4o6tk2qUyIiqmENpcGWGp48eRI3Nzfc3d2vei4uLg5PT09ef/110tPTiYmJwcPDgz/84Q8Wr3vhhRcskp/efPNNnJ2da/y8qpheAJPJVOv2KqZXRKRlUZ0SERFrapCJ18mTJ3n11VeJiooyth08eJDQ0FDatWvH5MmT6dmzJwBjx45l2LBhZGZmWvyS+Pvf/56tW7eyZ88eY1urVq24//77jcfJyclERkbi5eVl8fn+/v7W6pqIiDQDqlMiImJtVp14/VIE75o1a1izZg3//Oc/ad++PQCtW7dmyJAhHDp0iAsXLgAQERFRYwQvwN69e+nQoQNLliwhPz+f0aNHM27cuDq3XbGlIlIbGjNsk+qUiIjGk4Zi1XCNG4ngLS8vJy8vj6pmtG7dGmdnZxITEzly5EiNcbxTpkzhrbfeumr7mTNnWLBgAeXl5WRkZODt7Y2rqysAy5Ytu+oXRhERadlUp0REpKFY9YzXjUTwOjg4cPHiReLj43nmmWcZA979AAANSElEQVSM99rb2/PTTz8Zj1etWkVSUhJw+WLl0NBQ7OzsmDJlCsHBwQB07tzZKHTR0dGMGDHCWBoiIiJSneqUiIg0lAa5xis6OppVq1YZFyyfOnWKV155hZUrV1JWVsZLL73EhQsX2LdvnxHlGxwczF//+ldjH0VFRcbfVfG7BQUF7Nq1i+DgYDZu3EhsbCwFBQUAlJeX8/7772NnZ4evry9t2rRh9uzZBAQENESXRUTEhqhOiYiItTXIxKsqgnfAgAGUlpYa693hcpRvdHQ0Tz31FABJSUk888wz2Nvbk5aWZuzjxIkTRgRvlZSUFHbu3AlASEgIISEhV312SkoKe/bsITQ0tFZtVkyvSMujON2WS3VKRJoy1afmoUEmXosXL2bDhg28//77ODg4EBwcbCzhAOjYsSP//ve/OXnyJOvWrePVV18FICws7Lr7bd++Pb/+9a+v+5qCggI8PDxq3WbF9IqItByqUyIiYm0NMvFq27YtU6dOve5rrhflGxkZSY8ePQgNDSUvL4/i4mI6d+4MwJIlS66737S0NIt7qoiIiFSnOiUiItbWYDdQvpZfivKNiYmhoKCASZMmAbBv3z7S0tIYM2aM8bqSkhLmzJlTY5Rveno6vr6++Pn5/WLxu5JiNUVaHn3vpSaqUyLS2PR9bx6sGid/I34pyvfZZ5/l008/ve4+7rjjDiMxqkpYWBhr1qyp17aKiEjLozolIiL1odHOeOXk5ODl5fWLUb4dO3YkLCyM7777jri4OM6cOYPJZMLDw4Px48dfVchERETqg+qUiIjUJ6ue8SorKyMsLIzKyssX/1ZF8Lq4uBi/9B04cID27dszb948iyjf+fPnk52dTWxsLBUVFTz88MNERETg6+uLj48Pf//734mPj6dPnz488cQT+Pn5kZqayvLly4HL91CpivP19/dn7ty51uqmiIjYKNUpERFpKFY94+Xo6EhcXJzxODw8nIoKy+jb5ORkevfufVWU78mTJ+nRowcAX331FRcuXGDHjh34+PjwP//zP2RmZlJaWorJZDIKZlBQkBHlO3XqVOOXyap197WhmF4RqQ1F/dom1SkRkfqjWnh9DbbU8OTJk7i5uRm/FFZXPcq3oqKCwMBAkpOTKSsrY8qUKeTk5LB//3727t1LXl4e4eHhJCUl1bi/RYsWGX+bTKZat1cxvSIiLYvqlIiIWFODTLyuF8F71113AT9H+ebn51NaWsry5cvx9fWlXbt2nD9/Hg8PDyZMmGC8f+XKlRQXF3P+/HljW3JyMpGRkXh5eVl8vr+/v3U7KCIiNk11SkRErM2qE69fiuBds2YNe/bsYcWKFTg4OODo6Ejr1q0ZPny48RoPDw+OHTtW4/5PnTpFbm6u8Tg/P5/Ro0czbty4OrddsZ0iUhsaM2yT6pSISP3RuHR9Vp14lZWV8cADD9QYwVu1lOOOO+7ggw8+uOp5FxcXnJ2dgctLMEpKSiguLqawsJAffvgBs9nMkCFDKC4uNt7TvXt31q9fT3x8PBkZGXh7e+Pq6grAsmXLrvqF8Xq0PlVEpPlTnRIRkYZi1YnXL0XwVlm8eDEnTpzAweHn5mRmZvLOO+/QuXNn+vXrR2xsLCtWrMDd3Z2uXbtSUxhj586deeuttwCIjo5mxIgR9OzZ05pdFBERG6Y6JSIiDaVBbqA8YcIEiwjeU6dOsWzZMlauXElZWRm/+93v6NGjB3Z2dkaU74IFC8jJySEuLs6I8vX19TX2uXLlSs6dO0ebNm0YM2YM+/fvJzY2loKCAgDKy8uxt7fHzs4OX19f2rRpw+zZswkICLB2d0VExMaoTomIiLU1SLhG9QjevXv30qFDB+BylO8dd9zB4sWLcXV1NaJ8KyoqjJSnqijfKwual5cXJ06cwMPDA4CQkBBCQkKu+uyUlBT27NlDaGhordqsmF4RqaJ43OZPdUpEGopqSsvVIBOv6hG8wcHBFks47rjjDsLDwykqKuL06dNMnz4dd3d37O2vfYHeuHHjSE5OxtPTk5ycHLp06VLj6woKCoyiVxuK6RURaTlUp0RExNoaZOJVFcF7LWPHjiU4OJhXX32VTz75BE9PTwDuuusuiyjfKlVRvgC9evXC29v7mvtOS0ujT58+9dALERFprlSnRETE2hrsBsrXUl9RvhEREeTl5V21//T0dHx9ffHz82PJkiU33C7FYYpIFY0HLZvqlIjUJ313W64GCde4ntzcXM6cOVNjlO/MmTNZtmzZNd+bkJDAoEGDcHJyuuq5sLAw1qxZU69tFRGRlkd1SkRE6kOjn/Gqa5Tvp59+2uBtFhGRlkN1SkRE6kOjT7yqREdHXxXl+8orr7By5UoAcnJyWLt2La1atTLeM2vWLEpKSiy2iYiIWIPqlIiI1IVdYzegSlWUb2lpKYWFhRZRvgAmk4mKCsvY3CujfKu71nYREZGboTolIiJ10ejXeFXJy8tjw4YNHDt2zIjyHT16tLFk44MPPmDbtm1c2Vx3d3dWrFhRY5zvxYsXjdQpERGRulKdEhGRumgyEy8REREREZHmqsksNRQREREREWmuNPESERERERGxMk28RERERERErEwTLxERERERESvTxEtERERERMTKNPESERERERGxMofGbkBTtHnzZj7//HMcHBzo27cvkyZNauwm3ZRRo0Zx++23A+Do6Mi8efNs6oadFRUVxMTE8O2337Ju3TrAdo9NTX2x1eMzb9487OzsyMvL4w9/+AMPPfSQTR6Xmvphq8dkwYIFVFRUUFRURNeuXZk2bZpNHhOouS+2elysyVaPLzSvsb25jIfNaQwpLy/nxRdfxM3NjYULF9pkP6415tliXzIzM1mzZg1msxk7Ozuee+45EhMTba4faWlpvPPOO8bjgwcPsmjRIjIyMmrXF7NY+PHHH81PP/20ubKy0mw2m80vvPCCOT09vZFbdXOefPLJxm5CnWzdutV84MABox+2fGyq98Vstv3jU1FRYR47dqxNHxez+ed+mM22f0zMZrN55syZ5iNHjtj0Makyc+ZMc1paWrM4LvXJ1r9zzWlsr9JcxkOz2fbHkDfeeMO8c+dO85w5c2z2eNQ05tliXyorK83h4eHmixcvGttssR/VlZeXmydNmnRTfbF/+eWXX7buHNG2JCYm4ubmRv/+/QGorKwkPT2d2267rZFbVnsbN27k1KlTbNq0CTs7O7p169bYTaoVf39/OnXqxKZNmxg1apRNH5vqfQHbPz6lpaV8+eWXeHt72+xxgZ/7MXLkSJs/Jj/++COffPIJHTp0wNPT02aPCfzcl1GjRvHZZ5/Z9HGpb7Y8FkLzGturNJfx0NbHkM2bN+Pn50f37t3Zt28f7u7uNnk8aqpFtvg9SUlJ4dy5cyQmJvLFF19w8eJFCgsLba4f1X3xxRf4+vreVF+01LCavLw82rRpYzxu06YNGRkZjdiim/fuu+8Cl0+7P/fcc/j7+9O1a9fGbVQdNKdjA7Z/fN544w0mTpzImTNnbPq4VPUDbPeYZGRkEBMTQ0pKCvPmzSM/P99mj0n1vnh4eNjscbGW5jYWNof+2Pp42BzGkMOHD5Odnc2DDz7I6dOnAdv936ppzLPFvpw+fZoTJ04QGxuLk5MTCxYsICsri06dOhmvsYV+VPevf/2LVatW8d///rfWx0ThGtW0bduW/Px843F+fj5t27ZtxBbVnYODA4MGDeLkyZON3ZQ6aY7HBmzz+Lz99tsEBQUxYMAAmz4uV/bjSrZ2TLp06cLy5cv5/PPP+eijjygvL7fZY1K9LxcuXDCes7XjYi22/J2ria33pzmMh81hDPn888/5/vvviYyMJDo6mgMHDnDx4kWb68eVrhzzbPF/y8XFhUGDBuHk5ATA4MGDcXJysrl+XGn37t3069ePVq1a3dQx0cSrmr59+7J7927MZjMA8fHxBAcHN3Kr6i4pKYnAwMDGbkadNNdjA7Z1fN577z3c3NwYOXIkYLvHpXo/qrOlY1LFwcGByspKBg4caJPH5EpVfSkrK7PYbovHpb7Z6nfuWmy5P81lPKxiy2PIjBkzWLhwIQsXLuT555+nf//+PPzwwzbXj+qqxjxb/N/61a9+xaFDh4zHhw4dokuXLjbXjyutX7+ecePGATf3fddSw2o8PDwYNWoU06dPx97enttuu43u3bs3drNuyosvvkirVq0oLi5myJAh+Pr6NnaTboqjoyPQPI5NVV/ANo/PgQMHWLt2Lb/97W85ePAgAH/+859t7rhcqx/Lli2zuWNy+PBh/va3v+Hq6sqlS5cYOnQonTt3trljAtfuiy1+V6ypOYyFYPtje3MZD5vTGFLFwcEBe3t7m/3futaYZ2t9ad++Pffccw/PP/88rq6u+Pj4MGzYMMrKymyqH1WOHDlC586dadeuHXBzY5fJXDVNExEREREREavQUkMREREREREr08RLRERERETEyjTxEhERERERsTJNvERERERERKxMEy8REREREREr08RLRERERETEyjTxEhERERERsTJNvERERERERKzs/wGCaRmMM8lf8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# prepare the canvas frame\n",
    "fig, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, figsize=(14,5))\n",
    "# set the figure title\n",
    "fig.suptitle('Air Pollution in Seoul City', fontsize=14, fontweight='bold')\n",
    "# left (1st) sub-figure\n",
    "graphdf['pm25'].plot(kind='barh', ax=ax0)\n",
    "ax0.set(title='PM2.5')\n",
    "# right (2nd) sub-figure\n",
    "graphdf['pm10'].plot(kind='barh', ax=ax1)\n",
    "ax1.set(title='PM10')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://pbpython.com/images/matplotlib-pbpython-example-header.png\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 그 외"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 그래프를 표시하는 테마 자체를 설정 가능. 아래는, R에서 사용되는 ggplot 테마로 바꾸는 방법 </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:30.697052Z",
     "iopub.status.busy": "2020-08-04T11:05:30.697052Z",
     "iopub.status.idle": "2020-08-04T11:05:30.703053Z",
     "shell.execute_reply": "2020-08-04T11:05:30.702053Z",
     "shell.execute_reply.started": "2020-08-04T11:05:30.697052Z"
    }
   },
   "outputs": [],
   "source": [
    "# set the matplotlib default drawing style\n",
    "plt.style.use('ggplot')                   # plt.style.available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:30.706053Z",
     "iopub.status.busy": "2020-08-04T11:05:30.705056Z",
     "iopub.status.idle": "2020-08-04T11:05:31.849051Z",
     "shell.execute_reply": "2020-08-04T11:05:31.849051Z",
     "shell.execute_reply.started": "2020-08-04T11:05:30.705056Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'PM10')]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA14AAAFVCAYAAAD/gD4YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeVyU5f7/8dcMMuwoS6BCgjuuue8aFZk7HTU7paWppZbLKXM/p87vlGm0almpuaSlZppLZWnlEu5Legw1I5UCBAFBQdmH+f3hl/s4Kcg2cM/F5/l49Dgyc88915sHc33ONXPdnzFYLBYLQgghhBBCCCFsxljVAxBCCCGEEEII1cnCSwghhBBCCCFsTBZeQgghhBBCCGFjsvASQgghhBBCCBuThZcQQgghhBBC2JgsvIQQQgghhBDCxmThJYQQds5isfDAAw/QrFkz/vzzz1vuHzRoEM8++2yJz3fx4kWaNm2q/dezZ09eeukl0tPTS/T4WbNm0blzZwCOHDlC06ZN2blzZ4mf/6+OHj1Kly5d2LZtW5nPUZzr169z77338uqrr5b5HDExMYwfP54OHTpwzz338Pjjj1fgCItX0t/xV199xYgRI2jfvj0tW7akR48eLFmyBIBXX32V0NBQrl+/XhlDFkKIaqlGVQ9ACCFE+Rw4cIC4uDgANm7cyPPPP291f0REBC4uLiU+X0FBAQCDBw/m3nvvJTY2lvfee4/09HTefffdOz7eYrFgNputzlX4vyXx3XffkZSUxJNPPglA69ateffdd2nVqlWJz1Eabm5uvPvuu9StW7dMj79+/TqjR4/G1dWVGTNmYDAYSElJqeBRFq0kv+NZs2bx5Zdfct999zFjxgycnJy4ePEiHh4eAIwdO5YBAwbg5uYGwP79+/n555+ZOHGi7QMIIUQ1IQsvIYSwcxs3bsRkMhEUFMTmzZuZMmUKRuP/NjSEhISU6bxNmzalT58+ACQnJ7Nq1Sqys7NxdnaukHEXZdu2bURFRWkLL5PJRJcuXWz6nG3bti3zY48ePUp8fDwrV66ka9euFTiqirFhwwa+/PJLpk+fzpgxY257TO3ataldu7b2c2RkJMuXL5eFlxBCVCDZaiiEEHYsPT2d77//nvvuu49HH32UxMREIiMjrY555JFHGDlyJPC/bWnPPfccbdq0Yc6cOSV6nvr162OxWLh48SJZWVn8v//3/+jcuTP33HMPTz31FDExMSU6z+zZs7VtiABZWVmEhITw4YcfAjcWidu3byc+Pp6mTZuyefNmzp07p/270OrVq7n//vtp2bIlAwcOZM+ePdp9sbGxNG3alOeff57evXvTrl07xo0bx6VLl4ocV7du3Zg9ezZwY6HStGlT/vWvf9G9e3e6du3Kq6++Sl5e3m0fazKZADh8+HCR51+8eDFdu3blnnvuYdKkSVy5ckW7Ly0tjalTp9K+fXvatm3L5MmTSU5O1u5ftGgRISEhXLt2zWq8//znP4t8vputWrWKhg0bMmrUqCKPeeONN2jWrBkADzzwAMuXLwduLL7ff/99/va3vzFkyBDt+K+//pqmTZty5MiREo1BCCGELLyEEMKuffXVV+Tk5PDwww/Tv39/HB0d2bhxo9UxZrP5lu1o+/fvZ9KkSfTr169Ez5OQkABAzZo1efbZZ9mwYQPDhw/nxRdfJDY2lqFDh1otFopSUFCgbUMs/NlisZCfnw/AwoULadOmDd7e3ixYsIDOnTvfsm3xgw8+4NVXX6VTp0689NJL1KpVi3HjxmmLL4PBAMCPP/5IeHg4U6dO5fDhw7z22mtFjuvm31Hhp4WRkZFMnDiRIUOGsHr1atavX3/bx3bp0oUePXrwwQcfMGvWLK5evWp1/8qVK3nnnXd4+OGHmT59OkeOHOHFF18EID8/n8cff5zIyEjGjx/PxIkTOXz4MI8++ijZ2dlWv6ObtxKazWbtd1aczMxMfvvtN7p27YqDg0ORxxUUFGjn//e//819990HwIIFC+jbty99+/YlKiqK2NhYAL755hv8/f1p3779HccghBDiBll4CSGEHdu4cSPe3t706tULb29vevTowc6dO0lNTS32cU8++SRjxoyhe/fuRR6Tk5NDWloae/bsYd26ddx7773ExMSwf/9+pk2bxuTJk3niiSdYtmwZ169f1z4lKY/evXvj7++Pi4sLffr0oU6dOreMaenSpTz44IPMnz+fYcOGsXz5cu6++24WLlxodeyQIUN47rnnGD58OGFhYRw6dKhUY5kxYwaPPfYYL774InXq1Cny8QaDgQ8//JDhw4ezadMmBgwYwIkTJ7T7V6xYQVhYGBMmTGDgwIE8/fTTREZGkpSUxLZt2zh//jzz5s3j6aefZsyYMbz77rvEx8ffsoAui4yMDCwWi9U2wjvp2bMn9evXB6BPnz40bNiQ/v37YzAY2L59O9euXWPv3