climate_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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reflect Tables into SQLAlchemy ORM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine(\"sqlite:///Resources/hawaii.sqlite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['measurement', 'station']"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save references to each table\n",
    "Measurement = Base.classes.measurement\n",
    "Station = Base.classes.station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "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",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-08-23</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-23</th>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-23</th>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-23</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-23</th>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            precipitation\n",
       "date                     \n",
       "2016-08-23           0.00\n",
       "2016-08-23           0.15\n",
       "2016-08-23           0.05\n",
       "2016-08-23            NaN\n",
       "2016-08-23           0.02"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Design a query to retrieve the last 12 months of precipitation data and plot the results\n",
    "\n",
    "#latest date in database\n",
    "latest_date = session.query(Measurement.date).order_by(Measurement.date.desc()).first()\n",
    "\n",
    "# Calculate the date 1 year ago from the last data point in the database\n",
    "year_ago = dt.date(2017,8,23) - dt.timedelta(days=365)\n",
    "\n",
    "# Perform a query to retrieve the data and precipitation scores\n",
    "prcp_all_scores = session.query(Measurement.date, Measurement.prcp).\\\n",
    "                    order_by(Measurement.date.desc()).all()\n",
    "\n",
    "#Retrieve the last 12 months of precipitation data and plot the results\n",
    "prcp_data =session.query(Measurement.date, Measurement.prcp).\\\n",
    "            filter(Measurement.date >= year_ago).\\\n",
    "            order_by(Measurement.date).all()\n",
    "\n",
    "# Save the query results as a Pandas DataFrame and set the index to the date column\n",
    "# Sort the dataframe by date\n",
    "prcp_df = pd.DataFrame(prcp_data, columns=['date', 'precipitation']).sort_values('date')\n",
    "prcp_df = prcp_df.set_index('date')\n",
    "prcp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use Pandas Plotting with Matplotlib to plot the data\n",
    "prcp_df.plot(title=\"Figure 1.2016-2017 Hawaii Precipitation Data\", figsize=(12, 8))\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Precipitation\")\n",
    "plt.savefig(\"Images/Figure 1.2016-2017 Hawaii Precipitation Data.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use Pandas to calcualte the summary statistics for the precipitation data\n",
    "summ_stat = pd.DataFrame(prcp_df.describe())\n",
    "summ_stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Design a query to show how many stations are available in this dataset?\n",
    "station_count = session.query(Station.station).count()\n",
    "station_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What are the most active stations? (i.e. what stations have the most rows)?\n",
    "# List the stations and the counts in descending order.\n",
    "station_activity = session.query(Measurement.station, func.count(Measurement.station)).group_by(Measurement.station) \\\n",
    "                    .order_by(func.count(Measurement.station).desc()).all()\n",
    "\n",
    "station_activity\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The most active station is USC00519281, with temperature readings as follows: min, max, average = [(54.0, 85.0, 71.66378066378067)].\n"
     ]
    }
   ],
   "source": [
    "# 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",
    "most_active = station_activity[0][0]\n",
    "\n",
    "stats = [func.min(Measurement.tobs), \n",
    "       func.max(Measurement.tobs), \n",
    "       func.avg(Measurement.tobs)]\n",
    "\n",
    "temp_info = session.query(*stats).\\\n",
    "            filter(Measurement.station == most_active).all()\n",
    "\n",
    "print(f\"The most active station is {most_active}, with temperature readings as follows: min, max, average = {temp_info}.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 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",
    "max_observe_station = session.query(Measurement.tobs).filter(Measurement.station == most_active) \\\n",
    "                .filter(Measurement.date >= year_ago).all()\n",
    "tobs_df = pd.DataFrame(max_observe_station, columns=['tobs'])\n",
    "\n",
    "tobs_df.plot(kind='hist', bins=12, title=\"Figure 2. Temperature vs Frequency\", figsize=(10,5))\n",
    "plt.xlabel(\"Temperature\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Images/Figure 2. Temperature vs Frequency.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus Challenge Assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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, 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": 18,
   "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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>71.0</td>\n",
       "      <td>77.3125</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TMIN     TAVG  TMAX\n",
       "0  71.0  77.3125  81.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "trip = calc_temps(\"2017-05-29\",\"2017-06-10\")\n",
    "trip\n",
