# Licensed to the Apache Software Foundation (ASF) under one

# or more contributor license agreements. See the NOTICE file

# distributed with this work for additional information

# regarding copyright ownership. The ASF licenses this file

# to you under the Apache License, Version 2.0 (the

# "License"); you may not use this file except in compliance

# with the License. You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing,

# software distributed under the License is distributed on an

# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY

# KIND, either express or implied. See the License for the

# specific language governing permissions and limitations

# under the License.

""":py:class:`DataFrame` is one of the core concepts in DataFusion.

See :ref:`user_guide_concepts` in the online documentation for more information.

"""

from __future__ import annotations

import warnings

from collections.abc import AsyncIterator, Iterable, Iterator, Sequence

from typing import (

TYPE_CHECKING,

Any,

Literal,

overload,

)

try:

from warnings import deprecated # Python 3.13+

except ImportError:

from typing_extensions import deprecated # Python 3.12

from datafusion._internal import DataFrame as DataFrameInternal

from datafusion._internal import DataFrameWriteOptions as DataFrameWriteOptionsInternal

from datafusion._internal import InsertOp as InsertOpInternal

from datafusion._internal import ParquetColumnOptions as ParquetColumnOptionsInternal

from datafusion._internal import ParquetWriterOptions as ParquetWriterOptionsInternal

from datafusion.expr import (

Expr,

SortExpr,

SortKey,

ensure_expr,

ensure_expr_list,

expr_list_to_raw_expr_list,

sort_list_to_raw_sort_list,

)

from datafusion.plan import ExecutionPlan, LogicalPlan

from datafusion.record_batch import RecordBatch, RecordBatchStream

if TYPE_CHECKING:

import pathlib

from collections.abc import Callable

import pandas as pd

import polars as pl

import pyarrow as pa

from datafusion.catalog import Table

from enum import Enum

# excerpt from deltalake

# https://github.com/apache/datafusion-python/pull/981#discussion_r1905619163

class Compression(Enum):

"""Enum representing the available compression types for Parquet files."""

UNCOMPRESSED = "uncompressed"

SNAPPY = "snappy"

GZIP = "gzip"

BROTLI = "brotli"

LZ4 = "lz4"

# lzo is not implemented yet

# https://github.com/apache/arrow-rs/issues/6970

# LZO = "lzo" # noqa: ERA001

ZSTD = "zstd"

LZ4_RAW = "lz4_raw"

@classmethod

def from_str(cls: type[Compression], value: str) -> Compression:

"""Convert a string to a Compression enum value.

Args:

value: The string representation of the compression type.

Returns:

The Compression enum lowercase value.

Raises:

ValueError: If the string does not match any Compression enum value.

"""

try:

return cls(value.lower())

except ValueError as err:

valid_values = str([item.value for item in Compression])

error_msg = f"""

{value} is not a valid Compression.

Valid values are: {valid_values}

"""

raise ValueError(error_msg) from err

def get_default_level(self) -> int | None:

"""Get the default compression level for the compression type.

Returns:

The default compression level for the compression type.

"""

# GZIP, BROTLI default values from deltalake repo

# https://github.com/apache/datafusion-python/pull/981#discussion_r1905619163

# ZSTD default value from delta-rs

# https://github.com/apache/datafusion-python/pull/981#discussion_r1904789223

if self == Compression.GZIP:

return 6

if self == Compression.BROTLI:

return 1

if self == Compression.ZSTD:

return 4

return None

class ParquetWriterOptions:

"""Advanced parquet writer options.

Allows settings the writer options that apply to the entire file. Some options can

also be set on a column by column basis, with the field ``column_specific_options``

(see ``ParquetColumnOptions``).

