ChunML/web_scrapper.ipynb Secret

## web_scrapper.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from bs4 import BeautifulSoup\n",
    "import requests\n",
    "from datetime import datetime, timedelta\n",
    "from sqlalchemy import create_engine\n",
    "from sqlalchemy.ext.declarative import declarative_base\n",
    "from sqlalchemy import Column, Integer, String, Float, DateTime\n",
    "from sqlalchemy.schema import UniqueConstraint\n",
    "from sqlalchemy.orm import sessionmaker\n",
    "from sqlalchemy.exc import IntegrityError"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "url_template = 'https://tenki.jp/past/{}/{:02d}/{:02d}/amedas/3/17/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_weather_for_one_day(year, month, day):\n",
    "    url = url_template.format(year, month, day)\n",
    "    html_doc = requests.get(url).text\n",
    "    soup = BeautifulSoup(html_doc)\n",
    "    table = soup.find(lambda tag: tag.name == 'table' and 'amedas-point-detail-entries-table' in tag['class'])\n",
    "\n",
    "    try:\n",
    "        df = pd.read_html(str(table), na_values=['---'], header=0)[0]\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "        raise ValueError('No data is available for {}/{:02d}/{:02d}'.format(year, month, day))\n",
    "    df.drop(df.shape[0] - 1, inplace=True)\n",
    "    df.columns = ['city', 'max_temp', 'min_temp', 'rain', 'wind', 'daylight']\n",
    "    df['date'] = pd.to_datetime('{}/{}/{}'.format(year, month, day))\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_weather_from_date(year, month, day):\n",
    "    date = datetime(year, month, day)\n",
    "    today = datetime.utcnow()\n",
    "    df = []\n",
    "    while (today - date).days >= 1:\n",
    "        try:\n",
    "            df.append(get_weather_for_one_day(date.year, date.month, date.day))\n",
    "        except ValueError as e:\n",
    "            print(e)\n",
    "        finally:\n",
    "            date += timedelta(days=1)\n",
    "    return pd.concat(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = get_weather_from_date(2015, 1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 18810 entries, 0 to 10\n",
      "Data columns (total 7 columns):\n",
      "city        18810 non-null object\n",
      "max_temp    8550 non-null object\n",
      "min_temp    8550 non-null object\n",
      "rain        18810 non-null object\n",
      "wind        8550 non-null object\n",
      "daylight    8548 non-null object\n",
      "date        18810 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](1), object(6)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>city</th>\n",
       "      <th>max_temp</th>\n",
       "      <th>min_temp</th>\n",
       "      <th>rain</th>\n",
       "      <th>wind</th>\n",
       "      <th>daylight</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>横浜</td>\n",
       "      <td>8.5 (00:50)</td>\n",
       "      <td>0.6 (22:30)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.8 (01:10)</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>日吉</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>海老名</td>\n",
       "      <td>7.2 (10:10)</td>\n",
       "      <td>-4.3 (23:40)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.3 (12:50)</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>三浦</td>\n",
       "      <td>10.2 (00:10)</td>\n",
       "      <td>-0.5 (22:20)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.3 (14:00)</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>辻堂</td>\n",
       "      <td>8.3 (00:10)</td>\n",
       "      <td>-1.2 (23:00)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.4 (14:10)</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>相模原中央</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>平塚</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>相模湖</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>丹沢湖</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>小田原</td>\n",
       "      <td>8.2 (12:30)</td>\n",
