우리나라는 점점 여름과 겨울이 길어지고 있을까.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 시작하기) 데이터 교육에서 가장 중요한 것은 호기심을 유발하는 '데이터'입니다!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [ 데이터 분석을 이끄는 질문 ]  \n",
    "### - '우리나라는 점점 여름과 겨울이 길어지고 있을까?'   -> 데이터로 검증해보기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [ 분석 방법을 중심으로 본 데이터 교육 7단계 (출처 : 석리송의 생각) ]  \n",
    "- 1단계 : 데이터 탐색하기 with Web (data.go.kr 등 활용)  \n",
    "- 2단계 : 데이터 만져보기 with 엑셀  \n",
    "- 3단계 : 논리적으로 분석하기 with 파이썬  \n",
    "- 4단계 : 정형 데이터로 복잡한 분석하기 with pandas  \n",
    "- 5단계 : 정형 데이터로 머신러닝하기 with sckitlearn  \n",
    "- 6단계 : 다차원 데이터 이해하기 with numpy  \n",
    "- 7단계 : 비정형 데이터로 딥러닝하기 with tensorflow(pytorch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1단계 : 데이터 탐색하기 with Web"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 폭염일수 그래프로 확인해보기  \n",
    "https://data.kma.go.kr/climate/heatWave/selectHeatWaveDmap.do?pgmNo=106  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 기온 데이터 다운로드(csv)  \n",
    "https://data.kma.go.kr/stcs/grnd/grndTaList.do?pgmNo=70  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2단계 : 데이터 만져보기 with 엑셀 (feat.전처리)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 실제 데이터를 살펴보며 질문 생각하고, 분석에 필요없는 부분 다듬기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3단계 : 논리적으로 분석하기 with 파이썬"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 2단원의 데이터에 대한 내용 + 3단원의 문제해결 x 다른 과목의 데이터 맥락"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    " # 파일 입력 + for 반복문 + print 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1907-10-01', '108', '13.5', '7.9', '20.7']\n"
     ]
    }
   ],
   "source": [
    "import csv # csv 데이터를 불러오기 위한 라이브러리 import\n",
    "data = csv.reader(open('ta_20200721103344.csv')) # 필요없는 옵션 삭제\n",
    "\n",
    "for row in data : # csv 데이터에서 한 행 꺼내서 반복\n",
    "    print(row) # 행 출력\n",
    "    break # 일단 한 줄만 봅시다!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 리스트구나!  \n",
    "- 기온이 3가지로 나뉘어있구나?(평균, 최저, 최고)  \n",
    "- 숫자인 것 같지만 사실은 문자열이구나?!  \n",
    "- 날짜는 '-'로 구분되어 있구나?!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tip : split 메서드 사용하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1907', '10', '01']\n"
     ]
    }
   ],
   "source": [
    "a = '1907-10-01'\n",
    "print(a.split('-'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q1. 1980 ~ 1999년, 2000 ~ 2019년 중 30도가 넘는 날은 어떤 기간에 더 많았을까요?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[문제해결방법]  \n",
    "- 기온 데이터 입력\n",
    "- 두 기간의 30도가 넘는 날을 저장할 변수 2개 만들기  \n",
    "- 전체 데이터를 반복하며 1980 ~ 1999년 중 30도가 넘는 날을 카운트  \n",
    "- 전체 데이터를 반복하며 2000 ~ 2019년 중 30도가 넘는 날을 카운트  \n",
    "- 결과 출력하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "611 793\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "data = csv.reader(open('ta_20200721103344.csv'))\n",
    "summer20 = 0\n",
    "summer21 = 0\n",
    "\n",
    "for row in data : \n",
    "    date = row[0].split('-')\n",
    "    year = int(date[0])\n",
    "    if row[-1] == '' : # 6.25 전쟁 등 비어있는 기간 일부가 있어서 영향 주지 않는 값으로 변경\n",
    "        row[-1] = 0\n",
    "    if 1980 <= year < 2000 and float(row[-1]) > 30 :\n",
    "        summer20 += 1\n",
    "    elif 2000 <= year < 2020 and float(row[-1]) > 30 :\n",
    "        summer21 += 1\n",
    "        \n",
    "print(summer20, summer21)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q2. 1960, 1980, 2000년대로 20년씩 구간을 분석한다면 어떻게 될까요?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[문제해결방법 구상하기]  \n",
    "* 저는 이번엔 리스트와 딕셔너리를 써보았습니다 :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1960: 620, 1980: 611, 2000: 793}\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "data = csv.reader(open('ta_20200721103344.csv'))\n",
    "\n",
    "gap = [1960,1980,2000]\n",
    "result = {1960:0, 1980:0, 2000:0}\n",
    "\n",
    "for row in data : \n",
    "    date = row[0].split('-')\n",
    "    year = int(date[0])\n",
    "    if row[-1] == '' :\n",
    "        row[-1] = 0\n",
    "    for i in gap : \n",
    "        if i <= year < i + 20 and float(row[-1]) > 30 :\n",
    "            result[i] += 1\n",
    "            \n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 60년대, 80년대는 큰 차이가 없는데 2000년대에 큰 차이가 생겼네요?!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q3. 1960~2010년대로 10년씩 구간을 분석한다면 어떻게 될까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1960: 332, 1970: 288, 1980: 259, 1990: 352, 2000: 324, 2010: 469}\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "data = csv.reader(open('ta_20200721103344.csv'))\n",
    "\n",
    "gap = [1960,1970,1980,1990,2000,2010]\n",
    "result = {1960:0, 1970:0, 1980:0, 1990:0, 2000:0, 2010:0}\n",
    "\n",
    "for row in data : \n",
    "    date = row[0].split('-')\n",
    "    year = int(date[0])\n",
    "    if row[-1] == '' :\n",
    "        row[-1] = 0\n",
    "    for i in gap : \n",
    "        if i <= year < i + 10 and float(row[-1]) > 30 :\n",
    "            result[i] += 1\n",
    "            \n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q4. 1980년대부터 1년 단위로 분석해보면 어떨까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1980: 6, 1981: 29, 1982: 30, 1983: 24, 1984: 39, 1985: 36, 1986: 15, 1987: 15, 1988: 34, 1989: 31, 1990: 33, 1991: 30, 1992: 27, 1993: 13, 1994: 54, 1995: 24, 1996: 33, 1997: 58, 1998: 33, 1999: 47, 2000: 52, 2001: 56, 2002: 19, 2003: 16, 2004: 35, 2005: 30, 2006: 27, 2007: 35, 2008: 30, 2009: 24, 2010: 44, 2011: 36, 2012: 41, 2013: 48, 2014: 40, 2015: 57, 2016: 56, 2017: 50, 2018: 54, 2019: 43}\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "data = csv.reader(open('ta_20200721103344.csv'))\n",
