mrbkdad/Crawling_Nakdongriver_Data.ipynb

## Crawling_Nakdongriver_Data.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "from collections import namedtuple\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## 검색 조건\n",
    "select = dict([('2022670', '가산'), ('2006630', '가장'), ('2005640', '갈마'), ('2017630', '감암'),\n",
    " ('2013685', '개진1'), ('2013690', '개진2'), ('2019695', '거룡강'), ('2015635', '거창1'),\n",
    " ('2015620', '거창2'), ('2003650', '검암'), ('2101625', '경주2'), ('2013650', '고령'),\n",
    " ('2014640', '고령교'), ('2008621', '고로'), ('2002645', '광덕'), ('2010630', '교리'),\n",
    " ('2003670', '구담'), ('2011640', '구미'), ('2201653', '구영'), ('2022680', '구포'),\n",
    " ('2401650', '근남'), ('2021670', '금곡'), ('2012640', '금호'), ('2101668', '기계2'),\n",
    " ('2002685', '길안'), ('2010650', '김천'), ('2009620', '낙동'), ('2022696', '낙동강하구언(내)'),\n",
    " ('2022697', '낙동강하구언(외)'), ('2001615', '낙본A'), ('2018690', '내평'), ('2012605', '논골교'),\n",
    " ('2018674', '단성'), ('2012625', '단포교'), ('2007640', '달지'), ('2019635', '대곡'),\n",
    " ('2021650', '대리'), ('2002695', '대사'), ('2019620', '대산'), ('2014680', '대암'),\n",
    " ('2012652', '대평'), ('2019640', '덕곡'), ('2020640', '덕남'), ('2008631', '덕은'),\n",
    " ('2010623', '도곡2'), ('2001660', '도산'), ('2012645', '도천'), ('2008605', '동곡2'),\n",
    " ('2006675', '동문'), ('2012665', '동변'), ('2010660', '동부'), ('2012660', '동촌'),\n",
    " ('2012653', '동호'), ('2019610', '두문'), ('2020680', '마사'), ('2017670', '마수원'),\n",
    " ('2018630', '마천'), ('2007620', '말응'), ('2301650', '망양'), ('2302695', '명지'),\n",
    " ('2101650', '모아'), ('2008650', '무성'), ('2005650', '문경'), ('2019625', '문산'),\n",
    " ('2008630', '미성'), ('2004650', '미호'), ('2021675', '밀양'), ('2021685', '밀양2'),\n",
    " ('2101610', '배동'), ('2006625', '병성'), ('2201690', '병영'), ('2008635', '병천'),\n",
    " ('2004605', '봉화'), ('2020651', '부곡'), ('2101680', '부조'), ('2010625', '부항댐'),\n",
    " ('2001628', '분천'), ('2001625', '분천2'), ('2008632', '비안'), ('2008665', '비안2'),\n",
    " ('2010620', '사등'), ('2007660', '사벌'), ('2012670', '산격'), ('2021610', '산내'),\n",
    " ('2004695', '산양'), ('2018645', '산청'), ('2018655', '삼가'), ('2022610', '삼랑진'),\n",
    " ('2021677', '삼문'), ('2201660', '삼호'), ('2021665', '상동'), ('2402620', '상옥'),\n",
    " ('2006665', '상주'), ('2018640', '생초'), ('2012632', '서부'), ('2301630', '석천'),\n",
    " ('2010690', '선산'), ('2011665', '선원'), ('2012695', '성서'), ('2011660', '성주'),\n",
    " ('2017685', '세간'), ('2001630', '소천'), ('2022655', '소토'), ('2002630', '송강'),\n",
    " ('2020675', '수산'), ('2302650', '수영'), ('2002634', '신구'), ('2018665', '신안'),\n",
    " ('2013640', '쌍림'), ('2101675', '안강'), ('2003610', '안동'), ('2018608', '안의'),\n",
    " ('2018609', '안의2'), ('2012648', '압량'), ('2013630', '야로'), ('2022660', '양산'),\n",
    " ('2011625', '양포교'), ('2201611', '언양'), ('2402630', '영덕'), ('2002620', '영양'),\n",
    " ('2012628', '영천'), ('2004668', '예천'), ('2301670', '온산'), ('2011650', '왜관'),\n",
    " ('2008690', '용곡'), ('2001655', '운곡'), ('2003640', '운산'), ('2201670', '울산'),\n",
    " ('2401620', '울진'), ('2022640', '월촌'), ('2004655', '월포'), ('2004635', '월호'),\n",
    " ('2019680', '윤내'), ('2019653', '의령'), ('2014690', '이방'), ('2005680', '이안'),\n",