       "      <td>-3.7 (23:50)</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.2 (12:50)</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    city      max_temp      min_temp rain         wind daylight       date\n",
       "0     横浜   8.5 (00:50)   0.6 (22:30)  0.0  8.8 (01:10)      0.1 2015-01-01\n",
       "1     日吉           NaN           NaN  0.0          NaN      NaN 2015-01-01\n",
       "2    海老名   7.2 (10:10)  -4.3 (23:40)  0.5  5.3 (12:50)      3.2 2015-01-01\n",
       "3     三浦  10.2 (00:10)  -0.5 (22:20)  0.5  7.3 (14:00)      1.2 2015-01-01\n",
       "4     辻堂   8.3 (00:10)  -1.2 (23:00)  0.0  6.4 (14:10)      1.1 2015-01-01\n",
       "5  相模原中央           NaN           NaN  0.0          NaN      NaN 2015-01-01\n",
       "6     平塚           NaN           NaN  1.0          NaN      NaN 2015-01-01\n",
       "7    相模湖           NaN           NaN  0.5          NaN      NaN 2015-01-01\n",
       "8    丹沢湖           NaN           NaN  2.5          NaN      NaN 2015-01-01\n",
       "9    小田原   8.2 (12:30)  -3.7 (23:50)  3.0  8.2 (12:50)      5.3 2015-01-01"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>city</th>\n",
       "      <th>max_temp</th>\n",
       "      <th>min_temp</th>\n",
       "      <th>rain</th>\n",
       "      <th>wind</th>\n",
       "      <th>daylight</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>平塚</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-09-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>相模湖</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-09-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>丹沢湖</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-09-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>小田原</td>\n",
       "      <td>30.6 (12:50)</td>\n",
       "      <td>21.6 (04:50)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.3 (14:50)</td>\n",
       "      <td>10.6</td>\n",
       "      <td>2019-09-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>箱根</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-09-06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   city      max_temp      min_temp rain         wind daylight       date\n",
       "6    平塚           NaN           NaN  0.0          NaN      NaN 2019-09-06\n",
       "7   相模湖           NaN           NaN  0.0          NaN      NaN 2019-09-06\n",
       "8   丹沢湖           NaN           NaN  0.0          NaN      NaN 2019-09-06\n",
       "9   小田原  30.6 (12:50)  21.6 (04:50)  0.0  4.3 (14:50)     10.6 2019-09-06\n",
       "10   箱根           NaN           NaN  0.0          NaN      NaN 2019-09-06"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_data(df, city_mapping):\n",
    "    df = df.copy()\n",
    "    df.city = df.city.map(city_mapping)\n",
    "    temp = df.max_temp.str.split()\n",
    "    df.max_temp = pd.to_numeric(temp.str.get(0))\n",
    "    temp = df.min_temp.str.split()\n",
    "    df.min_temp = pd.to_numeric(temp.str.get(0))\n",
    "    temp = df.wind.str.split()\n",
    "    df.wind = pd.to_numeric(temp.str.get(0))\n",
    "    df.rain = pd.to_numeric(df.rain)\n",
    "    \n",
    "    df = df.fillna(-1e9)\n",
    "    \n",
    "    df = df.reset_index(drop=True)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_mapping = {\n",
    "    '横浜': 'Yokohama',\n",
    "    '日吉': 'Hiyoshi',\n",
    "    '海老名': 'Ebina',\n",
    "    '三浦': 'Miura',\n",
    "    '辻堂': 'Tsujido',\n",
    "    '相模原中央': 'Sagamihara-Chuo',\n",
    "    '平塚': 'Hiratsuka',\n",
    "    '相模湖': 'Sagamiko',\n",
    "    '丹沢湖': 'Tanzawako',\n",
    "    '小田原': 'Odawara',\n",
    "    '箱根': 'Hakone'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_df = clean_data(df, city_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>city</th>\n",
       "      <th>max_temp</th>\n",
       "      <th>min_temp</th>\n",
       "      <th>rain</th>\n",
       "      <th>wind</th>\n",
       "      <th>daylight</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Yokohama</td>\n",
       "      <td>8.500000e+00</td>\n",
       "      <td>6.000000e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.800000e+00</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Hiyoshi</td>\n",
