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scrapeTutorial.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Web Scraping\n",
"======\n",
"\n",
"** Lightly edited version of ** http://nbviewer.ipython.org/gist/johnb30/4743272\n",
"### Originally created by John Beieler, https://gist.github.com/johnb30\n",
"\n",
"> Even with the best of websites, I don’t think I’ve ever encountered a scraping job that couldn’t be described as *“A small and simple general model with heaps upon piles of annoying little exceptions”* \n",
"\n",
">> \\- Swizec Teller [http://swizec.com/blog/scraping-with-mechanize-and-beautifulsoup/swizec/5039](http://swizec.com/blog/scraping-with-mechanize-and-beautifulsoup/swizec/5039)\n",
"\n",
"## What is it?\n",
"\n",
"A large portion of the data that we as social scientists are interested in resides on the web in manner. Web scraping is a method for pulling data from the structured (or not so structured!) HTML that makes up a web page. Python has numerous libraries for approaching this type of problem, many of which are incredibly powerful. If there is something you want to do, there's usually a way to accomplish it. Perhaps not easily, but it can be done. \n",
"\n",
"\n",
"\n",
"## How is it accomplished?\n",
"\n",
"In general, there are three problems that you might face when undertaking a scraping task:\n",
"\n",
"1. You have a single page, or a set of pages, that you know of and you want to scrape.\n",
"2. You have a source that generates links, e.g., [RSS feeds](http://rss.nytimes.com/services/xml/rss/nyt/World.xml), to various pages with the same structure.\n",
"3. You have a page that contains many pages of interest that are scattered across the file system and you only have general rules for reaching these pages. \n",
"\n",
"The key is that you must identify which type of problem you have. After this, you must look at the HTML structure of a webpage and construct a script that will select the parts of the page that are of interest to you.\n",
"\n",
"\n",
"\n",
"## There's a library for that! (Yea, I know...)\n",
"\n",
"As mentioned previously, Python has various libraries for scraping tasks. The ones I have found the most useful are:\n",
"\n",
"- [pattern](http://www.clips.ua.ac.be/pages/pattern)\n",
"- [lxml](http://lxml.de/)\n",
"- [requests](http://docs.python-requests.org/en/latest/)\n",
"- [Scrapy](http://doc.scrapy.org/en/0.16/)\n",
"- [Beautiful Soup](http://www.crummy.com/software/BeautifulSoup/)\n",
"\n",
"\n",
"In addition you need some method to examine the source of a webpage in a structured manner. I use Chrome which, as a WebKit browser, allows for \"Inspect Element\" functionality. Alternatively there is [Firebug](https://getfirebug.com/) for Firefox. I have no idea about Safari, Opera, or any other browser you wish to use. \n",
"\n",
"So, let's look at some [webpage source](http://artsbeat.blogs.nytimes.com/2013/01/23/suspects-in-dutch-art-heist-if-not-the-art-itself-in-custody/). I'm going to pick on the New York Times throughout. \n",
"\n",
"\n",
"\n",
"## On to the Python"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Scraping a page that you know\n",
"\n",
"The easiest approach to webscraping is getting the content from a page that you know in advance. I'll go ahead and keep using that NYT page we looked at earlier. There are three basic steps to scraping a single page:\n",
"\n",
"1. Get (request) the page\n",
"2. Parse the page content\n",
"3. Select the content of interest using an XPath selector\n",
"\n",
"The following code executes these three steps and prints the result. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import requests\n",
"import lxml.html as lh # XML processing for HTML. see http://lxml.de/tutorial.html"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Three suspects in the theft of seven masterpieces from the Kunsthal Museum in Rotterdam, the Netherlands, have been arrested in Romania, but there is still no word on where the stolen art might be, the BBC reported. The paintings, which include works by Monet, Picasso, Matisse and Lucien Freud, were taken from the gallery on Oct. 16. A spokeswoman for the Rotterdam police, Yvette van den Heerik, said that the suspects’ involvement was still being investigated.The three suspects, all Romanians, deny links to the theft, the Associated Press reported, and a lawyer defending them argued against their extradition to the Netherlands, citing insufficient evidence.“There is no evidence that this was an organized criminal group,” said the lawyer, Doina Lupu, according to the A.P. “The arrests were based on assumptions and on simple declarations and these are not enough. ”On Wednesday, a spokeswoman for a Bucharest court told Reuters that prosecutors now believe that other people were also involved in the theft, but she would not provide names or nationalities of those suspects. In another part of the world, law enforcement officials have made more progress in closing a years-old Venezuelan art theft case. On Tuesday, Pedro Antonio Marcuello Guzman and Maria Martha Elisa Ornelas Lazo were sentenced in Miami to 21 months in prison for trying to sell a 1925 painting by Matisse that had been stolen from the Caracas Museum of Contemporary Art, Reuters reported. The pair offered the work, “Odalisque in Red Pants,” to an F.B.I. agent in July. Museum officials discovered in 2002 that the genuine Matisse had been replaced by a forgery. It is still unclear who engineered the switch or how the pair ended up with the painting.\n"
]
}
],
"source": [
"url = 'http://artsbeat.blogs.nytimes.com/2013/01/23/suspects-in-dutch-art-heist-if-not-the-art-itself-in-custody/'\n",
"page = requests.get(url)\n",
"doc = lh.fromstring(page.content)\n",
"text = doc.xpath('//p[@itemprop=\"articleBody\"]')\n",
"finalText = str()\n",
"for par in text:\n",
" finalText += par.text_content()\n",
"\n",
"print(finalText)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<Element html at 0x49ac090>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"doc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So we now have our lovely output. This output can be manipulated in various ways, or written to an output file.\n",
"\n",
"### Scraping generated links\n",
"\n",
"Let's say you want to get a stream of news stories in an easy manner. You could visit the homepage of the NYT and work from there, or you can use an [RSS feed](http://rss.nytimes.com/services/xml/rss/nyt/World.xml). RSS stands for Real Simple Syndication and is, at its heart, an XML document. This allows it to be easily parsed. The fantastic library `pattern` allows for easy parsing of RSS feeds. Using `pattern`'s `Newsfeed()` method, it is possible to parse a feed and obtain attributes of the XML document. The `search()` method returns an iterable composed of the individual stories. Each result has a variety of attributes such as `.url`, `.title`, `.description`, and more. The following code demonstrates these methods."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://rss.nytimes.com/c/34625/f/642565/s/4a8a5b0e/sc/11/l/0L0Snytimes0N0C20A150C10A0C10A0Cworld0Cmiddleeast0Cpentagon0Eprogram0Eislamic0Estate0Esyria0Bhtml0Dpartner0Frss0Gemc0Frss/story01.htm \n",
"\n",
" Obama Administration Ends Effort to Train Syrians to Combat ISIS \n",
"\n",
" The effort to build a rebel force against the Islamic State failed in part because many rebels focused on a campaign against President Bashar al-Assad, officials said.<br clear=\"all\" /><br /><br /><a href=\"http://rc.feedsportal.com/r/241225658459/u/0/f/642565/c/34625/s/4a8a5b0e/sc/11/rc/1/rc.htm\" rel=\"nofollow\"><img border=\"0\" src=\"http://rc.feedsportal.com/r/241225658459/u/0/f/642565/c/34625/s/4a8a5b0e/sc/11/rc/1/rc.img\" /></a><br /><br /><a href=\"http://rc.feedsportal.com/r/241225658459/u/0/f/642565/c/34625/s/4a8a5b0e/sc/11/rc/2/rc.htm\" rel=\"nofollow\"><img border=\"0\" src=\"http://rc.feedsportal.com/r/241225658459/u/0/f/642565/c/34625/s/4a8a5b0e/sc/11/rc/2/rc.img\" /></a><br /><br /><a href=\"http://rc.feedsportal.com/r/241225658459/u/0/f/642565/c/34625/s/4a8a5b0e/sc/11/rc/3/rc.htm\" rel=\"nofollow\"><img border=\"0\" src=\"http://rc.feedsportal.com/r/241225658459/u/0/f/642565/c/34625/s/4a8a5b0e/sc/11/rc/3/rc.img\" /></a><br /><br /><a href=\"http://da.feedsportal.com/r/241225658459/u/0/f/642565/c/34625/s/4a8a5b0e/sc/11/a2.htm\"><img border=\"0\" src=\"http://da.feedsportal.com/r/241225658459/u/0/f/642565/c/34625/s/4a8a5b0e/sc/11/a2.img\" /></a><br /><a href=\"http://adchoice.feedsportal.com/r/241225658459/u/0/f/642565/c/34625/s/4a8a5b0e/sc/11/ach.htm\"><img border=\"0\" src=\"http://adchoice.feedsportal.com/r/241225658459/u/0/f/642565/c/34625/s/4a8a5b0e/sc/11/ach.img\" /></a><img border=\"0\" height=\"1\" src=\"http://pi.feedsportal.com/r/241225658459/u/0/f/642565/c/34625/s/4a8a5b0e/sc/11/a2t.img\" width=\"1\" /><img border=\"0\" height=\"1\" src=\"http://pi2.feedsportal.com/r/241225658459/u/0/f/642565/c/34625/s/4a8a5b0e/sc/11/a2t2.img\" width=\"1\" /><img border=\"0\" height=\"1\" src=\"http://rss.nytimes.com/c/34625/f/642565/s/4a8a5b0e/sc/11/mf.gif\" width=\"1\" /> \n",
"\n",
"\n"
]
}
],
"source": [
"import pattern.web # not available for python 3\n",
"\n",
"url = 'http://rss.nytimes.com/services/xml/rss/nyt/World.xml'\n",
"results = pattern.web.Newsfeed().search(url, count=5)\n",
"results\n",
"\n",
"print('%s \\n\\n %s \\n\\n %s \\n\\n' % (results[0].url, results[0].title, results[0].description))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That looks pretty good, but the description looks nastier than we would generally prefer. Luckily, `pattern` provides functions to get rid of the HTML in a string. "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://rss.nytimes.com/c/34625/f/642565/s/4a8a5b0e/sc/11/l/0L0Snytimes0N0C20A150C10A0C10A0Cworld0Cmiddleeast0Cpentagon0Eprogram0Eislamic0Estate0Esyria0Bhtml0Dpartner0Frss0Gemc0Frss/story01.htm \n",
"\n",
" Obama Administration Ends Effort to Train Syrians to Combat ISIS \n",
"\n",
" The effort to build a rebel force against the Islamic State failed in part because many rebels focused on a campaign against President Bashar al-Assad, officials said. \n",
"\n",
"\n"
]
}
],
"source": [
"print('%s \\n\\n %s \\n\\n %s \\n\\n' % (results[0].url, results[0].title, pattern.web.plaintext(results[0].description)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"While it's all well and good to have the title and description of a story this is often insufficient (some descriptions are just the title, which isn't particularly helpful). To get further information on the story, it is possible to combine the single-page scraping discussed previously and the results from the RSS scrape. The following code implements a function to scrape the NYT article pages, which can be done easily since the NYT is wonderfully consistent in their HTML, and then iterates over the results applying the `scrape` function to each result."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import codecs\n",
"\n",
"outputFile = codecs.open('ScrapeOutput.txt', encoding='utf-8', mode='a')\n",
"\n",
"def scrape(url):\n",
" page = requests.get(url)\n",
" doc = lh.fromstring(page.content)\n",
" text = doc.xpath('//p[@itemprop=\"articleBody\"]')\n",
" finalText = \"\"\n",
" for par in text:\n",
" finalText += par.text_content()\n",
" return finalText\n",
"\n",
"for result in results:\n",
" outputText = scrape(result.url)\n",
" outputFile.write(outputText)\n",
"\n",
"outputFile.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Scraping arbitrary websites\n",
"\n",
"The final approach is for a webpage that contains information you want and the pages are spread around in a fairly consistent manner, but there is no simple, straightfoward manner in which the pages are named.\n",
"\n",
"I'll offer a brief aside here to mention that it is often possible to make slight modifications to the URL of a website and obtain many different pages. For example, a website that contains Indian parliament speeches has the URL `http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=` with differing values appended after the `=`. Thus, using a `for-loop` allows for the programatic creation of different URLs. Some sample code is below."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5175\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5176\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5177\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5178\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5179\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5180\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5181\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5182\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5183\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5184\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5185\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5186\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5187\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5188\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5189\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5190\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5191\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5192\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5193\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5194\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5195\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5196\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5197\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5198\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5199\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5200\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5201\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5202\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5203\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5204\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5205\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5206\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5207\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5208\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5209\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5210\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5211\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5212\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5213\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5214\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5215\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5216\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5217\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5218\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5219\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5220\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5221\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5222\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5223\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5224\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5225\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5226\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5227\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5228\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5229\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5230\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5231\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5232\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5233\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5234\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5235\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5236\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5237\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5238\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5239\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5240\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5241\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5242\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5243\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5244\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5245\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5246\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5247\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5248\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5249\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5250\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5251\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5252\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5253\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5254\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5255\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5256\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5257\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5258\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5259\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5260\n",
