sample-flask-pandas-dataframe VS seaborn

Hello guys, I'm looking around for Django boilerplates and I've just discovered https://appseed.us/

Usually, I'm writing about automation and seed projects. Well, this time the post is about a dummy video published on yTube that helps me to keep going with my startUp during the hard times. Thanks in advance!

This article explains how to use AppSeed to generate a simple Flask Dashboard using a visual interface. Users can access the service without an account, generate a new project based on their selections and download the code from Github (MIT License).

The new version of AppSeed is LIVE. The platform has been redesigned to offer a better user experience and complete refactoring over site structure. For newcomers, AppSeed is a platform that uses automation tools to generate tested, production-ready starters.

Thanks for reading! Content provided by App Generator

Has anyone used an appseed template to build their own flask dashboard/site? It seems great, I can download an example template and start adding in my own data etc. But it almost seems too good. It's free and the "reviews" of it online seem like plants. I'm worried there may be something hidden in it somewhere. I'm thinking of the phrase, "if you're not paying then you're the product".

The project that might help beginners to understand basic Flask concepts: - Create a simple Flask app - Download a public pandas DF - Create an SQLite DB and a table to save the information - Load pandas in DB using a new custom command - Visualize the data in the browser

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.

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

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/

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

Matplotlib Seaborn Example data sets

Seaborn - Statistical data visualization using Matplotlib.

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

Seaborn Documentation

You've done a great job presenting this. Maybe you already know, but seaborne is an extension of matplotlib that makes it pretty easy to "beautify" matplotlib charts