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apache / spark / 28951ed6681f9f0ad28908fd92b2c4a871fa5958 / . / python / pyspark / sql / dataframe.py
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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.
#
import json
import os
import sys
import random
import warnings
from collections.abc import Iterable
from functools import reduce
from typing import (
Any,
Callable,
Dict,
Iterator,
List,
Optional,
Sequence,
Tuple,
Type,
Union,
cast,
overload,
TYPE_CHECKING,
)
from py4j.java_gateway import JavaObject, JVMView
from pyspark import _NoValue
from pyspark._globals import _NoValueType
from pyspark.context import SparkContext
from pyspark.errors import (
PySparkTypeError,
PySparkValueError,
PySparkIndexError,
PySparkAttributeError,
)
from pyspark.rdd import (
RDD,
_load_from_socket,
_local_iterator_from_socket,
)
from pyspark.serializers import BatchedSerializer, CPickleSerializer, UTF8Deserializer
from pyspark.storagelevel import StorageLevel
from pyspark.traceback_utils import SCCallSiteSync
from pyspark.sql.column import Column, _to_seq, _to_list, _to_java_column
from pyspark.sql.readwriter import DataFrameWriter, DataFrameWriterV2
from pyspark.sql.streaming import DataStreamWriter
from pyspark.sql.types import (
StructType,
Row,
_parse_datatype_json_string,
)
from pyspark.sql.utils import get_active_spark_context, toJArray
from pyspark.sql.pandas.conversion import PandasConversionMixin
from pyspark.sql.pandas.map_ops import PandasMapOpsMixin
if TYPE_CHECKING:
from pyspark._typing import PrimitiveType
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.session import SparkSession
from pyspark.sql.group import GroupedData
from pyspark.sql.observation import Observation
__all__ = ["DataFrame", "DataFrameNaFunctions", "DataFrameStatFunctions"]
class DataFrame(PandasMapOpsMixin, PandasConversionMixin):
"""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"}).show()
+-------+------+-----------+--------+
| name|gender|avg(salary)|max(age)|
+-------+------+-----------+--------+
| ML| F| 150.0| 60|
|PySpark| M| 75.0| 50|
+-------+------+-----------+--------+
Notes
-----
A DataFrame should only be created as described above. It should not be directly
created via using the constructor.
"""
def __init__(
self,
jdf: JavaObject,
sql_ctx: Union["SQLContext", "SparkSession"],
):
from pyspark.sql.context import SQLContext
self._sql_ctx: Optional["SQLContext"] = None
if isinstance(sql_ctx, SQLContext):
assert not os.environ.get("SPARK_TESTING") # Sanity check for our internal usage.
assert isinstance(sql_ctx, SQLContext)
# We should remove this if-else branch in the future release, and rename
# sql_ctx to session in the constructor. This is an internal code path but
# was kept with a warning because it's used intensively by third-party libraries.
warnings.warn("DataFrame constructor is internal. Do not directly use it.")
self._sql_ctx = sql_ctx
session = sql_ctx.sparkSession
else:
session = sql_ctx
self._session: "SparkSession" = session
self._sc: SparkContext = sql_ctx._sc
self._jdf: JavaObject = jdf
self.is_cached = False
# initialized lazily
self._schema: Optional[StructType] = None
self._lazy_rdd: Optional[RDD[Row]] = None
# Check whether _repr_html is supported or not, we use it to avoid calling _jdf twice
# by __repr__ and _repr_html_ while eager evaluation opens.
self._support_repr_html = False
@property
def sql_ctx(self) -> "SQLContext":
from pyspark.sql.context import SQLContext
warnings.warn(
"DataFrame.sql_ctx is an internal property, and will be removed "
"in future releases. Use DataFrame.sparkSession instead."
