Climate_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Climate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Python and SQLAlchemy again, basic climate analysis and data exploration will be completed within this notebook. Tools used will be ORM queries via SQLAlchemy, Matplotlib, and Pandas. \n",
    "\n",
    "Analysis will be performed on precipitation, temperature, and stations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import dependencies\n",
    "import datetime as dt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import sqlalchemy\n",
    "from sqlalchemy.ext.automap import automap_base\n",
    "from sqlalchemy.orm import Session\n",
    "from sqlalchemy import create_engine, func, inspect, desc\n",
    "from sqlalchemy.sql import label\n",
    "\n",
    "from flask import Flask, jsonify\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting up and reflecting the existing sqlite database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Database setup\n",
    "engine = create_engine(\"sqlite:///hawaii.sqlite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# reflect an existing database into a new model\n",
    "Base = automap_base()\n",
    "# reflect the tables\n",
    "Base.prepare(engine, reflect=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Save reference to the tables\n",
    "Measurement = Base.classes.measurements\n",
    "Stations = Base.classes.stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create our session (link) from Python to the DB\n",
    "session = Session(bind = engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exploring the database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['measurements', 'stations']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the table names for each table\n",
    "inspector = inspect(engine)\n",
    "inspector.get_table_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id INTEGER\n",
      "station TEXT\n",
      "date DATE\n",
      "precip FLOAT\n",
      "tobs FLOAT\n"
     ]
    }
   ],
   "source": [
    "# Get a list of column names and types - measurements\n",
    "columns = inspector.get_columns('measurements')\n",
    "for c in columns:\n",
    "    print(c['name'], c[\"type\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id INTEGER\n",
      "station TEXT\n",
      "name TEXT\n",
      "latitude FLOAT\n",
      "longitude FLOAT\n",
      "elevation FLOAT\n"
     ]
    }
   ],
   "source": [
    "# Get a list of column names and types - stations\n",
    "columns = inspector.get_columns('stations')\n",
    "for c in columns:\n",
    "    print(c['name'], c[\"type\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 'USC00519397', 'WAIKIKI 717.2, HI US', 21.2716, -157.8168, 3.0),\n",
       " (2, 'USC00513117', 'KANEOHE 838.1, HI US', 21.4234, -157.8015, 14.6),\n",
       " (3, 'USC00514830', 'KUALOA RANCH HEADQUARTERS 886.9, HI US', 21.5213, -157.8374, 7.0),\n",
       " (4, 'USC00517948', 'PEARL CITY, HI US', 21.3934, -157.9751, 11.9),\n",
       " (5, 'USC00518838', 'UPPER WAHIAWA 874.3, HI US', 21.4992, -158.0111, 306.6)]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engine.execute('SELECT * FROM stations LIMIT 5').fetchall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 'USC00519397', '2010-01-01', None, 65.0),\n",
       " (2, 'USC00519397', '2010-01-02', None, 63.0),\n",
       " (3, 'USC00519397', '2010-01-03', None, 74.0),\n",
       " (4, 'USC00519397', '2010-01-04', None, 76.0),\n",
       " (5, 'USC00519397', '2010-01-07', None, 70.0)]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engine.execute('SELECT * FROM measurements LIMIT 5').fetchall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Climate Analysis: precipitation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Select prcp & date for the last 12 months\n",
    "results = session.query(Measurement.date, Measurement.precip).filter(Measurement.date >= '2017-01-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            prcp\n",
      "2017-01-01  None\n",
      "2017-01-02  None\n",
      "2017-01-03  None\n",
      "2017-01-04  None\n",
      "2017-01-05  None\n"
     ]
    }
   ],
   "source": [
    "# create empty dict to populate\n",
    "data = {'date': [], 'precip': []}\n",
