Web Scraping for Reddit & Predicting Comments¶
In this project, we will practice two major skills. Collecting data by scraping a website and then building a binary predictor.
As we discussed in week 2, and earlier today, there are two components to starting a data science problem: the problem statement, and acquiring the data.
For this article, your problem statement will be: What characteristics of a post on Reddit contribute most to the overall interaction (as measured by number of comments)?
Your method for acquiring the data will be scraping the 'hot' threads as listed on the Reddit homepage. You'll acquire AT LEAST FOUR pieces of information about each thread:
- The title of the thread
- The subreddit that the thread corresponds to
- The length of time it has been up on Reddit
- The number of comments on the thread
Once you've got the data, you will build a classification model that, using Natural Language Processing and any other relevant features, predicts whether or not a given Reddit post will have above or below the median number of comments.
BONUS PROBLEMS
- If creating a logistic regression, GridSearch Ridge and Lasso for this model and report the best hyperparameter values.
- Scrape the actual text of the threads using Selenium (you'll learn about this in Webscraping II).
- Write the actual article that you're pitching and turn it into a blog post that you host on your personal website.
Scraping Thread Info from Reddit.com¶
Set up a request (using requests) to the URL below. Use BeautifulSoup to parse the page and extract all results¶
In [1]:
import requests
from bs4 import BeautifulSoup
import time
import datetime
import pandas as pd
import os
from uuid import uuid4 as uuid # this is a randomly generated ID
import numpy as np
In [2]:
## YOUR CODE HERE
In [3]:
URL = "http://www.reddit.com"
r = requests.get(URL, headers={"User-agent": str(uuid())})
b = BeautifulSoup(r.text)
/Users/nmolivo/anaconda/lib/python3.5/site-packages/bs4/__init__.py:181: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system ("lxml"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.
The code that caused this warning is on line 184 of the file /Users/nmolivo/anaconda/lib/python3.5/runpy.py. To get rid of this warning, change code that looks like this:
BeautifulSoup([your markup])
to this:
BeautifulSoup([your markup], "lxml")
markup_type=markup_type))
In [49]:
print(b.prettify())
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<title>
reddit: the front page of the internet
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</i>
</dt>
<dd>
find submissions from "example.com"
</dd>
<dt>
url:
<i>
text
</i>
</dt>
<dd>
search for "text" in url
</dd>
<dt>
selftext:
<i>
text
</i>
</dt>
<dd>
search for "text" in self post contents
</dd>
<dt>
self:yes (or self:no)
</dt>
<dd>
include (or exclude) self posts
</dd>
<dt>
nsfw:yes (or nsfw:no)
</dt>
<dd>
include (or exclude) results marked as NSFW
</dd>
</dl>
<p>
e.g.
<code>
subreddit:aww site:imgur.com dog
</code>
</p>
<p>
<a href="https://www.reddit.com/wiki/search">
see the search faq for details.
</a>
</p>
</div>
<p>
<a href="https://www.reddit.com/wiki/search" id="search_showmore">
advanced search: by author, subreddit...
</a>
</p>
</div>
</form>
</div>
<div class="spacer">
<form action="https://www.reddit.com/post/login" class="login-form login-form-side" id="login_login-main" method="post">
<input name="op" type="hidden" value="login-main"/>
<input maxlength="20" name="user" placeholder="username" tabindex="1" type="text"/>
<input name="passwd" placeholder="password" tabindex="1" type="password"/>
<div class="g-recaptcha" data-sitekey="6LeTnxkTAAAAAN9QEuDZRpn90WwKk_R1TRW_g-JC">
</div>
<div class="status">
</div>
<div id="remember-me">
<input id="rem-login-main" name="rem" tabindex="1" type="checkbox"/>
<label for="rem-login-main">
remember me
</label>
<a class="recover-password" href="/password">
reset password
</a>
</div>
<div class="submit">
<span class="throbber">
</span>
<button class="btn" tabindex="1" type="submit">
login
</button>
</div>
<div class="clear">
</div>
</form>
</div>
<div class="spacer">
<div class="dfp-ad-container" id="ad_2">
</div>
<script>
r.hooks.get('reddit').register(function() { window.googletag = window.googletag || {}; googletag.cmd = googletag.cmd || []; window.pbjs = window.pbjs || {}; pbjs.que = pbjs.que || []; var adblock = r.utils.getAdblockLevel(); var frameId = "ad_2"; var sizes = [[300, 250], [300, 600]]; var keywords = ["s.brandlift.holdout_10", "k.music", "k.delivery", "k.hulu", "k.thanksgiving", "k.vote", "s.frontpage", "k.games", "k.30 rock", "k.movie", "k.congress", "k.today", "k.netflix", "k.acl", "k.us", "s.loggedout", "k.phone", "s.sfw", "k.love"]; var properties = {"placement": "atf", "whitelist_status": "all_ads", "subreddit_screen": false, "percentage": 18, "iaa": false, "random_number": 18, "environment": "production", "nsfw": false, "logged_in": false, "has_used_mobile_app": false, "full_url": "https://www.reddit.com/"}; var slotPrefix = "/32173961/desktop"; var frame = document.getElementById(frameId); var ads = window.__adslots = window.__adslots || []; if (adblock === 'whitelisted') { frame.className += ' acceptable-ads'; } function isWithinLoadDistance(el) { var rect = el.getBoundingClientRect(); var html = document.documentElement; return (rect.top - (window.innerHeight || html.clientHeight))< 300; } function getPageType() { var $body = $('body'); if ($body.hasClass('comments-page')) { return 'comments'; } else if ($body.hasClass('profile-page')) { return 'user' } else if ($body.hasClass('listing-page')) { return 'listing'; } } function getSlot(prefix, pageType) { var parts = [prefix]; if (pageType !== 'user') { var listing = r.config.cur_listing; if (listing !== 'frontpage') { listing = 'r.' + listing; } parts.push(listing); } if (pageType) { parts.push(pageType); } return parts.join('/'); } var adSlot; googletag.cmd.push(function() { var pageType = getPageType(); adSlot = googletag .defineSlot(getSlot(slotPrefix, pageType), sizes, frameId) .addService(googletag.pubads()); adSlot.setTargeting('keywords', keywords); adSlot.setTargeting('adblock', adblock); adSlot.setTargeting('listing', pageType); Object.keys(properties).forEach(function(key) { adSlot.setTargeting(key, properties[key]); }); googletag.display(frameId); }); var requestAd = function() { if (window._usePrebid) { pbjs.que.push(function() { pbjs.setTargetingForGPTAsync(); googletag.pubads().refresh([adSlot]); }); } else { googletag.pubads().refresh([adSlot]); } }; ads.push({ slotID: frameId, sizes: sizes, callback: function() { googletag.cmd.push(function() { if (isWithinLoadDistance(frame)) { requestAd(); } else { var checkDistance = _.throttle(function() { if (isWithinLoadDistance(frame)) { requestAd(); window.removeEventListener('scroll', checkDistance); } }, 100); window.addEventListener('scroll', checkDistance); } }); }, }) });
</script>
</div>
<div class="spacer">
<div class="sidebox submit submit-link">
<div class="morelink">
<a class="login-required access-required" data-event-action="submit" data-event-detail="link" data-type="subreddit" href="https://www.reddit.com/submit" target="_top">
Submit a new link
</a>
<div class="nub">
</div>
</div>
</div>
</div>
<div class="spacer">
<div class="sidebox submit submit-text">
<div class="morelink">
<a class="login-required access-required" data-event-action="submit" data-event-detail="self" data-type="subreddit" href="https://www.reddit.com/submit?selftext=true" target="_top">
Submit a new text post
</a>
<div class="nub">
</div>
</div>
</div>
</div>
<div class="spacer">
<div class="sidebox hohoho">
<div class="morelink">
<a href="https://www.redditgifts.com/exchanges/secret-santa-2017/?source=reddit-sidebar" target="_blank">
Join our Secret Santa tradition
</a>
<div class="nub">
</div>
</div>
</div>
</div>
<div class="spacer">
<div class="goldvertisement ">
<div class="inner">
<h2>
daily reddit gold goal
</h2>
<div class="progress">
<p>
77%
</p>
<div class="bar">
<span style="width: 77%">
</span>
</div>
</div>
<a href="/gold?goldtype=code&source=progressbar" target="_blank">
help support reddit
</a>
<div class="gold-bubble hover-bubble help-bubble anchor-top-centered">
<p>
<span class="gold-branding">
reddit gold
</span>
gives you extra features and helps keep our servers running. We believe the more reddit can be user-supported, the freer we will be to make reddit the best it can be.
</p>
<p class="buy-gold">
Buy gold for yourself to gain access to
<a href="/gold/about" target="_blank">
extra features
</a>
and
<a href="/r/goldbenefits" target="_blank">
special benefits
</a>
. A month of gold pays for
<b>
231.26 minutes
</b>
of reddit server time!
</p>
<p class="give-gold">
Give gold to thank exemplary people and encourage them to post more.
</p>
<p class="aside">
This daily goal updates every 10 minutes and is reset at midnight
<a href="https://en.wikipedia.org/wiki/Pacific_Time_Zone" target="_blank">
Pacific Time
</a>
(8 hours, 32 minutes from now).
</p>
<div class="history">
<p>
Yesterday's reddit gold goal
</p>
<div class="progress">
<p>
116%
</p>
<div class="bar">
<span style="width: 100%">
</span>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="spacer">
<div class="dfp-ad-container" id="ad_7">
</div>
<script>
r.hooks.get('reddit').register(function() { window.googletag = window.googletag || {}; googletag.cmd = googletag.cmd || []; window.pbjs = window.pbjs || {}; pbjs.que = pbjs.que || []; var adblock = r.utils.getAdblockLevel(); var frameId = "ad_7"; var sizes = [[300, 250]]; var keywords = ["s.brandlift.holdout_10", "k.music", "k.phone", "k.delivery", "k.hulu", "k.thanksgiving", "k.acl", "s.frontpage", "k.games", "k.30 rock", "k.congress", "k.today", "k.vote", "k.netflix", "k.us", "s.loggedout", "k.movie", "s.sfw", "k.love"]; var properties = {"placement": "btf", "whitelist_status": "all_ads", "subreddit_screen": false, "percentage": 77, "iaa": false, "random_number": 77, "environment": "production", "nsfw": false, "logged_in": false, "has_used_mobile_app": false, "full_url": "https://www.reddit.com/"}; var slotPrefix = "/32173961/desktop"; var frame = document.getElementById(frameId); var ads = window.__adslots = window.__adslots || []; if (adblock === 'whitelisted') { frame.className += ' acceptable-ads'; } function isWithinLoadDistance(el) { var rect = el.getBoundingClientRect(); var html = document.documentElement; return (rect.top - (window.innerHeight || html.clientHeight))< 300; } function getPageType() { var $body = $('body'); if ($body.hasClass('comments-page')) { return 'comments'; } else if ($body.hasClass('profile-page')) { return 'user' } else if ($body.hasClass('listing-page')) { return 'listing'; } } function getSlot(prefix, pageType) { var parts = [prefix]; if (pageType !== 'user') { var listing = r.config.cur_listing; if (listing !== 'frontpage') { listing = 'r.' + listing; } parts.push(listing); } if (pageType) { parts.push(pageType); } return parts.join('/'); } var adSlot; googletag.cmd.push(function() { var pageType = getPageType(); adSlot = googletag .defineSlot(getSlot(slotPrefix, pageType), sizes, frameId) .addService(googletag.pubads()); adSlot.setTargeting('keywords', keywords); adSlot.setTargeting('adblock', adblock); adSlot.setTargeting('listing', pageType); Object.keys(properties).forEach(function(key) { adSlot.setTargeting(key, properties[key]); }); googletag.display(frameId); }); var requestAd = function() { if (window._usePrebid) { pbjs.que.push(function() { pbjs.setTargetingForGPTAsync(); googletag.pubads().refresh([adSlot]); }); } else { googletag.pubads().refresh([adSlot]); } }; ads.push({ slotID: frameId, sizes: sizes, callback: function() { googletag.cmd.push(function() { if (isWithinLoadDistance(frame)) { requestAd(); } else { var checkDistance = _.throttle(function() { if (isWithinLoadDistance(frame)) { requestAd(); window.removeEventListener('scroll', checkDistance); } }, 100); window.addEventListener('scroll', checkDistance); } }); }, }) });
</script>
</div>
</div>
<a name="content">
</a>
<div class="content" role="main">
<div class="infobar welcome">
<h1>
reddit is a platform for internet communities
</h1>
<div class="button-row">
<h2>
where your votes shape what the world is talking about.
</h2>
<a href="/about">
learn more ›
</a>
</div>
</div>
<section class="infobar listingsignupbar">
<a class="login-required listingsignupbar__container" href="/login">
<h2 class="listingsignupbar__title">
Welcome to Reddit,
</h2>
<p class="listingsignupbar__desc">
the front page of the internet.
</p>
<div class="listingsignupbar__cta-container">
<span class="c-btn c-btn-primary c-pull-left listingsignupbar__cta-button">
Sign up
</span>
<p class="listingsignupbar__cta-desc">
and subscribe to one of thousands of communities.
</p>
</div>
</a>
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×
</a>
</section>
<div class="menuarea">
<div class="spacer">
<span class="dropdown-title lightdrop">
popular in:
</span>
<div class="dropdown lightdrop" onclick="open_menu(this)">
<span class="selected">
United States
</span>
</div>
<div class="drop-choices lightdrop">
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="GLOBAL"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Everywhere
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="AR"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Argentina
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="AU"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Australia
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="BG"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Bulgaria
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="CA"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Canada
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="CL"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Chile
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="CO"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Colombia
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="HR"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Croatia
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="CZ"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Czech Republic
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="FI"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Finland
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="GR"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Greece
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="HU"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Hungary
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="IS"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Iceland
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="IN"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
India
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="IE"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Ireland
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="JP"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Japan
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="MY"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Malaysia
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="MX"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Mexico
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="NZ"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
New Zealand
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="PH"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Philippines
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="PL"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Poland
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="PT"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Portugal
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="PR"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Puerto Rico
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="RO"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Romania
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="RS"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Serbia
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="SG"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Singapore
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="SE"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Sweden
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="TW"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Taiwan
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="TH"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Thailand
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="TR"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Turkey
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="GB"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
United Kingdom
</a>
</form>
</div>
</div>
<div class="spacer">
<span class="dropdown-title lightdrop">
select state:
</span>
<div class="dropdown lightdrop" onclick="open_menu(this)">
<span class="selected">
District of Columbia
</span>
</div>
<div class="drop-choices lightdrop">
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="US"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
All States
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="US_AK"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Alaska
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="US_AL"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Alabama
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="US_AR"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Arkansas
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="US_AZ"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Arizona
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="US_CA"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
California
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="US_CO"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Colorado
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="US_CT"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Connecticut
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="US_DE"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Delaware
</a>
</form>
<form action="https://www.reddit.com/prefs/update/geopopular" method="POST">
<input name="geo_filter" type="hidden" value="US_FL"/>
<a class="choice" href="https://www.reddit.com/prefs/update/geopopular" onclick="$(this).parent().submit(); return false;">
Florida
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[Rapoport] Sources: #Texans QB fear Deshaun Watson suffered a torn ACL in practice today. A non-contact injury. MRI coming to confirm. Awful, awful.
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Not quite what we were expecting - Our foster dog and Golden Mix gave birth yesterday. To baby cows. :) She is one proud Mama.
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NASA: Ozone hole smallest it's been since 1988
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<time class="live-timestamp" datetime="2017-11-02T19:01:30+00:00" title="Thu Nov 2 19:01:30 2017 UTC">
3 hours ago
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“Don’t worry I’ll be fine”
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<time class="live-timestamp" datetime="2017-11-02T16:39:26+00:00" title="Thu Nov 2 16:39:26 2017 UTC">
5 hours ago
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<div class=" thing id-t3_7ad1cj linkflair linkflair-13 odd gilded link " data-author="SwampAss13" data-author-fullname="t2_811qh" data-comments-count="1823" data-context="listing" data-domain="imgur.com" data-fullname="t3_7ad1cj" data-num-crossposts="0" data-permalink="/r/pics/comments/7ad1cj/cousin_gave_birth_on_halloween_night_this_was_the/" data-rank="5" data-score="46545" data-subreddit="pics" data-subreddit-fullname="t5_2qh0u" data-timestamp="1509643330000" data-type="link" data-url="https://imgur.com/hjXcjw9" data-whitelist-status="all_ads" id="thing_t3_7ad1cj" onclick="click_thing(this)">
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Cousin gave birth on Halloween night. This was the doc during the delivery.
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Halloween
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submitted
<time class="live-timestamp" datetime="2017-11-02T17:22:10+00:00" title="Thu Nov 2 17:22:10 2017 UTC">
5 hours ago
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Here comes the fish cuddle!
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submitted
<time class="live-timestamp" datetime="2017-11-02T19:20:32+00:00" title="Thu Nov 2 19:20:32 2017 UTC">
3 hours ago
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The FCC is expected to announce a vote to gut net neutrality rules the day before Thanksgiving. Only a big burst of phone calls to Congress can stop them from allowing ISPs to charge us extra fees to access sites, apps, games, and especially the music we all love. [discussion]
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submitted
<time class="live-timestamp" datetime="2017-11-02T13:29:02+00:00" title="Thu Nov 2 13:29:02 2017 UTC">
8 hours ago
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Since 30 Rock went from Netflix to Hulu, a Chris Brown reference has been edited out
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Quentin Tarantino Has Finished the Script for His Ninth Movie, About the Manson Family Murders
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<time class="live-timestamp" datetime="2017-11-02T15:45:24+00:00" title="Thu Nov 2 15:45:24 2017 UTC">
6 hours ago
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Mechanics of Reddit: What vehicles will you absolutely not buy/drive due to what you've seen at work?
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Experienced sandwiches only, please.