)
if self._sql_ctx is None:
self._sql_ctx = SQLContext._get_or_create(self._sc)
return self._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)
<class '...session.SparkSession'>
"""
return self._session
@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)
<class 'pyspark.rdd.RDD'>
"""
if self._lazy_rdd is None:
jrdd = self._jdf.javaToPython()
self._lazy_rdd = RDD(
jrdd, self.sparkSession._sc, BatchedSerializer(CPickleSerializer())
)
return self._lazy_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)
<class '...dataframe.DataFrameNaFunctions'>
Replace the missing values as 2.
>>> df.na.fill(2).show()
+---+---+
| c1| c2|
+---+---+
| 1| 2|
+---+---+
"""
return DataFrameNaFunctions(self)
@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)
<class '...dataframe.DataFrameStatFunctions'>
>>> df.stat.corr("id", "c")
1.0
"""
return DataFrameStatFunctions(self)
def toJSON(self, use_unicode: bool = True) -> RDD[str]:
"""Converts a :class:`DataFrame` into a :class:`RDD` of string.
Each row is turned into a JSON document as one element in the returned RDD.
.. versionadded:: 1.3.0
Parameters
----------
use_unicode : bool, optional, default True
Whether to convert to unicode or not.
Returns
-------
:class:`RDD`
Examples
--------
>>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
>>> df.toJSON().first()
'{"age":2,"name":"Alice"}'
"""
rdd = self._jdf.toJSON()
return RDD(rdd.toJavaRDD(), self._sc, UTF8Deserializer(use_unicode))
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
"""
warnings.warn("Deprecated in 2.0, use createOrReplaceTempView instead.", FutureWarning)
self._jdf.createOrReplaceTempView(name)
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|
+---+----+
"""
self._jdf.createTempView(name)
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
"""
self._jdf.createOrReplaceTempView(name)
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
"""
self._jdf.createGlobalTempView(name)
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
"""
self._jdf.createOrReplaceGlobalTempView(name)
@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)
<class '...readwriter.DataFrameWriter'>
Write the DataFrame as a table.
>>> _ = spark.sql("DROP TABLE IF EXISTS tab2")
>>> df.write.saveAsTable("tab2")
>>> _ = spark.sql("DROP TABLE tab2")
"""
return DataFrameWriter(self)
@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 tempfile
>>> df = spark.readStream.format("rate").load()
>>> type(df.writeStream)
<class '...streaming.readwriter.DataStreamWriter'>
>>> with tempfile.TemporaryDirectory() as d:
... # Create a table with Rate source.
... df.writeStream.toTable(
... "my_table", checkpointLocation=d)
<...streaming.query.StreamingQuery object at 0x...>
"""
return DataStreamWriter(self)
@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)])
"""
if self._schema is None:
try:
self._schema = cast(
StructType, _parse_datatype_json_string(self._jdf.schema().json())
)
except Exception as e:
raise PySparkValueError(
error_class="CANNOT_PARSE_DATATYPE",
message_parameters={"error": str(e)},
)
return self._schema
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)
"""
if level:
print(self._jdf.schema().treeString(level))
else:
print(self._jdf.schema().treeString())
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...
...
