DataFrame

Namespace: Polars.CSharp

DataFrame represents a 2-dimensional labeled data structure similar to a table or spreadsheet.

Properties & Indexers

Property	Type	Description
ColumnNames	string[]	Get column names in order.
Columns	string[]	Return DataFrame Columns' Name
DataTypes	IReadOnlyList<DataType>	Return DataFrame Columns' DataType
Height	long	Return DataFrame Height
IsEmpty	bool	True if the DataFrame contains no rows.
IsVoid	bool	True if the DataFrame has no columns and no rows (0x0). Represents a completely uninitialized or structurally void DataFrame.
Len	long	Return DataFrame Height
Schema	PolarsSchema	Gets the Schema of the DataFrame. Returns a disposable PolarsSchema object.
Shape	(long, long)	Return DataFrame Shape(Len,Width)
Width	long	Return DataFrame Width
this[Index rowIndex, IntoSelector columnSelector]	DataFrame	e.g. df[^2, Cs.Numeric()], df[5, typeof(double)], df[0, Pl.Col("Id")]
this[Index rowIndex, string colName]	object	e.g. df[^1, "A"]
this[Index rowIndex, string[] columnNames]	DataFrame	e.g. df[0, ["A", "B"]], df[^1, ["A", "B"]]
this[IntoSelector columnSelector]	DataFrame	e.g. df[Cs.Numeric()], df[typeof(int)], df[Pl.Col("Id")]
this[Range rowRange, IntoSelector columnSelector]	DataFrame	e.g. df[1..10, Cs.Numeric()], df[..^1, typeof(int)], df[1..5, "A"], df[0..2, Pl.Col("Id")]
this[Range rowRange, string columnName]	Series	e.g. df[1..10, "A"]
this[Range rowRange, string[] columnNames]	DataFrame	e.g. df[1..10, ["A", "B"]]
this[Range rowRange]	DataFrame	e.g. df[1..5] / df[..^1]
this[Series maskOrIndices, IntoSelector columnSelector]	DataFrame	e.g. df[df["Age"] > 18, Cs.Numeric()] e.g. df[Pl.Series("index", [1, 3, 5]), Cs.Numeric()]
this[Series maskOrIndices, string columnName]	Series	e.g. var adultNames = df[df["Age"] >= 18, "Name"]; e.g. var specificNames = df[Polars.Series(new[] { 1, 3, 5 }), "Name"];
this[Series maskOrIndices, string[] columnNames]	DataFrame	e.g. df[df["Age"] > 18, ["Name", "Score"]] e.g. df[Polars.Series(new[] { 1, 3, 5 }), ["Name", "Score"]]
this[Series maskOrIndices]	DataFrame	e.g. df[df["Age"] > 18]
this[int index]	Series	Indexer to get a column by position. Usage: var s = df[0];
this[int rowIndex, int columnIndex]	object	Syntax Sugar
this[int rowIndex, string colName]	object	df[rowIndex, colName] = value
this[string columnName]	Series	df["foo"] = series
this[string[] columnNames]	DataFrame	e.g. df[["A", "B"]] = df2[["X", "Y"]]; e.g. df[["A", "B"]] = [s1, s2];

Constructors & Static Methods

DataFrame

Create a DataFrame from a collection of Series.

public DataFrame(params Series[] series)
public DataFrame(ReadOnlySpan<Series> series)

Parameters

Name	Type	Description
series	Series[] / ReadOnlySpan<Series>	-

---

Concat

Concatenate multiple DataFrames either vertically (union) or horizontally.

public static DataFrame Concat(IEnumerable<DataFrame> dfs)

Parameters

Name	Type	Description
dfs	IEnumerable<DataFrame>	A collection of DataFrames to concatenate.

Returns: A new combined DataFrame.

Example

var df1 = DataFrame.FromColumns(new
{
    a = new[] { 1, 2 },
    b = new[] { 10, 20 }
});

var df2 = DataFrame.FromColumns(new
{
    a = new[] { 3, 4 },
    b = new[] { 30, 40 }
});

// Vertical Concat (Union rows)
var catVertical = DataFrame.ConcatVertical(new[] { df1, df2 });
catVertical.Show();
/* Output:
shape: (4, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i32 ┆ i32 │
╞═════╪═════╡
│ 1   ┆ 10  │
│ 2   ┆ 20  │
│ 3   ┆ 30  │
│ 4   ┆ 40  │
└─────┴─────┘
*/

// Horizontal Concat (Append columns)
var df3 = DataFrame.FromColumns(new { c = new[] { "x", "y" } });
var catHorizontal = DataFrame.ConcatHorizontal(new[] { df1, df3 });
catHorizontal.Show();
/* Output:
shape: (2, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ i32 ┆ i32 ┆ str │
╞═════╪═════╪═════╡
│ 1   ┆ 10  ┆ x   │
│ 2   ┆ 20  ┆ y   │
└─────┴─────┴─────┘
*/

---

ConcatDiagonal

Diagonal concatenation of DataFrames.

public static DataFrame ConcatDiagonal(IEnumerable<DataFrame> dfs)

Parameters

Name	Type	Description
dfs	IEnumerable<DataFrame>	-

---

ConcatHorizontal

Horizontal concatenation of DataFrames.

public static DataFrame ConcatHorizontal(
    IEnumerable<DataFrame> dfs,
    bool checkDuplicates = true,
    bool strict = true,
    bool unitLengthAsScalar = false
)

Parameters

Name	Type	Description
dfs	IEnumerable<DataFrame>	DataFrames to concat.
checkDuplicates	bool	If true, check that the column names are unique. If multiple columns have the same name, they will be dropped.
strict	bool	For Horizontal: if true, error on height mismatch.
unitLengthAsScalar	bool	For Horizontal: if true, broadcast length-1 DataFrames to match height.

---

From

Create a DataFrame from a collection of strongly-typed objects (POCOs).

This method uses reflection to inspect the properties of the class T and maps them to DataFrame columns.

public static DataFrame From<T>(IEnumerable<T> data)

Parameters

Name	Type	Description
data	IEnumerable<T>	The collection of objects to load.

Returns: A new DataFrame.

Example

public class Student
{
    public string Name { get; set; }
    public int Age { get; set; }
    public double GPA { get; set; }
}

var students = new List<Student>
{
    new Student { Name = "Alice", Age = 20, GPA = 3.5 },
    new Student { Name = "Bob", Age = 22, GPA = 3.8 },
    new Student { Name = "Charlie", Age = 19, GPA = 3.2 }
};

var df = DataFrame.From(students);
df.Show();
/* Output:
shape: (3, 3)
┌─────────┬─────┬─────┐
│ Name    ┆ Age ┆ GPA │
│ ---     ┆ --- ┆ --- │
│ str     ┆ i32 ┆ f64 │
╞═════════╪═════╪═════╡
│ Alice   ┆ 20  ┆ 3.5 │
│ Bob     ┆ 22  ┆ 3.8 │
│ Charlie ┆ 19  ┆ 3.2 │
└─────────┴─────┴─────┘
*/

---

FromArrow

Create DataFrame from Apache Arrow RecordBatch.

public static DataFrame FromArrow(RecordBatch batch)

Parameters

Name	Type	Description
batch	RecordBatch	-

---

FromArrowStream

Safely consume any foreign Arrow C Stream (Strict mode). Adapts physical memory layouts (e.g., Utf8View) automatically.

public static DataFrame FromArrowStream(IArrowArrayStream stream)

Parameters

Name	Type	Description
stream	IArrowArrayStream	-

---

FromColumns

Creates a DataFrame from an object where properties represent column data arrays or lists (Structure of Arrays layout). Leverages compiled expression trees to eliminate reflection on repeated container types.

Non-generic backward-compatible overload for Structure of Arrays data layout.

Create DataFrame from explicitly named columns. No reflection used. Best performance.

Create a DataFrame from a collection of Series.

This is the functional equivalent of the constructor new DataFrame(series), provided for consistency with other From... factory methods.

public static DataFrame FromColumns<T>(T columns)
public static DataFrame FromColumns(object columns)
public static DataFrame FromColumns(params (string, object)[] columns)
public static DataFrame FromColumns(params Series[] series)

Parameters

Name	Type	Description
columns	T / object / (string, object)[]	-
series	Series[]	The series to combine into a DataFrame.

Returns: A new DataFrame containing the provided series.

Example

var df = DataFrame.FromColumns(new { Time = new[] { dt1, dt2 }, Val = new[] { 1.0, 2.0 } });

var s1 = new Series("id", [1, 2, 3]);
var s2 = new Series("name", ["Alice", "Bob", "Charlie"]);

var df = DataFrame.FromSeries(s1, s2);
Console.WriteLine(df);

---

FromDict

Create a DataFrame from a single dictionary (represents 1 row).

public static DataFrame FromDict(
    IDictionary<string, object> data,
    IntoSchema? schema = null,
    IntoSchema? schemaOverrides = null,
    bool strict = true
)

Parameters

Name	Type	Description
data	IDictionary<string, object>	-
schema	IntoSchema?	-
schemaOverrides	IntoSchema?	-
strict	bool	-

---

FromDicts

Create a DataFrame from a collection of dictionaries.

public static DataFrame FromDicts(
    IEnumerable<IDictionary<string, object>> data,
    IntoSchema? schema = null,
    IntoSchema? schemaOverrides = null,
    bool strict = true,
    uint? inferSchemaLength = 100
)

Parameters

Name	Type	Description
data	IEnumerable<IDictionary<string, object>>	The collection of dictionaries representing rows.
schema	IntoSchema?	Strict blueprint. If provided, overrides dynamic inference. Extra keys in data are ignored.
schemaOverrides	IntoSchema?	Patch blueprint. Infers all keys, but overrides specific column types.
strict	bool	If true, throws on unpromotable type mismatch.
inferSchemaLength	uint?	Rows to inspect for C# type inference.

---

FromEnumerable

Stream C# objects into Polars by Arrow Interface

public static DataFrame FromEnumerable<T>(IEnumerable<T> data, int batchSize = 100000, Schema? providedSchema = null)

Parameters

Name	Type	Description
data	IEnumerable<T>	-
batchSize	int	-
providedSchema	Schema?	-

---

FromRows

Create a DataFrame from a collection of strongly-typed objects (POCOs).

This method uses reflection to inspect the properties of the class T and maps them to DataFrame columns.

public static DataFrame FromRows<T>(IEnumerable<T> data)

Parameters

Name	Type	Description
data	IEnumerable<T>	The collection of objects to load.

Returns: A new DataFrame.

Example

public class Student
{
    public string Name { get; set; }
    public int Age { get; set; }
    public double GPA { get; set; }
}

var students = new List<Student>
{
    new Student { Name = "Alice", Age = 20, GPA = 3.5 },
    new Student { Name = "Bob", Age = 22, GPA = 3.8 },
    new Student { Name = "Charlie", Age = 19, GPA = 3.2 }
};

var df = DataFrame.From(students);
df.Show();
/* Output:
shape: (3, 3)
┌─────────┬─────┬─────┐
│ Name    ┆ Age ┆ GPA │
│ ---     ┆ --- ┆ --- │
│ str     ┆ i32 ┆ f64 │
╞═════════╪═════╪═════╡
│ Alice   ┆ 20  ┆ 3.5 │
│ Bob     ┆ 22  ┆ 3.8 │
│ Charlie ┆ 19  ┆ 3.2 │
└─────────┴─────┴─────┘
*/

---

FromSeries

Create a DataFrame from a collection of Series.

This is the functional equivalent of the constructor new DataFrame(series), provided for consistency with other From... factory methods.

public static DataFrame FromSeries(params Series[] series)
public static DataFrame FromSeries(IEnumerable<Series> series)
public static DataFrame FromSeries(ReadOnlySpan<Series> series)

Parameters

Name	Type	Description
series	Series[] / IEnumerable<Series> / ReadOnlySpan<Series>	The series to combine into a DataFrame.

Returns: A new DataFrame containing the provided series.

Example

var s1 = new Series("id", [1, 2, 3]);
var s2 = new Series("name", ["Alice", "Bob", "Charlie"]);

var df = DataFrame.FromSeries(s1, s2);
Console.WriteLine(df);

---

ReadAdbc

Generate DataFrame from ADBC query results

Executes a SQL query directly against an ADBC connection and reads the result into a zero-copy Polars DataFrame. Pure syntactic sugar: automatically manages the creation and disposal of the underlying AdbcStatement.

public static DataFrame ReadAdbc(AdbcStatement statement)
public static DataFrame ReadAdbc(AdbcConnection connection, string sqlQuery)

Parameters

Name	Type	Description
statement	AdbcStatement	-
connection	AdbcConnection	The active ADBC connection (e.g., DuckDB, SQLite).
sqlQuery	string	The SQL query string to execute.

Returns: A fully materialized Polars DataFrame containing the query results.

---

ReadAvro

Read an Avro file into a DataFrame.

Read an Avro memory buffer into a DataFrame.

Read an Avro memory Stream into a DataFrame.

public static DataFrame ReadAvro(
    string path,
    ulong? nRows = null,
    string[]? columns = null,
    int[]? projection = null
)

public static DataFrame ReadAvro(
    byte[] buffer,
    ulong? nRows = null,
    string[]? columns = null,
    int[]? projection = null
)

public static DataFrame ReadAvro(
    Stream stream,
    ulong? nRows = null,
    string[]? columns = null,
    int[]? projection = null
)

Parameters

Name	Type	Description
path	string	The path to the Avro file.
nRows	ulong?	Stop reading when nRows are read.
columns	string[]?	Columns to select/project by name.
projection	int[]?	Columns to select/project by index.
buffer	byte[]	The byte array containing Avro data.
stream	Stream	The stream containing Avro data.

Returns: A new DataFrame.

---

ReadCsv

Read a DataFrame from a CSV file.

Read a DataFrame from a CSV memory buffer.

Read a DataFrame from a CSV memory stream.

public static DataFrame ReadCsv(
    string path,
    string[]? columns = null,
    bool hasHeader = true,
    char separator = ',',
    char? quoteChar = '"',
    char eolChar = '\n',
    bool ignoreErrors = false,
    bool tryParseDates = true,
    bool lowMemory = false,
    bool rechunk = false,
    ulong skipRows = 0,
    ulong skipRowsAfterHeader = 0,
    ulong? nRows = null,
    ulong? inferSchemaLength = null,
    ulong? inferSchemaFiles = 18446744073709551615,
    IntoSchema? schema = null,
    IntoSchema? dtypeOverride = null,
    CsvEncoding encoding = CsvEncoding.UTF8,
    string[]? nullValues = null,
    bool missingIsNull = true,
    string? commentPrefix = null,
    bool decimalComma = false,
    bool truncateRaggedLines = false,
    string? rowIndexName = null,
    ulong rowIndexOffset = 0,
    CloudOptions? cloudOptions = null
)

public static DataFrame ReadCsv(
    byte[] buffer,
    string[]? columns = null,
    bool hasHeader = true,
    char separator = ',',
    char? quoteChar = '"',
    char eolChar = '\n',
    bool ignoreErrors = false,
    bool tryParseDates = true,
    bool lowMemory = false,
    bool rechunk = false,
    ulong skipRows = 0,
    ulong skipRowsAfterHeader = 0,
    ulong? nRows = null,
    ulong? inferSchemaLength = null,
    ulong? inferSchemaFiles = 18446744073709551615,
    IntoSchema? schema = null,
    IntoSchema? dtypeOverride = null,
    CsvEncoding encoding = CsvEncoding.UTF8,
    string[]? nullValues = null,
    bool missingIsNull = true,
    string? commentPrefix = null,
    bool decimalComma = false,
    bool truncateRaggedLines = false,
    string? rowIndexName = null,
    ulong rowIndexOffset = 0
)

public static DataFrame ReadCsv(
    Stream stream,
    string[]? columns = null,
    bool hasHeader = true,
    char separator = ',',
    char? quoteChar = '"',
    char eolChar = '\n',
    bool ignoreErrors = false,
    bool tryParseDates = true,
    bool lowMemory = false,
    bool rechunk = false,
    ulong skipRows = 0,
    ulong skipRowsAfterHeader = 0,
    ulong? nRows = null,
    ulong? inferSchemaLength = null,
    ulong? inferSchemaFiles = null,
    IntoSchema? schema = null,
    IntoSchema? dtypeOverride = null,
    CsvEncoding encoding = CsvEncoding.UTF8,
    string[]? nullValues = null,
    bool missingIsNull = true,
    string? commentPrefix = null,
    bool decimalComma = false,
    bool truncateRaggedLines = false,
    string? rowIndexName = null,
    ulong rowIndexOffset = 0
)

Parameters

Name	Type	Description
path	string	Path to the CSV file.
columns	string[]?	Columns to select. If null, select all columns.
hasHeader	bool	Whether the CSV file has a header. Defaults to true.
separator	char	Character used as separator. Defaults to ','.
quoteChar	char?	Character used for quoting. Defaults to '"'. Set to '\0' to disable.
eolChar	char	Character used as End-Of-Line. Defaults to '\n'.
ignoreErrors	bool	Try to keep reading lines if some are invalid. Defaults to false.
tryParseDates	bool	Try to automatically parse dates. Defaults to true.
lowMemory	bool	Use valid JSON lines to reduce memory usage. Defaults to false.
rechunk	bool	Rechunk the memory to contiguous chunks after reading. Defaults to false.
skipRows	ulong	Number of rows to skip from the start. Defaults to 0.
skipRowsAfterHeader	ulong	Skip this number of rows after the header location. Defaults to 0.
nRows	ulong?	Stop reading after n rows. If null, read all.
inferSchemaLength	ulong?	Number of rows to scan for schema inference. If null, use Polars default (100).
inferSchemaFiles	ulong?	How many files to use when inferring schema.
schema	IntoSchema?	Provide a schema to ignore schema inference.
dtypeOverride	IntoSchema?	Optional PolarsSchema to specify column types or overwrite inference.
encoding	CsvEncoding	Encoding of the CSV file. Defaults to Utf8.
nullValues	string[]?	List of strings to consider as null values.
missingIsNull	bool	Treat missing fields as null. Defaults to true.
commentPrefix	string?	Lines starting with this prefix will be ignored.
decimalComma	bool	Use comma as decimal separator. Defaults to false.
truncateRaggedLines	bool	Truncate lines that are longer than the schema. Defaults to false.
rowIndexName	string?	If provided, add a column with the row index.
rowIndexOffset	ulong	Offset for the row index. Defaults to 0.
cloudOptions	CloudOptions?	Options for cloud storage authentication and configuration.
buffer	byte[]	Memory buffer with the CSV file.
stream	Stream	Memory stream with the CSV file.

