basic_python_pandas_ols.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Programming"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# jupyter notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook will introduce you to programming in Python, and using the Jupyter Notebook environment.\n",
    "\n",
    "To begin, Python is what is known as an interpreted language. In other words, the Python program executes your code by interpreting it and translating it to machine language to be executed. This type of language is typically closer to human language, making it easier to learn, but is slower to execute than compiled languages like C and Fortran. Compiled languages takes the code, compiles it into executeble machine code, which can then be run much faster. There are some technologies to improve Python speed (Just in Time compiler), but we will forgo that for now. Speed really isn't an issue yet, we need to first learn how to write code.\n",
    "\n",
    "The power of Python comes from the large open source community of developers that have done the heavy lifting for many tasks by creating well documented easy to use libraries to help you write useful code quickly. There are particularly many libraries for data analytic functions, which is why Python is often the weapon of choice for data scientists, digital marketers, and data intensive businesses. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download / Setup\n",
    "\n",
    "Please download Anaconda **with Python 2.7** from [https://www.anaconda.com/download/](https://www.anaconda.com/download/).\n",
    "\n",
    "Download the appropriate version for your computer.\n",
    "\n",
    "Once installed, open up command prompt in windows or terminal in OS X.\n",
    "\n",
    "Launch the jupyter notebook app with the command: `jupyter notebook`.\n",
    "\n",
    "<img src=\"https://www.dropbox.com/s/qvixuoc1ubo00os/terminal.png?dl=1\" alt=\"terminal\" style=\"width: 400px\"/>\n",
    "\n",
    "\n",
    "You should see a browser pop up launching the address: `loclahost:8888`\n",
    "\n",
    "<img src=\"https://www.dropbox.com/s/kpjb1zfxr2aycgh/jupyter.png?dl=1\" alt=\"jupyter\" style=\"width: 400px\"/>\n",
    "\n",
    "\n",
    "Basically, Jupyter opened a local server on the port 8888. This is how you will communicate with your Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# General features of the environment\n",
    "\n",
    "* Think of Jupyter notebooks as a notebook... duh. It is a place where you can play with code and document what you are doing in order to create a cohesive document with code that does stuff.\n",
    "* Cells. Each bit of code is confied to a cell. Cells can be independently executed. Once a bit of code is executed, a number indicating the order of execution will appear on the left. Cells are selected using the dropdown menu at the top of the notebook. <img src=\"./celltypes.png\" alt=\"jupyter\" style=\"width: 700px\"/>\n",
    "* To execute a cell, you can use the play button, or shift+enter (automatically goes to next cell or insert one if already last cell), or control+enter (executes without going to next cell).\n",
    "* There are basically 2 types of cells that you will be using. Code cells are the default, and markdown cells for notation. **Double click on this cell to see how markdown works**\n",
    "* We will get to code cells in just a bit. For now, a quick markdown tutorial.\n",
    "\n",
    "## Markdown tutorial (`#` indicates headers, the more you put, the smaller the header)\n",
    "Markdown is a type of markup language that allows you to write a document embedding images, code snippets, math equations, etc. It is super useful!\n",
    "\n",
    "1. Here's a list\n",
    "2. Part 2\n",
    "1. Look, it automatically keeps numbering even though I typed the wrong number.\n",
    "1. Oops I did it again.\n",
    "\n",
    "In the lines above the tutorial, I've used markdowns to do a bulleted list, and used HTML code to embed an image.\n",
    "\n",
    "**Everyone loves math!** (this is how you bold)\n",
    "\n",
    "For in line math, you can do something like this $y=3x^2 \\implies x = \\pm\\sqrt{\\frac{y}{3}}$. Note that math is typed using a flavor of LaTex (via Mathjax). This is a common typesetting language used by scientists to write professional looking equations.\n",
    "\n",
    "If you want to write a centered math block, you do this:\n",
    "\n",
    "$$\n",
    "y=3x^2 \\implies x = \\pm\\sqrt{\\frac{y}{3}} \\\\\n",
    "\\text{you can break lines and do more math! } \\sum_{i=1}^N{i}=(N+1)\\times\\frac{N}{/2}\n",
    "$$\n",
    "\n",
    "If you need to figure out how to do someting in LaTex, your best bet is just to google whatever it is you need to do + \"latex\". Just be careful what word you put before LaTeX.\n",
    "\n",
    "If you want to embed code, you just do this: `# this is a snippet of code`.\n",
    "\n",
    "Or, if you want Jupyter to auto highlight syntax for Python:\n",
    "\n",
    "```Python\n",
    "print \"hello world!\" # This code prints \"hello world\"\n",
    "```\n",
    "\n",
    "or Java if you're so inclined...\n",
    "\n",
    "```Java\n",
    "public class HelloWorld {\n",
    "   public static void main(String[] args) {\n",
    "      // Prints \"Hello, World\" in the terminal window.\n",
    "      System.out.println(\"Hello, World\");\n",
    "   }\n",
    "}```\n",
    "\n",
    "For more information about working with Mathjax please see [this cheatsheet](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet#videos) $\\leftarrow$ this is how you do a hyperlink."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's get programming\n",
    "\n",
    "## Hello World...right of passage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello world!\n"
     ]
    }
   ],
   "source": [
    "print('Hello world!') # print is a base python function that \"prints\" | \"hello world\" is just what print outputs..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example\n",
    "Okay, now that \"Hello World\" is out of the way, let's first begin with a simple example. Python can do some basic arithmetic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# comments follow \"#\" sign\n",
    "3+2 # we can do addition! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3-2 # we can do subtraction!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3*2 # we can do multiplication!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3/2 # we can do division... kind of (it works in python 3 as expected)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What happened here? \n",
    "This is a good time to introduce the concept of data types. 3 and 2 are both integers, so the default result of the division of 2 integers is to return another integer. In this case, it is the closest integer lower than the actual result. We can complete the operation as expected by changing one of the data types to \"float.\" A float is a decimal. This means the computer memory will keep track of as many digits as necessary up to a certain number (the machine limit)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3./2 # Look, now it worked! We can do division as expected because of the \".\" after the 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we can import the division from Python 3 that infers the proper data type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from __future__ import division # this means, from the __future__ module, import the function division\n",
    "3/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5/6 # now all of our division works as expected, as long as the above cell has been executed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 2 - variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The concept of a variable is essential to programming. A variable is anything that can take on some value. Valid variable names begin with a letter in Python (or an underscore, \"_\"). Variables are case sensitive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    }
   ],
   "source": [
    "x = 5 # assign x as 5, note in programming, = typically means set variable as target value\n",
    "y = 6\n",
    "z = x+y # assign a variable as a sum of 2 other variables\n",
    "print z # print is a reserved keyword function in Python that outputs the subsequent code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'q' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-42d626a8c231>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mq\u001b[0m \u001b[1;31m# this won't work because we haven't assigned anything to q\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'q' is not defined"
     ]
    }
   ],
   "source": [
    "x + q # this won't work because we haven't assigned anything to q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "q = z\n",
    "answer = q+z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "answer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Variables don't have to be just numbers, they can also be words, or in programming lingo, \"strings\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a string!!!! l33t skill2\n"
     ]
    }
   ],
   "source": [
    "q = 'this is a string!!!! l33t skill2' #Note that strings are inside quotes. They can be single or double.\n",
    "print q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "cannot concatenate 'str' and 'int' objects",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-08d2db7f6c15>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mq\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mz\u001b[0m \u001b[1;31m# you can't add strings with numbers... (remember q is defined as a string and z is defined as the sum of x and y which are integers)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: cannot concatenate 'str' and 'int' objects"
     ]
    }
   ],
   "source": [
