viz_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TEAMLAB Intern Researcher 서용득"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 품목별, 품종별, 산지별 등 다양한 기준에 따른 농산물들의 거래량과 거래 가격 파악\n",
    "- 시간의 흐름에 따른 농산물의 거래량과 거래 가격 변동 추이 파악"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 출처\n",
    "* DACON - 2021 농산물 가격예측 AI 경진대회 (https://dacon.io/competitions/official/235801/data)\n",
    "* 2016년 1월 ~ 2020년 9월의 전국 농산물 도매시장 거래정보 데이터"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 정보"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "기간 : 2016-01 ~ 2020-09\n",
    "\n",
    "- train.csv \n",
    "  - date : 일자\n",
    "  - 요일 : 요일\n",
    "  - 품목_거래량(kg) : 해당 품목의 거래량\n",
    "  - 품목_가격(원/kg) : 해당 품목의 kg당 가격\n",
    "  - 품목_가격 산출 방식 : 품목 또는 품종의 총 거래금액 / 총 거래량 (취소된 거래내역 제외)  \n",
    "- train_AT_TSALET_ALL : 월별 전국 도매시장 거래정보 데이터\n",
    "  - SALEDATE: 경락 일자\n",
    "  - WHSAL_NM: 도매시장\n",
    "  - CMP_NM: 법인\n",
    "  - PUM_NM: 품목\n",
    "  - KIND_NM: 품종\n",
    "  - DAN_NM: 단위\n",
    "  - POJ_NM: 포장\n",
    "  - SIZE_NM: 크기\n",
    "  - LV_NM: 등급\n",
    "  - SAN_NM: 산지\n",
    "  - DANQ: 단위중량\n",
    "  - QTY: 물량\n",
    "  - COST: 단가\n",
    "  - TOT_QTY: 총물량 (음수로 집계된 값은 거래 취소 내역)\n",
    "  - TOT_AMT: 총금액"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis\n",
    "- **가설 1** : 김장철이 되면 배추 가격이 상승할 것이다.  \n",
    "\n",
    "- **가설 2-1** : 귤의 주 생산지는 제주도일 것이다.\n",
    "- **가설 2-2** : 제주도에서는 다른 농산물보다 귤을 가장 많이 생산할 것이다.  \n",
    "    \n",
    "- **가설 3** : 제주도에서 멀리 있는 지역의 도매시장에서 일어난 거래일수록 거래 가격이 비쌀 것이다.  \n",
    "    \n",
    "- **가설 4** : 코로나 발생(2020년 1월 기준) 이후로 중국산 농산물의 거래량은 줄었을 것이고 가격은 올랐을 것이다.  \n",
    "\n",
    "- **가설 5** : 2016년에 비해 2019년에 열대과일의 수입량 대비 국내 생산량의 비율이 증가했을 것이다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 라이브러리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: plotly in c:\\miniconda3\\envs\\da\\lib\\site-packages (5.8.0)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in c:\\miniconda3\\envs\\da\\lib\\site-packages (from plotly) (8.0.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pickle\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import plotly.graph_objs as go\n",
    "import plotly.express as px\n",
    "\n",
    "from tqdm import tqdm\n",
    "from datetime import datetime\n",
    "from plotly.subplots import make_subplots as ms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 설정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 프레임의 모든 column을 볼 수 있도록 설정\n",
    "pd.set_option('display.max_columns', None) \n",
    "# warning 제거\n",
    "warnings.filterwarnings(action='ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 경로"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR = \"./data\"\n",
    "TRAIN_PATH = os.path.join(DATA_DIR, \"train.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 불러오기 function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 월별 도매시장 거래 DATA 불러오는 함수\n",
    "def TSALET_CSV(yearmonth):\n",
    "    return pd.read_csv(os.path.join(DATA_DIR, \"train_AT_TSALET_ALL/AT_TSALET_ALL_{}.csv\".format(yearmonth)), parse_dates = ['SALEDATE'])\n",
    "\n",
    "# 연별 도매시장 DataFrame을 concatenate하는 함수\n",
    "def TSALET_CONCAT_YEAR(year):\n",
    "    dt_index = pd.date_range(start=\"{}0101\".format(str(year)), end=\"{}1231\".format(str(year)), freq = \"M\")\n",
    "    dt_list = dt_index.strftime(\"%Y%m\").tolist()\n",
    "    return pd.concat([TSALET_CSV(int(i)) for i in tqdm(dt_list)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DATA - 품목별 거래량 합, 단가 평균 (2016-01 ~ 2020-09)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>요일</th>\n",
       "      <th>배추_거래량(kg)</th>\n",
       "      <th>배추_가격(원/kg)</th>\n",
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       "      <th>건고추_가격(원/kg)</th>\n",
       "      <th>마늘_거래량(kg)</th>\n",
       "      <th>마늘_가격(원/kg)</th>\n",
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       "      <th>대파_가격(원/kg)</th>\n",
       "      <th>얼갈이배추_거래량(kg)</th>\n",
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       "      <th>시금치_거래량(kg)</th>\n",
       "      <th>시금치_가격(원/kg)</th>\n",
       "      <th>미나리_거래량(kg)</th>\n",
       "      <th>미나리_가격(원/kg)</th>\n",
       "      <th>당근_거래량(kg)</th>\n",
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       "      <th>파프리카_거래량(kg)</th>\n",
       "      <th>파프리카_가격(원/kg)</th>\n",
       "      <th>새송이_거래량(kg)</th>\n",
       "      <th>새송이_가격(원/kg)</th>\n",
       "      <th>팽이버섯_거래량(kg)</th>\n",
       "      <th>팽이버섯_가격(원/kg)</th>\n",
       "      <th>토마토_거래량(kg)</th>\n",
       "      <th>토마토_가격(원/kg)</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>금요일</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>토요일</td>\n",
       "      <td>80860.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>80272.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>122787.5</td>\n",
       "      <td>1281.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>15019.0</td>\n",
       "      <td>5475.0</td>\n",
       "      <td>92334.0</td>\n",
       "      <td>1704.0</td>\n",
       "      <td>6359.0</td>\n",
       "      <td>1331.0</td>\n",
       "      <td>40028.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>4374.9</td>\n",
       "      <td>13242.0</td>\n",
       "      <td>16550.5</td>\n",
       "      <td>2339.0</td>\n",
       "      <td>10528.0</td>\n",
       "      <td>1729.0</td>\n",
       "      <td>13885.0</td>\n",
       "      <td>804.0</td>\n",
       "      <td>3853.0</td>\n",
       "      <td>3703.0</td>\n",
       "      <td>15797.0</td>\n",
       "      <td>2576.0</td>\n",
       "      <td>14634.0</td>\n",
       "      <td>1474.0</td>\n",
       "      <td>30950.0</td>\n",
       "      <td>1621.0</td>\n",
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       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>일요일</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>월요일</td>\n",
       "      <td>1422742.5</td>\n",
       "      <td>478.0</td>\n",
       "      <td>1699653.7</td>\n",
       "      <td>382.0</td>\n",
       "      <td>2315079.0</td>\n",
       "      <td>1235.0</td>\n",
       "      <td>699.0</td>\n",
       "      <td>4464.0</td>\n",
       "      <td>141638.0</td>\n",
       "      <td>5210.0</td>\n",
       "      <td>994328.1</td>\n",
       "      <td>1716.0</td>\n",
       "      <td>262615.5</td>\n",
       "      <td>1212.0</td>\n",
       "      <td>1074699.1</td>\n",
       "      <td>345.0</td>\n",
       "      <td>122613.5</td>\n",
       "      <td>9923.0</td>\n",
       "      <td>427435.1</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>82113.5</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>558950.2</td>\n",
       "      <td>794.0</td>\n",
       "      <td>104930.3</td>\n",
       "      <td>4871.0</td>\n",
       "      <td>277326.5</td>\n",
       "      <td>2440.0</td>\n",
       "      <td>159800.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>291057.0</td>\n",
       "      <td>1834.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>화요일</td>\n",
       "      <td>1167241.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>1423482.3</td>\n",
       "      <td>422.0</td>\n",
       "      <td>2092960.1</td>\n",
       "      <td>1213.0</td>\n",
       "      <td>1112.6</td>\n",
       "      <td>4342.0</td>\n",
       "      <td>126207.8</td>\n",
       "      <td>5387.0</td>\n",
       "      <td>787716.0</td>\n",
       "      <td>1715.0</td>\n",
       "      <td>221850.5</td>\n",
       "      <td>1197.0</td>\n",
       "      <td>825681.9</td>\n",
       "      <td>350.0</td>\n",
       "      <td>79055.9</td>\n",
       "      <td>9529.0</td>\n",
       "      <td>334636.8</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>80144.0</td>\n",
       "      <td>3333.0</td>\n",
       "      <td>444353.7</td>\n",
       "      <td>763.0</td>\n",
       "      <td>100699.5</td>\n",
       "      <td>5129.0</td>\n",
       "      <td>218465.2</td>\n",
       "      <td>2437.0</td>\n",
       "      <td>153084.0</td>\n",
       "      <td>1822.0</td>\n",
       "      <td>194626.5</td>\n",
       "      <td>1833.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "             요일  배추_거래량(kg)  배추_가격(원/kg)  무_거래량(kg)  무_가격(원/kg)  양파_거래량(kg)  \\\n",
       "date                                                                          \n",
       "2016-01-01  금요일         0.0          0.0        0.0         0.0         0.0   \n",
       "2016-01-02  토요일     80860.0        329.0    80272.0       360.0    122787.5   \n",
       "2016-01-03  일요일         0.0          0.0        0.0         0.0         0.0   \n",
       "2016-01-04  월요일   1422742.5        478.0  1699653.7       382.0   2315079.0   \n",
