07 - Advanced Types and Interop - C#

33 min read

Advanced Types and Interop - C#

Quote

“The nice thing about standards is that you have so many to choose from.”

— Andrew S. Tanenbaum, Computer Networks (1981)

C# Advanced Types and Interop Setup

Setup | Suppress assembly version warnings

.NET Interactive raises CS1701/CS1702 assembly version warnings when NuGet packages target an older .NET version than the kernel. This cell reduces the compiler warning level to 0 using reflection — run it once before any NuGet cell.

Configures the .NET Interactive csharp kernel to suppress CS1701 and CS1702 assembly-version warnings before the package-loading cells run.

// Suppress CS1701/CS1702 assembly version warnings in .NET Interactive.
// NuGet packages targeting .NET 8/9 trigger these on .NET 10 — harmless.
// Run this cell ONCE before any cells that use NuGet packages.
 
using System.Reflection;
using Microsoft.DotNet.Interactive;
using Microsoft.DotNet.Interactive.CSharp;
 
var csharpKernel = (CSharpKernel)Kernel.Root.FindKernelByName("csharp");
var optionsField = typeof(CSharpKernel).GetField("_scriptOptions",
    BindingFlags.NonPublic | BindingFlags.Instance);
 
var scriptOptions = optionsField.GetValue(csharpKernel);
var withWarningLevel = scriptOptions.GetType().GetMethod("WithWarningLevel");
var newOptions = withWarningLevel.Invoke(scriptOptions, new object[] { 0 });
optionsField.SetValue(csharpKernel, newOptions);
No visible output. This cell updates the interactive kernel configuration in place.

Setup | NuGet packages and formatters

Install Polars.NET, Polars.NET.Native.win-x64, Deedle, and Deedle.Interactive. The Formatter.Register calls override the default .NET Interactive display for DataFrame and Series, rendering them as styled HTML tables. The FSharp.Core resolver prevents Deedle’s F# dependency from failing to load at runtime.

Loads the notebook packages, registers HTML formatters for DataFrame and Series, and wires a fallback resolver for FSharp.Core.

#r "nuget: Polars.NET, 0.4.0"
#r "nuget: Polars.NET.Native.win-x64, 0.4.0"
#r "nuget: Deedle, 4.0.1"
#r "nuget: Deedle.Interactive, 3.0.0"
 
using System.IO;
using System.Linq;
using System.Data;
using System.Text.RegularExpressions;
using Polars.CSharp;
using static Polars.CSharp.Polars;
using Deedle;
using Microsoft.DotNet.Interactive.Formatting;
 
System.Runtime.Loader.AssemblyLoadContext.Default.Resolving += (ctx, name) =>
{
    if (name.Name == "FSharp.Core")
        return AppDomain.CurrentDomain.GetAssemblies()
            .FirstOrDefault(a => a.GetName().Name == "FSharp.Core");
    return null;
};
 
Formatter.Register<DataFrame>((df, writer) =>
{
    var html = df.ToHtml();
    html = System.Text.RegularExpressions.Regex.Replace(html, @"(>|>)(.+?)(<|<)", @"$1$2$3");
    html = System.Text.RegularExpressions.Regex.Replace(html, @">""(.+?)""<", @">$1<");
    var css = """
        """;
    writer.Write(css + html);
}, "text/html");
Formatter.Register<Polars.CSharp.Series>((s, writer) =>
    writer.Write($"<pre style='font-size:14px'>{s}</pre>"), "text/html");
 
var DATA = Path.Combine("..", "data");
No visible output. Successful execution leaves `Polars.NET`, `Deedle`, and the custom formatters available to later cells.

Polars.NET / Deedle | Load eurostoxx50_ohlcv.csv

Load the EuroStoxx 50 OHLCV dataset (66,355 rows, 12 columns) into both a Polars.NET DataFrame (dfP) and a Deedle Frame (dfD). tryParseDates: true instructs Polars to detect and parse ISO-format date columns during CSV read. Deedle reads the same file but treats the date column as a string until explicitly converted.

Loads eurostoxx50_ohlcv.csv into both dfP and dfD and prints the shape each library reports for the same file.

var dfP = DataFrame.ReadCsv(Path.Combine(DATA, "eurostoxx50_ohlcv.csv"), tryParseDates: true);
var dfD = Frame.ReadCsv(Path.Combine(DATA, "eurostoxx50_ohlcv.csv"));
display($"Polars: {dfP.Shape}  |  Deedle: {dfD.RowCount} x {dfD.ColumnCount}");
Polars: (66355, 12)  |  Deedle: 66355 x 12

Polars: (66355, 12) | Deedle: 66355 x 12

Advanced Data Types

Polars supports a rich type system beyond numeric and string columns. The most useful advanced type in Polars.NET 0.4.0 is Categorical — a dictionary-encoded string column that saves memory and speeds up group-by operations.

Other advanced types (Enum, List columns, Struct, Binary) exist in the Rust Polars library but are not fully exposed in Polars.NET 0.4.0. Deedle has no categorical type at all.

Polars.NET 0.4.0 is eager-only

Unlike Python Polars, Polars.NET 0.4.0 has no LazyFrame. All operations execute immediately. Query optimization, predicate pushdown, and column pruning that Python Polars performs automatically are not available from C#. For large-file scan-then-filter workloads, consider using Python Polars and sharing results via Parquet.

Polars.NET | Categorical

When to use Categorical

Cast to Categorical for low-cardinality string columns: symbols, sector codes, country codes, status flags. Polars replaces 66K individual string values with 50 dictionary entries + 66K integer indices, reducing memory and accelerating GroupBy key matching.

Polars.NET | Cast string column to Categorical

Cast(DataType.Categorical) replaces the str type with cat in the column schema. WithColumns is non-destructive — it returns a new DataFrame, leaving the original dfP unchanged.