"""

def __init__(

self,

data_pagesize_limit: int = 1024 * 1024,

write_batch_size: int = 1024,

writer_version: str = "1.0",

skip_arrow_metadata: bool = False,

compression: str | None = "zstd(3)",

compression_level: int | None = None,

dictionary_enabled: bool | None = True,

dictionary_page_size_limit: int = 1024 * 1024,

statistics_enabled: str | None = "page",

max_row_group_size: int = 1024 * 1024,

created_by: str = "datafusion-python",

column_index_truncate_length: int | None = 64,

statistics_truncate_length: int | None = None,

data_page_row_count_limit: int = 20_000,

encoding: str | None = None,

bloom_filter_on_write: bool = False,

bloom_filter_fpp: float | None = None,

bloom_filter_ndv: int | None = None,

allow_single_file_parallelism: bool = True,

maximum_parallel_row_group_writers: int = 1,

maximum_buffered_record_batches_per_stream: int = 2,

column_specific_options: dict[str, ParquetColumnOptions] | None = None,

) -> None:

"""Initialize the ParquetWriterOptions.

Args:

data_pagesize_limit: Sets best effort maximum size of data page in bytes.

write_batch_size: Sets write_batch_size in bytes.

writer_version: Sets parquet writer version. Valid values are ``1.0`` and

``2.0``.

skip_arrow_metadata: Skip encoding the embedded arrow metadata in the

KV_meta.

compression: Compression type to use. Default is ``zstd(3)``.

Available compression types are

- ``uncompressed``: No compression.

- ``snappy``: Snappy compression.

- ``gzip(n)``: Gzip compression with level n.

- ``brotli(n)``: Brotli compression with level n.

- ``lz4``: LZ4 compression.

- ``lz4_raw``: LZ4_RAW compression.

- ``zstd(n)``: Zstandard compression with level n.

compression_level: Compression level to set.

dictionary_enabled: Sets if dictionary encoding is enabled. If ``None``,

uses the default parquet writer setting.

dictionary_page_size_limit: Sets best effort maximum dictionary page size,

in bytes.

statistics_enabled: Sets if statistics are enabled for any column Valid

values are ``none``, ``chunk``, and ``page``. If ``None``, uses the

default parquet writer setting.

max_row_group_size: Target maximum number of rows in each row group

(defaults to 1M rows). Writing larger row groups requires more memory

to write, but can get better compression and be faster to read.

created_by: Sets "created by" property.

column_index_truncate_length: Sets column index truncate length.

statistics_truncate_length: Sets statistics truncate length. If ``None``,

uses the default parquet writer setting.

data_page_row_count_limit: Sets best effort maximum number of rows in a data

page.

encoding: Sets default encoding for any column. Valid values are ``plain``,

``plain_dictionary``, ``rle``, ``bit_packed``, ``delta_binary_packed``,

``delta_length_byte_array``, ``delta_byte_array``, ``rle_dictionary``,

and ``byte_stream_split``. If ``None``, uses the default parquet writer

setting.

bloom_filter_on_write: Write bloom filters for all columns when creating

parquet files.

bloom_filter_fpp: Sets bloom filter false positive probability. If ``None``,

uses the default parquet writer setting

bloom_filter_ndv: Sets bloom filter number of distinct values. If ``None``,

uses the default parquet writer setting.

allow_single_file_parallelism: Controls whether DataFusion will attempt to

speed up writing parquet files by serializing them in parallel. Each

column in each row group in each output file are serialized in parallel

leveraging a maximum possible core count of

``n_files * n_row_groups * n_columns``.

maximum_parallel_row_group_writers: By default parallel parquet writer is

tuned for minimum memory usage in a streaming execution plan. You may

see a performance benefit when writing large parquet files by increasing

``maximum_parallel_row_group_writers`` and

``maximum_buffered_record_batches_per_stream`` if your system has idle

cores and can tolerate additional memory usage. Boosting these values is

likely worthwhile when writing out already in-memory data, such as from

a cached data frame.

maximum_buffered_record_batches_per_stream: See

``maximum_parallel_row_group_writers``.

column_specific_options: Overrides options for specific columns. If a column

is not a part of this dictionary, it will use the parameters provided

here.