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7 hours ago
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<a class="title may-blank outbound" data-event-action="title" data-href-url="https://www.apnews.com/44f22ca8245441f9993ca76b894b8919/Child-sex-offenders-to-be-named-as-such-in-US-passports" data-outbound-expiration="1509665225000" data-outbound-url="https://out.reddit.com/t3_7abp34?url=https%3A%2F%2Fwww.apnews.com%2F44f22ca8245441f9993ca76b894b8919%2FChild-sex-offenders-to-be-named-as-such-in-US-passports&token=AQAAyan7WddNaNizbXMh7WUC2q5Xd8WSCal6ZqaVylzmMgOzg5Hr&app_name=reddit.com" href="https://www.apnews.com/44f22ca8245441f9993ca76b894b8919/Child-sex-offenders-to-be-named-as-such-in-US-passports" rel="" tabindex="1">
Child sex offenders to be named as such in US passports
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<time class="live-timestamp" datetime="2017-11-02T14:03:05+00:00" title="Thu Nov 2 14:03:05 2017 UTC">
8 hours ago
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Papa is out of touch
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<time class="live-timestamp" datetime="2017-11-02T15:50:13+00:00" title="Thu Nov 2 15:50:13 2017 UTC">
6 hours ago
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LPT: In a not-at-fault automobile accident, you are entitled to a check for the value your vehicle lost after being repaired. This is called "Diminished Value". But you will only get it if you ask.
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<time class="live-timestamp" datetime="2017-11-02T15:16:06+00:00" title="Thu Nov 2 15:16:06 2017 UTC">
7 hours ago
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Guys am I a Disney princess yet
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<time class="live-timestamp" datetime="2017-11-02T15:36:42+00:00" title="Thu Nov 2 15:36:42 2017 UTC">
6 hours ago
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<time class="live-timestamp" datetime="2017-11-02T15:56:59+00:00" title="Thu Nov 2 15:56:59 2017 UTC">
6 hours ago
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The objects authors most frequently use for size comparisons, past and present [OC]
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Nom
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submitted
<time class="live-timestamp" datetime="2017-11-02T15:05:35+00:00" title="Thu Nov 2 15:05:35 2017 UTC">
7 hours ago
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<div class=" thing id-t3_7abs0o linkflair linkflair-approve odd link " data-author="toast333" data-author-fullname="t2_839mx" data-comments-count="392" data-context="listing" data-domain="i.imgur.com" data-fullname="t3_7abs0o" data-num-crossposts="0" data-permalink="/r/interestingasfuck/comments/7abs0o/you_cant_stop_a_trash_panda/" data-rank="23" data-score="24482" data-subreddit="interestingasfuck" data-subreddit-fullname="t5_2qhsa" data-timestamp="1509632161000" data-type="link" data-url="https://i.imgur.com/wL24R78.gifv" data-whitelist-status="all_ads" id="thing_t3_7abs0o" onclick="click_thing(this)">
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<time class="live-timestamp" datetime="2017-11-02T14:16:01+00:00" title="Thu Nov 2 14:16:01 2017 UTC">
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<div class=" thing id-t3_7abk6l linkflair even gilded link " data-author="Dexter_russell" data-author-fullname="t2_sz9t9" data-comments-count="813" data-context="listing" data-domain="i.imgur.com" data-fullname="t3_7abk6l" data-num-crossposts="0" data-permalink="/r/evilbuildings/comments/7abk6l/gamers_agree_this_is_the_most_evilbuilding_in/" data-rank="24" data-score="37410" data-subreddit="evilbuildings" data-subreddit-fullname="t5_3ckh2" data-timestamp="1509630072000" data-type="link" data-url="https://i.imgur.com/QUfcxhX.jpg" data-whitelist-status="all_ads" id="thing_t3_7abk6l" onclick="click_thing(this)">
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Gamers agree, this is the most evilbuilding in existence
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<time class="live-timestamp" datetime="2017-11-02T13:41:12+00:00" title="Thu Nov 2 13:41:12 2017 UTC">
8 hours ago
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A realistic video game
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submitted
<time class="live-timestamp" datetime="2017-11-02T17:14:05+00:00" title="Thu Nov 2 17:14:05 2017 UTC">
5 hours ago
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While this has some more verbose elements removed, we can see that there is some structure to the above:
- The thread title is within an
<a>tag with the attributedata-event-action="title". - The time since the thread was created is within a
<time>tag with attributeclass="live-timestamp". - The subreddit is within an
<a>tag with the attributeclass="subreddit hover may-blank". - The number of comments is within an
<a>tag with the attribute data-event-action="comments"`.
Write 4 functions to extract these items (one function for each): title, time, subreddit, and number of comments.¶¶
Example
def extract_title_from_result(result):
return result.find ...
- Make sure these functions are robust and can handle cases where the data/field may not be available.¶
- Remember to check if a field is empty or None for attempting to call methods on it
- Remember to use try/except if you anticipate errors.
- Test the functions on the results above and simple examples
In [11]:
## YOUR CODE HERE to extract date
def extract_date(b):
quote_list =[]
for quote in b.find_all(name='div'):
if "datetime=" in str(quote):
quote_list.append(str(quote))
date_list =[]
for item in quote_list:
x = item.find("datetime=")+len("datetime=")
y = item.find("+", x)
unit = item[x:y].replace('"','').replace("T"," ")
if unit in date_list:
pass
else:
date_list.append(unit)
return date_list
In [12]:
# ^^ ok, this was NOT the most efficient way to do this.
In [13]:
extract_date(b)
Out[13]:
['2017-10-30 09:12:12', '2017-10-30 09:27:08', '2017-10-30 11:54:00', '2017-10-30 10:58:40', '2017-10-30 09:03:00', '2017-10-30 12:27:01', '2017-10-30 07:07:11', '2017-10-30 05:40:12', '2017-10-30 10:35:40', '2017-10-30 10:45:51', '2017-10-30 06:50:33', '2017-10-30 12:01:25', '2017-10-30 10:49:36', '2017-10-30 01:39:09', '2017-10-30 10:33:59', '2017-10-30 04:12:13', '2017-10-30 01:51:19', '2017-10-30 09:17:14', '2017-10-30 05:30:40', '2017-10-30 01:15:33', '2017-10-30 02:16:24', '2017-10-30 09:42:57', '2017-10-30 03:43:21', '2017-10-30 03:21:35', '2017-10-30 05:13:46']
In [14]:
## YOUR CODE HERE to extract title
def extract_title(b):
title_list =[]
links = b.findAll("a", {"class":"title"})
for link in links:
title_list.append(link.text)
return title_list
In [15]:
extract_title(b)
Out[15]:
["Why don't I ......", 'The lives of tens of thousands of premature babies could be saved by simply delaying clamping the umbilical cord by one minute, according to Australian doctors.', 'Paul Manafort, Who Once Ran Trump Campaign, Told to Surrender', 'Record surge in atmospheric CO2 in 2016', 'Anteater hit and run', 'Paul Manafort, Who Once Ran Trump Campaign, Told to Surrender', 'Would you?', 'Postgame Thread ⚾ Dodgers 12 @ Astros 13', "MRW It's October 30th and my wife still doesn't understand why it's too early for Christmas", 'Rich millennials are ditching the golf communities of their parents for a new kind of neighborhood - Agrihoods are built around working farms and are replacing the once-popular golf communities favored by Baby Boomers.', 'My goose attempting to dive but flipping herself upside down.', 'Paul Manafort and Rick Gates to be indicted', 'HMB while I do a solo keg stand', 'Star Trek Discovery Actor Anthony Rapp: Kevin Spacey Made A Sexual Advance Toward Me When I Was 14', 'First day of paw school!', 'Kevin Spacey Comes Out as Gay, Apologizes to Anthony Rapp', 'My cats are named Bill and Ted. Last night they fulfilled their destiny by meeting their other selves.', 'Google CEO makes fixing hamburger emoji his top priority - The Verge', 'PsBattle: Squirrel with snow on nose.', 'What is wrong with people?', 'TIL In Houston, Texas, a 19-year-old man named Rashaad died attempting to play Russian roulette with a semi-automatic pistol instead of a revolver.', 'Absolutely wholesome Death.', 'This half dented tree on a golf course', 'POTUS picks a twitter fight. Loses.', 'Throw it back!']
In [16]:
## YOUR CODE HERE to extract subreddit
def extract_subreddit(b):
taglist =[]
links = b.findAll("p", {"class":"tagline"})
for link in links:
taglist.append(link.text)
subred_list=[]
for item in taglist:
x = item.find("r/")+len("r/")
y = len(item)
unit = item[x:y]
subred_list.append(unit)
return subred_list
In [17]:
extract_subreddit(b)
Out[17]:
['PoliticalHumor', 'worldnews', 'worldnews', 'science', 'AnimalsBeingJerks', 'news', 'harrypotter', 'baseball', 'reactiongifs', 'Futurology', 'AnimalsBeingDerps', 'RussiaLago', 'holdmybeer', 'television', 'rarepuppers', 'news', 'pics', 'Android', 'photoshopbattles', 'BlackPeopleTwitter', 'todayilearned', 'wholesomememes', 'mildlyinteresting', 'MurderedByWords', 'sports']
In [18]:
## YOUR CODE HERE to extract comments
def extract_comments(b):
taglist =[]
links = b.findAll("li")
for link in links:
taglist.append(link.text)
comm_list=[]
for unit in taglist:
if unit.find("comments") >0:
space = unit.find(" ")
comm_list.append(unit[0:space])
else:
pass
return comm_list
In [19]:
extract_comments(b)
Out[19]:
['445', '354', '447', '196', '77', '250', '229', '3251', '44', '113', '68', '5', '25', '7400', '14', '5167', '340', '224', '138', '198', '848', '42', '141', '3361', '591']
In [20]:
## YOUR CODE HERE to extract author
def extract_user(b):
user_list =[]
aa = b.findAll("a", {"class":"author"})
for a in aa:
user_list.append(a.text)
return user_list
In [21]:
extract_user(b)
Out[21]:
['SMRNS2017', 'lapapinton', 'charging_bull', 'ripsgod', '-N3ptun3-', 'Spudgun888', 'brittany16691', 'BaseballBot', 'PapaNixon', 'mvea', 'GraeDaBoss', 'ghqwertt', 'Ima_Grab_Yo_Snatch', 'GavinGunhold', '-N3ptun3-', 'piede', 'MURPHtheSURF', 'Mearsu', 'derawin07', 'L1FE1SP01NTLESS', 'Rupert_YT', 'Thou_Art_God', 'yelnats87', 'TheTrueForester', 'FAX_ME_YOUR_BOTTOM']
Now, to scale up our scraping, we need to accumulate more results.
First, look at the source of a Reddit.com page: (https://www.reddit.com/). Try manually changing the page by clicking the 'next' button on the bottom. Look at how the url changes.
After leaving the Reddit homepage, the URLs should look something like this:
https://www.reddit.com/?count=25&after=t3_787ptcThe URL here has two query parameters
- count is the result number that the page starts with
- after is the unique id of the last result on the previous page
In order to scrape lots of pages from Reddit, we'll have to change these parameters every time we make a new request so that we're not just scraping the same page over and over again. Incrementing the count by 25 every time will be easy, but the bizarre code after after is a bit trickier.
To start off, let's look at a block of HTML from a Reddit page to see how we might solve this problem:
<div class=" thing id-t3_788tye odd gilded link " data-author="LordSneaux" data-author-fullname="t2_j3pty" data-comments-count="1548" data-context="listing" data-domain="v.redd.it" data-fullname="t3_788tye" data-kind="video" data-num-crossposts="0" data-permalink="/r/funny/comments/788tye/not_all_heroes_wear_capes/" data-rank="25" data-score="51468" data-subreddit="funny" data-subreddit-fullname="t5_2qh33" data-timestamp="1508775581000" data-type="link" data-url="https://v.redd.it/ush0rh2tultz" data-whitelist-status="all_ads" id="thing_t3_788tye" onclick="click_thing(this)">
<p class="parent">
</p>
<span class="rank">
25
</span>
<div class="midcol unvoted">
<div aria-label="upvote" class="arrow up login-required access-required" data-event-action="upvote" role="button" tabindex="0">
</div>
<div class="score dislikes" title="53288">
53.3k
</div>
<div class="score unvoted" title="53289">
53.3k
</div>
<div class="score likes" title="53290">
53.3k
</div>
<div aria-label="downvote" class="arrow down login-required access-required" data-event-action="downvote" role="button" tabindex="0">
</div>
</div>
Notice that within the div tag there is an attribute called id and it is set to "thing_t3_788tye". By finding the last ID on your scraped page, you can tell your next request where to start (pass everything after "thing_").
For more info on this, you can take a look at the Reddit API docs
Write one more function that finds the last id on the page, and stores it.¶
In [22]:
## YOUR CODE HERE
def extract_last_articleid(b):
id_list =[]
divs = b.findAll("div", {"class":"thing"})
for div in divs:
id_list.append(div["data-fullname"])
x = id_list[-1]
return str(x)
In [23]:
extract_last_articleid(b)
Out[23]:
't3_79lpti'
(Optional) Collect more information¶
While we only require you to collect four features, there may be other info that you can find on the results page that might be useful. Feel free to write more functions so that you have more interesting and useful data.
In [100]:
## YOUR CODE HERE
#I will add this after all the top 100's are scraped
In [ ]:
#meant to be run at a similar time to the previous cell, but not a requirement.
karmalist =[]
for user in list(df["user"]):
url = "https://www.reddit.com/user/" + str(user)
r = requests.get(url, headers={"User-agent": str(uuid())})
b = BeautifulSoup(r.text, "lxml")
if len(b) == 0:
karmalist.append((user, None, None))
continue
kpoints = b.findAll("span", {"class":"karma"})
comment_karma=None
post_karma=None
for kpoint in kpoints:
if "comment-karma" in kpoint["class"]:
comment_karma=kpoint.text.replace(",","")
else:
post_karma=kpoint.text.replace(",","")
karmalist.append((user, post_karma,comment_karma))
In [ ]:
karma_df = pd.DataFrame(karmalist,columns=["user","post-karma", 'comment-karma'])
In [ ]:
df = df.merge(karma_df.drop_duplicates(subset="user"), on="user", how = "left")
Now, let's put it all together.¶
Use the functions you wrote above to parse out the 4 fields - title, time, subreddit, and number of comments. Create a dataframe from the results with those 4 columns.
In [29]:
a_dict = {"title": extract_title(b), "subreddit": extract_subreddit(b),"comments": extract_comments(b), "user": extract_user(b)}
In [30]:
df = pd.DataFrame(a_dict)
In [31]:
df.head()
Out[31]:
| comments | subreddit | title | user | |
|---|---|---|---|---|
| 0 | 445 | PoliticalHumor | Why don't I ...... | SMRNS2017 |
| 1 | 354 | worldnews | The lives of tens of thousands of premature ba... | lapapinton |
| 2 | 447 | worldnews | Paul Manafort, Who Once Ran Trump Campaign, To... | charging_bull |
| 3 | 196 | science | Record surge in atmospheric CO2 in 2016 | ripsgod |
| 4 | 77 | AnimalsBeingJerks | Anteater hit and run | -N3ptun3- |
In [32]:
#meant to be run at a similar time to the previous cell, but not a requirement.
karmalist =[]
for user in list(df["user"]):
url = "https://www.reddit.com/user/" + str(user)
r = requests.get(url, headers={"User-agent": str(uuid())})
b = BeautifulSoup(r.text, "lxml")
if len(b) == 0:
karmalist.append((user, None, None))
continue
kpoints = b.findAll("span", {"class":"karma"})
comment_karma=None
post_karma=None
for kpoint in kpoints:
if "comment-karma" in kpoint["class"]:
comment_karma=kpoint.text.replace(",","")
else:
post_karma=kpoint.text.replace(",","")
karmalist.append((user, post_karma,comment_karma))
In [33]:
karma_df = pd.DataFrame(karmalist,columns=["user","post-karma", 'comment-karma'])
In [34]:
df = df.merge(karma_df.drop_duplicates(subset="user"), on="user", how = "left")
In [35]:
len(df)
Out[35]:
25
Save your results as a CSV¶
You may do this regularly while scraping data as well, so that if your scraper stops of your computer crashes, you don't lose all your data.
In [37]:
# Export to csv
file_name = time.strftime('top25_%Y%m%d%H%M.csv', time.gmtime())
df.to_csv(os.path.join('reddit', file_name), index=False)
Here is the template of the scraper we are using! It will look messier when implemented because we will be inserting some large blocks of code where there is ##
count = 0
while count < 1000:
# scrape top 100 reddit posts
##
# save as timestamped csv
##
count += 1
time.sleep(600) # 10 minutes
files = os.listdir('reddit')
data = pd.DataFrame()
for file in files:
df = pd.read_csv(os.path.join('reddit', file))
df['time_collected'] = file.split('_')[1].split('.')[0]
data = pd.concat(data, df)
unique_users = list(set(data.user)) #set is like .unique() but a vectorized method, so it's faster
# get user info for unique users as karma_df
##
full_data = data.merge(karma_df, on='user', how='left')
In [ ]:
count = 0
max_count = 1000
while count < max_count:
print(count)
# scrape top 100 reddit posts every X period of time
#url_template = "http://www.reddit.com/?count={}&after={}"
max_results = 100 # Set this to a high-value (5000) to generate more results.
