https://developers.nytimes.com/
https://developer.nytimes.com/article_search_v2.json
HTTP stands for Hyper Text Transfer Protocol. This protocol (like many) was proposed by the Internet Engineering Task Force (IETF) through a request for comments (RFC). We're going to start with a very simple HTTP method: the get method.
To learn more about HTTP methods see:
https://developer.mozilla.org/en-US/docs/Web/HTTP/Methods
The first thing to understand when dealing with APIs is how to make get requests in general. To do this, we'll use the Python requests package.
http://docs.python-requests.org/en/master/
import requestsresponse = requests.get('https://flatironschool.com')
print('Type:', type(response), '\n')
print('Response:', response, '\n')
print('Response text:\n', response.text)Type: <class 'requests.models.Response'>
Response: <Response [403]>
Response text:
<html>
<head><title>403 Forbidden</title></head>
<body bgcolor="white">
<center><h1>403 Forbidden</h1></center>
<hr><center>nginx</center>
</body>
</html>
Hmmm, well that was only partially helpful. You can see that our request was denied. (This is shown by the response itself, which has the code 403, meaning forbidden.) Most likely, this is caused by permissioning from Flatiron School's servers, which may be blocking requests that appear to be from an automated platform.
In general, here's some common HTTP response codes you might come across:
Let's try another get request in the hopes of getting a successful (200) response.
#The Electronic Frontier Foundation (EFF) website; advocating for data privacy and an open internet
response = requests.get('https://www.eff.org')
print(response)
print(response.text[:2500])<Response [200]>
<!DOCTYPE html>
<!--[if IEMobile 7]><html class="no-js ie iem7" lang="en" dir="ltr"><![endif]-->
<!--[if lte IE 6]><html class="no-js ie lt-ie9 lt-ie8 lt-ie7" lang="en" dir="ltr"><![endif]-->
<!--[if (IE 7)&(!IEMobile)]><html class="no-js ie lt-ie9 lt-ie8" lang="en" dir="ltr"><![endif]-->
<!--[if IE 8]><html class="no-js ie lt-ie9" lang="en" dir="ltr"><![endif]-->
<!--[if (gte IE 9)|(gt IEMobile 7)]><html class="no-js ie" lang="en" dir="ltr" prefix="fb: http://ogp.me/ns/fb# og: http://ogp.me/ns# article: http://ogp.me/ns/article# book: http://ogp.me/ns/book# profile: http://ogp.me/ns/profile# video: http://ogp.me/ns/video# product: http://ogp.me/ns/product#"><![endif]-->
<!--[if !IE]><!--><html class="no-js" lang="en" dir="ltr" prefix="fb: http://ogp.me/ns/fb# og: http://ogp.me/ns# article: http://ogp.me/ns/article# book: http://ogp.me/ns/book# profile: http://ogp.me/ns/profile# video: http://ogp.me/ns/video# product: http://ogp.me/ns/product#"><!--<![endif]-->
<head>
<meta charset="utf-8" />
<link href="https://www.eff.org/vi" rel="alternate" hreflang="vi" />
<link rel="apple-touch-icon-precomposed" href="https://www.eff.org/sites/all/themes/phoenix/apple-touch-icon-precomposed-114x114.png" sizes="114x114" />
<link href="https://www.eff.org/ur" rel="alternate" hreflang="ur" />
<link href="https://www.eff.org/tr" rel="alternate" hreflang="tr" />
<link href="https://www.eff.org/sh" rel="alternate" hreflang="sh" />
<link href="https://www.eff.org/sv" rel="alternate" hreflang="sv" />
<link href="https://www.eff.org/th" rel="alternate" hreflang="th" />
<link rel="apple-touch-icon-precomposed" href="https://www.eff.org/sites/all/themes/phoenix/apple-touch-icon-precomposed-72x72.png" sizes="72x72" />
<link rel="apple-touch-icon-precomposed" href="https://www.eff.org/sites/all/themes/phoenix/apple-touch-icon-precomposed-144x144.png" sizes="144x144" />
<link rel="profile" href="http://www.w3.org/1999/xhtml/vocab" />
<link rel="shortcut icon" href="https://www.eff.org/sites/all/themes/frontier/favicon.ico" type="image/vnd.microsoft.icon" />
<meta name="HandheldFriendly" content="true" />
<meta name="MobileOptimized" content="width" />
<link rel="apple-touch-icon-precomposed" href="https://www.eff.org/sites/all/themes/phoenix/apple-touch-icon-precomposed.png" />
<meta http-equiv="cleartype" content="on" />
<link href="https://www.eff.org/ru" rel="alternate" hreflang="ru" />
<link href="https://www.eff.org/es" rel="alternate" hreflang="es" />
<link href
Success! As you can see, the response.text is the html code for the given url that we requested. In the background, this forms the basis for web browsers themselves. Every time you put in a new url or click on a link your computer makes a get request for that particular page and then the browser itself renders that page into a visual display on screen.
Some requests are a bit more complicated. Often, websites require identity verification such as logins. This helps a variety of issues such as privacy concerns, limiting access to content and tracking users history. Going forward, OAuth has furthered this idea by allowing third parties such as apps access to user information without providing the underlying password itself.
In the words of the Internet Engineering Task Force, "The OAuth 2.0 authorization framework enables a third-party application to obtain limited access to an HTTP service, either on behalf of a resource owner by orchestrating an approval interaction between the resource owner and the HTTP service, or by allowing the third-party application to obtain access on its own behalf. This specification replaces and obsoletes the OAuth 1.0 protocol described in RFC 5849."
