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‎_sources/autoapi/datafusion/catalog/index.rst.txt‎

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This constructor is not typically called by the end user.
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.. py:method:: schema() -> pyarrow.Schema
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.. py:property:: kind
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:type: str
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Returns the schema associated with this table.
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Returns the kind of table.
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.. py:property:: kind
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:type: str
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.. py:property:: schema
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:type: pyarrow.Schema
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Returns the kind of table.
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Returns the schema associated with this table.
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.. py:attribute:: table

‎_sources/autoapi/datafusion/context/index.rst.txt‎

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Create and return a dataframe using the provided partitions.
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:param partitions: :py:class:`pyarrow.RecordBatch` partitions to register.
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:param partitions: :py:class:`pa.RecordBatch` partitions to register.
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:param name: Resultant dataframe name.
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:param schema: Schema for the partitions.
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.. py:method:: global_ctx() -> SessionContext
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:classmethod:
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Retrieve the global context as a `SessionContext` wrapper.
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:returns: A `SessionContext` object that wraps the global `SessionContextInternal`.
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.. py:method:: read_avro(path: str | pathlib.Path, schema: pyarrow.Schema | None = None, file_partition_cols: list[tuple[str, str]] | None = None, file_extension: str = '.avro') -> datafusion.dataframe.DataFrame
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Create a :py:class:`DataFrame` for reading Avro data source.
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.. py:method:: read_parquet(path: str | pathlib.Path, table_partition_cols: list[tuple[str, str]] | None = None, parquet_pruning: bool = True, file_extension: str = '.parquet', skip_metadata: bool = True, schema: pyarrow.Schema | None = None, file_sort_order: list[list[datafusion.expr.Expr]] | None = None) -> datafusion.dataframe.DataFrame
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.. py:method:: read_parquet(path: str | pathlib.Path, table_partition_cols: list[tuple[str, str]] | None = None, parquet_pruning: bool = True, file_extension: str = '.parquet', skip_metadata: bool = True, schema: pyarrow.Schema | None = None, file_sort_order: list[list[datafusion.expr.Expr | datafusion.expr.SortExpr]] | None = None) -> datafusion.dataframe.DataFrame
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Read a Parquet source into a :py:class:`~datafusion.dataframe.Dataframe`.
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.. py:method:: register_dataset(name: str, dataset: pyarrow.dataset.Dataset) -> None
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Register a :py:class:`pyarrow.dataset.Dataset` as a table.
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Register a :py:class:`pa.dataset.Dataset` as a table.
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:param name: Name of the table to register.
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:param dataset: PyArrow dataset.
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.. py:method:: register_parquet(name: str, path: str | pathlib.Path, table_partition_cols: list[tuple[str, str]] | None = None, parquet_pruning: bool = True, file_extension: str = '.parquet', skip_metadata: bool = True, schema: pyarrow.Schema | None = None, file_sort_order: list[list[datafusion.expr.Expr]] | None = None) -> None
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.. py:method:: register_parquet(name: str, path: str | pathlib.Path, table_partition_cols: list[tuple[str, str]] | None = None, parquet_pruning: bool = True, file_extension: str = '.parquet', skip_metadata: bool = True, schema: pyarrow.Schema | None = None, file_sort_order: list[list[datafusion.expr.SortExpr]] | None = None) -> None
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Register a Parquet file as a table.
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.. py:method:: register_view(name: str, df: datafusion.dataframe.DataFrame) -> None
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Register a :py:class: `~datafusion.detaframe.DataFrame` as a view.
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:param name: The name to register the view under.
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:type name: str
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:param df: The DataFrame to be converted into a view and registered.
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:type df: DataFrame
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.. py:method:: session_id() -> str
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Return an id that uniquely identifies this :py:class:`SessionContext`.

‎_sources/autoapi/datafusion/dataframe/index.rst.txt‎

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.. py:method:: __getitem__(key: str | List[str]) -> DataFrame
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.. py:method:: __getitem__(key: str | list[str]) -> DataFrame
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Return a new :py:class`DataFrame` with the specified column or columns.
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.. py:method:: into_view() -> pyarrow.Table
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Convert DataFrame as a ViewTable which can be used in register_table.
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.. py:method:: join(right: DataFrame, on: str | Sequence[str], how: Literal['inner', 'left', 'right', 'full', 'semi', 'anti'] = 'inner', *, left_on: None = None, right_on: None = None, join_keys: None = None) -> DataFrame
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join(right: DataFrame, on: None = None, how: Literal['inner', 'left', 'right', 'full', 'semi', 'anti'] = 'inner', *, left_on: str | Sequence[str], right_on: str | Sequence[str], join_keys: tuple[list[str], list[str]] | None = None) -> DataFrame
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join(right: DataFrame, on: None = None, how: Literal['inner', 'left', 'right', 'full', 'semi', 'anti'] = 'inner', *, join_keys: tuple[list[str], list[str]], left_on: None = None, right_on: None = None) -> DataFrame