r0MGDDAakurEEKI4smMKYQQdurXX3/l1KlT9OrVi5SUFBITE+nRowd5eXls3bq12MeW5Lqvt99+my5duvDMM8/QtGlTXnnlFU6fPg3A/fffrx0XFBREkyZNOHPmTPkClUBMTAyZmZlWz+/o6EhoaChnz561+jQtKChI+7evry9paWmleq6bH+/t7V3s400mEy+99BKrV6/GaDTy1FNP8eeff5KRkUFiYiLff/89HTt2pGPHjkRERAA3ukeePn0aBwcH7r33Xu1cXbp0wcPDQ/tdl4enpycGg+GOC/E7CQgIoE2bNmzfvp0ffviB3NxcBg0aVO7xCSFEdSLNNYQQwk5t2LABgM2bN1td/wQ3Pgkr7poeR0fHO55/+PDh9O3bl4CAgDt2/LNYLNoWv+L89ZiirpsqrZI+f2mU5XwdO3Zk8eLFhIeHs3XrVu3auoEDB/LII49YHduoUaNin6fw9tvdX9Lfm4uLi3bd3PPPP69dj1YWAwYM4JVXXiE/P58mTZqUuWmLEEJUV/KJlxBC2KHc3Fy++uorWrVqxUcffWT13+DBg/ntt984efJkuZ6jXr16dOzY0WrR1bx5cwB27dql3RYbG8vvv/+u3VccT09Prl+/rl2/dLtPyQwGAxaL5baPDw4OxtXV1er58/PziYyMJCQkpNjrmCqLl5cXAK6urnh4eODt7U1MTAydO3e2+s/d3Z3mzZuTn5/PTz/9pD3+yJEjZGRkaL9PT09PAK1FfWxsbKm+b+vJJ58kPj6et956q8jf618VLvZuPr5v3744ODhw+vRpBg4cWOLnF0IIcYN84iWEEHboxx9/5MqVK8yaNUtrhFCocePGbNq0iQ0bNtC6desKfd727dvTtWtX3njjDa5cuYK3tzerVq3C2dlZa/9enI4dO7Jy5Upefvll2rdvr32B7818fX1JSUlh5cqV+Pv707BhQ+0+Jycnnn76aRYsWMCsWbNo164d27ZtIyYmRuuMWNmio6PZsGEDzZo1Iy8vj88//xw3Nzf69+8PwIgRI1i4cCH/+Mc/CA0NJSkpiZSUFGbPnk2/fv348MMPmTlzJs888wwODg4sXbqU2rVrM3jwYAA6dOgAwPz58+nduzdr1qwp1fgGDx7MoUOHWLlyJadOnWLgwIG4ubmRlpaGp6cn4eHhtzzG19cXuNGN0cfHh0ceeQQfHx+6dOnC/v37GTBgQHl+ZUIIUS3JwksIIezQli1b8PT01L5n62aBgYF0796dbdu28c9//hMHBwetCUKNGjWs/vd27nTMhx9+yPz58/n000/Jysrinnvu4a233sLf3x8ABwcH7ZOnwv8tfP4HHniAZ555hi+//JLIyEj+/ve/8/HHH1t9UvX444+zf/9+FixYwBNPPEGTJk2szvHss8/i7u7OihUr+OqrrwgKCuKtt97SrvsqPNfN2ymNRmOxn4bd/Dv66+/qr5n+Kj8/n/3797N27VoMBgPt2rXj008/1X4fzzzzDNevX2fr1q388MMP+Pj4MHz4cO05PvvsM1555RU++OADLBYLnTt3Zvbs2dr3pTVv3pzZs2ezYsUKTpw4Qb9+/UhPTy/yd3w7r7/+Ot26dePzzz9n/vz55Ofn4+vrqy2WjUaj1eMHDRrEtm3b+Oijj+jdu7e2TdLV1fWWT0GFEEKUjMFS0n0HQgghhKiWzp49y7lz55g2bRqvvfbabT8lE0IIUTz5xEsIIYQQxfrss8/48ssv6dOnj2wzFEKIMpJPvIQQQgghhBDCxqSroRBCCCGEEELYmCy8hBBCCCGEEMLGZOElhBBCCCGEEDYmCy8hhBBCCCGEsDFZeAkhhBBCCCGEjcnCSwghhBBCCCFsTBZeQujERx99ROfOnRkwYADh4eEMHjyYzz77DICjR4/StGlTZs+eXeTjL126RMuWLRk1apR222+//caUKVPo27cv/fv354knnuDUqVN3HEvz5s3p168fAwYM0P5bvnx5uTMKIYSwf7aoV4VSUlL4+9//zvPPP3/bx37zzTeEh4czYMAAhg0bxs8//1whmYSoDPIFykLoRF5eHoMHD2bGjBkAXLt2jaeffhoPDw/8/f1p0KABP/30ExkZGXh4eNzy+I0bNxISEoLZbNZuKygoYMyYMbRu3RqA7du3M378eHbs2IGLi0uRYzGbzXzxxRe4ublVcEohhBD2zhb1CuDXX3/lhRdeoHHjxrfcBzcWde+99x6rVq3Cz8+PU6dOMX78eDZs2IC/v79twgpRgeQTLyF0yt3dnbFjx7Jp0yYAXFxceOCBB9i6destx1osFjZv3szgwYOtbg8JCdEWXQAPPfQQXl5e/Prrr7YdvBBCiGqjIuoV3PgkbMGCBdx77723fZ5PP/2UyZMn4+fnB0CLFi0YMGDAbZ9HCD2ShZcQOla7dm0SEhK0n//+97/z+eef33LcwYMHqVevHnXq1LnjOdPT03FycqrQcQohhKjeKqJe3XvvvTRu3LjI59i7dy9dunSxuq1Lly7s27evHCMXovLIwksIHYuLiyMgIED7uVmzZjg5OfHf//7X6rgNGzYwbNiwO57vhx9+wMvLi2bNmlX4WIUQQlRfFV2v/iozM5O8vDy8vb2tbq9bty5JSUllG7QQlUwWXkLo1KlTp3jrrbeYNGmS1e3Dhg2zehfx6tWrHD9+nPvuu6/Y82VkZPDaa68xY8YMDAbDHZ//2WefZeDAgQwePJgFCxaQmZlZtiBCCCGUVtH16naK2q3h7OxMenp66QctRBWQ5hpC6MjmzZvZv38/eXl5BAQE8Nprr9GmTRsOHTqkHdO/f38WLlzItWvXcHd356uvvqJPnz44OjoWeV6z2czUqVP529/+dss2jduJjIzU9tCnpKTwn//8h5deeok333yz/CGFEELYPVvVq6KYTCZyc3NvuT07OxuTyVSuLEJUFll4CaEjDz/8sNYlqiiurq6EhYWxdetWHn/8cTZu3HjHBdG8efNwcXFh4sSJJRpH4aILwNfXl3/961+EhYVhNptxcHAo0TmEEEKoy1b1qiheXl4ApKamWm03vHjxIrVr1y7TOYWobLLVUAg79Oijj/LFF18QFRWFq6srDRs2LPLYpUuXEhUVRURERIm2GN5Ofn4+RqMRo1GmDCGEECVXmnpVHIPBQLt27Th48KDV7QcPHqR9+/YVMVQhbE7+X5QQdigkJARHR0deffVVHnnkkSKP++qrr/jyyy/58MMPS9zJMDMz0+pC5aSkJP71r38xdOjQMi/chBBCVE8lrVclMWrUKBYuXEhycjIAp0+fZuvWrTz66KMVMVQhbE62GgqhE46OjtSocfuXpKOj4y172B977DHmzZvHQw89ZHXczXvn33nnHfLy8njiiSesHjt06FBGjRoFwMmTJ/n000+JiIgAbjThGDduHFlZWdrzPvzwwwwfPrwiYgohhLBztqhXfz3H7e7r1asXycnJPPXUU1gsFpycnHjrrbcIDAwsRxohKo/BYrFYqnoQQgghhBBCCKEy2WoohBBCCCGEEDYmCy8hhBBCCCGEsDFZeAkhhBBCCCGEjdn1wis7O7uqhyCEEEIUSeqUEEKIQrroapiUlMQXX3wBQIMGDejbty8Ab7/9Ni+88AI//fQTkZGRwI3vcejduzcdOnRg+fLljBo1CldXVwC2bNlCVFSU1bkNBgMPPfSQfMeDEEKIMpM6JYQQoryqfOF1+vRpTp48iY+PDwBXr15l3bp1DBo0iPz8fOBG+9BevXoBcOnSJb799ls6dOiAxWLh5qaM4eHhhIeHW53/4sWL7Nixo0wF7VLkj2WNpSsmJydyc3KqehgVQpUsquQAyVIk77swe/lWzLlKqW7duly8eLFKnrui1a1bt6qHIHWqEqgyj0gO/VEli81yVEGtUqVGFeYoTZ2q8oVXcHAwvr6+xMTEcPnyZRo3bkzNmjVxcXHRjsnIyCA2NhaLxUJqaipGY+XskMydP6NSnsfWcqt6ABVIlSyq5ADJUhTTzNehihZeomJJnbI9VeYRyaE/qmSxVQ6pVZWryhderq6ubN26FWdnZwIDA/n6668ZPHgwBoNBO2bTpk1YLBbc3d0xGAyEhoZq97399tt07tyZ3r17s3LlSmJjY60KXuEWDiGEqEwmJye8qvDTGj18UqQKqVNCCFVVVa1SpUaVNkeVL7wA4uLimDp1KgaDAWdnZ06fPk29evW4cOECixYtIi8vj44dO+Lh4UF2dja//PILp0+fBuCFF17Azc0NgCtXrjBjxoxbvjFdCCEqW25OTpVtpVBlGwfopzhLnRJCqKgqapUqNcoutxoC3HfffSxcuBAPDw8yMzMZOXIkAPXr1+e5554jPT2dmJgYatSoga+vLw0bNsTLy4u1a9fi4OCgnadNmza8+eabWCwWYmJiCA4OBiAoKIgRI0aUelymma9XSL6qpsr+ZlAniyo5QLIUyfuuijmP0AWpU7alyjwiOfRHlSy2vMZLVB5dLLzat29Pq1atmD59Or6+vrz55puMGTNGu9/T05O9e/dy6tQpcv7vj85gMODv70+NGv+LcPXqVe0i5sL/NRgMNGvWrEzjMjcs2+P0xkuRdxZAnSyq5ADJIqoHqVO2pcprT3LojypZVMlR3VX5wisyMpJdu3aRlZWFyWQiOzsbJycnvv76a+Li4khJScHX1xcfHx/Cw8Np3bo1AGazmX//+98kJydTp04dFi5cSEZGhnbemjVrAje+Q2XZsmUcPnyYCRMmVElGIYQQ9kvqlBBCiIpQ5Quvnj170rNnT37//Xe+/vprcnJyMBqN1K9fn2vXruHre6PTysGDB6lTpw4FBQU4OjqSlpaGp6cn3t7eAEyePPm25y9s0ztq1KhSj83h3Jky59KTtLjzOCjwMTuok0WVHCBZqkQVtqqvjqRO2Z7dvPbuQHLoj+6zyHxerVT5wqvQ6tWrmT59unYB8nvvvcf999+v3e/j40NKSgqbNm2ioKAAJycn7rrrLi5fvqxd1FbR3aKkTa/+qJJFlRwgWaqCtP+tGlKnbMdeXnt3Ijn0R+9ZZD6vXnSz8AoPD+f999/XtnDUrVuXtm3bkp+fz9WrV4vdfpGdnY2zs7N0ixJCVAslaf+rl26AKpE6JYSoaKVp567SvK5KltLmMFgKr+6tIj///DNxcXHazydPnqRly5bau4EFBQWkp6fj7Oxc5DlatmxJ8+bN2b17N/v376+wblGx/TuU+jFCCGFrppmvF9tUQZVWvaCP4ix1SghhK3eazwupNq+rkKUs7eSrfOFV6OTJkxw4cIDU1FQcHR0JCQmhd+/e2ruCycnJfPvttyQkJGCxWPD29ubBBx+kfv36tz1fREQE06dPL9eYLkX+WK7H64UqrVRBnSyq5ADJUiXucE2AKkUN9LHwKiR1ynbs5rV3B5JDf3SfpYTXeKk2r6uQxW6/x+v06dPs2bOHJ554glq1apGXl8eBAwf44IMP+Mc//gHA+++/z1NPPUVQUBAGg4HExESWLl3KlClT8PT0vKVbVGpqKnPnztV+9vb2LnW3KGnTqz+qZFElB0gWUT1InbItVV57kkN/VMoi7J/xzoeUz6VLl5g7dy5z585l+fLl2u0LFiwAYPPmzaxevZqoqCgWLFjAiRMnsFgs7Nq1C7PZDNzYxpGXl0dsbCx//PEHmZmZ7N69m5ycHLZs2UJSUhKTJ0/m8ccf185f2EUKICAgQFr0CiGEuC2pU0IIISqDTbcaxsXFWe2LL9SsWTMWL15stcXil19+YfPmzVy5coW6deuSnJzMzJkzqVWrFsePH+f7778nPz+f1NRUatWqRXZ2NkOGDOHs2bOEhoZSu3btIscxb948Zs2aVerxyxYO/VEliyo5QLJUuApoLazKNg6w/VZDqVP6oIvXXgWQHPqjShYlcvxffVOlRuluq6Gbmxt33XUXly5d4o8//qBZs2Z4eHjg5OR0y7EtWrTg22+/5eWXX8bT05NFixbh6OgIQF5eHp06dSI0NFQ7fv369fj5+XH27FmbjV/a9OqPKllUyQGSpaJJa+HKJXVKH/Tw2qsIkkN/VMmiQg6pbzZeeHl5eREbG0tMTAydOnXiu+++Y8SIETg7OxMdHc3cuXMZM2YMPj4+LFu2jF69euHp6ak9/u2336Zz587UqlXrtuf/448/SE1N1X7+/fffWbJkCTVr1rQ6LiAgwDYBhRCigpWmtXBx9NSUQs+kTgkhROW4ub6pUqNKm8PmzTX27t3L2LFjMZlM9OjRg2PHjvHAAw/QuHFjpk+fzoEDB1i5ciUDBw6kRYsWVo994YUXcHNz4/Dhw7c996VLl6wuVL527RphYWH07t3bppmEEMJWcnNyyr0FQ5VtHFA5xVnqlBBC2F5hfVOlRuluqyFAYGAgu3btolevXuzbt4+HHnrI6n4PDw+mTZuGg4OD1e1OTk7ad6QYjUZyc3PJzs4mKyuL5ORkADp16kTOTftdAwMD+e677zhy5AiJiYnUqlVL+16ViRMn3vIO452YZr5e6rx6pMS+4P+jShZVcoBkqXDed1Xt81dDUqeqni5eexVAcuiPKlmUyCH1zfYLr379+jFv3jxOnTpFly5daNSoEXCjkMGNL5XMzs5m2bJlJCcnYzQaKSgoICwsDBcXFwAaN27Mxo0bWbNmDa6urtStW5fb9QTx9fVl5syZAKxbt45u3bpRr169Mo9d2vTqjypZVMkBkkXYP6lTVU+V157k0B9VsqiSo7qz+cKrRo0axMbGEhQURG7u/y4NLLxwOTU1lc2bNxMQEMDgwYPx9fXls88+Y+fOnezdu5epU6dSs2ZNBg4cyDfffEN2djYNGzYkMTERgKioKEJDQ9m9ezc//PADCQkJAOTn57Njxw4MBgNOTk4EBAQwcuRIAgMDbR1ZCCGEHZE6JYQQojLYfOF1/PhxnJ2dSUtL4+uvv+ann37CwcGBCxcuMGTIEJKSksjKyuLEiRPs2rWL+fPnc+HCBcxms1b0CgoKOHfuHI0bNwYgMTERT09PTCaTtnc+NDTUqpvUzcraptfh3JmyhdaZtLjzONj7x9P/R5UsquQAyaJHGTmZ4ORa1cOwG1Knqp4qrz3JoT9VnqUCviJEqMPmCy8/Pz+mTJkC3Git++ijj9KgQQMiIiKoWbMmNWvWJCQkhP3797N48WKmTp3KtWvXmDdvHmvWrNHOU9gxKj8/n08//ZT//Oc/mEwm7rrrLnx9bfMHLW169UeVLKrkAMmiR6aX34XABlU9DLshdarqqfLakxz6U9VZpIW6uJnNF16FLXK3b99O06ZNSUpKYu3atcTExFgdV6tWLfr27Uu/fv1YsmQJfn5+2n1Go5GQkBDMZjNLly5lzJgx/PTTT8TFxWEwGKhR40YMadMrhBA3qNKqtzJInRJC2EpFfUUIqDWvq5JFd+3kk5OTWb9+Pb6+vsTFxdGkSRPmzJlDRESEdkxubi5fffUVQ4YMITExkaysrFvOEx0dzaZNm/D19eXw4cMMGTIEZ2dnq/NIm14hhLhBlYuwK6M4S50SQthKRXxFCKj3NSEqZNFdO/nc3Fy++eYbhg0bxl133UVubi67d++mefPm2rt/2dnZfPTRR4SHh5OdnU1KSsotBe38+fMcOXKEZ599Fnd3d6Kjo9m1axd9+/a1Ok7a9N6eEi1I/48qWVTJAZJFj2rcVbuqh2A3pE7pgyqvPcmhP1WeRVqoi5vYdOFlMpkYNWoUK1eu5OzZsxiNRmrWrMlvv/2mteD99NNPGTBgANnZ2WzZsgXglu9KCQ4OxsXFhS+++ILevXsTFBRETEwMKSkpJCQkkJSUhJ+fHxkZGZjNZgAsFovWyjcgIKDUxQykTa8eqZJFlRwgWfTIo25dMhTIURmkTumDKq89yaE/KmUR9s/mWw2joqJISEigRYsWNGrUiJYtW5KXl8fUqVMZN24cPXr0ICoqCoAWLVoAsHfvXjZv3kxqaioA//3vf9mzZw+xsbH4+fnRuXNn0tLSyMnJoXPnzhQUFLB792727dunPa+7uzuOjo4AREZGEh8fL216hRBC3ELqlBBCiMpg84WXu7s73t7e5Ofn8/333+Pt7Y3ZbMZoNGoXI4eEhBAZGcnx48cxm83UqlWLQYMGaRc25+Xl0aZNG65cucKJEyc4ceIEiYmJREVFkZaWRvfu3aVNbzGqvJVqBVIliyo5QLJUOGk9XOmkTlU9Xbz2KoDk0B/dZZE5vlqz+cIrODiYZ555hkWLFpGZmcmaNWvIz8/XvvsEYM+ePVy4cIHRo0dTo0YNduzYwYYNGxg+fLjVuf79739r/16/fj2BgYGcOHFCe8ewortFSZte/VEliyo5QLJUNGk9XPmkTlU9Pbz2KoLk0B+9ZZE5vnqz+cIrNTWVdevW4e7uTp06dXj88cfx9fW16vKUnJxM48aNcXd3ByAkJIQdO3Zw113FX5B46dIl7YspQbpFCSHsX0W1HlalVW9lkDolhKgs5ZnjVZrXVcmiq3byOTk5bNiwgfDwcAICArh8+TLbt2/n8ccftzquX79+LF26lF27dmnfdzJmzBjtfqPRSG5uLtnZ2WRlZZGcnAxAp06dyLnp4+OK7hYlhBCVrSJaD6vSqhdsX5ylTgkhKlNZ53jV5nUVsuiunbyTkxPPPPMMm00SUgMAACAASURBVDdvpnbt2vj4+GjFrLBNL4CrqytTpkzhxIkTJCUl3fJOYOPGjdm4cSNr1qzB1dWVunXrap2gbubr68vMmTMBWLduHd26daNevXplHr+06dUfVbKokgMkS4WT1sOVSuqUPujitVcBJIf+6C6LzPHVms23GgLExMRgNput2u++8MILnD9/nrVr12q35eTkYDabOXLkiHZbaGgo3bt3Z/To0VbnXL9+vdW7iH/tFpWRkcHZs2etCmdpu0VJm179USWLKjlAsgg1SJ2qWqq89iSH/qiURdi/Sll4eXl58frrr2M0Gq1uf/TRR5kzZw6XLl3i448/trqQ2cHBgaFDh9KoUSN++uknIiMjATAYDPTu3ZtatWqxefNmbX99aGgoSUlJREdHA+Dh4QHc2P4xcOBAWrZsWRlRhRBC2CGpU0IIIWzNYLndXogqZLFYOHLkCAcPHmTs2LG4urpa3X/p0iW+/fZbRo0axaJFiwgPD6d27dpW7xje7Ny5c5w6dYpBgwaVeiyXIn8sUwa90d3H7OWgShZVckA1z6LTtsCq7J8HfV6ALXWq4qkyj0gO/dFlljLUDtXmdRWy6O4ar5Iym81cuHCBY8eOkZCQQEJCAl27diU2NpYGDRqQnZ1NbGwsFouF1NRUq3ckvby8iixmANevX9e6UJWWtOnVH1WyqJIDqncWaQtcfUidsi1V5hHJoT96zCK1o/rSxcJrw4YN+Pj4EBoair+/P3DjHcPTp0/j7OzMnj17sFgsuLu7YzAYrL6A8u2336Zz585FtuaNi4ujUaNGlRFDCFHNVFTrd1vQ4ydF9kzqlBCiopS1dqg0r6uSpbQ5qnyr4ebNmzl16lSR9xsMBry9vWnVqhUeHh5kZ2eTnJyMg4MD586dY9SoUbi5ubFw4UKr70opFB8fj7+/P35+fkyYMKFUY4vt36HUeYQQ1Ydp5uu6bG6gyjYO0EdxljolhKhIZakdqs3rKmQpy1bDKl94/dXChQsZM2YMbm5u2m3p6enExMRQo0YNnJ2dqVmzJl5eXqxdu5YhQ4Zo34Fys4iICKZPn16uscjeef1RJYsqOaCaZ5FrvGxODwuvv5I6VfFUmUckh/7oMotc46VEFru9xgvgwoULHD9+nIyMDLZs2cLdd99Nt27dcHBwwNPTEy8vL1577TVyc2/s1jUYDPj6+tK5c2caNWpEZmYm7777rva9KRcuXGDu3Lm4uLgwadIkHB0dSz0mPb6TXRYqtVJVJYsqOUCyiOpD6pTtqPLakxz6o1IWYf90sfA6c+YMBw4c4IknnmDw4MEA/PbbbyxevJhnn30WgGPHjjF69Gg6duyIxWLh0KFDrFixQmvH6+rqyuzZs7VzFr6TuGzZMrKysspU0IQQQgiQOiWEEKL8dLHwys/PJzU1lcuXL+Pn50dWVhaJiYlkZmZqx3h4eHD06FF+/fVXkpOT+fPPP3F0dCQpKQlvb+9bCpbBYAAgMzPzlla/JeVw7kzZQ+lIWtx5HPT2MXsZqZJFlRxQTbLodEuhqDxSp2xLlXlEcuhPibLIHC8qiS4WXq1atSIvL48vv/yStLQ0nJycaNKkCZMmTdKOSUlJwWAwkJCQAEC7du1o164dycnJXLx4kaCgIKtzjhs3DrjRAri4Nr7FkTa9+qNKFlVyQPXIIq1/hdQp21JlHpEc+lOSLDLHi8qiu+Yat1OSjlJms9nqe1MKxcTEEBwcTGhoKN27dy/V80q3KCEEgPvL7+LVqUdVD0PomNQpIeyXzPGistjFwuuvbtdR6mYnT57k4sWL9OnTp1zPIwVNCAH6bRtfFFU6RoE+uxqWhNQpIeyHPczxqs3rKmSx666GJVFcRylbMM183SbnrWy6bKVaRqpkUSUHVJMs3ndV/mCEXZI6VTaqzCOSQ39KlEXmeFFJdLHwunTpEh9//LHVbQ4ODgwdOpRGjRphNpt56aWXSE1NpW7duhiNRi5cuMDZs2c5ceKEtsd+/fr1REdHk5GRQU5ODseOHcNoNDJw4EBatmxZ6nHp/d2PklKplaoqWVTJAZJFVA9Sp2xLldee5NAflbII+6eLhZe/vz9z5szRfv7555/ZvXu39tGdg4MDf//739m+fTsjRozQOkq99957pKWlaY8bNmwYAL/88gvx8fH06dOHc+fOcerUqTIVNCGEEAKkTgkhhCg/XSy8ABISEjhy5Ai//fYbsbGx9O3bl9TUVJydnTEajbftKJWTk8Njjz12y7maNm1Ko0aNALh+/Tru7u5lGpO06dUfVbKokgOqSRZpNSyQOmVLqswjkkN/is0ic7uoZLpYeK1ZswY3NzdatWqFp6cnkyZNIj4+nhMnTpCTk0P9+vWZNm0a3t7e2mNycnJwdXUlMDDwlvOZTCbt33FxcVpxKy1p06s/qmRRJQdUjyzSalhInbItVeYRyaE/xWWRuV1UtipfeF29epX7779f+/nw4cMEBQXh6upKhw43ujWZzWYCAwN5/vnnizzPwoULycjIuOX2+Ph4/P398fPzY8KECRUfQAihPJOTE1521l3PXrsB6pHUKSHUZG9zu0rzuipZSpujytvJHzt2jPj4+CLvNxgMhIWF8fHHH5Oens61a9fIzs7G1/fGOxQDBw6kdevWtzwuIiKC6dOnl2ts0qZXCAH20Wr4Zqq06gV9FGepU0KoyZ7mdtXmdRWy2GU7+fbt29O+fXv27dvH3r17yc3NxWKxEBQUxJAhQ7R974UdoSrqu09KQtr06o8qWVTJAdUki7QartakTtmeKvOI5NCfYrPI3C4qWZV/4gU33k08evQoI0eOxNnZGYvFwpkzZ/j6668ZOnQoa9euJTc3l7i4OAoKCrBYLDg4OGAwGPD396dfv3506dKFiIgICgoKgBvfpVK/fn1MJhPPPfccrq6upR6XCqtxUOedBVAniyo5QLLokSo5QB+feIHUKVtT5W9WcuiPKllUyQHqZLHLT7wA3NzcuH79OleuXNFa8F6+fBlnZ2caNGhg1cK3UGEr3/Hjx2vFatasWdr9b7zxBtOmTWPNmjUkJSURHBxcWXGEEEIoRuqUEEKI8tLFwiskJITc3Fw2bdpEamoqTk5OhISEMH78eKvj7tTK92bjxo0r97ikTa/+qJJFlRwgWaqMtEGuVFKnbMuuXnvFkBz6Y/MsMheLUtDFwgugdevWt734uFBZWvnCjZa9fn5+ZRqTtOnVH1WyqJIDJEtVkTbIlU/qlO3Y02uvOJJDf2ydReZiURq6WXgVp6Ja+QohhCru1AZZL9dGVRdSp4SoniqzJb1K87oqWeyunXxJ2KqV751civyxzGPWk2rTnciOqJIDJEuVKWZ7iyoXLoP9FGepU+VjV6+9YkgO/bF5lkraaqjavK5ClrI017CLhVehO7XyLVRRrXxV+KMAdf7AQZ0squQAyaJHquQA+1l4FZI6VTaq/M1KDv1RJYsqOUCdLHbb1bAoKSkp+Pj4YDAYOHbsGFFRUUyZMsWqle8HH3ygtfItlJubS35+PseOHdNuCw0NpXv37lURQwghhKKkTgkhhCgpXSy8VqxYoa18HRwcGDt2LL6+vnz++eeMGjUKNzc3fvjhB86dO0dCQgJOTk6YzWYyMzPx8/PTWvlu2bKFqKgoTCYTJpMJuLG946GHHqJ9+/ZVGVEIIYQdkzolhBCivHSx8Hrqqae0f8+fPx9nZ+dbjnnuuec4duwYx44d48qVKzg5OZGTk8PIkSO1Y8LDwwkPD7d63MWLF9mxY0eZCpq06dUfVbKokgMki7QSrh6kTtmWKvOI5NAfVbJUeg6pbTahi4VXof3799OgQQP27t3LsWPH+PPPP7X73nnnHXr27Em7du2021xcXPDy8rLZeKRNr/6okkWVHCBZpJVw9SJ1yjZUmUckh/6okqWyc0htsw3dLLwOHjzInj17MJlMPP300/Tp04dFixZp9xsMBg4ePHjL4+6++27toraVK1cSGxtr9SWVhVs4hBDCFiqzlXBp2FtTCnsgdUoIUV3YurapUqNKm6PKF17p6el89tln+Pn5MXPmTGJjY1myZAkTJkwAID8/n8uXL2s/3052djbOzs5cuXKFGTNmaPvmy8s08/UKOU9Vk7aw+qNKDpAsuW6euuvOpErHKNBHcZY6ZXuqzCOSQ39UyVLZOWxZ21SpUXbZTj4rK4usrCy8vb1vuW/VqlWEhobe9h3Em7Vs2ZLmzZuze/du9u/fj8ViISYmhuDgYACCgoIYMWJEqcemwh8FqPMHDupkUSUHSBY9UiUH6GPhJXXK9lT5m5Uc+qNKFlVygDpZ7LKdvIuLCy4uLqSnp7Ny5UquXbuGwWAAoH///tSrV4969eqRnJzMt99+S0JCAhaLBW9vb+6//34CAwO1i5xDQ0MJDQ0FICIigunTp1dVLCGEEIqQOiWEEKIiVPnCq9BHH32E0WjEYrHg5ORESEgIGzduJCQkhDVr1rB3715atGiBm5sbjo6O3HvvvSxdupTOnTszePBgFi5cSHR0NJmZmQDk5eUxZswYAPz8/KhXr16x20CEEEKI4kidEkIIUR66WHidPXuWuLg4unXrxsCBAzGbzXzzzTdcvHgRR0dHRo0aRWRkJH379uXMmTM0adKETZs2kZCQoJ1j8uTJtz33uXPnOHXqFIMGDSr1uKRNr/6okkWVHGBnWaQ9rigjqVO2ZVfzSDEkh/5UahapMeIOdLHwyszMpEWLFvj6+rJs2TJyc3Px9PQkMDBQ287RoEEDoqKiOHDgAMePH6dJkyZ07dqVa9euFXvu69ev4+7uXqZxSZte/VEliyo5wL6ySHtcUVZSp2zLnuaR4kgO/anMLFJjxJ3oYuHVtm1b0tLSOHToEGazGQB3d3emTJmiHePo6MjQoUMxmUw0adKEo0ePsn//flq3bl3suePi4mjUqJFNxy+EsA8laY+rh2YOFUGVHHohdUoIcSfSgr3kVMlS2hxV3tXwTnJycliyZAmnT5+mSZMmxMfH4+LiQufOnalfvz4xMTH079+fhQsXkpGRccvj4+Pj8ff3x8/Pr9R75y9F/lhRMaqUKq1UQZ0squQAO8tyh20gqnVaUoE9FGepU+VnV/NIMSSH/lRqFhtuNVRtXlchi112Ndy8eTMnT56koKAAR0fHW+43GAw8/fTTAJhMJlxdXbXjfv/9d9zc3IBb985XRLcoc8Nm5Xq8Xngp8gcO6mRRJQeolUWI25E6ZXuqzCOSQ39UyiLsX5UvvB5++GHq1avHyZMnSU9PJyMjA6PRSNOmTenXr5/WghfgpZdewmQy4eDggNlsxmg08vzzz2v3r1+/nujoaAAuXLjA3LlzMRqNDBw4kJYtW1Z6NiGEEPZP6pQQQoiKUOULL7hRfM6ePcucOXNwd3enoKCAqKgo3n//fV588UXgxh74xMRElixZoj1uzpw57Ny5k/79+wMwbNgw7b7CdxILu0VJQRNCCFFWUqeEEEKUly4WXj4+Ply8eJGIiAicnJwwm82kp6dz7do18vPzqVGjBklJSWRkZHDq1ClCQkK4evUqGRkZ5Ofnk5KSgq+v9Z7awi5T5ekWJW169UeVLKrkgGqWRVoFV1tSp2xLlXlEcuhPRk4mOLlW9TCEAHSy8AoNDcVkMvHNN9/g4OCAu7s7eXl5REREYDQaKSgo4IcffmDKlCl8+OGHBAQE4OXlhZ+fHyaTicuXL99S0MaNGweUr1uUtOnVH1WyqJIDqlcWaRVcfUmdsi1V5hHJoT+ml9+FwAZVPQwhAB0svLKzs8nNzcXR0ZH27dsTFhYGwHvvvae9k7hmzRp69+5NmzZtqF27NmvXrmXs2LEsXryYXr164ebmZpNuUUIIcTNbtwquSPbQDdBeSJ0Swr6pMh+qkgPUyWJ37eQPHz5MbGxskfcXFBTQsWNHgoODrW4zGo0sWrSIUaNGaR2jblYR3aKkTa/+qJJFlRxQzbLYyVZDVVr1gj6Ks9Qp21NlHpEc+uMaGESGAlsNVZvXVchil+3kO3XqRKdOndi3bx979+4lNzcXi8VCUFAQgwcPxsPDA4DVq1fTr18/fHx8MBqNADg4OGh75G1B2vTqjypZVMkBkkWoT+qU7any2pMc+uNRty4ZimQR9q/KFl7Z2dlaC95jx44RFRXFlClTcHZ2xmKxcObMGT744ANmzLixf91sNmM2m63OMX78+EoftxBCiOpB6pQQQoiKZPOF15UrV9i6dStXrlyhS5cudOrUCbixN37atGkAREZGcurUKeLj4zGZTBQUFJCenm710Z2XlxeLFi3CZDJZnb9Xr1707NkTuNHud82aNdq/586dC0BAQACjRo2ydVQhhBB2SOqUEEKIymDzhdeSJUsYPXo03t7erF27lm+//ZYaNWoQExOjHZOZmcmkSZPYt28fqampuLq60q5dO3r37q0dk5aWxuXLl/Hx8cFkMnHt2jWys7NJT0/Xjqlfvz5z5swBID09HU9PTwDmzZtXprFLm179USWLKjlAsuhRiXPYyTVrtiZ1qupVu9eezqmSA9TJYtc5pNZobLrwunbtGu7u7loL3QcffJCcnBxGjx5NRESE1bGtW7emdevWRZ5r1KhRODo68sADD1C7dm1OnjzJxYsX6dOnz22PLyxm5SFtevVHlSyq5ADJokclzSHt8aVO6UV1e+3pnSo5QJ0s9pxDas3/2HTh5erqSkJCAjk5OTg5OXH8+HFOnDjB3Llzrd5JvPvuu7XtFjcbP348Pj4+JX6+33//nSVLllCzZk2r2wMCAsqcQQghVGVP7fFtReqUEELY1u1qjR461lYE3bWTP3HiBJ9//jkGg4EmTZowcuRIDAYDERERjB8/nvPnzxf7+Dp16uDv78/8+fOJi4u7ZQtHixYtGDt2rPZcSUlJVls/ykPa9OqPKllUyQGSRY9KnMMOtn9URnGWOlX1qt1rT+dUyQHqZLHrHH+pNdJO3obatGlDmzZtsFgsVi11Cy8+/msHqL8qXBfOnDlTuy0iIuK2++EDAwP57rvvOHLkCImJidSqVUvrSDVx4sRb3mG8E2nTqz+qZFElB0gWPVIlR2WROlX1VPmblRz6o0oWVXJUd5XSTv7q1at88sknXL9+XbutT58+eHp60r59e86ePcumTZvIz88HbhSxsLAwunbtWqrn8fX11QrfunXr6NatG/Xq1au4IEIIIZQkdUoIIYStVUo7+VdffRVvb2/CwsLo1KkTubm5TJgwgSVLlmA0Gnn77bepW7cuNWrcGI7FYmHZsmXUq1ePgIAAFi5cSEZGhnbO1NRUq7323t7eNGvWjB07dnDp0iUA8vPz2bFjBwaDARcXF+rUqcPIkSMJDAy0dWQhhBB2ROqUEEKIylAp7eRbt26Nr68vp0+f5ptvviE3N5esrCwcHBwAyMvLY8CAAbRo0QKj0ci5c+dYt24d3t7eAEyePJmVK1dy9OhRfHx88Pb21vbOh4WF0b9/fwBCQ0NvO4Z33nmH559/vtRjlza9+qNKFlVygJ1msYNrm0TlkTpV9exyHrkNyaE/qmTRckj9smuV0k5+xIgR7Ny5k6SkJFJTU+nZsyfu7u7accHBwfzxxx/s2rWLgoICAgMDmThxIi4uLtoxpW3Te7Ob9+yXhrTp1R9VsqiSA+wzi7S2FYWkTumDPc4jtyM59EeVLIU5pH7Zt0ppJ5+Xl0dYWBhms5m4uDiio6Ot2vTWr1+fM2f+967dhQsXWLJkSanb9N5OZmYmbm5u5TqHEEItxbVRr64tbqsrqVNCCHuiyteAqFKjqm07+d27d7Nv375bHp+VlUVGRgZ+fn6l3jsvbXr1R5UsquQAO81SxFYN1VrcqkCldvJSp4pml/PIbUgO/VEli5ZDga2GqtSoat1OPjQ01GrvfEV0i5I2vfqjShZVcoBaWUT1JHWq6qkyj0gO/VEliyo5qrsqayc/aNAgrU3vmTNnWLVqFXl5eXh4eGA0GgkLCyMuLo727dsDcODAAc6ePWu1n/7MmTNkZWXRrl07ANavX090dDQACQkJREVF4ebmxsCBA2nZsmVlRBVCCGGHpE4JIYSwtUpZeK1bt45BgwYRHBwMQG5uLvPmzSMkJASj0cibb77J3/72N2rXrs327dt59tlnWbx4sfZ9Ka+//jopKSna+XJycpg7dy5Xr17FbDZz6NAhJkyYwLBhw6yes1u3buTl5XHq1CkpaEIIIYokdUoIIYStVcrCy8fHh19//RU/Pz9MJhPnz5/HwcEBBwcHUlJScHR0pE6dOrRo0YLc3Fy+/vprsrKyiI+PZ+7cuQwZMkQ71++//05qaipPPvkkhw8fJjMzs8j2vAaDgevXr1t1pioNadOrP6pkUSUHSJYyU2CfvkqkTlUtVeYRyaE/5coi87SoYJWy8Bo8eDA7d+7k448/Jjc3l3r16jFp0iTgRtG55557tDa9qampuLu7M3HiRFasWMH06dO18yQkJHD27FmeeeYZVq5cSWJiIl26dLntcw4YMAAXFxd++eUXGjVqVKZxS5te/VEliyo5QLKUlbQE1hepU1VLlXlEcuhPebLIPC0qWqUsvAr3woeFhd1yX2H3J19fXxo2bMh3333HpEmTcHV1JTo6mvnz5zNq1CiOHz/O2bNncXBw4OzZs4waNUp7JxFg4cKFZGRk3HL++Ph4/P398fPzY8KECTbPKoQQJWHrlsCqtOqtLFKnhBB/pbfW7SrN66pk0V07+ZL69ddfyczMpEWLFjg5OQGwdOlSnn76ab766it8fHzo1q0bBQUF7Nixg8aNG3P58uUit3BERERYvQtZFtKmV39UyaJKDpAsZWbDLSyqtOoFfRVnqVO2o8o8Ijn0p1xZdLTVULV5XYUsumwnX1IWi4Xt27ezfft2LBYLLi4uPPnkkwAMHDiQ8+fPc/z4cdq2bUufPn0ASEtLw2g02mxM0qZXf1TJokoOkCyi+pA6ZTuqvPYkh/6olEXYP90svNasWUNQUBBjx44FIDExkddff51XX30Vk8lEeno6UVFRHDp0CIBOnTrRoUMHq3Ns2bKFqKgoAC5cuMDcuXMxGAw89NBDWrtfIYQQoiykTgkhhCgP3Sy8DAYDaWlp5OXlkZeXx+nTp7V/m0wmDh06xL59+3B0dATgyJEj1KpVi+7duzN48GAAwsPDCQ8PB+CNN95g2rRpXLx4kR07dkhBE0IIUS5Sp4QQQpSHbhZeTZo0Ye/evSxYsIAaNWrQvHlzGjZsqN0/btw4xo0bR3Z2Nnv37iUqKophw4YVua9y3Lhx5R6TtOnVH1WyqJIDJEu56egaAlE8qVO2o8o8Ijn0R5Uspc4htUWXdLHwSklJITk5mXHjxhEVFcUTTzwBQHR0NABXr17lwIEDnDt3DoPBoO2tP3jwIB06dKBevXoArFy5ktjYWKv99IVbOMpC2vTqjypZVMkBkqW8pF2xfZA6ZVuqzCOSQ39UyVLaHFJb9KnKF16JiYmsXr2a0aNH4+PjQ0ZGBqtWrdIuWIYbRalRo0aEhYVx4cIF0tLS6NSpE6mpqVbnunLlCjNmzMBkMlV2DCGEKBNbtSvWUzdAeyd1Sghhb/TWCv+vVKlRdtdOPiUlBWdnZ9zd3bXbCgoKMBqNLFq0iL59+7J27VrtvoyMDHJzc/Hx8dFuCw0NpXv37uzevZv9+/djsViIiYkhODgYgKCgIEaMGFHqsUmbXv1RJYsqOUCylJsNtoOo0qoX9FGcpU7ZnirziOTQH1WylDqHjrcaqlKj7LKdvK/vjT+K1atX069fP3x8fLQtGA4ODtSuXZs5c+awb98+unfvXuy5QkNDte9KqYjvR5E2vfqjShZVcoBkEeqTOmV7qrz2JIf+qJJFlRzVXZUvvAqZzWbMZrPVbePHj9f+faeCtnDhQjIyMrSfU1NTmTt3rvazt7c3EyZMqMARCyGEqE6kTgkhhCgP3Sy8vLy8WLRoESkpKdSuXVt7N3HQoEE4ODiQnJzMrFmztONdXV2Jj49nwoQJ3HPPPUyePJn169drFzp7e3sDYDQaGThwIC1btqz8UEIIIZQhdUoIIUR56GbhFR4eTuPGjdm4cSPBwcFax6jc3FyOHj1KjRo1GDBgAOfOnSMvL49hw4axatUqGjVqpJ1j2LBht5z33LlznDp1qkwFTdr06o8qWVTJAXaQRcf73IV9kTplO7qfR0pIcuiPfE2I0BNdLLzS09P54YcfuHTpEjNnzuTnn3/mnXfe4cEHH6R58+Z069aNvXv30r17dxwdHcnOzsbDw6NE575+/brVBdGlIW169UeVLKrkAP1ncX/5XbxatC7x8Xpo5lARVMmhF1KnbEvv80hJSQ79qYospa07JaXSvK5KltLm0MXCa+fOnYSEhDB48GA2btxIeHg4LVu2JDIyEn9/f+66664iH3vu3Dnq1KlT5DFxcXFW7zYKIaqX3JycEl+QrFqnJRXopThLnRJClFRp6k5JqTavq5DFLrsaXr16lS5dugA3vislOjqaxMREatSoQZs2bTCbzRQUFBT5+ISEBFxcXFi7dq3VRcuF4uPj8ff3x8/Pr9QXLZtmvl66MDqlSitVUCeLKjnADrJ4F/1/iIUoCalTtqf7eaSEJIf+VNXXhAhxO1W+8Dp//rzVqrdVq1b897//1X42GAzcf//9wI3vTcnNzeXKlSvExMQA0KNHD9zc3GjcuLHVeaVN7/+o1IJUlSyq5AC1sghxO1KnbE+VeURy6I9KWYT9q/KFV9u2bWnbti379u1jw4YNeHt7Y7FYCAoKwmw2a19MmZqayooVK3B2dsbLy4usrCz+/PNPcnNzcXNzA7DqFnXhwgXmzp0r3aKEEEKUi9QpIYQQFaHKF14Ax44d4+TJkwQGBjJ16lQsFgtnzpzhvffe49FHH+Wpp55i1qxZJCYmAhATE8Px48eJj4+32t5xc7eowncSy9MtSgghhACp+JUvDAAAIABJREFUU0IIIcpPFwsvNzc3Ll68yJUrV8jPz+eTTz7hzJkzVnvhvb29mTJlitXjXnjhhSLPaTAYgPJ1i5I2vfqjShZVckA1zSKtgqsdqVO2pco8Ijn0x26ySF2pFgwWi8VS1YMwm828/PLLGI1GUlNTCQ4OJiQkhAsXLjB69Gjc3NzYv38/CQkJVo8rKChg6NChWvG6WXp6Op6enmzbto1GjRrRpEmTUo8rtn+HMmcSQqjL/eV38erUo6qHISqR1CkhhC1JXakeqvwTr5ycHD7++GOGDRtG69at2bVrF7GxsQwYMIBFixZx6dIl1q5dW+TjX3vtNUJDQzl27FiFd4sSQojbsUWr4IqkSqte0Ec7ealTQghbu1NdUW1eVyFLWdrJV/knXhcvXiQzM9PqO0wKCgowGo0sX76cxx57DBcXF+2+kydPEhsbS//+/Ys9b0V0i7oU+WO5Hq8X0hZWf1TJAdU0i863hKhS1EAfCy+pU7anyjwiOfTHbrLcoa6oNq+rkMUuv8ercLCZmZl88sknpKamYjQaKSgooG/fvri4uJCdnc3bb7+NxWLh2rVrZGdnc+LECVxcXPjHP/6B0Wi0ydikTa/+qJJFlRwgWYT6pE7ZniqvPcmhPyplEfavyhdehTZv3ozBYGDOnDkA5OfnM2XKFBo0aECtWrW4++67+fPPP3F3d9cuQo6OjiYhIYGAgABA2vQKIYSwHalTQgghykM3C69atWpx9OhRrl27hrOzMzExMeTm5uLg4ABAbGysVuwKLVu2DGdnZ+3nm9v0vvHGG0ybNk3a9AohhKgQUqeEEEKUh24WXn5+fly/fp3ly5cTHR1N4aVnxW3PaNWqFa6urre9b9y4cYC06QU7aqVaAqpkUSUHVJMsOr+mS1QOqVO2o8o8Ijn0x+6ySL1Rmi4WXmazmV27dvHYY48RHx/P5MmTAVi0aJF2TGBgIHPnztX2zvv63vijbNSokdVFzYU8PT0BiIuLs7ogujRy588o0+P0JreqB1CBVMmiSg6oHlncX34XrxatK3Us5aWHphQqkTplW6rMI5JDf+wtS3H1RqV5XZUspc1R5Quvwja9Dz30kNamd9WqVTz55JPAjWKXnp7Oww8/DMCZM2eIj48nLCxMO0dubi4fffSRtOkVQtiE3tvH/5UqHaNAH8VZ6pQQorIUVW9Um9dVyKJkO/n777+fY8eOFXuOFi1aEBISYnWbtOn9H7tppVoCqmRRJQdUkyx2tvVDlaIG+lh4SZ2yPVXmEcmhP3aXpYh6o9q8rkIWm7aT3717N6GhoVa37dy5k/vvv7/ET3Y7d2rTGxwcTHBwMBcvXmTLli2kpKQA4ObmRt++fWnWzHatdKVNr/6okkWVHCBZhH5InbJfqrz2JIf+qJRF2L8SL7z27NlzS0Hbs2dPuQtaoY0bN+Lr66tts8jPz+e5555j3rx5eHl58f7779OzZ09SU1MBaNmyJV988QUTJ07E29sbkDa9QghRnUmdEkIIoWclXngVFBSU6LaycnZ25ueff6Zv375am96CggIcHR0xGAw4OjoSGBjIAw88gNFo5M8//2Tfvn3UqPG/CNKmVwghqi+pU0IIIfSsxAsvHx8fTp8+TfPmzQH4+eefqVWrVoUNpFWrVsTFxbFixQqys7MJCAggJCREa9Pr7e3N4sWLyc7OxmKxYDAYaNq0qdYV6q+kTe//2F0r1WKokkWVHKDTLHZ2TZaoGFKn7Jcu55EykBz6o+ssUquqnRI310hLS+Ott97C3d0ds9lMRkYGL774otYut7w2bdpERkYGTz75JKtWrSI7O5tTp07x2muv4ebmph2Xl5fHnj17+P333xk+fDgeHh7Fnnfbtm00atSIJk2alHpMsf07lPoxQoiq5/7yu3h16lHVwxCVTOqUEMKeSK2qfkr8iZeXlxevvPIKCQkJANSpUweDwVAhgygoKODs2bM4OTlx9epVrUVv4fejpKenc/r0aX799VcKCgooKCjAYDBw4MABWrRoQUBAAAsXLpQ2vUIIoOzt31XrtKSC0nSLkjolhLAnpalVqs3rKmSxaVdDgOjoaA4fPozRaKRTp05l/sLHv/rxxx/p1KkTLVu25JNPPmHixIna1g2ApKQk3N3defzxx0lMTCQ9PZ0WLVrwxx9/kJaWRkBAgPZlloUqok2vaebr5Xq8XthdK9ViqJJFlRyg0yzed1X1CEQVkTpln3Q5j5SB5NAfXWeRWlXtlHjhtXfvXnbs2EGfPn0A+OSTT+jXrx9du3Yt1wCio6M5d+4c48ePByA0NJTvv/+ehx56SDvm5sKZmppKUlISLVu2JDg4uFzPfSfSpld/VMmiSg5QK4uwb1Kn7Jcq84jk0B+Vsgj7V+KF1/fff8/s2bNxdnYGoG3btsyfP79EBS0nJ4dffvmFDh3+txf9ww8/ZOTIkXh5edGvXz/i/3979x9XdX33f/zB758Cih5MELGk8EdWrktN+8GaujIzdq25dtWtLHdRNnVbJeXqe9u1ayO7dOputtbU1tpmZmxe2tqaudXUcivJLIwhAv5IEPyFoPwSgfP9w4tPHgT5eTifz5vn/S85HD58nh54v27vw+c8T0kJgYGBjB07ls8++wyAvLw8jh49ymuvvcaZM2c4fvw4TU1NuN1uXn/9dQBcLhczZsxg8uTJ7N27lw0bNgBf1PQCXHHFFdxzzz0djSoiIg6kOSUiInbW4Y2Xv7+/NcwAwsLCPC6zuJTy8nI+/fRTj4HWPJh+97vfkZycjJ+fH2FhYcTFxXHs2DEA3G43cXFxPP300/z+97/n2muvJTk52TrGnj17OHjwIJMnTwYgJSWFp59+GviiphdgxYoVHY0pIiIOpTklIiJ21qn38Tp9+rRVi1tRUUEHCxGprKy03jyypXPnzjFjxoxWPzd06FDCw8OB88Ot5QD19/dv8xyaa3qBLr+4WjW99mNKFlNygAOzqL7XWJpTzuW4daQNymE/jsiiudRndHjj9fWvf50f/ehHpKamArBt2zZmz57doa/Ny8uzmpyaK3j37dvX5v2bPx8YGGgNo6FDh7J27VqPN6JsaGjgrrvuavUYzYO3pqbGo+a3M+qfe7JLX2c39b4+gR5kShZTcoDzskT+8Gf0Hz22zc93pp3IzkzJ0RmaU87ltHWkLcphP07I0t5cambSum5Kls7m6PDGa+zYsWRkZPDJJ58AkJGRgcvlavfr2qvgbU19fT1HjhyhsbHRuu2GG2645HX6W7duZceOHRfdXltby5kzZ8jMzOSBBx4gISGh3XMWEXNdqr7XtIpbE3RmqGlOiYgTdaRW3rR13YQsPV4n//3vf59z58553NZ8ycQf//hHgoOD270uvb0KXn9/f+rq6nC73VRUVNDU1ERUVBSpqanW8Fy5ciUVFRUEBAQAcPDgQY+mqAEDBjB37lzrWU6A9evXM2nSJBITE9v/X2iDanrtx5QspuQAB2ZRfa9RNKc0p+xEOezHEVk0l/qMS268uvti345U8N56663WpRkxMTGMHj36ouMsWLCAX/ziF8yePZvw8PAeee+TjlBNr/2YksWUHGBWFnEezSnNKTtRDvsxKYs4X6feQLmz4uPj+fa3v219PHbsWMaO9byGddy4cYwbN46ioiLq6uqor6+nsrKSs//37MTy5ct57LHHcLvd1rOYkZGRbX7PrKwsCgoKKC0t5bPPPrNare68807GjBnjhZQiIuJUmlMiItJbvLrxam56ak9dXR2vvPKK9aaXtbW1fPjhh8D5FyY3H2v+/PnU1dURFhbGnDlzqK2tJTo6mh/84AcMHToUgFmzZgHnL+GYPHkyQ4cOpaioiNzcXA00ERHxoDklIiK9xasbr0uZNWuWNfAaGxsZPHiw9T4nQUFB1NTUcO+99/LrX/8agAcffJAHH3yQZcuW8fjjjwPnr6lPT0/3eN+WZjNmzCAsLAyA6urqSz772BbV9NqPKVlMyQHK0qNUKWwrmlO9x+e/ez1EOeyn21m0LksP8tnGa9Cg9l9IOGjQIKqrq9m3bx/Dhg0jJCSkw8e/cIAVFxczYsSITp+janrtx5QspuQAZelJHa0U7ghTqnp9SXOq9/j6d6+nKIf9dDdLT67L3WXSum5KFq/VyftKXV0dR44coby8nHfeeYeDBw+SmZkJwOeff87SpUv56le/yvjx41m5cqX1PiwXKikpIS4uDpfLxdy5c3s7gohIh3SkUrgjTKnqBWcMZ80pEXP11LrcXaat6yZk6fE6+d7i5+dHU1OT9fGF/46NjbXqdydOnAhATk4OR44c4emnn/Y4zoIFCzw+7m6rlGp67ceULKbkAGXpUaoUti3NKe/y+e9eD1EO++l2Fq3L0oNssfEKDQ0lOjqa1atXA+ffM2X69Ok+PivV9NqRKVlMyQHKIn2D5pR3mfK7pxz2Y1IWcT5bbLz8/f25//77PW47ceKEVcu7f/9+XnvtNetz9fX1NDQ0sGvXLuu21NRU60XPIiIiPUlzSkREusvrG6+TJ0+yZs0aGhsbPW5PTk5m1qxZvP/++2zbtg04fynHrbfeysSJE3n99deZPXs2AElJSTz00EPWpR2DBg1i27ZtXHfddbzzzjt8+ctfxuVyceDAAdatW2d9jwMHDpCZmUl8fLx1LBERkQtpTomISG/w+sbr6NGjpKSkkJaW1urnb7zxRm688UYANm3aZFXrNps9ezaffvop27ZtIzk5GYAJEyZw6tQpzp49i9vttgbd8OHDPa6nP336NFFRUSxevLhL566aXvsxJYspOUBZ7Mg2ORxSw6w55Xu2+ZntJuWwH1OyODKHQ2ZAb/L6xqv5Moz2FBUVkZ+f7zH4li9fzoQJE4iJieHaa6+1XrzcUVFRUZ26f0uq6bUfU7KYkgOUxY7sksNONcyXojnle3b5me0u5bAfU7I4McelZoATGms7wnZ18nFxcbz55ptkZmby+eefk5CQgL+/P0OHDrWul8/OziY7O5vY2FiysrKYOXMmAI899hgRERHs3Lmz1WMfOnSI8vJy6+PCwkJWr15NdHS0x/3i4+O9lE5ERNrSEzXMvTGcNadERHpeWzNAdfJeUldXR3BwMI8++igAL730Evfee691mcaZM2d49dVXGTZsGHPnzsXPz4/du3ezb9++Dh3/6NGjHu+HUlVVxZQpU5g2bVqPnL9qeu3HlCym5ABlsSPb5HBADbPmlD3Y5me2m5TDfkzJ4sgcDpgBvc2rG6+cnBwOHz5sfTxs2DDef/9962M/Pz/uv/9+wsPDrduuu+46AAICAggNDQXOt0nV19dTV1dHbW0tx48fB2D8+PGcveCHMCEhgc2bN5OdnU1ZWRkxMTHWMebNm3fRM4ztUU2v/ZiSxZQcoCx2ZEqO3qA5ZQ+m/Mwqh/2YksWUHH2dVzde48ePZ/z48ezYsYP333+f+vp63G43w4YN4+tf/zqRkZEArF27lkOHDnl8bXl5Oc899xwBAQEkJyezYcMG1q1bR3h4OEOGDMHtdnPq1CmPa/MHDhzIU089BcD69euZNGkSiYmJ3owoIiIOpjklIiK9xeuv8dq1axefffYZ3/3udwkNDcXtdpOXl8cvfvELMjIyALj99tsvqvKtqqri9ddf57777mPPnj2UlpYC5599TEpKws/Pj7/85S/ExcUBsHXrVjZs2EBNTQ0ADQ0NbNmyBT8/P1wuF5GRkTzwwAMkJCR4O7KIiDiI5pSIiPQGr2+8IiIiqK6upqKiApfLRW1tLSdPnrQurYDWq3xfeOEFvva1rwGtV/nGxMR4XB6SmpraaptUUVERubm51guhO0M1vfZjShZTcoAhWVR526dpTvmeEesIymFHl8yitV96mdc3XikpKdTX17Nx40bKy8sJCQkhJSWFRx55xLpPa1W+EydOJCQkxOO2llW+BQUFFBYW4nK5uO2221r9/tXV1dalIp2lml77MSWLKTnAjCwXVt721YrbvkxzyvdMWEdAOezoUlmc8pYXzUxa103J0tkcfu6OvoGJF504cYKXXnqJxsbGdqt8g4ODiYqKYubMmfzqV79i9uzZREREtHnst956ixEjRnDllVd2+rwO33F9lzOJiHMEP/U/NF4x0riKWxPYZThrTomYp3ntdwLT1nUTstiuTr4jeqrKd+XKlR6Vvc1KSkqIi4vD5XIxd+7cTp2banrtx5QspuQAQ7Ko8lYuQXPK+4xYR1AOO7pkFq390st8/hevnTt3elwD35Kfnx+33XabR5Vvs9zcXFJSUggICLjoc0uWLLFeFN1VJuzGwZxnFsCcLKbkAGWxI1NygD3+4qU55X2m/Mwqh/2YksWUHGBOFkf+xaunqnxFRES8QXNKRER6gtc3XidPnryoghcgOTmZWbNmUVNTw44dO9izZw/f+ta3SExM5NVXX+W6665j4cKFvPjiiwCMGTOGw4cP09TUZB2jurqanJwcvvSlLwGwd+9eNmzYAMCBAwfIzMwE4IorruCee+7xdlQREXEgzSkREekNXt94tVbBe6FVq1YRFRVFaWkpu3btIiEhgdLSUhITE/Hz87PuV1BQwN13301ycrJ1209+8hMOHjxoDbSUlBSefvppAJYuXcrChQsBWLFiRZfOXTW99mNKFlNygA2zqB5YOklzyvdst450kXLYj9ezaOZIJ3h949XeS8jOnTvHnDlzyMnJYceOHSxevJjDhw+TkpLCiBEjPI7j7+/v8bWjR49u87gPP/yw9e8LB2NnqKbXfkzJYkoOsF+W7tYD2+E1RT3BlBy9QXPK9+y2jnSVctiPt7P0ZiW9Seu6KVk6m8PrG6+4uDjefPNNMjMz26zgBRg7dixjx57/wX3uuee48sor2bt3r/X5oUOHsnbtWgIDvzjlhoYG7rrrrla/b1RUFAA1NTWXrPEVEbPUnz3b5RftmvaCXxP0xnDWnBKRrurOzOkM09Z1E7LYrlyjIxW8ramvr+fIkSMe19vfcMMN3HDDDW1+r61bt7Jjx46Lbq+treXMmTNkZmbywAMPkJCQ0OHzV02v/ZiSxZQcYMMsqgeWTtCcsgfbrSNdpBz24/UsmjnSCV7deOXk5HhU8A4bNoz333/f+tjPzw9/f3/q6upwu91UVFTQ1NREVFQUqampfPLJJ8D59z6pqKiwWqEOHjxIUlKSdZwBAwYwd+5cUlNTrdvWr1/PpEmTSExM7PL5O+VN9drT35BnFsCcLKbkALOySN+jOWUPpqwjymE/JmUR5/PqxqsjFbwff/wxa9euJS8vj5tuusm6Hv5vf/ub1Qw1b948VqxYQVpaGqGhoaxdu5Zx48Zx3XXX8c477/DlL38ZON8QtW7dOgBKS0v57LPPCAsLIz4+ntmzZ3szqoiIOJDmlIiI9Bavv8Zr7dq1/P3vf2fYsGEEBATgdrvZu3cvixYt4vnnn2fcuHGMGzeOH/3oRx6NUvn5+dZA+/TTT9m/fz85OTkEBwcTHBzMqVOnOHv2LG6327rf8OHDrbaoC59JXLx4cZfOXW1R9mNKFlNyQB/KouYqY2lO+Z4p64hy2I8pWTqcQ7PK1ry+8WpqaiI+Pp709HRcLhe1tbV8/PHH7N6927pPQUEBBw8e5NixY7hcLgYNGkRFRQXV1dWcOHGCc+fOkZiYSE5OjvWi5ffee4/PPvuMhoYGj0s3LtTVlqhmaouyH1OymJID+