    "result = {}\n",
    "for i in range(1980,2020) : \n",
    "    result[i] = 0 \n",
    "\n",
    "for row in data : \n",
    "    date = row[0].split('-')\n",
    "    year = int(date[0])\n",
    "    if row[-1] == '' :\n",
    "        row[-1] = 0\n",
    "    if 1980 <= year < 2020 and float(row[-1]) > 30 :\n",
    "        result[year] += 1\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 이걸 꺾은선 그래프로 그려보겠습니다!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(list(result.values()))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 이번엔 막대 그래프로 그려보겠습니다!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.bar(result.keys(), result.values()) # 딕셔너리의 키와 값을 따로 이렇게 묶을 수도 있습니다.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q5. 1960년대부터 1년 단위로 분석해보면 어떨까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import csv\n",
    "data = csv.reader(open('ta_20200721103344.csv'))\n",
    "result = {}\n",
    "for i in range(1960,2020) : \n",
    "    result[i] = 0 \n",
    "\n",
    "for row in data : \n",
    "    date = row[0].split('-')\n",
    "    year = int(date[0])\n",
    "    if row[-1] == '' :\n",
    "        row[-1] = 0\n",
    "    if 1960 <= year < 2020 and float(row[-1]) > 30 :\n",
    "        result[year] += 1\n",
    "import matplotlib.pyplot as plt\n",
    "plt.bar(result.keys(), result.values())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q6. 1960년대부터 1년 단위로 33도 이상인 날(폭염일수)을 분석해보면 어떨까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import csv\n",
    "data = csv.reader(open('ta_20200721103344.csv'))\n",
    "result = {}\n",
    "for i in range(1960,2020) : \n",
    "    result[i] = 0 \n",
    "\n",
    "for row in data : \n",
    "    date = row[0].split('-')\n",
    "    year = int(date[0])\n",
    "    if row[-1] == '' :\n",
    "        row[-1] = 0\n",
    "    if 1960 <= year < 2020 and float(row[-1]) > 33 :\n",
    "        result[year] += 1\n",
    "        \n",
    "import matplotlib.pyplot as plt\n",
    "plt.bar(result.keys(), result.values())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 이런 결과를 유의미하게 분석하려면 통계적 지식이 필요할 것 같습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q7. 15도에서 25도 사이인 날의 비율은 어떻게 변했을까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GelhmBncDb+imz+KJL0s9H6gkNyX5apL3dOWH0/tS1W7burJxMFNb+v0ecE03PYltuQ74AbAdeBj4QFX9H716f6tv+0loy9eB05MsS3I08JJu2VgdlySrgBcDG4HDqmo7QPd4aLfaTL//sTous2zLTCa9LefT+3QFQ7RlEsP9POCCJLfT+5jz4658GfCbwLnd4+8mOZlZ3AphEc3UFgCSvBT4YVXtHg+exLacAPwUeC5wNPCuJM9jMttyFb0/qk3Ah4EvArsYo7YkeQ7wGeDtVfX4nlYdUFZ7KN/r5tCWGXcxoGwi2pLkt+iF+0W7iwastse2LNS9ZRZMVd0HvBYgyfOB13WLtgH/UVXf6ZZ9jt5Y6ifp3f5gt7G5FcIe2rLb2TzRa4deGyetLW8EPl9VPwF2JPkvYIpeL2Qsb1ExU1uqahfwjt3rJfkisAX4LmNwXJI8k16AXF1V13fFjyZZWVXbk6wEdnTlM90iZBtw0rTyWxey3oPMsS0zGYvboMy1LUmOBa6gd97mf7viObdl4nruSQ7tHp8B/Cnwd92im4Bjk+yXZBnwKnpjpduB7yc5sbuC4S30xr0W3R7asrvsLHq3SwZ+/vFt0tryMPDq7uqM/emdvLuPMb5FxUxt6V5b+3fTrwF2VdVYvMa6570S2FxVH+xbdCOw+4qXNX31uhF4S3dcTgS+17XjJuC1SZZ3V3C8tivba4Zoy0wW/TU217YkOQq4HnhzVT3Qt/7c27KYJxdmcfLhGnpjtT+h9851PnAhvZNyDwCX05346tZ/E3APvTHT9/eVT3VlDwJ/07/NGLflJOC2AfuZqLYAzwH+oTsu9wJ/3Lef07r1HwTeO+6vMXonXu8HNgP/Ru92q2NxXOgNRRa9K8bu6H5Oo3cSewO9TxgbgIO69UPvBPGDwF3AVN++zgO2dj9vXYRjMte2/FJ37B4HHuumDxiH19gQbbmC3ifB3etuGvbvxdsPSFKDJm5YRpL09Ax3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1KD/B0TcJ2Sj4BlQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import csv\n",
    "data = csv.reader(open('ta_20200721103344.csv'))\n",
    "result = {}\n",
    "for i in range(1960,2020) : \n",
    "    result[i] = 0 \n",
    "\n",
    "for row in data : \n",
    "    date = row[0].split('-')\n",
    "    year = int(date[0])\n",
    "    if row[-1] == '' :\n",
    "        row[-1] = 0\n",
    "    if 1960 <= year < 2020 and 15 < float(row[-1]) < 25 :\n",
    "        result[year] += 1\n",
    "        \n",
    "import matplotlib.pyplot as plt\n",
    "plt.bar(result.keys(), result.values())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q8. 그렇다면 추운 날은 늘어나고 있을까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import csv\n",
    "data = csv.reader(open('ta_20200721103344.csv'))\n",
    "result = {}\n",
    "for i in range(1960,2020) : \n",
    "    result[i] = 0 \n",
    "\n",
    "for row in data : \n",
    "    date = row[0].split('-')\n",
    "    year = int(date[0])\n",
    "    if row[-2] == '' :\n",
    "        row[-2] = 0\n",
    "    if 1960 <= year < 2020 and  float(row[-2]) < -5 :\n",
    "        result[year] += 1\n",
    "        \n",
    "import matplotlib.pyplot as plt\n",
    "plt.bar(result.keys(), result.values())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4단계 : 정형 데이터로 복잡한 분석하기 with pandas   \n",
    "- 파이썬을 건너뛰고 pandas를 먼저 해보시면 아마 '되긴 하는데, 이게 뭐하는거지?' 싶으실 겁니다.  \n",
    "- 그래서 파이썬으로 표 형태의 정형 데이터를 먼저 이리저리 논리적으로 분석해본 후에 판다스를 사용하는 것을 추천합니다.  \n",
    "- 엑셀이 있는데 왜 굳이 파이썬을 쓰냐면, 교육적이기 때문입니다. \n",
    "- 그러면 파이썬이 있는데 왜 굳이 판다스를 쓰냐면, 효율적이기 때문입니다.  \n",
    "- 데이터가 1기가가 넘어가면 파이썬으로는 분석이 어렵습니다.  \n",
    "- 또, 위에서 분석을 직접해보시면 아시겠지만 뭔가 번거롭습니다. \n",
    "- 이럴 때 판다스를 시작하는 것이 좋습니다.  \n",
    "- 하지만, 판다스는 명령이 너무 많습니다. 많아도 너무 많아서 저는 그냥 구글링해서 찾습니다.  \n",
    "- 따라서, 오늘은 그냥 맛을 본다는 느낌으로 함께 해주시기 바랍니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [판다스 시작하기]  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "step 1) csv 데이터 불러오기(헤더 있는 데이터)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "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>날짜</th>\n",
       "      <th>지역코드</th>\n",
       "      <th>평균</th>\n",
       "      <th>최저</th>\n",
       "      <th>최고</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1907-10-01</td>\n",