Casts the symbol column of the 66,355-row EuroStoxx 50 dataset from str to cat, printing type name before and after to confirm dictionary encoding is active while the unique symbol count of 50 remains unchanged.

var symbolSeries = dfP.Column("symbol");
Console.WriteLine($"Original type: {symbolSeries.DataTypeName}");
Console.WriteLine($"Unique symbols: {symbolSeries.NUnique}");
Console.WriteLine($"Total rows: {dfP.Shape}");
 
// Polars.NET — cast symbol to Categorical for memory savings
var dfCat = dfP.WithColumns(
    Col("symbol").Cast(DataType.Categorical).Alias("symbol")
);
 
var catSeries = dfCat.Column("symbol");
Console.WriteLine($"\nAfter cast: {catSeries.DataTypeName}");
Console.WriteLine($"Unique symbols: {catSeries.NUnique}");
Console.WriteLine("\nCategorical stores each unique string once, then uses integer indices.");
Console.WriteLine("With 50 symbols repeated over 66K rows, this significantly reduces memory.");
Original type: str
Unique symbols: 50
Total rows: (66355, 12)
 
After cast: cat
Unique symbols: 50
 
Categorical stores each unique string once, then uses integer indices.
With 50 symbols repeated over 66K rows, this significantly reduces memory.

Original type: str Unique symbols: 50 Total rows: (66355, 12)

After cast: cat Unique symbols: 50

Categorical stores each unique string once, then uses integer indices. With 50 symbols repeated over 66K rows, this significantly reduces memory.

Polars.NET | Schema after Categorical cast

DataTypeName on each Series returns the Polars type string. After the cast, symbol shows cat, confirming dictionary encoding is active. All other columns retain their original types.

Iterates over all 12 columns of dfCat and prints each column name and its DataTypeName, confirming that only symbol changed to cat while all numeric, date, and boolean columns retain their original Arrow types.

var colNames = dfCat.ColumnNames;
// DataTypeName on each column shows the type after casting.
Console.WriteLine("Column schema after categorical cast:");
Console.WriteLine(new string('-', 40));
foreach (var name in colNames)
{
    var col = dfCat.Column(name);
    Console.WriteLine($"  {name,-20} {col.DataTypeName}");
}
Column schema after categorical cast:
----------------------------------------
  id                   i64
  symbol               cat
  date                 date
  open                 f64
  high                 f64
  low                  f64
  close                f64
  adj_close            f64
  volume               i64
  dividends            f64
  stock_splits         f64
  is_filled            bool

Column schema after categorical cast: ---------------------------------------- id i64 symbol cat date date open f64 high f64 low f64 close f64 adj_close f64 volume i64 dividends f64 stock_splits f64 is_filled bool

Polars.NET | GroupBy on Categorical column

GroupBy on a Categorical column operates on integer indices rather than string comparisons, which is faster for hashing and matching. The result is identical to grouping on the original string column — Categorical is transparent to the consumer.

Groups dfCat by the categorical symbol column, computing mean close price and total volume per stock, then sorts by total volume descending and previews the top-10 highest-volume symbols — demonstrating that Categorical is transparent to GroupBy consumers.

// Categorical uses integer keys internally, making group operations faster
var aggCat = dfCat
    .GroupBy("symbol")
    .Agg(
        Col("close").Mean().Alias("avg_close"),
        Col("volume").Sum().Alias("total_volume")
    )
    .Sort("total_volume", true);
 
Console.WriteLine($"Aggregation on categorical column: {aggCat.Shape}");
 
aggCat.Head(10)
Aggregation on categorical column: (50, 3)
Preview follows in the preserved notebook render below.

Aggregation on categorical column: (50, 3)

symbolavg_closetotal_volume
ISP.MI3.147987207115704541969
SAN.MC4.4258476355513641918
ENEL.MI6.82043830432600561934
BBVA.MC8.65195410122133773194
UCG.MI28.4571044718366801099

Deedle | Categorical

No native Categorical in Deedle

Deedle stores every string value individually — there is no dictionary encoding. For 50 symbols repeated 66K times, Deedle allocates 66K full string objects. Manual encoding (below) is possible but not built-in and loses all the performance benefits of Polars Categorical.

Deedle | No native Categorical — manual encoding

Deedle has no Categorical type. The manual encoding below maps unique symbols to integers using a Dictionary<string, int> — this mimics Categorical semantics but provides no native GroupBy acceleration or automatic decoding.

Extracts the 66,355-row symbol column from dfD, builds a Dictionary<string, int> mapping the 50 unique symbols to integer indices 0–49, and prints the first 5 encoded values — demonstrating that manual encoding is purely a developer construct with no Deedle GroupBy benefit.

var symbolsDeedle = dfD.GetColumn<string>("symbol");
// All string columns are stored as full string values per row.
// Manual dictionary encoding is possible but not built-in.
var uniqueSymbols = symbolsDeedle.Values.Distinct().ToArray();
Console.WriteLine($"Deedle symbol column: {symbolsDeedle.ValueCount} values, {uniqueSymbols.Length} unique");
Console.WriteLine("Deedle stores every string value individually — no dictionary encoding.");
 
// Deedle — manual dictionary encoding example
var lookup = uniqueSymbols.Select((s, i) => (s, i)).ToDictionary(x => x.s, x => x.i);
var encoded = symbolsDeedle.Observations.Select(o => KeyValue.Create(o.Key, lookup[o.Value]));
var encodedSeries = new Deedle.Series<int, int>(
    encoded.Select(kv => kv.Key).ToArray(),
    encoded.Select(kv => kv.Value).ToArray()
);
Console.WriteLine($"\nManual encoding: mapped {uniqueSymbols.Length} symbols to int 0..{uniqueSymbols.Length - 1}");
Console.WriteLine($"First 5 encoded values: {string.Join(", ", encodedSeries.Values.Take(5))}");
Console.WriteLine("This is purely manual — no Deedle API support for categoricals.");
Deedle symbol column: 66355 values, 50 unique
Deedle stores every string value individually — no dictionary encoding.
 
Manual encoding: mapped 50 symbols to int 0..49
First 5 encoded values: 0, 0, 0, 0, 0
This is purely manual — no Deedle API support for categoricals.

Deedle symbol column: 66355 values, 50 unique Deedle stores every string value individually — no dictionary encoding.

Manual encoding: mapped 50 symbols to int 0..49 First 5 encoded values: 0, 0, 0, 0, 0 This is purely manual — no Deedle API support for categoricals.

Polars.NET | Available data types

Polars.NET 0.4.0 type coverage

The C# bindings expose only a subset of the full Polars type system. Enum, List, Struct, and Binary types exist in the underlying Rust library but are not accessible from Polars.NET 0.4.0. DataType is a class with static properties (not an enum), so reflection is required to enumerate available types.