---

ReadCsvAsync

Asynchronously read a DataFrame from a CSV file.

public static Task<DataFrame> ReadCsvAsync(
    string path,
    string[]? columns = null,
    bool hasHeader = true,
    char separator = ',',
    char? quoteChar = '"',
    char eolChar = '\n',
    bool ignoreErrors = false,
    bool tryParseDates = true,
    bool lowMemory = false,
    bool rechunk = false,
    ulong skipRows = 0,
    ulong skipRowsAfterHeader = 0,
    ulong? nRows = null,
    ulong? inferSchemaLength = null,
    ulong? inferSchemaFiles = 18446744073709551615,
    IntoSchema? schema = null,
    IntoSchema? dtypeOverride = null,
    CsvEncoding encoding = CsvEncoding.UTF8,
    string[]? nullValues = null,
    bool missingIsNull = true,
    string? commentPrefix = null,
    bool decimalComma = false,
    bool truncateRaggedLines = false,
    string? rowIndexName = null,
    ulong rowIndexOffset = 0,
    CloudOptions? cloudOptions = null
)

Parameters

Name	Type	Description
path	string	-
columns	string[]?	-
hasHeader	bool	-
separator	char	-
quoteChar	char?	-
eolChar	char	-
ignoreErrors	bool	-
tryParseDates	bool	-
lowMemory	bool	-
rechunk	bool	-
skipRows	ulong	-
skipRowsAfterHeader	ulong	-
nRows	ulong?	-
inferSchemaLength	ulong?	-
inferSchemaFiles	ulong?	-
schema	IntoSchema?	-
dtypeOverride	IntoSchema?	-
encoding	CsvEncoding	-
nullValues	string[]?	-
missingIsNull	bool	-
commentPrefix	string?	-
decimalComma	bool	-
truncateRaggedLines	bool	-
rowIndexName	string?	-
rowIndexOffset	ulong	-
cloudOptions	CloudOptions?	-

---

ReadDatabase

Create a DataFrame directly from a System.Data.IDataReader.

This method streams data from the reader into Arrow batches, allowing for memory-efficient loading of large datasets from databases (e.g., SQL Server, PostgreSQL, SQLite).

It automatically maps C# types to Polars types (e.g., System.Decimal to Decimal128, System.DateTime to Timestamp).

public static DataFrame ReadDatabase(IDataReader reader, int batchSize = 50000)

Parameters

Name	Type	Description
reader	IDataReader	The open IDataReader instance.
batchSize	int	The number of rows to process per Arrow batch. Default is 50,000.

Returns: A new DataFrame.

Example

// Mocking a DataTable as a data source
var table = new System.Data.DataTable();
table.Columns.Add("Product", typeof(string));
table.Columns.Add("Price", typeof(decimal)); // Correctly maps to Polars Decimal128

table.Rows.Add("Laptop", 1234.56m);
table.Rows.Add("Mouse", 99.99m);

using IDataReader reader = table.CreateDataReader();

var df = DataFrame.ReadDatabase(reader);
df.Show();
/* Output:
shape: (2, 2)
┌─────────┬─────────────────────────┐
│ Product ┆ Price                   │
│ ---     ┆ ---                     │
│ str     ┆ decimal[38,18]          │
╞═════════╪═════════════════════════╡
│ Laptop  ┆ 1234.560000000000000000 │
│ Mouse   ┆ 99.990000000000000000   │
└─────────┴─────────────────────────┘
*/

---

ReadDelta

Read a delta table into a new DataFrame

public static DataFrame ReadDelta(
    string path,
    ulong? version = null,
    string? datetime = null,
    ulong? nRows = null,
    ParallelStrategy parallel = ParallelStrategy.Auto,
    bool lowMemory = false,
    bool useStatistics = true,
    bool glob = true,
    bool rechunk = false,
    bool cache = true,
    string? rowIndexName = null,
    uint rowIndexOffset = 0,
    string? includePathColumn = null,
    IntoSchema? schema = null,
    bool hivePartitioning = true,
    IntoSchema? hivePartitionSchema = null,
    bool tryParseHiveDates = true,
    CloudOptions? cloudOptions = null
)

Parameters

Name	Type	Description
path	string	Path to the Delta Lake table (folder containing _delta_log).
version	ulong?	The version of the table to read (e.g., 0, 1). Mutually exclusive with datetime.
datetime	string?	The timestamp to read (ISO-8601 string, e.g., "2026-02-09T12:00:00Z"). Mutually exclusive with version.
nRows	ulong?	Limit number of rows to read (optimization).
parallel	ParallelStrategy	Parallel strategy.
lowMemory	bool	Reduce memory usage at the expense of performance.
useStatistics	bool	Use parquet statistics to optimize the query plan.
glob	bool	Expand glob patterns (default: true).
rechunk	bool	Rechunk the memory to contiguous chunks when reading. (default: false)
cache	bool	Cache the result after reading. (default: true)
rowIndexName	string?	If provided, adds a column with the row number.
rowIndexOffset	uint	Offset for the row index.
includePathColumn	string?	If provided, adds a column with the source file path.
schema	IntoSchema?	Manually specify the schema of the file(s). Useful if the file footer is missing or to avoid I/O overhead of reading the schema.
hivePartitioning	bool	Enable Hive partitioning inference (default: false).
hivePartitionSchema	IntoSchema?	Manually specify the schema for Hive partitioning columns. Use this to ensure specific types for partition keys (e.g. string instead of int).
tryParseHiveDates	bool	Whether to try parsing dates in Hive partitioning paths (default: false).
cloudOptions	CloudOptions?	Options for cloud storage authentication (AWS S3, Azure, GCP, etc).

Returns: A new LazyFrame.

---

ReadExcel

Read an Excel file (.xlsx) into a DataFrame.

This uses the high-performance native Rust 'calamine' engine. It is significantly faster and more memory-efficient than traditional .NET Excel libraries.

public static DataFrame ReadExcel(
    string path,
    string? sheetName = null,
    ulong sheetIndex = 0,
    IntoSchema? schema = null,
    bool hasHeader = true,
    ulong inferSchemaLen = 100,
    bool dropEmptyRows = true,
    bool raiseIfEmpty = true
)

Parameters

Name	Type	Description
path	string	Path to the .xlsx file.
sheetName	string?	Name of the sheet to read. If provided, it takes precedence over sheetIndex.
sheetIndex	ulong	Index of the sheet to read (0-based). Default is 0 (the first sheet).
schema	IntoSchema?	Optional schema overrides. Provide this to strictly enforce specific column types (e.g., forcing a numeric column to be read as String).
hasHeader	bool	Indicates if the first row contains header names. Default is true.
inferSchemaLen	ulong	Number of rows to use for schema inference. (Note: The underlying engine scans implicitly, but this is kept for API alignment).
dropEmptyRows	bool	If true, rows where all cells are empty or null will be skipped. Default is true.
raiseIfEmpty	bool	If true, throws an exception if the sheet is empty or contains no data. Default is true.

Returns: A new DataFrame containing the Excel data.

---

ReadIpc

Read an Arrow IPC (Feather v2) file into a DataFrame.

Read Arrow IPC (Feather v2) from in-memory bytes.

Read Arrow IPC (Feather v2) from a Stream.

public static DataFrame ReadIpc(
    string path,
    string[]? columns = null,
    IntoSchema? schema = null,
    ulong? nRows = null,
    bool rechunk = false,
    bool cache = true,
    bool glob = true,
    string? rowIndexName = null,
    uint rowIndexOffset = 0,
    string? includePathColumn = null,
    bool hivePartitioning = false,
    IntoSchema? hivePartitionSchema = null,
    bool tryParseHiveDates = true,
    CloudOptions? cloudOptions = null
)

public static DataFrame ReadIpc(
    byte[] buffer,
    string[]? columns = null,
    IntoSchema? schema = null,
    ulong? nRows = null,
    bool rechunk = false,
    bool cache = true,
    string? rowIndexName = null,
    uint rowIndexOffset = 0,
    string? includePathColumn = null,
    bool hivePartitioning = false,
    IntoSchema? hivePartitionSchema = null,
    bool tryParseHiveDates = false
)

public static DataFrame ReadIpc(
    Stream stream,
    string[]? columns = null,
    IntoSchema? schema = null,
    ulong? nRows = null,
    bool rechunk = false,
    bool cache = true,
    string? rowIndexName = null,
    uint rowIndexOffset = 0,
    string? includePathColumn = null,
    bool hivePartitioning = false,
    IntoSchema? hivePartitionSchema = null,
    bool tryParseHiveDates = false
)

Parameters

Name	Type	Description
path	string	Path to the IPC file, glob pattern, or cloud path (e.g., "s3://...").
columns	string[]?	Columns to select. If null, select all columns. (Optimized via projection pushdown).
schema	IntoSchema?	Optional schema to enforce. If not provided, the schema is inferred from the file footer.
nRows	ulong?	Limit the number of rows to scan.
rechunk	bool	Make sure the DataFrame is contiguous in memory (default: false).
cache	bool	Cache the result of the scan (default: true).
glob	bool	Expand glob patterns (default: true).
rowIndexName	string?	If provided, adds a column with the row index.
rowIndexOffset	uint	Offset for the row index (default: 0).
includePathColumn	string?	If provided, adds a column with the source file path.
hivePartitioning	bool	Enable Hive partitioning inference (default: false).
hivePartitionSchema	IntoSchema?	Manually specify the schema for Hive partitioning columns.
tryParseHiveDates	bool	Whether to try parsing dates in Hive partitioning paths (default: true).
cloudOptions	CloudOptions?	Options for cloud storage (AWS S3, Azure Blob, GCS, etc.).
buffer	byte[]	The byte array containing the IPC data.
stream	Stream	The input stream containing the IPC data.

---

ReadIpcStream

Read into a DataFrame from Arrow IPC record batch stream.

public static DataFrame ReadIpcStream(
    string path,
    string[]? columns = null,
    uint[]? projection = null,
    ulong? nRows = null,
    string? rowIndexName = null,
    uint rowIndexOffset = 0,
    bool rechunk = true
)

public static DataFrame ReadIpcStream(
    ReadOnlySpan<byte> buffer,
    string[]? columns = null,
    uint[]? projection = null,
    ulong? nRows = null,
    string? rowIndexName = null,
    uint rowIndexOffset = 0,
    bool rechunk = true
)

Parameters

Name	Type	Description
path	string	Path to the IPC stream file or a byte buffer
columns	string[]?	Columns to select.
projection	uint[]?	Column indices to select.
nRows	ulong?	Stop reading from IPC stream after reading n rows
rowIndexName	string?	Insert a row index column with the given name into the DataFrame as the first column. If set to None (default), no row index column is created.
rowIndexOffset	uint	Start the row index at this offset.Only used if row_index_name is set.
rechunk	bool	Make sure that all data is contiguous.
buffer	ReadOnlySpan<byte>	-

---

ReadIpcStreamSchema

Get the schema of an IPC file without reading data.

public static PolarsSchema ReadIpcStreamSchema(string path)
public static PolarsSchema ReadIpcStreamSchema(ReadOnlySpan<byte> buffer)

Parameters

Name	Type	Description
path	string	-
buffer	ReadOnlySpan<byte>	-

---

ReadJson

Read a JSON file into a DataFrame.

Read JSON from an in-memory byte array.

Read JSON from a Stream.

public static DataFrame ReadJson(
    string path,
    string[]? columns = null,
    IntoSchema? schema = null,
    ulong? inferSchemaLen = null,
    ulong? batchSize = null,
    bool ignoreErrors = false,
    JsonFormat jsonFormat = JsonFormat.Json
)

public static DataFrame ReadJson(
    byte[] buffer,
    string[]? columns = null,
    IntoSchema? schema = null,
    ulong? inferSchemaLen = null,
    ulong? batchSize = null,
    bool ignoreErrors = false,
    JsonFormat jsonFormat = JsonFormat.Json
)

public static DataFrame ReadJson(
    Stream stream,
    string[]? columns = null,
    IntoSchema? schema = null,
    ulong? inferSchemaLen = null,
    ulong? batchSize = null,
    bool ignoreErrors = false,
    JsonFormat jsonFormat = JsonFormat.Json
)

Parameters

Name	Type	Description
path	string	Path to the JSON file.
columns	string[]?	Select specific columns to read.
schema	IntoSchema?	Manually specify the schema (recommended for stability).
inferSchemaLen	ulong?	Number of rows to scan for schema inference (default: scan all).
batchSize	ulong?	Batch size for reading (optimization).
ignoreErrors	bool	Ignore parsing errors (skip malformed lines).
jsonFormat	JsonFormat	Format: Json Array or Json Lines (NDJSON).
buffer	byte[]	-
stream	Stream	-

---

ReadParquet

Read a DataFrame from a Parquet file or multiple files via glob patterns.

Read Parquet from an in-memory byte array.

Read Parquet from a Stream.

public static DataFrame ReadParquet(
    string path,
    string[]? columns = null,
    ulong? nRows = null,
    ParallelStrategy parallel = ParallelStrategy.Auto,
    bool lowMemory = false,
    bool useStatistics = true,
    bool glob = true,
    bool allowMissingColumns = false,
    bool rechunk = false,
    bool cache = true,
    string? rowIndexName = null,
    uint rowIndexOffset = 0,
    string? includePathColumn = null,
    IntoSchema? schema = null,
    bool hivePartitioning = false,
    IntoSchema? hivePartitionSchema = null,
    bool tryParseHiveDates = false,
    CloudOptions? cloudOptions = null
)

public static DataFrame ReadParquet(
    byte[] buffer,
    string[]? columns = null,
    ulong? nRows = null,
    ParallelStrategy parallel = ParallelStrategy.Auto,
    bool lowMemory = false,
    bool useStatistics = true,
    bool allowMissingColumns = false,
    bool rechunk = false,
    bool cache = true,
    string? rowIndexName = null,
    uint rowIndexOffset = 0,
    string? includePathColumn = null,
    IntoSchema? schema = null,
    bool hivePartitioning = false,
    IntoSchema? hivePartitionSchema = null,
    bool tryParseHiveDates = false
)

public static DataFrame ReadParquet(
    Stream stream,
    string[]? columns = null,
    ulong? nRows = null,
    ParallelStrategy parallel = ParallelStrategy.Auto,
    bool lowMemory = false,
    bool useStatistics = true,
    bool allowMissingColumns = false,
    bool rechunk = false,
    bool cache = true,
    string? rowIndexName = null,
    uint rowIndexOffset = 0,
    string? includePathColumn = null,
    IntoSchema? schema = null,
    bool hivePartitioning = false,
    IntoSchema? hivePartitionSchema = null,
    bool tryParseHiveDates = false
)

Parameters

Name	Type	Description
path	string	Path to file or glob pattern (e.g. "data/*.parquet" or "s3://bucket/data.parquet").
columns	string[]?	Columns to select. If null, select all columns. (Optimized via projection pushdown).
nRows	ulong?	Limit number of rows to read (optimization).
parallel	ParallelStrategy	Parallel strategy.
lowMemory	bool	Reduce memory usage at the expense of performance.
useStatistics	bool	Use parquet statistics to optimize the query plan.
glob	bool	Expand glob patterns (default: true).
allowMissingColumns	bool	Allow missing columns when reading multiple files.
rechunk	bool	Rechunk the memory to contiguous chunks when reading. (default: false)
cache	bool	Cache the result after reading. (default: true)
rowIndexName	string?	If provided, adds a column with the row number.
rowIndexOffset	uint	Offset for the row index.
includePathColumn	string?	If provided, adds a column with the source file path.
schema	IntoSchema?	Manually specify the schema of the file(s).
hivePartitioning	bool	-
hivePartitionSchema	IntoSchema?	Manually specify the schema for Hive partitioning columns.
tryParseHiveDates	bool	Whether to try parsing dates in Hive partitioning paths (default: true).
cloudOptions	CloudOptions?	Options for cloud storage (AWS S3, Azure Blob, GCS, etc.).
buffer	byte[]	The byte array containing the parquet file.
stream	Stream	The input stream containing the parquet file.