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"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5901\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5902\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5903\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5904\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5905\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5906\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5907\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5908\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5909\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5910\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5911\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5912\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5913\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5914\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5915\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5916\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5917\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5918\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5919\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5920\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5921\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5922\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5923\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5924\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5925\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5926\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5927\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5928\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5929\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5930\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5931\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5932\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5933\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5934\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5935\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5936\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5937\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5938\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5939\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5940\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5941\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5942\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5943\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5944\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5945\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5946\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5947\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5948\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5949\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5950\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5951\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5952\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5953\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5954\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5955\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5956\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5957\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5958\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5959\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5960\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5961\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5962\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5963\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5964\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5965\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5966\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5967\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5968\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5969\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5970\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5971\n",
"Scraping: http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl=5972\n"
]
}
],
"source": [
"url = 'http://164.100.47.132/LssNew/psearch/Result13.aspx?dbsl='\n",
"\n",
"for i in xrange(5175,5973):\n",
" newUrl = url + str(i)\n",
" print('Scraping: %s' % newUrl)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Getting back on topic, it is often more difficult than the above to iterate over numerous webpages within a site. This is where the `Scrapy` library comes in. `Scrapy` allows for the creation of web spiders that crawl over a webpage, following any links that it finds. This is often far more difficult to implement than a simple scraper since it requires the identification of rules for link following. The [State Department](http://www.state.gov/r/pa/prs/dpb/2012/index.htm) offers a good example. I don't really have time to go into the depths of writing a `Scrapy` spider, but I thought I would put up some code to illustrate what it looks like."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from scrapy.contrib.spiders import CrawlSpider, Rule\n",
"from scrapy.contrib.linkextractors.sgml import SgmlLinkExtractor\n",
"from scrapy.selector import HtmlXPathSelector\n",
"from scrapy.item import Item\n",
"from BeautifulSoup import BeautifulSoup\n",
"import re\n",
"import codecs\n",
"\n",
"class MySpider(CrawlSpider):\n",
" name = 'statespider' #name is a name\n",
" start_urls = ['http://www.state.gov/r/pa/prs/dpb/2010/index.htm',\n",
" ] #defines the URL that the spider should start on. adjust the year.\n",
"\n",
" #defines the rules for the spider\n",
" rules = (Rule(SgmlLinkExtractor(allow=('/2010/'), restrict_xpaths=('//*[@id=\"local-nav\"]'),)), #allows only links within the navigation panel that have /year/ in them.\n",
"\n",
" Rule(SgmlLinkExtractor(restrict_xpaths=('//*[@id=\"dpb-calendar\"]',), deny=('/video/')), callback='parse_item'), #follows links within the caldendar on the index page for the individuals years, while denying any links with /video/ in them\n",
"\n",
" )\n",
"\n",
" def parse_item(self, response):\n",
" self.log('Hi, this is an item page! %s' % response.url) #prints the response.url out in the terminal to help with debugging\n",
" \n",
" #Insert code to scrape page content\n",
"\n",
" #opens the file defined above and writes 'texts' using utf-8\n",
" with codecs.open(filename, 'w', encoding='utf-8') as output:\n",
" output.write(texts)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##The Pitfalls of Webscraping\n",
"\n",
"Web scraping is much, much, *much*, more of an art than a science. It is often non-trivial to identify the XPath selector that will get you what you want. Also, some web programmers can't seem to decide how they want to structure the pages they write, so they just change the HTML every few pages. Notice that for the NYT example if `articleBody` gets changed to `articleBody1`, everything breaks. There are ways around this that are often convoluted, messy, and hackish. Usually, however, where there is a will there is a way.\n",
"\n",
"\n",
"##Workflow for real-time sraping\n",
"\n",
"As a wrap up, I thought I would show the workflow I have been using to perform real-time scraping from various news sites of stories pertaining to human atrocities. This is illustrative both of web scraping and of the issues that can accompany programming. \n",
"\n",
"The general flow of the scraper is:\n",
"\n",
"RSS feed -> identify relevant stories -> scrape story -> place results in mongoDB -> repeat every hour"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exercises for the class\n",
"0. Start a new notebook and load `ScrapeOutput.txt` there\n",
"1. Count the number of English words (advanced version: not including numerals, names, or abbreviations) in this text\n",
"2. Find the average word length (not including punctuation, e.g. periods or apostrophe-s)\n",
"3. Find the longest and shortest words used\n",
" - ADVANCED USERS: do the above by hand-coded python, otherwise try to use tools from Pandas\n",
"4. Plot a histogram of how many words at each word length from min to max length\n",
"5. Find the five most common words\n",
"6. Advanced: Install a word cloud library (e.g. https://github.com/amueller/word_cloud) and use it!\n",
"7. Advanced: Remove any mundane, common words like 'it' or 'the' (talk to me about this before attempting)\n",
"\n",
"** VERY IMPORTANT !! **\n",
"1. Make a note in Markdown cells of any assumptions or choices you make having first carefully observed the raw data\n",
"2. Make a plan of what steps you will take\n",
"3. Write utility functions and/or classes and test them before\n",
"\n",
"** HINTS **\n",
" - If you can, start writing your own version from scratch\n",
" - If you _can't_, feel free to try to adapt examples you find online\n",
" - Beware of capitalized first letters of sentences\n",
" - Beware of non-printing unicode characters\n",
" - Beware of abbreviations, numbers, proper nouns, or names that are not real words\n",
" - What could we do to verify that we have legitimate words to count?\n",
" - Adapt code from http://code.activestate.com/recipes/211886-retrieve-word-definitions-from-online-dictionary-s/\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Use this old example output if don't want to scrape first\n",
"outputText = u'TUNIS \\xe2\\x80\\x94 Hours before the Norwegian Nobel Committee gave its highest-profile honor to a coalition of Tunisian groups that had helped ease the country\\xe2\\x80\\x99s path to democracy, unknown gunmen attacked a member of Tunisia\\xe2\\x80\\x99s Parliament, firing seven or eight shots at his car as he drove to work in a seaside town.The assailants missed their target. But the attack, on Thursday, was an urgent reminder of the violence that still menaces Tunisia\\xe2\\x80\\x99s transition, one of many challenges to the country\\xe2\\x80\\x99s significant and celebrated political gains.The threat against prominent political figures, by shadowy militant groups, is among the government\\xe2\\x80\\x99s deepest worries: Twice in the last two years, high-profile assassinations have thrown Tunisia into political crisis. This year, the country has also grappled with an unprecedented wave of jihadist violence, including two large-scale attacks on tourists that killed at least 60 people and helped plunge the economy into recession.In a country still wrestling with its authoritarian past, the attacks have provoked anguished arguments about how much power the government and the police should wield to confront the threats. Other debates \\xe2\\x80\\x94 about the economic direction of the country, and its ability to come to terms with a legacy of past abuses \\xe2\\x80\\x94 have exposed divisions between old elites and newer political forces empowered by the uprising in late 2010 against the 23-year dictatorship of Zine el-Abidine Ben Ali.The challenges are testing not only the young government but also the compromise between secular and Islamist parties that is at the heart of Tunisia\\xe2\\x80\\x99s inchoate political system and is frequently held up as a model for the Arab world.Talk of Tunisia\\xe2\\x80\\x99s success is frequently attributed to its relatively peaceful transition, especially set against the violent struggles of other countries in the region, including Syria, Yemen and neighboring Libya. That contrast often overlooks an arduous road that began in December 2010, when a Tunisian fruit vendor named Mohammed Bouazizi lit himself on fire, in an act of despair that resonated throughout the Arab world.Days after Mr. Bouazizi died in January 2011, mass protests forced Mr. Ben Ali into exile. The Islamist Ennahda Party won the most votes in parliamentary elections that October but fell short of a majority. The group promised that its own Islamist program would not overwhelm the country\\xe2\\x80\\x99s deeply ingrained secular politics, and it also promised to build, as one Ennhada official put it, a \\xe2\\x80\\x9ccharismatic, democratic system.\\xe2\\x80\\x9dBut a backlash against Ennahda paralleled events in Egypt, where huge demonstrations led to a military coup in 2013 against the year-old government of President Mohamed Morsi, a leader of the Muslim Brotherhood, now banned.The four groups honored with the Nobel Peace Prize on Friday helped Tunisia avert the civil strife that led to hundreds of deaths in Egypt. They helped Tunisia negotiate its way through the most serious threat to its nascent transition: the crisis that followed the assassinations of two opposition politicians, Chokri Belaid and Mohamed Brahmi, in 2013.Giant protests that summer threatened to topple the Ennahda-led government. But the Islamists refused to cede power until they completed their mandate to pass a new Constitution.The impasse began to destabilize the country as the government grappled with jihadist militancy, popular unrest and strikes, and a worsening economy.After months of sometimes heated negotiations, a deal, concluded in December 2013, forged a new contract between the political parties, including a timetable for a democratic transition. The Islamist government agreed to step down and hand power to a caretaker government that would oversee the holding of parliamentary and presidential elections in October and November 2014.The adoption of the Constitution, in January 2014, was seen as a high point in the transition, producing a charter forged from robust debates between Tunisia\\xe2\\x80\\x99s disparate political currents, and that enshrined democratic principles and a separation of powers.Compromises by two men \\xe2\\x80\\x94 Rached Ghannouchi, the leader of the Islamist Ennahda party, and Beji Caid Essebsi, one of the founders of the secularist Nidaa Tounes party, and Tunisia\\xe2\\x80\\x99s current president \\xe2\\x80\\x94 ended the impasse, analysts say.Despite that achievement, the basis of their compromise remains fragile: \\xe2\\x80\\x9cIt is very much a consensus from the top \\xe2\\x80\\x94 often against elements of their base,\\xe2\\x80\\x9d said Issandr El Amrani, who oversees the North Africa Project for the International Crisis Group.As both leaders manage the pressures from within their own ranks, the government has been criticized for lacking a sense of direction and dynamism as well as for failing to tackle urgent issues, Mr. Amrani said. \\xe2\\x80\\x9cThis worries people.\\xe2\\x80\\x9dThe assassination attempt on Thursday, he said, could have been a sign of this tension, \\xe2\\x80\\x9cthe bigger underlying elite rivalries in Tunisia that are working themselves out.\\xe2\\x80\\x9dBut the motives for the attacks and other threats against political figures have been murky, given Tunisia\\xe2\\x80\\x99s overlapping rivalries.The Parliament member who survived the assassination attempt on Thursday, Ridha Charfeddine, 63, is a member of Nidaa Tounes and also a prominent businessman who owns a soccer team. The gunmen, riding in the back seat of a white car, attacked him in an industrial section of Sousse, on Tunisia\\xe2\\x80\\x99s eastern coast, according to the Interior Ministry. Sousse is the same beachside town where a 23-year-old Tunisian gunman slaughtered 38 people, mostly British tourists, in June. It was Tunisia\\xe2\\x80\\x99s worst terrorist attack in living memory.\\xe2\\x80\\x9cThis is not an isolated incident,\\xe2\\x80\\x9d Mohsen Marzouk, the general secretary of Nidaa Tounes, said in an interview with a local radio station after the gunfire. He said the gunmen belonged to an organized movement, but he did not identify it.As the political violence and jihadist attacks have unnerved the public, they have also given rise to fears about the state\\xe2\\x80\\x99s reaction. The police have reasserted themselves in response to the attacks, despite growing reports of human rights abuses and a lack of coherent strategy to reform the security services, Mr. Amrani said.In the aftermath of the attack on the tourists in Sousse, the government also started closing dozens of mosques \\xe2\\x80\\x94 prompting concern that Mr. Essebsi\\xe2\\x80\\x99s secular government, with its strong connections to the old dictatorship, was reviving the crackdowns on Islamists that occurred during Mr. Ben Ali\\xe2\\x80\\x99s rule.The government closed at least 80 mosques, though none of them had any connection to the gunman in Sousse, officials said.'"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"outputText = outputText.replace('.', ' ').replace(',', ' ').replace('?', ' ').replace('!', ' ')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tokenized = outputText.split()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[u'TUNIS',\n",
" u'\\xe2\\x80\\x94',\n",
" u'Hours',\n",
" u'before',\n",
" u'the',\n",
" u'Norwegian',\n",
" u'Nobel',\n",
" u'Committee',\n",
" u'gave',\n",
" u'its',\n",
" u'highest-profile',\n",
" u'honor',\n",
" u'to',\n",
" u'a',\n",
" u'coalition',\n",
" u'of',\n",
" u'Tunisian',\n",
" u'groups',\n",
" u'that',\n",
" u'had',\n",
" u'helped',\n",
" u'ease',\n",
" u'the',\n",
