climate_starter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import style\n",
    "style.use('fivethirtyeight')\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reflect Tables into SQLAlchemy ORM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine(\"sqlite:///Resources/hawaii.sqlite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "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": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['measurement', 'station']"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can view all of the classes that automap found\n",
    "base.classes.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save references to each table\n",
    "measurement=base.classes.measurement\n",
    "station=base.classes.station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sqlalchemy.ext.automap.measurement"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measurement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create our session (link) from Python to the DB\n",
    "session=Session(engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Climate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Design a query to retrieve the last 12 months of precipitation data and plot the results\n",
    "\n",
    "\n",
    "# Calculate the date 1 year ago from the last data point in the database\n",
    "\n",
    "# Perform a query to retrieve the data and precipitation scores\n",
    "\n",
    "# Save the query results as a Pandas DataFrame and set the index to the date column\n",
    "\n",
    "# Sort the dataframe by date\n",
    "\n",
    "# Use Pandas Plotting with Matplotlib to plot the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sqlalchemy.orm.query.Query at 0x1e0f2f14520>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Design a query to retrieve the last 12 months of precipitation data and plot the results\n",
    "last_year=session.query(measurement.date).order_by(measurement.date.desc()).first()\n",
    "for x in last_year:\n",
    "    split_date=x.split('-')\n",
    "split_date\n",
    "last_year=int(split_date[0])\n",
    "last_month=int(split_date[1])\n",
    "last_day=int(split_date[2])\n",
    "\n",
    "query_date=dt.date(last_year, last_month, last_day)-dt.timedelta(days=365)\n",
    "\n",
    "last_year_preci=session.query(measurement.date, measurement.prcp).filter(measurement.date>=query_date)\n",
    "last_year_preci"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the date 1 year ago from the last data point in the database\n",
    "query_date=dt.date(last_year, last_month, last_day)-dt.timedelta(days=365)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'session': <sqlalchemy.orm.session.Session at 0x1e0f2dd44c0>,\n",
       " '_polymorphic_adapters': {},\n",
       " '_entities': [<sqlalchemy.orm.query._ColumnEntity at 0x1e0f296fc40>,\n",
       "  <sqlalchemy.orm.query._ColumnEntity at 0x1e0f296f820>],\n",
       " '_primary_entity': None,\n",
       " '_has_mapper_entities': False,\n",
       " '_mapper_adapter_map': {<Mapper at 0x1e0f2dd4b50; measurement>: (<Mapper at 0x1e0f2dd4b50; measurement>,\n",
       "   None)},\n",
       " '_criterion': <sqlalchemy.sql.elements.BinaryExpression object at 0x000001E0F296FD00>}"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform a query to retrieve the data and precipitation scores\n",
    "last_preci=session.query(measurement.date, measurement.prcp).filter(measurement.date>=query_date)\n",
    "conn = engine.connect()\n",
    "last_preci.__dict__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>precipitation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-08-23</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-08-24</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-08-25</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-08-26</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-08-27</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2223</th>\n",
       "      <td>2017-08-17</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2225</th>\n",
       "      <td>2017-08-19</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2227</th>\n",
       "      <td>2017-08-21</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2228</th>\n",
       "      <td>2017-08-22</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2229</th>\n",
       "      <td>2017-08-23</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2021 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  precipitation\n",
       "0     2016-08-23           0.00\n",
       "1     2016-08-24           0.08\n",
       "2     2016-08-25           0.08\n",
       "3     2016-08-26           0.00\n",
       "4     2016-08-27           0.00\n",
       "...          ...            ...\n",
