awindnagel-practice-data-structures.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Earth Analytics Bootcamp - Practice Activity on Data Structures\n",
    "\n",
    "**Author:** Ann Windnagel\n",
    "**Date:** 13 August 2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2 - Import Python Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "import of packages successful\n"
     ]
    }
   ],
   "source": [
    "# import necessary Python packages\n",
    "import os\n",
    "import numpy as np\n",
    "import urllib.request\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# print message after packages imported successfully\n",
    "print(\"import of packages successful\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 3 - Create List of Data Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0, 2.0, 5.0, 9.56, 14.39, 21.72, 16.72, 11.61, 4.89, 0.99]\n"
     ]
    }
   ],
   "source": [
    "# create a list of average monthly temperature (Celsius) in Boulder, CO\n",
    "avg_monthly_temp_c = [0.0, 2.00, 5.0, 9.56, 14.39, 21.72, 16.72, 11.61, 4.89, 0.99]\n",
    "\n",
    "# print the list; note that there are only 10 data points because June and July were missing from the original data\n",
    "print(avg_monthly_temp_c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 4 - Insert Values Into a Python List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0, 2.0, 5.0, 9.56, 14.39, 19.56, 21.72, 16.72, 11.61, 4.89, 0.99]\n",
      "[0.0, 2.0, 5.0, 9.56, 14.39, 19.56, 22.78, 21.72, 16.72, 11.61, 4.89, 0.99]\n"
     ]
    }
   ],
   "source": [
    "# insert june data to list\n",
    "avg_monthly_temp_c.insert(5, 19.56)\n",
    "\n",
    "# print list with june data\n",
    "print(avg_monthly_temp_c)\n",
    "\n",
    "# insert june data to list\n",
    "avg_monthly_temp_c.insert(6, 22.78)\n",
    "\n",
    "# print list with june data\n",
    "print(avg_monthly_temp_c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 5 - Manually Create Numpy Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.2 2.3 3.4 4.5 5.6]\n",
      "[ 0.    2.    5.    9.56 14.39 19.56 22.78 21.72 16.72 11.61  4.89  0.99]\n",
      "(12,)\n"
     ]
    }
   ],
   "source": [
    "# manually create a numpy array with some random typed in values\n",
    "manual_array = np.array([1.2, 2.3, 3.4, 4.5, 5.6])\n",
    "\n",
    "# print the array\n",
    "print(manual_array)\n",
    "\n",
    "# manually create a numpy array from the list created in question 4\n",
    "array_from_list = np.array(avg_monthly_temp_c)\n",
    "\n",
    "# print the array\n",
    "print(array_from_list)\n",
    "\n",
    "# print the shape\n",
    "print(array_from_list.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quesion 6 - Download Text File and Import Into Numpy Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\windnage\\\\earth-analytics-bootcamp'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set the working directory to my `earth-analytics-bootcamp` directory\n",
    "os.chdir(\"C:/Users/windnage/earth-analytics-bootcamp/\")\n",
    "\n",
    "# print the current working directory\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datasets downloaded successfully\n"
     ]
    }
   ],
   "source": [
    "# use `urllib` to download files from Earth Lab figshare repository\n",
    "# download .txt containing monthly average temp for Boulder, CO\n",
    "urllib.request.urlretrieve(url = \"https://ndownloader.figshare.com/files/12732467\", \n",
    "                           filename = \"data/avg-monthly-temp.txt\")\n",
    "\n",
    "# print message that data downloads were successful\n",
    "print(\"datasets downloaded successfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.    2.    5.    9.56 14.39 19.56 22.78 21.72 16.72 11.61  4.89  0.99]\n",
      "(12,)\n"
     ]
    }
   ],
   "source": [
    "# create array from the average monthly temp text file\n",
    "avg_monthly_temp_c_array = np.loadtxt(fname = \"C:/Users/windnage/earth-analytics-bootcamp/data/avg-monthly-temp.txt\")\n",
    "\n",
    "# print the array\n",
    "print(avg_monthly_temp_c_array)\n",
    "\n",
    "# print the shape of the array\n",
    "print(avg_monthly_temp_c_array.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quesion 7 -Select and Summarize Data From Numpy Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5.    9.56 14.39]\n",
      "(3,)\n",
      "mean of average monthly precipitation: 9.65\n",
      "[16.72 11.61  4.89]\n",
      "(3,)\n",
      "mean of average monthly precipitation: 11.073333333333332\n"
     ]
    }
   ],
   "source": [
    "# select the data for Spring months (Mar, Apr, and May) into an array\n",
    "spring = avg_monthly_temp_c_array[2:5]\n",
    "\n",
    "# print spring\n",
    "print(spring)\n",
    "\n",
    "# print shape of spring\n",
    "print(spring.shape)\n",
    "\n",
    "# calculate and print the mean temp for spring months\n",
    "print(\"mean of average monthly precipitation:\", np.mean(spring))\n",
    "\n",