       "2016-01-05  화요일   1167241.0        442.0  1423482.3       422.0   2092960.1   \n",
       "\n",
       "            양파_가격(원/kg)  건고추_거래량(kg)  건고추_가격(원/kg)  마늘_거래량(kg)  마늘_가격(원/kg)  \\\n",
       "date                                                                          \n",
       "2016-01-01          0.0          0.0           0.0         0.0          0.0   \n",
       "2016-01-02       1281.0          3.0       11000.0     15019.0       5475.0   \n",
       "2016-01-03          0.0          0.0           0.0         0.0          0.0   \n",
       "2016-01-04       1235.0        699.0        4464.0    141638.0       5210.0   \n",
       "2016-01-05       1213.0       1112.6        4342.0    126207.8       5387.0   \n",
       "\n",
       "            대파_거래량(kg)  대파_가격(원/kg)  얼갈이배추_거래량(kg)  얼갈이배추_가격(원/kg)  \\\n",
       "date                                                                 \n",
       "2016-01-01         0.0          0.0            0.0             0.0   \n",
       "2016-01-02     92334.0       1704.0         6359.0          1331.0   \n",
       "2016-01-03         0.0          0.0            0.0             0.0   \n",
       "2016-01-04    994328.1       1716.0       262615.5          1212.0   \n",
       "2016-01-05    787716.0       1715.0       221850.5          1197.0   \n",
       "\n",
       "            양배추_거래량(kg)  양배추_가격(원/kg)  깻잎_거래량(kg)  깻잎_가격(원/kg)  시금치_거래량(kg)  \\\n",
       "date                                                                          \n",
       "2016-01-01          0.0           0.0         0.0          0.0          0.0   \n",
       "2016-01-02      40028.0         348.0      4374.9      13242.0      16550.5   \n",
       "2016-01-03          0.0           0.0         0.0          0.0          0.0   \n",
       "2016-01-04    1074699.1         345.0    122613.5       9923.0     427435.1   \n",
       "2016-01-05     825681.9         350.0     79055.9       9529.0     334636.8   \n",
       "\n",
       "            시금치_가격(원/kg)  미나리_거래량(kg)  미나리_가격(원/kg)  당근_거래량(kg)  당근_가격(원/kg)  \\\n",
       "date                                                                           \n",
       "2016-01-01           0.0          0.0           0.0         0.0          0.0   \n",
       "2016-01-02        2339.0      10528.0        1729.0     13885.0        804.0   \n",
       "2016-01-03           0.0          0.0           0.0         0.0          0.0   \n",
       "2016-01-04        2153.0      82113.5        3960.0    558950.2        794.0   \n",
       "2016-01-05        2220.0      80144.0        3333.0    444353.7        763.0   \n",
       "\n",
       "            파프리카_거래량(kg)  파프리카_가격(원/kg)  새송이_거래량(kg)  새송이_가격(원/kg)  \\\n",
       "date                                                                 \n",
       "2016-01-01           0.0            0.0          0.0           0.0   \n",
       "2016-01-02        3853.0         3703.0      15797.0        2576.0   \n",
       "2016-01-03           0.0            0.0          0.0           0.0   \n",
       "2016-01-04      104930.3         4871.0     277326.5        2440.0   \n",
       "2016-01-05      100699.5         5129.0     218465.2        2437.0   \n",
       "\n",
       "            팽이버섯_거래량(kg)  팽이버섯_가격(원/kg)  토마토_거래량(kg)  토마토_가격(원/kg)  \n",
       "date                                                                \n",
       "2016-01-01           0.0            0.0          0.0           0.0  \n",
       "2016-01-02       14634.0         1474.0      30950.0        1621.0  \n",
       "2016-01-03           0.0            0.0          0.0           0.0  \n",
       "2016-01-04      159800.0         1750.0     291057.0        1834.0  \n",
       "2016-01-05      153084.0         1822.0     194626.5        1833.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(TRAIN_PATH, parse_dates=[\"date\"], index_col = \"date\").iloc[:, 0:33] # 이후 5종은 품목이 아니라 품종이라 제외\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DATA - 전국 도매시장의 월별 거래정보"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SALEDATE</th>\n",
       "      <th>WHSAL_NM</th>\n",
       "      <th>CMP_NM</th>\n",
       "      <th>PUM_NM</th>\n",
       "      <th>KIND_NM</th>\n",
       "      <th>DAN_NM</th>\n",
       "      <th>POJ_NM</th>\n",
       "      <th>SIZE_NM</th>\n",
       "      <th>LV_NM</th>\n",
       "      <th>SAN_NM</th>\n",
       "      <th>DANQ</th>\n",
       "      <th>QTY</th>\n",
       "      <th>COST</th>\n",
       "      <th>TOT_QTY</th>\n",
       "      <th>TOT_AMT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>2016-01-09</td>\n",
       "      <td>부산엄궁도매</td>\n",
       "      <td>부산청과</td>\n",
       "      <td>우엉</td>\n",
       "      <td>우엉(일반)</td>\n",
       "      <td>kg</td>\n",
       "      <td>단</td>\n",
       "      <td>.</td>\n",
       "      <td>보통</td>\n",
       "      <td>경기도 여주군</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48</td>\n",
       "      <td>1622.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>77856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>2016-01-09</td>\n",
       "      <td>부산엄궁도매</td>\n",
       "      <td>부산청과</td>\n",
       "      <td>우엉</td>\n",
       "      <td>우엉(일반)</td>\n",
       "      <td>kg</td>\n",
       "      <td>단</td>\n",
       "      <td>.</td>\n",
       "      <td>보통</td>\n",
       "      <td>경기도 여주군</td>\n",
       "      <td>1.0</td>\n",
       "      <td>494</td>\n",
       "      <td>1889.0</td>\n",
       "      <td>494.0</td>\n",
       "      <td>933166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>2016-01-09</td>\n",
       "      <td>부산엄궁도매</td>\n",
       "      <td>부산청과</td>\n",
       "      <td>우엉</td>\n",
       "      <td>우엉(일반)</td>\n",
       "      <td>kg</td>\n",
       "      <td>단</td>\n",
       "      <td>.</td>\n",
       "      <td>보통</td>\n",
       "      <td>경기도 여주군</td>\n",
       "      <td>1.0</td>\n",
       "      <td>152</td>\n",
       "      <td>1411.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>214472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>2016-01-09</td>\n",
       "      <td>부산엄궁도매</td>\n",
       "      <td>부산청과</td>\n",
       "      <td>우엉</td>\n",
       "      <td>우엉(일반)</td>\n",
       "      <td>kg</td>\n",
       "      <td>단</td>\n",
       "      <td>.</td>\n",
       "      <td>보통</td>\n",
       "      <td>경기도 여주군</td>\n",
       "      <td>1.0</td>\n",
       "      <td>472</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>660800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>2016-01-09</td>\n",
       "      <td>부산엄궁도매</td>\n",
       "      <td>부산청과</td>\n",
       "      <td>사과</td>\n",
       "      <td>미시마</td>\n",
       "      <td>kg</td>\n",
       "      <td>상자</td>\n",
       "      <td>.</td>\n",
       "      <td>보통</td>\n",
       "      <td>경상북도 청송군</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>41600.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>41600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      SALEDATE WHSAL_NM CMP_NM PUM_NM KIND_NM DAN_NM POJ_NM SIZE_NM LV_NM  \\\n",
       "200 2016-01-09   부산엄궁도매   부산청과     우엉  우엉(일반)     kg      단       .    보통   \n",
       "201 2016-01-09   부산엄궁도매   부산청과     우엉  우엉(일반)     kg      단       .    보통   \n",
       "202 2016-01-09   부산엄궁도매   부산청과     우엉  우엉(일반)     kg      단       .    보통   \n",
       "203 2016-01-09   부산엄궁도매   부산청과     우엉  우엉(일반)     kg      단       .    보통   \n",
       "204 2016-01-09   부산엄궁도매   부산청과     사과     미시마     kg     상자       .    보통   \n",
       "\n",
       "       SAN_NM  DANQ  QTY     COST  TOT_QTY  TOT_AMT  \n",
       "200   경기도 여주군   1.0   48   1622.0     48.0    77856  \n",
       "201   경기도 여주군   1.0  494   1889.0    494.0   933166  \n",
       "202   경기도 여주군   1.0  152   1411.0    152.0   214472  \n",
       "203   경기도 여주군   1.0  472   1400.0    472.0   660800  \n",
       "204  경상북도 청송군  10.0    1  41600.0     10.0    41600  "
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TSALET_CSV(201601)[200:205]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 가설 1. 김장철이 되면 배추 가격은 상승할 것이다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **코드 수정**\n",
    "    - `drop()`을 최대한 사용하지 않고 코드를 짜는 것이 좋음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>요일</th>\n",
       "      <th>배추_거래량(kg)</th>\n",
       "      <th>배추_가격(원/kg)</th>\n",
       "      <th>무_거래량(kg)</th>\n",
       "      <th>무_가격(원/kg)</th>\n",
       "      <th>양파_거래량(kg)</th>\n",
       "      <th>양파_가격(원/kg)</th>\n",
       "      <th>건고추_거래량(kg)</th>\n",
       "      <th>건고추_가격(원/kg)</th>\n",
       "      <th>마늘_거래량(kg)</th>\n",
       "      <th>마늘_가격(원/kg)</th>\n",
       "      <th>대파_거래량(kg)</th>\n",
       "      <th>대파_가격(원/kg)</th>\n",
