SurfsUp_Challenge.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies\n",
    "import numpy as np\n",
    "\n",
    "# Python SQL toolkit and Object Relational Mapper\n",
    "import sqlalchemy\n",
    "from sqlalchemy.ext.automap import automap_base\n",
    "from sqlalchemy.orm import Session\n",
    "from sqlalchemy import create_engine, func\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "from matplotlib import style\n",
    "style.use('fivethirtyeight')\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine(\"sqlite:///hawaii.sqlite\")\n",
    "\n",
    "# reflect an existing database into a new model\n",
    "Base = automap_base()\n",
    "# reflect the tables\n",
    "Base.prepare(engine, reflect=True)\n",
    "\n",
    "# Save references to each table\n",
    "Measurement = Base.classes.measurement\n",
    "Station = Base.classes.station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create our session (link) from Python to the DB\n",
    "session = Session(engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## D1: Determine the Summary Statistics for June"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " ('2012-06-21', 74.0),\n",
       " ('2012-06-22', 78.0),\n",
       " ('2012-06-23', 76.0),\n",
       " ('2012-06-24', 77.0),\n",
       " ('2012-06-25', 72.0),\n",
       " ('2012-06-26', 72.0),\n",
       " ('2012-06-27', 73.0),\n",
       " ('2012-06-28', 75.0),\n",
       " ('2012-06-29', 79.0),\n",
       " ('2012-06-30', 81.0),\n",
       " ('2013-06-01', 76.0),\n",
       " ('2013-06-02', 76.0),\n",
       " ('2013-06-03', 77.0),\n",
       " ('2013-06-04', 78.0),\n",
       " ('2013-06-05', 79.0),\n",
       " ('2013-06-06', 79.0),\n",
       " ('2013-06-07', 75.0),\n",
       " ('2013-06-08', 79.0),\n",
       " ('2013-06-09', 79.0),\n",
       " ('2013-06-10', 75.0),\n",
       " ('2013-06-11', 77.0),\n",
       " ('2013-06-12', 81.0),\n",
       " ('2013-06-13', 76.0),\n",
       " ('2013-06-14', 75.0),\n",
       " ('2013-06-15', 78.0),\n",
       " ('2013-06-16', 81.0),\n",
       " ('2013-06-17', 76.0),\n",
       " ('2013-06-18', 76.0),\n",
       " ('2013-06-19', 76.0),\n",
       " ('2013-06-20', 72.0),\n",
       " ('2013-06-21', 73.0),\n",
       " ('2013-06-22', 74.0),\n",
       " ('2013-06-23', 74.0),\n",
       " ('2013-06-24', 75.0),\n",
       " ('2013-06-25', 73.0),\n",
       " ('2013-06-26', 73.0),\n",
       " ('2013-06-27', 76.0),\n",
       " ('2013-06-29', 73.0),\n",
       " ('2013-06-30', 79.0),\n",
       " ('2014-06-01', 78.0),\n",
       " ('2014-06-02', 76.0),\n",
       " ('2014-06-04', 76.0),\n",
       " ('2014-06-05', 76.0),\n",
       " ('2014-06-06', 77.0),\n",
       " ('2014-06-07', 79.0),\n",
       " ('2014-06-08', 80.0),\n",
       " ('2014-06-09', 78.0),\n",
       " ('2014-06-10', 76.0),\n",
       " ('2014-06-11', 78.0),\n",
       " ('2014-06-12', 78.0),\n",
       " ('2014-06-13', 77.0),\n",
       " ('2014-06-14', 79.0),\n",
       " ('2014-06-15', 79.0),\n",
       " ('2014-06-16', 75.0),\n",
       " ('2014-06-18', 76.0),\n",
       " ('2014-06-19', 77.0),\n",
       " ('2014-06-20', 74.0),\n",
       " ('2014-06-21', 77.0),\n",
       " ('2014-06-22', 76.0),\n",
       " ('2014-06-23', 76.0),\n",
       " ('2014-06-24', 75.0),\n",
       " ('2014-06-25', 74.0),\n",
