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\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reflect Tables into SQLAlchemy ORM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python SQL toolkit and Object Relational Mapper\n",
    "import sqlalchemy\n",
    "from sqlalchemy.ext.automap import automap_base\n",
    "from sqlalchemy.orm import Session\n",
    "from sqlalchemy import create_engine, func"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create engine to hawaii.sqlite\n",
    "engine = create_engine(\"sqlite:///Resources/hawaii.sqlite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['measurement', 'station']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View all of the classes that automap found\n",
    "Base.classes.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save references to each table\n",
    "Measure = Base.classes.measurement\n",
    "Station = Base.classes.station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create our session (link) from Python to the DB\n",
    "session = Session(bind = engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'_sa_instance_state': <sqlalchemy.orm.state.InstanceState at 0x2aac683d1c0>,\n",
       " 'date': '2010-01-01',\n",
       " 'tobs': 65.0,\n",
       " 'id': 1,\n",
       " 'station': 'USC00519397',\n",
       " 'prcp': 0.08}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ie = session.query(Measure).first()\n",
    "ie.__dict__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'_sa_instance_state': <sqlalchemy.orm.state.InstanceState at 0x2aac683da30>,\n",
       " 'id': 1,\n",
       " 'name': 'WAIKIKI 717.2, HI US',\n",
       " 'longitude': -157.8168,\n",
       " 'station': 'USC00519397',\n",
       " 'latitude': 21.2716,\n",
       " 'elevation': 3.0}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ie2 = session.query(Station).first()\n",
    "ie2.__dict__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Precipitation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the most recent date in the data set.\n",
    "last_date = session.query(Measure.date).order_by(Measure.date.desc()).first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2017-08-23',)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Design a query to retrieve the last 12 months of precipitation data and plot the results. \n",
    "# Starting from the most recent data point in the database. \n",
    "last_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(2016, 8, 24)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the date one year from the last date in data set.\n",
    "year_ago = dt.date(2017,8,23) - dt.timedelta(weeks=52)\n",
    "year_ago"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform a query to retrieve the date and precipitation scores\n",
    "prcp_1year = session.query(Measure.date, Measure.prcp).filter(Measure.date > year_ago).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Date</th>\n",
       "      <th>Precipitation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-08-25</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-08-26</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-08-27</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-08-28</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-08-29</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Precipitation\n",
       "0  2016-08-25           0.08\n",
       "1  2016-08-26           0.00\n",
       "2  2016-08-27           0.00\n",
       "3  2016-08-28           0.01\n",
       "4  2016-08-29           0.00"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the query results as a Pandas DataFrame \n",
    "prcp_df = pd.DataFrame(prcp_1year,columns=[\"Date\",\"Precipitation\"])\n",
    "prcp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>2016-08-25</th>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-26</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-27</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-28</th>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-29</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-30</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-31</th>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-01</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-02</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-03</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-04</th>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-05</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-06</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-07</th>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-08</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-09</th>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-10</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-11</th>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-12</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-13</th>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Precipitation\n",
       "Date                     \n",
       "2016-08-25           0.08\n",
       "2016-08-26           0.00\n",
       "2016-08-27           0.00\n",
       "2016-08-28           0.01\n",
       "2016-08-29           0.00\n",
       "2016-08-30           0.00\n",
       "2016-08-31           0.13\n",
       "2016-09-01           0.00\n",
       "2016-09-02           0.00\n",
       "2016-09-03           0.00\n",
       "2016-09-04           0.03\n",
       "2016-09-05            NaN\n",
       "2016-09-06            NaN\n",
       "2016-09-07           0.05\n",
