climate_starter.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 111,
   "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": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reflect Tables into SQLAlchemy ORM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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, inspect, func"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine(\"sqlite:///hawaii.sqlite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['measurement', 'station']"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can view all of the classes that automap found\n",
    "Base.classes.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save references to each table\n",
    "Measurement = Base.classes.measurement\n",
    "Station = Base.classes.station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create our session (link) from Python to the DB\n",
    "session = Session(engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Climate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2017-08-23')"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.query(Measurement.date).order_by(Measurement.date.desc()).first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
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       " ('2017-04-04', 0.0),\n",
       " ('2017-04-05', 0.07),\n",
       " ('2017-04-06', 0.0),\n",
       " ('2017-04-07', 0.0),\n",
       " ('2017-04-09', None),\n",
       " ('2017-04-10', 0.0),\n",
       " ('2017-04-11', 0.16),\n",
       " ('2017-04-12', 0.29),\n",
       " ('2017-04-13', 0.0),\n",
       " ('2017-04-14', 0.29),\n",
       " ('2017-04-17', None),\n",
       " ('2017-04-18', 0.12),\n",
       " ('2017-04-19', 0.0),\n",
       " ('2017-04-20', 0.0),\n",
       " ('2017-04-21', 1.05),\n",
       " ('2017-04-22', 0.7),\n",
       " ('2017-04-24', None),\n",
       " ('2017-04-25', 0.0),\n",
       " ('2017-04-26', 0.14),\n",
       " ('2017-04-27', 0.02),\n",
       " ('2017-04-28', 0.09),\n",
       " ('2017-04-29', 0.95),\n",
       " ('2017-04-30', 1.17),\n",
       " ('2017-05-01', 0.03),\n",
       " ('2017-05-02', 0.01),\n",
       " ('2017-05-03', 0.01),\n",
       " ('2017-05-04', 0.08),\n",
       " ('2017-05-05', 0.28),\n",
       " ('2017-05-06', 0.06),\n",
       " ('2017-05-08', 0.95),\n",
       " ('2017-05-09', 0.52),\n",
       " ('2017-05-10', 0.0),\n",
       " ('2017-05-12', None),\n",
       " ('2017-05-15', None),\n",
       " ('2017-05-16', 0.05),\n",
       " ('2017-05-17', 0.0),\n",
       " ('2017-05-18', 0.16),\n",
       " ('2017-05-19', 0.01),\n",
       " ('2017-05-20', 0.01),\n",
       " ('2017-05-22', None),\n",
       " ('2017-05-23', 0.11),\n",
       " ('2017-05-24', 0.1),\n",
       " ('2017-05-25', 0.07),\n",
       " ('2017-05-26', 0.0),\n",
       " ('2017-05-27', 0.0),\n",
       " ('2017-05-28', 0.02),\n",
       " ('2017-05-29', 0.0),\n",
       " ('2017-05-30', 0.04),\n",
       " ('2017-05-31', 0.0),\n",
       " ('2017-06-01', 0.0),\n",
       " ('2017-06-02', 0.15),\n",