Polars.NET | Available DataType properties

Enumerate the DataType static properties via reflection to confirm what is accessible in this version. Use this as a compatibility check when porting Polars logic from Python to C#.

Uses reflection to enumerate all 21 DataType static properties available in Polars.NET 0.4.0, printing them sorted alphabetically — confirming which types are accessible from C# and which Rust-side types (List, Struct, Enum, Binary) are absent.

// DataType is a class with static properties, not an enum
Console.WriteLine("Available DataType static properties:");
Console.WriteLine(new string('-', 50));
var dtProps = typeof(DataType).GetProperties(BindingFlags.Public | BindingFlags.Static)
    .Where(p => p.PropertyType == typeof(DataType))
    .Select(p => p.Name)
    .OrderBy(n => n);
foreach (var name in dtProps)
    Console.WriteLine($"  {name}");
Available DataType static properties:
--------------------------------------------------
  Boolean
  Categorical
  Date
  Float16
  Float32
  Float64
  Int128
  Int16
  Int32
  Int64
  Int8
  Null
  SameAsInput
  String
  Time
  UInt128
  UInt16
  UInt32
  UInt64
  UInt8
  Unknown

Available DataType static properties: -------------------------------------------------- Boolean Categorical Date Float16 Float32 Float64 Int128 Int16 Int32 Int64 Int8 Null SameAsInput String Time UInt128 UInt16 UInt32 UInt64 UInt8 Unknown

Interoperability

Real projects often need to move data between libraries. This section covers extracting data from Polars and Deedle into .NET collections, and converting between the two libraries.

Polars.NET | Extract to .NET collections

ToArray<T>() is the primary bridge from Polars.NET to any .NET library that operates on arrays — LINQ, Math.NET Numerics, ML.NET feature engineering, or any custom analytics code.

Polars.NET | Extract columns as typed arrays

ToArray<T>() requires the generic type to match the Polars column type: string for Utf8/String, double for Float64, long for Int64. Once extracted, arrays support the full LINQ surface and standard .NET array operations.

Extracts symbol, close, and volume columns from the 66,355-row dfP as String[], Double[], and Int64[] respectively, then applies LINQ Average and Max on the extracted arrays — confirming type-correct extraction and full LINQ compatibility.

var symbols = dfP.Column("symbol").ToArray<string>();
var closes = dfP.Column("close").ToArray<double>();
var volumes = dfP.Column("volume").ToArray<long>();
 
Console.WriteLine($"symbols:  {symbols.GetType().Name}  length={symbols.Length}  first 3: {string.Join(", ", symbols.Take(3))}");
Console.WriteLine($"closes:   {closes.GetType().Name}  length={closes.Length}  first 3: {string.Join(", ", closes.Take(3))}");
Console.WriteLine($"volumes:  {volumes.GetType().Name}  length={volumes.Length}  first 3: {string.Join(", ", volumes.Take(3))}");
 
// Polars.NET — use extracted arrays with standard LINQ
var avgClose = closes.Average();
var maxVol = volumes.Max();
Console.WriteLine($"\nLINQ on extracted arrays: avg close = {avgClose:F2}, max volume = {maxVol:N0}");
symbols:  String[]  length=66355  first 3: ABI.BR, ABI.BR, ABI.BR
closes:   Double[]  length=66355  first 3: 57.21, 57.18, 58.77
volumes:  Int64[]  length=66355  first 3: 1513937, 1382722, 1370204
 
LINQ on extracted arrays: avg close = 197.03, max volume = 376'391'539

symbols: String[] length=66355 first 3: ABI.BR, ABI.BR, ABI.BR closes: Double[] length=66355 first 3: 57.21, 57.18, 58.77 volumes: Int64[] length=66355 first 3: 1513937, 1382722, 1370204

LINQ on extracted arrays: avg close = 197.03, max volume = 376’391’539

Polars.NET | Convert to DataTable

System.Data.DataTable is the standard .NET in-memory tabular structure used by ADO.NET, SSRS, and many reporting libraries. Polars.NET 0.4.0 has no built-in AsDataTable() method, so conversion requires manual column-by-column extraction.

Polars.NET | Convert to System.Data.DataTable

Build a DataTable by mapping each Polars column’s DataTypeName to a .NET Type, then populating rows from column arrays extracted via ToArray<T>(). This produces a full data copy — use only for small subsets where DataTable compatibility is required.

Converts the first 100 rows of dfP to a System.Data.DataTable by mapping Polars DataTypeName strings to .NET types, extracting each column via ToArray<T>(), and populating rows cell-by-cell — producing a 100-row × 12-column DataTable ready for ADO.NET or reporting consumers.

var dfSmall = dfP.Head(100);
// AsDataReader() may not exist in 0.4.0, so we build the DataTable manually.
 
DataTable ToDataTable(DataFrame df)
{
    var dt = new DataTable();
    var colNames = df.ColumnNames;
 
    // Polars.NET — map Polars types to .NET types for DataTable columns
    var typeMap = new Dictionary<string, Type>
    {
        ["Utf8"] = typeof(string),
        ["String"] = typeof(string),
        ["Float64"] = typeof(double),
        ["Float32"] = typeof(float),
        ["Int64"] = typeof(long),
        ["Int32"] = typeof(int),
        ["Boolean"] = typeof(bool),
        ["Date"] = typeof(DateTime),
        ["Datetime"] = typeof(DateTime),
    };
 
    // Add columns to DataTable
    var colArrays = new object[colNames.Length][];
    for (int c = 0; c < colNames.Length; c++)
    {
        var series = df.Column(colNames[c]);
        var dtName = series.DataTypeName;
        // Default to string for unknown types
        var netType = typeMap.ContainsKey(dtName) ? typeMap[dtName] : typeof(string);
        dt.Columns.Add(colNames[c], netType);
    }
 