    "q + z # you can't add strings with numbers... (remember q is defined as a string and z is defined as the sum of x and y which are integers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'this is a string!!!! l33t skill2 oh this works!'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q + ' oh this works!' # You CAN \"add\" strings with strings, this is known as concatenation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'this is a string!!!! l33t skill211'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q + str(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Variables can be any data type. Here are some useful native Python data types: lists, sets, dictionaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A list is a Python data type that is an ordered collection of objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 5, 6, 8, 100, -3.0, -2.1, -10]\n"
     ]
    }
   ],
   "source": [
    "list1 = [2, 5, 6, 8, 100, -3.0, -2.1, -10]\n",
    "print list1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    }
   ],
   "source": [
    "# Lists have an attribute \"length\"\n",
    "print len(list1) # len is a default function in Python that computes how long an object is and returns an integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You can access a specific entry in a list, the entries start at 0.\n",
    "list1[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-10"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1[-1] # you can also use negative indices, where -1 is the last entry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2,\n",
       " 5,\n",
       " 6,\n",
       " 8,\n",
       " 100,\n",
       " -3.0,\n",
       " -2.1,\n",
       " -10,\n",
       " 2,\n",
       " 5,\n",
       " 6,\n",
       " 8,\n",
       " 100,\n",
       " -3.0,\n",
       " -2.1,\n",
       " -10,\n",
       " 2,\n",
       " 5,\n",
       " 6,\n",
       " 8,\n",
       " 100,\n",
       " -3.0,\n",
       " -2.1,\n",
       " -10]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1*3 # you can \"multiply\" lists, this appends (repeats) the same list to itself 3 times (or whatever you multiply by)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 5, 6, 8, 100, -3.0, -2.1, -10, 2, 5, 6, 8, 100, -3.0, -2.1, -10]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1+list1 # you can \"add\" lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 2, 'hello', [2, 5, 6, 8, 100, -3.0, -2.1, -10], [5, 6], -1]\n"
     ]
    }
   ],
   "source": [
    "# Lists can also include mixed data types, including other lists... what you put in a list is arbitrary\n",
    "list2 = [3, 2, 'hello', list1, [x, y], -1]\n",
    "print list2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list2[4][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 5, 6, 8, 100, -3.0, -2.1, -10, [3, 2, 'hello', [...], [5, 6], -1]]\n"
     ]
    }
   ],
   "source": [
    "# You can append lists (append is a functional attribute of a list), note that append replaces the list being appended with the new augmented list\n",
    "list1.append(list2)\n",
    "print list1 # the outtput is too big, so it omits some stuff as denoted by '...'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1[-1][4][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A set is a Python object that is similar to a list in that it is a collection of entries of objects. However, sets are unordered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{1, 1, 1, 2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "set0 = set([1, 1, 1, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set([2, 3, 4, 5, 9, -1])\n"
     ]
    }
   ],
   "source": [
    "set1 = {3,4,9,-1,5, 2} # sets are denoted by curly brackets with entries inside\n",
    "print set1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set1) #sets also have lengths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for *: 'set' and 'int'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-32-6a2c4d2a9055>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mset1\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m2\u001b[0m \u001b[1;31m# you can't multiiply sets\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for *: 'set' and 'int'"
     ]
    }
   ],
   "source": [
    "set1*2 # you can't multiiply sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'set' object does not support indexing",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-33-89e074f6ee46>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# since sets are unordered, you can't access specific items\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mset1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'set' object does not support indexing"
     ]
    }
   ],
   "source": [
    "# since sets are unordered, you can't access specific items\n",
    "set1[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for +: 'set' and 'set'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-34-f5b94ec38ee7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# you can't add sets\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mset1\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mset1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'set' and 'set'"
     ]
    }
   ],
   "source": [
    "# you can't add sets\n",
    "set1+set1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{-1, 2, 3, 4, 5, 9}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2, 4, 9}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# but you can subtract sets!\n",
    "set2 = {'a', 3, x, -1}\n",
    "set1 - set2 # finds only items in set1 but not set 2, note that 5 is in set1 and x, which = 5, is in set 2, is also omitted in this result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set([200, 1])\n"
     ]
    }
   ],
   "source": [
    "# sets only has unique objects\n",
    "set3 = {1,200,1,1,1}\n",
    "print set3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set(['a', 1, 2, 3, 4, 5, 200, 9, -1])\n"
     ]
    }
   ],
   "source": [
    "# You can get unions of sets, a union is all unique items in both sets.\n",
    "set4 = set1.union(set2).union(set3) # note you can chain these functions since the result of set1.union(set2) is a set, which also has the union property\n",
    "# unlike lists append function, you have to specify what to call the resulting union of sets\n",
    "print set4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why do we use sets? It is very useful for checking membership:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 30.69 times longer than the fastest. This could mean that an intermediate result is being cached \n",
      "10000000 loops, best of 3: 51.5 ns per loop\n"
     ]
    }
   ],
   "source": [
    "# % is an ipython magic indicator, they're \"magic\" commands that run only in iPython, not normal python % timeit times the code\n",
    "%timeit (-1 in set1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3, 4, 5, 9, -1]\n"
     ]
    }
   ],
   "source": [
    "list_1 = list(set1) # we can convert the set to a list\n",
    "print list_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 52.58 times longer than the fastest. This could mean that an intermediate result is being cached \n",
      "10000000 loops, best of 3: 105 ns per loop\n"
     ]
    }
   ],
   "source": [
    "# it's a little bit slower because the list is small, this becomes a big problem when the list is huge.\n",
    "%timeit (-1 in list_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, we will look at dictionaries. Dictionaries are objects that map keys to values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'a': 4, 'c': set([2, 3, 4, 5, 9, -1]), 'b': 7}\n"
     ]
    }
   ],
   "source": [
    "dict1 = {'a':4, 'b':7, 'c':set1} # dictionaries are defined by curly brackets with pair sof keys:values separated by commas\n",
    "print dict1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{-1, 2, 3, 4, 5, 9}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict1['c'] # we can look up the values associated with a key in a dictionary by usiing the indexing notation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict1['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'c', 'b']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can extract both the keys and values from the dictionary\n",
    "dict1.keys() #.keys is a function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, {-1, 2, 3, 4, 5, 9}, 7]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict1.values() #.values is a function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('a', 4), ('c', {-1, 2, 3, 4, 5, 9}), ('b', 7)]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict1.items() #we can get a list of tuples, another data type similar to lists, where each tuple is (key, value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tuples are like lists, but they are immutable (elements can't be changed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2, 3)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuple0 = (1, 2, 3) # let's specify a tuple (notice it uses parenthesis rather than square brackets)\n",
    "tuple0"
   ]
  },
  {
   "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[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-49-c9c6a9c53458>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtuple0\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m \u001b[1;31m# tules are immutable\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "tuple0[1]=5 # tules are immutable "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Programming principles: Loops, Booleans, and Functions\n",
    "\n",
    "A common thing that makes programming powerful is the ability to feed your system a sequence of generic instructions that are flexible enough to be computed over new data. To do this, we need to look at 3 things:\n",
