veyepar: Pandas on Jupyter: Improving Your Data Analysis Workflow

Author(s):	Nick Macro
Location	Cartoon 1
Date	jul Sun 29	Days Raw Files
Start	16:15	First Raw Start	15:51
Duration	0:30:0	Offset	0:23:29
End	16:45	Last Raw End	16:51
Chapters	00:00 0:06:26 0:09:00 0:23:25
Total cuts_time	25 min.

https://pyohio.org/2018/schedule/presentation/48/
raw-playlist raw-mp4-playlist encoded-files-playlist host archive tweet mp4 svg png

assets release.pdf Pandas_on_Jupyter_Improving_Your_Data_Analysis_Workflow.json
logs
Admin: episode episode list cut list raw files day marks day marks day image_files

State:

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

Normalise:

Channelcopy:

m=mono, 01=copy left to right, 10=right to left, 00=ignore.

Thumbnail:

filename.png

Description:

markdown

The need to clean, manipulate, and visualize data is a skill that is increasingly in demand due to the abundance of data available. There are many methods of performing data analysis, each with its own benefits and drawbacks. Of the many features that can be used to evaluate a data analysis method, we will focus on ease of use and reproducibility.

It is crucially important for data analysis to be reproducible from starting data. A reproducible workflow allows others to review your work and can help catch assumptions, bugs, and steps that are not transparent in the final result. Reproducibility improves the transparency of your analysis and therefore makes your results more resistant to misinterpretation.

Python and, more specifically, Jupyter Notebooks enable workflows to be reproducible with very little overhead. Due to these characteristics, Jupyter Notebooks are the current medium for most data science work.  
One of largest problems with switching to a reproducible workflow is amount of time you need to sink into converting to the new workflow. I will demonstrate how a data analysis workflow can be modified using python libraries and Jupyter Notebooks.

Comment:

production notes

2018-07-29/15_51_31.ts Apply: 16:04:17 - 16:15:04 ( 00:10:47 ) S: 15:51:31 - E: 16:21:30 D: 00:29:59 (Start: 766.0) (End: 1413.0) show more...

vlc ~/Videos/veyepar/pyohio/pyohio_2018/dv/cartoon1/2018-07-29/15_51_31.ts :start-time=0766.0 --audio-desync=0

	Raw File	Cut List
	15:51:31		seconds: 766.0 Wall: 16:04:17
Duration	00:29:59
	16:21:30		seconds: 1413.0 Wall: 16:15:04
Comments:
mp4 mp4.m3u	dv.m3u	Split: Sequence:: delete

2018-07-29/16_21_31.ts Apply: 16:21:31 - 16:24:05 ( 00:02:34 ) S: 16:21:31 - E: 16:51:30 D: 00:29:59 (End: 2:34) show more... 2:34 PA audio drops out

vlc ~/Videos/veyepar/pyohio/pyohio_2018/dv/cartoon1/2018-07-29/16_21_31.ts :start-time=00.0 --audio-desync=0

	Raw File	Cut List
	16:21:31		seconds: 0.0 Wall: 16:21:31
Duration	00:29:59
	16:51:30		seconds: 154.0 Wall: 16:24:05
Comments:		2:34 PA audio drops out
mp4 mp4.m3u	dv.m3u	Split: Sequence:: delete

2018-07-29/16_21_31.ts Apply: 16:24:05 - 16:38:30 ( 00:14:25 ) S: 16:21:31 - E: 16:51:30 D: 00:29:59 (Start: 2:34) (End: 1019.0) show more... channelcopy=10

vlc ~/Videos/veyepar/pyohio/pyohio_2018/dv/cartoon1/2018-07-29/16_21_31.ts :start-time=0154.0 --audio-desync=0

	Raw File	Cut List
	16:21:31		seconds: 154.0 Wall: 16:24:05
Duration	00:29:59
	16:51:30		seconds: 1019.0 Wall: 16:38:30
Comments:		channelcopy=10
mp4 mp4.m3u	dv.m3u	Split: Sequence:: delete

2018-07-29/16_21_31.ts Apply: 16:38:30 - 16:40:19 ( 00:01:49 ) S: 16:21:31 - E: 16:51:30 D: 00:29:59 (Start: 1019.0) (End: 1128.0) show more... channelcopy=10

vlc ~/Videos/veyepar/pyohio/pyohio_2018/dv/cartoon1/2018-07-29/16_21_31.ts :start-time=01019.0 --audio-desync=0

	Raw File	Cut List
	16:21:31		seconds: 1019.0 Wall: 16:38:30
Duration	00:29:59
	16:51:30		seconds: 1128.0 Wall: 16:40:19
Comments:		channelcopy=10
mp4 mp4.m3u	dv.m3u	Split: Sequence:: delete