       " ('2014-06-26', 79.0),\n",
       " ('2014-06-28', 76.0),\n",
       " ('2014-06-29', 74.0),\n",
       " ('2014-06-30', 76.0),\n",
       " ('2015-06-01', 77.0),\n",
       " ('2015-06-02', 78.0),\n",
       " ('2015-06-03', 77.0),\n",
       " ('2015-06-04', 75.0),\n",
       " ('2015-06-05', 75.0),\n",
       " ('2015-06-06', 80.0),\n",
       " ('2015-06-07', 77.0),\n",
       " ('2015-06-08', 77.0),\n",
       " ('2015-06-09', 77.0),\n",
       " ('2015-06-10', 80.0),\n",
       " ('2015-06-11', 76.0),\n",
       " ...]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Import the sqlalchemy extract function.\n",
    "from sqlalchemy import extract\n",
    "\n",
    "# 2. Write a query that filters the Measurement table to retrieve the temperatures for the month of June. \n",
    "session.query(Measurement.date, Measurement.tobs).filter(extract('month',Measurement.date)==6).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  3. Convert the June temperatures to a list.\n",
    "results = session.query(Measurement.tobs).filter(extract('month', Measurement.date) == 6).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>78.0</td>\n",
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       "    <tr>\n",
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       "      <td>79.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1697</th>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1698</th>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1700 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0\n",
       "0     78.0\n",
       "1     76.0\n",
       "2     78.0\n",
       "3     76.0\n",
       "4     77.0\n",
       "...    ...\n",
       "1695  79.0\n",
       "1696  74.0\n",
       "1697  74.0\n",
       "1698  76.0\n",
       "1699  75.0\n",
       "\n",
       "[1700 rows x 1 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4. Create a DataFrame from the list of temperatures for the month of June. \n",
    "df = pd.DataFrame(results)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>74.944118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.257417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>64.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>77.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>85.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0\n",
       "count  1700.000000\n",
       "mean     74.944118\n",
       "std       3.257417\n",
       "min      64.000000\n",
       "25%      73.000000\n",
       "50%      75.000000\n",
       "75%      77.000000\n",
       "max      85.000000"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5. Calculate and print out the summary statistics for the June temperature DataFrame.\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1ef5ec0af88>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.hist(bins=20)\n",
    "plt.tight_layout()\n",
    "plt.title(\"June\")\n",
    "plt.legend('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## D2: Determine the Summary Statistics for December"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('2010-12-01', 76.0),\n",
       " ('2010-12-03', 74.0),\n",
       " ('2010-12-04', 74.0),\n",
       " ('2010-12-06', 64.0),\n",
       " ('2010-12-07', 64.0),\n",
       " ('2010-12-08', 67.0),\n",
       " ('2010-12-09', 77.0),\n",