    "\n",
    "1. Loops - repeating the same tasks for a specified duration or until a condition is met.\n",
    "1. Booleans - evaluating truth values, allows us to compute different things depending on some conditions\n",
    "1. Functions - a set of instructions that takes input data and maps on to some output.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic loops\n",
    "\n",
    "There are basically 2 kinds of loops. The first is to loop for a prespecified number of iterations, this is a \"for loop.\" The second is to loop until some conditions are met, a \"while loop.\"\n",
    "\n",
    "Let's begin with an example: add the numbers 1... 1000\n",
    "\n",
    "First we write a for loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500500"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = 0 # Start with some s = 0\n",
    "for i in range(1, 1001): # this line tells Python, for each item, i, in the iterable range 1 to 1001,\n",
    "    #note this range function constructs a list from 1 to 1000 inclusive. Note the colon to signify an indented block.\n",
    "    s = s+i # add i to the previous total. Note that the instructions inside the loop are indented one level.\n",
    "s # this just displays the final value, this is not indented so it is outside of the loop."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we write a while loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500500"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = 1 # initiate an i\n",
    "s = 0 # initiate an s\n",
    "while i<1001: # note this condition for the while loop\n",
    "    s = s+i # add to previous sum\n",
    "    i += 1 # This specifies the next i to add to s. The += 1 notation is the same as i = i+1\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every while loop can be written as a for loop and vice versa.\n",
    "\n",
    "Of course there's a simpler way to do this, we can also use the sum function on lists to do the same thing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500500"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = sum(range(1,1001)) # sums up the entire list of numbers to add up\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Booleans\n",
    "\n",
    "Many times, we need to evaluate logical statements, these are statements that result in a truth value: True or False.\n",
    "\n",
    "Herea are some examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3+2==5 # Note the double equal sign to evaluate equality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3+2<5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3+2>5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3+2<=5 # Note <= is less than or equal to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(3+2==5)&(3+2>5) # you can combine booleans, this is an \"and\" condition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(3+2==5)|(3+2>5) # This is an or condition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can insert booleans into loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "250500"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = 0\n",
    "for i in range(1,1001):\n",
    "    if i % 2 == 0: # This evaluates the remainder (or modulo) function\n",
    "        s = s+i # note a second indent, since this bit of code is evaluated when the if condition is satisfied\n",
    "s # this is sum of all the even numbers from 1 to 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "250000"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = 0\n",
    "for i in range(1,1001):\n",
    "    if i % 2 != 0: # This evaluates the remainder (or modulo) function\n",
    "        s = s+i # note a second indent, since this bit of code is evaluated when the if condition is satisfied\n",
    "s # this is the sum of all the odd numbers from 1 to 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python allows us to write these simple loops more compactly using list literals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "250000"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum([v for v in range(1,1001) if (v % 2 != 0)]) #sum all divisible by 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions\n",
    "$f(x)=$do something with x and return the result\n",
    "\n",
    "Let's write a function that takes a beginning, $n$, and an end value, $m$, to evaluate a consecutive sum.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def add_consecutive(n,m): # This defines a function that takes n and m as inputs, also see the colon for indented block\n",
    "    s = 0 \n",
    "    for i in range(n, m+1): # note where n and m are being used inside the function.\n",
    "        s = s+i\n",
    "    return s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So... what happened?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.add_consecutive>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_consecutive # we've defined a function that's now in the memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4999950000L"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_consecutive(0,99999) # we need to give it values for n and m to execute."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python allows functions to have both ordered inputs (like the one above, where order matters) and named inputs. Let's write a function that allows us to add every $k^{\\text{th}}$ number in a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def add_consecutive_k(n,m, k=1): # We initialize the kth input as 1, so that if it is not explicitly specified, \n",
    "    # the function assumes k=1\n",
    "    s = 0 \n",
    "    for i,j in enumerate(range(n, m+1)): # enumerate takes the list and returns the order, value pair\n",
    "        # here that pair is i (order) and j (value)\n",
    "        if (i+1)%k==0: # See how we used i and k here. Remember that Python is zero indexed. So the first item in an \n",
    "            # iterable is actually 0. THe first item in the range n to m is the 0th item. \n",
    "            s = s+j # note we add j to s not i, since j corresponds to the value in the list.\n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4977874531L, 4977874531L, 4977874531L)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_consecutive(0,99778), add_consecutive_k(0,99778), add_consecutive_k(0,99778, k=1) # we get the same answers, yay!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1659258251"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_consecutive_k(0,99778, k = 3) # now we can specify to only add every 3rd number. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Okay, we said that Python was super useful because it has lots of libraries. Let's look at one useful one, Pandas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas is a dataframe library that allows us to read in / save data and manipulate it in a tabular format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd # we are importing the entire module, and giving it a nickname \"pd\", conda installs this library by default\n",
    "# If you find a package that may be useful, it will generally tell you how to install it. Typically it involves opening command prompt in windows\n",
    "#or console in Mac and typing conda install package_name or pip install package_name "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pandas has tons of functions and capaility to read lots of file formats. One of the most common is csv (or comma separated value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This dataset has 181 entries.\n"
     ]
    }
   ],
   "source": [
    "# let's read in an example csv file\n",
    "df = pd.read_csv('https://www.dropbox.com/s/4bbkolouwwrt0dj/data_sales_intro_programming.csv?dl=1', sep = ',') \n",
    "# this function takes a bunch of options, one of them is the separator, sep =, it specifies comma by default.\n",
    "# however, depending on how the file structured, you can specify an appropriate separator\n",
    "print 'This dataset has {} entries.'.format(len(df))\n",
    "# print 'This dataset has'+ str(len(df)) +'entries.'.format(len(df)) # This is equivalent to above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>sales</th>\n",
       "      <th>ad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>6/26/2017</td>\n",
       "      <td>587</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>6/27/2017</td>\n",
       "      <td>813</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>6/28/2017</td>\n",
       "      <td>799</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>6/29/2017</td>\n",
       "      <td>623</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>6/30/2017</td>\n",
       "      <td>1761</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  sales  ad\n",
       "176  6/26/2017    587   0\n",
       "177  6/27/2017    813   0\n",
       "178  6/28/2017    799   1\n",
       "179  6/29/2017    623   0\n",
       "180  6/30/2017   1761   1"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['date', 'sales', 'ad'], dtype=object)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>sales</th>\n",
       "      <th>ad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>618</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>762</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>628</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>849</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date  sales  ad\n",
       "0  1/1/2017    857   0\n",
       "1  1/2/2017    618   0\n",
       "2  1/3/2017    762   0\n",
       "3  1/4/2017    628   0\n",
       "4  1/5/2017    849   1"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() # .head() let's you see the first few rows of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>sales</th>\n",
       "      <th>ad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2/20/2017</td>\n",
