temp_analysis_bonus_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "handed-tissue",
   "metadata": {},
   "source": [
    "# Temperature Analysis 2: Bonus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "limiting-explanation",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dependencies for matplotlib\n",
    "%matplotlib inline\n",
    "from matplotlib import style\n",
    "style.use('fivethirtyeight')\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "undefined-substitute",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import other dependencies\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "optional-demand",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import SQL Alchemy dependencies\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": 4,
   "id": "corrected-seeking",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the engine to the Hawaii SQLite Database\n",
    "engine = create_engine(\"sqlite:///Resources/hawaii.sqlite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "initial-creek",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a base and prepare the base\n",
    "Base = automap_base()\n",
    "Base.prepare(engine, reflect = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "coated-anthropology",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['measurement', 'station']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See the classes in the database\n",
    "Base.classes.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "novel-possible",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save a reference to the tables\n",
    "Station = Base.classes.station\n",
    "Measurement = Base.classes.measurement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "drawn-editor",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start a session\n",
    "session = Session(engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "unnecessary-parks",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function from provided starter code:\n",
    "# This function called `calc_temps` will accept start date and end date in the format '%Y-%m-%d' \n",
    "# and return the minimum, maximum, and average 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, TAVG, 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()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "corrected-territory",
   "metadata": {},
   "source": [
    "## Temperature Statistics:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "secret-honduras",
   "metadata": {},
   "source": [
    "### Example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "hydraulic-layout",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(72.0, 79.25, 83.0)]\n"
     ]
    }
   ],
   "source": [
    "start_date = '2017-08-01'\n",
    "end_date = '2017-08-07'\n",
    "\n",
    "# Find temperature between two dates\n",
    "temperature_stats = calc_temps(start_date, end_date)\n",
    "print(temperature_stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "assumed-sheffield",
   "metadata": {},
   "source": [
    "### User Input Version:"
   ]
  },
  {
   "cell_type": "raw",
   "id": "amateur-permission",
   "metadata": {},
   "source": [
    "# Input from user\n",
    "print(\"Let's find temperature statistics for a range of dates!\")\n",
    "print(\"Input dates in the format YYYY-mm-dd\")\n",
    "start_date = input(\"What date would you like to start with? \")\n",
    "end_date = input(\"What date would you like to end with? \")\n",
    "\n",
    "temperature_stats = calc_temps(start_date, end_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ongoing-indian",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert tuple to a list\n",
    "temp_stats = list(np.ravel(temperature_stats))\n",
    "\n",
    "# Create variables for the values\n",
    "average_temp = temp_stats[1]\n",
    "min_temp = temp_stats[0]\n",
    "max_temp = temp_stats[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "sacred-hypothesis",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temperature Statistics for 2017-08-01 to 2017-08-07\n",
      "Minimum Temperature: 72.0\n",
      "Maximum Temperature: 83.0\n",
      "Average Temperature: 79.25\n"
     ]
    }
   ],
   "source": [
    "# Print out results\n",
    "print(f\"Temperature Statistics for {start_date} to {end_date}\")\n",
    "print(f\"Minimum Temperature: {min_temp}\")\n",
    "print(f\"Maximum Temperature: {max_temp}\")\n",
    "print(f\"Average Temperature: {average_temp}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "crazy-citation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the average temperature\n",
    "plt.figure(figsize=(4,5))\n",
    "plt.bar(1,average_temp, width=0.2, align=\"center\", yerr=(max_temp-min_temp), color=\"coral\")\n",
    "plt.xlim(0.5,1.5)\n",
    "plt.ylim(0,100)\n",
    "plt.title(\"Trip Avg Temperature\")\n",
    "plt.ylabel(\"Temperature (F)\")\n",
    "plt.savefig(\"Images/bonus_2_trip_avg_temp.png\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "classified-begin",
   "metadata": {},
   "source": [
    "## Daily Rainfall Average:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "growing-legend",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function that will find the details of the rainfall at each station\n",
    "def calc_rainfall(start_date, end_date):\n",
    "    \"\"\"Precipitation and Station details 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",
    "        AVG Precipitation, Station, Station Name, Latitude, Longitude, Elevation\n",
    "    \"\"\"\n",
