DataSciUF Tutorial - Instructor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Basics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Syntax\n",
    "Python is an object oriented scripting language and does not require a specific first or last line (such as <code>public static void main</code> in Java or <code>return</code> in C).\n",
    "\n",
    "There are no curly braces {} to define code blocks or semi-colons ; to end a line.  Instead of braces, indentation is rigidly enforced to create a block of code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "# This is a comment\n",
    "\n",
    "if (3 < 2):\n",
    "    print \"True\" # Another Comment.  This print syntax only works in Python 2, not 3\n",
    "else:\n",
    "        print \"False\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Arbitrary indentation can be used within a code block, as long as the indentation is consistent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "unexpected indent (<ipython-input-10-6e3880df02f0>, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-10-6e3880df02f0>\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m    print \"Deep Trouble:\"\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n"
     ]
    }
   ],
   "source": [
    "if (1 == 1):\n",
    "        print \"We're in \"\n",
    "            print \"Deep Trouble:\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This works \n",
      "just fine.\n"
     ]
    }
   ],
   "source": [
    "if (0 > -1):\n",
    "            print \"This works \"\n",
    "            print \"just fine.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variables and Types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variables can be given alphanumeric names beginning with an underscore or letter.  Variable types do not have to be declared and are inferred at run time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'int'>\n"
     ]
    }
   ],
   "source": [
    "a = 1\n",
    "print type(a) # Built in function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'float'>\n"
     ]
    }
   ],
   "source": [
    "b = 2.5 \n",
    "print type(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Strings can be declared with either single or double quotes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go Gators\n",
      "<type 'str'>\n"
     ]
    }
   ],
   "source": [
    "c1 = \"Go \"\n",
    "c2 = 'Gators'\n",
    "c3 = c1 + c2\n",
    "print c3\n",
    "print type(c3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The scope of variables is local to the function, class, and file in that increasing order of scope.  Global variables can also be declared."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b used to be 2.5\n",
      "Now b is 3.5\n"
     ]
    }
   ],
   "source": [
    "print \"b used to be\", b # Prints arguments with a space separator \n",
    "\n",
    "# Our first function declaration\n",
    "def sum():\n",
    "    global b\n",
    "    b = a + b\n",
    "    \n",
    "sum() # calling sum\n",
    "\n",
    "# using this syntax, the arguments can be of any type that supports a string representation.  No casting needed.\n",
    "print \"Now b is\", b "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Modules and Import\n",
    "Files with a .py extension are known as Modules in Python.  Modules are used to store functions, variables, and class definitions.  \n",
    "\n",
    "Modules that are not part of the standard Python library are included in your program using the <code>import</code> statement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'cos' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-0f2780fae532>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# To use Math, we must import it\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mcos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'cos' is not defined"
     ]
    }
   ],
   "source": [
    "# To use Math, we must import it\n",
    "import math\n",
    "print cos(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whoops.  Importing the <code>math</code> module allows us access to all of its functions, but we must call them in this way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print math.cos(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, you can use the <code>from</code> keyword"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.0\n"
     ]
    }
   ],
   "source": [
    "from math import cos\n",
    "print cos(math.pi) # we only imported cos, not the pi constant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the <code>from</code> statement we can import everything from the math module.  \n",
    "\n",
    "Disclaimer: many Pythonistas discourage doing this for performance reasons.  Just import what you need"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "from math import *\n",
    "print sin(pi/2) # now we don't have to make a call to math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Strings\n",
    "As you may expect, Python has a powerful, full featured string module.  \n",
    "\n",
    "###Substrings\n",
    "Python strings can be substringed using bracket syntax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Come on Gators\n"
     ]
    }
   ],
   "source": [
    "mystring = \"Go Gators, Come on Gators, Get up and go!\"\n",
    "print mystring[11:25]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python is a 0-index based language.  Generally whenever forming a range of values in Python, the first argument is inclusive whereas the second is not, i.e. <code>mystring[11:25]</code> returns characters 11 through 24.\n",
    "\n",
    "You can omit the first or second argument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go Gators\n"
     ]
    }
   ],
   "source": [
    "print mystring[:9] # all characters before the 9th index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Get up and go!\n"
     ]
    }
   ],
   "source": [
    "print mystring[27:] # all characters at or after the 27th"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go Gators, Come on Gators, Get up and go!\n"
     ]
    }
   ],
   "source": [
    "print mystring[:] # you can even omit both arguments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using negative values, you can count positions backwards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "go\n"
     ]
    }
   ],
   "source": [
    "print mystring[-3:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###String Functions\n",
    "Here are some more useful string functions\n",
    "####find"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "print mystring.find(\"Gators\") # returns the index of the first occurence of Gators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19\n"
     ]
    }
   ],
   "source": [
    "print mystring.find(\"Gators\", 4) # specify an index on which to begin searching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1\n"
     ]
    }
   ],
   "source": [
    "print mystring.find(\"Gators\", 4, 19) # specify both begin and end indexes to search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like nothing was found.  -1 is returned by default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1\n"
     ]
    }
   ],
   "source": [
    "print mystring.find(\"Seminoles\") # no Seminoles here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####lower and upper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "go gators, come on gators, get up and go!\n",
      "GO GATORS, COME ON GATORS, GET UP AND GO!\n"
     ]
    }
   ],
   "source": [
    "print mystring.lower()\n",
    "print mystring.upper()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####replace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go Seminoles, Come on Seminoles, Get up and go!\n"