       "      <td>885</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2/21/2017</td>\n",
       "      <td>899</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2/22/2017</td>\n",
       "      <td>691</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2/23/2017</td>\n",
       "      <td>901</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2/24/2017</td>\n",
       "      <td>1821</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2/25/2017</td>\n",
       "      <td>1476</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2/26/2017</td>\n",
       "      <td>513</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2/27/2017</td>\n",
       "      <td>798</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2/28/2017</td>\n",
       "      <td>664</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>3/1/2017</td>\n",
       "      <td>847</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  sales  ad\n",
       "50  2/20/2017    885   1\n",
       "51  2/21/2017    899   0\n",
       "52  2/22/2017    691   1\n",
       "53  2/23/2017    901   1\n",
       "54  2/24/2017   1821   1\n",
       "55  2/25/2017   1476   1\n",
       "56  2/26/2017    513   0\n",
       "57  2/27/2017    798   0\n",
       "58  2/28/2017    664   0\n",
       "59   3/1/2017    847   0"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[50:60]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note this is a dataset with 3 columns and 181 entries. Each entry consists of a date, the sales total, and whether or not an ad was shown that morning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1/1/2017'"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['date'][0] # we can index a dataframe in several methosd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1/1/2017'"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.date[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\ipykernel\\__main__.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "date     1/1/2017\n",
       "sales         857\n",
       "ad              0\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ix[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>857</td>\n",
       "      <td>1/1/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>618</td>\n",
       "      <td>1/2/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>762</td>\n",
       "      <td>1/3/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>628</td>\n",
       "      <td>1/4/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>849</td>\n",
       "      <td>1/5/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1670</td>\n",
       "      <td>1/6/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1298</td>\n",
       "      <td>1/7/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>778</td>\n",
       "      <td>1/8/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>874</td>\n",
       "      <td>1/9/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>510</td>\n",
       "      <td>1/10/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1164</td>\n",
       "      <td>1/11/2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sales       date\n",
       "0     857   1/1/2017\n",
       "1     618   1/2/2017\n",
       "2     762   1/3/2017\n",
       "3     628   1/4/2017\n",
       "4     849   1/5/2017\n",
       "5    1670   1/6/2017\n",
       "6    1298   1/7/2017\n",
       "7     778   1/8/2017\n",
       "8     874   1/9/2017\n",
       "9     510  1/10/2017\n",
       "10   1164  1/11/2017"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0:10, ['sales', 'date']] # this is the best way to reference slices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.date[0]) # It looks like the data we read in is fine,\n",
    "#but if we look at the data type for one of the date entries, we see that it is a string an not a date\n",
    "# type is a native Python function to tell you what is the data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.date = pd.to_datetime(df.date) # we can convert the dates using pandas' to_datetime function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas._libs.tslib.Timestamp"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.date[0]) # Now the data type is pandas' datetime index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2017-01-01 00:00:00')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0, 'date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "        date  sales  ad\n",
       "0 2017-01-01    857   0\n",
       "1 2017-01-02    618   0\n",
       "2 2017-01-03    762   0\n",
       "3 2017-01-04    628   0\n",
       "4 2017-01-05    849   1"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Converting to pandas timestamp allows us to use datetime modules\n",
    "import datetime\n",
    "df['dow'] = df.date.map(datetime.date.weekday) #the weekday function converts day of week from 0 to 6, where 0 is Monday and 6 is Sunday\n",
    "#  df.date is a pandas series (column), .map is an attribute of pandas dataframes/series that maps a function to each value in series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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      ],
      "text/plain": [
       "        date  sales  ad  dow\n",
       "0 2017-01-01    857   0    6\n",
       "1 2017-01-02    618   0    0\n",
       "2 2017-01-03    762   0    1\n",
       "3 2017-01-04    628   0    2\n",
       "4 2017-01-05    849   1    3"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's convert the 0-6 to words, to do so we first define a dictionary. First, we create a list of keys (number dow format) and a list of values (text dow format). We then \"zip\" them together into a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "numdow = range(0,7) # range creates a consecutive list of integers starting with the first entry and ending with the last entry - 1 (not incusive)\n",
    "textdow = ['Mon','Tue','Wed','Thu','Fri','Sat', 'Sun']\n",
    "dowdict = dict(zip(numdow, textdow)) # zip creates a \"zip\" object that zips 2 lists together, we convert this zipped list to a dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'Mon', 1: 'Tue', 2: 'Wed', 3: 'Thu', 4: 'Fri', 5: 'Sat', 6: 'Sun'}"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dowdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Sun'"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dowdict[6] # we can access the word associated with each entry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.dow = df.dow.apply(lambda x: dowdict[x]) # lambda is a temporary \"function\" that acts on x, \n",
    "# Dataframe allows us to also apply a function to each entry of a column\n",
    "# If each entry is x, then our temporary function of dowdict[x] will return the corresponding string format dow to the existing\n",
    "# numerical entry for dow\n",
    "# altogether, this syntax says, on df.dow apply the lambda function of evaluating dowdict for each value x of df.dow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Thu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  sales  ad  dow\n",
       "0 2017-01-01    857   0  Sun\n",
       "1 2017-01-02    618   0  Mon\n",
       "2 2017-01-03    762   0  Tue\n",
       "3 2017-01-04    628   0  Wed\n",
       "4 2017-01-05    849   1  Thu"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# we can also convert the dates to month\n",
    "df['ym'] = df.date.apply(lambda x: datetime.date(x.year, x.month, 1)) #datetime function takes 3 ordered arguments (year, mth, day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>0</td>\n",
       "      <td>Mon</td>\n",
       "      <td>2017-01-01</td>\n",
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       "      <td>2017-01-03</td>\n",
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       "      <td>Tue</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>628</td>\n",
       "      <td>0</td>\n",
       "      <td>Wed</td>\n",
       "      <td>2017-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-05</td>\n",
       "      <td>849</td>\n",
       "      <td>1</td>\n",
       "      <td>Thu</td>\n",
       "      <td>2017-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  sales  ad  dow          ym\n",
       "0 2017-01-01    857   0  Sun  2017-01-01\n",
       "1 2017-01-02    618   0  Mon  2017-01-01\n",
       "2 2017-01-03    762   0  Tue  2017-01-01\n",
       "3 2017-01-04    628   0  Wed  2017-01-01\n",
       "4 2017-01-05    849   1  Thu  2017-01-01"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() # now we can see that dow is comprised of strings, and we have the YM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### One awesome thing about dataframes is that we can group by variables to do conditional summaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th rowspan=\"2\" valign=\"top\">Fri</th>\n",
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       "      <th>1</th>\n",
       "      <td>1527.692308</td>\n",
       "    </tr>\n",
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       "      <th rowspan=\"2\" valign=\"top\">Mon</th>\n",
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       "      <td>749.277778</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">Sat</th>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Sun</th>\n",
       "      <th>0</th>\n",
       "      <td>704.933333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>866.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Thu</th>\n",
       "      <th>0</th>\n",
       "      <td>780.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>817.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Tue</th>\n",
       "      <th>0</th>\n",
       "      <td>708.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>923.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Wed</th>\n",
