climate_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reflect Tables into SQLAlchemy ORM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine(\"sqlite:///hawaii.sqlite\")\n",
    "Base = automap_base()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reflect an existing database into a new model\n",
    "Base.prepare(engine, reflect=True)\n",
    "\n",
    "# reflect the tables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['measurement', 'station']"
      ]
     },
     "execution_count": 34,
     "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": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "measurement.date\n"
     ]
    }
   ],
   "source": [
    "# Save references to each table\n",
    "Measurement = Base.classes.measurement\n",
    "Station = Base.classes.station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create our session (link) from Python to the DB\n",
    "\n",
    "session = Session(engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Climate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EmEdBhxDidBSsPQcFHUIIIbyhoGMG3X0Rwg9qyvYcFHTMoBOB2MqLhpkmxCwKOoQQQnhDQYcQ4nRUP/QcFHQIYRFdPO1DTdmeg4IOIYQQ3lDQIYQQwhsKOoQQQnhDQYcQFlGPaftQsXkOCjqEEKejjgSeg9Ogs3jxYohEIkyfPp3LzRBCCBEIzoLOqVOnsH79erRv356rTRBCCBEYToJOWVkZxo8fj2XLlkEkEnGxCUII8ThFVSqMPliMR34vxOZrlc7Ojl04CTrvvvsunn32WTz22GNcJE8IcTPUkcA6X58vx86calwoqcPk1FJIq1XOzpLNfNlOcP369cjOzsaqVausWl4ikTi8TTbSqNdY5y9FRQUkkhKW0nYd7JWX57C2zAoKfQAE2LWuuzG/37rnWpFUCokkn9sMuThrjpPvLt4rNxUDfHXsJsYmKLnMls3EYrHZ71kNOhKJBPPnz8cff/wBf39/q9axlEFrtuloGhppd3T+DAoOhlgczk7aLoLV8vIQtpTZP96VwJVSnc88sbwtlpneuRYREQGxOITjXLkuq48xvXILbRoOsTiUo1xxg9Wgc/LkSRQXF6Nnz56az1QqFY4fP461a9ciNzcXAQEBZlIghBDizlgNOoMHD0aXLl10PpsyZQpatWqF9957z+raDyGEEPfEatARiUQGvdUaN26MsLAwtGvXjs1N8YKhN9aIjeiBuH2o3DwHjUhACHE6ur/zHKz3XtO3Z88erjfBGRpHixBC2EU1HTOoeY3Yim5UCDGPgg4hhBDeUNAhhBDCGwo6ZlBTCSGEsIuCjhn0TIfYiu5TCDGPgg4hhBDeUNAhhDgd1RA9BwUdQojTUUu256Cgw6Ed1ysxfJ8Uc0+VoUZFp5UnoDt2wichPnfmfEQCT3WjXInXD9cPcX8wtwZRjX0wuX2wk3NFCCHORTUdjiw8W67z9+yTZU7KCSHEXQnxtQ4KOmY4MhWsrEbNYk4IcW8CvHa6BCE2r1HQMeNUUR0kZXXOzgYhbk+A105iJwo6Fsw9LXd2FgghxChqXnNDe29WOzsLRECEeBEghE8UdAghhPCGgg4hxOmogug5KOgQQpyOOhJ4Dgo6hLDIi+7ZCTGLgg4hhBDeUNAhhBDCG1aDzvfff49evXohPj4e8fHx6NevH/bt28fmJghxadRlmhDzWA06zZs3x7x583DkyBEcOnQIffr0wcsvv4wLFy6wuRlCCCECxWrQGTx4MPr164eWLVvi/vvvx0cffYTg4GCcOnWKzc0QIiiZJTSUEiENOHumo1Kp8Ouvv0KhUCApKYmrzRDi8vruLkSxA4PHEmKKEAf8ZH0+nczMTPTv3x/V1dUICgrCTz/9hPbt25tcXiKROLxNNtKo15i19BUKf+gXL3v5dIyr5ENIrC2zfKkPgACdz6pVwLyjt/DWfZ5V4zFfZrrnmlQqhUSSz22GXJx1x5huuZWUlEAiKeAmQ3YSi8Vmv2c96IjFYqSmpqKsrAw7d+7EpEmTsHv3brRr186uDFoikUgcTkMj7Y7Rj+1JPyinGCjRHbeNtXw6gNXy8hC2lNlFvyrgconB54fLGmGpuAXLOXNdFstM71yLiIiAWBzCca5cl9XHmF65hYc3hVgcylGuuMF60PH390fLli0BAF26dMHff/+NlStXYvny5WxvihDBuFlBzWuEfdS8ZoRarUZtbS3XmyEsUtSpkVd57yLZ2NcbzYN8nJgj4qrUDIP/Xa1ESY0ao1sHIdSfXv0j5rEadD755BP0798fsbGxqKiowLZt25CWloYtW7awuRnCsWP5tRjxV7Hm736xAdjaP8KJORIOT3tNZ/4ZOZb8WwEA2H69CgefaebkHHkWIb4XxmrQKSgowIQJE1BYWIjQ0FC0b98e27ZtQ9++fdncDCHERTQEHAD4W1qHi6V1aBfm58QceRaPb1777rvv2EyOOIkAj2PiIqTVamdngbg4aoAlFgmxCk+IJxDiuUlBhxhgqK5DCOEIBR1ikQBvpgghLoqCDjEgxIeTrkKIzR18oyGBPBvn7+kQN+BBV9K+uwpRdPdhuBeAP4dE2rS+55SUfRiGwZA/pM7OBnEiCjqEaLmlUKGw6l4PLDXV+lh1RlqHSzKls7NBnIia14gBus7eQzUXdhVVUdOap6OgQyzypAsvPc8ihFsUdIgBT77w6u+6Bz3OIoQXbvtM51aFEhV19944iQvyocEI7eTJ111n7TvDMMgqU0Lk743oxjTYqierVjLILlcizk0G3XXboDPjRBn23rw3n81PTzbFkMRAJ+ZIODy4ouNwLY+tIDUlTYafr1aikQ+w9vGmeDqBjl1PJK9VY9DeImSWuk/Q8Zhbf0++kDrKk5qYXKF57WpZHX6+WgmgftbRSaml/GeCuIT1WQpkltb39rutMOyEIcSmcLet6XjQddJjFFapkF5QPzeTF4DIQG/0jAowv5KDnHEcnSrSnda6rFZ4VxaGqZ9rB7hXhl6edPfCkh+zFM7OAuvcNujoE+IdgbO4alFdKKnDq4fuTQX9RPMA7BjAbtBxhXHnlG7wclC6zBtJ63I1fz/ZPADbB9CcTGwTYhx32+Y1e36LTuE0D4gxrnJc83Ep1r85sXXf2bgIqAQSc4y9c9NQfgbl6CoHkZsR4s20YGs61UoGC87KUVnmi7jqcoT6e+O1B4JMLi/A38ZpXPVAdjQg2MMZTUIqV/0B9BzKrbF6WYo5pIFgg055nfrurIX+wE05wgN0gw7dWbHHVYuSi9/YFS73SpoHjVhJiNc5wTavVem1QcjrHD9TBXKDyTlXLQZemtf0/nbGOa00sqN3jPRccjZbfg++yrGgUoXh+6RotzkP35wv52mrziPEa5Zwg47emVmnBrJk93r96B/kQvxxXIWr3k25YrbYyJPKSEeClZkVLKTMH2edbt9drMDB3BrkVqox74wc2XIaXNTVuE3QAYCPT8s1/3fVC6UQ7cqptrwQD3i5cXCBmxNjNR2h9Whz1vtO9U3u2n+7d21HiNc5VoPO119/jSeeeALx8fFo1aoVRo4ciYsXL7K5CQ1jp+C+W9X4/G85ntsnxe83qvWWF9ZJ62ryK53fvMNH05crvBxqrKYjdAK8NgqCEFtwWA06aWlpGDt2LPbt24edO3fC19cXw4YNQ2kp+29UmyrsRefKbepVo5OmA/lxdym3XKO2o42PgOAqF0uh3NHWqBisvliB7Xmu8fqBEC/K7o7V3mvbt2/X+XvVqlVISEhARkYGBg0axOambA4QdPBZz1hZFVc7v0sVH7VVVz1OXDHmGCur1w+XQKFkAOiNEyaUqCkwQixWTp/pVFRUQK1WQyQScbkZwoM6F2zycdfmNWMO3rGv9s43hbEHUoRo4fQ9nZkzZ6Jjx45ISkoyuYxEIrEr7Zvl3gAaWb18Xn4+JCrzzyVqahrBWBy2J48KhT/0i9fefWWbpXzkFvkA0B1epqSkGBJJAYe5suxOsW6+FIpK1stUrQ6EdjjLvnYNjX2s/+1yiw3LroG1aRQX+wLw1/ksq0zpMsdPg4JC0/uqr1JRAYmkxGT5SKVSSCT5LOSqsc5fcrkcEomUhXS5Z+z3raszfk1qUFJc4vTzUp9YLDb7PWdBZ/bs2cjIyEBKSgp8fEwPyW0pg6aUFdUC54qsXj4qOhrilo3NLuN/sRBQ1Bl8bk8eg3KKgRLd5yD27iubJBKJxXxc8K0EsnSfwzVtGg6xOJTLrFkkCagCLt0bey0oOAhicQKr2/DKyAW0anWtWrVC3o1rVv92V/XyqM3aNMIVcuCmYa8rVzh+tEV5VQJXrHteGxQUDLE43GT5REREQCwOcTxTaXd0/gwNDYVYHOZ4uhwzdV76ncuvH2rchKbhTZ1+XtqKk6Aza9YsbN++Hbt27UKLFi242ITLtr27A6GULSfNaw4OtSOQouOdi7RSEhfA+jOdGTNmYNu2bdi5cydat27NdvIatj5UFsqFlJjmjN/QGc90TI33dqPctV50tGlEAidFHXc/7YV4XWM16EybNg0///wz1qxZA5FIhIKCAhQUFKCiQlhvUxNDrvJAXRs3HQnsO4uz5UrWggJj4kryxT9yo58LgdBebiXcYbV5bc2aNQCAZ599VufzGTNmYNasWWxuyuYIb83ipk52T2OsFFyhaLjOQvftBQbN515WhLYPMmRYfYn7ybZkNc7vtq7NlqC//7Zzet+54L0Sq1zxZtASVoOOTCZjMzneucKFlZjGdXfm60ZqKpY2oVQzrAccocywKYTTRQh5dIQQr1mCHHvtdoUSA/ba1g3SqpqOfdlxO8bKwRWvg2xnyZ59rOTxvRQX/AkIsZkggw5Xpzk1O5vmCndUntKRwBO5wOFlVEWdGjNPyPDiX8U4lu96L+gK8fgU5CRu9pSzNRcs12oxdx5XCDDWYL2mY8c2BFJUxE5fni3Hfy/WN58ezq2B5MVoBPu5zr26UM5Vba5Tejawp83bmt9GLcRfkCeWivxMUS0WnZXjRAF3d4Nc/zrWdBrQR4cMO4yVPMMw+O16FRadlSPHSd3Fl1641/O2SsXgf1crnZIPdyLImg5XqHnNPpkldRiwpwhKBkg+W44DQyLRJcLf8ooOYrtpwVh6LtV84VKZ4b6357qsSkxNlwEA/ntRgQsjohHo69wyqKhzrYuEix