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# 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 Sequence
from typing import (
TYPE_CHECKING,
Any,
Iterable,
Literal,
Optional,
Union,
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 ParquetColumnOptions as ParquetColumnOptionsInternal
from datafusion._internal import ParquetWriterOptions as ParquetWriterOptionsInternal
from datafusion.expr import (
Expr,
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 RecordBatchStream
if TYPE_CHECKING:
import pathlib
from typing 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"
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) -> Optional[int]:
"""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: Optional[str] = "zstd(3)",
compression_level: Optional[int] = None,
dictionary_enabled: Optional[bool] = True,
dictionary_page_size_limit: int = 1024 * 1024,
statistics_enabled: Optional[str] = "page",
max_row_group_size: int = 1024 * 1024,
created_by: str = "datafusion-python",
column_index_truncate_length: Optional[int] = 64,
statistics_truncate_length: Optional[int] = None,
data_page_row_count_limit: int = 20_000,
encoding: Optional[str] = None,
bloom_filter_on_write: bool = False,
bloom_filter_fpp: Optional[float] = None,
bloom_filter_ndv: Optional[int] = 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: Optional[dict[str, ParquetColumnOptions]] = 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: Optional[str] = None,
dictionary_enabled: Optional[bool] = None,
compression: Optional[str] = None,
statistics_enabled: Optional[str] = None,
bloom_filter_enabled: Optional[bool] = None,
bloom_filter_fpp: Optional[float] = None,
bloom_filter_ndv: Optional[int] = 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.
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) -> 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)
>>> df.collect() # The DataFrame is still usable
>>> ctx.sql("SELECT value FROM values_view").collect()
"""
from datafusion.catalog import Table as _Table
return _Table(self.df.into_view())
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(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 do not require double quotes like
other operations such as `select`. Leading and trailing double quotes
are allowed and will be automatically stripped if present.
Args:
columns: Column names to drop from the dataframe. Both ``column_name``
and ``"column_name"`` are accepted.
Returns:
DataFrame with those columns removed in the projection.
Example Usage::
df.drop('ID_For_Students') # Works
df.drop('"ID_For_Students"') # Also works (quotes stripped)
"""
normalized_columns = []
for col in columns:
if col.startswith('"') and col.endswith('"'):
normalized_columns.append(col.strip('"')) # Strip double quotes
else:
normalized_columns.append(col)
return DataFrame(self.df.drop(*normalized_columns))
def filter(self, *predicates: 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. Each ``predicate`` must be an
:class:`~datafusion.expr.Expr` created using helper functions such as
:func:`datafusion.col` or :func:`datafusion.lit`.
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))
Args:
predicates: Predicate expression(s) to filter the DataFrame.
Returns:
DataFrame after filtering.
"""
df = self.df
for p in predicates:
df = df.filter(ensure_expr(p))
return DataFrame(df)
def with_column(self, name: str, expr: Expr) -> 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`.
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.
"""
return DataFrame(self.df.with_column(name, ensure_expr(expr)))
def with_columns(
self, *exprs: Expr | Iterable[Expr], **named_exprs: Expr
) -> DataFrame:
"""Add columns to the DataFrame.
By passing expressions, iterables of expressions, or named expressions.
All expressions must be :class:`~datafusion.expr.Expr` objects created via
:func:`datafusion.col` or :func:`datafusion.lit`.
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)
)
Args:
exprs: Either a single expression or an iterable of expressions to add.
named_exprs: Named expressions in the form of ``name=expr``
Returns:
DataFrame with the new columns added.
"""
expressions = ensure_expr_list(exprs)
for alias, expr in named_exprs.items():
ensure_expr(expr)
expressions.append(expr.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 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,
) -> 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,
) -> 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,
) -> 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,
) -> 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.
join_keys: Tuple of two lists of column names to join on. [Deprecated]
Returns:
DataFrame after join.
"""
# 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
and isinstance(on[0], list)
and isinstance(on[1], list)
):
# We know this is safe because we've checked the types
join_keys = on # type: ignore[assignment]
on = None
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]
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))
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, num: int) -> DataFrame:
"""Repartition a DataFrame using a hash partitioning scheme.
Args:
exprs: Expressions to evaluate and perform hashing on.
num: Number of partitions to repartition the DataFrame into.
Returns:
Repartitioned DataFrame.
"""
exprs = [expr.expr for expr in 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.
Args:
other: DataFrame to intersect with.
Returns:
DataFrame after intersection.
"""
return DataFrame(self.df.intersect(other.df))
def except_all(self, other: DataFrame) -> DataFrame:
"""Calculate the exception of two :py:class:`DataFrame`.
The two :py:class:`DataFrame` must have exactly the same schema.
Args:
other: DataFrame to calculate exception with.
Returns:
DataFrame after exception.
"""
return DataFrame(self.df.except_all(other.df))
def write_csv(self, path: str | pathlib.Path, with_header: bool = False) -> None:
"""Execute the :py:class:`DataFrame` and write the results to a CSV file.
Args:
path: Path of the CSV file to write.
with_header: If true, output the CSV header row.
"""
self.df.write_csv(str(path), with_header)
@overload
def write_parquet(
self,
path: str | pathlib.Path,
compression: str,
compression_level: int | None = None,
) -> None: ...
@overload
def write_parquet(
self,
path: str | pathlib.Path,
compression: Compression = Compression.ZSTD,
compression_level: int | None = None,
) -> None: ...
@overload
def write_parquet(
self,
path: str | pathlib.Path,
compression: ParquetWriterOptions,
compression_level: None = None,
) -> None: ...
def write_parquet(
self,
path: str | pathlib.Path,
compression: Union[str, Compression, ParquetWriterOptions] = Compression.ZSTD,
compression_level: int | None = None,
) -> None:
"""Execute the :py:class:`DataFrame` and write the results to a Parquet file.
Args:
path: Path of the Parquet file to write.
compression: Compression type to use. Default is "ZSTD".
Available compression types are:
- "uncompressed": No compression.
- "snappy": Snappy compression.
- "gzip": Gzip compression.
- "brotli": Brotli compression.
- "lz4": LZ4 compression.
- "lz4_raw": LZ4_RAW compression.
- "zstd": Zstandard compression.
Note: LZO is not yet implemented in arrow-rs and is therefore excluded.
compression_level: Compression level to use. For ZSTD, the
recommended range is 1 to 22, with the default being 4. Higher levels
provide better compression but slower speed.
"""
if isinstance(compression, ParquetWriterOptions):
if compression_level is not None:
You can’t perform that action at this time.