" u'country\\xe2\\x80\\x99s',\n",
" u'path',\n",
" u'to',\n",
" u'democracy,',\n",
" u'unknown',\n",
" u'gunmen',\n",
" u'attacked',\n",
" u'a',\n",
" u'member',\n",
" u'of',\n",
" u'Tunisia\\xe2\\x80\\x99s',\n",
" u'Parliament,',\n",
" u'firing',\n",
" u'seven',\n",
" u'or',\n",
" u'eight',\n",
" u'shots',\n",
" u'at',\n",
" u'his',\n",
" u'car',\n",
" u'as',\n",
" u'he',\n",
" u'drove',\n",
" u'to',\n",
" u'work',\n",
" u'in',\n",
" u'a']"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tokenized[:50]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Overkill solution\n",
"#from sklearn.feature_extraction.text import TfidfVectorizer\n",
"#vctr = TfidfVectorizer()\n",
"#pre_proc = vctr.build_preprocessor()(outputText)\n",
"#tokenized = vctr.build_tokenizer()(pre_proc)\n",
"#print(tokenized[:50])"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"new_words = []\n",
"for word in tokenized:\n",
" bytecodes = [ord(c) for c in word]\n",
" if any([b > 127 for b in bytecodes]):\n",
" # avoid `s` at end (for 's ) only if some bytecodes were > 127\n",
" if word[-1] == 's':\n",
" new_word = \"\".join([chr(b) for b in bytecodes[:-1] if b <= 127])\n",
" else:\n",
" new_word = \"\".join([chr(b) for b in bytecodes if b <= 127])\n",
" else:\n",
" new_word = \"\".join([chr(b) for b in bytecodes if b <= 127])\n",
" if new_word != '':\n",
" # ensure only lowercase, non-empty words\n",
" new_words.append(new_word.lower())"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['tunis',\n",
" 'hours',\n",
" 'before',\n",
" 'the',\n",
" 'norwegian',\n",
" 'nobel',\n",
" 'committee',\n",
" 'gave',\n",
" 'its',\n",
" 'highest-profile',\n",
" 'honor',\n",
" 'to',\n",
" 'a',\n",
" 'coalition',\n",
" 'of',\n",
" 'tunisian',\n",
" 'groups',\n",
" 'that',\n",
" 'had',\n",
" 'helped',\n",
" 'ease',\n",
" 'the',\n",
" 'country',\n",
" 'path',\n",
" 'to',\n",
" 'democracy,',\n",
" 'unknown',\n",
" 'gunmen',\n",
" 'attacked',\n",
" 'a',\n",
" 'member',\n",
" 'of',\n",
" 'tunisia',\n",
" 'parliament,',\n",
" 'firing',\n",
" 'seven',\n",
" 'or',\n",
" 'eight',\n",
" 'shots',\n",
" 'at',\n",
" 'his',\n",
" 'car',\n",
" 'as',\n",
" 'he',\n",
" 'drove',\n",
" 'to',\n",
" 'work',\n",
" 'in',\n",
" 'a',\n",
" 'seaside']"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_words[:50]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>words</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>tunis</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>hours</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>before</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>the</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>norwegian</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>nobel</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>committee</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>gave</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>its</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>highest-profile</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>honor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>to</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>a</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>coalition</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>of</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>tunisian</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>groups</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>that</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>had</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>helped</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>ease</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>the</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>country</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>path</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>to</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>democracy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>unknown</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>gunmen</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>attacked</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>a</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1013</th>\n",
" <td>on</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1014</th>\n",
" <td>islamists</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1015</th>\n",
" <td>that</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1016</th>\n",
" <td>occurred</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1017</th>\n",
" <td>during</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1018</th>\n",
" <td>mr</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1019</th>\n",
" <td>ben</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1020</th>\n",
" <td>ali</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1021</th>\n",
" <td>rule</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1022</th>\n",
" <td>the</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1023</th>\n",
" <td>government</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1024</th>\n",
" <td>closed</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1025</th>\n",
" <td>at</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1026</th>\n",
" <td>least</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1027</th>\n",
" <td>80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1028</th>\n",
" <td>mosques</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1029</th>\n",
" <td>though</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1030</th>\n",
" <td>none</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1031</th>\n",
" <td>of</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1032</th>\n",
" <td>them</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1033</th>\n",
" <td>had</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1034</th>\n",
" <td>any</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1035</th>\n",
" <td>connection</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1036</th>\n",
" <td>to</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1037</th>\n",
" <td>the</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1038</th>\n",
" <td>gunman</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1039</th>\n",
" <td>in</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1040</th>\n",
" <td>sousse</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1041</th>\n",
" <td>officials</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1042</th>\n",
" <td>said</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1043 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" words\n",
"0 tunis\n",
"1 hours\n",
"2 before\n",
"3 the\n",
"4 norwegian\n",
"5 nobel\n",
"6 committee\n",
"7 gave\n",
"8 its\n",
"9 highest-profile\n",
"10 honor\n",
"11 to\n",
"12 a\n",
"13 coalition\n",
"14 of\n",
"15 tunisian\n",
"16 groups\n",
"17 that\n",
"18 had\n",
"19 helped\n",
"20 ease\n",
"21 the\n",
"22 country\n",
"23 path\n",
"24 to\n",
"25 democracy\n",
"26 unknown\n",
"27 gunmen\n",
"28 attacked\n",
"29 a\n",
"... ...\n",
"1013 on\n",
"1014 islamists\n",
"1015 that\n",
"1016 occurred\n",
"1017 during\n",
"1018 mr\n",
"1019 ben\n",
"1020 ali\n",
"1021 rule\n",
"1022 the\n",
"1023 government\n",
"1024 closed\n",
"1025 at\n",
"1026 least\n",
"1027 80\n",
"1028 mosques\n",
"1029 though\n",
"1030 none\n",
"1031 of\n",
"1032 them\n",
"1033 had\n",
"1034 any\n",
"1035 connection\n",
"1036 to\n",
"1037 the\n",
"1038 gunman\n",
"1039 in\n",
"1040 sousse\n",
"1041 officials\n",
"1042 said\n",
"\n",
"[1043 rows x 1 columns]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import seaborn as sns\n",
"words = pd.DataFrame({'words': new_words})\n",
"words"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>words</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1043</td>\n",
" </tr>\n",
" <tr>\n",
" <th>unique</th>\n",
" <td>498</td>\n",
" </tr>\n",
" <tr>\n",
" <th>top</th>\n",
" <td>the</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq</th>\n",
" <td>88</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" words\n",
"count 1043\n",
"unique 498\n",
"top the\n",
"freq 88"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"words.describe()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index([u'words'], dtype='object')"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"words.columns"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"counts = {}\n",
"for word in words['words']:\n",
" if word in counts:\n",
" counts[word] += 1\n",
" else:\n",
" counts[word] = 1"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.series.Series'>\n",
"Index([u'the', u'of', u'a', u'in', u'to', u'and', u'that', u'tunisia',\n",
" u'government', u'as',\n",
" ...\n",
" u'held', u'violent', u'year-old', u'lit', u'committee', u'ennahda-led',\n",
" u'majority', u'before', u'isolated', u'serious'],\n",
" dtype='object', length=498)\n",
"[88 37 32 27 26 24 18 13 12 9 9 9 8 8 8 8 7 7 7 7 7 6 6 6 5\n",
" 5 5 5 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
" 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2\n",
" 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
" 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
" 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1\n",
" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n",
" count\n",
"word \n",
"the 88\n",
"of 37\n",
"a 32\n",
"in 27\n",
"to 26\n",
"and 24\n",
"that 18\n",
"tunisia 13\n",
"government 12\n",
"as 9\n",
"political 9\n",
"on 9\n",
"is 8\n",
"its 8\n",
"have 8\n",
"an 8\n",
"against 7\n",
"with 7\n",
"but 7\n",
"said 7\n",
"country 7\n",
"for 6\n",
"also 6\n",
"mr 6\n",
"islamist 5\n",
"their 5\n",
"attacks 5\n",
"it 5\n",
"was 4\n",
"he 4\n",
"... ...\n",
"now 1\n",
"killed 1\n",
"presidential 1\n",
"strong 1\n",
"success 1\n",
"adoption 1\n",
"late 1\n",
"well 1\n",
"23-year-old 1\n",
"nascent 1\n",
"rached 1\n",
"politicians 1\n",
"talk 1\n",
"coup 1\n",
"empowered 1\n",
"any 1\n",
"exile 1\n",
"survived 1\n",
"years 1\n",
"ease 1\n",
"held 1\n",
"violent 1\n",
"year-old 1\n",
"lit 1\n",
"committee 1\n",
"ennahda-led 1\n",
"majority 1\n",
"before 1\n",
"isolated 1\n",
"serious 1\n",
"\n",
"[498 rows x 1 columns]\n"
]
}
],
"source": [
"# easier alternative\n",
"wc = words['words'].value_counts()\n",
"print(type(wc))\n",
"print(wc.keys())\n",
"print(wc.values)\n",
"wc_df = pd.DataFrame({'word': list(wc.keys()), 'count': wc.values}).set_index(['word'])\n",
"print(wc_df)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" </tr>\n",
" <tr>\n",
" <th>word</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>the</th>\n",
" <td>88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>of</th>\n",
" <td>37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>in</th>\n",
" <td>27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>to</th>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>and</th>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>that</th>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>tunisia</th>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>government</th>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>as</th>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>political</th>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>on</th>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>is</th>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>its</th>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>have</th>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>an</th>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>against</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>with</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>but</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>said</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>country</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>for</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>also</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mr</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>islamist</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>their</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>attacks</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>it</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>was</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>he</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>now</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>killed</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>presidential</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>strong</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>success</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>adoption</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>late</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>well</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23-year-old</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>nascent</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rached</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>politicians</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>talk</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coup</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>empowered</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>any</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>exile</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>survived</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>years</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ease</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>held</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>violent</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>year-old</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lit</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>committee</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ennahda-led</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>majority</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>before</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>isolated</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>serious</th>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>498 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" count\n",
"word \n",
"the 88\n",
"of 37\n",
"a 32\n",
"in 27\n",
"to 26\n",
"and 24\n",
"that 18\n",
"tunisia 13\n",
"government 12\n",
"as 9\n",
"political 9\n",
"on 9\n",
"is 8\n",
"its 8\n",
"have 8\n",
"an 8\n",
"against 7\n",
"with 7\n",
"but 7\n",
"said 7\n",
"country 7\n",
"for 6\n",
"also 6\n",
"mr 6\n",
"islamist 5\n",
"their 5\n",
"attacks 5\n",
"it 5\n",
"was 4\n",
"he 4\n",
"... ...\n",
"now 1\n",
"killed 1\n",
"presidential 1\n",
"strong 1\n",
"success 1\n",
"adoption 1\n",
"late 1\n",
"well 1\n",
"23-year-old 1\n",
"nascent 1\n",
"rached 1\n",
"politicians 1\n",
"talk 1\n",
"coup 1\n",
"empowered 1\n",
"any 1\n",
"exile 1\n",
"survived 1\n",
"years 1\n",
"ease 1\n",
"held 1\n",
"violent 1\n",
"year-old 1\n",
"lit 1\n",
"committee 1\n",
"ennahda-led 1\n",
"majority 1\n",
"before 1\n",
"isolated 1\n",
"serious 1\n",
"\n",
"[498 rows x 1 columns]"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wc_df"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def filt_index_1(count_item):\n",
" return count_item[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, I could do this on one line with an equivalent lambda function definition: `key=lambda x: x[1]`"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sorted_word_counts = sorted(counts.items(), key=filt_index_1, reverse=True)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"[('the', 87),\n",
" ('of', 36),\n",
" ('in', 26),\n",
" ('to', 25),\n",
" ('and', 23),\n",
" ('that', 17),\n",
" ('tunisia', 12),\n",
" ('government', 11),\n",
" ('political', 8),\n",
" ('on', 8),\n",
" ('as', 8),\n",
" ('its', 7),\n",
" ('have', 7),\n",
" ('an', 7),\n",
" ('is', 7),\n",
" ('country', 6),\n",
" ('said', 6),\n",
" ('against', 6),\n",
" ('but', 6),\n",
" ('with', 6),\n",
" ('mr', 5),\n",
" ('also', 5),\n",
" ('for', 5),\n",
" ('islamist', 4),\n",
" ('transition', 4),\n",
" ('their', 4),\n",
" ('it', 4),\n",
" ('attacks', 4),\n",
" ('two', 3),\n",
" ('this', 3)]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted_word_counts[:30]"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.rcParams['figure.figsize'] = (14, 10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`sorted_word_counts` is just a list of pairs and we want to make a dataframe out of it. To do that, I first turn it into a numpy array, transpose it to make it headed by columns, then feed it to pandas while indicating the column name that matters ('count'). Sadly, I discovered that I have to coerce the object into floating point format in order to use the bar plot function below. In other words, I got stuck with an empty plot and used stackoverflow.com to find someone else who'd complained about it and received an acceptable answer. The bar plot function doesn't accept integer-based arrays, which I consider a \"bug\", but that's a situation you just have to learn to work around."