       " ('2010-12-10', 66.0),\n",
       " ('2010-12-11', 69.0),\n",
       " ('2010-12-12', 68.0),\n",
       " ('2010-12-13', 68.0),\n",
       " ('2010-12-14', 71.0),\n",
       " ('2010-12-15', 74.0),\n",
       " ('2010-12-16', 67.0),\n",
       " ('2010-12-17', 66.0),\n",
       " ('2010-12-18', 69.0),\n",
       " ('2010-12-19', 71.0),\n",
       " ('2010-12-23', 70.0),\n",
       " ('2010-12-24', 70.0),\n",
       " ('2010-12-26', 74.0),\n",
       " ('2010-12-27', 74.0),\n",
       " ('2010-12-28', 71.0),\n",
       " ('2010-12-29', 75.0),\n",
       " ('2010-12-30', 75.0),\n",
       " ('2010-12-31', 72.0),\n",
       " ('2011-12-01', 69.0),\n",
       " ('2011-12-02', 68.0),\n",
       " ('2011-12-03', 73.0),\n",
       " ('2011-12-04', 74.0),\n",
       " ('2011-12-05', 73.0),\n",
       " ('2011-12-06', 73.0),\n",
       " ('2011-12-07', 73.0),\n",
       " ('2011-12-08', 73.0),\n",
       " ('2011-12-09', 71.0),\n",
       " ('2011-12-10', 73.0),\n",
       " ('2011-12-11', 73.0),\n",
       " ('2011-12-12', 77.0),\n",
       " ('2011-12-13', 72.0),\n",
       " ('2011-12-14', 71.0),\n",
       " ('2011-12-15', 73.0),\n",
       " ('2011-12-16', 70.0),\n",
       " ('2011-12-17', 73.0),\n",
       " ('2011-12-18', 69.0),\n",
       " ('2011-12-19', 74.0),\n",
       " ('2011-12-20', 72.0),\n",
       " ('2011-12-21', 71.0),\n",
       " ('2011-12-22', 72.0),\n",
       " ('2011-12-23', 72.0),\n",
       " ('2011-12-24', 70.0),\n",
       " ('2011-12-25', 73.0),\n",
       " ('2011-12-26', 73.0),\n",
       " ('2011-12-27', 72.0),\n",
       " ('2011-12-28', 73.0),\n",
       " ('2011-12-29', 73.0),\n",
       " ('2011-12-30', 72.0),\n",
       " ('2011-12-31', 63.0),\n",
       " ('2012-12-01', 65.0),\n",
       " ('2012-12-02', 71.0),\n",
       " ('2012-12-03', 76.0),\n",
       " ('2012-12-04', 70.0),\n",
       " ('2012-12-05', 76.0),\n",
       " ('2012-12-06', 69.0),\n",
       " ('2012-12-07', 69.0),\n",
       " ('2012-12-08', 69.0),\n",
       " ('2012-12-09', 74.0),\n",
       " ('2012-12-10', 76.0),\n",
       " ('2012-12-11', 74.0),\n",
       " ('2012-12-12', 73.0),\n",
       " ('2012-12-13', 73.0),\n",
       " ('2012-12-14', 74.0),\n",
       " ('2012-12-15', 73.0),\n",
       " ('2012-12-16', 73.0),\n",
       " ('2012-12-17', 73.0),\n",
       " ('2012-12-18', 74.0),\n",
       " ('2012-12-19', 74.0),\n",
       " ('2012-12-20', 71.0),\n",
       " ('2012-12-21', 73.0),\n",
       " ('2012-12-22', 72.0),\n",
       " ('2012-12-23', 64.0),\n",
       " ('2012-12-24', 66.0),\n",
       " ('2012-12-26', 73.0),\n",
       " ('2012-12-27', 72.0),\n",
       " ('2012-12-28', 65.0),\n",
       " ('2012-12-29', 64.0),\n",
       " ('2012-12-30', 69.0),\n",
       " ('2012-12-31', 68.0),\n",
       " ('2013-12-01', 77.0),\n",
       " ('2013-12-02', 71.0),\n",
       " ('2013-12-03', 71.0),\n",
       " ('2013-12-04', 69.0),\n",
       " ('2013-12-05', 68.0),\n",
       " ('2013-12-06', 68.0),\n",
       " ('2013-12-07', 69.0),\n",
       " ('2013-12-08', 70.0),\n",
       " ('2013-12-09', 72.0),\n",
       " ('2013-12-10', 76.0),\n",
       " ('2013-12-11', 75.0),\n",
       " ('2013-12-12', 69.0),\n",
       " ('2013-12-13', 71.0),\n",
       " ('2013-12-14', 71.0),\n",
       " ('2013-12-15', 71.0),\n",
       " ('2013-12-16', 68.0),\n",