    " ('2401690', '인량'), ('2009670', '일선교'), ('2018635', '임천'), ('2002692', '임하'),\n",
    " ('2020650', '임해진'), ('2020605', '장마'), ('2001610', '장성'), ('2017620', '적포교'),\n",
    " ('2005660', '점촌'), ('2019655', '정암'), ('2022685', '정천'), ('2018692', '제수문(사천)'),\n",
    " ('2201630', '조동'), ('2004640', '조제'), ('2016680', '죽고'), ('2012696', '죽곡'),\n",
    " ('2004675', '죽전'), ('2003690', '지보'), ('2015655', '지산'), ('2019647', '지수'),\n",
    " ('2010635', '지품'), ('2020615', '진동'), ('2019615', '진주'), ('2013615', '창천'),\n",
    " ('2018685', '창촌'), ('2021640', '청도'), ('2002655', '청송'), ('2012690', '침산'),\n",
    " ('2018680', '태수'), ('2020603', '판곡'), ('2501620', '판문'), ('2101690', '포항'),\n",
    " ('2012650', '하양'), ('2019685', '함안'), ('2018620', '함양'), ('2016650', '합천'),\n",
    " ('2016640', '합천조정지방수로'), ('2004680', '향석'), ('2014660', '현풍'), ('2021690', '화성'),\n",
    " ('2014620', '화원'), ('2013620', '화죽'), ('2015630', '황강A'), ('2008645', '효령'),\n",
    " ('2101620', '효현'), ('2002635', '흥구')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# cmd:wlobser, dataType:downLoad, data_unit:WL, fbscd:02\n",
    "# obscd:2022670 - 지역 코드\n",
    "# sdt:201804032050\n",
    "# sdt_yy:2018, sdt_mm:04, sdt_dd:03, sdt_hh:20, sdt_mi:50\n",
    "# edt:201804042050\n",
    "# edt_yy:2018, edt_mm:04, edt_dd:04, edt_hh:20, edt_mi:50\n",
    "## 강수량 정보 : 'rfobser','RF', 수위 정보 : 'wlobser','WL', 댐자료 : 'dmobser','DM'\n",
    "class Condition:\n",
    "    def __init__(self,obscd,sdt,edt):\n",
    "        self.obscd = obscd\n",
    "        self.sdt = sdt\n",
    "        self.edt = edt\n",
    "    def getCondition(self,tp=1):\n",
    "        ## tp- 1:강수량, 2:수위, 3:댐\n",
    "        sch_cmd = {1:'rfobser',2:'wlobser',3:'dmobser'}\n",
    "        return {\n",
    "            'cmd':sch_cmd[tp],\n",
    "            'dataType':'downLoad',\n",
    "            'data_unit':sch_cmd[tp][:2].upper(),\n",
    "            'fbscd':'02',\n",
    "            'obscd':self.obscd,\n",
    "            'sdt':self.sdt,\n",
    "            'sdt_yy':self.sdt[:4],\n",
    "            'sdt_mm':self.sdt[4:6],\n",
    "            'sdt_dd':self.sdt[6:8],\n",
    "            'sdt_hh':self.sdt[8:10],\n",
    "            'sdt_mi':self.sdt[10:],\n",
    "            'edt':self.edt,\n",
    "            'edt_yy':self.edt[:4],\n",
    "            'edt_mm':self.edt[4:6],\n",
    "            'edt_dd':self.edt[6:8],\n",
    "            'edt_hh':self.edt[8:10],\n",
    "            'edt_mi':self.edt[10:]\n",
    "        }    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def today():\n",
    "    return datetime.today().strftime('%Y%m%d')\n",
    "\n",
    "## 크롤된 데이터 파서\n",
    "def dataParser(soup):\n",
    "    return [[td.text.strip() for td in tds]\n",
    "         for tds in [tr.select('td') for tr in soup.select('tbody > tr') ]]\n",
    "def headParser(soup):\n",
    "    return [td.text.strip() for td in soup.select('thead > tr > th') ]\n",
    "\n",
    "## 정보 조회\n",
    "def crawData(obscd,date,tp=1):\n",
    "    url = 'http://www.nakdongriver.go.kr/html/sumun/Sumun.jsp'\n",
    "    cond = Condition(obscd,date+'0000',date+'2350')\n",
    "    req = requests.post(url=url,data=cond.getCondition(tp))\n",