       "      <td>-1.000000e+09</td>\n",
       "      <td>-1.000000e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.000000e+09</td>\n",
       "      <td>-1e+09</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Ebina</td>\n",
       "      <td>7.200000e+00</td>\n",
       "      <td>-4.300000e+00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.300000e+00</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Miura</td>\n",
       "      <td>1.020000e+01</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.300000e+00</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Tsujido</td>\n",
       "      <td>8.300000e+00</td>\n",
       "      <td>-1.200000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.400000e+00</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Sagamihara-Chuo</td>\n",
       "      <td>-1.000000e+09</td>\n",
       "      <td>-1.000000e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.000000e+09</td>\n",
       "      <td>-1e+09</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Hiratsuka</td>\n",
       "      <td>-1.000000e+09</td>\n",
       "      <td>-1.000000e+09</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.000000e+09</td>\n",
       "      <td>-1e+09</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Sagamiko</td>\n",
       "      <td>-1.000000e+09</td>\n",
       "      <td>-1.000000e+09</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-1.000000e+09</td>\n",
       "      <td>-1e+09</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Tanzawako</td>\n",
       "      <td>-1.000000e+09</td>\n",
       "      <td>-1.000000e+09</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-1.000000e+09</td>\n",
       "      <td>-1e+09</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Odawara</td>\n",
       "      <td>8.200000e+00</td>\n",
       "      <td>-3.700000e+00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.200000e+00</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              city      max_temp      min_temp  rain          wind daylight  \\\n",
       "0         Yokohama  8.500000e+00  6.000000e-01   0.0  8.800000e+00      0.1   \n",
       "1          Hiyoshi -1.000000e+09 -1.000000e+09   0.0 -1.000000e+09   -1e+09   \n",
       "2            Ebina  7.200000e+00 -4.300000e+00   0.5  5.300000e+00      3.2   \n",
       "3            Miura  1.020000e+01 -5.000000e-01   0.5  7.300000e+00      1.2   \n",
       "4          Tsujido  8.300000e+00 -1.200000e+00   0.0  6.400000e+00      1.1   \n",
       "5  Sagamihara-Chuo -1.000000e+09 -1.000000e+09   0.0 -1.000000e+09   -1e+09   \n",
       "6        Hiratsuka -1.000000e+09 -1.000000e+09   1.0 -1.000000e+09   -1e+09   \n",
       "7         Sagamiko -1.000000e+09 -1.000000e+09   0.5 -1.000000e+09   -1e+09   \n",
       "8        Tanzawako -1.000000e+09 -1.000000e+09   2.5 -1.000000e+09   -1e+09   \n",
       "9          Odawara  8.200000e+00 -3.700000e+00   3.0  8.200000e+00      5.3   \n",
       "\n",
       "        date  \n",
       "0 2015-01-01  \n",
       "1 2015-01-01  \n",
       "2 2015-01-01  \n",
       "3 2015-01-01  \n",
       "4 2015-01-01  \n",
       "5 2015-01-01  \n",
       "6 2015-01-01  \n",
       "7 2015-01-01  \n",
       "8 2015-01-01  \n",
       "9 2015-01-01  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_df.to_csv('kanagawa_weather_20150101_20190904.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine('sqlite:///weather.sqlite')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "Base = declarative_base()\n",
    "\n",
    "class Weather(Base):\n",
    "    __tablename__ = 'weathers'\n",
    "    \n",
    "    id = Column(Integer, primary_key=True)\n",
    "    date = Column(DateTime, nullable=False)\n",
    "    city = Column(String(50), nullable=False)\n",
    "    max_temp = Column(Float)\n",
    "    min_temp = Column(Float)\n",
    "    wind = Column(Float)\n",
    "    rain = Column(Float)\n",
    "    daylight = Column(Float)\n",
    "    \n",