0SVhFmTceOdaw5QSjo1LO1y/Ssk2WaicfUDDDt7oWCa3ycb9S8xp69N6tsWn6q1nFUXKNGyi3b1gfc//cRYk1bmEGHo6sNPdOxT4Zek9oZaR1mn5Sxvh2uTzCubmZIvTdSTY/TZk0p5lbSGeoOPKZ5zbqXQx3bxvniWmSWKtE31rqRd10VG12ma1icaLRayUANxmC0ANab16z8TBuFHOvJax0rrSA7mtZcoVs9l1wtP9YQZNDhqpwd6UhwJLcG/9kvhZIBmgV6Q9xEkEXLGrZ+o503qjAptRS1aga9ovgP5pZOamqSZQdX5zTbP4+rVWxdLT/WEOSV0a5mECuWceQCMjm1VPNco7BKjcKqWvsTczI2DmS2LiLjj5Zoak1H8nSb8ezpbWaOPXeNfJ7zNH4ZcQf0TEeLI6f0nUoW25PcAUu/kblmOld4T4fPOOAKU4Z7OldrznK1/FhDkEHHHla9HOqCN5JqhsGaSxX4IEOGi6WGE8y5KgGeC0ZZeieMz2PG1Q5PNvNjTVruckyxSYjNa4IMOo40rynVDKYeL0XCplz8Z78UpVoj97pi0PnvRQWmZZRh9SUF+u0uQnkd93e7bBQDL92Z+ajqWEAvFPNHiHf1xJAwg44DB19afg1+zKqEvJbBgTs1WJ91b4TgYhcbOh4AZp8s0/xfoWTw42XDEY2rlQwUdWpUKtWoUjJQuUD05OMC4QrXID6PGFfYX64cyXW9cc0INwQZdBzx0SndibA+OVP/d5ZMGE1XdxSGDznGHSlB7E95aL4xDzEbc7H7ZrVD27Dl3ROlmkGtkSuvO18gtZ0pYr/DiKmyc+c7/SN5NSiqYv+5KNsVUVf7CYR4TAgy6HBRzhuvCGtMpQ1XFOi/uwjT0mUGw+vzeRxOMvHCHy81HRc44ZZf4G9WXHdvyVvGY1nay9V+AiEeE57TZdrCj+PtAhcwa0nK6vD2MRkA4KSRO21HL8bWHsd1agZbs40PTcJ2d2ZX5eMKkc9J2L7e5XPQA9SDfx6XJcyg48CRZGrNpQK4y2qw4J9ys9/zdZ4pzTzQsDcP806XoUrFwAuWLxiucD0R0s2Kq6vh4FmkEGsC2iydA4vOlWNK+2CIAoTTaCXIoGMPc8de3MZc3vLBhgoLPdhc4Tpo733BD5cVkFs5kq8r7CefQUfg10+LqgXwqpsrHHP61l9R4J2OIc7OhtWEEx61sP3DV/A45TAbLOWWr+Y1s3lw4rb5xGvQEVrh2KhGxc0OniqsxdjDJfjsjJyzbTjT3NNyywu5EM+p6bjfsWYSF9dBW9O0O+jY8ju5wG0nn8903P0QrtK6+bthZNI2e0paoVRjaIoUVXeDjZcXMKdrqL1ZJCxgvaZz7NgxjBo1Cm3btoVIJMKmTZvY3oRd3OWEterNbUdrOkY2YnSOHTO5sTcPQvudfKh5jTXavTDnsXT3/vuNak3AAeqfgTjCBe5zBI/1oKNQKNCuXTssWLAAgYGBbCcPwDk9UhiGwZZrlfjyrBy5d9+VKa5WYfG5cmy4YvjCpjNx0XMs+Z9yvHOsVOd5krlaCR/Na65wAWgRwl9jgbvP3aNd09lxw/YJ2/jA9y/gjj8562dM//790b9/fwDA5MmT2U4egH0Xm/fSZTiaV4PzJfa9BLryogJz7o4OsPayAv+OiMagvVJcKeN/7nZL++/oxdjUcb7+SiXaiPwwqX2w2eUAB2o6NpxkrhB0kpr5Y20WPzcd7ngB0lZl4dmqmgHmnirDzpwq9IwKwKIeTRDkJ8jH0h7No36x3xy4e5qjNRxNfpUas0+UOSXgWIPLmuAsrXIwG3TsTN9ck50r4iK3pn4/Vxukie0gqLKQ4LGCGnx7oQLXy1X4+Woltpl4R4y4Nqd3JJBIJDavo1ACQGPW82JKfR51t/e9kTHQrEvHOtJaYPjpQOhfvstkMiiqvQH4mFw3N/cOJFWmL1GW8lFQ4APA9IRpDetXmPkdSktLIZEUmt2OMWq14T6bUl5eDomk2OZtmNy2ynDbDftqqszyLZSVsbQskUp9AfgbfF5TU2vX+aLvpMwbOwt80TpIjZdjlXY/lyq0Yd8b5ObmmVynVqnS2j/D42rLNd0g885xGXp567/uYPm6YFsZ6qYnlUohkeTbsL71jOWrrq4RrKkbsHFcsEUsFpv93ulBx1IGjamoUwMZeRzkxjixWAyk3WEnHSuFVqpQddLw4G4iEkFWrgRKTQ+QGBcbC3FsI6PfSSQSi/mIYhTAVZnJ7xvWL6s1/Ts0bdoUYrEdvYSO37G6+rCn0Bff9YtHqD87FXbvk7mAXpdasVhstsyaQQFIZFalb+3vH15ZDuQYPkj39feHWBxvVRqm5FWq8NaxfKgZYF8RkBgdiXFtg+1Kq5mF48SY5s1jgEslRr/z9vG5V0ZWnm8GZWrFejZdc/TSi4yIgFjM/jsxpo4xv3P5Vr3AZM911FmcHnTs4Qpt+VxzpInM1lVLqlWoVtUP08/Actt6A1ubV2pUDNZeVsDLqz6PjXy88OoDQTrLGBs81JzP/pbjyx4i21YyQV5re3sRn81rbDRnfX2uXGcKj2kZZXYHHba5wODohAeCDDqewJHAauu6Y46U4rDW0PLDWjje69BYHqqUjM4zoSb+ukGn0I5RhldfUrASdKaly+xaz9KQRGxi43lXngvPcMvn3ERKNYNrciWiAn0ENYSMO2A96FRUVCA7OxsAoFarcfv2bZw/fx5hYWGIj3esaaCBUAfxK6xSoVmg6Wcx2sy+6W6hAGwtH/3FrT35be29pp+u/iKLHXyHwl7XypRYY8czOgC4bWSqCVNkNWqsulSBQB8vTGgbjEa+tv1Q2sV3u0KJH7MUiAvyxasPNIa3E04