climate_starter.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",
    "from sqlalchemy import create_engine, inspect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reflect Tables into SQLAlchemy ORM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python SQL toolkit and Object Relational Mapper\n",
    "import sqlalchemy\n",
    "from sqlalchemy.ext.automap import automap_base\n",
    "from sqlalchemy.orm import Session\n",
    "from sqlalchemy import create_engine, func"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine(\"sqlite:///Resources/hawaii.sqlite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reflect an existing database into a new model\n",
    "Base = automap_base()\n",
    "\n",
    "# reflect the tables\n",
    "Base.prepare(engine, reflect=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['measurement', 'station']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can view all of the classes that automap found\n",
    "Base.classes.keys()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save references to each table\n",
    "Measurement = Base.classes.measurement\n",
    "Station = Base.classes.station\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create our session (link) from Python to the DB\n",
    "session = Session(engine)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Climate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Design a query to retrieve the last 12 months of precipitation data and plot the results\n",
    "\n",
    "# Calculate the date 1 year ago from the last data point in the database\n",
    "\n",
    "# Perform a query to retrieve the data and precipitation scores\n",
    "\n",
    "# Save the query results as a Pandas DataFrame and set the index to the date column\n",
    "\n",
    "# Sort the dataframe by date\n",
    "\n",
    "# Use Pandas Plotting with Matplotlib to plot the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['measurement', 'station']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inspector = inspect(engine)\n",
    "inspector.get_table_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id INTEGER\n",
      "station TEXT\n",
      "date TEXT\n",
      "prcp FLOAT\n",
      "tobs FLOAT\n"
     ]
    }
   ],
   "source": [
    "# Get column names for Measurement \n",
    "columns = inspector.get_columns('measurement')\n",
    "for c in columns:\n",
    "    print(c['name'], c['type'])"
   ]
  },
  {
   "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": [
    "# Get column names for Station \n",
    "columns = inspector.get_columns('station')\n",
    "for c in columns:\n",
    "    print(c['name'], c['type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2017-08-23')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the most recent date:\n",
    "recent_date = session.query(Measurement.date).order_by(Measurement.date.desc()).first()\n",
    "recent_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('2016-08-24', 0.08),\n",
       " ('2016-08-24', 2.15),\n",
       " ('2016-08-24', 2.28),\n",
       " ('2016-08-24', None),\n",
       " ('2016-08-24', 1.22),\n",
       " ('2016-08-24', 2.15),\n",
       " ('2016-08-24', 1.45),\n",
       " ('2016-08-25', 0.08),\n",
       " ('2016-08-25', 0.08),\n",
       " ('2016-08-25', 0.0),\n",
       " ('2016-08-25', 0.0),\n",
       " ('2016-08-25', 0.21),\n",
       " ('2016-08-25', 0.06),\n",
       " ('2016-08-25', 0.11),\n",
       " ('2016-08-26', 0.0),\n",
       " ('2016-08-26', 0.03),\n",
       " ('2016-08-26', 0.02),\n",
       " ('2016-08-26', 0.04),\n",
       " ('2016-08-26', 0.0),\n",
       " ('2016-08-26', 0.01),\n",
       " ('2016-08-27', 0.0),\n",
       " ('2016-08-27', 0.18),\n",
       " ('2016-08-27', 0.02),\n",
       " ('2016-08-27', 0.0),\n",
       " ('2016-08-27', 0.12),\n",
       " ('2016-08-27', None),\n",
       " ('2016-08-28', 0.01),\n",
       " ('2016-08-28', 0.14),\n",
       " ('2016-08-28', 0.14),\n",
       " ('2016-08-28', 0.14),\n",
       " ('2016-08-28', 0.6),\n",
       " ('2016-08-28', 2.07),\n",
       " ('2016-08-29', 0.0),\n",
       " ('2016-08-29', 0.17),\n",
       " ('2016-08-29', 0.04),\n",
       " ('2016-08-29', None),\n",
       " ('2016-08-29', 0.0),\n",
       " ('2016-08-29', 0.35),\n",
       " ('2016-08-29', 0.9),\n",
       " ('2016-08-30', 0.0),\n",
       " ('2016-08-30', 0.0),\n",
       " ('2016-08-30', 0.02),\n",
       " ('2016-08-30', 0.0),\n",
       " ('2016-08-30', 0.0),\n",
       " ('2016-08-30', 0.05),\n",
       " ('2016-08-31', 0.13),\n",
       " ('2016-08-31', 0.1),\n",
       " ('2016-08-31', None),\n",
       " ('2016-08-31', None),\n",
       " ('2016-08-31', 0.25),\n",
       " ('2016-08-31', 0.24),\n",
       " ('2016-08-31', 2.46),\n",
       " ('2016-09-01', 0.0),\n",
       " ('2016-09-01', 0.0),\n",
       " ('2016-09-01', 0.0),\n",
       " ('2016-09-01', None),\n",
       " ('2016-09-01', 0.02),\n",
       " ('2016-09-01', 0.01),\n",
       " ('2016-09-02', 0.0),\n",
       " ('2016-09-02', 0.02),\n",
       " ('2016-09-02', 0.19),\n",
       " ('2016-09-02', None),\n",
       " ('2016-09-02', None),\n",
       " ('2016-09-02', 0.01),\n",
       " ('2016-09-02', 0.03),\n",
       " ('2016-09-03', 0.0),\n",
       " ('2016-09-03', 0.07),\n",
       " ('2016-09-03', 0.08),\n",
       " ('2016-09-03', 0.12),\n",
       " ('2016-09-03', 1.0),\n",
       " ('2016-09-04', 0.03),\n",
       " ('2016-09-04', 0.03),\n",
       " ('2016-09-04', 0.74),\n",
       " ('2016-09-04', 0.14),\n",
       " ('2016-09-04', 0.44),\n",
       " ('2016-09-05', None),\n",
       " ('2016-09-05', 0.11),\n",
       " ('2016-09-05', None),\n",
       " ('2016-09-05', 0.02),\n",
       " ('2016-09-05', 0.03),\n",
       " ('2016-09-05', 0.18),\n",
       " ('2016-09-06', None),\n",
       " ('2016-09-06', 0.05),\n",
       " ('2016-09-06', 0.04),\n",
       " ('2016-09-06', 0.03),\n",
       " ('2016-09-06', 0.11),\n",
       " ('2016-09-06', 1.0),\n",
       " ('2016-09-07', 0.05),\n",
       " ('2016-09-07', 0.1),\n",
       " ('2016-09-07', 0.23),\n",
       " ('2016-09-07', 0.11),\n",
       " ('2016-09-07', 0.16),\n",
       " ('2016-09-07', 1.35),\n",
       " ('2016-09-08', 0.0),\n",
       " ('2016-09-08', 0.22),\n",
       " ('2016-09-08', 0.01),\n",
       " ('2016-09-08', None),\n",
       " ('2016-09-08', 0.01),\n",
       " ('2016-09-08', 0.07),\n",
       " ('2016-09-08', 0.15),\n",
       " ('2016-09-09', 0.03),\n",
       " ('2016-09-09', 0.01),\n",
       " ('2016-09-09', 0.29),\n",
       " ('2016-09-09', None),\n",
       " ('2016-09-09', 0.23),\n",
       " ('2016-09-09', 0.16),\n",