# Crawling more results, will also take much longer. First test your code on a small number of results and then expand.
col1=[]
col2=[]
col3=[]
col4=[]
col5=[]
last_id = None
for start in range(0, max_results, 25):
if last_id is None:
url = "http://reddit.com/r/popular/?geo_filter=US_DC"
else:
url= "http://www.reddit.com/r/popular/?geo_filter=US_DC/?count=" + str(start) + "&after="+ str(last_id)
r = requests.get(url, headers={"User-agent": str(uuid())})
b = BeautifulSoup(r.text, "lxml")
col1+=extract_title(b)
col2+=extract_subreddit(b)
col3+=extract_date(b)
col4+=extract_comments(b)
col5+=extract_user(b)
#variables:
#col1 = title
#col2 = subreddit
#col3 = date
#col4 = comments
#col5 = user
last_id = extract_last_articleid(b)
df = pd.DataFrame(list(zip(col1, col2, col3, col4, col5)),columns=["title","subreddit", "date", "comments", "user"])
# save as timestamped csv
file_name = time.strftime('top100_%Y%m%d%H%M.csv', time.gmtime())
df.to_csv(os.path.join('reddit', file_name), index=False)
count += 1 #add one to the count so this stops
if count < max_count: # no need to sleep on last iteration
time.sleep(120) # 2 minutes
print('Done collecting post data')
In [40]:
#read in all files and merge.
print('Reading in data')
files = os.listdir('reddit')
for i, file in enumerate(files):
df = pd.read_csv(os.path.join('reddit', file))
df['time_collected'] = file.split('_')[1].split('.')[0]
df['rank'] = df.index
if i == 0:
data = pd.DataFrame(df)
else:
data = pd.concat([data, df])
Reading in data
In [75]:
#not every file got all 100. Perhaps the page updated and some exact duplicates were collected and our function removes
#exact duplicates.
data
Out[75]:
| Unnamed: 0 | comments | date | rank | subreddit | time_collected | title | user | |
|---|---|---|---|---|---|---|---|---|
| 0 | NaN | 441.0 | 2017-10-28 17:34:09 | 0 | CrappyDesign | 201710282341 | Requires 3 AAA batteries, in the most annoying... | Peef_Rimgar420 |
| 1 | NaN | 1859.0 | 2017-10-28 23:18:15 | 1 | CFB | 201710282341 | [Postgame Thread] Ohio State Defeats Penn Stat... | CFB_Referee |
| 2 | NaN | 711.0 | 2017-10-28 18:12:11 | 2 | mildlyinteresting | 201710282341 | Paint-chipping making the wheelchair guy look ... | Ribwichs |
| 3 | NaN | 1249.0 | 2017-10-28 15:51:41 | 3 | videos | 201710282341 | This guy made a video of various rappers count... | floopykid |
| 4 | NaN | 168.0 | 2017-10-28 18:52:24 | 4 | pics | 201710282341 | Lady at Yellowstone took our picture | nowthatssomeusername |
| 5 | NaN | 220.0 | 2017-10-28 17:02:43 | 5 | AnimalsBeingDerps | 201710282341 | Dog thinks it's a shark | 1Voice1Life |
| 6 | NaN | 2761.0 | 2017-10-28 14:11:03 | 6 | LifeProTips | 201710282341 | LPT: Most people at the gym are too busy with ... | lizardman3000000 |
| 7 | NaN | 963.0 | 2017-10-28 15:17:07 | 7 | funny | 201710282341 | So much respect for this guy. | toiletskidmarks |
| 8 | NaN | 293.0 | 2017-10-28 14:38:26 | 8 | thisismylifenow | 201710282341 | I AM THE Q-tip HOLDER | GallowBoob |
| 9 | NaN | 985.0 | 2017-10-28 22:56:37 | 9 | CFB | 201710282341 | [Postgame Thread] Iowa State Defeats TCU 14-7 | elefish92 |
| 10 | NaN | 1025.0 | 2017-10-28 15:03:36 | 10 | television | 201710282341 | “Stranger Things” star, Gaten Matarazzo, creat... | FordMasterTech |
| 11 | NaN | 799.0 | 2017-10-28 18:36:13 | 11 | apple | 201710282341 | Apple fired the engineer whose daughter releas... | JBeylovesyou |
| 12 | NaN | 280.0 | 2017-10-28 14:23:34 | 12 | standupshots | 201710282341 | Buzzfeed | samantharuddy |
| 13 | NaN | 289.0 | 2017-10-28 15:03:55 | 13 | AnimalsBeingBros | 201710282341 | Baby hippo and baby rhino are best friends | 1Voice1Life |
| 14 | NaN | 152.0 | 2017-10-28 15:24:07 | 14 | nostalgia | 201710282341 | My halloween costume this year. | mellabarbarella |
| 15 | NaN | 1124.0 | 2017-10-28 11:33:22 | 15 | BlackPeopleTwitter | 201710282341 | Fax | jvpyter |
| 16 | NaN | 105.0 | 2017-10-28 16:59:18 | 16 | insanepeoplefacebook | 201710282341 | This doesn't seem like an appropriate way to b... | pmyourevilplans |
| 17 | NaN | 143.0 | 2017-10-28 14:41:10 | 17 | EarthPorn | 201710282341 | Fall colors are amazing right now at the Portl... | kdsphotography |
| 18 | NaN | 45.0 | 2017-10-28 18:14:42 | 18 | KenM | 201710282341 | Ken M on Jesus | pm_your_pc_setup |
| 19 | NaN | 618.0 | 2017-10-28 15:30:42 | 19 | woahdude | 201710282341 | Heavenly pit, world's deepest sinkhole in China | catch22milo |
| 20 | NaN | 13143.0 | 2017-10-28 21:22:13 | 20 | CFB | 201710282341 | [Game Thread] Penn State @ Ohio State Second H... | CFB_Referee |
| 21 | NaN | 524.0 | 2017-10-28 15:22:33 | 21 | Showerthoughts | 201710282341 | In the tv series Supernatural nothing in that ... | dudeARama2 |
| 22 | NaN | 194.0 | 2017-10-28 15:20:45 | 22 | IASIP | 201710282341 | The Mcpoyle bloodline has been clean and pure ... | bequiet22 |
| 23 | NaN | 361.0 | 2017-10-28 14:36:00 | 23 | pics | 201710282341 | "Not a bug, a FEATURE!" | GallowBoob |
| 24 | NaN | 227.0 | 2017-10-28 15:13:47 | 24 | oldpeoplefacebook | 201710282341 | You can have my bingo ticket | Barack__Obama__ |
| 25 | NaN | 361.0 | 2017-10-28 18:27:52 | 25 | instant_regret | 201710282341 | Kid staples his ear | sil130 |
| 26 | NaN | 983.0 | 2017-10-28 15:35:45 | 26 | worldnews | 201710282341 | India: local government cuts power to 14 pharm... | eva01beast |
| 27 | NaN | 1105.0 | 2017-10-28 19:52:10 | 27 | baseball | 201710282341 | Gurriel to serve 5 game suspension in 2018 | gullibletrout |
| 28 | NaN | 362.0 | 2017-10-28 12:44:30 | 28 | NatureIsFuckingLit | 201710282341 | Swan babies, called Cygnets (Sig-nits), will r... | Nipru |
| 29 | NaN | 118.0 | 2017-10-28 14:45:47 | 29 | wholesomebpt | 201710282341 | Great couples costume idea 🔥 | GallowBoob |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 70 | NaN | 698.0 | 2017-10-29 18:01:59 | 70 | aww | 201710300953 | Whoever brought this child and this dog togeth... | dickfromaccounting |
| 71 | NaN | 85.0 | 2017-10-30 03:33:21 | 71 | steelers | 201710300953 | MARTAVIS WHO? JUJU FOR MVP UPVOTE PARTY!!! | phrausty |
| 72 | NaN | 186.0 | 2017-10-29 21:12:57 | 72 | Documentaries | 201710300953 | BBC One - Blue Planet II, Series 1, One Ocean ... | nffDionysos |
| 73 | NaN | 100.0 | 2017-10-29 23:42:53 | 73 | OldSchoolCool | 201710300953 | Some spooky skeletons riding horses for Hallow... | biranouk |
| 74 | NaN | 560.0 | 2017-10-29 20:46:52 | 74 | OutOfTheLoop | 201710300953 | What is up with Gordon Ramsay and cocaine? | realjohncenawwe |
| 75 | NaN | 150.0 | 2017-10-29 18:26:17 | 75 | calvinandhobbes | 201710300953 | I sure cant wait for these moment with my son | Dopplesoldner1 |
| 76 | NaN | 10851.0 | 2017-10-29 18:21:56 | 76 | videos | 201710300953 | YouTube user demonstrating how Facebook listen... | AnimalCrust |
| 77 | NaN | 227.0 | 2017-10-30 02:11:44 | 77 | gifs | 201710300953 | Girl using her head as table tennis racket | 1Voice1Life |
| 78 | NaN | 236.0 | 2017-10-29 18:53:49 | 78 | oldpeoplefacebook | 201710300953 | 65 is a great day! | assumetehposition |
| 79 | NaN | 3288.0 | 2017-10-29 14:27:57 | 79 | PoliticalHumor | 201710300953 | I'm sure Trump's administration won't add to t... | 13704 |
| 80 | NaN | 125.0 | 2017-10-29 23:20:22 | 80 | StartledCats | 201710300953 | Oh god, who let this thing in the house | H_G_Bells |
| 81 | NaN | 95.0 | 2017-10-29 22:40:05 | 81 | StrangerThings | 201710300953 | I heard you can get 1000 upvotes if you post a... | HephaestusXIII |
| 82 | NaN | 195.0 | 2017-10-30 04:06:20 | 82 | baseball | 201710300953 | [Haren] So thankful they juiced the ball after... | Jacked1703 |
| 83 | NaN | 363.0 | 2017-10-30 05:39:43 | 83 | baseball | 201710300953 | BREGMAN WALKS IT OFF! | Meatprint |
| 84 | NaN | 513.0 | 2017-10-29 16:10:14 | 84 | technology | 201710300953 | Company Added the Word ‘Blockchain’ to Its Nam... | kashevko |
| 85 | NaN | 212.0 | 2017-10-29 18:19:39 | 85 | StoppedWorking | 201710300953 | Puppy should have listened during geometry class | GallowBoob |
| 86 | NaN | 176.0 | 2017-10-30 02:50:19 | 86 | Whatcouldgowrong | 201710300953 | WCGW if I don't know what's lighter fluid? | TheNutsCracker |
| 87 | NaN | 299.0 | 2017-10-30 01:48:39 | 87 | NintendoSwitch | 201710300953 | Wall Kicks (and Triple Jumps) Will Work [SMO] | MatrixChicken |
| 88 | NaN | 86.0 | 2017-10-29 23:01:13 | 88 | EarthPorn | 201710300953 | Evening Mist Over Echo Lake, British Columbia,... | sndon |
| 89 | NaN | 57.0 | 2017-10-29 18:41:33 | 89 | BlackPeopleTwitter | 201710300953 | They made it happen | wwf4ever |
| 90 | NaN | 151.0 | 2017-10-30 03:52:14 | 90 | baseball | 201710300953 | Springer ties it with a HR | Meatprint |
| 91 | NaN | 19.0 | 2017-10-30 02:37:56 | 91 | BlackPeopleTwitter | 201710300953 | Aftermath of Cavs vs Knicks game 😔 | ParadiseProd |
| 92 | NaN | 145.0 | 2017-10-29 21:51:43 | 92 | Justfuckmyshitup | 201710300953 | Landing strip | Zaza9000 |
| 93 | NaN | 1164.0 | 2017-10-29 20:36:47 | 93 | formula1 | 201710300953 | Lewis Hamilton wins the 2017 Formula One World... | F1-Bot |
| 94 | NaN | 227.0 | 2017-10-29 18:03:30 | 94 | pics | 201710300953 | Will Poulter dressed as Sid from Toy Story | peace_among_worlds |
| 95 | NaN | 45.0 | 2017-10-29 19:18:55 | 95 | HistoryMemes | 201710300953 | kaiser Wilhelm ii (1917) | hazembazem |
| 96 | NaN | 91.0 | 2017-10-29 19:54:17 | 96 | funny | 201710300953 | My buddy was the United Airlines Passenger | justophicles |
| 97 | NaN | 620.0 | 2017-10-29 15:34:36 | 97 | aww | 201710300953 | This pic alone is worth the $9 I paid for this... | deemandaniels |
| 98 | NaN | 116.0 | 2017-10-29 18:37:31 | 98 | wholesomememes | 201710300953 | Every morning. | CaptainAmeriHeck |
| 99 | NaN | 6594.0 | 2017-10-29 22:07:31 | 99 | AskReddit | 201710300953 | What, in your opinion, is the single most beau... | GiveMeAllYourRupees |
99645 rows × 8 columns
In [59]:
len(list(set(data.user)))
Out[59]:
2424
In [58]:
# get user info for unique users as karma_df
unique_users = list(set(data.user)) #set is like .unique() but a vectorized method, so it's faster
print('Getting user data')
karmalist =[]
for user in unique_users:
url = "https://www.reddit.com/user/" + str(user)
r = requests.get(url, headers={"User-agent": str(uuid())})
b = BeautifulSoup(r.text, "lxml")
if len(b) == 0:
karmalist.append((user, None, None))
continue
kpoints = b.findAll("span", {"class":"karma"})
comment_karma=None
post_karma=None
for kpoint in kpoints:
if "comment-karma" in kpoint["class"]:
comment_karma=kpoint.text.replace(",","")
else:
post_karma=kpoint.text.replace(",","")
karmalist.append((user, post_karma,comment_karma))
karma_df = pd.DataFrame(karmalist,columns=["user","post-karma", 'comment-karma'])
Getting user data
In [77]:
full_data = data.merge(karma_df.drop_duplicates(subset="user"), on="user", how = "left")
#there's an unnamed column to remove.
full_data = full_data[["comments", "date", "rank", "subreddit", "time_collected","title", "user", "post-karma",
"comment-karma"]]
In [80]:
full_data.to_csv('full_reddit_data.csv')
In [68]:
