Climate_Analysis.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\n",
    "import seaborn as sns"
   ]
  },
  {
   "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, inspect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine(\"sqlite:///Resources/hawaii.sqlite\")\n",
    "conn = engine.connect()\n",
    "inspector = inspect(engine)"
   ]
  },
  {
   "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)\n",
    "\n",
    "# Create inspector & connect to engine\n",
    "inspector = inspect(engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Precipitation Analysis\n",
    "\n",
    "* Design a query to retrieve the last 12 months of precipitation data.\n",
    "\n",
    "* Select only the `date` and `prcp` values.\n",
    "\n",
    "* Load the query results into a Pandas DataFrame and set the index to the date column.\n",
    "\n",
    "* Sort the DataFrame values by `date`.\n",
    "\n",
    "* Plot the results using the DataFrame `plot` method.\n",
    "\n",
    "* Use Pandas to print the summary statistics for the precipitation data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id INTEGER\n",
      "station TEXT\n",
      "date TEXT\n",
      "prcp FLOAT\n",
      "tobs FLOAT\n"
     ]
    }
   ],
   "source": [
    "# Get data column name/types for ref.\n",
    "measurement_columns = inspector.get_columns('Measurement')\n",
    "for columns in measurement_columns: \n",
    "    print(columns[\"name\"], columns[\"type\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The last existing date in the Measurement data was: ('2017-08-23',)\n",
      "12 months from last existing date: 2016-08-23\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Precipitation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-08-23</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-08-23</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-08-23</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-08-23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-08-23</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-08-23</td>\n",
       "      <td>1.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-08-23</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-08-24</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-08-24</td>\n",
       "      <td>2.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-08-24</td>\n",
       "      <td>2.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2016-08-24</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2016-08-24</td>\n",
       "      <td>1.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2016-08-24</td>\n",
       "      <td>2.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2016-08-24</td>\n",
       "      <td>1.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016-08-25</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2016-08-25</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016-08-25</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2016-08-25</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2016-08-25</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2016-08-25</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2016-08-25</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2016-08-26</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2016-08-26</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2016-08-26</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2016-08-26</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2016-08-26</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2016-08-26</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2016-08-27</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2016-08-27</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2016-08-27</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2200</th>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2201</th>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2202</th>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2203</th>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2204</th>\n",
       "      <td>2017-08-17</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2205</th>\n",
       "      <td>2017-08-17</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2206</th>\n",
       "      <td>2017-08-17</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2207</th>\n",
       "      <td>2017-08-17</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2208</th>\n",
       "      <td>2017-08-18</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2209</th>\n",
       "      <td>2017-08-18</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2210</th>\n",
       "      <td>2017-08-18</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2211</th>\n",
       "      <td>2017-08-18</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2212</th>\n",
       "      <td>2017-08-19</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2213</th>\n",
       "      <td>2017-08-19</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2214</th>\n",
       "      <td>2017-08-19</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2215</th>\n",
       "      <td>2017-08-19</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2216</th>\n",
       "      <td>2017-08-20</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2217</th>\n",
       "      <td>2017-08-20</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2218</th>\n",