       " ('2016-09-09', 0.35),\n",
       " ('2016-09-10', 0.0),\n",
       " ('2016-09-10', 0.01),\n",
       " ('2016-09-10', 0.14),\n",
       " ('2016-09-10', 0.09),\n",
       " ('2016-09-10', 1.16),\n",
       " ('2016-09-11', 0.05),\n",
       " ('2016-09-11', 0.18),\n",
       " ('2016-09-11', 0.12),\n",
       " ('2016-09-11', 0.3),\n",
       " ('2016-09-11', 0.6),\n",
       " ('2016-09-12', 0.0),\n",
       " ('2016-09-12', 0.04),\n",
       " ('2016-09-12', None),\n",
       " ('2016-09-12', None),\n",
       " ('2016-09-12', 0.15),\n",
       " ('2016-09-12', 0.31),\n",
       " ('2016-09-12', 1.04),\n",
       " ('2016-09-13', 0.02),\n",
       " ('2016-09-13', 0.37),\n",
       " ('2016-09-13', 0.32),\n",
       " ('2016-09-13', None),\n",
       " ('2016-09-13', 0.46),\n",
       " ('2016-09-13', 0.34),\n",
       " ('2016-09-13', 1.2),\n",
       " ('2016-09-14', 1.32),\n",
       " ('2016-09-14', 0.9),\n",
       " ('2016-09-14', 1.84),\n",
       " ('2016-09-14', None),\n",
       " ('2016-09-14', 1.19),\n",
       " ('2016-09-14', 2.33),\n",
       " ('2016-09-14', 6.7),\n",
       " ('2016-09-15', 0.42),\n",
       " ('2016-09-15', 0.12),\n",
       " ('2016-09-15', 0.07),\n",
       " ('2016-09-15', None),\n",
       " ('2016-09-15', 0.17),\n",
       " ('2016-09-15', 0.83),\n",
       " ('2016-09-15', 3.35),\n",
       " ('2016-09-16', 0.06),\n",
       " ('2016-09-16', 0.01),\n",
       " ('2016-09-16', 0.07),\n",
       " ('2016-09-16', 0.0),\n",
       " ('2016-09-16', 0.01),\n",
       " ('2016-09-16', 0.06),\n",
       " ('2016-09-16', 0.61),\n",
       " ('2016-09-17', 0.05),\n",
       " ('2016-09-17', 0.04),\n",
       " ('2016-09-17', 0.0),\n",
       " ('2016-09-17', 0.36),\n",
       " ('2016-09-17', 0.23),\n",
       " ('2016-09-18', 0.0),\n",
       " ('2016-09-18', 0.0),\n",
       " ('2016-09-18', 0.04),\n",
       " ('2016-09-18', 0.07),\n",
       " ('2016-09-18', 0.42),\n",
       " ('2016-09-19', 0.0),\n",
       " ('2016-09-19', 0.01),\n",
       " ('2016-09-19', None),\n",
       " ('2016-09-19', None),\n",
       " ('2016-09-19', 0.05),\n",
       " ('2016-09-19', 0.01),\n",
       " ('2016-09-19', 0.25),\n",
       " ('2016-09-20', 0.0),\n",
       " ('2016-09-20', 0.09),\n",
       " ('2016-09-20', 0.25),\n",
       " ('2016-09-20', 0.0),\n",
       " ('2016-09-20', 0.04),\n",
       " ('2016-09-20', 0.22),\n",
       " ('2016-09-20', 0.43),\n",
       " ('2016-09-21', 0.0),\n",
       " ('2016-09-21', 0.06),\n",
       " ('2016-09-21', 0.02),\n",
       " ('2016-09-21', 0.0),\n",
       " ('2016-09-21', 0.07),\n",
       " ('2016-09-21', 1.02),\n",
       " ('2016-09-22', 0.02),\n",
       " ('2016-09-22', 0.09),\n",
       " ('2016-09-22', 0.17),\n",
       " ('2016-09-22', 0.06),\n",
       " ('2016-09-22', 0.01),\n",
       " ('2016-09-22', 0.34),\n",
       " ('2016-09-22', 0.75),\n",
       " ('2016-09-23', 0.0),\n",
       " ('2016-09-23', 0.15),\n",
       " ('2016-09-23', 0.15),\n",
       " ('2016-09-23', 0.0),\n",
       " ('2016-09-23', 0.0),\n",
       " ('2016-09-23', 0.94),\n",
       " ('2016-09-23', 0.33),\n",
       " ('2016-09-24', 0.0),\n",
       " ('2016-09-24', 0.0),\n",
       " ('2016-09-24', 0.0),\n",
       " ('2016-09-24', 0.0),\n",
       " ('2016-09-24', 0.01),\n",
       " ('2016-09-24', 0.27),\n",
       " ('2016-09-25', 0.0),\n",
       " ('2016-09-25', 0.02),\n",
       " ('2016-09-25', 0.0),\n",
       " ('2016-09-25', 0.0),\n",
       " ('2016-09-25', 0.03),\n",
       " ('2016-09-25', 0.04),\n",
       " ('2016-09-26', 0.06),\n",
       " ('2016-09-26', 0.06),\n",
       " ('2016-09-26', 0.02),\n",
       " ('2016-09-26', None),\n",
       " ('2016-09-26', 0.34),\n",
       " ('2016-09-26', 0.17),\n",
       " ('2016-09-26', 1.02),\n",
       " ('2016-09-27', 0.02),\n",
       " ('2016-09-27', 0.12),\n",
       " ('2016-09-27', 0.0),\n",
       " ('2016-09-27', 0.05),\n",
       " ('2016-09-27', 0.17),\n",
       " ('2016-09-27', 1.0),\n",
       " ('2016-09-28', 0.0),\n",
       " ('2016-09-28', 0.08),\n",
       " ('2016-09-28', 0.0),\n",
       " ('2016-09-28', 0.0),\n",
       " ('2016-09-28', 0.0),\n",
       " ('2016-09-28', 0.0),\n",
       " ('2016-09-28', 0.05),\n",
       " ('2016-09-29', 0.0),\n",
       " ('2016-09-29', 0.49),\n",
       " ('2016-09-29', 0.2),\n",
       " ('2016-09-29', 0.04),\n",
       " ('2016-09-29', 0.18),\n",
       " ('2016-09-29', 0.59),\n",
       " ('2016-09-29', 1.49),\n",
       " ('2016-09-30', 0.0),\n",
       " ('2016-09-30', 0.31),\n",
       " ('2016-09-30', 0.06),\n",
       " ('2016-09-30', None),\n",
       " ('2016-09-30', 0.15),\n",
       " ('2016-09-30', 0.25),\n",
       " ('2016-09-30', 0.38),\n",
       " ('2016-10-01', 0.0),\n",
       " ('2016-10-01', 0.14),\n",
       " ('2016-10-01', 0.08),\n",
       " ('2016-10-01', 0.07),\n",
       " ('2016-10-01', 0.14),\n",
       " ('2016-10-01', 1.02),\n",
       " ('2016-10-02', 0.0),\n",
       " ('2016-10-02', 0.02),\n",
       " ('2016-10-02', 0.03),\n",
       " ('2016-10-02', 0.0),\n",
       " ('2016-10-02', 0.06),\n",
       " ('2016-10-02', 0.61),\n",
       " ('2016-10-03', 0.0),\n",
       " ('2016-10-03', 0.04),\n",
       " ('2016-10-03', 0.03),\n",
       " ('2016-10-03', None),\n",
       " ('2016-10-03', 0.0),\n",
       " ('2016-10-03', 0.16),\n",
       " ('2016-10-03', 0.46),\n",
       " ('2016-10-04', 0.0),\n",
       " ('2016-10-04', 0.0),\n",
       " ('2016-10-04', 0.0),\n",
       " ('2016-10-04', None),\n",
       " ('2016-10-04', 0.0),\n",
       " ('2016-10-04', 0.03),\n",
       " ('2016-10-04', 3.46),\n",
       " ('2016-10-05', 0.0),\n",
       " ('2016-10-05', 0.0),\n",
       " ('2016-10-05', 0.0),\n",
       " ('2016-10-05', None),\n",
       " ('2016-10-05', 0.0),\n",
       " ('2016-10-05', 0.01),\n",
       " ('2016-10-05', 0.81),\n",
       " ('2016-10-06', 0.0),\n",
       " ('2016-10-06', 0.05),\n",
       " ('2016-10-06', 0.0),\n",
       " ('2016-10-06', 0.07),\n",
       " ('2016-10-06', 0.0),\n",
       " ('2016-10-06', 0.0),\n",
       " ('2016-10-06', 0.04),\n",
       " ('2016-10-07', 0.0),\n",
       " ('2016-10-07', 0.0),\n",
       " ('2016-10-07', 0.0),\n",
       " ('2016-10-07', None),\n",
       " ('2016-10-07', 0.0),\n",
       " ('2016-10-07', 0.0),\n",
       " ('2016-10-07', 0.01),\n",
       " ('2016-10-08', 0.0),\n",
       " ('2016-10-08', 0.0),\n",
       " ('2016-10-08', 0.0),\n",
       " ('2016-10-08', 0.0),\n",
       " ('2016-10-08', 0.04),\n",
       " ('2016-10-09', 0.0),\n",
       " ('2016-10-09', 0.0),\n",
       " ('2016-10-09', 0.0),\n",