       " ('2013-12-17', 65.0),\n",
       " ('2013-12-18', 70.0),\n",
       " ('2013-12-19', 69.0),\n",
       " ('2013-12-20', 69.0),\n",
       " ('2013-12-21', 71.0),\n",
       " ('2013-12-22', 67.0),\n",
       " ('2013-12-23', 73.0),\n",
       " ('2013-12-24', 74.0),\n",
       " ('2013-12-25', 73.0),\n",
       " ('2013-12-26', 67.0),\n",
       " ('2013-12-27', 74.0),\n",
       " ('2013-12-28', 75.0),\n",
       " ('2013-12-29', 75.0),\n",
       " ('2013-12-30', 74.0),\n",
       " ('2013-12-31', 67.0),\n",
       " ('2014-12-01', 73.0),\n",
       " ('2014-12-02', 73.0),\n",
       " ('2014-12-03', 71.0),\n",
       " ('2014-12-04', 72.0),\n",
       " ('2014-12-05', 71.0),\n",
       " ('2014-12-06', 68.0),\n",
       " ('2014-12-07', 65.0),\n",
       " ('2014-12-08', 69.0),\n",
       " ('2014-12-09', 70.0),\n",
       " ('2014-12-10', 70.0),\n",
       " ('2014-12-11', 74.0),\n",
       " ('2014-12-12', 71.0),\n",
       " ('2014-12-13', 74.0),\n",
       " ('2014-12-14', 73.0),\n",
       " ('2014-12-15', 72.0),\n",
       " ('2014-12-16', 74.0),\n",
       " ('2014-12-17', 72.0),\n",
       " ('2014-12-18', 74.0),\n",
       " ('2014-12-19', 75.0),\n",
       " ('2014-12-20', 74.0),\n",
       " ('2014-12-21', 70.0),\n",
       " ('2014-12-22', 74.0),\n",
       " ('2014-12-23', 70.0),\n",
       " ('2014-12-24', 66.0),\n",
       " ('2014-12-25', 61.0),\n",
       " ('2014-12-26', 63.0),\n",
       " ('2014-12-27', 63.0),\n",
       " ('2014-12-28', 63.0),\n",
       " ('2014-12-29', 63.0),\n",
       " ('2014-12-30', 67.0),\n",
       " ('2014-12-31', 56.0),\n",
       " ('2015-12-01', 75.0),\n",
       " ('2015-12-02', 77.0),\n",
       " ('2015-12-03', 75.0),\n",
       " ('2015-12-04', 75.0),\n",
       " ('2015-12-05', 75.0),\n",
       " ('2015-12-06', 75.0),\n",
       " ('2015-12-07', 71.0),\n",
       " ('2015-12-08', 70.0),\n",
       " ('2015-12-09', 74.0),\n",
       " ('2015-12-10', 74.0),\n",
       " ('2015-12-11', 75.0),\n",
       " ('2015-12-12', 72.0),\n",
       " ('2015-12-13', 70.0),\n",
       " ('2015-12-14', 66.0),\n",
       " ('2015-12-15', 74.0),\n",
       " ('2015-12-16', 75.0),\n",
       " ('2015-12-17', 74.0),\n",
       " ('2015-12-18', 75.0),\n",
       " ('2015-12-19', 76.0),\n",
       " ('2015-12-20', 74.0),\n",
       " ('2015-12-21', 75.0),\n",
       " ('2015-12-22', 74.0),\n",
       " ('2015-12-23', 73.0),\n",
       " ('2015-12-24', 68.0),\n",
       " ('2015-12-25', 73.0),\n",
       " ('2015-12-26', 74.0),\n",
       " ('2015-12-27', 74.0),\n",
       " ('2015-12-28', 77.0),\n",
       " ('2015-12-29', 70.0),\n",
       " ('2015-12-30', 70.0),\n",
       " ('2015-12-31', 69.0),\n",
       " ('2016-12-01', 75.0),\n",
       " ('2016-12-02', 72.0),\n",
       " ('2016-12-03', 69.0),\n",
       " ('2016-12-04', 71.0),\n",
       " ('2016-12-05', 68.0),\n",
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       " ('2014-12-04', 77.0),\n",
       " ('2014-12-05', 76.0),\n",
       " ('2014-12-08', 77.0),\n",
       " ('2014-12-09', 75.0),\n",
       " ('2014-12-10', 76.0),\n",
       " ('2014-12-11', 75.0),\n",
       " ('2014-12-15', 77.0),\n",
       " ('2014-12-16', 75.0),\n",
       " ('2014-12-17', 77.0),\n",
       " ('2014-12-18', 74.0),\n",
       " ('2014-12-19', 78.0),\n",
       " ('2014-12-22', 77.0),\n",