"""

self.data_pagesize_limit = data_pagesize_limit

self.write_batch_size = write_batch_size

self.writer_version = writer_version

self.skip_arrow_metadata = skip_arrow_metadata

if compression_level is not None:

self.compression = f"{compression}({compression_level})"

else:

self.compression = compression

self.dictionary_enabled = dictionary_enabled

self.dictionary_page_size_limit = dictionary_page_size_limit

self.statistics_enabled = statistics_enabled

self.max_row_group_size = max_row_group_size

self.created_by = created_by

self.column_index_truncate_length = column_index_truncate_length

self.statistics_truncate_length = statistics_truncate_length

self.data_page_row_count_limit = data_page_row_count_limit

self.encoding = encoding

self.bloom_filter_on_write = bloom_filter_on_write

self.bloom_filter_fpp = bloom_filter_fpp

self.bloom_filter_ndv = bloom_filter_ndv

self.allow_single_file_parallelism = allow_single_file_parallelism

self.maximum_parallel_row_group_writers = maximum_parallel_row_group_writers

self.maximum_buffered_record_batches_per_stream = (

maximum_buffered_record_batches_per_stream

)

self.column_specific_options = column_specific_options

class ParquetColumnOptions:

"""Parquet options for individual columns.

Contains the available options that can be applied for an individual Parquet column,

replacing the global options in ``ParquetWriterOptions``.

"""

def __init__(

self,

encoding: str | None = None,

dictionary_enabled: bool | None = None,

compression: str | None = None,

statistics_enabled: str | None = None,

bloom_filter_enabled: bool | None = None,

bloom_filter_fpp: float | None = None,

bloom_filter_ndv: int | None = None,

) -> None:

"""Initialize the ParquetColumnOptions.

Args:

encoding: Sets encoding for the column path. Valid values are: ``plain``,

``plain_dictionary``, ``rle``, ``bit_packed``, ``delta_binary_packed``,

``delta_length_byte_array``, ``delta_byte_array``, ``rle_dictionary``,

and ``byte_stream_split``. These values are not case-sensitive. If

``None``, uses the default parquet options

dictionary_enabled: Sets if dictionary encoding is enabled for the column

path. If `None`, uses the default parquet options

compression: Sets default parquet compression codec for the column path.

Valid values are ``uncompressed``, ``snappy``, ``gzip(level)``, ``lzo``,

``brotli(level)``, ``lz4``, ``zstd(level)``, and ``lz4_raw``. These

values are not case-sensitive. If ``None``, uses the default parquet

options.

statistics_enabled: Sets if statistics are enabled for the column Valid

values are: ``none``, ``chunk``, and ``page`` These values are not case

sensitive. If ``None``, uses the default parquet options.

bloom_filter_enabled: Sets if bloom filter is enabled for the column path.

If ``None``, uses the default parquet options.

bloom_filter_fpp: Sets bloom filter false positive probability for the

column path. If ``None``, uses the default parquet options.

bloom_filter_ndv: Sets bloom filter number of distinct values. If ``None``,

uses the default parquet options.

"""

self.encoding = encoding

self.dictionary_enabled = dictionary_enabled

self.compression = compression

self.statistics_enabled = statistics_enabled

self.bloom_filter_enabled = bloom_filter_enabled

self.bloom_filter_fpp = bloom_filter_fpp

self.bloom_filter_ndv = bloom_filter_ndv

class DataFrame:

"""Two dimensional table representation of data.

DataFrame objects are iterable; iterating over a DataFrame yields

:class:`datafusion.RecordBatch` instances lazily.

See :ref:`user_guide_concepts` in the online documentation for more information.

"""

def __init__(self, df: DataFrameInternal) -> None:

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

See :py:class:`~datafusion.context.SessionContext` for methods to

create a :py:class:`DataFrame`.

"""

self.df = df

def into_view(self, temporary: bool = False) -> Table:

"""Convert ``DataFrame`` into a :class:`~datafusion.Table`.

Examples:

>>> from datafusion import SessionContext

>>> ctx = SessionContext()

>>> df = ctx.sql("SELECT 1 AS value")

>>> view = df.into_view()

>>> ctx.register_table("values_view", view)

>>> result = ctx.sql("SELECT value FROM values_view").collect()

>>> result[0].column("value").to_pylist()

[1]

"""

from datafusion.catalog import Table as _Table

return _Table(self.df.into_view(temporary))

def __getitem__(self, key: str | list[str]) -> DataFrame:

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

Args:

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

Returns:

DataFrame with the specified column or columns.

"""

return DataFrame(self.df.__getitem__(key))

def __repr__(self) -> str:

"""Return a string representation of the DataFrame.