     ]
    }
   ],
   "source": [
    "print mystring.replace(\"Gators\", \"Seminoles\") # replaces all occurences of Gators with Seminoles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go Gators, Come on Gators, Get up and go!\n"
     ]
    }
   ],
   "source": [
    "print mystring"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that replace returned a new string.  Nothing was modified in place"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go Seminoles, Come on Gators, Get up and go!\n"
     ]
    }
   ],
   "source": [
    "print mystring.replace(\"Gators\", \"Seminoles\", 1) # limit the number of replacements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Go', 'Gators,', 'Come', 'on', 'Gators,', 'Get', 'up', 'and', 'go!']\n"
     ]
    }
   ],
   "source": [
    "print mystring.split() # returns a list of strings broken by a space by default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Go Gators', ' Come on Gators', ' Get up and go!']\n"
     ]
    }
   ],
   "source": [
    "print mystring.split(',') # you can also define the separator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####join\n",
    "\n",
    "The <code>join</code> is useful for building strings from lists or other iterables.  Call <code>join</code> on the desired separator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go Gators\n"
     ]
    }
   ],
   "source": [
    "print ' '.join([\"Go\", \"Gators\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more information on string functions:\n",
    "\n",
    "https://docs.python.org/2/library/stdtypes.html#string-methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Data Structures\n",
    "###Lists\n",
    "The Python standard library does not have traditional C-style fixed-memory fixed-type arrays.  Instead, lists are used and can contain a mix of any type.\n",
    "\n",
    "Lists are created with square brackets []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 'five']\n"
     ]
    }
   ],
   "source": [
    "mylist = [1, 2, 3, 4, 'five']\n",
    "print mylist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 'five', 6.0]\n"
     ]
    }
   ],
   "source": [
    "mylist.append(6.0) # add an item to the end of the list\n",
    "print mylist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 'five', 6.0, 8, 'nine']\n"
     ]
    }
   ],
   "source": [
    "mylist.extend([8, 'nine']) # extend the list with the contents of another list\n",
    "print mylist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 'five', 6.0, 7, 8, 'nine']\n"
     ]
    }
   ],
   "source": [
    "mylist.insert(6, 7) # insert the number 7 at index 6\n",
    "print mylist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 6.0, 7, 8, 'nine']\n"
     ]
    }
   ],
   "source": [
    "mylist.remove('five') # removes the first matching occurence \n",
    "print mylist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nine\n",
      "[1, 2, 3, 4, 6.0, 7, 8]\n"
     ]
    }
   ],
   "source": [
    "popped = mylist.pop() # by default, the last item in the list is removed and returned\n",
    "print popped\n",
    "print mylist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.0\n",
      "[1, 2, 3, 4, 7, 8]\n"
     ]
    }
   ],
   "source": [
    "popped2 = mylist.pop(4) # pops at at index\n",
    "print popped2\n",
    "print mylist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    }
   ],
   "source": [
    "print len(mylist) # returns the length of any iterable such as lists and strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 7, 8, -3, -2, -1]\n"
     ]
    }
   ],
   "source": [
    "mylist.extend(range(-3, 0)) # the range function returns a list from -3 inclusive to 0 non inclusive\n",
    "print mylist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-3, -2, -1, 1, 2, 3, 4, 7, 8]\n"
     ]
    }
   ],
   "source": [
    "# default list sorting. When more complex objects are in the list, arguments can be used to customize how to sort\n",
    "mylist.sort()\n",
    "print mylist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8, 7, 4, 3, 2, 1, -1, -2, -3]\n"
     ]
    }
   ],
   "source": [
    "mylist.reverse() # reverse the list\n",
    "print mylist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more information on Lists:\n",
    "\n",
    "https://docs.python.org/2/tutorial/datastructures.html#more-on-lists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Tuples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python supports n-tuple sequences.  These are non-mutable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Tim', 'Tebow', 15)\n",
      "<type 'tuple'>\n"
     ]
    }
   ],
   "source": [
    "mytuple = 'Tim', 'Tebow', 15 # Created with commas\n",
    "print mytuple\n",
    "print type(mytuple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tebow\n"
     ]
    }
   ],
   "source": [
    "print mytuple[1] # access an item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'tuple' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-49-4e1c006a78dd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmytuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Winston\"\u001b[0m \u001b[0;31m# results in error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "mytuple[1] = \"Winston\" # results in error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Sets\n",
    "Python includes the set data structure which is an unordered collection with no duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set(['Miami', 'Florida State', 'Florida'])\n"
     ]
    }
   ],
   "source": [
    "schools = ['Florida', 'Florida State', 'Miami', 'Florida']\n",
    "myset = set(schools) # the set is built from the schools list\n",
    "print myset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "print 'Georgia' in myset # membership test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print 'Florida' in myset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set(['Florida'])\n"
     ]
    }
   ],
   "source": [
    "badschools = set(['Florida State', 'Miami'])\n",
    "print myset - badschools # set arithmetic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set(['Miami', 'Florida State'])\n"
     ]
    }
   ],
   "source": [
    "print myset & badschools # AND"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set(['Miami', 'Florida State', 'Florida', 'Stetson'])\n"
     ]
    }
   ],
   "source": [
    "print myset | set(['Miami', 'Stetson']) # OR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set(['Stetson', 'Florida', 'Florida State'])\n"
     ]
    }
   ],
   "source": [
    "print myset ^ set(['Miami', 'Stetson']) # XOR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Dictionaries\n",
    "Python supports dictionaries which can be thought of as an unordered list of key, value pairs.  Keys can be any immutable type and are typically integers or strings.  Values can be any object, even dictionaries.\n",
    "\n",
    "Dictionaries are created with curly braces {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Georgia': 2, 'Florida': 1, 'Tennessee': 3}\n"
     ]
    }
   ],
   "source": [
    "mydict = {'Florida' : 1, 'Georgia' : 2, 'Tennessee' : 3}\n",
    "print mydict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "print mydict['Florida'] # access the value with key = 'Florida'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Georgia': 2, 'Florida': 1}\n"
     ]
    }
   ],
   "source": [
    "del mydict['Tennessee'] # funky syntax to delete a key, value pair\n",
    "print mydict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Georgia': 7, 'Florida': 1}\n"
     ]
    }
   ],
   "source": [
    "mydict['Georgia'] = 7 # assignment\n",
    "print mydict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Georgia': 7, 'Florida': 1, 'Kentucky': 6}\n"
     ]