    "    __table_args__ = (UniqueConstraint('date', 'city'),)\n",
    "    \n",
    "    def __init__(self, **kwargs):\n",
    "        super(Weather, self).__init__(**kwargs)\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return f'<Weather(date={self.date}, city={self.city}, max_temp={self.max_temp}, min_temp={self.min_temp}, wind={self.wind}, daylight={self.daylight})>'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "if Weather.__table__.exists(engine):\n",
    "    Weather.__table__.drop(engine)\n",
    "Weather.__table__.create(engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "Session = sessionmaker(bind=engine)\n",
    "session = Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_df = cleaned_df.to_dict('index')\n",
    "try:\n",
    "    session.add_all([Weather(**rec) for rec in dict_df.values()])\n",
    "    session.commit()\n",
    "except IntegrityError:\n",
    "    session.rollback()\n",
    "    print('Duplicate record!')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Weather(date=2015-01-01 00:00:00, city=Yokohama, max_temp=8.5, min_temp=0.6, wind=8.8, daylight=0.1)>,\n",
       " <Weather(date=2015-01-02 00:00:00, city=Yokohama, max_temp=7.7, min_temp=-0.8, wind=4.9, daylight=5.9)>,\n",
       " <Weather(date=2015-01-03 00:00:00, city=Yokohama, max_temp=8.1, min_temp=0.4, wind=6.1, daylight=9.6)>,\n",
       " <Weather(date=2015-01-04 00:00:00, city=Yokohama, max_temp=9.3, min_temp=1.0, wind=3.0, daylight=6.7)>,\n",
       " <Weather(date=2015-01-05 00:00:00, city=Yokohama, max_temp=13.2, min_temp=3.0, wind=6.3, daylight=9.3)>,\n",
       " <Weather(date=2015-01-06 00:00:00, city=Yokohama, max_temp=16.6, min_temp=6.5, wind=11.2, daylight=1.4)>,\n",
       " <Weather(date=2015-01-07 00:00:00, city=Yokohama, max_temp=9.5, min_temp=3.7, wind=8.7, daylight=4.1)>,\n",
       " <Weather(date=2015-01-08 00:00:00, city=Yokohama, max_temp=11.3, min_temp=2.9, wind=7.1, daylight=9.5)>,\n",
       " <Weather(date=2015-01-09 00:00:00, city=Yokohama, max_temp=11.1, min_temp=3.5, wind=5.4, daylight=9.2)>,\n",
       " <Weather(date=2015-01-10 00:00:00, city=Yokohama, max_temp=11.5, min_temp=2.4, wind=3.9, daylight=9.6)>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.query(Weather).filter_by(city='Yokohama').limit(10).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_yoko = pd.read_sql(\"SELECT * FROM weathers WHERE city = 'Yokohama'\", con=engine, parse_dates='date', index_col='id')\n",
    "df_yoko = df_yoko.set_index('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "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>city</th>\n",
       "      <th>max_temp</th>\n",
       "      <th>min_temp</th>\n",
       "      <th>wind</th>\n",
       "      <th>rain</th>\n",
       "      <th>daylight</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>Yokohama</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>Yokohama</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>Yokohama</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>Yokohama</td>\n",
       "      <td>9.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>Yokohama</td>\n",
       "      <td>13.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                city  max_temp  min_temp  wind  rain  daylight\n",
       "date                                                          \n",
       "2015-01-01  Yokohama       8.5       0.6   8.8   0.0       0.1\n",
       "2015-01-02  Yokohama       7.7      -0.8   4.9   0.0       5.9\n",
       "2015-01-03  Yokohama       8.1       0.4   6.1   0.0       9.6\n",
       "2015-01-04  Yokohama       9.3       1.0   3.0   0.0       6.7\n",
       "2015-01-05  Yokohama      13.2       3.0   6.3   0.0       9.3"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_yoko.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c6cb410>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_2015 = df_yoko.loc[df_yoko.index.year == 2015, :]\n",