‎_sources/autoapi/datafusion/expr/index.rst.txt‎

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.. py:attribute:: case_builder
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.. py:class:: Expr(expr: datafusion._internal.expr.Expr)
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.. py:class:: Expr(expr: datafusion._internal.expr.RawExpr)
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Expression object.
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.. py:method:: __eq__(rhs: Any) -> Expr
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.. py:method:: __eq__(rhs: object) -> Expr
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Equal to.
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.. py:method:: __ne__(rhs: Any) -> Expr
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.. py:method:: __ne__(rhs: object) -> Expr
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Not equal to.
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.. py:method:: cast(to: pyarrow.DataType[Any] | Type[float] | Type[int] | Type[str] | Type[bool]) -> Expr
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.. py:method:: cast(to: pyarrow.DataType[Any] | type[float | int | str | bool]) -> Expr
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Cast to a new data type.
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.. py:attribute:: _to_pyarrow_types
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:type: ClassVar[dict[type, pyarrow.DataType]]
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.. py:attribute:: expr
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.. py:method:: get_upper_bound()
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.. py:method:: get_upper_bound() -> WindowFrameBound
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Returns end bound.
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‎_sources/autoapi/datafusion/functions/index.rst.txt‎

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datafusion.functions.random
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datafusion.functions.range
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datafusion.functions.rank
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datafusion.functions.regexp_count
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datafusion.functions.regexp_like
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datafusion.functions.regexp_match
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datafusion.functions.regexp_replace
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datafusion.functions.to_timestamp
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datafusion.functions.to_timestamp_micros
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datafusion.functions.to_timestamp_millis
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datafusion.functions.to_timestamp_nanos
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datafusion.functions.to_timestamp_seconds
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datafusion.functions.to_unixtime
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datafusion.functions.translate
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This is an alias for :py:func:`date_trunc`.
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.. py:function:: decode(input: datafusion.expr.Expr, encoding: datafusion.expr.Expr) -> datafusion.expr.Expr
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.. py:function:: decode(expr: datafusion.expr.Expr, encoding: datafusion.expr.Expr) -> datafusion.expr.Expr
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Decode the ``input``, using the ``encoding``. encoding can be base64 or hex.
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This is an alias for :py:func:`array_empty`.
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.. py:function:: encode(input: datafusion.expr.Expr, encoding: datafusion.expr.Expr) -> datafusion.expr.Expr
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.. py:function:: encode(expr: datafusion.expr.Expr, encoding: datafusion.expr.Expr) -> datafusion.expr.Expr
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Encode the ``input``, using the ``encoding``. encoding can be base64 or hex.
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:param order_by: Set ordering within the window frame.
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.. py:function:: regexp_count(string: datafusion.expr.Expr, pattern: datafusion.expr.Expr, start: datafusion.expr.Expr, flags: datafusion.expr.Expr | None = None) -> datafusion.expr.Expr
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Returns the number of matches in a string.
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Optional start position (the first position is 1) to search for the regular
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expression.
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.. py:function:: regexp_like(string: datafusion.expr.Expr, regex: datafusion.expr.Expr, flags: datafusion.expr.Expr | None = None) -> datafusion.expr.Expr
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Find if any regular expression (regex) matches exist.
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Returns the last ``n`` characters in the ``string``.
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.. py:function:: round(value: datafusion.expr.Expr, decimal_places: datafusion.expr.Expr = Expr.literal(0)) -> datafusion.expr.Expr
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.. py:function:: round(value: datafusion.expr.Expr, decimal_places: datafusion.expr.Expr | None = None) -> datafusion.expr.Expr
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Round the argument to the nearest integer.
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See :py:func:`to_timestamp` for a description on how to use formatters.
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.. py:function:: to_timestamp_nanos(arg: datafusion.expr.Expr, *formatters: datafusion.expr.Expr) -> datafusion.expr.Expr
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Converts a string and optional formats to a ``Timestamp`` in nanoseconds.
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See :py:func:`to_timestamp` for a description on how to use formatters.
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.. py:function:: to_timestamp_seconds(arg: datafusion.expr.Expr, *formatters: datafusion.expr.Expr) -> datafusion.expr.Expr
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Converts a string and optional formats to a ``Timestamp`` in seconds.
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Converts a string to uppercase.
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.. py:function:: uuid(arg: datafusion.expr.Expr) -> datafusion.expr.Expr
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.. py:function:: uuid() -> datafusion.expr.Expr
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Returns uuid v4 as a string value.
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