PyGWalker: Turn your dataframe into an interactive UI for visual analysis
- data-analysis
- pandas
- tableau
- tableau-alternative
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PyGWalker: Turn your dataframe into an interactive UI for visual analysis
1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
Rich MySQL Terminal Client with AutoCompletion, Syntax Highlighting, and Dataframes
Modin: Scale your Pandas workflows by changing a single line of code
Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀
the portable Python dataframe library
Automatically visualize your pandas dataframe via a single print! 📊 💡
Mimesis is a Python library for generating fake but realistic data in multiple languages and locales.
A light-weight, flexible, and expressive statistical data testing library
Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.
Koalas: pandas API on Apache Spark
Visualize and compare datasets, target values and associations, with one line of code.
An interactive grid for sorting, filtering, and editing DataFrames in Jupyter notebooks
A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner
Lightweight and extensible compatibility layer between dataframe libraries!
Python Streaming DataFrames for Kafka
Supply a wrapper ``StockDataFrame`` based on the ``pandas.DataFrame`` with inline stock statistics/indicators support.
Clean APIs for data cleaning. Python implementation of R package Janitor
Jupyter magics and kernels for working with remote Spark clusters
Cloud-native genomic dataframes and batch computing
Python DataFrames as Interactive DataTables
Pandas, Polars, Spark, and Snowpark DataFrame comparison for humans and more!
We well know GANs for success in the realistic image generation. However, they can be applied in tabular data generation. We will review and examine some recent papers about tabular GANs in action.
Engine for AI/ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.