       "      <th>얼갈이배추_거래량(kg)</th>\n",
       "      <th>얼갈이배추_가격(원/kg)</th>\n",
       "      <th>양배추_거래량(kg)</th>\n",
       "      <th>양배추_가격(원/kg)</th>\n",
       "      <th>깻잎_거래량(kg)</th>\n",
       "      <th>깻잎_가격(원/kg)</th>\n",
       "      <th>시금치_거래량(kg)</th>\n",
       "      <th>시금치_가격(원/kg)</th>\n",
       "      <th>미나리_거래량(kg)</th>\n",
       "      <th>미나리_가격(원/kg)</th>\n",
       "      <th>당근_거래량(kg)</th>\n",
       "      <th>당근_가격(원/kg)</th>\n",
       "      <th>파프리카_거래량(kg)</th>\n",
       "      <th>파프리카_가격(원/kg)</th>\n",
       "      <th>새송이_거래량(kg)</th>\n",
       "      <th>새송이_가격(원/kg)</th>\n",
       "      <th>팽이버섯_거래량(kg)</th>\n",
       "      <th>팽이버섯_가격(원/kg)</th>\n",
       "      <th>토마토_거래량(kg)</th>\n",
       "      <th>토마토_가격(원/kg)</th>\n",
       "      <th>total_transaction</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",
       "      <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>2016-01-02</th>\n",
       "      <td>토요일</td>\n",
       "      <td>80860.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>80272.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>122787.5</td>\n",
       "      <td>1281.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>15019.0</td>\n",
       "      <td>5475.0</td>\n",
       "      <td>92334.0</td>\n",
       "      <td>1704.0</td>\n",
       "      <td>6359.0</td>\n",
       "      <td>1331.0</td>\n",
       "      <td>40028.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>4374.9</td>\n",
       "      <td>13242.0</td>\n",
       "      <td>16550.5</td>\n",
       "      <td>2339.0</td>\n",
       "      <td>10528.0</td>\n",
       "      <td>1729.0</td>\n",
       "      <td>13885.0</td>\n",
       "      <td>804.0</td>\n",
       "      <td>3853.0</td>\n",
       "      <td>3703.0</td>\n",
       "      <td>15797.0</td>\n",
       "      <td>2576.0</td>\n",
       "      <td>14634.0</td>\n",
       "      <td>1474.0</td>\n",
       "      <td>30950.0</td>\n",
       "      <td>1621.0</td>\n",
       "      <td>2390203.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>월요일</td>\n",
       "      <td>1422742.5</td>\n",
       "      <td>478.0</td>\n",
       "      <td>1699653.7</td>\n",
       "      <td>382.0</td>\n",
       "      <td>2315079.0</td>\n",
       "      <td>1235.0</td>\n",
       "      <td>699.0</td>\n",
       "      <td>4464.0</td>\n",
       "      <td>141638.0</td>\n",
       "      <td>5210.0</td>\n",
       "      <td>994328.1</td>\n",
       "      <td>1716.0</td>\n",
       "      <td>262615.5</td>\n",
       "      <td>1212.0</td>\n",
       "      <td>1074699.1</td>\n",
       "      <td>345.0</td>\n",
       "      <td>122613.5</td>\n",
       "      <td>9923.0</td>\n",
       "      <td>427435.1</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>82113.5</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>558950.2</td>\n",
       "      <td>794.0</td>\n",
       "      <td>104930.3</td>\n",
       "      <td>4871.0</td>\n",
       "      <td>277326.5</td>\n",
       "      <td>2440.0</td>\n",
       "      <td>159800.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>291057.0</td>\n",
       "      <td>1834.0</td>\n",
       "      <td>39913792.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>화요일</td>\n",
       "      <td>1167241.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>1423482.3</td>\n",
       "      <td>422.0</td>\n",
       "      <td>2092960.1</td>\n",
       "      <td>1213.0</td>\n",
       "      <td>1112.6</td>\n",
       "      <td>4342.0</td>\n",
       "      <td>126207.8</td>\n",
       "      <td>5387.0</td>\n",
       "      <td>787716.0</td>\n",
       "      <td>1715.0</td>\n",
       "      <td>221850.5</td>\n",
       "      <td>1197.0</td>\n",
       "      <td>825681.9</td>\n",
       "      <td>350.0</td>\n",
       "      <td>79055.9</td>\n",
       "      <td>9529.0</td>\n",
       "      <td>334636.8</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>80144.0</td>\n",
       "      <td>3333.0</td>\n",
       "      <td>444353.7</td>\n",
       "      <td>763.0</td>\n",
       "      <td>100699.5</td>\n",
       "      <td>5129.0</td>\n",
       "      <td>218465.2</td>\n",
       "      <td>2437.0</td>\n",
       "      <td>153084.0</td>\n",
       "      <td>1822.0</td>\n",
       "      <td>194626.5</td>\n",
       "      <td>1833.0</td>\n",
       "      <td>33173807.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>수요일</td>\n",
       "      <td>1045507.5</td>\n",
       "      <td>442.0</td>\n",
       "      <td>1904372.1</td>\n",
       "      <td>409.0</td>\n",
       "      <td>1860569.0</td>\n",
       "      <td>1263.0</td>\n",
       "      <td>1672.0</td>\n",
       "      <td>7041.0</td>\n",
       "      <td>91531.0</td>\n",
       "      <td>5013.0</td>\n",
       "      <td>793584.9</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>199151.0</td>\n",
       "      <td>1142.0</td>\n",
       "      <td>910812.3</td>\n",
       "      <td>327.0</td>\n",
       "      <td>78532.8</td>\n",
       "      <td>8123.0</td>\n",
       "      <td>357749.8</td>\n",
       "      <td>2219.0</td>\n",
       "      <td>76832.6</td>\n",
       "      <td>3133.0</td>\n",
       "      <td>396170.4</td>\n",
       "      <td>787.0</td>\n",
       "      <td>93051.3</td>\n",
       "      <td>5120.0</td>\n",
       "      <td>187900.4</td>\n",
       "      <td>2343.0</td>\n",
       "      <td>152693.1</td>\n",
       "      <td>1519.0</td>\n",
       "      <td>178811.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>33485764.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>목요일</td>\n",
       "      <td>1039925.0</td>\n",
       "      <td>448.0</td>\n",
       "      <td>1438990.2</td>\n",
       "      <td>428.0</td>\n",
       "      <td>1868011.7</td>\n",
       "      <td>1241.0</td>\n",
       "      <td>1191.0</td>\n",
       "      <td>3908.0</td>\n",
       "      <td>349913.8</td>\n",
       "      <td>4360.0</td>\n",
       "      <td>733312.0</td>\n",
       "      <td>1545.0</td>\n",
       "      <td>200119.0</td>\n",
       "      <td>992.0</td>\n",
       "      <td>836339.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>75987.7</td>\n",
       "      <td>6793.0</td>\n",
       "      <td>323944.6</td>\n",
       "      <td>2208.0</td>\n",
       "      <td>49040.9</td>\n",
       "      <td>2259.0</td>\n",
       "      <td>241115.0</td>\n",
       "      <td>770.0</td>\n",
       "      <td>60895.4</td>\n",
       "      <td>5095.0</td>\n",
       "      <td>181058.6</td>\n",
       "      <td>2291.0</td>\n",
       "      <td>148757.3</td>\n",
       "      <td>1365.0</td>\n",
       "      <td>107346.3</td>\n",
       "      <td>1921.0</td>\n",
       "      <td>30767546.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             요일  배추_거래량(kg)  배추_가격(원/kg)  무_거래량(kg)  무_가격(원/kg)  양파_거래량(kg)  \\\n",
       "date                                                                          \n",
       "2016-01-02  토요일     80860.0        329.0    80272.0       360.0    122787.5   \n",
       "2016-01-04  월요일   1422742.5        478.0  1699653.7       382.0   2315079.0   \n",
       "2016-01-05  화요일   1167241.0        442.0  1423482.3       422.0   2092960.1   \n",
       "2016-01-06  수요일   1045507.5        442.0  1904372.1       409.0   1860569.0   \n",
       "2016-01-07  목요일   1039925.0        448.0  1438990.2       428.0   1868011.7   \n",
       "\n",
       "            양파_가격(원/kg)  건고추_거래량(kg)  건고추_가격(원/kg)  마늘_거래량(kg)  마늘_가격(원/kg)  \\\n",
       "date                                                                          \n",
       "2016-01-02       1281.0          3.0       11000.0     15019.0       5475.0   \n",
       "2016-01-04       1235.0        699.0        4464.0    141638.0       5210.0   \n",
       "2016-01-05       1213.0       1112.6        4342.0    126207.8       5387.0   \n",
       "2016-01-06       1263.0       1672.0        7041.0     91531.0       5013.0   \n",
       "2016-01-07       1241.0       1191.0        3908.0    349913.8       4360.0   \n",
       "\n",
       "            대파_거래량(kg)  대파_가격(원/kg)  얼갈이배추_거래량(kg)  얼갈이배추_가격(원/kg)  \\\n",
       "date                                                                 \n",
       "2016-01-02     92334.0       1704.0         6359.0          1331.0   \n",
       "2016-01-04    994328.1       1716.0       262615.5          1212.0   \n",
       "2016-01-05    787716.0       1715.0       221850.5          1197.0   \n",
       "2016-01-06    793584.9       1620.0       199151.0          1142.0   \n",
       "2016-01-07    733312.0       1545.0       200119.0           992.0   \n",
       "\n",