Returns:

String representation of the DataFrame.

"""

return self.df.__repr__()

def _repr_html_(self) -> str:

return self.df._repr_html_()

@staticmethod

def default_str_repr(

batches: list[pa.RecordBatch],

schema: pa.Schema,

has_more: bool,

table_uuid: str | None = None,

) -> str:

"""Return the default string representation of a DataFrame.

This method is used by the default formatter and implemented in Rust for

performance reasons.

"""

return DataFrameInternal.default_str_repr(batches, schema, has_more, table_uuid)

def describe(self) -> 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.

"""

return DataFrame(self.df.describe())

def schema(self) -> pa.Schema:

"""Return the :py:class:`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

"""

return self.df.schema()

@deprecated(

"select_columns() is deprecated. Use :py:meth:`~DataFrame.select` instead"

)

def select_columns(self, *args: str) -> DataFrame:

"""Filter the DataFrame by columns.

Returns:

DataFrame only containing the specified columns.

"""

return self.select(*args)

def select_exprs(self, *args: str) -> DataFrame:

"""Project arbitrary list of expression strings into a new DataFrame.

This method will parse string expressions into logical plan expressions.

The output DataFrame has one column for each expression.

Returns:

DataFrame only containing the specified columns.

"""

return self.df.select_exprs(*args)

def select(self, *exprs: Expr | str) -> DataFrame:

"""Project arbitrary expressions into a new :py:class:`DataFrame`.

Args:

exprs: Either column names or :py:class:`~datafusion.expr.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"))

"""

exprs_internal = expr_list_to_raw_expr_list(exprs)

return DataFrame(self.df.select(*exprs_internal))

def drop(self, *columns: str) -> DataFrame:

"""Drop arbitrary amount of columns.

Column names are case-sensitive and require double quotes to be dropped

if the original name is not strictly lower case.

Args:

columns: Column names to drop from the dataframe.

Returns:

DataFrame with those columns removed in the projection.

Example Usage::

df.drop('a') # To drop a lower-cased column 'a'

df.drop('"a"') # To drop an upper-cased column 'A'

"""

return DataFrame(self.df.drop(*columns))

def filter(self, *predicates: Expr | str) -> 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. Each ``predicate`` can be an

:class:`~datafusion.expr.Expr` created using helper functions such as

:func:`datafusion.col` or :func:`datafusion.lit`, or a SQL expression string

that will be parsed against the DataFrame schema. If more complex logic is

required, see the logical operations in :py:mod:`~datafusion.functions`.

Example::

from datafusion import col, lit

df.filter(col("a") > lit(1))

df.filter("a > 1")

Args:

predicates: Predicate expression(s) or SQL strings to filter the DataFrame.

Returns:

DataFrame after filtering.

"""

df = self.df

for predicate in predicates:

expr = (

self.parse_sql_expr(predicate)

if isinstance(predicate, str)

else predicate

)

df = df.filter(ensure_expr(expr))

return DataFrame(df)

def parse_sql_expr(self, expr: str) -> Expr:

"""Creates logical expression from a SQL query text.

The expression is created and processed against the current schema.

Example::

from datafusion import col, lit

df.parse_sql_expr("a > 1")

should produce:

col("a") > lit(1)

Args:

expr: Expression string to be converted to datafusion expression

Returns:

Logical expression .

"""

return Expr(self.df.parse_sql_expr(expr))

def with_column(self, name: str, expr: Expr | str) -> DataFrame:

"""Add an additional column to the DataFrame.

The ``expr`` must be an :class:`~datafusion.expr.Expr` constructed with

:func:`datafusion.col` or :func:`datafusion.lit`, or a SQL expression

string that will be parsed against the DataFrame schema.

Example::

from datafusion import col, lit

df.with_column("b", col("a") + lit(1))

Args:

name: Name of the column to add.

expr: Expression to compute the column.

Returns:

DataFrame with the new column.

"""

expr = self.parse_sql_expr(expr) if isinstance(expr, str) else expr

return DataFrame(self.df.with_column(name, ensure_expr(expr)))

def with_columns(

self, *exprs: Expr | str | Iterable[Expr | str], **named_exprs: Expr | str

) -> DataFrame:

"""Add columns to the DataFrame.

