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balzer82/31C3: a new dawn-Top10-Talks.png

Last active February 24, 2021 07:47
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Top 10 Video views of CCC events from media.ccc.de
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CCCEventVideoViews.ipynb
{
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"name": "",
"signature": "sha256:57ff8faa4b969cc9b7886303f581329d5dd2711403c5571fe05208d04f5c50e1"
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"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Crawls 'media.ccc.de' and ranks the videos for views"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from bs4 import BeautifulSoup\n",
"import requests"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 118
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 124
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Some URLs you can use"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#url = 'http://media.ccc.de/browse/conferences/datenspuren/2014/index.html'\n",
"#url = 'http://media.ccc.de/browse/conferences/eh2014/index.html'\n",
"#url = 'http://media.ccc.de/browse/conferences/fiffkon/2014/index.html'\n",
"#url = 'http://media.ccc.de/browse/conferences/vcfb/2014/index.html'\n",
"#url = 'http://media.ccc.de/browse/conferences/hackover/2014/index.html'\n",
"#url = 'http://media.ccc.de/browse/conferences/mrmcd/mrmcd14/index.html'\n",
"#url = 'http://media.ccc.de/browse/conferences/froscon/2014/index.html'\n",
"\n",
"#url = 'http://media.ccc.de/browse/congress/2014/index.html'\n",
"\n",
"\n",
"# Or you can even try all years for the congresses:\n",
"for year in range(2000, 2015):\n",
" url = 'http://media.ccc.de/browse/congress/%i/index.html' % year\n",
" r = requests.get(url)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 119
},
{
"cell_type": "code",
"collapsed": false,
"input": [
" soup = BeautifulSoup(r.content)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 120
},
{
"cell_type": "code",
"collapsed": false,
"input": [
" eventname = soup.find('h2').text.encode('utf-8')\n",
" if len(eventname)==0:\n",
" eventname = soup.find('h1').text.encode('utf-8')\n",
" \n",
" print('%s...' % eventname)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 121
},
{
"cell_type": "code",
"collapsed": true,
"input": [
" talkurllist = []\n",
" for link in soup.find_all('a', attrs={'class': 'event-preview'}):\n",
" talkurllist.append(link.get('href'))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 122
},
{
"cell_type": "code",
"collapsed": false,
"input": [
" talks = {}\n",
"\n",
" for talkurl in talkurllist:\n",
" r = requests.get('http://media.ccc.de/' + talkurl)\n",
" soup = BeautifulSoup(r.content)\n",
" title = soup.find('h1')\n",
"\n",
" ul = soup.find('ul', attrs={'class': 'metadata'})\n",
" for i,li in enumerate(ul.find_all('li')):\n",
" if i==2:\n",
" talks[title.text.encode('utf-8').strip()[:60]] = int(li.text)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 123
},
{
"cell_type": "code",
"collapsed": false,
"input": [
" name = 'Views \\'media.ccc.de\\''\n",
" df = pd.DataFrame(data=talks.values(), index=talks.keys(), columns=[name])\n",
" df.sort(name, inplace='True')\n",
" df = df.tail(10)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 125
},
{
"cell_type": "code",
"collapsed": false,
"input": [
" df.plot(kind='barh', figsize=(11,5), title='Top 10 Most Viewed \\'%s\\' Talks' % eventname, color='#94C600')\n",
" plt.tight_layout()\n",
" plt.savefig('%s-Top10-Talks.png' % eventname, dpi=150)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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ngf8HPBoRB0o6Fbg0Is6fx/nWIBUqn2meIW4+vrT+baT+2+2By4HPRcStkk4D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\nJXr37k12djY/+tGPeP/993eZ06qi4lhkD7j7Wnfv4u5dgD8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"text": [
"<matplotlib.figure.Figure at 0x109a6aa90>"
]
}
],
"prompt_number": 126
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 126
}
],
"metadata": {}
}
]
}
Raw
CCCEventVideoViews.py
# -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>
# <headingcell level=1>
# Crawls 'media.ccc.de' and ranks the videos for views
# <codecell>
from bs4 import BeautifulSoup
import requests
# <codecell>
#%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
# <headingcell level=2>
# Some URLs you can use
# <codecell>
#url = 'http://media.ccc.de/browse/conferences/datenspuren/2014/index.html'
#url = 'http://media.ccc.de/browse/conferences/eh2014/index.html'
#url = 'http://media.ccc.de/browse/conferences/fiffkon/2014/index.html'
#url = 'http://media.ccc.de/browse/conferences/vcfb/2014/index.html'
#url = 'http://media.ccc.de/browse/conferences/hackover/2014/index.html'
#url = 'http://media.ccc.de/browse/conferences/mrmcd/mrmcd14/index.html'
#url = 'http://media.ccc.de/browse/conferences/froscon/2014/index.html'
#url = 'http://media.ccc.de/browse/congress/2014/index.html'
# Or you can even try all years for the congresses:
for year in range(2000, 2015):
url = 'http://media.ccc.de/browse/congress/%i/index.html' % year
r = requests.get(url)
# <codecell>
soup = BeautifulSoup(r.content)
# <codecell>
eventname = soup.find('h2').text.encode('utf-8')
if len(eventname)==0:
eventname = soup.find('h1').text.encode('utf-8')
print('%s...' % eventname)
# <codecell>
talkurllist = []
for link in soup.find_all('a', attrs={'class': 'event-preview'}):
talkurllist.append(link.get('href'))
# <codecell>
talks = {}
for talkurl in talkurllist:
r = requests.get('http://media.ccc.de/' + talkurl)
soup = BeautifulSoup(r.content)
title = soup.find('h1')
ul = soup.find('ul', attrs={'class': 'metadata'})
for i,li in enumerate(ul.find_all('li')):
if i==2:
talks[title.text.encode('utf-8').strip()[:60]] = int(li.text)
# <codecell>
name = 'Views \'media.ccc.de\''
df = pd.DataFrame(data=talks.values(), index=talks.keys(), columns=[name])
df.sort(name, inplace='True')
df = df.tail(10)
# <codecell>
df.plot(kind='barh', figsize=(11,5), title='Top 10 Most Viewed \'%s\' Talks' % eventname, color='#94C600')
plt.tight_layout()
plt.savefig('%s-Top10-Talks.png' % eventname, dpi=150)
# <codecell>
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