       "      <th>0</th>\n",
       "      <td>723.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>871.727273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sales\n",
       "dow ad             \n",
       "Fri 0   1108.461538\n",
       "    1   1527.692308\n",
       "Mon 0    749.277778\n",
       "    1    861.125000\n",
       "Sat 0   1178.700000\n",
       "    1   1572.533333\n",
       "Sun 0    704.933333\n",
       "    1    866.727273\n",
       "Thu 0    780.700000\n",
       "    1    817.687500\n",
       "Tue 0    708.750000\n",
       "    1    923.166667\n",
       "Wed 0    723.666667\n",
       "    1    871.727273"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's look at the average sales by dow\n",
    "df.groupby(['dow', 'ad'])[['sales']].mean() # we first groupby a variable (dow), then select a remaning column (sales) and apply the \n",
    "# summary function \"mean\" to calculate the conditional mean of sales by day of week\n",
    "# When are sales the highest?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Can we look at conditional averages of sales on both dow and advertising?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>Sat</th>\n",
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       "      <td>1572.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>704.933333</td>\n",
       "      <td>866.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thu</th>\n",
       "      <td>780.700000</td>\n",
       "      <td>817.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tue</th>\n",
       "      <td>708.750000</td>\n",
       "      <td>923.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wed</th>\n",
       "      <td>723.666667</td>\n",
       "      <td>871.727273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ad             0            1\n",
       "dow                          \n",
       "Fri  1108.461538  1527.692308\n",
       "Mon   749.277778   861.125000\n",
       "Sat  1178.700000  1572.533333\n",
       "Sun   704.933333   866.727273\n",
       "Thu   780.700000   817.687500\n",
       "Tue   708.750000   923.166667\n",
       "Wed   723.666667   871.727273"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_summary = df.groupby(['dow', 'ad'])['sales'].mean().unstack('ad')\n",
    "df_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What's the effect of advertising?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ad</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>ad_diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dow</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>1108.461538</td>\n",
       "      <td>1527.692308</td>\n",
       "      <td>419.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mon</th>\n",
       "      <td>749.277778</td>\n",
       "      <td>861.125000</td>\n",
       "      <td>111.847222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>1178.700000</td>\n",
       "      <td>1572.533333</td>\n",
       "      <td>393.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>704.933333</td>\n",
       "      <td>866.727273</td>\n",
       "      <td>161.793939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thu</th>\n",
       "      <td>780.700000</td>\n",
       "      <td>817.687500</td>\n",
       "      <td>36.987500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tue</th>\n",
       "      <td>708.750000</td>\n",
       "      <td>923.166667</td>\n",
       "      <td>214.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wed</th>\n",
       "      <td>723.666667</td>\n",
       "      <td>871.727273</td>\n",
       "      <td>148.060606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ad             0            1     ad_diff\n",
       "dow                                      \n",
       "Fri  1108.461538  1527.692308  419.230769\n",
       "Mon   749.277778   861.125000  111.847222\n",
       "Sat  1178.700000  1572.533333  393.833333\n",
       "Sun   704.933333   866.727273  161.793939\n",
       "Thu   780.700000   817.687500   36.987500\n",
       "Tue   708.750000   923.166667  214.416667\n",
       "Wed   723.666667   871.727273  148.060606"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_summary['ad_diff'] = df_summary[1]-df_summary[0]\n",
    "df_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Is there a difference by month?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ad</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ym</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>821.705882</td>\n",
       "      <td>1091.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-01</th>\n",
       "      <td>819.083333</td>\n",
       "      <td>1094.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-01</th>\n",
       "      <td>803.857143</td>\n",
       "      <td>1135.470588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-01</th>\n",
       "      <td>850.357143</td>\n",
       "      <td>967.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-01</th>\n",
       "      <td>830.480000</td>\n",
       "      <td>1185.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01</th>\n",
       "      <td>809.263158</td>\n",
       "      <td>1217.272727</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ad                   0            1\n",
       "ym                                 \n",
       "2017-01-01  821.705882  1091.857143\n",
       "2017-02-01  819.083333  1094.375000\n",
       "2017-03-01  803.857143  1135.470588\n",
       "2017-04-01  850.357143   967.625000\n",
       "2017-05-01  830.480000  1185.166667\n",
       "2017-06-01  809.263158  1217.272727"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['ym', 'ad'])['sales'].mean().unstack('ad')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## These tables are all fine and good, can we visualize it?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# to do so, we should import some more libraries %matplotlib allows the notebook to display plots\n",
    "% matplotlib inline\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let's plot the time series at the daily level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xac0a898>]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4hPFsHLzyLJ7nKlI8r0dpTQjw+8sNq21DHnYHSeWeKOO5hV0nS/IiyyDKc4797w+xUMmS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\nWUREREQko/8Hn//KTfByYDMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xab6c1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(df.date, df.sales, color = 'purple')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's look at the time series seprately for ad and no ad days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xad2bcc0>]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bJcKYp1lGdQyhRjIC9i/1VL8O97qfkfLi9IDQckR/s/zUNM6FAb2wDCEPIuoC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xad2b390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df.loc[df.ad == 1, 'date'], df.loc[df.ad == 1, 'sales'])\n",
    "plt.plot(df.loc[df.ad == 0, 'date'], df.loc[df.ad == 0, 'sales'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can do the same plot using only pandas. But Pandas does not \"fill in the missing values\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ad</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>857.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>618.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>762.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>628.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>849.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1670.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-07</th>\n",
       "      <td>1298.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>778.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>874.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>510.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>684.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-13</th>\n",
       "      <td>1084.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1406.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>934.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-16</th>\n",
       "      <td>880.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-17</th>\n",
       "      <td>742.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-18</th>\n",
       "      <td>703.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>736.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1686.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1932.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-22</th>\n",
       "      <td>848.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23</th>\n",
       "      <td>525.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>937.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-25</th>\n",
       "      <td>618.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-26</th>\n",
       "      <td>792.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-27</th>\n",
       "      <td>1096.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-28</th>\n",
       "      <td>1464.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>645.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>991.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01</th>\n",
       "      <td>832.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-02</th>\n",
       "      <td>1182.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-03</th>\n",
       "      <td>1304.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-04</th>\n",
       "      <td>594.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-05</th>\n",
       "      <td>860.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-06</th>\n",
       "      <td>810.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-07</th>\n",
