nrosenbloom-practice-data-structures.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Earth Analytics Bootcamp - Practice Activity on Data Structures\n",
    "**Author** Nan Rosenbloom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import the software packages that I need.  These include os, pandas, numpy, matplotlib, and urllib.request"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "import of packages successful\n"
     ]
    }
   ],
   "source": [
    "# Import the packages I'll need.\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "import urllib.request\n",
    "\n",
    "# print message after packages imported successfully\n",
    "print(\"import of packages successful\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a Python list of the average monthly temperatures, and a second list of the corresponding names of the months.  Notice that June and July are missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  create a Python list of the average monthly temperature (C) in Boulder.\n",
    "months = [\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Aug\",\"Sept\",\"Oct\",\"Nov\",\"Dec\"]\n",
    "temps = [0.0,2.00,5.0,9.56,14.39,21.72,16.72,11.61,4.89,0.99]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check what months are missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Aug', 'Sept', 'Oct', 'Nov', 'Dec']\n"
     ]
    }
   ],
   "source": [
    "# print the contents of the months list\n",
    "print(months)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": [
    "# print the contents of the temperatures list\n",
    "print(temps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the length of the months file.  Missing some months!\n",
    "len(months)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(temps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Insert the missing months (June, July), by referencing the appropriate indexing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# insert the missing months of June and July.\n",
    "months.insert(5,\"June\")\n",
    "months.insert(6,\"July\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Print the completed month listing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Jan', 'Feb', 'Mar', 'Apr', 'May', 'June', 'July', 'Aug', 'Sept', 'Oct', 'Nov', 'Dec']\n"
     ]
    }
   ],
   "source": [
    "print(months)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Insert the missing temperate values for June and July, using the appropriate indexing (zero-based)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# insert the missing temperatures for the months of June and July.\n",
    "temps.insert(5,19.56)\n",
    "temps.insert(6,22.78)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Complete monthly mean temperature values (degrees C)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[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": [
    "print(temps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a 1D array  using  numpy.array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "temparr = np.array([0.0, 2.0, 5.0, 9.56, 14.39, 19.56, 22.78, 21.72, 16.72, 11.61, 4.89, 0.99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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"
     ]
    }
   ],
   "source": [
    "print(temparr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  #### Create a new array using the one I just created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "temparr2 = np.array(temparr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "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"
     ]
    }
   ],
   "source": [
    "print(temparr2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download data - 2 step process\n",
    "### 1.   Set my working directory, then print working directory to confirm that it is set correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/nanr/work/earth-analytics-bootcamp'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set the working directory to the `earth-analytics-bootcamp` directory\n",
    "# replace `jpalomino` with your username here and all paths in this lesson\n",
    "os.chdir(\"/Users/nanr/work/earth-analytics-bootcamp/\")\n",
    "\n",
    "# print the current working directory\n",
    "os.getcwd()\n",
    "'/Users/nanr/work/earth-analytics-bootcamp'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.  Download the data from the figshare repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datasets downloaded successfully\n"
     ]
    }
   ],
   "source": [
    "# use `urllib` download files from Earth Lab figshare repository\n",
    "\n",
    "# download .csv containing monthly average temperature for Boulder, CO\n",
    "urllib.request.urlretrieve(url = \"https://ndownloader.figshare.com/files/12732467\", \n",
    "                           filename = \"data/avg-monthly-temp.txt\")\n",
    "\n",
    "\n",
    "# print message that data downloads were successful\n",
    "print(\"datasets downloaded successfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datasets downloaded successfully\n"
     ]
    }
   ],
   "source": [
    "# use `urllib` download files from Earth Lab figshare repository\n",
    "\n",
    "# download .csv containing monthly average temperature for Boulder, CO\n",
    "urllib.request.urlretrieve(url = \"https://ndownloader.figshare.com/files/12732467\", \n",
    "                           filename = \"data/avg-monthly-temp.txt\")\n",
    "\n",
    "\n",
    "# print message that data downloads were successful\n",
    "print(\"datasets downloaded successfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('data/months.txt', <http.client.HTTPMessage at 0x10e5ad550>)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "urllib.request.urlretrieve(url = \"https://ndownloader.figshare.com/files/12565619\", \n",
    "                           filename = \"data/months.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datasets downloaded successfully\n"
     ]
    }
   ],
   "source": [
    "tavg = np.loadtxt(fname=\"data/avg-monthly-temp.txt\")\n",
    "\n",
    "# print message that data downloads were successful\n",
    "print(\"datasets downloaded successfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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"
     ]
    }
   ],
   "source": [
    "print(tavg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(tavg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "mam_temp = np.array(tavg[2:5,])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5.    9.56 14.39]\n"
     ]
    }
   ],
   "source": [
    "print(mam_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "son_temp = np.array(tavg[8:11,])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16.72 11.61  4.89]\n"
     ]
    }
   ],
   "source": [
    "print(son_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "arraylist = [mam_temp,son_temp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([ 5.  ,  9.56, 14.39]), array([16.72, 11.61,  4.89])]\n"
     ]
    }
   ],
   "source": [
    "print(arraylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.65\n",
      "11.073333333333332\n"
     ]
    }
   ],
   "source": [
    "for m in arraylist:\n",
    "    mtmp = np.mean(m)\n",
    "    print(mtmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "mon_temp = pd.DataFrame( columns=[\"Months\", \"Temp\"], data=[[\"January\", 0.0], [\"February\", 2.0], [\"March\", 5.0],[\"April\",9.56],[\"May\",14.39],[\"June\",19.56],[\"July\",22.78],[\"August\",21.72],[\"September\",16.72],[\"October\",11.61],[\"November\",4.89],[\"December\",\"0.99\"]] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Months   Temp\n",
      "0     January      0\n",
      "1    February      2\n",
      "2       March      5\n",
      "3       April   9.56\n",
      "4         May  14.39\n",
      "5        June  19.56\n",
      "6        July  22.78\n",
      "7      August  21.72\n",
      "8   September  16.72\n",
      "9     October  11.61\n",
      "10   November   4.89\n",
      "11   December   0.99\n"
     ]
    }
   ],
   "source": [
    "print(mon_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datasets downloaded successfully\n"
     ]
    }
   ],
   "source": [
    "# use `urllib` download files from Earth Lab figshare repository\n",
    "\n",
    "# download .csv containing monthly average temperature for Boulder, CO\n",
    "urllib.request.urlretrieve(url = \"https://ndownloader.figshare.com/files/12739457\", \n",
    "                           filename = \"data/avg-temp-months-seasons.csv\")\n",
    "\n",
    "\n",
    "# print message that data downloads were successful\n",
    "print(\"datasets downloaded successfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_seas = pd.read_csv(\"data/avg-temp-months-seasons.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   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\n"
     ]
    }
   ],
   "source": [
    "print(temp_seas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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