       " ('2016-10-09', 0.0),\n",
       " ('2016-10-09', 0.0),\n",
       " ('2016-10-10', 0.0),\n",
       " ('2016-10-10', 0.0),\n",
       " ('2016-10-10', None),\n",
       " ('2016-10-10', 0.0),\n",
       " ('2016-10-10', 0.0),\n",
       " ('2016-10-10', 0.0),\n",
       " ('2016-10-11', 0.0),\n",
       " ('2016-10-11', 0.02),\n",
       " ('2016-10-11', 0.04),\n",
       " ('2016-10-11', None),\n",
       " ('2016-10-11', 0.0),\n",
       " ('2016-10-11', 0.28),\n",
       " ('2016-10-11', 0.35),\n",
       " ('2016-10-12', 0.0),\n",
       " ('2016-10-12', 0.03),\n",
       " ('2016-10-12', 0.0),\n",
       " ('2016-10-12', 0.0),\n",
       " ('2016-10-12', 0.03),\n",
       " ('2016-10-12', 0.02),\n",
       " ('2016-10-13', 0.0),\n",
       " ('2016-10-13', 0.0),\n",
       " ('2016-10-13', 0.02),\n",
       " ('2016-10-13', None),\n",
       " ('2016-10-13', 0.0),\n",
       " ('2016-10-13', 0.0),\n",
       " ('2016-10-13', 0.06),\n",
       " ('2016-10-14', 0.0),\n",
       " ('2016-10-14', 0.0),\n",
       " ('2016-10-14', 0.0),\n",
       " ('2016-10-14', 0.0),\n",
       " ('2016-10-14', 0.0),\n",
       " ('2016-10-14', 0.0),\n",
       " ('2016-10-15', 0.0),\n",
       " ('2016-10-15', 0.0),\n",
       " ('2016-10-15', 0.02),\n",
       " ('2016-10-15', 0.0),\n",
       " ('2016-10-15', 0.04),\n",
       " ('2016-10-15', 0.33),\n",
       " ('2016-10-16', 0.0),\n",
       " ('2016-10-16', 0.0),\n",
       " ('2016-10-16', 0.0),\n",
       " ('2016-10-16', 0.0),\n",
       " ('2016-10-16', 0.0),\n",
       " ('2016-10-17', 0.01),\n",
       " ('2016-10-17', 0.03),\n",
       " ('2016-10-17', None),\n",
       " ('2016-10-17', None),\n",
       " ('2016-10-17', 0.12),\n",
       " ('2016-10-17', 0.01),\n",
       " ('2016-10-17', 0.38),\n",
       " ('2016-10-18', 0.0),\n",
       " ('2016-10-18', 0.05),\n",
       " ('2016-10-18', 0.03),\n",
       " ('2016-10-18', None),\n",
       " ('2016-10-18', 0.02),\n",
       " ('2016-10-18', 0.02),\n",
       " ('2016-10-18', 0.48),\n",
       " ('2016-10-19', 0.0),\n",
       " ('2016-10-19', 0.06),\n",
       " ('2016-10-19', 0.0),\n",
       " ('2016-10-19', None),\n",
       " ('2016-10-19', 0.0),\n",
       " ('2016-10-19', 0.11),\n",
       " ('2016-10-19', 0.0),\n",
       " ('2016-10-20', 0.0),\n",
       " ('2016-10-20', 0.0),\n",
       " ('2016-10-20', 0.01),\n",
       " ('2016-10-20', None),\n",
       " ('2016-10-20', 0.0),\n",
       " ('2016-10-20', 1.0),\n",
       " ('2016-10-21', 0.05),\n",
       " ('2016-10-21', 0.15),\n",
       " ('2016-10-21', 0.03),\n",
       " ('2016-10-21', None),\n",
       " ('2016-10-21', None),\n",
       " ('2016-10-21', 0.0),\n",
       " ('2016-10-21', 0.09),\n",
       " ('2016-10-22', 0.15),\n",
       " ('2016-10-22', 0.1),\n",
       " ('2016-10-22', 0.0),\n",
       " ('2016-10-22', 0.15),\n",
       " ('2016-10-22', 1.37),\n",
       " ('2016-10-23', 0.01),\n",
       " ('2016-10-23', 0.01),\n",
       " ('2016-10-23', None),\n",
       " ('2016-10-23', 0.0),\n",
       " ('2016-10-23', 0.02),\n",
       " ('2016-10-23', 0.24),\n",
       " ('2016-10-24', 0.0),\n",
       " ('2016-10-24', 0.0),\n",
       " ('2016-10-24', 0.01),\n",
       " ('2016-10-24', None),\n",
       " ('2016-10-24', 0.0),\n",
       " ('2016-10-24', 0.08),\n",
       " ('2016-10-24', 0.7),\n",
       " ('2016-10-25', 0.03),\n",
       " ('2016-10-25', 0.04),\n",
       " ('2016-10-25', 0.0),\n",
       " ('2016-10-25', 0.4),\n",
       " ('2016-10-25', 0.12),\n",
       " ('2016-10-25', 0.11),\n",
       " ('2016-10-25', 0.4),\n",
       " ('2016-10-26', 0.0),\n",
       " ('2016-10-26', 0.06),\n",
       " ('2016-10-26', 0.2),\n",
       " ('2016-10-26', 0.02),\n",
       " ('2016-10-26', 0.01),\n",
       " ('2016-10-26', 0.0),\n",
       " ('2016-10-27', 0.0),\n",
       " ('2016-10-27', 0.11),\n",
       " ('2016-10-27', 0.2),\n",
       " ('2016-10-27', None),\n",
       " ('2016-10-27', 0.08),\n",
       " ('2016-10-27', 0.22),\n",
       " ('2016-10-27', 1.25),\n",
       " ('2016-10-28', 0.0),\n",
       " ('2016-10-28', 0.02),\n",
       " ('2016-10-28', 0.07),\n",
       " ('2016-10-28', None),\n",
       " ('2016-10-28', 0.06),\n",
       " ('2016-10-28', 0.05),\n",
       " ('2016-10-28', 0.37),\n",
       " ('2016-10-29', 0.0),\n",
       " ('2016-10-29', 0.02),\n",
       " ('2016-10-29', 0.26),\n",
       " ('2016-10-29', 0.01),\n",
       " ('2016-10-29', 0.1),\n",
       " ('2016-10-29', 0.25),\n",
       " ('2016-10-30', 0.24),\n",
       " ('2016-10-30', 0.1),\n",
       " ('2016-10-30', 0.14),\n",
       " ('2016-10-30', 0.0),\n",
       " ('2016-10-30', 0.16),\n",
       " ('2016-10-30', 0.95),\n",
       " ('2016-10-31', 0.03),\n",
       " ('2016-10-31', 0.03),\n",
       " ('2016-10-31', 0.0),\n",
       " ('2016-10-31', None),\n",
       " ('2016-10-31', 0.13),\n",
       " ('2016-10-31', 0.07),\n",
       " ('2016-10-31', 1.35),\n",
       " ('2016-11-01', 0.0),\n",
       " ('2016-11-01', 0.01),\n",
       " ('2016-11-01', 0.0),\n",
       " ('2016-11-01', 0.01),\n",
       " ('2016-11-01', 0.1),\n",
       " ('2016-11-01', 0.09),\n",
       " ('2016-11-02', 0.0),\n",
       " ('2016-11-02', 0.0),\n",
       " ('2016-11-02', 0.0),\n",
       " ('2016-11-02', 0.0),\n",
       " ('2016-11-02', 0.0),\n",
       " ('2016-11-02', 0.04),\n",
       " ('2016-11-03', 0.0),\n",
       " ('2016-11-03', 0.0),\n",
       " ('2016-11-03', 0.0),\n",
       " ('2016-11-03', 0.0),\n",
       " ('2016-11-03', 0.0),\n",
       " ('2016-11-03', 0.02),\n",
       " ('2016-11-04', 0.0),\n",
       " ('2016-11-04', 0.0),\n",
       " ('2016-11-04', 0.0),\n",
       " ('2016-11-04', None),\n",
       " ('2016-11-04', 0.0),\n",
       " ('2016-11-04', 0.0),\n",
       " ('2016-11-04', 0.06),\n",
       " ('2016-11-05', 0.0),\n",
       " ('2016-11-05', 0.02),\n",
       " ('2016-11-05', 0.0),\n",
       " ('2016-11-05', 0.02),\n",
       " ('2016-11-05', 0.03),\n",
       " ('2016-11-05', 0.38),\n",
       " ('2016-11-06', 0.0),\n",
       " ('2016-11-06', 0.02),\n",
       " ('2016-11-06', 0.0),\n",
       " ('2016-11-06', 0.0),\n",
       " ('2016-11-06', 0.01),\n",
       " ('2016-11-06', 0.05),\n",
       " ('2016-11-07', 0.0),\n",
       " ('2016-11-07', 0.0),\n",
       " ('2016-11-07', 0.13),\n",
       " ('2016-11-07', None),\n",
       " ('2016-11-07', 0.0),\n",
       " ('2016-11-07', 0.0),\n",
       " ('2016-11-07', 0.05),\n",
       " ('2016-11-08', 0.07),\n",
       " ('2016-11-08', 0.14),\n",
       " ('2016-11-08', 0.02),\n",
       " ('2016-11-08', 0.15),\n",
       " ('2016-11-08', 0.21),\n",