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"wc_array = np.array(sorted_word_counts).T"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>the</th>\n",
" <td>87</td>\n",
" </tr>\n",
" <tr>\n",
" <th>of</th>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>in</th>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>to</th>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>and</th>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>that</th>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>tunisia</th>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>government</th>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>political</th>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>on</th>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>as</th>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>its</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>have</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>an</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>is</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>country</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>said</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>against</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>but</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>with</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mr</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>also</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>for</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>islamist</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>transition</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>their</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>it</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>attacks</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>two</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>this</th>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count\n",
"the 87\n",
"of 36\n",
"in 26\n",
"to 25\n",
"and 23\n",
"that 17\n",
"tunisia 12\n",
"government 11\n",
"political 8\n",
"on 8\n",
"as 8\n",
"its 7\n",
"have 7\n",
"an 7\n",
"is 7\n",
"country 6\n",
"said 6\n",
"against 6\n",
"but 6\n",
"with 6\n",
"mr 5\n",
"also 5\n",
"for 5\n",
"islamist 4\n",
"transition 4\n",
"their 4\n",
"it 4\n",
"attacks 4\n",
"two 3\n",
"this 3"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wc = pd.DataFrame({'count': wc_array[1][:30]}, index=wc_array[0][:30]).astype(float)\n",
"wc"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>len</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>492.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>6.349593</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>2.435259</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>4.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>6.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>8.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>14.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" len\n",
"count 492.000000\n",
"mean 6.349593\n",
"std 2.435259\n",
"min 2.000000\n",
"25% 4.000000\n",
"50% 6.000000\n",
"75% 8.000000\n",
"max 14.000000"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"just_words = pd.DataFrame(pd.Series([len(w) for w in wc_array[0]]), columns=['len'])\n",
"just_words.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Find the index position of the longest word using `argmax`"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"143"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"max_pos = just_words['len'].argmax()\n",
"max_pos"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'assassinations'"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wc_array[0][max_pos]"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xc308a50>"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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JNid5dpL7tNaunGx/Z5KHJHnb2jQRAADYyKaZ03Jjkl9urT00yY8n+R9JNi3ZfkOS09eg\nbQAAAFOFll0Zgkpaax9M8qkkd1qyfVuS61e/aQAAAMmmhYWFZR9QVT+W5GtbaxdU1RlJ/izJ7iQv\nbq29u6peluTy1tqlx3uOgwcPLWzZsjlJsmvXrlQlyc7jPHpXWkt27jzedgAA4EvUpmPdOc2cllcn\neU1VXZFkIcmPZuhteVVVnZrkmiSXLfcE+/ffeNPP+/YdSLJ12Qr37TuQvXtvmKJpg+3bt416/GqW\n36h1z1te29df3fOW36h1z1te29df3fOW1/b1V/e85bV9/dU9b3ltP3757du3HfP+FUNLa+1gksce\nY9MDp2wbAADAzFxcEgAA6JrQAgAAdE1oAQAAuia0AAAAXRNaAACArgktAABA14QWAACga0ILAADQ\nNaEFAADomtACAAB0TWgBAAC6JrQAAABdE1oAAICuCS0AAEDXhBYAAKBrQgsAANA1oQUAAOia0AIA\nAHRNaAEAALomtAAAAF0TWgAAgK4JLQAAQNeEFgAAoGtCCwAA0DWhBQAA6JrQAgAAdE1oAQAAuia0\nAAAAXRNaAACArgktAABA14QWAACga0ILAADQNaEFAADomtACAAB0TWgBAAC6JrQAAABdE1oAAICu\nCS0AAEDXhBYAAKBrQgsAANA1oQUAAOia0AIAAHRNaAEAALomtAAAAF0TWgAAgK4JLQAAQNeEFgAA\noGtCCwAA0DWhBQAA6JrQAgAAdE1oAQAAuia0AAAAXRNaAACArgktAABA17ZM86Cq+vIkf5PkvCSH\nkrw2yeEkV7fWLliz1gEAABveij0tVbUlycuT3Di56+IkF7bWzk1ySlWdv4btAwAANrhphoe9JMnL\nknw8yaYk92mtXTnZ9s4MvS8AAABrYtnQUlU/muRfWmt/kiGwHF3mhiSnr03TAAAAVp7T8rgkh6vq\nIUnuneR1SbYv2b4tyfVr1DYAAIBsWlhYmOqBVXV5kqck+eUkv9Jau6KqXpbk8tbapcuVPXjw0MKW\nLZuTJLt27UpVkuw8zqN3pbVk587jbQcAAL5EbTrWnVOtHnaUZyW5pKpOTXJNkstWKrB//403/bxv\n34EkW5d9/L59B7J37w1TN2j79m2jHr+a5Tdq3fOW1/b1V/e85Tdq3fOW1/b1V/e85bV9/dU9b3lt\nX391z1te249ffvv2bce8f+rQ0lp78JKbD5y2HAAAwDxcXBIAAOia0AIAAHRNaAEAALomtAAAAF0T\nWgAAgK4JLQAAQNeEFgAAoGtCCwAA0DWhBQAA6JrQAgAAdE1oAQAAuia0AAAAXRNaAACArgktAABA\n17ac7AaMdejQoezZs/tm9+3fvzX79h246faOHWdl8+bNJ7ppAADAGlh3oWXPnt0555y9Sc48asvW\nyfdrc9VVydln3/0EtwwAAFgL6y60DM5MsnOZ7QeW2QYAAKwn5rQAAABdE1oAAICuCS0AAEDXhBYA\nAKBrQgsAANA1oQUAAOia0AIAAHRNaAEAALomtAAAAF0TWgAAgK4JLQAAQNeEFgAAoGtCCwAA0DWh\nBQAA6JrQAgAAdE1oAQAAuia0AAAAXRNaAACArgktAABA14QWAACga0ILAADQNaEFAADomtACAAB0\nTWgBAAC6JrQAAABdE1oAAICuCS0AAEDXhBYAAKBrW052A060Q4cOZc+e3Te7b//+rdm370CSZMeO\ns7J58+aT0TQAAOAYNlxo2bNnd845Z2+SM4/asjXJtbnqquTss+9+EloGAAAcy4YLLYMzk+w8zrYD\nJ7IhAADACsxpAQAAuia0AAAAXRNaAACArgktAABA14QWAACga0ILAADQtRWXPK6qU5JckqSSHE7y\nlCSfS/Laye2rW2sXrGEbAQCADWyanpZHJFlord0/yXOSXJTk4iQXttbOTXJKVZ2/hm0EAAA2sBVD\nS2vtbUmePLl5tyT7k9yntXbl5L53JjlvbZoHAABsdFPNaWmtHa6q307y60nekGTTks03JDl9DdoG\nAAAw/UT81trjkuxM8qokt1qyaVuS61e5XQAAAEmSTQsLC8s+oKoek+SurbUXVdWXJfm7JB9MclFr\n7d1V9bIkl7fWLj3ecxw8eGhhy5bNSZJdu3alKhnyz7HsSmvJzp3H3r625ZcvCwAArKlNx7pzxdXD\nklyW5LVV9e7J45+a5ANJXlVVpya5ZvKY49q//8abft6370CSrctWuG/fgezde8Nxt61l+eXKHsv2\n7dtGPX41y5/Muuctr+3rr+55y2/Uuuctr+3rr+55y2v7+qt73vLavv7qnre8th+//Pbt2455/4qh\npbX2b0l+4BibHjhl2wAAAGbm4pIAAEDXhBYAAKBrQgsAANA1oQUAAOia0AIAAHRNaAEAALomtAAA\nAF0TWgAAgK4JLQAAQNeEFgAAoGtCCwAA0DWhBQAA6JrQAgAAdE1oAQAAurblZDdgPTl06FD27Nl9\ns/v279+affsO3HR7x46