See https://oauth.net/2/ or https://tools.ietf.org/html/rfc6749 for more details.
In order to make requests to many APIs, you are required to login via an access token. As a result, the first step is to sign up through the web interface using your browser. Once you have an API key, you can then use it to make requests to the API. As with login passwords for your computer, these access tokens should be kept secret! For example, rather then including the passwords directly in this file, I have saved them to a seperate file called 'ny_times_api_keys.py'. The file would look something like this:
api_key = 'blah_blah_blah_YOUR_KEY_HERE'
Now it's time to start making some api calls!
from ny_times_api_keys import *import requestsurl = "https://api.nytimes.com/svc/search/v2/articlesearch.json"
url_params = {"api-key" : api_key,
'fq' : 'The New York Times',
'sort' : "newest"}
response = requests.get(url, params=url_params)response<Response [200]>
While you can see that we received an HTTP code of 200, indicating success, the actual data from the request is stored in a json file format. JSON stands for Javascript Object Notation and is the standard format for most data requests from the web these days. You can read more about json here. With that, let's take a quick look at our data:
response.json(){'status': 'OK',
'copyright': 'Copyright (c) 2018 The New York Times Company. All Rights Reserved.',
'response': {'docs': [{'web_url': 'https://www.nytimes.com/interactive/2018/upshot/elections-poll-ny22.html',
'snippet': 'The district stretches from Lake Ontario to the Pennsylvania border.',
'blog': {},
'source': 'The New York Times',
'multimedia': [{'rank': 0,
'subtype': 'xlarge',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-articleLarge.png',
'height': 368,
'width': 600,
'legacy': {'xlarge': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-articleLarge.png',
'xlargewidth': 600,
'xlargeheight': 368},
'subType': 'xlarge',
'crop_name': 'articleLarge'},
{'rank': 0,
'subtype': 'wide',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-thumbWide.png',
'height': 126,
'width': 190,
'legacy': {'wide': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-thumbWide.png',
'widewidth': 190,
'wideheight': 126},
'subType': 'wide',
'crop_name': 'thumbWide'},
{'rank': 0,
'subtype': 'thumbnail',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-thumbStandard.png',
'height': 75,
'width': 75,
'legacy': {'thumbnail': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-thumbStandard.png',
'thumbnailwidth': 75,
'thumbnailheight': 75},
'subType': 'thumbnail',
'crop_name': 'thumbStandard'},
{'rank': 0,
'subtype': 'articleInline',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-articleInline.png',
'height': 117,
'width': 190,
'legacy': {},
'subType': 'articleInline',
'crop_name': 'articleInline'},
{'rank': 0,
'subtype': 'blog225',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-blog225.png',
'height': 138,
'width': 225,
'legacy': {},
'subType': 'blog225',
'crop_name': 'blog225'},
{'rank': 0,
'subtype': 'blog427',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-blog427.png',
'height': 262,
'width': 427,
'legacy': {},
'subType': 'blog427',
'crop_name': 'blog427'},
{'rank': 0,
'subtype': 'blog480',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-blog480.png',
'height': 295,
'width': 480,
'legacy': {},
'subType': 'blog480',
'crop_name': 'blog480'},
{'rank': 0,
'subtype': 'blog533',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-blog533.png',
'height': 327,
'width': 533,
'legacy': {},
'subType': 'blog533',
'crop_name': 'blog533'},
{'rank': 0,
'subtype': 'blogSmallInline',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-blogSmallInline.png',
'height': 93,
'width': 151,
'legacy': {},
'subType': 'blogSmallInline',
'crop_name': 'blogSmallInline'},
{'rank': 0,
'subtype': 'blogSmallThumb',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-blogSmallThumb.png',
'height': 50,
'width': 50,
'legacy': {},
'subType': 'blogSmallThumb',
'crop_name': 'blogSmallThumb'},
{'rank': 0,
'subtype': 'facebookJumbo',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-facebookJumbo.png',
'height': 550,
'width': 1050,
'legacy': {},
'subType': 'facebookJumbo',
'crop_name': 'facebookJumbo'},
{'rank': 0,
'subtype': 'filmstrip',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-filmstrip.png',
'height': 190,
'width': 190,
'legacy': {},
'subType': 'filmstrip',
'crop_name': 'filmstrip'},
{'rank': 0,
'subtype': 'hpLarge',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-hpLarge.png',
'height': 288,
'width': 511,
'legacy': {},
'subType': 'hpLarge',
'crop_name': 'hpLarge'},
{'rank': 0,
'subtype': 'hpSmall',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-hpSmall.png',
'height': 100,
'width': 163,
'legacy': {},
'subType': 'hpSmall',
'crop_name': 'hpSmall'},
{'rank': 0,
'subtype': 'jumbo',
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'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-jumbo.png',
'height': 629,
'width': 1024,
'legacy': {},
'subType': 'jumbo',
'crop_name': 'jumbo'},
{'rank': 0,
'subtype': 'largeHorizontal375',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-largeHorizontal375.png',
'height': 250,
'width': 375,
'legacy': {},
'subType': 'largeHorizontal375',
'crop_name': 'largeHorizontal375'},
{'rank': 0,
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'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-largeHorizontalJumbo.png',
