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PUG – Python User’s Group

Python User’ Group (or PUG for short) is an open and informal collaborative space for experimentation and exploration with the Python programming language. It is an opportunity for those interested in Python to work together virtually and find support. Whether you are looking for advice or assistance with new or current projects, looking to discuss and learn new skills using Python tools, or to join us to play around with our collection of sample datasets, PUG is your place!

PUG is open to people of all skill levels, disciplines, and backgrounds. Complete beginners to Python will find a place here. Come, and let’s learn together.

Join PUG Slack here: https://join.slack.com/t/pug-world/shared_invite/zt-iube7uch-nVkvtIyIbpaqtQSZcMB2Ig

PUG is cosponsored by the MA in Digital Humanities / MS in Data Analytics and Visualization programs and the Mina Rees Library.

To learn more, visit http://cuny.is/pug

Admins:

Discussion of Structured Data Visualization (Zoom session is available)

  • Posted by Yuxiao Luo on November 6, 2020 at 1:51 pm

    In data visualization, we often come across these two data types: structured data and unstructured data. In this post, we will briefly talk about structured data visualization. Usually, structured data includes table, database, network, geo-spatial, time series, and 3D model.

    A. For network data, you can check out some creative and interesting demos in FlowingData. As an R user, I would recommend these widely used packages in R for network visualization:

    1. igraph for Network Analysis and Visualization.
    2. network for Classes for Relational Data.
    3. sna for Social Network Analysis.
    4. visNetwork for Interactive Network Visualization.
    5. networkD3 for D3 JavaScript Network Graphs from R.
    6. ndtv for Network Dynamic Temporal Visualizations.
    7. ggnet2 is the network visualization version

    B. For geospatial data visualization like maps, maybe you can check out this useful tool Mapbox Studio, which is simple and easy to get started with. Moreover, the RUG group is discussing the mapping applications in the R environment this semester and I would highly recommend you join if you are interested in R or you are already an R user.

    C. For time-series data visualization, check out these nice and inspiring examples:

    1. Coronavirus
    2. Humans of data
    3. Hottest year on record 2014
    4. Streamgraph Transitions
    5. Hottest year on record 2016

    After reading these beautiful cases, if there is anything coming to your mind or any experience about structured data visualization that you would like to discuss, post your comment below, and share it with us!

    You are also very welcome to join us in the Zoom Casual Meeting held every other week. (We have one today on Nov 6th)

    Yuxiao Luo is inviting you to a scheduled Zoom meeting.
    Join Zoom Meeting
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    Meeting ID: 335 553 2575
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