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"
   ]
  },
  {
   "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\n",
    "from sqlalchemy import inspect"
   ]
  },
  {
   "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",
    "\n",
    "tables = Base.classes.keys()\n",
    "tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save references to each table\n",
    "measurement_table=Base.classes.measurement\n",
    "station_table=Base.classes.station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Design a query to retrieve the last 12 months of precipitation data and plot the results\n",
    "precipitation12months = session.query(measurement_table.prcp).filter(measurement_table.date == \"2010-01-01\")\n",
    "precipitation12months\n",
    "\n",
    "# Calculate the date 1 year ago from the last data point in the database\n",
    "##find the last date\n",
    "last_date = session.query(measurement_table.date).order_by(measurement_table.date.desc()).first()\n",
    "last_date\n",
    "\n",
    "##find date greater than one year ago (2016-08-23)\n",
    "twelve_months_date=session.query(measurement_table.date).\\\n",
    "filter(measurement_table.date >= '2016-08-23')\n",
    "twelve_months_date\n",
    "\n",
    "#date_1year_ago= session.query(measurement_table.date).order_by(measurement_table.date).all()\n",
    "#date_1year_agoa\n",
    "\n",
    "# Perform a query to retrieve the data and precipitation scores\n",
    "precipitation_12months = session.query(measurement_table.date,measurement_table.prcp).filter(measurement_table.date > '2016-08-23').all()\n",
    "precipitation_12months \n",
    "\n",
    "# Save the query results as a Pandas DataFrame and set the index to the date column\n",
    "precipitation_df=pd.DataFrame(precipitation_12months,columns=['Date', 'Precipitation'])\n",
    "\n",
    "precipitation_df\n",
    "\n",
    "# Sort the dataframe by date\n",
    "precipitation_df = precipitation_df.sort_index()\n",
    "precipitation_df\n",
    "\n",
    "# Use Pandas Plotting with Matplotlib to plot the data\n",
    "\n",
    "precipitation_df.plot(title=\"Precipitation for the Last 12 Months\", color='blue', alpha=0.8, figsize=(10,6))\n",
    "\n",
    "plt.savefig(\"Images_Output/Precipitation.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Precipitation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>2015.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>0.176462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.460288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>0.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>0.130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>6.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Precipitation\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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use Pandas to calcualte the summary statistics for the precipitation data\n",
    "precipitation_summary=precipitation_df.describe()\n",
    "precipitation_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Design a query to show how many stations are available in this dataset?\n",
    "session.query(station_table).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What are the most active stations? (i.e. what stations have the most rows)?\n",
    "\n",
    "\n",
    "# List the stations and the counts in descending order.\n",
    "most_active_session=session.query(measurement_table.station, func.count(measurement_table.station)).\\\n",
    "group_by(measurement_table.station).order_by(func.count(measurement_table.station).desc()).all()\n",
    "most_active_session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(54.0, 85.0, 71.66378066378067)]"
      ]
     },
     "execution_count": 126,
     "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",
    "\n",
    "lowest_highest_avg_temp=session.query(func.min(measurement_table.tobs),func.max(measurement_table.tobs),func.avg(measurement_table.tobs)).\\\n",
    "filter(measurement_table.station==\"USC00519281\").all()   \n",
    "lowest_highest_avg_temp\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Choose the station with the highest number of temperature observations.\n",
    "\n",
    "\n",
    "# Query the last 12 months of temperature observation data for this station and plot the results as a histogram\n",
    "temp=session.query(measurement_table.tobs).filter(measurement_table.station==\"USC00519281\").\\\n",
    "filter(measurement_table.date >= '2016-08-23').all()\n",
    "temp\n",
    "\n",
    "#create dataframe\n",