'height': 682,
'width': 1024,
'legacy': {},
'subType': 'largeHorizontalJumbo',
'crop_name': 'largeHorizontalJumbo'},
{'rank': 0,
'subtype': 'largeWidescreen573',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-largeWidescreen573.png',
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'legacy': {},
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{'rank': 0,
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'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-largeWidescreen1050.png',
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'width': 1050,
'legacy': {},
'subType': 'largeWidescreen1050',
'crop_name': 'largeWidescreen1050'},
{'rank': 0,
'subtype': 'master1050',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-master1050.png',
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'width': 1050,
'legacy': {},
'subType': 'master1050',
'crop_name': 'master1050'},
{'rank': 0,
'subtype': 'master180',
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'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-master180.png',
'height': 111,
'width': 180,
'legacy': {},
'subType': 'master180',
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{'rank': 0,
'subtype': 'master315',
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'legacy': {},
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{'rank': 0,
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'legacy': {},
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{'rank': 0,
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'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-master768.png',
'height': 472,
'width': 768,
'legacy': {},
'subType': 'master768',
'crop_name': 'master768'},
{'rank': 0,
'subtype': 'master675',
'caption': None,
'credit': None,
'type': 'image',
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'width': 675,
'legacy': {},
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'crop_name': 'master675'},
{'rank': 0,
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'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-mediumFlexible177.png',
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'legacy': {},
'subType': 'mediumFlexible177',
'crop_name': 'mediumFlexible177'},
{'rank': 0,
'subtype': 'mediumSquare149',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-mediumSquare149.png',
'height': 149,
'width': 149,
'legacy': {},
'subType': 'mediumSquare149',
'crop_name': 'mediumSquare149'},
{'rank': 0,
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'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-mediumThreeByTwo210.png',
'height': 140,
'width': 210,
'legacy': {},
'subType': 'mediumThreeByTwo210',
'crop_name': 'mediumThreeByTwo210'},
{'rank': 0,
'subtype': 'mediumThreeByTwo225',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-mediumThreeByTwo225.png',
'height': 150,
'width': 225,
'legacy': {},
'subType': 'mediumThreeByTwo225',
'crop_name': 'mediumThreeByTwo225'},
{'rank': 0,
'subtype': 'mediumThreeByTwo440',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-mediumThreeByTwo440.png',
'height': 293,
'width': 440,
'legacy': {},
'subType': 'mediumThreeByTwo440',
'crop_name': 'mediumThreeByTwo440'},
{'rank': 0,
'subtype': 'mediumThreeByTwo378',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-mediumThreeByTwo378.png',
'height': 252,
'width': 378,
'legacy': {},
'subType': 'mediumThreeByTwo378',
'crop_name': 'mediumThreeByTwo378'},
{'rank': 0,
'subtype': 'mediumThreeByTwo252',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-mediumThreeByTwo252.png',
'height': 168,
'width': 252,
'legacy': {},
'subType': 'mediumThreeByTwo252',
'crop_name': 'mediumThreeByTwo252'},
{'rank': 0,
'subtype': 'miniMoth',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-miniMoth.png',
'height': 70,
'width': 151,
'legacy': {},
'subType': 'miniMoth',
'crop_name': 'miniMoth'},
{'rank': 0,
'subtype': 'moth',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-moth.png',
'height': 151,
'width': 151,
'legacy': {},
'subType': 'moth',
'crop_name': 'moth'},
{'rank': 0,
'subtype': 'popup',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-popup.png',
'height': 399,
'width': 650,
'legacy': {},
'subType': 'popup',
'crop_name': 'popup'},
{'rank': 0,
'subtype': 'sfSpan',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-sfSpan.png',
'height': 263,
'width': 395,
'legacy': {},
'subType': 'sfSpan',
'crop_name': 'sfSpan'},
{'rank': 0,
'subtype': 'slide',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-slide.png',
'height': 368,
'width': 600,
'legacy': {},
'subType': 'slide',
'crop_name': 'slide'},
{'rank': 0,
'subtype': 'smallSquare168',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-smallSquare168.png',
'height': 168,
'width': 168,
'legacy': {},
'subType': 'smallSquare168',
'crop_name': 'smallSquare168'},
{'rank': 0,
'subtype': 'smallSquare252',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-smallSquare252.png',
'height': 252,
'width': 252,
'legacy': {},
'subType': 'smallSquare252',
'crop_name': 'smallSquare252'},
{'rank': 0,
'subtype': 'square320',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-square320.png',
'height': 320,
'width': 320,
'legacy': {},
'subType': 'square320',
'crop_name': 'square320'},
{'rank': 0,
'subtype': 'square640',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-square640.png',
'height': 640,
'width': 640,
'legacy': {},
'subType': 'square640',
