climate.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\n",
    "import datetime\n",
    "from dateutil.relativedelta import relativedelta\n",
    "from pandas import DataFrame\n",
    "from matplotlib.dates import drange\n",
    "from sqlalchemy import func, and_\n",
    "from sqlalchemy import desc"
   ]
  },
  {
   "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, inspect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine(\"sqlite:///Resources/hawaii.sqlite\", echo=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['measurement', 'station']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View all of the classes that automap found\n",
    "Base.classes.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save references to each table\n",
    "Measurement = Base.classes.measurement\n",
    "Station = Base.classes.station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create our session (link) from Python to the DB\n",
    "session = Session(engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn = engine.connect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Precipitation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>id</th>\n",
       "      <th>station</th>\n",
       "      <th>date</th>\n",
       "      <th>prcp</th>\n",
       "      <th>tobs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>USC00519397</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>USC00519397</td>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>USC00519397</td>\n",
       "      <td>2010-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>USC00519397</td>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>USC00519397</td>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19545</th>\n",
       "      <td>19545</td>\n",
       "      <td>USC00516128</td>\n",
       "      <td>2017-08-19</td>\n",
       "      <td>2.3</td>\n",
       "      <td>21.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19546</th>\n",
       "      <td>19546</td>\n",
       "      <td>USC00516128</td>\n",
       "      <td>2017-08-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19547</th>\n",
       "      <td>19547</td>\n",
       "      <td>USC00516128</td>\n",
       "      <td>2017-08-21</td>\n",
       "      <td>14.2</td>\n",
       "      <td>24.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19548</th>\n",
       "      <td>19548</td>\n",
       "      <td>USC00516128</td>\n",
       "      <td>2017-08-22</td>\n",
       "      <td>12.7</td>\n",
       "      <td>24.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19549</th>\n",
       "      <td>19549</td>\n",
       "      <td>USC00516128</td>\n",
       "      <td>2017-08-23</td>\n",
       "      <td>11.4</td>\n",
       "      <td>24.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19550 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id      station        date  prcp  tobs\n",
       "0          0  USC00519397  2010-01-01   2.0  18.3\n",
       "1          1  USC00519397  2010-01-02   0.0  17.2\n",
       "2          2  USC00519397  2010-01-03   0.0  23.3\n",
       "3          3  USC00519397  2010-01-04   0.0  24.4\n",
       "4          4  USC00519397  2010-01-06   NaN  22.8\n",
       "...      ...          ...         ...   ...   ...\n",
       "19545  19545  USC00516128  2017-08-19   2.3  21.7\n",
       "19546  19546  USC00516128  2017-08-20   NaN  25.6\n",
       "19547  19547  USC00516128  2017-08-21  14.2  24.4\n",
       "19548  19548  USC00516128  2017-08-22  12.7  24.4\n",
       "19549  19549  USC00516128  2017-08-23  11.4  24.4\n",
       "\n",
       "[19550 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Measurement_data = pd.read_sql(\"SELECT * FROM measurement\", conn)\n",
    "Measurement_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>id</th>\n",
       "      <th>station</th>\n",
       "      <th>name</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>elevation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>USC00519397</td>\n",
       "      <td>WAIKIKI 717.2, HI US</td>\n",
       "      <td>21.27160</td>\n",
       "      <td>-157.81680</td>\n",
       "      <td>0.914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>USC00513117</td>\n",
       "      <td>KANEOHE 838.1, HI US</td>\n",
       "      <td>21.42340</td>\n",
       "      <td>-157.80150</td>\n",
       "      <td>4.450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>USC00514830</td>\n",
       "      <td>KUALOA RANCH HEADQUARTERS 886.9, HI US</td>\n",
       "      <td>21.52130</td>\n",
       "      <td>-157.83740</td>\n",
       "      <td>2.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>USC00517948</td>\n",
       "      <td>PEARL CITY, HI US</td>\n",
       "      <td>21.39340</td>\n",
       "      <td>-157.97510</td>\n",
       "      <td>3.627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>USC00518838</td>\n",
       "      <td>UPPER WAHIAWA 874.3, HI US</td>\n",
       "      <td>21.49920</td>\n",
