spark/python/pyspark/sql/dataframe.py at master · apache/spark · GitHub

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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.

# mypy: disable-error-code="empty-body"

import random

import sys

from typing import (

TYPE_CHECKING,

Any,

Callable,

Dict,

Iterator,

List,

Optional,

Sequence,

Tuple,

Union,

overload,

)

from pyspark import _NoValue

from pyspark._globals import _NoValueType

from pyspark.resource import ResourceProfile

from pyspark.sql.column import Column

from pyspark.sql.merge import MergeIntoWriter

from pyspark.sql.readwriter import DataFrameWriter, DataFrameWriterV2

from pyspark.sql.streaming import DataStreamWriter

from pyspark.sql.table_arg import TableArg

from pyspark.sql.types import Row, StructType

from pyspark.sql.utils import dispatch_df_method

from pyspark.storagelevel import StorageLevel

from pyspark.util import is_remote_only

if TYPE_CHECKING:

import pyarrow as pa

from py4j.java_gateway import JavaObject

from pyspark._typing import PrimitiveType

from pyspark.core.context import SparkContext

from pyspark.core.rdd import RDD

from pyspark.pandas.frame import DataFrame as PandasOnSparkDataFrame

from pyspark.sql._typing import (

ColumnOrName,

ColumnOrNameOrOrdinal,

LiteralType,

OptionalPrimitiveType,

)

from pyspark.sql.context import SQLContext

from pyspark.sql.group import GroupedData

from pyspark.sql.metrics import ExecutionInfo

from pyspark.sql.observation import Observation

from pyspark.sql.pandas._typing import (

ArrowMapIterFunction,

PandasMapIterFunction,

)

from pyspark.sql.pandas._typing import (

DataFrameLike as PandasDataFrameLike,

)

from pyspark.sql.plot import PySparkPlotAccessor

from pyspark.sql.session import SparkSession

__all__ = ["DataFrame", "DataFrameNaFunctions", "DataFrameStatFunctions"]

class DataFrame:

"""A distributed collection of data grouped into named columns.

.. versionadded:: 1.3.0

.. versionchanged:: 3.4.0

Supports Spark Connect.

Examples

--------

A :class:`DataFrame` is equivalent to a relational table in Spark SQL,

and can be created using various functions in :class:`SparkSession`:

>>> people = spark.createDataFrame([

... {"deptId": 1, "age": 40, "name": "Hyukjin Kwon", "gender": "M", "salary": 50},

... {"deptId": 1, "age": 50, "name": "Takuya Ueshin", "gender": "M", "salary": 100},

... {"deptId": 2, "age": 60, "name": "Xinrong Meng", "gender": "F", "salary": 150},

... {"deptId": 3, "age": 20, "name": "Haejoon Lee", "gender": "M", "salary": 200}

... ])

Once created, it can be manipulated using the various domain-specific-language

(DSL) functions defined in: :class:`DataFrame`, :class:`Column`.

To select a column from the :class:`DataFrame`, use the apply method:

>>> age_col = people.age

A more concrete example:

>>> # To create DataFrame using SparkSession

... department = spark.createDataFrame([

... {"id": 1, "name": "PySpark"},

... {"id": 2, "name": "ML"},

... {"id": 3, "name": "Spark SQL"}

... ])

>>> people.filter(people.age > 30).join(

... department, people.deptId == department.id).groupBy(

... department.name, "gender").agg(

... {"salary": "avg", "age": "max"}).sort("max(age)").show()

+-------+------+-----------+--------+

+-------+------+-----------+--------+

|PySpark| M| 75.0| 50|

| ML| F| 150.0| 60|

+-------+------+-----------+--------+

Notes

-----

A DataFrame should only be created as described above. It should not be directly

created via using the constructor.