'crop_name': 'square640'},
{'rank': 0,
'subtype': 'superJumbo',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2018/11/01/upshot/elections-poll-ny22-1541083888183/elections-poll-ny22-1541083888183-superJumbo.png',
'height': 861,
'width': 1402,
'legacy': {},
'subType': 'superJumbo',
'crop_name': 'superJumbo'},
{'rank': 0,
'subtype': 'thumbLarge',
'caption': None,
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type(response.json())dict
response.json()['response']['docs'][0]['web_url']'https://www.nytimes.com/1989/10/21/world/poland-s-premier-in-rome-seeks-aid.html'
response.json()['response']['docs'][0]['headline']{'main': "POLAND'S PREMIER, IN ROME, SEEKS AID",
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import pandas as pdpd.DataFrame(response.json()['response']['docs'])
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| _id | abstract | blog | byline | document_type | headline | keywords | multimedia | news_desk | print_page | pub_date | score | section_name | snippet | source | type_of_material | web_url | word_count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 4fd1aa418eb7c8105d6c7bc7 | NaN | {} | {'original': 'By ALAN RIDING, Special to The N... | article | {'main': 'POLAND'S PREMIER, IN ROME, SEEKS AID... | [{'name': 'persons', 'value': 'POPE', 'rank': ... | [] | Foreign Desk | 4 | 1989-10-21T00:00:00Z | 1.0 | NaN | LEAD: On his first trip abroad since taking of... | The New York Times | News | https://www.nytimes.com/1989/10/21/world/polan... | 694 |
| 1 | 4fc04afd45c1498b0d22d8e2 | Cable opened | {} | NaN | article | {'main': 'PRESIDENT OPENS NEW MANILA CABLE; Me... | [{'name': 'persons', 'value': 'ROOSEVELT, THEO... | [] | NaN | 1 | 1903-07-05T00:00:00Z | 1.0 | NaN | With the completion of the Commercial Pacific ... | The New York Times | Front Page | https://query.nytimes.com/gst/abstract.html?re... | 1140 |
| 2 | 50193d971c22dfde670b384a | The N.H.L. gave the players' union thousands o... | {} | {'original': 'By JEFF Z. KLEIN', 'person': [{'... | blogpost | {'main': 'In N.H.L. Negotiation, 76,000 Pages,... | [{'name': 'persons', 'value': 'Bettman, Gary',... | [] | NaN | NaN | 2012-08-01T10:24:31Z | 1.0 | Hockey | The N.H.L. gave the players' union thousands o... | The New York Times | Blog | https://slapshot.blogs.nytimes.com/2012/08/01/... | 306 |
| 3 | 4fc0b42c45c1498b0d417411 | Leaves for Portsmouth, Me | {} | NaN | article | {'main': 'Birth Notice 1 -- No Title', 'kicker... | [{'name': 'persons', 'value': 'MEADOWCROFT, WM... | [] | NaN | 19 | 1931-07-18T00:00:00Z | 1.0 | NaN | Leaves for Portsmouth, Me | The New York Times | Birth Notice | https://query.nytimes.com/gst/abstract.html?re... | 139 |
| 4 | 5360131038f0d87ca6edfc10 | chrysanthemum show, NY Botanical Garden; illus | {} | NaN | article | {'main': 'MUMS DISPLAYED IN MOTIF OF JAPAN', '... | [{'name': 'subject', 'value': 'HORTICULTURE', ... | [] | NaN | NaN | 1964-11-09T00:00:00Z | 1.0 | NaN | chrysanthemum show, NY Botanical Garden; illus | The New York Times | News | https://www.nytimes.com/1964/11/09/mums-displa... | 198 |
| 5 | 4fc04afd45c1498b0d22d8e7 | Warner, J. De W., protest water fee tales | {} | {'original': 'JOHN DE WITT WARNER', 'person': ... | article | {'main': 'John De Witt Warner's Compensation.'... | [{'name': 'persons', 'value': 'WARNER, J. DE W... | [] | NaN | 16 | 1903-12-15T00:00:00Z | 1.0 | NaN | Warner, J. De W., protest water fee tales | The New York Times | Letter | https://query.nytimes.com/gst/abstract.html?re... | 212 |
| 6 | 4fbfdf8145c1498b0d03fa63 | NaN | {} | NaN | article | {'main': 'Amusements this Evening.', 'kicker':... | [] | [] | NaN | 4 | 1863-09-23T00:03:58Z | 1.0 | NaN | The New York Times | Article | https://www.nytimes.com/1863/09/23/news/amusem... | 74 | |
| 7 | 501945961c22dfde670b3882 | A new study shows less mislabeling of sturgeon... | {} | {'original': 'By FLORENCE FABRICANT', 'person'... | blogpost | {'main': 'A Caviar Crackdown Has Worked, Resea... | [{'name': 'glocations', 'value': 'New York Cit... | [] | NaN | NaN | 2012-08-01T11:02:20Z | 1.0 | Dining & Wine | A new study shows less mislabeling of sturgeon... | The New York Times | Blog | https://dinersjournal.blogs.nytimes.com/2012/0... | 412 |
| 8 | 4fc0b42c45c1498b0d417415 | Wilson E; kidnapping feared | {} | NaN | article | {'main': 'ACTRESS VANISHES; KIDNAPPING FEARED;... | [{'name': 'persons', 'value': 'WILSON, EVELYN'... | [] | NaN | 15 | 1931-07-06T00:00:00Z | 1.0 | NaN | With a black silk purse as their most tangible... | The New York Times | Article | https://query.nytimes.com/gst/abstract.html?re... | 713 |
| 9 | 4fc4782845c1498b0d9f334b | State Farm Mutual Auto Ins Co announces it wil... | {} | NaN | article | {'main': 'STATE FARM PLANS DIVIDEND PAYMENTS',... | [{'name': 'subject', 'value': 'AUTOMOBILE INSU... | [] | NaN | 52 | 1971-08-17T00:00:00Z | 1.0 | NaN | The State Farm Mutual Automobile Insurance Com... | The New York Times | Article | https://query.nytimes.com/gst/abstract.html?re... | 171 |
import timeresponses = []
for i in range(10**3):
url = "https://api.nytimes.com/svc/search/v2/articlesearch.json"
url_params = {"api-key" : api_key,
'fq' : 'The New York Times',
'q' : 'politics',
'sort' : "newest",
'page': i}
response = requests.get(url, params=url_params)
if response.status_code == 200:
responses.append(response)
else:
print('Request Failed.')
print(response)
print('Pausing for 60 seconds.')