zsnnz5hPdNAAA+JIltIywZ8/unHPO3iRnHrVl6+T7tbnqquTss+9+glsG\nAABfuoSW0c5MsnOZ7QeW2QYAAIxlTgsAANA1oQUAAOia0AIAAHTNnJYTyOpjAAAwntByAll9DAAA\nxhNaTjirjwEAwBjmtAAAAF0TWgAAgK4JLQAAQNeEFgAAoGtCCwAA0DWhBQAA6JrQAgAAdE1oAQAA\nuia0AAAAXRNaAACArm052Q1gOocOHcqePbtvdt/+/Vuzb9+Bm27v2HFWNm/efKKbBgAAa0poWSf2\n7Nmdc87Zm+TMo7ZsnXy/NlddlZx99t1PcMsAAGBtCS3ryplJdi6z/cAy2wAAYH0ypwUAAOia0AIA\nAHRNaAEAALomtAAAAF0TWgAAgK4JLQAAQNeEFgAAoGtCCwAA0DWhBQAA6JrQAgAAdE1oAQAAuia0\nAAAAXRNaAACArgktAABA14QWAACga0ILAADQNaEFAADomtACAAB0bctyG6tqS5LXJNmR5LQkv5Dk\n/Ulem+RwkqtbaxesbRMBAICNbKWelkcn+WRr7VuSfFuS30xycZILW2vnJjmlqs5f4zYCAAAb2Eqh\n5U1JnjP5eXOSg0nu01q7cnLfO5Oct0ZtAwAAWH54WGvtxiSpqm1JLk3y7CQvWfKQG5KcvmatAwAA\nNrwVJ+JX1VcmuTzJ77TW3phhLsuibUmuX6O2AQAAZNPCwsJxN1bVnZL8eZILWmt/PrnvbUl+pbV2\nRVW9LMnlrbVLl6vk4MFDC1u2bE6S7Nq1K1VJsvM4j96V1pKdO4+9fW3Ln8y6ly8/b90AALAObDrW\nncsOD0vyc0lum+Q5VfXcJAtJnpbkN6rq1CTXJLlspZr377/xpp/37TuQZOuyj9+370D27r3huNvW\nsvzJrHu58vPWfSzbt28b9fjVLH8y6563/Eate97yG7Xuectr+/qre97y2r7+6p63vLavv7rnLa/t\nxy+/ffu2Y96/0pyWpyd5+jE2PXBE2wAAAGbm4pIAAEDXhBYAAKBrQgsAANA1oQUAAOia0AIAAHRN\naAEAALomtAAAAF0TWgAAgK4te3FJvnQcOnQoe/bsvtl9+/dvzb59B266vWPHWdm8efOJbhoAACxL\naNkg9uzZnXPO2ZvkzKO2bJ18vzZXXZWcffbdT3DLAABgeULLhnJmkp3LbD+wzDYAADg5zGkBAAC6\nJrQAAABdE1oAAICuCS0AAEDXhBYAAKBrQgsAANA1oQUAAOia0AIAAHRNaAEAALomtAAAAF0TWgAA\ngK4JLQAAQNeEFgAAoGtCCwAA0DWhBQAA6JrQAgAAdE1oAQAAuia0AAAAXRNaAACArgktAABA14QW\nAACga0ILAADQNaEFAADomtACAAB0TWgBAAC6JrQAAABdE1oAAICuCS0AAEDXtpzsBrA+HDp0KHv2\n7L7Zffv3b82+fQduur1jx1nZvHnziW4aAABf4oQWprJnz+6cc87eJGcetWXr5Pu1ueqq5Oyz736C\nWwYAwJc6oYURzkyyc5ntB5bZBgAAszGnBQAA6JrQAgAAdE1oAQAAumZOC2vOymMAAMxDaGHNWXkM\nAIB5CC2cIFYeAwBgNua0AAAAXRNaAACArgktAABA14QWAACga0ILAADQNaEFAADo2lRLHlfVNyX5\nxdbag6rq7CSvTXI4ydWttQvWsH0AAMAGt2JPS1X95ySXJLnF5K6Lk1zYWjs3ySlVdf4atg8AANjg\nphke9qEk373k9n1ba1dOfn5nkvNWvVUAAAATK4aW1trvJzm45K5NS36+Icnpq90oAACARbNMxD+8\n5OdtSa5fpbYAAAB8kakm4h/lb6vqW1prVyT59iSXr1Tgdre7dbZs2Zwk2b9/64oV3P72W7N9+7Zj\nblvr8iez7uXK9/66LVd+3rqPZ+zjV7P8Rq173vIbte55y2v7+qt73vLavv7qnre8tq+/uuctr+3j\nzBJanpXkkqo6Nck1SS5bqcD+/Tfe9PO+fQeSLH8Qu2/fgezde8Nxt61l+ZNZ93Lle3/dlis/b93H\nsn37tlGPX83yG7Xuectv1LrnLa/t66/uectr+/qre97y2r7+6p63vLYfv/zxAs1UoaW19pEk3zz5\n+YNJHji6hQAAADNwcUkAAKBrQgsAANC1Wea0wAl16NCh7Nmz+2b37d+/dTJXZrBjx1nZvHnzTOXn\nKbtSeQAA5ie00L09e3bnnHP2JjnzqC2Lk/uvzVVXJWefffcZys9TduW6AQCYn9DCOnFmkp3LbD+w\nzLaVys9TdpryAADMw5wWAACga0ILAADQNaEFAADomjktsIbmWX1srVdNW+vyAACrRWiBNTTP6mNr\nu2ra2pcHAFgtQgusuXlWH1vLVdNORHkAgPmZ0wIAAHRNaAEAALomtAAAAF0zpwVYdSd75bKjy1t1\nDQDWN6EFWHUne+WyY5e36hoArFdCC7BGTvbKZcuVt+oaAKwn5rQAAABdE1oAAICuCS0AAEDXzGkB\n6MTJXrnMymcA9EpoAejEyV65zMpnAPRKaAHoysleuczKZwD0x5wWAACga0ILAADQNaEFAADomtAC\nAAB0zUR8AOZ2spdbPrq8pZ4BvrQILQDM7WQvt3zs8pZ6BvhSIbQAsEpO9nLLy5W31DPAemZOCwAA\n0DWhBQAA6JrQAgAAdM2cFgCYwzyrj/W26tp6bvt6ed2A2QgtADCHeVYf63PVtfXc9v5fN2A2QgsA\nzG2e1cd6XnVtpfI9t73n1w0Yy5wWAACga0ILAADQNaEFAADomjktAAAnyMleucyKcSe+7b29bkeX\n7/V1O5rQAgBwgpzslcusGOd1u3n5fl+3owktAAAn1MleucyKcSe+vNdt9vIDc1oAAICuCS0AAEDX\nhBYAAKBrQgsAANA1oQUAAOia0AIAAHRNaAEAALomtAAAAF0TWgAAgK4JLQAAQNeEFgAAoGtCCwAA\n0LUtsxSqqk1JXprk3kk+m+SJrbXdq9kwAACAZPaelu9KcovW2jcn+bkkF69ekwAAAI6YNbTcP8m7\nkqS19t4kX79qLQIAAFhi1tDyZUk+veT2waoyPwYAAFh1M81pSfKZJNuW3D6ltXZ4+uLXrrBt+0kq\nfzLrnqZ8r6/bNOV7bbvXbe3K99p2r9vale+17V63tSvfa9u9bmtXvte2e91mK9/763bEpoWFhake\nuFRVfU+Sh7fWHl9V90vynNbaw0Y/EQAAwApm7Wn5/SQPqar3TG4/bpXaAwAAcDMz9bQAAACcKCbP\nAwAAXRNaAACArgktAABA14QWAACga0JLkqo69WS3AQAAjqWqvv6o2+eerLacLOsutFTVL0y+nz/H\nczylqnZV1e6qujbJ365aA6dvw/aqWnev/8lWVadU1eaqekBVnXay27NeVNWXVdXXVtVtTnC9Tzzq\n9lNPZP0bWVXdvqq+oaruOEPZX1mLNk1Z9w+frLrZeOY9EKyqr66qR1bV181Q95ajbt927HNsVEf/\n3WYof/eq+o6qumtVbVqtdk1Rb81Q5gFV9WNJXl9VT558/XiS31r9Fq6dqnpWVU13FcnjmPU6Laui\nqv5TkrOTXJXkg621z05R7Pur6uNJfqqq7rR0Q2vtlVNW/RNJzk3yX5NcmuQ7p2/1oKq+PMktl9T9\n0SnLPShQgUi2AAAgAElEQVTJq5N8Jsltq+pJrbU/maLcVx1v27R1T57nv7bWXrjk9otaaz83Zdn7\nZbgmz6lJNiU5o7X20CnKbU6yOckbk/zApOwpSf6otfbgEW3/tSTXJLlbkvsk+eckPzJFuX9KsjCp\nd6mF1toZU9b9lUl+MDf/m/+36VqeVNV5Gf7fTknyGxkuyPqGKct+bZLbJDmc5KIkF7XW/mxE3d+X\n5NmT+t9UVQtL3wMrlJ3p966qH8zwf/Wgqlr8G29Ocq8kvz5l3ZuT/Ickt15S9xXTlF3yHL/ZWvvJ\nJbdf11p77Apl/iHD+2VzktOS7M1wud59rbVvGlH33yR5fZLXtdb2jWz3G1prPzSmzFHlvz/JC5O8\nP8m9qup5rbXXj3iKe1bVbVtr189Q98z7mIknJ/kfY+ud1HWXJKcnOZjkvyT5jdba301R7lj7qM1J\n/nDafdTkhMDtknxh8ju8rrX2kRFt35zkRzPs3/40yftba5+cse2z7F8vTPIzSW6cPMeK+8dV/Fza\nluHvdUaSP0hydWvtQ1OWnXUf9YAk90zyjKq6eHL35iQXZNhPTVP3U5P8UJL/k+Q/V9WbWmsvmaLc\nnZN8WZLXVdVjcuRv9rok3zhN3ZPnmen9Pin7oNban09+vlWSX22tPWVE3V+b4VjmK5P8U5IntNZG\nnQCuqi/L8Ln23Une0VrbP6L4s6pqR4Z97OvH7Kuq6icndd4+ye8mOSvJTy5b6EjZTUm+ITd/v435\nXHp1kvuPeHyS7E9y5yS3SPIVk/sOZ/h/nVpV/WaSV037HjlG+ZmPYyYOJPn9qvpEhtfhXa21Uddd\nOWmhpaouSnLXJPfI8A93YYYdz0p+OMlDM/zx7pUh9Fyb5F9GVP/x1to/VdW21tpfVNXPjmz7S5N8\nR5KPZ7JzT/LNUxZ/QZL7t9Y+PtnhvCXJiqElRxL1XZNsTfI3GQ7oPpkp/gGq6glJnpjkHlX1HZO7\nN2cIINMeULwsyYuTfF+Sf0gy7YfS4zP8fe+cpGV4zQ4nuXLK8ou+obX29Kr689bag6pqqgP31tpX\nrPyoFV2a4UDi/81Y/hcyfLj9VpL/mORNSab9Z395hh3q8zOEjxcnmTq0JHlGkvsleVeG0PNXGQ5o\npzHr7/2uDB9kd0jyisl9h5N8eMRzXJbktkk+Mbm9kGSqD4equiDDSYnbV9X3ZHjPbUryjyuVba19\nzeQ5fjvJL7bWWlWdneH1H+O8DH/zt1fV/8vwYfGnU5a9xeSgYFeG1y2ttc+PqPsZSe7TWjswOSC8\nPMOH+7TukeSTVfXJSf0LrbW7LFdglfYxyfC7/98M+4rF333aAPeGJM/LcOB5WZJfTfKgKcot7qPu\nNKk3Gb+PuizD/+r3ZgiLr8zwWTWtV2T4THlIht7/12X4nFnJ0v3rrsl9s+xffyDDiagbR5SZ63Np\nidckeWeGk4n7MhzQTNvjMes+aumB4J0n9x3OuPfqD2b4PD84GWb+v5OsGFoy7I+flqQyvE8W6/7j\nEXUns7/fk+QFVfX0DMeBr8pw8D7Gf0/yxNba3096mRY/26ZSVW9M8o4Mx06nJPmeDEFiKq21R1XV\n7TLsYy+tqn9Jcklr7S+mKP6oJN+S5M9aaxdPTjBN681JvjxH3m9Tfy5N/GtV/Wpuvn9b9oR7a+3q\nJFdX1SWttY8nQ1hvrY19z78jyYWTY8/XJ/kfrbXPjCg/z3FMWmsvT/LyqvrqDMcyr6iq1yT579MG\n1pPZ03L/1tq3TA5AX1NVT56mUGvtr5L8VVV9LsMH5PuTfHuGf9xpfbqqvivJwqTLbdkP42P4xiRn\ntdYOjyyXJIcW33SttY9V1TS9S2mtPSJJqurtSR7ZWvtsDcOj3jplva/PcKB7YYY3XjL8w4wJe59s\nrf1eVX1ra+15VfWHU7b9kiSXVNXjW2uvGVHf0TZX1X2T7Jn87tvGFJ61p2jihtbafx3V2pu7MUPP\n0MHW2ieqaszZhc9mONg+rbX2f6rq0Mi6D7XWPjfpYTlUVf86ouxMv/dkB/QXVfXuDH+nxbNpV494\nmju21h4wtu5J/b+V5Leq6sLW2kWzPEeG//E2eb4PV9XdRrbh+iQvrao/T/KcJG+oYTjqL7bWfn+F\n4juTvG3J7YUMZwOndbi1dmDSjhum3c8s8dQM/yu3XOmBS6zGPiYZzhjP6nCGA4hnt9beWFVPmrLc\nQmvtzMmBz/9cev+Ium+doZfgaa21x07OSo5xdmvtiVV1/9baW6vqP09TaBX3r9cm+bcxBVbhc2nR\nHSbHAY9urV1R44ZOz7qPWjwQ/EKGHq7FM8g3JHn7lE+zqbV2cPJ8X5g81zR1vzXJW6vqO1prfzS2\n7UvM+n5Pku/K8H49LcPf7pqRdW9qrf19krTW/q6qDo4sf0Zr7fVV9YTJSchpT+gsdackX5XkjhmO\nBb+3qp7YWnv0CuVOyfC/vfj/PWb/eOfW2rQnqY/lf0++32nZRx3bD1fV9RlO5j2uqt7VWnvmtIVb\na+9K8q4ahmj99yS/XFWXJXlBa22aE4rzHMcsDn98VJLHJrk+Q3A/JUOYmirwnszQsqWqbpkhOGxO\nMvZA7LvzxWcSpz1T8MQk/y7DGZWfTvJTI+v+cIYP8zFnpBZ9pqp+KsOO5luSfGpk+Tu3I8PovpAh\n8a+otfa5DAf7FyT5+hw5cL9/kt+bsu7Dk4R866qqDDuLMf6kqn4mMw6xSvI7SV6a4WDql5JcMrL+\nWXuKkuHD7VFJ/m8mO7rW2q7li9zMZzL0Pryiqn4iwz/+tBYynHX9o8mwn6k+GJf4y6r6vSR3raqX\nJ/nrEWXn/b1/L7OfTfvIjGeTlvpYVd1sOFhr7XVTlv1kVb0gw+t1/4x7v2Tyd35shr/9JRmGMm5J\n8t4ky4aWJb09X57kU621sfvH3TXMS1ncz4zp4UqSX84wxGnqIRdL9jHPyFHDpJKsOEyqqh7eWntH\nhrPPR3v3lM04NcP/+BU1DMWddt7b4ntsqqBwHKdl+BB+X1XdM8OQzjG21GT+0eQzbexJsT+pqksz\nDHnaleQZrbU9I8qfluQf6sjwyDE9XDN9Li1VVf9+8v2uGUZfTGvefdSjMvtw8fdMDvquzLCPeM+I\nsknyyBqG796ktfb4EeVHv9+r6kU5crD+gSTfluQxVZXW2oUj6j5UVQ/P8Lt/S5LPjSibJKdNesHf\nP3nfjz0J+d4MYeOVSZ472f+kqqbprXpDhn3j3arqjzIuZH+gqs5YPPk8Vmvt+ZMTGmdlMjViRPHv\nzfBav6u1ds+qunxM3VV1jwwB/RFJ/iLJAzL0hr8pyX2neIrF45hXTo4lx56Q+usMJ7cetXT4aFXd\nZ9onOJmh5VeTvC/DWPH3Tm6PMfOZxNbaDRl2cMkQWsb6ygwHVB/KkZ3ktMn7ryblX5hhfsbekXX/\n0eTs9fuSfFOGnewYb8mwo7tLhjfr32b60PLMJF+dYU7CGzJ06Y8x7xCrhQwHQ+/IcAB8XsYFl5l6\niia+LsniHKzF4YgrjhefnPV5VYazQGdnGHazM0eGcUzjBzL07r0zyQMnt6fWWruwqr4tw9/6A621\nac8iJsPvfe8lt2+REUMAMsPZtDoyB+mWGeawLQb7qecgLfHvJ983Zfhd9mU4iJ7Go5M8JcnDMvR0\nPWdk3V+d5Adba9cuue8Lk97dZVXVAzP8f306ye1qyrlvSzwuyY9lGGr0/iSjhsAm+cfW2rRB4Wiz\nDpO6w+T7PMM5H5fhd351kvMzhMYVtdb+ePL9d+ao+1mTOn8hw3vnaSPLPzvJX2b4/a9K8vSR5V+Z\n4cTMFRn2E6/OsM+a1i+NrG+peT+XnprktzPsHy/LMO90Wl83+Vq0kCn2zUvMPFy8tfbTVfWwDO1+\nbWttzGdKMsxDSob9030yzOkZ4+j3+4pzPDMElUUt058QONrjMwyF+8UM/+dPXP7hX+SXMgyve2aG\nv/+YE5jJcPD7jAwnGp47+ay9cprRE62136xhePm9hpvt/xtR7/2TfLSqFo/dRn0u1exTI5Lh5P6d\nc+Sk562XeeyxXDL5ev7SYaCTIVrT+P4MPcLvr6p7ZRhWOMbOtmQOS1V9RWvtn1prz572CU5aaGmt\nXTo5gPl3SXa31sb2OMx7JnG0JQegH8nNzxyu2EVWS8Z7ZwgryXB2Z9Ryy621n6+q/5DhYOx3Frtn\nR7hja+2cqnpVhh6mFce5V9WWSRf4B3PkrMA5I+tN5h9iNe8CCvP0FL0sQ9D8kyRfk+EDdhqLAe0D\nOfJh0Y7z2JupIxNsX5nhbOCpGSZ8/mFGfCjXMF73NUleMXL8ajIMlXlmjvTM3TCy/OizaW0yB+no\nXpbFM7FjtCUTwGuYQPmOlcpU1de31v4mw1mof8yReTDnJvlfI6q/91GBZbFNV01R9oWZbe7boltk\nGF725iRPynAwNPWk8CRvq6qrcmRfNeYM8EzDpBYDw5xnIu/aWntpklTVO5JcnCF4rrnW2nuqaneG\nCdZvz/gD0MrQu7Ilw//JKzNuSOAtW2t/MPn5rZMerzH+NkdNhp+24JLPpcoMn0uToVrnJOPH6k9O\nhtwhw0mh3W2KxQuOMvNw8ap6X4Z5KG9urb1vZL03heWJd1XVVPuXJfuoM5N8KMO+6fpMjqdWqPN3\nJs9xvyTf2Fr79ar63Yw/cfyQ1tojl7TpqZlykZWJO7TWvn/y83Nr/MqSP5zkfq21f5nsIy/LlMcl\nNSze8IgMJ8buUVXfNe2oj9bazpHtPNpMUyMm/mLy9ega5sWMCsmttftP9q2PnuzfP9ha+2wbhlNP\n444Z5sR8eYbAfZsMnQ7Ten4Nq56dluFz4n0Z5ndN7WROxH9Ykh/PJClOuibHnB1Zeibxmow/kziL\nxR3pu2YouyrjvSf/bA/J8M9296o6f+QQq8V0fZvW2r/VdMuhvi7D5KuWIwFtcQGCMR+q83bjz7WA\nQoaD73vmSE/Rq0eUPdbE5hWHI855Bnfp5ODFwDPLBNuHJXlMkj+rqn/MMFlx2mEMRwfFR4ys+8UZ\nAtfi2bQXrFRgcgbnjCQvrmFc/+LKOr+Ym59RXVHdfFnsMzJ8yK/kP2WYUHz02a+FjAst+6rqabn5\nhMtpy880922JyzIE7e/LbJPCn5rhbzd69bDMOUxqzjOR804unllVvTrDQdNtktwqQ6/6w0c8xVMy\nzM/8xEoPPI4tVfU1rbV/qKqvmaH8zJPhJweNP51hWNgbq+qWrbWpD2Ym/+czjdWvqkfmyMiFWVbK\nm2e4+DkZ9hdPqKrfSPLe1trUYbGqvnXJza/I9PMcVmMf9RsZ9s1J8vNJXpvhBPCy6tgrQ56S4WTe\niqHlOOVHrSw58a+ttX9JbtpHjhmuP/Ooj5pvbmwyx9SISY/Esyft+OvW2qih4nPuW5Phc+RXMow6\n+KsM+9gxoeM7J/X/6uR5xix6keTkDg97QYYDwZl20JMz/yd0jep5DkAXx3tnGOM9j3mHWL2lqp6b\n5O+r6v8kWXFS9uK45tbaTQd8VbV5hnH283bjz7uAwieSfMXkjOhrMm5Z1XknNs9iNSYHp7X2z0le\nUlVvynAg+vYMSz1OY66g2Fp7S4ZegiR57pTFbpdhR3qnDGE5GQ76Xzqm7sUmZPhguUOS6zLFEJjW\n2i9Nvj9u6f1VNXbY0qdy8/f8igcUVXV6a+3T+eK5b6OWTM5wMujtSZ4+prdjiU+01v7nyg87pp/O\nMMl31mFS85yJnHdy8TzunWFI4CsyHAyMOQBLhuGrY3rDjvZTSV5dVWdkWIVszKTsZL7J8PMezMwz\nVv+ZSe7bZlwpr803XPw2k68tGXo3x87lWXrA+NkMJ6pWtHQfNTnw3ZQhQI056/2FNpl83VrbXVXT\nzqGad2XIucpX1WKYPVRVr88wvO2cjBsFMM+oj3nmxiZfPDXi4uUffmTp/knvyNLhVWOmJiTz7VuT\n5FattctrWNb+6hmOg/6pDYsCbWvD4jZj50Wf1NCyr80+Znojm2uI1dJuwBrmdEw99KKGi74dyrBz\nfnFV/XKbYk36JXU/qKpOT7IjyYcXQ8AI8y6g8MYMK2YkwwHl6zP9mdATPhwxqzM5ODVMRP+RDGez\nXpPhLNG05gqKNcO1H1prVya5sqru00au+38MP5Hh5MaHMxzILzt0Yqmq+m8ZeoMXu7L/JuOGRb5n\nMpx08fmmGf7whxnGTH8sN5/7NuZvlsw/KfzfqupduXmv6LKTdKvqrq216zIsd/uqDAdwY3qmFo0+\nE1mrN7l4HvtaawtVdZvW2ifrqOuIHc/k7GcyDKX84wzDtKZ6zY9y7wzDyr6Q4YDo9zOuJ3yeyfDz\nHszMM1b/ZJxQWrQ3w4Hrs1tro09ITkLHvTJZPKGNvH5GffG1yz6RYaL1ND4yee9dlWHO5MembPP+\nTIYp1c2vVzfV8WSbf2XJxakES086jj2WnGfUxzxzY5Nh3tr9MxzLXDvlcMbFEQqPWvZRK5t3AazP\nVtVDM6zker+MW3UtSa6rqsdnWPb5RZlhwY4THlqWJLvPVdUrMyTOxTfNtBeH3MjmGmJ19LC8iWl7\nO56WYfjCGzPMB/lfmW5N+sW6vzfDMKPRFzlMVmUBhdu0YXWitGF5yBUnRC9xwocjzjm0bKl7J/nJ\nGc86zxsUR1/7oY5cEPKSGpY2v8nIs0rJMOxhpnHPmbEre87hE1+oqr9OcvccmU/ygCR/lOmvBZXM\n39sxZrGGRc+cfL3iqPvH9qjOskjLak0unsffVNWzkny8hmtQTLsaUjvq+6x+JsPwzVl74eeZDD/v\nwcxfZPax+ifjhNKir8ww7PLRk2GJ72sjLqQ66U39oQzv82fVlBenXGKma5dNPC7DkMTvyDCEdMWh\nu0tV1W9lGHo8y/XqkhlXllyFz8NkxoV1JuZdRfW1GU78vj3DCZ5pQstT6vjLC4+ZHjDvAlhPznDM\nd8cMC4/8+MjyP5bhf+bSDOF69AWUT0ZPy+IQi4dmeLEXz0bd6iS0ZT2adzWneYblLa7hf8Oki2/s\n++eZmf0ih6vh81X1kAyT2b8x48aSnvDhiKvooiTfWlXfkCNjcF80TcFVCIqjr/2QIx+eZ2aY5Pq+\nDAftY64vs2iecc+zdmXPM/zhvAy9WS/LuIPGJElVLU4Snau3Y8YhsM+cfJ/24nbHe57Ri7S01Ztc\nPI+HZni//16GA+iphuqs0kFYMrxWU11F/jjtuLqGVQZ3ZHxP+FwHM3OO1Z93pbx5/HOGifA7M/R2\n7BhZ/oeSPKCNvzjlonmuXXYww3v0bzN8Lnxvpl9JNBlWiZv1enXJ6lynZVazLqyTzLmKamvtoVX1\nZRlOAL++qm7dWvsPKxRb7IX6kQw9e1dkOJa6x8i651oAa9KTPrq3p4667MDEpzNcfuP9Y57rZISW\n6zKcvf3XDH+0ZEjZY6+avFHNu5rTPMPydmc44H9GVf18kjHLBCbzXeRwNTwxwwfCr2f4RxnT07Ke\nvSXDWfuvzRAg5j2jO8bSaz8kw/CwZc+uTObgpLV2+xrWlf/ODMs0/kumvMbLKo17nqkre57hE5N5\nYh/NcAZzFosh6eizcmNPbsysql6Y5AlL27DSkMCjys+zSMtMk4tXQ2vtvkver9+Z4YD2e05E3RM3\nVtU7k/xdZhheNk9PeGvtuqr6pQwH7//QjrFq3gp1jx56XEdW0Hpwbr6y5YMy27DEWXw+w3vsLRku\ncP38keVnujjlEq/LMNfv8RnmWRzdy7mc38/slz9I5rteXTLndVrmNHphnVqlVVQnw63PyxD6Pprh\nxNyyWmtvnpR9cjuyPPAfV9WYFSXnXgBrluHeE9sn3781wwm1KzL8/rfP9JcgSHJyQsvrM0wkf3bm\nu2ryRjXTak6rMSxvMv526+Qf/a8XDy5HmOcihzNbsrP5aIZ1xhe7sjeKTa21p9Sw+MATs8KFDVfZ\nzNd+qKqvy7BzX9ypjhnethrjnl+QZGuG9+nfZ+RB/yoMnxhtsZejqp6S+U5uzOPhSXa0ycXeZjBP\nb/Csk4vndoz36weWefhamOfK6skcPeE1zP86b1LmaVV1aWttTC/XLEOPV2uVv9HqyCUMbsxw1v2r\nMyw0clrGnXz9yzpyccoHZOTFKduwvPfiAiVjr+sz+vIHR5nnenXJDCtLrqJZ5kGt1iqqL8pwIc5f\nzLDwxJgVGm9XVf+utfahGuYqjg16cy2AlRmGeydJa+1XkmG1vNbaD0/ufsXY0JWchNAy+SD7SOZf\nRWujmnU1p5mH5U0mV75wEjgWhmGcN6X0qccktptf5PCaxfklJ8Bq7WzWq4OTyXdbM/zO0y6rObNa\nnaubvztD796zW2ujDshWacjN72Y4e3pBhvfPRRnO4k5r3uET85h3qep5/N8MZ2BnDS3z9AbPNLl4\nlcz8fl0Nq/Cen6cn/GEZhuUdmkzwvSrjhuaNHnrcJitoZbYFL+a1KpcwaK09a3L2+98nec2075uq\nuqy19n115EK8ybgz38lslz+Y63p1k/KLJxHfkSPXzTqRw8STGeZBtVVaRbW1do+q2pHhWOwtk+Fh\n06609/Qkl9WwkuV1Gb9C4LwLYM0y3HupO1bVbVtr11fV9gwrhY5yMlcPYzazruY0z7C8xUm5Lx/Z\n1pup/7+9+w+yq6zvOP7ekBggQBUqGFqgHQa+kkKlpkopAU0LMkwpTkdQsLQlFaeMxB9Y1JFMbBjK\naO0UbJFWqEEpoAyj8qvYgKVAgrQwhDJBkC8iP4r8CBBMKW34lWz/+J7DvbuE3fuc5+w55+5+XjM7\ne7N7zz1Pkrtnz/M83x9mv0qED4wAC8xsgbt/Oec1B9E3sfqgu7+2u2PRdXwmOJ+42F1P7DYlreZV\n9EbdzVN2uHYhqqwcaWZ/Djzt7ik15XNtIX6pLSsKN6T+gniQvPCJHLk9jXL8CHjSzJ6idyM16eJA\nHbvBZCYXZ2r7/ZorZyf8KWKXYRPxf57cLJrE0GPL7BeSw2tqYWDRFPN3iMWdnc1sTZFHONn5jy0e\n/qG7p5SH7vc9M1tOr/3BoDlMOf3qoBuLiJUL61QJZRx3/DuJ69MRxO+GKwY91t1vo69tRJEHNcg5\n6yqAlRzuPc5ZwF1mthH4BSrkbWrSMnyqVnOqHJbnve7GdxETnm0nePpEriZif39e8fhKzGwRsX1/\nmpmVNdFnAUuJhlbT3c5Ec8ntidK3B031CX1sd/P59DXiSniZNxOT8r2Icef0sKhiDhHCsNrMFhMX\n7BR70gufgLjAT2l4WJ/cnkY5PkQUUUhtTFlHkZbc5OIcbb9fs4zbCb/f3VMqyG0P3Fvc/B5IvO+u\nKV73mAHO3R96fKe7DxK+ktsvpAu+SVRKu4RY8b+YtDyoFUQ+RhVXA497lOm+jgFLXHtmVcu+m9zl\nntYEtDaeV1gnq4oqsfv9PeAYj55cAyuu5f1hvy8Qk/TJ1FUAq3K4N4C7X2Vm1xL5oeurRCFo0jJk\nvGI1p5rC8q4m4vPLlZbUvJDH3H1Fxvmr2kj0AJhL74d3C5FQNhOUK89V41grs7wu4auAq4Cz3f3e\nqRnhhJYQq2ErgfcTlVtStLnKnluqOsejRNW21PCwOoq05CYX52j7/ZrFzNYSu7Hfdfe1iYeflHnu\nb/D60OPJGi3OK3YRnxj39R1yxtKwee5eRjDcbWYDFRrpM2pmVxK7FltgoH5K+xM/H38FfLb4N9+G\nyLU4cIJD6/ZR0vNouiC3iuoSYuJygpk9AJzl7oM2Dz4VeC+9sN9JFwQKdRXA2ivhua+Tu0sFmrRI\nmlnufmLG8dea2ZfoK3Hn7kmVI6pw9x8R/W0udPcnp/p8HZTbaTtH5S7h7v6bUzWoAc/fXyVm4C38\nPlub5KTU1K+s6uJGTfYAfmpmZSPPQXeY6ijSkptcXFnb79caHEwkt3/EzM4Dbnf30yY6wMY29Rxj\nspvncS4vPo8QTRIH2ZGtqy9Q48xs5+Lh4xZl+G8miiCkVnb8F+ImeiORFzJIHtFbiAT43egtrGyh\nl9DflLlm9p+MnXAl9+1oQW4V1ZVE/ttlRN7hNxl88lE17LeuAlhlieURYoL7HGnVv3J3qTRpkSTr\nzOwgxpbUfDnh+OOJ+NHyjd9IBa8yaZGIpayatDh0rL5O2zk2eIUu4dNEWV2vvBGb1eJYmvShKgfV\ntBtcKblYgNgNnUfcF8xlsBLftVRIK0OOCqvMbNLqX76VvkBmtoe7V22u2aTvEtfiEWKlu1ztnpv4\nOh8AjvfoI7WGuAE+Z6ID3H0NsMbM3u3ud5RfN7P3JJ471+caPl8tPL+K6i7ufl7x+G4zO3bCZ49V\nKey3rgJY3tc41cxG6BVSGFRZpa3qLpUmLZLkPYytQpSaOPeSu6d2UM1WJi26+/ik8Omurk7bOdZa\ntS7hQ8/dx6wAW/TQmAnmAMcxNo+pqZ5IVZOLBZ4hGtctc/eBbm6819RzNhEitiexopvUMM7M3tf3\nx/kkVDg0s88QOw1vBpaY2apyQtNVXl9p8uQS30WO5wLg0305ntsQoUdN5nhmhRo1zWqqogpsZ2Zv\nc/enzOxtxL/9oMaH/S5NODabRQPT0nwidzHFg+TtUmnSIoNz93dkvsSjZvZ5xq74T3kTsPIis7Xv\nDcl2dCU1lECtYwxnmNkOxArLUQzYJXw6sF53eogL/FD9ks7wLSK3ZBGRA/dMg+eulFwsQIT1HQmc\naGafAtb2r6xO4mvE//URxPX9n4g8ukH153+9SDRLHNQHiCT2Ve6+wMxuSji2bbmlyauU+N5IXI/m\nErmeEKFCTTf3zg01alotVVSJ/+sfmtnLRHGXlKqUs4C9ieT7dTT/O6V/AXQT8NeJx18CfCKx4MYY\nmrTIwIpfBmNu/j2hmyqxmrRv8QENNAEr5F5kpCKLhpb9jjazx4DzPTrHT2f9Oy0v0nxuSVtecPcv\nmtk+7v6nRbWYKdWh5OJhtp7I4dqXuBn6lYRj93b3k83s0KJC0GdSTlyE3OxP7AA84O53Jxy+mbj5\nLhqqn/EAAArlSURBVMN0UisitSm3NHl/ie8fM0C/k74cz1eI3bHZxM3w/9C7MZ9yNYQaNarGKqo7\nENemzfRCBAd1JRHmVd7sN9ok24seNWa2K5Erm1r960x3P6x4rUqFgTRpkRSnFJ9HgIUk3gy4+5La\nRzTYeW8BMLOdgOUUvxhptofDTLUdUYJ0DZFo+i4i+e9iBk8+HEruvrjow7A38JC7P9v2mBoyWoQ9\n7Ghm82im3HJXkouHmRMJwlcCKxLzFWcX+UOjZrYjRWL1oMzs40TvjtuB083sioSqQjcXHyea2blE\nCeFhkVWa3N1fBL5S8dzHM3aXp9HrcQ2hRm3JraL6F8BvFeFhuwHXMHgbgpG27qPgtd52K4HngbeY\n2UfdPaWrfXK1u/E0aZGBuXv/1uD9ZvaRlOOL0LDPEcmybSTCX0T1qh1SzVu912DvejO7wd2Xm9nq\nVkfVADM7jlj5/DGwv5mtaKsvQcPOBP6ACAV4qPg8pTqUXDzMLnH316rbmdkXE8LDlgG3Ejef/040\ns03xYeBQd3/VomHebQxYVcjdlxXnp0iMfiXx3G1qszR5mw1oIT/UqC25VVQ3lLsM7r7ezCbNY+qb\n4D1kZgczNsQ+ZXEh118SP6dPmNkvEf1mUiYt4yMvkmnSIgOzXldViOTaeYkvcTywu7u30SEc8qp2\nSDU7mdnb3f1+M9uPWH3fheHqpVDVp4GFRfzujkQTuGk/aXH31UA5Kb2miXN2KLl46BSLTycD+1k0\nl4T4t0vp4WDEyulsotjGhaQVaRnxaPiHu79ShC4NduIoGXwaRbhOkRjd6ZLHpZZLk7fZgLaOUKO2\n5FZRfcbMriAKVrwbmGNmZSW8N6r85vRCyfrf26nFkHJtdvcnANz9cTN7cbIDxnk4dwCatEiKv6fX\nZ2ITsYqa4mF6jZnakFO1Q6o5FbjUzHYnttOXEiVxz57wqOlhi7u/AHFzUuECP1TM7EleHyrR1I5q\nV5KLh9GlwI1EH6WqPRxOIeL8qzawvdXMvkOEkS4idm0GdS6xszMMpY67pM1dnjpCjdqSW0V1Vd/j\nNcXHhMoJXgc8X4RyriaKX2xIPL6sHjtC9G97hN4C10A0aZFJ9a3Eba2baoo3AfeY2T30ViiarN6V\nU7VDqlkI7AS8ROQbXOTuB7Q7pMY8ZGZ/Q1yUDyVye6atNkuKdyW5eBgVPRweMbOPEQ1R9yJWgTcR\nP7eDyG1g+xXg/URu0kLSFjX+y93/NePcM1LLuzyQH2rUitwqqlWqeprZV919qZndNu5bo+5+SM54\nEt1BVBksw56TKkP2hYqXIW/JTZs1aZFB1LESBxEy0GbFqJyqHVLN+JKeMymH6ALi734Esbt01MRP\nnx7M7HB6k4bzgOXu/q2GTt9qcvGQSy5bbPU1sL0MWEHszJ5B7J4snuiAPk+b2deIG/Dy3BcmnFva\nkRtq1IoaqqhWUVbGe93CgJmtAG5w9/ETmtr0h5ASkxWI62zqwnW/2VQIbdOkRSZVrsSR2U0VON3d\nF+WPqLKcqh1STdvJnm06l1636nOIwg+HtTukRpxNJFafDxxCrKY1NWmZye+3XGXZ4kUJZYvramC7\nhdiRPMPdLzezlF3wMk6+DAtstAysVJYbatSWrCqqFZXFRVZt5XtziAWHX5/C89eycN0XQjxCzD/+\nNnUgmrRIk54zs08yttxdE31aSslVOyRbq8meLUvuVj1N/B+xMvhqsUDQ5E3kTH6/5SrLFjNo2eIa\nG9jOAb5MVIBbTITvDsTdzzSz+fS6yjdZkVKqywo1aktuFdWK57y++LzVnzcz+9kUn7+Whes6Qog1\naZEmbSBWJcqViaaaS5aqVO2QPK0me7asSrfq6eB5YkXwgiJPYv0kz6/TTH6/5SrLFu8B/AfwyQbP\nvYQIS1tJ5Lb8yaAHmtlK4GCimuV2xM3w0VMwRqnBFIUaNaaGKqq1c/et7cB0jpn9HpGMv335tdTQ\nOk1apDFF1+N9iZuKdUT8dJOSq3ZIng4ke7YpuVv1MDOzk93968B9REPN/Yju6g80NYYZ/n7LNY/Y\n4XiUuIFsrP+Du/8E+Enxx9Tk3HcQlYguIMJX/q7GoUn96sqRbUtuFdWZ7CyiPHnVKoOatEhzzGwp\n0XRuZyLJc2+iBG4jagxlEJlUZrfqYVSWnL2/+ID8XAdpTpnz93RRzek7xA5G121w91Ezm+fuzxb5\nitJRNebINqrGKqoz2XPufkvOC2jSIk06nki4u9HdzzWzO9sekIjUY7K4a+m8/3X3p+G1ak5tNQFO\ntdbMTgeeMLPLmRmNa6V5w75D1Jq+kLqXzOxCYC0VK/1p0iJNmkW8UcvE3KEocSgiMl2VeX3AZjO7\nFLiF2GEZlkIlFxOhxpuIFfA7Jn66SLph3SHqiDIB/0gitK7cDd0u9YU0aZEmfZsob7iXmX0fuKrl\n8YiIzHRlqdnL+r6WFcLRsJV9pfTVSFSke37GG4fWfT7lhUZGR1XSXJphZrOBfYD9icqB61oekoiI\nDLGiqeV9jC2lr+aSIh1hZnOJPkrLGBdaV+xgDUw7LdKke4iVsK+7e2MVhUREZNo6HPghsGvx5+SQ\nExGZOsXE5FFqCK3TTos0xszeBBwDnARsC3zD3S+b8CAREZFx+qo5LSB2WqAIOXH3d7Y2MBGZMpq0\nSOPMbBHwKeDX3H2/tscjIiLDpQg5mc9WqjmlhpyIyHDQpEUaY2ZfAD4I3EWEiK1ueUgiIiIiMgSU\n0yJN+jlwiLv/d9sDEREREZHhoZ0WaYyZ7QGcQ8QgPwCc5u6PtDooEREREem8WW0PQGaUC4FLgEOI\nhmAr2x2OiIiIiAwDhYdJk7Z192uKx1eZ2WmtjkZEREREhoJ2WqRJs83sAIDys4iIiIjIZLTTIk36\nOLDSzHYHHqeGRkMiIiIiMv0pEV8aY2aPA7sBzwC/CGwC1gMfc/cftDk2EREREekuhYdJk1YTDSXn\nA28HrgKOAs5qdVQiIiIi0mmatEiTftndHcDdfwrs6e4PAq+2OywRERER6TLltEiTnjSzLwG3Ab8N\nPGVmRwAvtzssEREREeky7bRIk/4YeIIICXsMOAl4ATihxTGJiIiISMcpEV9ERERERDpNOy0iIiIi\nItJpmrSIiIiIiEinadIiIiIiIiKdpkmLiIh0lplda2aHtT0OERFplyYtIiIiIiLSaerTIiIitTCz\ndcBx7u5mdhmw0d1PNbODgC8AtwInEg1lbwA+C+wJrAKeBTYBRwP/CLyLKI2+c+N/ERER6RzttIiI\nSF3+Gfjd4vEBwKLi8VHF934f+I3iYx/glOL7+wIfdvf3AUuBbdx9AfBngDUzdBER6TJNWkREpC7f\nBw43s/2Ae4HNZvZWYtKyEPi2u7/s7luAi+hNcJ5298eKx4uBywHc/RHgxgbHLyIiHaVJi4iI1OU2\n4EBiMnITcAtwLDAH2DjuuSP0QpQ39X19tPheafOUjFRERIaKJi0iIlKLYgflduATwM3ExGUZsQNz\nE3CCmW1rZrOBJcC/FYf2T1J+APyRmY2Y2Xzgvc2MXkREukyTFhERqdN1wDx3f4DYadkVuNbdryu+\ndydwD/Aw8NXimNG+4/8B2ADcB1wCrGto3CIi0mEjo6Ojkz9LRERERESkJdppERERERGRTtOkRURE\nREREOk2TFhERERER6TRNWkREREREpNM0aRERERERkU7TpEVERERERDpNkxYREREREek0TVpERERE\nRKTT/h9MyLuG+VptswAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xaca3bd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"wc_df[:50].plot(kind='bar')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** I don't recommend running this next line yourself: it will take several minutes! **\n",
"Just enjoy the crazy picture result of when I ran it..."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes.AxesSubplot at 0x105ffc90>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ASJKEw+HgjDPOYP/+/cpMka6uLqU/xOVyKUmH1+tVZpKEE5b09HTq6+tRq9VY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\nE6x+AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x73e3550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(y=\"words\", data=words, palette=\"Greens_d\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Possible next strategies:\n",