    }
   ],
   "source": [
    "mydict['Kentucky'] = 6 # you can append a new key\n",
    "print mydict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Georgia', 'Florida', 'Kentucky']\n"
     ]
    }
   ],
   "source": [
    "print mydict.keys() # get a list of keys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Conditionals\n",
    "Python supports the standard if-else-if conditional expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a is greater than b\n"
     ]
    }
   ],
   "source": [
    "a = 2; b = 1;\n",
    "\n",
    "if a > b: print \"a is greater than b\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b is less than or equal to a\n"
     ]
    }
   ],
   "source": [
    "if b > a:\n",
    "    print \"b is greater than a\"\n",
    "else:\n",
    "    print \"b is less than or equal to a\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a is equal to b\n"
     ]
    }
   ],
   "source": [
    "b = 2\n",
    "\n",
    "if a > b:\n",
    "    print \"a is greater than b\"\n",
    "elif a < b:\n",
    "    print \"a is less than b\"\n",
    "else:\n",
    "    print \"a is equal to b\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Loops\n",
    "Python supports for, foreach, and while loops\n",
    "###For (counting)\n",
    "Traditional counting loops are accomplished in Python with a combination of the <code>for</code> key word and the <code>range</code> function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "for x in range(10): # with one argument, range produces integers from 0 to 9\n",
    "    print x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "for y in range(5, 12): # with two argumentts, range produces integers from 5 to 11\n",
    "    print y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "4\n",
      "7\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "for z in range(1, 12, 3): # with three arguments, range starts at 1 and goes in steps of 3 until greater than 12\n",
    "    print z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "for a in range(10, 1, -5): # can use a negative step size as well\n",
    "    print a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for b in range(2, 1, 1): # with a positive step, all values are less than 1. No integers are produced\n",
    "    print b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for c in range(1, 2, -1): # same goes for a negative step as all values are less than 2\n",
    "    print c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Foreach\n",
    "As it turns out, counting loops are just foreach loops in Python.  The <code>range</code> function returns a list of integers over which <code>for in</code> iterates.  This can be extended to any other iterable type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "foo\n",
      "bar\n"
     ]
    }
   ],
   "source": [
    "for i in ['foo', 'bar']: # iterate over a list of strings\n",
    "    print i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "three 3\n",
      "two 2\n",
      "one 1\n"
     ]
    }
   ],
   "source": [
    "anotherdict = {'one' : 1, 'two' : 2, 'three' : 3}\n",
    "\n",
    "for key in anotherdict.keys(): # iterate over a dictionary.  Order is not guaranteed\n",
    "    print key, anotherdict[key]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###While\n",
    "Python supports standard <code>while</code> loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "a = 1; b = 4; c = 7; d = 5;\n",
    "\n",
    "while (a < b) and (c > d): # example of and condition\n",
    "    print c - a\n",
    "    a += 1 # example of incrementing\n",
    "    c -= 1 # decrementing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python does not have a construct for a do-while loop, though it can be accomplished using the <code>break</code> statement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "4\n",
      "8\n",
      "16\n"
     ]
    }
   ],
   "source": [
    "a = 1; b = 10\n",
    "\n",
    "while True: # short circuit the while condition\n",
    "    a *= 2\n",
    "    print a\n",
    "    if a > b:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Functions\n",
    "Functions in Python do not have a distinction between those that do and do not return a value.  If a value is returned, the type is not declared.\n",
    "\n",
    "Functions can be declared in any module without any distinction between static and non-static.  Functions can even be declared within other functions\n",
    "\n",
    "The syntax is the following"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello there!\n"
     ]
    }
   ],
   "source": [
    "def hello():\n",
    "    print \"Hello there!\"\n",
    "    \n",
    "hello()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#0 Kasey Hill\n"
     ]
    }
   ],
   "source": [
    "def player(name, number): # use some arguments\n",
    "    print \"#\" + str(number), name # cast number to a string when concatenating\n",
    "    \n",
    "player(\"Kasey Hill\", 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Functions can have optional arguments if a default value is provided in the function signature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#0 Kasey Hill Florida\n"
     ]
    }
   ],
   "source": [
    "def player(name, number, team = 'Florida'): # optional team argument\n",
    "    print \"#\" + str(number), name, team\n",
    "    \n",
    "player(\"Kasey Hill\", 0) # no team argument supplied"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#2 Aaron Harrison Kentucky\n"
     ]
    }
   ],
   "source": [
    "player(\"Aaron Harrison\", 2, \"Kentucky\") # supplying all three arguments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python functions can be called using named arguments, instead of positional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#23 Chris Walker Florida\n"
     ]
    }
   ],
   "source": [
    "player(number = 23, name = 'Chris Walker')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###\\*args and \\**kwargs\n",
    "In Python, there is a special deferencing scheme that allows for defining and calling functions with argument lists or dictionaries.\n",
    "\n",
    "####\\*args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#20 Michael Frazier II Florida\n"
     ]
    }
   ],
   "source": [
    "args = ['Michael Frazier II', 20, 'Florida']\n",
    "\n",
    "player(*args) # calling player with the dereferenced argument list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Argument lists can also be used in defining a function as such"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "la\n",
      "dee\n",
      "da\n"
     ]
    }
   ],
   "source": [
    "def foo(*args): \n",
    "    for someFoo in args:\n",
    "        print someFoo\n",
    "        \n",
    "foo('la', 'dee', 'da') # supports an arbitrary number of arguments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####**kwargs\n",
    "Similarly, we can define a dictionary of named parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#20 Michael Frazier II Florida\n"
     ]
    }
   ],
   "source": [
    "kwargs = {'name' : 'Michael Frazier II', 'number' : 20}\n",
    "\n",
    "player(**kwargs) # calling player with the dereferenced kwargs dictionary.  The team argument will be defaulted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just as before, we can define a function taking an arbitrary dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name Michael Frazier II\n",
      "number 20\n"
     ]
    }
   ],
   "source": [
    "def foo(**kwargs):\n",
    "    for key in kwargs.keys():\n",
    "        print key, kwargs[key]\n",
    "        \n",
    "foo(**kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###return\n",