       " ('2017-06-03', 0.16),\n",
       " ('2017-06-04', 0.05),\n",
       " ('2017-06-05', 0.02),\n",
       " ('2017-06-06', 0.0),\n",
       " ('2017-06-07', 0.0),\n",
       " ('2017-06-08', 0.01),\n",
       " ('2017-06-09', 0.0),\n",
       " ('2017-06-10', 0.53),\n",
       " ('2017-06-11', 0.14),\n",
       " ('2017-06-12', 0.35),\n",
       " ('2017-06-13', 0.1),\n",
       " ('2017-06-14', 0.21),\n",
       " ('2017-06-15', 0.3),\n",
       " ('2017-06-16', 0.02),\n",
       " ('2017-06-17', 0.02),\n",
       " ('2017-06-18', 0.18),\n",
       " ('2017-06-19', 0.19),\n",
       " ('2017-06-20', 0.17),\n",
       " ('2017-06-23', None),\n",
       " ('2017-06-26', None),\n",
       " ('2017-06-29', None),\n",
       " ('2017-06-30', 0.0),\n",
       " ('2017-07-03', None),\n",
       " ('2017-07-05', None),\n",
       " ('2017-07-07', None),\n",
       " ('2017-07-08', 0.06),\n",
       " ('2017-07-09', 0.0),\n",
       " ('2017-07-10', 0.0),\n",
       " ('2017-07-11', 0.0),\n",
       " ('2017-07-12', 0.02),\n",
       " ('2017-07-13', 0.3),\n",
       " ('2017-07-14', 0.0),\n",
       " ('2017-07-15', 0.01),\n",
       " ('2017-07-16', 0.12),\n",
       " ('2017-07-17', 0.16),\n",
       " ('2017-07-18', 0.0),\n",
       " ('2017-07-19', 0.09),\n",
       " ('2017-07-20', 0.0),\n",
       " ('2017-07-21', 0.0),\n",
       " ('2017-07-22', 0.12),\n",
       " ('2017-07-23', 0.07),\n",
       " ('2017-07-24', 1.19),\n",
       " ('2017-07-25', 0.12),\n",
       " ('2017-07-26', 0.02),\n",
       " ('2017-07-27', 0.0),\n",
       " ('2017-07-28', 0.14),\n",
       " ('2017-07-29', 0.02),\n",
       " ('2017-07-31', None),\n",
       " ('2017-08-01', 0.12),\n",
       " ('2017-08-02', 0.05),\n",
       " ('2017-08-03', 0.01),\n",
       " ('2017-08-04', 0.04),\n",
       " ('2017-08-06', 0.0),\n",
       " ('2017-08-07', 0.0),\n",
       " ('2017-08-08', 0.1),\n",
       " ('2017-08-09', 0.0),\n",
       " ('2017-08-10', 0.0),\n",
       " ('2017-08-11', 0.0),\n",
       " ('2017-08-13', None),\n",
       " ...]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Design a query to retrieve the last 12 months of precipitation data and plot the results\n",
    "# Calculate the date 1 year ago from the last data point in the database\n",
    "\n",
    "prev_year = dt.date(2017,8,23) - dt.timedelta(days=365)\n",
    "# Perform a query to retrieve the data and precipitation scores\n",
    "sel = [Measurement.date, Measurement.prcp]\n",
    "rainfall = session.query(*sel).\\\n",
    "    filter(Measurement.date >= prev_year).all()\n",
    "rainfall\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>precip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-08-23</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-08-24</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-08-25</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-08-26</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-08-27</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2225</th>\n",