KtkMEXyGnTs1gaIoU6QW1iGzkjR0DItChqZ/m+xoVgzkny3Ao17EpQohxrIf4f/75B3369EGfPn1QVVWF5ORk9OnTB1988QVr2xDqCMZL/7V+UFE+azr6Ac7aZg5z740Yy4P+0vrXyYxC9uemscbRPH4mEHvxQDGS/ynHx6flePu48SkhzGloeVSqGfTbU4TF5yswNV3m8AuProKvis7OG1VIL6g/1oqq1fjwVJnB92suK3BN7vxaoVBvsM1hvabz6KOPQiaTsZ2sW1ieWYHPkppYtawjI2nbGnYMajpWrJNRUIMsmeku49YEHW+9pdz9PZSGCx1Q3xNrdR/b1m8onz9uVSOv8t6vlPxPOWZ0Fv7QLi1D+Wnt15+O47DerKXjj9p+Q0CsJ8jGTDcM/gZM7ePma5WaERFMrutg85o1F//F58rxznGZTXnQr0HpL+OsmJNyWxjNKA1hhot5Z1wBX0HHnLNS87Xtv6V1+OVqJWQuOLW9UAgz6HhA1DG1j2W1DP61MKqCw81rVqxjaRmjNR29qKK/XUsvB3Jl3y1hBJ0YK58HmmOshIftk2JdloKXYXbMbaFh887s7DD7ZJnZ73fcqMKk1FI8trMQtRyMWF1YpcKEoyV4Yb+Uk6nQXYHzby3s4AExx7FnOjYPCa3fzGX+ZMqWKyGxMBqDsSzoByp7aliewFQ59I2z7UVMax3OrcHh3Bp0CvdDlwjDl1KN2SRRaGavtYW554UNXzmzJnfdyOjWxuRUqPC/a5UY3TrI8sI2mHWiDL9er2/+O19SjEAbO5sIgSBrOp6Az/d0bL34d/21ADcrLDXxGebCUk2H3tOoZ6r3IBuXH3PzyVi6y29wo1yJKWkyu7ZvPujcnX7ArpQdJ6tR6zwrs+Rvlmsi18qUmoADAAVVatwod7+mVEEGHU9oXnPkh+Gjec2ePFjqMu1Ia0VRlQoj/pSiw5Z8fH1e2L25FBZezp2eYV1w0Fdep8Zfd0z31Cuutu6XX+JA+VrTvGb/YLGOdfVfdcm2KevZfqpT6yF3XcIMOs7OAA8cquk4/J6O/ds2lweD3mt6CznycuCqiwrsv12D2woV5p+R44rMvtHE9dWqGPx4yxdvpZVafJbGliUmutZfNtNb0BrrWBoN21JQNMfcb9xw3NnyzlGZ1hAWjpZPso0v+rIdIxyZhVZu61AeTiTIoOMJHHkXSX/NXTlVSNyUixabcnGk2PBhNBfNXF6ob5t/+1gpJqWWIqdcaTFdR06br/Tuvtmq7Sw+X46VOf7YKKnE03uLrB4iiAvbr1dhW3al3ev/IjG/rrV75shNnzXPdGy5KOVoPYOp47mm0FAzr1UxqFExUKkZVCkZozWuhmXMcaRcHaklMQyDurt5L6lWoVLJbQCjjgQuiq23dBiGwVtppSi7O65Y8lV/jOthfltsDEfiBWBSaikO3X0H4lJpHdY90VRnGcPeaw5vVoONF1wBYOHZe8GrvI7B1mz2Hx7bYtwR+98hcYXGG2uCji01de30rJ46nKUeK2qGwZ6cKkw8WooKrZuR51sGYnWfME2NbX2WAtMyZAj08cJ/+4Th6QTjw0w5UtNxpPZw4E4Nnv9Td7T2W/8XgxCO5ioSZE3HI57psNS8VqsGZFoDWRbXeRlcaPXLk437HFmtWhNwAOBscZ1OUwhgJNjZueEPMmT2rQjba3XWPvewhqvPBKpSM9gkUWD1xQrW7n7NpaJ5pmNDei8eKMbQFClOF9Xy3uWeYYBJaboBBwC2ZVfhyN1jv07NYFqGDHVqQF7H4C0zHTAcGcrIsXUNP9t81f4atSVU07HC53+zM1+7tb48K0e1A7f92gegsRNRzUBnDhUuRgYw9lxCpXfFMQx29m149SXDZxXWpmTrpZTNY0//d3A1s0+WYdXdst19sxo7B0bUf+FAns0F+YbfwpYbrrxKNfIqa/DaoRIMbWF8Og+uMDA9Mvl/LynwRGwjFFWpoTXDO4rNvFTK1o2mNdQMg6/PVyD5Hzl6Rxt2xf/4tJyz0ccFGXT4xvfYVl84OHJxw/FXUKnC2WLDh98qRncKOP0D9ngBNy+l6bc761ezndF5x9ZtslnLVsPcVHzss7V4V2kF86N5NchVqNA8yLEcm226vfudPYfBbYUKKzP5mTa8gf5wOsYYa6GqVTHwN3K34cih9bOkEqsuVSAx2BfLHxEhPtj8pX3h2XJN07GxsQcrlQxulCvRIoT9ECHQ5jUXvj100Bf/OF6r8gJwoaQO3XcUYORfhjNrKi10XebKP1LdAKj/M7L5TMfapGyt1emXFVcP1R3BMAzSC2qQXlDDahOefvOoPax5puMWPYfvlruxY7rSRGcURy5rs06W4Ua5CkfyaqwarV37WaUpXL2kK8ig444uldZhwpESfGnFwWCJlxcw52SZzrMcbfongiPVeltclet2aTXsMs1PPnS2aeN9NdvNa1dkddieXYliK2aHtNYnp+UYtFeKQXul+OT0vZsYR/POxr2eNe/pcH0c8HmYKfV2pnljb5Pz91xnaVTrdVcMn8dskiiQ8FMu2m/OR0aBdaOqW90xw0bUvOYk27MrcbNChRB/LwT4eGF6ehmqWLrV9wJwxMxw/QbPVljZqmXG3iWQ16rx+40qJAT7sjqJl7VJ2XyBY7GwThTWYORfxahRAdGB3sh4LgpN/B3bAMMw+PbCvedp316owCfdQuHl5cXaxdaRHOofe9oY1De/sRmAnc3wBs946TEM8J/9Uta2+9TuQqQ8HQlfby9UKxm8e7yhM4NKZyJFc7h63khBxwkYhsEYB7q+Osrg4s5T1NFvA5eUKZGwKY+fjZtg8zMd/b+97O948d5xGWruXl/zq9RYfakC7z