# what does this code do?
files = os.listdir('reddit')
data = pd.DataFrame()
current_id = 0
for i, file in enumerate(files):
df = pd.read_csv(os.path.join('reddit', file))
df['time_collected'] = file.split('_')[1].split('.')[0]
df['rank'] = df.index
#df.set_index(list(range(current_id, current_id + len(df))))
current_id += len(df)
data = pd.concat([data, df])
In [82]:
full_data.to_csv('full_reddit_data.csv', index=False)
Predicting comments using Random Forests + Another Classifier¶
Load in the the data of scraped results¶
In [ ]:
## YOUR CODE HERE
In [93]:
full_data = pd.read_csv('full_reddit_data.csv')
In [94]:
full_data.head(10)
Out[94]:
| comments | date | rank | subreddit | time_collected | title | user | post-karma | comment-karma | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 441.0 | 2017-10-28 17:34:09 | 0 | CrappyDesign | 201710282341 | Requires 3 AAA batteries, in the most annoying... | Peef_Rimgar420 | 8560 | 325.0 |
| 1 | 1859.0 | 2017-10-28 23:18:15 | 1 | CFB | 201710282341 | [Postgame Thread] Ohio State Defeats Penn Stat... | CFB_Referee | 313858 | 2845.0 |
| 2 | 711.0 | 2017-10-28 18:12:11 | 2 | mildlyinteresting | 201710282341 | Paint-chipping making the wheelchair guy look ... | Ribwichs | 7018 | 61.0 |
| 3 | 1249.0 | 2017-10-28 15:51:41 | 3 | videos | 201710282341 | This guy made a video of various rappers count... | floopykid | 22034 | 38536.0 |
| 4 | 168.0 | 2017-10-28 18:52:24 | 4 | pics | 201710282341 | Lady at Yellowstone took our picture | nowthatssomeusername | 7001 | 64.0 |
| 5 | 220.0 | 2017-10-28 17:02:43 | 5 | AnimalsBeingDerps | 201710282341 | Dog thinks it's a shark | 1Voice1Life | NaN | NaN |
| 6 | 2761.0 | 2017-10-28 14:11:03 | 6 | LifeProTips | 201710282341 | LPT: Most people at the gym are too busy with ... | lizardman3000000 | 25710 | 186.0 |
| 7 | 963.0 | 2017-10-28 15:17:07 | 7 | funny | 201710282341 | So much respect for this guy. | toiletskidmarks | 13816 | 541.0 |
| 8 | 293.0 | 2017-10-28 14:38:26 | 8 | thisismylifenow | 201710282341 | I AM THE Q-tip HOLDER | GallowBoob | NaN | NaN |
| 9 | 985.0 | 2017-10-28 22:56:37 | 9 | CFB | 201710282341 | [Postgame Thread] Iowa State Defeats TCU 14-7 | elefish92 | 24850 | 17525.0 |
In [95]:
new_profiles = full_data[pd.isnull(full_data["comment-karma"])]
In [96]:
# get user info for unique users as karma_df
unique_users = list(set(new_profiles["user"])) #set is like .unique() but a vectorized method, so it's faster
karmalist =[]
for user in unique_users:
url = "https://www.reddit.com/user/" + str(user)
r = requests.get(url, headers={"User-agent": str(uuid())})
b = BeautifulSoup(r.text, "lxml")
if len(b) == 0:
karmalist.append((user, None, None))
continue
kpoints = b.findAll("div", {"class":"ProfileSidebar__counterInfo"})
comment_karma=None
post_karma=None
for kpoint in kpoints:
s = kpoint.text.find(" ")
x = str(kpoint).find("<br/>")+len("<br/>")
y = str(kpoint).find(" ", x)
post_karma=kpoint.text[0:s].replace(",","")
comment_karma=str(kpoint)[x:y].replace(",","")
karmalist.append((user, post_karma,comment_karma))
new_karma_df = pd.DataFrame(karmalist,columns=["user","post-karma", 'comment-karma'])
In [97]:
new_karma_df
Out[97]:
| user | post-karma | comment-karma | |
|---|---|---|---|
| 0 | 51isnotprime | 7214 | 3014 |
| 1 | ganggxnggvng | 126027 | 363025 |
| 2 | Cortimer | 1869 | 293 |
| 3 | I_AM_STILL_A_IDIOT | 357428 | 351660 |
| 4 | Redzit69 | 53899 | 1727 |
| 5 | ExpFilm_Student | None | None |
| 6 | Moni3 | 10402 | 34623 |
| 7 | IfaqYurmama | 70509 | 89154 |
| 8 | BrandonKatrena | 54086 | 19 |
| 9 | F1NAC | 5404 | 3980 |
| 10 | TheVineyard00 | 9369 | 93223 |
| 11 | Lorix_In_Oz | 30552 | 5174 |
| 12 | Dormponk | 2023 | 1 |
| 13 | MatiasUK | 20469 | 36870 |
| 14 | GallowBoob | 17536350 | 833693 |
| 15 | Radical-Momo | 9468 | 11636 |
| 16 | TakesJonToKnowJuan | 21038 | 36343 |
| 17 | DarkUnix | 375 | 2144 |
| 18 | erindizmo | 5262 | 27948 |
| 19 | sellingspams | 275010 | 3678 |
| 20 | Dragon029 | 25889 | 38804 |
| 21 | tchikboom | 2910 | 2582 |
| 22 | A10A10A10 | 28444 | 3259 |
| 23 | pectoid | 858 | 2182 |
| 24 | kashevko | 5703 | 2214 |
| 25 | mosen2 | None | None |
| 26 | StratosPhere15 | 3697 | 4645 |
| 27 | Zetice | 33182 | 82283 |
| 28 | Gamersville101 | 11369 | 1256 |
| 29 | GrandAdmiralMellacus | None | None |
| ... | ... | ... | ... |
| 191 | remain_unaltered | 332322 | 30010 |
| 192 | MichaelTen | 109763 | 1746 |
| 193 | manikfox | 39565 | 14092 |
| 194 | st3dy | 3004 | 380 |
| 195 | KarlMarxIsntDead | 76720 | 18608 |
| 196 | unphilievable | 11892 | 1322 |
| 197 | IceCreamBeavers | None | None |
| 198 | Ibleedcarrots | 2790846 | 21481 |
| 199 | denvit | 14775 | 11681 |
| 200 | ihasRyzen | None | None |
| 201 | gntrr | 70636 | 13800 |
| 202 | solateor | 1347934 | 180541 |
| 203 | Mega_Buster_Mk-17 | 101711 | 31710 |
| 204 | Snudge | 8382 | 9777 |
| 205 | Offlinelol | 8415 | 1847 |
| 206 | MyAngelKami | 7278 | 8268 |
| 207 | gDisasters | 970817 | 65802 |
| 208 | MyRSSbot | 186056 | 1502 |
| 209 | Fourteen_of_Twelve | 134251 | 169262 |
| 210 | diversity_is_racism | 92814 | 28536 |
| 211 | OwnTheKnight | 528976 | 98929 |
| 212 | ByterBit | 5615 | 18500 |
| 213 | furrutia29 | 73988 | 4978 |
| 214 | liamkr | 190514 | 20382 |
| 215 | AliChawal | 26631 | 5488 |
| 216 | THEBIGC01 | 16956 | 73137 |
| 217 | MagicalScarf | 113304 | 341 |
| 218 | TheGangMakesaPodcast | 277682 | 19102 |
| 219 | taghere | 3574 | 504 |
| 220 | Anyun | 7901 | 10995 |
221 rows × 3 columns
In [98]:
new_profiles = full_data[pd.isnull(full_data["comment-karma"])]
In [99]:
a = full_data.merge(new_karma_df, how='left', left_on='user', right_on='user')
In [100]:
def convert_float(x):
try:
return float(x)
except:
return np.nan
In [101]:
a['post-karma_x'] = a['post-karma_x'].apply(lambda x: convert_float(x))
a['post-karma_y'] = a['post-karma_y'].apply(lambda x: convert_float(x))
a['post-karma'] = a['post-karma_x']
a['post-karma'].fillna(a['post-karma_y'], inplace=True)
In [102]:
a['comment-karma_x'] = a['comment-karma_x'].apply(lambda x: convert_float(x))
a['comment-karma_y'] = a['comment-karma_y'].apply(lambda x: convert_float(x))
a['comment-karma'] = a['comment-karma_x']
a['comment-karma'].fillna(a['comment-karma_y'], inplace=True)
In [103]:
a
Out[103]:
| comments | date | rank | subreddit | time_collected | title | user | post-karma_x | comment-karma_x | post-karma_y | comment-karma_y | post-karma | comment-karma | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 441.0 | 2017-10-28 17:34:09 | 0 | CrappyDesign | 201710282341 | Requires 3 AAA batteries, in the most annoying... | Peef_Rimgar420 | 8560.0 | 325.0 | NaN | NaN | 8560.0 | 325.0 |
| 1 | 1859.0 | 2017-10-28 23:18:15 | 1 | CFB | 201710282341 | [Postgame Thread] Ohio State Defeats Penn Stat... | CFB_Referee | 313858.0 | 2845.0 | NaN | NaN | 313858.0 | 2845.0 |
| 2 | 711.0 | 2017-10-28 18:12:11 | 2 | mildlyinteresting | 201710282341 | Paint-chipping making the wheelchair guy look ... | Ribwichs | 7018.0 | 61.0 | NaN | NaN | 7018.0 | 61.0 |
| 3 | 1249.0 | 2017-10-28 15:51:41 | 3 | videos | 201710282341 | This guy made a video of various rappers count... | floopykid | 22034.0 | 38536.0 | NaN | NaN | 22034.0 | 38536.0 |
| 4 | 168.0 | 2017-10-28 18:52:24 | 4 | pics | 201710282341 | Lady at Yellowstone took our picture | nowthatssomeusername | 7001.0 | 64.0 | NaN | NaN | 7001.0 | 64.0 |
| 5 | 220.0 | 2017-10-28 17:02:43 | 5 | AnimalsBeingDerps | 201710282341 | Dog thinks it's a shark | 1Voice1Life | NaN | NaN | 6476268.0 | 34958.0 | 6476268.0 | 34958.0 |
| 6 | 2761.0 | 2017-10-28 14:11:03 | 6 | LifeProTips | 201710282341 | LPT: Most people at the gym are too busy with ... | lizardman3000000 | 25710.0 | 186.0 | NaN | NaN | 25710.0 | 186.0 |
| 7 | 963.0 | 2017-10-28 15:17:07 | 7 | funny | 201710282341 | So much respect for this guy. | toiletskidmarks | 13816.0 | 541.0 | NaN | NaN | 13816.0 | 541.0 |
| 8 | 293.0 | 2017-10-28 14:38:26 | 8 | thisismylifenow | 201710282341 | I AM THE Q-tip HOLDER | GallowBoob | NaN | NaN | 17536350.0 | 833693.0 | 17536350.0 | 833693.0 |
| 9 | 985.0 | 2017-10-28 22:56:37 | 9 | CFB | 201710282341 | [Postgame Thread] Iowa State Defeats TCU 14-7 | elefish92 | 24850.0 | 17525.0 | NaN | NaN | 24850.0 | 17525.0 |
| 10 | 1025.0 | 2017-10-28 15:03:36 | 10 | television | 201710282341 | “Stranger Things” star, Gaten Matarazzo, creat... | FordMasterTech | 6496.0 | 4484.0 | NaN | NaN | 6496.0 | 4484.0 |
| 11 | 799.0 | 2017-10-28 18:36:13 | 11 | apple | 201710282341 | Apple fired the engineer whose daughter releas... | JBeylovesyou | 36113.0 | 4010.0 | NaN | NaN | 36113.0 | 4010.0 |
| 12 | 280.0 | 2017-10-28 14:23:34 | 12 | standupshots | 201710282341 | Buzzfeed | samantharuddy | 159018.0 | 9419.0 | NaN | NaN | 159018.0 | 9419.0 |
| 13 | 289.0 | 2017-10-28 15:03:55 | 13 | AnimalsBeingBros | 201710282341 | Baby hippo and baby rhino are best friends | 1Voice1Life | NaN | NaN | 6476268.0 | 34958.0 | 6476268.0 | 34958.0 |
| 14 | 152.0 | 2017-10-28 15:24:07 | 14 | nostalgia | 201710282341 | My halloween costume this year. | mellabarbarella | 6506.0 | 378.0 | NaN | NaN | 6506.0 | 378.0 |
| 15 | 1124.0 | 2017-10-28 11:33:22 | 15 | BlackPeopleTwitter | 201710282341 | Fax | jvpyter | 7417.0 | 71.0 | NaN | NaN | 7417.0 | 71.0 |
| 16 | 105.0 | 2017-10-28 16:59:18 | 16 | insanepeoplefacebook | 201710282341 | This doesn't seem like an appropriate way to b... | pmyourevilplans | 10033.0 | 553.0 | NaN | NaN | 10033.0 | 553.0 |
| 17 | 143.0 | 2017-10-28 14:41:10 | 17 | EarthPorn | 201710282341 | Fall colors are amazing right now at the Portl... | kdsphotography | 48784.0 | 1772.0 | NaN | NaN | 48784.0 | 1772.0 |
| 18 | 45.0 | 2017-10-28 18:14:42 | 18 | KenM | 201710282341 | Ken M on Jesus | pm_your_pc_setup | 6837.0 | 401.0 | NaN | NaN | 6837.0 | 401.0 |
| 19 | 618.0 | 2017-10-28 15:30:42 | 19 | woahdude | 201710282341 | Heavenly pit, world's deepest sinkhole in China | catch22milo | NaN | NaN | 282245.0 | 1164236.0 | 282245.0 | 1164236.0 |
| 20 | 13143.0 | 2017-10-28 21:22:13 | 20 | CFB | 201710282341 | [Game Thread] Penn State @ Ohio State Second H... | CFB_Referee | 313858.0 | 2845.0 | NaN | NaN | 313858.0 | 2845.0 |
| 21 | 524.0 | 2017-10-28 15:22:33 | 21 | Showerthoughts | 201710282341 | In the tv series Supernatural nothing in that ... | dudeARama2 | 16637.0 | 42029.0 | NaN | NaN | 16637.0 | 42029.0 |
| 22 | 194.0 | 2017-10-28 15:20:45 | 22 | IASIP | 201710282341 | The Mcpoyle bloodline has been clean and pure ... | bequiet22 | 5618.0 | 1071.0 | NaN | NaN | 5618.0 | 1071.0 |
| 23 | 361.0 | 2017-10-28 14:36:00 | 23 | pics | 201710282341 | "Not a bug, a FEATURE!" | GallowBoob | NaN | NaN | 17536350.0 | 833693.0 | 17536350.0 | 833693.0 |
| 24 | 227.0 | 2017-10-28 15:13:47 | 24 | oldpeoplefacebook | 201710282341 | You can have my bingo ticket | Barack__Obama__ | 7517.0 | 31771.0 | NaN | NaN | 7517.0 | 31771.0 |
| 25 | 361.0 | 2017-10-28 18:27:52 | 25 | instant_regret | 201710282341 | Kid staples his ear | sil130 | 42108.0 | 2896.0 | NaN | NaN | 42108.0 | 2896.0 |
| 26 | 983.0 | 2017-10-28 15:35:45 | 26 | worldnews | 201710282341 | India: local government cuts power to 14 pharm... | eva01beast | NaN | NaN | 16120.0 | 26638.0 | 16120.0 | 26638.0 |
| 27 | 1105.0 | 2017-10-28 19:52:10 | 27 | baseball | 201710282341 | Gurriel to serve 5 game suspension in 2018 | gullibletrout | NaN | NaN | 32546.0 | 44546.0 | 32546.0 | 44546.0 |
| 28 | 362.0 | 2017-10-28 12:44:30 | 28 | NatureIsFuckingLit | 201710282341 | Swan babies, called Cygnets (Sig-nits), will r... | Nipru | 146687.0 | 48972.0 | NaN | NaN | 146687.0 | 48972.0 |
| 29 | 118.0 | 2017-10-28 14:45:47 | 29 | wholesomebpt | 201710282341 | Great couples costume idea 🔥 | GallowBoob | NaN | NaN | 17536350.0 | 833693.0 | 17536350.0 | 833693.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 99615 | 698.0 | 2017-10-29 18:01:59 | 70 | aww | 201710300953 | Whoever brought this child and this dog togeth... | dickfromaccounting | 228186.0 | 217270.0 | NaN | NaN | 228186.0 | 217270.0 |
| 99616 | 85.0 | 2017-10-30 03:33:21 | 71 | steelers | 201710300953 | MARTAVIS WHO? JUJU FOR MVP UPVOTE PARTY!!! | phrausty | 11416.0 | 5129.0 | NaN | NaN | 11416.0 | 5129.0 |
| 99617 | 186.0 | 2017-10-29 21:12:57 | 72 | Documentaries | 201710300953 | BBC One - Blue Planet II, Series 1, One Ocean ... | nffDionysos | 26170.0 | 2147.0 | NaN | NaN | 26170.0 | 2147.0 |
| 99618 | 100.0 | 2017-10-29 23:42:53 | 73 | OldSchoolCool | 201710300953 | Some spooky skeletons riding horses for Hallow... | biranouk | 18400.0 | 275.0 | NaN | NaN | 18400.0 | 275.0 |
| 99619 | 560.0 | 2017-10-29 20:46:52 | 74 | OutOfTheLoop | 201710300953 | What is up with Gordon Ramsay and cocaine? | realjohncenawwe | 35748.0 | 17563.0 | NaN | NaN | 35748.0 | 17563.0 |
| 99620 | 150.0 | 2017-10-29 18:26:17 | 75 | calvinandhobbes | 201710300953 | I sure cant wait for these moment with my son | Dopplesoldner1 | 5336.0 | 384.0 | NaN | NaN | 5336.0 | 384.0 |
| 99621 | 10851.0 | 2017-10-29 18:21:56 | 76 | videos | 201710300953 | YouTube user demonstrating how Facebook listen... | AnimalCrust | 16710.0 | 38204.0 | NaN | NaN | 16710.0 | 38204.0 |
| 99622 | 227.0 | 2017-10-30 02:11:44 | 77 | gifs | 201710300953 | Girl using her head as table tennis racket | 1Voice1Life | NaN | NaN | 6476268.0 | 34958.0 | 6476268.0 | 34958.0 |
| 99623 | 236.0 | 2017-10-29 18:53:49 | 78 | oldpeoplefacebook | 201710300953 | 65 is a great day! | assumetehposition | 13906.0 | 23087.0 | NaN | NaN | 13906.0 | 23087.0 |
| 99624 | 3288.0 | 2017-10-29 14:27:57 | 79 | PoliticalHumor | 201710300953 | I'm sure Trump's administration won't add to t... | 13704 | 15004.0 | 57097.0 | NaN | NaN | 15004.0 | 57097.0 |
| 99625 | 125.0 | 2017-10-29 23:20:22 | 80 | StartledCats | 201710300953 | Oh god, who let this thing in the house | H_G_Bells | 66271.0 | 3814.0 | NaN | NaN | 66271.0 | 3814.0 |
| 99626 | 95.0 | 2017-10-29 22:40:05 | 81 | StrangerThings | 201710300953 | I heard you can get 1000 upvotes if you post a... | HephaestusXIII | 8247.0 | 17661.0 | NaN | NaN | 8247.0 | 17661.0 |
| 99627 | 195.0 | 2017-10-30 04:06:20 | 82 | baseball | 201710300953 | [Haren] So thankful they juiced the ball after... | Jacked1703 | 7196.0 | 18827.0 | NaN | NaN | 7196.0 | 18827.0 |
| 99628 | 363.0 | 2017-10-30 05:39:43 | 83 | baseball | 201710300953 | BREGMAN WALKS IT OFF! | Meatprint | 90554.0 | 11564.0 | NaN | NaN | 90554.0 | 11564.0 |
| 99629 | 513.0 | 2017-10-29 16:10:14 | 84 | technology | 201710300953 | Company Added the Word ‘Blockchain’ to Its Nam... | kashevko | NaN | NaN | 5703.0 | 2214.0 | 5703.0 | 2214.0 |
| 99630 | 212.0 | 2017-10-29 18:19:39 | 85 | StoppedWorking | 201710300953 | Puppy should have listened during geometry class | GallowBoob | NaN | NaN | 17536350.0 | 833693.0 | 17536350.0 | 833693.0 |
| 99631 | 176.0 | 2017-10-30 02:50:19 | 86 | Whatcouldgowrong | 201710300953 | WCGW if I don't know what's lighter fluid? | TheNutsCracker | NaN | NaN | 497072.0 | 1326.0 | 497072.0 | 1326.0 |
| 99632 | 299.0 | 2017-10-30 01:48:39 | 87 | NintendoSwitch | 201710300953 | Wall Kicks (and Triple Jumps) Will Work [SMO] | MatrixChicken | 6217.0 | 4439.0 | NaN | NaN | 6217.0 | 4439.0 |
| 99633 | 86.0 | 2017-10-29 23:01:13 | 88 | EarthPorn | 201710300953 | Evening Mist Over Echo Lake, British Columbia,... | sndon | 6864.0 | 483.0 | NaN | NaN | 6864.0 | 483.0 |