       "      <td>2017-08-19</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2226</th>\n",
       "      <td>2017-08-20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2227</th>\n",
       "      <td>2017-08-21</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2228</th>\n",
       "      <td>2017-08-22</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2229</th>\n",
       "      <td>2017-08-23</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2230 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  precip\n",
       "0     2016-08-23    0.00\n",
       "1     2016-08-24    0.08\n",
       "2     2016-08-25    0.08\n",
       "3     2016-08-26    0.00\n",
       "4     2016-08-27    0.00\n",
       "...          ...     ...\n",
       "2225  2017-08-19    0.09\n",
       "2226  2017-08-20     NaN\n",
       "2227  2017-08-21    0.56\n",
       "2228  2017-08-22    0.50\n",
       "2229  2017-08-23    0.45\n",
       "\n",
       "[2230 rows x 2 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the query results as a Pandas DataFrame and set the index to the date column\n",
    "rainfall_df = pd.DataFrame(rainfall, columns=['date','precip'])\n",
    "#rainfall_df.reset_index('date')\n",
    "rainfall_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date       object\n",
       "precip    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the dataframe by date\n",
    "rainfall_df.sort_values(\"date\", inplace=True)\n",
    "rainfall_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Rainfall Amounts for Previous 12mos')"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use Pandas Plotting with Matplotlib to plot the data\n",
    "rainfall_df.plot(x=\"date\", y=\"precip\",rot=90)\n",
    "plt.xlabel(\"date\")\n",
    "plt.ylabel(\"inches\")\n",
    "plt.title(\"Rainfall Amounts for Previous 12mos\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>precip</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": [
       "            precip\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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use Pandas to calcualte the summary statistics for the precipitation data\n",
    "rainfall_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(9)]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Design a query to show how many stations are available in this dataset?\n",
    "#stn_cnt=(session.query(func.count(Measurement.station).label('count')).group_by(Measurement.station)).all\n",
    "#stn_df=pd.DataFramestn\n",
    "session.query(func.count(Station.station)).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What are the most active stations? (i.e. what stations have the most rows)?\n",
    "# List the stations and the counts in descending order.\n",
    "stn_cnt=(session.query(Measurement.station,func.count(Measurement.station)).group_by(Measurement.station).order_by(func.count(Measurement.station).desc()).all())\n",
    "#session.query(func.(Measurment.station))\n",
