Hi
user
Admin Login:
Username:
Password:
Name:
pandas: Powerful data analysis tools for Python
--client
psf
--show
pycon_2012
--room E2 735 --force
Next: 6 The Journey to Give Every Scientist a Supercomputer
show more...
Marks
Author(s):
Wes McKinney
Location
Track III (E2)
Date
mar Fri 09
Days Raw Files
Start
March 9, 2012, 5:20 p.m.
First Raw Start
March 8, 2012, 5:19 p.m.
Duration
00:40:00
Offset
1 day, 0:00:17
End
March 9, 2012, 6 p.m.
Last Raw End
March 8, 2012, 5:54 p.m.
Chapters
00:00
Total cuts_time
34 min.
http://us.pycon.org/2012/schedule/presentation/416/
raw-playlist
raw-mp4-playlist
encoded-files-playlist
host
public
mp4
svg
png
assets
release.pdf
pandas_Powerful_data_analysis_tools_for_Python.json
logs
Admin:
episode
episode list
cut list
raw files day
marks day
marks day
image_files
State:
---------
borked
edit
encode
push to queue
post
richard
review 1
email
review 2
make public
tweet
to-miror
conf
done
Locked:
clear this to unlock
Locked by:
user/process that locked.
Start:
initially scheduled time from master, adjusted to match reality
Duration:
length in hh:mm:ss
Name:
Video Title (shows in video search results)
Emails:
email(s) of the presenter(s)
Released:
has someone authorised pubication
Unknown
Yes
No
Normalise:
Channelcopy:
m=mono, 01=copy left to right, 10=right to left, 00=ignore.
Thumbnail:
filename.png
Description:
markdown
pandas is a Python library providing fast, expressive data structures for working with structured or relational data sets. In addition to being used for general purpose data manipulation and data analysis, it has also been designed to enable Python to become a competitive statistical computing platform. In this talk, I will discuss the library's features and show a variety of topical examples.
Comment:
production notes
<?xml version='1.0' encoding='UTF-8'?> <ns0:entry xmlns:ns0="http://www.w3.org/2005/Atom" xmlns:ns1="http://schemas.google.com/g/2005" xmlns:ns2="http://search.yahoo.com/mrss/" xmlns:ns3="http://gdata.youtube.com/schemas/2007" xmlns:ns4="http://purl.org/atom/app#"><ns0:category scheme="http://schemas.google.com/g/2005#kind" term="http://gdata.youtube.com/schemas/2007#video" /><ns0:category label="Education" scheme="http://gdata.youtube.com/schemas/2007/categories.cat" term="Education" /><ns0:category scheme="http://gdata.youtube.com/schemas/2007/keywords.cat" term="psf" /><ns0:category scheme="http://gdata.youtube.com/schemas/2007/keywords.cat" term="python pycon pycon2012" /><ns0:category scheme="http://gdata.youtube.com/schemas/2007/keywords.cat" term="pycon_2012" /><ns0:category scheme="http://gdata.youtube.com/schemas/2007/keywords.cat" term="WesMcKinney" /><ns0:id>http://gdata.youtube.com/feeds/api/videos/qbYYamU42Sw</ns0:id><ns0:author><ns0:name>NextDayVideo</ns0:name><ns0:uri>https://gdata.youtube.com/feeds/api/users/NextDayVideo</ns0:uri></ns0:author><ns0:content type="text">Wes McKinney pandas is a Python library providing fast, expressive data structures for working with structured or relational data sets. In addition to being used for general purpose data manipulation and data analysis, it has also been designed to en</ns0:content><ns0:updated>2012-03-13T03:10:04.000Z</ns0:updated><ns0:published>2012-03-13T03:10:04.000Z</ns0:published><ns1:comments><ns1:feedLink countHint="0" href="https://gdata.youtube.com/feeds/api/videos/qbYYamU42Sw/comments?client=ndv" rel="http://gdata.youtube.com/schemas/2007#comments" /></ns1:comments><ns2:group><ns2:keywords>psf, python pycon pycon2012, pycon_2012, WesMcKinney</ns2:keywords><ns2:description type="plain">Wes McKinney pandas is a Python library providing fast, expressive data structures for working with structured or relational data sets. In addition to being used for general purpose data manipulation and data analysis, it has also