| 99634 | 57.0 | 2017-10-29 18:41:33 | 89 | BlackPeopleTwitter | 201710300953 | They made it happen | wwf4ever | 308181.0 | 69855.0 | NaN | NaN | 308181.0 | 69855.0 |
| 99635 | 151.0 | 2017-10-30 03:52:14 | 90 | baseball | 201710300953 | Springer ties it with a HR | Meatprint | 90554.0 | 11564.0 | NaN | NaN | 90554.0 | 11564.0 |
| 99636 | 19.0 | 2017-10-30 02:37:56 | 91 | BlackPeopleTwitter | 201710300953 | Aftermath of Cavs vs Knicks game 😔 | ParadiseProd | 43587.0 | 29131.0 | NaN | NaN | 43587.0 | 29131.0 |
| 99637 | 145.0 | 2017-10-29 21:51:43 | 92 | Justfuckmyshitup | 201710300953 | Landing strip | Zaza9000 | 17319.0 | 2892.0 | NaN | NaN | 17319.0 | 2892.0 |
| 99638 | 1164.0 | 2017-10-29 20:36:47 | 93 | formula1 | 201710300953 | Lewis Hamilton wins the 2017 Formula One World... | F1-Bot | 44022.0 | 2627.0 | NaN | NaN | 44022.0 | 2627.0 |
| 99639 | 227.0 | 2017-10-29 18:03:30 | 94 | pics | 201710300953 | Will Poulter dressed as Sid from Toy Story | peace_among_worlds | 5886.0 | 1411.0 | NaN | NaN | 5886.0 | 1411.0 |
| 99640 | 45.0 | 2017-10-29 19:18:55 | 95 | HistoryMemes | 201710300953 | kaiser Wilhelm ii (1917) | hazembazem | 7299.0 | 818.0 | NaN | NaN | 7299.0 | 818.0 |
| 99641 | 91.0 | 2017-10-29 19:54:17 | 96 | funny | 201710300953 | My buddy was the United Airlines Passenger | justophicles | 5776.0 | 897.0 | NaN | NaN | 5776.0 | 897.0 |
| 99642 | 620.0 | 2017-10-29 15:34:36 | 97 | aww | 201710300953 | This pic alone is worth the $9 I paid for this... | deemandaniels | 9383.0 | 344.0 | NaN | NaN | 9383.0 | 344.0 |
| 99643 | 116.0 | 2017-10-29 18:37:31 | 98 | wholesomememes | 201710300953 | Every morning. | CaptainAmeriHeck | 8892.0 | 479.0 | NaN | NaN | 8892.0 | 479.0 |
| 99644 | 6594.0 | 2017-10-29 22:07:31 | 99 | AskReddit | 201710300953 | What, in your opinion, is the single most beau... | GiveMeAllYourRupees | 31910.0 | 10035.0 | NaN | NaN | 31910.0 | 10035.0 |
99645 rows × 13 columns
In [104]:
full_data = a[["comments", "date", "rank", "subreddit", "time_collected", "title", "user", "post-karma", "comment-karma"]]
In [105]:
full_data
Out[105]:
| comments | date | rank | subreddit | time_collected | title | user | post-karma | comment-karma | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 441.0 | 2017-10-28 17:34:09 | 0 | CrappyDesign | 201710282341 | Requires 3 AAA batteries, in the most annoying... | Peef_Rimgar420 | 8560.0 | 325.0 |
| 1 | 1859.0 | 2017-10-28 23:18:15 | 1 | CFB | 201710282341 | [Postgame Thread] Ohio State Defeats Penn Stat... | CFB_Referee | 313858.0 | 2845.0 |
| 2 | 711.0 | 2017-10-28 18:12:11 | 2 | mildlyinteresting | 201710282341 | Paint-chipping making the wheelchair guy look ... | Ribwichs | 7018.0 | 61.0 |
| 3 | 1249.0 | 2017-10-28 15:51:41 | 3 | videos | 201710282341 | This guy made a video of various rappers count... | floopykid | 22034.0 | 38536.0 |
| 4 | 168.0 | 2017-10-28 18:52:24 | 4 | pics | 201710282341 | Lady at Yellowstone took our picture | nowthatssomeusername | 7001.0 | 64.0 |
| 5 | 220.0 | 2017-10-28 17:02:43 | 5 | AnimalsBeingDerps | 201710282341 | Dog thinks it's a shark | 1Voice1Life | 6476268.0 | 34958.0 |
| 6 | 2761.0 | 2017-10-28 14:11:03 | 6 | LifeProTips | 201710282341 | LPT: Most people at the gym are too busy with ... | lizardman3000000 | 25710.0 | 186.0 |
| 7 | 963.0 | 2017-10-28 15:17:07 | 7 | funny | 201710282341 | So much respect for this guy. | toiletskidmarks | 13816.0 | 541.0 |
| 8 | 293.0 | 2017-10-28 14:38:26 | 8 | thisismylifenow | 201710282341 | I AM THE Q-tip HOLDER | GallowBoob | 17536350.0 | 833693.0 |
| 9 | 985.0 | 2017-10-28 22:56:37 | 9 | CFB | 201710282341 | [Postgame Thread] Iowa State Defeats TCU 14-7 | elefish92 | 24850.0 | 17525.0 |
| 10 | 1025.0 | 2017-10-28 15:03:36 | 10 | television | 201710282341 | “Stranger Things” star, Gaten Matarazzo, creat... | FordMasterTech | 6496.0 | 4484.0 |
| 11 | 799.0 | 2017-10-28 18:36:13 | 11 | apple | 201710282341 | Apple fired the engineer whose daughter releas... | JBeylovesyou | 36113.0 | 4010.0 |
| 12 | 280.0 | 2017-10-28 14:23:34 | 12 | standupshots | 201710282341 | Buzzfeed | samantharuddy | 159018.0 | 9419.0 |
| 13 | 289.0 | 2017-10-28 15:03:55 | 13 | AnimalsBeingBros | 201710282341 | Baby hippo and baby rhino are best friends | 1Voice1Life | 6476268.0 | 34958.0 |
| 14 | 152.0 | 2017-10-28 15:24:07 | 14 | nostalgia | 201710282341 | My halloween costume this year. | mellabarbarella | 6506.0 | 378.0 |
| 15 | 1124.0 | 2017-10-28 11:33:22 | 15 | BlackPeopleTwitter | 201710282341 | Fax | jvpyter | 7417.0 | 71.0 |
| 16 | 105.0 | 2017-10-28 16:59:18 | 16 | insanepeoplefacebook | 201710282341 | This doesn't seem like an appropriate way to b... | pmyourevilplans | 10033.0 | 553.0 |
| 17 | 143.0 | 2017-10-28 14:41:10 | 17 | EarthPorn | 201710282341 | Fall colors are amazing right now at the Portl... | kdsphotography | 48784.0 | 1772.0 |
| 18 | 45.0 | 2017-10-28 18:14:42 | 18 | KenM | 201710282341 | Ken M on Jesus | pm_your_pc_setup | 6837.0 | 401.0 |
| 19 | 618.0 | 2017-10-28 15:30:42 | 19 | woahdude | 201710282341 | Heavenly pit, world's deepest sinkhole in China | catch22milo | 282245.0 | 1164236.0 |
| 20 | 13143.0 | 2017-10-28 21:22:13 | 20 | CFB | 201710282341 | [Game Thread] Penn State @ Ohio State Second H... | CFB_Referee | 313858.0 | 2845.0 |
| 21 | 524.0 | 2017-10-28 15:22:33 | 21 | Showerthoughts | 201710282341 | In the tv series Supernatural nothing in that ... | dudeARama2 | 16637.0 | 42029.0 |
| 22 | 194.0 | 2017-10-28 15:20:45 | 22 | IASIP | 201710282341 | The Mcpoyle bloodline has been clean and pure ... | bequiet22 | 5618.0 | 1071.0 |
| 23 | 361.0 | 2017-10-28 14:36:00 | 23 | pics | 201710282341 | "Not a bug, a FEATURE!" | GallowBoob | 17536350.0 | 833693.0 |
| 24 | 227.0 | 2017-10-28 15:13:47 | 24 | oldpeoplefacebook | 201710282341 | You can have my bingo ticket | Barack__Obama__ | 7517.0 | 31771.0 |
| 25 | 361.0 | 2017-10-28 18:27:52 | 25 | instant_regret | 201710282341 | Kid staples his ear | sil130 | 42108.0 | 2896.0 |
| 26 | 983.0 | 2017-10-28 15:35:45 | 26 | worldnews | 201710282341 | India: local government cuts power to 14 pharm... | eva01beast | 16120.0 | 26638.0 |
| 27 | 1105.0 | 2017-10-28 19:52:10 | 27 | baseball | 201710282341 | Gurriel to serve 5 game suspension in 2018 | gullibletrout | 32546.0 | 44546.0 |
| 28 | 362.0 | 2017-10-28 12:44:30 | 28 | NatureIsFuckingLit | 201710282341 | Swan babies, called Cygnets (Sig-nits), will r... | Nipru | 146687.0 | 48972.0 |
| 29 | 118.0 | 2017-10-28 14:45:47 | 29 | wholesomebpt | 201710282341 | Great couples costume idea 🔥 | GallowBoob | 17536350.0 | 833693.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 99615 | 698.0 | 2017-10-29 18:01:59 | 70 | aww | 201710300953 | Whoever brought this child and this dog togeth... | dickfromaccounting | 228186.0 | 217270.0 |
| 99616 | 85.0 | 2017-10-30 03:33:21 | 71 | steelers | 201710300953 | MARTAVIS WHO? JUJU FOR MVP UPVOTE PARTY!!! | phrausty | 11416.0 | 5129.0 |
| 99617 | 186.0 | 2017-10-29 21:12:57 | 72 | Documentaries | 201710300953 | BBC One - Blue Planet II, Series 1, One Ocean ... | nffDionysos | 26170.0 | 2147.0 |
| 99618 | 100.0 | 2017-10-29 23:42:53 | 73 | OldSchoolCool | 201710300953 | Some spooky skeletons riding horses for Hallow... | biranouk | 18400.0 | 275.0 |
| 99619 | 560.0 | 2017-10-29 20:46:52 | 74 | OutOfTheLoop | 201710300953 | What is up with Gordon Ramsay and cocaine? | realjohncenawwe | 35748.0 | 17563.0 |
| 99620 | 150.0 | 2017-10-29 18:26:17 | 75 | calvinandhobbes | 201710300953 | I sure cant wait for these moment with my son | Dopplesoldner1 | 5336.0 | 384.0 |
| 99621 | 10851.0 | 2017-10-29 18:21:56 | 76 | videos | 201710300953 | YouTube user demonstrating how Facebook listen... | AnimalCrust | 16710.0 | 38204.0 |
| 99622 | 227.0 | 2017-10-30 02:11:44 | 77 | gifs | 201710300953 | Girl using her head as table tennis racket | 1Voice1Life | 6476268.0 | 34958.0 |
| 99623 | 236.0 | 2017-10-29 18:53:49 | 78 | oldpeoplefacebook | 201710300953 | 65 is a great day! | assumetehposition | 13906.0 | 23087.0 |
| 99624 | 3288.0 | 2017-10-29 14:27:57 | 79 | PoliticalHumor | 201710300953 | I'm sure Trump's administration won't add to t... | 13704 | 15004.0 | 57097.0 |
| 99625 | 125.0 | 2017-10-29 23:20:22 | 80 | StartledCats | 201710300953 | Oh god, who let this thing in the house | H_G_Bells | 66271.0 | 3814.0 |
| 99626 | 95.0 | 2017-10-29 22:40:05 | 81 | StrangerThings | 201710300953 | I heard you can get 1000 upvotes if you post a... | HephaestusXIII | 8247.0 | 17661.0 |
| 99627 | 195.0 | 2017-10-30 04:06:20 | 82 | baseball | 201710300953 | [Haren] So thankful they juiced the ball after... | Jacked1703 | 7196.0 | 18827.0 |
| 99628 | 363.0 | 2017-10-30 05:39:43 | 83 | baseball | 201710300953 | BREGMAN WALKS IT OFF! | Meatprint | 90554.0 | 11564.0 |
| 99629 | 513.0 | 2017-10-29 16:10:14 | 84 | technology | 201710300953 | Company Added the Word ‘Blockchain’ to Its Nam... | kashevko | 5703.0 | 2214.0 |
| 99630 | 212.0 | 2017-10-29 18:19:39 | 85 | StoppedWorking | 201710300953 | Puppy should have listened during geometry class | GallowBoob | 17536350.0 | 833693.0 |
| 99631 | 176.0 | 2017-10-30 02:50:19 | 86 | Whatcouldgowrong | 201710300953 | WCGW if I don't know what's lighter fluid? | TheNutsCracker | 497072.0 | 1326.0 |
| 99632 | 299.0 | 2017-10-30 01:48:39 | 87 | NintendoSwitch | 201710300953 | Wall Kicks (and Triple Jumps) Will Work [SMO] | MatrixChicken | 6217.0 | 4439.0 |
| 99633 | 86.0 | 2017-10-29 23:01:13 | 88 | EarthPorn | 201710300953 | Evening Mist Over Echo Lake, British Columbia,... | sndon | 6864.0 | 483.0 |
| 99634 | 57.0 | 2017-10-29 18:41:33 | 89 | BlackPeopleTwitter | 201710300953 | They made it happen | wwf4ever | 308181.0 | 69855.0 |
| 99635 | 151.0 | 2017-10-30 03:52:14 | 90 | baseball | 201710300953 | Springer ties it with a HR | Meatprint | 90554.0 | 11564.0 |
| 99636 | 19.0 | 2017-10-30 02:37:56 | 91 | BlackPeopleTwitter | 201710300953 | Aftermath of Cavs vs Knicks game 😔 | ParadiseProd | 43587.0 | 29131.0 |
| 99637 | 145.0 | 2017-10-29 21:51:43 | 92 | Justfuckmyshitup | 201710300953 | Landing strip | Zaza9000 | 17319.0 | 2892.0 |
| 99638 | 1164.0 | 2017-10-29 20:36:47 | 93 | formula1 | 201710300953 | Lewis Hamilton wins the 2017 Formula One World... | F1-Bot | 44022.0 | 2627.0 |
| 99639 | 227.0 | 2017-10-29 18:03:30 | 94 | pics | 201710300953 | Will Poulter dressed as Sid from Toy Story | peace_among_worlds | 5886.0 | 1411.0 |
| 99640 | 45.0 | 2017-10-29 19:18:55 | 95 | HistoryMemes | 201710300953 | kaiser Wilhelm ii (1917) | hazembazem | 7299.0 | 818.0 |
| 99641 | 91.0 | 2017-10-29 19:54:17 | 96 | funny | 201710300953 | My buddy was the United Airlines Passenger | justophicles | 5776.0 | 897.0 |
| 99642 | 620.0 | 2017-10-29 15:34:36 | 97 | aww | 201710300953 | This pic alone is worth the $9 I paid for this... | deemandaniels | 9383.0 | 344.0 |
| 99643 | 116.0 | 2017-10-29 18:37:31 | 98 | wholesomememes | 201710300953 | Every morning. | CaptainAmeriHeck | 8892.0 | 479.0 |
| 99644 | 6594.0 | 2017-10-29 22:07:31 | 99 | AskReddit | 201710300953 | What, in your opinion, is the single most beau... | GiveMeAllYourRupees | 31910.0 | 10035.0 |
99645 rows × 9 columns
In [106]:
full_data['time_collected'] = full_data['time_collected'].astype(str)
full_data['time_collected'] = full_data['time_collected'].apply(lambda x: x[0:4] + '-' + x[4:6] + '-' + x[6:8] + ' ' + x[8:10] + ':' + x[10:])
full_data['time_collected'] = pd.to_datetime(full_data['time_collected'])
/Users/nmolivo/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy if __name__ == '__main__': /Users/nmolivo/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy from ipykernel import kernelapp as app /Users/nmolivo/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy app.launch_new_instance()
In [107]:
full_data['date'] = pd.to_datetime(full_data['date'])
/Users/nmolivo/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy if __name__ == '__main__':
In [118]:
full_data['date'] = full_data['date'].apply(lambda x: x.tz_localize('US/Eastern'))
full_data['time_collected'] = full_data['time_collected'].apply(lambda x: x.tz_localize('UTC').tz_convert('US/Eastern'))
/Users/nmolivo/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy if __name__ == '__main__': /Users/nmolivo/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy from ipykernel import kernelapp as app
In [119]:
full_data.head()
Out[119]:
| comments | date | rank | subreddit | time_collected | title | user | post-karma | comment-karma | age-in-hrs | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 441.0 | 2017-10-28 17:34:09-04:00 | 0 | CrappyDesign | 2017-10-28 19:41:00-04:00 | Requires 3 AAA batteries, in the most annoying... | Peef_Rimgar420 | 8560.0 | 325.0 | 17.885833 |
| 1 | 1859.0 | 2017-10-28 23:18:15-04:00 | 1 | CFB | 2017-10-28 19:41:00-04:00 | [Postgame Thread] Ohio State Defeats Penn Stat... | CFB_Referee | 313858.0 | 2845.0 | 23.620833 |
| 2 | 711.0 | 2017-10-28 18:12:11-04:00 | 2 | mildlyinteresting | 2017-10-28 19:41:00-04:00 | Paint-chipping making the wheelchair guy look ... | Ribwichs | 7018.0 | 61.0 | 18.519722 |
| 3 | 1249.0 | 2017-10-28 15:51:41-04:00 | 3 | videos | 2017-10-28 19:41:00-04:00 | This guy made a video of various rappers count... | floopykid | 22034.0 | 38536.0 | 16.178056 |
| 4 | 168.0 | 2017-10-28 18:52:24-04:00 | 4 | pics | 2017-10-28 19:41:00-04:00 | Lady at Yellowstone took our picture | nowthatssomeusername | 7001.0 | 64.0 | 19.190000 |
In [136]:
from datetime import datetime
def hours_between(d1, d2):
td = d2 - d1
return abs(td.total_seconds()/3600)
In [137]:
full_data["age-in-hrs"] = full_data.apply(lambda x: hours_between(x["time_collected"], x['date']), axis =1)
/Users/nmolivo/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy if __name__ == '__main__':
In [138]:
full_data.head()
Out[138]:
| comments | date | rank | subreddit | time_collected | title | user | post-karma | comment-karma | age-in-hrs | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 441.0 | 2017-10-28 17:34:09-04:00 | 0 | CrappyDesign | 2017-10-28 19:41:00-04:00 | Requires 3 AAA batteries, in the most annoying... | Peef_Rimgar420 | 8560.0 | 325.0 | 2.114167 |
| 1 | 1859.0 | 2017-10-28 23:18:15-04:00 | 1 | CFB | 2017-10-28 19:41:00-04:00 | [Postgame Thread] Ohio State Defeats Penn Stat... | CFB_Referee | 313858.0 | 2845.0 | 3.620833 |
| 2 | 711.0 | 2017-10-28 18:12:11-04:00 | 2 | mildlyinteresting | 2017-10-28 19:41:00-04:00 | Paint-chipping making the wheelchair guy look ... | Ribwichs | 7018.0 | 61.0 | 1.480278 |
| 3 | 1249.0 | 2017-10-28 15:51:41-04:00 | 3 | videos | 2017-10-28 19:41:00-04:00 | This guy made a video of various rappers count... | floopykid | 22034.0 | 38536.0 | 3.821944 |
| 4 | 168.0 | 2017-10-28 18:52:24-04:00 | 4 | pics | 2017-10-28 19:41:00-04:00 | Lady at Yellowstone took our picture | nowthatssomeusername | 7001.0 | 64.0 | 0.810000 |
In [139]:
full_data.to_csv('full_reddit_data.csv', index=False)
We want to predict a binary variable - whether the number of comments was low or high. Compute the median number of comments and create a new binary variable that is true when the number of comments is high (above the median)¶
We could also perform Linear Regression (or any regression) to predict the number of comments here. Instead, we are going to convert this into a binary classification problem, by predicting two classes, HIGH vs LOW number of comments.
