temp_analysis_bonus_2_starter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "from pprint import pprint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reflect Tables into SQLALchemy ORM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create engine to hawaii.sqlite\n",
    "engine = create_engine(\"sqlite:///Resources/hawaii.sqlite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['measurement', 'station']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View all of the classes that automap found\n",
    "Base.classes.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save references to each table\n",
    "measurement = Base.classes.measurement\n",
    "Station = Base.classes.station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create our session (link) from Python to the DB\n",
    "session = Session(engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus Challenge Assignment: Temperature Analysis II"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(62.0, 69.57142857142857, 74.0)]\n"
     ]
    }
   ],
   "source": [
    "# This function called `calc_temps` will accept start date and end date in the format '%Y-%m-%d' \n",
    "# and return the minimum, maximum, and average 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",
    "# For example\n",
    "print(calc_temps('2012-02-28', '2012-03-05'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(72.0, 78.0677966101695, 82.0)]\n"
     ]
    }
   ],
   "source": [
    "# Use the function `calc_temps` to calculate the tmin, tavg, and tmax \n",
    "# for a year in the data set\n",
    "prev_yr_trip_start = '2016-08-15'\n",
    "prev_yr_trip_end = '2016-08-23'\n",
    "print(calc_temps(prev_yr_trip_start, prev_yr_trip_end))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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 bar height (y value)\n",
    "# Use the peak-to-peak (tmax-tmin) value as the y error bar (yerr)\n",
    "trip_temps = calc_temps(prev_yr_trip_start, prev_yr_trip_end)\n",
    "\n",
    "trip_avg = trip_temps[0][1]\n",
    "trip_min = trip_temps[0][0]\n",
    "trip_max = trip_temps[0][2]\n",
    "yerr = [trip_max - trip_min]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=plt.figaspect(2.))\n",
    "\n",
    "bar= ax.bar(1, trip_max, yerr=yerr,alpha=0.5, color=\"blue\")\n",
    "ax.set(xticks=range(1), xticklabels=\"a\", title=\"Trip Avg Temp\", ylabel = \"Temp (F)\")\n",
    "ax.margins(.2,.2)\n",
    "fig.tight_layout()\n",
    "plt.savefig(\"avg_temp.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Daily Rainfall Average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('USC00516128', 'MANOA LYON ARBO 785.2, HI US', 21.3331, -157.8025, 152.4, 0.95), ('USC00519281', 'WAIHEE 837.5, HI US', 21.45167, -157.84888999999998, 32.9, 0.23), ('USC00513117', 'KANEOHE 838.1, HI US', 21.4234, -157.8015, 14.6, 0.17), ('USC00519523', 'WAIMANALO EXPERIMENTAL FARM, HI US', 21.33556, -157.71139, 19.5, 0.0), ('USC00519397', 'WAIKIKI 717.2, HI US', 21.2716, -157.8168, 3.0, 0.0), ('USC00517948', 'PEARL CITY, HI US', 21.3934, -157.9751, 11.9, None), ('USC00514830', 'KUALOA RANCH HEADQUARTERS 886.9, HI US', 21.5213, -157.8374, 7.0, None)]\n"
     ]
    }
   ],
   "source": [
    "# Calculate the total amount of rainfall per weather station for your trip dates using the previous year's \n",
    "# matching dates.\n",
    "# Sort this in descending order by precipitation amount and list the station, name, latitude, longitude, and elevation\n",
    "def precipitation(start_date, end_date):\n",
    "    sel = [measurement.station, \n",
    "           Station.name, \n",
    "           Station.latitude, \n",
    "           Station.longitude, \n",
    "           Station.elevation, \n",
    "           measurement.prcp]\n",
    "    \n",
    "    return session.query(*sel).\\\n",
    "            filter(measurement.station == Station.station).\\\n",
    "            filter(measurement.date >= start_date).\\\n",
    "            filter(measurement.date <= end_date).\\\n",
    "            group_by(measurement.station).\\\n",
    "            order_by(measurement.prcp.desc()).all()\n",
    "\n",
    "print(precipitation(prev_yr_trip_start, prev_yr_trip_end))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(62.0, 69.15384615384616, 77.0)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use this function to 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",
    "# For example\n",
    "daily_normals(\"01-01\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(67.0, 75.54, 83.0),\n",
      " (68.0, 75.60377358490567, 84.0),\n",
      " (70.0, 76.61111111111111, 85.0),\n",
      " (69.0, 76.71153846153847, 84.0),\n",
      " (69.0, 76.14814814814815, 82.0),\n",
      " (67.0, 76.25, 83.0),\n",
      " (71.0, 77.15686274509804, 83.0)]\n"
     ]
    }
   ],
   "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",
    "start_date = '2017-08-01'\n",
    "end_date = '2017-08-07'\n",
    "\n",
    "# Use the start and end date to create a range of dates\n",
    "date_range = pd.date_range(start_date, end_date)\n",
    "\n",
    "# Strip off the year and save a list of strings in the format %m-%d\n",
    "date_list = [d.strftime(\"%m-%d\") for d in date_range]\n",
    "\n",
    "# Use the `daily_normals` function to calculate the normals for each date string \n",
    "# and append the results to a list called `normals`.\n",
    "daily_normal = [daily_normals(d)[0] for d in date_list]\n",
    "\n",
    "pprint(daily_normal)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>tmin</th>\n",
       "      <th>tavg</th>\n",
       "      <th>tmax</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-08-01</th>\n",
       "      <td>67.0</td>\n",
       "      <td>75.540000</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-02</th>\n",
       "      <td>68.0</td>\n",
       "      <td>75.603774</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-03</th>\n",
       "      <td>70.0</td>\n",
       "      <td>76.611111</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-04</th>\n",
       "      <td>69.0</td>\n",
       "      <td>76.711538</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-05</th>\n",
       "      <td>69.0</td>\n",
       "      <td>76.148148</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-06</th>\n",
       "      <td>67.0</td>\n",
       "      <td>76.250000</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-07</th>\n",
       "      <td>71.0</td>\n",
       "      <td>77.156863</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            tmin       tavg  tmax\n",
       "Date                             \n",
       "2017-08-01  67.0  75.540000  83.0\n",
       "2017-08-02  68.0  75.603774  84.0\n",
       "2017-08-03  70.0  76.611111  85.0\n",
       "2017-08-04  69.0  76.711538  84.0\n",
       "2017-08-05  69.0  76.148148  82.0\n",
       "2017-08-06  67.0  76.250000  83.0\n",
       "2017-08-07  71.0  77.156863  83.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the previous query results into a Pandas DataFrame and add the `trip_dates` range as the `date` index\n",
    "normal_df = pd.DataFrame(\n",
    "    daily_normal, columns=[\"tmin\", \"tavg\", \"tmax\"], index=date_range\n",
    ")\n",
    "normal_df.index.name = \"Date\"\n",
    "normal_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the daily normals as an area plot with `stacked=False`\n",
    "normal_df.plot(kind=\"area\", stacked=False, x_compat=True, alpha=0.333, figsize=(9,8))\n",
    "plt.title(f\"Temperature Records for Trip Dates\\n {start_date} to {end_date}\")\n",
    "plt.xlabel(\"Dates\")\n",
    "plt.ylabel(\"Temperature\")\n",
    "plt.savefig(\"area_plot.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Close Session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "session.close()"
   ]
  },
  {
   "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}