       "            양배추_거래량(kg)  양배추_가격(원/kg)  깻잎_거래량(kg)  깻잎_가격(원/kg)  시금치_거래량(kg)  \\\n",
       "date                                                                          \n",
       "2016-01-02      40028.0         348.0      4374.9      13242.0      16550.5   \n",
       "2016-01-04    1074699.1         345.0    122613.5       9923.0     427435.1   \n",
       "2016-01-05     825681.9         350.0     79055.9       9529.0     334636.8   \n",
       "2016-01-06     910812.3         327.0     78532.8       8123.0     357749.8   \n",
       "2016-01-07     836339.0         315.0     75987.7       6793.0     323944.6   \n",
       "\n",
       "            시금치_가격(원/kg)  미나리_거래량(kg)  미나리_가격(원/kg)  당근_거래량(kg)  당근_가격(원/kg)  \\\n",
       "date                                                                           \n",
       "2016-01-02        2339.0      10528.0        1729.0     13885.0        804.0   \n",
       "2016-01-04        2153.0      82113.5        3960.0    558950.2        794.0   \n",
       "2016-01-05        2220.0      80144.0        3333.0    444353.7        763.0   \n",
       "2016-01-06        2219.0      76832.6        3133.0    396170.4        787.0   \n",
       "2016-01-07        2208.0      49040.9        2259.0    241115.0        770.0   \n",
       "\n",
       "            파프리카_거래량(kg)  파프리카_가격(원/kg)  새송이_거래량(kg)  새송이_가격(원/kg)  \\\n",
       "date                                                                 \n",
       "2016-01-02        3853.0         3703.0      15797.0        2576.0   \n",
       "2016-01-04      104930.3         4871.0     277326.5        2440.0   \n",
       "2016-01-05      100699.5         5129.0     218465.2        2437.0   \n",
       "2016-01-06       93051.3         5120.0     187900.4        2343.0   \n",
       "2016-01-07       60895.4         5095.0     181058.6        2291.0   \n",
       "\n",
       "            팽이버섯_거래량(kg)  팽이버섯_가격(원/kg)  토마토_거래량(kg)  토마토_가격(원/kg)  \\\n",
       "date                                                                 \n",
       "2016-01-02       14634.0         1474.0      30950.0        1621.0   \n",
       "2016-01-04      159800.0         1750.0     291057.0        1834.0   \n",
       "2016-01-05      153084.0         1822.0     194626.5        1833.0   \n",
       "2016-01-06      152693.1         1519.0     178811.0        1999.0   \n",
       "2016-01-07      148757.3         1365.0     107346.3        1921.0   \n",
       "\n",
       "            total_transaction  \n",
       "date                           \n",
       "2016-01-02          2390203.6  \n",
       "2016-01-04         39913792.0  \n",
       "2016-01-05         33173807.2  \n",
       "2016-01-06         33485764.8  \n",
       "2016-01-07         30767546.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 일요일인 것 제외하기\n",
    "df = df[df[\"요일\"] != \"일요일\"]\n",
    "# 행 전체가 0인 행 제외하기\n",
    "df[\"total_transaction\"] = df.sum(axis = 1)\n",
    "df = df[df.total_transaction != 0]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# 2016년 ~ 2020년 9월의 배추 거래정보에서 거래가 일어나지 않았거나 일요일의 데이터는 제외\n",
    "df_baechu = df[[\"배추_거래량(kg)\", \"배추_가격(원/kg)\"]].replace(0, np.NaN).dropna(axis = 0)\n",
    "sunday = []\n",
    "for i in range(df_baechu.shape[0]):\n",
    "    if df_baechu.index[i].weekday() == 6:\n",
    "        sunday.append(df_baechu.index[i])\n",
    "        \n",
    "df_baechu = df_baechu.drop(index = sunday, axis = 0)\n",
    "\n",
    "# 2016년 ~ 2020년 9월의 주별 배추 거래량, 가격\n",
    "df_baechu_vol = df_baechu['배추_거래량(kg)'].resample('W').sum()[\"2016\":\"2020\"]\n",
    "df_baechu_price = df_baechu['배추_가격(원/kg)'].resample('W').sum()[\"2016\":\"2020\"]\n",
    "\n",
    "# save pickle\n",
    "with open(\"./pickles/hypo1_baechu_vol_data.pickle\", \"wb\") as fw:\n",
    "    pickle.dump(df_baechu_vol, fw)\n",
    "with open(\"./pickles/hypo1_baechu_price_data.pickle\", \"wb\") as fw:\n",
    "    pickle.dump(df_baechu_price, fw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **코드 수정**\n",
    "    - 읽는 사람이 편하게 이해할 수 있도록 코드를 짜는 것이 중요함\n",
    "    - shape 설정시 줄바꿈을 하든지 설명을 하든지 해야함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
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   "source": [
    "# load pickle\n",
    "with open(\"./pickles/hypo1_baechu_vol_data.pickle\", \"rb\") as fr:\n",
    "    df_baechu_vol = pickle.load(fr)\n",
    "with open(\"./pickles/hypo1_baechu_price_data.pickle\", \"rb\") as fr:\n",
    "    df_baechu_price = pickle.load(fr)\n",
    "\n",
    "# 8,9,11,12월 세로영역\n",
    "shapes_8_9 = [{'type' : 'rect',\n",
    "               'xref' : 'x',\n",
    "               'yref' : 'paper',\n",
    "               'x0' : '{}-08-01'.format(str(year)),\n",
    "               'y0' : 0,\n",
    "               'x1' : '{}-09-30'.format(str(year)),\n",
    "               'y1' : 1,\n",
    "               'fillcolor' : 'blue',\n",
    "               'opacity' : 0.1,\n",
    "               'line' : {'width' : 0}}\n",
    "                for year in range(2016, 2020)]\n",
    "shapes_11_12 = [{'type' : 'rect',\n",
    "                 'xref' : 'x',\n",
    "                 'yref' : 'paper',\n",
    "                 'x0' : '{}-11-01'.format(str(year)),\n",
    "                 'y0' : 0,\n",
    "                 'x1' : '{}-12-31'.format(str(year)),\n",
    "                 'y1' : 1,\n",
    "                 'fillcolor' : 'red',\n",
    "                 'opacity' : 0.1,\n",
    "                 'line' : {'width' : 0}}\n",
    "                 for year in range(2016, 2020)]\n",
    "\n",
    "df_baechu_vol = df_baechu_vol.reset_index()\n",
    "df_baechu_price = df_baechu_price.reset_index()\n",
    "\n",
    "# 시각화\n",
    "fig1 = px.line(df_baechu_vol, x = 'date', y = '배추_거래량(kg)', \n",
    "       title = '2016 ~ 2020년 9월의 배추 거래량(kg) 변화')\n",
    "fig1.update_layout(shapes = shapes_8_9 + shapes_11_12)\n",
    "fig1.show()\n",
    "\n",
    "fig2 = px.line(df_baechu_price, x = 'date', y = '배추_가격(원/kg)', \n",
    "       title = '2016 ~ 2020년 9월의 배추 가격(원/kg) 변화')\n",
    "fig2.update_layout(shapes = shapes_8_9 + shapes_11_12)\n",
    "fig2.show()"
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   "source": [
    "* 위의 그래프를 통해서,\n",
    "  * 배추의 거래량은 11, 12월에 급증한다. 또한 그보다 3개월 이전인 8, 9월에도 급증하는 추세를 보인다.\n",
    "  * 배추의 가격은 11, 12월에는 낮은 반면, 오히려 그보다 3개월 이전인 8, 9월에 급증하는 추세를 보인다.\n",
    "* 그래서 배추의 품종별로 연간 거래량과 가격이 어떻게 변화하는지 확인해보자. (2016년을 예시로 함)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* ***여기서 2016년을 예시로 든 이유는,***\n",
    "  * 본인이 프로젝트를 진행하면서 많은 양의 데이터를 한 번에 처리하는데 어려움을 겪었는데, 그에 대한 나름대로의 해결 방안이 전체 데이터 중 1개 년의 데이터로만 확인해보는 것이었다.\n",
    "  * 많은 양의 데이터를 처리하는 여러 방식에 대해서는 프로젝트 진행 도중에 알게 되었는데, 시간 부족으로 인해 수정하지 못했다.\n",
    "  * 추후에 이와 비슷한 일을 겪는다면, 좀 더 매끄러운 데이터 처리를 할 수 있도록 해야겠다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 12/12 [01:34<00:00,  7.86s/it]\n"
     ]
    }
   ],
   "source": [
    "# 2016년 도매시장 데이터\n",
    "df_2016 = TSALET_CONCAT_YEAR(2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2016년 배추 거래 정보에서 COST, QTY, DANQ가 0 이하이거나 빈 칸이 있는 행은 제거\n",
    "# 도매시장에서 낱개 거래가 이루어져 단가의 이상치가 된다고 판단되는 행은 제거\n",
    "df_2016_baechu = df_2016[df_2016[\"PUM_NM\"]==\"배추\"].replace('', np.NaN).dropna()\n",
    "df_2016_baechu = df_2016_baechu.drop(df_2016_baechu[(df_2016_baechu[\"COST\"] <= 0) | (df_2016_baechu[\"QTY\"] <= 0) | (df_2016_baechu[\"DANQ\"] <= 0)].index, axis = 0)\n",
    "# df_2016_baechu = df_2016_baechu.drop(2976757, axis = 0)\n",
    "df_2016_baechu = df_2016_baechu.drop(df_2016_baechu[df_2016_baechu[\"POJ_NM\"]==\"상자\"].index, axis = 0)\n",
    "df_2016_baechu = df_2016_baechu.drop(df_2016_baechu[df_2016_baechu[\"QTY\"]==1].index, axis = 0)\n",
    "\n",
    "# 배추의 품종별로 단가의 평균을 계산해 column을 추가\n",
    "df_2016_baechu[\"COST_PER_DANQ\"] = df_2016_baechu[\"TOT_AMT\"] / df_2016_baechu[\"TOT_QTY\"]\n",
    "grouped_df_2016_baechu_DANQ = df_2016_baechu.groupby([\"KIND_NM\"])[\"COST_PER_DANQ\"].mean().sort_values(ascending = False)\n",
    "\n",
    "# 2016년의 배추 품종별 거래량의 합\n",
    "grouped_2016_baechu_kind_TOT_QTY = pd.DataFrame(df_2016_baechu.groupby([\"KIND_NM\", \"SALEDATE\"])[\"TOT_QTY\"].sum()).reset_index()\n",
    "df_2016_baechu_kind_TOT_QTY = pd.DataFrame(columns = [\"TOT_QTY\", \"KIND_NM\"])\n",
    "\n",
    "for kind in grouped_2016_baechu_kind_TOT_QTY[\"KIND_NM\"].unique():\n",
    "    df_temp = grouped_2016_baechu_kind_TOT_QTY[grouped_2016_baechu_kind_TOT_QTY[\"KIND_NM\"]==kind].drop([\"KIND_NM\"],\n",
    "                                                                                             axis = 1).set_index('SALEDATE')\n",
    "    df_temp = pd.DataFrame(df_temp[\"TOT_QTY\"].resample(\"W\").sum()).replace(0, np.NaN).dropna()\n",
    "    df_temp[\"KIND_NM\"] = [kind for i in range(df_temp.shape[0])]\n",