While performing regression may be better, performing classification may help remove some of the noise of the extremely popular threads. We don't have to choose the median as the splitting point - we could also split on the 75th percentile or any other reasonable breaking point.
In fact, the ideal scenario may be to predict many levels of comment numbers.
In [140]:
## YOUR CODE HERE
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
import sklearn.datasets as datasets
In [141]:
full_data["comments"].quantile(q=0.85)
Out[141]:
890.0
In [142]:
full_data["high-comments"] = [1 if x > full_data["comments"].quantile(q=0.85) else 0 for x in full_data['comments']]
/Users/nmolivo/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy if __name__ == '__main__':
In [148]:
full_data["hour-posted"] = full_data.apply(lambda x: x['date'].hour, axis =1)
In [149]:
full_data
Out[149]:
| comments | date | rank | subreddit | time_collected | title | user | post-karma | comment-karma | age-in-hrs | high-comments | hour | hour-posted | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 441.0 | 2017-10-28 17:34:09-04:00 | 0 | CrappyDesign | 2017-10-28 19:41:00-04:00 | Requires 3 AAA batteries, in the most annoying... | Peef_Rimgar420 | 8560.0 | 325.0 | 2.114167 | 0 | 10 | 17 |
| 1 | 1859.0 | 2017-10-28 23:18:15-04:00 | 1 | CFB | 2017-10-28 19:41:00-04:00 | [Postgame Thread] Ohio State Defeats Penn Stat... | CFB_Referee | 313858.0 | 2845.0 | 3.620833 | 1 | 10 | 23 |
| 2 | 711.0 | 2017-10-28 18:12:11-04:00 | 2 | mildlyinteresting | 2017-10-28 19:41:00-04:00 | Paint-chipping making the wheelchair guy look ... | Ribwichs | 7018.0 | 61.0 | 1.480278 | 0 | 10 | 18 |
| 3 | 1249.0 | 2017-10-28 15:51:41-04:00 | 3 | videos | 2017-10-28 19:41:00-04:00 | This guy made a video of various rappers count... | floopykid | 22034.0 | 38536.0 | 3.821944 | 1 | 10 | 15 |
| 4 | 168.0 | 2017-10-28 18:52:24-04:00 | 4 | pics | 2017-10-28 19:41:00-04:00 | Lady at Yellowstone took our picture | nowthatssomeusername | 7001.0 | 64.0 | 0.810000 | 0 | 10 | 18 |
| 5 | 220.0 | 2017-10-28 17:02:43-04:00 | 5 | AnimalsBeingDerps | 2017-10-28 19:41:00-04:00 | Dog thinks it's a shark | 1Voice1Life | 6476268.0 | 34958.0 | 2.638056 | 0 | 10 | 17 |
| 6 | 2761.0 | 2017-10-28 14:11:03-04:00 | 6 | LifeProTips | 2017-10-28 19:41:00-04:00 | LPT: Most people at the gym are too busy with ... | lizardman3000000 | 25710.0 | 186.0 | 5.499167 | 1 | 10 | 14 |
| 7 | 963.0 | 2017-10-28 15:17:07-04:00 | 7 | funny | 2017-10-28 19:41:00-04:00 | So much respect for this guy. | toiletskidmarks | 13816.0 | 541.0 | 4.398056 | 1 | 10 | 15 |
| 8 | 293.0 | 2017-10-28 14:38:26-04:00 | 8 | thisismylifenow | 2017-10-28 19:41:00-04:00 | I AM THE Q-tip HOLDER | GallowBoob | 17536350.0 | 833693.0 | 5.042778 | 0 | 10 | 14 |
| 9 | 985.0 | 2017-10-28 22:56:37-04:00 | 9 | CFB | 2017-10-28 19:41:00-04:00 | [Postgame Thread] Iowa State Defeats TCU 14-7 | elefish92 | 24850.0 | 17525.0 | 3.260278 | 1 | 10 | 22 |
| 10 | 1025.0 | 2017-10-28 15:03:36-04:00 | 10 | television | 2017-10-28 19:41:00-04:00 | “Stranger Things” star, Gaten Matarazzo, creat... | FordMasterTech | 6496.0 | 4484.0 | 4.623333 | 1 | 10 | 15 |
| 11 | 799.0 | 2017-10-28 18:36:13-04:00 | 11 | apple | 2017-10-28 19:41:00-04:00 | Apple fired the engineer whose daughter releas... | JBeylovesyou | 36113.0 | 4010.0 | 1.079722 | 0 | 10 | 18 |
| 12 | 280.0 | 2017-10-28 14:23:34-04:00 | 12 | standupshots | 2017-10-28 19:41:00-04:00 | Buzzfeed | samantharuddy | 159018.0 | 9419.0 | 5.290556 | 0 | 10 | 14 |
| 13 | 289.0 | 2017-10-28 15:03:55-04:00 | 13 | AnimalsBeingBros | 2017-10-28 19:41:00-04:00 | Baby hippo and baby rhino are best friends | 1Voice1Life | 6476268.0 | 34958.0 | 4.618056 | 0 | 10 | 15 |
| 14 | 152.0 | 2017-10-28 15:24:07-04:00 | 14 | nostalgia | 2017-10-28 19:41:00-04:00 | My halloween costume this year. | mellabarbarella | 6506.0 | 378.0 | 4.281389 | 0 | 10 | 15 |
| 15 | 1124.0 | 2017-10-28 11:33:22-04:00 | 15 | BlackPeopleTwitter | 2017-10-28 19:41:00-04:00 | Fax | jvpyter | 7417.0 | 71.0 | 8.127222 | 1 | 10 | 11 |
| 16 | 105.0 | 2017-10-28 16:59:18-04:00 | 16 | insanepeoplefacebook | 2017-10-28 19:41:00-04:00 | This doesn't seem like an appropriate way to b... | pmyourevilplans | 10033.0 | 553.0 | 2.695000 | 0 | 10 | 16 |
| 17 | 143.0 | 2017-10-28 14:41:10-04:00 | 17 | EarthPorn | 2017-10-28 19:41:00-04:00 | Fall colors are amazing right now at the Portl... | kdsphotography | 48784.0 | 1772.0 | 4.997222 | 0 | 10 | 14 |
| 18 | 45.0 | 2017-10-28 18:14:42-04:00 | 18 | KenM | 2017-10-28 19:41:00-04:00 | Ken M on Jesus | pm_your_pc_setup | 6837.0 | 401.0 | 1.438333 | 0 | 10 | 18 |
| 19 | 618.0 | 2017-10-28 15:30:42-04:00 | 19 | woahdude | 2017-10-28 19:41:00-04:00 | Heavenly pit, world's deepest sinkhole in China | catch22milo | 282245.0 | 1164236.0 | 4.171667 | 0 | 10 | 15 |
| 20 | 13143.0 | 2017-10-28 21:22:13-04:00 | 20 | CFB | 2017-10-28 19:41:00-04:00 | [Game Thread] Penn State @ Ohio State Second H... | CFB_Referee | 313858.0 | 2845.0 | 1.686944 | 1 | 10 | 21 |
| 21 | 524.0 | 2017-10-28 15:22:33-04:00 | 21 | Showerthoughts | 2017-10-28 19:41:00-04:00 | In the tv series Supernatural nothing in that ... | dudeARama2 | 16637.0 | 42029.0 | 4.307500 | 0 | 10 | 15 |
| 22 | 194.0 | 2017-10-28 15:20:45-04:00 | 22 | IASIP | 2017-10-28 19:41:00-04:00 | The Mcpoyle bloodline has been clean and pure ... | bequiet22 | 5618.0 | 1071.0 | 4.337500 | 0 | 10 | 15 |
| 23 | 361.0 | 2017-10-28 14:36:00-04:00 | 23 | pics | 2017-10-28 19:41:00-04:00 | "Not a bug, a FEATURE!" | GallowBoob | 17536350.0 | 833693.0 | 5.083333 | 0 | 10 | 14 |
| 24 | 227.0 | 2017-10-28 15:13:47-04:00 | 24 | oldpeoplefacebook | 2017-10-28 19:41:00-04:00 | You can have my bingo ticket | Barack__Obama__ | 7517.0 | 31771.0 | 4.453611 | 0 | 10 | 15 |
| 25 | 361.0 | 2017-10-28 18:27:52-04:00 | 25 | instant_regret | 2017-10-28 19:41:00-04:00 | Kid staples his ear | sil130 | 42108.0 | 2896.0 | 1.218889 | 0 | 10 | 18 |
| 26 | 983.0 | 2017-10-28 15:35:45-04:00 | 26 | worldnews | 2017-10-28 19:41:00-04:00 | India: local government cuts power to 14 pharm... | eva01beast | 16120.0 | 26638.0 | 4.087500 | 1 | 10 | 15 |
| 27 | 1105.0 | 2017-10-28 19:52:10-04:00 | 27 | baseball | 2017-10-28 19:41:00-04:00 | Gurriel to serve 5 game suspension in 2018 | gullibletrout | 32546.0 | 44546.0 | 0.186111 | 1 | 10 | 19 |
| 28 | 362.0 | 2017-10-28 12:44:30-04:00 | 28 | NatureIsFuckingLit | 2017-10-28 19:41:00-04:00 | Swan babies, called Cygnets (Sig-nits), will r... | Nipru | 146687.0 | 48972.0 | 6.941667 | 0 | 10 | 12 |
| 29 | 118.0 | 2017-10-28 14:45:47-04:00 | 29 | wholesomebpt | 2017-10-28 19:41:00-04:00 | Great couples costume idea 🔥 | GallowBoob | 17536350.0 | 833693.0 | 4.920278 | 0 | 10 | 14 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 99615 | 698.0 | 2017-10-29 18:01:59-04:00 | 70 | aww | 2017-10-30 05:53:00-04:00 | Whoever brought this child and this dog togeth... | dickfromaccounting | 228186.0 | 217270.0 | 11.850278 | 0 | 10 | 18 |
| 99616 | 85.0 | 2017-10-30 03:33:21-04:00 | 71 | steelers | 2017-10-30 05:53:00-04:00 | MARTAVIS WHO? JUJU FOR MVP UPVOTE PARTY!!! | phrausty | 11416.0 | 5129.0 | 2.327500 | 0 | 10 | 3 |
| 99617 | 186.0 | 2017-10-29 21:12:57-04:00 | 72 | Documentaries | 2017-10-30 05:53:00-04:00 | BBC One - Blue Planet II, Series 1, One Ocean ... | nffDionysos | 26170.0 | 2147.0 | 8.667500 | 0 | 10 | 21 |
| 99618 | 100.0 | 2017-10-29 23:42:53-04:00 | 73 | OldSchoolCool | 2017-10-30 05:53:00-04:00 | Some spooky skeletons riding horses for Hallow... | biranouk | 18400.0 | 275.0 | 6.168611 | 0 | 10 | 23 |
| 99619 | 560.0 | 2017-10-29 20:46:52-04:00 | 74 | OutOfTheLoop | 2017-10-30 05:53:00-04:00 | What is up with Gordon Ramsay and cocaine? | realjohncenawwe | 35748.0 | 17563.0 | 9.102222 | 0 | 10 | 20 |
| 99620 | 150.0 | 2017-10-29 18:26:17-04:00 | 75 | calvinandhobbes | 2017-10-30 05:53:00-04:00 | I sure cant wait for these moment with my son | Dopplesoldner1 | 5336.0 | 384.0 | 11.445278 | 0 | 10 | 18 |
| 99621 | 10851.0 | 2017-10-29 18:21:56-04:00 | 76 | videos | 2017-10-30 05:53:00-04:00 | YouTube user demonstrating how Facebook listen... | AnimalCrust | 16710.0 | 38204.0 | 11.517778 | 1 | 10 | 18 |
| 99622 | 227.0 | 2017-10-30 02:11:44-04:00 | 77 | gifs | 2017-10-30 05:53:00-04:00 | Girl using her head as table tennis racket | 1Voice1Life | 6476268.0 | 34958.0 | 3.687778 | 0 | 10 | 2 |
| 99623 | 236.0 | 2017-10-29 18:53:49-04:00 | 78 | oldpeoplefacebook | 2017-10-30 05:53:00-04:00 | 65 is a great day! | assumetehposition | 13906.0 | 23087.0 | 10.986389 | 0 | 10 | 18 |
| 99624 | 3288.0 | 2017-10-29 14:27:57-04:00 | 79 | PoliticalHumor | 2017-10-30 05:53:00-04:00 | I'm sure Trump's administration won't add to t... | 13704 | 15004.0 | 57097.0 | 15.417500 | 1 | 10 | 14 |
| 99625 | 125.0 | 2017-10-29 23:20:22-04:00 | 80 | StartledCats | 2017-10-30 05:53:00-04:00 | Oh god, who let this thing in the house | H_G_Bells | 66271.0 | 3814.0 | 6.543889 | 0 | 10 | 23 |
| 99626 | 95.0 | 2017-10-29 22:40:05-04:00 | 81 | StrangerThings | 2017-10-30 05:53:00-04:00 | I heard you can get 1000 upvotes if you post a... | HephaestusXIII | 8247.0 | 17661.0 | 7.215278 | 0 | 10 | 22 |
| 99627 | 195.0 | 2017-10-30 04:06:20-04:00 | 82 | baseball | 2017-10-30 05:53:00-04:00 | [Haren] So thankful they juiced the ball after... | Jacked1703 | 7196.0 | 18827.0 | 1.777778 | 0 | 10 | 4 |
| 99628 | 363.0 | 2017-10-30 05:39:43-04:00 | 83 | baseball | 2017-10-30 05:53:00-04:00 | BREGMAN WALKS IT OFF! | Meatprint | 90554.0 | 11564.0 | 0.221389 | 0 | 10 | 5 |
| 99629 | 513.0 | 2017-10-29 16:10:14-04:00 | 84 | technology | 2017-10-30 05:53:00-04:00 | Company Added the Word ‘Blockchain’ to Its Nam... | kashevko | 5703.0 | 2214.0 | 13.712778 | 0 | 10 | 16 |
| 99630 | 212.0 | 2017-10-29 18:19:39-04:00 | 85 | StoppedWorking | 2017-10-30 05:53:00-04:00 | Puppy should have listened during geometry class | GallowBoob | 17536350.0 | 833693.0 | 11.555833 | 0 | 10 | 18 |
| 99631 | 176.0 | 2017-10-30 02:50:19-04:00 | 86 | Whatcouldgowrong | 2017-10-30 05:53:00-04:00 | WCGW if I don't know what's lighter fluid? | TheNutsCracker | 497072.0 | 1326.0 | 3.044722 | 0 | 10 | 2 |
| 99632 | 299.0 | 2017-10-30 01:48:39-04:00 | 87 | NintendoSwitch | 2017-10-30 05:53:00-04:00 | Wall Kicks (and Triple Jumps) Will Work [SMO] | MatrixChicken | 6217.0 | 4439.0 | 4.072500 | 0 | 10 | 1 |
| 99633 | 86.0 | 2017-10-29 23:01:13-04:00 | 88 | EarthPorn | 2017-10-30 05:53:00-04:00 | Evening Mist Over Echo Lake, British Columbia,... | sndon | 6864.0 | 483.0 | 6.863056 | 0 | 10 | 23 |
| 99634 | 57.0 | 2017-10-29 18:41:33-04:00 | 89 | BlackPeopleTwitter | 2017-10-30 05:53:00-04:00 | They made it happen | wwf4ever | 308181.0 | 69855.0 | 11.190833 | 0 | 10 | 18 |
| 99635 | 151.0 | 2017-10-30 03:52:14-04:00 | 90 | baseball | 2017-10-30 05:53:00-04:00 | Springer ties it with a HR | Meatprint | 90554.0 | 11564.0 | 2.012778 | 0 | 10 | 3 |
| 99636 | 19.0 | 2017-10-30 02:37:56-04:00 | 91 | BlackPeopleTwitter | 2017-10-30 05:53:00-04:00 | Aftermath of Cavs vs Knicks game 😔 | ParadiseProd | 43587.0 | 29131.0 | 3.251111 | 0 | 10 | 2 |
| 99637 | 145.0 | 2017-10-29 21:51:43-04:00 | 92 | Justfuckmyshitup | 2017-10-30 05:53:00-04:00 | Landing strip | Zaza9000 | 17319.0 | 2892.0 | 8.021389 | 0 | 10 | 21 |
| 99638 | 1164.0 | 2017-10-29 20:36:47-04:00 | 93 | formula1 | 2017-10-30 05:53:00-04:00 | Lewis Hamilton wins the 2017 Formula One World... | F1-Bot | 44022.0 | 2627.0 | 9.270278 | 1 | 10 | 20 |
| 99639 | 227.0 | 2017-10-29 18:03:30-04:00 | 94 | pics | 2017-10-30 05:53:00-04:00 | Will Poulter dressed as Sid from Toy Story | peace_among_worlds | 5886.0 | 1411.0 | 11.825000 | 0 | 10 | 18 |
| 99640 | 45.0 | 2017-10-29 19:18:55-04:00 | 95 | HistoryMemes | 2017-10-30 05:53:00-04:00 | kaiser Wilhelm ii (1917) | hazembazem | 7299.0 | 818.0 | 10.568056 | 0 | 10 | 19 |
| 99641 | 91.0 | 2017-10-29 19:54:17-04:00 | 96 | funny | 2017-10-30 05:53:00-04:00 | My buddy was the United Airlines Passenger | justophicles | 5776.0 | 897.0 | 9.978611 | 0 | 10 | 19 |
| 99642 | 620.0 | 2017-10-29 15:34:36-04:00 | 97 | aww | 2017-10-30 05:53:00-04:00 | This pic alone is worth the $9 I paid for this... | deemandaniels | 9383.0 | 344.0 | 14.306667 | 0 | 10 | 15 |
| 99643 | 116.0 | 2017-10-29 18:37:31-04:00 | 98 | wholesomememes | 2017-10-30 05:53:00-04:00 | Every morning. | CaptainAmeriHeck | 8892.0 | 479.0 | 11.258056 | 0 | 10 | 18 |
| 99644 | 6594.0 | 2017-10-29 22:07:31-04:00 | 99 | AskReddit | 2017-10-30 05:53:00-04:00 | What, in your opinion, is the single most beau... | GiveMeAllYourRupees | 31910.0 | 10035.0 | 7.758056 | 1 | 10 | 22 |
99645 rows × 13 columns
Thought experiment: What is the baseline accuracy for this model?¶
Baseline accuracy for this model will be if it can predict a 'viral' reddit post, definition of 'viral' being the top 15% comment count.
