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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## all imports\n",
      "from IPython.display import HTML\n",
      "import numpy as np\n",
      "import urllib2\n",
      "import bs4 #this is beautiful soup\n",
      "\n",
      "from pandas import Series\n",
      "import pandas as pd\n",
      "from pandas import DataFrame\n",
      "\n",
      "import matplotlib\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "\n",
      "import seaborn as sns\n",
      "sns.set_context(\"talk\")\n",
      "sns.set_style(\"white\")"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "CS109\n",
      "=====\n",
      "\n",
      "Verena Kaynig-Fittkau\n",
      "\n",
      "* vkaynig@seas.harvard.edu\n",
      "* staff@cs109.org"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Announcements\n",
      "==============\n",
      "\n",
      "* Nice page to promote your projects\n",
      "\n",
      "http://sites.fas.harvard.edu/~huit-apps/archive/index.html"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Announcements\n",
      "==============\n",
      "\n",
      "* HW1 solutions are online\n",
      "* Awesome TFs are trying to get the grading done this week\n",
      "* be proactive:\n",
      "    - start early\n",
      "    - use office hours and Piazza\n",
      "    - if resources are not enough: let us know!"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Announcements\n",
      "==============\n",
      "\n",
      "* homework submission format\n",
      "    - create a folder lastname_firstinitial_hw# \n",
      "    - place the notebook and any others files into that folder\n",
      "    - notebooks should be executed\n",
      "    - compress the folder\n",
      "    - submit to iSites"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Todays lecture:\n",
      "===============\n",
      "\n",
      "* all about data scraping\n",
      "* ***What is it? ***\n",
      "* How to do it:\n",
      "    - from a website\n",
      "    - with an API\n",
      "* Plus: Some more SVD!\n",
      "\n",
      "Answer: Data scraping is about obtaining data from webpages. There is low level scraping where you parse the data out of the html code of the webpage. There also is scraping over APIs from websites who try to make your life a bit easier.\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "API registrations\n",
      "=================\n",
      "\n",
      "* Rotten Tomatoes\n",
      "\n",
      "http://developer.rottentomatoes.com/member/register\n",
      "\n",
      "* Twitter\n",
      "\n",
      "https://apps.twitter.com/app/new\n",
      "\n",
      "* Twitter instructions\n",
      "\n",
      "https://twittercommunity.com/t/how-to-get-my-api-key/7033"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Python data scraping\n",
      "====================\n",
      "\n",
      "* Why scrape the web?\n",
      "    - vast source of information\n",
      "    - automate tasks\n",
      "    - keep up with sites\n",
      "    - fun!\n",
      "\n",
      "*** Can you think of examples ? ***\n",
      "  \n",
      "Answer: Some examples we had were stock market monitoring, sports data, or airline prices."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Read and Tweet!\n",
      "=================\n",
      "\n",
      "![ReadTweet](http://developer.nytimes.com/files/readtweet.jpg\n",
      " \"We read we tweet\")\n",
      "\n",
      "* by Justin Blinder\n",
      "* http://projects.justinblinder.com/We-Read-We-Tweet"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Twitter Sentiments\n",
      "=================\n",
      "\n",
      "![TwitterSentiments](http://www.csc.ncsu.edu/faculty/healey/tweet_viz/figs/tweet-viz-ex.png\n",
      " \"Twitter Sentiments\")\n",
      "\n",
      "* by Healey and Ramaswamy\n",
      "* http://www.csc.ncsu.edu/faculty/healey/tweet_viz/tweet_app/"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "L.A. Happy Hours\n",
      "===============\n",
      "\n",
      "* http://www.downtownla.com/3_10_happyHours.asp?action=ALL\n",
      "\n",
      "* by Katharine Jarmul"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Python data scraping\n",
      "====================\n",
      "\n",
      "* copyrights and permission:\n",
      "    - be careful and polite\n",
      "    - give credit\n",
      "    - care about media law\n",
      "    - don't be evil (no spam, overloading sites, etc.)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Robots.txt\n",
      "==========\n",
      "\n",
      "![Robots.txt](images/robots_txt.jpg \"Robots.txt\")"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Robots.txt\n",
      "==========\n",
      "\n",
      "* specified by web site owner\n",
      "* gives instructions to web robots (aka your script)\n",
      "* is located at the top-level directory of the web server\n",
      "\n",
      "http://www.example.com/robots.txt\n",
      "\n",
      "If you want you can also have a look at\n",
      "\n",
      "http://google.com/robots.txt"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Robots.txt\n",
      "==========\n",
      "\n",
      "*** What does this one do? ***\n",
      "\n",
      "Answer: This file allows google to search through everything on the server, while all others should stay completely away."