"""

# HACK ALERT!! this is to reduce the backward compatibility concern, and returns

# Spark Classic DataFrame by default. This is NOT an API, and NOT supposed to

# be directly invoked. DO NOT use this constructor.

_sql_ctx: Optional["SQLContext"]

_session: "SparkSession"

_sc: "SparkContext"

_jdf: "JavaObject"

is_cached: bool

_schema: Optional[StructType]

_lazy_rdd: Optional["RDD[Row]"]

_support_repr_html: bool

def __new__(

cls,

jdf: "JavaObject",

sql_ctx: Union["SQLContext", "SparkSession"],

) -> "DataFrame":

from pyspark.sql.classic.dataframe import DataFrame

return DataFrame(jdf, sql_ctx)

@property

def sparkSession(self) -> "SparkSession":

"""Returns Spark session that created this :class:`DataFrame`.

.. versionadded:: 3.3.0

.. versionchanged:: 3.4.0

Supports Spark Connect.

Returns

-------

:class:`SparkSession`

Examples

--------

>>> df = spark.range(1)

>>> type(df.sparkSession)

"""

...

if not is_remote_only():

@property

def rdd(self) -> "RDD[Row]":

"""Returns the content as an :class:`pyspark.RDD` of :class:`Row`.

.. versionadded:: 1.3.0

Returns

-------

:class:`RDD`

Examples

--------

>>> df = spark.range(1)

>>> type(df.rdd)

"""

...

@property

def na(self) -> "DataFrameNaFunctions":

"""Returns a :class:`DataFrameNaFunctions` for handling missing values.

.. versionadded:: 1.3.1

.. versionchanged:: 3.4.0

Supports Spark Connect.

Returns

-------

:class:`DataFrameNaFunctions`

Examples

--------

>>> df = spark.sql("SELECT 1 AS c1, int(NULL) AS c2")

>>> type(df.na)

Replace the missing values as 2.

>>> df.na.fill(2).show()

+---+---+

| c1| c2|

+---+---+

| 1| 2|

+---+---+

"""

...

@property

def stat(self) -> "DataFrameStatFunctions":

"""Returns a :class:`DataFrameStatFunctions` for statistic functions.

.. versionadded:: 1.4.0

.. versionchanged:: 3.4.0

Supports Spark Connect.

Returns

-------

:class:`DataFrameStatFunctions`

Examples

--------

>>> import pyspark.sql.functions as f

>>> df = spark.range(3).withColumn("c", f.expr("id + 1"))

>>> type(df.stat)

>>> df.stat.corr("id", "c")

1.0

"""

...

@dispatch_df_method

def toJSON(self, use_unicode: bool = True) -> Union["RDD[str]", "DataFrame"]:

"""Converts a :class:`DataFrame` into a :class:`RDD` of string or :class:`DataFrame`.

Each row is turned into a JSON document as one element in the returned RDD

or DataFrame.

.. versionadded:: 1.3.0

.. versionchanged:: 4.2.0

Supports Spark Connect.

Parameters

----------

use_unicode : bool, optional, default True

Whether to convert to unicode or not. Note that this argument is disallowed

in Spark Connect mode.

Returns

-------

:class:`RDD` (in Classic mode) or :class:`DataFrame` (in connect mode)

Examples

--------

>>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])

>>> df.toJSON().first() # doctest: +SKIP

'{"age":2,"name":"Alice"}'

"""

...

@dispatch_df_method

def registerTempTable(self, name: str) -> None:

"""Registers this :class:`DataFrame` as a temporary table using the given name.

The lifetime of this temporary table is tied to the :class:`SparkSession`

that was used to create this :class:`DataFrame`.

.. versionadded:: 1.3.0

.. versionchanged:: 3.4.0

Supports Spark Connect.

.. deprecated:: 2.0.0

Use :meth:`DataFrame.createOrReplaceTempView` instead.

Parameters

----------

name : str

Name of the temporary table to register.