       "2016-09-08           0.00\n",
       "2016-09-09           0.03\n",
       "2016-09-10           0.00\n",
       "2016-09-11           0.05\n",
       "2016-09-12           0.00\n",
       "2016-09-13           0.02"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And set the index to the date column  # Sort the dataframe by date\n",
    "prcp_df.set_index([\"Date\"],inplace=True)\n",
    "prcp_df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use Pandas Plotting with Matplotlib to plot the data\n",
    "plot= prcp_df.plot(kind=\"line\",\n",
    "                    figsize=(8,4),\n",
    "                    rot=60,\n",
    "                    title=\"Precipitation during one year (Aug 2016 to Aug 2017)\",\n",
    "                    ylabel=\"Precipitation\",\n",
    "                    xlabel=\"Date\",\n",
    "                    color=\"green\", \n",
    "                    alpha=0.6\n",
    "                )\n",
    "plot.axes.xaxis.set_ticks([])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I have grouped by month for better plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "prcp_by_month = session.query(func.strftime(\"%m\",Measure.date), func.sum(Measure.prcp)).\\\n",
    "                filter(Measure.prcp != None).\\\n",
    "                group_by(func.strftime(\"%m\",Measure.date)).\\\n",
    "                all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('01', 210.56000000000003),\n",
       " ('02', 201.23999999999995),\n",
       " ('03', 319.82000000000005),\n",
       " ('04', 240.27000000000004),\n",
       " ('05', 236.5800000000001),\n",
       " ('06', 214.6299999999999),\n",
       " ('07', 263.47000000000014),\n",
       " ('08', 218.81999999999994),\n",
       " ('09', 226.50000000000017),\n",
       " ('10', 224.85000000000016),\n",
       " ('11', 246.76),\n",
       " ('12', 304.6299999999995)]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prcp_by_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>01</th>\n",
       "      <td>210.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02</th>\n",
       "      <td>201.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03</th>\n",
       "      <td>319.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04</th>\n",
       "      <td>240.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05</th>\n",
       "      <td>236.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06</th>\n",
       "      <td>214.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07</th>\n",
       "      <td>263.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08</th>\n",
       "      <td>218.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09</th>\n",
       "      <td>226.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>224.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Precipitation\n",
       "Month               \n",
       "01            210.56\n",
       "02            201.24\n",
       "03            319.82\n",
       "04            240.27\n",
       "05            236.58\n",
       "06            214.63\n",
       "07            263.47\n",
       "08            218.82\n",
       "09            226.50\n",
       "10            224.85"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prcp_by_month_df = pd.DataFrame(prcp_by_month,columns=[\"Month\",\"Precipitation\"])\n",
    "prcp_by_month_df.set_index([\"Month\"],inplace=True)\n",
    "prcp_by_month_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot= prcp_by_month_df.plot(kind=\"bar\",\n",
    "                    figsize=(8,4),\n",
    "                    rot=60,\n",
    "                    title=\"Accumulated precipitation by Month (2016 - 2017)\",\n",
    "                    ylabel=\"Precipitation\",\n",
    "                    xlabel=\"Month\",\n",
    "                    color=\"green\", \n",
    "                    alpha=0.6\n",
    "                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(prcp_df.index, pr)\n",
    "prcp_df.plot(kind=\"bar\",\n",
    "                figsize=(8,4),\n",
    "                rot=60,\n",
    "                title=\"Precipitation during one year\",\n",
    "                ylabel=\"Precipitation\",\n",
    "                xlabel=\"Date\",\n",
    "                color=\"green\", \n",
    "                alpha=0.6\n",
    "               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>2009.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.172344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.452818</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    2009.000000\n",
       "mean        0.172344\n",
       "std         0.452818\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         0.020000\n",
       "75%         0.130000\n",
       "max         6.700000"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use Pandas to calcualte the summary statistics for the precipitation data\n",
    "prcp_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>242.344167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>36.882724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>201.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>217.772500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>231.540000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>250.937500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>319.820000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Precipitation\n",
       "count      12.000000\n",
       "mean      242.344167\n",
       "std        36.882724\n",
