climate_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "checked-candy",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import SQL alchemy dependencies\n",
    "from sqlalchemy import create_engine\n",
    "from sqlalchemy.ext.automap import automap_base\n",
    "from sqlalchemy.orm import Session\n",
    "from sqlalchemy import func, inspect\n",
    "\n",
    "# Import other dependencies\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "\n",
    "# Import dependencies for graphs\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from matplotlib import style\n",
    "style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "coordinate-nelson",
   "metadata": {},
   "source": [
    "### Reflect Tables into SQLAlchemy ORM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "toxic-warrior",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create engine to the hawaii database\n",
    "engine = create_engine(\"sqlite:///Resources/hawaii.sqlite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dangerous-judge",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create automap base\n",
    "Base = automap_base()\n",
    "\n",
    "# Prepare base to reflect tables\n",
    "Base.prepare(engine, reflect=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "macro-thumbnail",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['measurement', 'station']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print classes from base\n",
    "# These are the tables in the database\n",
    "Base.classes.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "anonymous-ambassador",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save a reference to the classes (tables)\n",
    "Station = Base.classes.station\n",
    "Measurement = Base.classes.measurement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "matched-saskatchewan",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Begin a session\n",
    "session = Session(engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eight-entity",
   "metadata": {},
   "source": [
    "### Exploratory Precipitation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "narrow-costume",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['measurement', 'station']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use inspector to see the class/table names (same as keys above)\n",
    "inspector = inspect(engine)\n",
    "inspector.get_table_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "weekly-hudson",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id INTEGER\n",
      "station TEXT\n",
      "date TEXT\n",
      "prcp FLOAT\n",
      "tobs FLOAT\n"
     ]
    }
   ],
   "source": [
    "# Use inspector to see the column names for measurement\n",
    "columns_measurement = inspector.get_columns('measurement')\n",
    "\n",
    "# Print out column names to see the name and type\n",
    "for c in columns_measurement:\n",
    "    print(c[\"name\"], c[\"type\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "common-thing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 'USC00519397', '2010-01-01', 0.08, 65.0),\n",
       " (2, 'USC00519397', '2010-01-02', 0.0, 63.0),\n",
       " (3, 'USC00519397', '2010-01-03', 0.0, 74.0),\n",
       " (4, 'USC00519397', '2010-01-04', 0.0, 76.0),\n",
       " (5, 'USC00519397', '2010-01-06', None, 73.0)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See the first five rows of data in measurement\n",
    "engine.execute(\"SELECT * FROM measurement LIMIT 5\").fetchall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dental-sustainability",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2017-08-23',)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the most recent date in measurement\n",
    "session.query(Measurement.date).order_by(Measurement.date.desc()).first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "institutional-satellite",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define selection values for query\n",
    "sel = [Measurement.date,\n",
    "       func.sum(Measurement.prcp)\n",
    "]\n",
    "\n",
    "# Retrieve the last 12 months of precipitation data\n",
    "last_twelve_months = session.query(*sel).\\\n",
    "    filter(Measurement.date <= '2017-08-23').\\\n",
    "    filter(Measurement.date >= '2016-08-23').\\\n",
    "    order_by(Measurement.date).group_by(Measurement.date).all()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "immune-jewel",
   "metadata": {
    "scrolled": false
   },
   "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>2.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-08-24</td>\n",
       "      <td>9.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-08-25</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-08-26</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-08-27</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>2017-08-19</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>2017-08-20</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>2017-08-21</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>2017-08-22</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>2017-08-23</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>366 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  precipitation\n",