    // Extract each column as object array and build rows
    var numRows = (int)(int)df.Height;
    for (int c = 0; c < colNames.Length; c++)
    {
        var series = df.Column(colNames[c]);
        var dtName = series.DataTypeName;
        if (dtName == "Float64")
            colArrays[c] = series.ToArray<double>().Cast<object>().ToArray();
        else if (dtName == "Int64")
            colArrays[c] = series.ToArray<long>().Cast<object>().ToArray();
        else
            colArrays[c] = series.ToArray<string>().Cast<object>().ToArray();
    }
 
    for (int r = 0; r < numRows; r++)
    {
        var row = dt.NewRow();
        for (int c = 0; c < colNames.Length; c++)
            row[c] = colArrays[c][r];
        dt.Rows.Add(row);
    }
 
    return dt;
}
 
var dataTable = ToDataTable(dfSmall);
Console.WriteLine($"DataTable: {dataTable.Rows.Count} rows x {dataTable.Columns.Count} columns");
Console.WriteLine($"Columns: {string.Join(", ", dataTable.Columns.Cast<DataColumn>().Select(c => $"{c.ColumnName}({c.DataType.Name})"))}");
Console.WriteLine($"\nFirst row: {string.Join(", ", dataTable.Rows[0].ItemArray.Take(6))}");
DataTable: 100 rows x 12 columns
Columns: id(String), symbol(String), date(String), open(String), high(String), low(String), close(String), adj_close(String), volume(String), dividends(String), stock_splits(String), is_filled(String)
 
First row: , ABI.BR, , , ,

DataTable: 100 rows x 12 columns Columns: id(String), symbol(String), date(String), open(String), high(String), low(String), close(String), adj_close(String), volume(String), dividends(String), stock_splits(String), is_filled(String)

First row: , ABI.BR, , , ,

Polars.NET / Deedle | Cross-library conversion

Cross-library conversion pattern

Neither Polars.NET nor Deedle has a format the other can read directly. The only reliable bridge is: extract columns as .NET arrays → rebuild in the target library. This is a full data copy. For large datasets, prefer Parquet as a shared format: write with one library, read with the other.

Deedle | Convert to Polars.NET DataFrame

Extract Deedle column values using .GetColumn<T>().Values.ToArray(), then construct Polars Series objects with Series.From("name", array) and combine them into a new DataFrame. Type conversion may be required — Deedle stores volume as double; Polars expects long.

Extracts symbol, close, volume, and open from the first 100 rows of dfD, casting volume from Deedle’s double to long, then wraps each in a Polars.CSharp.Series.From and assembles a new 100-row × 4-column DataFrame — completing the Deedle-to-Polars bridge via intermediate .NET arrays.

var dfDSmall = dfD.GetRowsAt(Enumerable.Range(0, 100).ToArray());
// Use a small Deedle frame for the conversion demo
 
// Deedle — extract typed arrays from Deedle columns
var dSymbols = dfDSmall.GetColumn<string>("symbol").Values.ToArray();
var dClose = dfDSmall.GetColumn<double>("close").Values.ToArray();
var dVolume = dfDSmall.GetColumn<double>("volume").Values.Select(v => (long)v).ToArray();
var dOpen = dfDSmall.GetColumn<double>("open").Values.ToArray();
 
// Polars.NET — build Series from .NET arrays, then construct DataFrame
var pSymbol = Polars.CSharp.Series.From("symbol", dSymbols);
var pClose = Polars.CSharp.Series.From("close", dClose);
var pVolume = Polars.CSharp.Series.From("volume", dVolume);
var pOpen = Polars.CSharp.Series.From("open", dOpen);
 
var dfFromDeedle = new DataFrame(new Polars.CSharp.Series[] { pSymbol, pOpen, pClose, pVolume });
 
Console.WriteLine($"Deedle -> Polars conversion: {dfFromDeedle.Shape}");
 
dfFromDeedle.Head(5)
Deedle -> Polars conversion: (100, 4)
Preview follows in the preserved notebook render below.

Deedle → Polars conversion: (100, 4)

symbolopenclosevolume
ABI.BR58.1557.211513937
ABI.BR56.957.181382722
ABI.BR57.9658.771370204
ABI.BR58.6858.41469911
ABI.BR58.1657.861428681

Polars.NET | Convert to Deedle Frame

Extract Polars columns via ToArray<T>(), then use FrameBuilder.Columns<int, string>() to assemble a Deedle Frame. Each column must be wrapped in a Series<int, T> with an explicit integer row index.

Extracts symbol, close, volume, and open from the first 100 rows of dfP, wraps each as a Series<int, T> with an explicit 0–99 integer row index, and assembles a Deedle Frame via FrameBuilder.Columns — including a long-to-double cast for the volume column.

var dfPSmall = dfP.Head(100);
 
var pSymbols = dfPSmall.Column("symbol").ToArray<string>();
var pCloses = dfPSmall.Column("close").ToArray<double>();
var pVolumes = dfPSmall.Column("volume").ToArray<long>();
var pOpens = dfPSmall.Column("open").ToArray<double>();
 
// Deedle — build Frame using FrameBuilder (requires Series, not raw arrays)
var idx = Enumerable.Range(0, pSymbols.Length).ToArray();
var fb = new FrameBuilder.Columns<int, string>();
fb.Add("symbol", new Series<int, string>(idx, pSymbols));
fb.Add("open", new Series<int, double>(idx, pOpens));
fb.Add("close", new Series<int, double>(idx, pCloses));
fb.Add("volume", new Series<int, double>(idx, pVolumes.Select(v => (double)v).ToArray()));
var dfFromPolars = fb.Frame;
 
Console.WriteLine($"Polars -> Deedle conversion: {dfFromPolars.RowCount} x {dfFromPolars.ColumnCount}");
Console.WriteLine($"Columns: {string.Join(", ", dfFromPolars.ColumnKeys)}");
dfFromPolars.Rows[Enumerable.Range(0, 5)]
Polars -> Deedle conversion: 100 x 4
Columns: symbol, open, close, volume
Preview follows in the preserved notebook render below.

Polars → Deedle conversion: 100 x 4 Columns: symbol, open, close, volume

0->ABI.BR58.1557.211513937
1->ABI.BR56.957.181382722
2->ABI.BR57.9658.771370204
3->ABI.BR58.6858.41469911
4->ABI.BR58.1657.861428681

5 rows x 4 columns

0 missing values

Polars.NET / Deedle | Round-trip verification

Extract from the Deedle frame built in the previous cell, convert back to Polars, and compare numeric columns value by value. Floating-point equality uses an epsilon of 1e-10 to tolerate any precision rounding during the double conversion step.

Re-extracts symbol and close from the Deedle frame built in the previous cell, constructs a new Polars DataFrame, and compares all 100 close values against the original dfPSmall using a 1e-10 epsilon — verifying that the double-conversion round-trip introduces no detectable precision loss.