k6W4Kf+BzTMjKQ55XumrCPKYT+mZOloDs0qe/P6xmvw4MH069ePjRs3Ul5eTkhICCkpKTzyyCPA+YG3ceNGfvSjH/Gb3/yG+fPnc++99/LCCy8watQoTp48CcCECRM8BldWVhYJCQnW9fUAhYWFrF69mujoaBoaGigsLMTPz4/4+HhvxxQRwwWHhNDfQfW3plT19gbNKRExhVNmlSkzynZ18pGRkbzzzjtWbe7Zs2f59NNP+fzzz/n2t7/N6tWrmTZtGomJiXzjG99gxYoV1htKTp8+nYiICHbu3NnqsY8ePerROFVVVcWUKVOYNm2at2OJSB/TW5XBPcGUql7oneGsOSUipnDCrDJlRtmuTh5g0qRJTJo0CTjf+pSenk5oaChw/v1N0tLSrOrcpKQknnzyyYvegNLf35/6+nrq6uqora3l+PHjwPkXRZ+94HrXhIQENm/eTHZ2NmVlZcTExFjfa968eURHR3fq3FXTaz+mZDElB/ShLKoMNpbmlO+Zso4oh/2YkqXDOTSrbM3rG69LfvPAQBISEvjd737H9OnTiY2NtYZZQECAde17cnIymZmZFBcXEx4ezpAhQ3C73bz11luEh4dbx3v33Xet91Rxu9243W78/f258847Oz3MQDW9dmRKFlNygLKI2TSneocpv3vKYT+mZDElR1/n1Y3Xpk2byM3NtT6uqKhg2bJl1sd+fn7Mnz+fw4cPk5mZSWxsrMfXZ2VlMXv2bKKjowkICKC0tBSAoqIiysrKqK2tta6n37p1KwUFBdbXRkZGEhQURFVVFWvWrMHlcnX6jSlFRMRsmlMiItJbvLrxSktL86jebUtQUFCrzU5XXHGF9e+BAwfy+OOPWx9v2bKFPXv2WB+npqa22hqVk5PDiRMnuPXWWzt59qrptSNTspiSAxyURRW70grNKXtwzDrSDuWwH69n0WyRTvDppYbNrrnmGqZOncq1117b5n3q6+vJzMz0uDSjvLyc+++/n/79+7f5dcXFxYwYMaJL56WaXvsxJYspOcA5WVSxK92hOeVdTllH2qMc9uPtLJot0hl+brfb7euT+Mc//sEbb7xhNUo169+/P+np6VRVVVm35eXlUVJSwpQpU6zbwsLCWLVqlUdzVLOSkhLi4uJwuVzMnTu3U+d1+I7rO5lEROwq8oc/o//4G319GuJQmlMi0hrNFukMW2y8LtSyUero0aP885//vOTXjBw5kquuusrjtiVLlpCRkdGtc9FAEzFH8FP/024RgWkVtyaw43u9aE6JSLOOzJaeYNq6bkIWW9bJd1dcXBxpaWkcOXKEN954gxMnTgAQERHB7bffzsiR3vthV02v/ZiSxZQc4KAsqtgVL9Gc6j7HrCPtUA778XoWzRbpBJ//xWvTpk3k5OTQ1NREUFAQFRUVxMTEWJ9vbpR66qmnuOyyy4iPj2fmzJn4+/vz7LPPMnXqVOuNKLOysqzGqAMHDjB8+HCrpnfMmDGdPjcTduNgzjMLYE4WU3KAstiRKTnAHn/x0pzyPlN+ZpXDfkzJYkoOMCeLI//ilZaWRlJSEseOHbMGU0s7d+7E7XYzffp0YmJiWLNmDbNmzaKxsdF6PxSAWbNmWf9eunQpCxcupKioiNzc3C4NNBEREc0pERHpCT7feHVEZWUls2bNYu/evRw+fJjCwkK2b9/O1KlTCQsLa/VrHn74YQCqq6uJjIzs0vdVTa/9mJLFlBxgcBZVBEsnaE51jynriHLYT7tZtNZLL3LExuuWW25h9erVnDlzhoaGBh599FHGjRvHzp07qampafVrmpunVNOrWlg7MiUHmJtFFcHSGZpT3WPKOqIc9tNeFq310ptssfEaOHAgr7/+OtnZ2dTV1VlNUQEBATzxxBNUVFRw9913U1tbS2VlJeXl5bz66qvExsZa9125cmWP1/SKSN8VHBJCfxu8vqir7PDaKJNoTomYyUlrvUnruilZOpvD5+UaLbWs6S0vL2fTpk0EBQWRl5fHuHHjSExMZOjQoRw+fJiamhpSU1MvOo5qekWkO3qrItgbTHnhMthzOGtOiZjDKWu9aeu6CVkcWa7RngEDBvDQQw/x+eef09jYSGpqKocOHeLll1/m9OnT3H777V773qrptR9TspiSAwzOoopg6SDNqe4zZR1RDvtpN4vWeulFPv+L16ZNm8jOzqasrAyAc+fOERgYiJ+fH/369cPlcjF//nyeffZZrrjiCvr168c3v/lNAN577z2qq6u57bbbANX0tsWUZxbAnCym5ABlsSNTcoA9/uKlOeV9pvzMKof9mJLFlBxgThZH/sUrLS2NtLS0i253u90sW7aMJ554grKyMgIDA8nPzwdgz549hIWFUVlZSUJCgjXQVNMrIiI9TXNKRER6gs83Xm1xu90EBwcDEBoayr333gtAYWEh5eXl3H///Zdsi1JN7xf6VC2sQ5iSA/pQFlUOSwuaUz3HlHVEOexHdfJiJ7bdeJWXlxMbGwtATEwMMTExlJaWkp+fT3p6Oq+88gplZWVMnDix1a9XTe8X+lItrFOYkgP6ThZVDktLmlM9x5R1RDnsR3XyYic+33ht2rSJ3NzcNj//7LPPMn/+fN5//33y8/MJCAggPz+f2bNnezyTqJpeEfEmJ1UOgz1eG2UKzSkRczlpbTdpXTcli+Pr5Fvz5ptvEhsby6RJk2hqamLLli0kJydz8uRJ1fSKSK9wSuUwmPPCZXDOcNacEnEmp6ztpq3rJmRxZLkGwL59+1i1ahUDBgzwuD0qKor58+dz5513sn//fnbv3s11111nvUj51KlT+Pv7e+28VNNrP6ZkMSUH9KEsqhzu0zSnvMuUdUQ57Ed18mIntth4ffTRR7T8w5ufnx+HDh0CoLGxkTVr1lBXV8dbb71l3Sc4OJjvfOc71scta3ozMzO7VdPrhGdAOqK/Ic8sgDlZTMkByiJ9g+aUd5nyu6cc9mNSFnE+W2y8UlJSGDRoEFOnTrVuc7vdPPLIIwAEBASQnp7Oz3/+c4+vO3ToEOvXr+ehhx4CVNMrIiLeoTklIiLdZYuN1+DBg3n77bfZuXOndZvb7fa4pGP48OEsW7bM4+vWrVvHpEmTWj2manq/0KdqYR3ClBxgYJaIKFULy0U0p7zLlHVEOezHFllUWS//xxYbryFDhrBo0SKP2w4dOsTHH38MQFNTEwsXLrzo2vrg4GBcLlerx1RN7xf6Ui2sU5iSA8zLomphaY3mlHeZso4oh/3YIYvmijQyHRIsAAAe8UlEQVTz+carrZreyspK3G43eXl5PProoyQkJPD973+/zeOopldEeoKTqoUvxSltgE6gOSUi3dFTc8Wkdd2ULI6vk1+5ciXp6emEhoZ63P78889z+vRpqqqqqKurY+DA888c3HnnnYwdO/ai46imV0S6winVwpdiSlUv2HM4a06JSGf0xFwxbV03IYtj6+Q7Yv78+QDk5ORw5MgRq6rXm1TTaz+mZDElBxiYJSLK16chDqU51XWmrCPKYT+2yKLKevk/tt54VVZWcvLkSV577TXrtvr6ehoaGti1a5d1W2pqKpMnT+7x7+/0Z72bmVSlakoWU3KAskjfpjnVM0z53VMO+zEpizifLTZeq1at4sSJEwB8/vnnbN++nWnTprFq1SoyMjJYtGgR9fVfvDwyJCQEPz8/iouLOXbsGOPGjQN6/v1RREREQHNKRES6zxYbr+ZKXYCKigpefvllpk2bZt32+eefWxW+5eXlhISE8OCDD1JZWUlpaal1vwvfH+X06dNERUV16/1RVNNrP6ZkMSUH9LEsqgTuszSnvMuUdUQ57KfTWbTOixfZYuMFcOzYMU6fPs1ll11Gv379PD6XlJREUlIScP5FzTNmzGj3eM01vd15fxTV9NqPKVlMyQF9K4sqgfs2zSnvMWUdUQ776WwWrfPiTbbZeJWVlXHkyBHCw8MZPXo0cP4ZxMzMTO69914SExNZu3YtJ0+evGjgXUp33h9FRORCTqqat2MboNNpTomYz87rvEnruilZOpvD5xuvwsJC9u/fz5EjR6ioqMDtdlNbW8s777xDYmIiGRkZlJSUsHLlSm699VZmzJjBqlWruOOOOzyOo/dHERFvqz971hEv0jalqhfsMZw1p0T6Druu86at6yZkcWSd/IABA/Dz82PEiBEEBAQQFBREaGgo0dHRHD58GDj/AuT09HTCw8MBeOyxxzh+/Djl5eXWcRYsWOBx3J54fxTV9NqPKVlMyQF9LIsqgfskzSnvM2UdUQ776XQWrfPiRbbYeA0YMICPPvqIt99+27o9PDyce++9F4Abb7yRkpIStm7dyvTp0wkMDOSyyy7j6NGj+Pv7e+3cVNNrP6ZkMSUHKIuYT3PK+0z53VMO+zEpizifzzdeALm5uWRlZXHfffcRGBjIkCFDaGpq4plnnuEXv/gFTU1N/P3vf+ejjz6iurqau+66i+DgYD755BOPSzlU0ysiIt6gOSUiIt1li43XmTNnqK+vJz8/n5iYGN5++20mTJhgfX7Lli00NDTgdrv58MMPeffdd0lISKCkpITGxkbmzJkDeNb0Ll26lIULF6qmlz5eC2tTpuQAZekW1RY7huaUd5myjiiH/Xg9i9Zx6QRbbLwmTpzI8OHD2bJlCx999BHFxcWMHDmSxMREAPbs2cP3v/99Zs+eDcDixYtZtGgRr7zyCtOnT2/1mM3vuaKa3r5dC2tXpuQAZekO1RY7h+aUd5myjiiH/Xg7i9Zx6QxbbLzgfKvT4MGDmTx5Mjk5Odx22228+eabrF69moiICGpqaoiKiqKpqYmAgADr61544QW+9KUvMXPmTI/jNb8/imp6RcSuvFlbbIc2QNNoTolIS71ZP2/Sum5KFsfVyTdzu9243W6SkpIYMmQINTU1DBs2jPT0dMrKyvjtb3/L2LFjycvL4/bbb7e+7jvf+Q4ul0s1vSLiON6qLTalqhfsNZw1p0Skpd6qnzdtXTchiyPr5Pfs2cO+fftobGzE7XazadMmAgMDSUpKsu4zePBgHn74YcrKyhg/fjyhoaEAXHPNNdblGarpbVufroW1KVNygLJ0i2qLHUFzyvtMWUeUw368nkXruHSCzzdeV199NVdffTVZWVl885vf9Pjc3/72N+vfGzdutJqgmh07dozFixd77dxU02s/pmQxJQcoi5hPc8r7TPndUw77MSmLOJ/XN1779u3j97///UW3X3PNNcyYMQOAl156iQ8++ICCggLS09MZNGgQ69at49FHHwWgqamJm266icmTJwMwaNAgtm3bRmFhIevXr2fGjBm4XC4OHDjAunXrgC9qegHi4+OtFzyLiIhcSHNKRER6g5/b7Xb74hv/9Kc/Zd68eWzfvh2A//3f/yU8PByAkJAQysvLSUhIYMGCBezfv59t27aRnJwMwIQJE3j33Xc5duwYYWFh3HHHHQwePNjj+M01vfBFu1RnHX3vne5EtA1dMmA/puQAZbEjn+fowXplX77GS3Oq9/j8Z7aHKIf9mJLFlBxgsyzdmFe2f43X9u3bufnmm2loaCAoKIigoCBSUlI4ceIEN910EzExMVx99dVERkby2muvMXv2bCIiIjh37hzXXnstqampHscbNWoUJSUlrX6v5pre7lBNr/2YksWUHKAsduTrHE6uV9ac8g1f/8z2FOWwH1OymJID7JWlt+dVr268srOzufnmm6moqODKK6+06nbff/99br/9do4ePcpf//pX640mly9fzoQJE4iJiWn1eKGhoR4NUYWFhaxevZro6GiP+8XHx3spkYiItNSb9co9TXNKRKTv6O68slWd/KZNm8jNzbU+PnjwoHU9O8Du3bu59dZbGTNmDMnJySQlJfHhhx9an3/ssceIiIhg586drR7/6NGjHgOtqqqKKVOmMG3aNC+kERGRjujJemVvX2qoOSUi0nd1Z17Z7lLDtLQ00tLSLnkft9vNG2+8wdq1a2loaOAb3/gGAH5+fvj5+QHg7+9PfX09dXV11NbWcvz4cQDGjx/P2QuuEU1ISGDz5s1kZ2dTVlZGTEyMVek7b968i55hbI9qeu3HlCym5ABlsSOf53BQvbLmlD34/Ge2hyiH/ZiSxZQcYLMsvTyveqVco3nI+Pn50dTURP/+/bnvvvvo37+/dZ8PPviA3bt3U1tby2WXXcZNN91Ev379iI6OprKykg0bNgAQHh7OkCFDKC0t5cYbb+S9994jNTX1ohctr1+/nkmTJpGYmNjl8zalftSUN6oDc7KYkgOUxY5MyQG9V66hOeVbpvzMKof9mJLFlBxgThbb/cUL4KOPPqKsrIxnnnnGembw0KFDvPjii/zgBz+goaGBJ598kvr6emJjYwkLC+O6665jzZo1hIaGsmjRIqKjo3nooYfYsWOHVdWblZVFUVERhYWFpKamsnXrVjZs2EBNTQ0ADQ0NbNmyBT8/P1wuF5GRkTzwwAMkJCR4O7KIiDiI5pSIiPQGr2+8oqKiKCsro7S0lLi4OGpra8nPz7deiBwYGMhtt91GdXU1U6ZM4cUXXyQgIIDg4OCLjrV161ZroMXExBAQEEDzH+xSU1MvapMCKCoqIjc3l5kzZ3b63AOK8jr9NXZ0qng/AXb5k243mZLFlBzg8Cw9WHsuzqU55XuOXkcuoBz247gsmktG8/rG68orr+Tuu+/mrbfe4vjx44SGhjJ69GiPGt2pU6fywQcf8Mtf/pKDBw+ya9cuHn74YV5++WWPY/n7+1v/njZtGrm5uaSkpDBwYNs/oNXV1URGRnbp3FXTaz+mZDElBzg7i5Nrz6XnaE75npPXkQsph/04LYvmktl6pU5+xIgRjBgx4pL3SUhI4OzZsyxevJioqCgACgoKyMzMZM6cOYSEhHDgwAEaGxvJzs4mJyeHU6dOMWbMGAID245RXFzc7vcWkb6ptRpZX75hb08yJUdv0ZwSETu4VL25Seu6KVlsVSffETU1NWzcuJGKigq++93vejzrl5ycTEZGBgBvvPEG119/Pe+++y5Tp05l4sSJ5ObmcvDgQQBWrlzpUdnbrKSkhLi4OFwuF3Pnzu2VTCLiDC1rZE17wa8J7DCcNadEpLe0VW9u2rpuQhZblmu0p7GxkRtuuIHLL7/8os+Fh4cDcOrUKfbu3cuTTz7J888/z+jRoy8KuWDBAo+PlyxZYg3DrlJNr/2YksWUHODwLA6qPRff0ZzyPkevIxdQDvtxXBbNJaP5fOPVr18/+vXrR0FBAX/4wx+A8++ZEhYWRlpaGo2NjaxZs4Y5c+YAMGfOHF555RUeffRRr59b4xUjvf49ekN/Q55ZAHOymJIDzMoi0hrNKe8zZR1RDvsxKYs4n883Xg0NDSxdupSCggKSkpIICwtjwYIFVFRU8P/+3/9j9erVPPzww7z99tvMmjWL8PBwa5i99957Hs8oZmVlUVBQAMCBAwfIzMzE39+fO++8kzFjxvgkn4iIOJvmlIiI9ASfb7wCAwNZtGgRP/3pT7ntttv4y1/+QnV1NXl5eVZVb3R0NLt377aGVbOioiKP+t1Zs2ZZ/166dCkLFy60anq7MtBU02s/pmQxJQf4MIsqd6WXaE55nylronJ4mdZ9cTifb7yaPfLII2RlZXHgwAF++9vfMmrUKIYOHWp9fsCAAXz3u9/1+JoXXniBSZMmtXq85hpg1fQ6r0r1UkzJYkoO8F0WVe5Kb9Oc8h5T1kTl8C6t++J0ttl4VVRUUFpaynPPPWfV9P7+97+3anonT57Mm2++6fE1KSkpuFyuVo/XfAzV9IqY6VKVu91hhxa9nmBKDjvRnBLxra6u+6ash6bkAHOyGFcnf/fdd/OrX/2qza//+OOPSU1NZdeuXarpFelD2qrc7Q7TKm5NYIfhrDklYg9dWfdNWQ9NyQHmZDGyTv7yyy/n6aeftm7Lycnh8OHD3HHHHR73nTx5ssfHqun9guOqVC/BlCym5AAfZlHlrvQSzSnvM2VNVA4v07ovDuezjVdlZSXR0dGXrOm9//77Aairq2P58uW43W6qqqqoq6vjk08+ISwsjO9973v4+/t75RxV02s/pmQxJQeYlUXkQppTvceUdUQ5RORSvLrxaq7gbWpqAiA4OJgFCxYQEhLCqlWryMjIID8/n/j4eNavX8/jjz/O3r17GTduHJ9++ik/+9nP+PGPf0xoaCg333wzl19+ucef81asWMHf//53vvKVrwDnq3nXrVtn/TszMxOA+Ph4Zs+e7c2oIiLiQJpTIiLSW7y68Wqu4G22bNkya7g12717N2FhYYSFhbF//37+/Oc/ExQUxPbt2z2eITxx4gSbN28mLCzMuu3AgQMkJiZaHw8fPty63KO5phdg8eLFXTp/1fTajylZTMkByuJBVceOozllD6asI8phP6Zk6ZUcmmFe12uXGhYXF1uDq6U33niDqKgoXnvtNUpKSsjKyiIiIoLrr7/e43733XefxzX2GzdutN5DpaXmmt7uUE2v/ZiSxZQcoCwXUtWxs2lO+Y4p64hy2I8pWXojh2aY9/XKxqu4uJjf/OY3zJ8/37qtoKCAzMxM+vXrR1pamvWM4DPPPMPEiRMpKyvzeCbx+uuvZ+fOneTm5lq3BQUFMWHCBOvjwsJCVq9eTXR0tMf3j4+P91Y0ERGLtyruu8IObYBOojklIn1db84wU2aUrerk26vgzcjIYMOGDfztb39jwIABAERERPBv//ZvFBQUUFFRAcDKlStbreAFyMvLY8CAAcydO5eqqiqmTJnCtGnTvBlLRKRV3qi47wpTqnrB+8NZc0pE5LzemmGmzCjb1cm3V8ELkJaW5jGspk+fTnBwMMePH7cG2oIFCzy+9rnnnuOpp5666JgJCQls3ryZ7OxsysrKiImJITQ0FIB58+Zd9Axje1TTaz+mZDElByiLB1UdO47mlD2Yso4oh/2YkqVXcmiGeZ1XN14dqeANCAjg9OnTfPTRR/z7v/+79bX+/v6cveAH7A9/+AP5+fnAF01Q/v7+pKWlMXLk+TrdgQMHWoNu/fr1TJo0yeNFzZ2lml77MSWLKTlAWcTZNKfswZTfPeWwH1OymJKjr+uV13g1V/A2P3t49OhRfv3rX/PEE0/Q0NDAmjVrqKioIC8vz6ryHTlyJG+++aZ1jNraWuvfw4cPB6CqqopPP/2UkSNHsnXrVjZs2EBNTQ1wviJ4y5Yt+Pn54XK5iIyM5IEHHiAhIaE3IouIiINoTomIiLf1ysaruYL3qquuoqGhgby8PGJjY8+fQGAg3/ve9/jJT34CwL59+1iyZAn+/v4UFxdbxzh8+LBVwdusqKiITz75BIDU1FRSU1Mv+t5FRUXk5uYyc+bMTp+3anrtx5QspuQAA7NERKlOtw/SnPItU9YR5bAfU7JoPpmhVzZejzzyCJs3b+btt98mICCAUaNGWZdwAMTGxrJixQqKi4v54x//yKOPPgrAkiVLLnnc/v37c+WVV17yPtXV1R4vlu4M1fTajylZTMkB5mVRnW7fpDnlW6asI8phP6Zk0XwyQ69svCIjI7n77rsveZ9LVfnOmTOHhIQEMjMzqaqqoq6ujoEDz//gzZ07t93jjhgxovshRKRPsFMlfHeYUtXbWzSnRMTuTJlPYM6MslWdfEe0V+X7+OOPU11dTVpaGnC+lrekpIQpU6ZY96uvr+eXv/xlq1W+JSUlxMXF4XK52h1+IiJ2qYTvDlOqesEew1lzSkTswIT5BObMKNvVyXdEe1W+x48fZ8eOHRd97q9//av179GjR19U5btkyRIyMjK6dW6q6bUfU7KYkgMMzBIR5evTEJvRnPI+U9YR5bAfU7JoPpnBZxuvyspKoqOj263yjY2N5etf/zpHjhzhjTfe4MSJE8D5N7C8/fbbrYpeb1BNr/2YksWUHKAsYi7Nqd5jyu+ectiPKVlMydHXeXXj1dDQwNKlS2lqagKwKnhDQkJYtWoVGRkZ5Ofn079//4uqfFetWsWvfvUrMjIyaGxsZMWKFfzHf/wHLpeLQYMG8Ze//IV169ZxxRVXMH36dFwuFwcOHGDdunXAF++hAhAfH8/s2bO9GVVERBxIc0pERHqLVzdegYGBLFq0yPp42bJl1nBrVlhYSFJS0kVVvsXFxQwdOhSAnJwcTp06xa5duxg0aBATJ06krKyMc+fO4efnZx1z+PDhVpXv0qVLWbhwIQCLFy/u0vmrptd+TMliSg5QFg8DBqnq12E0p+zBlHVEOezHlCyOyqFZ2KZeu9SwuLiYsLAwwsLCWv18yyrfpqYmkpKSKCws5Ny5c3zta1+jsrKSvXv38q9//YszZ84wa9Ys9u3b1+rxHn744W6fs2p67ceULKbkAGW5kKp+nU1zyndMWUeUw35MyeKkHJqFbeuVjdelKnivvvpq4Isq36qqKhoaGnj11VdxuVz069eP8vJyIiIiuOOOO6yvz8rKoq6ujvLycuu2wsJCVq9eTXR0tMf3j4+P93JCERF7Vf3aoQ3QSTSnRER6RkdmoSkzylZ18u1V8GZkZPCvf/2L1157DX9/fwIDA4mIiGDSpEnWfSIiIjh06FCrxz969KhHNW9VVRVTpkxh2rRp3owlItIqu1T9mlLVC94fzppTIiI9q71ZaMqMsl2dfHsVvACjRo3ixz/+8UWfDw4OJiQkBAB/f3/q6+upq6ujtraW48ePAzB+/HjOXnC9a0JCAps3byY7O5uysjJiYmIIDQ0FYN68eRc9w9ge1fTajylZTMkByuJhwKCeOxnpFZpT9mDKOqIc9mNKFkfl0Cxsk1c3Xu1V8DZ7+eWXKS4uJiAgwLqtrKyMH/7whwwcOJDk5GQ2bNjAunXrCA8PZ8iQIbjd7ou+38CBA3nqqacAWL9+PZMmTSIxMbHL56+aXvsxJYspOUBZxNk0p+zBlN895bAfU7KYkqOv65XXeLWs4D169Ci//vWveeKJJ2hoaOCDDz6wmqGaq3xfeuklVq9ezQ9+8APKysqYMWMGLpfLOmZWVhZ//OMf6devHwBbt25lw4YN1NTUAOcrgrds2YKfnx8ul4vIyEgeeOABEhISeiOyiIg4iOaUiIh4W69svFpW8Obl5REbG3v+BAIDGTVqFI888gihoaFWle+Fdb7NVb4XDrSYmBgOHz5MREQEAKmpqaSmpl70vYuKisjNzWXmzJmdPm/V9NqPKVlMyQF9KIvqcY2mOeVbpqwjymE/ts2imdIn9crGq2UF76hRozwu4Rg9ejTLly+ntraWY8eOsXz5csLCwjwu6Whp2rRpFBQUEB0dTWVlJYMHD271ftXV1R4vlu4M1fTajylZTMkBfSeL6nHNpjnlW6asI8phP3bNopnSN/XKxqu5grctU6dOZeTIkfzmN79h6dKlREVFAfCf//mfHlW+zZqrfAGGDRtGTExMm8cuLi5mxIgRPZBCRPoyO1XFd4QpVb29RXNKRHpTV2aKSeu6KVlsVSffET1V5bty5UqPyt5mJSUlxMXF4XK5mDt3bq/lEhGz2KUqviNMqeoFewxnzSkR6WmdnSmmresmZLFdnXxH9FSV74IFCzw+t2TJEjIyMrp1bqrptR9TspiSA/pQFtXj9lmaU95nyjqiHPZj2yyaKX2Szzde3a3yXbZsmdfOLe6mr3jt2CIi3mSHvxSZQnNKROzApHXdlCyOu9Sw2aWqfAFOnz7NU089RXBwsPU1L7zwAufOnfO4TURExBs0p0REpDv8fX0CzZqrfM+dO0dtba1HlW+zC6t7mz/28/Nr9Xht3S4iItIVmlMiItIdfm632+3rk4DzDVCbN2/m0KFDVpXvV77yFeuSjb/+9a9kZ2dz4emGhYXxve99D3//i/ePp0+ftlqnREREuktzSkREusM2Gy8RERERERFT2eZSQxEREREREVNp4yUiIiIiIuJl2niJiIiIiIh4mTZeIiIiIiIiXqaNl4iIiIiIiJdp4yUiIiIiIuJlgb4+ATt67733+Mc//kFAQADJycncddddvj6lLsnIyGDEiBEABAYG8uCDDzrqDTubmprIysqiqKiIp59+GnDuY9NaFqc+Pr/85S/x9/enqqqK66+/nptvvtmRj0trOZz6mLz00ks0NTVRW1vLZZddxqxZsxz5mEDrWZz6uHiTUx9fMGttN2U9NGkNaWxs5Oc//zlhYWGkp6c7Mkdba54Ts5SVlbFhwwbcbjf+/v7cc8895ObmOi5HSUkJf/7zn62PCwoKSE9Pp6ysrHNZ3OKhpqbG/ZOf/MTd1NTkdrvd7pUrV7pLSkp8fFZd89///d++PoVu+fDDD935+flWDic/Ni2zuN3Of3waGxvdzzzzjKMfF7f7ixxut/MfE7fb7X7++efdBw4ccPRj0uz55593l5SUGPG49CSn/86ZtLY3M2U9dLudv4a8/vrr7k8++cT94osvOvbxaG3Nc2KWpqYm97Jly9ynT5+2bnNijpYaGxvdzz77bJeyBPzXf/3Xf3l3j+gsubm5hIWFcdVVVwHgdrspKSnh8ssv9/GZdd7WrVs5duwY27Ztw8/PjyFDhvj6lDolPj6e2NhYtm/fzi233OLox6ZlFnD+43Pu3Dl2795NTEyMYx8X+CLH5MmTHf+Y1NTUsH37dmJjY4mKinLsYwJfZLnlllvYsWOHox+XnubktRDMWtubmbIeOn0Nee+994iLiyM+Pp68vDzCwsIc+Xi0Nouc+HtSVFTEyZMnyc3N5YMPPuDMmTPU1NQ4LkdL//znP3G5XNTW1nY6iy41bKGqqorIyEjr48jISEpLS314Rl33wx/+EDj/Z/cVK1YQHx/PZZdd5uOz6jqTHhtw/uOTlZXFzJkzOXHihKMfl+Yc4NzHpKysjKysLAoLC3nooYcc/bvSMktERIRjHxdvcfLj2xoT8jh9PTRhDdm/fz8VFRXcdNNNHDt2DHDuz1Zra54Tsxw7dozDhw+TkZFBUFAQL730EidPnmTgwIHWfZyQo6Vt27bx+OOPs3Pnzk4/JirXaCEyMpKqqirr46qqKvr16+fDM+q+gIAAxowZQ3Fxsa9PpVtMfGzAmY/Pn/70J5KSkkhJSXH043Jhjgs57TEZPHgwCxYsYMWKFbz77rs0NDQ49jFpmaWiosL6nNMeF29x8u9ca5yex4T10IQ15B//+AelpaWsXr2a9evXk5+fz+nTpx2X40IXrnlO/NkKCQnh6quvJigoCIAvfelLBAUFOS7HhXJyckhOTiY4OLhLj4k2Xi0kJyezZ88e3G43ANnZ2YwcOdLHZ9V9+/btY9iwYb4+jW4x9bEBZz0+W7ZsITw8nBtvvBFw7uPSMkdLTnpMmgUEBNDU1MSYMWMc+ZhcqDlLQ0ODx+1OfFx6mlN/59ri5DymrIfNnLyG3HfffaSnp5Oens4999zDVVddxS233OK4HC01r3lO/Nm6/PLL2bdvn/VxQUEBgwcPdlyOC23evJmvfvWrQNd+33WpYQsRERHcfPPNLF++nICAAC6//HLi4+N9fVpd8vOf/5zg4GDq6uoYP348LpfL16fUJYGB539MTXhsmrOAMx+f/Px8Nm7cyDXXXGMtpt/61rcc97i0leN3v/ud4x6T/fv386c//YnQ0FDOnj3LhAkTGDhwoOMeE2g7ixN/V7zJhLUQnL+2m7IemrSGNAsICMDf39+xP1ttrXlOy9K/f3+uvfZafvaznxEaGsqgQYOYOHEiDQ0NjsrR7ODBgwwcOND6q1ZXfr783M3bNBEREREREfEKXWooIiIiIiLiZdp4iYiIiIiIeJk2XiIiIiIiIl6mjZeIiIiIiIiXaeMlIiIiIiLiZdp4iYiIiIiIeJk2XiIiIiIiIl6mjZeIiIiIiIiX/X/Ta4UcC8IgJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# prepare the canvas frame\n",
    "fig, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, figsize=(14,5))\n",
    "# set the figure title\n",
    "fig.suptitle('Air Pollution in Seoul City', fontsize=14, fontweight='bold')\n",
    "# left (1st) sub-figure\n",
    "graphdf['pm25'].plot(kind='barh', ax=ax0)\n",
    "ax0.set(title='PM2.5')\n",
    "# right (2nd) sub-figure\n",
    "graphdf['pm10'].plot(kind='barh', ax=ax1)\n",
    "ax1.set(title='PM10')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 그래프를 화면에 표시하는 것 외에도, 이미지 파일로 저장 가능. 이미지 타입은 파일 확장자로부터 자동 선택 </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:31.851050Z",
     "iopub.status.busy": "2020-08-04T11:05:31.851050Z",
     "iopub.status.idle": "2020-08-04T11:05:32.212050Z",
     "shell.execute_reply": "2020-08-04T11:05:32.211053Z",
     "shell.execute_reply.started": "2020-08-04T11:05:31.851050Z"
    }
   },
   "outputs": [],
   "source": [
    "#\n",
    "#  fig 작업을 수행\n",
    "#\n",
    "#\n",
    "# save the figure into file\n",
    "fig.savefig('piechart.png', dpi=125)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:32.213054Z",
     "iopub.status.busy": "2020-08-04T11:05:32.213054Z",
     "iopub.status.idle": "2020-08-04T11:05:32.678056Z",
     "shell.execute_reply": "2020-08-04T11:05:32.677055Z",
     "shell.execute_reply.started": "2020-08-04T11:05:32.213054Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Active code page: 65001\n",
      " Volume in drive D is D30G\n",
      " Volume Serial Number is 701B-706E\n",
      "\n",
      " Directory of d:\\jupyternotebook\\python_anal_atoz\n",
      "\n",
      "2020-08-04  �삤�썑 08:04    <DIR>          .\n",
      "2020-08-04  �삤�썑 08:04    <DIR>          ..\n",
      "2020-08-03  �삤�썑 10:07    <DIR>          .ipynb_checkpoints\n",
      "2020-08-04  �삤�썑 08:04           479,078 advanced_pandas.ipynb\n",
      "2020-08-01  �삤�썑 07:31         1,651,865 basic_pandas.ipynb\n",
      "2020-08-04  �삤�쟾 12:40            31,739 financial_analysis.ipynb\n",
      "2020-08-01  �삤�썑 07:31            46,474 jupyter_python.ipynb\n",
      "2020-07-31  �삤�썑 11:42               764 new_result.csv\n",
      "2020-08-04  �삤�썑 08:05            72,463 piechart.png\n",
      "2020-08-03  �삤�썑 10:07           462,578 python_modules.ipynb\n",
      "2020-08-03  �삤�썑 08:39               407 README.md\n",
      "2020-08-03  �삤�썑 08:26         3,649,764 report.html\n",
      "2017-10-09  �삤�썑 05:07           902,377 seoul_air_polution.csv\n",
      "2020-08-01  �삤�쟾 08:17             1,043 seoul_greenpark.tsv\n",
      "2020-07-29  �삤�썑 09:02         1,134,011 seoul_municipalities_geo.json\n",
      "2020-08-04  �삤�썑 08:04         1,075,223 SWEETVIZ_REPORT.html\n",
      "2020-08-01  �삤�썑 07:31           790,283 Untitled.ipynb\n",
      "2020-08-03  �삤�썑 09:57           284,790 �긽�옣踰뺤씤紐⑸줉.xlsx\n",
      "              15 File(s)     10,582,859 bytes\n",
      "               3 Dir(s)   6,923,018,240 bytes free\n"
     ]
    }
   ],
   "source": [
    "!dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Appendix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b> Tip:</b> 데이터에 대한 간략한 통계, 분포를 통한 EDA를 자동으로 report 해주는 모듈 </b>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-03T11:02:30.205142Z",
     "iopub.status.busy": "2020-08-03T11:02:30.204141Z",
     "iopub.status.idle": "2020-08-03T11:03:23.815139Z",
     "shell.execute_reply": "2020-08-03T11:03:23.809142Z",
     "shell.execute_reply.started": "2020-08-03T11:02:30.205142Z"
    }
   },
   "source": [
    "!conda install -c conda-forge pandas-profiling  -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:08:12.432051Z",
     "iopub.status.busy": "2020-08-04T11:08:12.431051Z",
     "iopub.status.idle": "2020-08-04T11:08:26.158051Z",
     "shell.execute_reply": "2020-08-04T11:08:26.157053Z",