time.sleep(60)
time.sleep(2) #Always include a 2 second pause
print(len(responses))dfs = []
for r in responses:
dfs.append(pd.DataFrame(r.json()['response']['docs']))
df = pd.concat(dfs, ignore_index=True)
print(len(df))
df.head()2010
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| _id | abstract | blog | byline | document_type | headline | keywords | multimedia | news_desk | print_page | pub_date | score | section_name | snippet | source | type_of_material | uri | web_url | word_count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 5bdb3ba600a1bc2872e91b3c | NaN | {} | {'original': 'By MICHAEL TACKETT', 'person': [... | article | {'main': 'Writing Postcards Brings Voters Back... | [{'name': 'subject', 'value': 'Politics and Go... | [{'rank': 0, 'subtype': 'xlarge', 'caption': N... | Washington | NaN | 2018-11-01T17:45:04+0000 | 16.240780 | Politics | A grass roots army of almost 40,000 is hand wr... | The New York Times | News | nyt://article/5de9ee53-d584-5ef8-bbb4-7ee60a67... | https://www.nytimes.com/2018/11/01/us/politics... | 1001 |
| 1 | 5bdb39f600a1bc2872e91b39 | NaN | {} | {'original': 'By MICHAEL S. SCHMIDT, MARK MAZZ... | article | {'main': 'Read the Emails: The Trump Campaign ... | [{'name': 'subject', 'value': 'Presidential El... | [{'rank': 0, 'subtype': 'xlarge', 'caption': N... | Washington | NaN | 2018-11-01T17:37:56+0000 | 15.122690 | Politics | Newly revealed messages show how the political... | The New York Times | News | nyt://article/02569d9a-d5b6-5e19-89c6-d74fc500... | https://www.nytimes.com/2018/11/01/us/politics... | 964 |
| 2 | 5bdb39b100a1bc2872e91b38 | NaN | {} | {'original': 'By SHARON LaFRANIERE, MICHAEL S.... | article | {'main': 'Roger Stone Sold Himself to Trump’s ... | [{'name': 'subject', 'value': 'Presidential El... | [{'rank': 0, 'subtype': 'xlarge', 'caption': N... | Washington | NaN | 2018-11-01T17:36:36+0000 | 13.804007 | Politics | The special counsel is investigating whether M... | The New York Times | News | nyt://article/4ee43518-63b8-5f83-9077-c59eed29... | https://www.nytimes.com/2018/11/01/us/politics... | 1762 |
| 3 | 5bdb391e00a1bc2872e91b32 | NaN | {} | {'original': 'By JASON FARAGO', 'person': [{'f... | article | {'main': 'How Conspiracy Theories Shape Art', ... | [{'name': 'subject', 'value': 'Art', 'rank': 1... | [{'rank': 0, 'subtype': 'xlarge', 'caption': N... | Weekend | NaN | 2018-11-01T17:34:14+0000 | 9.288733 | Art & Design | At the Met Breuer, the crackpot exhibition “Ev... | The New York Times | Review | nyt://article/1deb977a-def7-5508-999d-9394c566... | https://www.nytimes.com/2018/11/01/arts/design... | 1436 |
| 4 | 5bdb391f00a1bc2872e91b33 | NaN | {} | {'original': 'By ALAN RAPPEPORT', 'person': [{... | article | {'main': 'Democrats Eye Trump’s Tax Returns, W... | [{'name': 'persons', 'value': 'Trump, Donald J... | [{'rank': 0, 'subtype': 'xlarge', 'caption': N... | Washington | NaN | 2018-11-01T17:34:12+0000 | 16.596770 | Politics | Democrats intend to request the president’s ta... | The New York Times | News | nyt://article/08b8183d-f48a-5027-8d1b-286cf30a... | https://www.nytimes.com/2018/11/01/us/politics... | 1016 |
df['main_headline'] = df.headline.map(lambda x: x['main'])text = ''
for h in df.main_headline:
text += str(h)
print(len(text), text[:50], text[-50:])117759 Writing Postcards Brings Voters Back From the Edge ard Channing Is a Mother to Remember in ‘Apologia’
df.to_csv('Pulls_Nov1_2018_recent.csv', index=False)import pandas as pd
df = pd.read_csv('Pulls_Nov1_2018_recent.csv')
df.head()
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| _id | abstract | blog | byline | document_type | headline | keywords | multimedia | news_desk | print_page | pub_date | score | section_name | snippet | source | type_of_material | uri | web_url | word_count | main_headline | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 5bdb3ba600a1bc2872e91b3c | NaN | {} | {'original': 'By MICHAEL TACKETT', 'person': [... | article | {'main': 'Writing Postcards Brings Voters Back... | [{'name': 'subject', 'value': 'Politics and Go... | [{'rank': 0, 'subtype': 'xlarge', 'caption': N... | Washington | NaN | 2018-11-01T17:45:04+0000 | 16.240780 | Politics | A grass roots army of almost 40,000 is hand wr... | The New York Times | News | nyt://article/5de9ee53-d584-5ef8-bbb4-7ee60a67... | https://www.nytimes.com/2018/11/01/us/politics... | 1001 | Writing Postcards Brings Voters Back From the ... |
| 1 | 5bdb39f600a1bc2872e91b39 | NaN | {} | {'original': 'By MICHAEL S. SCHMIDT, MARK MAZZ... | article | {'main': 'Read the Emails: The Trump Campaign ... | [{'name': 'subject', 'value': 'Presidential El... | [{'rank': 0, 'subtype': 'xlarge', 'caption': N... | Washington | NaN | 2018-11-01T17:37:56+0000 | 15.122690 | Politics | Newly revealed messages show how the political... | The New York Times | News | nyt://article/02569d9a-d5b6-5e19-89c6-d74fc500... | https://www.nytimes.com/2018/11/01/us/politics... | 964 | Read the Emails: The Trump Campaign and Roger ... |