       "      <td>108</td>\n",
       "      <td>13.5</td>\n",
       "      <td>7.9</td>\n",
       "      <td>20.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1907-10-02</td>\n",
       "      <td>108</td>\n",
       "      <td>16.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1907-10-03</td>\n",
       "      <td>108</td>\n",
       "      <td>16.2</td>\n",
       "      <td>13.1</td>\n",
       "      <td>21.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1907-10-04</td>\n",
       "      <td>108</td>\n",
       "      <td>16.5</td>\n",
       "      <td>11.2</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1907-10-05</td>\n",
       "      <td>108</td>\n",
       "      <td>17.6</td>\n",
       "      <td>10.9</td>\n",
       "      <td>25.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40766</th>\n",
       "      <td>2020-07-16</td>\n",
       "      <td>108</td>\n",
       "      <td>25.0</td>\n",
       "      <td>20.4</td>\n",
       "      <td>29.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40767</th>\n",
       "      <td>2020-07-17</td>\n",
       "      <td>108</td>\n",
       "      <td>26.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>31.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40768</th>\n",
       "      <td>2020-07-18</td>\n",
       "      <td>108</td>\n",
       "      <td>26.1</td>\n",
       "      <td>22.8</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40769</th>\n",
       "      <td>2020-07-19</td>\n",
       "      <td>108</td>\n",
       "      <td>24.0</td>\n",
       "      <td>20.6</td>\n",
       "      <td>26.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40770</th>\n",
       "      <td>2020-07-20</td>\n",
       "      <td>108</td>\n",
       "      <td>23.8</td>\n",
       "      <td>22.4</td>\n",
       "      <td>26.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40771 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               날짜  지역코드    평균    최저    최고\n",
       "0      1907-10-01   108  13.5   7.9  20.7\n",
       "1      1907-10-02   108  16.2   7.9  22.0\n",
       "2      1907-10-03   108  16.2  13.1  21.3\n",
       "3      1907-10-04   108  16.5  11.2  22.0\n",
       "4      1907-10-05   108  17.6  10.9  25.4\n",
       "...           ...   ...   ...   ...   ...\n",
       "40766  2020-07-16   108  25.0  20.4  29.7\n",
       "40767  2020-07-17   108  26.0  21.5  31.6\n",
       "40768  2020-07-18   108  26.1  22.8  31.0\n",
       "40769  2020-07-19   108  24.0  20.6  26.6\n",
       "40770  2020-07-20   108  23.8  22.4  26.6\n",
       "\n",
       "[40771 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('ta_20200721103344_header.csv', encoding = 'cp949') \n",
    "df # 판다스에서 사용되는 표 형태의 데이터 형식을 data frame이라고 부릅니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "보통은 head 메서드를 사용해서 가장 위 5개 데이터만 살펴봅니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "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>날짜</th>\n",
       "      <th>지역코드</th>\n",
       "      <th>평균</th>\n",
       "      <th>최저</th>\n",
       "      <th>최고</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1907-10-01</td>\n",
       "      <td>108</td>\n",
       "      <td>13.5</td>\n",
       "      <td>7.9</td>\n",
       "      <td>20.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1907-10-02</td>\n",
       "      <td>108</td>\n",
       "      <td>16.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1907-10-03</td>\n",
       "      <td>108</td>\n",
       "      <td>16.2</td>\n",
       "      <td>13.1</td>\n",
       "      <td>21.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1907-10-04</td>\n",
       "      <td>108</td>\n",
       "      <td>16.5</td>\n",
       "      <td>11.2</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1907-10-05</td>\n",
       "      <td>108</td>\n",
       "      <td>17.6</td>\n",
       "      <td>10.9</td>\n",
       "      <td>25.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           날짜  지역코드    평균    최저    최고\n",
       "0  1907-10-01   108  13.5   7.9  20.7\n",
       "1  1907-10-02   108  16.2   7.9  22.0\n",
       "2  1907-10-03   108  16.2  13.1  21.3\n",
       "3  1907-10-04   108  16.5  11.2  22.0\n",
       "4  1907-10-05   108  17.6  10.9  25.4"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "숫자로 이루어진 데이터의 특성을 쭈욱 살펴봅니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>지역코드</th>\n",
       "      <th>평균</th>\n",
       "      <th>최저</th>\n",
       "      <th>최고</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>40771.0</td>\n",
       "      <td>40015.000000</td>\n",
       "      <td>40014.000000</td>\n",
       "      <td>40013.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>108.0</td>\n",
       "      <td>11.729294</td>\n",
       "      <td>7.432399</td>\n",
       "      <td>16.741204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.656384</td>\n",
       "      <td>10.878462</td>\n",
       "      <td>10.985124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>108.0</td>\n",
       "      <td>-19.200000</td>\n",
       "      <td>-23.100000</td>\n",
       "      <td>-16.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>108.0</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>-1.400000</td>\n",
       "      <td>7.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>108.0</td>\n",
       "      <td>12.900000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>18.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>108.0</td>\n",
