[−][src]Trait datafusion::dataframe::DataFrame

pub trait DataFrame {
    fn select_columns(&self, columns: Vec<&str>) -> Result<Arc<dyn DataFrame>>;

    fn select(&self, expr: Vec<Expr>) -> Result<Arc<dyn DataFrame>>;

    fn filter(&self, expr: Expr) -> Result<Arc<dyn DataFrame>>;

    fn aggregate(
        &self, 
        group_expr: Vec<Expr>, 
        aggr_expr: Vec<Expr>
    ) -> Result<Arc<dyn DataFrame>>;

    fn limit(&self, n: usize) -> Result<Arc<dyn DataFrame>>;

    fn sort(&self, expr: Vec<Expr>) -> Result<Arc<dyn DataFrame>>;

#[must_use]    fn collect<'life0, 'async_trait>(
        &'life0 self
    ) -> Pin<Box<dyn Future<Output = Result<Vec<RecordBatch>>> + Send + 'async_trait>>
    where
        'life0: 'async_trait,
        Self: 'async_trait;

    fn schema(&self) -> &Schema;

    fn to_logical_plan(&self) -> LogicalPlan;

    fn explain(&self, verbose: bool) -> Result<Arc<dyn DataFrame>>;

    fn registry(&self) -> &dyn FunctionRegistry;
}

DataFrame represents a logical set of rows with the same named columns. Similar to a Pandas DataFrame or Spark DataFrame

DataFrames are typically created by the read_csv and read_parquet methods on the ExecutionContext and can then be modified by calling the transformation methods, such as filter, select, aggregate, and limit to build up a query definition.

The query can be executed by calling the collect method.

let mut ctx = ExecutionContext::new();
let df = ctx.read_csv("tests/example.csv", CsvReadOptions::new())?;
let df = df.filter(col("a").lt_eq(col("b")))?
           .aggregate(vec![col("a")], vec![min(col("b"))])?
           .limit(100)?;
let results = df.collect();

Required methods

fn select_columns(&self, columns: Vec<&str>) -> Result<Arc<dyn DataFrame>>

Filter the DataFrame by column. Returns a new DataFrame only containing the specified columns.

let mut ctx = ExecutionContext::new();
let df = ctx.read_csv("tests/example.csv", CsvReadOptions::new())?;
let df = df.select_columns(vec!["a", "b"])?;

fn select(&self, expr: Vec<Expr>) -> Result<Arc<dyn DataFrame>>

Create a projection based on arbitrary expressions.

let mut ctx = ExecutionContext::new();
let df = ctx.read_csv("tests/example.csv", CsvReadOptions::new())?;
let df = df.select(vec![col("a") * col("b"), col("c")])?;

fn filter(&self, expr: Expr) -> Result<Arc<dyn DataFrame>>

Filter a DataFrame to only include rows that match the specified filter expression.

let mut ctx = ExecutionContext::new();
let df = ctx.read_csv("tests/example.csv", CsvReadOptions::new())?;
let df = df.filter(col("a").lt_eq(col("b")))?;

fn aggregate(
    &self,
    group_expr: Vec<Expr>,
    aggr_expr: Vec<Expr>
) -> Result<Arc<dyn DataFrame>>

Perform an aggregate query with optional grouping expressions.

let mut ctx = ExecutionContext::new();
let df = ctx.read_csv("tests/example.csv", CsvReadOptions::new())?;

// The following use is the equivalent of "SELECT MIN(b) GROUP BY a"
let _ = df.aggregate(vec![col("a")], vec![min(col("b"))])?;

// The following use is the equivalent of "SELECT MIN(b)"
let _ = df.aggregate(vec![], vec![min(col("b"))])?;

fn limit(&self, n: usize) -> Result<Arc<dyn DataFrame>>

Limit the number of rows returned from this DataFrame.

let mut ctx = ExecutionContext::new();
let df = ctx.read_csv("tests/example.csv", CsvReadOptions::new())?;
let df = df.limit(100)?;

fn sort(&self, expr: Vec<Expr>) -> Result<Arc<dyn DataFrame>>

Sort the DataFrame by the specified sorting expressions. Any expression can be turned into a sort expression by calling its sort method.

let mut ctx = ExecutionContext::new();
let df = ctx.read_csv("tests/example.csv", CsvReadOptions::new())?;
let df = df.sort(vec![col("a").sort(true, true), col("b").sort(false, false)])?;

#[must_use]fn collect<'life0, 'async_trait>(
    &'life0 self
) -> Pin<Box<dyn Future<Output = Result<Vec<RecordBatch>>> + Send + 'async_trait>> where
    'life0: 'async_trait,
    Self: 'async_trait, 

Executes this DataFrame and collects all results into a vector of RecordBatch.

let mut ctx = ExecutionContext::new();
let df = ctx.read_csv("tests/example.csv", CsvReadOptions::new())?;
let batches = df.collect().await?;

fn schema(&self) -> &Schema

Returns the schema describing the output of this DataFrame in terms of columns returned, where each column has a name, data type, and nullability attribute.

let mut ctx = ExecutionContext::new();
let df = ctx.read_csv("tests/example.csv", CsvReadOptions::new())?;
let schema = df.schema();

fn to_logical_plan(&self) -> LogicalPlan

Return the logical plan represented by this DataFrame.

fn explain(&self, verbose: bool) -> Result<Arc<dyn DataFrame>>

Return a DataFrame with the explanation of its plan so far.

let mut ctx = ExecutionContext::new();
let df = ctx.read_csv("tests/example.csv", CsvReadOptions::new())?;
let batches = df.limit(100)?.explain(false)?.collect().await?;

fn registry(&self) -> &dyn FunctionRegistry

Return a FunctionRegistry used to plan udf's calls

let mut ctx = ExecutionContext::new();
let df = ctx.read_csv("tests/example.csv", CsvReadOptions::new())?;
let f = df.registry();
// use f.udf("name", vec![...]) to use the udf
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Implementors

impl DataFrame for DataFrameImpl[src]

fn select_columns(&self, columns: Vec<&str>) -> Result<Arc<dyn DataFrame>>[src]

Apply a projection based on a list of column names

fn select(&self, expr_list: Vec<Expr>) -> Result<Arc<dyn DataFrame>>[src]

Create a projection based on arbitrary expressions

fn filter(&self, predicate: Expr) -> Result<Arc<dyn DataFrame>>[src]

Create a filter based on a predicate expression

fn aggregate(
    &self,
    group_expr: Vec<Expr>,
    aggr_expr: Vec<Expr>
) -> Result<Arc<dyn DataFrame>>[src]

Perform an aggregate query

fn limit(&self, n: usize) -> Result<Arc<dyn DataFrame>>[src]

Limit the number of rows

fn sort(&self, expr: Vec<Expr>) -> Result<Arc<dyn DataFrame>>[src]

Sort by specified sorting expressions

fn to_logical_plan(&self) -> LogicalPlan[src]

Convert to logical plan

fn schema(&self) -> &Schema[src]

Returns the schema from the logical plan

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