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "\n",
      "User-agent: Google\n",
      "Disallow:\n",
      "\n",
      "User-agent: *\n",
      "Disallow: /"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Things to consider:\n",
      "-------------------\n",
      "\n",
      "* can be just ignored\n",
      "* can be a security risk - *** Why? ***\n",
      "\n",
      "Answer: You are basically telling everybody who cares to look into the file where you have stored sensitive information.\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Scraping with Python:\n",
      "=====================\n",
      "\n",
      "* scraping is all about HTML tags\n",
      "* bad news: \n",
      "    - need to learn about tags\n",
      "    - websites can be ugly"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "HTML\n",
      "=====\n",
      "\n",
      "* HyperText Markup Language\n",
      "\n",
      "* standard for creating webpages\n",
      "\n",
      "* HTML tags \n",
      "    - have angle brackets\n",
      "    - typically come in pairs"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is an example for a minimal webpage defined in HTML tags. The root tag is `<html>` and then you have the `<head>` tag. This part of the page typically includes the title of the page and might also have other meta information like the author or keywords that are important for search engines. The `<body>` tag marks the actual content of the page. You can play around with the `<h2>` tag trying different header levels. They range from 1 to 6. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = \"\"\"<!DOCTYPE html>\n",
      "<html>\n",
      "  <head>\n",
      "    <title>This is a title</title>\n",
      "  </head>\n",
      "  <body>\n",
      "    <h2> Test </h2>\n",
      "    <p>Hello world!</p>\n",
      "  </body>\n",
      "</html>\"\"\"\n",
      "\n",
      "h = HTML(s)\n",
      "h"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [
      {
       "html": [
        "<!DOCTYPE html>\n",
        "<html>\n",
        "  <head>\n",
        "    <title>This is a title</title>\n",
        "  </head>\n",
        "  <body>\n",
        "    <h2> Test </h2>\n",
        "    <p>Hello world!</p>\n",
        "  </body>\n",
        "</html>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<IPython.core.display.HTML at 0x16a70470>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Useful Tags\n",
      "===========\n",
      "\n",
      "* heading\n",
      "`<h1></h1> ... <h6></h6>`\n",
      "\n",
      "* paragraph\n",
      "`<p></p>` \n",
      "\n",
      "* line break\n",
      "`<br>` \n",
      "\n",
      "* link with attribute\n",
      "\n",
      "`<a href=\"http://www.example.com/\">An example link</a>`\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Scraping with Python:\n",
      "=====================\n",
      "\n",
      "* example of a beautifully simple webpage:\n",
      "\n",
      "http://www.crummy.com/software/BeautifulSoup"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Scraping with Python:\n",
      "=====================\n",
      "\n",
      "* good news: \n",
      "    - some browsers help\n",
      "    - look for: inspect element\n",
      "    - need only basic html\n",
      "    \n",
      "*** Try 'Ctrl-Shift I' in Chrome ***\n",
      "\n",
      "*** Try 'Command-Option I' in Safari ***\n",
      "\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Scraping with Python\n",
      "==================\n",
      "\n",
      "* different useful libraries:\n",
      "    - urllib\n",
      "    - beautifulsoup\n",
      "    - pattern\n",
      "    - LXML\n",
      "    - ...\n",
      "    "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The following cell just defines a url as a string and then reads the data from that url using the `urllib` library. If you uncomment the print command you see that we got the whole HTML content of the page into the string variable source."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url = 'http://www.crummy.com/software/BeautifulSoup'\n",
      "source = urllib2.urlopen(url).read()\n",
      "#print source"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Quiz :\n",
      "======\n",
      "\n",
      "* Is the word 'Alice' mentioned on the beautiful soup homepage?\n",
      "* How often does the word 'Soup' occur on the site?\n",
      "    - hint: use `.count()`\n",
      "* At what index occurs the substring 'alien video games' ?\n",
      "    - hint: use `.find()`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## is 'Alice' in source?\n",
      "print 'Alice' in source\n",
      "\n",
      "## count occurences of 'Soup'\n",
      "print source.count('Soup')\n",
      "\n",
      "## find index of 'alien video games'\n",
      "position =  source.find('alien video games')\n",
      "print position\n",
      "\n",
      "## quick test to see the substring in the source variable\n",
      "## you can access strings like lists\n",
      "print source[position:position + 20]\n",
      "\n",
      "## or the tidier version:\n",
      "print source[position:position + len('alien video games')]"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "False\n",
        "41\n",
        "1695\n",
        "alien video games</a\n",
        "alien video games\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Beautiful Soup\n",
      "==============\n",
      "\n",
      "* designed to make your life easier\n",
      "* many good functions for parsing html code"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Some examples\n",
      "=============\n",
      "\n",
      "Now we create a beautiful soup object from the string variable source. Note that the `prettify()` function formats the output to show the different levels of the HTML code. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## get bs4 object\n",
      "soup = bs4.BeautifulSoup(source)\n",
      " \n",
      "## compare the two print statements\n",
      "# print soup\n",
      "# print soup.prettify()\n",
      "\n",
      "## show how to find all a tags\n",
      "soup.findAll('a')\n",
      "\n",
      "## ***Why does this not work? ***\n",
      "soup.findAll('Soup')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "[]"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The last command only returns an empty list, because `Soup` is not an HTML tag. It is just a string that occours in the webpage."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Some examples\n",
      "============="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## get attribute value from an element:\n",
      "## find tag: this only returns the first occurrence, not all tags in the string\n",