    "    \n",
    "    return session.query(func.avg(Measurement.prcp), Station.station, Station.name, Station.latitude, Station.longitude, Station.elevation).\\\n",
    "        filter(Measurement.date >= start_date).filter(Station.station == Measurement.station).\\\n",
    "        filter(Measurement.date <= end_date).order_by(func.avg(Measurement.prcp).desc()).group_by(Station.station).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "graduate-serbia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.12, 'USC00516128', 'MANOA LYON ARBO 785.2, HI US', 21.3331, -157.8025, 152.4),\n",
       " (0.03666666666666667, 'USC00514830', 'KUALOA RANCH HEADQUARTERS 886.9, HI US', 21.5213, -157.8374, 7.0),\n",
       " (0.02, 'USC00519281', 'WAIHEE 837.5, HI US', 21.45167, -157.84888999999998, 32.9),\n",
       " (0.005714285714285714, 'USC00519397', 'WAIKIKI 717.2, HI US', 21.2716, -157.8168, 3.0),\n",
       " (0.0, 'USC00519523', 'WAIMANALO EXPERIMENTAL FARM, HI US', 21.33556, -157.71139, 19.5)]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_date = \"2017-08-01\"\n",
    "end_date = \"2017-08-07\"\n",
    "\n",
    "# Find the rainfall for a range of dates\n",
    "rainfall = (calc_rainfall(start_date, end_date))\n",
    "rainfall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "confidential-excerpt",
   "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>avg prcp</th>\n",
       "      <th>station</th>\n",
       "      <th>name</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>elevation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.120000</td>\n",
       "      <td>USC00516128</td>\n",
       "      <td>MANOA LYON ARBO 785.2, HI US</td>\n",
       "      <td>21.33310</td>\n",
       "      <td>-157.80250</td>\n",
       "      <td>152.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.036667</td>\n",
       "      <td>USC00514830</td>\n",
       "      <td>KUALOA RANCH HEADQUARTERS 886.9, HI US</td>\n",
       "      <td>21.52130</td>\n",
       "      <td>-157.83740</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.020000</td>\n",
       "      <td>USC00519281</td>\n",
       "      <td>WAIHEE 837.5, HI US</td>\n",
       "      <td>21.45167</td>\n",
       "      <td>-157.84889</td>\n",
       "      <td>32.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.005714</td>\n",
       "      <td>USC00519397</td>\n",
       "      <td>WAIKIKI 717.2, HI US</td>\n",
       "      <td>21.27160</td>\n",
       "      <td>-157.81680</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>USC00519523</td>\n",
       "      <td>WAIMANALO EXPERIMENTAL FARM, HI US</td>\n",
       "      <td>21.33556</td>\n",
       "      <td>-157.71139</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avg prcp      station                                    name       lat  \\\n",
       "0  0.120000  USC00516128            MANOA LYON ARBO 785.2, HI US  21.33310   \n",
       "1  0.036667  USC00514830  KUALOA RANCH HEADQUARTERS 886.9, HI US  21.52130   \n",
       "2  0.020000  USC00519281                     WAIHEE 837.5, HI US  21.45167   \n",
       "3  0.005714  USC00519397                    WAIKIKI 717.2, HI US  21.27160   \n",
       "4  0.000000  USC00519523      WAIMANALO EXPERIMENTAL FARM, HI US  21.33556   \n",
       "\n",
       "         lng  elevation  \n",
       "0 -157.80250      152.4  \n",
       "1 -157.83740        7.0  \n",
       "2 -157.84889       32.9  \n",
       "3 -157.81680        3.0  \n",
       "4 -157.71139       19.5  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe out of the queried data\n",
    "rain_df = pd.DataFrame(rainfall, columns=[\"avg prcp\", \"station\", \"name\", \"lat\", \"lng\", \"elevation\"])\n",
    "rain_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "specific-isolation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a list for station ids\n",
    "list_of_stations = []\n",
    "\n",
    "# Create a list for avg precipitation\n",
    "avg_prcp = rain_df[\"avg prcp\"]\n",
    "\n",
    "# Append to the list of station ids\n",
    "for x in rain_df[\"station\"]:\n",
    "    list_of_stations.append(x)\n",
    "\n",
    "# Create a bar chart to show the difference in avg precipitation between each station\n",
    "plt.figure(figsize=(7,4))\n",
    "plt.bar(list_of_stations, avg_prcp)\n",
    "plt.title(\"Avg Precipitation by Station ID\")\n",
    "plt.xlabel(\"Station\")\n",
    "plt.ylabel(\"Avg Precipitation (Inches)\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.savefig(\"Images/bonus_2_avg_prcp.png\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "occupational-chase",
   "metadata": {},
   "source": [
    "## Daily Temperature Normals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "formal-teens",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function provided from starter notebook\n",
    "# Use this function to 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",
    "# For example\n",
    "normal_temp = daily_normals(\"01-01\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "employed-macedonia",
   "metadata": {},
   "source": [
    "### User Input Version:"
   ]
  },
  {
   "cell_type": "raw",
   "id": "simplified-arrow",
   "metadata": {},
   "source": [
    "# Input from user\n",
    "print(\"Let's find daily normal temperature statistics for a month and date!\")\n",
    "print(\"Input dates in the format mm-dd\")\n",
    "date = input(\"What date would you like to look at? \")\n",
    "\n",
    "normal_temp = daily_normals(date)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "rising-pointer",