       " ('2014-12-23', 76.0),\n",
       " ('2014-12-24', 69.0),\n",
       " ('2014-12-26', 70.0),\n",
       " ('2014-12-29', 64.0),\n",
       " ('2014-12-30', 75.0),\n",
       " ('2015-12-01', 69.0),\n",
       " ('2015-12-02', 65.0),\n",
       " ('2015-12-03', 76.0),\n",
       " ('2015-12-04', 81.0),\n",
       " ('2015-12-08', 71.0),\n",
       " ('2015-12-09', 71.0),\n",
       " ('2015-12-10', 71.0),\n",
       " ('2015-12-16', 82.0),\n",
       " ('2015-12-17', 74.0),\n",
       " ('2015-12-18', 75.0),\n",
       " ('2015-12-24', 77.0),\n",
       " ('2015-12-27', 74.0),\n",
       " ('2015-12-28', 68.0),\n",
       " ('2016-12-01', 72.0),\n",
       " ('2016-12-02', 70.0),\n",
       " ('2016-12-05', 68.0),\n",
       " ('2016-12-06', 74.0),\n",
       " ('2016-12-07', 71.0),\n",
       " ('2016-12-08', 70.0),\n",
       " ('2016-12-09', 70.0),\n",
       " ('2016-12-12', 70.0),\n",
       " ('2016-12-13', 68.0),\n",
       " ('2016-12-14', 68.0),\n",
       " ('2016-12-15', 68.0),\n",
       " ('2016-12-16', 66.0),\n",
       " ('2016-12-19', 71.0),\n",
       " ('2016-12-20', 76.0),\n",
       " ('2016-12-21', 73.0),\n",
       " ('2016-12-22', 70.0),\n",
       " ('2016-12-23', 68.0),\n",
       " ('2016-12-28', 71.0),\n",
       " ('2016-12-29', 71.0),\n",
       " ('2016-12-30', 68.0),\n",
       " ('2010-12-02', 71.0),\n",
       " ('2010-12-03', 70.0),\n",
       " ('2010-12-05', 73.0),\n",
       " ('2010-12-06', 78.0),\n",
       " ('2010-12-07', 76.0),\n",
       " ('2010-12-08', 73.0),\n",
       " ('2010-12-09', 74.0),\n",
       " ('2010-12-10', 63.0),\n",
       " ('2010-12-11', 72.0),\n",
       " ('2010-12-13', 75.0),\n",
       " ('2010-12-14', 74.0),\n",
       " ('2010-12-15', 72.0),\n",
       " ('2010-12-16', 68.0),\n",
       " ('2012-12-03', 74.0),\n",
       " ('2012-12-06', 73.0),\n",
       " ('2013-12-17', 73.0),\n",
       " ('2013-12-19', 72.0),\n",
       " ('2013-12-20', 73.0),\n",
       " ('2013-12-23', 72.0),\n",
       " ('2010-12-01', 78.0),\n",
       " ('2010-12-02', 74.0),\n",
       " ('2010-12-03', 74.0),\n",
       " ('2010-12-04', 78.0),\n",
       " ('2010-12-05', 78.0),\n",
       " ('2010-12-06', 61.0),\n",
       " ('2010-12-07', 69.0),\n",
       " ('2010-12-08', 65.0),\n",
       " ('2010-12-09', 77.0),\n",
       " ('2010-12-10', 67.0),\n",
       " ('2010-12-11', 69.0),\n",
       " ('2010-12-12', 75.0),\n",
       " ('2010-12-13', 67.0),\n",
       " ('2010-12-14', 71.0),\n",
       " ('2010-12-15', 74.0),\n",
       " ('2010-12-16', 66.0),\n",
       " ('2010-12-17', 66.0),\n",
       " ('2010-12-18', 77.0),\n",
       " ('2010-12-19', 70.0),\n",
       " ('2010-12-20', 73.0),\n",
       " ('2010-12-21', 71.0),\n",
       " ('2010-12-22', 71.0),\n",
       " ('2010-12-23', 71.0),\n",
       " ('2010-12-24', 67.0),\n",
       " ('2010-12-25', 71.0),\n",
       " ('2010-12-26', 74.0),\n",
       " ('2010-12-27', 76.0),\n",
       " ('2010-12-28', 72.0),\n",
       " ('2010-12-29', 74.0),\n",
       " ('2010-12-30', 75.0),\n",
       " ('2010-12-31', 69.0),\n",
       " ('2011-12-01', 69.0),\n",
       " ('2011-12-02', 69.0),\n",
       " ('2011-12-03', 75.0),\n",
       " ('2011-12-04', 73.0),\n",
       " ('2011-12-05', 73.0),\n",
       " ('2011-12-06', 72.0),\n",
       " ('2011-12-07', 72.0),\n",