       " ('2016-11-08', 0.53),\n",
       " ('2016-11-09', 0.0),\n",
       " ('2016-11-09', 0.08),\n",
       " ('2016-11-09', 0.17),\n",
       " ('2016-11-09', 0.0),\n",
       " ('2016-11-09', 0.0),\n",
       " ('2016-11-09', 0.11),\n",
       " ('2016-11-09', 0.04),\n",
       " ('2016-11-10', 0.0),\n",
       " ('2016-11-10', 0.0),\n",
       " ('2016-11-10', 0.0),\n",
       " ('2016-11-10', 0.0),\n",
       " ('2016-11-10', 0.0),\n",
       " ('2016-11-10', 0.01),\n",
       " ('2016-11-11', 0.0),\n",
       " ('2016-11-11', 0.0),\n",
       " ('2016-11-11', 0.0),\n",
       " ('2016-11-11', 0.0),\n",
       " ('2016-11-11', 0.0),\n",
       " ('2016-11-11', 0.0),\n",
       " ('2016-11-12', 0.0),\n",
       " ('2016-11-12', 0.0),\n",
       " ('2016-11-12', 0.0),\n",
       " ('2016-11-12', 0.0),\n",
       " ('2016-11-12', 0.0),\n",
       " ('2016-11-12', 0.0),\n",
       " ('2016-11-13', 0.0),\n",
       " ('2016-11-13', 0.0),\n",
       " ('2016-11-13', 0.0),\n",
       " ('2016-11-13', 0.0),\n",
       " ('2016-11-13', 0.0),\n",
       " ('2016-11-13', 0.0),\n",
       " ('2016-11-14', 0.0),\n",
       " ('2016-11-14', 0.06),\n",
       " ('2016-11-14', 0.05),\n",
       " ('2016-11-14', 0.02),\n",
       " ('2016-11-14', 0.0),\n",
       " ('2016-11-14', 0.0),\n",
       " ('2016-11-14', 0.02),\n",
       " ('2016-11-15', 0.0),\n",
       " ('2016-11-15', 0.0),\n",
       " ('2016-11-15', 0.0),\n",
       " ('2016-11-15', None),\n",
       " ('2016-11-15', 0.0),\n",
       " ('2016-11-15', 0.0),\n",
       " ('2016-11-15', 0.05),\n",
       " ('2016-11-16', 0.0),\n",
       " ('2016-11-16', 0.14),\n",
       " ('2016-11-16', 0.18),\n",
       " ('2016-11-16', None),\n",
       " ('2016-11-16', 0.07),\n",
       " ('2016-11-16', 0.24),\n",
       " ('2016-11-16', 0.91),\n",
       " ('2016-11-17', 0.0),\n",
       " ('2016-11-17', 0.03),\n",
       " ('2016-11-17', 0.0),\n",
       " ('2016-11-17', None),\n",
       " ('2016-11-17', 0.0),\n",
       " ('2016-11-17', 0.01),\n",
       " ('2016-11-17', 0.02),\n",
       " ('2016-11-18', 0.0),\n",
       " ('2016-11-18', 0.01),\n",
       " ('2016-11-18', None),\n",
       " ('2016-11-18', 0.02),\n",
       " ('2016-11-18', 0.0),\n",
       " ('2016-11-19', 0.03),\n",
       " ('2016-11-19', 0.11),\n",
       " ('2016-11-19', 0.13),\n",
       " ('2016-11-19', 0.11),\n",
       " ('2016-11-20', 0.05),\n",
       " ('2016-11-20', 0.11),\n",
       " ('2016-11-20', 0.4),\n",
       " ('2016-11-20', 0.39),\n",
       " ('2016-11-20', None),\n",
       " ('2016-11-21', 0.01),\n",
       " ('2016-11-21', 0.02),\n",
       " ('2016-11-21', None),\n",
       " ('2016-11-21', 0.07),\n",
       " ('2016-11-21', 0.11),\n",
       " ('2016-11-21', 2.87),\n",
       " ('2016-11-22', 0.13),\n",
       " ('2016-11-22', 0.41),\n",
       " ('2016-11-22', None),\n",
       " ('2016-11-22', None),\n",
       " ('2016-11-22', 0.31),\n",
       " ('2016-11-22', 2.05),\n",
       " ('2016-11-22', 2.11),\n",
       " ('2016-11-23', 0.14),\n",
       " ('2016-11-23', 0.03),\n",
       " ('2016-11-23', None),\n",
       " ('2016-11-23', 0.03),\n",
       " ('2016-11-23', 0.25),\n",
       " ('2016-11-23', 0.22),\n",
       " ('2016-11-24', 0.05),\n",
       " ('2016-11-24', 0.2),\n",
       " ('2016-11-24', 0.21),\n",
       " ('2016-11-24', 0.3),\n",
       " ('2016-11-24', 0.72),\n",
       " ('2016-11-25', 0.05),\n",
       " ('2016-11-25', 0.05),\n",
       " ('2016-11-25', None),\n",
       " ('2016-11-25', 0.11),\n",
       " ('2016-11-25', 0.08),\n",
       " ('2016-11-25', 1.03),\n",
       " ('2016-11-26', 0.05),\n",
       " ('2016-11-26', 0.05),\n",
       " ('2016-11-26', 0.02),\n",
       " ('2016-11-26', 0.03),\n",
       " ('2016-11-26', 0.06),\n",
       " ('2016-11-26', 0.3),\n",
       " ('2016-11-27', 0.0),\n",
       " ('2016-11-27', 0.06),\n",
       " ('2016-11-27', 0.03),\n",
       " ('2016-11-27', 0.0),\n",
       " ('2016-11-27', 0.17),\n",
       " ('2016-11-27', 0.29),\n",
       " ('2016-11-28', 0.01),\n",
       " ('2016-11-28', 0.02),\n",
       " ('2016-11-28', 0.0),\n",
       " ('2016-11-28', None),\n",
       " ('2016-11-28', 0.0),\n",
       " ('2016-11-28', 0.0),\n",
       " ('2016-11-28', 0.69),\n",
       " ('2016-11-29', 0.0),\n",
       " ('2016-11-29', 0.04),\n",
       " ('2016-11-29', 0.04),\n",
       " ('2016-11-29', None),\n",
       " ('2016-11-29', 0.06),\n",
       " ('2016-11-29', 0.09),\n",
       " ('2016-11-29', 0.2),\n",
       " ('2016-11-30', 0.14),\n",
       " ('2016-11-30', 0.05),\n",
       " ('2016-11-30', 0.03),\n",
       " ('2016-11-30', None),\n",
       " ('2016-11-30', 0.0),\n",
       " ('2016-11-30', 0.05),\n",
       " ('2016-11-30', 0.79),\n",
       " ('2016-12-01', 0.12),\n",
       " ('2016-12-01', 0.33),\n",
       " ('2016-12-01', 0.07),\n",
       " ('2016-12-01', None),\n",
       " ('2016-12-01', 0.16),\n",
       " ('2016-12-01', 0.37),\n",
       " ('2016-12-01', 0.72),\n",
       " ('2016-12-02', 0.03),\n",
       " ('2016-12-02', 0.3),\n",
       " ('2016-12-02', 0.4),\n",
       " ('2016-12-02', None),\n",
       " ('2016-12-02', 0.01),\n",
       " ('2016-12-02', 0.35),\n",
       " ('2016-12-02', 1.27),\n",
       " ('2016-12-03', 0.0),\n",
       " ('2016-12-03', 0.04),\n",
       " ('2016-12-03', 0.26),\n",
       " ('2016-12-03', 0.02),\n",
       " ('2016-12-03', 0.77),\n",
       " ('2016-12-03', 1.62),\n",
       " ('2016-12-04', 0.03),\n",
       " ('2016-12-04', 0.1),\n",
       " ('2016-12-04', 0.0),\n",
       " ('2016-12-04', 0.32),\n",
       " ('2016-12-04', 0.04),\n",
       " ('2016-12-04', 0.31),\n",
       " ('2016-12-05', 0.43),\n",
       " ('2016-12-05', 0.34),\n",
       " ('2016-12-05', 0.2),\n",
       " ('2016-12-05', None),\n",
       " ('2016-12-05', 0.45),\n",
       " ('2016-12-05', 0.22),\n",
       " ('2016-12-05', 1.6),\n",
       " ('2016-12-06', 0.02),\n",
       " ('2016-12-06', 0.02),\n",
       " ('2016-12-06', None),\n",
       " ('2016-12-06', 0.0),\n",
       " ('2016-12-06', 0.0),\n",
       " ('2016-12-06', 0.0),\n",
       " ('2016-12-07', 0.0),\n",
       " ('2016-12-07', 0.17),\n",
       " ('2016-12-07', None),\n",
       " ('2016-12-07', None),\n",
       " ('2016-12-07', 0.07),\n",
       " ('2016-12-07', 0.12),\n",
       " ('2016-12-07', 0.02),\n",
       " ('2016-12-08', 0.03),\n",
       " ('2016-12-08', 0.03),\n",
       " ('2016-12-08', 0.02),\n",
       " ('2016-12-08', 0.27),\n",
       " ('2016-12-08', 0.01),\n",
       " ('2016-12-08', 0.07),\n",
       " ('2016-12-08', 0.03),\n",
       " ('2016-12-09', 0.52),\n",
       " ('2016-12-09', 0.34),\n",
