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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
from typing import Any, List, TYPE_CHECKING
from datafusion.record_batch import RecordBatchStream
from typing_extensions import deprecated
if TYPE_CHECKING:
import pyarrow as pa
import pandas as pd
import polars as pl
import pathlib
from datafusion._internal import DataFrame as DataFrameInternal
from datafusion.expr import Expr
from datafusion._internal import (
LogicalPlan,
ExecutionPlan,
)
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 __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 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()
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 = [
arg.expr if isinstance(arg, Expr) else Expr.column(arg).expr
for arg in exprs
]
return DataFrame(self.df.select(*exprs))
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. If more complex logic is required, see the
logical operations in :py:mod:`~datafusion.functions`.
Args:
predicates: Predicate expression(s) to filter the DataFrame.
Returns:
DataFrame after filtering.
"""
df = self.df
for p in predicates:
df = df.filter(p.expr)
return DataFrame(df)
def with_column(self, name: str, expr: Expr) -> DataFrame:
"""Add an additional column to the DataFrame.
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, expr.expr))
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: list[Expr], aggs: list[Expr]) -> DataFrame:
"""Aggregates the rows of the current DataFrame.
Args:
group_by: List of expressions to group by.
aggs: List of expressions to aggregate.
Returns:
DataFrame after aggregation.
"""
group_by = [e.expr for e in group_by]
aggs = [e.expr for e in aggs]
return DataFrame(self.df.aggregate(group_by, aggs))
def sort(self, *exprs: 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.
Args:
exprs: Sort expressions, applied in order.
Returns:
DataFrame after sorting.
"""
exprs = [expr.expr for expr in exprs]
return DataFrame(self.df.sort(*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 collect(self) -> list[pa.RecordBatch]:
"""Execute this :py:class:`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 :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())
def join(
self,
right: DataFrame,
join_keys: tuple[list[str], list[str]],
how: str,
) -> DataFrame:
"""Join this :py:class:`DataFrame` with another :py:class:`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.
Args:
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.
"""
return DataFrame(self.df.join(right.df, join_keys, how))
def explain(self, 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.
Args:
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.
"""
return DataFrame(self.df.explain(verbose, analyze))
def logical_plan(self) -> LogicalPlan:
"""Return the unoptimized ``LogicalPlan``.
Returns:
Unoptimized logical plan.
"""
return self.df.logical_plan()
def optimized_logical_plan(self) -> LogicalPlan:
"""Return the optimized ``LogicalPlan``.
Returns:
Optimized logical plan.
"""
return self.df.optimized_logical_plan()
def execution_plan(self) -> ExecutionPlan:
"""Return the execution/physical plan.
Returns:
Execution plan.
"""
return 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)
def write_parquet(
self,
path: str | pathlib.Path,
compression: str = "uncompressed",
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.
compression_level: Compression level to use.
"""
self.df.write_parquet(str(path), compression, compression_level)
def write_json(self, path: str | pathlib.Path) -> None:
"""Execute the :py:class:`DataFrame` and write the results to a JSON file.
Args:
path: Path of the JSON file to write.
"""
self.df.write_json(str(path))
def to_arrow_table(self) -> pa.Table:
"""Execute the :py:class:`DataFrame` and convert it into an Arrow Table.
Returns:
Arrow Table.
"""
return self.df.to_arrow_table()
def execute_stream(self) -> RecordBatchStream:
"""Executes this DataFrame and returns a stream over a single partition.
Returns:
Record Batch Stream over a single partition.
"""
return RecordBatchStream(self.df.execute_stream())
def execute_stream_partitioned(self) -> list[RecordBatchStream]:
"""Executes this DataFrame and returns a stream for each partition.
Returns:
One record batch stream per partition.
"""
streams = self.df.execute_stream_partitioned()
return [RecordBatchStream(rbs) for rbs in streams]
def to_pandas(self) -> pd.DataFrame:
"""Execute the :py:class:`DataFrame` and convert it into a Pandas DataFrame.
Returns:
Pandas DataFrame.
"""
return self.df.to_pandas()
def to_pylist(self) -> list[dict[str, Any]]:
"""Execute the :py:class:`DataFrame` and convert it into a list of dictionaries.
Returns:
List of dictionaries.
"""
return self.df.to_pylist()
def to_pydict(self) -> dict[str, list[Any]]:
"""Execute the :py:class:`DataFrame` and convert it into a dictionary of lists.
Returns:
Dictionary of lists.
"""
return self.df.to_pydict()
def to_polars(self) -> pl.DataFrame:
"""Execute the :py:class:`DataFrame` and convert it into a Polars DataFrame.
Returns:
Polars DataFrame.
"""
return self.df.to_polars()
def count(self) -> int:
"""Return the total number of rows in this :py:class:`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.
"""
return self.df.count()
@deprecated("Use :py:func:`unnest_columns` instead.")
def unnest_column(self, column: str, preserve_nulls: bool = True) -> DataFrame:
"""See :py:func:`unnest_columns`."""
return DataFrame(self.df.unnest_column(column, preserve_nulls=preserve_nulls))
def unnest_columns(self, *columns: str, preserve_nulls: bool = True) -> DataFrame:
"""Expand columns of arrays into a single row per array element.
Args:
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.
"""
columns = [c for c in columns]
return DataFrame(self.df.unnest_columns(columns, preserve_nulls=preserve_nulls))