   "metadata": {},
   "source": [
    "# calculate the daily normals for your trip\n",
    "# push each tuple of calculations into a list called `normals`\n",
    "daily_normals_temp = list(np.ravel(normal_temp))\n",
    "\n",
    "# Create variables for the values\n",
    "average_temp = normals[1]\n",
    "min_temp = normals[0]\n",
    "max_temp = normals[2]"
   ]
  },
  {
   "cell_type": "raw",
   "id": "assumed-witness",
   "metadata": {},
   "source": [
    "# Print out results\n",
    "print(f\"Temperature Statistics for {date}\")\n",
    "print(f\"Minimum Temperature: {min_temp}\")\n",
    "print(f\"Maximum Temperature: {max_temp}\")\n",
    "print(f\"Average Temperature: {average_temp}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "modified-rings",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the start and end date of the trip\n",
    "start_date = '2017-08-01'\n",
    "end_date = '2017-08-07'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "first-maine",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['08-01', '08-02', '08-03', '08-04', '08-05', '08-06', '08-07']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the start and end date to create a range of dates\n",
    "start = dt.datetime.strptime(start_date, \"%Y-%m-%d\")\n",
    "end = dt.datetime.strptime(end_date, \"%Y-%m-%d\")\n",
    "\n",
    "# Find the number of dates in the range\n",
    "num_of_dates = (end - start).days + 1\n",
    "\n",
    "# Create a list of the dates range\n",
    "dates_range = []\n",
    "\n",
    "# Loop through and append each date to the list\n",
    "# Strip off the year and save a list of strings in the format %m-%d\n",
    "for x in range(num_of_dates):\n",
    "    dates = start + dt.timedelta(days=x)\n",
    "    dates_month_day = dt.datetime.strftime(dates, \"%m-%d\")\n",
    "    dates_range.append(dates_month_day)\n",
    "    \n",
    "dates_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "several-classic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[67.0, 75.54, 83.0],\n",
       " [68.0, 75.60377358490567, 84.0],\n",
       " [70.0, 76.61111111111111, 85.0],\n",
       " [69.0, 76.71153846153847, 84.0],\n",
       " [69.0, 76.14814814814815, 82.0],\n",
       " [67.0, 76.25, 83.0],\n",
       " [71.0, 77.15686274509804, 83.0]]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the `daily_normals` function to calculate the normals for each date string \n",
    "# and append the results to a list called `normals`.\n",
    "normals = []\n",
    "\n",
    "# Loop through and calculate the data, and append it to a list without the tuple\n",
    "for x in dates_range:\n",
    "    normal_temp = daily_normals(x)\n",
    "    normals.append(list(np.ravel(normal_temp)))\n",
    "\n",
    "normals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "novel-interest",
   "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>min</th>\n",
       "      <th>avg</th>\n",
       "      <th>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",
       "      <th>08-01</th>\n",
       "      <td>67.0</td>\n",
       "      <td>75.540000</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-02</th>\n",
       "      <td>68.0</td>\n",
       "      <td>75.603774</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-03</th>\n",
       "      <td>70.0</td>\n",
       "      <td>76.611111</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-04</th>\n",
       "      <td>69.0</td>\n",
       "      <td>76.711538</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-05</th>\n",
       "      <td>69.0</td>\n",
       "      <td>76.148148</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-06</th>\n",
       "      <td>67.0</td>\n",
       "      <td>76.250000</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-07</th>\n",
       "      <td>71.0</td>\n",
       "      <td>77.156863</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        min        avg   max\n",
       "date                        \n",
       "08-01  67.0  75.540000  83.0\n",
       "08-02  68.0  75.603774  84.0\n",
       "08-03  70.0  76.611111  85.0\n",
       "08-04  69.0  76.711538  84.0\n",
       "08-05  69.0  76.148148  82.0\n",
       "08-06  67.0  76.250000  83.0\n",
       "08-07  71.0  77.156863  83.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe of temperature averages\n",
    "df = pd.DataFrame(normals, columns=[\"min\", \"avg\", \"max\"])\n",
    "df_date = pd.DataFrame(dates_range, columns=[\"date\"])\n",
    "df_merged = pd.concat([df, df_date], axis=1)\n",
    "\n",
    "df_merged.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "expensive-professor",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list for the xtick positions\n",
    "x = [x for x in range(num_of_dates)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "spiritual-oklahoma",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create an area plot to show the daily temperatures\n",
    "df_merged.plot.area(stacked=False)\n",
    "plt.xticks(x, dates_range, rotation=\"vertical\")\n",
    "plt.title(\"Daily Temperature\")\n",
    "plt.xlabel(\"Dates\")\n",
    "plt.ylabel(\"Temperature\")\n",
    "plt.savefig(\"Images/bonus_2_daily_temp.png\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "convenient-former",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Close the session \n",
    "session.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:PythonData] *",
   "language": "python",
   "name": "conda-env-PythonData-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}