       "min       201.240000\n",
       "25%       217.772500\n",
       "50%       231.540000\n",
       "75%       250.937500\n",
       "max       319.820000"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prcp_by_month_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Station Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(9,)]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Design a query to calculate the total number stations in the dataset\n",
    "total_stations = session.query(func.count(Station.station.distinct())).all()\n",
    "total_stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('USC00519397',),\n",
       " ('USC00513117',),\n",
       " ('USC00514830',),\n",
       " ('USC00517948',),\n",
       " ('USC00518838',),\n",
       " ('USC00519523',),\n",
       " ('USC00519281',),\n",
       " ('USC00511918',),\n",
       " ('USC00516128',)]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations = session.query(Station.station.distinct()).all()\n",
    "stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('USC00519281', 7, 2772),\n",
       " ('USC00519397', 1, 2724),\n",
       " ('USC00513117', 2, 2709),\n",
       " ('USC00519523', 6, 2669),\n",
       " ('USC00516128', 9, 2612),\n",
       " ('USC00514830', 3, 2202),\n",
       " ('USC00511918', 8, 1979),\n",
       " ('USC00517948', 4, 1372),\n",
       " ('USC00518838', 5, 511)]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Design a query to find the most active stations (i.e. what stations have the most rows?)\n",
    "# List the stations and the counts in descending order.\n",
    "most_active_st = session.query(Measure.station, Station.id, func.count(Measure.station)).\\\n",
    "                filter(Measure.station==Station.station).\\\n",
    "                group_by(Measure.station).\\\n",
    "                order_by(func.count(Measure.station).desc()).\\\n",
    "                all()\n",
    "most_active_st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Station: USC00519281\n",
      "Min: 54.0\n",
      "Max: 85.0\n",
      "Avg: 71.7\n"
     ]
    }
   ],
   "source": [
    "# Using the most active station id from the previous query, calculate the lowest, highest, and average temperature.\n",
    "mostact=most_active_st[0][1]\n",
    "active_station_temp = session.query(Measure.station, \n",
    "                                    func.min(Measure.tobs), \n",
    "                                    func.max(Measure.tobs), \n",
    "                                    func.avg(Measure.tobs)).\\\n",
    "                                    filter(Measure.station==Station.station).\\\n",
    "                                    filter(Station.id == mostact).all()\n",
    "\n",
    "print(f\"Station: {active_station_temp[0][0]}\")\n",
    "print(f\"Min: {active_station_temp[0][1]}\")\n",
    "print(f\"Max: {active_station_temp[0][2]}\")\n",
    "print(f\"Avg: {round(active_station_temp[0][3],1)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using the most active station id\n",
    "# Query the last 12 months of temperature observation data for this station\n",
    "mostact_year_temp = session.query(Measure.date, Measure.tobs).\\\n",
    "                    filter(Measure.station==Station.station).\\\n",
    "                    filter(Station.id == mostact).\\\n",
    "                    filter(Measure.date > year_ago).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-08-25</th>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-26</th>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-27</th>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-28</th>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-29</th>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-30</th>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-31</th>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-01</th>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-02</th>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-03</th>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Temperature\n",
       "Date                   \n",
       "2016-08-25         80.0\n",
       "2016-08-26         80.0\n",
       "2016-08-27         75.0\n",
       "2016-08-28         73.0\n",
       "2016-08-29         78.0\n",
       "2016-08-30         77.0\n",
       "2016-08-31         78.0\n",
       "2016-09-01         80.0\n",
       "2016-09-02         80.0\n",
       "2016-09-03         78.0"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "mostact_year_temp_df = pd.DataFrame(mostact_year_temp,columns=[\"Date\",\"Temperature\"])\n",
    "mostact_year_temp_df.set_index([\"Date\"],inplace=True)\n",
    "mostact_year_temp_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# And plot the results as a histogram\n",
    "mostact_year_temp_df.plot.hist(bins=12, color=\"green\")\n",
    "plt.title(f\"Temperature for {active_station_temp[0][0]} station\")\n",
    "plt.xlabel(\"Temperature\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Close session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Close Session\n",
    "session.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernel_info": {
   "name": "python3"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "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": "3.9.7"
  },
  "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"
   ],
   "window_display": false
  },
  "vscode": {
   "interpreter": {
    "hash": "1d4b4d99b5e84493afab03ed593eb1d1bc23c15333c006dc20fe35c1b08d0cba"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}