      "first_tag = soup.find('a')\n",
      "\n",
      "## get attribute `href`\n",
      "first_tag.get('href')\n",
      "\n",
      "## get all links in the page\n",
      "link_list = [l.get('href') for l in soup.findAll('a')]\n",
      "\n",
      "## filter all external links\n",
      "# create an empty list to collect the valid links\n",
      "external_links = []\n",
      "\n",
      "# write a loop to filter the links\n",
      "# if it starts with 'http' we are happy\n",
      "for l in link_list:\n",
      "    if l[:4] == 'http':\n",
      "        external_links.append(l)\n",
      "\n",
      "# this throws an error! It says something about 'NoneType'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "TypeError",
       "evalue": "'NoneType' object has no attribute '__getitem__'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-50-cad0ad5333f8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[1;31m# if it starts with 'http' we are happy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0ml\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlink_list\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m     \u001b[1;32mif\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'http'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     19\u001b[0m         \u001b[0mexternal_links\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mTypeError\u001b[0m: 'NoneType' object has no attribute '__getitem__'"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# lets investigate. Have a close look at the link_list:\n",
      "link_list\n",
      "\n",
      "# Seems that there are None elements!\n",
      "# Let's verify\n",
      "print sum([l is None for l in link_list])\n",
      "\n",
      "# So there are two elements in the list that are None!"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2\n"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's filter those objects out in the for loop\n",
      "external_links = []\n",
      "\n",
      "# write a loop to filter the links\n",
      "# if it is not None and starts with 'http' we are happy\n",
      "for l in link_list:\n",
      "    if l is not None and l[:4] == 'http':\n",
      "        external_links.append(l)\n",
      "        \n",
      "external_links"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "['http://www.awaretek.com/python/index.html',\n",
        " 'https://code.launchpad.net/beautifulsoup',\n",
        " 'https://groups.google.com/forum/?fromgroups#!forum/beautifulsoup',\n",
        " 'http://www.candlemarkandgleam.com/shop/constellation-games/',\n",
        " 'http://constellation.crummy.com/Constellation%20Games%20excerpt.html',\n",
        " 'https://groups.google.com/forum/?fromgroups#!forum/beautifulsoup',\n",
        " 'https://bugs.launchpad.net/beautifulsoup/',\n",
        " 'http://lxml.de/',\n",
        " 'http://code.google.com/p/html5lib/',\n",
        " 'http://www.crummy.com/software/BeautifulSoup/bs3/documentation.html',\n",
        " 'http://www.nytimes.com/2007/10/25/arts/design/25vide.html',\n",
        " 'https://github.com/reddit/reddit/blob/85f9cff3e2ab9bb8f19b96acd8da4ebacc079f04/r2/r2/lib/media.py',\n",
        " 'http://www.harrowell.org.uk/viktormap.html',\n",
        " 'http://svn.python.org/view/tracker/importer/',\n",
        " 'http://www2.ljworld.com/',\n",
        " 'http://www.b-list.org/weblog/2010/nov/02/news-done-broke/',\n",
        " 'http://esrl.noaa.gov/gsd/fab/',\n",
        " 'http://laps.noaa.gov/topograbber/',\n",
        " 'http://groups.google.com/group/beautifulsoup/',\n",
        " 'https://launchpad.net/beautifulsoup',\n",
        " 'https://code.launchpad.net/beautifulsoup/',\n",
        " 'https://bugs.launchpad.net/beautifulsoup/',\n",
        " 'http://creativecommons.org/licenses/by-sa/2.0/',\n",
        " 'http://creativecommons.org/licenses/by-sa/2.0/',\n",
        " 'http://www.crummy.com/',\n",
        " 'http://www.crummy.com/software/',\n",
        " 'http://www.crummy.com/software/BeautifulSoup/']"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note: The above `if` condition works because of lazy evaluation in Python. The `and` statement becomes `False` if the first part is `False`, so there is no need to ever evaluate the second part. Thus a `None` entry in the list gets never asked about its first four characters. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# another option for the if statement\n",
      "# didn't know about the startswith function until it was pointed out in class. Thanks!\n",
      "# and we can put this in a list comprehension as well, it almost reads like sentence.\n",
      "\n",
      "[l for l in link_list if l is not None and l.startswith('http')]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 84,
       "text": [
        "['http://www.awaretek.com/python/index.html',\n",
        " 'https://code.launchpad.net/beautifulsoup',\n",
        " 'https://groups.google.com/forum/?fromgroups#!forum/beautifulsoup',\n",
        " 'http://www.candlemarkandgleam.com/shop/constellation-games/',\n",
        " 'http://constellation.crummy.com/Constellation%20Games%20excerpt.html',\n",
        " 'https://groups.google.com/forum/?fromgroups#!forum/beautifulsoup',\n",
        " 'https://bugs.launchpad.net/beautifulsoup/',\n",
        " 'http://lxml.de/',\n",
        " 'http://code.google.com/p/html5lib/',\n",
        " 'http://www.crummy.com/software/BeautifulSoup/bs3/documentation.html',\n",
        " 'http://www.nytimes.com/2007/10/25/arts/design/25vide.html',\n",
        " 'https://github.com/reddit/reddit/blob/85f9cff3e2ab9bb8f19b96acd8da4ebacc079f04/r2/r2/lib/media.py',\n",
        " 'http://www.harrowell.org.uk/viktormap.html',\n",
        " 'http://svn.python.org/view/tracker/importer/',\n",
        " 'http://www2.ljworld.com/',\n",
        " 'http://www.b-list.org/weblog/2010/nov/02/news-done-broke/',\n",
        " 'http://esrl.noaa.gov/gsd/fab/',\n",
        " 'http://laps.noaa.gov/topograbber/',\n",
        " 'http://groups.google.com/group/beautifulsoup/',\n",
        " 'https://launchpad.net/beautifulsoup',\n",
        " 'https://code.launchpad.net/beautifulsoup/',\n",
        " 'https://bugs.launchpad.net/beautifulsoup/',\n",
        " 'http://creativecommons.org/licenses/by-sa/2.0/',\n",
        " 'http://creativecommons.org/licenses/by-sa/2.0/',\n",
        " 'http://www.crummy.com/',\n",
        " 'http://www.crummy.com/software/',\n",
        " 'http://www.crummy.com/software/BeautifulSoup/']"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Parsing the Tree\n",
      "================\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# redifining `s` without any line breaks\n",
