Polars.NET

June 8, 2026 · View on GitHub

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F# & C# E2E examples repo:Polars.NET-Cookbook

High-Performance, DataFrame Engine for .NET, powered by Rust Polars & Apache Arrow. With cloud and deltalake features.

icon

Supported Platforms: Windows (x64), Linux (x64/ARM64, glibc/musl), macOS (ARM64). Cloud: AWS, Azure and GCP Data Lake: Delta Lake

99% API in python Polars now is available in both Polars.NET and Polars.FSharp.

Why Polars.NET exists

This is the game I'd like to play: binding the lightning-fast Polars engine to the .NET ecosystem. And it brings a lot of fun.

  • Polars.NET vs Python Ecosystem

    Polars.NET vs Python

  • Speedup vs Legacy .NET

    Speedup Summary

  • Delta Lake Read pic

  • Delta Lake Write pic

Installation

C# Users:

dotnet add package Polars.NET 
# And then add the native runtime for your current environment:
dotnet add package Polars.NET.Native.win-x64
# Add LINQ extension package once you need to write LINQ
dotnet add package Polars.NET.Linq
# Add ML.NET integration if needed
dotnet add package Polars.NET.ML

F# Users:

dotnet add package Polars.FSharp
# And then add the native runtime for your current environment:
dotnet add package Polars.NET.Native.win-x64
# Add LINQ extension package once you need to write LINQ or computation expressions
dotnet add package Polars.NET.Linq
# Add ML.NET integration if needed
dotnet add package Polars.NET.ML
  • Requirements: .NET 8+.
  • Hardware: CPU with AVX2 support (x86-64-v3). Roughly Intel Haswell (2013+) or AMD Excavator (2015+). If you have AVX-512 supported CPU, please try to compile Rust core on your machine use RUSTFLAGS='-C target-cpu=native'

Built Specially for .NET

Bringing .NET to Polars is not enough, it is the time to bring Polars to .NET.

  • ML.NET & Tensor Interop

Prepare data with polars, then train it with ML.NET & ONNX, finally analyze results back with polars.

Notice: Tensor Interop doesn't need any extra package but ML.NET integration requires Polars.NET.ML extension nuget package.

// ==========================================
// Data Loading
// ==========================================
var hfUrl = "https://huggingface.co/datasets/scikit-learn/iris/resolve/refs%2Fconvert%2Fparquet/default/train/0000.parquet";
var options = CloudOptions.Http(new Dictionary<string, string>
{
    { "User-Agent", "Polars.NET-Test" }
});
using var lf = LazyFrame.ScanParquet(hfUrl, cloudOptions: options);

// sepal length (cm), sepal width (cm), petal length (cm), petal width (cm)     
using var cleanlf = lf.Cast((typeof(double),typeof(float)));
using var cleanDf = cleanlf.WithColumns(Pl.ConcatArray(Cs.Float().ToExpr().Alias("Features"))).Collect();
// ==========================================
// Polars -> ML.NET
// ==========================================
var dataView = cleanDf.AsDataView();

var mlContext = new MLContext(seed: 42);

// ==========================================
// ML.NET Pipeline
// ==========================================
// Form VBuffer<float> tensor
var pipeline = mlContext.Clustering.Trainers.KMeans("Features", numberOfClusters: 3);
var model = pipeline.Fit(dataView);
// ==========================================
// ML.NET Transform and Read Back
// ==========================================
var predictions = model.Transform(dataView);

// ML.NET -> Polars
using var resultDf = predictions.ToDataFrame();

// ==========================================
// TensorInterop
// ==========================================
float[,] matrix = new float[,]
{
    { 1.1f, 1.2f, 1.3f },
    { 2.1f, 2.2f, 2.3f }
};

using var series = Series.From("ffi_matrix", matrix);

var (ptr, shape) = series.AsDangerousUnmanagedTensor<float>();

int totalElements = (int)(shape[0] * shape[1]); 

float* rawFloatPtr = (float*)ptr.ToPointer();

var nativeSpan = new ReadOnlySpan<float>(rawFloatPtr, totalElements);
  • ADO.NET

Polars.NET DataReader is generic typed without boxing/unboxing on hot path.

