Flask JSONDash VS seaborn

Flask JSONDash

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seaborn

• Apache Superset
  14 projects | news.ycombinator.com | 26 Feb 2024

  If you are doing data analysis I don't think any of the 3 pieces of software you mentioned are going to be that helpful.

  I see these products as tools for data visualization and reporting i.e. presenting prepared datasets to users in a visually appealing way. They aren't as well suited for serious analytics.

  I can't comment on Superset or Tableau but I am familiar with Power BI (it has been rolled out across my org), the type of statistics you can do with it are fairly rudimentary. If you need to do any thing beyond summarizing (counts, averages, min, max etc). It is not particularly easy.

  For data analysis I use SAS or R. This software allows you do things like multivariate regression, timeseries forecasting, PCA, Cluster analysis etc. There is also plotting capability.

  Both these products are kind of old school, I've been using them since early 2000's, the "new school" seems to be Python. Pretty much all the recent data science people in my organization use Python. Particularly Pandas and libraries like Seaborn (https://seaborn.pydata.org/).

  The "power" users of Power BI in my organization tend to be finance/HR people for use cases like drill down into cost figures or Interactively presenting KPI's and other headline figures to management things like that.

• Seaborn bug responsible for finding of declining disruptiveness in science
  2 projects | news.ycombinator.com | 25 Feb 2024

  It's referring to the seaborn library (https://seaborn.pydata.org/), a Python library for data visualization (built on top of matplotlib).

  2 projects | news.ycombinator.com | 25 Feb 2024

  The seaborn bug linked in the paper: “Treat binwidth as approximate to avoid dropping outermost datapoints” (https://github.com/mwaskom/seaborn/pull/3489)

• Why Pandas feels clunky when coming from R
  2 projects | news.ycombinator.com | 23 Feb 2024

  While it’s not perfect and it’s not ggplot2, Seaborn is definitely a big improvement over bare matplotlib. You can still use matplotlib to modify the plots it spits out if you want to but the defaults are pretty good most of the time.

  https://seaborn.pydata.org/

• Releasing The Force Of Machine Learning: A Novice’s Guide 😃
  3 projects | dev.to | 22 Feb 2024

  Seaborn: A statistical data visualization library based on Matplotlib, enhancing the aesthetics and visual appeal of statistical graphics.

• Seven Python Projects to Elevate Your Coding Skills
  3 projects | dev.to | 15 Feb 2024

  Matplotlib Seaborn Example data sets

• Mastering Matplotlib: A Step-by-Step Tutorial for Beginners
  11 projects | dev.to | 25 Dec 2023

  Seaborn - Statistical data visualization using Matplotlib.

• Top 10 growing data visualization libraries in Python in 2023
  10 projects | dev.to | 11 Oct 2023

  Github: https://github.com/mwaskom/seaborn

• Best Portfolio Projects for Data Science
  3 projects | dev.to | 19 Sep 2023

  Seaborn Documentation

• Introducing seaborn-polars, a package allowing to use Polars DataFrames and LazyFrames with Seaborn
  3 projects | /r/Python | 15 May 2023

  I'm sure that your package is great, but seaborn will soon support the interchange protocol and will work relatively seamlessly with polars. https://github.com/mwaskom/seaborn/pull/3340