"""
if extended is not None and mode is not None:
raise PySparkValueError(
error_class="CANNOT_SET_TOGETHER",
message_parameters={"arg_list": "extended and mode"},
)
# For the no argument case: df.explain()
is_no_argument = extended is None and mode is None
# For the cases below:
# explain(True)
# explain(extended=False)
is_extended_case = isinstance(extended, bool) and mode is None
# For the case when extended is mode:
# df.explain("formatted")
is_extended_as_mode = isinstance(extended, str) and mode is None
# For the mode specified:
# df.explain(mode="formatted")
is_mode_case = extended is None and isinstance(mode, str)
if not (is_no_argument or is_extended_case or is_extended_as_mode or is_mode_case):
if (extended is not None) and (not isinstance(extended, (bool, str))):
raise PySparkTypeError(
error_class="NOT_BOOL_OR_STR",
message_parameters={
"arg_name": "extended",
"arg_type": type(extended).__name__,
},
)
if (mode is not None) and (not isinstance(mode, str)):
raise PySparkTypeError(
error_class="NOT_STR",
message_parameters={"arg_name": "mode", "arg_type": type(mode).__name__},
)
# Sets an explain mode depending on a given argument
if is_no_argument:
explain_mode = "simple"
elif is_extended_case:
explain_mode = "extended" if extended else "simple"
elif is_mode_case:
explain_mode = cast(str, mode)
elif is_extended_as_mode:
explain_mode = cast(str, extended)
assert self._sc._jvm is not None
print(self._sc._jvm.PythonSQLUtils.explainString(self._jdf.queryExecution(), explain_mode))
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`
Examples
--------
>>> df1 = spark.createDataFrame(
... [("a", 1), ("a", 1), ("a", 1), ("a", 2), ("b", 3), ("c", 4)], ["C1", "C2"])
>>> df2 = spark.createDataFrame([("a", 1), ("b", 3)], ["C1", "C2"])
>>> df1.exceptAll(df2).show()
+---+---+
| C1| C2|
+---+---+
| a| 1|
| a| 1|
| a| 2|
| c| 4|
+---+---+
"""
return DataFrame(self._jdf.exceptAll(other._jdf), self.sparkSession)
def isLocal(self) -> bool:
"""Returns ``True`` if the :func:`collect` and :func:`take` methods can be run locally
(without any Spark executors).
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
bool
Examples
--------
>>> df = spark.sql("SHOW TABLES")
>>> df.isLocal()
True
"""
return self._jdf.isLocal()
@property
def isStreaming(self) -> bool:
"""Returns ``True`` if this :class:`DataFrame` contains one or more sources that
continuously return data as it arrives. A :class:`DataFrame` that reads data from a
streaming source must be executed as a :class:`StreamingQuery` using the :func:`start`
method in :class:`DataStreamWriter`. Methods that return a single answer, (e.g.,
:func:`count` or :func:`collect`) will throw an :class:`AnalysisException` when there
is a streaming source present.
.. versionadded:: 2.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Notes
-----
This API is evolving.
Returns
-------
bool
Whether it's streaming DataFrame or not.
Examples
--------
>>> df = spark.readStream.format("rate").load()
>>> df.isStreaming
True
"""
return self._jdf.isStreaming()
def isEmpty(self) -> bool:
"""
Checks if the :class:`DataFrame` is empty and returns a boolean value.
.. versionadded:: 3.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
bool
Returns ``True`` if the DataFrame is empty, ``False`` otherwise.
See Also
--------
DataFrame.count : Counts the number of rows in DataFrame.
Notes
-----
- Unlike `count()`, this method does not trigger any computation.
- An empty DataFrame has no rows. It may have columns, but no data.
Examples
--------
Example 1: Checking if an empty DataFrame is empty
>>> df_empty = spark.createDataFrame([], 'a STRING')
>>> df_empty.isEmpty()
True
Example 2: Checking if a non-empty DataFrame is empty
>>> df_non_empty = spark.createDataFrame(["a"], 'STRING')
>>> df_non_empty.isEmpty()
False
Example 3: Checking if a DataFrame with null values is empty
>>> df_nulls = spark.createDataFrame([(None, None)], 'a STRING, b INT')
>>> df_nulls.isEmpty()
False
Example 4: Checking if a DataFrame with no rows but with columns is empty
>>> df_no_rows = spark.createDataFrame([], 'id INT, value STRING')
>>> df_no_rows.isEmpty()
True
"""
return self._jdf.isEmpty()
def show(self, n: int = 20, truncate: Union[bool, int] = True, vertical: bool = False) -> None:
"""
Prints the first ``n`` rows of the DataFrame to the console.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
n : int, optional, default 20
Number of rows to show.
truncate : bool or int, optional, default True
If set to ``True``, truncate strings longer than 20 chars.