       "      <td>517.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>977.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1537.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1237.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-11</th>\n",
       "      <td>541.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-12</th>\n",
       "      <td>598.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-14</th>\n",
       "      <td>558.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-15</th>\n",
       "      <td>817.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1073.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-17</th>\n",
       "      <td>1292.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-18</th>\n",
       "      <td>699.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1094.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1064.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>876.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-22</th>\n",
       "      <td>812.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-23</th>\n",
       "      <td>1178.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1842.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-25</th>\n",
       "      <td>759.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26</th>\n",
       "      <td>587.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-27</th>\n",
       "      <td>813.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>799.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-29</th>\n",
       "      <td>623.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1761.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "ad               0       1\n",
       "date                      \n",
       "2017-01-01   857.0     NaN\n",
       "2017-01-02   618.0     NaN\n",
       "2017-01-03   762.0     NaN\n",
       "2017-01-04   628.0     NaN\n",
       "2017-01-05     NaN   849.0\n",
       "2017-01-06     NaN  1670.0\n",
       "2017-01-07  1298.0     NaN\n",
       "2017-01-08     NaN   778.0\n",
       "2017-01-09     NaN   874.0\n",
       "2017-01-10   510.0     NaN\n",
       "2017-01-11     NaN  1164.0\n",
       "2017-01-12     NaN   684.0\n",
       "2017-01-13  1084.0     NaN\n",
       "2017-01-14     NaN  1406.0\n",
       "2017-01-15     NaN   934.0\n",
       "2017-01-16   880.0     NaN\n",
       "2017-01-17   742.0     NaN\n",
       "2017-01-18   703.0     NaN\n",
       "2017-01-19     NaN   736.0\n",
       "2017-01-20     NaN  1686.0\n",
       "2017-01-21     NaN  1932.0\n",
       "2017-01-22   848.0     NaN\n",
       "2017-01-23   525.0     NaN\n",
       "2017-01-24     NaN   937.0\n",
       "2017-01-25   618.0     NaN\n",
       "2017-01-26   792.0     NaN\n",
       "2017-01-27  1096.0     NaN\n",
       "2017-01-28  1464.0     NaN\n",
       "2017-01-29     NaN   645.0\n",
       "2017-01-30     NaN   991.0\n",
       "...            ...     ...\n",
       "2017-06-01   832.0     NaN\n",
       "2017-06-02  1182.0     NaN\n",
       "2017-06-03  1304.0     NaN\n",
       "2017-06-04   594.0     NaN\n",
       "2017-06-05   860.0     NaN\n",
       "2017-06-06   810.0     NaN\n",
       "2017-06-07   517.0     NaN\n",
       "2017-06-08     NaN   977.0\n",
       "2017-06-09     NaN  1537.0\n",
       "2017-06-10     NaN  1237.0\n",
       "2017-06-11   541.0     NaN\n",
       "2017-06-12   598.0     NaN\n",
       "2017-06-13     NaN  1130.0\n",
       "2017-06-14   558.0     NaN\n",
       "2017-06-15   817.0     NaN\n",
       "2017-06-16     NaN  1073.0\n",
       "2017-06-17  1292.0     NaN\n",
       "2017-06-18   699.0     NaN\n",
       "2017-06-19     NaN  1094.0\n",
       "2017-06-20     NaN  1064.0\n",
       "2017-06-21     NaN   876.0\n",
       "2017-06-22   812.0     NaN\n",
       "2017-06-23  1178.0     NaN\n",
       "2017-06-24     NaN  1842.0\n",
       "2017-06-25   759.0     NaN\n",
       "2017-06-26   587.0     NaN\n",
       "2017-06-27   813.0     NaN\n",
       "2017-06-28     NaN   799.0\n",
       "2017-06-29   623.0     NaN\n",
       "2017-06-30     NaN  1761.0\n",
       "\n",
       "[181 rows x 2 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['date','ad'])['sales'].mean().unstack('ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xad2b908>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vmSlPBL7HFr4mB54PlNL7KKWnUUrPppSeDRacX0wpPQrgJgBvDd3oFwNYppQ+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\nUofcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xabf1908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['date','ad'])['sales'].mean().unstack('ad').plot(figsize=(8,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the ad vs. no ad by day of week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ad</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dow</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>704.933333</td>\n",
       "      <td>866.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tue</th>\n",
       "      <td>708.750000</td>\n",
       "      <td>923.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wed</th>\n",
       "      <td>723.666667</td>\n",
       "      <td>871.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mon</th>\n",
       "      <td>749.277778</td>\n",
       "      <td>861.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thu</th>\n",
       "      <td>780.700000</td>\n",
       "      <td>817.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>1108.461538</td>\n",
       "      <td>1527.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>1178.700000</td>\n",
       "      <td>1572.533333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ad             0            1\n",
       "dow                          \n",
       "Sun   704.933333   866.727273\n",
       "Tue   708.750000   923.166667\n",
       "Wed   723.666667   871.727273\n",
       "Mon   749.277778   861.125000\n",
       "Thu   780.700000   817.687500\n",
       "Fri  1108.461538  1527.692308\n",
       "Sat  1178.700000  1572.533333"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['dow', 'ad'])['sales'].mean().unstack('ad').sort_values(by = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\pandas\\plotting\\_core.py:179: UserWarning: 'colors' is being deprecated. Please use 'color'instead of 'colors'\n",
      "  warnings.warn((\"'colors' is being deprecated. Please use 'color'\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb5123c8>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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voqqunaTtO33Usjme03cBM8iaYZRLaGwYZQs4su8CerY3g3vevw/cB4wDIzUP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\nh1o57AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb50de48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['dow', 'ad'])['sales'].mean().unstack('ad').sort_values(0).plot(kind='bar', colors = ['green', 'red'], figsize=[8,6]) # we also sort the table on values for no ad days"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's say your boss wants to see the graph above for each month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How do you do this?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To do this effectively, we ought to introduce the concept of loops. There are 2 types: for loops and while loops. \n",
    "\n",
    "For loops execute some code \"for\" some list of values.\n",
    "\n",
    "While loops execute \"while\" some condition is satisfied."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## So which type of loop should we use to solve the problem posed by your boss?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The for loop is the most natural, we know exactly which values to loop over (months)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([datetime.date(2017, 1, 1), datetime.date(2017, 2, 1),\n",
       "       datetime.date(2017, 3, 1), datetime.date(2017, 4, 1),\n",
       "       datetime.date(2017, 5, 1), datetime.date(2017, 6, 1)], dtype=object)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mths = df.ym.unique()\n",
    "mths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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x/v9W4FiKswnviYgFZdbWG+laOmdRbAW0UNzTYHFEPFNmXTmQNJ1dQxB/Sm1H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\nZplyAJiZZer/A8vDthHVd5BeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb3d8860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb8d5b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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E3BgRs4CpwCqK67qU+guih15Pcc7FH4G1QCvQSNdhavTPT/sQxCIG2BCElSur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\ngJlZpv4TJ/IYdX4j3J0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc828e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Ki8SOp/iKztkUF7j9ZCiLNusvjwDMquvPBTI/oQiNVwMrgaOBv6b4tjOzpuUA\nMNvdbcA7JL1S0v7A2yluY/GMpONSn/cBP67o/15gfbotxFZgDsWtIsyalncBmfUQEXdL+hbwS+A3\n7NqVMxe4Ih3YfYT0XcYR8Vhxs9D//4n/p0BHK31fgOXJt4IwM8uUdwGZmWXKAWBmlikHgJlZphwA\nZmaZcgCYmWXKAWBmlikHgJlZphwAZmaZ+n+acWINf+3bnAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9a144a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SVqT5x0kKSePS9BOS9irxxzDrlUPArBNJJwKzKB6F+V5gapp1PfCZ9CSxR4AF\nEfEsMELSfsApwHLgFEmHAc9GxAsD/gOY1cDXCZjt6hTg1o4/4Ok2zHsD+0dEx6NIrwNuTp/vo7gQ\n7lSKx33OoLgI6ycDWbRZPbwnYNa1Wi6g+QlFcBwG3AYcB/w5xVO7zAY1h4DZru4FzpO0p6R9gfdQ\n3HLh95JOSX0+BNxT0f+DwOPpFhhbgZkUt8UwG9Q8HGTWSUQ8JOlG4GHgaXYO68wBrkoHe58kPZs3\nItYVN0l9/Zv/T4HWJnpegmXMt40wM8uYh4PMzDLmEDAzy5hDwMwsYw4BM7OMOQTMzDLmEDAzy5hD\nwMwsYw4BM7OM/X9gSK3m56qpuAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xca9a198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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opmuhdG5GLmWQbUZWIyK2RMTNETEbmA60U1wTqNRfQFU6uuwCGtTs9Vs/yepS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\nygFgZpap/w8OLe6k289rBQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb90f208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mths = df.ym.unique() # first we grab the unique months in our dataset\n",