    "# select the data for Fall months (Sep, Oct, and Nov) into an array\n",
    "fall = avg_monthly_temp_c_array[8:11]\n",
    "\n",
    "# print spring\n",
    "print(fall)\n",
    "\n",
    "# print shape of spring\n",
    "print(fall.shape)\n",
    "\n",
    "# calculate and print the mean temp for spring months\n",
    "print(\"mean of average monthly precipitation:\", np.mean(fall))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 8 - Manually Create Pandas Dataframes"
   ]
  },
  {
   "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>month</th>\n",
       "      <th>temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr</td>\n",
       "      <td>9.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</td>\n",
       "      <td>14.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jun</td>\n",
       "      <td>19.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jul</td>\n",
       "      <td>22.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug</td>\n",
       "      <td>21.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sep</td>\n",
       "      <td>16.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oct</td>\n",
       "      <td>11.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nov</td>\n",
       "      <td>4.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dec</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   month   temp\n",
       "0    Jan   0.00\n",
       "1    Feb   2.00\n",
       "2    Mar   5.00\n",
       "3    Apr   9.56\n",
       "4    May  14.39\n",
       "5    Jun  19.56\n",
       "6    Jul  22.78\n",
       "7    Aug  21.72\n",
       "8    Sep  16.72\n",
       "9    Oct  11.61\n",
       "10   Nov   4.89\n",
       "11   Dec   0.99"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# manuallly create a dataframe\n",
    "avg_temp_dataframe = pd.DataFrame( columns=[\"month\", \"temp\"], data=[ [\"Jan\", 0.0], [\"Feb\", 2.00], [\"Mar\", 5.00], [\"Apr\", 9.56\n",
    "], [\"May\", 14.39], [\"Jun\", 19.56], [\"Jul\", 22.78], [\"Aug\", 21.72], [\"Sep\", 16.72], [\"Oct\", 11.61], [\"Nov\", 4.89], [\"Dec\", 0.99] ] )\n",
    "\n",
    "# print\n",
    "avg_temp_dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 9 - Download CSV File and Import Into Pandas Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datasets downloaded successfully\n"
     ]
    }
   ],
   "source": [
    "# use `urllib` to download files from Earth Lab figshare repository\n",
    "# download .txt containing monthly average precipitation for Boulder, CO\n",
    "urllib.request.urlretrieve(url = \"https://ndownloader.figshare.com/files/12739457\", \n",
    "                           filename = \"data/avg-temp-months-seasons.csv\")\n",
    "\n",
    "# print message that data downloads were successful\n",
    "print(\"datasets downloaded successfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>months</th>\n",
       "      <th>temp</th>\n",
       "      <th>seasons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar</td>\n",
       "      <td>5.00</td>\n",
       "      <td>Spring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr</td>\n",
       "      <td>9.56</td>\n",
       "      <td>Spring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</td>\n",
       "      <td>14.39</td>\n",
       "      <td>Spring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>June</td>\n",
       "      <td>19.56</td>\n",
       "      <td>Summer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>July</td>\n",
       "      <td>22.78</td>\n",
       "      <td>Summer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug</td>\n",
       "      <td>21.72</td>\n",
       "      <td>Summer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sept</td>\n",
       "      <td>16.72</td>\n",
       "      <td>Fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oct</td>\n",
       "      <td>11.61</td>\n",
       "      <td>Fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nov</td>\n",
       "      <td>4.89</td>\n",
       "      <td>Fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dec</td>\n",
       "      <td>0.99</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   months   temp seasons\n",
       "0     Jan   0.00  Winter\n",
       "1     Feb   2.00  Winter\n",
       "2     Mar   5.00  Spring\n",
       "3     Apr   9.56  Spring\n",
       "4     May  14.39  Spring\n",
       "5    June  19.56  Summer\n",
       "6    July  22.78  Summer\n",
       "7     Aug  21.72  Summer\n",
       "8    Sept  16.72    Fall\n",
       "9     Oct  11.61    Fall\n",
       "10    Nov   4.89    Fall\n",
       "11    Dec   0.99  Winter"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import the seasonal average temp values as a pandas dataframe\n",
    "seasonal_temp = pd.read_csv(\"C:/Users/windnage/earth-analytics-bootcamp/data/avg-temp-months-seasons.csv\")\n",
    "\n",
    "# print the data frame\n",