       "      <td>21.200000</td>\n",
       "      <td>17.100000</td>\n",
       "      <td>26.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>108.0</td>\n",
       "      <td>33.700000</td>\n",
       "      <td>30.300000</td>\n",
       "      <td>39.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          지역코드            평균            최저            최고\n",
       "count  40771.0  40015.000000  40014.000000  40013.000000\n",
       "mean     108.0     11.729294      7.432399     16.741204\n",
       "std        0.0     10.656384     10.878462     10.985124\n",
       "min      108.0    -19.200000    -23.100000    -16.300000\n",
       "25%      108.0      2.700000     -1.400000      7.200000\n",
       "50%      108.0     12.900000      8.000000     18.600000\n",
       "75%      108.0     21.200000     17.100000     26.300000\n",
       "max      108.0     33.700000     30.300000     39.600000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "전체 데이터에 비해 평균, 최저, 최고 데이터가 숫자가 다른 것을 볼 수 있습니다.   \n",
    "비어있는 데이터는 간단히 전처리해줍니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다시 보면 이제 같아진 것을 확인할 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>지역코드</th>\n",
       "      <th>평균</th>\n",
       "      <th>최저</th>\n",
       "      <th>최고</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>40771.0</td>\n",
       "      <td>40771.000000</td>\n",
       "      <td>40771.000000</td>\n",
       "      <td>40771.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>108.0</td>\n",
       "      <td>11.511803</td>\n",
       "      <td>7.294400</td>\n",
       "      <td>16.429958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.675045</td>\n",
       "      <td>10.823598</td>\n",
       "      <td>11.115003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>108.0</td>\n",
       "      <td>-19.200000</td>\n",
       "      <td>-23.100000</td>\n",
       "      <td>-16.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>108.0</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>-1.200000</td>\n",
       "      <td>6.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>108.0</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>18.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>108.0</td>\n",
       "      <td>21.100000</td>\n",
       "      <td>16.900000</td>\n",
       "      <td>26.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>108.0</td>\n",
       "      <td>33.700000</td>\n",
       "      <td>30.300000</td>\n",
       "      <td>39.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          지역코드            평균            최저            최고\n",
       "count  40771.0  40771.000000  40771.000000  40771.000000\n",
       "mean     108.0     11.511803      7.294400     16.429958\n",
       "std        0.0     10.675045     10.823598     11.115003\n",
       "min      108.0    -19.200000    -23.100000    -16.300000\n",
       "25%      108.0      2.200000     -1.200000      6.600000\n",
       "50%      108.0     12.500000      7.600000     18.200000\n",
       "75%      108.0     21.100000     16.900000     26.200000\n",
       "max      108.0     33.700000     30.300000     39.600000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "판다스에서 원하는 컬럼을 선택하는 방식은 딕셔너리 사용법과 유사합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        20.7\n",
       "1        22.0\n",
       "2        21.3\n",
       "3        22.0\n",
       "4        25.4\n",
       "         ... \n",
       "40766    29.7\n",
       "40767    31.6\n",
       "40768    31.0\n",
       "40769    26.6\n",
       "40770    26.6\n",
       "Name: 최고, Length: 40771, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['최고']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "판다스는 이렇게 한 번의 명령으로 전체 데이터에 같은 명령을 적용하는 장점이 있습니다.  \n",
    "아래처럼 이렇게 조건을 적용하면 참과 거짓인 데이터가 구분이 됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        False\n",
       "1        False\n",
       "2        False\n",
       "3        False\n",
       "4        False\n",
       "         ...  \n",
       "40766    False\n",
       "40767     True\n",
       "40768     True\n",
       "40769    False\n",
       "40770    False\n",
       "Name: 최고, Length: 40771, dtype: bool"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['최고'] > 30"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이런 것을 mask라고 하는데, 마스크를 적용하면 전체 데이터 프레임 중 참인 것들만 따로 모을 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>날짜</th>\n",
       "      <th>지역코드</th>\n",
       "      <th>평균</th>\n",
       "      <th>최저</th>\n",
       "      <th>최고</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>1908-06-23</td>\n",
       "      <td>108</td>\n",
       "      <td>24.4</td>\n",
       "      <td>18.3</td>\n",
       "      <td>32.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>1908-06-24</td>\n",
       "      <td>108</td>\n",
       "      <td>23.7</td>\n",
       "      <td>19.5</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>1908-07-07</td>\n",
       "      <td>108</td>\n",
       "      <td>23.5</td>\n",
       "      <td>17.3</td>\n",
       "      <td>30.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>1908-07-12</td>\n",
       "      <td>108</td>\n",
       "      <td>24.8</td>\n",
       "      <td>19.8</td>\n",
       "      <td>30.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>1908-07-14</td>\n",
       "      <td>108</td>\n",
       "      <td>25.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>31.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40758</th>\n",
       "      <td>2020-07-08</td>\n",