      "s = \"\"\"<!DOCTYPE html><html><head><title>This is a title</title></head><body><h3> Test </h3><p>Hello world!</p></body></html>\"\"\"\n",
      "## get bs4 object\n",
      "tree = bs4.BeautifulSoup(s)\n",
      "\n",
      "## get html root node\n",
      "root_node = tree.html\n",
      "\n",
      "## get head from root using contents\n",
      "head = root_node.contents[0]\n",
      "\n",
      "## get body from root\n",
      "body = root_node.contents[1]\n",
      "\n",
      "## could directly access body\n",
      "tree.body"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 67,
       "text": [
        "<body><h3> Test </h3><p>Hello world!</p></body>"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Quiz:\n",
      "=====\n",
      "\n",
      "* Find the `h3` tag by parsing the tree starting at `body`\n",
      "* Create a list of all __Hall of Fame__ entries listed on the Beautiful Soup webpage\n",
      "    - hint: it is the only unordered list in the page (tag `ul`)\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## get h3 tag from body\n",
      "body.contents[0]"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "<h3> Test </h3>"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## use ul as entry point\n",
      "entry_point = soup.find('ul')\n",
      "\n",
      "## get hall of fame list from entry point\n",
      "## skip the first entry \n",
      "hall_of_fame_list = entry_point.contents[1:]\n",
      "\n",
      "## reformat into a list containing strings\n",
      "tmp = []\n",
      "for li in hall_of_fame_list:\n",
      "    tmp.append(li.contents)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [],
     "prompt_number": 89
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`tmp` now is actually a list of lists containing the hall of fame entries. \n",
      "I had to ask a collegue to solve this for me, so thanks to Ray, here is some \n",
      "advanced Pythonvon how to print really just one entry per list item.\n",
      "\n",
      "The cool things about this are: \n",
      "* The use of `\"\"` to just access the `join` function of strings.\n",
      "* The `join` function itself\n",
      "* that you can actually have two nested for loops in a list comprehension"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test =  [\"\".join(str(a) for a in sublist) for sublist in tmp]\n",
      "print '\\n'.join(test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<a href=\"http://www.nytimes.com/2007/10/25/arts/design/25vide.html\">\"Movable\n",
        " Type\"</a>, a work of digital art on display in the lobby of the New\n",
        " York Times building, uses Beautiful Soup to scrape news feeds.\n",
        "\n",
        "\n",
        "Reddit uses Beautiful Soup to <a href=\"https://github.com/reddit/reddit/blob/85f9cff3e2ab9bb8f19b96acd8da4ebacc079f04/r2/r2/lib/media.py\">parse\n",
        "a page that's been linked to and find a representative image</a>.\n",
        "\n",
        "\n",
        "Alexander Harrowell uses Beautiful Soup to <a href=\"http://www.harrowell.org.uk/viktormap.html\">track the business\n",
        " activities</a> of an arms merchant.\n",
        "\n",
        "\n",
        "The developers of Python itself used Beautiful Soup to <a href=\"http://svn.python.org/view/tracker/importer/\">migrate the Python\n",
        "bug tracker from Sourceforge to Roundup</a>.\n",
        "\n",
        "\n",
        "The <a href=\"http://www2.ljworld.com/\">Lawrence Journal-World</a>\n",
        "uses Beautiful Soup to <a href=\"http://www.b-list.org/weblog/2010/nov/02/news-done-broke/\">gather\n",
        "statewide election results</a>.\n",
        "\n",
        "\n",
        "The <a href=\"http://esrl.noaa.gov/gsd/fab/\">NOAA's Forecast\n",
        "Applications Branch</a> uses Beautiful Soup in <a href=\"http://laps.noaa.gov/topograbber/\">TopoGrabber</a>, a script for\n",
        "downloading \"high resolution USGS datasets.\"\n",
        "\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 90
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Advanced Example\n",
      "================\n",
      "\n",
      "Designed by Katharine Jarmul\n",
      "----------------------------\n",
      "\n",
      "https://github.com/kjam/python-web-scraping-tutorial\n",
      "\n",
      "\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Scraping Happy Hours\n",
      "====================\n",
      "\n",
      "Scrape the happy hour list of LA for personal preferences\n",
      "\n",
      "http://www.downtownla.com/3_10_happyHours.asp?action=ALL\n",
      "\n",
      "This example is part of her talk about data scraping at PyCon2014. She is a really good speaker and I enjoyed watching her talk. Check it out: http://www.youtube.com/watch?v=p1iX0uxM1w8"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stuff_i_like = ['burger', 'sushi', 'sweet potato fries', 'BBQ']\n",
      "found_happy_hours = []\n",
      "my_happy_hours = []\n",
      "# First, I'm going to identify the areas of the page I want to look at\n",
      "url = 'http://www.downtownla.com/3_10_happyHours.asp?action=ALL'\n",
      "source = urllib2.urlopen(url).read()\n",
      "tables = bs4.BeautifulSoup(source)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Then, I'm going to sort out the *exact* parts of the page\n",
      "# that match what I'm looking for...\n",
      "for t in tables.findAll('p', {'class': 'calendar_EventTitle'}):\n",
      "    text = t.text\n",
      "    for s in t.findNextSiblings():\n",
      "        text += '\\n' + s.text\n",
      "    found_happy_hours.append(text)\n",
      "\n",
      "print \"The scraper found %d happy hours!\" % len(found_happy_hours)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The scraper found 66 happy hours!\n"
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now I'm going to loop through the food I like\n",
      "# and see if any of the happy hour descriptions match\n",
      "for food in stuff_i_like:\n",
      "    for hh in found_happy_hours:\n",
      "        # checking for text AND making sure I don't have duplicates\n",
      "        if food in hh and hh not in my_happy_hours:\n",
      "            print \"YAY! I found some %s!\" % food\n",
      "            my_happy_hours.append(hh)\n",
      "\n",
      "print \"I think you might like %d of them, yipeeeee!\" % len(my_happy_hours)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "YAY! I found some burger!\n",
        "YAY! I found some sushi!\n",
        "YAY! I found some sushi!\n",
        "YAY! I found some sushi!\n",
        "I think you might like 4 of them, yipeeeee!\n"
       ]
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now, let's make a mail message we can read:\n",