    "\n",
    "# populate dict with rows from results\n",
    "for row in results:\n",
    "    data['date'].append(row.date)\n",
    "    data['precip'].append(row.precip)\n",
    "\n",
    "# create pandas df from dict\n",
    "precip17 = pd.DataFrame(data['precip'], columns = ['prcp'], index = data['date'])\n",
    "print(precip17.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prcp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            prcp\n",
       "2017-01-01  None\n",
       "2017-01-02  None\n",
       "2017-01-03  None\n",
       "2017-01-04  None\n",
       "2017-01-05  None"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precip17.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Plot the results using the DataFrame `plot` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use Pandas to print the summary statistics for the precipitation data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Climate Analysis: temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(64.0, 72.0, 78.0)]\n"
     ]
    }
   ],
   "source": [
    "# Write a function called `calc_temps` that will accept start date and end date in the format '%Y-%m-%d' \n",
    "# and return the minimum, average, and maximum temperatures for that range of dates\n",
    "\n",
    "#Use the `calc_temps` function to calculate the min, avg, and max temperatures for your trip using the \n",
    "#matching dates from the previous year (i.e. use \"2017-01-01\" if your trip start date was \"2018-01-01\")\n",
    "\n",
    "def calc_temps(start_date, end_date):\n",
    "    \"\"\"TMIN, TAVG, and TMAX for a list of dates.\n",
    "    \n",
    "    Args:\n",
    "        start_date (string): A date string in the format %Y-%m-%d\n",
    "        end_date (string): A date string in the format %Y-%m-%d\n",
    "        \n",
    "    Returns:\n",
    "        TMIN, TAVE, and TMAX\n",
    "    \"\"\"\n",
    "    \n",
    "    return session.query(func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)).\\\n",
    "        filter(Measurement.date >= start_date).filter(Measurement.date <= end_date).all()\n",
    "print(calc_temps('2017-02-28', '2017-03-05'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Plot the min, avg, and max temperature from your previous query as a bar chart.\n",
    "# Use the average temperature as the bar height.\n",
    "# Use the peak-to-peak (tmax-tmin) value as the y error bar (yerr)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "temperatures = (calc_temps('2017-02-28', '2017-03-05'))\n",
    "labels = ['TMIN', 'TAVE', 'TMAX']\n",
    "df = pd.DataFrame.from_records(temperatures, columns=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2IhbVoj4zsyLqcgw0fRTIUcBC4Fhgfvu8NGyXpO1mZqVVkx5olqQBJAMzXxsR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c3eaf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "temp_chart = df[['TAVE']].plot(kind='bar', title =\"Temperatures in Hawaii\", figsize=(5, 7), legend=True, fontsize=12, grid=True, color='lightblue')\n",
    "temp_chart.set_xlabel(\"Temprature Avg\", fontsize=12)\n",
    "temp_chart.set_ylabel(\"Temperatures in Farenheit\", fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Climate Analysis: stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are (9,) stations.\n"
     ]
    }
   ],
   "source": [
    "# Total number of stations\n",
    "stations_results = session.query(func.count(Stations.station)).all()\n",
    "print(\"There are \" + str(stations_results[0]) + \" stations.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('USC00511918', 1932)\n",
      "('USC00513117', 2696)\n",
      "('USC00514830', 1937)\n",
      "('USC00516128', 2484)\n",
      "('USC00517948', 683)\n",
      "('USC00518838', 342)\n",
      "('USC00519281', 2772)\n",
      "('USC00519397', 2685)\n",
      "('USC00519523', 2572)\n"
     ]
    }
   ],
   "source": [
    "# Most active stations\n",
    "active_results = session.query(Measurement.station,\n",
    "    label('date', func.count(Measurement.date))).group_by(Measurement.station).all()\n",
    "for result in active_results:\n",
    "    print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('USC00519281', 2772)\n",
      "('USC00513117', 2696)\n",
      "('USC00519397', 2685)\n",
