seaborn
Apache Superset
seaborn | Apache Superset | |
---|---|---|
77 | 3 | |
12,003 | 34,745 | |
- | - | |
8.4 | 9.9 | |
13 days ago | over 3 years ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
seaborn
- "No" is not an actionable error message
-
Apache Superset
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
It's referring to the seaborn library (https://seaborn.pydata.org/), a Python library for data visualization (built on top of matplotlib).
-
Why Pandas feels clunky when coming from R
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 😃
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
Matplotlib Seaborn Example data sets
-
Mastering Matplotlib: A Step-by-Step Tutorial for Beginners
Seaborn - Statistical data visualization using Matplotlib.
-
Top 10 growing data visualization libraries in Python in 2023
Github: https://github.com/mwaskom/seaborn
-
Best Portfolio Projects for Data Science
Seaborn Documentation
-
[OC] Nationwide Public Transit Ridership is down 30% from pre-lockdown levels; San Francisco's BART ridership is down almost 70%
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
Apache Superset
-
Using KeyCloak(OpenID Connect) with Apache SuperSet
The first difference is that after pull request 4565 was merged, you can no longer do:
-
Open Source Analytics Stack: Bringing Control, Flexibility, and Data-Privacy to Your Analytics
Open-source BI platforms such as Metabase (website, GitHub) and Apache SuperSet (website, GitHub) are easy to deploy without IT involvement. Metabase lets you build dashboards from the data in your warehouse easily, with no SQL, or, if you have data engineering or science know-how, inside more powerful and flexible notebooks or with SQL itself. Similarly, Apache SuperSet helps businesses explore and visualize data from simple line charts to detailed geospatial charts.
-
Ask HN: What low-code “dashboarding“ SaaS would you recommend in 2021?
Check out Superset. https://github.com/apache/incubator-superset
It’s modern, easy to extend. From the same author of apache airflow.
What are some alternatives?
bokeh - Interactive Data Visualization in the browser, from Python
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
Altair - Declarative statistical visualization library for Python
Grafana - The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.
Metabase - The simplest, fastest way to get business intelligence and analytics to everyone in your company :yum:
ggplot - ggplot port for python
Redash - Make Your Company Data Driven. Connect to any data source, easily visualize, dashboard and share your data.
plotnine - A Grammar of Graphics for Python
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
matplotlib - matplotlib: plotting with Python
cube.js - 📊 Cube — The Semantic Layer for Building Data Applications