    "In Python functions, an arbitrary number of values can be returned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "def sum(x,y):\n",
    "    return x + y # return a single value\n",
    "\n",
    "print sum(1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 2\n"
     ]
    }
   ],
   "source": [
    "def sum_and_product(x,y):\n",
    "    return x + y, x * y # return two values\n",
    "\n",
    "mysum, myproduct = sum_and_product(1,2)\n",
    "print mysum, myproduct"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Data Science Tutorial\n",
    "Now that we've covered some Python basics, we will begin a tutorial going through many tasks a data scientist may perform.  We will obtain real world data and go through the process of auditing, analyzing, visualing, and building classifiers from the data.\n",
    "\n",
    "We will use a database of breast cancer data obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg.  The data is a collection of samples from Dr. Wolberg's clinical cases with attributes pertaining to tumors and a class labeling the sample as benign or malignant.\n",
    "\n",
    "| Attribute                      | Domain                          |\n",
    "|--------------------------------|---------------------------------|\n",
    "| 1. Sample code number          | id number                       |\n",
    "| 2. Clump Thickness             | 1 - 10                          |\n",
    "| 3. Uniformity of Cell Size     | 1 - 10                          |\n",
    "| 4. Uniformity of Cell Shape    | 1 - 10                          |\n",
    "| 5. Marginal Adhesion           | 1 - 10                          |\n",
    "| 6. Single Epithelial Cell Size | 1 - 10                          |\n",
    "| 7. Bare Nuclei                 | 1 - 10                          |\n",
    "| 8. Bland Chromatin             | 1 - 10                          |\n",
    "| 9. Normal Nucleoli             | 1 - 10                          |\n",
    "| 10. Mitoses                    | 1 - 10                          |\n",
    "| 11. Class                      | (2 for benign, 4 for malignant) |\n",
    "\n",
    "For more information on this data set:\n",
    "https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29\n",
    "\n",
    "##Obtaining the Data\n",
    "Lets begin by programmatically obtaining the data.  Here I'll define a function we can use to make HTTP requests and download the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def download_file(url, local_filename):\n",
    "    import requests\n",
    "    \n",
    "    # stream = True allows downloading of large files; prevents loading entire file into memory\n",
    "    r = requests.get(url, stream = True)\n",
    "    with open(local_filename, 'wb') as f:\n",
    "        for chunk in r.iter_content(chunk_size=1024): \n",
    "            if chunk: # filter out keep-alive new chunks\n",
    "                f.write(chunk)\n",
    "                f.flush()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll specify the url of the file and the file name we will save to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data'\n",
    "filename = 'breast-cancer-wisconsin.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And make a call to <code>download_file</code>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "download_file(url, filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now this might seem like overkill for downloading a single, small csv file, but we can use this same function to access countless APIs available on the World Wide Web by building an API request in the url.\n",
    "\n",
    "##Wrangling the Data\n",
    "Now that we have some data, lets get it into a useful form.  For this task we will use a package called pandas. pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for Python.  The most fundamental data structure in pandas is the dataframe, which is similar to the data.frame data structure found in the R statistical programming language.\n",
    "\n",
    "For more information: http://pandas.pydata.org\n",
    "\n",
    "pandas dataframes are a 2-dimensional labeled data structures with columns of potentially different types.  Dataframes can be thought of as similar to a spreadsheet or SQL table.\n",
    "\n",
    "There are numerous ways to build a dataframe with pandas.  Since we have already attained a csv file, we can use a parser built into pandas called <code>read_csv</code> which will read the contents of a csv file directly into a data frame.\n",
    "\n",
    "For more information: http://pandas.pydata.org/pandas-docs/dev/generated/pandas.io.parsers.read_csv.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "</div>"
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      "text/plain": [
       "   1000025  5   1  1.1  1.2  2 1.3  3  1.4  1.5  2.1\n",
       "0  1002945  5   4    4    5  7  10  3    2    1    2\n",
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       "2  1016277  6   8    8    1  3   4  3    7    1    2\n",
       "3  1017023  4   1    1    3  2   1  3    1    1    2\n",
       "4  1017122  8  10   10    8  7  10  9    7    1    4"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd # import the module and alias it as pd\n",
    "\n",
    "cancer_data = pd.read_csv('breast-cancer-wisconsin.csv')\n",
    "cancer_data.head() # show the first few rows of the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whoops, looks like our csv file did not contain a header row.  <code>read_csv</code> assumes the first row of the csv is the header by default.\n",
    "\n",
    "Lets check out the file located here: https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.names\n",
    "\n",
    "This contains information about the data set including the names of the attributes.\n",
    "\n",
    "Lets create a list of these attribute names to use when reading the csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# \\ allows multi line wrapping\n",
    "cancer_header = [ \\\n",
    "                 'sample_code_number', \\\n",
    "                 'clump_thickness', \\\n",
    "                 'uniformity_cell_size', \\\n",
    "                 'uniformity_cell_shape', \\\n",
    "                 'marginal_adhesion', \\\n",
    "                 'single_epithelial_cell_size', \\\n",
    "                 'bare_nuclei', \\\n",
    "                 'bland_chromatin', \\\n",
    "                 'normal_nucleoli', \\\n",
    "                 'mitoses', \\\n",
    "                 'class']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets try the import again, this time specifying the names.  When specifying names, the <code>read_csv</code> function requires us to set the <code>header</code> row number to <code>None</code>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample_code_number</th>\n",
       "      <th>clump_thickness</th>\n",
       "      <th>uniformity_cell_size</th>\n",
       "      <th>uniformity_cell_shape</th>\n",
       "      <th>marginal_adhesion</th>\n",
       "      <th>single_epithelial_cell_size</th>\n",
       "      <th>bare_nuclei</th>\n",
       "      <th>bland_chromatin</th>\n",
       "      <th>normal_nucleoli</th>\n",
       "      <th>mitoses</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 1000025</td>\n",
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       "      <td> 2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sample_code_number  clump_thickness  uniformity_cell_size  \\\n",
       "0             1000025                5                     1   \n",
       "1             1002945                5                     4   \n",
       "2             1015425                3                     1   \n",
       "3             1016277                6                     8   \n",
       "4             1017023                4                     1   \n",