    "    soup = BeautifulSoup(req.text,'lxml')\n",
    "    return pd.DataFrame(dataParser(soup),columns=headParser(soup))\n",
    "    #return (dataParser(soup),headParser(soup))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "가산\n"
     ]
    }
   ],
   "source": [
    "## 정보 읽어오기\n",
    "obscd = '2022670'\n",
    "date = '20180403'\n",
    "print(select[obscd])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>시간</th>\n",
       "      <th>강수량</th>\n",
       "      <th>누가강수량</th>\n",
       "      <th>수위(m)</th>\n",
       "      <th>유량(cms)</th>\n",
       "      <th>해발표고(m)</th>\n",
       "      <th>저수위(EL.m)</th>\n",
       "      <th>유입량(㎥/s)</th>\n",
       "      <th>방류량(㎥/s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>04-03 23:50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.77</td>\n",
       "      <td>44.66</td>\n",
       "      <td>0.78</td>\n",
       "      <td>54.46</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>04-03 23:40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.77</td>\n",
       "      <td>44.66</td>\n",
       "      <td>0.78</td>\n",
       "      <td>54.46</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>04-03 23:30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.77</td>\n",
       "      <td>44.66</td>\n",
       "      <td>0.78</td>\n",
       "      <td>54.47</td>\n",
       "      <td>0.60</td>\n",
       "      <td>2.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04-03 23:20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.77</td>\n",
       "      <td>44.66</td>\n",
       "      <td>0.78</td>\n",
       "      <td>54.47</td>\n",
       "      <td>0.60</td>\n",
       "      <td>2.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>04-03 23:10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.77</td>\n",
       "      <td>44.66</td>\n",
       "      <td>0.78</td>\n",
       "      <td>54.47</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            시간  강수량 누가강수량 수위(m) 유량(cms) 해발표고(m) 저수위(EL.m) 유입량(㎥/s) 방류량(㎥/s)\n",
       "0  04-03 23:50  0.0   0.0  1.77   44.66    0.78     54.46     1.00     2.00\n",
       "1  04-03 23:40  0.0   0.0  1.77   44.66    0.78     54.46     1.00     2.00\n",
       "2  04-03 23:30  0.0   0.0  1.77   44.66    0.78     54.47     0.60     2.30\n",
       "3  04-03 23:20  0.0   0.0  1.77   44.66    0.78     54.47     0.60     2.30\n",
       "4  04-03 23:10  0.0   0.0  1.77   44.66    0.78     54.47     0.00     2.30"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = crawData(obscd,date,tp=1)\n",
    "df = pd.merge(df,crawData(obscd,date,tp=2),how='left',on='시간')\n",
    "df = pd.merge(df,crawData(obscd,date,tp=3),how='left',on='시간')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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So8t8Ix/fekd1L3+q1i/2KeMHQ2VLcUiSjN1GWh0AAIhJBEcAAnhaXHLvbQ2Yb+RjH5go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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cd3bffcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plot_df = df.set_index('시간')\n",
    "plot_df = plot_df.astype('float') \n",
    "plot_df.plot(figsize=(14,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}