       "      <td>2017-08-20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2219</th>\n",
       "      <td>2017-08-21</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2220</th>\n",
       "      <td>2017-08-21</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2221</th>\n",
       "      <td>2017-08-21</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2222</th>\n",
       "      <td>2017-08-21</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2223</th>\n",
       "      <td>2017-08-22</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2224</th>\n",
       "      <td>2017-08-22</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2225</th>\n",
       "      <td>2017-08-22</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2226</th>\n",
       "      <td>2017-08-23</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2227</th>\n",
       "      <td>2017-08-23</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2228</th>\n",
       "      <td>2017-08-23</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2229</th>\n",
       "      <td>2017-08-23</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2230 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  Precipitation\n",
       "0     2016-08-23           0.00\n",
       "1     2016-08-23           0.15\n",
       "2     2016-08-23           0.05\n",
       "3     2016-08-23            NaN\n",
       "4     2016-08-23           0.02\n",
       "5     2016-08-23           1.79\n",
       "6     2016-08-23           0.70\n",
       "7     2016-08-24           0.08\n",
       "8     2016-08-24           2.15\n",
       "9     2016-08-24           2.28\n",
       "10    2016-08-24            NaN\n",
       "11    2016-08-24           1.22\n",
       "12    2016-08-24           2.15\n",
       "13    2016-08-24           1.45\n",
       "14    2016-08-25           0.08\n",
       "15    2016-08-25           0.08\n",
       "16    2016-08-25           0.00\n",
       "17    2016-08-25           0.00\n",
       "18    2016-08-25           0.21\n",
       "19    2016-08-25           0.06\n",
       "20    2016-08-25           0.11\n",
       "21    2016-08-26           0.00\n",
       "22    2016-08-26           0.03\n",
       "23    2016-08-26           0.02\n",
       "24    2016-08-26           0.04\n",
       "25    2016-08-26           0.00\n",
       "26    2016-08-26           0.01\n",
       "27    2016-08-27           0.00\n",
       "28    2016-08-27           0.18\n",
       "29    2016-08-27           0.02\n",
       "...          ...            ...\n",
       "2200  2017-08-16           0.00\n",
       "2201  2017-08-16           0.07\n",
       "2202  2017-08-16           0.12\n",
       "2203  2017-08-16           0.42\n",
       "2204  2017-08-17           0.00\n",
       "2205  2017-08-17           0.05\n",
       "2206  2017-08-17           0.01\n",
       "2207  2017-08-17           0.13\n",
       "2208  2017-08-18           0.00\n",
       "2209  2017-08-18           0.00\n",
       "2210  2017-08-18           0.06\n",
       "2211  2017-08-18            NaN\n",
       "2212  2017-08-19           0.00\n",
       "2213  2017-08-19           0.00\n",
       "2214  2017-08-19            NaN\n",
       "2215  2017-08-19           0.09\n",
       "2216  2017-08-20           0.00\n",
       "2217  2017-08-20           0.01\n",
       "2218  2017-08-20            NaN\n",
       "2219  2017-08-21           0.00\n",
       "2220  2017-08-21           0.02\n",
       "2221  2017-08-21            NaN\n",
       "2222  2017-08-21           0.56\n",
       "2223  2017-08-22           0.00\n",
       "2224  2017-08-22           0.00\n",
       "2225  2017-08-22           0.50\n",
       "2226  2017-08-23           0.00\n",
       "2227  2017-08-23           0.00\n",
       "2228  2017-08-23           0.08\n",
       "2229  2017-08-23           0.45\n",
       "\n",
       "[2230 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1). Finding the last date in the data - - select only date/prcp values\n",
    "last_date = session.query(Measurement.date).order_by(Measurement.date.desc()).first()\n",
    "print(f\"The last existing date in the Measurement data was: {last_date}\")\n",
    "\n",
    "# 2). Finding the last 12 months FROM last date in data (last 12 mo - 365d) \n",
    "last_12_months = dt.date(2017, 8, 23) - dt.timedelta(days=365)\n",
    "print(f\"12 months from last existing date: {last_12_months}\")\n",
    "\n",
    "# 3). Perform a query to retrieve the data and precipitation scores for 'last12mo'\n",
    "last_year_precipitation = session.query(Measurement.date, Measurement.prcp)\\\n",
    ".filter(Measurement.date >= last_12_months).order_by(Measurement.date).all()\n",
    "    # last_year_precipitation\n",
    "\n",
    "# 4). Save the query results as a Pandas DataFrame and set the index to the date column\n",
    "prcp_1617_df = pd.DataFrame(last_year_precipitation, columns=['Date', 'Precipitation'])\n",
    "\n",
    "prcp_1617_df.sort_values(by=['Date'])\n",
    "prcp_1617_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precipitation Plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 6). Plot the results using the DataFrame `plot` method.\n",
    "plt.title = (\"Precipitation from 2016-08-23 to 2016-08-23\")\n",
    "plt.xlabel = (\"Date\")\n",