    "df_2015.resample('W').mean().plot(y='max_temp', figsize=(20, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b994d50>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_2015_7 = df_yoko.loc[(datetime(2015, 7, 1) <= df_yoko.index) & (df_yoko.index < datetime(2015, 8, 1)), :]\n",
    "df_2015_7.plot(y='max_temp', figsize=(20, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11caaab50>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_yoko[:'2018'].resample('Y').mean().plot(y='max_temp', figsize=(20, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "month_average = df_yoko['2015':'2019'].resample('M').mean()\n",
    "# smoothed_temp = unsmoothed_temp.rolling(window=90).mean()\n",
    "\n",
    "# compare_df = pd.DataFrame({'smoothed': smoothed_temp, 'unsmoothed': unsmoothed_temp})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15a17aa8dc8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "month_average[month_average.index.month == 7]['min_temp'].plot(kind='bar', figsize=(20, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15a18183e08>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "month_average[month_average.index.month == 7]['max_temp'].plot(kind='bar', figsize=(20, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>city</th>\n",
       "      <th>max_temp</th>\n",
       "      <th>min_temp</th>\n",
       "      <th>wind</th>\n",
       "      <th>rain</th>\n",
       "      <th>daylight</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2015-07-01</td>\n",
       "      <td>Yokohama</td>\n",
       "      <td>26.4</td>\n",
       "      <td>19.2</td>\n",
       "      <td>10.2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-07-02</td>\n",
       "      <td>Yokohama</td>\n",
       "      <td>24.4</td>\n",
       "      <td>21.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-07-03</td>\n",
       "      <td>Yokohama</td>\n",
       "      <td>24.4</td>\n",
       "      <td>20.5</td>\n",
       "      <td>6.4</td>\n",
       "      <td>103.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-07-04</td>\n",
       "      <td>Yokohama</td>\n",
       "      <td>25.3</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-07-05</td>\n",
       "      <td>Yokohama</td>\n",
       "      <td>21.5</td>\n",
       "      <td>19.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-07-06</td>\n",
       "      <td>Yokohama</td>\n",
       "      <td>20.4</td>\n",
       "      <td>19.5</td>\n",
       "      <td>3.6</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-07-07</td>\n",
       "      <td>Yokohama</td>\n",
       "      <td>23.4</td>\n",
       "      <td>19.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-07-08</td>\n",
       "      <td>Yokohama</td>\n",
       "      <td>25.1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>41.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-07-09</td>\n",
       "      <td>Yokohama</td>\n",
       "      <td>20.8</td>\n",
       "      <td>19.2</td>\n",
       "      <td>4.4</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-07-10</td>\n",
       "      <td>Yokohama</td>\n",
       "      <td>28.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>12.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                city  max_temp  min_temp  wind   rain  daylight\n",
       "date                                                           \n",
       "2015-07-01  Yokohama      26.4      19.2  10.2   17.0       0.0\n",
       "2015-07-02  Yokohama      24.4      21.5   5.4    7.0       1.3\n",
       "2015-07-03  Yokohama      24.4      20.5   6.4  103.0       0.1\n",
       "2015-07-04  Yokohama      25.3      21.0   5.4    5.0       0.1\n",
       "2015-07-05  Yokohama      21.5      19.1   3.0   21.5       0.0\n",
       "2015-07-06  Yokohama      20.4      19.5   3.6   34.0       0.0\n",
       "2015-07-07  Yokohama      23.4      19.5   3.2    2.5       0.0\n",
       "2015-07-08  Yokohama      25.1      20.0   3.7   41.5       0.0\n",
       "2015-07-09  Yokohama      20.8      19.2   4.4   22.5       0.0\n",
       "2015-07-10  Yokohama      28.0      18.6   4.4    0.5      12.2"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_yoko_july = df_yoko[df_yoko.index.month == 7]\n",