       "      <td>108</td>\n",
       "      <td>25.5</td>\n",
       "      <td>21.3</td>\n",
       "      <td>30.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40759</th>\n",
       "      <td>2020-07-09</td>\n",
       "      <td>108</td>\n",
       "      <td>27.5</td>\n",
       "      <td>21.8</td>\n",
       "      <td>32.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40761</th>\n",
       "      <td>2020-07-11</td>\n",
       "      <td>108</td>\n",
       "      <td>25.8</td>\n",
       "      <td>22.2</td>\n",
       "      <td>30.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40767</th>\n",
       "      <td>2020-07-17</td>\n",
       "      <td>108</td>\n",
       "      <td>26.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>31.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40768</th>\n",
       "      <td>2020-07-18</td>\n",
       "      <td>108</td>\n",
       "      <td>26.1</td>\n",
       "      <td>22.8</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3876 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               날짜  지역코드    평균    최저    최고\n",
       "266    1908-06-23   108  24.4  18.3  32.4\n",
       "267    1908-06-24   108  23.7  19.5  31.0\n",
       "280    1908-07-07   108  23.5  17.3  30.3\n",
       "285    1908-07-12   108  24.8  19.8  30.4\n",
       "287    1908-07-14   108  25.3  20.0  31.2\n",
       "...           ...   ...   ...   ...   ...\n",
       "40758  2020-07-08   108  25.5  21.3  30.4\n",
       "40759  2020-07-09   108  27.5  21.8  32.9\n",
       "40761  2020-07-11   108  25.8  22.2  30.2\n",
       "40767  2020-07-17   108  26.0  21.5  31.6\n",
       "40768  2020-07-18   108  26.1  22.8  31.0\n",
       "\n",
       "[3876 rows x 5 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['최고'] > 30]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그리고 이 결과도 데이터 프레임 형식이기 때문에 여기에서 다시 컬럼 이름을 뽑아낼 수 있겠죠?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "266      1908-06-23\n",
       "267      1908-06-24\n",
       "280      1908-07-07\n",
       "285      1908-07-12\n",
       "287      1908-07-14\n",
       "            ...    \n",
       "40758    2020-07-08\n",
       "40759    2020-07-09\n",
       "40761    2020-07-11\n",
       "40767    2020-07-17\n",
       "40768    2020-07-18\n",
       "Name: 날짜, Length: 3876, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['최고'] > 30]['날짜']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "또한 판다스에서는 다양한 날짜 선택 기능을 제공하는데, 이렇게 간단히 연도만 뽑아낼 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1907, 1907, 1907, 1907, 1907, 1907, 1907, 1907, 1907, 1907,\n",
       "            ...\n",
       "            2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020],\n",
       "           dtype='int64', name='날짜', length=40771)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DatetimeIndex(df['날짜']).year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그리고 딕셔너리와 마찬가지로 새로운 컬럼을 만들고 싶을 땐 이렇게 만들 수 있습니다.  \n",
    "아래는 연도만 뽑아서 별도의 year라는 컬럼을 만드는 명령입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['year'] = pd.DatetimeIndex(df['날짜']).year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "head 메서드를 확인해보니 새로운 year라는 컬럼이 생긴 것을 볼 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "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>날짜</th>\n",
       "      <th>지역코드</th>\n",
       "      <th>평균</th>\n",
       "      <th>최저</th>\n",
       "      <th>최고</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1907-10-01</td>\n",
       "      <td>108</td>\n",
       "      <td>13.5</td>\n",
       "      <td>7.9</td>\n",
       "      <td>20.7</td>\n",
       "      <td>1907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1907-10-02</td>\n",
       "      <td>108</td>\n",
       "      <td>16.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1907-10-03</td>\n",
       "      <td>108</td>\n",
       "      <td>16.2</td>\n",
       "      <td>13.1</td>\n",
       "      <td>21.3</td>\n",
       "      <td>1907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1907-10-04</td>\n",
       "      <td>108</td>\n",
       "      <td>16.5</td>\n",
       "      <td>11.2</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1907-10-05</td>\n",
       "      <td>108</td>\n",
       "      <td>17.6</td>\n",
       "      <td>10.9</td>\n",
       "      <td>25.4</td>\n",
       "      <td>1907</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           날짜  지역코드    평균    최저    최고  year\n",
       "0  1907-10-01   108  13.5   7.9  20.7  1907\n",
       "1  1907-10-02   108  16.2   7.9  22.0  1907\n",
       "2  1907-10-03   108  16.2  13.1  21.3  1907\n",
       "3  1907-10-04   108  16.5  11.2  22.0  1907\n",
       "4  1907-10-05   108  17.6  10.9  25.4  1907"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그리고 아직 시작도 하지 않았습니다ㅎㅎ 판다스는 데이터 분석에 필요한 엄청난 기능들을 갖고 있습니다.  \n",
    "예를 들면, 이렇게 한 줄의 코드로 연도별 30도가 넘는 날의 숫자를 카운트해줍니다. 그것도 정렬해서 말이죠!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1943    71\n",
       "1939    69\n",
       "1942    66\n",
       "1949    58\n",
       "1997    58\n",
       "        ..\n",
       "1936    14\n",
       "1993    13\n",
       "1913    12\n",
       "1934    11\n",
       "1980     6\n",
       "Name: year, Length: 110, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['최고'] > 30]['year'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "하지만 우리는 시간 순서대로 결과를 확인하고 싶으니 이렇게 메서드를 하나 더 써서 연도별로 정렬을 다시 해보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1908    23\n",
       "1909    31\n",
       "1910    24\n",