| 2 | 5bdb39b100a1bc2872e91b38 | NaN | {} | {'original': 'By SHARON LaFRANIERE, MICHAEL S.... | article | {'main': 'Roger Stone Sold Himself to Trump’s ... | [{'name': 'subject', 'value': 'Presidential El... | [{'rank': 0, 'subtype': 'xlarge', 'caption': N... | Washington | NaN | 2018-11-01T17:36:36+0000 | 13.804007 | Politics | The special counsel is investigating whether M... | The New York Times | News | nyt://article/4ee43518-63b8-5f83-9077-c59eed29... | https://www.nytimes.com/2018/11/01/us/politics... | 1762 | Roger Stone Sold Himself to Trump’s Campaign a... |
| 3 | 5bdb391e00a1bc2872e91b32 | NaN | {} | {'original': 'By JASON FARAGO', 'person': [{'f... | article | {'main': 'How Conspiracy Theories Shape Art', ... | [{'name': 'subject', 'value': 'Art', 'rank': 1... | [{'rank': 0, 'subtype': 'xlarge', 'caption': N... | Weekend | NaN | 2018-11-01T17:34:14+0000 | 9.288734 | Art & Design | At the Met Breuer, the crackpot exhibition “Ev... | The New York Times | Review | nyt://article/1deb977a-def7-5508-999d-9394c566... | https://www.nytimes.com/2018/11/01/arts/design... | 1436 | How Conspiracy Theories Shape Art |
| 4 | 5bdb391f00a1bc2872e91b33 | NaN | {} | {'original': 'By ALAN RAPPEPORT', 'person': [{... | article | {'main': 'Democrats Eye Trump’s Tax Returns, W... | [{'name': 'persons', 'value': 'Trump, Donald J... | [{'rank': 0, 'subtype': 'xlarge', 'caption': N... | Washington | NaN | 2018-11-01T17:34:12+0000 | 16.596770 | Politics | Democrats intend to request the president’s ta... | The New York Times | News | nyt://article/08b8183d-f48a-5027-8d1b-286cf30a... | https://www.nytimes.com/2018/11/01/us/politics... | 1016 | Democrats Eye Trump’s Tax Returns, With Mnuchi... |
import matplotlib.pyplot as plt
import seaborn as sns
# sns.set_style('darkgrid')
%matplotlib inlinedf.word_count.hist(figsize=(10,10))
plt.title('Distribution of Words Per Article')
plt.xlabel('Number of Words in Articles')
plt.ylabel('Number of Articles')Text(0,0.5,'Number of Articles')
word_counts = {}
for h in df.main_headline:
for word in h.split():
word = word.lower()
word_counts[word] = word_counts.get(word, 0) + 1
word_counts = pd.DataFrame.from_dict(word_counts, orient='index')
word_counts.columns = ['count']
word_counts = word_counts.sort_values(by='count', ascending=False)
word_counts.head(10)
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| count | |
|---|---|
| to | 500 |
| in | 445 |
| the | 443 |
| of | 301 |
| a | 283 |
| trump | 227 |
| for | 218 |
| on | 185 |
| and | 161 |
| is | 127 |
word_counts.head(25).plot(kind='barh', figsize=(12,10))
plt.title('Most Frequent Headline Words', fontsize=18)
plt.xlabel('Frequency', fontsize=14)
plt.ylabel('Word', fontsize=14)
plt.xticks( fontsize=14)
plt.yticks( fontsize=14)(array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24]),
<a list of 25 Text yticklabel objects>)
In order to perform topic modelling on our data we will use two primary tools. The first is to turn our text into a vector of word frequency counts; each possible word will be a feature and the number of times that word occurs will be represented by a number. From there, we can then apply mathematical operations to this numerical representation. In our case, we will be applying a common Natural Language Processing Algorithm known as Latent Dirichlet Allocation (LDA). For more technical details, start here.
from sklearn.feature_extraction.text import CountVectorizer
corpus = [
'This is the first document.',
'This document is the second document.',
'And this is the third one.',
'Is this the first document?',
]
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(corpus)
print(vectorizer.get_feature_names())
print(X.toarray()) ['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this']
[[0 1 1 1 0 0 1 0 1]
[0 2 0 1 0 1 1 0 1]
[1 0 0 1 1 0 1 1 1]
[0 1 1 1 0 0 1 0 1]]
#Installing a new python package on the fly
!pip install ldahttps://en.wikipedia.org/wiki/Latent_Dirichlet_allocation
http://jmlr.org/papers/volume3/blei03a/blei03a.pdf
Latent dirichlet allocation is a probabilistic model for classifying documents. It works by viewing documents as mixtures of topics. In turn, topics can be viewed of as probability distributions of words. As we'll see, this allows us to model topics of a corpus and then visualize these topics by the top words associated with the topics as word clouds. While the mathematics behind LDA is fairly complex and outside the scope of this presentation, you can easily implement this powerful concept using prebuilt tools based on this academic research.