       "\n",
       "   uniformity_cell_shape  marginal_adhesion  single_epithelial_cell_size  \\\n",
       "0                      1                  1                            2   \n",
       "1                      4                  5                            7   \n",
       "2                      1                  1                            2   \n",
       "3                      8                  1                            3   \n",
       "4                      1                  3                            2   \n",
       "\n",
       "  bare_nuclei  bland_chromatin  normal_nucleoli  mitoses  class  \n",
       "0           1                3                1        1      2  \n",
       "1          10                3                2        1      2  \n",
       "2           2                3                1        1      2  \n",
       "3           4                3                7        1      2  \n",
       "4           1                3                1        1      2  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_data = pd.read_csv('breast-cancer-wisconsin.csv', header=None, names=cancer_header)\n",
    "cancer_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets take a look at some simple statistics for the **clump_thickness** column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    699.000000\n",
       "mean       4.417740\n",
       "std        2.815741\n",
       "min        1.000000\n",
       "25%        2.000000\n",
       "50%        4.000000\n",
       "75%        6.000000\n",
       "max       10.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_data[\"clump_thickness\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Referring to the documentation link above about the data, the count, range of values (min = 1, max = 10), and data type (dtype = float64) look correct.\n",
    "\n",
    "Lets take a look at another column, this time **bare_nuclei**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     699\n",
       "unique     11\n",
       "top         1\n",
       "freq      402\n",
       "dtype: object"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_data[\"bare_nuclei\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well at least the count is correct.  We were expecting no more than 10 unique values and now the data type is an object.  \n",
    "\n",
    "Whats up with our data?\n",
    "\n",
    "We have arrived at arguably the most important part of performing data science: dealing with messy data.  One of most important tools in a data scientist's toolbox is the ability to audit, clean, and reshape data.  The real world is full of messy data and your sources may not always have data in the exact format you desire.\n",
    "\n",
    "In this case we are working with csv data, which is a relatively straightforward format, but this will not always be the case when performing real world data science.  Data comes in all varieties from csv all the way to something as unstructured as a collection of emails or documents.  A data scientist must be versed in a wide variety of technologies and methodologies in order to be successful.\n",
    "\n",
    "Now, lets do a little bit of digging into why were are not getting a numeric pandas column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1', '10', '2', '4', '3', '9', '7', '?', '5', '8', '6'], dtype=object)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_data[\"bare_nuclei\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using <code>unique</code> we can see that '?' is one of the distinct values that appears in this series.  Looking again at the documentation for this data set, we find the following:\n",
    "\n",
    ">Missing attribute values: 16\n",
    ">\n",
    ">   There are 16 instances in Groups 1 to 6 that contain a single missing \n",
    ">   (i.e., unavailable) attribute value, now denoted by \"?\". \n",
    "\n",
    "It was so nice of them to tell us to expect these missing values, but as a data scientist that will almost never be the case.  Lets see what we can do with these missing values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cancer_data[\"bare_nuclei\"] = cancer_data[\"bare_nuclei\"].convert_objects(convert_numeric=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we have attempted to convert the **bare_nuclei** series to a numeric type.  Lets see what the unique values are now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.,  10.,   2.,   4.,   3.,   9.,   7.,  nan,   5.,   8.,   6.])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_data[\"bare_nuclei\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The decimal point after each number means that it is an integer value being represented by a floating point number.  Now instead of our pesky '?' we have <code>nan</code> (not a number).  <code>nan</code> is a construct used by pandas to represent the absence of value.  It is a data type that comes from the package numpy, used internally by pandas, and is not part of the standard Python library.\n",
    "\n",
    "Now that we have <code>nan</code> values in place of '?', we can use some nice features in pandas to deal with these missing values.\n",
    "\n",
    "What we are about to do is what is called \"imputing\" or providing a replacement for missing values so the data set becomes easier to work with.  There are a number of strategies for imputing missing values, all with their own pitfalls.  In general, imputation introduces some degree of bias to the data, so the imputation strategy taken should be in an attempt to minimize that bias.\n",
    "\n",
    "Here, we will simply use the mean of all of the non-nan values in the series as a replacement.  Since we already know that the data is integer in possible values, we will round the mean to the nearest whole number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.,  10.,   2.,   4.,   3.,   9.,   7.,   5.,   8.,   6.])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_data.fillna(cancer_data.mean().round(), inplace=True)\n",
    "cancer_data[\"bare_nuclei\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<code>fillna</code> is a dataframe function that replaces all nan values with either a scalar value, a series of values with the same indices as found in the dataframe, or a dataframe that is indexed by the columns of the target dataframe.\n",
    "\n",
    "<code>cancer_data.mean().round()</code> will take the mean of each column (this computation ignores the currently present nan values), then round, and return a dataframe indexed by the columns of the original dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sample_code_number             1071704\n",
       "clump_thickness                      4\n",
       "uniformity_cell_size                 3\n",
       "uniformity_cell_shape                3\n",
       "marginal_adhesion                    3\n",
       "single_epithelial_cell_size          3\n",
       "bare_nuclei                          4\n",
       "bland_chromatin                      3\n",
       "normal_nucleoli                      3\n",
       "mitoses                              2\n",
       "class                                3\n",
       "dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_data.mean().round()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<code>inplace=True</code> allows us to make this modification directly on the dataframe, without having to do any assignment.  \n",
    "\n",
    "Now that we have figured out how to impute these missing values in a single column, lets start over and quickly apply this technique to the entire dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    699.000000\n",
       "mean       3.555079\n",
       "std        3.602496\n",