    "temp_df=pd.DataFrame(temp,columns=['Temperature'])\n",
    "\n",
    "#Create Histogram\n",
    "\n",
    "temp_df.plot.hist(bins=12, title=\"Temperature vs. Frequency Histogram\",color='blue', alpha=0.8, figsize=(8,4))\n",
    "\n",
    "plt.savefig(\"Images_Output/TemperaturevsFrequency.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus Challenge Assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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_table.tobs), func.avg(measurement_table.tobs), func.max(measurement_table.tobs)).\\\n",
    "        filter(measurement_table.date >= start_date).filter(measurement_table.date <= end_date).all()\n",
    "\n",
    "# function usage example\n",
    "print(calc_temps('2012-02-28', '2012-03-05'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(58.0, 70.08333333333333, 79.0)]"
      ]
     },
     "execution_count": 30,
     "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",
    "\n",
    "start_date = dt.date(2016, 2, 3) - dt.timedelta(days=365)\n",
    "end_date = dt.date(2016, 2, 14) - dt.timedelta(days=365)\n",
    "\n",
    "temp_data=calc_temps(start_date, end_date)\n",
    "temp_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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",
    "temp_data_df=pd.DataFrame(temp_data,columns=[\"T min\",\"T avg\",\"T max\"])\n",
    "temp_data_df.plot.bar(title=\"Trip Avg Temp\",color='orange',alpha=0.6, figsize=(5,8),\n",
    "                      y=\"T avg\",yerr=(temp_data_df[\"T max\"]-temp_data_df[\"T min\"]))\n",
    "                      \n",
    "plt.xticks(np.arange(1, 1.0))\n",
    "plt.ylabel(\"Temp (F)\")\n",
    "\n",
    "\n",
    "plt.savefig(\"Images_Output/TripAvgTemp.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('USC00519397',\n",
       "  5.579999999999999,\n",
       "  'WAIMANALO EXPERIMENTAL FARM, HI US',\n",
       "  21.33556,\n",
       "  -157.71139,\n",
       "  19.5),\n",
       " ('USC00519397',\n",
       "  5.579999999999999,\n",
       "  'WAIKIKI 717.2, HI US',\n",
       "  21.2716,\n",
       "  -157.8168,\n",
       "  3.0),\n",
       " ('USC00519397',\n",
       "  5.579999999999999,\n",
       "  'WAIHEE 837.5, HI US',\n",
       "  21.45167,\n",
       "  -157.84888999999998,\n",
       "  32.9),\n",
       " ('USC00519397',\n",
       "  5.579999999999999,\n",
       "  'UPPER WAHIAWA 874.3, HI US',\n",
       "  21.4992,\n",
       "  -158.0111,\n",
       "  306.6),\n",
       " ('USC00519397',\n",
       "  5.579999999999999,\n",
       "  'PEARL CITY, HI US',\n",
       "  21.3934,\n",
       "  -157.9751,\n",
       "  11.9),\n",
       " ('USC00519397',\n",
       "  5.579999999999999,\n",
       "  'MANOA LYON ARBO 785.2, HI US',\n",
       "  21.3331,\n",
       "  -157.8025,\n",
       "  152.4),\n",
       " ('USC00519397',\n",
       "  5.579999999999999,\n",
       "  'KUALOA RANCH HEADQUARTERS 886.9, HI US',\n",
       "  21.5213,\n",
       "  -157.8374,\n",
       "  7.0),\n",
       " ('USC00519397',\n",
       "  5.579999999999999,\n",
       "  'KANEOHE 838.1, HI US',\n",
       "  21.4234,\n",
       "  -157.8015,\n",
       "  14.6),\n",
       " ('USC00519397',\n",
       "  5.579999999999999,\n",
       "  'HONOLULU OBSERVATORY 702.2, HI US',\n",
       "  21.3152,\n",
       "  -157.9992,\n",
       "  0.9)]"
      ]
     },
     "execution_count": 177,
     "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",
    "\n",
    "total_rainfall = session.query(measurement_table.station,func.sum(measurement_table.prcp),\\\n",
    "                               station_table.name,station_table.latitude,\\\n",
    "                                station_table.longitude,station_table.elevation).\\\n",
    "                                filter(measurement_table.date >='2015-02-13').\\\n",
    "                                filter(measurement_table.date <='2015-02-20').\\\n",
    "                                group_by(station_table.name).order_by(func.sum(measurement_table.prcp).desc()).all()\n",
    "total_rainfall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(62.0, 69.15384615384616, 77.0)]"
      ]
     },
     "execution_count": 138,
     "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_table.tobs), func.avg(measurement_table.tobs), func.max(measurement_table.tobs)]\n",
    "    return session.query(*sel).filter(func.strftime(\"%m-%d\", measurement_table.date) == date).all()\n",
    "    \n",
    "daily_normals(\"01-01\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(58.0, 67.79661016949153, 77.0),\n",
       " (61.0, 68.14035087719299, 76.0),\n",