---

ReadParquetAsync

Read a Parquet file asynchronously.

public static Task<DataFrame> ReadParquetAsync(
    string path,
    string[]? columns = null,
    ulong? nRows = null,
    ParallelStrategy parallel = ParallelStrategy.Auto,
    bool lowMemory = false,
    bool useStatistics = true,
    bool glob = true,
    bool allowMissingColumns = false,
    bool rechunk = false,
    bool cache = true,
    string? rowIndexName = null,
    uint rowIndexOffset = 0,
    string? includePathColumn = null,
    IntoSchema? schema = null,
    bool hivePartitioning = false,
    IntoSchema? hivePartitionSchema = null,
    bool tryParseHiveDates = false,
    CloudOptions? cloudOptions = null
)

Parameters

Name	Type	Description
path	string	-
columns	string[]?	-
nRows	ulong?	-
parallel	ParallelStrategy	-
lowMemory	bool	-
useStatistics	bool	-
glob	bool	-
allowMissingColumns	bool	-
rechunk	bool	-
cache	bool	-
rowIndexName	string?	-
rowIndexOffset	uint	-
includePathColumn	string?	-
schema	IntoSchema?	-
hivePartitioning	bool	-
hivePartitionSchema	IntoSchema?	-
tryParseHiveDates	bool	-
cloudOptions	CloudOptions?	-

---

Operators

operator + (Addition)

e.g. df + 1

public static DataFrame operator +(DataFrame df, int scalar)
public static DataFrame operator +(DataFrame df, double scalar)

Parameters

Name	Type	Description
df	DataFrame	-
scalar	int / double	-

---

operator / (Division)

public static DataFrame operator /(DataFrame df, int scalar)
public static DataFrame operator /(DataFrame df, double scalar)

Parameters

Name	Type	Description
df	DataFrame	-
scalar	int / double	-

---

operator == (Equality)

public static bool operator ==(DataFrame? left, DataFrame? right)

Parameters

Name	Type	Description
left	DataFrame?	-
right	DataFrame?	-

---

operator != (Inequality)

public static bool operator !=(DataFrame? left, DataFrame? right)

Parameters

Name	Type	Description
left	DataFrame?	-
right	DataFrame?	-

---

operator * (Multiply)

public static DataFrame operator *(DataFrame df, int scalar)
public static DataFrame operator *(DataFrame df, double scalar)

Parameters

Name	Type	Description
df	DataFrame	-
scalar	int / double	-

---

operator - (Subtraction)

public static DataFrame operator -(DataFrame df, int scalar)
public static DataFrame operator -(DataFrame df, double scalar)

Parameters

Name	Type	Description
df	DataFrame	-
scalar	int / double	-

---

Instance Methods

Add

Appends a new column to the end of the DataFrame, or replaces an existing column if the name already exists.

public void Add(Series series)

Parameters

Name	Type	Description
series	Series	The Series to add or update. The column name is determined by the Series name.

NOTE

This method enables the C# collection initializer syntax (e.g., var df = new DataFrame { s1, s2 };). If the DataFrame already contains a column with the same name as the provided series, it performs an highly efficient in-place replacement. Otherwise, it appends the new column to the right.

---

AlignChunks

Align chunks

public DataFrame AlignChunks()

---

AsDataReader

Export DataFrame As DbDataReader (Zero-Copy Enabled)

public DbDataReader AsDataReader(int bufferSize = 5, Dictionary<string, Type>? typeOverrides = null)

Parameters

Name	Type	Description
bufferSize	int	-
typeOverrides	Dictionary<string, Type>?	-

---

AsEnumerable

Returns a lazy heap-allocated IEnumerable over DataFrame rows. Ideal for LINQ composition, interface boundaries, and streaming scenarios.

public PolarsRowEnumerable<T> AsEnumerable<T>()

---

AsTensor

Converts selected DataFrame columns into a managed 2D Row-Major Tensor [Rows, Columns]. Automatically handles memory fragmentation and performs the Column-Major to Row-Major transposition required by most Machine Learning frameworks (like ONNX, TorchSharp, ML.NET).

public Tensor<T> AsTensor<T>(params string[] columnNames)

Parameters

Name	Type	Description
columnNames	string[]	Specific columns to extract. If empty, extracts all columns.

Returns: A 2D System.Numerics.Tensors.Tensor representing the feature matrix.

---

BottomK

Get the top k rows according to the given expressions.

This selects the largest values.

Get the Bottom k rows according to a single expression.

Master method: Get the Bottom k rows by multiple Expressions with optional parallel boolean arrays. Usage: df.BottomK(10, myColumns.Select(c => Pl.Col(c)))

Get the Bottom k rows with specific reverse directions using Tuples. Usage: df.BottomK(5, ("Score", false), ("Age", true), (Cs.Numeric(), false))

public DataFrame BottomK(int k, IEnumerable<Expr> by, bool[] reverse)
public DataFrame BottomK(int k, IntoExprColumn by, bool reverse = false)
public DataFrame BottomK(int k, IEnumerable<string> colName, bool reverse = false)
public DataFrame BottomK(int k, params (IntoExprColumn, bool)[] configs)

Parameters

Name	Type	Description
k	int	-
by	IEnumerable<Expr> / IntoExprColumn	-
reverse	bool[]	-
colName	IEnumerable<string>	-
configs	(IntoExprColumn, bool)[]	-

---

Cast

Cast all columns to the specified target type.

Cast a specific Expression, String, Selector, or Type to a DataType.

Cast multiple targets using tuples. Supports String, Expr, Selector mixed with Type, DataType, or DataTypeExpr! Usage: df.Cast( ("Age", typeof(int)), (Cs.Numeric(), DataType.Float32), (DataType.Float64, DataType.Float32) )

Cast columns to the types specified in a Schema, DataFrame, or LazyFrame.

public DataFrame Cast(IntoDataTypeExpr dtype, bool strict = true, bool wrapNumerical = false)
public DataFrame Cast(
    IntoSelector selector,
    IntoDataTypeExpr dtype,
    bool strict = true,
    bool wrapNumerical = false
)

public DataFrame Cast(params (IntoSelector, IntoDataTypeExpr)[] casts)
public DataFrame Cast(IntoSchema schemaWrapper, bool strict = true, bool wrapNumerical = false)

Parameters

Name	Type	Description
dtype	IntoDataTypeExpr	-
strict	bool	-
wrapNumerical	bool	-
selector	IntoSelector	-
casts	(IntoSelector, IntoDataTypeExpr)[]	-
schemaWrapper	IntoSchema	-

---

Clear

Create an empty (n=0) or n-row null-filled (n>0) copy of the DataFrame. Returns a n-row null-filled DataFrame with an identical schema.

public DataFrame Clear(long n = 0)

Parameters

Name	Type	Description
n	long	Number of (null-filled) rows to return in the cleared frame.

Returns: A new DataFrame.

---

Clone

Clone the DataFrame

public DataFrame Clone()

---

CollectSchema

Gets the Schema of the DataFrame.Same as Schema field.

public PolarsSchema CollectSchema()

---

Column

Get a column as a Series by name.

Get a column by its positional index (0-based).

public Series Column(string name)
public Series Column(long index)

Parameters

Name	Type	Description
name	string	-
index	long	-

---

Count

Return the number of non-null elements for each column.

public DataFrame Count()

---

Describe

Generate a summary statistics DataFrame (count, mean, std, min, 25%, 50%, 75%, max). Similar to pandas/polars describe().

public DataFrame Describe()

---

Drop

Drop one or more columns from the DataFrame. Returns a new DataFrame.

Drop columns using Polars Selectors or Expressions. Example: df.Drop(Cs.EndsWith("_tmp").ToExpr())

public DataFrame Drop(params string[] columns)
public DataFrame Drop(params IntoSelector[] exprs)

Parameters

Name	Type	Description
columns	string[]	-
exprs	IntoSelector[]	-

---

DropInPlace

Drop a column in-place and return it as a Series. This mutates the original DataFrame.

public Series DropInPlace(string name)

Parameters

Name	Type	Description
name	string	The name of the column to drop.

Returns: The dropped column as a Series.

---

DropNans

Drop rows containing one or more NaN values.

Drop rows with NaN in specific columns.

Drop rows with NaN by specific Expressions.

public DataFrame DropNans()
public DataFrame DropNans(params IntoSelector[] subsets)
public DataFrame DropNans(IEnumerable<string> subsets)

Parameters

Name	Type	Description
subsets	IntoSelector[] / IEnumerable<string>	-

---

DropNulls

Drop rows containing one or more Null values.

Drop rows with Nulls in specific columns.

Drop rows with Nulls by specific Expressions.

public DataFrame DropNulls()
public DataFrame DropNulls(IEnumerable<string> subsets)
public DataFrame DropNulls(params IntoSelector[] subsets)

Parameters

Name	Type	Description
subsets	IEnumerable<string> / IntoSelector[]	-

---

EstimatedSize

Return an estimation of the total (heap) allocated size of the Series/DataFrame. Estimated size is given in the specified unit (bytes by default).

public double EstimatedSize(SizeUnit unit = SizeUnit.Bytes)

Parameters

Name	Type	Description
unit	SizeUnit	Scale the returned size to the given unit (uses 1024 base).

Returns: The estimated size as a double.

---

Explode

Explode list/array columns into multiple rows.

This un-nests list columns. If multiple columns are provided, they are exploded in parallel. The list columns being exploded must have the same length for each row.

Explode list/array columns into multiple rows using selector.

Bridge overload to support C# 12 collection expressions. Usage: df.Explode(["list1", "list2"])

public DataFrame Explode(params string[] columns)
public DataFrame Explode(IntoSelector columns, bool emptyAsNull = true, bool keepNulls = true)
public DataFrame Explode(IEnumerable<string> columns, bool emptyAsNull = true, bool keepNulls = true)

Parameters

Name	Type	Description
columns	string[] / IntoSelector / IEnumerable<string>	Expressions selecting the columns to explode.
emptyAsNull	bool	If true, empty lists are exploded into a single null value. If false, rows with empty lists are removed from the result.
keepNulls	bool	If true, null values in the column are preserved as null in the result. If false, rows with null values are removed.

Returns: A new DataFrame where list elements are expanded into rows.

Example

var df = DataFrame.FromColumns(new
{
    id = new[] { 1, 2 },
    tags = new[] { new[] { "apple", "orange" }, new[] { "banana" } },
    scores = new[] { new[] { 10, 20 }, new[] { 30 } }
});

// Explode "tags" and "scores" columns simultaneously
var exploded = df.Explode(["tags","scores"]);

exploded.Show();
/* Output:
shape: (3, 3)
┌─────┬────────┬────────┐
│ id  ┆ tags   ┆ scores │
│ --- ┆ ---    ┆ ---    │
│ i32 ┆ str    ┆ i32    │
╞═════╪════════╪════════╡
│ 1   ┆ apple  ┆ 10     │
│ 1   ┆ orange ┆ 20     │
│ 2   ┆ banana ┆ 30     │
└─────┴────────┴────────┘
*/

---

ExportBatches

Export DataFrame to Record Batch

public void ExportBatches(Action<RecordBatch> onBatchReceived)

Parameters

Name	Type	Description
onBatchReceived	Action<RecordBatch>	Receive RecordBatch Callback

---

Extend

Extend the memory backed by this DataFrame with the values from other. Different from vstack which adds the chunks from other to the chunks of this DataFrame, extend appends the data from other to the underlying memory locations and thus may cause a reallocation. If this does not cause a reallocation, the resulting data structure will not have any extra chunks and thus will yield faster queries.

public DataFrame Extend(DataFrame other)

Parameters

Name	Type	Description
other	DataFrame	The DataFrame to extend.

---

FillNan

Fill NaN values in floating point columns with a specified Expression.

public DataFrame FillNan(Expr fillValue)

Parameters

Name	Type	Description
fillValue	Expr	-

---

FillNull

Fill null values in all columns with a specified Expression.

Fill null values in all columns with a specified literal value.

public DataFrame FillNull(Expr fillValue)
public DataFrame FillNull(object value)

Parameters

Name	Type	Description
fillValue	Expr	-
value	object	-

---

Filter

Filter rows based on a boolean expression (predicate).

Retains only the rows where the expression evaluates to true.

Filter rows based on a boolean series.

Filter rows based on a boolean array.

public DataFrame Filter(Expr expr)
public DataFrame Filter(Series series)
public DataFrame Filter(IEnumerable<bool> mask)

Parameters

Name	Type	Description
expr	Expr	A boolean expression to filter by (e.g., Col("a") > 5).
series	Series	A boolean series as mask
mask	IEnumerable<bool>	A boolean IEnumerable as mask

Returns: A new DataFrame containing only the matching rows.

Example

var df = DataFrame.FromColumns(new
{
    foo = new[] { 1, 2, 3 },
    bar = new[] { 6, 7, 8 },
    ham = new[] { "a", "b", "c" }
});

// Keep rows where "foo" is greater than 1
var filtered = df.Filter(Col("foo") > 1);

filtered.Show();
/* Output:
shape: (2, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i32 ┆ i32 ┆ str │
╞═════╪═════╪═════╡
│ 2   ┆ 7   ┆ b   │
│ 3   ┆ 8   ┆ c   │
└─────┴─────┴─────┘
*/

---

Fold

Apply a horizontal reduction on a DataFrame.

Fold over all columns with a custom starting accumulator.

public Series Fold(Func<Series, Series, Series> operation)
public Series Fold(Series initial, Func<Series, Series, Series> operation)

Parameters

Name	Type	Description
operation	Func<Series, Series, Series>	function that takes two Series and returns a Series.
initial	Series	-

---

Gather

Selects rows from this DataFrame at the given indices.

public DataFrame Gather(IntoIndexLazyFrame index, bool nullOnOob = false)

Parameters

Name	Type	Description
index	IntoIndexLazyFrame	The indices of the rows to select.
nullOnOob	bool	If true when an index is out-of-bounds a null row will be generated instead of raising an error.

---

GatherEvery

Take every nth row in the Frame and return as a new Frame.

public DataFrame GatherEvery(int n, int offset = 0)

Parameters

Name	Type	Description
n	int	Gather every n-th row.
offset	int	Starting Index

---

GetColumns

Get all columns as a list of Series. Order is guaranteed to match the physical column order.

public Series[] GetColumns()

---

GetEnumerator

Enable foreach (var series in df)

public IEnumerator<Series> GetEnumerator()

---

GetRow

Fills the destination span with all values of a single row.

public void GetRow(long index, Span<object> destination)

Parameters

Name	Type	Description
index	long	The 64-bit row index.
destination	Span<object>	The destination span whose length must be at least the DataFrame width.

---

GetValue

Get a value from the DataFrame at the specified row and column. This is efficient for single-value lookups (no Arrow conversion).

public T GetValue<T>(long rowIndex, string colName)
public T GetValue<T>(string colName, long rowIndex)

Parameters

Name	Type	Description
rowIndex	long	-
colName	string	-

---

Glimpse

Print a dense preview of the DataFrame to the standard output. The formatting shows one line per column so that wide dataframes display cleanly.

public void Glimpse(int maxItemsPerColumn = 10, int maxColnameLength = 50)

Parameters

Name	Type	Description
maxItemsPerColumn	int	Maximum number of items to show per column.
maxColnameLength	int	Maximum length of the displayed column names.

---

GlimpseFrame

Return a dense preview of the DataFrame as a new DataFrame. Schema: "column" (String), "dtype" (String), "values" (List[String])

public DataFrame GlimpseFrame(int maxItemsPerColumn = 10, int maxColnameLength = 50)

Parameters

Name	Type	Description
maxItemsPerColumn	int	-
maxColnameLength	int	-

---

GlimpseString

Return a dense preview of the DataFrame as a formatted string.

public string GlimpseString(int maxItemsPerColumn = 10, int maxColnameLength = 50)

Parameters

Name	Type	Description
maxItemsPerColumn	int	-
maxColnameLength	int	-

---

GroupBy

Start a GroupBy operation to perform aggregations on groups of data.

This returns a GroupByBuilder which allows you to specify the aggregation functions (like Sum, Min, Max, Count) using the .Agg() method.

Group by a single key with explicit control over maintainOrder.

The core GroupBy implementation. All other overloads route here.

public GroupByBuilder GroupBy(IntoExprColumn key, bool maintainOrder = true)
public GroupByBuilder GroupBy(IEnumerable<IntoExprColumn> keys, bool maintainOrder = true)

Parameters

Name	Type	Description
key	IntoExprColumn	-
maintainOrder	bool	-
keys	IEnumerable<IntoExprColumn>	-

Returns: A builder object to define aggregations.

