pygwalker
seaborn
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pygwalker | seaborn | |
---|---|---|
22 | 76 | |
9,759 | 11,958 | |
8.4% | - | |
9.6 | 8.4 | |
11 days ago | 2 days ago | |
Python | Python | |
Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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.
pygwalker
- Show HN: Use an "eraser" to clean data on flight without breaking your workflow
- Show HN: Data Painter – different way to interact with data in Jupyter notebook
- PyGWalker: a Python library for data engineer that turns your dataframe into tableau-like data app.
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Top 10 growing data visualization libraries in Python in 2023
The most popular data visualization python library in 2023. It turns your dataframe into an interactive data exploration app like tableau/powerBI with one line of code. It provides simple drag-and-drop/chat interface for you to build charts. It can run in juypter notebook, which means you do not need to switch between your code and the visualization app. Besides, you can also build interactive spitial visualization on maps with it. And it also has Javascript and R version.
- Boost pygwalker's speed for visual analysis with duckDB
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Turn your data frame into a tableau-style interactive visualization interface in R
GWalkR is the R binding of Graphic-Walker, if you want to use it in python, check the python version: PyGWalker: https://github.com/Kanaries/pygwalker
- FLaNK Stack Weekly on 26 June 2023
- A blocky based CAD program
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Show HN: RATH – Open-Source Copilot and Autopilot for Data Analysis
+ Graphic Walker (https://github.com/Kanaries/graphic-walker): A lite embeddable component for visual analysis.
+ PyGWalker (https://github.com/Kanaries/pygwalker): turning your pandas dataframe into a Tableau-style User Interface for visual exploration.
RATH is a collection of interesting ideas that we think the next generation of data analysis software should be, so there might be many features that not well organized to be a united app. Tell me which feature you prefer and which is not. Looking forward for your ideas and advice.
- Converting a huge CSV file into a custom made table
seaborn
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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.
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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).
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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/
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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.
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Matplotlib Seaborn Example data sets
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Seaborn - Statistical data visualization using Matplotlib.
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Top 10 growing data visualization libraries in Python in 2023
Github: https://github.com/mwaskom/seaborn
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Seaborn Documentation
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[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
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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
What are some alternatives?
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
bokeh - Interactive Data Visualization in the browser, from Python
Rath - Next generation of automated data exploratory analysis and visualization platform.
Altair - Declarative statistical visualization library for Python
graphic-walker - An open source alternative to Tableau. Embeddable visual analytic
RasgoQL - Write python locally, execute SQL in your data warehouse
ggplot - ggplot port for python
devpod - Codespaces but open-source, client-only and unopinionated: Works with any IDE and lets you use any cloud, kubernetes or just localhost docker.
plotnine - A Grammar of Graphics for Python
ai - Build AI-powered applications with React, Svelte, Vue, and Solid
matplotlib - matplotlib: plotting with Python