Data-Analytics/4-climate-analysis-SQLAlchemy/climate_starter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import style\n",
    "style.use('fivethirtyeight')\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python SQL toolkit and Object Relational Mapper\n",
    "from sqlalchemy.ext.automap import automap_base\n",
    "from sqlalchemy.orm import Session\n",
    "from sqlalchemy import create_engine, inspect, desc, func"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create engine to hawaii.sqlite\n",
    "engine = create_engine(\"sqlite:///Resources/hawaii.sqlite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#inspector\n",
    "inspector = inspect(engine)\n",
    "table_names = inspector.get_table_names()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['station', 'measurement']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reflect an existing database into a new model\n",
    "Base = automap_base()\n",
    "Base.prepare(autoload_with = engine)\n",
    "\n",
    "# reflect the tables\n",
    "measurement = Base.classes.measurement\n",
    "station = Base.classes.station\n",
    "Base.classes.keys()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": [
    "# Save references to each table\n",
    "columns = inspector.get_columns('station')\n",
    "for col in columns:\n",
    "    print (col['name'], col['type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create our session (link) from Python to the DB\n",
    "session = Session(engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Precipitation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2017-08-23',)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the most recent date in the data set.\n",
    "session.query(measurement.date).order_by(desc(measurement.date)).first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>year</th>\n",
       "      <th>precipitation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-08-24</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-08-25</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-08-26</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-08-27</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-08-28</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2216</th>\n",
       "      <td>2017-08-17</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2218</th>\n",
       "      <td>2017-08-19</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2220</th>\n",
       "      <td>2017-08-21</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2221</th>\n",
       "      <td>2017-08-22</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2222</th>\n",
       "      <td>2017-08-23</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2015 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           year  precipitation\n",
       "0    2016-08-24           0.08\n",
       "1    2016-08-25           0.08\n",
       "2    2016-08-26           0.00\n",
       "3    2016-08-27           0.00\n",
       "4    2016-08-28           0.01\n",
       "...         ...            ...\n",
       "2216 2017-08-17           0.13\n",
       "2218 2017-08-19           0.09\n",
       "2220 2017-08-21           0.56\n",
       "2221 2017-08-22           0.50\n",
       "2222 2017-08-23           0.45\n",
       "\n",
       "[2015 rows x 2 columns]"
      ]
     },
     "execution_count": 37,
     "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": [
    "# Design a query to retrieve the last 12 months of precipitation data and plot the results. \n",
    "\n",
    "# Starting from the most recent data point in the database. \n",
    "date = session.query(measurement.date).order_by(desc(measurement.date)).first()\n",
    "dt.datetime.strptime(date[0], '%Y-%m-%d')\n",
    "# Calculate the date one year from the last date in data set.\n",
    "past_year = dt.datetime.strptime(date[0], '%Y-%m-%d') - dt.timedelta(365)\n",
    "\n",
    "# Perform a query to retrieve the data and precipitation scores\n",
    "past_year_data = session.query(measurement.date, measurement.prcp).\\\n",
    "    filter(measurement.date >= past_year).all()\n",
    "\n",
    "# Save the query results as a Pandas DataFrame. Explicitly set the column names\n",
    "prcp_df = pd.DataFrame(past_year_data, columns = ['year', 'precipitation'])\n",
    "prcp_df['year'] = pd.to_datetime(prcp_df['year'])\n",
    "prcp_df = prcp_df.dropna(how = 'any')\n",
    "\n",
    "# Sort the dataframe by date\n",
    "prcp_df.sort_values(by = 'year')\n",
    "\n",
    "# Use Pandas Plotting with Matplotlib to plot the data\n",
    "prcp_df.plot(x = 'year', y = 'precipitation', rot = 90,\n",
    "             xlabel = 'Date',\n",
    "             ylabel = 'Inches')\n",
    "plt.legend(loc = 'upper right')\n",
    "prcp_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>2015.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.176462</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",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.460288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       precipitation\n",
       "count    2015.000000\n",
       "mean        0.176462\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         0.020000\n",
       "75%         0.130000\n",
       "max         6.700000\n",
       "std         0.460288"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(prcp_df.describe()['precipitation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prcp_df['precipitation'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Station Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "columns = inspector.get_columns('station')\n",
    "for col in columns:\n",
    "    print (col['name'], col['type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Design a query to calculate the total number of stations in the dataset\n",
    "session.query(measurement.station).distinct().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 15,
     "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",
    "session.query(measurement.station, func.count(measurement.station)).group_by(measurement.station).order_by(desc(func.count(measurement.station))).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('USC00519281', 54.0, 85.0, 71.66378066378067)]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the most active station id from the previous query, calculate the lowest, highest, and average temperature.\n",
    "most_active_station = session.query(measurement.station, func.count(measurement.station)).group_by(measurement.station).order_by(desc(func.count(measurement.station))).first()\n",
    "most_active_station[0]\n",
    "session.query(measurement.station, func.min(measurement.tobs), func.max(measurement.tobs), func.avg(measurement.tobs)).filter(measurement.station == most_active_station[0]).all()\n",
    "# session.query(station.name).filter(station.station == measurement.station[0]).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-08-19</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-08-20</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-08-21</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-08-22</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-08-23</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>2017-08-14</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>2017-08-17</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>2017-08-18</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>356 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  temp\n",
       "0   2016-08-19  79.0\n",
       "1   2016-08-20  81.0\n",
       "2   2016-08-21  79.0\n",
       "3   2016-08-22  78.0\n",
       "4   2016-08-23  77.0\n",
       "..         ...   ...\n",
       "351 2017-08-14  77.0\n",
       "352 2017-08-15  77.0\n",
       "353 2017-08-16  76.0\n",
       "354 2017-08-17  76.0\n",
       "355 2017-08-18  79.0\n",
       "\n",
       "[356 rows x 2 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "most_active_station_id = most_active_station[0]\n",
    "most_active_station_latest_date = session.query(measurement.date).order_by(desc(measurement.date)).filter(measurement.station == most_active_station[0]).first()\n",
    "most_active_station_latest_date = dt.datetime.strptime(most_active_station_latest_date[0], '%Y-%m-%d')\n",
    "most_active_station_past_year = most_active_station_latest_date - dt.timedelta(365)\n",
    "most_active_station_past_year_data = session.query(measurement.date, measurement.tobs).\\\n",
    "    filter(measurement.date >= most_active_station_past_year).\\\n",
    "    filter(measurement.station == most_active_station[0]).all()\n",
    "prcp_df = pd.DataFrame(most_active_station_past_year_data, columns = ['date', 'temp'])\n",
    "prcp_df.values.tolist()\n",
    "prcp_df['date'] = pd.to_datetime(prcp_df['date'])\n",
    "prcp_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 5.,  7.,  7., 12., 27., 41., 40., 57., 66., 54., 31.,  9.]),\n",
       " array([59., 61., 63., 65., 67., 69., 71., 73., 75., 77., 79., 81., 83.]),\n",
       " <BarContainer object of 12 artists>)"
      ]
     },
     "execution_count": 18,
     "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": [
    "plt.hist(prcp_df['temp'], bins = 12, label='tobs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Close Session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": "3.11.7"
  },
  "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
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}