from sklearn.feature_extraction.text import CountVectorizer
import lda
import numpy as np
tf_vectorizer = CountVectorizer(max_df=0.95, min_df=2, max_features=10000,
stop_words='english');
tf = tf_vectorizer.fit_transform(df.main_headline);
model = lda.LDA(n_topics=6, n_iter=1500, random_state=1);
model.fit(tf);
topic_word = model.topic_word_ # model.components_ also works
vocab = tf_vectorizer.get_feature_names();INFO:lda:n_documents: 2010
INFO:lda:vocab_size: 1943
INFO:lda:n_words: 10657
INFO:lda:n_topics: 6
INFO:lda:n_iter: 1500
WARNING:lda:all zero row in document-term matrix found
/Users/matthew.mitchell/anaconda3/lib/python3.6/site-packages/lda/utils.py:55: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.
if sparse and not np.issubdtype(doc_word.dtype, int):
INFO:lda:<0> log likelihood: -123726
INFO:lda:<10> log likelihood: -86737
INFO:lda:<20> log likelihood: -85052
INFO:lda:<30> log likelihood: -84339
INFO:lda:<40> log likelihood: -83882
INFO:lda:<50> log likelihood: -83550
INFO:lda:<60> log likelihood: -83251
INFO:lda:<70> log likelihood: -83088
INFO:lda:<80> log likelihood: -83000
INFO:lda:<90> log likelihood: -82910
INFO:lda:<100> log likelihood: -82774
INFO:lda:<110> log likelihood: -82743
INFO:lda:<120> log likelihood: -82632
INFO:lda:<130> log likelihood: -82702
INFO:lda:<140> log likelihood: -82690
INFO:lda:<150> log likelihood: -82461
INFO:lda:<160> log likelihood: -82646
INFO:lda:<170> log likelihood: -82474
INFO:lda:<180> log likelihood: -82298
INFO:lda:<190> log likelihood: -82392
INFO:lda:<200> log likelihood: -82188
INFO:lda:<210> log likelihood: -82018
INFO:lda:<220> log likelihood: -82038
INFO:lda:<230> log likelihood: -82137
INFO:lda:<240> log likelihood: -82150
INFO:lda:<250> log likelihood: -82047
INFO:lda:<260> log likelihood: -82133
INFO:lda:<270> log likelihood: -82167
INFO:lda:<280> log likelihood: -82184
INFO:lda:<290> log likelihood: -82194
INFO:lda:<300> log likelihood: -82094
INFO:lda:<310> log likelihood: -82118
INFO:lda:<320> log likelihood: -82132
INFO:lda:<330> log likelihood: -82106
INFO:lda:<340> log likelihood: -82076
INFO:lda:<350> log likelihood: -82059
INFO:lda:<360> log likelihood: -82114
INFO:lda:<370> log likelihood: -82048
INFO:lda:<380> log likelihood: -82075
INFO:lda:<390> log likelihood: -82065
INFO:lda:<400> log likelihood: -81971
INFO:lda:<410> log likelihood: -81969
INFO:lda:<420> log likelihood: -82055
INFO:lda:<430> log likelihood: -81902
INFO:lda:<440> log likelihood: -81975
INFO:lda:<450> log likelihood: -81882
INFO:lda:<460> log likelihood: -81949
INFO:lda:<470> log likelihood: -81984
INFO:lda:<480> log likelihood: -81976
INFO:lda:<490> log likelihood: -81993
INFO:lda:<500> log likelihood: -81877
INFO:lda:<510> log likelihood: -81906
INFO:lda:<520> log likelihood: -81755
INFO:lda:<530> log likelihood: -81895
INFO:lda:<540> log likelihood: -81868
INFO:lda:<550> log likelihood: -81955
INFO:lda:<560> log likelihood: -81835
INFO:lda:<570> log likelihood: -81833
INFO:lda:<580> log likelihood: -81820
INFO:lda:<590> log likelihood: -81932
INFO:lda:<600> log likelihood: -81841
INFO:lda:<610> log likelihood: -81967
INFO:lda:<620> log likelihood: -81864
INFO:lda:<630> log likelihood: -81857
INFO:lda:<640> log likelihood: -81826
INFO:lda:<650> log likelihood: -81723
INFO:lda:<660> log likelihood: -81934
INFO:lda:<670> log likelihood: -81899
INFO:lda:<680> log likelihood: -81789
INFO:lda:<690> log likelihood: -81729
INFO:lda:<700> log likelihood: -81708
INFO:lda:<710> log likelihood: -81775
INFO:lda:<720> log likelihood: -81884
INFO:lda:<730> log likelihood: -81748
INFO:lda:<740> log likelihood: -81817
INFO:lda:<750> log likelihood: -81759
INFO:lda:<760> log likelihood: -81732
INFO:lda:<770> log likelihood: -81775
INFO:lda:<780> log likelihood: -81661
INFO:lda:<790> log likelihood: -81726
INFO:lda:<800> log likelihood: -81665
INFO:lda:<810> log likelihood: -81591
INFO:lda:<820> log likelihood: -81816
INFO:lda:<830> log likelihood: -81830
INFO:lda:<840> log likelihood: -81821
INFO:lda:<850> log likelihood: -81712
INFO:lda:<860> log likelihood: -81636
INFO:lda:<870> log likelihood: -81758
INFO:lda:<880> log likelihood: -81730
INFO:lda:<890> log likelihood: -81702
INFO:lda:<900> log likelihood: -81667
INFO:lda:<910> log likelihood: -81703
INFO:lda:<920> log likelihood: -81716
INFO:lda:<930> log likelihood: -81766
INFO:lda:<940> log likelihood: -81760
INFO:lda:<950> log likelihood: -81726
INFO:lda:<960> log likelihood: -81705
INFO:lda:<970> log likelihood: -81814
INFO:lda:<980> log likelihood: -81702
INFO:lda:<990> log likelihood: -81696
INFO:lda:<1000> log likelihood: -81667