      "message = 'Hey Katharine,\\n\\n\\n'\n",
      "message += 'OMG, I found some stuff for you in Downtown, take a look.\\n\\n'\n",
      "message += '==============================\\n'.join(my_happy_hours)\n",
      "message = message.encode('utf-8')\n",
      "# To read more about encoding:\n",
      "# http://diveintopython.org/xml_processing/unicode.html\n",
      "message = message.replace('\\t', '').replace('\\r', '')\n",
      "message += '\\n\\nXOXO,\\n Your Py Script'\n",
      "\n",
      "#print message"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [],
     "prompt_number": 94
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Getting Data with an API\n",
      "=========================\n",
      "\n",
      "* API: application programming interface\n",
      "* some sites try to make your life easier\n",
      "* Twitter, New York Times, ImDB, rotten Tomatoes, Yelp, ..."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Rotten Tomatoes\n",
      "===============\n",
      "\n",
      "![The Wizard of Oz](images/wiz_oz.png \"The wizard of Oz\")\n",
      "\n",
      "http://www.rottentomatoes.com/top/\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "API keys\n",
      "=========\n",
      "\n",
      "* required for data access\n",
      "* identifies application (you)\n",
      "* monitors usage\n",
      "* limits rates"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Rotten Tomatoes Key\n",
      "===================\n",
      "\n",
      "http://developer.rottentomatoes.com/member/register"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import json\n",
      "import requests\n",
      "\n",
      "api_key = 'your key here'\n",
      "\n",
      "url = 'http://api.rottentomatoes.com/api/public/v1.0/lists/dvds/top_rentals.json?apikey=' + api_key\n",
      "data = urllib2.urlopen(url).read()\n",
      "#print data"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [
      {
       "ename": "NameError",
       "evalue": "name 'urllib2' is not defined",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-2-893523031a48>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'http://api.rottentomatoes.com/api/public/v1.0/lists/dvds/top_rentals.json?apikey='\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mapi_key\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0murllib2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0murlopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[1;31m#print data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mNameError\u001b[0m: name 'urllib2' is not defined"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Python Dictonaries\n",
      "==================\n",
      "\n",
      "* build in data type\n",
      "* uses key: value pairs"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = {'a': 1, 'b':2}\n",
      "print a\n",
      "\n",
      "#show keys\n",
      "print a.keys()\n",
      "\n",
      "#show values\n",
      "print a.values()\n",
      "\n",
      "#show for loop over all entries\n",
      "# option 1 using zip\n",
      "# this works also for iterating over any\n",
      "# other two lists\n",
      "for k,v in zip(a.keys(), a.values()):\n",
      "    print k,v\n",
      "\n",
      "# option 2 using the dictionary `iteritems()` function\n",
      "for k,v in a.iteritems():\n",
      "    print k,v"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'a': 1, 'b': 2}\n",
        "['a', 'b']\n",
        "[1, 2]\n",
        "a 1\n",
        "b 2\n",
        "a 1\n",
        "b 2\n"
       ]
      }
     ],
     "prompt_number": 101
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "JSON\n",
      "======\n",
      "\n",
      "* JavaScript Object Notation\n",
      "* human readable\n",
      "* transmit attribute-value pairs"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = {'a': 1, 'b':2}\n",
      "s = json.dumps(a)\n",
      "a2 = json.loads(s)\n",
      "\n",
      "## a is a dictionary\n",
      "#a\n",
      "## vs s is a string containing a in JSON encoding\n",
      "#s\n",
      "## reading back the keys are now in unicode\n",
      "#a2"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 102,
       "text": [
        "{'a': 1, 'b': 2}"
       ]
      }
     ],
     "prompt_number": 102
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## create dictionary from JSON \n",
      "dataDict = json.loads(data)\n",
      "\n",
      "## expore dictionary\n",
      "print dataDict.keys()\n",
      "\n",
      "## there is a key named `movies` containing a list of movies as a value\n",
      "movies = dataDict['movies']\n",
      "\n",
      "## each element of the list `movies` is a dictionary\n",
      "print movies[0].keys()\n",
      "\n",
      "## one of the keys is called `ratings`\n",
      "## the value is yet another dictionary\n",
      "print movies[0]['ratings'].keys()\n",
      "\n",
      "## so we made it all the way to find the critics score\n",
      "print movies[0]['ratings']['critics_score']\n"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[u'movies', u'link_template', u'links']\n",
        "[u'ratings', u'links', u'title', u'critics_consensus', u'release_dates', u'abridged_cast', u'synopsis', u'mpaa_rating', u'year', u'alternate_ids', u'posters', u'runtime', u'id']\n",
        "[u'critics_score', u'audience_score', u'critics_rating', u'audience_rating']\n",
        "89\n"
       ]
      }
     ],
     "prompt_number": 116
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Quiz\n",
      "=====\n",
      "\n",
      "* build a list with critics scores\n",
      "* build a list with audience scores"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# critics scores list\n",
      "critics_scores = [m['ratings']['critics_score'] for m in movies]\n",
      "\n",
      "# audience scores list\n",
      "audience_scores = [m['ratings']['audience_score'] for m in movies]"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [],
     "prompt_number": 119
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The following code shows how to create a pandas data frame with the data we gathered from the webpage.\n",
      "Beware of the `set_index()` function in pandas. Per default it does not change the actual data frame! You need to either reassign the output or set the `inplace` argument to `True`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## create pandas data frame with critics and audience score\n",
      "scores = pd.DataFrame(data=[critics_scores, audience_scores]).transpose()\n",
      "scores.columns = ['critics', 'audience']\n",
      "\n",
      "## also create a list with all movie titles\n",
      "movie_titles = [m['title'] for m in movies]\n",
      "\n",
