climate_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reflect Tables into SQLAlchemy ORM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create engine to hawaii.sqlite\n",
    "engine = create_engine(\"sqlite:///Resources/hawaii.sqlite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reflect an existing database into a new model\n",
    "Base = automap_base()\n",
    "\n",
    "# reflect the tables\n",
    "Base.prepare(autoload_with=engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['station', 'measurement']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View all of the classes that automap found\n",
    "Base.classes.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save references to each table\n",
    "measurement = Base.classes.measurement\n",
    "station = Base.classes.station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create our session (link) from Python to the DB\n",
    "session = Session(engine)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Precipitation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-08-23\n"
     ]
    }
   ],
   "source": [
    "# Find the most recent date in the data set.\n",
    "last_date = session.query(func.max(measurement.date)).scalar()\n",
    "\n",
    "print(last_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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",
    "\n",
    "# Calculate the date one year from the last date in the dataset\n",
    "one_year_ago = (pd.to_datetime(last_date) - pd.DateOffset(years=1)).strftime('%Y-%m-%d')\n",
    "\n",
    "# Perform a query to retrieve the data and precipitation scores\n",
    "results = session.query(measurement.date, measurement.prcp).\\\n",
    "    filter(measurement.date >= one_year_ago).\\\n",
    "    order_by(measurement.date).all()\n",
    "\n",
    "# Save the query results as a Pandas DataFrame. Explicitly set the column names\n",
    "df = pd.DataFrame(results, columns=['Date', 'Precipitation'])\n",
    "\n",
    "# Sort the dataframe by date\n",
    "df = df.sort_values('Date')\n",
    "\n",
    "# Use Pandas Plotting with Matplotlib to plot the data\n",
    "df.plot(x='Date', y='Precipitation', rot=90)\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Precipitation (inches)')\n",
    "plt.title('Precipitation in the Last 12 Months')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use Pandas to calculate the summary statistics for the precipitation data\n",
    "df.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Station Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n"
     ]
    }
   ],
   "source": [
    "# Design a query to calculate the total number of stations in the dataset\n",
    "number_of_stations = session.query(station.name).count()\n",
    "print(number_of_stations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Design a query to find the most active stations (i.e. which stations have the most rows?)\n",
    "# List the stations and their counts in descending order.\n",
    "sel = [measurement.station,func.count(measurement.id)]\n",
    "active_stations = session.query(*sel).\\\n",
    "    group_by(measurement.station).\\\n",
    "    order_by(func.count(measurement.id).desc()).all()\n",
    "active_stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(54.0, 85.0, 71.66378066378067)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the most active station id from the previous query, calculate the lowest, highest, and average temperature.\n",
    "sel = [func.min(measurement.tobs),func.max(measurement.tobs),func.avg(measurement.tobs)]\n",
    "most_active_station = session.query(*sel).\\\n",
    "    group_by(measurement.station).\\\n",
    "    order_by(func.count(measurement.id).desc()).first()\n",
    "most_active_station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2093d903490>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using the most active station id\n",
    "# Query the last 12 months of temperature observation data for this station and plot the results as a histogram\n",
    "query_result = session.query(measurement.tobs).\\\n",
    "    filter(measurement.station == active_stations[0][0]).\\\n",
    "    filter(measurement.date >= one_year_ago).all()\n",
    "temperatures = list(np.ravel(query_result))\n",
    "\n",
    "sel = [station.station,station.name,station.latitude,station.longitude,station.elevation]\n",
    "query_result = session.query(*sel).all()\n",
    "stations_desc = pd.DataFrame(query_result, columns=['Station','Name','Latitude','Longitude','Elevation'])\n",
    "\n",
    "station_name = stations_desc.loc[stations_desc[\"Station\"] == active_stations[0][0],\"Name\"].tolist()[0]\n",
    "\n",
    "plt.hist(temperatures, bins=12,rwidth=1.0,label='tobs')\n",
    "plt.grid(axis='both', alpha=0.75)\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend()\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Close Session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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 (ipykernel)",
   "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": "3.10.10"
  },
  "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
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}