      "('USC00519523', 2572)\n",
      "('USC00516128', 2484)\n",
      "('USC00514830', 1937)\n",
      "('USC00511918', 1932)\n",
      "('USC00517948', 683)\n",
      "('USC00518838', 342)\n"
     ]
    }
   ],
   "source": [
    "### List stations and obs counts in descending order\n",
    "desc_order = session.query(Measurement.station, func.count(Measurement.tobs)).group_by(Measurement.station).order_by(func.count(Measurement.tobs).desc())\n",
    "for result in desc_order:\n",
    "    print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('USC00519281', 2772)\n"
     ]
    }
   ],
   "source": [
    "### Which station has highest number of observations\n",
    "obsv_num = session.query(Measurement.station, func.count(Measurement.tobs)).group_by(Measurement.station).order_by(func.count(Measurement.tobs).desc()).first()\n",
    "print(obsv_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('USC00519397', datetime.date(2017, 1, 7), 66.0),\n",
       " ('USC00513117', datetime.date(2017, 1, 7), 72.0),\n",
       " ('USC00514830', datetime.date(2017, 1, 7), 71.0),\n",
       " ('USC00519523', datetime.date(2017, 1, 7), 72.0),\n",
       " ('USC00519281', datetime.date(2017, 1, 7), 70.0),\n",
       " ('USC00516128', datetime.date(2017, 1, 7), 70.0),\n",
       " ('USC00519397', datetime.date(2017, 1, 8), 60.0),\n",
       " ('USC00513117', datetime.date(2017, 1, 8), 62.0),\n",
       " ('USC00514830', datetime.date(2017, 1, 8), 74.0),\n",
       " ('USC00519523', datetime.date(2017, 1, 8), 74.0),\n",
       " ('USC00519281', datetime.date(2017, 1, 8), 70.0),\n",
       " ('USC00516128', datetime.date(2017, 1, 8), 66.0),\n",
       " ('USC00519397', datetime.date(2017, 1, 9), 64.0),\n",
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       " ('USC00519397', datetime.date(2017, 6, 20), 78.0),\n",
       " ('USC00513117', datetime.date(2017, 6, 20), 75.0),\n",
       " ('USC00514830', datetime.date(2017, 6, 20), 79.0),\n",
       " ('USC00519281', datetime.date(2017, 6, 20), 72.0),\n",
       " ('USC00516128', datetime.date(2017, 6, 20), 74.0),\n",
       " ('USC00519397', datetime.date(2017, 6, 21), 79.0),\n",
       " ('USC00513117', datetime.date(2017, 6, 21), 82.0),\n",
       " ('USC00519281', datetime.date(2017, 6, 21), 74.0),\n",
       " ('USC00516128', datetime.date(2017, 6, 21), 75.0),\n",
       " ('USC00519397', datetime.date(2017, 6, 22), 80.0),\n",
       " ('USC00513117', datetime.date(2017, 6, 22), 77.0),\n",
       " ('USC00517948', datetime.date(2017, 6, 22), 81.0),\n",
       " ('USC00519523', datetime.date(2017, 6, 22), 80.0),\n",
       " ('USC00519281', datetime.date(2017, 6, 22), 72.0),\n",
       " ('USC00516128', datetime.date(2017, 6, 22), 76.0),\n",
       " ('USC00519397', datetime.date(2017, 6, 23), 78.0),\n",
       " ('USC00513117', datetime.date(2017, 6, 23), 76.0),\n",
       " ('USC00517948', datetime.date(2017, 6, 23), 76.0),\n",
       " ('USC00519523', datetime.date(2017, 6, 23), 79.0),\n",
       " ('USC00519281', datetime.date(2017, 6, 23), 72.0),\n",
       " ('USC00516128', datetime.date(2017, 6, 23), 71.0),\n",
       " ('USC00519397', datetime.date(2017, 6, 24), 80.0),\n",
       " ('USC00513117', datetime.date(2017, 6, 24), 74.0),\n",
       " ('USC00519523', datetime.date(2017, 6, 24), 82.0),\n",
       " ('USC00519281', datetime.date(2017, 6, 24), 77.0),\n",
       " ('USC00516128', datetime.date(2017, 6, 24), 73.0),\n",
       " ('USC00519397', datetime.date(2017, 6, 25), 80.0),\n",
       " ('USC00513117', datetime.date(2017, 6, 25), 73.0),\n",
       " ('USC00519523', datetime.date(2017, 6, 25), 80.0),\n",
       " ('USC00519281', datetime.date(2017, 6, 25), 71.0),\n",
       " ('USC00516128', datetime.date(2017, 6, 25), 79.0),\n",
       " ('USC00519397', datetime.date(2017, 6, 26), 81.0),\n",
       " ('USC00513117', datetime.date(2017, 6, 26), 77.0),\n",
       " ('USC00519523', datetime.date(2017, 6, 26), 81.0),\n",
       " ('USC00519281', datetime.date(2017, 6, 26), 73.0),\n",
       " ('USC00516128', datetime.date(2017, 6, 26), 79.0),\n",
       " ('USC00519397', datetime.date(2017, 6, 27), 80.0),\n",
       " ('USC00513117', datetime.date(2017, 6, 27), 77.0),\n",
       " ('USC00517948', datetime.date(2017, 6, 27), 80.0),\n",