    "for m in mths:\n",
    "    df[df.ym == m].groupby(['dow', 'ad'])['sales'].mean().unstack('ad').sort_values(by=0).plot(kind='bar', title = str(m))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Putting it all together!\n",
    "### What if we want to be able to streamline the whole process from reading a file to plotting so we can do this task easily in the future?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's write a function that takes the file path as an input and saves the file in some output path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def read_file_make_mthly_plot(infile, outpath): # note that the folder of outpath must already exist!\n",
    "    df = pd.read_csv(infile, sep = ',') # read in file\n",
    "    df.date = pd.to_datetime(df.date) # convert date to proper data type\n",
    "    df['dow'] = df.date.map(datetime.date.weekday) # create dow variable\n",
    "    numdow = range(0,7)\n",
    "    textdow = ['Mon','Tue','Wed','Thu','Fri','Sat', 'Sun']\n",
    "    dowdict = dict(zip(numdow, textdow)) # create conversion dictionary to text format\n",
    "    df.dow = df.dow.apply(lambda x: dowdict[x]) # convert dow data to text format\n",
    "    df['ym'] = df.date.apply(lambda x: datetime.date(x.year, x.month, 1)) # create ym variable\n",
    "    # next we loop through each month and plot the sales split on advertising\n",
    "    mths = df.ym.unique() # first we grab the unique months in our dataset\n",
    "    out = list() # we will collect where the files are saved in this out list, first we define an empty list\n",
    "    for m in mths:\n",
    "        df[df.ym == m].groupby(['dow', 'ad'])['sales'].mean().unstack('ad').sort_values(by = 0).plot(kind='bar', title = str(m), figsize = (15,10))\n",
    "        fname = outpath + str(m.year)+'_'+str(m.month)+'.png'\n",
    "        plt.savefig(fname, dpi = 230, bbox_inches='tight') # this saves the figure with a specified file name = fname and the dpi argument specifies the resolution \n",
    "        plt.close() # close the plot so it won't be displayed in the notebook\n",
    "        out.append(fname) # we append out with newest file\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['./figures/2017_1.png', './figures/2017_2.png', './figures/2017_3.png', './figures/2017_4.png', './figures/2017_5.png', './figures/2017_6.png']\n"
     ]
    }
   ],
   "source": [
    "out = read_file_make_mthly_plot('https://www.dropbox.com/s/4bbkolouwwrt0dj/data_sales_intro_programming.csv?dl=1', './figures/')\n",
    "print out # The '.' means the current directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Voila!\n",
    "Now your monthly task has gotten a lot easier!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merging datasets\n",
    "\n",
    "One of the most common tasks in data analysis is combining data from multiple sources. This requires merging of datasets. Pandas provides a simple merging function.\n",
    "\n",
    "Conceptually, merging requires two datasets to have matching columns (variables) on which to merge. For example, I may want to merge individual firms' revenue data from tax collecting agencies with their online reviews data at the quarterly level. This would require both datasets to have a unique business identifier (perhaps street address?) and quarterly timestamps. Using these 2 variables, we can match up the rows of tax data with corresponding rows of review data.\n",
    "\n",
    "There are basically 3 types of merges:\n",
    "\n",
    "1. **Left merge**. Left dataset A stays unchanged, matched columns from right dataset B are appended to A. Unmatched rows of A will have null (missing) values for B columns.\n",
    "2. **Inner merge**. Only matched rows from both A and B are kept.\n",
    "3. **Outer merge**. All rows from both datasets are kept. Matched rows will have data from both A&B, unmatched rows of A will have null values in B columns, unmatched rows of B will have null alues in A columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple merge examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a dataset A\n",
    "index_a = ['a','b', 'c', 'd']\n",
    "data_a = [1,2,3,4]\n",
    "index_b = ['b', 'c', 'e', 'f']\n",
    "data_b = [5,2,5,8]\n",
    "A = pd.DataFrame(zip(index_a, data_a))\n",
    "A.columns = ['ind', 'a']\n",
    "B = pd.DataFrame(zip(index_b, data_b))\n",
    "B.columns = ['ind', 'b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame A: \n",
      "  ind  a\n",
      "0   a  1\n",
      "1   b  2\n",
      "2   c  3\n",
      "3   d  4\n",
      "DataFrame B: \n",
      "  ind  b\n",
      "0   b  5\n",
      "1   c  2\n",
      "2   e  5\n",
      "3   f  8\n"
     ]
    }
   ],
   "source": [
    "print('DataFrame A: ')\n",
    "print A\n",
    "print('DataFrame B: ')\n",
    "print B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Left merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Left merge result: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
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       "      <td>NaN</td>\n",
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      "text/plain": [
       "  ind  a    b\n",
       "0   a  1  NaN\n",
       "1   b  2  5.0\n",
       "2   c  3  2.0\n",
       "3   d  4  NaN"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C = A.merge(B, how = 'left', on = 'ind')\n",
    "# C = A.merge(B, how = 'left', left_on = 'ind', right_on = 'ind') # this also works in case you have different column names\n",
    "print('Left merge result: ')\n",
    "C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inner merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner merge result: \n"
     ]
    },
    {
     "data": {
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       "  ind  a  b\n",
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     "metadata": {},
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   "source": [
    "C = A.merge(B, how = 'inner', on = 'ind')\n",
    "# C = A.merge(B, how = 'inner', left_on = 'ind', right_on = 'ind') # this also works in case you have different column names\n",
    "print('Inner merge result: ')\n",
    "C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outer merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer merge result: \n"
     ]
    },
    {
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       "      <th>2</th>\n",
       "      <td>c</td>\n",
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       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>e</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ind    a    b\n",
       "0   a  1.0  NaN\n",
       "1   b  2.0  5.0\n",
       "2   c  3.0  2.0\n",
       "3   d  4.0  NaN\n",
       "4   e  NaN  5.0\n",
       "5   f  NaN  8.0"
      ]
     },
     "execution_count": 111,
     "metadata": {},
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   ],
   "source": [
    "C = A.merge(B, how = 'outer', on = 'ind')\n",
    "# C = A.merge(B, how = 'outer', left_on = 'ind', right_on = 'ind') # this also works in case you have different column names\n",
    "print('Outer merge result: ')\n",
    "C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge example with multiple index columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "A['ind2'] = ['x', 'y', 'beta', 'alpha']\n",
    "B['ind2'] = ['y', 'y', 'beta', 'alpha']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame A: \n",
      "  ind  a   ind2\n",
      "0   a  1      x\n",
      "1   b  2      y\n",
      "2   c  3   beta\n",
      "3   d  4  alpha\n",
      "DataFrame B: \n",
      "  ind  b   ind2\n",
      "0   b  5      y\n",
      "1   c  2      y\n",
      "2   e  5   beta\n",
      "3   f  8  alpha\n"
     ]
    }
   ],
   "source": [
    "print('DataFrame A: ')\n",
    "print A\n",
    "print('DataFrame B: ')\n",
    "print B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Left merge on ind and ind2: \n"
     ]
    },
    {
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       "      <td>beta</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
       "      <td>d</td>\n",
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       "  ind  a   ind2    b\n",
       "0   a  1      x  NaN\n",
       "1   b  2      y  5.0\n",
       "2   c  3   beta  NaN\n",
       "3   d  4  alpha  NaN"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C = A.merge(B, how = 'left', on = ['ind', 'ind2'])\n",
    "print ('Left merge on ind and ind2: ')\n",
    "C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner merge result: \n"
     ]
    },
    {
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       "  ind  a ind2  b\n",
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     "execution_count": 115,
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   ],
   "source": [
    "C = A.merge(B, how = 'inner', on = ['ind', 'ind2'])\n",
    "print('Inner merge result: ')\n",
    "C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer merge result: \n"
     ]
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       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>2.0</td>\n",
       "      <td>y</td>\n",
       "      <td>5.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>3.0</td>\n",
       "      <td>beta</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d</td>\n",
       "      <td>4.0</td>\n",
       "      <td>alpha</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c</td>\n",
       "      <td>NaN</td>\n",
       "      <td>y</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>e</td>\n",
       "      <td>NaN</td>\n",
       "      <td>beta</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alpha</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ind    a   ind2    b\n",