Examples

--------

>>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])

>>> df.registerTempTable("people")

>>> df2 = spark.sql("SELECT * FROM people")

>>> sorted(df.collect()) == sorted(df2.collect())

True

>>> spark.catalog.dropTempView("people")

True

"""

...

@dispatch_df_method

def createTempView(self, name: str) -> None:

"""Creates a local temporary view with this :class:`DataFrame`.

The lifetime of this temporary table is tied to the :class:`SparkSession`

that was used to create this :class:`DataFrame`.

throws :class:`TempTableAlreadyExistsException`, if the view name already exists in the

catalog.

.. versionadded:: 2.0.0

.. versionchanged:: 3.4.0

Supports Spark Connect.

Parameters

----------

name : str

Name of the view.

Examples

--------

Example 1: Creating and querying a local temporary view

>>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])

>>> df.createTempView("people")

>>> spark.sql("SELECT * FROM people").show()

+---+-----+

|age| name|

+---+-----+

| 2|Alice|

| 5| Bob|

+---+-----+

Example 2: Attempting to create a temporary view with an existing name

>>> df.createTempView("people") # doctest: +IGNORE_EXCEPTION_DETAIL

Traceback (most recent call last):

...

AnalysisException: "Temporary table 'people' already exists;"

Example 3: Creating and dropping a local temporary view

>>> spark.catalog.dropTempView("people")

True

>>> df.createTempView("people")

Example 4: Creating temporary views with multiple DataFrames with

:meth:`SparkSession.table`

>>> df1 = spark.createDataFrame([(1, "John"), (2, "Jane")], schema=["id", "name"])

>>> df2 = spark.createDataFrame([(3, "Jake"), (4, "Jill")], schema=["id", "name"])

>>> df1.createTempView("table1")

>>> df2.createTempView("table2")

>>> result_df = spark.table("table1").union(spark.table("table2"))

>>> result_df.show()

+---+----+

| id|name|

+---+----+

| 1|John|

| 2|Jane|

| 3|Jake|

| 4|Jill|

+---+----+

"""

...

@dispatch_df_method

def createOrReplaceTempView(self, name: str) -> None:

"""Creates or replaces a local temporary view with this :class:`DataFrame`.

.. versionadded:: 2.0.0

.. versionchanged:: 3.4.0

Supports Spark Connect.

Parameters

----------

name : str

Name of the view.

Notes

-----

The lifetime of this temporary table is tied to the :class:`SparkSession`

that was used to create this :class:`DataFrame`.

Examples

--------

Example 1: Creating a local temporary view named 'people'.

>>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])

>>> df.createOrReplaceTempView("people")

Example 2: Replacing the local temporary view.

>>> df2 = df.filter(df.age > 3)

>>> # Replace the local temporary view with the filtered DataFrame

>>> df2.createOrReplaceTempView("people")

>>> # Query the temporary view

>>> df3 = spark.sql("SELECT * FROM people")

>>> # Check if the DataFrames are equal

... assert sorted(df3.collect()) == sorted(df2.collect())

Example 3: Dropping the temporary view.

>>> # Drop the local temporary view

... spark.catalog.dropTempView("people")

True

"""

...

@dispatch_df_method

def createGlobalTempView(self, name: str) -> None:

"""Creates a global temporary view with this :class:`DataFrame`.

.. versionadded:: 2.1.0

.. versionchanged:: 3.4.0

Supports Spark Connect.

Parameters

----------

name : str

Name of the view.

Notes

-----

The lifetime of this temporary view is tied to this Spark application.

throws :class:`TempTableAlreadyExistsException`, if the view name already exists in the

catalog.

Examples

--------

Example 1: Creating and querying a global temporary view

>>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])

>>> df.createGlobalTempView("people")

>>> df2 = spark.sql("SELECT * FROM global_temp.people")

>>> df2.show()

+---+-----+

|age| name|

+---+-----+

| 2|Alice|

| 5| Bob|

+---+-----+

Example 2: Attempting to create a duplicate global temporary view

>>> df.createGlobalTempView("people") # doctest: +IGNORE_EXCEPTION_DETAIL

Traceback (most recent call last):

...