       "2223  2017-08-17           0.13\n",
       "2225  2017-08-19           0.09\n",
       "2227  2017-08-21           0.56\n",
       "2228  2017-08-22           0.50\n",
       "2229  2017-08-23           0.45\n",
       "\n",
       "[2021 rows x 2 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the query results as a Pandas DataFrame and set the index to the date column\n",
    "data = pd.DataFrame(last_preci, columns=['date','precipitation'])\n",
    "data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the dataframe by date, already sorted by date \n",
    "type(data['precipitation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e0f48f9af0>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CCwUSQgghvFAgIYQQwgsFEkIIIbxQICEkgjE0ZpsTyiV2FEgIIYTwQoGEkAimAC3azgXlEju/A8kHH3wApVKJ2bNni5EeQgiRHaraYudXIDl69ChWr16N3r17i5UeQkgQURsJEQLnQFJRUYEXXngBS5cuhVKpFDNNhBAiK6EQbu0Mg4xTlbh7sxZ/y9Gjxhq8VHMOJH/9618xduxY3HvvvWKmhxBCSAAOF1uw+GQVzumsWHnRiI251UE7dgyXjVavXo3c3Fx8/vnnnHes0WgCTpQQn480lF/+C688S3L6q7i4BJroQp/bMQzjVz6EV56xcc6nstJSaDRFfu8lmPn16skENC4bvHJAj6GKAkH2rVarWd/3GUg0Gg0WLVqEHTt2IC4uTrAD+zomn89HGsov/4VdnmXnO/3Zrl1bqNVNfW6nUCg450PY5Rkbl3xq3aYN1Opmfu0i2PllP6MFYHV6LVjH9xlIjhw5grKyMgwdOtTxms1mw8GDB7Fq1SoUFBQgPj5e1EQSQgiRL5+BZMyYMejfv7/Tay+//DJSU1Px+uuv+1VKIYQQEn58BhKlUunWSyspKQktW7ZEWlqaaAkjhBASGmhkOyGEEF449dpytW3bNqHTQQghskUrJLKjEgkhYYhufCSYKJAQQkgYUEg4syQFEkLCENebChVcwoeUpVAKJIQQ4gMFXHYUSAgJQ1yfTmmdjfBBVVuEEEJCFgUSQsIQtZGQYKJAQgghhBcKJISEIRpHIqx3TlTif34uxc83TFInRZYokBASwaixnbv/5pvx5H/LUG6ySZ0U2aFAQkgYojYScdgY4MuLRqmTITsUSAghxA9VFnmGXylLlxRICAlD1EYSeaT8ySmQEEII4YUCCSFhSMpRzuFOroU9qtoihBASsiiQEBKGqI2EBBMFEkIIIbxQICEkDFEbiXgY2baSSIcCCSGEEF4okBAShqiNhASTz0Dy5ZdfYtiwYejUqRM6deqEkSNHYteuXcFIGyGEyA4FaXc+A0mHDh2wcOFC7N27F1lZWRg+fDiefvpp/Pbbb8FIHyEkAJzn2qKbIhFAjK8NxowZ4/T3m2++iZUrV+Lo0aPo06ePaAkjhJAcrRm7bphwd/t4jOyYIHVyiBc+A0ljNpsNmzdvhtFoxKBBg8RKEyGEJ85rtsu4d9dFfS3G7CiFnQE+PmvAlofaYHhyvNTJIh5wCiTnzp3DqFGjYDKZ0KRJE3z99dfo3bs362c0Gg2vhPH9fKSh/PJfeOVZktNfxSUl0MQU+tyOsTN+5UMw8+z1c/GwM9GOv1/MKsaPA4O1sFSS13d0ej00mhJOewlmflksCXBtrRDq+Gq1mvV9ToFErVZj//79qKiowJYtW/DSSy/hp59+QlpaWsAHZqPRaHh9PtI0zi+GYfDXg3qsvlyN3i1j8M2fWiOlqV8Fz4gQdudYdr7Tn6p2baFWN/W5HRQKzvkQ7Dy7crwQgN3xd4E5KnjHd82nRlq0UEKtVvrcRbDzK+6sFqixOr0WrONz6v4bFxeHrl27on///liwYAH69u2L5cuXi502EoBDxRasvlwNADins+LD01USp4gQEu4CGkdit9thsViETgsRwL/PG5z+/upWUCGRJRx6Y4XCd9BW2/B7hRVXKmpxWV+LSovd94c4+PmGCR+dqUJeldX3xjLgs87jH//4B0aNGoXbbrsNBoMBmZmZyM7OxoYNG4KRPuKnULj4iHzIubE9FMw6pMfWvIZ2m9X3t8LYLom89rkptxqT9+oAAB+frcJvT7RHs1jfz/xS/pQ+A4lWq8XUqVNRXFyM5s2bo3fv3sjMzMSIESOCkT5CSABoHIl4GmdZlEs+C5Gf9UEEACosDL44b8Tf7mjGf8ci8hlIPvvss2CkgxBCnIRCjItyKQfYRYjMF/S1gu9TaDTXFiFhiEoaweFa8hOmhST0UCAJM3T/IOFCrsFQ7Kott+PJNB8ai9hAUm6yhUyPCEL8FQ6N6CFw/3Rr4PZWIsk32vDM7jKM2VGCA0VmsZMVdBEZSH69aULfjVrckanFrBy91MmRVI2VwdLfqvDJ2SpUWyO1YE5IYFwDtrfSw/wjFdiaZ8KBIguezSqH1c49TIZCQI3IQDJ1nw5Ga93Ps+KiEdfCqGTibzF42r5yvHm0Em8dq8Tze3S+P0BCQihUh4QD1xuot8b2zddqHP8uNdlxuDi8xuFFZCApNzs/eR8rCa8f1R9bGvWB33HDBJsfT0qERKRGl4jCpUjCtUwfboX/iAwkhIS78Ggjkf9DTaCN7eHw+zRGgYQQQhox1nIvLrjeQLmGPn/iSCgEJwokYYbvM5z8nwEJF+HQRiLFd/jxWg3U3xaxbtM4WW7jSCS86Uv5m9P84hGMCYe7DSECen5POax+XBZuJRKugYT7IULi4Y5KJMSJHE7aDb9XY8D3WozdWYrrhvDpURdMnOfaEjcZIcefIAJ4GtnesIMbNQqcLLV4fGDzL5BwS9SlCumuFSqRhBl/rgM53kQqLHbMyNbBYgeuVFrx7skqLP9jS6mT5ea38lqU1Ngcedi7ZSxUSdGsnyH+keP5CbiObHcOCfUxY8Pv1XjpeAJsx0swIZXfbMBcWGzS5haVSIgTqWu7Nl+tQeMlHdZfked6Kh+crsJjP5dh/K3/5DZa2dPvyPfJ2JNykw1jd5aiw9oCzMjW+TXQTkh2hhFlwkRf3MeR1P1/6j4dbLdy97vfa+DKnzYSLl/L4EcHATFQIIlgfK87vdmO9RojDmuFu4mG6jCWUEj2snMG3xv5aeVFI/YWmlFtZfC1php7CoQ7F7jm6X8uGqFaU4Bu3xRhr4DH5yTASRvDrPcvBRLijOvFW2tncP/WYkzP1uPB7aXYfNX9qSsQrv3y5cptagxpkuEXT2nkm+7FJ52Xcl50vJLnHhvhkLhqqx2v5ehRa68baPzqweDOziCXxnapzz8KJBGMz8m36WoNrlbZHH//ZU85/wQhhAKJ1AnwIdwGvHlzWe/cwHyt0TkplsbBwvV85VwiCbPfhwJJmPH0RDRssxa/3DS5v8Hx856cKRNnsZ1QCSSupG5bcuUxPUFIo8yyQRSNe1G5jyPhlgMKgR9FpL5sKJAgeD/CBV0tDhaZg94oeF5nxbR9OtS6NEBIdK9h5doLRq5CJJlOPP22gX6NS/pa7C+UvoOB5Oeraw6KOCDxor4W928tRt+NRcjMlVcnFAokQfK1xohhm4vx8I5STJZglt1ysx0nQmByypAtkUidgAAFku7M3GoM21yMR3eWCp6exjylbeeNGsl6hnkS6AqJgUyRsvBYJU6W1uKGwYZXD+hR4++gFxFRIAmSGdl6x4Wx+VoNciuDP3jItau5xy6iEt8SQ+WEdL0RyOeSruPpiVeoNE7Zq3M7l4Q+hjcTfy3Hk7+WiXwU7gJtbA/EjhsN1dNGK4O9hQ1/S33++bxuP/zwQ9x///3o1KkTUlNTMWHCBJw/fz4YaQtrGpFGobKdUEKebGIFnFApkcg9mZ7HkQQ/Ha70Zjv0Zm7P7d7S+0u+Ged14rTR+cv1fOU6LjDipkjJzs7G888/j127dmHLli2IiYnBuHHjoNPRIkihxvWE9NhGIvFZGyqBxJXU+SYXbPO3rbxoQOo3hVB/W4h1GiOv44j1IMZF468YaFuZ0G1sUl82PqdI2bRpk9Pfn3/+OVJSUnDo0CGMHj1atIQR4Ql5sxO610m9UGlsD7CNVVJSptFmZ/C3nIq6fzPAy9l6PK1uEvD+2NpJTpdZcEfrOL/3+eM1/mOh/JlG3s4wWHu5GgXVNjzXownae5lix9s+5fTw4neVtMFggN1uh1KpFCM9hCe2c8v12vPcRiKtEAkjsk+nVPHY2/ljCmAuKLZPsLUzzz1c4fexAHCvcmv0b7e2Mj/u7u+fqsKrB/V471QVRm4rkWSKF6H4PWnj3Llz0bdvXwwaNIh1O41GE3CihPg8uySnv4qKiqCxiT2QyfmYBQX50JiEmx+nPr+MhngAnp9s8vNvQmNsOGbddeOcritXfkcTDmeFThcLINZjGvjQlkUDiBd8v57w2W9VZRwaXz5FRUXQMOIPhvPO+XfUFpdAE13o9FpJaQwA5yd1hmH8yoe6bZO8vm82Wzzur8bmnkZfx7XbE+EtZN8sLILGbsP1qigACU7v5Wg9p8GX4mL3c8+TiopKaDR1PdZcr4OysjJoNFqw5REA3LxxHe+eapjM8YbBhrVHczGspd3tswaDARpNudvrBQWF0Jjrzjl9LTweU6hrR61Ws77vVyB54403cOjQIezcuRPR0ewznfo6MBuNRsPr8z5l5zv92b59e6i7sv/wQh+zQ4fboO6U4GVj/zTOryZ5ZYDO8+DD2267DeoODcessTLAwQKnbVK7paJZrO+CaktdBVDgPHeTEL+ZJr4GuOA8Sl6Mc4HvOda8sBwoaagKUalUUPOoquHN5fxq17Yt1OqmTq+1rq4C8pynMFEoFJzzwZFnLsdqLD4uDmp1J7fXjbV2IMc5sPk6ruJQgdfJ19q0rctvQ6kFOF3i9n4gv62KMQJX9D63a9GiOdTquhmpW1VWAjcapolp1bo11OrmrHkEACkpKcAp53RHt2xfdw65fLZJk6ZQq1u7vZ7cIRnqlLpgVGayAYfdF+QS9T7aCOdAMm/ePGzatAlbt25Fly5dREwSEQuX7vdSl65DtrFd6gSEIbZz0XVwrRACOvUCbmznf6I7Nfrz3hs/nNpI5syZg8zMTGzZsgXdu3cXO02ED5arz73XVuAXo6fPfnbOwLme2Ru3kcIy5XojkFsgEXMcCZtg5YMYs6YL8RDD9fvnVXHvdSbFmDN/+Qwks2bNwvr167FixQoolUpotVpotVoYDMJPSU3EJfYsovOOVODh7SURsYSv3MMd1/VIQpVNhO/C9Tdlb2znto//t9t9klNvn71UYUVRNXv7m9S/rM9AsmLFClRVVWHs2LHo0aOH47+lS5cGI31EQK4nqhj3lfN6K3K08p+KRWhS3qOlXtSosWBng6Bd2gOobgpW77glp6vcXmO8/FsKPttI9HrfjU9EPviObBfiwizw8fQUDuS0Hsmay9wm8JPjAFQ2wZ6uh3OJhCVZfFLMFpSOynyevFCZ2ogIwK1EIk0yWIXoeERJeRolHgr5KLeqtkDyLFhzrvnqWyB1VlIgiSDyumyJmLgONhU64Hi7oQVy7gX7fA3kZhiseO1psOKkrHJM369DcY1N8mubAkkEca0qkON6JKEi0EZWKQUjjV6n8/BjW8f7PKqQAintcA2qrNXHIuWxp93aGWD9lWq8flD65gcKJGGG9eLjcJL/lCfM2uskNEgZAMU8tjmAZrpASheuwefjs1UoNwnfRsiWVz9dN0n+IEOBJIK4jSPxcPLNPKDHN1f4rb4WAtXzvMmpsd0TuY0jCfa8bge1ZuQb/buhBxRIXP6utQNdv3EfYc6XjNby8ogCSQThejK+tJ+WCPAlFIOllPeiYB97/M9lGLhJi70F3JcDlnrmabZSha8O3lLHGQokYcafE0qsk49XF0jBUhFcUlctuOKanmA1tnvcluf7vraptjJ4OZv7Q1EgbSRSNrY3JvXpR4Ekgkh9soUTuS+1W1XLwOZSBJVy1cRAjs36Psd03/SzestfQsyZ1bAv7+9R1RaRDS7rkQghVEsV/pD7OI2FxysxZkcp77nP5E7In0HOPymNIyEkAgR7FDYXh4otWH25YbBicBrbuR9FiPQI+Z1+zfe8/ILbMSWYdVfujwMUSCKI1E8t4cR1qWG55u2CY43WH5Gyq6+n12SWZ1ynmmksWCVTn9WAwUmGV36vkEhClxyfmkOV3Ku2xOJroJ+QwUGuZ+v6K9VQJUbhlb7NgnZM3+1J0uYWBRLIu27UX/6MupXjhRqqN2g55qUrIR4kAu1p5elGxyc1Uuf3R2cN+Ohs8JbSsEv+jdnJtmpr1w0TuqwrQKevC/DDVX4D5EgdeZ+KoSUU450gbRIB7sTzFCnsO7PRCevgKy+kzirZBpKZB3TQWxhU1TKYtk/nsx818c19ZDvlqVAoK+sIlQ2+RqWHYiAXk9Snn2wDSXFNQz8Fix2otEidVaGBdXRskLIwEi5yuY8j8USIaUoC/W9B1LYAABvQSURBVJ7+NrZvucY+51so5LeQ5P6gIttAQsQn83NT3vyIllY7g4XHKjDsBy3mHtbDIlGdjZTdbf0NYnRuOuMzU3IwhEwgEbNqa/JeHR7ZUYK8Kqtox3AlxVN7KFyc/qw1Xmay4UCRGRUW9l72VyutGL29BP0zi7Dhd3Ha29jydne+GR+dNeC83op/nzdi4CYt+m4swpS95agSeZlcX+eZ0OdEsG9ooXBOC4FGtgtE7HzMLrJ4XBdZLFLMc+V6Mm67zm0AltQ8faerlVYM+aEYY3aU4p4fi1HKMnX34pOVyNFacLXKhpkHdILcvP1Zj2TmAef5nvIMNtww2JCZW4NvNMHrSCLEFCnCNrYTrnx1TJA6L0MmkATD2iBe1FJo/GRvszN4TQYL4rjiekEsOl6JElNdQLhhsOGzc967YmbmNtS3m23AdgECqD8lymqr92/1f4creKeFja/u1EK3kfg1aagAdz8uv8P0/Tq8dlCHkhpx590Sk84sdahgFzKBRN7ZKB/FLBdL4zwsqLaFTPdKTzecH1waY/1ZQ0WM6hd/xu8EU+MbraTdfwXOA8bl/2zWX6nGfy5V+zUTsBSkbufgg1MgOXDgACZOnIhevXpBqVRi3bp1YqfLTShncrCYbQzO6by38zTOwrgoefat8vQ7P7yjFA9vL2FdeS4myN8nFAdOSnkJyaFq6+eb3NcmCTVS3x85BRKj0Yi0tDS8++67SExMFDtNHoVbHBHjPpSjZb9QGp9s0SFTFgUOF1twUGtBfdPGlxfcq7FiJL6xy/X8FDpb5Fa1FU5C8eGkHqcpUkaNGoVRo0YBAKZPny5qggBp100IFjG+TlE1eyNy42PKNT/ZklVf6Jh9yL1dITbYJRKXv+U6uLPxzUmIJPps9PXydrDGq0QqqfNLls+lnjJF7tMohxq5dyf0hO2JrTzI62748/QolyfNsGojYcTZb6iSOh9Em7RRo9EE/FlPefJ77lUY4oXKrSSv7/BJtz/HLCjIh8Yk3M1Po9FAWxwNIN7rNoVaLTSKunaGUot7mlz3x0aviwUQ6/G9oqIiaGyB9ZDJL48CkODxvau5udDFAp7SXWKyI+vMFXRM9HSOOG9fVFQEqPj91q7fv6SkFBpNkcdt7fZEsFUy8T3nLOYEeH0mZBjH/nVefjN/jv/777+D7by5abRh1+kr6Jrk/DsUmxUAnKvFf/3tKkotCgxU2tDc5U5UWhIDIM7rcY7eKMfTN8pRYFIAiOacfvbv6v17BYNWq4UGtoDScS3vGlzzFxDufqZWq1nfFy2Q+DowmwuX3b985y5d0KmpQMnNzvf6lqd02+wMtuaZEKUAHumcgKhAHjFdjvl1cTPsqorGk92SMLYLv3YnjUYDtVoNlaIauOy9Z4qqnQpqdRMAQBOjDTji+cYH+P79WpTrgQKjx/eSk9tD/YfALsqTv1cD8PwdUrt2RauEaK+/35qyVvjP/a3c33DZXtW+PcDk8zpHW+oqgIKGtpo2bdpArfY8rXjU4QLWWff4pAMA4s5pgWrPnSyiohSO/St1nn8zrsfXaDTompoK5BSybvd1WUusvaO102tJRhtw1Pl8m3Km7oGhY5NoHH6sHZrENgTDNhYDcNV71+htxYHdC1i/K8t9IRhUqlvXZwDp6Ny5M3C82O11vucWV7Ks2vJUJJGyKmbGAT3+sqcck7LK8X8e6ucDcby0FjtvmPBsVjlyK4Mzor5x8VfM7ORTzF7YeCEmF77Wx3btEiwmmdRWBR2Xn3Zrnn/jdG4abZh5IDhjmuTalhXqZBlI5NBVsF6tnXEao7DiouencD7ST3q/eQqp8ZoGfKecEaveP7/ae5WYnG/ebLkpZbqdxpFIeA/1dQPfdLUGh3z0OhSCnMdO8fl9pP5anAKJwWDAmTNncObMGdjtdty8eRNnzpzBjRs3REmUx8Z2iXKqNggHLhOorcRXSs83GmMi5reSS+OymELlOzZeEljSxnYO27xyq1RiZxicqmvEE1wodjLhQuqCFqdAcvLkSQwfPhzDhw9HTU0N0tPTMXz4cPzzn/8UJVGeA4k0OeW6NjcgfPE4WDellReNKLs1qC8ULyiheviKkd0hmJ0BEfN7Xq6oe9B5Znc5NuaKU00p5/M+VB5OPOHUYvXHP/4Ren3w5mXydJ+W6gTw1G/ezgDRAv7oQu2Ky34+OWvAwoEtBDpicMnpQvNn0kYpCT6ORMQSCQCcKrWIOpno4pOVmN+/ORJcRrCGetuJ1KmXZRuJJ1KNI/EUwISuZxXq/sglWZpbDftyfjLzJpj55IuMYhoroefaChTX+/ThYnGqtOot/c2Af3pokwzF66ExqZMvy0AipzaSoAQSCe5KH5zhN2W+FA9wcr55s2aHhAnncugrFbWip4OrYJxWn/zmPsWOHAY8X6uyYdLusoA+e71K2pmNKZD4EIz2Gl/dWoWmN9uxLgSnzJdV1ZZLWqR+IuTCWxqn7uM+K26g31Pu+SOHEsmS01XY4mfX6XpP/BpYABKKPAOJxzYSaX5pT4eVQ9XWvkIz7t1SjNHbS/C7kfseTpfW4uebobGglav6jg89laKNo+VMRjGNFZfge6K0Fu+cqGRdgiBYpLjMt1+vwaLjwemCH66kvyI9kH+JRNhj+Nsbyc4weHFfOQpuTdJYa4rDQ/24fTa/2sbpCfSdE5WY2acpWsS5P2swDIP1LOt/eOrp5s3VSiumZ+uQb7Th//p5HhnesN/643PefdDIMU2AS8BjSeOS01X48VoNjjzWjrWEzLVRWlttgyqJ+9QlUtmWV4Ond5dLnYyQJ8sSiSfBuk5dSz6eSkI2oau2/Nw+t9LqCCIAcKxC+At2yekqTNnj+QL75ko1Kiz882DXDRP6f69FjtaC6wYbXvexYmN9wJXDPdufqi25lF585Zumwor9ReyN3VzzfpnLipVcL5lgV18+v5eCiBBCJpBwKQUYa+1482gFnssqx4mSwHp/uFZbybHXlre8ELoL4y/5Zo/7nJ7Nvys4wzCYdch5PxYfLZ71Nxm2c2Hz1RqkrCvAH9YXYNcNz1V4od7V0y9+nlxXfUzXwzXnPDVocxGMAcBAQwcDlrXSwoadYVBusuGSvla0JgJZBpJAq5PST1Zh6W8G/HCtBmN3lcLkYa3s4z4CjM3lZhaMqq2frpuw4GiFx/R6EsynNo5J8ltVLYMbBv+u4gNFdVNosK2JMSNbh0oLA52ZwSsHxFtaNWTWI2n0by4prKoVp/8S19xZfSk4nUDeOcGv12KoGLRJi3arC9D1myIM/qEYOpGWWwihQOL7VPy0UXG6qpbxOInf33LYn6Zdq62CUSIBgH/9ZsDKS9zm8Qpm6d8s0uREgYxSn5VTN2Em26lgaBT5tDWeLxohesn50w7El8XG4KtLRvznohEWHr8Hl1jnaxOx1yO5EqQJTK9VBec4UrMxjNPDYLGXa4IveTa2e+q15fMz7h+q8FBXcqqMvc88t6otcW6u849U4OXeTd1ev1JRi5goBbo0q/u5gnkTE+kBNaB2jvqbjByf/cVM08vZOseUIfsKzVh1X0v8eM2EC/paTExln66/wsJg2GYthqniUSPAQ4Ec8z4QcupKLqbfK51L/SUmO3qJcBx5BhIPr/mqTjpR6h4gAjlXXK81qXuQvXOiEktOVyFKASwZosTknk08bidW3aeQJZLiGhtmZutwUW/FxG6BLyLkT4pEq3IKQmP78nMG/Pu8AdcbVQH+cK0G92viHRMcfnbegFbx7BUL53VWnNdZ4aEDXtD4WqI32CIkjjj55G4lerQQ55Yvy6otT3ydhn8/Ksw6Ie5VW57n2gqGaqsdS05XOY75+q1qOU8lIrGmx371oB53fV+Eg0X8p/j+9DcDdt00I89gw3unAq+j9ic2BJovvgKQ61xrQud/XpUVbxypcAoi9V5ptHZHpYXBNY6jmn11ZuAi0Lgc7KWQfTlRWsurmjAUqRKjReuSHTYlEmOtMCeFzmxHm4SGzK720NosVtWWq3IP08sr/+N59TSrXZxqh/qeTw/vKMVPo9uglsfFF2hPHlf+3JIsHKM+wzD4w/pCWOx1bRJWBih7tgOivTTmxLgGEh5PFy/sLceye1oirlF0Wn5OmLzii2EYrLxoRI7WgoHx0Rh3W2D7WX5O+HV8+BLq4TNUVArxJOGFPAOJh2sykCqKQC7tz88bsWSoEkBdaWTE1hK3beT4IGNlxK88eGRHqSyqBPzp7TXyJ/ffz9O5pFAoUG1lnJ7aa+1AtJcye6xLgLHyuEY35tZgbJdEPNK5YcllsdqmfHLJmi15Jsy6tSro94hHaifu83JZ7QxibuXT70FqRPfHFxfkF9zEJGa7kCyrtqRsl9ia19DT66MzBqdeQPVsduCXmyZ8ds4gi2klgLo0BaOgxOUQ9ffYWjuDNZeNWHPZKNj4gE/O+lcldk7H/QYW5xIc2EozrgGGrZs0lwt4/hHnp2Op2hSKauyONWsAYOo+5wF7rulkM+KnEpTcuj5k+OwVccS82YdOIGn0b01FLcbtKsWon0oEqbtvrL60oamoxdsnPM+/87XGiMd/KcO8IxW4d0uxLOpag1Ei4ar+fjx9vw6vHNDjlQN6vLRfmDEdb7Gs6c5XrEv1MVvwi1G4lkiEzX2phqUsO2dA2oYifJ9bN57D7PKclGfgHphPl9Vi/tEKXKuy4pJePjMMR6ooEYsksgwknjS+TmcfqsCeAjOOlFjwwl6doD2W6ts/FrLcsD4731AkLqy2Y0OuOIOo/KndsAapRMLF/9tdjnyjzWmVu0yRVrwTUrxbicT7tjEuVw7fZwnXjwvU5BcQsw2YecDzeCt/z7ENv9egX6ZWuqo64iDmxLcybSNxj5yNA8megoZSSH61DVcqfD8lMQyDj8/6bsCsH9ne+Bi+XPSj+sQf/jzkWu3yKZEAdSNqQ02sS1cstq7PQpdIXHtn8enUIARPnUwAqqIKZWI+nMgukNjsDPaXu3dRq4+mX3kY/X20xIIz5e5F59xKK2x2BlEKYM7hCk6Na2Jcv/50FOj5bSFq7cDknk2w0Y+Szl/2lONYiXyqD4xiza0iAJMNgIdekAvuag6TjUFclAJxUQq0SfBeYPenjSQKCnC5BdvsDPQWO6bt0+HXfGGrbAPh6byVS6mX+E/o6tfGZBdIJmWVY9v1OLfX7UzdPEDzDrs39r3sZRLBLy4Y8cUFIx7oEI/dHEsYVhF6h/mzx6JbUxhknPavUVlOQUTuXs/RY/9Q99f/tyu3QZJZ+SansRwAe3tKLMcK5NarC7htGCRVUtavEcGJWciVVSBZfcmIbdc9z9habrYju9Ac0DQPXIMI4D5pIxfLzhnQqWk0NuXWoHmcAr1axqJ7ixhMSE1CXLSCV9dQIo6nTybgdA8GCoUCOrMdVjuDtoncBmvluVRDdW0Wjb/d7ryWis5c1/spMSZK1qUzNp+dd68KDtGvEvF6KWNEXRCOc2P7ihUrcPvtt0OlUuHee+/FwYMHBU2IodaOV1nWo5h5QI+tAS5D6Y/6OOVvB4e5hytwpMSCX/PNWPqbATMP6NFuTQGm7StHJbU0ys51UxROlNZiU241en1XiO7fFuEDH6XAa1VWPPFLKf7qcp4ObBeH7spYp9e+z63GgE3F6L2hKGSf7NNPRsYMuZHgzbuaY1C7eNH2zymQbNq0CXPnzsXf/vY37Nu3D4MGDcLjjz+OGzduCJaQ8zrfVTNsq/IJhQHQYW2BYBf/d7/XoNs3RYLsiwhrxE8lmLxXB5Ot7nd/+0QljCxB/+VsHX6+6V663VdoRla+CWsuN7TB1dCjO5GRXi4POkLjFEiWLVuGp556Cs8++yx69OiBjIwMqFQqrFq1SrCEGGT01OatxwoJfy/t1+Gml7ESB7ysHlhYbcdjP5c5tZtU2xgMaReHrs3kv9wsCW+n/1eFPzQXtxXDZyCxWCw4deoUHnjgAafXH3jgARw+fFiwhLD1kCEkWPYXmbHsnAGTdpfh5WydY34ibz3vbm9V96T3eNdENI9tqA8d3SkB/7pb6XWuLkKCpXMz8ZvCfR6hrKwMNpsNbdu2dXq9bdu2KC4u9vo5jUbjV0ISAfRrHo9TlfQER5w92s6KrcXiXAxTOtViWmfXalUj0AowWIHCa6XQ3ooFD7aNw66ShnS0j7djZVoFrlUr0CWpGu3tMY7zPhHAtWoFLJZ4hNC4X9JI/+Y2LO9jxr7yaMy5KF77QqASohiY7N4fVKLB4KdBNX7fiz1Rq9Ws73O+Ol1XlWMYhnWlOV8H9mRrFzsGfq91dIF1dX+HeGT50QOrXv82sWgWG4V9heyfTWka7RgYlto8GoZaxusqe3ylKWNwXs99IOOFCe3RPjEKP98042SpBRO7JWHNZSM+PBP4LLGPpCSgV8tYZBWYcKykFhNSExGlUOCbW21RqsQoPNI5ER2Sor1OFwMAreKjkDGkBXbcMPEawd5LGYMLeivaJESh9NbMx0+rk/Bo5wR0LDBj/ZVqVFgaSgZ9W8XiuR5NcKzE4tZ+1ksZgy/ubYVpe8s95nPvpjZM7NUS09Oaci41vN/eiuQzVfjqcjWe6paE1glRUKs7Iclow21NojEgtgbqLg0TLyYZbXjEbMCgdnHYnW/C6st1aRyeHI/sIrPTgNORt8WjSWwUNntY1dOblKbRSG0e43ZNPN+zCaIAfHnR6La9p2npvRmmisNBbUN1XstYBoPbJyJaUbevrXkmVNbaseXBNlAogBnZepz1MJ7LVXJSFAqrG66rP90Wj5bxUU4zIfjrkZQE3JMcj8zcahwrqcWk7kno3TIW849UOHqa9VTGoENSNPYXmR0j7ROjFTj3hAqjtpU6Fk6bfUczDGoXh/s6xCM2SoFeAB653YbeG5zbOhfc1RwWO4PLeiv+t2siEmMU+Pd5Iw4WmTFUFQdzjRF7yrg/AKkSoxz3m6GqOORonatSvxjeElEKIONUFXQWO17r2wzT0prggs6Ku3+se6iPjQLevLM5ciuteLJbEgarghMAFXq9nrVBwGKxIDk5GStXrsS4ceMcr8+aNQvnz5/H9u3bBU+URqMJKBBFKsov/1Ge+Y/yzD+RlF8+y9xxcXHo168fsrKynF7PysrC4MGDRUsYIYSQ0MCp3PXyyy9j2rRpuOuuuzB48GCsWrUKRUVFeO6558ROHyGEEJnjFEjGjx+P8vJyZGRkQKvVolevXtiwYQNSUlLETh8hhBCZ49wSNGXKFEyZMkXMtBBCCAlB1C+REEIILxRICCGE8OKz+y8hhBDChkokhBBCeKFAQgghhBcKJIQQQnihQEIIIYQXCiSEEEJ4kVUgEXs531CRnp4OpVLp9F/37t0d7zMMg/T0dPTs2RPt27fHmDFjcOHCBad9mM1mzJ49G127dkWHDh0wceJE5OfnB/uriObAgQOYOHEievXqBaVSiXXr1jm9L1Qe6fV6TJ06FSkpKUhJScHUqVOh13tfElqufOXXSy+95HbO/