       " ('2016-12-09', 0.26),\n",
       " ('2016-12-09', None),\n",
       " ('2016-12-09', 0.31),\n",
       " ('2016-12-09', 0.42),\n",
       " ('2016-12-10', 0.05),\n",
       " ('2016-12-10', 0.02),\n",
       " ('2016-12-10', 0.0),\n",
       " ('2016-12-10', None),\n",
       " ('2016-12-10', 0.02),\n",
       " ('2016-12-10', 0.04),\n",
       " ('2016-12-11', 0.04),\n",
       " ('2016-12-11', 0.02),\n",
       " ('2016-12-11', 0.06),\n",
       " ('2016-12-11', 0.0),\n",
       " ('2016-12-11', 0.13),\n",
       " ('2016-12-12', 0.01),\n",
       " ('2016-12-12', 0.01),\n",
       " ('2016-12-12', None),\n",
       " ('2016-12-12', 0.02),\n",
       " ('2016-12-12', 0.0),\n",
       " ('2016-12-12', 0.0),\n",
       " ('2016-12-12', 0.01),\n",
       " ('2016-12-13', 0.05),\n",
       " ('2016-12-13', 0.1),\n",
       " ('2016-12-13', 0.34),\n",
       " ('2016-12-13', None),\n",
       " ('2016-12-13', 0.15),\n",
       " ('2016-12-13', 0.04),\n",
       " ('2016-12-13', 0.09),\n",
       " ('2016-12-14', 0.03),\n",
       " ('2016-12-14', 0.05),\n",
       " ('2016-12-14', 0.12),\n",
       " ('2016-12-14', None),\n",
       " ('2016-12-14', 0.05),\n",
       " ('2016-12-14', 0.92),\n",
       " ('2016-12-14', 0.33),\n",
       " ('2016-12-15', 0.0),\n",
       " ('2016-12-15', 0.02),\n",
       " ('2016-12-15', 0.07),\n",
       " ('2016-12-15', None),\n",
       " ('2016-12-15', 0.0),\n",
       " ('2016-12-15', 0.14),\n",
       " ('2016-12-15', 0.03),\n",
       " ('2016-12-16', 0.0),\n",
       " ('2016-12-16', 0.01),\n",
       " ('2016-12-16', 0.0),\n",
       " ('2016-12-16', None),\n",
       " ('2016-12-16', 0.0),\n",
       " ('2016-12-16', 0.03),\n",
       " ('2016-12-16', 0.0),\n",
       " ('2016-12-17', 0.01),\n",
       " ('2016-12-17', 0.11),\n",
       " ('2016-12-17', 0.0),\n",
       " ('2016-12-17', 0.16),\n",
       " ('2016-12-17', 0.07),\n",
       " ('2016-12-18', 0.13),\n",
       " ('2016-12-18', 0.29),\n",
       " ('2016-12-18', 0.04),\n",
       " ('2016-12-18', 0.27),\n",
       " ('2016-12-18', 0.16),\n",
       " ('2016-12-18', None),\n",
       " ('2016-12-19', 0.01),\n",
       " ('2016-12-19', 0.21),\n",
       " ('2016-12-19', 0.0),\n",
       " ('2016-12-19', None),\n",
       " ('2016-12-19', 0.02),\n",
       " ('2016-12-19', 0.03),\n",
       " ('2016-12-19', 0.15),\n",
       " ('2016-12-20', 0.0),\n",
       " ('2016-12-20', 0.02),\n",
       " ('2016-12-20', 0.0),\n",
       " ('2016-12-20', None),\n",
       " ('2016-12-20', 0.01),\n",
       " ('2016-12-20', 0.0),\n",
       " ('2016-12-20', 0.0),\n",
       " ('2016-12-21', 0.0),\n",
       " ('2016-12-21', 0.03),\n",
       " ('2016-12-21', 0.09),\n",
       " ('2016-12-21', 0.06),\n",
       " ('2016-12-21', 0.06),\n",
       " ('2016-12-21', 0.11),\n",
       " ('2016-12-21', 0.55),\n",
       " ('2016-12-22', 0.01),\n",
       " ('2016-12-22', 0.17),\n",
       " ('2016-12-22', 0.05),\n",
       " ('2016-12-22', None),\n",
       " ('2016-12-22', 0.14),\n",
       " ('2016-12-22', 0.86),\n",
       " ('2016-12-22', 1.24),\n",
       " ('2016-12-23', 0.01),\n",
       " ('2016-12-23', 0.1),\n",
       " ('2016-12-23', 0.03),\n",
       " ('2016-12-23', None),\n",
       " ('2016-12-23', 0.02),\n",
       " ('2016-12-23', 0.24),\n",
       " ('2016-12-23', 0.83),\n",
       " ('2016-12-24', 0.01),\n",
       " ('2016-12-24', 0.14),\n",
       " ('2016-12-24', 0.13),\n",
       " ('2016-12-24', 0.06),\n",
       " ('2016-12-24', 0.2),\n",
       " ('2016-12-24', 1.08),\n",
       " ('2016-12-25', 0.0),\n",
       " ('2016-12-25', 0.03),\n",
       " ('2016-12-25', 0.0),\n",
       " ('2016-12-25', 0.02),\n",
       " ('2016-12-25', 0.38),\n",
       " ('2016-12-26', 0.02),\n",
       " ('2016-12-26', 0.26),\n",
       " ('2016-12-26', None),\n",
       " ('2016-12-26', 0.06),\n",
       " ('2016-12-26', 0.22),\n",
       " ('2016-12-26', 1.48),\n",
       " ('2016-12-27', 0.0),\n",
       " ('2016-12-27', 0.03),\n",
       " ('2016-12-27', 0.02),\n",
       " ('2016-12-27', 0.0),\n",
       " ('2016-12-27', 0.05),\n",
       " ('2016-12-27', 0.14),\n",
       " ('2016-12-28', 0.02),\n",
       " ('2016-12-28', 0.09),\n",
       " ('2016-12-28', 0.01),\n",
       " ('2016-12-28', None),\n",
       " ('2016-12-28', 0.06),\n",
       " ('2016-12-28', 0.09),\n",
       " ('2016-12-28', 0.14),\n",
       " ('2016-12-29', 0.04),\n",
       " ('2016-12-29', 0.18),\n",
       " ('2016-12-29', 0.56),\n",
       " ('2016-12-29', None),\n",
       " ('2016-12-29', 0.05),\n",
       " ('2016-12-29', 0.52),\n",
       " ('2016-12-29', 1.03),\n",
       " ('2016-12-30', 0.12),\n",
       " ('2016-12-30', 0.21),\n",
       " ('2016-12-30', 0.29),\n",
       " ('2016-12-30', None),\n",
       " ('2016-12-30', 0.07),\n",
       " ('2016-12-30', 0.29),\n",
       " ('2016-12-30', 2.37),\n",
       " ('2016-12-31', 0.01),\n",
       " ('2016-12-31', 0.62),\n",
       " ('2016-12-31', 0.36),\n",
       " ('2016-12-31', 0.25),\n",
       " ('2016-12-31', 0.9),\n",
       " ('2017-01-01', 0.0),\n",
       " ('2017-01-01', 0.29),\n",
       " ('2017-01-01', 0.0),\n",
       " ('2017-01-01', None),\n",
       " ('2017-01-01', 0.03),\n",
       " ('2017-01-01', 0.03),\n",
       " ('2017-01-02', 0.0),\n",
       " ('2017-01-02', 0.0),\n",
       " ('2017-01-02', 0.01),\n",
       " ('2017-01-02', 0.01),\n",
       " ('2017-01-02', 0.0),\n",
       " ('2017-01-03', 0.0),\n",
       " ('2017-01-03', 0.0),\n",
       " ('2017-01-03', 0.0),\n",
       " ('2017-01-03', None),\n",
       " ('2017-01-03', 0.0),\n",
       " ('2017-01-03', 0.0),\n",
       " ('2017-01-04', 0.0),\n",
       " ('2017-01-04', 0.0),\n",
       " ('2017-01-04', 0.0),\n",
       " ('2017-01-04', 0.18),\n",
       " ('2017-01-04', 0.0),\n",
       " ('2017-01-04', 0.0),\n",
       " ('2017-01-05', 0.0),\n",
       " ('2017-01-05', 0.0),\n",
       " ('2017-01-05', 0.0),\n",
       " ('2017-01-05', 0.42),\n",
       " ('2017-01-05', 0.06),\n",
       " ('2017-01-05', 0.47),\n",
       " ('2017-01-06', 0.0),\n",
       " ('2017-01-06', 0.0),\n",
       " ('2017-01-06', 0.59),\n",
       " ('2017-01-06', 0.01),\n",
       " ('2017-01-06', 0.1),\n",
       " ('2017-01-06', 0.1),\n",
       " ('2017-01-07', 0.0),\n",
       " ('2017-01-07', 0.06),\n",
       " ('2017-01-07', 0.0),\n",
       " ('2017-01-07', 0.0),\n",
       " ('2017-01-07', 0.0),\n",
       " ('2017-01-07', 0.0),\n",
       " ('2017-01-08', 0.0),\n",
       " ('2017-01-08', 0.0),\n",
       " ('2017-01-08', 0.03),\n",
       " ('2017-01-08', 0.0),\n",
       " ('2017-01-08', 0.0),\n",