       " (59.0, 69.6842105263158, 80.0),\n",
       " (58.0, 70.38983050847457, 77.0),\n",
       " (60.0, 69.0, 77.0),\n",
       " (56.0, 66.75438596491227, 74.0),\n",
       " (57.0, 67.58928571428571, 75.0),\n",
       " (56.0, 68.41666666666667, 76.0),\n",
       " (61.0, 69.25, 79.0),\n",
       " (60.0, 69.15789473684211, 78.0),\n",
       " (58.0, 70.36206896551724, 80.0),\n",
       " (62.0, 70.89473684210526, 79.0)]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "start_date = \"2016-02-03\" \n",
    "end_date = \"2016-02-14\"\n",
    "\n",
    "\n",
    "# Use the start and end date to create a range of dates\n",
    "\n",
    "trip_date=pd.date_range(start_date,end_date)\n",
    "trip_date\n",
    "\n",
    "# Stip off the year and save a list of %m-%d strings\n",
    "\n",
    "trip_date_one=trip_date.strftime(\"%m-%d\")\n",
    "trip_date_one\n",
    "\n",
    "trip_list=[]\n",
    "\n",
    "# Loop through the list of %m-%d strings and calculate the normals for each date\n",
    "for x in trip_date_one:\n",
    "    trip_list.append(*daily_normals(x))\n",
    "trip_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "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>temp min</th>\n",
       "      <th>temp average</th>\n",
       "      <th>temp max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2016-02-03</td>\n",
       "      <td>58.0</td>\n",
       "      <td>67.796610</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-02-04</td>\n",
       "      <td>61.0</td>\n",
       "      <td>68.140351</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-02-05</td>\n",
       "      <td>59.0</td>\n",
       "      <td>69.684211</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-02-06</td>\n",
       "      <td>58.0</td>\n",
       "      <td>70.389831</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-02-07</td>\n",
       "      <td>60.0</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-02-08</td>\n",
       "      <td>56.0</td>\n",
       "      <td>66.754386</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-02-09</td>\n",
       "      <td>57.0</td>\n",
       "      <td>67.589286</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-02-10</td>\n",
       "      <td>56.0</td>\n",
       "      <td>68.416667</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-02-11</td>\n",
       "      <td>61.0</td>\n",
       "      <td>69.250000</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-02-12</td>\n",
       "      <td>60.0</td>\n",
       "      <td>69.157895</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-02-13</td>\n",
       "      <td>58.0</td>\n",
       "      <td>70.362069</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-02-14</td>\n",
       "      <td>62.0</td>\n",
       "      <td>70.894737</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            temp min  temp average  temp max\n",
       "date                                        \n",
       "2016-02-03      58.0     67.796610      77.0\n",
       "2016-02-04      61.0     68.140351      76.0\n",
       "2016-02-05      59.0     69.684211      80.0\n",
       "2016-02-06      58.0     70.389831      77.0\n",
       "2016-02-07      60.0     69.000000      77.0\n",
       "2016-02-08      56.0     66.754386      74.0\n",
       "2016-02-09      57.0     67.589286      75.0\n",
       "2016-02-10      56.0     68.416667      76.0\n",
       "2016-02-11      61.0     69.250000      79.0\n",
       "2016-02-12      60.0     69.157895      78.0\n",
       "2016-02-13      58.0     70.362069      80.0\n",
       "2016-02-14      62.0     70.894737      79.0"
      ]
     },
     "execution_count": 160,
     "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",
    "trip_list_df=pd.DataFrame(trip_list,columns=[\"temp min\",\"temp average\",\"temp max\"])\n",
    "trip_list_df['date']=trip_date\n",
    "trip_list_df.set_index(['date'],inplace=True)\n",
    "trip_list_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the daily normals as an area plot with `stacked=False`\n",
    "trip_list_df.plot(kind=\"area\")\n",
    "\n",
    "plt.ylabel(\"Temperature\")\n",
    "plt.savefig(\"Images_Output/AreaPlot.png\")"
   ]
  }
 ],
 "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": "3.7.4"
  },
  "nteract": {
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  },
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    "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"
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  }
 },
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}