       " ('USC00519523', datetime.date(2017, 6, 27), 81.0),\n",
       " ('USC00519281', datetime.date(2017, 6, 27), 76.0),\n",
       " ('USC00516128', datetime.date(2017, 6, 27), 74.0),\n",
       " ('USC00519397', datetime.date(2017, 6, 28), 79.0),\n",
       " ('USC00513117', datetime.date(2017, 6, 28), 77.0),\n",
       " ('USC00517948', datetime.date(2017, 6, 28), 80.0),\n",
       " ('USC00519523', datetime.date(2017, 6, 28), 79.0),\n",
       " ('USC00519281', datetime.date(2017, 6, 28), 77.0),\n",
       " ('USC00516128', datetime.date(2017, 6, 28), 74.0),\n",
       " ('USC00519397', datetime.date(2017, 6, 29), 79.0),\n",
       " ('USC00513117', datetime.date(2017, 6, 29), 76.0),\n",
       " ('USC00517948', datetime.date(2017, 6, 29), 79.0),\n",
       " ('USC00519523', datetime.date(2017, 6, 29), 78.0),\n",
       " ('USC00519281', datetime.date(2017, 6, 29), 76.0),\n",
       " ('USC00516128', datetime.date(2017, 6, 29), 76.0),\n",
       " ('USC00519397', datetime.date(2017, 6, 30), 75.0),\n",
       " ('USC00513117', datetime.date(2017, 6, 30), 74.0),\n",
       " ('USC00514830', datetime.date(2017, 6, 30), 81.0),\n",
       " ('USC00517948', datetime.date(2017, 6, 30), 74.0),\n",
       " ('USC00519523', datetime.date(2017, 6, 30), 75.0),\n",
       " ('USC00519281', datetime.date(2017, 6, 30), 76.0),\n",
       " ('USC00516128', datetime.date(2017, 6, 30), 75.0),\n",
       " ('USC00519397', datetime.date(2017, 7, 1), 80.0),\n",
       " ('USC00513117', datetime.date(2017, 7, 1), 76.0),\n",
       " ('USC00519281', datetime.date(2017, 7, 1), 79.0),\n",
       " ('USC00516128', datetime.date(2017, 7, 1), 74.0),\n",
       " ('USC00519397', datetime.date(2017, 7, 2), 80.0),\n",
       " ('USC00513117', datetime.date(2017, 7, 2), 77.0),\n",
       " ('USC00519281', datetime.date(2017, 7, 2), 81.0),\n",
       " ('USC00516128', datetime.date(2017, 7, 2), 78.0),\n",
       " ('USC00519397', datetime.date(2017, 7, 3), 78.0),\n",
       " ('USC00513117', datetime.date(2017, 7, 3), 77.0),\n",
       " ('USC00519523', datetime.date(2017, 7, 3), 81.0),\n",
       " ('USC00519281', datetime.date(2017, 7, 3), 76.0),\n",
       " ('USC00516128', datetime.date(2017, 7, 3), 77.0),\n",
       " ('USC00519397', datetime.date(2017, 7, 4), 80.0),\n",
       " ('USC00513117', datetime.date(2017, 7, 4), 77.0),\n",
       " ('USC00519281', datetime.date(2017, 7, 4), 78.0),\n",
       " ('USC00516128', datetime.date(2017, 7, 4), 77.0),\n",
       " ('USC00519397', datetime.date(2017, 7, 5), 80.0),\n",
       " ('USC00513117', datetime.date(2017, 7, 5), 77.0),\n",
       " ('USC00519281', datetime.date(2017, 7, 5), 77.0),\n",
       " ('USC00516128', datetime.date(2017, 7, 5), 75.0),\n",
       " ('USC00519397', datetime.date(2017, 7, 6), 81.0),\n",
       " ('USC00513117', datetime.date(2017, 7, 6), 76.0),\n",
       " ...]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retrieve last 12 months of temp obs data (tobs)\n",
    "temps = session.query(Measurement.station, Measurement.date, Measurement.tobs).\\\n",
    "    filter(Measurement.date > '2017-01-06').\\\n",
    "    order_by(Measurement.date).all()\n",
    "temps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('USC00519397', 81.0),\n",
       " ('USC00519281', 79.0),\n",
       " ('USC00513117', 77.0),\n",
       " ('USC00516128', 76.0),\n",
       " ('USC00519523', 82.0),\n",
       " ('USC00514830', 82.0),\n",
       " ('USC00517948', 80.0)]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Filter by station with highest number of observations\n",
    "highest_obs = session.query(Measurement.station, Measurement.tobs).\\\n",
    "    filter(Measurement.date > '2017-01-01').\\\n",
    "    group_by(Measurement.station).\\\n",
    "    order_by(func.count(Measurement.tobs).desc()).all()\n",
    "highest_obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Station</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>USC00519397</th>\n",
       "      <td>2017-01-07</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USC00513117</th>\n",
       "      <td>2017-01-07</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USC00514830</th>\n",
       "      <td>2017-01-07</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USC00519523</th>\n",