       " ('2017-01-08', 0.03),\n",
       " ('2017-01-09', 0.0),\n",
       " ('2017-01-09', 0.0),\n",
       " ('2017-01-09', 0.0),\n",
       " ('2017-01-09', None),\n",
       " ('2017-01-09', 0.0),\n",
       " ('2017-01-09', 0.0),\n",
       " ('2017-01-09', 0.0),\n",
       " ('2017-01-10', 0.0),\n",
       " ('2017-01-10', 0.0),\n",
       " ('2017-01-10', 0.0),\n",
       " ('2017-01-10', None),\n",
       " ('2017-01-10', 0.0),\n",
       " ('2017-01-10', 0.0),\n",
       " ('2017-01-10', 0.0),\n",
       " ('2017-01-11', 0.0),\n",
       " ('2017-01-11', 0.0),\n",
       " ('2017-01-11', 0.0),\n",
       " ('2017-01-11', None),\n",
       " ('2017-01-11', 0.0),\n",
       " ('2017-01-11', 0.0),\n",
       " ('2017-01-12', 0.0),\n",
       " ('2017-01-12', 0.0),\n",
       " ('2017-01-12', None),\n",
       " ('2017-01-12', None),\n",
       " ('2017-01-12', 0.0),\n",
       " ('2017-01-12', 0.0),\n",
       " ('2017-01-13', 0.0),\n",
       " ('2017-01-13', 0.0),\n",
       " ('2017-01-13', None),\n",
       " ('2017-01-13', None),\n",
       " ('2017-01-13', 0.0),\n",
       " ('2017-01-13', 0.0),\n",
       " ('2017-01-14', 0.0),\n",
       " ('2017-01-14', 0.0),\n",
       " ('2017-01-14', 0.0),\n",
       " ('2017-01-14', 0.01),\n",
       " ('2017-01-14', 0.0),\n",
       " ('2017-01-15', 0.0),\n",
       " ('2017-01-15', 0.0),\n",
       " ('2017-01-15', None),\n",
       " ('2017-01-15', 0.0),\n",
       " ('2017-01-15', 0.01),\n",
       " ('2017-01-16', 0.0),\n",
       " ('2017-01-16', 0.0),\n",
       " ('2017-01-16', None),\n",
       " ('2017-01-16', 0.0),\n",
       " ('2017-01-16', 0.0),\n",
       " ('2017-01-16', 0.0),\n",
       " ('2017-01-17', 0.0),\n",
       " ('2017-01-17', 0.0),\n",
       " ('2017-01-17', 0.0),\n",
       " ('2017-01-17', 0.0),\n",
       " ('2017-01-17', 0.0),\n",
       " ('2017-01-17', 0.0),\n",
       " ('2017-01-18', 0.0),\n",
       " ('2017-01-18', 0.0),\n",
       " ('2017-01-18', 0.0),\n",
       " ('2017-01-18', 0.0),\n",
       " ('2017-01-18', None),\n",
       " ('2017-01-18', 0.0),\n",
       " ('2017-01-18', 0.07),\n",
       " ('2017-01-19', 0.0),\n",
       " ('2017-01-19', 0.0),\n",
       " ('2017-01-19', 0.0),\n",
       " ('2017-01-19', None),\n",
       " ('2017-01-19', 0.0),\n",
       " ('2017-01-19', 0.02),\n",
       " ('2017-01-19', 0.0),\n",
       " ('2017-01-20', 0.0),\n",
       " ('2017-01-20', 0.0),\n",
       " ('2017-01-20', 0.0),\n",
       " ('2017-01-20', None),\n",
       " ('2017-01-20', 0.0),\n",
       " ('2017-01-20', 0.0),\n",
       " ('2017-01-20', 0.0),\n",
       " ('2017-01-21', 0.0),\n",
       " ('2017-01-21', 0.04),\n",
       " ('2017-01-21', 0.02),\n",
       " ('2017-01-21', 0.11),\n",
       " ('2017-01-21', 0.03),\n",
       " ('2017-01-21', 0.08),\n",
       " ('2017-01-22', 0.16),\n",
       " ('2017-01-22', 0.01),\n",
       " ('2017-01-22', 0.04),\n",
       " ('2017-01-22', 0.09),\n",
       " ('2017-01-22', 0.72),\n",
       " ('2017-01-23', 0.0),\n",
       " ('2017-01-23', 0.08),\n",
       " ('2017-01-23', None),\n",
       " ('2017-01-23', None),\n",
       " ('2017-01-23', 0.0),\n",
       " ('2017-01-23', 0.01),\n",
       " ('2017-01-23', 0.85),\n",
       " ('2017-01-24', 0.04),\n",
       " ('2017-01-24', 0.15),\n",
       " ('2017-01-24', None),\n",
       " ('2017-01-24', 0.08),\n",
       " ('2017-01-24', 0.13),\n",
       " ('2017-01-24', 1.85),\n",
       " ('2017-01-25', 0.03),\n",
       " ('2017-01-25', 0.12),\n",
       " ('2017-01-25', None),\n",
       " ('2017-01-25', None),\n",
       " ('2017-01-25', 0.0),\n",
       " ('2017-01-25', 0.79),\n",
       " ('2017-01-25', 2.64),\n",
       " ('2017-01-26', 0.0),\n",
       " ('2017-01-26', 0.0),\n",
       " ('2017-01-26', 0.01),\n",
       " ('2017-01-26', 0.0),\n",
       " ('2017-01-26', 0.0),\n",
       " ('2017-01-26', 0.0),\n",
       " ('2017-01-26', 0.1),\n",
       " ('2017-01-27', 0.0),\n",
       " ('2017-01-27', 0.0),\n",
       " ('2017-01-27', 0.0),\n",
       " ('2017-01-27', 0.0),\n",
       " ('2017-01-27', 0.0),\n",
       " ('2017-01-27', 0.03),\n",
       " ('2017-01-27', 0.03),\n",
       " ('2017-01-28', 0.0),\n",
       " ('2017-01-28', 0.14),\n",
       " ('2017-01-28', 0.0),\n",
       " ('2017-01-28', 0.0),\n",
       " ('2017-01-28', 0.0),\n",
       " ('2017-01-29', 0.18),\n",
       " ('2017-01-29', 0.0),\n",
       " ...]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Design a query to retrieve the last 12 months of precipitation data and plot the results\n",
    "percipitation_last_12_months = session.query(Measurement.date, Measurement.prcp).\\\n",
    "filter(Measurement.date >='2016-08-24').filter(Measurement.date <='2017-08-23').order_by(Measurement.date).all()\n",
    "\n",
    "percipitation_last_12_months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>percipitation</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>2016-08-24</td>\n",
       "      <td>2.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-08-24</td>\n",
       "      <td>2.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-08-24</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-08-24</td>\n",
       "      <td>1.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-08-24</td>\n",
       "      <td>2.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-08-24</td>\n",
       "      <td>1.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-08-25</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-08-25</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-08-25</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  percipitation\n",
       "0  2016-08-24           0.08\n",
       "1  2016-08-24           2.15\n",
       "2  2016-08-24           2.28\n",
       "3  2016-08-24            NaN\n",
       "4  2016-08-24           1.22\n",
       "5  2016-08-24           2.15\n",
       "6  2016-08-24           1.45\n",
       "7  2016-08-25           0.08\n",
       "8  2016-08-25           0.08\n",
       "9  2016-08-25           0.00"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Save the query results as a Pandas DataFrame and set the index to the date column\n",
    "percipitation_last_12_months_df = pd.DataFrame(percipitation_last_12_months, columns=[\"date\", \"percipitation\"])\n",
    "percipitation_last_12_months_df.set_index(\"date\")\n",
    "percipitation_last_12_months_df.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>percipitation</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>2016-08-24</td>\n",
       "      <td>2.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-08-24</td>\n",