       "0    2016-08-23           2.71\n",
       "1    2016-08-24           9.33\n",
       "2    2016-08-25           0.54\n",
       "3    2016-08-26           0.10\n",
       "4    2016-08-27           0.32\n",
       "..          ...            ...\n",
       "361  2017-08-19           0.09\n",
       "362  2017-08-20           0.01\n",
       "363  2017-08-21           0.58\n",
       "364  2017-08-22           0.50\n",
       "365  2017-08-23           0.53\n",
       "\n",
       "[366 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the query results into a DataFrame\n",
    "measurement_df = pd.DataFrame(last_twelve_months, columns = [\"date\", \"precipitation\"])\n",
    "measurement_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adequate-hartford",
   "metadata": {},
   "source": [
    "### Generate Labels for Graphs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "stone-astrology",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "366"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the number of dates\n",
    "date_count = measurement_df['date'].count()\n",
    "date_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "documentary-posting",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Figure out the number to increment x-ticks\n",
    "divided = date_count / 12\n",
    "divided"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "subsequent-identification",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find date labels for dates every 31 days\n",
    "date_labels = []\n",
    "date = dt.date(2016,8,23)\n",
    "start_date_str = date.strftime(\"%Y-%m-%d\")\n",
    "date_labels.append(start_date_str)\n",
    "\n",
    "# Loop through and append dates to a list\n",
    "for x in range(0,11):\n",
    "    date = date + dt.timedelta(days=31)\n",
    "    date_str = date.strftime(\"%Y-%m-%d\")\n",
    "    date_labels.append(date_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "stunning-lafayette",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2016-08-23',\n",
       " '2016-09-23',\n",
       " '2016-10-24',\n",
       " '2016-11-24',\n",
       " '2016-12-25',\n",
       " '2017-01-25',\n",
       " '2017-02-25',\n",
       " '2017-03-28',\n",
       " '2017-04-28',\n",
       " '2017-05-29',\n",
       " '2017-06-29',\n",
       " '2017-07-30']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print date labels to check\n",
    "date_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "coordinated-rochester",
   "metadata": {
    "scrolled": true
   },
   "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>2.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-24</th>\n",
       "      <td>9.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-25</th>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-26</th>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-27</th>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-19</th>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-20</th>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-21</th>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-22</th>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-23</th>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>366 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            precipitation\n",
       "date                     \n",
       "2016-08-23           2.71\n",
       "2016-08-24           9.33\n",
       "2016-08-25           0.54\n",
       "2016-08-26           0.10\n",
       "2016-08-27           0.32\n",
       "...                   ...\n",
       "2017-08-19           0.09\n",
       "2017-08-20           0.01\n",
       "2017-08-21           0.58\n",
       "2017-08-22           0.50\n",
       "2017-08-23           0.53\n",
       "\n",
       "[366 rows x 1 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the date as the index and SORT BY date\n",
    "measurement_df.set_index(\"date\", inplace=True)\n",
    "measurement_df.sort_values(\"date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "intense-canada",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results using the DataFrame plot method\n",
    "ax = measurement_df.plot(rot = 90, kind=\"bar\", figsize=(10, 6), width=3)\n",
    "plt.tight_layout()\n",
    "plt.xticks(np.arange(0,date_count,31), date_labels)\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Precipitation (Inches)\")\n",
    "plt.ylim(-1,14.5)\n",
    "plt.title(\"Precipitation in the Last 12 Months\")\n",
    "plt.savefig(\"Images/last_year_precipitation.png\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caroline-observer",
   "metadata": {},
   "source": [
    "### Summary Statisitcs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "statewide-variance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    366.000000\n",
       "mean       0.978907\n",
       "std        1.776349\n",
       "min        0.000000\n",
       "25%        0.052500\n",
       "50%        0.405000\n",
       "75%        1.087500\n",
       "max       14.280000\n",
       "Name: precipitation, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary statistics\n",