If set to a number greater than one, truncates long strings to length ``truncate``
and align cells right.
vertical : bool, optional
If set to ``True``, print output rows vertically (one line per column value).
Examples
--------
>>> df = spark.createDataFrame([
... (14, "Tom"), (23, "Alice"), (16, "Bob"), (19, "This is a super long name")],
... ["age", "name"])
Show :class:`DataFrame`
>>> df.show()
+---+--------------------+
|age| name|
+---+--------------------+
| 14| Tom|
| 23| Alice|
| 16| Bob|
| 19|This is a super l...|
+---+--------------------+
Show only top 2 rows.
>>> df.show(2)
+---+-----+
|age| name|
+---+-----+
| 14| Tom|
| 23|Alice|
+---+-----+
only showing top 2 rows
Show full column content without truncation.
>>> df.show(truncate=False)
+---+-------------------------+
|age|name |
+---+-------------------------+
|14 |Tom |
|23 |Alice |
|16 |Bob |
|19 |This is a super long name|
+---+-------------------------+
Show :class:`DataFrame` where the maximum number of characters is 3.
>>> df.show(truncate=3)
+---+----+
|age|name|
+---+----+
| 14| Tom|
| 23| Ali|
| 16| Bob|
| 19| Thi|
+---+----+
Show :class:`DataFrame` vertically.
>>> df.show(vertical=True)
-RECORD 0--------------------
age | 14
name | Tom
-RECORD 1--------------------
age | 23
name | Alice
-RECORD 2--------------------
age | 16
name | Bob
-RECORD 3--------------------
age | 19
name | This is a super l...
"""
print(self._show_string(n, truncate, vertical))
def _show_string(
self, n: int = 20, truncate: Union[bool, int] = True, vertical: bool = False
) -> str:
if not isinstance(n, int) or isinstance(n, bool):
raise PySparkTypeError(
error_class="NOT_INT",
message_parameters={"arg_name": "n", "arg_type": type(n).__name__},
)
if not isinstance(vertical, bool):
raise PySparkTypeError(
error_class="NOT_BOOL",
message_parameters={"arg_name": "vertical", "arg_type": type(vertical).__name__},
)
if isinstance(truncate, bool) and truncate:
return self._jdf.showString(n, 20, vertical)
else:
try:
int_truncate = int(truncate)
except ValueError:
raise PySparkTypeError(
error_class="NOT_BOOL",
message_parameters={
"arg_name": "truncate",
"arg_type": type(truncate).__name__,
},
)
return self._jdf.showString(n, int_truncate, vertical)
def __repr__(self) -> str:
if not self._support_repr_html and self.sparkSession._jconf.isReplEagerEvalEnabled():
vertical = False
return self._jdf.showString(
self.sparkSession._jconf.replEagerEvalMaxNumRows(),
self.sparkSession._jconf.replEagerEvalTruncate(),
vertical,
)
else:
return "DataFrame[%s]" % (", ".join("%s: %s" % c for c in self.dtypes))
def _repr_html_(self) -> Optional[str]:
"""Returns a :class:`DataFrame` with html code when you enabled eager evaluation
by 'spark.sql.repl.eagerEval.enabled', this only called by REPL you are
using support eager evaluation with HTML.
"""
if not self._support_repr_html:
self._support_repr_html = True
if self.sparkSession._jconf.isReplEagerEvalEnabled():
return self._jdf.htmlString(
self.sparkSession._jconf.replEagerEvalMaxNumRows(),
self.sparkSession._jconf.replEagerEvalTruncate(),
)
else:
return None
def checkpoint(self, eager: bool = True) -> "DataFrame":
"""Returns a checkpointed version of this :class:`DataFrame`. Checkpointing can be used to
truncate the logical plan of this :class:`DataFrame`, which is especially useful in
iterative algorithms where the plan may grow exponentially. It will be saved to files
inside the checkpoint directory set with :meth:`SparkContext.setCheckpointDir`.