    "stn_cnt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(54.0, 85.0, 71.66378066378067)]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the station id from the previous query, calculate the lowest temperature recorded, \n",
    "# highest temperature recorded, and average temperature of the most active station?\n",
    "session.query(func.min(Measurement.tobs),func.max(Measurement.tobs),func.avg(Measurement.tobs)).\\\n",
    "    filter(Measurement.station == \"USC00519281\").all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Temperature')"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ErK2ta/0Ylif57rvvkJ+fjz/84Q/6trKyMpw4cQJbtmzBqVOnAAC5ublwcnLSr5OXl1dpdPaomj5wY9FoNKLt29SZRd/UYXIGUV1/L8zid6qWjJ5iHxwcjG+++abeOw4KCsKJEydw9OhR/U///v0RGhqKo0ePws3NDUqlEhkZGfr3lJSU4OTJk/Dx8an3/omISPqMHom9/vrrmD59Ol577TVMnz4dnTt3hqWlZaX1njRaAgC5XF7p+zRbW1u0adMGHh4eAIDw8HCsXbsWKpUKbm5uSEhIgJ2dHcLCwowtm4iImiGjQ8zLywsymQzff/89du/eXe16tZ2d+CQLFixAcXExIiMjodPp4OXlhfT0dF4jRkREAOoQYkuWLHnixIr6+PLLLw1ey2QyREVFISoqqlH2R0RE0mZ0iDFQiIjIVNTp3okVysrKcOvWLZSWljZUPURERLVWpxA7c+YMxo4di44dO8LNzQ3Hjx8HAOTn5+Oll17C4cOHG7RIIiKiqhgdYt999x2ef/55/Pzzz5gwYYLBfRPbtm2LoqIi7Nixo0GLJCIiqorRIbZ69Wq4uroiMzMTy5cvr7R8yJAh+Oc//9kgxRERET2J0SF25swZTJkyBS1btqxylmKnTp309z0kIiJqTEaHmIWFBSwsqn+bVquFjY1NvYoiIiKqDaNDrF+/fvj73/9e5bL79+8jNTUV3t7e9S6MiIioJkaH2KJFi3DkyBHMmzcPZ8+eBQDcuHED3377LcaMGYOff/4ZixcvbvBCiYiIHmf0xc7PPfccNm3ahMjISHz88ccAHt7jUBAEtG7dGikpKRg4cGCDF0pERPQ4o0MMAMLCwvD888/j4MGDyM7ORnl5Obp27Qp/f3/Y29s3dI1ERERVqlOIAQ/vOP/CCy80ZC1ERERGMTrErl69Wqv1OnfubHQxRERExjA6xPr27Vuru9g3xKNYiIiInsToEPvggw8qhVhZWRlycnLwySefoH379pgxY0aDFUhERFQdo0Ns8uTJ1S6LiIiAn58fioqK6lUUERFRbdTrUSyPs7e3x+TJk7Fx48aG3CwREVGVGjTEAKBFixa4fv16Q2+WiIiokgYNsbNnz+Ivf/kLevTo0ZCbJSIiqlKDzU4sKCjAnTt3YG9vj6SkpAYpjoiI6EmMDrHBgwdXCjGZTAa5XI5u3bohNDQUcrm8wQokIiKqjtEhplarG6MOIiIiozX4xA4iIqKmYvRIbM2aNUbvRCaTYcmSJQZtH374IbZu3aq/jZW7uzveeOMNBAQEAAAEQUBsbCy2b98OnU4HLy8vJCQkoGfPnkbvn4iImiejQyw2Nlb/nZggCAbLntT+eIh17NgRK1euhKurK8rLy/HXv/4VkydPxqFDh9C7d28kJiYiKSkJSUlJUKlUiIuLQ0hICLKysuDg4GBs2URE1AwZfTrxp59+Qu/evREWFoaDBw/il19+wS+//IJ//OMfCA0NRe/evfHTTz/h9u3b+p+q7qMYFBSEESNGoFu3bnBzc8Of/vQn2NvbIysrC4IgQK1WIyIiAsHBwfDw8IBarUZRURHS0tIa5IMTEZH0GR1iS5YsgYuLC5KTk9G/f384ODjAwcEBnp6e+PDDD+Hs7Fxp1FWTsrIy7NmzB3fv3oW3tzdycnKg1Wrh5+enX8fGxga+vr7IzMw0tmQiImqmjD6dmJGRgRUrVlS7/LnnnsPKlStrta3z589j5MiRKCkpgZ2dHXbu3IlevXrpg0qhUBisr1AoarwbiEajqdW+G4OY+zZ1zb9vbMUugCSoPr8Xzf936iGVSvXE5UaHmKWlJc6dO1ft8h9//BEWFrUb4KlUKhw9ehQFBQXYt28fwsPDsX//fv3yx69HEwShxsfA1PSBG4tGoxFt36bOLPrm2K9iV0ASVNffC7P4naolo08njhkzBh999BHWrl2LwsJCfXthYSESEhKwc+dOBAcH12pb1tbW6NatG/r3748VK1agT58+2LhxI5RKJQAgNzfXYP28vLxKozMiIjJfRo/EoqOj8fPPPyM6OhoxMTFo3749ZDIZtFotysrKMHjwYERHR9epmPLycty/fx8uLi5QKpXIyMiAp6cnAKCkpAQnT57EqlWr6rRtIiJqfowOMQcHB+zbtw9/+9vfcODAAVy7dg2CIGDkyJEYOXIkAgMDa7Wdd955ByNHjkSnTp30sw6PHTuG3bt3QyaTITw8HGvXroVKpYKbmxsSEhJgZ2eHsLAwoz8kEZEpkm+t62lo2zqfwtZN61THfZomo0OsQmBgYK0DqyparRazZs1Cbm4uWrVqhV69eiEtLQ3+/v4AgAULFqC4uBiRkZH6i53T09N5jRgREenJdDqdUPNqlV29ehXHjx/HzZs3ERISAicnJ5SWluL27dto06YNrKzqnI+SxC9aq2cOfVP3/6MmalociQF48803kZycjLKyMshkMvTt2xdOTk64d+8ePD09sWzZMsydO7ehayUiIjJg9OzE9evXQ61WY+7cufjss88MbjHVqlUrBAUFGUyTJyIiaixGh9j27dvx0ksvYeXKlejTp0+l5b169cLly5cbpDgiIqInMfp04rVr1zB//vxqlzs4OKCgoKBeRRHVB7+fIjIfRo/Enn76ady4caPa5efPn0eHDh3qVRQREVFtGB1iI0eOxPbt25Gfn19p2Q8//ICdO3ciKCioQYojIiJ6EqND7M0334SFhQV8fX3xzjvvQCaTYdeuXXj11VcxYsQIdOzYEZGRkY1RKxERkQGjQ0ypVOLQoUMYNWoUvvjiCwiCgNTUVHz77bcYP348vv76a8jl8saolYiIyIBREzsePHiA7777Do6OjkhMTERiYiLy8vJQXl6Odu3a1fru9URERA3BqNSxtLTE2LFjcfDgQX1bu3bt0L59ewYYERE1OaOSx8LCAs7OzigqKmqseoiIiGrN6OHTnDlzsG3bNty8ebMx6iEiIqo1oy92Lioqgp2dHTw9PREUFIQuXbrAxsbGYB2ZTPbEC6KJiIgagtF3sW/Tpk3NG5XJcOvWrToXJUXmcKf2umrqvuEdO4iqZ5Z3sV+8eDGmTJmC/v3744cffoAgCLh9+zZatWpldo9cISIi01GrBNqyZQsGDRqE/v37w9nZGbdu3UL//v2xd+9eDB06tLFrJCIiqlKd58U/+ggWIiIiMfDiLiIikiyGGBERSVatZ2VcuXIFp0+fBgDcuXMHwMNZZ/b29lWu7+Xl1QDlERERVa9WU+zbtGkDmUxm0CYIQqW2R9s5xZ4qcIo9kekwyyn2SUlJjV0HERGR0WoVYpMmTWrsOoiIiIwm2sSO9957D8899xw6d+4MV1dXjB8/Hv/+978N1hEEATExMXB3d4ejoyOCgoJw4cIFkSomIiJTI1qIHTt2DNOnT8eBAwewb98+WFlZYezYsbh9+7Z+ncTERCQlJWHNmjU4ePAgFAoFQkJCUFhYKFbZRERkQkS7Z1R6errB602bNsHZ2RmnTp1CYGAgBEGAWq1GREQEgoODAQBqtRoqlQppaWmYNm2aGGUTEZEJMZnrxIqKilBeXg65XA4AyMnJgVarhZ+fn34dGxsb+Pr6IjMzU6wyiYjIhJjM3XuXLVuGPn36wNvbGwCg1WoBAAqFwmA9hUKB69evV7sdjUbTeEXWQMx9m7qm7RvbJtwXkbRI7e9UTZfnmESIvfnmmzh16hT+/ve/w9LS0mBZba9PqyDWtVq8Tqx6Td43x3idGFF1mtvfKdFPJ0ZFRWHPnj3Yt28funTpom9XKpUAgNzcXIP18/LyKo3OiIjIPIkaYkuXLkVaWhr27duH7t27GyxzcXGBUqlERkaGvq2kpAQnT56Ej49PU5dKREQmSLTTiW+88QY+/fRT7Ny5E3K5XP8dmJ2dHezt7SGTyRAeHo61a9dCpVLBzc0NCQkJsLOzQ1hYmFhlExGRCREtxFJSUgBAP32+wtKlSxEVFQUAWLBgAYqLixEZGQmdTgcvLy+kp6fDwcGhyeslIiLTU6sbAFPNOLGjerwBMJHpaG43ABZ9YgcREVFdMcSIiEiyGGJERCRZDDEiIpIshhgREUkWQ4yIiCSLIUZERJLFECMiIsliiBERkWQxxIiISLIYYkREJFkMMSIikiyGGBERSRZDjIiIJIshRkREksUQIyIiyWKIERGRZDHEiIhIshhiREQkWQwxIiKSLCuxC6Dmb+AxW+DYr2KXQUTNEEdiREQkWaKG2PHjxzFhwgT07NkTcrkcu3btMlguCAJiYmLg7u4OR0dHBAUF4cKFCyJVS0REpkbUELt79y48PDwQGxsLGxubSssTExORlJSENWvW4ODBg1AoFAgJCUFhYaEI1RIRkakRNcRGjhyJ5cuXIzg4GBYWhqUIggC1Wo2IiAgEBwfDw8MDarUaRUVFSEtLE6liIiIyJSb7nVhOTg60Wi38/Pz0bTY2NvD19UVmZqaIlRERkakw2RDTarUAAIVCYdCuUCiQm5srRklERGRiTH6KvUwmM3gtCEKltkdpNJrGLskk923abMUugIj+S2p/p1Qq1ROXm2yIKZVKAEBubi6cnJz07Xl5eZVGZ4+q6QM3Fo1GI9q+TR6vESMyGc3t75TJnk50cXGBUqlERkaGvq2kpAQnT56Ej4+PiJUREZGpEHUkVlRUhOzsbABAeXk5rl27hh9//BFt2rRB586dER4ejrVr10KlUsHNzQ0JCQmws7NDWFiYmGUTEZGJkOl0OkGsnR89ehSjR4+u1D5x4kSo1WoIgoDY2Fhs27YNOp0OXl5eSEhIgIeHhwjVPhlPJ1ZPvpWnE4lMhW5aJ7FLaFCihlhzwhCrHkOMyHQ0txAz2YkdRETU8Jr6fyobOzRNdmIHERFRTRhiREQkWQwxIiKSLH4nJjJOeiAiqjuOxIiISLIYYkREJFkMMSIikiyGGBERSRZDjIiIJIuzEx9T99mCtnzkCBFRE+NIjIiIJIshRkREksUQIyIiyWKIERGRZDHEiIhIshhiREQkWQwxIiKSLIYYERFJFkOMiIgkiyFGRESSxRAjIiLJYogREZFkSSLEUlJS0LdvXyiVSgwdOhQnTpwQuyQiIjIBJh9i6enpWLZsGRYvXowjR47A29sbL774Iq5evSp2aUREJDKTD7GkpCRMmjQJr7zyCnr06IH4+HgolUps2bJF7NKIiEhkJv08sfv37+P777/H66+/btDu5+eHzMzMRtmnblqnRtkuERE1PJMeieXn56OsrAwKhcKgXaFQIDc3V6SqiIjIVJh0iFWQyWQGrwVBqNRGRETmx6RDrG3btrC0tKw06srLy6s0OiMiIvNj0iFmbW2Nfv36ISMjw6A9IyMDPj4+IlVFRESmwqQndgDA3LlzMXv2bHh5ecHHxwdbtmzBjRs3MG3aNLFLIyIikZn0SAwAxo0bh5iYGMTHx2PIkCE4deoUdu/eDWdn5yav5caNG3jttdfg6