    "measurement_df[\"precipitation\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "speaking-advice",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate stats for table\n",
    "max_prcp = measurement_df[\"precipitation\"].max()\n",
    "min_prcp = measurement_df[\"precipitation\"].min()\n",
    "std_prcp = measurement_df[\"precipitation\"].std()\n",
    "avg_prcp = measurement_df[\"precipitation\"].mean()\n",
    "median_prcp = measurement_df[\"precipitation\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "proper-panic",
   "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>Mean</th>\n",
       "      <th>Max</th>\n",
       "      <th>Min</th>\n",
       "      <th>Standard Dev</th>\n",
       "      <th>Median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.978907</td>\n",
       "      <td>14.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.776349</td>\n",
       "      <td>0.405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Mean    Max  Min  Standard Dev  Median\n",
       "0  0.978907  14.28  0.0      1.776349   0.405"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Stats Table\n",
    "stats_table = pd.DataFrame({\n",
    "    \"Mean\": avg_prcp,\n",
    "    \"Max\": max_prcp,\n",
    "    \"Min\": min_prcp,\n",
    "    \"Standard Dev\": std_prcp,\n",
    "    \"Median\": median_prcp\n",
    "}, index=[0])\n",
    "\n",
    "stats_table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "missing-revelation",
   "metadata": {},
   "source": [
    "### Exploratory Station Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "burning-kazakhstan",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id INTEGER\n",
      "station TEXT\n",
      "name TEXT\n",
      "latitude FLOAT\n",
      "longitude FLOAT\n",
      "elevation FLOAT\n"
     ]
    }
   ],
   "source": [
    "# Use inspector to see the column names for station\n",
    "columns_station = inspector.get_columns('station')\n",
    "\n",
    "# Print out the name and type of each column\n",
    "for c in columns_station:\n",
    "    print(c[\"name\"], c[\"type\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "extensive-medicine",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 'USC00519397', 'WAIKIKI 717.2, HI US', 21.2716, -157.8168, 3.0),\n",
       " (2, 'USC00513117', 'KANEOHE 838.1, HI US', 21.4234, -157.8015, 14.6),\n",
       " (3, 'USC00514830', 'KUALOA RANCH HEADQUARTERS 886.9, HI US', 21.5213, -157.8374, 7.0),\n",
       " (4, 'USC00517948', 'PEARL CITY, HI US', 21.3934, -157.9751, 11.9),\n",
       " (5, 'USC00518838', 'UPPER WAHIAWA 874.3, HI US', 21.4992, -158.0111, 306.6),\n",
       " (6, 'USC00519523', 'WAIMANALO EXPERIMENTAL FARM, HI US', 21.33556, -157.71139, 19.5),\n",
       " (7, 'USC00519281', 'WAIHEE 837.5, HI US', 21.45167, -157.84888999999998, 32.9),\n",
       " (8, 'USC00511918', 'HONOLULU OBSERVATORY 702.2, HI US', 21.3152, -157.9992, 0.9),\n",
       " (9, 'USC00516128', 'MANOA LYON ARBO 785.2, HI US', 21.3331, -157.8025, 152.4)]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See the rows of data in station\n",
    "engine.execute(\"SELECT * FROM station\").fetchall()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "early-rogers",
   "metadata": {},
   "source": [
    "### Number of Stations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "tested-magazine",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Stations: 9\n"
     ]
    }
   ],
   "source": [
    "# Query to calculate the total number of stations\n",
    "number_of_stations = session.query(func.count(Station.station)).all()\n",
    "\n",
    "# Convert tuple to a list\n",
    "num_of_stations = list(np.ravel(number_of_stations))\n",
    "\n",
    "print(f\"Number of Stations: {num_of_stations[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "light-bacteria",
   "metadata": {},
   "source": [
    "### Stations Ordered by Activity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "medium-personality",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(7, 'USC00519281', 'WAIHEE 837.5, HI US', 2772),\n",
       " (1, 'USC00519397', 'WAIKIKI 717.2, HI US', 2724),\n",
       " (2, 'USC00513117', 'KANEOHE 838.1, HI US', 2709),\n",
       " (6, 'USC00519523', 'WAIMANALO EXPERIMENTAL FARM, HI US', 2669),\n",
       " (9, 'USC00516128', 'MANOA LYON ARBO 785.2, HI US', 2612),\n",
       " (3, 'USC00514830', 'KUALOA RANCH HEADQUARTERS 886.9, HI US', 2202),\n",
       " (8, 'USC00511918', 'HONOLULU OBSERVATORY 702.2, HI US', 1979),\n",
       " (4, 'USC00517948', 'PEARL CITY, HI US', 1372),\n",
       " (5, 'USC00518838', 'UPPER WAHIAWA 874.3, HI US', 511)]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Query to find the most active stations \n",
    "sel = [Station.id,\n",
    "    Station.station,\n",
    "    Station.name,\n",
    "    func.count(Measurement.id)\n",
    "]\n",
    "\n",
    "# Create query that filters by the join between the tables\n",
    "session.query(*sel).filter(Station.station == Measurement.station).\\\n",
    "    group_by(Station.id).order_by(func.count(Measurement.id).desc()).all()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sharp-crest",
   "metadata": {},
   "source": [