       "      <td>-158.01110</td>\n",
       "      <td>93.447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>USC00519523</td>\n",
       "      <td>WAIMANALO EXPERIMENTAL FARM, HI US</td>\n",
       "      <td>21.33556</td>\n",
       "      <td>-157.71139</td>\n",
       "      <td>5.943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>USC00519281</td>\n",
       "      <td>WAIHEE 837.5, HI US</td>\n",
       "      <td>21.45167</td>\n",
       "      <td>-157.84889</td>\n",
       "      <td>10.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>USC00511918</td>\n",
       "      <td>HONOLULU OBSERVATORY 702.2, HI US</td>\n",
       "      <td>21.31520</td>\n",
       "      <td>-157.99920</td>\n",
       "      <td>0.274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>USC00516128</td>\n",
       "      <td>MANOA LYON ARBO 785.2, HI US</td>\n",
       "      <td>21.33310</td>\n",
       "      <td>-157.80250</td>\n",
       "      <td>46.449</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id      station                                    name  latitude  \\\n",
       "0   0  USC00519397                    WAIKIKI 717.2, HI US  21.27160   \n",
       "1   1  USC00513117                    KANEOHE 838.1, HI US  21.42340   \n",
       "2   2  USC00514830  KUALOA RANCH HEADQUARTERS 886.9, HI US  21.52130   \n",
       "3   3  USC00517948                       PEARL CITY, HI US  21.39340   \n",
       "4   4  USC00518838              UPPER WAHIAWA 874.3, HI US  21.49920   \n",
       "5   5  USC00519523      WAIMANALO EXPERIMENTAL FARM, HI US  21.33556   \n",
       "6   6  USC00519281                     WAIHEE 837.5, HI US  21.45167   \n",
       "7   7  USC00511918       HONOLULU OBSERVATORY 702.2, HI US  21.31520   \n",
       "8   8  USC00516128            MANOA LYON ARBO 785.2, HI US  21.33310   \n",
       "\n",
       "   longitude  elevation  \n",
       "0 -157.81680      0.914  \n",
       "1 -157.80150      4.450  \n",
       "2 -157.83740      2.133  \n",
       "3 -157.97510      3.627  \n",
       "4 -158.01110     93.447  \n",
       "5 -157.71139      5.943  \n",
       "6 -157.84889     10.027  \n",
       "7 -157.99920      0.274  \n",
       "8 -157.80250     46.449  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Station_data = pd.read_sql(\"SELECT * FROM station\", conn)\n",
    "Station_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2017-08-23'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the most recent date in the data set.\n",
    "Measurement_data[\"date\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Designing a query to retrieve the last 12 months of precipitation data and plot the results. \n",
    "d = dt.datetime(2017, 8, 23)\n",
    "One_year_prior = d - relativedelta(years=1)\n",
    "\n",
    "\n",
    "# Perform a query to retrieve the data and precipitation scores\n",
    "Precip_filter = session.query(Measurement.date, Measurement.prcp).filter(Measurement.date > One_year_prior).all()\n",
    "Precip_filter                   \n",
    "\n",
    "# Save the query results as a Pandas DataFrame and set the index to the date column\n",
    "Precip_filter_df = pd.DataFrame(Precip_filter, columns=['Date', 'Precipitation'])\n",
    "Precip_filter_df.set_index('Date', inplace = True)\n",
    "\n",
    "# Sort the dataframe by date\n",
    "Precip_filter_sort_df = Precip_filter_df.sort_values('Date',ascending = True)\n",
    "\n",
    "\n",
    "# Use Pandas Plotting with Matplotlib to plot the data\n",
    "\n",
    "Precip_filter_sort_df.plot(figsize = (10,6))\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"mm\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.title(\"Precipitation data for the last 12 months\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2015.000000\n",
       "mean        4.485112\n",
       "std        11.690532\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         0.500000\n",
       "75%         3.300000\n",
       "max       170.200000\n",
       "Name: Precipitation, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use Pandas to calcualte the summary statistics for the precipitation data\n",
    "Precip_filter_df['Precipitation'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Station Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>id</th>\n",
       "      <th>date</th>\n",
       "      <th>prcp</th>\n",
       "      <th>tobs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>USC00511918</th>\n",
       "      <td>1979</td>\n",
       "      <td>1979</td>\n",
       "      <td>1932</td>\n",
       "      <td>1979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USC00513117</th>\n",
       "      <td>2709</td>\n",
       "      <td>2709</td>\n",
       "      <td>2696</td>\n",
       "      <td>2709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USC00514830</th>\n",
       "      <td>2202</td>\n",
       "      <td>2202</td>\n",
       "      <td>1937</td>\n",