    "seasonal_temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 10: Select and Summarize Data From Pandas Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.996667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.495000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.495000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           temp\n",
       "count  3.000000\n",
       "mean   0.996667\n",
       "std    1.000017\n",
       "min    0.000000\n",
       "25%    0.495000\n",
       "50%    0.990000\n",
       "75%    1.495000\n",
       "max    2.000000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select the winter months and assign to new data frame\n",
    "winter = seasonal_temp[seasonal_temp.seasons == \"Winter\"]\n",
    "\n",
    "# print summary stats for winter\n",
    "winter.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.695647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11.975000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14.390000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            temp\n",
       "count   3.000000\n",
       "mean    9.650000\n",
       "std     4.695647\n",
       "min     5.000000\n",
       "25%     7.280000\n",
       "50%     9.560000\n",
       "75%    11.975000\n",
       "max    14.390000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select the spring months and assign to new data frame\n",
    "spring = seasonal_temp[seasonal_temp.seasons == \"Spring\"]\n",
    "\n",
    "# print spring stats\n",
    "spring.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>21.353333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.641016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>19.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>21.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22.780000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            temp\n",
       "count   3.000000\n",
       "mean   21.353333\n",
       "std     1.641016\n",
       "min    19.560000\n",
       "25%    20.640000\n",
       "50%    21.720000\n",
       "75%    22.250000\n",
       "max    22.780000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select the summer months and assign to new data frame\n",
    "summer = seasonal_temp[seasonal_temp.seasons == \"Summer\"]\n",
    "\n",
    "# print summer stats\n",
    "summer.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.073333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.933231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.890000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11.610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>14.165000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16.720000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            temp\n",
       "count   3.000000\n",
       "mean   11.073333\n",
       "std     5.933231\n",
       "min     4.890000\n",
       "25%     8.250000\n",
       "50%    11.610000\n",
       "75%    14.165000\n",
       "max    16.720000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select the fall months and assign to new data frame\n",
    "fall = seasonal_temp[seasonal_temp.seasons == \"Fall\"]\n",
    "\n",
    "# print fall stats\n",
    "fall.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 11: Plot Data From Pandas Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Season</th>\n",
       "      <th>mean_temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Winter</td>\n",
       "      <td>0.996667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spring</td>\n",
       "      <td>9.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Summer</td>\n",
       "      <td>21.353333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fall</td>\n",
       "      <td>11.073333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Season  mean_temp\n",
       "0  Winter   0.996667\n",
       "1  Spring   9.650000\n",
       "2  Summer  21.353333\n",
       "3    Fall  11.073333"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a new dataframe with the mean temps of the seasons\n",
    "seasonal_mean_temps = pd.DataFrame( columns=[\"Season\", \"mean_temp\"], data=[ [\"Winter\", 0.996667], [\"Spring\", 9.650000], [\"Summer\", 21.353333], [\"Fall\", 11.073333] ] )\n",
    "\n",
    "# print mean temps\n",
    "seasonal_mean_temps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a scatter plot of monthly snowfall for 2017 for Boulder, CO\n",
    "# set plot size for all plots that follow\n",
    "plt.rcParams[\"figure.figsize\"] = (8, 8)\n",
    "\n",
    "# create the plot space upon which to plot the data\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# add the x-axis and the y-axis to the plot\n",
    "ax.bar(seasonal_mean_temps.Season, seasonal_mean_temps.mean_temp, color=\"grey\")\n",
    "\n",
    "# set plot title\n",
    "ax.set(title=\"Average Seasonal Temp\")\n",
    "\n",
    "# add labels to the axes\n",
    "ax.set(xlabel=\"Season\", ylabel=\"Temp (C)\");"
   ]
  }
 ],
 "metadata": {
  "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}