EDA¶
In [154]:
%matplotlib inline
In [150]:
full_data['age-in-hrs-int'] = full_data['age-in-hrs'].astype(int)
In [152]:
by_time = full_data.groupby('age-in-hrs-int')['comments'].mean().reset_index()
by_time.head()
Out[152]:
| age-in-hrs-int | comments | |
|---|---|---|
| 0 | 0 | 338.827723 |
| 1 | 1 | 377.472455 |
| 2 | 2 | 422.340780 |
| 3 | 3 | 474.231038 |
| 4 | 4 | 515.989068 |
In [170]:
by_time.plot(x='age-in-hrs-int', y='comments', kind='line', figsize = (10,10), fontsize=24, legend=14)
Out[170]:
<matplotlib.axes._subplots.AxesSubplot at 0x12b0dfe80>
In [209]:
by_subreddit = full_data.groupby('subreddit')['comments'].mean().reset_index()
pop_subreddits = by_subreddit.sort_values("comments", ascending=False)[0:10]
In [210]:
pop_subreddits.plot(x='subreddit', y='comments', kind='bar', figsize = (10,10), fontsize=24, legend=14)
Out[210]:
<matplotlib.axes._subplots.AxesSubplot at 0x122019e10>
In [160]:
subreddit_by_time = full_data.groupby(['subreddit', 'age-in-hrs-int'])['comments'].mean().reset_index()
subreddit_by_time.head()
Out[160]:
| subreddit | age-in-hrs-int | comments | |
|---|---|---|---|
| 0 | 13or30 | 5 | 35.500000 |
| 1 | 2healthbars | 1 | 11.000000 |
| 2 | 2healthbars | 2 | 17.840000 |
| 3 | 2healthbars | 3 | 37.107143 |
| 4 | 2healthbars | 4 | 66.206897 |
In [163]:
#https://pandas.pydata.org/pandas-docs/stable/generated/pandas.pivot_table.html
subreddit_by_time_2 = pd.pivot_table(subreddit_by_time, values='comments', columns='subreddit', index='age-in-hrs-int')
subreddit_by_time_2.head()
Out[163]:
| subreddit | 13or30 | 2healthbars | 2meirl4meirl | 30ROCK | 49ers | 90DayFiance | AFCON | ANormalDayInRussia | ATBGE | AbandonedPorn | ... | writing | wtfstockphotos | xboxone | xkcd | xmen | xxfitness | youdontsurf | youseeingthisshit | youtubehaiku | zelda |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| age-in-hrs-int | |||||||||||||||||||||
| 0 | NaN | NaN | 63.596154 | NaN | 52.0 | 2063.0 | NaN | 28.461538 | NaN | NaN | ... | NaN | NaN | NaN | NaN | 8.0 | NaN | 7.000000 | 634.0 | 34.829268 | NaN |
| 1 | NaN | 11.000000 | 132.967742 | NaN | NaN | 9.0 | 25.0 | 19.615385 | NaN | 86.0 | ... | 373.0 | NaN | NaN | NaN | 6.0 | NaN | 6.666667 | NaN | 71.333333 | NaN |
| 2 | NaN | 17.840000 | 179.562500 | NaN | NaN | 212.0 | NaN | 10.571429 | NaN | 6.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 4.000000 | NaN | 62.500000 | NaN |
| 3 | NaN | 37.107143 | 194.857143 | NaN | 5.0 | NaN | NaN | 11.666667 | 142.000000 | NaN | ... | NaN | 5.0 | NaN | NaN | 107.0 | NaN | 14.666667 | NaN | 223.333333 | NaN |
| 4 | NaN | 66.206897 | 210.766667 | NaN | NaN | NaN | NaN | NaN | 116.444444 | 68.0 | ... | NaN | NaN | 351.0 | NaN | NaN | NaN | 16.000000 | NaN | 189.500000 | NaN |
5 rows × 1051 columns
In [197]:
max_values = pd.DataFrame(subreddit_by_time_2.apply(lambda x: max(x.fillna(0)), axis=0), columns=['max_value'])
In [198]:
max_values.head()
Out[198]:
| max_value | |
|---|---|
| subreddit | |
| 13or30 | 35.500000 |
| 2healthbars | 130.000000 |
| 2meirl4meirl | 266.687500 |
| 30ROCK | 21.333333 |
| 49ers | 52.000000 |
In [199]:
max_values.sort_values(by='max_value', inplace=True, ascending=False)
In [200]:
max_values.head()
Out[200]:
| max_value | |
|---|---|
| subreddit | |
| ImaginaryWitches | 9919.000000 |
| AskReddit | 8850.000000 |
| television | 5452.457143 |
| videos | 5329.583333 |
| Accounting | 5142.000000 |
In [193]:
max_subreddits = list(max_values[:10].index)
In [201]:
subreddit_by_time_3 = subreddit_by_time_2[max_subreddits]
In [205]:
subreddit_by_time_3.plot(style='.-', figsize=(10,10), fontsize = 24, )
Out[205]:
<matplotlib.axes._subplots.AxesSubplot at 0x120ce2ef0>
In [220]:
unique_posts = full_data.drop_duplicates(subset='title')
pop_auth_unique_posts = unique_posts.groupby('user')['title'].count()
pop_auth_unique_posts.head()
Out[220]:
user -N3ptun3- 10 -melodymelodymelody- 1 13704 1 14chickc 1 19djafoij02 1 Name: title, dtype: int64
In [232]:
pop_auth_1 = pd.DataFrame(pop_auth_unique_posts).sort_values("title", ascending = False)
In [235]:
len(pop_auth_1[pop_auth_1["title"] ==1])
Out[235]:
2273
In [236]:
len(pop_auth_1)
Out[236]:
2397
In [237]:
2273/2397
Out[237]:
0.9482686691697956
In [227]:
pop_auth_2 = pd.DataFrame(pop_auth_unique_posts).sort_values("title", ascending = False)[0:10]
In [230]:
pop_auth_2.plot(kind='bar', fontsize=24, figsize=(10,10))
Out[230]:
<matplotlib.axes._subplots.AxesSubplot at 0x12fd46cc0>
In [213]:
#ok, now to post the most popular author:
pop_auth = full_data.groupby(['user', 'post-karma','comment-karma', 'age-in-hrs-int'])['comments'].mean().reset_index()
In [215]:
full_data.head()
Out[215]:
| comments | date | rank | subreddit | time_collected | title | user | post-karma | comment-karma | age-in-hrs | high-comments | hour | hour-posted | age-in-hrs-int | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 441.0 | 2017-10-28 17:34:09-04:00 | 0 | CrappyDesign | 2017-10-28 19:41:00-04:00 | Requires 3 AAA batteries, in the most annoying... | Peef_Rimgar420 | 8560.0 | 325.0 | 2.114167 | 0 | 10 | 17 | 2 |
| 1 | 1859.0 | 2017-10-28 23:18:15-04:00 | 1 | CFB | 2017-10-28 19:41:00-04:00 | [Postgame Thread] Ohio State Defeats Penn Stat... | CFB_Referee | 313858.0 | 2845.0 | 3.620833 | 1 | 10 | 23 | 3 |
| 2 | 711.0 | 2017-10-28 18:12:11-04:00 | 2 | mildlyinteresting | 2017-10-28 19:41:00-04:00 | Paint-chipping making the wheelchair guy look ... | Ribwichs | 7018.0 | 61.0 | 1.480278 | 0 | 10 | 18 | 1 |
| 3 | 1249.0 | 2017-10-28 15:51:41-04:00 | 3 | videos | 2017-10-28 19:41:00-04:00 | This guy made a video of various rappers count... | floopykid | 22034.0 | 38536.0 | 3.821944 | 1 | 10 | 15 | 3 |
| 4 | 168.0 | 2017-10-28 18:52:24-04:00 | 4 | pics | 2017-10-28 19:41:00-04:00 | Lady at Yellowstone took our picture | nowthatssomeusername | 7001.0 | 64.0 | 0.810000 | 0 | 10 | 18 | 0 |
In [214]:
pop_auth
Out[214]:
| user | post-karma | comment-karma | age-in-hrs-int | comments | |
|---|---|---|---|---|---|
| 0 | -Mavoko- | 12230.0 | 42473.0 | 7 | 20.000000 |
| 1 | -N3ptun3- | 611097.0 | 4930.0 | 0 | 64.796875 |
| 2 | -N3ptun3- | 611097.0 | 4930.0 | 1 | 149.011905 |
| 3 | -N3ptun3- | 611097.0 | 4930.0 | 2 | 289.250000 |
| 4 | -N3ptun3- | 611097.0 | 4930.0 | 3 | 334.954545 |
| 5 | -N3ptun3- | 611097.0 | 4930.0 | 4 | 344.862069 |
| 6 | -N3ptun3- | 611097.0 | 4930.0 | 5 | 336.611650 |
| 7 | -N3ptun3- | 611097.0 | 4930.0 | 6 | 347.864662 |
| 8 | -N3ptun3- | 611097.0 | 4930.0 | 7 | 352.454545 |
| 9 | -N3ptun3- | 611097.0 | 4930.0 | 8 | 381.621622 |
| 10 | -N3ptun3- | 611097.0 | 4930.0 | 9 | 467.505618 |
| 11 | -N3ptun3- | 611097.0 | 4930.0 | 10 | 637.073171 |
| 12 | -N3ptun3- | 611097.0 | 4930.0 | 11 | 442.272727 |
| 13 | -N3ptun3- | 611097.0 | 4930.0 | 12 | 374.333333 |
| 14 | -N3ptun3- | 611097.0 | 4930.0 | 13 | 254.000000 |
| 15 | -N3ptun3- | 611097.0 | 4930.0 | 14 | 493.000000 |
| 16 | -N3ptun3- | 611097.0 | 4930.0 | 15 | 250.000000 |
| 17 | -N3ptun3- | 611097.0 | 4930.0 | 17 | 484.000000 |
| 18 | -melodymelodymelody- | 1121.0 | 6868.0 | 6 | 17.000000 |
| 19 | 13704 | 15004.0 | 57097.0 | 0 | 524.603175 |
| 20 | 13704 | 15004.0 | 57097.0 | 1 | 906.613636 |
| 21 | 13704 | 15004.0 | 57097.0 | 2 | 1746.433333 |
| 22 | 13704 | 15004.0 | 57097.0 | 3 | 2149.700000 |
| 23 | 13704 | 15004.0 | 57097.0 | 4 | 2305.593750 |
| 24 | 13704 | 15004.0 | 57097.0 | 5 | 2536.000000 |
| 25 | 13704 | 15004.0 | 57097.0 | 6 | 2742.193548 |
| 26 | 13704 | 15004.0 | 57097.0 | 7 | 2877.733333 |
| 27 | 13704 | 15004.0 | 57097.0 | 8 | 2984.222222 |
| 28 | 13704 | 15004.0 | 57097.0 | 9 | 3063.592593 |
| 29 | 13704 | 15004.0 | 57097.0 | 10 | 3116.520000 |
| ... | ... | ... | ... | ... | ... |
| 7864 | zibsha | 6141.0 | 2307.0 | 9 | 497.111111 |
| 7865 | zibsha | 6141.0 | 2307.0 | 10 | 561.333333 |
| 7866 | zibsha | 6141.0 | 2307.0 | 11 | 602.500000 |
| 7867 | zibsha | 6141.0 | 2307.0 | 12 | 609.500000 |
| 7868 | zibsha | 6141.0 | 2307.0 | 13 | 611.000000 |
| 7869 | zydeco | 6766.0 | 5103.0 | 0 | 83.466667 |
| 7870 | zydeco | 6766.0 | 5103.0 | 1 | 132.000000 |
| 7871 | zydeco | 6766.0 | 5103.0 | 2 | 215.133333 |
| 7872 | zydeco | 6766.0 | 5103.0 | 3 | 383.468750 |
| 7873 | zydeco | 6766.0 | 5103.0 | 4 | 450.757576 |
| 7874 | zydeco | 6766.0 | 5103.0 | 5 | 410.562500 |
| 7875 | zydeco | 6766.0 | 5103.0 | 6 | 456.032258 |
| 7876 | zydeco | 6766.0 | 5103.0 | 7 | 491.321429 |
| 7877 | zydeco | 6766.0 | 5103.0 | 8 | 503.482759 |
| 7878 | zydeco | 6766.0 | 5103.0 | 9 | 517.360000 |
| 7879 | zydeco | 6766.0 | 5103.0 | 10 | 526.206897 |
| 7880 | zydeco | 6766.0 | 5103.0 | 11 | 537.468750 |
| 7881 | zydeco | 6766.0 | 5103.0 | 12 | 551.263158 |
| 7882 | zydeco | 6766.0 | 5103.0 | 13 | 564.000000 |
| 7883 | zydeco | 6766.0 | 5103.0 | 17 | 652.000000 |
| 7884 | zyyp | 49895.0 | 345.0 | 2 | 25.333333 |
| 7885 | zyyp | 49895.0 | 345.0 | 3 | 32.000000 |
| 7886 | zyyp | 49895.0 | 345.0 | 4 | 38.275862 |
| 7887 | zyyp | 49895.0 | 345.0 | 5 | 51.200000 |
| 7888 | zyyp | 49895.0 | 345.0 | 6 | 64.538462 |
| 7889 | zyyp | 49895.0 | 345.0 | 7 | 73.533333 |
| 7890 | zyyp | 49895.0 | 345.0 | 8 | 81.652174 |
| 7891 | zyyp | 49895.0 | 345.0 | 9 | 89.692308 |
| 7892 | zyyp | 49895.0 | 345.0 | 10 | 105.566667 |
| 7893 | zyyp | 49895.0 | 345.0 | 11 | 14.000000 |
7894 rows × 5 columns
Create a Random Forest model to predict High/Low number of comments using Sklearn. Start by ONLY using the subreddit as a feature.¶
In [238]:
## YOUR CODE HERE
from sklearn.preprocessing import LabelEncoder
y = LabelEncoder().fit_transform(full_data['high-comments'])
X = pd.get_dummies(full_data["subreddit"])
In [239]:
from sklearn.cross_validation import cross_val_score, StratifiedKFold
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, BaggingClassifier
cv = StratifiedKFold(y, n_folds=3, shuffle=True, random_state=41)
/Users/nmolivo/anaconda/lib/python3.5/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20. "This module will be removed in 0.20.", DeprecationWarning)
In [240]:
rf = RandomForestClassifier(class_weight='balanced')
s = cross_val_score(rf, X, y, cv=cv, n_jobs=-1)
print("{} Score:\t{:0.3} ± {:0.3}".format("Random Forest", s.mean().round(3), s.std().round(3)))
Random Forest Score: 0.83 ± 0.001
it does pretty well just based on subreddit. It'd be interesting to include ...
- hours since post (age)
- user
- and of course subreddit.
In [269]:
model_data = full_data[["high-comments", "post-karma", 'comment-karma', "age-in-hrs-int", "subreddit"]].dropna()
In [270]:
len(model_data)
Out[270]:
97261
In [271]:
## YOUR CODE HERE
from sklearn.preprocessing import LabelEncoder
y = model_data['high-comments']
X = pd.concat([model_data[["post-karma", 'comment-karma', "age-in-hrs-int"]], pd.get_dummies(model_data["subreddit"])]
, axis=1)
In [272]:
X.head(1)
Out[272]:
| post-karma | comment-karma | age-in-hrs-int | 13or30 | 2healthbars | 2meirl4meirl | 30ROCK | 49ers | 90DayFiance | AFCON | ... | writing | wtfstockphotos | xboxone | xkcd | xmen | xxfitness | youdontsurf | youseeingthisshit | youtubehaiku | zelda | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 8560.0 | 325.0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 rows × 1039 columns
In [273]:
cv = StratifiedKFold(y, n_folds=3, shuffle=True, random_state=41)
In [274]:
rf = RandomForestClassifier(class_weight='balanced')
s = cross_val_score(rf, X, y, cv=cv, n_jobs=-1)
print("{} Score:\t{:0.3} ± {:0.3}".format("Random Forest", s.mean().round(3), s.std().round(3)))
Random Forest Score: 0.979 ± 0.0
In [275]:
# once you've cv'd the model, train the whole data and get feature importances
rf.fit(X, y)
imp = pd.DataFrame({'importance': list(rf.feature_importances_)}, index=X.columns)
imp.sort_values(by='importance', ascending=False, inplace=True)
imp_top10 = imp.head(10)
imp_top10.sort_values(by='importance', ascending=True, inplace=True)
/Users/nmolivo/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:6: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
In [276]:
imp_top10.plot(kind='barh', fontsize=24, figsize=(10,10))
Out[276]:
<matplotlib.axes._subplots.AxesSubplot at 0x134c51a58>
In [253]:
full_data.loc[full_data['user']=='2martin4u', 'comments'].max()
Out[253]:
3232.0
In [277]:
from sklearn.linear_model import LogisticRegression
In [279]:
lg = LogisticRegression()
s = cross_val_score(lg, X, y, cv=cv, n_jobs=-1)
print("{} Score:\t{:0.3} ± {:0.3}".format("Logistic Regression", s.mean().round(3), s.std().round(3)))
Logistic Regression Score: 0.852 ± 0.0
In [282]:
lg.fit(X,y) # https://stackoverflow.com/questions/34052115/how-to-find-the-importance-of-the-features-for-a-logistic-regression-model
imp = pd.DataFrame({'importance':abs(np.std(X,0)*lg.coef_[0])}, index=X.columns)
imp.sort_values(by='importance', ascending=False, inplace=True)
imp_top10 = imp.head(10)
imp_top10.sort_values(by='importance', ascending=True, inplace=True)
/Users/nmolivo/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:5: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
In [283]:
imp_top10.plot(kind='barh', fontsize=24, figsize=(10,10))
Out[283]:
<matplotlib.axes._subplots.AxesSubplot at 0x133bf7da0>
Create a few new variables in your dataframe to represent interesting features of a thread title.¶
- For example, create a feature that represents whether 'cat' is in the title or whether 'funny' is in the title.