       "      <td>2.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-08-24</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-08-24</td>\n",
       "      <td>1.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  percipitation\n",
       "0  2016-08-24           0.08\n",
       "1  2016-08-24           2.15\n",
       "2  2016-08-24           2.28\n",
       "3  2016-08-24            NaN\n",
       "4  2016-08-24           1.22"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the dataframe by date\n",
    "percipitation_last_12_months_df.sort_values(by=[\"date\"])\n",
    "percipitation_last_12_months_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>percipitation</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>std</th>\n",
       "      <td>0.460288</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": [
       "       percipitation\n",
       "count    2015.000000\n",
       "mean        0.176462\n",
       "std         0.460288\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         0.020000\n",
       "75%         0.130000\n",
       "max         6.700000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use Pandas to calcualte the summary statistics for the precipitation data\n",
    "percipitation_last_12_months_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "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",
    "percipitation_last_12_months_df.plot()\n",
    "plt.xlabel(\"date\")\n",
    "plt.ylabel(\"percipitation\")\n",
    "plt.title(\"12 Months Percipitation Data\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.savefig(\"12 Months Percipitation Data.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Design a query to show how many stations are available in this dataset?\n",
    "station_count = session.query(Station.id).count()\n",
    "\n",
    "station_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 21,
     "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",
    "session.query(Measurement.station, func.count(Measurement.station)).\\\n",
    "group_by(Measurement.station).order_by(func.count(Measurement.station).desc()).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(54.0, 85.0, 71.66378066378067)]"
      ]
     },
     "execution_count": 22,
     "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()\n",
    "# Station USC00519281 is the most active station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\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",
    "highest_temp = session.query(Measurement.tobs).\\\n",
    "filter(Measurement.date >='2016-08-23').all()\n",
    "\n",
    "station_highest_temp = pd.DataFrame(highest_temp, columns=[\"tobs\"])\n",
    "station_highest_temp.plot.hist(bins=12)\n",
    "plt.savefig(\"Last 12 Months Temperature.png\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus Challenge Assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(71.0, 78.00877192982456, 83.0)]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "trip_begin = dt.date(2016, 9, 15)\n",
    "trip_end = dt.date(2016, 9, 30)\n",
    "\n",
    "start_date = trip_begin - dt.timedelta(days=365)\n",
    "end_date = trip_end - dt.timedelta(days=365)\n",
    "\n",
    "trip_temp = calc_temps(start_date, end_date)\n",
    "trip_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x576 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",
    "fig, ax = plt.subplots(figsize=plt.figaspect(2.))\n",
    "trip_avg_temp = trip_temp[0][1]\n",
    "xpos = 1\n",
    "error = trip_temp[0][2]- trip_temp[0][1]\n",
    "bar_chart = ax.bar(xpos, trip_avg_temp, yerr= error,alpha=0.5, color='orange')\n",
    "ax.set(xticks= range(xpos), title=\"Trip Avg Temp\", ylabel=\"Average Temperature (F)\")\n",
    "fig.tight_layout()\n",
    "plt.savefig(\"Trip Avg Temp.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "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>Station</th>\n",
       "      <th>Location</th>\n",
       "      <th>Total Precipitation</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>USC00516128</td>\n",
       "      <td>MANOA LYON ARBO 785.2, HI US</td>\n",
       "      <td>21.3331</td>\n",
       "      <td>-157.8025</td>\n",
       "      <td>152.4</td>\n",
       "      <td>11.640000000000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USC00519281</td>\n",
       "      <td>WAIHEE 837.5, HI US</td>\n",
       "      <td>21.45167</td>\n",
       "      <td>-157.84888999999998</td>\n",
       "      <td>32.9</td>\n",
       "      <td>4.119999999999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USC00513117</td>\n",
       "      <td>KANEOHE 838.1, HI US</td>\n",
       "      <td>21.4234</td>\n",
       "      <td>-157.8015</td>\n",
       "      <td>14.6</td>\n",
       "      <td>1.6500000000000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>USC00519523</td>\n",
       "      <td>WAIMANALO EXPERIMENTAL FARM, HI US</td>\n",
       "      <td>21.33556</td>\n",
       "      <td>-157.71139</td>\n",
       "      <td>19.5</td>\n",
       "      <td>1.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>USC00514830</td>\n",
       "      <td>KUALOA RANCH HEADQUARTERS 886.9, HI US</td>\n",
       "      <td>21.5213</td>\n",
       "      <td>-157.8374</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0100000000000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>WAIKIKI 717.2, HI US</td>\n",
       "      <td>21.2716</td>\n",
       "      <td>-157.8168</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.6300000000000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>USC00517948</td>\n",
       "      <td>PEARL CITY, HI US</td>\n",
       "      <td>21.3934</td>\n",
       "      <td>-157.9751</td>\n",
       "      <td>11.9</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Station                                Location Total Precipitation  \\\n",
       "0  USC00516128            MANOA LYON ARBO 785.2, HI US             21.3331   \n",
       "1  USC00519281                     WAIHEE 837.5, HI US            21.45167   \n",
       "2  USC00513117                    KANEOHE 838.1, HI US             21.4234   \n",
       "3  USC00519523      WAIMANALO EXPERIMENTAL FARM, HI US            21.33556   \n",
       "4  USC00514830  KUALOA RANCH HEADQUARTERS 886.9, HI US             21.5213   \n",
       "5  USC00519397                    WAIKIKI 717.2, HI US             21.2716   \n",
       "6  USC00517948                       PEARL CITY, HI US             21.3934   \n",
       "\n",
       "              Latitude Longitude           Elevation  \n",