       " ('2011-12-08', 72.0),\n",
       " ('2011-12-09', 74.0),\n",
       " ('2011-12-10', 72.0),\n",
       " ('2011-12-11', 73.0),\n",
       " ('2011-12-12', 73.0),\n",
       " ('2011-12-13', 73.0),\n",
       " ('2011-12-14', 73.0),\n",
       " ('2011-12-15', 74.0),\n",
       " ('2011-12-16', 73.0),\n",
       " ('2011-12-17', 71.0),\n",
       " ('2011-12-18', 72.0),\n",
       " ('2011-12-19', 73.0),\n",
       " ('2011-12-20', 74.0),\n",
       " ('2011-12-21', 72.0),\n",
       " ('2011-12-22', 73.0),\n",
       " ('2011-12-23', 73.0),\n",
       " ('2011-12-24', 71.0),\n",
       " ('2011-12-25', 73.0),\n",
       " ('2011-12-26', 75.0),\n",
       " ('2011-12-27', 71.0),\n",
       " ('2011-12-28', 72.0),\n",
       " ('2011-12-29', 73.0),\n",
       " ('2011-12-30', 71.0),\n",
       " ('2011-12-31', 65.0),\n",
       " ('2012-12-01', 77.0),\n",
       " ('2012-12-02', 72.0),\n",
       " ('2012-12-03', 78.0),\n",
       " ('2012-12-04', 71.0),\n",
       " ('2012-12-05', 72.0),\n",
       " ('2012-12-06', 71.0),\n",
       " ('2012-12-07', 72.0),\n",
       " ('2012-12-08', 76.0),\n",
       " ('2012-12-09', 75.0),\n",
       " ('2012-12-10', 75.0),\n",
       " ('2012-12-11', 76.0),\n",
       " ('2012-12-12', 73.0),\n",
       " ('2012-12-13', 72.0),\n",
       " ('2012-12-14', 74.0),\n",
       " ('2012-12-15', 74.0),\n",
       " ('2012-12-16', 74.0),\n",
       " ('2012-12-17', 73.0),\n",
       " ('2012-12-18', 77.0),\n",
       " ('2012-12-19', 73.0),\n",
       " ('2012-12-20', 70.0),\n",
       " ('2012-12-21', 72.0),\n",
       " ('2012-12-22', 76.0),\n",
       " ('2012-12-23', 65.0),\n",
       " ('2012-12-24', 74.0),\n",
       " ('2012-12-25', 73.0),\n",
       " ('2012-12-26', 77.0),\n",
       " ('2012-12-27', 76.0),\n",
       " ('2012-12-28', 69.0),\n",
       " ('2012-12-29', 78.0),\n",
       " ('2012-12-30', 76.0),\n",
       " ('2012-12-31', 68.0),\n",
       " ('2013-12-01', 76.0),\n",
       " ('2013-12-02', 68.0),\n",
       " ('2013-12-03', 79.0),\n",
       " ('2013-12-04', 69.0),\n",
       " ('2013-12-05', 64.0),\n",
       " ('2013-12-06', 70.0),\n",
       " ('2013-12-07', 69.0),\n",
       " ('2013-12-08', 73.0),\n",
       " ('2013-12-09', 70.0),\n",
       " ('2013-12-10', 74.0),\n",
       " ('2013-12-11', 76.0),\n",
       " ('2013-12-12', 76.0),\n",
       " ('2013-12-13', 69.0),\n",
       " ('2013-12-14', 73.0),\n",
       " ('2013-12-15', 73.0),\n",
       " ('2013-12-16', 65.0),\n",
       " ('2013-12-17', 70.0),\n",
       " ('2013-12-18', 72.0),\n",
       " ('2013-12-19', 72.0),\n",
       " ('2013-12-20', 75.0),\n",
       " ('2013-12-21', 74.0),\n",
       " ('2013-12-22', 69.0),\n",
       " ('2013-12-23', 73.0),\n",
       " ('2013-12-24', 75.0),\n",
       " ('2013-12-25', 76.0),\n",
       " ('2013-12-26', 74.0),\n",
       " ('2013-12-27', 73.0),\n",
       " ('2013-12-28', 74.0),\n",
       " ('2013-12-29', 71.0),\n",
       " ('2013-12-30', 72.0),\n",
       " ('2013-12-31', 73.0),\n",
       " ('2014-12-01', 72.0),\n",
       " ('2014-12-02', 72.0),\n",
       " ('2014-12-03', 73.0),\n",
       " ('2014-12-04', 72.0),\n",
       " ('2014-12-05', 75.0),\n",
       " ('2014-12-06', 76.0),\n",
       " ('2014-12-07', 67.0),\n",
       " ('2014-12-08', 69.0),\n",
       " ('2014-12-09', 70.0),\n",