AnalysisException: "Temporary table 'people' already exists;"

Example 3: Dropping a global temporary view

>>> spark.catalog.dropGlobalTempView("people")

True

"""

...

@dispatch_df_method

def createOrReplaceGlobalTempView(self, name: str) -> None:

"""Creates or replaces a global temporary view using the given name.

The lifetime of this temporary view is tied to this Spark application.

.. versionadded:: 2.2.0

.. versionchanged:: 3.4.0

Supports Spark Connect.

Parameters

----------

name : str

Name of the view.

Examples

--------

Example 1: Creating a global temporary view with a DataFrame

>>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])

>>> df.createOrReplaceGlobalTempView("people")

Example 2: Replacing a global temporary view with a filtered DataFrame

>>> df2 = df.filter(df.age > 3)

>>> df2.createOrReplaceGlobalTempView("people")

>>> df3 = spark.table("global_temp.people")

>>> sorted(df3.collect()) == sorted(df2.collect())

True

Example 3: Dropping a global temporary view

>>> spark.catalog.dropGlobalTempView("people")

True

"""

...

@property

def write(self) -> DataFrameWriter:

"""

Interface for saving the content of the non-streaming :class:`DataFrame` out into external

storage.

.. versionadded:: 1.4.0

.. versionchanged:: 3.4.0

Supports Spark Connect.

Returns

-------

:class:`DataFrameWriter`

Examples

--------

>>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])

>>> type(df.write)

Write the DataFrame as a table.

>>> _ = spark.sql("DROP TABLE IF EXISTS tab2")

>>> df.write.saveAsTable("tab2")

>>> _ = spark.sql("DROP TABLE tab2")

"""

...

@property

def writeStream(self) -> DataStreamWriter:

"""

Interface for saving the content of the streaming :class:`DataFrame` out into external

storage.

.. versionadded:: 2.0.0

.. versionchanged:: 3.5.0

Supports Spark Connect.

Notes

-----

This API is evolving.

Returns

-------

:class:`DataStreamWriter`

Examples

--------

>>> import time

>>> import tempfile

>>> df = spark.readStream.format("rate").load()

>>> type(df.writeStream)

>>> with tempfile.TemporaryDirectory(prefix="writeStream") as d:

... # Create a table with Rate source.

... query = df.writeStream.toTable(

... "my_table", checkpointLocation=d)

... time.sleep(3)

... query.stop()

"""

...

@property

def schema(self) -> StructType:

"""Returns the schema of this :class:`DataFrame` as a :class:`pyspark.sql.types.StructType`.

.. versionadded:: 1.3.0

.. versionchanged:: 3.4.0

Supports Spark Connect.

Returns

-------

:class:`StructType`

Examples

--------

Example 1: Retrieve the inferred schema of the current DataFrame.

>>> df = spark.createDataFrame(

... [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])

>>> df.schema

StructType([StructField('age', LongType(), True),

StructField('name', StringType(), True)])

Example 2: Retrieve the schema of the current DataFrame (DDL-formatted schema).

>>> df = spark.createDataFrame(

... [(14, "Tom"), (23, "Alice"), (16, "Bob")],

... "age INT, name STRING")

>>> df.schema

StructType([StructField('age', IntegerType(), True),

StructField('name', StringType(), True)])

Example 3: Retrieve the specified schema of the current DataFrame.

>>> from pyspark.sql.types import StructType, StructField, StringType

>>> df = spark.createDataFrame(

... [("a",), ("b",), ("c",)],

... StructType([StructField("value", StringType(), False)]))

>>> df.schema

StructType([StructField('value', StringType(), False)])

"""

...

@dispatch_df_method

def printSchema(self, level: Optional[int] = None) -> None:

"""Prints out the schema in the tree format.

