datafusion.dataframe

DataFrame is one of the core concepts in DataFusion.

See Concepts in the online documentation for more information.

Classes

DataFrame

Two dimensional table representation of data.

Module Contents

class datafusion.dataframe.DataFrame(df: datafusion._internal.DataFrame)

Two dimensional table representation of data.

See Concepts in the online documentation for more information.

This constructor is not to be used by the end user.

See SessionContext for methods to create a DataFrame.

__arrow_c_stream__(requested_schema: pyarrow.Schema) Any

Export an Arrow PyCapsule Stream.

This will execute and collect the DataFrame. We will attempt to respect the requested schema, but only trivial transformations will be applied such as only returning the fields listed in the requested schema if their data types match those in the DataFrame.

Parameters:

requested_schema – Attempt to provide the DataFrame using this schema.

Returns:

Arrow PyCapsule object.

__getitem__(key: str | List[str]) DataFrame

Return a new :py:class`DataFrame` with the specified column or columns.

Parameters:

key – Column name or list of column names to select.

Returns:

DataFrame with the specified column or columns.

__repr__() str

Return a string representation of the DataFrame.

Returns:

String representation of the DataFrame.

_repr_html_() str
aggregate(group_by: list[datafusion.expr.Expr] | datafusion.expr.Expr, aggs: list[datafusion.expr.Expr] | datafusion.expr.Expr) DataFrame

Aggregates the rows of the current DataFrame.

Parameters:
  • group_by – List of expressions to group by.

  • aggs – List of expressions to aggregate.

Returns:

DataFrame after aggregation.

cache() DataFrame

Cache the DataFrame as a memory table.

Returns:

Cached DataFrame.

collect() list[pyarrow.RecordBatch]

Execute this DataFrame and collect results into memory.

Prior to calling collect, modifying a DataFrme simply updates a plan (no actual computation is performed). Calling collect triggers the computation.

Returns:

List of pyarrow.RecordBatch collected from the DataFrame.

collect_partitioned() list[list[pyarrow.RecordBatch]]

Execute this DataFrame and collect all partitioned results.

This operation returns pyarrow.RecordBatch maintaining the input partitioning.

Returns:

List of list of RecordBatch collected from the

DataFrame.

count() int

Return the total number of rows in this DataFrame.

Note that this method will actually run a plan to calculate the count, which may be slow for large or complicated DataFrames.

Returns:

Number of rows in the DataFrame.

describe() DataFrame

Return the statistics for this DataFrame.

Only summarized numeric datatypes at the moments and returns nulls for non-numeric datatypes.

The output format is modeled after pandas.

Returns:

A summary DataFrame containing statistics.

distinct() DataFrame

Return a new DataFrame with all duplicated rows removed.

Returns:

DataFrame after removing duplicates.

except_all(other: DataFrame) DataFrame

Calculate the exception of two DataFrame.

The two DataFrame must have exactly the same schema.

Parameters:

other – DataFrame to calculate exception with.

Returns:

DataFrame after exception.

execute_stream() datafusion.record_batch.RecordBatchStream

Executes this DataFrame and returns a stream over a single partition.

Returns:

Record Batch Stream over a single partition.

execute_stream_partitioned() list[datafusion.record_batch.RecordBatchStream]

Executes this DataFrame and returns a stream for each partition.

Returns:

One record batch stream per partition.

execution_plan() datafusion._internal.ExecutionPlan

Return the execution/physical plan.

Returns:

Execution plan.

explain(verbose: bool = False, analyze: bool = False) DataFrame

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

If analyze is specified, runs the plan and reports metrics.

Parameters:
  • verbose – If True, more details will be included.

  • analyze – If Tru`e, the plan will run and metrics reported.

Returns:

DataFrame with the explanation of its plan.

filter(*predicates: datafusion.expr.Expr) DataFrame

Return a DataFrame for which predicate evaluates to True.

Rows for which predicate evaluates to False or None are filtered out. If more than one predicate is provided, these predicates will be combined as a logical AND. If more complex logic is required, see the logical operations in functions.

Parameters:

predicates – Predicate expression(s) to filter the DataFrame.

Returns:

DataFrame after filtering.

intersect(other: DataFrame) DataFrame

Calculate the intersection of two DataFrame.

The two DataFrame must have exactly the same schema.

Parameters:

other – DataFrame to intersect with.

Returns:

DataFrame after intersection.

join(right: DataFrame, join_keys: tuple[list[str], list[str]], how: str) DataFrame

Join this DataFrame with another DataFrame.

Join keys are a pair of lists of column names in the left and right dataframes, respectively. These lists must have the same length.

Parameters:
  • right – Other DataFrame to join with.

  • join_keys – Tuple of two lists of column names to join on.

  • how – Type of join to perform. Supported types are “inner”, “left”, “right”, “full”, “semi”, “anti”.

Returns:

DataFrame after join.

limit(count: int, offset: int = 0) DataFrame

Return a new DataFrame with a limited number of rows.

Parameters:
  • count – Number of rows to limit the DataFrame to.

  • offset – Number of rows to skip.

Returns:

DataFrame after limiting.

logical_plan() datafusion._internal.LogicalPlan

Return the unoptimized LogicalPlan.

Returns:

Unoptimized logical plan.

optimized_logical_plan() datafusion._internal.LogicalPlan

Return the optimized LogicalPlan.

