This blog post will introduce Hasktorch, a Haskell binding for deep learning. We'll explore how Hasktorch leverages Foreign Function Interface (FFI) to integrate with Libtorch. The post will demonstra...

Thank you for pointing me to DataHaskell! The vision outlined on the website—building a complete data science ecosystem in Haskell with pillars like dataframes, Hasktorch, and distributed computing resonates with me. My interest is particularly strong because I see a natural alignment with **bioinformatics and computational biology** , a field built on complex data transformation pipelines. Last year, I completed a dedicated training program (BioNT Training) focused on bioinformatics, RNA-seq analysis, high-performance computing, and data management. This experience showed me both the power and the pain points of current workflows. I’m very keen to explore whether DataHaskell has, or is contemplating, any initiatives or sub-projects applied to **life sciences data**. The promise of Haskell’s strong typing and pure functional pipelines for ensuring reproducibility and correctness in biological data analysis is incredibly compelling. I’ll spend the coming days exploring the website https://www.datahaskell.org/ to understand the current development focus. If there’s a working group or discussion channel where applications in bioinformatics are being considered, I’d be grateful to be pointed in that direction. Regards Leonardo

### DataFrame update dump (0.3.1.1 → 0.4.0.5): faster, safer, and a lot more expressive I’ve been heads-down shipping a _pile_ of improvements to **DataFrame** over the last few releases, and I wanted to share a “why you should care” summary (with some highlights + a couple examples). #### Highlights * **Ecosystem** * You can now read SQL tables using the dataframe-persistent (written by @junjihashimoto) * Convert DataFrames into hasktorch tensors and back with dataframe-hasktorch. * Symbolic regression (based on srtree) to discover mathematical relationships between columns in dataframe-symbolic-regression (thanks to Fabricio Olivetti) * **Performance wins across the board** * Faster **folds/reductions** , **sum** , **mean** , improved filtering + index selection, more efficient groupby paths. * Better join performance (incl. **hashmap-based inner joins**) + fixes for right/full outer join edge cases. * **Decision trees** * We now have a decision tree implementation that also does enumerative bottom up search for split predicates to capture complex decision boundaries (performs well out the box on Kaggle classification tasks). This will likely be split into a separate library for symbolic machine learning proposed here. * **Much nicer data cleaning + missing data ergonomics** * `meanMaybe`, `medianMaybe`, `stddevMaybe`, plus **genericPercentile / percentile**. * “Maybe-aware” functions promoted into **DataFrame-level functions**. * Null-handling helpers like `filterAllNothing`, `filterNothing`, plus better “NA” handling. * New `recodeWithCondition` (change values based on a predicate) + `recodeWithDefault`. * APIs to create splits in data (kFolds, randomSplit, sample dataframes). * **Expressions / schema evolution got smoother** * More APIs now operate on **expressions instead of strings**. * A growing **monadic interface (state monad)** to make “derive / inspect / impute / filter” pipelines readable and resilient as schemas evolve. This helps you use DataFrames in scripts (prior they were mostly designed for notebooks). * **I/O + parsing hardening** * CSV: strip header/title whitespace, auto bool parsing, better type logic, fast CSV improvements. * Parquet: fixes and improvements. * JSON Lines: initial support. #### Example: expressive aggregation with conditionals (0.4.0.1) df |> D.groupBy [F.name ocean_proximity] |> D.aggregate [ "rand" .= F.sum (F.ifThenElse (ocean_proximity .== "ISLAND") 1 0) ] #### Example: schema-friendly transformation pipeline (0.4.0.0+) print $ execFrameM df $ do is_expensive <- deriveM "is_expensive" (median_house_value .>= 500000) meanBedrooms <- inspectM (D.meanMaybe total_bedrooms) totalBedrooms <- imputeM total_bedrooms meanBedrooms filterWhereM (totalBedrooms .>= 200 .&& is_expensive) If you’re doing ETL-y cleaning, feature engineering, quick stats, or want a Haskell-y dataframe that’s getting faster and more ergonomic every release: **this is a really good time to try the latest (0.4.0.4).** Hoping to get a GSOC proposal for either Parquet writers or Arrow support so if you’d like to co-mentor please reach out.

Google Summer of Code is a long-running program that supports Open Source projects. Haskell has taken part in this program almost since its inception! It allows newcomers to open source to contribute to projects for a stipend. However, in order to do that, we need to have some **ideas of what to contribute to**. In the past, this has led to many improvements for GHC, Cabal, HLS, Hasktorch… and it can include your project as well! This is a great way to find contributors for your project (even after the summer ends) – many past participants have become involved long-term. You can find more info and instructions on how to participate here: Summer of Haskell - ideas

Bangalore FP June 2026 meetup

Summary: Hands-on synthesis of features for Iris in Haskell using bottom-up enumeration and tooling like dataframe, hasktorch, IHaskell