Example

var df = DataFrame.FromColumns(new
{
    group = new[] { "A", "A", "B", "B", "B" },
    val1 = new[] { 1, 2, 3, 4, 5 },
    val2 = new[] { 10, 20, 10, 20, 30 }
});

// Group by "group" and calculate:
// 1. Sum of val1
// 2. Max of val2 (aliased to "max_val2")
// 3. Count of elements in the group
var grouped = df.GroupBy(Col("group")).Agg(
    Pl.Col("val1").Sum(),
    Pl.Col("val2").Max().Alias("max_val2"),
    Pl.Col("val1").Count().Alias("count")
);

grouped.Sort("group").Show();
/* Output:
shape: (2, 4)
┌───────┬──────┬──────────┬───────┐
│ group ┆ val1 ┆ max_val2 ┆ count │
│ ---   ┆ ---  ┆ ---      ┆ ---   │
│ str   ┆ i32  ┆ i32      ┆ u32   │
╞═══════╪══════╪══════════╪═══════╡
│ A     ┆ 3    ┆ 20       ┆ 2     │
│ B     ┆ 12   ┆ 30       ┆ 3     │
└───────┴──────┴──────────┴───────┘
*/

---

GroupByDynamic

Group based on a time index using dynamic windows (Rolling/Resampling).

This is essential for time-series analysis, allowing you to downsample data (e.g., 1-minute data to 1-hour bars).

public GroupByBuilder GroupByDynamic(
    IntoSelector indexColumn,
    IntoDuration every,
    IntoDuration? period = null,
    IntoDuration? offset = null,
    IEnumerable<IntoExprColumn>? groupBy = null,
    Label label = Label.Left,
    bool includeBoundaries = false,
    ClosedInterval ClosedInterval = ClosedInterval.Left,
    StartBy startBy = StartBy.WindowBound
)

Parameters

Name	Type	Description
indexColumn	IntoSelector	The column containing time/date data (must be sorted).
every	IntoDuration	The interval at which to start a new window (e.g., "1h", "1d"). Also known as the step size.
period	IntoDuration?	The duration of each window. If null, defaults to every.
offset	IntoDuration?	Offset to shift the window start times.
groupBy	IEnumerable<IntoExprColumn>?	Optional extra columns to group by (e.g., group by "stock_symbol" AND time window).
label	Label	Which label to use for the window (Left boundary, Right boundary, or Datapoint).
includeBoundaries	bool	Whether to include the window boundaries in the output.
ClosedInterval	ClosedInterval	Which side of the window interval is closed (inclusive).
startBy	StartBy	Strategy to determine the start of the first window.

Returns: A GroupByBuilder object to define aggregations.

Example

var df = DataFrame.FromColumns(new
{
    time = new[] 
    { 
        new DateTime(2024, 1, 1, 10, 0, 0),
        new DateTime(2024, 1, 1, 10, 30, 0), // Inside 10:00-11:00 window
        new DateTime(2024, 1, 1, 11, 0, 0), // Start of next window
        new DateTime(2024, 1, 1, 11, 15, 0),
        new DateTime(2024, 1, 2, 09, 0, 0)
    },
    val = new[] { 1, 2, 3, 4, 5 }
});

// Group into 1-hour windows based on "time"
var dynamicGrouped = df.GroupByDynamic(
    indexColumn: "time", 
    every: TimeSpan.FromHours(1)
).Agg(
    Pl.Col("val").Sum().Alias("total_val"),
    Pl.Col("val").Count().Alias("count")
);

dynamicGrouped.Show();
/* Output:
shape: (3, 3)
┌─────────────────────┬───────────┬───────┐
│ time                ┆ total_val ┆ count │
│ ---                 ┆ ---       ┆ ---   │
│ datetime[μs]        ┆ i32       ┆ u32   │
╞═════════════════════╪═══════════╪═══════╡
│ 2024-01-01 10:00:00 ┆ 3         ┆ 2     │ // 10:00 + 10:30
│ 2024-01-01 11:00:00 ┆ 7         ┆ 2     │ // 11:00 + 11:15
│ 2024-01-02 09:00:00 ┆ 5         ┆ 1     │
└─────────────────────┴───────────┴───────┘
*/

---

HStack

Horizontally stack columns to the DataFrame. Returns a new DataFrame with the new columns appended.

Horizontally stack columns to the DataFrame.

public DataFrame HStack(IEnumerable<Series> columns)
public DataFrame HStack(params Series[] columns)

Parameters

Name	Type	Description
columns	IEnumerable<Series> / Series[]	The series to stack.

---

HashRows

Hash and combine the rows in this DataFrame.

public Series HashRows(ulong? seed = 42)

Parameters

Name	Type	Description
seed	ulong?	Random seed parameter. Defaults to 42 for reproducible hashing.

Returns: A Series containing the UInt64 hashes.

---

Head

Return head lines from a DataFrame

public DataFrame Head(int n = 5)

Parameters

Name	Type	Description
n	int	-

---

InsertColumn

Insert a column into the DataFrame at a specified index. Accepts a string (column name), a Series, a primitive value, or an Expr.

public DataFrame InsertColumn(int index, IntoExprColumn column)

Parameters

Name	Type	Description
index	int	-
column	IntoExprColumn	-

---

Interpolate

Interpolate intermediate values. The interpolation method is linear. Nulls at the beginning and end of the series remain null.

public DataFrame Interpolate()

---

IsDuplicated

Get a mask of all duplicated rows in this DataFrame.

public Series IsDuplicated()

---

IsSorted

Check whether the DataFrame is sorted by the given columns.

Overload for checking if DataFrame is sorted by a single column.

public bool IsSorted(IEnumerable<string> by, IEnumerable<bool>? descending = null, IEnumerable<bool>? nullsLast = null)
public bool IsSorted(string by, bool descending = false, bool nullsLast = false)

Parameters

Name	Type	Description
by	IEnumerable<string>	Column name(s) to check.
descending	IEnumerable<bool>?	Sort in descending order. When sorting by multiple columns, can be specified per column by passing a sequence of booleans.
nullsLast	IEnumerable<bool>?	Place null values last. When sorting by multiple columns, can be specified per column by passing a sequence of booleans.

---

IsUnique

Get a mask of all unique rows in this DataFrame.

public Series IsUnique()

---

IterColumns

Returns an iterator over the columns of this DataFrame.

public IEnumerator<Series> IterColumns()

---

IterRows

Iterates over all rows in the DataFrame as strongly-typed models using a zero-allocation stack enumerator, executing the specified action for each row without allocating heap objects.

public void IterRows<T>(Action<T> action)

Parameters

Name	Type	Description
action	Action<T>	The action to execute for each row.

---

IterSlices

Returns an iterator over slices of this DataFrame.

public IEnumerable<DataFrame> IterSlices(int nRows = 10000)

Parameters

Name	Type	Description
nRows	int	The number of rows per slice. Default is 10,000.

Returns: An enumerable collection of DataFrame slices.

---

Join

Join with another DataFrame using expression expressions.

Join with another DataFrame using column names.

Join using specific multiple expressions or column names. Usage: lf.Join(other, leftOn: ["lid", "ldate"], rightOn: ["rid", "rdate"])

Join with another DataFrame using a single column pair (Convenience overload).

Join using specific multiple expressions. (C# Array Covariance Helper) Usage: lf.Join(other, leftOn: [Pl.Col("A")], rightOn: [Pl.Col("B")])

Join using multiple shared expressions. (C# Array Covariance Helper) Usage: df.Join(other, on: [Pl.Col("A"), Pl.Col("B")])

public DataFrame Join(
    DataFrame other,
    IntoExprColumn on,
    JoinType how = JoinType.Inner,
    string? suffix = null,
    JoinValidation validation = JoinValidation.ManyToMany,
    JoinCoalesce coalesce = JoinCoalesce.JoinSpecific,
    JoinMaintainOrder maintainOrder = JoinMaintainOrder.None,
    JoinSide joinSide = JoinSide.LetPolarsDecide,
    bool nullsEqual = false,
    long? sliceOffset = null,
    ulong sliceLen = 0
)

public DataFrame Join(
    DataFrame other,
    IntoExprColumn leftOn,
    IntoExprColumn rightOn,
    JoinType how = JoinType.Inner,
    string? suffix = null,
    JoinValidation validation = JoinValidation.ManyToMany,
    JoinCoalesce coalesce = JoinCoalesce.JoinSpecific,
    JoinMaintainOrder maintainOrder = JoinMaintainOrder.None,
    JoinSide joinSide = JoinSide.LetPolarsDecide,
    bool nullsEqual = false,
    long? sliceOffset = null,
    ulong sliceLen = 0
)

public DataFrame Join(
    DataFrame other,
    IEnumerable<IntoExprColumn> leftOn,
    IEnumerable<IntoExprColumn> rightOn,
    JoinType how = JoinType.Inner,
    string? suffix = null,
    JoinValidation validation = JoinValidation.ManyToMany,
    JoinCoalesce coalesce = JoinCoalesce.JoinSpecific,
    JoinMaintainOrder maintainOrder = JoinMaintainOrder.None,
    JoinSide joinSide = JoinSide.LetPolarsDecide,
    bool nullsEqual = false,
    long? sliceOffset = null,
    ulong sliceLen = 0
)

public DataFrame Join(
    DataFrame other,
    IEnumerable<string> on,
    JoinType how = JoinType.Inner,
    string? suffix = null,
    JoinValidation validation = JoinValidation.ManyToMany,
    JoinCoalesce coalesce = JoinCoalesce.JoinSpecific,
    JoinMaintainOrder maintainOrder = JoinMaintainOrder.None,
    JoinSide joinSide = JoinSide.LetPolarsDecide,
    bool nullsEqual = false,
    long? sliceOffset = null,
    ulong sliceLen = 0
)

public DataFrame Join(
    DataFrame other,
    IEnumerable<IntoExprColumn> on,
    JoinType how = JoinType.Inner,
    string? suffix = null,
    JoinValidation validation = JoinValidation.ManyToMany,
    JoinCoalesce coalesce = JoinCoalesce.JoinSpecific,
    JoinMaintainOrder maintainOrder = JoinMaintainOrder.None,
    JoinSide joinSide = JoinSide.LetPolarsDecide,
    bool nullsEqual = false,
    long? sliceOffset = null,
    ulong sliceLen = 0
)

public DataFrame Join(
    DataFrame other,
    IEnumerable<Expr> leftOn,
    IEnumerable<Expr> rightOn,
    JoinType how = JoinType.Inner,
    string? suffix = null,
    JoinValidation validation = JoinValidation.ManyToMany,
    JoinCoalesce coalesce = JoinCoalesce.JoinSpecific,
    JoinMaintainOrder maintainOrder = JoinMaintainOrder.None,
    JoinSide joinSide = JoinSide.LetPolarsDecide,
    bool nullsEqual = false,
    long? sliceOffset = null,
    ulong sliceLen = 0
)

public DataFrame Join(
    DataFrame other,
    IEnumerable<Expr> on,
    JoinType how = JoinType.Inner,
    string? suffix = null,
    JoinValidation validation = JoinValidation.ManyToMany,
    JoinCoalesce coalesce = JoinCoalesce.JoinSpecific,
    JoinMaintainOrder maintainOrder = JoinMaintainOrder.None,
    JoinSide joinSide = JoinSide.LetPolarsDecide,
    bool nullsEqual = false,
    long? sliceOffset = null,
    ulong sliceLen = 0
)

Parameters

Name	Type	Description
other	DataFrame	DataFrame to join with.
on	IntoExprColumn / IEnumerable<string> / IEnumerable<IntoExprColumn> / IEnumerable<Expr>	Join Key
how	JoinType	Join type (Inner, Left, etc.).
suffix	string?	Suffix to append to columns with same name in right DataFrame. Default "_right".
validation	JoinValidation	Check if join keys are unique.
coalesce	JoinCoalesce	How to coalesce the join keys.
maintainOrder	JoinMaintainOrder	How to maintain the order of the join.
joinSide	JoinSide	Specifies the strategy for the hash join build side.
nullsEqual	bool	Consider nulls as equal.
sliceOffset	long?	Slice the result starting at this offset.
sliceLen	ulong	Length of the slice.
leftOn	IntoExprColumn / IEnumerable<IntoExprColumn> / IEnumerable<Expr>	Column names in the left DataFrame to join on.
rightOn	IntoExprColumn / IEnumerable<IntoExprColumn> / IEnumerable<Expr>	Column names in the right DataFrame to join on.

Returns: A new DataFrame resulting from the join.

Example

var dfCustomers = DataFrame.FromColumns(new
{
    id = new[] { 1, 2, 3 },
    name = new[] { "Alice", "Bob", "Charlie" }
});

var dfOrders = DataFrame.FromColumns(new
{
    id = new[] { 1, 2, 4 },
    amount = new[] { 100, 200, 500 }
});

// Perform an Inner Join on the "id" column
var joined = dfCustomers.Join(
    dfOrders, 
    leftOn: new[] { "id" }, 
    rightOn: new[] { "id" }, 
    how: JoinType.Inner
);

joined.Show();
/* Output:
shape: (2, 3)
┌─────┬───────┬────────┐
│ id  ┆ name  ┆ amount │
│ --- ┆ ---   ┆ ---    │
│ i32 ┆ str   ┆ i32    │
╞═════╪═══════╪════════╡
│ 1   ┆ Alice ┆ 100    │
│ 2   ┆ Bob   ┆ 200    │
└─────┴───────┴────────┘
*/

---

JoinAsOf

Join AsOf using a shared 'on' column and optional shared 'by' columns. Usage: lf.JoinAsOf(other, on: "time", tolerance: "2h", by: ["ticker"])

Join AsOf using independent 'leftOn'/'rightOn' and 'leftBy'/'rightBy' columns. Usage: lf.JoinAsOf(other, leftOn: "time_L", rightOn: "time_R", tolerance: TimeSpan.FromMinutes(5))

public DataFrame JoinAsOf(
    DataFrame other,
    IntoExprColumn on,
    AsOfTolerance? tolerance = null,
    IEnumerable<string>? by = null,
    AsofStrategy strategy = AsofStrategy.Backward,
    bool allowEq = true,
    bool checkSorted = true,
    string? suffix = null,
    JoinValidation validation = JoinValidation.ManyToMany,
    JoinCoalesce coalesce = JoinCoalesce.JoinSpecific,
    JoinMaintainOrder maintainOrder = JoinMaintainOrder.None,
    JoinSide joinSide = JoinSide.LetPolarsDecide,
    bool nullsEqual = false,
    long? sliceOffset = null,
    ulong sliceLen = 0
)

public DataFrame JoinAsOf(
    DataFrame other,
    IntoExprColumn leftOn,
    IntoExprColumn rightOn,
    AsOfTolerance? tolerance = null,
    IEnumerable<string>? leftBy = null,
    IEnumerable<string>? rightBy = null,
    AsofStrategy strategy = AsofStrategy.Backward,
    bool allowEq = true,
    bool checkSorted = true,
    string? suffix = null,
    JoinValidation validation = JoinValidation.ManyToMany,
    JoinCoalesce coalesce = JoinCoalesce.JoinSpecific,
    JoinMaintainOrder maintainOrder = JoinMaintainOrder.None,
    JoinSide joinSide = JoinSide.LetPolarsDecide,
    bool nullsEqual = false,
    long? sliceOffset = null,
    ulong sliceLen = 0
)

Parameters

Name	Type	Description
other	DataFrame	The right LazyFrame to join with.
on	IntoExprColumn	-
tolerance	AsOfTolerance?	Tolerance as a time duration string (e.g., "2h", "10s", "1d"), int or double or TimeSpan. Matches that are further away than this duration are discarded.
by	IEnumerable<string>?	-
strategy	AsofStrategy	The strategy to determine which value is "nearest" (Backward, Forward, or Nearest). Defaults to AsofStrategy.Backward.
allowEq	bool	If true, allow exact matches to be included in the result. If false, a match must be strictly unequal (e.g. less than for Backward strategy) to the key.
checkSorted	bool	Check if the join keys are sorted. If false, the user must ensure keys are sorted; otherwise results are undefined (but execution is faster).
suffix	string?	Suffix to append to columns with name conflicts. Defaults to "_right".
validation	JoinValidation	Check if join keys are unique (mostly relevant for the 'by' columns).
coalesce	JoinCoalesce	How to coalesce the join keys.
maintainOrder	JoinMaintainOrder	How to maintain the order of the join.
joinSide	JoinSide	pecifies the strategy for the hash join build side.
nullsEqual	bool	Consider nulls as equal.
sliceOffset	long?	Slice the result starting at this offset (optimization).
sliceLen	ulong	Length of the slice to keep.
leftOn	IntoExprColumn	Join key of the left LazyFrame. Must be sorted.
rightOn	IntoExprColumn	Join key of the right LazyFrame. Must be sorted.
leftBy	IEnumerable<string>?	Columns to match exactly (equivalence join) before performing the as-of join. Useful for joining separate time-series per group (e.g., by "Symbol").
rightBy	IEnumerable<string>?	Columns to match exactly in the right DataFrame.