       "min        1.000000\n",
       "25%        1.000000\n",
       "50%        1.000000\n",
       "75%        5.000000\n",
       "max       10.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_data = pd.read_csv('breast-cancer-wisconsin.csv', header=None, names=cancer_header)\n",
    "cancer_data = cancer_data.convert_objects(convert_numeric=True)\n",
    "cancer_data.fillna(cancer_data.mean().round(), inplace=True)\n",
    "cancer_data[\"bare_nuclei\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.,  10.,   2.,   4.,   3.,   9.,   7.,   5.,   8.,   6.])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_data[\"bare_nuclei\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Structurally, Pandas dataframes are a collection of Series objects sharing a common index.  In general, the Series object and Dataframe object share a large number of functions with some behavioral differences.  In other words, whatever computation you can do on a single column can generally be applied to the entire dataframe.\n",
    "\n",
    "Now we can use the dataframe version of <code>describe</code> to get an overview of all of our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample_code_number</th>\n",
       "      <th>clump_thickness</th>\n",
       "      <th>uniformity_cell_size</th>\n",
       "      <th>uniformity_cell_shape</th>\n",
       "      <th>marginal_adhesion</th>\n",
       "      <th>single_epithelial_cell_size</th>\n",
       "      <th>bare_nuclei</th>\n",
       "      <th>bland_chromatin</th>\n",
       "      <th>normal_nucleoli</th>\n",
       "      <th>mitoses</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>      699.000000</td>\n",
       "      <td> 699.000000</td>\n",
       "      <td> 699.000000</td>\n",
       "      <td> 699.000000</td>\n",
       "      <td> 699.000000</td>\n",
       "      <td> 699.000000</td>\n",
       "      <td> 699.000000</td>\n",
       "      <td> 699.000000</td>\n",
       "      <td> 699.000000</td>\n",
       "      <td> 699.000000</td>\n",
       "      <td> 699.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>  1071704.098712</td>\n",
       "      <td>   4.417740</td>\n",
       "      <td>   3.134478</td>\n",
       "      <td>   3.207439</td>\n",
       "      <td>   2.806867</td>\n",
       "      <td>   3.216023</td>\n",
       "      <td>   3.555079</td>\n",
       "      <td>   3.437768</td>\n",
       "      <td>   2.866953</td>\n",
       "      <td>   1.589413</td>\n",
       "      <td>   2.689557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>   617095.729819</td>\n",
       "      <td>   2.815741</td>\n",
       "      <td>   3.051459</td>\n",
       "      <td>   2.971913</td>\n",
       "      <td>   2.855379</td>\n",
       "      <td>   2.214300</td>\n",
       "      <td>   3.602496</td>\n",
       "      <td>   2.438364</td>\n",
       "      <td>   3.053634</td>\n",
       "      <td>   1.715078</td>\n",
       "      <td>   0.951273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>    61634.000000</td>\n",
       "      <td>   1.000000</td>\n",
       "      <td>   1.000000</td>\n",
       "      <td>   1.000000</td>\n",
       "      <td>   1.000000</td>\n",
       "      <td>   1.000000</td>\n",
       "      <td>   1.000000</td>\n",
       "      <td>   1.000000</td>\n",
       "      <td>   1.000000</td>\n",
       "      <td>   1.000000</td>\n",
       "      <td>   2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>   870688.500000</td>\n",
       "      <td>   2.000000</td>\n",
       "      <td>   1.000000</td>\n",
       "      <td>   1.000000</td>\n",
       "      <td>   1.000000</td>\n",
       "      <td>   2.000000</td>\n",
       "      <td>   1.000000</td>\n",
       "      <td>   2.000000</td>\n",
       "      <td>   1.000000</td>\n",
       "      <td>   1.000000</td>\n",
       "      <td>   2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>  1171710.000000</td>\n",
       "      <td>   4.000000</td>\n",
       "      <td>   1.000000</td>\n",
       "      <td>   1.000000</td>\n",
       "      <td>   1.000000</td>\n",
       "      <td>   2.000000</td>\n",
       "      <td>   1.000000</td>\n",
       "      <td>   3.000000</td>\n",
       "      <td>   1.000000</td>\n",
       "      <td>   1.000000</td>\n",
       "      <td>   2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>  1238298.000000</td>\n",
       "      <td>   6.000000</td>\n",
       "      <td>   5.000000</td>\n",
       "      <td>   5.000000</td>\n",
       "      <td>   4.000000</td>\n",
       "      <td>   4.000000</td>\n",
       "      <td>   5.000000</td>\n",
       "      <td>   5.000000</td>\n",
       "      <td>   4.000000</td>\n",
       "      <td>   1.000000</td>\n",
       "      <td>   4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td> 13454352.000000</td>\n",
       "      <td>  10.000000</td>\n",
       "      <td>  10.000000</td>\n",
       "      <td>  10.000000</td>\n",
       "      <td>  10.000000</td>\n",
       "      <td>  10.000000</td>\n",
       "      <td>  10.000000</td>\n",
       "      <td>  10.000000</td>\n",
       "      <td>  10.000000</td>\n",
       "      <td>  10.000000</td>\n",
       "      <td>   4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sample_code_number  clump_thickness  uniformity_cell_size  \\\n",
       "count          699.000000       699.000000            699.000000   \n",
       "mean       1071704.098712         4.417740              3.134478   \n",
       "std         617095.729819         2.815741              3.051459   \n",
       "min          61634.000000         1.000000              1.000000   \n",
       "25%         870688.500000         2.000000              1.000000   \n",
       "50%        1171710.000000         4.000000              1.000000   \n",
       "75%        1238298.000000         6.000000              5.000000   \n",
       "max       13454352.000000        10.000000             10.000000   \n",
       "\n",
       "       uniformity_cell_shape  marginal_adhesion  single_epithelial_cell_size  \\\n",
       "count             699.000000         699.000000                   699.000000   \n",
       "mean                3.207439           2.806867                     3.216023   \n",
       "std                 2.971913           2.855379                     2.214300   \n",
       "min                 1.000000           1.000000                     1.000000   \n",
       "25%                 1.000000           1.000000                     2.000000   \n",
       "50%                 1.000000           1.000000                     2.000000   \n",
       "75%                 5.000000           4.000000                     4.000000   \n",
       "max                10.000000          10.000000                    10.000000   \n",
       "\n",
       "       bare_nuclei  bland_chromatin  normal_nucleoli     mitoses       class  \n",
       "count   699.000000       699.000000       699.000000  699.000000  699.000000  \n",
       "mean      3.555079         3.437768         2.866953    1.589413    2.689557  \n",
       "std       3.602496         2.438364         3.053634    1.715078    0.951273  \n",
       "min       1.000000         1.000000         1.000000    1.000000    2.000000  \n",
       "25%       1.000000         2.000000         1.000000    1.000000    2.000000  \n",
       "50%       1.000000         3.000000         1.000000    1.000000    2.000000  \n",
       "75%       5.000000         5.000000         4.000000    1.000000    4.000000  \n",
       "max      10.000000        10.000000        10.000000   10.000000    4.000000  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "##Visualizing the Data\n",