    "\n",
    "trip_df = pd.DataFrame(trip, columns=[\"TMIN\", \"TAVG\", \"TMAX\"])\n",
    "trip_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x504 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 the y value\n",
    "# Use the peak-to-peak (tmax-tmin) value as the y error bar (yerr)\n",
    "trip_df.plot.bar(y=\"TAVG\", yerr=(trip_df[\"TMAX\"] - trip_df[\"TMIN\"]),\n",
    "                title='Figure 3. Trip Avg Temp',\n",
    "                color='limegreen',\n",
    "                alpha=0.5,\n",
    "                figsize=(5,7),\n",
    "                legend=None)\n",
    "plt.xticks([])\n",
    "plt.ylabel(\"Temperature (F)\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Images/Figure 3. Trip Avg Temp.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('USC00519281',\n",
       "  'WAIHEE 837.5, HI US',\n",
       "  2.7600000000000002,\n",
       "  21.45167,\n",
       "  -157.84888999999998,\n",
       "  32.9),\n",
       " ('USC00513117', 'KANEOHE 838.1, HI US', 1.05, 21.4234, -157.8015, 14.6),\n",
       " ('USC00514830',\n",
       "  'KUALOA RANCH HEADQUARTERS 886.9, HI US',\n",
       "  0.96,\n",
       "  21.5213,\n",
       "  -157.8374,\n",
       "  7.0),\n",
       " ('USC00516128',\n",
       "  'MANOA LYON ARBO 785.2, HI US',\n",
       "  0.85,\n",
       "  21.3331,\n",
       "  -157.8025,\n",
       "  152.4),\n",
       " ('USC00519523',\n",
       "  'WAIMANALO EXPERIMENTAL FARM, HI US',\n",
       "  0.39,\n",
       "  21.33556,\n",
       "  -157.71139,\n",
       "  19.5),\n",
       " ('USC00519397', 'WAIKIKI 717.2, HI US', 0.34, 21.2716, -157.8168, 3.0),\n",
       " ('USC00517948', 'PEARL CITY, HI US', None, 21.3934, -157.9751, 11.9)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "def totalRainfall(start_date, end_date):\n",
    "    return session.query(Measurement.station,\n",
    "                         Station.name,\n",
    "                         func.sum(Measurement.prcp),\n",
    "                         Station.latitude,\n",
    "                         Station.longitude,\n",
    "                         Station.elevation).\\\n",
    "                            group_by(Measurement.station).\\\n",
    "                            join(Station, Measurement.station == Station.station).\\\n",
    "                            filter(Measurement.date >= start_date).\\\n",
    "                            filter(Measurement.date <= end_date).\\\n",
    "                            order_by(func.sum(Measurement.prcp).desc()).all()\n",
    "\n",
    "rainfall = totalRainfall(\"2017-05-29\",\"2017-06-10\")\n",
    "rainfall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Station</th>\n",
       "      <th>Location</th>\n",
       "      <th>Total Precipitation</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Elevation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>USC00519281</td>\n",
       "      <td>WAIHEE 837.5, HI US</td>\n",
       "      <td>2.76</td>\n",
       "      <td>21.4517</td>\n",
       "      <td>-157.849</td>\n",
       "      <td>32.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USC00513117</td>\n",
       "      <td>KANEOHE 838.1, HI US</td>\n",
       "      <td>1.05</td>\n",
       "      <td>21.4234</td>\n",
       "      <td>-157.802</td>\n",
       "      <td>14.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USC00514830</td>\n",
       "      <td>KUALOA RANCH HEADQUARTERS 886.9, HI US</td>\n",
       "      <td>0.96</td>\n",
       "      <td>21.5213</td>\n",
       "      <td>-157.837</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>USC00516128</td>\n",
       "      <td>MANOA LYON ARBO 785.2, HI US</td>\n",
       "      <td>0.85</td>\n",
       "      <td>21.3331</td>\n",
       "      <td>-157.803</td>\n",
       "      <td>152.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>USC00519523</td>\n",
       "      <td>WAIMANALO EXPERIMENTAL FARM, HI US</td>\n",
       "      <td>0.39</td>\n",
       "      <td>21.3356</td>\n",
       "      <td>-157.711</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>WAIKIKI 717.2, HI US</td>\n",
       "      <td>0.34</td>\n",
       "      <td>21.2716</td>\n",
       "      <td>-157.817</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>USC00517948</td>\n",
       "      <td>PEARL CITY, HI US</td>\n",
       "      <td>None</td>\n",
       "      <td>21.3934</td>\n",
       "      <td>-157.975</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Station                                Location Total Precipitation  \\\n",
       "0  USC00519281                     WAIHEE 837.5, HI US                2.76   \n",
       "1  USC00513117                    KANEOHE 838.1, HI US                1.05   \n",
       "2  USC00514830  KUALOA RANCH HEADQUARTERS 886.9, HI US                0.96   \n",
       "3  USC00516128            MANOA LYON ARBO 785.2, HI US                0.85   \n",
       "4  USC00519523      WAIMANALO EXPERIMENTAL FARM, HI US                0.39   \n",
       "5  USC00519397                    WAIKIKI 717.2, HI US                0.34   \n",
       "6  USC00517948                       PEARL CITY, HI US                None   \n",
       "\n",
       "  Latitude Longitude Elevation  \n",
       "0  21.4517  -157.849      32.9  \n",
       "1  21.4234  -157.802      14.6  \n",
       "2  21.5213  -157.837         7  \n",
       "3  21.3331  -157.803     152.4  \n",
       "4  21.3356  -157.711      19.5  \n",
       "5  21.2716  -157.817         3  \n",