       " ('2014-12-10', 75.0),\n",
       " ('2014-12-11', 74.0),\n",
       " ('2014-12-12', 71.0),\n",
       " ('2014-12-13', 74.0),\n",
       " ('2014-12-14', 71.0),\n",
       " ('2014-12-15', 72.0),\n",
       " ('2014-12-16', 74.0),\n",
       " ('2014-12-17', 75.0),\n",
       " ('2014-12-18', 73.0),\n",
       " ('2014-12-19', 75.0),\n",
       " ('2014-12-20', 76.0),\n",
       " ('2014-12-22', 68.0),\n",
       " ('2014-12-23', 71.0),\n",
       " ('2014-12-24', 68.0),\n",
       " ('2014-12-25', 66.0),\n",
       " ('2014-12-26', 63.0),\n",
       " ('2014-12-27', 62.0),\n",
       " ('2014-12-28', 67.0),\n",
       " ('2014-12-29', 68.0),\n",
       " ('2014-12-30', 63.0),\n",
       " ('2014-12-31', 56.0),\n",
       " ('2015-12-02', 76.0),\n",
       " ('2015-12-03', 75.0),\n",
       " ('2015-12-04', 74.0),\n",
       " ('2015-12-05', 76.0),\n",
       " ('2015-12-07', 69.0),\n",
       " ('2015-12-08', 71.0),\n",
       " ('2015-12-09', 72.0),\n",
       " ('2015-12-10', 74.0),\n",
       " ('2015-12-11', 75.0),\n",
       " ('2015-12-12', 72.0),\n",
       " ('2015-12-13', 78.0),\n",
       " ('2015-12-14', 69.0),\n",
       " ('2015-12-15', 75.0),\n",
       " ('2015-12-16', 75.0),\n",
       " ('2015-12-17', 74.0),\n",
       " ('2015-12-18', 74.0),\n",
       " ('2015-12-19', 76.0),\n",
       " ('2015-12-20', 73.0),\n",
       " ('2015-12-22', 75.0),\n",
       " ('2015-12-23', 75.0),\n",
       " ('2015-12-24', 74.0),\n",
       " ('2015-12-25', 79.0),\n",
       " ('2015-12-26', 75.0),\n",
       " ('2015-12-27', 76.0),\n",
       " ('2015-12-28', 76.0),\n",
       " ('2015-12-29', 69.0),\n",
       " ('2015-12-31', 72.0),\n",
       " ('2016-12-01', 76.0),\n",
       " ('2016-12-02', 75.0),\n",
       " ('2016-12-03', 75.0),\n",
       " ('2016-12-04', 75.0),\n",
       " ('2016-12-05', 70.0),\n",
       " ('2016-12-06', 70.0),\n",
       " ('2016-12-07', 74.0),\n",
       " ('2016-12-08', 73.0),\n",
       " ('2016-12-10', 72.0),\n",
       " ('2016-12-11', 78.0),\n",
       " ('2016-12-12', 67.0),\n",
       " ('2016-12-13', 69.0),\n",
       " ('2016-12-14', 71.0),\n",
       " ('2016-12-15', 74.0),\n",
       " ('2016-12-16', 63.0),\n",
       " ('2016-12-17', 76.0),\n",
       " ('2016-12-18', 76.0),\n",
       " ('2016-12-19', 71.0),\n",
       " ('2016-12-20', 76.0),\n",
       " ('2016-12-21', 77.0),\n",
       " ('2016-12-22', 73.0),\n",
       " ('2016-12-23', 71.0),\n",
       " ('2016-12-24', 78.0),\n",
       " ('2016-12-25', 78.0),\n",
       " ('2016-12-26', 75.0),\n",
       " ('2016-12-27', 75.0),\n",
       " ('2016-12-28', 72.0),\n",
       " ('2016-12-29', 77.0),\n",
       " ('2016-12-30', 72.0),\n",
       " ('2010-12-01', 72.0),\n",
       " ('2010-12-02', 71.0),\n",
       " ('2010-12-03', 72.0),\n",
       " ('2010-12-04', 74.0),\n",
       " ('2010-12-05', 66.0),\n",
       " ('2010-12-06', 61.0),\n",
       " ('2010-12-07', 58.0),\n",
       " ('2010-12-08', 60.0),\n",
       " ('2010-12-09', 71.0),\n",
       " ('2010-12-10', 65.0),\n",
       " ('2010-12-11', 72.0),\n",
       " ('2010-12-12', 70.0),\n",
       " ('2010-12-13', 68.0),\n",
       " ('2010-12-14', 67.0),\n",
       " ('2010-12-15', 71.0),\n",
       " ('2010-12-16', 65.0),\n",
       " ('2010-12-17', 64.0),\n",
       " ('2010-12-18', 65.0),\n",
       " ('2010-12-19', 69.0),\n",