      "## set index of dataFrame BEWARE of inplace!\n",
      "scores.set_index([movie_titles])\n",
      "\n",
      "## the line above does not changes scores!\n",
      "## You need to either reassign\n",
      "\n",
      "scores = scores.set_index([movie_titles])\n",
      "\n",
      "## or set the inplace argument to True\n",
      "scores.set_index([movie_titles], inplace=True)\n",
      "scores.head(3)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>critics</th>\n",
        "      <th>audience</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Captain America: The Winter Soldier</th>\n",
        "      <td> 89</td>\n",
        "      <td> 93</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>The Amazing Spider-Man 2</th>\n",
        "      <td> 53</td>\n",
        "      <td> 68</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Godzilla</th>\n",
        "      <td> 73</td>\n",
        "      <td> 69</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 135,
       "text": [
        "                                     critics  audience\n",
        "Captain America: The Winter Soldier       89        93\n",
        "The Amazing Spider-Man 2                  53        68\n",
        "Godzilla                                  73        69"
       ]
      }
     ],
     "prompt_number": 135
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## create a bar plot with the data\n",
      "scores.plot(kind = 'bar')\n",
      "\n",
      "## set the title to Score Comparison\n",
      "plt.title('Score Comparison')\n",
      "\n",
      "## set the x label\n",
      "plt.xlabel('Movies')\n",
      "\n",
      "## set the y label\n",
      "plt.ylabel('Scores')\n",
      "\n",
      "## show the plot\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SPHU4TuvQxiUxGI7TxdXfnzXTp09f6PMDIQhOAIZExNKZ+XK5bAjwV2DbiFgj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\nERERERERERERETGw/j9cINf+kNWiPgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x17cacb38>"
       ]
      }
     ],
     "prompt_number": 138
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Twitter Example:\n",
      "================\n",
      "\n",
      "* API a bit more complicated\n",
      "* libraries make life easier\n",
      "* python-twitter\n",
      "\n",
      "https://github.com/bear/python-twitter\n",
      "\n",
      "What we are going to do is scrape Rafael's twitter account, and then filter it for the interesting tweets. Defining interesting as tweets that have be re-tweeted at least 10 times. \n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import twitter\n",
      "\n",
      "## define the necessary keys\n",
      "cKey = 'your consumer key here'\n",
      "cSecret = 'your consumer secret here'\n",
      "aKey = 'your access token key here'\n",
      "aSecret = 'your access token secret here'\n",
      "\n",
      "## create the api object with the twitter-python library\n",
      "api = twitter.Api(consumer_key=cKey, consumer_secret=cSecret, access_token_key=aKey, access_token_secret=aSecret)\n"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [],
     "prompt_number": 139
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## get the user timeline with screen_name = 'rafalab'\n",
      "twitter_statuses = api.GetUserTimeline(screen_name = 'rafalab')\n",
      "\n",
      "## create a data frame\n",
      "## first get a list of panda Series or dict\n",
      "pdSeriesList = [pd.Series(t.AsDict()) for t in twitter_statuses]\n",
      "\n",
      "## then create the data frame\n",
      "data = pd.DataFrame(pdSeriesList)\n",
      "\n",
      "data.head(2)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>created_at</th>\n",
        "      <th>favorite_count</th>\n",
        "      <th>favorited</th>\n",
        "      <th>hashtags</th>\n",
        "      <th>id</th>\n",
        "      <th>in_reply_to_screen_name</th>\n",
        "      <th>in_reply_to_status_id</th>\n",
        "      <th>in_reply_to_user_id</th>\n",
        "      <th>lang</th>\n",
        "      <th>retweet_count</th>\n",
        "      <th>retweeted</th>\n",
        "      <th>retweeted_status</th>\n",
        "      <th>source</th>\n",
        "      <th>text</th>\n",
        "      <th>truncated</th>\n",
        "      <th>urls</th>\n",
        "      <th>user</th>\n",
        "      <th>user_mentions</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> Wed Sep 24 13:56:29 +0000 2014</td>\n",
        "      <td>  7</td>\n",
        "      <td> False</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 514775379941548033</td>\n",
        "      <td> rstudio</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 235261861</td>\n",
        "      <td> en</td>\n",
        "      <td>  1</td>\n",
        "      <td> False</td>\n",
        "      <td> NaN</td>\n",
        "      <td> &lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
        "      <td> @rstudio spell checker is suggesting I correct...</td>\n",
        "      <td> False</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td> {u'id': 177729631, u'profile_sidebar_fill_colo...</td>\n",
        "      <td> [{u'screen_name': u'rstudio', u'id': 235261861...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> Mon Sep 22 13:09:38 +0000 2014</td>\n",
        "      <td> 14</td>\n",
        "      <td> False</td>\n",
        "      <td> [gi2014]</td>\n",
        "      <td> 514038816387387392</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td> en</td>\n",
        "      <td> 10</td>\n",
        "      <td> False</td>\n",
        "      <td> NaN</td>\n",
        "      <td> &lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
        "      <td> #gi2014 if you want more info on how to deal w...</td>\n",
        "      <td> False</td>\n",
        "      <td> {u'http://t.co/YdYAv8DLqo': u'http://genomicsc...</td>\n",
        "      <td> {u'id': 177729631, u'profile_sidebar_fill_colo...</td>\n",
        "      <td>                                               NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 141,
       "text": [
        "                       created_at  favorite_count favorited  hashtags  \\\n",
        "0  Wed Sep 24 13:56:29 +0000 2014               7     False       NaN   \n",
        "1  Mon Sep 22 13:09:38 +0000 2014              14     False  [gi2014]   \n",
        "\n",
        "                   id in_reply_to_screen_name  in_reply_to_status_id  \\\n",
        "0  514775379941548033                 rstudio                    NaN   \n",
        "1  514038816387387392                     NaN                    NaN   \n",
        "\n",
        "   in_reply_to_user_id lang  retweet_count retweeted retweeted_status  \\\n",