       "0   a  1.0      x  NaN\n",
       "1   b  2.0      y  5.0\n",
       "2   c  3.0   beta  NaN\n",
       "3   d  4.0  alpha  NaN\n",
       "4   c  NaN      y  2.0\n",
       "5   e  NaN   beta  5.0\n",
       "6   f  NaN  alpha  8.0"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C = A.merge(B, how = 'outer', on = ['ind', 'ind2'])\n",
    "print('Outer merge result: ')\n",
    "C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analytics Basics: Regression\n",
    "\n",
    "Line of best fit example (assumes you understand linear regression). If not: https://en.wikipedia.org/wiki/Linear_regression\n",
    "\n",
    "![Regression Figure](https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Linear_regression.svg/438px-Linear_regression.svg.png)\n",
    "\n",
    "Basically, we want to find the (linear) relationship between some explanatory variable(s), X, and some dependent variable, y.\n",
    "\n",
    "The relationship between the 2 variables in the simplest case of one explanatory variable is assumed to be of the form:\n",
    "$$\n",
    "y_i = a+bX_i+e_i\n",
    "$$\n",
    "\n",
    "Where $a$ and $b$ are the intercept and slope of the relationship, $e_i$ is a random disturbance (noise), $i$ is the observation index $i\\in\\{1,...,N\\}$ for a dataset with $N$ observations.\n",
    "\n",
    "We will first simulate some data with pre-specified relationship. We will then show that the relationship can be recovered using linear regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np # Numpy is one of the fundamental packages in Python on which the analytics muscle of Python is built"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Simulate data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "obs = 5000 # Specifiy number of observations to simulate\n",
    "# Specifiy True Relationship Parameters\n",
    "A = 10\n",
    "b = -4\n",
    "S = 12. # Noise parameter (how disperse the distribution from which noise, e_i, is drawn)\n",
    "# simulate data\n",
    "np.random.seed(8989) # This sets the random seed so the \"random\" draw is the same for everyone who executes the code\n",
    "x = np.random.uniform(-20,20, obs) # this creates a uniform random draw between -20 and 20 for specified number of observations\n",
    "y = A+b*x+np.random.normal(0,S,obs)\n",
    "# Put x and y into a dataframe\n",
    "data = pd.DataFrame(zip(x, y))\n",
    "data.columns = ['x', 'y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,u'Scatterplot of x vs y')"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bkuxMsnP//v2HWIYkaTbj/jI2M7TVTB2raltVbaiqDStXrhxzGZKkKYca9PdPTcm0532t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\nL0mdM+glqXP/H8vGz8T4luyYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcbf37b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xcbf33c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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saPXhtJkZWUry/fPL+JxW7t3eXAvWVt0+/QEvR8ZWqHNaSGQL6CVw20w8MNDK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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcb04630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.hist(data.x) # fairly uniform\n",
    "plt.title('Distribution of X')\n",
    "plt.figure()\n",
    "plt.hist(data.y) # looks more normally distrbuted\n",
    "plt.title('Distribution of y')\n",
    "plt.figure()\n",
    "plt.scatter(data.x, data.y, s=2, alpha =.5) # We can kind of eyeball a line of best fit\n",
    "plt.title('Scatterplot of x vs y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run regression with Statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf # import the formula api from statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ols = smf.ols('y~x', data) # specify the model\n",
    "# SMF models are specified as dependent ~ independent1+independent2...etc.\n",
    "# Here, we have just y and x, so y~x\n",
    "# We also have to specify which datasets to look for the variable names\n",
    "# Here, our dataset is called data\n",
    "results = ols.fit() # This runs the regression, stores output as results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall our A is 10 and b is -4\n",
      "Our estimated A is 9.83 and b is -4.02\n"
     ]
    }
   ],
   "source": [
    "print('Recall our A is {} and b is {}'.format(A,b))\n",
    "print('Our estimated A is {:.2f} and b is {:.2f}'.format(results.params[0], results.params[1])) # Nailed it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept    9.831341\n",
       "x           -4.021471\n",
       "dtype: float64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.params # this returns estimated parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.83134146262 9.83134146262\n",
      "-4.02147053671 -4.02147053671\n"
     ]
    }
   ],
   "source": [
    "#  parameter tables are like positional dictionaries\n",
    "print results.params['Intercept'], results.params[0]\n",
    "print results.params['x'], results.params[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.937</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.937</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>7.463e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 29 Nov 2017</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:46:26</td>     <th>  Log-Likelihood:    </th> <td> -19468.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  5000</td>      <th>  AIC:               </th> <td>3.894e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  4998</td>      <th>  BIC:               </th> <td>3.895e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    9.8313</td> <td>    0.168</td> <td>   58.482</td> <td> 0.000</td> <td>    9.502</td> <td>   10.161</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x</th>         <td>   -4.0215</td> <td>    0.015</td> <td> -273.192</td> <td> 0.000</td> <td>   -4.050</td> <td>   -3.993</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.042</td> <th>  Durbin-Watson:     </th> <td>   1.992</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.594</td> <th>  Jarque-Bera (JB):  </th> <td>   0.988</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.025</td> <th>  Prob(JB):          </th> <td>   0.610</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.047</td> <th>  Cond. No.          </th> <td>    11.4</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.937\n",
       "Model:                            OLS   Adj. R-squared:                  0.937\n",
       "Method:                 Least Squares   F-statistic:                 7.463e+04\n",
       "Date:                Wed, 29 Nov 2017   Prob (F-statistic):               0.00\n",
       "Time:                        17:46:26   Log-Likelihood:                -19468.\n",
       "No. Observations:                5000   AIC:                         3.894e+04\n",
       "Df Residuals:                    4998   BIC:                         3.895e+04\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      9.8313      0.168     58.482      0.000       9.502      10.161\n",
       "x             -4.0215      0.015   -273.192      0.000      -4.050      -3.993\n",
       "==============================================================================\n",
       "Omnibus:                        1.042   Durbin-Watson:                   1.992\n",
       "Prob(Omnibus):                  0.594   Jarque-Bera (JB):                0.988\n",
       "Skew:                          -0.025   Prob(JB):                        0.610\n",
       "Kurtosis:                       3.047   Cond. No.                         11.4\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.summary() # this returns summary table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data['yhat'] = results.predict(data.x) # model predictions for some (possibly new) explanatory variable observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xcf4fc88>]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ByDJ1LcMUmxIJR2SarBN8ed08tCqJZ44P8uQRK/MtBgLhCIvzDJwcnGDTIjNd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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcd0f710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the line of best fit\n",
    "plt.scatter(data.x, data.y, alpha = .5, s=1)\n",
    "plt.plot(data.x, data.yhat, c = 'orange')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run regression using sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols = LinearRegression()\n",
    "ols.fit(data[['x']], data[['y']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-4.02147054]])"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols.coef_ # returns coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9.83134146])"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols.intercept_ # returns intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.93723630161406501"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols.score(data[['x']], data[['y']]) # Get R^2 of regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data['y_hat'] = ols.predict(data[['x']]) # Get model predicted values for some (possibly new) data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1089eda0>]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ByDJ1LcMUmxIJR2SarBN8ed08tCqJZ44P8uQRK/MtBgLhCIvzDJwcnGDTIjNd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jkWDDp17Pj2E1Z2nuplXXV2TD11yzst/LimkZvn5FCencLt8/PGnDNeC+hgm4MfvXGaJJ2aJL2a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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10826978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the line of best fit\n",
    "plt.scatter(data.x, data.y, alpha = .5, s=1)\n",
    "plt.plot(data.x, data.y_hat, c = 'orange')"
   ]
  }
 ],
 "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": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}