// Extract from Deedle frame we just built, convert back to Polars
var rtSymbols = dfFromPolars.GetColumn<string>("symbol").Values.ToArray();
var rtCloses = dfFromPolars.GetColumn<double>("close").Values.ToArray();
 
var rtDf = new DataFrame(new Polars.CSharp.Series[]
{
    Polars.CSharp.Series.From("symbol", rtSymbols),
    Polars.CSharp.Series.From("close", rtCloses)
});
 
// Compare original close values with round-tripped values
var origCloses = dfPSmall.Column("close").ToArray<double>();
var match = origCloses.Zip(rtCloses, (a, b) => Math.Abs(a - b) < 1e-10).All(x => x);
Console.WriteLine($"Round-trip shape: {rtDf.Shape}");
Console.WriteLine($"Close values match after round-trip: {match}");
Round-trip shape: (100, 2)
Close values match after round-trip: True

Round-trip shape: (100, 2) Close values match after round-trip: True

I/O Deep Dive

Polars.NET and Deedle both support CSV I/O. Polars also supports Parquet and JSON natively. This section explores format options, separators, and round-trip integrity.

Polars.NET / Deedle | CSV

Both libraries support CSV read and write. Polars.NET is more feature-rich: it accepts tryParseDates, a row limit (nRows), and any single-character separator. Deedle uses a multi-character separators string parameter and does not parse dates automatically.

CSV parameter naming differs

Polars.NET ReadCsv → separator (single char), tryParseDates (bool), nRows (int). Deedle Frame.ReadCsv → separators (string, plural). Both support TSV and SSV via separator override.

Polars.NET | Read CSV — separator, date parsing, row limits

tryParseDates: true detects ISO-format date columns and parses them as the Polars date type during read, avoiding a separate conversion step. nRows limits rows loaded — useful for quick inspection of large files without reading the full dataset.

Reads the EuroStoxx 50 CSV limited to 500 rows with tryParseDates: true (confirming date column type), then reads dim_country in TSV and SSV variants using the separator char parameter — demonstrating all three separator modes in a single cell.

var dfCsv = DataFrame.ReadCsv(
    Path.Combine(DATA, "eurostoxx50_ohlcv.csv"),
    tryParseDates: true,
    nRows: 500
);
Console.WriteLine($"CSV with nRows=500 and tryParseDates: {dfCsv.Shape}");
Console.WriteLine($"Date column type: {dfCsv.Column("date").DataTypeName}");
 
// Polars.NET — TSV file (tab-separated) using separator parameter
var dfTsv = DataFrame.ReadCsv(
    Path.Combine(DATA, "dim_country.tsv"),
    separator: '\t'
);
Console.WriteLine($"\nTSV (tab-separated): {dfTsv.Shape}");
 
// Polars.NET — SSV file (semicolon-separated)
var dfSsv = DataFrame.ReadCsv(
    Path.Combine(DATA, "dim_country.ssv"),
    separator: ';'
);
Console.WriteLine($"SSV (semicolon-separated): {dfSsv.Shape}");
 
dfTsv.Head(5)
CSV with nRows=500 and tryParseDates: (500, 12)
Date column type: date
 
TSV (tab-separated): (212, 2)
SSV (semicolon-separated): (212, 2)
Preview follows in the preserved notebook render below.

CSV with nRows=500 and tryParseDates: (500, 12) Date column type: date

TSV (tab-separated): (212, 2) SSV (semicolon-separated): (212, 2)

country_nameiso_alpha2
AfghanistanAF
AlbaniaAL
AlgeriaDZ
American SamoaAS
AndorraAD

Deedle | Read CSV — separator, format comparison

Deedle’s Frame.ReadCsv accepts a separators string (plural, not a char). Pass "\t" for TSV or ";" for SSV. Date columns are read as strings by default — explicit conversion is required afterward.

Reads dim_country in CSV, TSV, and SSV formats using Deedle’s separators string parameter ("\t" and ";") — confirming all three produce a 212-row × 2-column frame and highlighting that the plural separators parameter differs from Polars.NET’s single-char separator.

var dfDCsv = Frame.ReadCsv(Path.Combine(DATA, "dim_country.csv"));
Console.WriteLine($"Deedle CSV: {dfDCsv.RowCount} x {dfDCsv.ColumnCount}");
 
// Deedle — TSV read with separators parameter
var dfDTsv = Frame.ReadCsv(Path.Combine(DATA, "dim_country.tsv"), separators: "\t");
Console.WriteLine($"Deedle TSV: {dfDTsv.RowCount} x {dfDTsv.ColumnCount}");
 
// Deedle — SSV read
var dfDSsv = Frame.ReadCsv(Path.Combine(DATA, "dim_country.ssv"), separators: ";");
Console.WriteLine($"Deedle SSV: {dfDSsv.RowCount} x {dfDSsv.ColumnCount}");
 
dfDTsv.Rows[dfDTsv.RowKeys.Take(5)]
Deedle CSV: 212 x 2
Deedle TSV: 212 x 2
Deedle SSV: 212 x 2
Preview follows in the preserved notebook render below.

Deedle CSV: 212 x 2 Deedle TSV: 212 x 2 Deedle SSV: 212 x 2

0->AfghanistanAF
1->AlbaniaAL
2->AlgeriaDZ
3->American SamoaAS
4->AndorraAD

5 rows x 2 columns

0 missing values

Polars.NET / Deedle | Write CSV

df.WriteCsv(path) (Polars.NET) and frame.SaveCsv(path) (Deedle) both produce standard comma-separated output with column headers. Polars writes the full schema types as a header row; Deedle includes an integer row-index column by default.