     "shell.execute_reply.started": "2020-08-04T11:08:12.432051Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "67ce8eacf092416983680e747e2154ec",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Summarize dataset', max=25.0, style=ProgressStyle(descrip…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:84: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4d5027cfcc494ad787e518d36043669c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Generate report structure', max=1.0, style=ProgressStyle(…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b81432adce61447589587ba0333a1fed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Render HTML', max=1.0, style=ProgressStyle(description_wi…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c4286e517a15400885b32705168e2ef0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Export report to file', max=1.0, style=ProgressStyle(desc…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "report = airdf.profile_report()\n",
    "report.to_file(output_file=\"report.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:06:00.590053Z",
     "iopub.status.busy": "2020-08-04T11:06:00.589053Z",
     "iopub.status.idle": "2020-08-04T11:06:01.698052Z",
     "shell.execute_reply": "2020-08-04T11:06:01.697052Z",
     "shell.execute_reply.started": "2020-08-04T11:06:00.590053Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "webbrowser.open(\"report.html\", new=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:06:01.700052Z",
     "iopub.status.busy": "2020-08-04T11:06:01.700052Z",
     "iopub.status.idle": "2020-08-04T11:06:08.309051Z",
     "shell.execute_reply": "2020-08-04T11:06:08.308052Z",
     "shell.execute_reply.started": "2020-08-04T11:06:01.700052Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Active code page: 65001\n",
      "Requirement already satisfied: sweetviz in c:\\programdata\\anaconda3\\lib\\site-packages (1.0b5)\n",
      "Requirement already satisfied: numpy>=1.16.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from sweetviz) (1.18.5)\n",
      "Requirement already satisfied: tqdm>=4.43.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from sweetviz) (4.47.0)\n",
      "Requirement already satisfied: jinja2>=2.11.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from sweetviz) (2.11.2)\n",
      "Requirement already satisfied: pandas!=1.0.0,!=1.0.1,!=1.0.2,>=0.25.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from sweetviz) (1.0.5)\n",
      "Requirement already satisfied: matplotlib>=3.1.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from sweetviz) (3.2.2)\n",
      "Requirement already satisfied: importlib-resources>=1.2.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from sweetviz) (3.0.0)\n",
      "Requirement already satisfied: scipy>=1.3.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from sweetviz) (1.5.0)\n",
      "Requirement already satisfied: MarkupSafe>=0.23 in c:\\programdata\\anaconda3\\lib\\site-packages (from jinja2>=2.11.1->sweetviz) (1.1.1)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas!=1.0.0,!=1.0.1,!=1.0.2,>=0.25.3->sweetviz) (2020.1)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas!=1.0.0,!=1.0.1,!=1.0.2,>=0.25.3->sweetviz) (2.8.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib>=3.1.3->sweetviz) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib>=3.1.3->sweetviz) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib>=3.1.3->sweetviz) (2.4.7)\n",
      "Requirement already satisfied: zipp>=0.4; python_version < \"3.8\" in c:\\programdata\\anaconda3\\lib\\site-packages (from importlib-resources>=1.2.0->sweetviz) (3.1.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-dateutil>=2.6.1->pandas!=1.0.0,!=1.0.1,!=1.0.2,>=0.25.3->sweetviz) (1.15.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install --trusted-host pypi.org --trusted-host files.pythonhosted.org sweetviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:06:08.311052Z",
     "iopub.status.busy": "2020-08-04T11:06:08.310051Z",
     "iopub.status.idle": "2020-08-04T11:06:08.316052Z",
     "shell.execute_reply": "2020-08-04T11:06:08.315056Z",
     "shell.execute_reply.started": "2020-08-04T11:06:08.311052Z"
    }
   },
   "outputs": [],
   "source": [
    "import sweetviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:09:52.857062Z",
     "iopub.status.busy": "2020-08-04T11:09:52.857062Z",
     "iopub.status.idle": "2020-08-04T11:09:58.226051Z",
     "shell.execute_reply": "2020-08-04T11:09:58.225052Z",
     "shell.execute_reply.started": "2020-08-04T11:09:52.857062Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ":loc:                              |███▌                 | [ 17%]   00:00  -> (00:02 left)C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 51008 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 54217 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 44396 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 50689 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 46321 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 54252 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 44277 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 54637 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 45824 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 47196 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 50577 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 52380 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 51008 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 54217 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 44396 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 50689 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 46321 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 54252 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 44277 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 54637 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 45824 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 47196 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 50577 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 52380 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 51473 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 46993 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 44396 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 44053 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 45224 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 45824 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 47196 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 46020 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 48393 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 47560 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 54252 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 51333 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 52380 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 54840 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 50857 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 49328 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 54868 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 49888 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 52492 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 52397 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 44228 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 49436 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 47928 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 51008 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 54217 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 50689 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 46321 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 44277 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 54637 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 50577 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 51473 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 46993 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 44396 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 44053 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 45224 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 45824 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 47196 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 46020 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 48393 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 47560 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 54252 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 51333 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 52380 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 54840 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 50857 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 49328 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 54868 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 49888 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 52492 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 52397 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 44228 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 49436 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 47928 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 51008 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 54217 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 50689 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 