    "### Station ID: USC00519281 has the highest number of observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "greenhouse-crack",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'USC00519281'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the station name of the most active station\n",
    "most_active_station = session.query(Station.station).filter(Station.station == Measurement.station).\\\n",
    "    group_by(Station.id).order_by(func.count(Measurement.id).desc()).first()\n",
    "\n",
    "most_active_station[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "identified-panama",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The lowest temperature at USC00519281 is 53.0.\n",
      "The highest temperature at USC00519281 is 87.0.\n",
      "The average temperature at USC00519281 is 73.098.\n"
     ]
    }
   ],
   "source": [
    "# Define what we will be selecting\n",
    "sel = [\n",
    "    func.min(Measurement.tobs),\n",
    "    func.max(Measurement.tobs),\n",
    "    func.avg(Measurement.tobs)\n",
    "]\n",
    "\n",
    "# Query the database to get the above selections\n",
    "calculations = session.query(*sel).all()\n",
    "\n",
    "# Pull the data out of the tuple\n",
    "for record in calculations:\n",
    "    (min_temp, max_temp, avg_temp) = record\n",
    "\n",
    "# Print the most active station's stats\n",
    "print(f\"The lowest temperature at {most_active_station[0]} is {min_temp}.\")\n",
    "print(f\"The highest temperature at {most_active_station[0]} is {max_temp}.\")\n",
    "print(f\"The average temperature at {most_active_station[0]} is {avg_temp:.3f}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "supposed-gasoline",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2017-08-18',)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the last date of data for the most active station\n",
    "session.query(Measurement.date).filter(Measurement.station == \"USC00519281\").order_by(Measurement.date.desc()).first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "looking-humanity",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retrieve the last 12 months of data for most active\n",
    "sel = [Measurement.tobs,\n",
    "   Measurement.date\n",
    "]\n",
    "\n",
    "twelve_months_active = session.query(*sel).\\\n",
    "    filter(Measurement.date <= '2017-08-18').\\\n",
    "    filter(Measurement.date >= '2016-08-18').\\\n",
    "    filter(Measurement.station == \"USC00519281\").\\\n",
    "    order_by(Measurement.date).all()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ideal-raise",
   "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>temperature</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80.0</td>\n",
       "      <td>2016-08-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79.0</td>\n",
       "      <td>2016-08-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>81.0</td>\n",
       "      <td>2016-08-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>79.0</td>\n",
       "      <td>2016-08-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78.0</td>\n",
       "      <td>2016-08-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>77.0</td>\n",
       "      <td>2017-08-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>77.0</td>\n",
       "      <td>2017-08-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>76.0</td>\n",
       "      <td>2017-08-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>76.0</td>\n",
       "      <td>2017-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>79.0</td>\n",
       "      <td>2017-08-18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>357 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     temperature        date\n",
       "0           80.0  2016-08-18\n",
       "1           79.0  2016-08-19\n",
       "2           81.0  2016-08-20\n",
       "3           79.0  2016-08-21\n",
       "4           78.0  2016-08-22\n",
       "..           ...         ...\n",
       "352         77.0  2017-08-14\n",
       "353         77.0  2017-08-15\n",
       "354         76.0  2017-08-16\n",
       "355         76.0  2017-08-17\n",
       "356         79.0  2017-08-18\n",
       "\n",
       "[357 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe of the query data\n",
    "active_df = pd.DataFrame(twelve_months_active, columns=[\"temperature\", \"date\"])\n",
    "active_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "induced-boards",
   "metadata": {},
   "source": [
    "### Histogram of Temperature Frequency at Most Active Station:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "reflected-swaziland",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results as a histogram\n",
    "active_df.hist(bins = 12, figsize=(10,6))\n",
    "plt.xlabel(\"Temperature\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(f\"Temperature of {most_active_station[0]} in the Last 12 Months\")\n",
    "plt.savefig(\"Images/most_active_station_temp_frequency.png\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "disciplinary-scout",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Close the session\n",
    "session.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:PythonData] *",
   "language": "python",
   "name": "conda-env-PythonData-py"
  },
  "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": 5
}