elPf3LaJpLy68MPP8T999+PTp06ITU1FRMmTMD58+edtqFzrIFsAkkwlvMNJWq1GpcuXXL81zio/utf/8KyZcvw3nvvYffu3Wjbti0ee+wxVFU1rLE9b948bN26FStXrsT27dtRVVWFCRMmwGbjPo24nBmNRqSlpeHdd99FYmKi2/tC5dGUKVNw5swZbNy4EZmZmThz5gymTZsWlO8oJF/5BQD33Xef0zm3ceNGp/cjKb+ys7Px/PPPY9euXdiyZQtiYmIwbtw46HQ6xzZ0jjWQzTiSESNGoHfv3vjkk08cr915550YO3YsFixYIGHKgi89PR1btmxBTk6O23sMw6Bnz5544YUXMGvWLABATU0N1Go13n77bTz33HOoqKhAt27dsGzZMjzxxBMAgJs3b6Jv377IzMzEiBEjgvp9xHbbbbfh/fffx9NPPw1AuDy6dOkSBg8ejJ07d2LIkCEAgJycHIwePRpHjx4N2SnCXfMLqCuRlJeX47vvvvP4mUjOLwAwGAxISUnBunXrMHr0aDrHXMiiRBKs5XxDybVr19CrVy/cfvvtmDx5Mq5duwYAyMvLg1ardcqrxMREDBs2zJFXp06dQm1trdM2HTt2RI8ePSIiP4XKoyNHjqBp06ZOyyUMGTIETZo0Cct8zMnJQbdu3XDXXXfhlVdeQUlJieO9SM8vg8EAu90OpVIJgM4xV+Iv5stBoMv5hqsBAwZg+fLlUKvVKC0tRUZGBkaNGoVDhw5Bq9UCgMe8KiwsBAAUFxcjOjoarVu3dtsmEvJTqDwqLi5G69atnVYCVSgUaNOmTdjl45/+9Cc8+uij6Ny5M65fv4533nkHf/7zn7Fnzx7Ex8dHfH7NnTsXffv2xaBBgwDQOeZKFoGknr/L+YarkSNHOv09YMAA9OvXD+vXr8fAgQMBBJZXkZafQuSRp+3DMR//53/+x/Hv3r17o1+/fujbty927dqFP//5z14/Fwn59cYbb+DQoUPYuXMnoqOjnd6jc6yOLKq2WrdujejoaLcIXFpa6hbxI1HTpk3Rs2dP5ObmQqVSAQBrXrVr1w42mw1lZWVetwlnQuVRu3btUFpaCoZpaEZkGAZlZWVhn4/Jycno0KEDcnNzAURufs2bNw/ff/89tmzZgi5dujhep3PMmSwCCS3ny85kMkGj0UClUqFz585QqVROeWUymZCTk+PIq379+iE2NtZpm/z8fEfDXrgTKo8GDRoEg8GAI0eOOLY5cuQIjEZj2OdjWVkZCgsLHTfMSMyvOXPmIDMzE1u2bHHqfg/QOeYqeu7cuf+QOhEA0KxZM6Snp6N9+/ZISEhARkYGDh48iE8//RQtWrSQOnlB9fe//x1xcXGw2+24cuUKZs+ejdzcXHz00UdQKpWw2Wz46KOP0K1bN9hsNsyfPx9arRYff/wx4uPjkZCQgKKiInz55Zfo06cPKioq8Nprr6F58+ZYuHAhoqJk8fzAi8FgwMWLF6HVarF27VqkpaWhefPmsFgsaNGihSB51KZNGxw7dgyZmZm4/fbbkZ+fj9deew133nlnyHXPZMuv6OhoLFq0CE2bNoXVasXZs2cxc+ZM2Gw2ZGRkRGR+zZo1C99++y2++uordOzYEUajEUajEUDdg69CoaBzrBHZdP8F6gYk/utf/3Is5/vPf/4Td999t9TJCrrJkyfj4MGDKCsrQ5s2bTBgwADMnz8fPXv2BFBX9H333Xfx1VdfQa/X46677sKSJUuQlpbm2IfJZMKbb76JzMxMmEwmDB8+HB988AE6duwo1dcS1P79+/Hoo4+6vf7kk0/is88+EyyPdDod5syZgx07dgAARo8ejffff9/ReydUsOXXhx9+iKeffhpnzpxBRUUFVCoV/vjHP2L+/PlOeRFJ+eUtvXPmzMG8efMACHcdhkOeySqQEEIICT2hX8dBCCFEUhRICCGE8EKBhBBCCC8USAghhPBCgYQQQggvFEgIIYTwQoGEEEIILxRICCGE8EKBhBBCCC//H6jVzXSMGn7kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use Pandas Plotting with Matplotlib to plot the data\n",
    "data.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use Pandas to calcualte the summary statistics for the precipitation data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Design a query to show how many stations are available in this dataset?\n",
    "total_stations=session.query(station).count()\n",
    "total_stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('USC00519281', 2772),\n",
       " ('USC00519397', 2724),\n",
       " ('USC00513117', 2709),\n",
       " ('USC00519523', 2669),\n",
       " ('USC00516128', 2612),\n",
       " ('USC00514830', 2202),\n",
       " ('USC00511918', 1979),\n",
       " ('USC00517948', 1372),\n",
       " ('USC00518838', 511)]"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What are the most active stations? (i.e. what stations have the most rows)?\n",
    "\n",
    "active_station=session.query(measurement.station, func.count(measurement.station)).group_by(measurement.station).\\\n",
    "order_by(func.count(measurement.station).desc()).all()\n",
    "# List the stations and the counts in descending order\n",
    "active_station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('USC00519397', 53.0, 87.0, 73.09795396419437)"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "low_high_avg_temp=session.query(measurement.station, func.min(measurement.tobs),func.max(measurement.tobs), func.avg(measurement.tobs)). \\\n",
    "order_by(func.count(measurement.station).desc()).first()\n",
    "low_high_avg_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ignore this code\n",
    "#Using the station id from the previous query, calculate the lowest temperature recorded, \n",
    "# highest temperature recorded, and average temperature of the most active station?\n",
    "# lowest_temp=session.query(measurement.station, measurement.tobs). \\\n",
    "#                          order_by(func.count(measurement.station).desc()).first()\n",
    "# for x in lowest_temp:\n",
    "#     print(x)\n",
    "# # highest_temp=session.query(measurement.station, func.(measurement.tobs))\n",
    "# for x in highest_temp:\n",
    "#     print(x)\n",
    "# avg_temp=("
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('2016-08-23', 81.0)\n",
      "('2016-08-24', 79.0)\n",
      "('2016-08-25', 80.0)\n",
      "('2016-08-26', 79.0)\n",
      "('2016-08-27', 77.0)\n",
      "('2016-08-28', 78.0)\n",
      "('2016-08-29', 78.0)\n",
      "('2016-08-30', 79.0)\n",
      "('2016-08-31', 80.0)\n",
      "('2016-09-01', 81.0)\n",
      "('2016-09-02', 80.0)\n",
      "('2016-09-03', 79.0)\n",
      "('2016-09-04', 75.0)\n",
      "('2016-09-05', 79.0)\n",
