US_Births_DOW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## U.S. Births for Each Day of the Week from 1994 - 2003\n",
    "\n",
    "In this project, I'll be working with the data set from the Centers for Disease Control and Prevention's National Center for Health Statistics to tally the births for each corresponding day of the week from 1994 - 2003.\n",
    "\n",
    "The data set has the following structure:\n",
    "\n",
    " - year - Year\n",
    " - month - Month\n",
    " - date_of_month - Day number of the month\n",
    " - day_of_week - Day of week, where 1 is Monday and 7 is Sunday\n",
    " - births - Number of births\n",
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Open the CSV file in read mode and assign to a variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "f = open(\"births.csv\").read()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the data on the new-line character to create a list of lists and assign to a variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rows = f.split(\"\\n\")    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a new dictionary and add extracted values from day of the week and corresponding total births for that day to the dictionary. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('Monday', '1'), ('Tuesday', '2'), ('Wednesday', '3'), ('Thursday', '4'), ('Friday', '5'), ('Saturday', '6'), ('Sunday', '7')])\n",
      "\n",
      "OrderedDict([('1', 5789166), ('2', 6446196), ('3', 6322855), ('4', 6288429), ('5', 6233657), ('6', 4562111), ('7', 4079723)])\n",
      "\n",
      "OrderedDict([('Monday', 5789166), ('Tuesday', 6446196), ('Wednesday', 6322855), ('Thursday', 6288429), ('Friday', 6233657), ('Saturday', 4562111), ('Sunday', 4079723)])\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import collections\n",
    "\n",
    "# new empty dictionary created\n",
    "days_counts = {}\n",
    "\n",
    "rows_no_header = rows[1:]\n",
    "for row in rows_no_header:\n",
    "    split_row = row.split(\",\")\n",
    "    # extract the number of the day of the week (i.e., 0 = Sunday, 1 = Monday)\n",
    "    dow = split_row[3]\n",
    "    # extract the total births for that day of the week\n",
    "    births = int(split_row[4])\n",
    "    # check if there is a day (number) corresponding to the total births\n",
    "    # if so, add day and total births to new dictionary, or just add total births\n",
    "    if dow in days_counts:\n",
    "        days_counts[dow] = days_counts[dow] + births\n",
    "    else:\n",
    "        days_counts[dow] = births\n",
    "\n",
    "# created new dictionary of days of the week as key sorted by numbered value\n",
    "day_names = {'Monday': '1', 'Tuesday': '2', 'Wednesday': '3', 'Thursday': '4', 'Friday': '5', 'Saturday': '6', 'Sunday': '7'}\n",
    "\n",
    "# turned 'day_names' and 'days_counts' dictionaries into a sorted OrderedDict\n",
    "sorted_dn = collections.OrderedDict(sorted(day_names.items(), key=lambda x: x[1]))\n",
    "sorted_dc = collections.OrderedDict(sorted(days_counts.items(), key=lambda x: x[0]))\n",
    "\n",
    "# used key from 'sorted_dn' and value from 'sorted_dc' to create a new OrderedDict\n",
    "sorted_count = collections.OrderedDict((k, sorted_dc.get(v)) for k, v in sorted_dn.items() if v in sorted_dc)\n",
    "\n",
    "print(sorted_dn)\n",
    "print()\n",
    "print(sorted_dc)\n",
    "print()\n",
    "print(sorted_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total births for each day of the week from 1994 - 2003:\n",
    "Monday: 5,789,166\n",
    "\n",
    "Tuesday:\t6,446,196\n",
    "\n",
    "Wednesday:\t6,322,855\n",
    "\n",
    "Thursday:\t6,288,429\n",
    "\n",
    "Friday:\t    6,233,657\n",
    "\n",
    "Saturday:\t4,562,111\n",
    "\n",
    "Sunday:\t    4,079,723\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar chart showing counts by days of the week\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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NDaKiomAymZCTk6OUezwepKWlwWg0Ys6cOThy5IiyzW63w2QyITw8HEVFRUp5Q0MD4uPj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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61244a6e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(9, 3))\n",
    "plt.bar(range(len(sorted_count)), list(sorted_count.values()), align='center', width=0.3)\n",
    "plt.xticks(range(len(sorted_count)), list(sorted_count.keys()))\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}