       "      <td>2017-01-07</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USC00519281</th>\n",
       "      <td>2017-01-07</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   date  temp\n",
       "Station                      \n",
       "USC00519397  2017-01-07  66.0\n",
       "USC00513117  2017-01-07  72.0\n",
       "USC00514830  2017-01-07  71.0\n",
       "USC00519523  2017-01-07  72.0\n",
       "USC00519281  2017-01-07  70.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Plot results as histogram with bins=12\n",
    "df = pd.DataFrame(temps, columns=['Station', 'date', 'temp'])\n",
    "df.set_index('Station', inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1yZ2t871VdUSG931MhnPjbsjwvi8Y69+p0aDuviHJb1XVqzJc3P3HM4SG/8pwPcg/TfL8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c467940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist_plot = df['temp'].hist(bins=12, figsize=(15,10))\n",
    "hist_plot.set_title('Temperature Observations', fontsize=20)\n",
    "hist_plot.set_ylabel('Frequency', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Flask Setup\n",
    "app = Flask(__name__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "@app.route(\"/\")\n",
    "def welcome1():\n",
    "    \"\"\"Listing of the available API routes\"\"\"\n",
    "    return(\n",
    "        f\"Available Routes: <br/>\"\n",
    "        f\"/api/v1.0/precipitation <br/>\"\n",
    "        f\"/api/v1.0/stations <br/>\"\n",
    "        f\"/api/v1.0/tobs <br/>\"\n",
    "        f\"/api/v1.0/<start><br/>\"\n",
    "        f\"/api/v1.0/<start>/<end>\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "@app.route(\"/api/v1.0/precipitation\")\n",
    "def precipitation():\n",
    "    \"\"\"Query for the dates and temps for the last year\"\"\"\n",
    "    \n",
    "    # Query the database for dates and tobs\n",
    "    prcp_results = session.query(Measurement.date, Measurement.tobs).\\\n",
    "    filter(Measurement.date > '2017-01-01').all()\n",
    "    \n",
    "    # Create dictionary from row data and append to the list 'all_prcp'\n",
    "    all_prcp = []\n",
    "    for prcp in prcp_results:\n",
    "        prcp_dict = {}\n",
    "        prcp_dict[\"Date\"] = Measurement.date\n",
    "        prcp_dict[\"TOBS\"] = Measurement.tobs\n",
    "        all_prcp.append(prcp_dict)\n",
    "    return jsonify(all_prcp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "@app.route(\"/api/v1.0/stations\")\n",
    "def stations():\n",
    "    \"\"\"Returns a list of stations from the dataset in JSON format\"\"\"\n",
    "    \n",
    "    station_results = session.query(Station.station).all()\n",
    "    \n",
    "    # Convert the list of tuples into a normal list:\n",
    "    all_stations = list(np.ravel(station_results))\n",
    "    \n",
    "    return jsonify(all_stations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "@app.route(\"/api/v1.0/tobs\")\n",
    "def tobs():\n",
    "    \"\"\"Returns a list of temperature observations from the last year in JSON format \"\"\"\n",
    "    \n",
    "    #Query database for tobs for last year\n",
    "    tobs_results = session.query(Measurement.tobs).filter(Measurement.date > '2017-01-01').all()\n",
    "    \n",
    "    # Convert the list of tuples into normal list:\n",
    "    all_tobs = list(np.ravel(tobs_results))\n",
    "    \n",
    "    return jsonify(all_tobs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)\n",
      " * Restarting with stat\n"
     ]
    },
    {
     "ename": "SystemExit",
     "evalue": "1",
     "output_type": "error",
     "traceback": [
      "An exception has occurred, use %tb to see the full traceback.\n",
      "\u001b[0;31mSystemExit\u001b[0m\u001b[0;31m:\u001b[0m 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/arwen/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2870: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n",
      "  warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n"
     ]
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "    app.run(debug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
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 },
 "nbformat": 4,
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}