8YYl9idxlruWnosGDtPc1ZaS2OGXmux8ZhYq6HWUmNGs/vL8bBXG7nN+Kzk5t+mfuaaFu6rPAy+tvZ63RRHT49I8e4tkEGnRn0m7lN4aoFhIKOE5zkeLIyS7Vi/YPbHdtYrX6mY+Y7LyPfs/k8MVtvXK0Dd2rwbkcHg46RmkRDxxGVFRH2fHEtnthVxFkTlLzOdMp/W3kxdBTXQU2bfpCV1zLouaMAPt5e8PMGwgO8sa1/BH6542ciBft9e6EC316oQPsw+y7zvhxFHQo6TvC/a9z1gQcs35Fq330xDMPqHZbQmO1MZeQzNjsSGEvb0ecZ+p1EGj7zh+U7aUmZEn12FlncxgkHbprmn+H39QNjOJiRwKTfb+hOm6FkGFzSGq4nKrD+lu+PIu4uxZml9g0PxFXzmjve5Ho8S3fj5VrPVopr1NhmRddPobH+mY5tVyCur1f2vqvUwNgzk4baj6MBrWF9dxz5mHVeXki5VYXP9N/x029uc+GeuBR03AjXbcqWjpUpafeeJ7H5hr0Q2fpTVGtVFxiGYfWu2csLqLXzet7QNdrY+GMN02Q72lFDxbj+KAoug2HwppHRB/SbF00943EFXDWvufAuE3tZ6v1zquhe2zkb714Ima1BIy743suRJSxPWZxeUIsWP9vXsWLs3Y4pxprQVl2sgKxGjVwb5ooxRsUwZntFkntOFtVCasUNXU6FCkP+sNyk6QxcdSSgoMOzoioV5000z+4zfCHUlIIqzws60moVVl2swF+3q7Enx7apqrXPQ1NDoDjD9utVqFPXv0Wu72Bujd3BTFulksGxfPecQpltpTXWHxtpLlqmguoyvWbNGixduhQFBQVo06YNkpOT0atXLy42JTji/+U7OwsAgIuldahTM3jlYImzs8KJHTeq8KORz+vUDB7fWYTbCvvasf6R1mJEq8YATL9Z7ixDU6SapjQuDNorxbM8j29GnEcwvde2b9+OmTNnYvHixejRowfWrFmDF154ARkZGYiPj2d7c8ROvX4rdHYWOCf68Q4ODIlEs0Bv7L1ZjfMldVCqGbsDDgB8d1GBR2PqB0g8zfJ0xY7iMuA00O+N5S5WZNo2a6gn8OeoHcxLJpOxervWt29ftG/fHkuXLtV81rVrVzz77LOYO3cua9sR/XiHtbSI++rQ1A/Rgd5mp2kmhBgqerU5/Dio7bAay2pra3H27Fk8+eSTOp8/+eSTOHHiBJubwpAEquYTy7wADG0RiP/cVz9xVqiDw8wQ98XXGIRCwUXAAVhuXisuLoZKpUJkZKTO55GRkSgsNN6cI5FI7NrW9FhAVh6AtFJ2B4f3AYPoRgzuVFMfCwCIb6TG6gerkVvtjbHnhRfow1CF0zcq8esdP3QJVeG5aCU+vmI4f4i9OoeqcFbO3wQFvl4MlIzwro4+YJAUpkY6y+crm9xidGuWPBKmsvvaLBaLzX7PSUcC/ZcTGYYx+cKipQyas7sNcPjfqxh22vj0r9qaBXpjWItAvNsxBM2DfHC6qBaFVSrsyqnGycIaPN68EZRqBi/e3xhtRH54I7UUKbe4a79uF+aLiybeFJ7eKQSbr9UPCAoAjX29jD60/uShUPSK9se8M3K7ehU92NQP50uMDz0y7cEQ9Iz2R9cIf4TdHRW3VWItHt91r3tn53A/DIhvhDc7BGPLtUq8n14/kGBSpD/6NA/AhLZBiGzkjX23qzHvtBw3K1R4pXVjtAjxRWpeDfbcrC/fEa0Cca1MiTNaw6D80rcpdlyvwhatF1dHtAzEqj5h8PLywpdn5RbnHRrfJgjNAr3xcDN/hIXV4JZChUcfCEaXEplm/KkPu4Zi87VKSMrqf4sX72+MQfGNMPqQbgcLf2/gyVjDoDsksRHeOSYz6Hr9YFM/BPp6mX17/8vuTXC+pA4/SepHqOga4YeEYF8UVKk0z2e2PBWOsABvrM1SQKVm8LK4MW4pVPjoVJlOD6mmAd7oFxeAzdd0X/TtHO6nmVOpU7gfzhmZXynEzwvlZoan0d6n/7QMxNzTpkcV8PbSvXh/9nAo3uwQAoZhcE2uxHcXFfjhsgKtm/jiyt0y/+GxME2Xb2tEB3oj30yvy8RgHzzRPMDoaMsNVvUJw4Hb1diSXQUfL2BiuyCcKKjFvyV1uP5SDAJ8vLD4fDmS7x5jPZr5429pLfTfMAj197LYi7FTuB++7xOG7dercL1cibggH8jrGHxvZPJBWzzRPEBndl42tAr1wcD4QEzrFKI579nG6jOd2tpaxMTE4IcffsCwYcM0n0+bNg0XL17E3r172dqUhkQicShweRoqL9tRmdmOysw2nlRerIYyf39/dO7cGYcOHdL5/NChQ+jevTubmyKEECJArDevTZkyBRMnTsRDDz2E7t27Y+3atcjPz8frr7/O9qYIIYQIDOtBZ/jw4SgpKcGiRYtQUFCAtm3bYsuWLUhISGB7U4QQQgSGk44E48aNw7hx47hImhBCiIBRv2BCCCG8oaBDCCGEN6wPg0MIIYSYQjUdQgghvKGgQwghhDcUdAghhPCGgg4hhBDeUNAhhBDCG4tB5+uvv8YTTzyB+Ph4tGrVCiNHjsTFixd1lmEYBsnJyWjTpg2io6MxePBgXLp0SWeZdevWYciQIUhISIBIJEJOTo7R7R04cAD9+vVDTEwMEhISMHToUIs7kZmZiaeffhrR0dFo27YtFi5cCIbR7ZS3detWPPLII4iJiUHr1q0xYcIEFBQUOLzvn332GR5++GE0b94ciYmJGDp0KKZOnerSZVZdXY1JkyahV69eiIiIwODBgw2Wyc/Px7hx4/Dwww+jadOmmDRpktk0ta1ZswYPPvggoqKi8Nhjj+H48eM63xcWFmLSpElo06YNYmJi8NBDD6FXr16cl1dqaipEIpHRf7/99pvZfbJ0jNlbXtYcYxUVFZg+fTratWuH6OhodOvWDSNGjODlGOOyzLSlp6cjPDwcPXv2tFhmx44dw6hRo9C2bVuIRCJs2rTJYBl3LbNJkyYZTbd58+YOl5k1+84Gi0EnLS0NY8eOxb59+7Bz5074+vpi2LBhKC29NxT5t99+ixUrVmDhwoU4ePAgIiMj8dxzz6G8/N7Q85WVlXjyyScxc+ZMk9vavXs3xowZg5EjR+Lo0aP4888/8X//939m8yeXy/Hcc8+hWbNmOHjwIBYsWIBly5Zh+fLlmmUyMjIwceJEvPjii0hPT8emTZtw+fJljB8/3uF9F4vF+Oqrr3D8+HGkpKQgMTERGzZswIgRI1y2zFQqFRo1aoQJEyagf//+RpepqalB06ZN8e6776Jbt25m09PWMF35+++/j6NHjyIpKQkvvPACbt26BaD+wH755ZeRnZ2NTZs24ejRo6iqqkJeXh5+++03Tsure/fuyMrK0vn33nvvITg4GE899ZTJfbLmGLO3vKw5xubMmYP9+/fjv//9L06cOIH3338ff/75Jzp27Mj5McZlmTWQyWR444038Nhjj1lVZgqFAu3atcOCBQsQGGh8WhN3LbMFCxYYpN2iRQudUf3tLTNr9p0VMpmMseXf7du3GW9vb+aXX35hZDIZU1paykRFRTEffvihZpm8vDwmODiY+eabbwzWP3ToEAOAOXfunM7nxcXFTFxcHPPtt9/alJ/FixczISEhTF5enuazOXPmMDExMUxpaSkjk8mYTz/9lImLi9NZb/ny5UxQUJBD+27s382bNxkAzK+//uqyZab9b/z48Uzv3r3NLjNgwADmxRdftCq9hx56iBk9erTOZy1btmSmTp3KyGQy5vTp0wwAJjU1VfN9SUkJExERwSxdupTT8jL27/7772deffVVh48xe8vLmmOsbdu2zAcffKCzXK9evZjx48dzfozxUWZDhgxhZs6cycyYMYNp27atTeUVFBTErFixwuBzdy+zhn8pKSkMAGbfvn0OlZmt++7IP5uf6VRUVECtVkMkEgEAcnJyUFBQoDNFdWBgIHr16mXTFNVnz57F7du34e/vjz59+qB169Z47rnncO7cObPrnTx5Ej179tSJ3n379kVeXp6m2tu9e3cUFBTgjz/+AMMwKC4uxvbt29GvXz8b9txw3/XV1tZi/fr1CA0NRceOHU2u5+wy44o105XX1NRPOtWo0b0J0by9vREQEID09HQA3JWXvtTUVFy9ehWvvfaa2eWsOcbYYuwY69GjB1JSUnD79m0AwIkTJ3DhwgX07dvX5HpCKbM1a9agsLAQ06dPtztPxrhzmWlbv3492rZt6/DUMVztuzE2B52ZM2eiY8eOSEpKAgDNcxFbpqg25saNGwCAzz//HO+//z62bNmC5s2bY8iQIcjLyzO5XmFhodFtN3wHAElJSVizZg0mTJiAyMhItGrVCgzD4LvvvrM6f4DhvjdISUlBbGwsoqKisHLlSuzYsQPNmjUzuZ6zy4wr1kxX3rp1a8THx2P+/PkoLS1FbW0tlixZgjt37mjKhavy0rd+/Xp06NABXbp0MbucNccYW4wdYwsXLkTHjh3RoUMHzTO4Tz75BAMHDjS5nhDKLDMzEwsXLsTq1avh48PuNNbuWmbaysrK8Pvvv2P06NF2560BV/tujE1BZ/bs2cjIyMDGjRsNDhJbpqg2Rq2unwd22rRpePbZZ9G5c2d8++23aNKkCTZv3gyg/u4lNjYWsbGxeP75581uW/vzy5cvY+bMmZg+fToOHz6MX3/9FQUFBXj33XcBAMePH9ekGxsbiy1btti0748++ihSU1Oxf/9+9O3bF6+99hry8/NduswcZa7MzO2Xn58fNm7ciOvXr+O+++5DTEwMUlNT0a9fP/j4+HBaXtpKS0uxa9cug7tPe48xSxw5xlatWoUTJ07gl19+weHDh/HFF1/go48+wl9//WV2PVP5doUyq6mpwdixY/Hpp5+iRYsWRrdnTZmZ4o5lpm/Lli1QqVQYNWqU5jNHyszU9u3dd1Osntpg1qxZ2L59O3bt2qVzkERFRQGoj8RxcXGaz6VSqUHUNKchnQceeOBe5nx90bJlS00VecuWLVAq6+dVb2ieadasmUEklkqlAO5F7a+//hpdu3bF22+/DQDo0KEDGjdujEGDBuGjjz5Cly5dkJqaqllfP9+m9r1BUFAQWrZsiZYtW+Lhhx9G165dsWHDBpSWlrpkmbHBWJkFBATAx8fH6O+hvV+dO3dGWloaysrKUFdXh4iICPTt2xc1NTU4f/48Z+Wl7eeff4a3tzdeeOEFnc/tPcYssfcYq6qqwvz587Fu3ToMGjQIQP3x+++//2LZsmU4cOAAp8eYNjbLLD8/H5cvX8aUKVMwZcoUAPU3UQzDIDw8HFu3bkXPnj3Nlpkp7lpm+tavX4+hQ4ciLCxM85ml48wULvbdFKtqOjNmzMC2bduwc+dOtG7dWue7xMREREVF6UxRXV1djfT0dJvaGTt37oyAgABIJBLNZ2q1GtevX0d8fDwAICEhQXNxb+gimJSUhPT0dFRXV2vWO3ToEGJiYpCYmAig/iDUv5tp+JthGAQGBmrSbdmyJUJCQqzad1PUajVSUlJctszYYKzMbJ2uvEmTJoiIiMC1a9dw5swZ3Lp1i9Py0rZx40YMGzYMTZo00fnc3mPMEnuPsbq6OtTV1Rk9fiUSCefHmDY2y6x58+Y4fvw4UlNTNf/GjBmDli1bIjU1FUlJSWbLzBx3LTNtZ86cwYULFwya1uwtMy723RSLNZ1p06Zh8+bN+OmnnyASiTRtf0FBQQgODoaXlxcmTZqExYsXQywW4/7778dXX32FoKAgnWpjQUEBCgoKcPXqVQBAVlYWysrKEB8fj7CwMISGhuL111/HggULEBsbi4SEBKxevRplZWUYMWKEyfw9//zzWLhwISZPnoxp06bh6tWrWLJkCT744ANNtXDgwIF455138MMPP6Bv377Iz8/HrFmz0KlTJ83F2Z59l8vlWLp0KQYOHIioqCgUFxfj+++/x82bN1FYWIhffvnFJcsMqG9yrK2tRXFxMRQKBc6fPw8AePDBBzXLNHwml8vh5eWF8+fPw9/fH23atDGZrjXTlf/2229o2rQpEhISkJmZiYkTJ8LX1xcbNmzgtLwapKen4/Lly1iyZInZMmpgzTFmb3lZOsZCQ0PRu3dvzJs3D0FBQYiPj8exY8ewceNG+Pr6YsuWLYIsMz8/P7Rr105nnYiICAQEBBh8rq+iogLZ2dkA6m+ybt++jfPnzyMsLAzx8fFuW2ba1q1bh1atWuGRRx6xKm1LZWbtvrPCUvc2AEb/zZgxQ6e73YwZM5ioqCgmICCA6dWrF3P8+HGddGbMmGE0He2ue0VFRczbb7/NNGvWjAkJCWF69+7NHD582GIXvGPHjjE9e/ZkAgICmKioKGbmzJkGXQwXLlzItGnThgkMDGSioqKY559/nsnMzHRo33Nzc5nBgwcz0dHRjL+/PxMdHc0MGjRIEGUWHx9vNG1L+x8fH28x7a+++oqJj49n/P39mU6dOjF79uzR+X7BggVMbGws4+fnx8TFxfFaXjKZjBk1ahTzwAMP2NTN05pjzJ7ysmbfs7KymJdeeomJiYlhGjVqxIjFYrcpM/28WNNleteuXUbzrN1N3Z3L7NatW0xQUBAzb948q9O1psys2Xc2/tF8OoQQQnhDY68RQgjhDQUdQgghvKGgQwghhDcUdAghhPCGgg4hhBDeUNAhhBDCGwo6hNhp06ZNZifXI4QYoqBDCM9SUlKQnJzs7GwQ4hQUdAjh2b59+7Bw4UJnZ4MQp6CgQwghhDcUdAixwqlTp9C/f39ERUWhQ4cO+OabbzRznTTYu3cvRo4cibZt26JZs2bo0KED5s6dq5ktFQAmTZqEH3/8EQAgEok0/7SfC/3666/o27cvYmJikJCQgJEjR+Ly5cv87CghHLN6Ph1CPNXly5cxbNgwhISEYNq0afD398e6desQFBSks9xPP/0EHx8fTJgwASKRCCdOnMCyZctw584drFmzBgDw+uuv486dOzh69ChWrVqlWTciIgIAsGTJEnzyySd45plnMGrUKCgUCqxZswYDBgzAkSNHTE54RohQ0ICfhFjwyiuvICUlBSdPnsR9990HoH5yq65du0Iul+PcuXNITExEZWUlGjdurLPuokWL8MUXX+DChQuIjY0FAEydOhU//vgjZDKZzrK3bt1Cly5d8P7772PWrFmaz/Pz85GUlIShQ4di+fLl3O4sIRyj5jVCzFCpVDhw4AAGDhyoCThAfc1Ef86ihoCjVqtRVlaG4uJi9OrVCwzD4Ny5cxa3tWvXLiiVSvznP/9BcXGx5p+fnx+6deuGo0ePsrtzhDgBNa8RYoZUKkVlZSXEYrHBd/fff7/O35cuXcLHH3+MtLQ0VFVV6XxXVlZmcVvXrl0DUD+DpDH6tShChIiCDiFmNHQW0J+5Ufs7oD6oPPPMMwgMDMRHH32E++67D4GBgcjNzcXkyZOhVqstbqthmW3btsHX1/DU9PamhgkifBR0CDEjMjISjRs3xpUrVwy+a6iZAEBqaiqkUil2796tM4Ww9pzzDYwFMACa5ru4uDizU1wTImR060SIGT4+PnjyySeRkpKC69evaz6XSqXYunWrznKAbu1HrVZjxYoVBmk2NJPpdyQYOnQofH19kZycbLRmJJVKHdoXQlwB1XQIsWD27Nk4ePAgBg0ahHHjxsHPzw/r1q1DfHy85llNjx490LRpU0yaNAkTJ06Er68vdu7ciYqKCoP0unTpAgCYPn06nnrqKfj6+mLgwIFo0aIF5s2bhzlz5uCpp57CM888g7CwMNy6dQv79+9Ht27d8M033/C674SwjbpME2KFEydO4MMPP8T58+cRGRmJsWPHIjIyEm+++aamy/Tp06c1ywQFBWHo0KEYM2YMevfujRUrVuDll18GUN8jbvbs2dixYweKioo0vdsSExMB1A+Ts2zZMpw7dw5KpRIxMTHo0aMHxo0bh65duzqzGAhxGAUdQgghvKFnOoQQQnhDQYcQQghvKOgQQgjhDQUdQgghvKGgQwghhDcUdAghhPCGgg4hhBDeUNAhhBDCGwo6hBBCeENBhxBCCG/+H6jM+v1MXvi8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Design a query to retrieve the last 12 months of precipitation data and plot the results. \n",
    "#Starting from the last data point in the database. \n",
    "prev_year = dt.date(2017, 8, 23)\n",
    "\n",
    "# Calculate the date one year from the last date in data set.\n",
    "prev_year = dt.date(2017, 8, 23) - dt.timedelta(days=365)\n",
    "\n",
    "# Perform a query to retrieve the data and precipitation scores\n",
    "results = []\n",
