SurfsUp_Challenge.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 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\n",
    "\n",
    "# Import DataFrame image\n",
    "import dataframe_image as dfi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine(\"sqlite:///hawaii.sqlite\")\n",
    "\n",
    "# reflect an existing database into a new model\n",
    "Base = automap_base()\n",
    "\n",
    "# reflect the tables\n",
    "Base.prepare(engine, reflect=True)\n",
    "\n",
    "# Save references to each table\n",
    "Measurement = Base.classes.measurement\n",
    "Station = Base.classes.station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create our session (link) from Python to the DB\n",
    "session = Session(engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## D1: Determine the Summary Statistics for June"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Import the sqlalchemy extract function.\n",
    "from sqlalchemy import extract\n",
    "\n",
    "# 2. Write a query that filters the Measurement table to retrieve the temperatures for the month of June. \n",
    "june_temps = session.query(Measurement).filter(extract('month', Measurement.date) == 6).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  3. Convert the June temperatures to a list.\n",
    "june_temps_list = [temp.tobs for temp in june_temps]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>June Temps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1695</th>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1696</th>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1697</th>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1698</th>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1700 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      June Temps\n",
       "0           78.0\n",
       "1           76.0\n",
       "2           78.0\n",
       "3           76.0\n",
       "4           77.0\n",
       "...          ...\n",
       "1695        79.0\n",
       "1696        74.0\n",
       "1697        74.0\n",
       "1698        76.0\n",
       "1699        75.0\n",
       "\n",
       "[1700 rows x 1 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4. Create a DataFrame from the list of temperatures for the month of June. \n",
    "june_temps_df = pd.DataFrame(june_temps_list, columns=['June Temps'])\n",
    "june_temps_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>June Temps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>74.944118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.257417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>64.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>77.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>85.000000</td>\n",
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      ],
      "text/plain": [
       "        June Temps\n",
       "count  1700.000000\n",
       "mean     74.944118\n",
       "std       3.257417\n",
       "min      64.000000\n",
       "25%      73.000000\n",
       "50%      75.000000\n",
       "75%      77.000000\n",
       "max      85.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5. Calculate and print out the summary statistics for the June temperature DataFrame.\n",
    "june_temp_sum_stats = june_temps_df.describe()\n",
    "\n",
    "# Export as an image\n",
    "dfi.export(june_temp_sum_stats, \"Images/june_temp_summary_stats.png\")\n",
    "\n",
    "june_temp_sum_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## D2: Determine the Summary Statistics for December"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6. Write a query that filters the Measurement table to retrieve the temperatures for the month of December.\n",
    "dec_temps = session.query(Measurement).filter(extract('month', Measurement.date) == 12).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 7. Convert the December temperatures to a list.\n",
    "dec_temps_list = [temp.tobs for temp in dec_temps]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>December Temps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1512</th>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1513</th>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1514</th>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515</th>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1516</th>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1517 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      December Temps\n",
       "0               76.0\n",
       "1               74.0\n",
       "2               74.0\n",
       "3               64.0\n",
       "4               64.0\n",
       "...              ...\n",
       "1512            71.0\n",
       "1513            71.0\n",
       "1514            69.0\n",
       "1515            65.0\n",
       "1516            65.0\n",
       "\n",
       "[1517 rows x 1 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 8. Create a DataFrame from the list of temperatures for the month of December. \n",
    "dec_temps_df = pd.DataFrame(dec_temps_list, columns=['December Temps'])\n",
    "dec_temps_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>December Temps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1517.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>71.041529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.745920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>56.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>69.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>71.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>83.000000</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "       December Temps\n",
       "count     1517.000000\n",
       "mean        71.041529\n",
       "std          3.745920\n",
       "min         56.000000\n",
       "25%         69.000000\n",
       "50%         71.000000\n",
       "75%         74.000000\n",
       "max         83.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 9. Calculate and print out the summary statistics for the Decemeber temperature DataFrame.\n",
    "dec_temp_sum_stats = dec_temps_df.describe()\n",
    "\n",
    "# Export as an image\n",
    "dfi.export(dec_temp_sum_stats, \"Images/dec_temp_summary_stats.png\")\n",
    "\n",
    "dec_temp_sum_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "# Divide temperature observations into 10 different bins and display data in a histogram.\n",
    "june_temps_df.plot.hist(bins=10)\n",
    "\n",
    "# Save the histogram.\n",
    "plt.savefig(\"Images/June_Temps_hist.png\")\n",
    "\n",
    "# Display the figure.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "# Divide temperature observations into 10 different bins and display data in a histogram.\n",
    "dec_temps_df.plot.hist(bins=10)\n",
    "\n",
    "# Save the histogram.\n",
    "plt.savefig(\"Images/December_Temps_hist.png\")\n",
    "\n",
    "# Display the figure.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PythonData",
   "language": "python",
   "name": "pythondata"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}