Example

// Trades: Events happening at specific times
var trades = DataFrame.FromColumns(new
{
    time = new[] { 10, 20, 30 },
    stock = new[] { "A", "A", "A" }
});

// Quotes: Price updates (irregular intervals)
// 9->100, 15->101, 25->102, 40->103
var quotes = DataFrame.FromColumns(new
{
    time = new[] { 9, 15, 25, 40 },
    bid = new[] { 100, 101, 102, 103 }
});

// Find the latest quote BEFORE or AT the trade time
var asof = trades.JoinAsOf(
    quotes, 
    leftOn: Pl.Col("time"), 
    rightOn: Pl.Col("time"),
    strategy: AsofStrategy.Backward
);

asof.Show();
/* Output:
shape: (3, 3)
┌──────┬───────┬─────┐
│ time ┆ stock ┆ bid │
│ ---  ┆ ---   ┆ --- │
│ i32  ┆ str   ┆ i32 │
╞══════╪═══════╪═════╡
│ 10   ┆ A     ┆ 100 │ // Matches time 9
│ 20   ┆ A     ┆ 101 │ // Matches time 15
│ 30   ┆ A     ┆ 102 │ // Matches time 25
└──────┴───────┴─────┘
*/

// Trades: Events happening at specific times
var trades = DataFrame.FromColumns(new
{
    time = new[] { 10, 20, 30 },
    stock = new[] { "A", "A", "A" }
}).Lazy();

// Quotes: Price updates (irregular intervals)
// 9->100, 15->101, 25->102, 40->103
var quotes = DataFrame.FromColumns(new
{
    time = new[] { 9, 15, 25, 40 },
    bid = new[] { 100, 101, 102, 103 }
}).Lazy();

// Find the latest quote BEFORE or AT the trade time
var asof = trades.JoinAsOf(
    quotes, 
    leftOn: Pl.Col("time"), 
    rightOn: Pl.Col("time"),
    strategy: AsofStrategy.Backward
);

var df = asof.Collect();
df.Show();
/* Output:
shape: (3, 3)
┌──────┬───────┬─────┐
│ time ┆ stock ┆ bid │
│ ---  ┆ ---   ┆ --- │
│ i32  ┆ str   ┆ i32 │
╞══════╪═══════╪═════╡
│ 10   ┆ A     ┆ 100 │ // Matches time 9
│ 20   ┆ A     ┆ 101 │ // Matches time 15
│ 30   ┆ A     ┆ 102 │ // Matches time 25
└──────┴───────┴─────┘
*/

---

JoinWhere

Join with another DataFrame using multiple boolean expressions. Usage: lf.JoinWhere(other, [Pl.Col("start") > Pl.Col("end"), Pl.Col("id") != null], how: JoinType.Left)

Join using a single condition (Most common use case). Usage: lf.JoinWhere(other, Pl.Col("A") > Pl.Col("B"), how: JoinType.Inner)

Usage: lf.JoinWhere(other, Pl.Col("A") > Pl.Col("B"), Pl.Col("C") > 0)

public DataFrame JoinWhere(
    DataFrame other,
    IEnumerable<IntoExprColumn> predicates,
    JoinType how = JoinType.Inner,
    string? suffix = null,
    JoinValidation validation = JoinValidation.ManyToMany,
    JoinCoalesce coalesce = JoinCoalesce.JoinSpecific,
    JoinMaintainOrder maintainOrder = JoinMaintainOrder.None,
    bool nullsEqual = false
)

public DataFrame JoinWhere(
    DataFrame other,
    IntoExprColumn predicate,
    JoinType how = JoinType.Inner,
    string? suffix = null,
    JoinValidation validation = JoinValidation.ManyToMany,
    JoinCoalesce coalesce = JoinCoalesce.JoinSpecific,
    JoinMaintainOrder maintainOrder = JoinMaintainOrder.None,
    bool nullsEqual = false
)

public DataFrame JoinWhere(DataFrame other, params IntoExprColumn[] predicates)

Parameters

Name	Type	Description
other	DataFrame	-
predicates	IEnumerable<IntoExprColumn> / IntoExprColumn[]	-
how	JoinType	-
suffix	string?	-
validation	JoinValidation	-
coalesce	JoinCoalesce	-
maintainOrder	JoinMaintainOrder	-
nullsEqual	bool	-
predicate	IntoExprColumn	-

---

Lazy

Convert the DataFrame into a LazyFrame. This allows building a query plan and optimizing execution.

public LazyFrame Lazy()

---

MapRows

Transforms DataFrame rows into a new DataFrame using a strongly-typed mapping function.

Operates entirely in managed memory with pre-compiled property extraction and ingestion delegates, eliminating per-row reflection and intermediate boxing allocations.

public DataFrame MapRows<TIn, TOut>(Func<TIn, TOut> mapFunc)

Parameters

Name	Type	Description
mapFunc	Func<TIn, TOut>	The transformation delegate applied to each row.

Returns: A new DataFrame materialized from the mapped output records.

---

MatchToSchema

Align the Frame's schema to the target schema. Columns not in the target schema are handled according to the extraColumns policy.

public DataFrame MatchToSchema(
    PolarsSchema schema,
    ExtraColumnsPolicy extraColumns = ExtraColumnsPolicy.Raise,
    MatchSchemaConfig? defaultConfig = null,
    IReadOnlyDictionary<string, MatchSchemaConfig>? columnOverrides = null
)

Parameters

Name	Type	Description
schema	PolarsSchema	The target schema to match against.
extraColumns	ExtraColumnsPolicy	Policy for handling top-level columns present in the frame but not in the target schema.
defaultConfig	MatchSchemaConfig?	The default configuration applied to all columns for handling missing data and type casting.
columnOverrides	IReadOnlyDictionary<string, MatchSchemaConfig>?	Specific configuration overrides mapped by column name.

Returns: A new LazyFrame with the aligned schema.

---

Max

Aggregate the columns in the Frame to their maximum value.

public DataFrame Max()

---

MaxHorizontal

Get the maximum value horizontally across columns.

public Series MaxHorizontal()

---

Mean

Aggregate the columns in the Frame to their mean value.

public DataFrame Mean()

---

MeanHorizontal

Compute the mean of all values horizontally across columns.

public Series MeanHorizontal(bool ignoreNulls = true)

Parameters

Name	Type	Description
ignoreNulls	bool	Ignore null values (default). If set to False, any null value in the input will lead to a null output.

---

Median

Aggregate the columns in the Frame to their median value.

public DataFrame Median()

---

Melt

Unpivot (Melt) the DataFrame from wide to long format.

Unpivot (Melt) the DataFrame from wide to long format.

This is the reverse of DataFrame.Pivot. It collapses multiple columns into key-value pairs.

public DataFrame Melt(
    IntoSelector index,
    IntoSelector? on = null,
    string variableName = "variable",
    string valueName = "value"
)

public DataFrame Melt(
    IEnumerable<string> index,
    IEnumerable<string>? on = null,
    string variableName = "variable",
    string valueName = "value"
)

Parameters

Name	Type	Description
index	IntoSelector / IEnumerable<string>	Column names to keep as identifiers (id_vars).
on	IntoSelector? / IEnumerable<string>?	Column names to unpivot/melt (value_vars).
variableName	string	Name for the new variable column (default "variable").
valueName	string	Name for the new value column (default "value").

Returns: A long-format DataFrame.

Example

// Using the 'pivoted' DataFrame from the Pivot example
// shape: (2, 3) [date, US, CN]

// Unpivot back to long format
var melted = pivoted.Unpivot(
    index: new[] { "date" },
    on: new[] { "CN", "US" },
    variableName: "country",
    valueName: "sales"
);

melted.Sort(new[] { "date", "country" }).Show();
/* Output:
shape: (4, 3)
┌────────────┬─────────┬───────┐
│ date       ┆ country ┆ sales │
│ ---        ┆ ---     ┆ ---   │
│ str        ┆ str     ┆ i32   │
╞════════════╪═════════╪═══════╡
│ 2024-01-01 ┆ CN      ┆ 200   │
│ 2024-01-01 ┆ US      ┆ 100   │
│ 2024-01-02 ┆ CN      ┆ 220   │
│ 2024-01-02 ┆ US      ┆ 110   │
└────────────┴─────────┴───────┘
*/

---

Merge

Initiates a Merge (Upsert) builder to apply changes from a source DataFrame.

Initiates an eager Merge builder using a Selector or Column Expression.

Initiates a strongly-typed Merge operation between two DataFrames.

public DataFrameMergeBuilder Merge(DataFrame source, params string[] on)
public DataFrameMergeBuilder Merge(DataFrame source, IntoSelector on)
public TypedMergeBuilder<TTarget, TSource> Merge<TTarget, TSource, TKey>(DataFrame source, Expression<Func<TTarget, TKey>> targetKey, Expression<Func<TSource, TKey>> sourceKey)

Parameters

Name	Type	Description
source	DataFrame	The source DataFrame containing updates/inserts.
on	string[] / IntoSelector	The column names to merge on.
targetKey	Expression<Func<TTarget, TKey>>	-
sourceKey	Expression<Func<TSource, TKey>>	-

---

MergeCatalogRecords

Starts building a Merge (Upsert) operation using this DataFrame as the source.

public DeltaMergeBuilder MergeCatalogRecords(
    UnityCatalog catalog,
    string catalogName,
    string schemaName,
    string tableName,
    string[] mergeKeys,
    bool canEvolve = false,
    CloudOptions? cloudOptions = null
)

Parameters

Name	Type	Description
catalog	UnityCatalog	-
catalogName	string	The Unity Catalog name (e.g., "main").
schemaName	string	The schema/database name within the catalog.
tableName	string	The target Delta table name registered in the catalog.
mergeKeys	string[]	An array of column names used as join keys to match source rows with target rows.
canEvolve	bool	If true, enables Schema Evolution to handle new columns in the source data.
cloudOptions	CloudOptions?	Cloud-specific configurations and credentials.

Returns: A DeltaMergeBuilder used to configure "WhenMatched" and "WhenNotMatched" clauses.

NOTE

This is a builder pattern. No data is modified until .Execute() is called on the resulting DeltaMergeBuilder.

---

MergeDeltaOrdered

Starts a fluent builder to merge a DataFrame into a Delta Lake table with strict, order-preserving SQL MERGE semantics.

Unlike traditional merge methods, this builder guarantees that chained actions (Update, Delete, Insert) are evaluated exactly in the order they are defined. If no actions are specified before execution, it intelligently defaults to a standard Upsert (WhenMatchedUpdate + WhenNotMatchedInsert).

public DeltaMergeBuilder MergeDeltaOrdered(
    string path,
    string[] mergeKeys,
    bool canEvolve = false,
    CloudOptions? cloudOptions = null
)

Parameters

Name	Type	Description
path	string	The URI to the target Delta Lake table (local or cloud).
mergeKeys	string[]	The column names to join on (must exist in both the Source DataFrame and Target Delta table).
canEvolve	bool	If set to true, allows schema evolution (e.g., adding new columns from the Source to the Target). Default is false.
cloudOptions	CloudOptions?	Cloud storage credentials and configuration (e.g., AWS S3, Azure Blob).

Returns: A DeltaMergeBuilder instance used to chain match conditions, culminating in a call to .Execute().

Example

df.MergeDeltaOrdered("s3://bucket/my_table", new[] { "Id" })
  .WhenMatchedDelete(Delta.Source("Status") == "Deleted")      // Evaluated 1st
  .WhenMatchedUpdate(Delta.Source("Stock") > Delta.Target("Stock")) // Evaluated 2nd
  .WhenNotMatchedInsert()                                      // Evaluated 3rd
  .Execute();

---

MergeSorted

Take two sorted DataFrames and merge them by the sorted key. The output of this operation will also be sorted. It is the callers responsibility that the frames are sorted in ascending order by the key, with null keys at the end, otherwise the order of the output will not make sense. The schemas of both LazyFrames must be equal.

public DataFrame MergeSorted(DataFrame other, string key, bool maintainOrder = false)
public DataFrame MergeSorted(DataFrame other, string[] key, bool maintainOrder = false)

Parameters

Name	Type	Description
other	DataFrame	Other DataFrame that must be merged
key	string / string[]	Key that is sorted.
maintainOrder	bool	If True, the output is guaranteed to have left-biased ordering for equal keys: rows from the left frame appear before rows from the right frame when their keys are equal.

---

Min

Aggregate the columns in the Frame to their minimum value.

public DataFrame Min()

---

MinHorizontal

Get the Minimum value horizontally across columns.

public Series MinHorizontal()

---

NChunks

Get the number of chunks used by the first Column of this DataFrame. (Equivalent to strategy='first')

public long NChunks()

---

NChunksAll

Get an array containing the number of chunks for all columns in this DataFrame. (Equivalent to strategy='all')

public long[] NChunksAll()

---

NUnique

Return the number of unique rows (based on all columns).

Return the number of unique rows based on a subset of columns (Selector, Type, string, etc.).

Return the number of unique rows based on specific column names (supports ["A", "B"] syntax).

public long NUnique()
public long NUnique(IntoSelector subset)
public long NUnique(IEnumerable<string> columns)

Parameters

Name	Type	Description
subset	IntoSelector	-
columns	IEnumerable<string>	-

---

NullCount

Aggregate the columns in the Frame as the sum of their null value count.

public DataFrame NullCount()

---

PartitionBy

Split into multiple DataFrames partitioned by groups.

Convenience overload for Params usage.

Group by the given columns and return the groups as separate dataframes.

public DataFrame[] PartitionBy(params IntoSelector[] by)
public DataFrame[] PartitionBy(IntoSelector by, bool maintainOrder = true, bool includeKey = true)
public DataFrame[] PartitionBy(IEnumerable<IntoSelector> by, bool maintainOrder = true, bool includeKey = true)

Parameters

Name	Type	Description
by	IntoSelector[] / IEnumerable<IntoSelector>	Column name(s) or selector(s) to group by.
maintainOrder	bool	Ensure that the order of the groups is consistent with the input data. This is slower than a default partition by operation.
includeKey	bool	Include the columns used to partition the DataFrame in the output.

---

PartitionByAsDict

Split into multiple DataFrames, returning a dictionary mapping the group keys to the DataFrames.

Partitions the DataFrame by specified columns into a strongly typed dictionary. Supports domain model keys (record struct / record class), scalar identifiers (string, int), and tuples.

public Dictionary<object[], DataFrame> PartitionByAsDict(params IntoSelector[] by)
public Dictionary<object[], DataFrame> PartitionByAsDict(IntoSelector by, bool maintainOrder = true, bool includeKey = true)
public Dictionary<object[], DataFrame> PartitionByAsDict(IEnumerable<IntoSelector> by, bool maintainOrder = true, bool includeKey = true)
public Dictionary<TKey, DataFrame> PartitionByAsDict<TKey>(
    IEnumerable<IntoSelector> by,
    bool maintainOrder = true,
    bool includeKey = true,
    IEqualityComparer<TKey>? comparer = null
)

Parameters

Name	Type	Description
by	IntoSelector[] / IEnumerable<IntoSelector>	Column name(s) or selector(s) to group by.
maintainOrder	bool	Ensure that the order of the groups is consistent with the input data. This is slower than a default partition by operation.
includeKey	bool	Include the columns used to partition the DataFrame in the output.
comparer	IEqualityComparer<TKey>?	Optional equality comparer for custom domain key hashing.

Returns: A dictionary mapping strongly typed domain keys to their respective partition DataFrames.

---

Pipe

Offers a structured way to apply a sequence of user-defined functions (UDFs).

public T Pipe<T>(Func<DataFrame, T> function)

Parameters

Name	Type	Description
function	Func<DataFrame, T>	A function that receives the current expression and returns a value of type T. Typically this function wraps several Polars API calls that operate on the given expression.

Returns: The result of applying function to this DataFrame.

---

Pivot

Pivot the DataFrame using Selectors for column selection. This is the most flexible pivot method.

Pivot the DataFrame from long to wide format.

This creates a new column for each unique value in the on argument. The values in the new columns come from the values column.

public DataFrame Pivot(
    IntoSelector on,
    IntoSelector? index = null,
    IntoSelector? values = null,
    Expr? aggregateExpr = null,
    PivotAgg aggregateFunction = PivotAgg.First,
    bool maintainOrder = false,
    string separator = "_",
    PivotColumnNaming columnNaming = PivotColumnNaming.Auto
)

public DataFrame Pivot(
    IEnumerable<string> on,
    IEnumerable<string>? index = null,
    IEnumerable<string>? values = null,
    Expr? aggregateExpr = null,
    PivotAgg aggregateFunction = PivotAgg.First,
    bool maintainOrder = false,
    string separator = "_",
    PivotColumnNaming columnNaming = PivotColumnNaming.Auto
)

Parameters

Name	Type	Description
on	IntoSelector / IEnumerable<string>	Selector for the column(s) to pivot.
index	IntoSelector? / IEnumerable<string>?	Selector for the index column(s).
values	IntoSelector? / IEnumerable<string>?	Selector for the value column(s).
aggregateExpr	Expr?	Optional expression to aggregate the values. If null, uses aggregateFunction.
aggregateFunction	PivotAgg	Aggregation function to use if aggregateExpr is null. Default is First.
maintainOrder	bool	Keep the original order of the rows (index).
separator	string	Separator used to combine column names when multiple value columns are selected.
columnNaming	PivotColumnNaming	How resulting column names will be constructed.

Returns: A wide-format DataFrame.