      "('2016-09-06', 76.0)\n",
      "('2016-09-07', 76.0)\n",
      "('2016-09-08', 80.0)\n",
      "('2016-09-09', 79.0)\n",
      "('2016-09-10', 78.0)\n",
      "('2016-09-11', 76.0)\n",
      "('2016-09-12', 78.0)\n",
      "('2016-09-13', 78.0)\n",
      "('2016-09-14', 75.0)\n",
      "('2016-09-15', 79.0)\n",
      "('2016-09-16', 80.0)\n",
      "('2016-09-17', 79.0)\n",
      "('2016-09-18', 81.0)\n",
      "('2016-09-19', 77.0)\n",
      "('2016-09-20', 77.0)\n",
      "('2016-09-21', 78.0)\n",
      "('2016-09-22', 76.0)\n",
      "('2016-09-23', 82.0)\n",
      "('2016-09-24', 81.0)\n",
      "('2016-09-25', 78.0)\n",
      "('2016-09-26', 80.0)\n",
      "('2016-09-27', 80.0)\n",
      "('2016-09-28', 79.0)\n",
      "('2016-09-29', 80.0)\n",
      "('2016-09-30', 80.0)\n",
      "('2016-10-01', 80.0)\n",
      "('2016-10-02', 79.0)\n",
      "('2016-10-03', 79.0)\n",
      "('2016-10-04', 79.0)\n",
      "('2016-10-05', 80.0)\n",
      "('2016-10-06', 80.0)\n",
      "('2016-10-07', 79.0)\n",
      "('2016-10-08', 78.0)\n",
      "('2016-10-09', 76.0)\n",
      "('2016-10-10', 75.0)\n",
      "('2016-10-11', 80.0)\n",
      "('2016-10-12', 80.0)\n",
      "('2016-10-13', 79.0)\n",
      "('2016-10-14', 80.0)\n",
      "('2016-10-15', 79.0)\n",
      "('2016-10-16', 78.0)\n",
      "('2016-10-17', 78.0)\n",
      "('2016-10-18', 80.0)\n",
      "('2016-10-19', 81.0)\n",
      "('2016-10-20', 79.0)\n",
      "('2016-10-21', 77.0)\n",
      "('2016-10-22', 79.0)\n",
      "('2016-10-23', 78.0)\n",
      "('2016-10-24', 77.0)\n",
      "('2016-10-25', 77.0)\n",
      "('2016-10-26', 78.0)\n",
      "('2016-10-27', 77.0)\n",
      "('2016-10-28', 78.0)\n",
      "('2016-10-29', 77.0)\n",
      "('2016-10-30', 76.0)\n",
      "('2016-10-31', 77.0)\n",
      "('2016-11-01', 77.0)\n",
      "('2016-11-02', 77.0)\n",
      "('2016-11-03', 77.0)\n",
      "('2016-11-04', 77.0)\n",
      "('2016-11-05', 76.0)\n",
      "('2016-11-06', 75.0)\n",
      "('2016-11-07', 71.0)\n",
      "('2016-11-08', 71.0)\n",
      "('2016-11-09', 71.0)\n",
      "('2016-11-10', 71.0)\n",
      "('2016-11-11', 72.0)\n",
      "('2016-11-12', 71.0)\n",
      "('2016-11-13', 72.0)\n",
      "('2016-11-14', 77.0)\n",
      "('2016-11-15', 77.0)\n",
      "('2016-11-16', 77.0)\n",
      "('2016-11-17', 77.0)\n",
      "('2016-11-18', 77.0)\n",
      "('2016-11-19', 75.0)\n",
      "('2016-11-20', 76.0)\n",
      "('2016-11-21', 76.0)\n",
      "('2016-11-22', 75.0)\n",
      "('2016-11-23', 72.0)\n",
      "('2016-11-24', 74.0)\n",
      "('2016-11-25', 75.0)\n",
      "('2016-11-26', 73.0)\n",
      "('2016-11-27', 76.0)\n",
      "('2016-11-28', 75.0)\n",
      "('2016-11-29', 68.0)\n",
      "('2016-11-30', 75.0)\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",
      "('2016-12-06', 67.0)\n",
      "('2016-12-07', 69.0)\n",
      "('2016-12-08', 73.0)\n",
      "('2016-12-09', 70.0)\n",
      "('2016-12-10', 70.0)\n",
      "('2016-12-11', 70.0)\n",
      "('2016-12-12', 66.0)\n",
      "('2016-12-13', 67.0)\n",
      "('2016-12-14', 70.0)\n",
      "('2016-12-15', 66.0)\n",
      "('2016-12-16', 62.0)\n",
      "('2016-12-17', 71.0)\n",
      "('2016-12-18', 73.0)\n",
      "('2016-12-19', 69.0)\n",
      "('2016-12-20', 77.0)\n",
      "('2016-12-21', 74.0)\n",
      "('2016-12-22', 71.0)\n",
      "('2016-12-23', 72.0)\n",
      "('2016-12-24', 74.0)\n",
      "('2016-12-25', 74.0)\n",
      "('2016-12-26', 74.0)\n",
      "('2016-12-27', 74.0)\n",
      "('2016-12-28', 71.0)\n",
      "('2016-12-29', 73.0)\n",
      "('2016-12-30', 69.0)\n",
      "('2016-12-31', 66.0)\n",
      "('2017-01-01', 62.0)\n",
      "('2017-01-02', 66.0)\n",
      "('2017-01-03', 63.0)\n",
      "('2017-01-04', 62.0)\n",
      "('2017-01-05', 63.0)\n",
      "('2017-01-06', 64.0)\n",
      "('2017-01-07', 66.0)\n",
      "('2017-01-08', 60.0)\n",
      "('2017-01-09', 64.0)\n",
      "('2017-01-10', 63.0)\n",
      "('2017-01-11', 78.0)\n",
      "('2017-01-12', 67.0)\n",
      "('2017-01-13', 67.0)\n",
      "('2017-01-14', 65.0)\n",
      "('2017-01-15', 66.0)\n",
      "('2017-01-16', 67.0)\n",
      "('2017-01-17', 66.0)\n",
      "('2017-01-18', 72.0)\n",
      "('2017-01-19', 73.0)\n",
      "('2017-01-20', 71.0)\n",
      "('2017-01-21', 72.0)\n",
      "('2017-01-22', 72.0)\n",
      "('2017-01-23', 72.0)\n",
      "('2017-01-24', 69.0)\n",
      "('2017-01-25', 72.0)\n",
      "('2017-01-26', 71.0)\n",
      "('2017-01-27', 65.0)\n",
      "('2017-01-28', 64.0)\n",
      "('2017-01-29', 67.0)\n",
      "('2017-01-30', 67.0)\n",
      "('2017-01-31', 68.0)\n",
      "('2017-02-01', 70.0)\n",
      "('2017-02-02', 61.0)\n",
      "('2017-02-03', 65.0)\n",
      "('2017-02-04', 72.0)\n",
      "('2017-02-05', 73.0)\n",