       "      <td>2202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USC00516128</th>\n",
       "      <td>2612</td>\n",
       "      <td>2612</td>\n",
       "      <td>2484</td>\n",
       "      <td>2612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USC00517948</th>\n",
       "      <td>1372</td>\n",
       "      <td>1372</td>\n",
       "      <td>683</td>\n",
       "      <td>1372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USC00518838</th>\n",
       "      <td>511</td>\n",
       "      <td>511</td>\n",
       "      <td>342</td>\n",
       "      <td>511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USC00519281</th>\n",
       "      <td>2772</td>\n",
       "      <td>2772</td>\n",
       "      <td>2772</td>\n",
       "      <td>2772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USC00519397</th>\n",
       "      <td>2724</td>\n",
       "      <td>2724</td>\n",
       "      <td>2685</td>\n",
       "      <td>2724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USC00519523</th>\n",
       "      <td>2669</td>\n",
       "      <td>2669</td>\n",
       "      <td>2572</td>\n",
       "      <td>2669</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               id  date  prcp  tobs\n",
       "station                            \n",
       "USC00511918  1979  1979  1932  1979\n",
       "USC00513117  2709  2709  2696  2709\n",
       "USC00514830  2202  2202  1937  2202\n",
       "USC00516128  2612  2612  2484  2612\n",
       "USC00517948  1372  1372   683  1372\n",
       "USC00518838   511   511   342   511\n",
       "USC00519281  2772  2772  2772  2772\n",
       "USC00519397  2724  2724  2685  2724\n",
       "USC00519523  2669  2669  2572  2669"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Design a query to calculate the total number stations in the dataset\n",
    "active_stations = Measurement_data.groupby(['station']).count()\n",
    "active_stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Design a query to find the most active stations\n",
    "top_station = active_stations.sort_values(\"id\", axis = 0, ascending = False).head(1)\n",
    "top_station\n",
    "\n",
    "# List the stations and the counts in descending order.\n",
    "station_count = session.query(Measurement.station,\\\n",
    "             func.count(Measurement.date)).group_by(Measurement.station).order_by(func.count(Measurement.date).desc()).all()\n",
    "station_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('USC00519281')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the most active station id from the previous query, calculate the lowest, highest, and average temperature.\n",
    "active_station = session.query(Measurement.station\\\n",
    "                ).group_by(Measurement.station).order_by(func.count(Measurement.date).desc()).first()\n",
    "active_station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(22.03582251082252)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_lowest = session.query(func.min(Measurement.tobs),\\\n",
    "                ).filter(Measurement.station == active_station[0]).all()\n",
    "active_lowest\n",
    "active_highest = session.query(func.max(Measurement.tobs),\\\n",
    "                ).filter(Measurement.station == active_station[0]).all()\n",
    "active_highest\n",
    "active_mean = session.query(func.avg(Measurement.tobs),\\\n",
    "                ).filter(Measurement.station == active_station[0]).all()\n",
    "active_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(28.3),\n",
       " (27.8),\n",
       " (27.8),\n",
       " (27.8),\n",
       " (27.2),\n",
       " (27.2),\n",
       " (27.2),\n",
       " (27.2),\n",
       " (27.2),\n",
       " (26.7),\n",
       " (26.7),\n",
       " (26.7),\n",
       " (26.7),\n",
       " (26.7),\n",
       " (26.7),\n",
       " (26.7),\n",
       " (26.7),\n",
       " (26.7),\n",
       " (26.7),\n",
       " (26.7),\n",
       " (26.1),\n",
       " (26.1),\n",
       " (26.1),\n",
       " (26.1),\n",
       " (26.1),\n",
       " (26.1),\n",
       " (26.1),\n",
       " (26.1),\n",
       " (26.1),\n",
       " (26.1),\n",
       " (26.1),\n",
       " (26.1),\n",
       " (26.1),\n",
       " (26.1),\n",
       " (26.1),\n",
       " (26.1),\n",
       " (26.1),\n",
       " (26.1),\n",
       " (26.1),\n",
       " (26.1),\n",
       " (25.6),\n",
       " (25.6),\n",
       " (25.6),\n",
       " (25.6),\n",
       " (25.6),\n",
       " (25.6),\n",
       " (25.6),\n",
       " (25.6),\n",
       " (25.6),\n",
       " (25.6),\n",
       " (25.6),\n",
       " (25.6),\n",
       " (25.6),\n",
       " (25.6),\n",
       " (25.6),\n",
       " (25.6),\n",
       " (25.6),\n",
       " (25.6),\n",
       " (25.6),\n",
       " (25.6),\n",
       " (25.6),\n",
       " (25.6),\n",
       " (25.6),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (25.0),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (24.4),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.9),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (23.3),\n",