       " ('2010-12-20', 70.0),\n",
       " ('2010-12-21', 70.0),\n",
       " ('2010-12-22', 68.0),\n",
       " ('2010-12-23', 69.0),\n",
       " ('2010-12-24', 71.0),\n",
       " ('2010-12-25', 70.0),\n",
       " ('2010-12-26', 71.0),\n",
       " ('2010-12-27', 76.0),\n",
       " ('2010-12-28', 71.0),\n",
       " ('2010-12-29', 72.0),\n",
       " ('2010-12-30', 73.0),\n",
       " ('2010-12-31', 77.0),\n",
       " ('2011-12-01', 67.0),\n",
       " ('2011-12-02', 69.0),\n",
       " ('2011-12-03', 72.0),\n",
       " ('2011-12-04', 74.0),\n",
       " ('2011-12-05', 71.0),\n",
       " ('2011-12-06', 70.0),\n",
       " ('2011-12-07', 68.0),\n",
       " ('2011-12-08', 70.0),\n",
       " ('2011-12-09', 66.0),\n",
       " ('2011-12-10', 72.0),\n",
       " ('2011-12-11', 70.0),\n",
       " ('2011-12-12', 70.0),\n",
       " ('2011-12-13', 70.0),\n",
       " ('2011-12-14', 70.0),\n",
       " ('2011-12-15', 73.0),\n",
       " ('2011-12-16', 71.0),\n",
       " ('2011-12-17', 69.0),\n",
       " ('2011-12-18', 73.0),\n",
       " ('2011-12-19', 72.0),\n",
       " ('2011-12-20', 71.0),\n",
       " ('2011-12-21', 69.0),\n",
       " ('2011-12-22', 70.0),\n",
       " ('2011-12-23', 69.0),\n",
       " ('2011-12-24', 70.0),\n",
       " ('2011-12-25', 75.0),\n",
       " ('2011-12-26', 71.0),\n",
       " ('2011-12-27', 70.0),\n",
       " ...]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 6. Write a query that filters the Measurement table to retrieve the temperatures for the month of December.\n",
    "session.query(Measurement.date, Measurement.tobs).filter(extract('month',Measurement.date)==12).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 7. Convert the December temperatures to a list.\n",
    "results = session.query(Measurement.date, Measurement.tobs).filter(extract('month',Measurement.date)==12).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 8. Create a DataFrame from the list of temperatures for the month of December. \n",
    "df = pd.DataFrame(results, columns=['date','temperature'])\n",
    "df.set_index(df['date'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1517.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>71.041529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.745920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>56.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>69.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>71.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>83.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       temperature\n",
       "count  1517.000000\n",
       "mean     71.041529\n",
       "std       3.745920\n",
       "min      56.000000\n",
       "25%      69.000000\n",
       "50%      71.000000\n",
       "75%      74.000000\n",
       "max      83.000000"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 9. Calculate and print out the summary statistics for the Decemeber temperature DataFrame.\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Dec')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.hist(bins=20)\n",
    "plt.tight_layout()\n",
    "plt.title('Dec')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PythonData",
   "language": "python",
   "name": "pythondata"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}