uoKpVIJHx8fHDt2TL9cEATExMTA3d0djo6OCAoKwoULF5q8TjHU1Dfh4eGQy+UGP8OHDxex4qbRp0+fSp9bLpfjpZdeAmDexwxQc/+Y63EDAGVlZYiOjtbf6KFv376Ijo5GaWmpfh1zP34ACYzEAGDGjBmYMWOGqDXodDoEBARg0KBB2L17N9q2bYucnByD7+YSExORlJSEpKQkqFQqxMXFISQkBFlZWXBwcBCx+sZVm74BgGHDhmHTpk3619bW1k1dapPLyMhAWVmZ/vWNGzcwbNgwjB07FoD5HjMVauofwDyPGwBYt24dUlJSoFar4eHhgfPnzyM8PBzW1tZYsmQJAB4/gERCzBSsX78ejo6OBr9MXbp00f+3IAhQq9WIiIhAcHAwAECtVkOlUiEtLa1Zn/6sqW8qPPXUU1AqlU1YmfjatWtn8HrHjh1wcHDA2LFjzfqYqfCk/qlgjscNAHz33XcYNWoUAgMDAQAuLi4IDAzE6dOnAZj335xHmfzpRFPx5ZdfwsvLC9OmTYObmxueffZZJCcnQxAEAEBOTg60Wi38/Pz077GxsYGvr2+jXZhtKmrqmwonT56Em5sbvLy8MH/+fNy8eVOkisUhCAJ27NiB8ePHw9bW1qyPmao83j8VzPW4GTRoEI4dO3IhsakAAAs8SURBVIb//Oc/AICffvoJR48exYgRIwCY99+cR3EkVktXrlzB5s2bMWfOHERERODs2bNYunQpAGDWrFnQarUAUOWF2devX2/yeptSTX0DAMOHD8fo0aPh4uKCX375BdHR0RgzZgwOHTqEp556Sszym0xGRgZycnIwdepUADDrY6Yqj/cPYN7HTUREBIqKiuDj4wNLS0uUlpbijTfe0H+1wuPnIYZYLZWXl6N///5YsWIFAOCZZ55BdnY2UlJS9H+oAfO8MLs2fRMaGqpfv1evXujXrx/69OmDAwcOYMyYMaLU3dS2b98OT09P9O3b16DdHI+ZqlTVP+Z83KSnp+OTTz5BSkoK3N3dcfbsWSxbtgzOzs54+eWX9euZ+/HD04m1pFQq0aNHD4O27t2749q1a/rlAMzywuya+qYqHTp0QMeOHZGdnd3Y5ZmEmzdv4quvvsIrr7yibzPnY+ZxVfVPVczpuFm+fDnmzZuH0NBQ9OrVCxMmTMDcuXPx/vvvA+DxU4EhVkuDBg3CpUuXDNouXbqEzp07A3j4patSqTS4MLukpAQnT55s9hdm19Q3VcnPz8f169fN5gv7Xbt24amnnsK4ceP0beZ8zDyuqv6pijkdN/fu3YOlpaVBm6WlJcrLywHw+KlguWzZsnfELkIKnJycsGbNGlhYWMDR0RGHDx9GdHQ0Fi5cCC8vL8hkMpSVleH999+Hm5sbysrK8NZbb0Gr1WLdunXN+vx9TX1TVFSEVatWwd7eHqWlpTh79ixef/11lJWVIT4+vln3DfDw9M7cuXMREBBgMOvOnI+ZR1XXP+Z+3Fy8eBGffvop3Nzc0KJFCxw9ehSrV6/GuHHj4O/vz+Pnv2Q6nU6oeTUCgAMHDmDVqlW4dOkSnJycMHPmTMyePVt//lkQBMTGxmLbtm3Q6XTw8vJCQkICPDw8RK688T2pb4qLizF58mT8+OOPKCgogFKpxJAhQ/DWW2/ByclJ7NIb3ZEjRzBmzBj84x//gJeXl8Eycz5mKlTXP+Z+3BQWFuLdd9/F/v37kZeXB6VSidDQUCxZsgQtW7YEwOMHYIgREZGE8TsxIiKSLIYYERFJFkOMiIgkiyFGRESSxRAjIiLJYogREZFkMcSIiEiyGGLU7FX15OCqfnbt2iV2qaKKj4/H/v37xS6DyCi82JmavU8//dTg9bZt2/DPf/4TH3zwgUG7j49PlQ/zNBdKpRLjxo2DWq0WuxSiWuOjWKjZGz9+vMHrQ4cO4cyZM5Xam5P79+/D0tKy0g1kxXDv3j2Dh1wSNSSeTiT6rz179sDf3x8dOnSAs7Mzxo8fj59++slgnfDwcCiVSvz222+YNGkSnJyc0KNHD/2o7vLlywgNDUWnTp3Qs2dPbN261eD9R48ehVwux+7du/HnP/8Z7u7u6NChA8aOHQuNRlOppsuXL+PVV1+Fq6sr2rdvD19fX+zcubPabcbFxaF3795wdHTEtWvXcP/+fbz77rsYNmwYXFxc4OjoCH9/f3z11VcG25DL5fj999/x17/+VX96NSgoCAAQExMDuVxeqbaK/R49elTfFhQUhIEDB+LcuXMYPXo0OnbsiMWLFwN4eJ+/5ORk+Pr6QqlUomvXrpg5cyZ+/fXX2v4TEVXCkRgRgHXr1uGdd97B6NGjMWHCBNy9excpKSkICAjA4cOHDU4zlpeXIywsDAMHDsTKlSuRnp6Ot99+G61atUJ8fDxeeOEFBAYGYufOnVi4cCE8PT3xzDPPVNpfeXk55s2bB51Oh02bNmH06NE4ceIEnn76aQAP72IeEBCAtm3bYu7cuWjdujW+/vprzJs3D3fu3MGcOXMMtvn+++9DJpNh9uzZAAB7e3sUFhZi69atCAkJwZQpU1BcXIzU1FRMnjwZaWlp8Pf3BwBs2rQJ8+bNw4ABA/DHP/4RANC+ffs69WVBQQHGjRuH0aNHIzQ0FK1btwYALFq0CB999BHGjx+PGTNmQKvVIjk5GZmZmThy5EiVQUlUE4YYmb2rV68iOjoaS5cuRVRUlL59woQJ8Pb2RkJCgsH3Zw8ePMDYsWOxZMkSAA9PV/bs2RMLFizA+vXrMXXqVAAPRyW9e/fGzp07K4XYzZs3kZWVpf/DPWTIEAQHB+ODDz7A8uXLAQDLli3TPy+q4nTc9OnTMW3aNMTExOCVV16BnZ2dfpuFhYXIzMw0aCsrK8P58+cNHssxe/ZsDBkyBBs2bNCH2Pjx4zF//nx06dKl3qdZc3NzERsbi9dee03flpmZia1btyIpKQmTJ0/Wt48ePRrDhg1DcnKyvj+JjMHTiWT2vvjiC5SWliI0NBT5+fn6nxYtWmDAgAE4cuRIpfc8+nh4e3t7uLu7w9LSEhMmTNC3d+jQAZ06dcKVK1cqvX/ChAkGI4+hQ4eiZ8+e+PrrrwEAOp0Ohw4dwtixY1FcXGxQ1/Dhw1FYWIh//etflbb5aIABDx+iWBFg9+/fx+3bt1FYWIjBgwfj+++/N76zasHKyko/mquwd+9e2NvbY+TIkQafpUOHDnB1da2yj4lqgyMxMnuXL18GAHh7e1e5/PFJCS1atICjo6NBW6tWraBUKtGiRYtK7TqdrtI2XV1dq2yr+H7p8uXLEAQBa9aswZo1a6qsKy8vz+B1dTMrP/roI2zcuBEXL16EIPxvMnLFc/AamqOjo/55VxUuX76MoqIiqFSqKt/TWLVQ88cQI7NX8bj3tLQ0WFlV/pWwsLB44uua2h8NjgpV/dF+dL2KmubMmYORI0dWud3HH3xoY2NTaZ20tDTMnz8fgYGBWLBgARQKBaysrLBr1y6kpqZWud3a1PpojY+rqo7y8nI8/fTT2LJlS5Xv4exFqiuGGJm9rl27AgCcnJzg7u7eJPu8dOlSpbbs7Gx07twZwP9GVVZWVhg2bFid95Oeno4uXbrg448/Ngijqi7sri6sKk576nQ6g1OgOTk5ta6ja9euyMjIgJeXFxwcHGr9PqKa8DsxMntjxoyBlZUVYmJiqhxdPH7ariF88sknBqcZDx8+jAsXLmDEiBEAAIVCgf/7v//Dtm3bcO3atTrXVHGd2KOjvCtXrlR5Zw5bW9sqT31269YNAAym0peWlla6fOBJxo0bh/LycsTGxlZaJggC8vPza70tokdxJEZmr0uXLli5ciXeeustDB8+HKNHj0abNm1w9epVfP311xgwYADef//9Bt2nQqHAqFGjMGXKFBQUFOAvf/kL2rdvj3nz5unXee+99xAQEIDBgwfjlVdegaurK/Lz8/HDDz/g4MGDuHr1ao37CQwMxBdffIGJEyciMDAQv/32GzZv3gxXV1ecO3fOYN3+/fvj8OHD2LBhAzp27Ih27dph6NCh8PPzg7OzM+bPnw+NRoOWLVsiLS2tytOk1fH19cXs2bORlJSEc+fOYfjw4bC1tUVOTg7279+PqVOnYuHChbXvQKL/YogRAZg7dy7c3NywYcMGvPfeeygtLUWHDh0waNAg/ZT5hhQREQGNRoMPPvgAOp0OPj4+iIuLQ9u2bfXruLm54dChQ4iLi0Nqairy8vLQtm1b9OjRA6tXr67VfiZNmoS8vDxs3rwZhw4dQrdu3fDnP/8Z2dnZlUIsNjYWixYtQmxsLO7evYvBgwdj6NCh+u/QIiMjERsbi6effhpTpkzBs88+i7Fjx9b6M69Zswb9+vXD5s2bERMTAwsLC3Ts2BH+/v544YUXar0dokfx3olETejo0aMYPXo0Nm/ejNDQULHLIZI8fidGRESSxRAjIiLJYogREZFk8TsxIiKSLI7EiIhIshhiREQkWQwxIiKSLIYYERFJFkOMiIgkiyFGRESS9f/VnsFzprfH7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Choose the station with the highest number of temperature observations.\n",
    "# Query the last 12 months of temperature observation data for this station and plot the results as a histogram\n",
    "tobs = session.query(Measurement.tobs).\\\n",
    "    filter(Measurement.date >= prev_year).\\\n",
    "    filter(Measurement.station == \"USC00519281\").all()\n",
    "tobs_df = pd.DataFrame(tobs,columns=[\"tobs\"])\n",
    "plot=tobs_df.plot.hist(bins=12)\n",
    "plot.set_xlabel(\"Temperature\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus Challenge Assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(62.0, 69.57142857142857, 74.0)]\n"
     ]
    }
   ],
   "source": [
    "# This function called `calc_temps` will accept start date and end date in the format '%Y-%m-%d' \n",
    "# and return the minimum, average, and maximum temperatures for that range of dates\n",
    "def calc_temps(start_date, end_date):\n",
    "    \"\"\"TMIN, TAVG, and TMAX for a list of dates.\n",
    "    \n",
    "    Args:\n",
    "        start_date (string): A date string in the format %Y-%m-%d\n",
    "        end_date (string): A date string in the format %Y-%m-%d\n",
    "        \n",
    "    Returns:\n",
    "        TMIN, TAVE, and TMAX\n",
    "    \"\"\"\n",
    "    \n",
    "    return session.query(func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)).\\\n",
    "        filter(Measurement.date >= start_date).filter(Measurement.date <= end_date).all()\n",
    "\n",