Example

var dfLong = DataFrame.FromColumns(new
{
    date = new[] { "2024-01-01", "2024-01-01", "2024-01-02", "2024-01-02" },
    country = new[] { "US", "CN", "US", "CN" },
    sales = new[] { 100, 200, 110, 220 }
});

// Pivot: rows=date, cols=country, values=sales
var pivoted = dfLong.Pivot(
    index: new[] { "date" }, 
    columns: new[] { "country" }, 
    values: new[] { "sales" }
);

pivoted.Show();
/* Output:
shape: (2, 3)
┌────────────┬─────┬─────┐
│ date       ┆ US  ┆ CN  │
│ ---        ┆ --- ┆ --- │
│ str        ┆ i32 ┆ i32 │
╞════════════╪═════╪═════╡
│ 2024-01-01 ┆ 100 ┆ 200 │
│ 2024-01-02 ┆ 110 ┆ 220 │
└────────────┴─────┴─────┘
*/

---

PrintSchema

Prints the schema of the DataFrame to the standard output.

public void PrintSchema()

---

Quantile

Aggregate the columns in the Frame to their quantile value.

public DataFrame Quantile(double quantile, QuantileMethod method = QuantileMethod.Linear)

Parameters

Name	Type	Description
quantile	double	Quantile between 0.0 and 1.0.
method	QuantileMethod	['nearest’, ‘higher’, ‘lower’, ‘midpoint’, ‘linear’] Interpolation method.

---

Rechunk

Rechunk the data in this DataFrame to a contiguous allocation. This will make sure all subsequent operations have optimal and predictable performance.

public DataFrame Rechunk()

---

Remove

Remove rows that match the predicate. This is the exact opposite of Filter. Rows where the predicate evaluates to False or Null are kept.

Remove rows based on a boolean series.

Remove rows based on a boolean array.

public DataFrame Remove(Expr predicate)
public DataFrame Remove(Series predicate)
public DataFrame Remove(IEnumerable<bool> mask)

Parameters

Name	Type	Description
predicate	Expr / Series	-
mask	IEnumerable<bool>	-

---

Rename

Rename a column.

Rename a list of columns.

Rename columns using a dictionary mapping old names to new names.

Rename columns using a list of (OldName, NewName) tuples.

public DataFrame Rename(string oldName, string newName)
public DataFrame Rename(string[] oldNames, string[] newNames)
public DataFrame Rename(IReadOnlyDictionary<string, string> mapping)
public DataFrame Rename(params (string, string)[] renames)

Parameters

Name	Type	Description
oldName	string	-
newName	string	-
oldNames	string[]	-
newNames	string[]	-
mapping	IReadOnlyDictionary<string, string>	-
renames	(string, string)[]	-

Example

df.Rename(("old1", "new1"), ("old2", "new2"));

---

ReplaceColumn

Replace a column by its index in-place. Supports negative indexing (e.g., -1 replaces the last column).

Replace a column by its name in-place.

public DataFrame ReplaceColumn(int index, Series newColumn, bool keepName = false)
public DataFrame ReplaceColumn(string columnName, Series newColumn, bool keepName = true)

Parameters

Name	Type	Description
index	int	The index of the column to replace. Negative values count from the end.
newColumn	Series	The new Series to insert.
keepName	bool	If true, keeps the original column name. If false, uses the new Series's name. Default is false.
columnName	string	The name of the column to replace.

Returns: The current DataFrame instance to support method chaining.

---

Reverse

Reverse the DataFrame.

public DataFrame Reverse()

---

Rolling

Group based on a time index using rolling windows.

public GroupByBuilder Rolling(
    IntoSelector indexColumn,
    IntoDuration period,
    IntoDuration? offset = null,
    IEnumerable<IntoExprColumn>? groupBy = null,
    ClosedInterval ClosedInterval = ClosedInterval.Left
)

Parameters

Name	Type	Description
indexColumn	IntoSelector	-
period	IntoDuration	-
offset	IntoDuration?	-
groupBy	IEnumerable<IntoExprColumn>?	-
ClosedInterval	ClosedInterval	-

---

Row

Get data for selected row.

public object[] Row(long index)

Parameters

Name	Type	Description
index	long	-

---

Rows

Enumerates DataFrame rows as strongly-typed objects using pre-compiled Fast Path mappings.

public DataFrameRowEnumerator<T> Rows<T>()

Returns: An IEnumerable of hydrated objects.

---

Sample

Sample from this DataFrame.

public DataFrame Sample(
    ulong n = 1,
    bool withReplacement = false,
    bool? shuffle = null,
    ulong? seed = null
)

public DataFrame Sample(
    double fraction,
    bool withReplacement = false,
    bool? shuffle = null,
    ulong? seed = null
)

Parameters

Name	Type	Description
n	ulong	Number of items to return. Defaults to 1
withReplacement	bool	Allow values to be sampled more than once.
shuffle	bool?	If set to True, the order of the sampled rows will be shuffled. If set to False (default), the order of the returned rows will be neither stable nor fully random.
seed	ulong?	Seed for the random number generator. If set to None (default), a random seed is generated for each time the sample is called.
fraction	double	Fraction of items to return.

---

Select

Select columns from the DataFrame and apply expressions to them.

This is the primary way to project data, rename columns, or compute new columns based on existing ones. The result will only contain the columns specified in the expression list.

Select columns by name (convenience overload).

This is a shortcut for creating Polars.Col expressions for each column name.

Select columns by name (convenience overload).

Select columns by expressions (convenience overload).

Bridge overload to support IEnumerable IntoExpr exprs. Usage: lf.Select(["Id", "Name", "Date"])

public DataFrame Select(params IntoExprColumn[] columns)
public DataFrame Select(IEnumerable<string> columns)
public DataFrame Select(params string[] columns)
public DataFrame Select(IEnumerable<Expr> exprs)
public DataFrame Select(IEnumerable<IntoExprColumn> exprs)

Parameters

Name	Type	Description
columns	IntoExprColumn[] / IEnumerable<string> / string[]	A list of expressions defining the columns to select or compute.
exprs	IEnumerable<Expr> / IEnumerable<IntoExprColumn>	-

Returns: A new DataFrame containing only the selected/computed columns. / A new DataFrame containing only the selected columns.

Example

var df = DataFrame.FromColumns(new
{
    foo = new[] { 1, 2, 3 },
    bar = new[] { 6, 7, 8 },
    ham = new[] { "a", "b", "c" }
});

// Select "foo" column and compute a new column "bar_x_2"
var selected = df.Select(
    Col("foo"),
    (Col("bar") * 2).Alias("bar_x_2")
);

selected.Show();
/* Output:
shape: (3, 2)
┌─────┬─────────┐
│ foo ┆ bar_x_2 │
│ --- ┆ ---     │
│ i32 ┆ i32     │
╞═════╪═════════╡
│ 1   ┆ 12      │
│ 2   ┆ 14      │
│ 3   ┆ 16      │
└─────┴─────────┘
*/

NOTE

For more advanced selections (renaming, calculations), use DataFrame.Select.

---

SetSorted

Mark one or multiple columns as sorted. This is an optimizer hint and will not actually execute a sorting operation.

public DataFrame SetSorted(IntoSelector columns, bool descending = false, bool nullsLast = false)

Parameters

Name	Type	Description
columns	IntoSelector	The columns to mark as sorted. Accepts strings, arrays, or Selectors (e.g. Cs.Temporal()).
descending	bool	Whether the columns are sorted in descending order.
nullsLast	bool	Whether null values appear last.

Returns: A new Frame with the sorted hints applied to the query plan.

---

Show

Print the DataFrame to Console.

public void Show()

---

ShrinkToFit

Shrink DataFrame memory usage.

public DataFrame ShrinkToFit()

Returns: A new DataFrame

---

ShrinkToFitInplace

Shrink DataFrame memory usage.This won't return a new DataFrame

public void ShrinkToFitInplace()

---

Slice

Slice the DataFrame along the rows.

e.g. df.Slice(1..5) or df.Slice(..^10)

public DataFrame Slice(long offset, ulong? length = null)
public DataFrame Slice(Range range)

Parameters

Name	Type	Description
offset	long	Start index. Negative values work as expected (counting from the end).
length	ulong?	Length of the slice.
range	Range	-

Returns: A new sliced DataFrame.

---

Sort

Sort the LazyFrame by a single column name, selector, or column expression.

Sort the DataFrame by multiple columns (all ascending or all descending).

Sort the LazyFrame using specific sorting configurations for each column/selector.

public DataFrame Sort(
    IntoSelector by,
    bool descending = false,
    bool nullsLast = false,
    bool maintainOrder = false
)

public DataFrame Sort(
    IEnumerable<IntoSelector> by,
    bool[]? descending = null,
    bool[]? nullsLast = null,
    bool maintainOrder = false
)

public DataFrame Sort(params (IntoSelector, bool)[] sortConfigs)

Parameters

Name	Type	Description
by	IntoSelector / IEnumerable<IntoSelector>	-
descending	bool / bool[]?	-
nullsLast	bool / bool[]?	-
maintainOrder	bool	-
sortConfigs	(IntoSelector, bool)[]	-

---

Std

Aggregate the columns in the Frame to their standard deviation value.

public DataFrame Std(byte ddof = 1)

Parameters

Name	Type	Description
ddof	byte	“Delta Degrees of Freedom”: the divisor used in the calculation is N - ddof, where N represents the number of elements. By default ddof is 1.

---

Sum

Aggregate the columns in the Frame to their sum value.

public DataFrame Sum()

---

SumHorizontal

Sum all values horizontally across columns. (ignore_nulls defaults to true)

public Series SumHorizontal(bool ignoreNulls = true)

Parameters

Name	Type	Description
ignoreNulls	bool	-

---

Tail

Return tail lines from a DataFrame

public DataFrame Tail(int n = 5)

Parameters

Name	Type	Description
n	int	-

---

Take

Alias for gather

public DataFrame Take(IntoIndexLazyFrame index, bool nullOnOob = false)

Parameters

Name	Type	Description
index	IntoIndexLazyFrame	-
nullOnOob	bool	-

---

ToArrow

Transfer a DataFrame to Arrow

public RecordBatch ToArrow()

---

ToArrowStream

Export DataFrame to Arrow C Data Interface Stream. Supports zero-copy and lazy chunked reading.

public IArrowArrayStream ToArrowStream(ReadOnlySpan<int> columnIndices = default, ulong? seed = null)

Parameters

Name	Type	Description
columnIndices	ReadOnlySpan<int>	Optional column indices to prune the export (Projection Pushdown).
seed	ulong?	Optional random seed to shuffle chunks.

Returns: Standard IArrowArrayStream

---

ToAsyncEnumerable

Exposes in-memory DataFrame rows as a cooperative, cancellation-aware IAsyncEnumerable.

public IAsyncEnumerable<T> ToAsyncEnumerable<T>(int chunkSize = 10000, CancellationToken cancellationToken = default)

Parameters

Name	Type	Description
chunkSize	int	Number of rows hydrated synchronously per cooperative slice.
cancellationToken	CancellationToken	Optional cooperative cancellation token.

Returns: An asynchronous stream of hydrated row objects.

---

ToDict

Convert the DataFrame to a dictionary of column name to Series.

public IReadOnlyDictionary<string, Series> ToDict()

---

ToDicts

Convert every row to a dictionary of column name to value. Corresponds to Python's DataFrame.to_dicts().

public List<Dictionary<string, object>> ToDicts()

Returns: A list of dictionaries, one per row, mapping column names to their values as object?.

---

ToDummies

Convert categorical/string variables into dummy/indicator variables (One-Hot Encoding).

public DataFrame ToDummies(
    IntoSelector? columns = null,
    string separator = "_",
    bool dropFirst = false,
    bool dropNulls = false
)

Parameters

Name	Type	Description
columns	IntoSelector?	Optional. The columns to encode. Accepts strings, string arrays, or Selectors (e.g. Cs.String()). If null, all string/categorical columns are encoded.
separator	string	The separator used in the generated column names.
dropFirst	bool	Whether to drop the first dummy variable to avoid collinearity (k-1 dummies).
dropNulls	bool	Whether to ignore null values when creating dummies.

Returns: A new DataFrame with one-hot encoded columns.

---

ToHtml

Generates a rich HTML table representation of the DataFrame for interactive environments (e.g., LINQPad, Jupyter).

public string ToHtml()

Returns: An HTML string representing the preview of the DataFrame.

---

ToStruct

Convert a DataFrame to a Series of type Struct.

public Series ToStruct(string name = "")

Parameters

Name	Type	Description
name	string	Name for the struct Series.

---

TopK

Get the top k rows according to the given expressions.

This selects the largest values.

Get the top k rows according to a single expression.

Master method: Get the top k rows by multiple Expressions with optional parallel boolean arrays. Usage: df.TopK(10, myColumns.Select(c => Pl.Col(c)))

Get the top k rows with specific reverse directions using Tuples. Usage: df.TopK(5, ("Score", false), ("Age", true), (Cs.Numeric(), false))

public DataFrame TopK(int k, IEnumerable<Expr> by, bool[] reverse)
public DataFrame TopK(int k, IntoExprColumn by, bool reverse = false)
public DataFrame TopK(int k, IEnumerable<string> colName, bool reverse = false)
public DataFrame TopK(int k, params (IntoExprColumn, bool)[] configs)

Parameters

Name	Type	Description
k	int	-
by	IEnumerable<Expr> / IntoExprColumn	-
reverse	bool[]	-
colName	IEnumerable<string>	-
configs	(IntoExprColumn, bool)[]	-

---

Transpose

Transpose a DataFrame over the diagonal.

public DataFrame Transpose(bool includeHeader = false, string headerName = "column")
public DataFrame Transpose(string columnName, bool includeHeader = false, string headerName = "column")
public DataFrame Transpose(IEnumerable<string> customNames, bool includeHeader = false, string headerName = "column")

Parameters

Name	Type	Description
includeHeader	bool	If set, the column names will be added as first column.
headerName	string	If include_header is set, this determines the name of the column that will be inserted.
columnName	string	Optional iterable yielding strings or a string naming an existing column. These will name the value (non-header) columns in the transposed data.
customNames	IEnumerable<string>	Optional iterable yielding strings or a string naming an existing column. These will name the value (non-header) columns in the transposed data.

---

Unique

Returns a new DataFrame with unique rows based on ALL columns.

Returns a new DataFrame with unique rows based on specific column names. Supports C# 12 collection expressions like ["Id", "Date"].

Returns a new DataFrame with unique rows based on a subset of columns (Selector, Type, DataType).

public DataFrame Unique(
    UniqueKeepStrategy keep = UniqueKeepStrategy.First,
    bool maintainOrder = false,
    long? offset = null,
    long? len = null
)

public DataFrame Unique(
    IEnumerable<string> subset,
    UniqueKeepStrategy keep = UniqueKeepStrategy.First,
    bool maintainOrder = false,
    long? offset = null,
    long? len = null
)

public DataFrame Unique(
    IntoSelector subset,
    UniqueKeepStrategy keep = UniqueKeepStrategy.First,
    bool maintainOrder = false,
    long? offset = null,
    long? len = null
)

Parameters

Name	Type	Description
keep	UniqueKeepStrategy	The strategy for which duplicate rows to retain (First, Last, Any, or None).
maintainOrder	bool	Keep the same order as the original DataFrame.
offset	long?	The starting index from which to begin the slice of unique results.
len	long?	The maximum number of rows to include in the result.
subset	IEnumerable<string> / IntoSelector	-

---

Unnest

Decompose a struct column into multiple columns.

This is useful for flattening nested JSON-like data.

Decompose a struct column into multiple columns (one for each field in the struct).

This is useful for flattening nested JSON-like data or composite types. The original struct column is replaced by its fields.

Decompose a struct column into multiple columns (one for each field in the struct).

public DataFrame Unnest(IntoSelector columns, string? separator = null)
public DataFrame Unnest(IEnumerable<string> columns, string? separator = null)
public DataFrame Unnest(params string[] columns)

Parameters

Name	Type	Description
columns	IntoSelector / IEnumerable<string> / string[]	The names of the struct columns to unnest.
separator	string?	Optional separator to append to the struct field names (e.g., "struct_col.field"). If null, the field names replace the struct column name directly.

Returns: A new DataFrame with the struct columns expanded.

Example

var data = new[] { new { User = new { Name = "Alice", Age = 30 } } };
var df = DataFrame.From(data);

// Unnest with a separator to avoid name collisions
// Result columns: "User_Name", "User_Age"
var unnested = df.Unnest(new[] { "User" }, separator: "_");
unnested.Show();
/* Output:
shape: (1, 2)
┌───────────┬──────────┐
│ User_Name ┆ User_Age │
│ ---       ┆ ---      │
│ str       ┆ i32      │
╞═══════════╪══════════╡
│ Alice     ┆ 30       │
└───────────┴──────────┘
*/

// Create a DataFrame with a nested structure (simulating JSON)
var nestedData = new[]
{
    new { Id = 1, User = new { Name = "Alice", City = "NY" } },
    new { Id = 2, User = new { Name = "Bob",   City = "LA" } }
};

var df = DataFrame.From(nestedData);

// Unnest the "User" column into "Name" and "City"
var unnested = df.Unnest("User");

unnested.Show();
/* Output:
shape: (2, 3)
┌─────┬───────┬──────┐
│ Id  ┆ Name  ┆ City │
│ --- ┆ ---   ┆ ---  │
│ i32 ┆ str   ┆ str  │
╞═════╪═══════╪══════╡
│ 1   ┆ Alice ┆ NY   │
│ 2   ┆ Bob   ┆ LA   │
└─────┴───────┴──────┘
*/

---

Unpivot

Unpivot (Melt) the DataFrame from wide to long format.