       "6  21.3934  -157.975      11.9  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating the Numpy array \n",
    "rainfall_df = pd.DataFrame(np.array(rainfall))\n",
    "rainfall_df = rainfall_df.rename(columns={0: \"Station\", \n",
    "                                          1: \"Location\", \n",
    "                                          2: \"Total Precipitation\", \n",
    "                                          3: \"Latitude\", \n",
    "                                          4: \"Longitude\", \n",
    "                                          5: \"Elevation\"})\n",
    "\n",
    "rainfall_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(62.0, 69.15384615384616, 77.0)]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(68.0, 74.4, 81.0),\n",
       " (64.0, 74.61, 81.0),\n",
       " (65.0, 74.4, 81.0),\n",
       " (65.0, 74.03, 80.0),\n",
       " (65.0, 73.82, 81.0),\n",
       " (68.0, 74.85, 81.0),\n",
       " (70.0, 75.18, 80.0),\n",
       " (68.0, 75.07, 81.0),\n",
       " (70.0, 75.59, 81.0),\n",
       " (69.0, 75.15, 81.0),\n",
       " (68.0, 75.14, 81.0)]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the daily normals for your trip\n",
    "# push each tuple of calculations into a list called `normals`\n",
    "# Set the start and end date of the trip\n",
    "# Use the start and end date to create a range of dates\n",
    "# Stip off the year and save a list of %m-%d strings\n",
    "# Loop through the list of %m-%d strings and calculate the normals for each date\n",
    "\n",
    "\n",
    "dates=['05-31','06-01','06-02','06-03','06-04',\n",
    "            '06-05','06-06','06-07','06-08','06-09','06-10']\n",
    "       \n",
    "normals=[]\n",
    "    \n",
    "def daily_normals(date):\n",
    "    sel = [func.min(Measurement.tobs), \n",
    "           func.round(func.avg(Measurement.tobs),2), \n",
    "           func.max(Measurement.tobs)]\n",
    "    return session.query(*sel).filter(func.strftime(\"%m-%d\", Measurement.date) == date).all()\n",
    "\n",
    "for i in dates:\n",
    "    normals.append(daily_normals(i)[0])\n",
    "\n",
    "normals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>min_temp</th>\n",
       "      <th>avg_temp</th>\n",
       "      <th>max_temp</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>05-31</th>\n",
       "      <td>68.0</td>\n",
       "      <td>74.40</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06-01</th>\n",
       "      <td>64.0</td>\n",
       "      <td>74.61</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06-02</th>\n",
       "      <td>65.0</td>\n",
       "      <td>74.40</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06-03</th>\n",
       "      <td>65.0</td>\n",
       "      <td>74.03</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06-04</th>\n",
       "      <td>65.0</td>\n",
       "      <td>73.82</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06-05</th>\n",
       "      <td>68.0</td>\n",
       "      <td>74.85</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06-06</th>\n",
       "      <td>70.0</td>\n",
       "      <td>75.18</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06-07</th>\n",
       "      <td>68.0</td>\n",
       "      <td>75.07</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06-08</th>\n",
       "      <td>70.0</td>\n",
       "      <td>75.59</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06-09</th>\n",
       "      <td>69.0</td>\n",
       "      <td>75.15</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06-10</th>\n",
       "      <td>68.0</td>\n",
       "      <td>75.14</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       min_temp  avg_temp  max_temp\n",
       "date                               \n",
       "05-31      68.0     74.40      81.0\n",
       "06-01      64.0     74.61      81.0\n",
       "06-02      65.0     74.40      81.0\n",
       "06-03      65.0     74.03      80.0\n",
       "06-04      65.0     73.82      81.0\n",
       "06-05      68.0     74.85      81.0\n",
       "06-06      70.0     75.18      80.0\n",
       "06-07      68.0     75.07      81.0\n",
       "06-08      70.0     75.59      81.0\n",
       "06-09      69.0     75.15      81.0\n",
       "06-10      68.0     75.14      81.0"
      ]
     },
     "execution_count": 26,
     "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",
    "normals_df=pd.DataFrame(normals,columns=['min_temp','avg_temp','max_temp'],\n",
    "                                   index=dates)\n",
    "\n",
    "normals_df.index.name='date'\n",
    "normals_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the daily normals as an area plot with `stacked=False`\n",
    "\n",
    "plt.rcParams['figure.figsize']=(11,7)\n",
    "colors=['xkcd:lime green','xkcd:azure','xkcd:cobalt']\n",
    "normals_df.plot.area(linewidth=5,stacked=False,rot=0,alpha=0.5,color=colors);\n",
    "\n",
    "plt.ylim(-5,100)\n",
    "plt.yticks(size=14)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.xlabel('Date (mm-dd)',fontsize=16,color='black',labelpad=20)\n",
    "plt.ylabel('Temperature (F)',fontsize=16,color='black',labelpad=20)\n",
    "plt.title('Daily Temperature Normals\\nHonolulu, Hawaii',fontsize=20,pad=40)\n",
    "plt.gca().legend(loc='center left', bbox_to_anchor=(1.02, 0.91),shadow=True,borderpad=1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernel_info": {
   "name": "python3"
  },
  "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": "3.8.3"
  },
  "nteract": {
   "version": "0.12.3"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}