Optionally allows to specify how many levels to print if schema is nested.

.. versionadded:: 1.3.0

.. versionchanged:: 3.4.0

Supports Spark Connect.

Parameters

----------

level : int, optional

How many levels to print for nested schemas.

.. versionadded:: 3.5.0

Examples

--------

Example 1: Printing the schema of a DataFrame with basic columns

>>> df = spark.createDataFrame(

... [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])

>>> df.printSchema()

root

|-- age: long (nullable = true)

|-- name: string (nullable = true)

Example 2: Printing the schema with a specified level for nested columns

>>> df = spark.createDataFrame([(1, (2, 2))], ["a", "b"])

>>> df.printSchema(1)

root

|-- a: long (nullable = true)

|-- b: struct (nullable = true)

Example 3: Printing the schema with deeper nesting level

>>> df.printSchema(2)

root

|-- a: long (nullable = true)

|-- b: struct (nullable = true)

| |-- _1: long (nullable = true)

| |-- _2: long (nullable = true)

Example 4: Printing the schema of a DataFrame with nullable and non-nullable columns

>>> df = spark.range(1).selectExpr("id AS nonnullable", "NULL AS nullable")

>>> df.printSchema()

root

|-- nonnullable: long (nullable = false)

|-- nullable: void (nullable = true)

"""

...

@dispatch_df_method

def explain(

self, extended: Optional[Union[bool, str]] = None, mode: Optional[str] = None

) -> None:

"""Prints the (logical and physical) plans to the console for debugging purposes.

.. versionadded:: 1.3.0

.. versionchanged:: 3.4.0

Supports Spark Connect.

Parameters

----------

extended : bool, optional

default ``False``. If ``False``, prints only the physical plan.

When this is a string without specifying the ``mode``, it works as the mode is

specified.

mode : str, optional

specifies the expected output format of plans.

* ``simple``: Print only a physical plan.

* ``extended``: Print both logical and physical plans.

* ``codegen``: Print a physical plan and generated codes if they are available.

* ``cost``: Print a logical plan and statistics if they are available.

* ``formatted``: Split explain output into two sections: a physical plan outline \

and node details.

.. versionchanged:: 3.0.0

Added optional argument `mode` to specify the expected output format of plans.

Examples

--------

Example 1: Print out the physical plan only (default).

>>> df = spark.createDataFrame(

... [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])

>>> df.explain() # doctest: +SKIP

== Physical Plan ==

*(1) Scan ExistingRDD[age...,name...]

Example 2: Print out all parsed, analyzed, optimized, and physical plans.

>>> df.explain(extended=True)

== Parsed Logical Plan ==

...

== Analyzed Logical Plan ==

...

== Optimized Logical Plan ==

...

== Physical Plan ==

...

Example 3: Print out the plans with two sections: a physical plan outline and node details.

>>> df.explain(mode="formatted") # doctest: +SKIP

== Physical Plan ==

* Scan ExistingRDD (...)

(1) Scan ExistingRDD [codegen id : ...]

Output [2]: [age..., name...]

...

Example 4: Print a logical plan and statistics if they are available.

>>> df.explain(mode="cost")

== Optimized Logical Plan ==

...Statistics...

...

"""

...

@dispatch_df_method

def exceptAll(self, other: "DataFrame") -> "DataFrame":

"""Return a new :class:`DataFrame` containing rows in this :class:`DataFrame` but

not in another :class:`DataFrame` while preserving duplicates.

This is equivalent to `EXCEPT ALL` in SQL.

As standard in SQL, this function resolves columns by position (not by name).

.. versionadded:: 2.4.0

.. versionchanged:: 3.4.0

Supports Spark Connect.

Parameters

----------

other : :class:`DataFrame`

The other :class:`DataFrame` to compare to.

Returns

-------

:class:`DataFrame`

spark/python/pyspark/sql/dataframe.py at master · apache/spark · GitHub

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