This is the reverse of DataFrame.Pivot. It collapses multiple columns into key-value pairs.

Unpivot (Melt) the DataFrame from wide to long format.

public DataFrame Unpivot(
    IEnumerable<string> index,
    IEnumerable<string>? on = null,
    string variableName = "variable",
    string valueName = "value"
)

public DataFrame Unpivot(
    IntoSelector index,
    IntoSelector? on = null,
    string variableName = "variable",
    string valueName = "value"
)

Parameters

Name	Type	Description
index	IEnumerable<string> / IntoSelector	Column names to keep as identifiers (id_vars).
on	IEnumerable<string>? / IntoSelector?	Column names to unpivot/melt (value_vars).
variableName	string	Name for the new variable column (default "variable").
valueName	string	Name for the new value column (default "value").

Returns: A long-format DataFrame.

Example

// Using the 'pivoted' DataFrame from the Pivot example
// shape: (2, 3) [date, US, CN]

// Unpivot back to long format
var melted = pivoted.Unpivot(
    index: new[] { "date" },
    on: new[] { "CN", "US" },
    variableName: "country",
    valueName: "sales"
);

melted.Sort(new[] { "date", "country" }).Show();
/* Output:
shape: (4, 3)
┌────────────┬─────────┬───────┐
│ date       ┆ country ┆ sales │
│ ---        ┆ ---     ┆ ---   │
│ str        ┆ str     ┆ i32   │
╞════════════╪═════════╪═══════╡
│ 2024-01-01 ┆ CN      ┆ 200   │
│ 2024-01-01 ┆ US      ┆ 100   │
│ 2024-01-02 ┆ CN      ┆ 220   │
│ 2024-01-02 ┆ US      ┆ 110   │
└────────────┴─────────┴───────┘
*/

---

Unstack

Unstack a long table to a wide form without doing an aggregation. This can be much faster than a pivot, because it can skip the grouping phase.

Unstack specific columns by names.

Unstack all columns

public DataFrame Unstack(
    long step,
    IntoSelector columns,
    UnstackDirection how = UnstackDirection.Vertical,
    object[]? fillValues = null
)

public DataFrame Unstack(
    long step,
    IEnumerable<string> columns,
    UnstackDirection how = UnstackDirection.Vertical,
    object[]? fillValues = null
)

public DataFrame Unstack(long step, UnstackDirection how = UnstackDirection.Vertical, object[]? fillValues = null)

Parameters

Name	Type	Description
step	long	Number of rows in the unstacked frame.
columns	IntoSelector / IEnumerable<string>	Column name(s) or selector(s) to include in the operation. If set to None (default), use all columns.
how	UnstackDirection	Direction of the unstack.
fillValues	object[]?	Fill values that don’t fit the new size with this value.

---

Update

Update the values in this Frame with the values in other.

public DataFrame Update(
    DataFrame other,
    IEnumerable<string>? on = null,
    JoinType how = JoinType.Left,
    IEnumerable<string>? leftOn = null,
    IEnumerable<string>? rightOn = null,
    bool includeNulls = false,
    JoinMaintainOrder maintainOrder = JoinMaintainOrder.Left
)

Parameters

Name	Type	Description
other	DataFrame	Frame that will be used to update the values
on	IEnumerable<string>?	Column names that will be joined on. If set to Null (default), the implicit row index of each frame is used as a join key.
how	JoinType	'Left' will keep all rows from the left table; rows may be duplicated if multiple rows in the right frame match the left row’s key. ‘Inner’: keeps only those rows where the key exists in both frames. ‘Full’: will update existing rows where the key matches while also adding any new rows contained in the given frame.
leftOn	IEnumerable<string>?	Join column(s) of the left Frame.
rightOn	IEnumerable<string>?	Join column(s) of the right Frame.
includeNulls	bool	Overwrite values in the left frame with null values from the right frame. If set to False (default), null values in the right frame are ignored.
maintainOrder	JoinMaintainOrder	Which order of rows from the inputs to preserve. See join() for details. Unlike join this function preserves the left order by default.

---

Upsample

Upsample a DataFrame at a regular frequency.

public DataFrame Upsample(
    IntoSelector timeColumn,
    IntoDuration every,
    IntoSelector? groupBy = null,
    bool maintainOrder = false
)

Parameters

Name	Type	Description
timeColumn	IntoSelector	The column used for the time/datetime index.
every	IntoDuration	The interval to upsample to (e.g., "1d", "1h", or a TimeSpan).
groupBy	IntoSelector?	Optional. Group by these columns before upsampling. Accepts strings, Selectors, or Exprs.
maintainOrder	bool	If true, maintains the original order of the groups.

Returns: A new DataFrame with missing time steps filled with nulls.

---

VStack

Vertically stack another DataFrame to this one. Checks that the schema matches.

public DataFrame VStack(DataFrame other)

Parameters

Name	Type	Description
other	DataFrame	The DataFrame to stack vertically.

---

Var

Aggregate the columns in the Frame to their variance value.

public DataFrame Var(byte ddof = 1)

Parameters

Name	Type	Description
ddof	byte	“Delta Degrees of Freedom”: the divisor used in the calculation is N - ddof, where N represents the number of elements. By default ddof is 1.

---

WithColumns

Add new columns to the DataFrame or replace existing ones using expressions.

Unlike DataFrame.Select, this method keeps all original columns in the DataFrame and appends the new ones (or replaces them if the names match).

Add or overwrite multiple columns from a collection of Expressions.

Add or overwrite multiple columns from a collection of mixed IntoExpr types.

Add or overwrite multiple columns from a collection of string column names.

public DataFrame WithColumns(params IntoExprColumn[] exprs)
public DataFrame WithColumns(IEnumerable<Expr> exprs)
public DataFrame WithColumns(IEnumerable<IntoExprColumn> exprs)
public DataFrame WithColumns(IEnumerable<string> columns)

Parameters

Name	Type	Description
exprs	IntoExprColumn[] / IEnumerable<Expr> / IEnumerable<IntoExprColumn>	Expressions defining the new columns to add.
columns	IEnumerable<string>	-

Returns: A new DataFrame with the original columns plus the new/modified columns.

Example

var df = DataFrame.FromColumns(new
{
    foo = new[] { 1, 2, 3 },
    bar = new[] { 6, 7, 8 },
    ham = new[] { "a", "b", "c" }
});

// Add a "sum" column (foo + bar) while keeping others
var withCols = df.WithColumns(
    (Col("foo") + Col("bar")).Alias("sum")
);

withCols.Show();
/* Output:
shape: (3, 4)
┌─────┬─────┬─────┬─────┐
│ foo ┆ bar ┆ ham ┆ sum │
│ --- ┆ --- ┆ --- ┆ --- │
│ i32 ┆ i32 ┆ str ┆ i32 │
╞═════╪═════╪═════╪═════╡
│ 1   ┆ 6   ┆ a   ┆ 7   │
│ 2   ┆ 7   ┆ b   ┆ 9   │
│ 3   ┆ 8   ┆ c   ┆ 11  │
└─────┴─────┴─────┴─────┘
*/

---

WithRowIndex

Add a column at index 0 that counts the rows.

This can be useful to generate a unique index or to maintain order in operations that would otherwise discard it.

public DataFrame WithRowIndex(string name = "index", int? offset = null)

Parameters

Name	Type	Description
name	string	The name of the new row index column. Defaults to "index".
offset	int?	The starting value of the row index. Defaults to 0.

Returns: A new DataFrame with the row index column added.

---

WriteAvro

Write the DataFrame to an Apache Avro file.

public void WriteAvro(string path, AvroCompression compression = AvroCompression.Uncompressed, string name = "")

Parameters

Name	Type	Description
path	string	The file path to write to.
compression	AvroCompression	The compression algorithm to use.
name	string	The name of the Avro record.

---

WriteAvroMemory

Write the DataFrame to an Apache Avro memory buffer.

public byte[] WriteAvroMemory(AvroCompression compression = AvroCompression.Uncompressed, string name = "")

Parameters

Name	Type	Description
compression	AvroCompression	The compression algorithm to use.
name	string	The name of the Avro record.

Returns: A byte array containing the Avro data.

---

WriteCatalogTable

Writes the DataFrame into a Unity Catalog table by converting it to a LazyFrame.

public void WriteCatalogTable(
    UnityCatalog catalog,
    string catalogName,
    string schemaName,
    string tableName,
    IntoSelector? partitionBy = null,
    DeltaSaveMode mode = DeltaSaveMode.Append,
    bool canEvolve = false,
    bool includeKeys = true,
    bool keysPreGrouped = false,
    int maxRowsPerFile = 0,
    long approxBytesPerFile = 0,
    ParquetCompression compression = ParquetCompression.Snappy,
    int compressionLevel = -1,
    bool statistics = true,
    uint rowGroupSize = 0,
    uint dataPageSize = 0,
    int compatLevel = -1,
    bool maintainOrder = true,
    SyncOnClose syncOnClose = SyncOnClose.None,
    bool mkdir = false,
    CloudOptions? cloudOptions = null
)

Parameters

Name	Type	Description
catalog	UnityCatalog	-
catalogName	string	The name of the catalog (e.g., "main").
schemaName	string	The name of the schema/database (e.g., "default").
tableName	string	The name of the table to sink.
partitionBy	IntoSelector?	The selector(s) to partition the data by. Directories will be created in the format "col=value".
mode	DeltaSaveMode	Save mode (Append, Overwrite, ErrorIfExists, Ignore). Default is Append.
canEvolve	bool	Define whether schema evolution is allowed, default: false
includeKeys	bool	Whether to include the partition keys in the Parquet files themselves. Default is true (recommended for Delta Lake compatibility).
keysPreGrouped	bool	Assert that the keys are already pre-grouped. This can speed up the operation if true.
maxRowsPerFile	int	Maximum number of rows per file. 0 means no limit.
approxBytesPerFile	long	Approximate size in bytes per file. 0 means no limit.
compression	ParquetCompression	Compression codec to use (Snappy, Zstd, etc.).
compressionLevel	int	Compression level (depends on the codec).
statistics	bool	Write statistics to the Parquet file. Delta Lake uses these stats for data skipping, so 'true' is highly recommended.
rowGroupSize	uint	Target row group size (in rows).
dataPageSize	uint	Target data page size (in bytes).
compatLevel	int	IPC format compatibility.
maintainOrder	bool	Maintain the order of the data.
syncOnClose	SyncOnClose	Whether to sync the file to disk on close.
mkdir	bool	Create parent directories if they don't exist.
cloudOptions	CloudOptions?	Options for cloud storage authentication and configuration.

---

WriteCsv

Write DataFrame to a comma-separated values (CSV) file.

This uses the Lazy execution engine internally to support streaming and cloud storage.

public void WriteCsv(
    string path,
    bool includeHeader = true,
    bool includeBom = false,
    char separator = ',',
    char quoteChar = '"',
    QuoteStyle quoteStyle = QuoteStyle.Necessary,
    string? nullValue = null,
    string? lineTerminator = "\n",
    bool? floatScientific = null,
    int? floatPrecision = null,
    bool decimalComma = false,
    string? dateFormat = null,
    string? timeFormat = null,
    string? datetimeFormat = null,
    bool checkExtension = true,
    ExternalCompression compression = ExternalCompression.Uncompressed,
    int compressionLevel = -1,
    bool maintainOrder = true,
    SyncOnClose syncOnClose = SyncOnClose.None,
    bool mkdir = false,
    int batchSize = 0,
    CloudOptions? cloudOptions = null
)

Parameters

Name	Type	Description
path	string	The output file path.
includeHeader	bool	Whether to include the header row. Defaults to true.
includeBom	bool	Whether to include the UTF-8 Byte Order Mark (BOM). Defaults to false.
separator	char	The character used as a field separator. Defaults to ','.
quoteChar	char	The character used for quoting fields. Defaults to '"'.
quoteStyle	QuoteStyle	The quoting style to use. Defaults to QuoteStyle.Necessary.
nullValue	string?	The string representation for null values. Defaults to empty string.
lineTerminator	string?	The character sequence used to terminate lines. Defaults to "\n".
floatScientific	bool?	Whether to always use scientific notation for floats. If null (default), formatting is automatic.
floatPrecision	int?	The number of decimal places to write for floats. If null (default), uses full precision.
decimalComma	bool	Whether to use a comma ',' as the decimal separator. Defaults to false.
dateFormat	string?	Format string for Date columns. If null, uses ISO 8601.
timeFormat	string?	Format string for Time columns. If null, uses ISO 8601.
datetimeFormat	string?	Format string for Datetime columns. If null, uses ISO 8601.
checkExtension	bool	Whether to check if the file extension matches '.csv'. Defaults to true.
compression	ExternalCompression	Compression method (Gzip/Zstd). Defaults to None.
compressionLevel	int	Compression level (depends on the codec). -1 for default.
maintainOrder	bool	Whether to maintain the order of the data. Setting this to false can improve performance in streaming mode. Defaults to true.
syncOnClose	SyncOnClose	File synchronization behavior on close (e.g., flush to disk). Defaults to None.
mkdir	bool	Recursively create the output directory if it does not exist. Defaults to false.
batchSize	int	The batch size for writing rows. 0 means use the Polars default.
cloudOptions	CloudOptions?	Options for cloud storage (AWS S3, Azure Blob, GCS, etc.).

---

WriteCsvMemory

Sink the LazyFrame to a CSV format in memory.

This allows for streaming execution directly into a byte array without writing to disk.

public byte[] WriteCsvMemory(
    bool includeBom = false,
    bool includeHeader = true,
    int batchSize = 1024,
    bool checkExtension = false,
    ExternalCompression compressionCode = ExternalCompression.Uncompressed,
    int compressionLevel = 0,
    string? dateFormat = null,
    string? timeFormat = null,
    string? datetimeFormat = null,
    int floatScientific = -1,
    int floatPrecision = -1,
    bool decimalComma = false,
    byte separator = 44,
    byte quoteChar = 34,
    string? nullValue = null,
    string? lineTerminator = "\n",
    QuoteStyle quoteStyle = QuoteStyle.Necessary,
    bool maintainOrder = true
)

Parameters

Name	Type	Description
includeBom	bool	-
includeHeader	bool	-
batchSize	int	-
checkExtension	bool	-
compressionCode	ExternalCompression	-
compressionLevel	int	-
dateFormat	string?	-
timeFormat	string?	-
datetimeFormat	string?	-
floatScientific	int	-
floatPrecision	int	-
decimalComma	bool	-
separator	byte	-
quoteChar	byte	-
nullValue	string?	-
lineTerminator	string?	-
quoteStyle	QuoteStyle	-
maintainOrder	bool	-

---

WriteCsvPartitioned

Write DataFrame to a partitioned comma-separated values (CSV) file.

This uses the Lazy execution engine internally to support streaming and cloud storage.

public void WriteCsvPartitioned(
    string path,
    IntoSelector partitionBy,
    bool includeKeys = true,
    bool keysPreGrouped = false,
    int maxRowsPerFile = 0,
    long approxBytesPerFile = 0,
    bool includeHeader = true,
    bool includeBom = false,
    char separator = ',',
    char quoteChar = '"',
    QuoteStyle quoteStyle = QuoteStyle.Necessary,
    string? nullValue = null,
    string? lineTerminator = "\n",
    bool? floatScientific = null,
    int? floatPrecision = null,
    bool decimalComma = false,
    string? dateFormat = null,
    string? timeFormat = null,
    string? datetimeFormat = null,
    bool checkExtension = true,
    ExternalCompression compression = ExternalCompression.Uncompressed,
    int compressionLevel = -1,
    bool maintainOrder = true,
    SyncOnClose syncOnClose = SyncOnClose.None,
    bool mkdir = false,
    int batchSize = 0,
    CloudOptions? cloudOptions = null
)

Parameters

Name	Type	Description
path	string	The output base directory path.
partitionBy	IntoSelector	The selector(s) to partition the data by.
includeKeys	bool	Whether to include the partition keys in the output files.
keysPreGrouped	bool	Assert that the keys are already pre-grouped. This can speed up the operation if true. Use with caution: if the data is not grouped, the output may be incorrect.
maxRowsPerFile	int	Maximum number of rows per file. 0 means no limit.
approxBytesPerFile	long	Approximate size in bytes per file. 0 means no limit.
includeHeader	bool	Whether to include the header row. Defaults to true.
includeBom	bool	Whether to include the UTF-8 Byte Order Mark (BOM). Defaults to false.
separator	char	The character used as a field separator. Defaults to ','.
quoteChar	char	The character used for quoting fields. Defaults to '"'.
quoteStyle	QuoteStyle	The quoting style to use. Defaults to QuoteStyle.Necessary.
nullValue	string?	The string representation for null values. Defaults to empty string.
lineTerminator	string?	The character sequence used to terminate lines. Defaults to "\n".
floatScientific	bool?	Whether to always use scientific notation for floats. If null (default), formatting is automatic.
floatPrecision	int?	The number of decimal places to write for floats. If null (default), uses full precision.
decimalComma	bool	Whether to use a comma ',' as the decimal separator. Defaults to false.
dateFormat	string?	Format string for Date columns. If null, uses ISO 8601.
timeFormat	string?	Format string for Time columns. If null, uses ISO 8601.
datetimeFormat	string?	Format string for Datetime columns. If null, uses ISO 8601.
checkExtension	bool	Whether to check if the file extension matches '.csv'. Defaults to true.
compression	ExternalCompression	Compression method (Gzip/Zstd). Defaults to None.
compressionLevel	int	Compression level (depends on the codec). -1 for default.
maintainOrder	bool	Whether to maintain the order of the data. Setting this to false can improve performance in streaming mode. Defaults to true.
syncOnClose	SyncOnClose	File synchronization behavior on close (e.g., flush to disk). Defaults to None.
mkdir	bool	Recursively create the output directory if it does not exist. Defaults to false.
batchSize	int	The batch size for writing rows. 0 means use the Polars default.
cloudOptions	CloudOptions?	Options for cloud storage (AWS S3, Azure Blob, GCS, etc.).

