climate_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "# Import SQLAlchemy `automap` and other dependencies\n",
    "import sqlalchemy\n",
    "from sqlalchemy.ext.automap import automap_base\n",
    "from sqlalchemy.orm import Session\n",
    "from sqlalchemy import create_engine, inspect, Column, Integer, String, Float, ForeignKey, func\n",
    "import sqlite3\n",
    "\n",
    "from sqlalchemy.ext.declarative import declarative_base\n",
    "Base = declarative_base()\n",
    "\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from dateutil.parser import parse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Connect to the SQLite database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine(\"sqlite:///output/hawaii.sqlite\")\n",
    "\n",
    "#Create the database\n",
    "Base.metadata.create_all(engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reflect Database into ORM classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['measurement', 'station']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Base = automap_base()\n",
    "Base.prepare(engine, reflect=True)\n",
    "Base.classes.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Map Measurement class\n",
    "Measurement = Base.classes.measurement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Map Station class\n",
    "Station = Base.classes.station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a session\n",
    "session = Session(engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Climate Analysis and Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type in start date (Month-day-Year7-21-2018\n"
     ]
    }
   ],
   "source": [
    "start_date = input(\"type in start date (Month-day-Year\").lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type in end date (Month-day-Year7-28-2018\n"
     ]
    }
   ],
   "source": [
    "end_date = input(\"type in end date (Month-day-Year\").lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precipitation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(datetime.date(2017, 8, 23),)\n"
     ]
    }
   ],
   "source": [
    "# last date recorded\n",
    "for row in session.query(func.max(Measurement.date).label('maxdate')).all():\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " #### Therefore, last 12 months would be from '2016-08-23' to '2017-08-23'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " '('USC00519397', datetime.date(2016, 8, 23), 0.0)'\n",
      " '('USC00519397', datetime.date(2016, 9, 1), 0.0)'\n",
      " '('USC00519397', datetime.date(2016, 10, 1), 0.0)'\n",
      " '('USC00519397', datetime.date(2016, 11, 1), 0.0)'\n",
      " '('USC00519397', datetime.date(2016, 12, 1), 0.12)'\n",
      " '('USC00519397', datetime.date(2017, 1, 1), 0.0)'\n",
      " '('USC00519397', datetime.date(2017, 2, 1), 0.0)'\n",
      " '('USC00519397', datetime.date(2017, 3, 1), 1.19)'\n",
      " '('USC00519397', datetime.date(2017, 4, 1), 0.0)'\n",
      " '('USC00519397', datetime.date(2017, 5, 1), 0.26)'\n",
      " '('USC00519397', datetime.date(2017, 6, 1), 0.0)'\n",
      " '('USC00519397', datetime.date(2017, 7, 1), 0.02)'\n",
      " '('USC00519397', datetime.date(2017, 8, 1), 0.02)'\n"
     ]
    }
   ],
   "source": [
    "precipitation_data = session.query(Station, Measurement).filter(Station.station == Measurement.station).\\\n",
    "filter( Measurement.date >= dt.date(2016,8,23)).\\\n",
    "       filter(Measurement.date <=dt.date(2017,8,23)).\\\n",
    "       group_by(func.strftime(\"%Y-%m\", Measurement.date)).\\\n",
    "       order_by(Measurement.date).all()\n",
    "\n",
    "annual_precipitation = []\n",
    "\n",
    "for record in precipitation_data:\n",
    "    (s,m) = record\n",
    "    annual_precipitation.append([s.station,m.date, m.prcp])\n",
    "    print( f\" '{s.station,m.date, m.prcp}'\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>name</th>\n",
       "      <th>date</th>\n",
       "      <th>prcp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>2016-08-23</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>2016-11-01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>2017-08-01</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name        date  prcp\n",
       "0   USC00519397  2016-08-23  0.00\n",
       "1   USC00519397  2016-09-01  0.00\n",
       "2   USC00519397  2016-10-01  0.00\n",
       "3   USC00519397  2016-11-01  0.00\n",
       "4   USC00519397  2016-12-01  0.12\n",
       "5   USC00519397  2017-01-01  0.00\n",
       "6   USC00519397  2017-02-01  0.00\n",
       "7   USC00519397  2017-03-01  1.19\n",
       "8   USC00519397  2017-04-01  0.00\n",
       "9   USC00519397  2017-05-01  0.26\n",
       "10  USC00519397  2017-06-01  0.00\n",
       "11  USC00519397  2017-07-01  0.02\n",
       "12  USC00519397  2017-08-01  0.02"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(annual_precipitation, columns=['name', 'date','prcp'])\n",
    "df"
   ]
  },
  {
   "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": [
    "# Plot the Results in a Matplotlib bar chart\n",
    "df = pd.DataFrame(annual_precipitation, columns=['name', 'date','prcp'])\n",
    "df.set_index('date', inplace=True)\n",
    "df.plot.bar()\n",
    "plt.tight_layout()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>prcp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.123846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.329078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.190000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            prcp\n",
       "count  13.000000\n",
       "mean    0.123846\n",
       "std     0.329078\n",
       "min     0.000000\n",
       "25%     0.000000\n",
