#python #data_science #exploratory_data_analysis #jupyter #pandas #visualization #visualization_tools

https://github.com/lux-org/lux

#python #cleandata #data_engineering #data_profilers #data_profiling #data_quality #data_science #data_unit_tests #datacleaner #datacleaning #dataquality #dataunittest #eda #exploratory_analysis #exploratory_data_analysis #exploratorydataanalysis #mlops #pipeline #pipeline_debt #pipeline_testing #pipeline_tests

https://github.com/great-expectations/great_expectations

#python #active_learning #annotations #classification #crowdsourcing #data_centric_ai #data_cleaning #data_labeling #data_quality #data_science #data_validation #entity_recognition #exploratory_data_analysis #image_tagging #label_errors #machine_learning #noisy_labels #out_of_distribution_detection #outlier_detection #robust_machine_learning #weak_supervision

https://github.com/cleanlab/cleanlab

#python #cleandata #data_engineering #data_profilers #data_profiling #data_quality #data_science #data_unit_tests #datacleaner #datacleaning #dataquality #dataunittest #eda #exploratory_analysis #exploratory_data_analysis #exploratorydataanalysis #mlops #pipeline #pipeline_debt #pipeline_testing #pipeline_tests

GX Core is a powerful tool for ensuring data quality. It allows you to write simple tests, called "Expectations," to check if your data meets certain standards. This helps teams work together more effectively and keeps everyone informed about the data's quality. You can automatically generate reports, making it easy to share results and preserve your organization's knowledge about its data. To get started, you just need to install GX Core in a Python virtual environment and follow some simple steps. This makes managing data quality much simpler and more efficient.

https://github.com/great-expectations/great_expectations