From 6e8e40dd093073e5dd3d67d204ab10579df4c742 Mon Sep 17 00:00:00 2001 From: Evgeny Ivanov Date: Sat, 14 Dec 2024 23:28:59 +0300 Subject: [PATCH] Update readme --- README.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/README.md b/README.md index 4275c44..4f9258c 100644 --- a/README.md +++ b/README.md @@ -18,7 +18,7 @@ - Power analysis. - Multiple hypothesis testing (family-wise error rate and false discovery rate). -**tea-tasting** calculates statistics directly within data backends such as BigQuery, ClickHouse, PostgreSQL, Snowflake, Spark, and 20+ other backends supported by [Ibis](https://ibis-project.org/). This approach eliminates the need to import granular data into a Python environment, though Pandas DataFrames are also supported. +**tea-tasting** calculates statistics directly within data backends such as BigQuery, ClickHouse, DuckDB, PostgreSQL, Snowflake, Spark, and many other backends supported by [Ibis](https://github.com/ibis-project/ibis) and [Narwhals](https://github.com/narwhals-dev/narwhals). This approach eliminates the need to import granular data into a Python environment. **tea-tasting** also accepts dataframes supported by [Narwhals](https://github.com/narwhals-dev/narwhals): cuDF, Dask, Modin, pandas, Polars, PyArrow. Check out the [blog post](https://e10v.me/tea-tasting-analysis-of-experiments/) explaining the advantages of using **tea-tasting** for the analysis of A/B tests. @@ -56,7 +56,6 @@ Learn more in the detailed [user guide](https://tea-tasting.e10v.me/user-guide/) ## Roadmap -- Support more dataframes with [Narwhals](https://github.com/narwhals-dev/narwhals). - A/A tests and simulations. - More statistical tests: - Asymptotic and exact tests for frequency data.