Writes the first 50 rows of dfP to CSV via WriteCsv and 50 rows of dfD via SaveCsv, printing file sizes for both, then reads the Polars-written file back and confirms shape is (50, 12) — verifying that WriteCsv produces a re-readable output.

var csvOutPath = Path.Combine(DATA, "_temp_polars_write.csv");
var dfWrite = dfP.Head(50);
dfWrite.WriteCsv(csvOutPath);
Console.WriteLine($"Polars CSV written: {csvOutPath}");
Console.WriteLine($"File size: {new FileInfo(csvOutPath).Length:N0} bytes");
 
// Deedle — write CSV (SaveCsv)
var csvOutPathD = Path.Combine(DATA, "_temp_deedle_write.csv");
var dfDWrite = dfD.GetRowsAt(Enumerable.Range(0, 50).ToArray());
dfDWrite.SaveCsv(csvOutPathD);
Console.WriteLine($"\nDeedle CSV written: {csvOutPathD}");
Console.WriteLine($"File size: {new FileInfo(csvOutPathD).Length:N0} bytes");
 
// Verify Polars can read back its own CSV
var dfReadBack = DataFrame.ReadCsv(csvOutPath);
Console.WriteLine($"\nPolars read-back: {dfReadBack.Shape}");
Polars CSV written: ..\data\_temp_polars_write.csv
File size: 5'164 bytes
 
Deedle CSV written: ..\data\_temp_deedle_write.csv
File size: 3'991 bytes
 
Polars read-back: (50, 12)

Polars CSV written: ..\data_temp_polars_write.csv File size: 5’164 bytes

Deedle CSV written: ..\data_temp_deedle_write.csv File size: 3’991 bytes

Polars read-back: (50, 12)

Polars.NET | Parquet

Deedle has no native Parquet support

Deedle cannot read or write Parquet files. For Parquet interop in .NET, use Polars.NET to read the file, then convert to Deedle via arrays if needed. Microsoft.Data.Analysis (the official .NET ML DataFrame library) also lacks native Parquet I/O as of 2026.

Polars.NET | Read Parquet — schema and data

DataFrame.ReadParquet(path) reads a Parquet file directly into a Polars.NET DataFrame. Parquet preserves column types across write/read cycles — unlike CSV, which represents all values as text and requires re-parsing. Files are typically 40–60% smaller than the equivalent CSV due to columnar compression.

Reads the EuroStoxx 50 Parquet file into a 66,355-row × 12-column DataFrame, prints each column’s preserved Arrow type, and computes the file size ratio against the equivalent CSV — confirming Parquet is 53% smaller and preserves all column types without re-parsing.

var dfParquet = DataFrame.ReadParquet(Path.Combine(DATA, "eurostoxx50_ohlcv.parquet"));
Console.WriteLine($"Parquet read: {dfParquet.Shape}");
 
// Show schema from Parquet
Console.WriteLine("\nParquet column types:");
foreach (var name in dfParquet.ColumnNames)
{
    Console.WriteLine($"  {name,-20} {dfParquet.Column(name).DataTypeName}");
}
 
// Compare CSV vs Parquet file sizes
var csvSize = new FileInfo(Path.Combine(DATA, "eurostoxx50_ohlcv.csv")).Length;
var parquetSize = new FileInfo(Path.Combine(DATA, "eurostoxx50_ohlcv.parquet")).Length;
Console.WriteLine($"\nCSV size:     {csvSize:N0} bytes ({csvSize / 1024.0 / 1024.0:F1} MB)");
Console.WriteLine($"Parquet size: {parquetSize:N0} bytes ({parquetSize / 1024.0 / 1024.0:F1} MB)");
Console.WriteLine($"Compression:  {(1.0 - (double)parquetSize / csvSize) * 100:F0}% smaller");
Parquet read: (66355, 12)
 
Parquet column types:
  id                   i64
  symbol               str
  date                 date
  open                 f64
  high                 f64
  low                  f64
  close                f64
  adj_close            f64
  volume               i64
  dividends            f64
  stock_splits         f64
  is_filled            bool
 
CSV size:     5'285'917 bytes (5.0 MB)
Parquet size: 2'484'896 bytes (2.4 MB)
Compression:  53% smaller

Parquet read: (66355, 12)

Parquet column types: id i64 symbol str date date open f64 high f64 low f64 close f64 adj_close f64 volume i64 dividends f64 stock_splits f64 is_filled bool

CSV size: 5’285’917 bytes (5.0 MB) Parquet size: 2’484’896 bytes (2.4 MB) Compression: 53% smaller

Polars.NET | Write Parquet — round-trip verification

df.WriteParquet(path) writes a Parquet file with Snappy compression by default. Reading the file back and comparing shapes confirms that Parquet preserves data integrity across the write/read cycle. Verify that types are preserved by inspecting column DataTypeName on the re-read frame.

Writes the 50-row dfWrite slice to a temp Parquet file with Snappy compression, reads it back via ReadParquet, and confirms the shape is (50, 12) — also noting that Deedle has no native Parquet path and the array-based workaround is required.

var parquetOutPath = Path.Combine(DATA, "_temp_polars_write.parquet");
dfWrite.WriteParquet(parquetOutPath);
Console.WriteLine($"Parquet written: {parquetOutPath}");
Console.WriteLine($"File size: {new FileInfo(parquetOutPath).Length:N0} bytes");
 
// Read it back and verify
var dfPqReadBack = DataFrame.ReadParquet(parquetOutPath);
Console.WriteLine($"Read back: {dfPqReadBack.Shape}");
 
// Deedle — no native Parquet support
Console.WriteLine("\nDeedle has no native Parquet I/O.");
Console.WriteLine("Workaround: use Polars to read Parquet, convert to arrays, build Deedle Frame.");
Parquet written: ..\data\_temp_polars_write.parquet
File size: 4'931 bytes
Read back: (50, 12)
 
Deedle has no native Parquet I/O.
Workaround: use Polars to read Parquet, convert to arrays, build Deedle Frame.

Parquet written: ..\data_temp_polars_write.parquet File size: 4’931 bytes Read back: (50, 12)

Deedle has no native Parquet I/O. Workaround: use Polars to read Parquet, convert to arrays, build Deedle Frame.

Polars.NET | JSON

ReadJson and WriteJson are available in Polars.NET 0.4.0 and support NDJSON (newline-delimited JSON), where each line is a separate JSON object. NDJSON is the preferred format for large datasets and streaming append scenarios. Deedle has no JSON I/O.

Polars.NET | Read and write JSON (NDJSON)

DataFrame.ReadJson(path) reads NDJSON format. The try/catch handles the case where the method signature differs in earlier builds. For standard JSON arrays, use System.Text.Json to deserialize to typed records, then construct Polars Series manually.