        "0            235261861   en              1     False              NaN   \n",
        "1                  NaN   en             10     False              NaN   \n",
        "\n",
        "                                              source  \\\n",
        "0  <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
        "1  <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
        "\n",
        "                                                text truncated  \\\n",
        "0  @rstudio spell checker is suggesting I correct...     False   \n",
        "1  #gi2014 if you want more info on how to deal w...     False   \n",
        "\n",
        "                                                urls  \\\n",
        "0                                                NaN   \n",
        "1  {u'http://t.co/YdYAv8DLqo': u'http://genomicsc...   \n",
        "\n",
        "                                                user  \\\n",
        "0  {u'id': 177729631, u'profile_sidebar_fill_colo...   \n",
        "1  {u'id': 177729631, u'profile_sidebar_fill_colo...   \n",
        "\n",
        "                                       user_mentions  \n",
        "0  [{u'screen_name': u'rstudio', u'id': 235261861...  \n",
        "1                                                NaN  "
       ]
      }
     ],
     "prompt_number": 141
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## filter tweets with enough retweet_count\n",
      "maybe_interesting = data[data.retweet_count>10]\n",
      "\n",
      "## get the text of these tweets\n",
      "tweet_text = maybe_interesting.text\n",
      "\n",
      "## print them out\n",
      "text = tweet_text.values\n",
      "\n",
      "for t in text:\n",
      "    print '######'\n",
      "    print t"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "######\n",
        "RT @kwbroman: Official announce\u2019t of new members of R core and R foundation: https://t.co/7HIjPVUFa3\n",
        "Yay @eddelbuettel @hadleywickham @lawr\u2026\n",
        "######\n",
        "Post inspired by @hadleywickham's critique of academia. Features @kwbroman, @jtleek, @stat110 and @hpfister efforts http://t.co/owH6C55IHj\n",
        "######\n",
        "RT @simplystats: Applied Statisticians: people want to learn what we do. Let's teach them. http://t.co/qpvO8jEmxL\n",
        "######\n",
        "Harvard Data Science course (CS109) starts today. Follow along here:  http://t.co/IqDw4lo8YX Videos, code, and slides will be made available\n"
       ]
      }
     ],
     "prompt_number": 162
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Extracting columns:\n",
      "===================\n",
      "\n",
      "__Warning:__ The returned column `tweet_text` is a `view` on the data\n",
      "    \n",
      "* it is not a copy\n",
      "* you change the Series => you change the DataFrame\n",
      "\n",
      "Below is another example of such a view:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## create a view for favorite_count on maybe_interesting\n",
      "view = maybe_interesting['favorite_count']\n",
      "print '-----------------'\n",
      "print \"This is view:\"\n",
      "print view\n",
      "\n",
      "\n",
      "## change a value\n",
      "view[8] = 9999\n",
      "\n",
      "## look at original frame\n",
      "print '-----------------'\n",
      "print \"This is view after changing view[8]\"\n",
      "print view\n",
      "\n",
      "print '-----------------'\n",
      "print \"This is maybe_interesting after changing view[8]\"\n",
      "print \"It changed too!\"\n",
      "print maybe_interesting['favorite_count']\n",
      "\n",
      "## to avoid this you can use copy\n",
      "independent_data = maybe_interesting['favorite_count'].copy()\n",
      "independent_data[10] = 999\n",
      "print '-----------------'\n",
      "print \"This is independent_data after changed at 10:\"\n",
      "print independent_data\n",
      "print '-----------------'\n",
      "print \"This is maybe_interesting after changing independent_data:\"\n",
      "print \"It did not change because we only changed a copy of it\"\n",
      "print maybe_interesting['favorite_count']"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-----------------\n",
        "This is view:\n",
        "8     9999\n",
        "9       24\n",
        "10     NaN\n",
        "13     101\n",
        "Name: favorite_count, dtype: float64\n",
        "-----------------"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "This is view after changing view[8]\n",
        "8     9999\n",
        "9       24\n",
        "10     NaN\n",
        "13     101\n",
        "Name: favorite_count, dtype: float64\n",
        "-----------------\n",
        "This is maybe_interesting after changing view[8]\n",
        "It changed too!\n",
        "8     9999\n",
        "9       24\n",
        "10     NaN\n",
        "13     101\n",
        "Name: favorite_count, dtype: float64\n",
        "-----------------\n",
        "This is independent_data after changed at 10:\n",
        "8     9999\n",
        "9       24\n",
        "10     999\n",
        "13     101\n",
        "Name: favorite_count, dtype: float64\n",
        "-----------------\n",
        "This is maybe_interesting after changing independent_data:\n",
        "It did not change because we only changed a copy of it\n",
        "8     9999\n",
        "9       24\n",
        "10     NaN\n",
        "13     101\n",
        "Name: favorite_count, dtype: float64\n"
       ]
      }
     ],
     "prompt_number": 169
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Singular Value Decomposition\n",
      "============================\n",
      "\n",
      "* remember Rafael's nice illustration last week\n",
      "\n",
      "* some more python details\n",
      "\n",
      "http://cs109.github.io/2014/pages/lectures/04-distance.html#/11\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import scipy\n",
      "\n",
      "## seeding the random generate so we always get the same random numbers\n",
      "np.random.seed(seed=99)\n",
      "\n",
      "# make some data up\n",
      "mean = [0,0]\n",
      "cov = [[1.0,0.7],[0.7,1.0]] \n",
      "x,y = np.random.multivariate_normal(mean,cov,500).T"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [],
     "prompt_number": 171
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# plot the data\n",
      "fig = plt.figure()\n",
      "plt.scatter(x,y)\n",
      "plt.axis('equal')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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KEAR4AuDkdJlSpUq9dn+KonDyZBDQGfDAxGQG7dunUq7cswPW1Wo1Q4e2T78+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\niIiIiIiIiIiIiNyl/wccYUka6G1RHwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x17d14198>"