By passing expressions, iterables of expressions, string SQL expressions,

or named expressions.

All expressions must be :class:`~datafusion.expr.Expr` objects created via

:func:`datafusion.col` or :func:`datafusion.lit`, or SQL expression strings.

To pass named expressions use the form ``name=Expr``.

Example usage: The following will add 4 columns labeled ``a``, ``b``, ``c``,

and ``d``::

from datafusion import col, lit

df = df.with_columns(

col("x").alias("a"),

[lit(1).alias("b"), col("y").alias("c")],

d=lit(3)

)

Equivalent example using just SQL strings:

df = df.with_columns(

"x as a",

["1 as b", "y as c"],

d="3"

)

Args:

exprs: Either a single expression, an iterable of expressions to add or

SQL expression strings.

named_exprs: Named expressions in the form of ``name=expr``

Returns:

DataFrame with the new columns added.

"""

expressions = []

for expr in exprs:

if isinstance(expr, str):

expressions.append(self.parse_sql_expr(expr).expr)

elif isinstance(expr, Iterable) and not isinstance(

expr, Expr | str | bytes | bytearray

):

expressions.extend(

[

self.parse_sql_expr(e).expr

if isinstance(e, str)

else ensure_expr(e)

for e in expr

]

)

else:

expressions.append(ensure_expr(expr))

for alias, expr in named_exprs.items():

e = self.parse_sql_expr(expr) if isinstance(expr, str) else expr

ensure_expr(e)

expressions.append(e.alias(alias).expr)

return DataFrame(self.df.with_columns(expressions))

def with_column_renamed(self, old_name: str, new_name: str) -> DataFrame:

r"""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 \`).

Args:

old_name: Old column name.

new_name: New column name.

Returns:

DataFrame with the column renamed.

"""

return DataFrame(self.df.with_column_renamed(old_name, new_name))

def aggregate(

self,

group_by: Sequence[Expr | str] | Expr | str,

aggs: Sequence[Expr] | Expr,

) -> DataFrame:

"""Aggregates the rows of the current DataFrame.

Args:

group_by: Sequence of expressions or column names to group by.

aggs: Sequence of expressions to aggregate.

Returns:

DataFrame after aggregation.

"""

group_by_list = (

list(group_by)

if isinstance(group_by, Sequence) and not isinstance(group_by, Expr | str)

else [group_by]

)

aggs_list = (

list(aggs)

if isinstance(aggs, Sequence) and not isinstance(aggs, Expr)

else [aggs]

)

group_by_exprs = expr_list_to_raw_expr_list(group_by_list)

aggs_exprs = ensure_expr_list(aggs_list)

return DataFrame(self.df.aggregate(group_by_exprs, aggs_exprs))

def sort(self, *exprs: SortKey) -> DataFrame:

"""Sort the DataFrame by the specified sorting expressions or column names.

Note that any expression can be turned into a sort expression by

calling its ``sort`` method.

Args:

exprs: Sort expressions or column names, applied in order.

Returns:

DataFrame after sorting.

"""

exprs_raw = sort_list_to_raw_sort_list(exprs)

return DataFrame(self.df.sort(*exprs_raw))

def cast(self, mapping: dict[str, pa.DataType[Any]]) -> DataFrame:

"""Cast one or more columns to a different data type.

Args:

mapping: Mapped with column as key and column dtype as value.

Returns:

DataFrame after casting columns

"""

exprs = [Expr.column(col).cast(dtype) for col, dtype in mapping.items()]

return self.with_columns(exprs)

def limit(self, count: int, offset: int = 0) -> DataFrame:

"""Return a new :py:class:`DataFrame` with a limited number of rows.

Args:

count: Number of rows to limit the DataFrame to.

offset: Number of rows to skip.

Returns:

DataFrame after limiting.

"""

return DataFrame(self.df.limit(count, offset))

def head(self, n: int = 5) -> DataFrame:

"""Return a new :py:class:`DataFrame` with a limited number of rows.

Args:

n: Number of rows to take from the head of the DataFrame.

Returns:

DataFrame after limiting.