    "    df_2016_baechu_kind_TOT_QTY = pd.concat([df_2016_baechu_kind_TOT_QTY, df_temp])\n",
    "\n",
    "df_2016_baechu_kind_TOT_QTY = df_2016_baechu_kind_TOT_QTY.reset_index().rename(columns = {\"index\":\"SALEDATE\"})\n",
    "\n",
    "# 2016년 배추 품종별 단가의 평균\n",
    "grouped_2016_baechu_kind_DANQ = pd.DataFrame(df_2016_baechu.groupby([\"KIND_NM\", \"SALEDATE\"])[\"COST_PER_DANQ\"].mean()).reset_index()\n",
    "df_2016_baechu_kind_DANQ = pd.DataFrame(columns = [\"COST_PER_DANQ\", \"KIND_NM\"])\n",
    "\n",
    "for kind in grouped_2016_baechu_kind_DANQ[\"KIND_NM\"].unique():\n",
    "    df_temp = grouped_2016_baechu_kind_DANQ[grouped_2016_baechu_kind_DANQ[\"KIND_NM\"]==kind].drop([\"KIND_NM\"],\n",
    "                                                                                             axis = 1).set_index('SALEDATE')\n",
    "    df_temp = pd.DataFrame(df_temp[\"COST_PER_DANQ\"].resample(\"W\").mean()).replace(0, np.NaN).dropna()\n",
    "    df_temp[\"KIND_NM\"] = [kind for i in range(df_temp.shape[0])]\n",
    "    df_2016_baechu_kind_DANQ = pd.concat([df_2016_baechu_kind_DANQ, df_temp])\n",
    "    \n",
    "df_2016_baechu_kind_DANQ = df_2016_baechu_kind_DANQ.reset_index().rename(columns = {\"index\":\"SALEDATE\"})\n",
    "\n",
    "# save pickle\n",
    "with open(\"./pickles/hypo1_baechu_kind_TOT_QTY_data.pickle\", \"wb\") as fw:\n",
    "    pickle.dump(df_2016_baechu_kind_TOT_QTY, fw)\n",
    "with open(\"./pickles/hypo1_baechu_kind_DANQ_data.pickle\", \"wb\") as fw:\n",
    "    pickle.dump(df_2016_baechu_kind_DANQ, fw)"
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   "source": [
    "# load pickle\n",
    "with open(\"./pickles/hypo1_baechu_kind_TOT_QTY_data.pickle\", \"rb\") as fr:\n",
    "    df_2016_baechu_kind_TOT_QTY = pickle.load(fr)\n",
    "with open(\"./pickles/hypo1_baechu_kind_DANQ_data.pickle\", \"rb\") as fr:\n",
    "    df_2016_baechu_kind_DANQ = pickle.load(fr)\n",
    "\n",
    "# 컬럼명 변경\n",
    "df_2016_baechu_kind_TOT_QTY = df_2016_baechu_kind_TOT_QTY.rename(columns={'SALEDATE' : 'date', 'TOT_QTY' : '거래량(kg)', 'KIND_NM' : '품종'})\n",
    "df_2016_baechu_kind_DANQ = df_2016_baechu_kind_DANQ.rename(columns={'SALEDATE' : 'date', 'COST_PER_DANQ' : '가격(kg/원)', 'KIND_NM' : '품종'})\n",
    "\n",
    "# 시각화\n",
    "fig1 = px.line(df_2016_baechu_kind_TOT_QTY, x = 'date', y = '거래량(kg)', color = \"품종\",\n",
    "              title = \"2016년 배추 품종별 거래량(kg) 변화\")\n",
    "fig1.update_layout(\n",
    "    shapes = [{'type' : 'rect','xref' : 'x','yref' : 'paper','x0' : '2016-08-01','y0' : 0,'x1' : '2016-09-30',\n",
    "                'y1' : 1,'fillcolor' : 'blue','opacity' : 0.1,'line' : {'width' : 0}},\n",
    "              {'type' : 'rect','xref' : 'x','yref' : 'paper','x0' : '2016-11-01','y0' : 0,'x1' : '2016-12-31',\n",
    "                'y1' : 1,'fillcolor' : 'red','opacity' : 0.1,'line' : {'width' : 0}}])\n",
    "fig1.show()\n",
    "\n",
    "fig2 = px.line(df_2016_baechu_kind_DANQ, x = 'date', y = '가격(kg/원)', color = \"품종\",\n",
    "              title = \"2016년 배추 품종별 가격(원/kg) 변화\")\n",
    "fig2.update_layout(\n",
    "    shapes = [{'type' : 'rect','xref' : 'x','yref' : 'paper','x0' : '2016-08-01','y0' : 0,'x1' : '2016-09-30',\n",
    "                'y1' : 1,'fillcolor' : 'blue','opacity' : 0.1,'line' : {'width' : 0}},\n",
    "              {'type' : 'rect','xref' : 'x','yref' : 'paper','x0' : '2016-11-01','y0' : 0,'x1' : '2016-12-31',\n",
    "                'y1' : 1,'fillcolor' : 'red','opacity' : 0.1,'line' : {'width' : 0}}])\n",
    "fig2.show()"
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   "metadata": {},
   "source": [
    "* **위 두 그래프에서,**\n",
    "  * 거래량은 8, 9월에 고냉지배추가 증가했고, 11, 12월에는 김장(가을)배추가 크게 증가하는 추세를 보인다.\n",
    "  * 가격은 우거지와 절임배추를 제외하면, 대부분 품종의 배추들이 8,9월에 증가하는 추세를 보인다.\n",
    "* 김장철인 11, 12월에 김장배추의 거래량이 증가한다는 사실은 알 수 있지만,\n",
    "* 가격은 전체적으로 8, 9월을 중심으로 증가하며, 김장철에는 크게 영향을 받지 않는다는 사실을 알 수 있다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 가설 2-1. 귤의 주 생산지는 제주도일 것이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": [
    "# 2016년 도매시장 데이터\n",
    "df_2016 = TSALET_CONCAT_YEAR(2016)"
   ]
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    "# 데이터 전처리\n",
    "df_2016_gamgul = df_2016[df_2016[\"PUM_NM\"]==\"감귤\"]\n",
    "df_2016_gamgul[\"SAN_NM\"].replace('', np.NaN, inplace = True)\n",
    "df_2016_gamgul = df_2016_gamgul.dropna(subset = [\"SAN_NM\"], axis = 0)\n",
    "df_2016_gamgul = df_2016_gamgul.drop((df_2016_gamgul[df_2016_gamgul['COST'] <= 0]).index, axis=0)\n",
    "df_2016_gamgul = df_2016_gamgul.drop((df_2016_gamgul[df_2016_gamgul['QTY'] <= 0]).index, axis=0)\n",
    "df_2016_gamgul = df_2016_gamgul.drop((df_2016_gamgul[df_2016_gamgul['DANQ'] <= 0]).index, axis=0)\n",
    "df_2016_gamgul = df_2016_gamgul.replace(\"제주 서귀포시\", \"제주도 서귀포시\")\n",
    "df_2016_gamgul = df_2016_gamgul.replace(\"제주 제주시\", \"제주도 제주시\")\n",
    "\n",
    "# 2016년도 전국 감귤 생산지별 거래량 합\n",
    "df_2016_gamgul_san = df_2016_gamgul.groupby([\"SAN_NM\"])[\"TOT_QTY\"].sum().sort_values(ascending = False)\n",
    "\n",
    "# 제주도를 제외한 생산지는 기타로 묶음\n",
    "df_2016_gamgul_san_etc_name = '기타(150여 개 지역)'\n",
    "df_2016_gamgul_san_etc_value = df_2016_gamgul_san[5:].sum()\n",
    "\n",
    "# 2016년도 전국 감귤 생산지별 거래량 합에 제주도를 제외한 생산지는 모두 묶어 기타 row로 처리\n",
    "df_2016_gamgul_san = df_2016_gamgul_san[0:5]\n",
    "df_2016_gamgul_san.loc[df_2016_gamgul_san_etc_name] = df_2016_gamgul_san_etc_value\n",
    "\n",
    "# save pickle\n",
    "with open(\"./pickles/hypo2_2016_gamgul_san_data.pickle\", \"wb\") as fw:\n",
    "    pickle.dump(df_2016_gamgul_san, fw)"
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         "text": "2016년도 전국 감귤 생산지별 거래량 합(kg)"
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    "# load pickle\n",
    "with open(\"./pickles/hypo2_2016_gamgul_san_data.pickle\", \"rb\") as fr:\n",
    "    df_2016_gamgul_san = pickle.load(fr)\n",
    "    \n",
    "# 시각화 - 2016년도 전국 감귤 생산지별 거래량 합\n",
    "colors = [\"orange\",] * 6\n",
    "colors[5] = 'lightslategray'\n",
    "\n",
    "fig = go.Figure(data = [go.Bar(x = df_2016_gamgul_san.index, y = df_2016_gamgul_san.values,\n",
    "                               marker_color = colors)])\n",
    "fig.update_layout(title_text = '2016년도 전국 감귤 생산지별 거래량 합(kg)')\n",
    "\n",
    "fig.show()"
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   "metadata": {},
   "source": [
    "* **위 그래프에서,**\n",
    "  * 감귤 생산량에 있어 전국의 기타 150여 개 지역이 차지하는 양에 비해 제주도가 차지하는 양이 압도적으로 많았다.\n",
    "* 따라서, 감귤의 최대 생산지는 제주도라고 할 수 있다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 가설 2-2. 제주도에서는 다른 농산물보다 귤을 가장 많이 생산할 것이다."
   ]
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  {
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   "metadata": {
    "scrolled": false
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    "# 데이터 전처리\n",
    "df_2016[\"SAN_NM\"].replace('', np.NaN, inplace = True)\n",
    "df_2016 = df_2016.dropna(subset = [\"SAN_NM\"], axis = 0)\n",
    "\n",
    "# 2016년 제주도산 농산물의 품목별 전체 거래중량\n",
    "df_2016_san_jeju = df_2016[df_2016[\"SAN_NM\"].isin([i for i in df_2016[\"SAN_NM\"].unique() if \"제주\" in i])]\n",
    "grouped_san_jeju = df_2016_san_jeju.groupby([\"PUM_NM\"])[\"TOT_QTY\"].sum().sort_values()\n",
    "grouped_san_jeju = pd.DataFrame(data = grouped_san_jeju)\n",
    "\n",
    "# 합이 1이 되도록 정규화\n",
    "grouped_san_jeju[\"TOT_QTY\"] /= grouped_san_jeju[\"TOT_QTY\"].sum()\n",
    "\n",
    "# save pickle\n",
    "with open(\"./pickles/hypo2_piechart_data.pickle\", \"wb\") as fw:\n",
    "    pickle.dump(grouped_san_jeju, fw)\n",
    "\n",
    "## 가설 3 : 제주도에서 멀리 있는 지역의 도매시장에서 일어난 거래일수록 거래 가격이 비쌀 것이다."
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         "text": "2016년도 제주도산 농산물 품목 비율(%) - 상위 10개 품목"
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   "source": [
    "# load pickle\n",
    "with open(\"./pickles/hypo2_piechart_data.pickle\", \"rb\") as fr:\n",
    "    grouped_san_jeju = pickle.load(fr)\n",
    "\n",
    "grouped_san_jeju = grouped_san_jeju.sort_values(by = \"TOT_QTY\", ascending = False).head(10)\n",
    "\n",
    "# 시각화 - 제주도가 주로 생산하는 농산물에 대한 비율\n",
    "fig = px.pie(grouped_san_jeju, values = grouped_san_jeju[\"TOT_QTY\"], names = grouped_san_jeju.index,\n",
    "              color_discrete_sequence=px.colors.sequential.RdBu, title = \"2016년도 제주도산 농산물 품목 비율(%) - 상위 10개 품목\")\n",
    "fig.update_traces(textposition='inside', textinfo='percent+label')\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 2016년도의 제주도산 농산물 중 가장 많은 비율을 차지하는 품목은 40.9%로 감귤이었다. \n",
    "* 감귤, 무, 양배추가 2016년 제주도산 농산물 중 약 80%를 차지한다.\n",
    "  * 따라서 이후 가설 3에서 감귤, 무, 양배추에 대해서 거래지역별 거래 가격의 차이에 대해서 알아보기로 한다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 가설 3 : 제주도에서 멀리 있는 지역의 도매시장에서 일어난 거래일수록 거래 가격이 비쌀 것이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# 2016년 제주도산 감귤 중 DAMQ, COST, QTY가 0이거나 음수인 행 제거\n",