       ]
      }
     ],
     "prompt_number": 172
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We only created the data points as two vectors `x` and `y`, now we want to put them into the same matrix using `column_stack()`. It does exactly what you would expect. Stack the two column vectors together.\n",
      "Then we use the linear algebra library of `scipy` to compute the singular value decomposition. The function gives us the matrices `U` and `Vh`, but `s` is only returned as a vector. In order to use `s` to reconstruct the data or project it down to one dimension we need to convert `s` into a diagonal matrix containing only zeros and the values of `s` in the diagonal. As this is a common computation, the library provides us with a convenient function for this: `diagsvd`. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## create a data matrix\n",
      "matrix = np.column_stack((x,y))\n",
      "## compute SVD\n",
      "U,s,Vh = scipy.linalg.svd(matrix)\n",
      "## blow up s\n",
      "S = scipy.linalg.diagsvd(s, 500, 2)\n",
      "## reconstruct the data (sanity test)\n",
      "reconstruction = np.dot(U, np.dot(S, Vh))\n",
      "\n",
      "## check the results\n",
      "print matrix[1,:]\n",
      "print reconstruction[1,:]\n",
      "## the allclose() function allows for the data points to deviate by a small\n",
      "## epsilon and still be considered correctly reconstructed.\n",
      "print np.allclose(matrix, reconstruction)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[-0.77618857  0.25387936]\n",
        "[-0.77618857  0.25387936]\n",
        "True\n"
       ]
      }
     ],
     "prompt_number": 173
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In the following plot you can see the columns of V visualized along with the data points. The thing to note is that the vectors are orthogonal to each other and that the first vector runs along the direction of the most variance in the data. In a way it is capturing the main source of information. These vectors are also called the principal components of the data. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# show the column vectors of V\n",
      "V = Vh.T\n",
      "plt.scatter(x, y)\n",
      "plt.plot([0, V[0,0]], [0,V[1,0]], c='r', linewidth=10.0)\n",
      "plt.plot([0, V[0,1]], [0,V[1,1]], c='y', linewidth=10.0)\n",
      "plt.axis('equal')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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XKY4evQV4Ay4A2NvfpHz58pnuT1EULlzwBnoCzhgZLaBr1wSqVn15wLparWbM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\nXfp/XOZdrqUgvAwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x18136710>"
       ]
      }
     ],
     "prompt_number": 174
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The following code shows how to project the two dimensional data points down into one dimension. We basically project the data onto the red line in the plot above. The second option shown also works to apply the same projection to new data points that were generated from the same source as the data we used to calculate the SVD. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# two ways to project the data\n",
      "projection = np.dot(U, S[:,:1])\n",
      "projection2 = np.dot(matrix, V[:,:1])\n",
      "np.allclose(projection, projection2)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 175,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 175
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The following plot shows where the original two dimensional data points end up in the projected version. What you can see is that the points at the outer ends of the data cloud also are at the outer ends of the projected points. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# compare the plots\n",
      "plt.clf()\n",
      "zeros = np.zeros_like(projection)\n",
      "plt.scatter(projection, zeros, c='r', zorder=2)\n",
      "plt.scatter(x,y,c='b', zorder=2)\n",
      "\n",
      "for px, py, proj in zip(x,y,projection):\n",
      "    plt.plot([px,proj],[py,0],c='0.5', linewidth=1.0, zorder=1)\n",
      "    \n",
      "plt.axis('equal')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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uruS996JJSLiPJUue5je/8cPe/vIelm1YWw9cpDU+Pp6cnBxiY2P7DT49PT0x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\nJ13DJsgcHuEAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1839d208>"
       ]
      }
     ],
     "prompt_number": 176
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "When we calculated the SVD we did a sanity check by reconstructing the original data from the three matrices. All we needed to do was multiply them and we got the original data back. Now we have the points projected down to one dimension. We can still reconstruct them back into the two dimensional space by multiplying with Vh, but because we threw some information away, we cannot completely get the original data points back."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## try to reconstruct back to 2D\n",
      "## just a reminder\n",
      "projection = np.dot(U, S[:,:1])\n",
      "## now the reconstruction\n",
      "reconstruction = np.dot(projection, Vh[:1,:])\n",
      "reconstruction.shape"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 179,
       "text": [
        "(500L, 2L)"
       ]
      }
     ],
     "prompt_number": 179
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What you can see in the plot below is that the reconstructed data still is a one dimensional line, but now rotated in the two dimensional space of the original data points. The loss of variance in the data actually can be a feature, because the variance along the other dimensions might have been caused by noise in the data. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# compare the plots\n",
      "plt.clf()\n",
      "zeros = np.zeros_like(projection)\n",
      "plt.scatter(reconstruction[:,0], reconstruction[:,1], c='r', zorder=2)\n",
      "plt.scatter(x,y,c='b', zorder=2)\n",
      "\n",
      "for px, py, rx,ry in zip(x,y,reconstruction[:,0], \n",
      "                         reconstruction[:,1]):\n",
      "    plt.plot([px,rx],[py,ry],c='0.5', linewidth=1.0, zorder=1)\n",
      "    \n",
      "plt.axis('equal')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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pd3tlB4vf36icl5cbCxa8x5dfTuLq1Ss895wp1tbvY2IyB1/fv9Gjh5Li4uIq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       "text": [
        "<matplotlib.figure.Figure at 0x183aacf8>"
       ]
      }
     ],
     "prompt_number": 180
    }
   ],
   "metadata": {}
  }
 ]
}