       " (22.8),\n",
       " (22.8),\n",
       " (22.8),\n",
       " (22.8),\n",
       " (22.8),\n",
       " (22.8),\n",
       " (22.8),\n",
       " (22.8),\n",
       " (22.8),\n",
       " (22.8),\n",
       " (22.8),\n",
       " (22.8),\n",
       " (22.8),\n",
       " (22.8),\n",
       " (22.8),\n",
       " (22.8),\n",
       " (22.8),\n",
       " (22.8),\n",
       " (22.2),\n",
       " (22.2),\n",
       " (22.2),\n",
       " (22.2),\n",
       " (22.2),\n",
       " (22.2),\n",
       " (22.2),\n",
       " (22.2),\n",
       " (22.2),\n",
       " (22.2),\n",
       " (22.2),\n",
       " (22.2),\n",
       " (22.2),\n",
       " (22.2),\n",
       " (22.2),\n",
       " (22.2),\n",
       " (22.2),\n",
       " (22.2),\n",
       " (22.2),\n",
       " (22.2),\n",
       " (22.2),\n",
       " (22.2),\n",
       " (21.7),\n",
       " (21.7),\n",
       " (21.7),\n",
       " (21.7),\n",
       " (21.7),\n",
       " (21.7),\n",
       " (21.7),\n",
       " (21.7),\n",
       " (21.7),\n",
       " (21.7),\n",
       " (21.7),\n",
       " (21.7),\n",
       " (21.7),\n",
       " (21.7),\n",
       " (21.7),\n",
       " (21.7),\n",
       " (21.7),\n",
       " (21.7),\n",
       " (21.1),\n",
       " (21.1),\n",
       " (21.1),\n",
       " (21.1),\n",
       " (21.1),\n",
       " (21.1),\n",
       " (21.1),\n",
       " (21.1),\n",
       " (21.1),\n",
       " (21.1),\n",
       " (21.1),\n",
       " (21.1),\n",
       " (21.1),\n",
       " (21.1),\n",
       " (21.1),\n",
       " (21.1),\n",
       " (21.1),\n",
       " (21.1),\n",
       " (21.1),\n",
       " (21.1),\n",
       " (21.1),\n",
       " (21.1),\n",
       " (20.6),\n",
       " (20.6),\n",
       " (20.6),\n",
       " (20.6),\n",
       " (20.6),\n",
       " (20.6),\n",
       " (20.6),\n",
       " (20.6),\n",
       " (20.6),\n",
       " (20.6),\n",
       " (20.6),\n",
       " (20.6),\n",
       " (20.6),\n",
       " (20.6),\n",
       " (20.6),\n",
       " (20.6),\n",
       " (20.6),\n",
       " (20.6),\n",
       " (20.6),\n",
       " (20.0),\n",
       " (20.0),\n",
       " (20.0),\n",
       " (20.0),\n",
       " (20.0),\n",
       " (20.0),\n",
       " (20.0),\n",
       " (20.0),\n",
       " (20.0),\n",
       " (20.0),\n",
       " (20.0),\n",
       " (20.0),\n",
       " (19.4),\n",
       " (19.4),\n",
       " (19.4),\n",
       " (19.4),\n",
       " (19.4),\n",
       " (19.4),\n",
       " (19.4),\n",
       " (19.4),\n",
       " (19.4),\n",
       " (19.4),\n",
       " (19.4),\n",
       " (19.4),\n",
       " (19.4),\n",
       " (19.4),\n",
       " (19.4),\n",
       " (18.9),\n",
       " (18.9),\n",
       " (18.9),\n",
       " (18.9),\n",
       " (18.9),\n",
       " (18.3),\n",
       " (18.3),\n",
       " (18.3),\n",
       " (18.3),\n",
       " (18.3),\n",
       " (18.3),\n",
       " (18.3),\n",
       " (17.8),\n",
       " (17.8),\n",
       " (17.8),\n",
       " (17.2),\n",
       " (17.2),\n",
       " (17.2),\n",
       " (17.2),\n",
       " (16.7),\n",
       " (16.7),\n",
       " (16.7),\n",
       " (16.7),\n",
       " (16.7),\n",
       " (16.7),\n",
       " (16.1),\n",
       " (15.6),\n",
       " (15.6),\n",
       " (15.0),\n",
       " (15.0),\n",
       " (15.0)]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the most active station id\n",
    "active_max_date = session.query(func.max(Measurement.date),\\\n",
    "                ).filter(Measurement.station == active_station[0]).all()\n",
    "active_max_date\n",
    "d2 = dt.datetime(2017, 8, 18)\n",
    "active_one_year_prior = d2 - relativedelta(years=1)\n",
    "active_one_year_prior  \n",
    "active_last_year = session.query(Measurement.tobs)\\\n",
    "                   .filter(Measurement.date >= active_one_year_prior).filter(Measurement.station == active_station[0])\\\n",
    "                   .order_by(Measurement.tobs.desc()).all()\n",
    "\n",
    "active_last_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "active_last_year_df = pd.DataFrame(active_last_year)\n",
    "active_last_year_df\n",
    "fig, ax = plt.subplots()\n",
    "ax.hist(active_last_year_df['tobs'], bins=12, linewidth=0.5, edgecolor=\"white\")\n",
    "\n",
    "ax.set(xlim=(15, 29), xticks=np.arange(16, 30,2),\n",
    "       ylim=(0, 70), yticks=np.linspace(0, 70, 8))\n",
    "plt.xlabel(\"Temperature\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Station Analysis last 12 months for Station \" + active_station[0])\n",
    "plt.legend([\"tobs\"])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Close session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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 [conda env:PythonData] *",
   "language": "python",
   "name": "conda-env-PythonData-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "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
}