       "50%     0.000000\n",
       "75%     0.020000\n",
       "max     1.190000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Station Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of stations are: '9'\n"
     ]
    }
   ],
   "source": [
    "# total_number_of_stations\n",
    "total_number_of_stations = session.query(Station, Measurement).\\\n",
    "filter(Station.station == Measurement.station).\\\n",
    "group_by(Measurement.station).count()\n",
    "print(f\"The total number of stations are: '{total_number_of_stations}'\")"
   ]
  },
  {
   "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>name</th>\n",
       "      <th>#Obs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WAIHEE 837.5, HI US</td>\n",
       "      <td>2772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KANEOHE 838.1, HI US</td>\n",
       "      <td>2696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WAIKIKI 717.2, HI US</td>\n",
       "      <td>2685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WAIMANALO EXPERIMENTAL FARM, HI US</td>\n",
       "      <td>2572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MANOA LYON ARBO 785.2, HI US</td>\n",
       "      <td>2484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KUALOA RANCH HEADQUARTERS 886.9, HI US</td>\n",
       "      <td>1937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HONOLULU OBSERVATORY 702.2, HI US</td>\n",
       "      <td>1932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PEARL CITY, HI US</td>\n",
       "      <td>683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>UPPER WAHIAWA 874.3, HI US</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     name  #Obs\n",
       "0                     WAIHEE 837.5, HI US  2772\n",
       "1                    KANEOHE 838.1, HI US  2696\n",
       "2                    WAIKIKI 717.2, HI US  2685\n",
       "3      WAIMANALO EXPERIMENTAL FARM, HI US  2572\n",
       "4            MANOA LYON ARBO 785.2, HI US  2484\n",
       "5  KUALOA RANCH HEADQUARTERS 886.9, HI US  1937\n",
       "6       HONOLULU OBSERVATORY 702.2, HI US  1932\n",
       "7                       PEARL CITY, HI US   683\n",
       "8              UPPER WAHIAWA 874.3, HI US   342"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# most_active_stations\n",
    "Measurement_list = session.query(Station.name,func.count(Measurement.date).label('#Obs')).\\\n",
    "filter(Station.station == Measurement.station).\\\n",
    "group_by(Measurement.station).\\\n",
    "order_by(func.count(Measurement.tobs).desc()).all()\n",
    "\n",
    "\n",
    "# tests = [result for result in Measurement_list]\n",
    "most_active_stations_df = pd.DataFrame(Measurement_list)\n",
    "most_active_stations_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### WAIHEE had highest number of observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Therefore, last 12 months would be from '2016-08-23' to '2017-08-23'\n",
    "twelve_month_precipitation_data = session.query(Measurement.tobs).filter(Station.station == Measurement.station).\\\n",
    "    filter( Measurement.date >= dt.date(2016,8,23)).\\\n",
    "    filter(Measurement.date <=dt.date(2017,8,23)).\\\n",
    "    filter(Station.name == 'WAIHEE 837.5, HI US').all()\n",
    "    \n",
    "twelve_month_precipitation_data_list = [result for result in twelve_month_precipitation_data]\n",
    "twelve_month_precipitation_data_list\n",
    "\n",
    "sns.set(style=\"whitegrid\")\n",
    "f, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "sns.distplot(twelve_month_precipitation_data_list, bins=20, kde=False,label=\"Temperature Observations\");\n",
    "\n",
    "ax.legend(ncol=2, loc=\"upper right\", frameon=True);\n",
    "ax.set(title=\"WAIHEE Station Observations\", ylabel=\"Frequency\",xlabel=\"Temperature, F\");\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Temperature Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query Date:  strftime(:strftime_1, :strftime_2) - :strftime_3\n"
     ]
    }
   ],
   "source": [
    "def calc_temps(start_date, end_date):\n",
    "    return func.strftime(\"%Y-%m\", start_date)\n",
    "calc_temps(\"2016-03-4\",\"2016-03-4\")\n",
    "\n",
    "query_date = func.strftime(\"%Y-%m\", start_date) - dt.timedelta(days=365)\n",
    "print(\"Query Date: \", query_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(86.0, 53.0, 73.07489214840379)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#assumes format `%Y-%m-%d`\n",
    "def calc_temps(start_date, end_date):\n",
    "    result = session.query(func.max(Measurement.tobs),func.min(Measurement.tobs),func.avg(Measurement.tobs)).\\\n",
    "        filter(Measurement.date >= start_date).\\\n",
    "        filter(Measurement.date <= end_date).first()        \n",
    "    return result\n",
    "\n",
    "calc_temps(\"2011-3-4\",\"2016-7-4\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(81.0, 67.0, 74.48407643312102)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trip_stats = calc_temps(\"2011-06-15\",\"2011-07-4\")\n",
    "trip_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(87.0, 58.0, 73.67077922077922)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calc the prior year\n",
    "prior_year_trip_stats = calc_temps(\"2010-06-15\",\"2010-7-4\")\n",
    "prior_year_trip_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(6, 6))\n",
    "ax.bar(1,prior_year_trip_stats[2], yerr=prior_year_trip_stats[1]-prior_year_trip_stats[0], align='center',color='orange', ecolor='black');\n",
    "ax.set(title=\"Trip Average Temperature\", ylabel=\"Temperature, F\",xlabel=\"\");"
   ]
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}