Struct DataFrame 

pub struct DataFrame {
    pub columns: Vec<String>,
    pub data: Vec<Series>,
}

Fields

columns: Vec<String>

data: Vec<Series>

Implementations

impl DataFrame

pub fn window<'a>(&'a self, window_size: usize) -> Window<'a>

Create window for rolling operations

pub fn pivot_table( &self, index: &str, columns: &str, values: &str, aggfunc: PivotAggFunc, ) -> DataFrame

Pivot table functionality

pub fn melt( &self, id_vars: &[&str], value_vars: &[&str], var_name: Option<&str>, value_name: Option<&str>, ) -> DataFrame

Melt operation (unpivot)

pub fn cv_split( &self, n_folds: usize, shuffle: bool, ) -> Vec<(DataFrame, DataFrame)>

Cross-validation split

pub fn sample(&self, n: usize, replace: bool) -> DataFrame

Sample rows randomly

impl DataFrame

pub fn from_csv_map(path: &str) -> Result<Self, Box<dyn Error>>

Memory-mapped CSV reading for large files

impl DataFrame

pub fn to_arrow(&self) -> Result<RecordBatch, Box<dyn Error>>

Convert DataFrame to Apache Arrow RecordBatch

pub fn from_arrow(batch: &RecordBatch) -> Result<Self, Box<dyn Error>>

Create DataFrame from Apache Arrow RecordBatch

pub fn from_parquet(path: &str) -> Result<Self, Box<dyn Error>>

Read Parquet file using Arrow

pub fn to_parquet(&self, path: &str) -> Result<(), Box<dyn Error>>

Write DataFrame to Parquet file

pub fn from_ipc(path: &str) -> Result<Self, Box<dyn Error>>

Create DataFrame from Arrow IPC (Feather) file

pub fn to_ipc(&self, path: &str) -> Result<(), Box<dyn Error>>

Write DataFrame to Arrow IPC (Feather) file

pub fn arrow_filter( &self, column: &str, predicate: ArrowPredicate, ) -> Result<DataFrame, Box<dyn Error>>

Convert to Arrow and perform operations using Arrow Compute

pub fn arrow_agg( &self, column: &str, agg_func: ArrowAggFunc, ) -> Result<f64, Box<dyn Error>>

Aggregation using Arrow compute

pub fn arrow_slice( &self, offset: usize, length: usize, ) -> Result<DataFrame, Box<dyn Error>>

Zero-copy slice using Arrow

impl DataFrame

pub fn new(columns: Vec<(String, Series)>) -> Self

pub fn empty(columns: Vec<(String, SeriesType)>) -> Self

Create empty DataFrame with specified column names and types

pub fn len(&self) -> usize

Get number of rows

pub fn is_empty(&self) -> bool

Check if DataFrame is empty

pub fn shape(&self) -> (usize, usize)

Get shape (rows, columns)

pub fn head(&self, n: usize) -> DataFrame

Get first n rows

pub fn tail(&self, n: usize) -> DataFrame

Get last n rows

pub fn select(&self, cols: &[&str]) -> DataFrame

Select specific columns

pub fn get_column(&self, name: &str) -> Option<&Series>

Get a single column as a Series

pub fn filter(&self, mask: &[bool]) -> DataFrame

Filter rows based on a boolean mask

pub fn sort_by(&self, column: &str, ascending: bool) -> DataFrame

Sort by column

pub fn with_column(&self, name: String, series: Series) -> DataFrame

Add a new column

pub fn drop(&self, cols: &[&str]) -> DataFrame

Drop columns

pub fn join(&self, other: &DataFrame, on: &str, how: JoinType) -> DataFrame

Inner join with another DataFrame

pub fn describe(&self) -> DataFrame

Describe numeric columns (basic statistics)

impl DataFrame

pub fn groupby<'a>(&'a self, by: &str) -> GroupBy<'a>

Group DataFrame by column

pub fn groupby_count(&self, by: &str) -> DataFrame

Convenience method for groupby count (maintains backward compatibility)

impl DataFrame

pub fn from_csv(path: &str) -> Result<Self, Box<dyn Error>>

Read CSV with automatic type inference

pub fn from_csv_with_options( path: &str, options: CsvReadOptions, ) -> Result<Self, Box<dyn Error>>

Read CSV with custom options

pub fn to_csv(&self, path: &str) -> Result<(), Box<dyn Error>>

Write DataFrame to CSV

pub fn to_csv_with_options( &self, path: &str, options: CsvWriteOptions, ) -> Result<(), Box<dyn Error>>

Write DataFrame to CSV with custom options

pub fn from_jsonl(path: &str) -> Result<Self, Box<dyn Error>>

Read from JSON Lines format

pub fn to_jsonl(&self, path: &str) -> Result<(), Box<dyn Error>>

Write DataFrame to JSON Lines format

pub fn to_json(&self, path: &str) -> Result<(), Box<dyn Error>>

Write DataFrame to regular JSON format (array of objects)

pub fn infer_column_type(data: &[String]) -> SeriesType

Infer the type of a column from string data

pub fn parse_bool(s: &str) -> Result<bool, BoolParseError>

Parse boolean from string

Trait Implementations

impl Clone for DataFrame

fn clone(&self) -> DataFrame

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for DataFrame

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PartialEq for DataFrame

fn eq(&self, other: &DataFrame) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for DataFrame