       "1911    31\n",
       "1912    22\n",
       "        ..\n",
       "2016    56\n",
       "2017    50\n",
       "2018    54\n",
       "2019    43\n",
       "2020    19\n",
       "Name: year, Length: 110, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['최고'] > 30]['year'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그리고 이렇게 메서드를 하나 더 추가해주면, 짜잔하고 그래프까지 그려줍니다.  \n",
    "참고로 이 그래프는 matplotlib을 사용해서 그리는 것이라 matplotlib은 잘 알아두시면 좋습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x235143c1c08>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jqty57r7YzE7P+kwGngDc3R/PO+LHAkvc/Z/N7C7iWVfHAL9K3CXfBfy7LHcFsNLdHzSzTwIfAPYCLwFHAxuzLj8vu642K/Iql6rr09dlxa8lFtBYYC0wEWgjGm4vUfk5RCNOAuYDtwL35N//Ag4DXgH+BngQ+AownlhgZwE7c9oVwEqi8e4E/h64iFigm/J7XsuyB6vcVwADXga20gIrSs7zicBC4FPAD5tc7hbiR1a9jBe4+3fN7KGDtOMLwOeB38lpWmUZtvK6M5jLsOh5PgI4ClgKPJztPQm4Kr/7pfy+8cRNji8CFwDLiN/QvwLvIHohHPgB8AfZlguJmyKPAFZnm/5WlnUWsC/rcwLwd4Src7zTgMXA5Vn3bdmep+bwW/P1JOJy7j3AbcDnsu0q3zsKWERsZNqBR4gHHY4G/i3L+Xp+/9XAd9z9Fmpp5LbSRv+AbwJ3Az8E/h/wNPAckbC6cqHcmwvjE1nxFUSy/yLwejbIXGKl6s5Grfywdme8DZieC2JbLuCN+T235zSVBLGZWDFnEQv/s4NU7oac7vvESvRlYCbwVJazFPjH/K6lQAewPMtZQqwMi4HtxI/hdWIl2Z/f+3SWvy7b+7Gsy/dyml3AQ1m3v8qy1wCdWe6WLKOZ5XbldPdme67NZb6vjnb856zTrS20DFt93Sl6GQ7m73Q3keg/mt//eH7/G7kM5ubrfuDD+bol41eI9awzv+dPMh91EfnnozlPe4E7sj4n5DgrgW0ZL8syngGezTKeq4o7gP+e028B/gM9G6j1WfYVVWU/C7yH2Dh1E0cb+4DjcpzlwPaMTwaezHgMsLyunFtwQn8mX0cTK+WzxFbLcmYfzs+35Yy9ka/V8X7gS7nCvAacDjxPrKiv5Pec3qvRJmeD7szx9gFvq1pIlUZ7Y4jKfZ5IsEOxoizO+j2TddhdFe8vsNx5Ve34HJEcDtaOn836LCH3bqrWq1ZYhkNV7nH0ve4MxjIcqt/pVmJD9q58XZPxMiJpLyI2RhuBb+V3PwmsIjYGF+b3bAGeq/odPA/cSOzBr8n3lwK7Mt6QbXcKcF7O2ynEHvVe4ujnR/RslObl9M9mPV8DPpLftZk4UqrM2yLgmix7U1Vb76pazyt1PYXoRir0WS71GJXdLkcBbyKS+WjikMmJww2Ihl7m7keTW+mMnyEaaizRONuJvaNjiI2C5XvXmtkRxAo31t2XEyvNE8Af53gPVtWrcu5gK7HlG4xyD835h3iC2lrisO0/Ez+k9+XwPuJHeDGxEnQBn8n3u4gfxg7isA3ix3Couz9A7A09TRyqjSJWkN7zfHxV2euJH8flxA9+Q5PL3Vsp190vyXaaRXSzdddox59kudcTh9WjzezsPuZnMJfhUJX783XH3bdkHT7ML687hS5DBvd3Oht4k5ndnvN0NJEA3wx8yd0fITYmS4jfy6os72+I7qSzicR7BPB5M/txztfp2dV3fLbLNcQRyRFm9gDRd46Z3Z/fPx74v8BdxNHDce6+mthQ7Ad+m0jqbwY+QnQLnZmvrwOXmtlK4HDgNDNblfWYANxCdAUdbmaLicTdbWb3Z/fYCWb2I6JL6wbqUfAe+n/Lhn4pF9gyYkV7jVg5VmYjrc7GW5zvfzSnvx+4LeP/mQ14JLHy7CX2SB4G/jexgu0hNhTbgEeJrfeGXMg7s+xu4NWqcjcNUrn7iCRwP/FjWUOsCCuIQ+cpWd4mYuM3mliJ9hErx46Mu4m927uz/Sonblbld63J9v4+8SNanPXbXlX2piy7C/hYlrswv7OZ5e7OZf0CsCDbeEm+v7VGO34L+Hi2Y6ssw4GUu24A5VavO3cRv5Wf8cvrTtHLcDWD+zudRXTv/DmR9K8m+skne0+3xKEZHwr8XsYnEt1S36jKRWcQXTmfBn6DSKrvBKbl5/+J6G76PLHxnEacQ7g640OyLd+c4x/WK9edn6/jgI8BX6j67Kj8rsuBCfneW4C3ZDwt2+aDWa9pwPtzmmnAIfXm3MJv/TeztwC4+zozO5Y4MXE48I/uvsTM3kwsACNWqNdz/J19fNeJ7r7WzI4Efp04hPtq1eeTcto1OXwIceK328zaiBOyj3mc1R70colkem6vch8nTg53e5ypP8zdd1d99/nu/qiZjQM+BExy9z/Lz44iTubszvna0Ku9KyvlQuJH2rvsdcCeIst19+/1buss72zgnQdrx0FehidmOVp36liGgzHP2dYT6LnKZQ89R/dFx+vcfUOv+Rrr7tt7xwf7rFlxvQYjoRs9K6LT84jd6uHHvKoiZna6uy8pMibO5u/tFY8DXi8wbgM2u/v+/PFMJPaYtgO/Bqx2981mdhhx2PgisRfVjHh7xqvd/TUz+5i7/122x6DEZjYWeBuxR9jdSOzuW6umf4XYk6vEvwq8nei/3JHxspznZsZdxBHG24Eud7895+8sd3+mqDiHTwbeyHZoJ5LeU+7+MzM7j7hS5Qmia6LZ8XZig7A7l8sYYi+8u8D4ZKJL6VBiD/4MYkdwTzbJmILiXcQyPoY4KvqYuz+Ry+Bldz+5d3ywzxqJzewsYiO9kTjquczdT8pxHnP3c6ml9y57M/+Iky0rsnJ35OuO/Ku8V+l2eE/VdC8XFQPvJlaazmy8fVXx3gLjJ7Pc9cAXiI3ZbuKwfS2xUu0DvpvT7CYOb7ubEG/PlWQ5kQh/lu89l39FxS8Sl7dtIvpZtxBdL11Zn4PFG3J4AZG0/404XF9Q1aYL6LnE7H/kvK4tMPZs0wUZbyEO0fcR63BR8W3ZlkuAP8w22kbPFSp7iOTzOj3dM82KO7O91+Z7O/P9bmIdKyp24sjkUWJdutx7TiguLzD+Us7rjcCPsy3m5N+eA8TPVQ0PJN5MbFD+jLiKpxt4a9brybpybsEJfTHQ3nuYuGbzNaKf68tE/9wuoj+wM2ekqLgrG/DLueLsI/qpVhA/kKLiF3Me/yM9lyaeSvwwdxInVS4gVuTz87N92WYDjT3n9c9z3ruyzG4icRYVbyMuldyS7fDVXA+2EyfUDhb/FNiZ8a/k/Lw9h7uqxtsFLMp4J/BsgXEX8eP/q5yfyqWgu3N5FhXvITaIf5lttIQ42XdSLtuTiBOd1ddPNzNeVvX7rVzStxrYWGD8KrAh4zXAAxkvJ/6XcVHxLiI3fY7YkHXlMngo2/qRPuJ9Oe5A40ou+Fz+bc+6TQOeqCfnFn2Vy2hiYfQeXgscS2ydFhFJZw+xRT6GSApFxTuJH8siYm/gDeKM+iHEiaKi4srJp1uIhbjD3V8kTkK5u7/q7j/J+NH8bA+R1AYaL8vyjyS6J3bn35nEDRhFxSvc/UIioe9z9z/K9WBUfn6weEzWGXdfRXQPPpGf7Sf6csnlOTbj3cQJ5aLibqK/+jPETshaYiehchluUXHFUWQi9+i7rnRfrqVnr7a7gLjSJVF9xcsbWZ+i4u8B48zsd4mjswvM7IvEOnxkgfEeoptpCZFQf+juF+Z6vAe4sY94IdGVOtB4VcY3u/vNxO/m/cQ9PKdQj4L30D9NdDXcBFwLfIdY+dYSe3Gfy8+2AbfnNA8RJySKijvo6X7pIA7jJ9FzA0WR8baMd9GzpzONSEqjiA3AbmBUfraUnmtRBxJPIzaeVxKHsC8Te0CPEie+ior3ERusylUNzxPX6Hq2wcHibUTifqbqs8r0O3K8u4hD0/3E0VDlxpqi4srRx9dyeV6b7fsk8K4C468Tl/PNAb5N/H4ezbatdG1VdlC2FxBvI+4wrXSHXJtl7i4wvifL/Spxk9lOYvkvzL+i4qeBB7LcjxKPGKjks/Mqw73i04AzmxBfS/SbV8qrXBFzMvD39eTcwTgpeiZwBT1nyCtb+7HED3E1cejytLt3mdnxxMX1RcUXA53u/nSv+ErgYnf/REHxu4HfdPebe8XtxD/Y/mui2+U64K/dfZeZXUHcwfYPA4zbgQvc/RtmdgZxidRRxK3Gf0FcxlVEPIO4dHVKvm4kkuOZxFHZfQeJjyN+NJX4MmKvbW8On0YkmHMyXkf0d3YTXQVFxO3ETsAa4Ch3vwPAzK51928VGI8mLmlzItGdR1xfvom4tPOP871HiKOJX29yPIE491S5P+CtxB7scflZEfELwP3u/qLFtevj3f0l5KD0H4tEpOVYPJjr08RR5QSiq2kUsRPIEMW7iKNOy/cOL7i8ncQR4RzgFnffSg1FP5xrLPGwoPcTXQ17iEMqI/bQR9PTF+ojON6B2kXtUn9caReII60ytsthREJbSpxP+QbR9fL9/Px3Bjl+L/CnxDNnAP4LcVRdVJ3eS9zxex3x/JuLPe62PqiiT4p+k+jo/y3gZuIQ8SvESY49xHMj3kOsnK+M0Ph5YiGqXdQujbbLT0vcLnuIPvyriT7kY9y9ci5l/2DH7n4rcLS7f9bdP5vxrQWXd7LHxRK3ZhvUVk9He3//iH7x6uGufB1FHL48XhmPnkvURlo8Khei2kXtonbpiR8gzpdMyHgj8ejdZURf/mDHNxHXif8lPU/e/FTB5T2Y838T8GA9Obfof3Cxw8wucPefmNnlwO6qYaOnz2hM1TQjLX4f4GqXX4rVLn3HI6VdvkY8dOsR4mKBo8n/E5CfD0W8i3hOixHdXDcTyb2o8nYSz8CZS9ypW1PRXS5/BNxmZluJrcyHc/h1Yot7RH62F3hthMafIe6YVbuoXdQuPfEncz4/Tlwl9QViYzYu3xuKuPJwsN8l+rXPKbi8a9z9DHe/iXh8Sm1FdLXU80c+J7ivYcVqF7XLyG4X4lLM9cQ/7thM3A9QifcMQbyaOAe4NIe7ifsBiixvZVW71HWn6JAk86zgywcaVqx2UbuM7HYhbiB7JePKI5dvyLhrCOJ2omvrT7NOu4iukSLL6wJuyPLqepZL0ZctPtPrrclV8eFEo0BcomQ5PNLi3m2hdlG7qF0ihrjD+W35/gLiZqe1xI1kgxm/QPSfP1wVjyNu9CqqvIur4gvdfQq11JP1+/tHXBQ/hXgOwSnEQ7IuIx4+tS/j84iH4WweoXF1W6hd1C5ql4gXZXwKcdnqRuJE5Pqc58GO7yJOVL6jKp5ScHm/ENeVcwtO6HcSt5z/0jDx0KoLqt6fNxLjPtpC7aJ2UbvEjYg/OEB8+WDHOXwVPf+xqDouqrzz+4oP9qdb/0VESqLoyxZFRGSQKKGLiJSEErqISEkooYuIlIQSuohISfx/ZsJg1zo75rAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['최고'] > 30]['year'].value_counts().sort_index().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그리고 데이터 조건도 이렇게 아주 간단하게 추가해주면 원하는 조건에 맞게 딱 그래프를 그려줄 수 있습니다.  \n",
