modin
PandasGUI
Our great sponsors
| modin | PandasGUI | |
|---|---|---|
| 11 | 8 | |
| 9,476 | 3,131 | |
| 1.3% | - | |
| 9.6 | 4.3 | |
| 4 days ago | 5 months ago | |
| Python | Python | |
| Apache License 2.0 | 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.
modin
- The Distributed Tensor Algebra Compiler (2022)
-
A Polars exploration into Kedro
The interesting thing about Polars is that it does not try to be a drop-in replacement to pandas, like Dask, cuDF, or Modin, and instead has its own expressive API. Despite being a young project, it quickly got popular thanks to its easy installation process and its “lightning fast” performance.
-
Modern Polars: an extensive side-by-side comparison of Polars and Pandas
Yeah, tried Polars a couple of times: the API seems worse than Pandas to me too. eg the decision only to support autoincrementing integer indexes seems like it would make debugging "hmmm, that answer is wrong, what exactly did I select?" bugs much more annoying. Polars docs write "blazingly fast" all over them but I doubt that is a compelling point for people using single-node dataframe libraries. It isn't for me.
Modin (https://github.com/modin-project/modin) seems more promising at this point, particularly since a migration path for standing Pandas code is highly desirable.
-
Polars: The Next Big Python Data Science Library... written in RUST?
If anyone wants a faster version of pandas it’s not hard to find, modin for example uses multiple cores to speed it up, so if you have 4 cores it’s about 4 times faster than pandas, and has the same API as pandas.
-
Working with more than 10gb csv
Modin should fit. It implements Pandas APIs with e.g. Ray as backend. https://github.com/modin-project/modin
- Modern Python Performance Considerations
-
I made a video about efficient memory use in pandas dataframes!
If you really want speed you should try modin.pandas which makes pandas multi-threaded.
-
Almost no one knows how easily you can optimize your AI models
I am guessing XGB is fairly optimised as it is. If you would want to use the sklearn libraries with pandas, look into Modin
-
TIL about modin.pandas which significantly speeds up pandas if you import modin.pandas instead of pandas.
Source
- How to Speed Up Pandas with 1 Line of Code
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?
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
dtale - Visualizer for pandas data structures
swifter - A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner
pandastable - Table analysis in Tkinter using pandas DataFrames.
fugue - A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.
seaborn - Statistical data visualization in Python
mars - Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
plotnine - A Grammar of Graphics for Python
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Altair - Declarative statistical visualization library for Python
pyjanitor - Clean APIs for data cleaning. Python implementation of R package Janitor
koalas - Koalas: pandas API on Apache Spark