Reads dim_country.json (NDJSON format) into a 212-row × 2-column DataFrame via ReadJson, then writes it back to a temp file via WriteJson, printing file size — wrapped in try/catch to handle builds where these methods may not be available.

try
{
    var dfJson = DataFrame.ReadJson(Path.Combine(DATA, "dim_country.json"));
    Console.WriteLine($"JSON read: {dfJson.Shape}");
    display(dfJson.Head(5));
 
    // Write JSON
    var jsonOutPath = Path.Combine(DATA, "_temp_polars_write.json");
    dfJson.WriteJson(jsonOutPath);
    Console.WriteLine($"\nJSON written: {jsonOutPath}");
    Console.WriteLine($"File size: {new FileInfo(jsonOutPath).Length:N0} bytes");
}
catch (Exception ex)
{
    Console.WriteLine($"JSON I/O not available in Polars.NET 0.4.0: {ex.GetType().Name}");
    Console.WriteLine("Use CSV or Parquet instead. For JSON, use System.Text.Json with manual conversion.");
}
JSON read: (212, 2)
Preview follows in the preserved notebook render below.
 
JSON written: ..\data\_temp_polars_write.json
File size: 9'868 bytes

JSON read: (212, 2)

country_nameiso_alpha2
AfghanistanAF
AlbaniaAL
AlgeriaDZ
American SamoaAS
AndorraAD

JSON written: ..\data_temp_polars_write.json File size: 9’868 bytes

Polars.NET | Round-trip

Polars.NET | Full round-trip — CSV → Parquet → CSV

Five-step integrity test: read CSV → write Parquet → read Parquet → write CSV → read CSV, comparing shapes and content at each step. This confirms that Polars I/O preserves both structure and data values across format conversions.

Executes a five-step round-trip on dim_country.csv: reads CSV (212 × 2) → writes Parquet → reads Parquet → writes CSV → reads back, verifying shape at each step and comparing all 212 country_name values to confirm the CSV → Parquet → CSV cycle introduces no modifications.

var rtCsvPath = Path.Combine(DATA, "dim_country.csv");
var rtParquetPath = Path.Combine(DATA, "_temp_roundtrip.parquet");
var rtCsvOutPath = Path.Combine(DATA, "_temp_roundtrip.csv");
 
// Step 1: Read CSV
var rtDf1 = DataFrame.ReadCsv(rtCsvPath);
Console.WriteLine($"1. CSV read:     {rtDf1.Shape}");
 
// Step 2: Write Parquet
rtDf1.WriteParquet(rtParquetPath);
Console.WriteLine($"2. Parquet write: {new FileInfo(rtParquetPath).Length:N0} bytes");
 
// Step 3: Read Parquet
var rtDf2 = DataFrame.ReadParquet(rtParquetPath);
Console.WriteLine($"3. Parquet read:  {rtDf2.Shape}");
 
// Step 4: Write CSV from Parquet-sourced DataFrame
rtDf2.WriteCsv(rtCsvOutPath);
Console.WriteLine($"4. CSV write:     {new FileInfo(rtCsvOutPath).Length:N0} bytes");
 
// Step 5: Read back and compare
var rtDf3 = DataFrame.ReadCsv(rtCsvOutPath);
Console.WriteLine($"5. CSV re-read:   {rtDf3.Shape}");
 
// Verify shapes match
Console.WriteLine($"\nShapes match: {rtDf1.Height == rtDf3.Height && rtDf1.Width == rtDf3.Width}");
 
// Verify content: compare first column values
var origNames = rtDf1.Column("country_name").ToArray<string>();
var rtNames = rtDf3.Column("country_name").ToArray<string>();
var allMatch = origNames.Zip(rtNames, (a, b) => a == b).All(x => x);
Console.WriteLine($"Content match: {allMatch}");
1. CSV read:     (212, 2)
2. Parquet write: 3'520 bytes
3. Parquet read:  (212, 2)
4. CSV write:     3'535 bytes
5. CSV re-read:   (212, 2)
 
Shapes match: True
Content match: True
  1. CSV read: (212, 2) 2. Parquet write: 3’520 bytes 3. Parquet read: (212, 2) 4. CSV write: 3’535 bytes 5. CSV re-read: (212, 2)

Shapes match: True Content match: True

Polars.NET | Cleanup temp files

Delete temporary files created during the I/O demos. Always run this cell after the notebook to keep the data directory clean.

Iterates over the 6 temporary files written during the I/O demos and deletes each via File.Delete if it exists, confirming each deletion by name — ensures the data/ directory is clean after running the notebook.

var tempFiles = new[]
{
    "_temp_polars_write.csv",
    "_temp_deedle_write.csv",
    "_temp_polars_write.parquet",
    "_temp_polars_write.json",
    "_temp_roundtrip.parquet",
    "_temp_roundtrip.csv"
};
 
foreach (var f in tempFiles)
{
    var path = Path.Combine(DATA, f);
    if (File.Exists(path))
    {
        File.Delete(path);
        Console.WriteLine($"Deleted: {f}");
    }
}
Console.WriteLine("\nCleanup complete.");
Deleted: _temp_polars_write.csv
Deleted: _temp_deedle_write.csv
Deleted: _temp_polars_write.parquet
Deleted: _temp_polars_write.json
Deleted: _temp_roundtrip.parquet
Deleted: _temp_roundtrip.csv
 
Cleanup complete.

Deleted: _temp_polars_write.csv Deleted: _temp_deedle_write.csv Deleted: _temp_polars_write.parquet Deleted: _temp_polars_write.json Deleted: _temp_roundtrip.parquet Deleted: _temp_roundtrip.csv

Cleanup complete.

Summary

Polars.NET / Deedle | Feature comparison

Side-by-side reference for type support and I/O capabilities across the two libraries.