       "0            -157.8025     152.4  11.640000000000002  \n",
       "1  -157.84888999999998      32.9   4.119999999999999  \n",
       "2            -157.8015      14.6  1.6500000000000001  \n",
       "3           -157.71139      19.5                1.04  \n",
       "4            -157.8374       7.0  1.0100000000000002  \n",
       "5            -157.8168       3.0  0.6300000000000001  \n",
       "6            -157.9751      11.9                 0.1  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "trip_begin = dt.date(2016, 9, 15)\n",
    "trip_end = dt.date(2016, 9, 30)\n",
    "\n",
    "sel = [Station.station, Station.name, Station.latitude, Station.longitude,Station.elevation, func.sum(Measurement.prcp)]\n",
    "results = session.query(*sel).\\\n",
    "filter(Measurement.station ==Station.station).\\\n",
    "filter(Measurement.date >=trip_begin).\\\n",
    "filter(Measurement.date <=trip_end).\\\n",
    "group_by(Station.name).order_by(func.sum(Measurement.prcp).desc()).all()\n",
    "\n",
    "result_output = pd.DataFrame(np.array(results))\n",
    "result_output = result_output.rename(columns={0:'Station', 1: 'Location',\n",
    "                                            2: 'Total Precipitation', 3: 'Latitude',\n",
    "                                             4: 'Longitude', 5: 'Elevation'})\n",
    "\n",
    "result_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(62.0, 69.15384615384616, 77.0)]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 29,
   "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",
    "trip_begin = 15\n",
    "trip_end = 30\n",
    "\n",
    "# Use the start and end date to create a range of dates\n",
    "dates = [dt.date(2016,9,i) for i in range(trip_begin,trip_end)]\n",
    "\n",
    "# Stip off the year and save a list of %m-%d strings\n",
    "stripped_dates = [date.strftime('%m-%d') for date in dates]\n",
    "\n",
    "# Loop through the list of %m-%d strings and calculate the normals for each date\n",
    "date_normals = list()\n",
    "\n",
    "for stripped_date in stripped_dates:\n",
    "    date_normals.append(*daily_normals(stripped_date))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(67.0, 76.14, 81.0),\n",
       " (70.0, 77.40384615384616, 86.0),\n",
       " (68.0, 76.57692307692308, 83.0),\n",
       " (67.0, 75.85106382978724, 84.0),\n",
       " (68.0, 76.34693877551021, 87.0),\n",
       " (69.0, 76.14583333333333, 83.0),\n",
       " (67.0, 76.44897959183673, 84.0),\n",
       " (69.0, 76.37254901960785, 84.0),\n",
       " (67.0, 75.82692307692308, 83.0),\n",
       " (69.0, 77.02, 85.0),\n",
       " (69.0, 76.14893617021276, 83.0),\n",
       " (66.0, 75.64, 85.0),\n",
       " (68.0, 75.64, 83.0),\n",
       " (67.0, 76.13461538461539, 84.0),\n",
       " (68.0, 75.97872340425532, 83.0)]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_normals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>tmin</th>\n",
       "      <th>tavg</th>\n",
       "      <th>tmax</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dates</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-09-15</th>\n",
       "      <td>67.0</td>\n",
       "      <td>76.140000</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-16</th>\n",
       "      <td>70.0</td>\n",
       "      <td>77.403846</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-17</th>\n",
       "      <td>68.0</td>\n",
       "      <td>76.576923</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-18</th>\n",
       "      <td>67.0</td>\n",
       "      <td>75.851064</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-19</th>\n",
       "      <td>68.0</td>\n",
       "      <td>76.346939</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-20</th>\n",
       "      <td>69.0</td>\n",
       "      <td>76.145833</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-21</th>\n",
       "      <td>67.0</td>\n",
       "      <td>76.448980</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-22</th>\n",
       "      <td>69.0</td>\n",
       "      <td>76.372549</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-23</th>\n",
       "      <td>67.0</td>\n",
       "      <td>75.826923</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-24</th>\n",
       "      <td>69.0</td>\n",
       "      <td>77.020000</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>69.0</td>\n",
       "      <td>76.148936</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>66.0</td>\n",
       "      <td>75.640000</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-27</th>\n",
       "      <td>68.0</td>\n",
       "      <td>75.640000</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-28</th>\n",
       "      <td>67.0</td>\n",
       "      <td>76.134615</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-29</th>\n",
       "      <td>68.0</td>\n",
       "      <td>75.978723</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            tmin       tavg  tmax\n",
       "dates                            \n",
       "2016-09-15  67.0  76.140000  81.0\n",
       "2016-09-16  70.0  77.403846  86.0\n",
       "2016-09-17  68.0  76.576923  83.0\n",
       "2016-09-18  67.0  75.851064  84.0\n",
       "2016-09-19  68.0  76.346939  87.0\n",
       "2016-09-20  69.0  76.145833  83.0\n",
       "2016-09-21  67.0  76.448980  84.0\n",
       "2016-09-22  69.0  76.372549  84.0\n",
       "2016-09-23  67.0  75.826923  83.0\n",
       "2016-09-24  69.0  77.020000  85.0\n",
       "2016-09-25  69.0  76.148936  83.0\n",
       "2016-09-26  66.0  75.640000  85.0\n",
       "2016-09-27  68.0  75.640000  83.0\n",
       "2016-09-28  67.0  76.134615  84.0\n",
       "2016-09-29  68.0  75.978723  83.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the previous query results into a Pandas DataFrame and add the `trip_dates` range as the `date` index\n",
    "\n",
    "date_normals_df = pd.DataFrame(date_normals, columns=['tmin', 'tavg', 'tmax'])\n",
    "date_normals_df['dates']= dates\n",
    "date_normals_df = date_normals_df.set_index('dates')\n",
    "\n",
    "date_normals_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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 daily normals as an area plot with `stacked=False`\n",
    "\n",
    "date_normals_df.plot.area(stacked = False,xticks = list(date_normals_df.index)[::1],rot = 'vertical')\n",
    "plt.savefig(\"Daily Normals.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernel_info": {
   "name": "python3"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  },
  "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
}