      "('2017-02-06', 76.0)\n",
      "('2017-02-07', 64.0)\n",
      "('2017-02-08', 62.0)\n",
      "('2017-02-09', 61.0)\n",
      "('2017-02-10', 71.0)\n",
      "('2017-02-11', 69.0)\n",
      "('2017-02-12', 68.0)\n",
      "('2017-02-13', 74.0)\n",
      "('2017-02-14', 75.0)\n",
      "('2017-02-15', 75.0)\n",
      "('2017-02-16', 71.0)\n",
      "('2017-02-17', 71.0)\n",
      "('2017-02-18', 68.0)\n",
      "('2017-02-19', 69.0)\n",
      "('2017-02-20', 71.0)\n",
      "('2017-02-21', 70.0)\n",
      "('2017-02-22', 72.0)\n",
      "('2017-02-23', 74.0)\n",
      "('2017-02-24', 70.0)\n",
      "('2017-02-25', 64.0)\n",
      "('2017-02-26', 65.0)\n",
      "('2017-02-27', 72.0)\n",
      "('2017-02-28', 74.0)\n",
      "('2017-03-01', 66.0)\n",
      "('2017-03-02', 73.0)\n",
      "('2017-03-03', 69.0)\n",
      "('2017-03-04', 74.0)\n",
      "('2017-03-05', 71.0)\n",
      "('2017-03-06', 69.0)\n",
      "('2017-03-07', 68.0)\n",
      "('2017-03-08', 71.0)\n",
      "('2017-03-09', 72.0)\n",
      "('2017-03-10', 68.0)\n",
      "('2017-03-11', 71.0)\n",
      "('2017-03-12', 74.0)\n",
      "('2017-03-13', 71.0)\n",
      "('2017-03-14', 71.0)\n",
      "('2017-03-15', 71.0)\n",
      "('2017-03-16', 70.0)\n",
      "('2017-03-17', 72.0)\n",
      "('2017-03-18', 69.0)\n",
      "('2017-03-19', 73.0)\n",
      "('2017-03-20', 75.0)\n",
      "('2017-03-21', 75.0)\n",
      "('2017-03-22', 74.0)\n",
      "('2017-03-23', 70.0)\n",
      "('2017-03-24', 73.0)\n",
      "('2017-03-25', 76.0)\n",
      "('2017-03-26', 76.0)\n",
      "('2017-03-27', 75.0)\n",
      "('2017-03-28', 77.0)\n",
      "('2017-03-29', 76.0)\n",
      "('2017-03-30', 77.0)\n",
      "('2017-03-31', 76.0)\n",
      "('2017-04-01', 76.0)\n",
      "('2017-04-02', 76.0)\n",
      "('2017-04-03', 77.0)\n",
      "('2017-04-04', 76.0)\n",
      "('2017-04-05', 78.0)\n",
      "('2017-04-06', 77.0)\n",
      "('2017-04-07', 77.0)\n",
      "('2017-04-08', 77.0)\n",
      "('2017-04-09', 72.0)\n",
      "('2017-04-10', 75.0)\n",
      "('2017-04-11', 79.0)\n",
      "('2017-04-12', 78.0)\n",
      "('2017-04-13', 77.0)\n",
      "('2017-04-14', 75.0)\n",
      "('2017-04-15', 73.0)\n",
      "('2017-04-16', 74.0)\n",
      "('2017-04-17', 74.0)\n",
      "('2017-04-18', 75.0)\n",
      "('2017-04-19', 74.0)\n",
      "('2017-04-20', 78.0)\n",
      "('2017-04-21', 73.0)\n",
      "('2017-04-22', 76.0)\n",
      "('2017-04-23', 77.0)\n",
      "('2017-04-24', 77.0)\n",
      "('2017-04-25', 77.0)\n",
      "('2017-04-26', 77.0)\n",
      "('2017-04-27', 78.0)\n",
      "('2017-04-28', 76.0)\n",
      "('2017-04-29', 72.0)\n",
      "('2017-04-30', 63.0)\n",
      "('2017-05-01', 75.0)\n",
      "('2017-05-02', 78.0)\n",
      "('2017-05-03', 78.0)\n",
      "('2017-05-04', 78.0)\n",
      "('2017-05-05', 79.0)\n",
      "('2017-05-06', 79.0)\n",
      "('2017-05-07', 79.0)\n",
      "('2017-05-08', 77.0)\n",
      "('2017-05-10', 78.0)\n",
      "('2017-05-11', 78.0)\n",
      "('2017-05-12', 79.0)\n",
      "('2017-05-13', 79.0)\n",
      "('2017-05-14', 77.0)\n",
      "('2017-05-15', 78.0)\n",
      "('2017-05-16', 78.0)\n",
      "('2017-05-17', 78.0)\n",
      "('2017-05-18', 73.0)\n",
      "('2017-05-19', 76.0)\n",
      "('2017-05-20', 78.0)\n",
      "('2017-05-21', 76.0)\n",
      "('2017-05-22', 77.0)\n",
      "('2017-05-23', 87.0)\n",
      "('2017-05-24', 77.0)\n",
      "('2017-05-25', 77.0)\n",
      "('2017-05-27', 74.0)\n",
      "('2017-05-28', 76.0)\n",
      "('2017-05-29', 75.0)\n",
      "('2017-05-30', 76.0)\n",
      "('2017-05-31', 79.0)\n",
      "('2017-06-01', 79.0)\n",
      "('2017-06-02', 79.0)\n",
      "('2017-06-03', 79.0)\n",
      "('2017-06-04', 79.0)\n",
      "('2017-06-05', 80.0)\n",
      "('2017-06-06', 79.0)\n",
      "('2017-06-07', 79.0)\n",
      "('2017-06-08', 80.0)\n",
      "('2017-06-09', 80.0)\n",
      "('2017-06-10', 77.0)\n",
      "('2017-06-11', 79.0)\n",
      "('2017-06-12', 83.0)\n",
      "('2017-06-13', 80.0)\n",
      "('2017-06-14', 80.0)\n",
      "('2017-06-15', 78.0)\n",
      "('2017-06-16', 79.0)\n",
      "('2017-06-17', 80.0)\n",
      "('2017-06-18', 77.0)\n",
      "('2017-06-19', 80.0)\n",
      "('2017-06-20', 78.0)\n",
      "('2017-06-21', 79.0)\n",
      "('2017-06-22', 80.0)\n",
      "('2017-06-23', 78.0)\n",
      "('2017-06-24', 80.0)\n",
      "('2017-06-25', 80.0)\n",
      "('2017-06-26', 81.0)\n",
      "('2017-06-27', 80.0)\n",
      "('2017-06-28', 79.0)\n",
      "('2017-06-29', 79.0)\n",
      "('2017-06-30', 75.0)\n",
      "('2017-07-01', 80.0)\n",
      "('2017-07-02', 80.0)\n",
      "('2017-07-03', 78.0)\n",
      "('2017-07-04', 80.0)\n",
      "('2017-07-05', 80.0)\n",
      "('2017-07-06', 81.0)\n",
      "('2017-07-07', 79.0)\n",