Returns:

Optimized logical plan.

repartition(num: int) DataFrame

Repartition a DataFrame into num partitions.

The batches allocation uses a round-robin algorithm.

Parameters:

num – Number of partitions to repartition the DataFrame into.

Returns:

Repartitioned DataFrame.

repartition_by_hash(*exprs: datafusion.expr.Expr, num: int) DataFrame

Repartition a DataFrame using a hash partitioning scheme.

Parameters:
  • exprs – Expressions to evaluate and perform hashing on.

  • num – Number of partitions to repartition the DataFrame into.

Returns:

Repartitioned DataFrame.

schema() pyarrow.Schema

Return the pyarrow.Schema of this DataFrame.

The output schema contains information on the name, data type, and nullability for each column.

Returns:

Describing schema of the DataFrame

select(*exprs: datafusion.expr.Expr | str) DataFrame

Project arbitrary expressions into a new DataFrame.

Parameters:

exprs – Either column names or Expr to select.

Returns:

DataFrame after projection. It has one column for each expression.

Example usage:

The following example will return 3 columns from the original dataframe. The first two columns will be the original column a and b since the string “a” is assumed to refer to column selection. Also a duplicate of column a will be returned with the column name alternate_a:

df = df.select("a", col("b"), col("a").alias("alternate_a"))
select_columns(*args: str) DataFrame

Filter the DataFrame by columns.

Returns:

DataFrame only containing the specified columns.

show(num: int = 20) None

Execute the DataFrame and print the result to the console.

Parameters:

num – Number of lines to show.

sort(*exprs: datafusion.expr.Expr) DataFrame

Sort the DataFrame by the specified sorting expressions.

Note that any expression can be turned into a sort expression by calling its` sort method.

Parameters:

exprs – Sort expressions, applied in order.

Returns:

DataFrame after sorting.

to_arrow_table() pyarrow.Table

Execute the DataFrame and convert it into an Arrow Table.

Returns:

Arrow Table.

to_pandas() pandas.DataFrame

Execute the DataFrame and convert it into a Pandas DataFrame.

Returns:

Pandas DataFrame.

to_polars() polars.DataFrame

Execute the DataFrame and convert it into a Polars DataFrame.

Returns:

Polars DataFrame.

to_pydict() dict[str, list[Any]]

Execute the DataFrame and convert it into a dictionary of lists.

Returns:

Dictionary of lists.

to_pylist() list[dict[str, Any]]

Execute the DataFrame and convert it into a list of dictionaries.

Returns:

List of dictionaries.

transform(func: Callable[Ellipsis, DataFrame], *args: Any) DataFrame

Apply a function to the current DataFrame which returns another DataFrame.

This is useful for chaining together multiple functions. For example:

def add_3(df: DataFrame) -> DataFrame:
    return df.with_column("modified", lit(3))

def within_limit(df: DataFrame, limit: int) -> DataFrame:
    return df.filter(col("a") < lit(limit)).distinct()

df = df.transform(modify_df).transform(within_limit, 4)
Parameters:
  • func – A callable function that takes a DataFrame as it’s first argument

  • args – Zero or more arguments to pass to func

Returns:

After applying func to the original dataframe.

Return type:

DataFrame

union(other: DataFrame, distinct: bool = False) DataFrame

Calculate the union of two DataFrame.

The two DataFrame must have exactly the same schema.

Parameters:
  • other – DataFrame to union with.

  • distinct – If True, duplicate rows will be removed.

Returns:

DataFrame after union.

union_distinct(other: DataFrame) DataFrame

Calculate the distinct union of two DataFrame.

The two DataFrame must have exactly the same schema. Any duplicate rows are discarded.

Parameters:

other – DataFrame to union with.

Returns:

DataFrame after union.

unnest_columns(*columns: str, preserve_nulls: bool = True) DataFrame

Expand columns of arrays into a single row per array element.

Parameters:
  • columns – Column names to perform unnest operation on.

  • preserve_nulls – If False, rows with null entries will not be returned.

Returns:

A DataFrame with the columns expanded.

with_column(name: str, expr: datafusion.expr.Expr) DataFrame

Add an additional column to the DataFrame.

Parameters:
  • name – Name of the column to add.

  • expr – Expression to compute the column.

Returns:

DataFrame with the new column.

with_column_renamed(old_name: str, new_name: str) DataFrame

Rename one column by applying a new projection.

This is a no-op if the column to be renamed does not exist.

The method supports case sensitive rename with wrapping column name into one the following symbols (” or ‘ or `).

Parameters:
  • old_name – Old column name.

  • new_name – New column name.

Returns:

DataFrame with the column renamed.

write_csv(path: str | pathlib.Path, with_header: bool = False) None

Execute the DataFrame and write the results to a CSV file.

Parameters:
  • path – Path of the CSV file to write.

  • with_header – If true, output the CSV header row.

write_json(path: str | pathlib.Path) None

Execute the DataFrame and write the results to a JSON file.

Parameters:

path – Path of the JSON file to write.

write_parquet(path: str | pathlib.Path, compression: str = 'uncompressed', compression_level: int | None = None) None

Execute the DataFrame and write the results to a Parquet file.

Parameters:
  • path – Path of the Parquet file to write.

  • compression – Compression type to use.

  • compression_level – Compression level to use.

df