- Then build a new Random Forest with these features. Do they add any value?
- After creating these variables, use count-vectorizer to create features based on the words in the thread titles.
- Build a new random forest model with subreddit and these new features included.
In [285]:
## YOUR CODE HERE
full_data["cat"]=full_data["title"].str.contains("cat")
full_data["funny"]=full_data["title"].str.contains("funny")
In [378]:
model_data = full_data[["high-comments", "post-karma", 'comment-karma', "age-in-hrs-int", "subreddit", "cat",
"funny", "title"]].dropna()
y = model_data['high-comments']
X_1 = pd.concat([model_data[["post-karma", 'comment-karma', "age-in-hrs-int", "cat", "funny"]], pd.get_dummies(model_data["subreddit"])]
, axis=1)
In [290]:
rf = RandomForestClassifier(class_weight='balanced')
s = cross_val_score(rf, X_1, y, cv=cv, n_jobs=-1)
print("{} Score:\t{:0.3} ± {:0.3}".format("Random Forest", s.mean().round(3), s.std().round(3))) # doesn't help
Random Forest Score: 0.979 ± 0.001
In [ ]:
#My accuracy is the same and it increases my margin of error.
In [295]:
from sklearn.feature_extraction.text import CountVectorizer
In [315]:
title_data = model_data['title'].drop_duplicates()
title_data.head()
Out[315]:
0 Requires 3 AAA batteries, in the most annoying... 1 [Postgame Thread] Ohio State Defeats Penn Stat... 2 Paint-chipping making the wheelchair guy look ... 3 This guy made a video of various rappers count... 4 Lady at Yellowstone took our picture Name: title, dtype: object
In [321]:
len(title_data)
Out[321]:
2587
In [354]:
# words appearing in a minimum of 10 titles and a maximum of 80% of titles, with english stop words removed
# binary counts 1 for every title the word is in, 0 otherwise (means column sums of the dtm will equal document frequencies)
vectorizer = CountVectorizer(min_df=10, max_df=0.8, binary=True, stop_words='english')
tvec = vectorizer.fit_transform(title_data) #creates document term matrix (dtm)
In [355]:
# get document frequencies of term
vocab = pd.DataFrame(list(zip(vectorizer.get_feature_names(), np.asarray(tvec.sum(axis=0)).ravel()))) # unravels 2d numpy array
In [362]:
# 20 most common terms
vocab.columns = ['word', 'doc_freq']
vocab.sort_values(by='doc_freq', ascending=False, inplace=True)
vocab.head(20)
Out[362]:
| word | doc_freq | |
|---|---|---|
| 52 | halloween | 99 |
| 20 | costume | 67 |
| 60 | just | 64 |
| 66 | like | 60 |
| 86 | new | 58 |
| 169 | year | 52 |
| 144 | time | 49 |
| 40 | game | 46 |
| 103 | post | 46 |
| 31 | don | 42 |
| 48 | got | 39 |
| 50 | guy | 34 |
| 80 | man | 34 |
| 10 | best | 32 |
| 146 | today | 31 |
| 75 | love | 31 |
| 90 | oc | 30 |
| 77 | make | 30 |
| 142 | thread | 29 |
| 93 | old | 29 |
In [379]:
# create new columns for top 20 words
for word in vocab.word[:20]:
model_data[word] = model_data['title'].str.contains(word)
model_data.head()
Out[379]:
| high-comments | post-karma | comment-karma | age-in-hrs-int | subreddit | cat | funny | title | halloween | costume | ... | got | guy | man | best | today | love | oc | make | thread | old | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 8560.0 | 325.0 | 2 | CrappyDesign | False | False | Requires 3 AAA batteries, in the most annoying... | False | False | ... | False | False | False | False | False | False | False | False | False | False |
| 1 | 1 | 313858.0 | 2845.0 | 3 | CFB | False | False | [Postgame Thread] Ohio State Defeats Penn Stat... | False | False | ... | False | False | False | False | False | False | False | False | False | False |
| 2 | 0 | 7018.0 | 61.0 | 1 | mildlyinteresting | False | False | Paint-chipping making the wheelchair guy look ... | False | False | ... | False | True | True | False | False | False | False | False | False | True |
| 3 | 1 | 22034.0 | 38536.0 | 3 | videos | False | False | This guy made a video of various rappers count... | False | False | ... | False | True | False | False | False | False | False | False | False | False |
| 4 | 0 | 7001.0 | 64.0 | 0 | pics | False | False | Lady at Yellowstone took our picture | False | False | ... | False | False | False | False | False | False | False | False | False | False |
5 rows × 28 columns
In [365]:
y = model_data['high-comments']
X_2 = pd.concat([model_data[["post-karma", 'comment-karma', "age-in-hrs-int", "cat", "funny"] + list(vocab.word[:20])],
pd.get_dummies(model_data["subreddit"])], axis=1)
In [367]:
rf = RandomForestClassifier(class_weight='balanced')
s = cross_val_score(rf, X_2, y, cv=cv, n_jobs=-1)
print("{} Score:\t{:0.3} ± {:0.3}".format("Random Forest", s.mean().round(3), s.std().round(3)))
# doesn't help (no change), if the models are equally good, we should use the model with the fewest variables
Random Forest Score: 0.979 ± 0.0
In [368]:
# what if we look at only top terms in the titles and none of the other variables
y = model_data['high-comments']
X_3 = model_data[list(vocab.word[:20])]
rf = RandomForestClassifier(class_weight='balanced')
s = cross_val_score(rf, X_3, y, cv=cv, n_jobs=-1)
print("{} Score:\t{:0.3} ± {:0.3}".format("Random Forest", s.mean().round(3), s.std().round(3))) # performs worse
Random Forest Score: 0.779 ± 0.002
In [380]:
# what if we look at only top terms in the titles and just subreddit
y = model_data['high-comments']
X_4 = pd.concat([model_data[list(vocab.word[:20])], pd.get_dummies(model_data["subreddit"])], axis=1)
rf = RandomForestClassifier(class_weight='balanced')
s = cross_val_score(rf, X_4, y, cv=cv, n_jobs=-1)
print("{} Score:\t{:0.3} ± {:0.3}".format("Random Forest", s.mean().round(3), s.std().round(3)))
# performs better than just the title-based variables, but worse than the best performing model
Random Forest Score: 0.885 ± 0.002
Use cross-validation in scikit-learn to evaluate the model above.¶
- Evaluate the accuracy of the model, as well as any other metrics you feel are appropriate.
In [369]:
from sklearn.metrics import precision_score, recall_score, f1_score
In [370]:
## YOUR CODE HERE
# See cross validation above for accuracy, I'll check some other metrics for my best performing model
model_data = full_data[["high-comments", "post-karma", 'comment-karma', "age-in-hrs-int", "subreddit"]].dropna()
y = model_data['high-comments']
X = pd.concat([model_data[["post-karma", 'comment-karma', "age-in-hrs-int"]], pd.get_dummies(model_data["subreddit"])]
, axis=1)
cv = StratifiedKFold(y, n_folds=3, shuffle=True, random_state=41)
rf = RandomForestClassifier(class_weight='balanced')
In [375]:
precisions=[]
recalls =[]
for train_ind, test_ind in cv:
X_train = X.iloc[train_ind]
X_test = X.iloc[test_ind]
y_train = y.iloc[train_ind]
y_test = y.iloc[test_ind]
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
precisions.append(precision_score(y_test, y_pred)) # number true positive that are classified as positive over
# all classified as positive
recalls.append(recall_score(y_test, y_pred)) # number true positive that are classified as positive over all
# positive (classified or not)
print('Average precision {}, recall {}'.format(round(np.mean(precisions), 3), round(np.mean(recalls), 3),))
Average precision 0.889, recall 0.979
Repeat the model-building process with a non-tree-based method.¶
In [ ]:
## YOUR CODE HERE
# See Logistic Regression implemented above
Use Count Vectorizer from scikit-learn to create features from the thread titles.¶
- Examine using count or binary features in the model
- Re-evaluate your models using these. Does this improve the model performance?
- What text features are the most valuable?
In [ ]:
## YOUR CODE HERE
# Completed above, when taking into account karma and subreddit, these variables do not improve the predictive power of
# the mode
We know that 'viral'-ness is a function of engagement and time since being posted. Our study determines that there are a few controllable factors that can help reach your viral goals.
While timing is important across the board, it's important to understand that specific subject matters have different peak times. For instance, current events peak at their inception or very close to right after they are posted. More static events or continual events (like College Football during college football season) can have a following with consistent engagement.
We apply Logistic Regression to our variables "comments karma", "post karma", "age in hours", an "subreddit and get a pretty accurate model (about 77%) with little variance. However, an approach with fewer assumptions on the distribution of independent variables may yeild a more accurage graph.
Turns out Random Forest reigns supreme out of the two. Its flexibility, lack of assumptions about the data and that it doesn't rely on all features being important to the output variable is key as Reddit post attributes are not neccessarily linearly related to engagement. We get a model with cross validation 97+% accuracy and 0 variance. Pretty good in the spectrum of online sensations.
We learned that who posts the post, matters. We learned that timely relevant posts have the capability to get a good spike. We also learned that particular words in the title did not improve our model projecting a post's ability to go viral.
I suggest 538 do one or many of the following options:
- Start building a brand now; the top influencers of posts with the most engagement were 'comment-karma' and 'post-karma'
- While posting in preparation, plan a 'real' debut on Thanksgiving because it will take time to amass followers to take you to that sensational level.
2a. Consider posting about sports. During football season, College Football and Fantasy Football are most popular subreddits and they consistently have a large following.
2b. Consider posting what data science can reveal about Thanksgiving
2c. Consider posting a cute picture
2d. Maybe do all 3! Would not be unheard of in the Reddit Community - Consider if your goal to go viral is on brand. Some of the content on Reddit isn't what 538 does, and that's ok. The most rewarding experience will be acheived by staying true to who you are and finding your very own corner of Reddit that may never see sensational comment volume.
*Please see my powerpoint slides for a few examples of viral Reddit posts.
In [390]:
full_data['total'] = full_data["post-karma"]+full_data["comment-karma"]
In [389]:
full_data.sort_values("comments", ascending=False)[["title", "user", "comments", "subreddit"]].drop_duplicates("title")
Out[389]:
| title | user | comments | subreddit | |
|---|---|---|---|---|
| 88843 | Game Thread: World Series Game 5 ⚾ Dodgers (2-... | BaseballBot | 35205.0 | baseball |
| 37302 | [Game Thread] Penn State @ Ohio State Second H... | CFB_Referee | 13340.0 | CFB |
| 35169 | Happy Halloween 🎃 | 666Fearbot666 | 13338.0 | trashy |
| 36349 | BoJack Horseman and Mr. Peanutbutter in the sa... | senor_flojo | 13337.0 | BoJackHorseman |
| 12788 | Party on, Single life. | oliverclothessoff | 13320.0 | funny |
| 99621 | YouTube user demonstrating how Facebook listen... | AnimalCrust | 10851.0 | videos |
| 24988 | Brigmore Witch by Eric Tan | net-diver | 9919.0 | ImaginaryWitches |
| 1399 | [Game Thread] Penn State @ Ohio State (3:30PM ET) | CFB_Referee | 9654.0 | CFB |
| 79385 | ‘MURICA! | sacredvampire | 9541.0 | MURICA |
| 39873 | Redditors with a Ph.D./Master's, what is a TL;... | Emprythius | 8850.0 | AskReddit |
| 39697 | Umm I really don't think you should be bending... | masonprendergast | 8835.0 | EarthPorn |
| 6462 | What's your "I hated that person, but they did... | OvertOperation | 8622.0 | AskReddit |
| 35876 | 100% done with your sh*t, Greg | GallowPlaceholder | 8612.0 | youseeingthisshit |
| 86569 | Slow burn revenge on ex, which I'll sadly neve... | ChewbaccaTheRookie | 7998.0 | pettyrevenge |
| 82417 | What is the biggest men/women double standard? | the_dank_muslim | 7260.0 | AskReddit |
| 99644 | What, in your opinion, is the single most beau... | GiveMeAllYourRupees | 6594.0 | AskReddit |
| 90538 | This is definitely shitty x) | AGN_187 | 6262.0 | Shitty_Car_Mods |
| 83244 | Facebook executive denied the social network u... | maxwellhill | 6212.0 | worldnews |
| 99545 | Star Trek Discovery Actor Anthony Rapp: Kevin ... | GavinGunhold | 6121.0 | television |
| 69771 | TIL that when the 24-7-365 restaurant Denny's ... | HeadphoneJackal | 6031.0 | todayilearned |
| 69804 | Golden Retriever Hugs Dad | ripahe | 5351.0 | AnimalsBeingBros |
| 94405 | tax_irl | catofillomens | 5142.0 | Accounting |
| 13185 | Firearm fatalities climbed in West Virginia af... | DoremusJessup | 5050.0 | GunsAreCool |
| 46312 | My Nephew's Costume for Chocolate! | TRUEfoe-X | 5042.0 | disney |
| 44432 | Elephant crossing in Kruger Park 🐘🐘🐘 | awkwardtheturtle | 5010.0 | babyelephantgifs |
| 43844 | [Postgame Thread] Ohio State Defeats Penn Stat... | CFB_Referee | 4994.0 | CFB |
| 67108 | Made it out here before they cut this magical ... | Awayiflew | 4970.0 | EarthPorn |
| 39324 | Smell, Jan Miense Molenaar, Oil on Canvas, 1637 | 2emotional4u | 4895.0 | Art |
| 93407 | My play opens this month. Feeling inspired. | AGirlOfThrones | 4881.0 | bulletjournal |
| 24318 | /r/all has not seen Robert Mueller in over a m... | US_Senate_SgtAtArms | 4751.0 | The_Mueller |
| ... | ... | ... | ... | ... |
| 67177 | "Bob Ross Birthday Tribute," Oil, 18x24in canvas | tutusdaddy23 | 2.0 | Art |
| 15476 | Husband offered 2x salary but we just bought o... | nowwhat_tho | 2.0 | personalfinance |
| 34084 | We'll always have the music. We'll always have... | MattJ_33 | 2.0 | LinkinPark |
| 34090 | Iron Fist Costume On A Budget | thecursedham | 2.0 | Defenders |
| 39668 | Chef Skills Sunday | AutoModerator | 2.0 | leangains |
| 24208 | Koreans Hold 3rd 'Most Powerful' Passport in t... | MellowYellons | 2.0 | korea |
| 24922 | 1965, Times sure have changed | str8_outta_bed | 2.0 | fakehistoryporn |
| 83757 | “From this bullshit called reality” | KissMeImKappazoid | 2.0 | CringeAnarchy |
| 24935 | Found on /r/skyrim. Worth investing in? | PhyrexianAngel | 2.0 | MemeEconomy |
| 15642 | Election results of the 1912 Imperial Election... | Priamosish | 2.0 | europe |
| 79361 | Cute Megumin [x-post /r/awwnime] | CannonGerbil | 2.0 | Megumin |
| 12742 | The pizza omelette in all its glory, and alway... | oppurtunityisnowhere | 2.0 | KitchenConfidential |
| 15768 | Carrot in a more traditional anime style. | AlrongPilates | 2.0 | OnePiece |
| 25107 | Colored the last page of chapter 158 | LeinCaster | 2.0 | BokuNoHeroAcademia |
| 24303 | A TM 5.1 I built for a friend | Itchy-Areola | 2.0 | airsoft |
| 93395 | Katruck | MadDogDelpho | 2.0 | kachow |
| 44208 | Rose and Hydrangea flower cupcakes🌹 | CelestialIndia | 2.0 | Baking |
| 44219 | Poison Summer Madness | rmendis | 2.0 | MechanicalKeyboards |
| 68115 | Blondey McCoy hit by taxi in London | oscarsmith279 | 2.0 | skateboarding |
| 44222 | Silky smooth | acemz | 2.0 | Tinder |
| 44224 | Are you free? | pokegoing | 2.0 | dankchristianmemes |
| 93401 | /u/titanicman119 shamelessly stole my Spongebo... | DUTCHWIZARD | 2.0 | KarmaCourt |
| 40551 | Not mines | dazed_out | 2.0 | HistoryMemes |
| 93406 | Shoutout to Steve from Stranger Things for dre... | Wonderfulwonderful6 | 2.0 | arcticmonkeys |
| 92225 | Professor Frink, Professor Frink, he’ll make y... | Tubbsie | 2.0 | TheSimpsons |
| 23250 | /r/HeckCorporate/ - OBEY. CONSUME SCHMACKOS. B... | RainyPug | 2.0 | wowthissubexists |
| 37141 | This bag should have three bottles in it. Scre... | bens_crazy | 2.0 | mildlyinfuriating |
| 57591 | Prettyboye doin a heckin waffle nom. | whorecrux_1029 | 2.0 | rarepuppers |
| 40567 | Armenian Fashion Illustrator Creates Stunning ... | Millsjack | 2.0 | ArtPorn |
| 23550 | Fox News describes Obama's jury duty. | MWM2 | 2.0 | PoliticalHumor |
2641 rows × 4 columns
BONUS¶
Refer to the README for the bonus parts
In [ ]:
## YOUR CODE HERE