// To DataReader
using var bulkReader = df.AsDataReader(bufferSize: 100, typeOverrides: overrides);
// From DataReader
using var sourceReader = sourceTable.CreateDataReader();
var df = DataFrame.ReadDatabase(sourceReader);
  • C# LINQ & F# Computation Expression

With Polars.NET.Linq Extension package(Thanks to Linq2DB), playing DataFrame/Series with LINQ/Query block is available now.

using var dfDepts = DataFrame.From(depts);
using var dfEmps = DataFrame.From(emps);

using var db = new PolarsDataContext(new SqlContext(), ownsContext: true);
var deptQuery = dfDepts.AsQueryable<DeptDto>(db);
var empQuery = empQuery.AsQueryable<EmpDto>(db);

var query = deptQuery
    .LeftJoin(
        empQuery,
        d => d.DeptId,
        e => e.DeptId,
        (d, e) => new 
        {
            d.DeptId,
            d.DeptName,
            EmployeeName = e != null ? e.Name : "NO_EMPLOYEE" 
        })
    .OrderBy(x => x.DeptId)
    .ThenBy(x => x.EmployeeName)
    .Select(x => new JoinResult
    {
        DeptName = x.DeptName,
        EmployeeName = x.EmployeeName
    });

var results = query.ToList();

let queryResult = 
    query {
        for d in deptQuery do
        leftOuterJoin e in empQuery on (d.DeptId = e.DeptId) into empGroup
        for e in empGroup.DefaultIfEmpty() do
        sortBy d.DeptId
        thenBy e.Name
        
        select {|
            DeptName = d.DeptName

            EmployeeName = if box e = null then "NO_EMPLOYEE" else e.Name
        |}
    }
    |> Seq.toList

Quick Start

C# Example

using Polars.CSharp;
using Pl = Polars.CSharp.Polars;

// 1. Create a DataFrame
var data = new[] {
    new { Name = "Alice", Age = 25, Dept = "IT" },
    new { Name = "Bob", Age = 30, Dept = "HR" },
    new { Name = "Charlie", Age = 35, Dept = "IT" }
};
var df = DataFrame.From(data);

// 2. Filter & Aggregate
var res = df
    .Filter(Pl.Col("Age") > 28)
    .GroupBy("Dept")
    .Agg(
        Pl.Col("Age").Mean().Alias("AvgAge"),
        Pl.Col("Name").Count().Alias("Count")
    )
    .Sort("AvgAge", descending: true);

// 3. Output
res.Show();
// shape: (2, 3)
// ┌──────┬────────┬───────┐
// │ Dept ┆ AvgAge ┆ Count │
// │ ---  ┆ ---    ┆ ---   │
// │ str  ┆ f64    ┆ u32   │
// ╞══════╪════════╪═══════╡
// │ IT   ┆ 35.0   ┆ 1     │
// │ HR   ┆ 30.0   ┆ 1     │
// └──────┴────────┴───────┘

F# Example


open Polars.FSharp

// 1. Scan CSV (Lazy)
let lf = LazyFrame.ScanCsv "users.csv"

// 2. Transform Pipeline
let res = 
    lf
    |> pl.filterLazy (pl.col "age" .> pl.lit 28)
    |> pl.groupByLazy [ pl.col "dept" ]
    |> pl.aggLazy
        [ 
            pl.col("age").Mean().Alias "AvgAge" 
            pl.col("name").Count().Alias "Count"
        ]
    |> pl.sortAscendingLazy [pl.col "AvgAge"]
    |> pl.collect
    |> pl.show

Benchmark

Extension Nuget Package

  • Polars.NET.Linq
  • Polars.NET.ML

Architecture

3-Layer Architecture ensures API stability.

  1. Hand-written Rust C ABI layer bridging .NET and Polars. (native_shim)
  2. .NET Core layer for dirty works like unsafe ops, wrappers, LibraryImports. (Polars.NET.Core)
  3. High level C# and F# API layer here. No unsafe blocks. (Polars.CSharp & Polars.FSharp)

Roadmap

  • More code examples, user cases

  • Native LINQ Provider

Contributing

Contributions are welcome. Whether it's adding new expression mappings, improving documentation, or optimizing the FFI layer.

  1. Fork the repo.

  2. Create your feature branch.

  3. Submit a Pull Request.

License

MIT License. See LICENSE for details. For Third party licenses please check THIRD_PARTY_LICENSES.html