      "('2017-07-08', 80.0)\n",
      "('2017-07-09', 80.0)\n",
      "('2017-07-10', 80.0)\n",
      "('2017-07-11', 78.0)\n",
      "('2017-07-12', 81.0)\n",
      "('2017-07-13', 79.0)\n",
      "('2017-07-14', 81.0)\n",
      "('2017-07-15', 80.0)\n",
      "('2017-07-16', 78.0)\n",
      "('2017-07-17', 80.0)\n",
      "('2017-07-18', 80.0)\n",
      "('2017-07-20', 80.0)\n",
      "('2017-07-21', 80.0)\n",
      "('2017-07-22', 81.0)\n",
      "('2017-07-23', 81.0)\n",
      "('2017-07-24', 78.0)\n",
      "('2017-07-25', 80.0)\n",
      "('2017-07-26', 82.0)\n",
      "('2017-07-27', 79.0)\n",
      "('2017-07-28', 81.0)\n",
      "('2017-07-29', 81.0)\n",
      "('2017-07-30', 81.0)\n",
      "('2017-07-31', 80.0)\n",
      "('2017-08-01', 77.0)\n",
      "('2017-08-02', 73.0)\n",
      "('2017-08-03', 79.0)\n",
      "('2017-08-04', 80.0)\n",
      "('2017-08-05', 81.0)\n",
      "('2017-08-06', 80.0)\n",
      "('2017-08-07', 80.0)\n",
      "('2017-08-08', 80.0)\n",
      "('2017-08-09', 80.0)\n",
      "('2017-08-10', 81.0)\n",
      "('2017-08-11', 78.0)\n",
      "('2017-08-12', 80.0)\n",
      "('2017-08-13', 81.0)\n",
      "('2017-08-14', 79.0)\n",
      "('2017-08-15', 78.0)\n",
      "('2017-08-18', 80.0)\n",
      "('2017-08-19', 79.0)\n",
      "('2017-08-20', 81.0)\n",
      "('2017-08-21', 81.0)\n",
      "('2017-08-22', 82.0)\n",
      "('2017-08-23', 81.0)\n"
     ]
    }
   ],
   "source": [
    "# Choose the station with the highest number of temperature observations.\n",
    "temp_obs=session.query(measurement.date, measurement.tobs).\\\n",
    "filter(measurement.station==low_high_avg_temp[0]).\\\n",
    "filter(measurement.date>=query_date).order_by(measurement.date).all()\n",
    "for x in temp_obs:\n",
    "    print(x)\n",
    "# Query the last 12 months of temperature observation data for this station and plot the results as a histogram\n",
    "df_station=pd.DataFrame(temp_obs, columns=['date','temp'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_station.plot.hist(bins=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus Challenge Assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function called `calc_temps` 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",
    "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",
    "\n",
    "# function usage example\n",
    "print(calc_temps('2012-02-28', '2012-03-05'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use your previous function `calc_temps` to calculate the tmin, tavg, and tmax \n",
    "# for your trip using the previous year's data for those same dates.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the results from your previous query as a bar chart. \n",
    "# Use \"Trip Avg Temp\" as your Title\n",
    "# Use the average temperature for the y value\n",
    "# Use the peak-to-peak (tmax-tmin) value as the y error bar (yerr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the total amount of rainfall per weather station for your trip dates using the previous year's matching dates.\n",
    "# Sort this in descending order by precipitation amount and list the station, name, latitude, longitude, and elevation\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a query that will calculate the daily normals \n",
    "# (i.e. the averages for tmin, tmax, and tavg for all historic data matching a specific month and day)\n",
    "\n",
    "def daily_normals(date):\n",
    "    \"\"\"Daily Normals.\n",
    "    \n",
    "    Args:\n",
    "        date (str): A date string in the format '%m-%d'\n",
    "        \n",
    "    Returns:\n",
    "        A list of tuples containing the daily normals, tmin, tavg, and tmax\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    sel = [func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)]\n",
    "    return session.query(*sel).filter(func.strftime(\"%m-%d\", Measurement.date) == date).all()\n",
    "    \n",
    "daily_normals(\"01-01\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the daily normals for your trip\n",
    "# push each tuple of calculations into a list called `normals`\n",
    "\n",
    "# Set the start and end date of the trip\n",
    "\n",
    "# Use the start and end date to create a range of dates\n",
    "\n",
    "# Stip off the year and save a list of %m-%d strings\n",
    "\n",
    "# Loop through the list of %m-%d strings and calculate the normals for each date\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the previous query results into a Pandas DataFrame and add the `trip_dates` range as the `date` index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the daily normals as an area plot with `stacked=False`\n"
   ]
  }
 ],
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