Summary Statistics - Distributions - Traveling to Acadia.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f9a8fe37",
   "metadata": {},
   "source": [
    "# Traveling to Acadia\n",
    "\n",
    "Imagine you work at a travel agency and want to inform your customers of the best time to visit one of your favorite vacation locations, Acadia, Maine. \n",
    "\n",
    "In this project, you will use flower bloom data, and flight information to recommend the best time of year for someone to make a trip to Maine.\n",
    "\n",
    "\n",
    "-    `in_bloom` is a NumPy array containing 900 numbers. Each number is a day of the year, which represents the average start date of a flower blooming.\n",
    "-    `flights` is a NumPy array with 11,000 numbers. Each number is a day of the year, which represents a flight from your hometown to airports near Acadia, Maine.\n",
    "\n",
    "Your goal is to create two histograms, each displaying the frequency of an occurrence each day of the year (either flights or flower blooms).\n",
    "\n",
    "You will use the `in_bloom` variable to find a count of the number of flowers that start blooming each day of the year.\n",
    "\n",
    "You will use the `flights` variable to find a count of the number of flights that occur each day of the year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f91420db",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bfaf32d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load in data\n",
    "in_bloom = np.loadtxt(open(\"in-bloom.csv\"), delimiter=\",\")\n",
    "flights = np.loadtxt(open(\"flights.csv\"), delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "82e68743",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45.0, 349.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bloom_min = np.amin(in_bloom)\n",
    "bloom_max = np.amax(in_bloom)\n",
    "bloom_min, bloom_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "566b8adb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0, 365.0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights_min = np.amin(flights)\n",
    "flights_max = np.amax(flights)\n",
    "flights_min, flights_max"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44447cdf",
   "metadata": {},
   "source": [
    "## Making a Histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4ac81ed",
   "metadata": {},
   "source": [
    "1. Use Matplotlib to create a flights histogram.\n",
    "2. If you haven’t done so already, set the range of your histogram to `(0, 365)`\n",
    "3. Set the number of bins in your plot to 365, so you have a separate bin for each day of the year.\n",
    "4. Add an x-label, y-label, and title to your figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0b7394ab",
   "metadata": {
    "scrolled": true
   },
   "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": [
    "# Use Matplotlib to create a flights histogram.\n",
    "plt.hist(flights, range = (0, 365), bins = 365)\n",
    "plt.title('Number of flights to Acadia per day of year')\n",
    "plt.xlabel('Day of the year')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b464bdb6",
   "metadata": {},
   "source": [
    "## Multiple Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8183344c",
   "metadata": {},
   "source": [
    "5. Now, we’re going to set up our figure so it displays two plots at once. Above your `plt.hist()` line, add the following:\n",
    "\n",
    "        plt.figure(1)\n",
    "        plt.subplot(211)\n",
    "\n",
    "6. Between the last line for plotting your histogram and the show command, add `plt.subplot(212)`.\n",
    "\n",
    "    At this point, your code should look something like:\n",
    "\n",
    "        plt.figure(1)\n",
    "        plt.subplot(211)\n",
    "\n",
    "        plt.hist(flights, range=(0, 365), bins=365)\n",
    "        plt.title(\"Flights by Day\")\n",
    "        plt.xlabel(\"Day of the Year\")\n",
    "        plt.ylabel(\"Flight Count\")\n",
    "\n",
    "        plt.subplot(212)\n",
    "        # Next histogram below\n",
    " \n",
    " \n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d75f6dc6",
   "metadata": {},
   "source": [
    "## Second Histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdc41c79",
   "metadata": {},
   "source": [
    "7. Under `plt.subplot(212)`, use `lt.hist()` to make a histogram that displays the number of flowers that begin to bloom each day of the year.\n",
    "8. Label the y-axis of the plot. Right before calling `plt.show()` call `plt.tight_layout()`. This will prevent the labels from overlapping with the graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a7694cd1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(211)\n",
    "\n",
    "plt.figure(1)\n",
    "plt.subplot(211)\n",
    " \n",
    "plt.hist(flights, range=(0, 365), bins=365)\n",
    "plt.title(\"Flights to Acadia by Day\")\n",
    "plt.xlabel(\"Day of the Year\")\n",
    "plt.ylabel(\"Flight Count\")\n",
    " \n",
    "plt.subplot(212)\n",
    "# Next histogram below\n",
    "\n",
    "plt.hist(in_bloom, range = (0, 365), bins = 365)\n",
    "plt.title(\"Flowers starting to bloom by Day\")\n",
    "plt.xlabel('Day of the Year')\n",
    "plt.ylabel('Bloom Count')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61b03d22",
   "metadata": {},
   "source": [
    "## Thinking About the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fe239cf",
   "metadata": {},
   "source": [
    "9. How would you use these histograms to help inform customers when they should go to Acadia, Maine. For example, if someone said they wanted to visit Acadia while there weren’t many people there, but while flowers were blooming, when would you recommend?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d41e1b0",
   "metadata": {},
   "source": [
    "*Convert the day of the year to month/week, go between day 250 and day 300 to get the less busy flights and most blooms, or the days leading up to 150 to get the most blooms, but crowded flights.*\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}