    "plt.ylabel = (\"Precipitation\")\n",
    "plt.legend = (\"Precipitation\")\n",
    "prcp_1617_df.plot()\n",
    "plt.savefig('Images/Precipitation_Plot.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use Pandas to calcualte the summary statistics for the precipitation data\n",
    "prcp_1617_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Station Analysis\n",
    "\n",
    "* Design a query to calculate the total number of stations.\n",
    "\n",
    "* Design a query to find the most active stations.\n",
    "\n",
    "  * List the stations and observation counts in descending order.\n",
    "\n",
    "  * Which station has the highest number of observations?\n",
    "  \n",
    "  * Hint: You may need to use functions such as `func.min`, `func.max`, `func.avg`, and `func.count` in your queries.\n",
    "  \n",
    "\n",
    "* Design a query to retrieve the last 12 months of temperature observation data (tobs).\n",
    "\n",
    "  * Filter by the station with the highest number of observations.\n",
    "\n",
    "  * Plot the results as a histogram with `bins=12`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "# Get Station columns\n",
    "station_columns = inspector.get_columns('Station')\n",
    "for columns in station_columns:\n",
    "    print(columns[\"name\"], columns[\"type\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of stations in this data set is: 9\n"
     ]
    }
   ],
   "source": [
    "# 1). Design a query to calculate the total number of stations.\n",
    "stations_total = session.query(Station).count()\n",
    "print(f\"The total number of stations in this data set is: {stations_total}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most active stations:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Station Name</th>\n",
       "      <th>Observation Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>USC00519281</td>\n",
       "      <td>2772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>2724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USC00513117</td>\n",
       "      <td>2709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>USC00519523</td>\n",
       "      <td>2669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>USC00516128</td>\n",
       "      <td>2612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>USC00514830</td>\n",
       "      <td>2202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>USC00511918</td>\n",
       "      <td>1979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>USC00517948</td>\n",
       "      <td>1372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>USC00518838</td>\n",
       "      <td>511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Station Name  Observation Count\n",
       "0  USC00519281               2772\n",
       "1  USC00519397               2724\n",
       "2  USC00513117               2709\n",
       "3  USC00519523               2669\n",
       "4  USC00516128               2612\n",
       "5  USC00514830               2202\n",
       "6  USC00511918               1979\n",
       "7  USC00517948               1372\n",
       "8  USC00518838                511"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2). Design a query to find the most active stations.\n",
    "    # List the stations and observation counts in descending order.\n",
    "    # Which station has the highest number of observations?\n",
    "active_stations_locations = session.query(Measurement.station, func.count(Measurement.tobs)).\\\n",
    "group_by(Measurement.station).order_by(func.count(Measurement.tobs).desc()).all()\n",
    "\n",
    "print(f\"Most active stations:\")\n",
    "active_stations_df = pd.DataFrame(active_stations_locations, columns=['Station Name', \"Observation Count\"])\n",
    "active_stations_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(54.0, 85.0, 71.66378066378067)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_active_station = session.query(Measurement.station).\\\n",
    "    group_by(Measurement.station).\\\n",
    "    order_by(func.count(Measurement.station).desc()).\\\n",
    "    limit(1).scalar()\n",
    "\n",
    "sel = [func.min(Measurement.tobs), func.max(Measurement.tobs), func.avg(Measurement.tobs)]\n",
    "session.query(*sel).\\\n",
    "    filter(Measurement.station == most_active_station).\\\n",
    "    group_by(Measurement.station).\\\n",
    "    all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_active_station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3). Design a query to retrieve the last 12 months of temperature observation data (tobs).\n",
    "    # Filter by the station with the highest number of observations.\n",
    "    # Plot the results as a histogram with `bins=12`.\n",
    "\n",
    "measurement_tobs = session.query(Measurement.station, Measurement.tobs).\\\n",
    "                filter(Measurement.station == 'USC00519281').\\\n",
    "                filter(Measurement.date>= last_12_months).statement\n",
    "\n",
    "tobs_df = pd.read_sql(measurement_tobs, conn)\n",
    "tobs_df.plot(kind='hist', bins=12)\n",
    "plt.show()\n",
    "\n",
    "plt.savefig('Images/Histogram.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python[conda env: KCDataVis]",
   "language": "python",
   "name": "env"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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