    "df_2016_san_jeju_gamgul = df_2016_san_jeju[df_2016_san_jeju[\"PUM_NM\"]==\"감귤\"]\n",
    "df_2016_san_jeju_gamgul = df_2016_san_jeju_gamgul.drop((df_2016_san_jeju_gamgul[df_2016_san_jeju_gamgul['COST'] <= 0]).index, axis=0)\n",
    "df_2016_san_jeju_gamgul = df_2016_san_jeju_gamgul.drop((df_2016_san_jeju_gamgul[df_2016_san_jeju_gamgul['QTY'] <= 0]).index, axis=0)\n",
    "df_2016_san_jeju_gamgul = df_2016_san_jeju_gamgul.drop((df_2016_san_jeju_gamgul[df_2016_san_jeju_gamgul['DANQ'] <= 0]).index, axis=0)\n",
    "\n",
    "\n",
    "# 2016년 제주도산 감귤의 단위가격 평균 \n",
    "df_2016_san_jeju_gamgul['COST_PER_DANQ'] = df_2016_san_jeju_gamgul[\"TOT_AMT\"] / df_2016_san_jeju_gamgul[\"TOT_QTY\"]\n",
    "grouped_2016_san_jeju_gamgul_WHSAL = pd.DataFrame(df_2016_san_jeju_gamgul.groupby([\"WHSAL_NM\"])[\"COST_PER_DANQ\"].mean()).reset_index()\n",
    "\n",
    "# 도매시장의 좌표\n",
    "latitude = [37.73613, 35.18385, 35.11620, 37.61263, 36.15712, 35.90339, 36.36528, 36.35779, 35.21407, 35.12858,\n",
    "            37.49403, 37.55287, 37.25562, 34.91683, 36.58981, 37.30957, 37.38298, 35.53989, 37.35449, 35.91878, \n",
    "            37.44229, 37.52219, 35.86807, 35.58939, 35.21068, 35.25519, 35.22901, 36.86107, 36.64783, 37.91020, \n",
    "            37.00551, 36.08150]\n",
    "longitude = [128.91824, 126.93501, 126.85632, 127.14465, 128.35023, 128.54235, 127.32074, 127.40658, 129.12180, 128.96510,\n",
    "             127.11482, 126.82083, 127.03111, 127.53796, 128.60896, 126.85733, 126.97009, 129.34266, 127.92328, 126.91714,\n",
    "             126.70494, 126.74920, 127.11701, 126.84576, 128.12295, 128.51132, 128.63746, 127.14993, 127.46439, 127.72691,\n",
    "             127.92131, 129.33650]\n",
    "\n",
    "# 도매시장의 좌표와 제주도와의 거리 column 추가\n",
    "jeju = [33.52088, 126.53885]\n",
    "\n",
    "grouped_2016_san_jeju_gamgul_WHSAL = grouped_2016_san_jeju_gamgul_WHSAL.assign(latitude = latitude, longitude = longitude)\n",
    "grouped_2016_san_jeju_gamgul_WHSAL = grouped_2016_san_jeju_gamgul_WHSAL.assign(\n",
    "    distance = [((grouped_2016_san_jeju_gamgul_WHSAL.iloc[i, 2] - jeju[0])**2 + \n",
    "                 (grouped_2016_san_jeju_gamgul_WHSAL.iloc[i, 3] - jeju[1])**2)**0.5 for i in range(0, 32)])\n",
    "\n",
    "######################################################################\n",
    "\n",
    "# 2016년 제주도산 무 중 DAMQ, COST, QTY가 0이거나 음수인 행 제거\n",
    "df_2016_san_jeju_radish = df_2016_san_jeju[df_2016_san_jeju[\"PUM_NM\"]==\"무\"]\n",
    "df_2016_san_jeju_radish = df_2016_san_jeju_radish.drop((df_2016_san_jeju_radish[df_2016_san_jeju_radish['COST'] <= 0]).index, axis=0)\n",
    "df_2016_san_jeju_radish = df_2016_san_jeju_radish.drop((df_2016_san_jeju_radish[df_2016_san_jeju_radish['QTY'] <= 0]).index, axis=0)\n",
    "df_2016_san_jeju_radish = df_2016_san_jeju_radish.drop((df_2016_san_jeju_radish[df_2016_san_jeju_radish['DANQ'] <= 0]).index, axis=0)\n",
    "\n",
    "\n",
    "# 2016년 제주도산 무의 단위가격 평균 확인\n",
    "df_2016_san_jeju_radish['COST_PER_DANQ'] = df_2016_san_jeju_radish[\"TOT_AMT\"] / df_2016_san_jeju_radish[\"TOT_QTY\"]\n",
    "grouped_2016_san_jeju_radish_WHSAL = pd.DataFrame(df_2016_san_jeju_radish.groupby([\"WHSAL_NM\"])[\"COST_PER_DANQ\"].mean()).reset_index()\n",
    "\n",
    "# 도매시장의 좌표와 제주도와의 거리 column 추가\n",
    "latitude2 = latitude[0:14] + latitude[15:29] + latitude[30:]\n",
    "longitude2 = longitude[0:14] + longitude[15:29] + longitude[30:]\n",
    "grouped_2016_san_jeju_radish_WHSAL = grouped_2016_san_jeju_radish_WHSAL.assign(latitude = latitude2,longitude = longitude2)\n",
    "grouped_2016_san_jeju_radish_WHSAL = grouped_2016_san_jeju_radish_WHSAL.assign(\n",
    "    distance = [((grouped_2016_san_jeju_radish_WHSAL.iloc[i, 2] - jeju[0])**2 + \n",
    "                 (grouped_2016_san_jeju_radish_WHSAL.iloc[i, 3] - jeju[1])**2)**0.5 for i in range(0, 30)])\n",
    "\n",
    "######################################################################\n",
    "\n",
    "# 2016년 제주도산 양배추 중 DAMQ, COST, QTY가 0이거나 음수인 행 제거\n",
    "df_2016_san_jeju_cabbage = df_2016_san_jeju[df_2016_san_jeju[\"PUM_NM\"]==\"양배추\"]\n",
    "df_2016_san_jeju_cabbage = df_2016_san_jeju_cabbage.drop((df_2016_san_jeju_cabbage[df_2016_san_jeju_cabbage['COST'] <= 0]).index, axis=0)\n",
    "df_2016_san_jeju_cabbage = df_2016_san_jeju_cabbage.drop((df_2016_san_jeju_cabbage[df_2016_san_jeju_cabbage['QTY'] <= 0]).index, axis=0)\n",
    "df_2016_san_jeju_cabbage = df_2016_san_jeju_cabbage.drop((df_2016_san_jeju_cabbage[df_2016_san_jeju_cabbage['DANQ'] <= 0]).index, axis=0)\n",
    "\n",
    "\n",
    "# 2016년 제주도산 양배추의 단위가격 평균 \n",
    "df_2016_san_jeju_cabbage['COST_PER_DANQ'] = df_2016_san_jeju_cabbage[\"TOT_AMT\"] / df_2016_san_jeju_cabbage[\"TOT_QTY\"]\n",
    "grouped_2016_san_jeju_cabbage_WHSAL = pd.DataFrame(df_2016_san_jeju_cabbage.groupby([\"WHSAL_NM\"])[\"COST_PER_DANQ\"].mean()).reset_index()\n",
    "\n",
    "\n",
    "# 도매시장의 좌표와 제주도와의 거리 column 추가\n",
    "grouped_2016_san_jeju_cabbage_WHSAL = grouped_2016_san_jeju_cabbage_WHSAL.assign(latitude = latitude2,longitude = longitude2)\n",
    "grouped_2016_san_jeju_cabbage_WHSAL = grouped_2016_san_jeju_cabbage_WHSAL.assign(\n",
    "    distance = [((grouped_2016_san_jeju_cabbage_WHSAL.iloc[i, 2] - jeju[0])**2 + \n",
    "                 (grouped_2016_san_jeju_cabbage_WHSAL.iloc[i, 3] - jeju[1])**2)**0.5 for i in range(0, 30)])\n",
    "\n",
    "# save pickle\n",
    "with open(\"./pickles/hypo3_map_gamgul_data.pickle\", \"wb\") as fw:\n",
    "    pickle.dump(grouped_2016_san_jeju_gamgul_WHSAL, fw)\n",
    "with open(\"./pickles/hypo3_map_radish_data.pickle\", \"wb\") as fw:\n",
    "    pickle.dump(grouped_2016_san_jeju_radish_WHSAL, fw)\n",
    "with open(\"./pickles/hypo3_map_cabbage_data.pickle\", \"wb\") as fw:\n",
    "    pickle.dump(grouped_2016_san_jeju_cabbage_WHSAL, fw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
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   "source": [
    "# load pickle\n",
    "with open(\"./pickles/hypo3_map_gamgul_data.pickle\", \"rb\") as fr:\n",
    "    grouped_2016_san_jeju_gamgul_WHSAL = pickle.load(fr)\n",
    "with open(\"./pickles/hypo3_map_radish_data.pickle\", \"rb\") as fr:\n",
    "    grouped_2016_san_jeju_radish_WHSAL = pickle.load(fr)\n",
    "with open(\"./pickles/hypo3_map_cabbage_data.pickle\", \"rb\") as fr:\n",
    "    grouped_2016_san_jeju_cabbage_WHSAL = pickle.load(fr)\n",
    "    \n",
    "# 지도 시각화\n",
    "px.set_mapbox_access_token(open(\"mapbox_token.py\").read())\n",
    "\n",
    "# 감귤\n",
    "fig1 = px.scatter_mapbox(grouped_2016_san_jeju_gamgul_WHSAL, lat = 'latitude', lon = 'longitude', size = 'distance',\n",
    "                        color = 'COST_PER_DANQ', hover_name = 'WHSAL_NM',\n",
    "                        color_continuous_scale = px.colors.sequential.Oranges)\n",
    "fig1.add_trace(go.Scattermapbox(\n",
    "    mode = \"markers\",\n",
    "    name = \"제주\",\n",
    "    lon = [126.53885],\n",
    "    lat = [33.52088],\n",
    "    marker = {'size': 15, 'color': \"yellow\"}))\n",
    "fig1.update_layout(mapbox_style='carto-darkmatter', mapbox_zoom=6, showlegend = False, \n",
    "                  title_text = \"2016년 제주도산 감귤의 거래지역별 단가(원/kg)\", width=1100, height=750, title_x=0.5, mapbox_center = {\"lat\": 35.8, \"lon\": 127.5})\n",
    "\n",
    "# 무\n",
    "fig2 = px.scatter_mapbox(grouped_2016_san_jeju_radish_WHSAL, lat = 'latitude', lon = 'longitude', size = 'distance',\n",
    "                        color = 'COST_PER_DANQ', hover_name = 'WHSAL_NM',\n",
    "                        color_continuous_scale = px.colors.sequential.Reds)\n",
    "fig2.add_trace(go.Scattermapbox(\n",
    "    mode = \"markers\",\n",
    "    name = \"제주\",\n",
    "    lon = [126.53885],\n",
    "    lat = [33.52088],\n",
    "    marker = {'size': 15, 'color': \"yellow\"}))\n",
    "fig2.update_layout(mapbox_style='carto-darkmatter', mapbox_zoom=6, showlegend = False, \n",
    "                  title_text = \"2016년 제주도산 무의 거래지역별 단가(원/kg)\", width=1100, height=750, title_x=0.5, mapbox_center = {\"lat\": 35.8, \"lon\": 127.5})\n",
    "\n",
    "# 양배추\n",
    "fig3 = px.scatter_mapbox(grouped_2016_san_jeju_cabbage_WHSAL, lat = 'latitude', lon = 'longitude', size = 'distance',\n",
    "                        color = 'COST_PER_DANQ', hover_name = 'WHSAL_NM',\n",
    "                        color_continuous_scale = px.colors.sequential.Greens)\n",
    "fig3.add_trace(go.Scattermapbox(\n",
    "    mode = \"markers\",\n",
    "    name = \"제주\",\n",
    "    lon = [126.53885],\n",
    "    lat = [33.52088],\n",