been designed to en</ns2:description><ns2:title type="plain">pandas: Powerful data analysis tools for Python</ns2:title><ns3:duration seconds="0" /><ns2:content duration="0" expression="full" isDefault="true" medium="video" type="application/x-shockwave-flash" url="https://www.youtube.com/v/qbYYamU42Sw?version=3&f=user_uploads&c=ndv&d=Aarb2r5skm2_yNTuKEinXdAO88HsQjpE1a8d1GxQnGDm&app=youtube_gdata" ns3:format="5" /><ns2:thumbnail height="360" time="00:00:00" url="http://i.ytimg.com/vi/qbYYamU42Sw/0.jpg" width="480" /><ns2:thumbnail height="90" time="00:00:00" url="http://i.ytimg.com/vi/qbYYamU42Sw/1.jpg" width="120" /><ns2:thumbnail height="90" time="00:00:00" url="http://i.ytimg.com/vi/qbYYamU42Sw/2.jpg" width="120" /><ns2:thumbnail height="90" time="00:00:00" url="http://i.ytimg.com/vi/qbYYamU42Sw/3.jpg" width="120" /><ns2:category label="Education" scheme="http://gdata.youtube.com/schemas/2007/categories.cat">Education</ns2:category><ns2:category scheme="http://gdata.youtube.com/schemas/2007/developertags.cat">psf</ns2:category><ns2:category scheme="http://gdata.youtube.com/schemas/2007/developertags.cat">python pycon pycon2012</ns2:category><ns2:category scheme="http://gdata.youtube.com/schemas/2007/developertags.cat">pycon_2012</ns2:category><ns2:category scheme="http://gdata.youtube.com/schemas/2007/developertags.cat">WesMcKinney</ns2:category><ns2:player url="https://www.youtube.com/watch?v=qbYYamU42Sw&feature=youtube_gdata_player" /></ns2:group><ns0:title type="text">pandas: Powerful data analysis tools for Python</ns0:title><ns4:control><ns4:draft>yes</ns4:draft><ns3:state name="processing" /></ns4:control><ns0:link href="https://www.youtube.com/watch?v=qbYYamU42Sw&feature=youtube_gdata" rel="alternate" type="text/html" /><ns0:link href="https://gdata.youtube.com/feeds/api/videos/qbYYamU42Sw/responses?client=ndv" rel="http://gdata.youtube.com/schemas/2007#video.responses" type="application/atom+xml" /><ns0:link href="https://gdata.youtube.com/feeds/api/videos/qbYYamU42Sw/ratings?client=ndv" rel="http://gdata.youtube.com/schemas/2007#video.ratings" type="application/atom+xml" /><ns0:link href="https://gdata.youtube.com/feeds/api/videos/qbYYamU42Sw/complaints?client=ndv" rel="http://gdata.youtube.com/schemas/2007#video.complaints" type="application/atom+xml" /><ns0:link href="https://gdata.youtube.com/feeds/api/videos/qbYYamU42Sw/related?client=ndv" rel="http://gdata.youtube.com/schemas/2007#video.related" type="application/atom+xml" /><ns0:link href="https://gdata.youtube.com/feeds/api/users/nextdayvideo/uploads/qbYYamU42Sw?client=ndv" rel="self" type="application/atom+xml" /><ns0:link href="https://gdata.youtube.com/feeds/api/users/nextdayvideo/uploads/qbYYamU42Sw?client=ndv" rel="edit" type="application/atom+xml" /></ns0:entry>
2012-03-08/17:19:43.dv
Apply:
17:19:43 - 17:54:19 ( 00:34:36 )
S:
17:19:43 -
E:
17:54:19
D:
00:34:36
show more...
vlc ~/Videos/veyepar/psf/pycon_2012/dv/E2/2012-03-08/17:19:43.dv :start-time=00.0 --audio-desync=0
Raw File
Cut List
March 8, 2012, 5:19 p.m.
seconds: 0.0
Wall: 17:19:43
Duration
00:34:36
March 8, 2012, 5:54 p.m.
seconds: 0.0
Wall: 17:19:43
Comments:
mp4
mp4.m3u
dv.m3u
Split:
Sequence:
:
delete
2012-03-08/17:54:20.dv
Apply:
17:54:20 - 17:54:49 ( 00:00:29 )
S:
17:54:20 -
E:
17:54:49
D:
00:00:29
show more...
vlc ~/Videos/veyepar/psf/pycon_2012/dv/E2/2012-03-08/17:54:20.dv :start-time=00.0 --audio-desync=0
Raw File
Cut List
March 8, 2012, 5:54 p.m.
seconds: 0.0
Wall: 17:54:20
Duration
00:00:29
March 8, 2012, 5:54 p.m.
seconds: 0.0
Wall: 17:54:20
Comments:
mp4
mp4.m3u
dv.m3u
Split:
Sequence:
:
delete
Rf filename:
root is .../show/dv/location/, example: 2013-03-13/13:13:30.dv
Sequence:
get this:
check and save to add this
2012-03-08/17:19:43.dv
2012-03-08/17:54:20.dv
Veyepar
Video Eyeball Processor and Review