"- remove all 2- and 3-letter words (that are not nouns)\n",
"- remove the top most common words (but how many?)\n",
"- remove all prepositions and articles\n",
"\n",
"E.g. use tagged corpus `wordnet` from the `nltk` library: http://www.nltk.org/howto/wordnet.html\n",
"Notice that nouns made up of > 1 word contain '_' to represent the space"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from nltk.corpus import wordnet as wn\n",
"# build a set object from the words stored in the form `Synset('carpet_bombing.n.01')`\n",
"nouns = {x.name().split('.', 1)[0] for x in wn.all_synsets('n')}"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"67176"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(nouns)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"only_noun_counts = [(w[0], w[1][0]) for w in wc_df.iterrows() if w[0] in nouns and len(w[0]) > 2]"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# derp\n",
"'at' in nouns"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"w[1][0]"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"[('tunisia', 13),\n",
" ('government', 12),\n",
" ('country', 7),\n",
" ('islamist', 5),\n",
" ('sousse', 4),\n",
" ('transition', 4),\n",
" ('two', 4),\n",
" ('crisis', 3),\n",
" ('attack', 3),\n",
" ('one', 3),\n",
" ('tunisian', 3),\n",
" ('power', 3),\n",
" ('jihadist', 3),\n",
" ('thursday', 3),\n",
" ('member', 3),\n",
" ('party', 3),\n",
" ('people', 3),\n",
" ('violence', 3),\n",
" ('ben', 3),\n",
" ('ali', 3),\n",
" ('economy', 2),\n",
" ('still', 2),\n",
" ('january', 2),\n",
" ('threat', 2),\n",
" ('dictatorship', 2),\n",
" ('police', 2),\n",
" ('parliament', 2),\n",
" ('despite', 2),\n",
" ('much', 2),\n",
" ('nobel', 2),\n",
" ('assassination', 2),\n",
" ('gunman', 2),\n",
" ('are', 2),\n",
" ('past', 2),\n",
" ('direction', 2),\n",
" ('leader', 2),\n",
" ('group', 2),\n",
" ('attempt', 2),\n",
" ('old', 2),\n",
" ('president', 2),\n",
" ('compromise', 2),\n",
" ('car', 2),\n",
" ('december', 2),\n",
" ('arab', 2),\n",
" ('october', 2),\n",
" ('town', 2),\n",
" ('constitution', 2),\n",
" ('given', 2),\n",
" ('system', 2),\n",
" ('egypt', 2),\n",
" ('least', 2),\n",
" ('world', 2),\n",
" ('eight', 1),\n",
" ('brotherhood', 1),\n",
" ('point', 1),\n",
" ('military', 1),\n",
" ('region', 1),\n",
" ('contract', 1),\n",
" ('honor', 1),\n",
" ('africa', 1),\n",
" ('fire', 1),\n",
" ('ministry', 1),\n",
" ('services', 1),\n",
" ('working', 1),\n",
" ('opposition', 1),\n",
" ('businessman', 1),\n",
" ('worsening', 1),\n",
" ('work', 1),\n",
" ('recession', 1),\n",
" ('dynamism', 1),\n",
" ('back', 1),\n",
" ('four', 1),\n",
" ('fell', 1),\n",
" ('none', 1),\n",
" ('program', 1),\n",
" ('basis', 1),\n",
" ('remains', 1),\n",
" ('libya', 1),\n",
" ('june', 1),\n",
" ('timetable', 1),\n",
" ('muslim', 1),\n",
" ('last', 1),\n",
" ('rule', 1),\n",
" ('rise', 1),\n",
" ('wave', 1),\n",
" ('worst', 1),\n",
" ('seat', 1),\n",
" ('prize', 1),\n",
" ('model', 1),\n",
" ('mohammed', 1),\n",
" ('team', 1),\n",
" ('lack', 1),\n",
" ('unrest', 1),\n",
" ('radio', 1),\n",
" ('connection', 1),\n",
" ('mass', 1),\n",
" ('young', 1),\n",
" ('plunge', 1),\n",
" ('riding', 1),\n",
" ('local', 1),\n",
" ('secretary', 1),\n",
" ('international', 1),\n",
" ('public', 1),\n",
" ('contrast', 1),\n",
" ('movement', 1),\n",
" ('strategy', 1),\n",
" ('growing', 1),\n",
" ('hours', 1),\n",
" ('incident', 1),\n",
" ('politics', 1),\n",
" ('separation', 1),\n",
" ('path', 1),\n",
" ('november', 1),\n",
" ('backlash', 1),\n",
" ('state', 1),\n",
" ('charter', 1),\n",
" ('set', 1),\n",
" ('testing', 1),\n",
" ('wrestling', 1),\n",
" ('sign', 1),\n",
" ('pass', 1),\n",
" ('concern', 1),\n",
" ('reform', 1),\n",
" ('section', 1),\n",
" ('current', 1),\n",
" ('summer', 1),\n",
" ('soccer', 1),\n",
" ('coalition', 1),\n",
" ('friday', 1),\n",
" ('step', 1),\n",
" ('base', 1),\n",
" ('say', 1),\n",
" ('consensus', 1),\n",
" ('elite', 1),\n",
" ('ability', 1),\n",
" ('closing', 1),\n",
" ('security', 1),\n",
" ('aftermath', 1),\n",
" ('deal', 1),\n",
" ('year', 1),\n",
" ('out', 1),\n",
" ('days', 1),\n",
" ('british', 1),\n",
" ('station', 1),\n",
" ('way', 1),\n",
" ('white', 1),\n",
" ('coast', 1),\n",
" ('north', 1),\n",
" ('project', 1),\n",
" ('living', 1),\n",
" ('reaction', 1),\n",
" ('tunis', 1),\n",
" ('target', 1),\n",
" ('official', 1),\n",
" ('militant', 1),\n",
" ('caretaker', 1),\n",
" ('secularist', 1),\n",
" ('act', 1),\n",
" ('syria', 1),\n",
" ('road', 1),\n",
" ('top', 1),\n",
" ('down', 1),\n",
" ('tackle', 1),\n",
" ('drove', 1),\n",
" ('unknown', 1),\n",
" ('strife', 1),\n",
" ('seaside', 1),\n",
" ('interview', 1),\n",
" ('reminder', 1),\n",
" ('tension', 1),\n",
" ('giant', 1),\n",
" ('hand', 1),\n",
" ('norwegian', 1),\n",
" ('fruit', 1),\n",
" ('memory', 1),\n",
" ('brahmi', 1),\n",
" ('response', 1),\n",
" ('short', 1),\n",
" ('general', 1),\n",
" ('high', 1),\n",
" ('yemen', 1),\n",
" ('gunfire', 1),\n",
" ('heart', 1),\n",
" ('seven', 1),\n",
" ('sense', 1),\n",
" ('authoritarian', 1),\n",
" ('elements', 1),\n",
" ('terrorist', 1),\n",
" ('democracy', 1),\n",
" ('failing', 1),\n",
" ('despair', 1),\n",
" ('peace', 1),\n",
" ('mandate', 1),\n",
" ('now', 1),\n",
" ('success', 1),\n",
" ('adoption', 1),\n",
" ('well', 1),\n",
" ('talk', 1),\n",
" ('coup', 1),\n",
" ('exile', 1),\n",
" ('ease', 1),\n",
" ('committee', 1),\n",
" ('majority', 1)]"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"only_noun_counts"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wc_noun_array = np.array(only_noun_counts).T"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1abc1c30>"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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AwNCEFgAAYGhCCwAAMDShBQAAGJrQAgAADE1oAQAAhia0AAAAQxNaAACAoQktAADA0Pbs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\nqVi3TD+/meMW6cfpjYvdp64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1aqZYbEv30VXVo9PnQl+a+bsGLqaq7pUeMq5YhzlzZOTQ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\nIYQQQgihaP8Pa6QjUS4Xdb8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1abb8950>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"new_wc_df = pd.DataFrame({'count': wc_noun_array[1][:40]}, index=wc_noun_array[0][:40]).astype(float)\n",
"new_wc_df.plot(kind='bar')"
]
}
],
"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": "2.7.5"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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