    "    marker = {'size': 15, 'color': \"yellow\"}))\n",
    "fig3.update_layout(mapbox_style='carto-darkmatter', mapbox_zoom=6, showlegend = False, \n",
    "                  title_text = \"2016년 제주도산 양배추의 거래지역별 단가(원/kg)\", width=1100, height=750, title_x=0.5, mapbox_center = {\"lat\": 35.8, \"lon\": 127.5})\n",
    "\n",
    "fig1.show()\n",
    "fig2.show()\n",
    "fig3.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 위 지도에서 원의 크기는 제주도와의 거리를, 색은 단가를 의미한다.\n",
    "* 제주도산 감귤, 무, 양배추의 거래지역별 단가에 대해서 알아봤는데,\n",
    "  * **제주도와의 거리와 단가 사이에는 눈에 띄는 관계가 발견되지는 않았다.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **시각화를 해 본 후**\n",
    "  * 분석 시 지도상 일직선 거리를 이용해 알아보고자 했기 때문에 뚜렷한 관계가 나타나지 않은 것 같다.\n",
    "  * 제주도와의 거리보다는 지역별로 존재하는 구매방식이나 유통구조의 차이와 같은 다른 요인에 의해 단가의 차이가 나타날 수도 있을 것 같다는 생각을 했다.\n",
    "  * 거래지역별로 단가가 차이가 나는 이유에 대해서 제대로 알아보고자 한다면, 추가적인 정보가 필요할 것 같다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2016년도 제주산 감귤의 가격에 대한 상관관계분석\n",
    "grouped_2016_san_jeju_gamgul_WHSAL = pd.DataFrame(df_2016_san_jeju_gamgul.groupby([\"WHSAL_NM\"])[\"COST_PER_DANQ\", \"TOT_QTY\"].mean()).reset_index()\n",
    "grouped_2016_san_jeju_gamgul_WHSAL = grouped_2016_san_jeju_gamgul_WHSAL.assign(latitude = latitude, longitude = longitude)\n",
    "grouped_2016_san_jeju_gamgul_WHSAL = grouped_2016_san_jeju_gamgul_WHSAL.assign(\n",
    "    distance = [((grouped_2016_san_jeju_gamgul_WHSAL.iloc[i, 3] - jeju[0])**2 + \n",
    "                 (grouped_2016_san_jeju_gamgul_WHSAL.iloc[i, 4] - jeju[1])**2)**0.5 for i in range(0, 32)])\n",
    "corr_gamgul = grouped_2016_san_jeju_gamgul_WHSAL[[\"COST_PER_DANQ\", \"TOT_QTY\", \"distance\"]].corr()\n",
    "\n",
    "# 2016년도 제주산 무의 가격에 대한 상관관계분석\n",
    "grouped_2016_san_jeju_radish_WHSAL = pd.DataFrame(df_2016_san_jeju_radish.groupby([\"WHSAL_NM\"])[\"COST_PER_DANQ\", \"TOT_QTY\"].mean()).reset_index()\n",
    "grouped_2016_san_jeju_radish_WHSAL = grouped_2016_san_jeju_radish_WHSAL.assign(latitude = latitude2, longitude = longitude2)\n",
    "grouped_2016_san_jeju_radish_WHSAL = grouped_2016_san_jeju_radish_WHSAL.assign(\n",
    "    distance = [((grouped_2016_san_jeju_radish_WHSAL.iloc[i, 3] - jeju[0])**2 + \n",
    "                 (grouped_2016_san_jeju_radish_WHSAL.iloc[i, 4] - jeju[1])**2)**0.5 for i in range(0, 30)])\n",
    "corr_radish = grouped_2016_san_jeju_radish_WHSAL[[\"COST_PER_DANQ\", \"TOT_QTY\", \"distance\"]].corr()\n",
    "\n",
    "# 2016년도 제주산 양배추의 가격에 대한 상관관계분석\n",
    "grouped_2016_san_jeju_cabbage_WHSAL = pd.DataFrame(df_2016_san_jeju_cabbage.groupby([\"WHSAL_NM\"])[\"COST_PER_DANQ\", \"TOT_QTY\"].mean()).reset_index()\n",
    "grouped_2016_san_jeju_cabbage_WHSAL = grouped_2016_san_jeju_cabbage_WHSAL.assign(latitude = latitude2, longitude = longitude2)\n",
    "grouped_2016_san_jeju_cabbage_WHSAL = grouped_2016_san_jeju_cabbage_WHSAL.assign(\n",
    "    distance = [((grouped_2016_san_jeju_cabbage_WHSAL.iloc[i, 3] - jeju[0])**2 + \n",
    "                 (grouped_2016_san_jeju_cabbage_WHSAL.iloc[i, 4] - jeju[1])**2)**0.5 for i in range(0, 30)])\n",
    "corr_cabbage = grouped_2016_san_jeju_cabbage_WHSAL[[\"COST_PER_DANQ\", \"TOT_QTY\", \"distance\"]].corr()\n",
    "\n",
    "\n",
    "# save pickle\n",
    "with open(\"./pickles/hypo3_map_gamgul_corr_data.pickle\", \"wb\") as fw:\n",
    "    pickle.dump(corr_gamgul, fw)\n",
    "with open(\"./pickles/hypo3_map_radish_corr_data.pickle\", \"wb\") as fw:\n",
    "    pickle.dump(corr_radish, fw)\n",
    "with open(\"./pickles/hypo3_map_cabbage_corr_data.pickle\", \"wb\") as fw:\n",
    "    pickle.dump(corr_cabbage, fw)"
   ]
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   ],
   "source": [
    "# load pickle\n",
    "with open(\"./pickles/hypo3_map_gamgul_corr_data.pickle\", \"rb\") as fr:\n",
    "    corr_gamgul = pickle.load(fr)\n",
    "with open(\"./pickles/hypo3_map_radish_corr_data.pickle\", \"rb\") as fr:\n",
    "    corr_radish = pickle.load(fr)\n",
    "with open(\"./pickles/hypo3_map_cabbage_corr_data.pickle\", \"rb\") as fr:\n",
    "    corr_cabbage = pickle.load(fr)\n",
    "    \n",
    "# heatmap 시각화\n",
    "fig1 = px.imshow(corr_gamgul.round(4),\n",
    "                color_continuous_scale = px.colors.diverging.RdBu, text_auto=True,\n",
    "                color_continuous_midpoint = 0,\n",
    "                title = \"2016년도 제주산 감귤의 가격에 대한 상관관계분석\")\n",
    "fig1.show()\n",
    "\n",
    "fig2 = px.imshow(corr_radish.round(4),\n",
    "                color_continuous_scale = px.colors.diverging.RdBu, text_auto=True,\n",
    "                color_continuous_midpoint = 0,\n",
    "                title = \"2016년도 제주산 무의 가격에 대한 상관관계분석\")\n",
    "fig2.show()\n",
    "\n",
    "fig3 = px.imshow(corr_cabbage.round(4),\n",
    "                color_continuous_scale = px.colors.diverging.RdBu, text_auto=True,\n",
    "                color_continuous_midpoint = 0,\n",
    "                title = \"2016년도 제주산 양배추의 가격에 대한 상관관계분석\")\n",
    "fig3.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **추가적으로 단가, 거래중량, 거래지역과의 거리에 대한 상관관계분석을 진행**\n",
    "  * 유의미한 관계를 찾지 못했다.\n",
    "  * 산지로부터 거래지역과의 거리 이외에 나타나지 않은 요인에 의해 거래 가격이 변화하는 것으로 생각할 수 있다.\n",
    "  * 거래 가격이 변화하게끔 하는 요인이 어떤 것이 있는지에 대해서는 더 구체적인 데이터 분석을 통해 알 수 있을 것이라고 판단된다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 가설 4 : 코로나 발생(2020년 1월 기준) 이후로 중국산 농산물의 거래량은 줄었을 것이고 가격은 올랐을 것이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2019 = TSALET_CONCAT_YEAR(2019)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_index = pd.date_range(start=\"20200101\", end=\"20200930\", freq = \"M\")\n",
    "dt_list = dt_index.strftime(\"%Y%m\").tolist()\n",
    "df_2020 = pd.concat([TSALET_CSV(int(i)) for i in tqdm(dt_list)])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
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   "source": [
    "# 2019년 ~ 2020년의 중국산 농산물에 대한 가격과 거래량 확인\n",
    "df_2019_china = df_2019[df_2019[\"SAN_NM\"]==\"중국\"]\n",
    "df_2019_china = df_2019_china.drop(df_2019_china[(df_2019_china[\"COST\"] <= 0) | (df_2019_china[\"DANQ\"] <= 0) |\n",
    "                                                 (df_2019_china[\"QTY\"] <= 0)].index, axis = 0)\n",
    "df_2019_china.groupby([\"SALEDATE\"])[\"TOT_QTY\"].sum()\n",
    "\n",
    "df_2020_china = df_2020[df_2020[\"SAN_NM\"] == \"중국\"]\n",
    "df_2020_china = df_2020_china.drop(df_2020_china[(df_2020_china[\"COST\"] <= 0) | (df_2020_china[\"DANQ\"] <= 0) |\n",
    "                                                 (df_2020_china[\"QTY\"] <= 0)].index, axis = 0)\n",
    "\n",
    "df_19_20_china = pd.concat([df_2019_china, df_2020_china])\n",
    "\n",
    "# 2019년 ~ 2020년 중국산 농산물의 단가 column 추가\n",
    "df_19_20_china[\"COST_PER_DANQ\"] = df_19_20_china[\"TOT_AMT\"] / df_19_20_china[\"TOT_QTY\"]\n",
    "\n",
    "# 2019년 ~ 2020년 전체 농산물 거래량의 합\n",
    "df_2019_2020_TOT_QTY = pd.concat([df_2019.groupby([\"SALEDATE\"])[\"TOT_QTY\"].sum(),\n",
    "                                  df_2020.groupby([\"SALEDATE\"])[\"TOT_QTY\"].sum()])\n",
    "\n",
    "# 2019년 전체 농산물 단가 평균\n",
    "df_2019_SD_TQ_TA = df_2019[[\"SALEDATE\", \"TOT_QTY\", \"TOT_AMT\"]]\n",
    "df_2019_SD_TQ_TA = df_2019_SD_TQ_TA.replace(0, np.NaN).dropna(axis = 0)\n",
    "df_2019_SD_TQ_TA[\"COST_PER_DANQ\"] = df_2019_SD_TQ_TA[\"TOT_AMT\"] / df_2019_SD_TQ_TA[\"TOT_QTY\"]\n",
    "grouped_2019_all_DANQ = df_2019_SD_TQ_TA.groupby([\"SALEDATE\"])[\"COST_PER_DANQ\"].mean()\n",
    "\n",
    "# 2020년 전체 농산물 단가 평균\n",
    "df_2020_SD_TQ_TA = df_2020[[\"SALEDATE\", \"TOT_QTY\", \"TOT_AMT\"]]\n",
    "df_2020_SD_TQ_TA = df_2020_SD_TQ_TA.replace(0, np.NaN).dropna(axis = 0)\n",
    "df_2020_SD_TQ_TA[\"COST_PER_DANQ\"] = df_2020_SD_TQ_TA[\"TOT_AMT\"] / df_2020_SD_TQ_TA[\"TOT_QTY\"]\n",
    "grouped_2020_all_DANQ = df_2020_SD_TQ_TA.groupby([\"SALEDATE\"])[\"COST_PER_DANQ\"].mean()\n",
    "\n",
    "# 2019년 ~ 2020년 전체 농산물 단가 평균\n",
    "grouped_19_20_all_DANQ = pd.concat([grouped_2019_all_DANQ, grouped_2020_all_DANQ])\n",
    "\n",
    "# 2019년 ~ 2020년 (전체 / 중국산) 농산물 거래량 합 변화\n",
    "grouped_19_20_TOT_QTY_all = pd.DataFrame(df_2019_2020_TOT_QTY)[\"TOT_QTY\"].resample(\"2W\").sum()\n",
    "grouped_19_20_TOT_QTY_china = pd.DataFrame(df_19_20_china.groupby([\"SALEDATE\"])[\"TOT_QTY\"].sum())[\"TOT_QTY\"].resample(\"2W\").sum()\n",
    "\n",
    "# 2019년 ~ 2020년 (전체 / 중국산) 농산물 단가 평균 변화\n",
    "grouped_19_20_DANQ_all = pd.DataFrame(grouped_19_20_all_DANQ)[\"COST_PER_DANQ\"].resample(\"2W\").mean()\n",
    "grouped_19_20_DANQ_china = pd.DataFrame(df_19_20_china.groupby([\"SALEDATE\"])[\"COST_PER_DANQ\"].mean())[\"COST_PER_DANQ\"].resample(\"2W\").mean()\n",
    "\n",
    "# save pickle\n",
    "with open(\"./pickles/hypo4_grouped_1920_TOT_QTY_all_data.pickle\", \"wb\") as fw:\n",
    "    pickle.dump(grouped_19_20_TOT_QTY_all, fw)\n",