---

WriteDelta

Write a DataFrame into a delta table

public void WriteDelta(
    string path,
    IntoSelector? partitionBy = null,
    DeltaSaveMode mode = DeltaSaveMode.Append,
    bool canEvolve = false,
    bool includeKeys = true,
    bool keysPreGrouped = false,
    int maxRowsPerFile = 0,
    long approxBytesPerFile = 0,
    ParquetCompression compression = ParquetCompression.Snappy,
    int compressionLevel = -1,
    bool statistics = true,
    uint rowGroupSize = 0,
    uint dataPageSize = 0,
    int compatLevel = -1,
    bool maintainOrder = true,
    SyncOnClose syncOnClose = SyncOnClose.None,
    bool mkdir = false,
    CloudOptions? cloudOptions = null
)

Parameters

Name	Type	Description
path	string	Path to the root of the Delta Table. Can be local (e.g. "./data/table") or remote (e.g. "s3://bucket/table").
partitionBy	IntoSelector?	The selector(s) to partition the data by. Directories will be created in the format "col=value".
mode	DeltaSaveMode	Save mode (Append, Overwrite, ErrorIfExists, Ignore). Default is Append.
canEvolve	bool	Define whether schema evolution is allowed, default: false
includeKeys	bool	Whether to include the partition keys in the Parquet files themselves. Default is true (recommended for Delta Lake compatibility).
keysPreGrouped	bool	Assert that the keys are already pre-grouped. This can speed up the operation if true.
maxRowsPerFile	int	Maximum number of rows per file. 0 means no limit.
approxBytesPerFile	long	Approximate size in bytes per file. 0 means no limit.
compression	ParquetCompression	Compression codec to use (Snappy, Zstd, etc.).
compressionLevel	int	Compression level (depends on the codec).
statistics	bool	Write statistics to the Parquet file. Delta Lake uses these stats for data skipping, so 'true' is highly recommended.
rowGroupSize	uint	Target row group size (in rows).
dataPageSize	uint	Target data page size (in bytes).
compatLevel	int	IPC format compatibility.
maintainOrder	bool	Maintain the order of the data.
syncOnClose	SyncOnClose	Whether to sync the file to disk on close.
mkdir	bool	Create parent directories if they don't exist.
cloudOptions	CloudOptions?	Options for cloud storage authentication and configuration.

---

WriteExcel

Writes the DataFrame to an Excel file (.xlsx) using the native high-performance engine.

Performance: Uses columnar writing strategies for maximum speed (via rust_xlsxwriter).

Data Integrity:- UInt64, Int128, UInt128 will be automatically written as Text to prevent Excel's 53-bit floating-point precision loss. - Date and Datetime are written as native Excel date objects with specified formatting.

public void WriteExcel(
    string path,
    string? sheetName = null,
    string? dateFormat = null,
    string? datetimeFormat = null
)

Parameters

Name	Type	Description
path	string	The file path to save the .xlsx file.
sheetName	string?	Name of the worksheet. Defaults to "Sheet1" if null.
dateFormat	string?	Excel format string for Date columns (e.g., "yyyy-mm-dd"). If null, defaults to "yyyy-mm-dd".
datetimeFormat	string?	Excel format string for Datetime columns (e.g., "yyyy-mm-dd hh:mm:ss"). If null, defaults to "yyyy-mm-dd hh:mm:ss".

---

WriteIpc

Write DataFrame to an IPC (Arrow/Feather) file.

This uses the Lazy execution engine internally to support streaming and cloud storage.

public void WriteIpc(
    string path,
    IpcCompression compression = IpcCompression.None,
    int compatLevel = -1,
    int recordBatchSize = 0,
    bool recordBatchStatistics = true,
    bool maintainOrder = true,
    SyncOnClose syncOnClose = SyncOnClose.None,
    bool mkdir = false,
    CloudOptions? cloudOptions = null
)

Parameters

Name	Type	Description
path	string	The output file path.
compression	IpcCompression	Compression method (None, LZ4, ZSTD). Defaults to None.
compatLevel	int	Compatibility level (default -1 = newest).
recordBatchSize	int	Number of rows per record batch (0 = default).
recordBatchStatistics	bool	Write statistics to the record batch header (default = true).
maintainOrder	bool	Maintain the order of the data.
syncOnClose	SyncOnClose	Whether to sync the file to disk on close.
mkdir	bool	Create parent directories if they don't exist (Local file system only).
cloudOptions	CloudOptions?	Options for cloud storage.

---

WriteIpcMemory

Sink the LazyFrame to an IPC (Arrow) format in memory.

This allows for streaming execution directly into a byte array without writing to disk.

public byte[] WriteIpcMemory(
    IpcCompression compression = IpcCompression.None,
    int compatLevel = -1,
    int recordBatchSize = 0,
    bool recordBatchStatistics = true,
    bool maintainOrder = true
)

Parameters

Name	Type	Description
compression	IpcCompression	Compression method to use.
compatLevel	int	Compatibility level (default -1 = newest).
recordBatchSize	int	Number of rows per record batch (0 = default).
recordBatchStatistics	bool	Write statistics to the record batch header (default = true).
maintainOrder	bool	Maintain the order of the data.

Returns: A byte array containing the serialized IPC data.

---

WriteIpcPartitioned

Sink the LazyFrame to an IPC (Arrow) file.

This allows for streaming execution.

public void WriteIpcPartitioned(
    string path,
    IntoSelector partitionBy,
    bool includeKeys = true,
    bool keysPreGrouped = false,
    int maxRowsPerFile = 0,
    long approxBytesPerFile = 0,
    IpcCompression compression = IpcCompression.None,
    int compatLevel = -1,
    int recordBatchSize = 0,
    bool recordBatchStatistics = true,
    bool maintainOrder = true,
    SyncOnClose syncOnClose = SyncOnClose.None,
    bool mkdir = false,
    CloudOptions? cloudOptions = null
)

Parameters

Name	Type	Description
path	string	Path to the output file.
partitionBy	IntoSelector	The selector(s) to partition the data by.
includeKeys	bool	Whether to include the partition keys in the output files.
keysPreGrouped	bool	Assert that the keys are already pre-grouped. This can speed up the operation if true. Use with caution: if the data is not grouped, the output may be incorrect.
maxRowsPerFile	int	Maximum number of rows per file. 0 means no limit.
approxBytesPerFile	long	Approximate size in bytes per file. 0 means no limit.
compression	IpcCompression	Compression method to use.
compatLevel	int	Compatibility level (default -1 = newest).
recordBatchSize	int	Number of rows per record batch (0 = default).
recordBatchStatistics	bool	Write statistics to the record batch header (default = true).
maintainOrder	bool	Maintain the order of the data.
syncOnClose	SyncOnClose	Whether to sync the file to disk on close.
mkdir	bool	Create parent directories if they don't exist (Local file system only).
cloudOptions	CloudOptions?	Options for cloud storage.

---

WriteIpcStream

Write to Arrow IPC record batch stream.

public void WriteIpcStream(string path, IpcCompression compression = IpcCompression.None, int compatLevel = -1)

Parameters

Name	Type	Description
path	string	Path to the IPC stream file or a byte buffer
compression	IpcCompression	Compression method. Defaults to None.
compatLevel	int	Use a specific compatibility level when exporting Polars’ internal data structures.

---

WriteIpcStreamMemory

Write to Arrow IPC record batch stream.

public byte[] WriteIpcStreamMemory(IpcCompression compression = IpcCompression.None, int compatLevel = -1)

Parameters

Name	Type	Description
compression	IpcCompression	Compression method. Defaults to None.
compatLevel	int	Use a specific compatibility level when exporting Polars’ internal data structures.

---

WriteJson

Write DataFrame to a JSON file.

public void WriteJson(string path, JsonFormat format = JsonFormat.Json)

Parameters

Name	Type	Description
path	string	Output file path.
format	JsonFormat	JSON format (Json Array or JsonLines). Defaults to Json.

---

WriteJsonMemory

Writes the DataFrame to a JSON format in memory.

public byte[] WriteJsonMemory(JsonFormat jsonFormat = JsonFormat.Json)

Parameters

Name	Type	Description
jsonFormat	JsonFormat	The JSON format to use (Json or JsonLines).

Returns: A byte array containing the JSON data.

---

WriteJsonPartitioned

Sink the LazyFrame to a NDJSON (Newline Delimited JSON) file.

public void WriteJsonPartitioned(
    string path,
    IntoSelector partitionBy,
    bool includeKeys = true,
    bool keysPreGrouped = false,
    int maxRowsPerFile = 0,
    long approxBytesPerFile = 0,
    ExternalCompression compression = ExternalCompression.Uncompressed,
    int compressionLevel = -1,
    bool checkExtension = true,
    bool maintainOrder = true,
    SyncOnClose syncOnClose = SyncOnClose.None,
    bool mkdir = false,
    CloudOptions? cloudOptions = null
)

Parameters

Name	Type	Description
path	string	Output file path.
partitionBy	IntoSelector	The selector(s) to partition the data by.
includeKeys	bool	Whether to include the partition keys in the output files.
keysPreGrouped	bool	Assert that the keys are already pre-grouped. This can speed up the operation if true. Use with caution: if the data is not grouped, the output may be incorrect.
maxRowsPerFile	int	Maximum number of rows per file. 0 means no limit.
approxBytesPerFile	long	Approximate size in bytes per file. 0 means no limit.
compression	ExternalCompression	Compression method (Gzip/Zstd).
compressionLevel	int	Compression level.
checkExtension	bool	Whether to check if the file extension matches '.json' or '.ndjson'.
maintainOrder	bool	Maintain the order of data.
syncOnClose	SyncOnClose	Sync to disk on close.
mkdir	bool	Create parent directories.
cloudOptions	CloudOptions?	Cloud storage options.

---

WriteNdJson

Write DataFrame to a Newline Delimited JSON (NDJSON) file.

This uses the Lazy execution engine internally to support streaming, compression, and cloud storage.

public void WriteNdJson(
    string path,
    ExternalCompression compression = ExternalCompression.Uncompressed,
    int compressionLevel = -1,
    bool checkExtension = true,
    bool maintainOrder = true,
    SyncOnClose syncOnClose = SyncOnClose.None,
    bool mkdir = false,
    CloudOptions? cloudOptions = null
)

Parameters

Name	Type	Description
path	string	Output file path.
compression	ExternalCompression	Compression method (Gzip/Zstd). Defaults to None.
compressionLevel	int	Compression level. -1 for default.
checkExtension	bool	Whether to check if the file extension matches '.json' or '.ndjson'.
maintainOrder	bool	Maintain the order of data.
syncOnClose	SyncOnClose	Sync to disk on close.
mkdir	bool	Create parent directories.
cloudOptions	CloudOptions?	Cloud storage options.

---

WriteParquet

Write DataFrame to a Parquet file.

This uses the Lazy execution engine internally to support streaming, statistics, and cloud storage.

public void WriteParquet(
    string path,
    ParquetCompression compression = ParquetCompression.Snappy,
    int compressionLevel = -1,
    bool statistics = false,
    int rowGroupSize = 0,
    int dataPageSize = 0,
    int compatLevel = -1,
    bool maintainOrder = true,
    SyncOnClose syncOnClose = SyncOnClose.None,
    bool mkdir = false,
    CloudOptions? cloudOptions = null
)

Parameters

Name	Type	Description
path	string	Output file path.
compression	ParquetCompression	Compression method. Defaults to Snappy.
compressionLevel	int	Compression level. -1 means default.
statistics	bool	Compute and write column statistics. Defaults to false.
rowGroupSize	int	Number of rows per row group. 0 means use default.
dataPageSize	int	Size of data page in bytes. 0 means use default.
compatLevel	int	-
maintainOrder	bool	-
syncOnClose	SyncOnClose	-
mkdir	bool	-
cloudOptions	CloudOptions?	Options for cloud storage (AWS S3, Azure Blob, GCS, etc.).

---

WriteParquetMemory

Sink the LazyFrame to a Parquet format in memory.

This allows for streaming execution directly into a byte array without writing to disk.

public byte[] WriteParquetMemory(
    ParquetCompression compression = ParquetCompression.ZSTD,
    int compressionLevel = 3,
    bool statistics = true,
    int rowGroupSize = 0,
    int dataPageSize = 0,
    int compatLevel = -1,
    bool maintainOrder = true
)

Parameters

Name	Type	Description
compression	ParquetCompression	-
compressionLevel	int	-
statistics	bool	-
rowGroupSize	int	-
dataPageSize	int	-
compatLevel	int	-
maintainOrder	bool	-

---

WriteParquetPartitioned

Sink the LazyFrame to a set of Parquet files, partitioned by the specified selector.

This writes the dataset to a directory, splitting the data into multiple files based on the partition key(s) defined in partitionBy.

public void WriteParquetPartitioned(
    string path,
    IntoSelector partitionBy,
    bool includeKeys = true,
    bool keysPreGrouped = false,
    int maxRowsPerFile = 0,
    long approxBytesPerFile = 0,
    ParquetCompression compression = ParquetCompression.Snappy,
    int compressionLevel = -1,
    bool statistics = false,
    int rowGroupSize = 0,
    int dataPageSize = 0,
    int compatLevel = -1,
    bool maintainOrder = true,
    SyncOnClose syncOnClose = SyncOnClose.None,
    bool mkdir = false,
    CloudOptions? cloudOptions = null
)

Parameters

Name	Type	Description
path	string	Base path to the output directory.
partitionBy	IntoSelector	The selector(s) to partition the data by.
includeKeys	bool	Whether to include the partition keys in the output files.
keysPreGrouped	bool	Assert that the keys are already pre-grouped. This can speed up the operation if true. Use with caution: if the data is not grouped, the output may be incorrect.
maxRowsPerFile	int	Maximum number of rows per file. 0 means no limit.
approxBytesPerFile	long	Approximate size in bytes per file. 0 means no limit.
compression	ParquetCompression	Compression codec to use.
compressionLevel	int	Compression level (depends on the codec).
statistics	bool	Write statistics to the parquet file.
rowGroupSize	int	Target row group size (in rows).
dataPageSize	int	Target data page size (in bytes).
compatLevel	int	IPC format compatibility, -1: oldest, 0: default, 1: newest.
maintainOrder	bool	Maintain the order of the data.
syncOnClose	SyncOnClose	Whether to sync the file to disk on close.
mkdir	bool	Create parent directories if they don't exist (Local file system only).
cloudOptions	CloudOptions?	Options for cloud storage (AWS S3, Azure Blob, GCS, etc.).

---

WriteTo

Common Write Interface:Transform DataFrame to IDataReader

public void WriteTo(Action<IDataReader> writerAction, int bufferSize = 5, Dictionary<string, Type>? typeOverrides = null)

Parameters

Name	Type	Description
writerAction	Action<IDataReader>	-
bufferSize	int	-
typeOverrides	Dictionary<string, Type>?	-

---

WriteToAdbc

Zero-copy bulk ingest of the current DataFrame into an ADBC database (e.g., DuckDB, SQLite).

Zero-copy bulk ingest of the current DataFrame into an ADBC database table. Pure syntactic sugar: automatically manages the creation, configuration, and disposal of the underlying AdbcStatement.

public UpdateResult WriteToAdbc(AdbcStatement statement)
public UpdateResult WriteToAdbc(AdbcConnection connection, string tableName, AdbcIngestMode ingestMode = AdbcIngestMode.Create)

Parameters

Name	Type	Description
statement	AdbcStatement	An AdbcStatement configured with ingest options (e.g., target table).
connection	AdbcConnection	The active ADBC connection (e.g., DuckDB, SQLite).
tableName	string	The name of the target table to ingest data into.
ingestMode	AdbcIngestMode	The ingestion mode (e.g., "adbc.ingest.mode.create" or "adbc.ingest.mode.append"). Defaults to create.

Returns: The UpdateResult containing the number of rows affected.

---