INFO:lda:<1010> log likelihood: -81652
INFO:lda:<1020> log likelihood: -81644
INFO:lda:<1030> log likelihood: -81563
INFO:lda:<1040> log likelihood: -81723
INFO:lda:<1050> log likelihood: -81769
INFO:lda:<1060> log likelihood: -81665
INFO:lda:<1070> log likelihood: -81680
INFO:lda:<1080> log likelihood: -81746
INFO:lda:<1090> log likelihood: -81734
INFO:lda:<1100> log likelihood: -81642
INFO:lda:<1110> log likelihood: -81810
INFO:lda:<1120> log likelihood: -81738
INFO:lda:<1130> log likelihood: -81574
INFO:lda:<1140> log likelihood: -81712
INFO:lda:<1150> log likelihood: -81701
INFO:lda:<1160> log likelihood: -81643
INFO:lda:<1170> log likelihood: -81570
INFO:lda:<1180> log likelihood: -81564
INFO:lda:<1190> log likelihood: -81667
INFO:lda:<1200> log likelihood: -81701
INFO:lda:<1210> log likelihood: -81626
INFO:lda:<1220> log likelihood: -81538
INFO:lda:<1230> log likelihood: -81568
INFO:lda:<1240> log likelihood: -81535
INFO:lda:<1250> log likelihood: -81578
INFO:lda:<1260> log likelihood: -81556
INFO:lda:<1270> log likelihood: -81620
INFO:lda:<1280> log likelihood: -81572
INFO:lda:<1290> log likelihood: -81618
INFO:lda:<1300> log likelihood: -81565
INFO:lda:<1310> log likelihood: -81632
INFO:lda:<1320> log likelihood: -81570
INFO:lda:<1330> log likelihood: -81655
INFO:lda:<1340> log likelihood: -81523
INFO:lda:<1350> log likelihood: -81748
INFO:lda:<1360> log likelihood: -81692
INFO:lda:<1370> log likelihood: -81647
INFO:lda:<1380> log likelihood: -81637
INFO:lda:<1390> log likelihood: -81680
INFO:lda:<1400> log likelihood: -81692
INFO:lda:<1410> log likelihood: -81674
INFO:lda:<1420> log likelihood: -81672
INFO:lda:<1430> log likelihood: -81723
INFO:lda:<1440> log likelihood: -81790
INFO:lda:<1450> log likelihood: -81680
INFO:lda:<1460> log likelihood: -81663
INFO:lda:<1470> log likelihood: -81623
INFO:lda:<1480> log likelihood: -81612
INFO:lda:<1490> log likelihood: -81633
INFO:lda:<1499> log likelihood: -81516
n_top_words = 10
for i, topic_dist in enumerate(topic_word):
topic_words = np.array(vocab)[np.argsort(topic_dist)][:-(n_top_words):-1]
print('Topic {}: {}'.format(i, ' '.join(topic_words)))Topic 0: trump bombs court bomb suspect ex political pipe charged
Topic 1: says trump china caravan migrant border vote mexico eu
Topic 2: new brazil president pm election pittsburgh right political sri
Topic 3: saudi khashoggi election midterm poll briefing vs district elections
Topic 4: trump democrats house race latest senate republicans governor campaign
Topic 5: trump new york debate tax america media white plan
from wordcloud import WordCloud
topic = "new soviet russia national talks party music world minister"
# Generate a word cloud image
wordcloud = WordCloud().generate(topic)
# Display the generated image:
# the matplotlib way:
import matplotlib.pyplot as plt
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
# lower max_font_size
wordcloud = WordCloud(max_font_size=40).generate(topic)
plt.figure()
plt.imshow(wordcloud, interpolation="bilinear")
plt.axis("off")
plt.show()
sns.set_style(None)fig, axes = plt.subplots(3,2, figsize=(15,15))
fig.tight_layout()
for i, topic_dist in enumerate(topic_word):
topic_words = list(np.array(vocab)[np.argsort(topic_dist)][:-(n_top_words):-1])
topic_words = ' '.join(topic_words)
row = i//2
col = i%2
ax = axes[row, col]
wordcloud = WordCloud().generate(topic_words)
ax.imshow(wordcloud, interpolation='bilinear')
ax.set_title('Topic {}'.format(i))
plt.tight_layout()
Congratulations! We've covered a lot here! We started with HTTP requests, one of the fundamental protocols underlying the internet that we know and love. From there, we further investigated OAuth and saw how to get an access token to use in an API such as yelp. Then we made some requests to retrieve information that came back as a json format. We then transformed this data into a dataframe using the Pandas package. Finally, we created an initial visualization of the data that we retrieved using matplotlib!
from bs4 import BeautifulSoupdef scrape_full_article_text(url):
response = requests.get(url)
page = response.text
soup = BeautifulSoup(page, 'html.parser')
paragraphs = soup.find_all('p', attrs={'class': 'story-body-text'})
full_text=str()
for paragraph in paragraphs:
raw_paragraph = paragraph.contents
cleaned_paragraph=str()
for piece in raw_paragraph:
if piece.string:
cleaned_paragraph += piece.string
cleaned_paragraph = cleaned_paragraph.replace(r"<.*?>","")
cleaned_paragraph = cleaned_paragraph.encode('ascii','ignore')
print(cleaned_paragraph, type(cleaned_paragraph))
full_text += str(cleaned_paragraph)
return full_text