    "with open(\"./pickles/hypo4_grouped_1920_TOT_QTY_china_data.pickle\", \"wb\") as fw:\n",
    "    pickle.dump(grouped_19_20_TOT_QTY_china, fw)\n",
    "with open(\"./pickles/hypo4_grouped_1920_DANQ_all_data.pickle\", \"wb\") as fw:\n",
    "    pickle.dump(grouped_19_20_DANQ_all, fw)\n",
    "with open(\"./pickles/hypo4_grouped_1920_DANQ_china_data.pickle\", \"wb\") as fw:\n",
    "    pickle.dump(grouped_19_20_DANQ_china, fw)"
   ]
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   "source": [
    "# load pickle\n",
    "with open(\"./pickles/hypo4_grouped_1920_TOT_QTY_all_data.pickle\", \"rb\") as fr:\n",
    "    grouped_19_20_TOT_QTY_all = pickle.load(fr)\n",
    "with open(\"./pickles/hypo4_grouped_1920_TOT_QTY_china_data.pickle\", \"rb\") as fr:\n",
    "    grouped_19_20_TOT_QTY_china = pickle.load(fr)\n",
    "with open(\"./pickles/hypo4_grouped_1920_DANQ_all_data.pickle\", \"rb\") as fr:\n",
    "    grouped_19_20_DANQ_all = pickle.load(fr)\n",
    "with open(\"./pickles/hypo4_grouped_1920_DANQ_china_data.pickle\", \"rb\") as fr:\n",
    "    grouped_19_20_DANQ_china = pickle.load(fr)\n",
    "\n",
    "# 시각화 - 2019년 ~ 2020년 (전체 / 중국산) 농산물 거래량 합 변화\n",
    "fig1= ms(specs=[[{'secondary_y': True}]])\n",
    "fig1.add_trace(go.Scatter(x = grouped_19_20_TOT_QTY_all.index, y = grouped_19_20_TOT_QTY_all.values[:-1], name = \"All\"),\n",
    "             secondary_y = False)\n",
    "fig1.add_trace(go.Scatter(x = grouped_19_20_TOT_QTY_china.index, y = grouped_19_20_TOT_QTY_china.values[:-1], name = \"China\"),\n",
    "             secondary_y = True)\n",
    "\n",
    "fig1.add_shape(type = 'rect', x0 = '2020-01-06', y0 = 0, x1 = '2020-10-04', y1 =1, \n",
    "              fillcolor = 'green',opacity = 0.1, line = {'width' : 0}, xref='x',yref='paper')\n",
    "fig1.add_shape(type = 'line', x0 = '2020-01-06', y0 = 0, x1 = '2020-01-06', y1 =1, xref='x', yref='paper',\n",
    "              line = {'color' : 'green'})\n",
    "\n",
    "fig1.update_layout(title = '2019년 ~ 2020년 (전체 / 중국산) 농산물 거래량 합 변화')\n",
    "\n",
    "fig2= ms(specs=[[{'secondary_y': True}]])\n",
    "fig2.add_trace(go.Scatter(x = grouped_19_20_DANQ_all.index, y = grouped_19_20_DANQ_all.values[:-1], name = \"All\"),\n",
    "             secondary_y = False)\n",
    "fig2.add_trace(go.Scatter(x = grouped_19_20_DANQ_china.index, y = grouped_19_20_DANQ_china.values[:-1], name = \"China\"),\n",
    "             secondary_y = True)\n",
    "\n",
    "fig2.add_shape(type = 'rect', x0 = '2020-01-06', y0 = 0, x1 = '2020-10-04', y1 =1, \n",
    "              fillcolor = 'green',opacity = 0.1, line = {'width' : 0}, xref='x',yref='paper')\n",
    "fig2.add_shape(type = 'line', x0 = '2020-01-06', y0 = 0, x1 = '2020-01-06', y1 =1, xref='x', yref='paper',\n",
    "              line = {'color' : 'green'})\n",
    "\n",
    "fig2.update_layout(title = '2019년 ~ 2020년 (전체 / 중국산) 농산물 단가 평균 변화')\n",
    "\n",
    "fig1.show()\n",
    "fig2.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 코로나 바이러스가 전파되기 시작한 시기인 2020년 1월 초를 기준으로 전후 변화를 알아보기 위해 2019년과 2020년 데이터를 활용하였다.\n",
    "\n",
    "* **중국산 농산물의 거래량은 2020년 1월을 기준으로 하여 전과 다르게 큰 변화가 생겼다고 할 수 없다고 판단된다.**\n",
    "  * 2020년 1월 중순부터 3월 초까지 거래량이 한달 반의 기간동안 감소하긴 하지만, 그 후 비슷한 폭으로 증감을 반복하는 추세를 보이기 때문에 코로나 바이러스 발생을 기준으로 거래량에 큰 변화가 생겼다고 할 수 없다고 판단된다.\n",
    "* **중국산 농산물의 단가 또한 2020년 1월을 기준으로 하여 전과 다르게 큰 변화가 생겼다고 할 수 없다.**\n",
    "  * 2020년 1월 초부터 2월 중순까지 단가가 한달 반의 기간동안 증가하긴 하지만, 그 후 비슷한 폭으로 증감을 반복하는 추세를 보이기 때문에 코로나 바이러스 발생을 기준으로 단가에 큰 변화가 생겼다고 할 수 없다고 판단된다.\n",
    "  \n",
    "\n",
    "* **시각화를 해 본 후**\n",
    "  * 그래프를 육안으로 관찰해 볼 때에는 거래량과 가격이 2020년 1월을 기준으로 크게 변화가 나타나지 않는다고 판단이 되는데, 이 판단이 통계적으로 유의한지, 즉 통계적으로 변화가 일어났는지의 유무에 대해서는 알아보지 못했다.\n",
    "  * 이는 추가적으로 진행해야 할 필요가 있어 보인다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 가설 5. 2016년에 비해 2019년에 열대과일의 수입량 대비 국내 생산량의 비율이 증가했을 것이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2016 = TSALET_CONCAT_YEAR(2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2019 = TSALET_CONCAT_YEAR(2019)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 국내 생산 중인 열대과일만을 선별\n",
    "tropical_fruits = ['파파야', '바나나', '용과', '파인애플', '참다래(키위)', '자몽', '람부탄']\n",
    "\n",
    "# 2016년 열대과일의 생산지 추출\n",
    "trop_2016_san_TOT_QTY = df_2016[df_2016[\"PUM_NM\"].isin(tropical_fruits)]\n",
    "trop_2016_san_TOT_QTY = pd.DataFrame(trop_2016_san_TOT_QTY.groupby([\"PUM_NM\", \"SAN_NM\"])[\"TOT_QTY\"].sum()).reset_index()\n",
    "\n",
    "# 2016년 열대과일의 생산지를 국내와 수입으로 분류\n",
    "for i in range(560):\n",
    "    if trop_2016_san_TOT_QTY.iloc[i,1][-1] in [\"군\", \"구\", \"도\", \"시\"]:\n",
    "        trop_2016_san_TOT_QTY.iloc[i,1] = \"국내\"\n",
    "    else:\n",
    "        trop_2016_san_TOT_QTY.iloc[i,1] = \"수입\"\n",
    "        \n",
    "# 품목별 국내, 수입산 거래량\n",
    "grouped_2016_trop_san_TOT_QTY = pd.DataFrame(trop_2016_san_TOT_QTY.groupby([\"PUM_NM\", \"SAN_NM\"])[\"TOT_QTY\"].sum()).reset_index()\n",
    "\n",
    "# 정규화\n",
    "for i in range(0, 7):\n",
    "    sum = (grouped_2016_trop_san_TOT_QTY.iloc[2*i, 2] + grouped_2016_trop_san_TOT_QTY.iloc[2*i+1, 2])\n",
    "    grouped_2016_trop_san_TOT_QTY.iloc[2*i, 2] /= sum\n",
    "    grouped_2016_trop_san_TOT_QTY.iloc[2*i+1, 2] /= sum\n",
    "    \n",
    "# 2019년 열대과일의 생산지 추출\n",
    "trop_2019_san_TOT_QTY = df_2019[df_2019[\"PUM_NM\"].isin(tropical_fruits)]\n",
    "trop_2019_san_TOT_QTY = pd.DataFrame(trop_2019_san_TOT_QTY.groupby([\"PUM_NM\", \"SAN_NM\"])[\"TOT_QTY\"].sum()).reset_index()\n",
    "\n",
    "# 2019년 열대과일의 생산지를 국내와 수입으로 분류\n",
    "for i in range(568):\n",
    "    if trop_2019_san_TOT_QTY.iloc[i,1][-1] in [\"군\", \"구\", \"도\", \"시\"]:\n",
    "        trop_2019_san_TOT_QTY.iloc[i,1] = \"국내\"\n",
    "    else:\n",
    "        trop_2019_san_TOT_QTY.iloc[i,1] = \"수입\"\n",
    "        \n",
    "# 품목별 국내, 수입산 거래량\n",
    "grouped_2019_trop_san_TOT_QTY = pd.DataFrame(trop_2019_san_TOT_QTY.groupby([\"PUM_NM\", \"SAN_NM\"])[\"TOT_QTY\"].sum()).reset_index()\n",
    "\n",
    "# 정규화\n",
    "for i in range(0, 7):\n",
    "    sum = (grouped_2019_trop_san_TOT_QTY.iloc[2*i, 2] + grouped_2019_trop_san_TOT_QTY.iloc[2*i+1, 2])\n",
    "    grouped_2019_trop_san_TOT_QTY.iloc[2*i, 2] /= sum\n",
    "    grouped_2019_trop_san_TOT_QTY.iloc[2*i+1, 2] /= sum\n",
    "    \n",
    "# save pickle\n",
    "with open(\"./pickles/hypo5_grouped_2016_trop_san_TOT_QTY_data.pickle\", \"wb\") as fw:\n",
    "    pickle.dump(grouped_2016_trop_san_TOT_QTY, fw)\n",
    "with open(\"./pickles/hypo5_grouped_2019_trop_san_TOT_QTY_data.pickle\", \"wb\") as fw:\n",
    "    pickle.dump(grouped_2019_trop_san_TOT_QTY, fw)"
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   "source": [
    "# load pickle\n",
    "with open(\"./pickles/hypo5_grouped_2016_trop_san_TOT_QTY_data.pickle\", \"rb\") as fr:\n",
    "    grouped_2016_trop_san_TOT_QTY = pickle.load(fr)\n",
    "with open(\"./pickles/hypo5_grouped_2019_trop_san_TOT_QTY_data.pickle\", \"rb\") as fr:\n",
    "    grouped_2019_trop_san_TOT_QTY = pickle.load(fr)\n",
    "\n",
    "# 품목_연도\n",
    "for i in range(14):\n",
    "    grouped_2016_trop_san_TOT_QTY.iloc[i, 0] = grouped_2016_trop_san_TOT_QTY.iloc[i, 0] + \"_2016\"\n",
    "for i in range(14):\n",
    "    grouped_2019_trop_san_TOT_QTY.iloc[i, 0] = grouped_2019_trop_san_TOT_QTY.iloc[i, 0] + \"_2019\"\n",
    "\n",
    "# 2016년과 2019년을 동시에 표현\n",
    "grouped_2016_2019_trop_san_TOT_QTY = pd.concat([grouped_2016_trop_san_TOT_QTY,\n",
    "                                                grouped_2019_trop_san_TOT_QTY]).sort_values(\"PUM_NM\")\n",
    "\n",
    "# 소수점 4자리까지 표현\n",
    "grouped_2016_2019_trop_san_TOT_QTY['TOT_QTY'] = grouped_2016_2019_trop_san_TOT_QTY['TOT_QTY'].round(4)\n",
    "\n",
    "# 컬럼명 변경\n",
    "grouped_2016_2019_trop_san_TOT_QTY = grouped_2016_2019_trop_san_TOT_QTY.rename(columns={'PUM_NM' : '품종', 'SAN_NM' : '산지', 'TOT_QTY' : '거래량 비율'})\n",
    "\n",
    "# 시각화 - 2016년과 2019년의 열대과일 국내, 수입산 거래량 비율\n",
    "fig = px.bar(grouped_2016_2019_trop_san_TOT_QTY, x = '품종', y = \"거래량 비율\", color = '산지', text_auto = True)\n",
    "fig.update_layout(title = \"2016년과 2019년의 열대과일 7종의 국내, 수입산 거래량 비율\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 세로 2줄씩 2016년과 2019년의 품목별 국내산, 수입산 거래량 비율을 나타내었다.\n",
    "* 기술 발전으로 인해 시간이 지나면서 열대과일의 국내 생산량이 늘어났을 것이라고 예측하여 가설을 세웠지만,\n",
    "* 2016년 대비 2019년에 열대과일의 국내 생산량 비율이 더 높아진 품목은 7개 품목 중 람부탄 하나 뿐이었다.\n",
    "* 나머지 6종은 국내 생산량 비율이 소폭 감소했다.\n",
    "* \"일반적으로 2016년과 2019년 사이에 열대과일의 국내산 거래량이 수입산 거래량보다 적어졌다\" 라고 단정짓기에는 통계적 검정 과정이 필요할 것이지만, 본 데이터 분석 결과로는 2016년과 2019년 사이에 열대과일의 국내산 거래량이 수입산 거래량보다 적어졌다고 판단했다.  \n",
    "* **시각화를 해 본 후**\n",
    "  * 3년이 지난 후 열대과일의 수입산 대비 국내산 거래량 비율이 줄어든 이유에 대해서는 외부 데이터를 이용하여 추가적인 분석이 필요해 보인다."
   ]
  }
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