    "df_yoko_july.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15a18249ec8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.figure(figsize=(20, 8))\n",
    "sns.barplot(x=df_yoko_july.index.day, y='max_temp', hue=df_yoko_july.index.year, data=df_yoko_july)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2015-07-01    19.2\n",
       "2015-07-02    21.5\n",
       "2015-07-03    20.5\n",
       "2015-07-04    21.0\n",
       "2015-07-05    19.1\n",
       "2015-07-06    19.5\n",
       "2019-07-07    18.1\n",
       "2019-07-08    18.2\n",
       "2019-07-09    19.1\n",
       "2015-07-10    18.6\n",
       "2019-07-11    17.8\n",
       "2019-07-12    17.9\n",
       "2019-07-13    20.9\n",
       "2019-07-14    20.4\n",
       "2019-07-15    19.9\n",
       "2019-07-16    19.3\n",
       "2019-07-17    20.6\n",
       "2019-07-18    22.4\n",
       "2017-07-19    23.4\n",
       "2016-07-20    24.0\n",
       "2016-07-21    21.2\n",
       "2016-07-22    20.9\n",
       "2016-07-23    20.5\n",
       "2016-07-24    20.8\n",
       "2016-07-25    22.2\n",
       "2016-07-26    21.8\n",
       "2017-07-27    22.4\n",
       "2018-07-28    20.3\n",
       "2016-07-29    23.4\n",
       "2016-07-30    23.4\n",
       "2017-07-31    24.7\n",
       "Name: min_temp, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = df_yoko_july.groupby(df_yoko_july.index.day)['min_temp'].idxmin()\n",
    "min_temp_july = df_yoko_july.loc[idx]\n",
    "min_temp_july['min_temp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019    11\n",
       "2016     9\n",
       "2015     7\n",
       "2017     3\n",
       "2018     1\n",
       "Name: date, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_temp_july.index.year.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2018-07-01    30.8\n",
       "2018-07-02    31.2\n",
       "2016-07-03    33.3\n",
       "2016-07-04    32.4\n",
       "2017-07-05    30.6\n",
       "2017-07-06    30.0\n",
       "2016-07-07    34.5\n",
       "2017-07-08    32.5\n",
       "2017-07-09    31.4\n",
       "2018-07-10    31.9\n",
       "2018-07-11    33.0\n",
       "2017-07-12    31.2\n",
       "2018-07-13    33.1\n",
       "2018-07-14    35.3\n",
       "2018-07-15    34.1\n",
       "2017-07-16    33.1\n",
       "2017-07-17    33.3\n",
       "2018-07-18    33.6\n",
       "2018-07-19    33.6\n",
       "2018-07-20    33.4\n",
       "2018-07-21    33.2\n",
       "2018-07-22    34.7\n",
       "2018-07-23    36.9\n",
       "2018-07-24    34.8\n",
       "2015-07-25    33.2\n",
       "2015-07-26    35.0\n",
       "2015-07-27    33.5\n",
       "2015-07-28    32.8\n",
       "2015-07-29    32.9\n",
       "2015-07-30    33.6\n",
       "2015-07-31    34.6\n",
       "Name: max_temp, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = df_yoko_july.groupby(df_yoko_july.index.day)['max_temp'].idxmax()\n",
    "max_temp_july = df_yoko_july.loc[idx]\n",
    "max_temp_july['max_temp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018    14\n",
       "2015     7\n",
       "2017     7\n",
       "2016     3\n",
       "Name: date, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_temp_july.index.year.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15a17abcf88>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_yoko_july.pivot_table(values='min_temp', columns=df_yoko_july.index.year, index=df_yoko_july.index.day).plot(kind='box', figsize=(20, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c99cc10>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_yoko_july.pivot_table(values='max_temp', columns=df_yoko_july.index.year, index=df_yoko_july.index.day).plot(kind='box', figsize=(20, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15a17c79e88>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_yoko_july.pivot_table(values='min_temp', columns=df_yoko_july.index.year, index=df_yoko_july.index.day).plot( figsize=(20, 8))"
   ]
  }
 ],
 "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": 2
}