    "Another important tool in the data scientist's toolbox is the ability to create visualizations from data.  Visualizing data is often the most logical place to start getting a deeper intuition of the data.  This intuition will shape and drive your analysis.\n",
    "\n",
    "Even more important than visualizing data for your own personal benefit, it is often the job of the data scientist to use the data to tell a story.  Creating illustrative visuals that succinctly convey an idea are the best way to tell that story, especially to stakeholders with less technical skillsets.\n",
    "\n",
    "Here we will be using a Python package called ggplot (https://ggplot.yhathq.com).  The ggplot package is an attempt to bring visuals following the guidelines outlayed in the grammar of graphics (http://vita.had.co.nz/papers/layered-grammar.html) to Python.  It is based off of and intended to mimic the features of the ggplot2 library found in R.  Additionally, ggplot is designed to work with Pandas dataframes, making things nice and simple. \n",
    "\n",
    "We'll start by doing a bit of setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# The following line is NOT Python code, but a special syntax for enabling inline plotting in IPython\n",
    "%matplotlib inline \n",
    "\n",
    "from ggplot import *\n",
    "\n",
    "import warnings\n",
    "\n",
    "# ggplot usage of pandas throws a future warning\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we enabled plotting in IPython and imported everything from the ggplot package.  Now we'll create a plot and then break down the components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "QoAEAACAEQIkAAAAjBAgAQAAYOT/ASdrfEyb1/hZAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bdfaad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (286348185)>\n"
     ]
    }
   ],
   "source": [
    "plt = ggplot(aes(x = 'clump_thickness'), data = cancer_data) + \\\n",
    "        geom_histogram(binwidth = 1, fill = 'steelblue')\n",
    "\n",
    "# using print gets the plot to show up here within the notebook.  \n",
    "# In normal Python environment without using print, the plot appears in a window\n",
    "print plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A plot begins with the <code>ggplot</code> function.  Here, we pass in the cancer_data pandas dataframe and a special function called <code>aes</code> (short for aesthetic).  The values provided to <code>aes</code> change depending on which type of plot is being used.  Here we are going to make a histogram from the **clump_thickness** column in cancer_data, so that column name needs to be passed as the x parameter to <code>aes</code>.\n",
    "\n",
    "The grammar of graphics is based off of a concept of \"geoms\" (short for geometric objects).  These geoms provide granular control of the plot and are progressively added to the base call to <code>ggplot</code> with + syntax.\n",
    "\n",
    "\n",
    "Lets say we wanted to show the mean clump_thickness on this plot.  We could do something like the following"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bdd0dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (284385069)>\n"
     ]
    }
   ],
   "source": [
    "plt = ggplot(aes(x = 'clump_thickness'), data = cancer_data) + \\\n",
    "        geom_histogram(binwidth = 1, fill = 'steelblue') + \\\n",
    "        geom_vline(xintercept = [cancer_data['clump_thickness'].mean()], linetype='dashed')\n",
    "print plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, each geom has its own set of parameters specific to the appearance of that geom (also called aesthetics).\n",
    "\n",
    "Lets try a scatter plot to get some multi-variable action"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "/wCCxslYPuUNAgAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bdfad50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (284217657)>\n"
     ]
    }
   ],
   "source": [
    "plt = ggplot(aes(x = 'uniformity_cell_shape', y = 'bare_nuclei'), data = cancer_data) + \\\n",
    "        geom_point()\n",
    "print plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes when working with integer data, or data that takes on a limited range of values, it is easier to visualize the plot with added jitter to the points.  We can do that by adding an aesthetic to <code>geom_point</code>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "hBCtShJOIYQQQgjRqiThFEIIIYQQrep/q9cm7ZocvusAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bdfa190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (284609961)>\n"
     ]
    }
   ],
   "source": [
    "plt = ggplot(aes(x = 'uniformity_cell_shape', y = 'bare_nuclei'), data = cancer_data) + \\\n",
    "        geom_point(position = 'jitter')\n",
    "print plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a simple aesthetic addition, we can see how these two variables play into our cancer classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "UEIIIYQQEZICSgghhBAiQlJACSGEEEJESAooIYQQQogISQElhBBCCBGh/w90RLl4Yif3+AAAAABJ\n",
       "RU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f1e0a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (284256349)>\n"
     ]
    }
   ],
   "source": [
    "plt = ggplot(aes(x = 'uniformity_cell_shape', y = 'bare_nuclei', color = 'class'), data = cancer_data) + \\\n",
    "        geom_point(position = 'jitter')\n",
    "print plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By adding <code>color = 'class'</code> as a parameter to the aes function, we now give a color to each unique value found in that column and automatically get a legend.  Remember, 2 is benign and 4 is malignant. \n",
    "\n",
    "We can also do things such as add a title or change the axis labeling with geoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "VCoVlMtltFqtdadCRL8JO1BE9CWTyQTFYhHRaHTdqRAR/TUsoIjol11dXcHhcMDlcuHo6Gjd6fyz\n",
       "lm1PEtH/iVt4RERERAaxA0VERERkEAsoIiIiIoNYQBEREREZxAKKiIiIyCAWUEREREQGfQPdsBK5\n",
       "pC4wiQAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f2c0a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (284331857)>\n"
     ]
    }
   ],
   "source": [
    "plt = ggplot(aes(x = 'uniformity_cell_shape', y = 'bare_nuclei', color = 'class'), data = cancer_data) + \\\n",
    "        geom_point(position = 'jitter') + \\\n",
    "        ggtitle(\"The Effect of the Bare Nuclei and Cell Shape Uniformity on Classification\") + \\\n",
    "        ylab(\"Amount of Bare Nuclei\") + \\\n",
    "        xlab(\"Uniformity in Cell shape\")\n",
    "print plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is definitely some patterning going on in that plot.\n",
    "\n",
    "A slightly different way to convey this idea is to use faceting.  Faceting is the creation of multiple related plots arranged by the values of a given faceted variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f2c0b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (284331841)>\n"
     ]
    }
   ],
   "source": [
    "plt = ggplot(aes(x = 'uniformity_cell_shape', y = 'bare_nuclei'), data = cancer_data) + \\\n",
    "        geom_point(position = 'jitter') + \\\n",
    "        ggtitle(\"The Effect of the Bare Nuclei and Cell Shape Uniformity on Classification\") + \\\n",
    "        facet_grid('class')\n",
    "print plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rather than set the color equal to the class, we have created two plots based off of the class.  With a facet, we can get very detailed.  Lets through some more variables into the mix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "AAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f760890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (284282745)>\n"
     ]
    }
   ],
   "source": [
    "plt = ggplot(aes(x = 'uniformity_cell_shape', y = 'bare_nuclei', color = 'class'), data = cancer_data) + \\\n",