    "눈치 채셨겠지만 and가 &, or는 | 입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2351618a508>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAEJCAYAAABi9hcIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAbPElEQVR4nO3de9RcVZ3m8e+PJGhIICTkJSIxBG2utnLxHcDGGYGIItgQF6IwDkYHTa/pC7Ta3SJ9cXSxHJzpRmbNtNIRkDgqiCiGbgXBCKjYBJIQyA3kYgwBTKIQFS9g4Jk/9i5TKety3rcuyX7zfNaqVVVn79p71z7n/GrXrnNOhSTMzKxMu+3oBpiZ2eg5iJuZFcxB3MysYA7iZmYFcxA3MyuYg7iZWcEqBfGI2Dsiro+IByJibUS8NiKmRcStEfFQvp/a78aamdn2ospx4hGxEPiupCsiYndgD+Ai4ClJl0TEhcBUSR9qV8706dM1e/bsHjTbzGzXsWzZsp9IGmqW1jGIR8RewH3Ay1WXOSIeBE6Q9GRE7AfcLumQdmUNDw9r6dKlI34DZma7sohYJmm4WVqV6ZSXA5uBz0bEvRFxRURMAmZIehIg3+/bsxabmVklVYL4eOBo4NOSjgJ+CVxYtYKImB8RSyNi6ebNm0fZTDMza6ZKEN8AbJC0JD+/nhTUN+ZpFPL9pmYvlrRA0rCk4aGhplM6ZmY2Sh2DuKQfA49FRG2+ew6wBrgRmJeXzQMW9aWFZmbW0viK+f4C+EI+MuVR4D2kD4DrIuI8YD1wVn+aaGZmrVQK4pJWAM1+GZ3T2+aYmdlI+IxNM7OCOYibmRWs6py4Wd/NvvDr2z1fd8lpO6glZuXwSNzMrGAO4mZmBXMQNzMrmOfEzWxMa/ytBcbW7y0eiZuZFcxB3MysYJ5OMasz1r96W3MlH97qkbiZWcEcxM3MCuYgbmZWMAdxM7OCOYibmRXMQdzMrGA+xNAqKfkQLLNB2FH7iEfiZmYFcxA3MyuYg7iZWcEcxM3MCuYgbmZWMAdxM7OCOYibmRXMx4nbLsXHu9tY45G4mVnBHMTNzApWaTolItYBvwCeB7ZKGo6IacCXgNnAOuDtkp7uTzPNzKyZkYzET5R0pKTh/PxCYLGkg4DF+bmZmQ1QN9MpZwAL8+OFwNzum2NmZiNRNYgLuCUilkXE/LxshqQnAfL9vv1ooJmZtVb1EMPjJT0REfsCt0bEA1UryEF/PsCsWbNG0UQzsx1rZz40tdJIXNIT+X4TcANwDLAxIvYDyPebWrx2gaRhScNDQ0O9abWZmQEVgnhETIqIPWuPgTcCq4AbgXk52zxgUb8aaWZmzVWZTpkB3BARtfxflHRzRNwDXBcR5wHrgbP610wzM2umYxCX9ChwRJPlPwXm9KNRZmZWjc/YNDMrmIO4mVnBfBVDM9up7cyH9+0MPBI3MyuYg7iZWcE8nWJjir96267GI3Ezs4I5iJuZFcxB3MysYA7iZmYFcxA3MyuYg7iZWcEcxM3MCuYgbmZWMAdxM7OCOYibmRXMp92b2aj5Mgc7nkfiZmYFcxA3MyuYg7iZWcE8J25mNgD9+v3AI3Ezs4I5iJuZFcxB3MysYA7iZmYFcxA3MyuYg7iZWcEqH2IYEeOApcDjkt4SEQcC1wLTgOXAuZKe608zzWwsajzsDnzq/kiNZCR+AbC27vkngE9KOgh4Gjivlw0zM7POKgXxiJgJnAZckZ8HcBJwfc6yEJjbjwaamVlrVadTLgP+BtgzP98H2CJpa36+Adi/2QsjYj4wH2DWrFmjb6mZWRO7+pUUO47EI+ItwCZJy+oXN8mqZq+XtEDSsKThoaGhUTbTzMyaqTISPx44PSJOBV4M7EUame8dEePzaHwm8ET/mmlmZs10HIlL+rCkmZJmA2cD35b0TuA24G052zxgUd9aaWZmTXVzFcMPAddGxMXAvcCVvWlSb+zq82RmtmsYURCXdDtwe378KHBM75tkZmZV+YxNM7OCOYibmRXMQdzMrGAO4mZmBXMQNzMrmIO4mVnBHMTNzArmIG5mVjAHcTOzgnVz2n3R/I8iZv3ny1/0n0fiZmYFcxA3MyvYLjudYrajDGoqz1MZuwaPxM3MCuYgbmZWMAdxM7OCeU68zzwvaWb95JG4mVnBHMTNzArm6ZSd3FiZjunFYXVjpS/MeskjcTOzgjmIm5kVzEHczKxgDuJmZgVzEDczK5iDuJlZwToG8Yh4cUTcHRH3RcTqiPhoXn5gRCyJiIci4ksRsXv/m2tmZvWqHCf+LHCSpGciYgLwvYi4CfgA8ElJ10bE5cB5wKf72NYxycc+m1k3Oo7ElTyTn07INwEnAdfn5QuBuX1poZmZtVRpTjwixkXECmATcCvwCLBF0tacZQOwf3+aaGZmrVQ67V7S88CREbE3cANwWLNszV4bEfOB+QCzZs2q1ChPMYyM+2vsGcQ69XYzNozo6BRJW4DbgeOAvSOi9iEwE3iixWsWSBqWNDw0NNRNW83MrEGVo1OG8giciJgIvAFYC9wGvC1nmwcs6lcjzcysuSrTKfsBCyNiHCnoXyfp3yJiDXBtRFwM3Atc2cd2mplZEx2DuKT7gaOaLH8UOGY0lXouzsysN3zGpplZwRzEzcwK5iBuZlYwB3Ezs4I5iJuZFcxB3MysYP63+12AD+kce7xOrcYjcTOzgjmIm5kVzEHczKxgDuJmZgVzEDczK5iDuJlZwRzEzcwK5iBuZlYwB3Ezs4I5iJuZFazY0+592vHOxetj7PE6LYNH4mZmBXMQNzMrmIO4mVnBHMTNzArmIG5mVjAHcTOzgjmIm5kVzEHczKxgDuJmZgXrGMQj4mURcVtErI2I1RFxQV4+LSJujYiH8v3U/jfXzMzqVRmJbwU+KOkw4DjgzyLicOBCYLGkg4DF+bmZmQ1QxyAu6UlJy/PjXwBrgf2BM4CFOdtCYG6/GmlmZs2NaE48ImYDRwFLgBmSnoQU6IF9e904MzNrr3IQj4jJwFeAv5T08xG8bn5ELI2IpZs3bx5NG83MrIVKQTwiJpAC+BckfTUv3hgR++X0/YBNzV4raYGkYUnDQ0NDvWizmZllVY5OCeBKYK2kS+uSbgTm5cfzgEW9b56ZmbVT5U8hjgfOBVZGxIq87CLgEuC6iDgPWA+c1Z8mmu1c/GcJ1g+N2xVU27Y6BnFJ3wOiRfKcjjWYmVnf+IxNM7OCOYibmRXMQdzMrGAO4mZmBXMQNzMrmIO4mVnBHMTNzArmIG5mVjAHcTOzglU57d7MRsCn5dsgeSRuZlYwB3Ezs4I5iJuZFcxz4m10mtv03KeZ7WgeiZuZFcxB3MysYA7iZmYFcxA3MyuYg7iZWcEcxM3MCuZDDG3U/7JtZjueR+JmZgVzEDczK5iDuJlZwRzEzcwK5iBuZlYwB3Ezs4J1DOIRcVVEbIqIVXXLpkXErRHxUL6f2t9mmplZM1VG4lcDpzQsuxBYLOkgYHF+bmZmA9YxiEv6DvBUw+IzgIX58UJgbo/bZWZmFYx2TnyGpCcB8v2+vWuSmZlV1fcfNiNifkQsjYilmzdv7nd1Zma7lNEG8Y0RsR9Avt/UKqOkBZKGJQ0PDQ2NsjozM2tmtEH8RmBefjwPWNSb5piZ2UhUOcTwGuDfgUMiYkNEnAdcApwcEQ8BJ+fnZmY2YB0vRSvpnBZJc3rcFjMzGyGfsWlmVjAHcTOzgjmIm5kVzEHczKxgDuJmZgVzEDczK5iDuJlZwRzEzcwK5iBuZlYwB3Ezs4I5iJuZFcxB3MysYA7iZmYFcxA3MyuYg7iZWcEcxM3MCuYgbmZWMAdxM7OCOYibmRXMQdzMrGAO4mZmBXMQNzMrmIO4mVnBHMTNzArmIG5mVjAHcTOzgnUVxCPilIh4MCIejogLe9UoMzOrZtRBPCLGAf8MvBk4HDgnIg7vVcPMzKyzbkbixwAPS3pU0nPAtcAZvWmWmZlV0U0Q3x94rO75hrzMzMwGJCSN7oURZwFvkvTe/Pxc4BhJf9GQbz4wPz89BHiwLnk68JM21XRK70UZg6ijF2WMlTp6UcZYqaMXZbiOwZaxo+o4QNJQ09ySRnUDXgt8s+75h4EPj7CMpd2k96KMQdRRSjvdF+6LsVxHKe2sUkf9rZvplHuAgyLiwIjYHTgbuLGL8szMbITGj/aFkrZGxJ8D3wTGAVdJWt2zlpmZWUejDuIAkr4BfKOLIhZ0md6LMgZRRy/KGCt19KKMsVJHL8pwHYMtY2ep43dG/cOmmZnteD7t3sysYA7iZmYFcxA3MyuYg7iZWcG6OjrFrBQRMQU4hXRpCAFPkE5W29LhdSdLujU/3gsYkvRIQ55XS7o/Il4CIOnHETEE/EfgwVaH3kbExyVd1KbuA4GjgDWSHoiIWcAmSb+JiADeDRwNrAE+A5wK3CLpNx3e038CNkp6MCJeBxwHrJX09Zw+mdRXLwO2Ag/lcl/I6YeSrpNU35c3Slrbrt782vdI+mwuY39giaRn6tJPkXRzfnwMIEn35IvrnQI8kI+Ka1b25yS9q0Xa60jXe1ol6Za87Nj8vn8eEROBC9nWnx8H5gE3SHqsRZm182OekPStiPjPwB8Ba4EFkn6b870CeGtDf14j6Wfd9ifsJEen1FZsftx25fZyxeb07VZutys2l9lx5VZYsW8C5rL9il1U28DbiYh/kPSxXMZMYLGkdXXp/1XSVTkQnJXLvx44ibQxPQBcXttpG8r+tqST8uPpkn5Sl/Zfan0JfEaSIuKtwB2SnsqB7Z/IgQn4IPAB4CuS7mzxXqYBf57f/5XARaSzhdcCH5f0dM53InBmQ39eIenhiHgX8BHgFuDxXPRM4GTgo5I+16Yv10uaFRFvBy4DNgETgHdLuifnWQ78C2lbCeATpAC7Gjge+J/AEY1FA+cCnwOQdH5EfE3S3FzmGbm+20nbzv8A/op0aYtfRcQngFcAXyOtN4BzgF8CNwHXkD6knm94P5eR1tF40jkec3L+1wP3kk7i+2vgPuBE4Pukb+yvAt5J+qA4h3TBuw11fXk2cK2kS1r1Za0/gX8E/oy0Do8ELpC0qNaXko6OiI+QrpA6HrgVODb3xRtyu49t0p8nAt/Oz18i6Zhc5vtyfTcAbwT+VdIlEbEaOCKf87IA+BVpP5hDWl9zcn8+kvvzy5I2172XL+T27QFsASYDX82vC0nzIuJ84I+BO3LfrQCeJu37f5rfx6j7E3aeIF7bUc6nzcoFFtHFipV0ekTc3W7lknasUa/YXG7blQsso/2KnQscTNrB61fsu4CHJF3QqT+BzwOvA5bnui6T9H9qfZl3lE8B+wK7Az8HXpT74FRgY+67xv48mG3Xv9kq6ehc5t+RRp5fBN4CbJD0/ohYI+nwnOdLwF3Al0nr7J2k9fwjYAj4EumD7N669/INYCWwF3BYfnwdKQAfIemMiLgEmAEszn33Q+AHuS8/DlwMHNs46o6IqcAS0odW064ETpI0KSJWAG+W9GQeSHwOuEjSVyPiXtL6PhaYmN/PH+QR+VTgNtL1MG4nfZBELv8fSYEZSQsj4l5JR+W2fR94p6QfRsT0/N4m1PXlMuA/1I2O7wNeIAX0t5GCwB+Stu1rJN2R863OyyeSPtD2zx8KE0hB/AXguLxsOvAFSW+KiFcDl+f38craKLOuL3cHVks6KCLub9OfB+d181pJz0TEbNL+9f8k/e9aH0TEStK28SLgx8DMuoHVEtIH9RrgCtIgJEj749m5rsvq+vIe4FRJmyNiEnCXpFdFxFpJh+U8y2vbcn6+Ipf7GtK2+g7gdNK+ew1pf75T0qsjYnzuy5dKej4Pju7LaSuBI/PyPYBvSDohf6taBEzq1J8t+nKbVufj9/oG3N/ithJ4NudZCUzOj2cDS0mBHNIGtpJ0dugepKCzV06bmMtaTgpeJ5BGFicAT+bHr6+VU9eme0hfj8mduZI0Cq+lL294DytyO3YjBf0rgc3AzaQR+p6195rvx5OC4bj8POrec23ZHsDt+fGsXP4PWvRhkII4+f03u/2CtIGvBMbnvHuTTsr6ZH0fACvz/QTgp8Dude1eSbqMwueBQ4ED8jp5LD8+oKEvlwOT6sqrlf1gXZ5lzfozPz4I+HvS6PUB0sj5YGBF3Xt/vPH19e+jru135sdTSd8KfgBMadKfU0gj9qeB02rbSd3tBNK0w3Z15Of7kXbo8/N7X16Xdl9D3nuBPUkj6y+SAifAow356su4u0kZ3yR9qAB8hXRRJIB9SCPnxu31Jbl9/w48lpetyvcvzu97Yn4+jhQUV7JtcDexYR2vyuvmgCZ9eUBtXZO2+SPrtpPabTbpG9WahtdOJu1Dl9at0/p6723Iv4K0D76fNJg7srE/c39MzX2ztOH1tW3uy8B78uPPAsP58cGk2NDYnxNIgfwa0n6/ijQAmkra76bV9e3aupj2orrtcdlI+7NjbK2SqRe3Tis252m7crtdsVVWbrcrtm7ltFy5FVbs/aSvzY19eAzbguN6YEaLvn6Mug+jup30yvz+VjfZUW5u7M98/1bgO8DpTXaUB0hTI6/h9wNX7fX/AnyMFBD+CZibl59I+iayvEn7X02aPng498VU0gfcz4DZOc8+te0lr9NaH88ijbRqZa0mfcA+AnyaNB1zEWlU+Qhp2uMm4MQWffmdfP994BUNaXuSRsjPkgYcE/LymXV5XlzfN7mvbiONwNc1lPc82z6InyNNCZC3pftJU0W35fXxr6Qg/G3SdjuHhn2iMSjk+08A3yVty/8rl/O3pG8Il+f0b+Y++i7p2wbAtNyXp+T1chPpzMIFpH30YeCUnPdK4HUt2vHF3OYjG5aPJ327eT4/XwLskR/vVpdvCtt/2M0kbdP/F1hft3wd8CjpW9mjdX05mW3b5hTg6rwdLAF+m/PeQfrW3a4/J5JizaOkb17n523hM6T9+yM53wV53S0g7S+12DKU12PH/uwYW6tk6sWt04rN921XbrcrtsrK7XbF5vu2K7fCij06172GtHPdQgr+S4DX5LwX0yTQ1+2o/0b+9tGQdjHwQn58E/mbT0Oel1A3EiR9S7mUNDLfULf8tobbfnn57z4gSR9y/530obOe9HX9F6SdeVa7/syvP4c0ANhImvP+FulD+nFgfs7zjtzXt+Q6Tqvrz9q2NZX0VfuDpAB6NjB1BNvvEcBBTZZPIE0LzSIH8Yb0/YE3NCwL0jTe5yvWvTdp+qH2/DDSbxdnkqZwdsvLT6hY3mtJUyaQ5tX/Cnh7XTmn5mUn171mN7YNPHYj/Rh6Jmnq5jjyN8uK9c8k73dN0o7P9y9qkT4deFWT5aeRfiPpVPcewIENy/bM6/c11A2MgIMrlPdS0jRKbT29jYb9EnhlXn5oizK66s+dYk68JiJmkuZZf9wk7XhSYHi2Sdp0UgBZ2bD8NNJG0fIIgLq8e5BW4A/z8z2Bl5M+RDZI2piXHyzpBxXKeymApCciYm/SvNp6SXfn9FeSdsZVkprOyeajHfYn7fQbmvVLm/on5vp/3SRtf0mP//6rfpc+iTQ1sqlh+RGkYHJ5h7rHkXbCXzUsn0Ka4vlp3bLJqvsBu015ofQ7xXjSN7rHJT1Zl2caaX09rBZHnETEDOp+KK6t06rpvShjrNTRTMV12TbPIMoopY6qBhrE82T+zyVtyT9oDJOOLFlVNU+36aXUkfMMU3e0RbNg3ynPIMrY2euIiCNJUwVTSD8UB2k0uIX046dIUy1T2P7olS3An0pa3lBGY57/lssYbXqtjqPatKNjGaRvOZ3qaPc+KpXR2O81tQMUWqVXyTOIMnamOkgHASwgfWDeBHxI2464+t1BGO0M7DjxiLgQ+BPg2Yio/Sp/J/DRiLhS0qWd8pDmBkedXlAdy0jzx1tIX/HuBKZGxG+BcyU9FhGvb5eHNCrtaxkF1XE18CeSltRtkkTEcaTfPdQh/YgOZVzdoYxO6bU6PttlGd2+j45lRMRCmgvSlCQR8YF2eTql96KMUuoAPkWabrwLeC/wvYg4XelchAktXrt9QYMaiUc6tGmYNCe1Dni5th3ys0TSH3bKQ3rjo04vqI7fAm/Myw4ELpX01og4GfhrSW+MdFhbyzyk+eC+llFQHQeqxaFaEfEw6byDlumS/iAiHuqmjDFUx0zSD6Jbm2R5v6S9I+I37fKQfuztaxkF1bFO0pG1BZHOd1hAGnx8SnWHPbYyyDM2n5f064h4Dvg16ZA2JP0yIqrm6Ta9lDrGadux5+tJR/Eg6dZIJ2tUyfP8AMoopY6bIuLrpB/IaydpvYx03P3NwAsd0ulBGWOljqOBr0laRoOIeG9+uLxDnjUDKKOUOiIipiif5Cfptog4k3QI6bTG1zQzyJH41aRphEmkE2i2kjaKk0jHV7+9U568bNTpBdXxDOlr7WLSUQiPS/pApB9fl0s6NCKuapeHdEhcX8sopY6c581sO7U5SHPjNyqf6dspvUqeXaGOiDgEeEoNJ7jl182QtLFTHtJRHH0to6A65pAO272rIW0W8PeS3tf4ut8rZ4BBfDzbn+JdO910PfDPeRTaNg/peNxRpxdUx3PA+4DDScdAX6V0xtdEYF9JP4p0hl3LPKSTKvpaRil1SPoRZmOVKh6L6Jtvpd5IR1pcQjrW/qf5tjYv27tTei/KGIN1PFChjqZ5BlHGWKmjyvY9sEvRRsTkiPhYRKyOiJ9FxOaIuCsi5lXN0216gXWsakh/d5M6muYZRBml1EG61srTpLMy95G0D+mM0S2kk8I6pfeijLFWxwkN6U83qaNVnkGUMVbq6GiQ0ymLSBfj+Rbp7LBJpCt3/R1pDvOiTnlIZz6NOt11lNfOHtVxpqRDaCIiHgRoly7pkNr9aMtwHWOvnYOoo1Xadgb4lbbx2hr35PvdSCe5dMzTbbrrKK+dParjFuBv2P6U6hnAh0iBv216ft5VGa5j7LVzUH3R6TbIf/b5ZaRrdxMRfww8BaB0Kc2omKfbdNdRXjt7Ucc7SNdzuSMino6Ip0iXhZ1GGrl3SqcHZbiOsdfOQfVFe1UifS9upCvT3U2aW/se+eIypJM5zq+Sp9t011FeO3tRR358KOn6NZMbtstTqqT3ogzXMfbaOai+aBtbq2Tq9418Fb9u8nSb7jrKa2fVOkgfBg+S/gVnHXBGXfryTun5vqsyXMfYa+eg+qLjNl4lU79vNFwudjR5uk13HeW1s2odVPuzkZbp+b6rMlzH2GvnoPqi022QF8Bq95dNM6rk6TbddZTXzl7UATyjfMlPSesi4gTg+og4IOcZ1yGdCnm6TXcd5bVzUH3RXpVI34sb1f7Zp9PfOnWV7jrKa2eP6uj0ZyNV/mmmqzJcx9hr56D6omNsHWAQr/LPPp3+1qmrdNdRXjt7VEfbf5LplJ7vuyrDdYy9dg6qLzrddqp/9jEzs5EZ5HHiZmbWYw7iZmYFcxA3MyuYg7iZWcEcxM3MCvb/Ac4fR2D8e7c7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[(df['최고'] > 30) & (df['year'] > 1980)]['year'].value_counts().sort_index().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5줄로 데이터 분석하기!  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23516311f48>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd # 판다스 라이브러리 불러오기\n",
    "df = pd.read_csv('ta_20200721103344_header.csv', encoding = 'cp949')  # 서울의 기온데이터 입력하기\n",
    "df = df.fillna(0) # 누락된 값 0으로 채우기\n",
    "df['year'] = pd.DatetimeIndex(df['날짜']).year # year 칼럼 추가하기\n",
    "df[(df['최고'] > 30) & (df['year'] > 1980)]['year'].value_counts().sort_index().plot.bar() \n",
    "# 81년부터 연도별 최고 기온이 30도 넘는 날의 일수 막대그래프로 출력하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 마무으리ㅎㅎ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "지금까지 데이터 분석의 전체적인 흐름을 다양한 툴을 활용해서 쭈욱 살펴보았습니다.  \n",
    "머신러닝 딥러닝까지 가지는 못했지만, 정형 데이터를 기반으로 하는 머신러닝을 하려면 판다스를 잘 사용하는 것이 아주 중요하답니다.  \n",
    "\n",
    "오늘 하루만에 모든 것을 소화하시느라 수고 많으셨고, 관심이 생기신 선생님들께서는 시중에 있는 책들을 공부해보시면 재밌으실 겁니다.  \n",
    "\n",
    "하지만, 중요한 것은 함수만 사용하는 데이터 교육 이전에 표 형태의 데이터를 어떻게 처리할 것인지 알고리즘을 생각하고,  \n",
    "그 알고리즘을 논리적, 절차적으로 구현할 수 있는 능력을 키워주셔야한다는 것을 기억하시기 바랍니다! :)"
   ]
  }
 ],
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