    "results = session.query(Measurement.date, Measurement.prcp).filter(Measurement.date >= prev_year).all()\n",
    "\n",
    "# Save the query results as a Pandas DataFrame and set the index to the date column\n",
    "df = pd.DataFrame(results, columns=['date','precipitation'])\n",
    "df.set_index(df['date'], inplace=True)\n",
    "\n",
    "\n",
    "\n",
    "# Sort the dataframe by date\n",
    "df = df.sort_index()\n",
    "#print(df.to_string(index=False))\n",
    "\n",
    "# Use Pandas Plotting with Matplotlib to plot the data\n",
    "df.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>precipitation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2021.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.177279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.461190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       precipitation\n",
       "count    2021.000000\n",
       "mean        0.177279\n",
       "std         0.461190\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         0.020000\n",
       "75%         0.130000\n",
       "max         6.700000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use Pandas to calcualte the summary statistics for the precipitation data\n",
    "df.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(9,)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many stations are available in this dataset?\n",
    "session.query(func.count(Station.station)).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What are the most active stations?\n",
    "# List the stations and the counts in descending order.\n",
    "session.query(Measurement.station, func.count(Measurement.station)).\\\n",
    "group_by(Measurement.station).order_by(func.count(Measurement.station).desc()).all()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(54.0, 85.0, 71.66378066378067)]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the station id from the previous query, calculate the lowest temperature recorded, \n",
    "# highest temperature recorded, and average temperature most active station?\n",
    "session.query(func.min(Measurement.tobs), func.max(Measurement.tobs), func.avg(Measurement.tobs)).\\\n",
    "filter(Measurement.station == 'USC00519281').all()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Choose the station with the highest number of temperature observations.\n",
    "# Query the last 12 months of temperature observation data for this station and plot the results as a histogram\n",
    "results = session.query(Measurement.tobs).\\\n",
    "filter(Measurement.station == 'USC00519281').\\\n",
    "filter(Measurement.date >= prev_year).all()\n",
    "df = pd.DataFrame(results, columns=['tobs'])\n",
    "df.plot.hist(bins=12)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Challenge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}