Day13SeoulWheather.ipynb

{
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  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>날짜</th>\n",
       "      <th>지점</th>\n",
       "      <th>평균기온(℃)</th>\n",
       "      <th>최저기온(℃)</th>\n",
       "      <th>최고기온(℃)</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   지점  평균기온(℃)  최저기온(℃)  최고기온(℃)\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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('seoul.csv', encoding='cp949')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>avertemp</th>\n",
       "      <th>mintemp</th>\n",
       "      <th>maxtemp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1907-10-01</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>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>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>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>17.6</td>\n",
       "      <td>10.9</td>\n",
       "      <td>25.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  avertemp  mintemp  maxtemp\n",
       "0  1907-10-01      13.5      7.9     20.7\n",
       "1  1907-10-02      16.2      7.9     22.0\n",
       "2  1907-10-03      16.2     13.1     21.3\n",
       "3  1907-10-04      16.5     11.2     22.0\n",
       "4  1907-10-05      17.6     10.9     25.4"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop('지점', axis=1)\n",
    "df.columns = ['date','avertemp', 'mintemp', 'maxtemp']\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>avertemp</th>\n",
       "      <th>mintemp</th>\n",
       "      <th>maxtemp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31277</th>\n",
       "      <td>1994-07-24</td>\n",
       "      <td>33.1</td>\n",
       "      <td>28.1</td>\n",
       "      <td>38.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date  avertemp  mintemp  maxtemp\n",
       "31277  1994-07-24      33.1     28.1     38.4"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 서울 역대 최고기온인 날은 언제인가\n",
    "df[df.avertemp == df.avertemp.max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>avertemp</th>\n",
       "      <th>mintemp</th>\n",
       "      <th>maxtemp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31277</th>\n",
       "      <td>1994-07-24</td>\n",
       "      <td>33.1</td>\n",
       "      <td>28.1</td>\n",
       "      <td>38.4</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "             date  avertemp  mintemp  maxtemp\n",
       "31277  1994-07-24      33.1     28.1     38.4"
      ]
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     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# 서울 역대 최고기온인 날은 언제인가\n",
    "df[df.maxtemp == df.maxtemp.max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>avertemp</th>\n",
       "      <th>mintemp</th>\n",
       "      <th>maxtemp</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31299</th>\n",
       "      <td>1994-08-15</td>\n",
       "      <td>30.3</td>\n",
       "      <td>28.8</td>\n",
       "      <td>33.5</td>\n",
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      "text/plain": [
       "             date  avertemp  mintemp  maxtemp\n",
       "31299  1994-08-15      30.3     28.8     33.5"
      ]
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     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# 서울 역대 최고기온인 날은 언제인가\n",
    "df[df.mintemp == df.mintemp.max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
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       "<matplotlib.axes._subplots.AxesSubplot at 0x9b22f48>"
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     "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": [
    "# 8월의 최고기온데이터의 라인차트\n",
    "df['month']=df.date.apply(lambda x:x.split('-')[1])\n",
    "df[df.month=='01'].avertemp.plot(kind='hist', bins=30)\n",
    "df[df.month=='08'].avertemp.plot(kind='hist', bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>date</th>\n",
       "      <th>avertemp</th>\n",
       "      <th>mintemp</th>\n",
       "      <th>maxtemp</th>\n",
       "      <th>month</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>13.5</td>\n",
       "      <td>7.9</td>\n",
       "      <td>20.7</td>\n",
       "      <td>10</td>\n",
       "      <td>1907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1907-10-02</td>\n",
       "      <td>16.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>22.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1907-10-03</td>\n",
       "      <td>16.2</td>\n",
       "      <td>13.1</td>\n",
       "      <td>21.3</td>\n",
       "      <td>10</td>\n",
       "      <td>1907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1907-10-04</td>\n",
       "      <td>16.5</td>\n",
       "      <td>11.2</td>\n",
       "      <td>22.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1907-10-05</td>\n",
       "      <td>17.6</td>\n",
       "      <td>10.9</td>\n",
       "      <td>25.4</td>\n",
       "      <td>10</td>\n",
       "      <td>1907</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  avertemp  mintemp  maxtemp month  year\n",
       "0  1907-10-01      13.5      7.9     20.7    10  1907\n",
       "1  1907-10-02      16.2      7.9     22.0    10  1907\n",
       "2  1907-10-03      16.2     13.1     21.3    10  1907\n",
       "3  1907-10-04      16.5     11.2     22.0    10  1907\n",
       "4  1907-10-05      17.6     10.9     25.4    10  1907"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# year컬럼 만들기\n",
    "df['year'] = df.date.apply(lambda x:x.split('-')[0])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa9a06c8>"
      ]
     },
     "execution_count": 41,
     "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": [
    "# 평균기온이 상슷추세인가?\n",
    "# df.groupby('year').avertemp.mean().plot()\n",
    "df[df.month=='03'].groupby('year').avertemp.mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "10    10.923733\n",
       "05    10.868827\n",
       "04    10.837664\n",
       "06     9.760561\n",
       "03     9.566308\n",
       "09     9.521132\n",
       "11     9.407477\n",
       "02     8.858164\n",
       "01     8.424522\n",
       "12     8.254391\n",
       "08     7.985951\n",
       "07     7.375550\n",
       "Name: difftemp, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 일교차가 가장 큰 월은 언제인가\n",
    "# 일교차 컬럼 만들기\n",
    "df['difftemp'] = df.maxtemp - df.mintemp\n",
    "df.head()\n",
    "# 각 월당 일교차 평균 구하기\n",
    "df.groupby('month').difftemp.mean().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}