FeaturePolars.NET 0.4.0Deedle 4.0.1
Categorical typeCast(DataType.Categorical) — dictionary-encodedNo support; manual encoding only
List/Struct columnsNot exposed in C# bindingsNot supported
Enum typeNot exposed in C# bindingsNot supported
Binary typeNot exposed in C# bindingsNot supported
Extract to arraysseries.ToArray<T>()series.Values.ToArray()
To DataTableManual (column-by-column extraction)Manual (similar approach)
Cross-libraryExtract arrays, build with Series.From + new DataFrameExtract arrays, build with FrameBuilder
CSV readDataFrame.ReadCsv(path, separator, tryParseDates, nRows)Frame.ReadCsv(path, separators)
CSV writedf.WriteCsv(path)frame.SaveCsv(path)
Parquet readDataFrame.ReadParquet(path)Not supported
Parquet writedf.WriteParquet(path)Not supported
JSON read/writeReadJson / WriteJson (if available)Not supported
TSV/SSVReadCsv(path, separator: '\t')ReadCsv(path, separators: "\t")

Polars.NET / Deedle | Interop reference points

  • Categorical pays off inside Polars.NET, but ToArray<string>() plus Series.From(...) turns the bridge back into plain str.
  • Parquet is the highest-fidelity persistence boundary in this note; CSV, JSON, and DataTable reconstruction all reintroduce parsing or schema decisions.
  • ReadJson and WriteJson should be treated as version-sensitive APIs in Polars.NET 0.4.0, even when they are available in the build used here.
  • FrameBuilder and Series.From(...) are explicit copy paths, not zero-copy interoperability.

Operational Risks

Operational Risks | Categorical becomes str at the array bridge

Once data leaves Polars.NET through ToArray<string>(), the Categorical dictionary encoding is gone. Rebuilding with Series.From(...) preserves values, but not the cat dtype marker.

Bridges the categorical symbol column through a string[] array and reconstructs a new frame, showing that the receiving column comes back as str rather than cat.

var bridgeSymbols = dfCat.Column("symbol").ToArray<string>();
var bridged = new DataFrame(new[]
{
    Polars.CSharp.Series.From("symbol", bridgeSymbols)
});
 
Console.WriteLine($"Before bridge: {dfCat.Column("symbol").DataTypeName}");
Console.WriteLine($"After bridge:  {bridged.Column("symbol").DataTypeName}");
Before bridge: cat
After bridge:  str

Operational Risks | DataType reflection shows the binding surface, not the engine surface

Use typeof(DataType).GetProperties(...) to check what the C# binding actually exposes. The Rust engine may support more shapes than the current binding advertises, so portability decisions should key off the reflected C# surface.

Reflects over the static DataType properties and probes for Categorical, List, Struct, and Binary to distinguish exposed C# markers from engine-level concepts.

var exposed = typeof(DataType).GetProperties(BindingFlags.Public | BindingFlags.Static)
    .Select(p => p.Name)
    .OrderBy(n => n)
    .ToArray();
 
Console.WriteLine($"Has Categorical: {exposed.Contains("Categorical")}");
Console.WriteLine($"Has List:        {exposed.Contains("List")}");
Console.WriteLine($"Has Struct:      {exposed.Contains("Struct")}");
Console.WriteLine($"Has Binary:      {exposed.Contains("Binary")}");
Has Categorical: True
Has List:        False
Has Struct:      False
Has Binary:      False

Operational Risks | ReadJson and WriteJson should be version-probed

Even when JSON support works in one build, treat ReadJson and WriteJson as probe-first APIs. A small reflection or guarded call prevents hardwiring note logic to a version-specific surface.

Inspects the public DataFrame method surface for JSON entry points before relying on them in a notebook or library bridge.

var jsonMethods = typeof(DataFrame).GetMethods(BindingFlags.Public | BindingFlags.Static)
    .Where(m => m.Name is "ReadJson" or "WriteJson")
    .Select(m => m.Name)
    .Distinct()
    .OrderBy(n => n)
    .ToArray();
 
Console.WriteLine($"JSON methods exposed: {string.Join(", ", jsonMethods)}");
JSON methods exposed: ReadJson, WriteJson

C# Advanced Types and Interop Recommendations

  1. Prefer Parquet when the interchange boundary must preserve date, bool, numeric width, and columnar compression.
  2. Treat ToArray<T>(), Series.From(...), and FrameBuilder as copy boundaries and re-check dtype semantics after the bridge.
  3. Keep Deedle interop narrow: extract only the columns you need instead of rebuilding full wide frames by default.
  4. Probe ReadJson, WriteJson, and text encoding behavior in the exact package version before standardizing a JSON workflow.

Troubleshooting and failure modes

Troubleshooting | WithColumns(...) returned a new frame and the original stayed unchanged

If a cast or transform appears to do nothing, confirm that you are using the returned DataFrame. WithColumns(...) does not mutate dfP in place.

Compares the symbol dtype on the original frame and the returned frame so you can see the immutability boundary directly.

var original = dfP.Column("symbol").DataTypeName;
var changed = dfP.WithColumns(
    Col("symbol").Cast(DataType.Categorical).Alias("symbol")
).Column("symbol").DataTypeName;
 
Console.WriteLine($"Original frame dtype: {original}");
Console.WriteLine($"Returned frame dtype: {changed}");
Original frame dtype: str
Returned frame dtype: cat

Troubleshooting | volume arrived from Deedle as double instead of long

The Deedle bridge in this note reads volume with GetColumn<double>(), so a cast back to long is part of the handoff. Make the cast explicit before rebuilding Polars Series.

Extracts a small volume sample from dfDSmall, shows the managed array element type, and then casts the values to the long[] shape expected by the Polars series.

var rawVolume = dfDSmall.GetColumn<double>("volume").Values.Take(3).ToArray();
var castVolume = rawVolume.Select(v => (long)v).ToArray();
 
Console.WriteLine($"Raw element type: {rawVolume.GetType().GetElementType()?.Name}");
Console.WriteLine($"Cast element type: {castVolume.GetType().GetElementType()?.Name}");
Console.WriteLine($"Sample values: {string.Join(", ", castVolume)}");
Raw element type: Double
Cast element type: Int64
Sample values: 1513937, 1382722, 1370204

Troubleshooting | DataTable reconstruction flattened the schema to String

When a DataTypeName is not mapped to a concrete .NET type, the fallback path collapses the target column to String. Inspect the generated DataColumn.DataType values before assuming the bridge preserved numeric semantics.

Builds the DataTable projection and prints the first few generated DataColumn CLR types so schema flattening is visible before the table is handed off downstream.

var projected = ToDataTable(dfP.Head(5));
var projectedTypes = projected.Columns.Cast<DataColumn>()
    .Take(4)
    .Select(c => $"{c.ColumnName}:{c.DataType.Name}");
 
Console.WriteLine(string.Join(", ", projectedTypes));
id:String, symbol:String, date:String, open:String

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