    "        geom_point(position = 'jitter') + \\\n",
    "        ggtitle(\"The Effect of the Bare Nuclei and Cell Shape Uniformity on Classification\") + \\\n",
    "        facet_grid('clump_thickness', 'marginal_adhesion')\n",
    "print plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, legends for faceting are not yet implemented in the Python ggplot package.  In this example we faceted on the x-axis with **clump_thickness** and along the y-axis with **marginal_adhesion**, then created 100 plots of **uniformity_cell_shape** vs. **bare_nuclei** effect on **class**.\n",
    "\n",
    "I highly encourage you to check out https://ggplot.yhathq.com/docs/index.html to see all of the available geoms.  The best way to learn is to play with and visualize the data with many different plots and aesthetics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Machine Learning\n",
    "So now that we've acquired, audited, cleaned, and visualized our data, we have arrived at machine learning.  By formal definition from Tom Mitchell:\n",
    "\n",
    ">A computer program is set to learn from an experience E with \n",
    ">respect to some task T and some performance measure P if its performance \n",
    ">on T as measured by P improves with experience E.\n",
    "\n",
    "Okay, thats a bit ridiculous.  Essentially machine learning is the science of building algorithms that learn from data in order make predictions about the data.  There are two main classes of machine learning: supervised and unsupervised.\n",
    "\n",
    "In supervised learning, an algorithm will use the features of the data given to make a prediction about a known label.  For example, we will use supervised learning here to take features such as bare_nuclei and uniformity_cell_shape and predict a tumor class (benign or malignant).  This type of machine learning is called supervised because the class labels (benign or malignant) are a known quantity during learning, so we are *supervising* the algorithm with the \"correct\" answer.\n",
    "\n",
    "In unsupervised learning, an algorithm will use the features of the data to discover what types of labels there could be.  The \"correct\" answer is not known.\n",
    "\n",
    "In this session we will be mostly focused on supervised learning as we attempt to predict whether a tumor is benign or malignant.  We will also be focused on doing some practical machine learning, and will glaze over the algorithmic details.\n",
    "\n",
    "The first thing we have to do is to extract the class labels and features from <code>cancer_data</code> and store them as separate arrays.  In our first classifier we will only choose two features from <code>cancer_data</code> to keep things simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cancer_features = ['uniformity_cell_shape', 'bare_nuclei']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we call <code>values</code> on the dataframe to extract the values stored in the dataframe as an array of numpy arrays with the same dimensions as our subsetted dataframe.  Numpy is a powerful, high performance scientific computing package that implements arrays.  It is used internally by pandas.  We will use <code>labels</code> and <code>features</code> later on in our machine learning classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "labels = cancer_data['class'].values\n",
    "features = cancer_data[cancer_features].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An important concept in machine learning is to split the data set into training data and testing data.  The machine learning algorithm will use the subset of training data to build a classifier to predict labels.  We then test the accuracy of this classifier on the subset of testing data.  This is done in order to prevent overfitting the classifier to one given set of data.\n",
    "\n",
    "Overfitting is a major concern in the design of machine learning algorithms.  Conceptually, overfitting is when a classifier is really good at predicting the data used to build it, but isn't robust or general enough to predict new, yet unseen data all that well.\n",
    "\n",
    "To perform machine learning, we will use a package called sci-kit learn (sklearn for short).  The sklearn cross_validation module contains a function called <code>train_test_split</code> that will take in features and labels, and randomly select values into the training and testing subsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "features_train, features_test, labels_train, labels_test = train_test_split(features, \n",
    "                                                                            labels, \n",
    "                                                                            test_size = 0.3, \n",
    "                                                                            random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this example, we will build a Decision Tree Classifier.  The goal of a decision tree is to create a prediction by outlining a simple tree of decision rules.  These rules are built from the training data by slicing the data on simple boundaries and trying to minimize the prediction error of that boundary.  More details on decision trees can be found here: http://scikit-learn.org/stable/modules/tree.html\n",
    "\n",
    "The first step is to import the classifier from the <code>sklearn.tree</code> module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we create a variable to store the classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we have to fit the classifier to the training data. Both the training features (uniformity_cell_shape and bare_nuclei) and the labels (benign vs. malignant) are passed to the fit function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf.fit(features_train, labels_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The classifier is now ready to make some predictions.  We can use the score function to see how accurate the classifier is on the test data.  The score function will take the data in <code>features_test</code>, make a prediction of benign or malignant based on the decision tree that was fit to the training data, and compare that prediction to the true values in <code>labels_test</code>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.938095238095\n"
     ]
    }
   ],
   "source": [
    "print \"Accuracy score:\", clf.score(features_test,labels_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nearly all classifiers, decision trees included, will have paremeters that can be tuned to build a more accurate model.  Without any parameter tuning and using just two features we have made a pretty accurate prediction.  Good job!\n",
    "\n",
    "To get a better idea of what is going on, I have included a helper function to plot our test data along with the decision boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "mSYAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1143cb310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from class_vis import prettyPicture # helper class\n",
    "\n",
    "prettyPicture(clf, features_test, labels_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The area in red is where the classifier predicts a malignant tumor, whereas the blue area predicts a benign tumor.  The color of the points on the plot represents the true label of the data point.  Remember, there is no jitter included in this plot, so a number of data points are plotted on top of one another.\n",
    "\n",
    "The vertical and horizontal lines represent what is called the decision boundary.  For example, our classifier predicts all data points with uniformity_cell_shape greater than around 6.25 to be malignant."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}