"""

return DataFrame(self.df.limit(n, 0))

def tail(self, n: int = 5) -> DataFrame:

"""Return a new :py:class:`DataFrame` with a limited number of rows.

Be aware this could be potentially expensive since the row size needs to be

determined of the dataframe. This is done by collecting it.

Args:

n: Number of rows to take from the tail of the DataFrame.

Returns:

DataFrame after limiting.

"""

return DataFrame(self.df.limit(n, max(0, self.count() - n)))

def collect(self) -> list[pa.RecordBatch]:

"""Execute this :py:class:`DataFrame` and collect results into memory.

Prior to calling ``collect``, modifying a DataFrame simply updates a plan

(no actual computation is performed). Calling ``collect`` triggers the

computation.

Returns:

List of :py:class:`pyarrow.RecordBatch` collected from the DataFrame.

"""

return self.df.collect()

def collect_column(self, column_name: str) -> pa.Array | pa.ChunkedArray:

"""Executes this :py:class:`DataFrame` for a single column."""

return self.df.collect_column(column_name)

def cache(self) -> DataFrame:

"""Cache the DataFrame as a memory table.

Returns:

Cached DataFrame.

"""

return DataFrame(self.df.cache())

def collect_partitioned(self) -> list[list[pa.RecordBatch]]:

"""Execute this DataFrame and collect all partitioned results.

This operation returns :py:class:`pyarrow.RecordBatch` maintaining the input

partitioning.

Returns:

List of list of :py:class:`RecordBatch` collected from the

DataFrame.

"""

return self.df.collect_partitioned()

def show(self, num: int = 20) -> None:

"""Execute the DataFrame and print the result to the console.

Args:

num: Number of lines to show.

"""

self.df.show(num)

def distinct(self) -> DataFrame:

"""Return a new :py:class:`DataFrame` with all duplicated rows removed.

Returns:

DataFrame after removing duplicates.

"""

return DataFrame(self.df.distinct())

@overload

def join(

self,

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,

coalesce_duplicate_keys: bool = True,

) -> DataFrame: ...

@overload

def join(

self,

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,

coalesce_duplicate_keys: bool = True,

) -> DataFrame: ...

@overload

def join(

self,

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,

coalesce_duplicate_keys: bool = True,

) -> DataFrame: ...

def join(

self,

right: DataFrame,

on: str | Sequence[str] | tuple[list[str], list[str]] | None = None,

how: Literal["inner", "left", "right", "full", "semi", "anti"] = "inner",

*,

left_on: str | Sequence[str] | None = None,

right_on: str | Sequence[str] | None = None,

join_keys: tuple[list[str], list[str]] | None = None,

coalesce_duplicate_keys: bool = True,

) -> DataFrame:

"""Join this :py:class:`DataFrame` with another :py:class:`DataFrame`.

`on` has to be provided or both `left_on` and `right_on` in conjunction.

Args:

right: Other DataFrame to join with.

on: Column names to join on in both dataframes.

how: Type of join to perform. Supported types are "inner", "left",

"right", "full", "semi", "anti".

left_on: Join column of the left dataframe.

right_on: Join column of the right dataframe.

coalesce_duplicate_keys: When True, coalesce the columns

from the right DataFrame and left DataFrame

that have identical names in the ``on`` fields.

join_keys: Tuple of two lists of column names to join on. [Deprecated]

Returns:

DataFrame after join.

"""

if join_keys is not None:

warnings.warn(

"`join_keys` is deprecated, use `on` or `left_on` with `right_on`",

category=DeprecationWarning,

stacklevel=2,

)

left_on = join_keys[0]

right_on = join_keys[1]

# This check is to prevent breaking API changes where users prior to

# DF 43.0.0 would pass the join_keys as a positional argument instead

# of a keyword argument.

if (

isinstance(on, tuple)

and len(on) == 2 # noqa: PLR2004

and isinstance(on[0], list)

and isinstance(on[1], list)

):

# We know this is safe because we've checked the types

left_on = on[0]

right_on = on[1]

on = None

if on is not None:

if left_on is not None or right_on is not None:

error_msg = "`left_on` or `right_on` should not provided with `on`"

raise ValueError(error_msg)

left_on = on

right_on = on

elif left_on is not None or right_on is not None:

if left_on is None or right_on is None:

error_msg = "`left_on` and `right_on` should both be provided."