46321 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 44277 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 54637 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 50577 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      ":district:                         |███████████████▊     | [ 75%]   00:03  -> (00:01 left)C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 51473 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 46993 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 44396 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 46041 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 45824 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 47928 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 50577 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 52380 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 48324 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 46020 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 52769 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 51221 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 49548 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 51473 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 46993 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 44396 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 46041 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 45824 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 47928 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 50577 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 52380 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 48324 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 46020 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 52769 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 51221 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 49548 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 44305 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 51652 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 44396 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 44053 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 45224 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 44552 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 52380 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 49436 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 52488 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 46041 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 51089 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 50689 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 46321 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 54252 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 51008 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 54217 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 45824 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 47928 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 50857 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 49328 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 47196 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 47560 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 46020 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 48393 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 51473 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 46993 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 50577 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 48324 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 52769 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 51221 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 49548 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 44305 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 51652 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 44396 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 44053 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 45224 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 44552 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 52380 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 49436 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 52488 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 46041 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 51089 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 50689 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 46321 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 54252 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 51008 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 54217 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 45824 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 47928 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 50857 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 49328 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 47196 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 47560 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 46020 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 48393 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 51473 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 46993 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 50577 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 48324 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 52769 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 51221 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 49548 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      ":FEATURES DONE:                    |█████████████████████| [100%]   00:03  -> (00:00 left)\n",
      ":PAIRWISE DONE:                    |█████████████████████| [100%]   00:00  -> (00:00 left)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating Associations graph... DONE!\n",
      "Report SWEETVIZ_REPORT.html was generated! NOTEBOOK/COLAB USERS: no browser will pop up, the report is saved in your notebook/colab files.\n"
     ]
    }
   ],
   "source": [
    "analysis_report = sweetviz.analyze(airdf);\n",
    "analysis_report.show_html();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:06:15.663052Z",
     "iopub.status.busy": "2020-08-04T11:06:15.663052Z",
     "iopub.status.idle": "2020-08-04T11:06:23.674051Z",
     "shell.execute_reply": "2020-08-04T11:06:23.673053Z",
     "shell.execute_reply.started": "2020-08-04T11:06:15.663052Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ":loc:                              |███▌                 | [ 17%]   00:00  -> (00:04 left)C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 44396 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 47196 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 51473 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 44396 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 47196 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 51473 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 44396 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 47196 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 51473 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 44396 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 47196 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 51473 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      ":district:                         |███████████████▊     | [ 75%]   00:04  -> (00:01 left)C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 44396 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 47196 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 51473 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 44396 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 47196 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 51473 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 44396 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 47196 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 51473 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 44396 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 47196 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 51473 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      ":FEATURES DONE:                    |█████████████████████| [100%]   00:06  -> (00:00 left)\n",
      ":PAIRWISE DONE:                    |█████████████████████| [100%]   00:00  -> (00:00 left)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating Associations graph... DONE!\n",
      "Report SWEETVIZ_REPORT.html was generated! NOTEBOOK/COLAB USERS: no browser will pop up, the report is saved in your notebook/colab files.\n"
     ]
    }
   ],
   "source": [
    "analysis_report = sweetviz.compare(airdf[airdf['loc'] == '중구'], airdf[airdf['loc'] == '구로구'])\n",
    "analysis_report.show_html()"
   ]
  }
 ],
 "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-autonumbering": true
 },
 "nbformat": 4,
 "nbformat_minor": 4
}