seaborn
PandasGUI
Our great sponsors
| seaborn | PandasGUI | |
|---|---|---|
| 76 | 8 | |
| 11,910 | 3,125 | |
| - | - | |
| 8.5 | 4.3 | |
| 10 days ago | 4 months ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | MIT No Attribution |
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
-
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.
-
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
-
Introducing seaborn-polars, a package allowing to use Polars DataFrames and LazyFrames with Seaborn
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
PandasGUI
- PandasGUI: A GUI for Pandas DataFrames
-
GUI for a Dynamically Created Dataframe
This works with plotly but does a lot on its own if visualization isn’t the only thing you need, https://github.com/adamerose/PandasGUI
-
Error Message Software Installation
the errors aren't exactly the same, but possible solutions on these two suggest it might be an issue with the version of qt or pyqt that was installed: https://github.com/adamerose/pandasgui/issues/56
-
Low-code GUI tools for PySpark?
Similar to the several pandas low-code GUI tools such as [bamboolib](https://bamboolib.8080labs.com) or [PandasGUI](https://github.com/adamerose/PandasGUI), is there something available for PySpark?
- What's the best architecture for communication between a localhost React GUI and local Python app?
-
When you've created a finalised dataframe, do any of you convert it into an excel document to help you visualise your data, or am I being inefficient in doing this?
I usually use PandasGui to view or plot DataFrames. If I do export a CSV I'm bringing it into Tabeau or JMP. Excel isn't very good for plotting.
-
Plotting in R's ggplot2 vs Python's Matplotlib: Is it just me or is ggplot2 WAY smoother of an experience than Matplotlib?
I'll take this excuse to plug my open source project with a drag and drop UI for quickly making EDA graphs in Plotly https://github.com/adamerose/PandasGUI
What are some alternatives?
bokeh - Interactive Data Visualization in the browser, from Python
dtale - Visualizer for pandas data structures
Altair - Declarative statistical visualization library for Python
pandastable - Table analysis in Tkinter using pandas DataFrames.
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
modin - Modin: Scale your Pandas workflows by changing a single line of code
ggplot - ggplot port for python
plotnine - A Grammar of Graphics for Python
matplotlib - matplotlib: plotting with Python
koalas - Koalas: pandas API on Apache Spark