raise ValueError(error_msg)

else:

error_msg = "either `on` or `left_on` and `right_on` should be provided."

raise ValueError(error_msg)

if isinstance(left_on, str):

left_on = [left_on]

if isinstance(right_on, str):

right_on = [right_on]

return DataFrame(

self.df.join(right.df, how, left_on, right_on, coalesce_duplicate_keys)

)

def join_on(

self,

right: DataFrame,

*on_exprs: Expr,

how: Literal["inner", "left", "right", "full", "semi", "anti"] = "inner",

) -> DataFrame:

"""Join two :py:class:`DataFrame` using the specified expressions.

Join predicates must be :class:`~datafusion.expr.Expr` objects, typically

built with :func:`datafusion.col`. On expressions are used to support

in-equality predicates. Equality predicates are correctly optimized.

Example::

from datafusion import col

df.join_on(other_df, col("id") == col("other_id"))

Args:

right: Other DataFrame to join with.

on_exprs: single or multiple (in)-equality predicates.

how: Type of join to perform. Supported types are "inner", "left",

"right", "full", "semi", "anti".

Returns:

DataFrame after join.

"""

exprs = [ensure_expr(expr) for expr in on_exprs]

return DataFrame(self.df.join_on(right.df, exprs, how))

def explain(self, verbose: bool = False, analyze: bool = False) -> None:

"""Print an explanation of the DataFrame's plan so far.

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

Args:

verbose: If ``True``, more details will be included.

analyze: If ``True``, the plan will run and metrics reported.

"""

self.df.explain(verbose, analyze)

def logical_plan(self) -> LogicalPlan:

"""Return the unoptimized ``LogicalPlan``.

Returns:

Unoptimized logical plan.

"""

return LogicalPlan(self.df.logical_plan())

def optimized_logical_plan(self) -> LogicalPlan:

"""Return the optimized ``LogicalPlan``.

Returns:

Optimized logical plan.

"""

return LogicalPlan(self.df.optimized_logical_plan())

def execution_plan(self) -> ExecutionPlan:

"""Return the execution/physical plan.

Returns:

Execution plan.

"""

return ExecutionPlan(self.df.execution_plan())

def repartition(self, num: int) -> DataFrame:

"""Repartition a DataFrame into ``num`` partitions.

The batches allocation uses a round-robin algorithm.

Args:

num: Number of partitions to repartition the DataFrame into.

Returns:

Repartitioned DataFrame.

"""

return DataFrame(self.df.repartition(num))

def repartition_by_hash(self, *exprs: Expr | str, num: int) -> DataFrame:

"""Repartition a DataFrame using a hash partitioning scheme.

Args:

exprs: Expressions or a SQL expression string to evaluate

and perform hashing on.

num: Number of partitions to repartition the DataFrame into.

Returns:

Repartitioned DataFrame.

"""

exprs = [self.parse_sql_expr(e) if isinstance(e, str) else e for e in exprs]

exprs = expr_list_to_raw_expr_list(exprs)

return DataFrame(self.df.repartition_by_hash(*exprs, num=num))

def union(self, other: DataFrame, distinct: bool = False) -> DataFrame:

"""Calculate the union of two :py:class:`DataFrame`.

The two :py:class:`DataFrame` must have exactly the same schema.

Args:

other: DataFrame to union with.

distinct: If ``True``, duplicate rows will be removed.

Returns:

DataFrame after union.

"""

return DataFrame(self.df.union(other.df, distinct))

def union_distinct(self, other: DataFrame) -> DataFrame:

"""Calculate the distinct union of two :py:class:`DataFrame`.

The two :py:class:`DataFrame` must have exactly the same schema.

Any duplicate rows are discarded.

Args:

other: DataFrame to union with.

Returns:

DataFrame after union.

"""

return DataFrame(self.df.union_distinct(other.df))

def intersect(self, other: DataFrame) -> DataFrame:

"""Calculate the intersection of two :py:class:`DataFrame`.

The two :py:class:`DataFrame` must have exactly the same schema.