    "# function usage example\n",
    "print(calc_temps('2012-02-28', '2012-03-05'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(63.0, 74.1547619047619, 79.0)]\n"
     ]
    }
   ],
   "source": [
    "# Use your previous function `calc_temps` to calculate the tmin, tavg, and tmax \n",
    "# for your trip using the previous year's data for those same dates.\n",
    "print(calc_temps('2016-11-04', '2016-11-16'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "trip = calc_temps('2016-11-04', '2016-11-16')\n",
    "trip_list = list(np.ravel(trip))\n",
    "tmin= trip_list[0]\n",
    "tave=trip_list[1]\n",
    "tmax=trip_list[2]\n",
    "yerr=tmax-tmin\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results from your previous query as a bar chart. \n",
    "# Use \"Trip Avg Temp\" as your Title\n",
    "# Use the average temperature for the y value\n",
    "# Use the peak-to-peak (tmax-tmin) value as the y error bar (yerr)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "x = [' ']\n",
    "ax.bar(x,tave, yerr = yerr)\n",
    "ax.set_title('Trip Avg Temp')\n",
    "ax.set_ylabel('Temp(F)')\n",
    "ax.set_yticks(np.arange(0,110,20))\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the total amount of rainfall per weather station for your trip dates using the previous year's matching dates.\n",
    "# Sort this in descending order by precipitation amount and list the station, name, latitude, longitude, and elevation\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a query that will calculate the daily normals \n",
    "# (i.e. the averages for tmin, tmax, and tavg for all historic data matching a specific month and day)\n",
    "\n",
    "def daily_normals(date):\n",
    "    \"\"\"Daily Normals.\n",
    "    \n",
    "    Args:\n",
    "        date (str): A date string in the format '%m-%d'\n",
    "        \n",
    "    Returns:\n",
    "        A list of tuples containing the daily normals, tmin, tavg, and tmax\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    sel = [func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)]\n",
    "    return session.query(*sel).filter(func.strftime(\"%m-%d\", Measurement.date) == date).all()\n",
    "    \n",
    "daily_normals(\"01-01\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the daily normals for your trip\n",
    "# push each tuple of calculations into a list called `normals`\n",
    "\n",
    "# Set the start and end date of the trip\n",
    "\n",
    "# Use the start and end date to create a range of dates\n",
    "\n",
    "# Stip off the year and save a list of %m-%d strings\n",
    "\n",
    "# Loop through the list of %m-%d strings and calculate the normals for each date\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the previous query results into a Pandas DataFrame and add the `trip_dates` range as the `date` index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the daily normals as an area plot with `stacked=False`\n"
   ]
  }
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