.. versionadded:: 2.1.0
Parameters
----------
eager : bool, optional, default True
Whether to checkpoint this :class:`DataFrame` immediately.
Returns
-------
:class:`DataFrame`
Checkpointed DataFrame.
Notes
-----
This API is experimental.
Examples
--------
>>> import tempfile
>>> df = spark.createDataFrame([
... (14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
>>> with tempfile.TemporaryDirectory() as d:
... spark.sparkContext.setCheckpointDir("/tmp/bb")
... df.checkpoint(False)
DataFrame[age: bigint, name: string]
"""
jdf = self._jdf.checkpoint(eager)
return DataFrame(jdf, self.sparkSession)
def localCheckpoint(self, eager: bool = True) -> "DataFrame":
"""Returns a locally checkpointed version of this :class:`DataFrame`. Checkpointing can be
used to truncate the logical plan of this :class:`DataFrame`, which is especially useful in
iterative algorithms where the plan may grow exponentially. Local checkpoints are
stored in the executors using the caching subsystem and therefore they are not reliable.
.. versionadded:: 2.3.0
Parameters
----------
eager : bool, optional, default True
Whether to checkpoint this :class:`DataFrame` immediately.
Returns
-------
:class:`DataFrame`
Checkpointed DataFrame.
Notes
-----
This API is experimental.
Examples
--------
>>> df = spark.createDataFrame([
... (14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
>>> df.localCheckpoint(False)
DataFrame[age: bigint, name: string]
"""
jdf = self._jdf.localCheckpoint(eager)
return DataFrame(jdf, self.sparkSession)
def withWatermark(self, eventTime: str, delayThreshold: str) -> "DataFrame":
"""Defines an event time watermark for this :class:`DataFrame`. A watermark tracks a point
in time before which we assume no more late data is going to arrive.
Spark will use this watermark for several purposes:
- To know when a given time window aggregation can be finalized and thus can be emitted
when using output modes that do not allow updates.
- To minimize the amount of state that we need to keep for on-going aggregations.
The current watermark is computed by looking at the `MAX(eventTime)` seen across
all of the partitions in the query minus a user specified `delayThreshold`. Due to the cost
of coordinating this value across partitions, the actual watermark used is only guaranteed
to be at least `delayThreshold` behind the actual event time. In some cases we may still
process records that arrive more than `delayThreshold` late.
.. versionadded:: 2.1.0
.. versionchanged:: 3.5.0
Supports Spark Connect.
Parameters
----------
eventTime : str
the name of the column that contains the event time of the row.
delayThreshold : str
the minimum delay to wait to data to arrive late, relative to the
latest record that has been processed in the form of an interval
(e.g. "1 minute" or "5 hours").
Returns
-------
:class:`DataFrame`
Watermarked DataFrame
Notes
-----
This is a feature only for Structured Streaming.
This API is evolving.
Examples
--------
>>> from pyspark.sql import Row
>>> from pyspark.sql.functions import timestamp_seconds
>>> df = spark.readStream.format("rate").load().selectExpr(
... "value % 5 AS value", "timestamp")
>>> df.select("value", df.timestamp.alias("time")).withWatermark("time", '10 minutes')
DataFrame[value: bigint, time: timestamp]
Group the data by window and value (0 - 4), and compute the count of each group.
>>> import time
>>> from pyspark.sql.functions import window
>>> query = (df
... .withWatermark("timestamp", "10 minutes")
... .groupBy(
... window(df.timestamp, "10 minutes", "5 minutes"),
... df.value)
... ).count().writeStream.outputMode("complete").format("console").start()
>>> time.sleep(3)
>>> query.stop()
"""
if not eventTime or type(eventTime) is not str:
raise PySparkTypeError(
error_class="NOT_STR",
message_parameters={"arg_name": "eventTime", "arg_type": type(eventTime).__name__},
)
if not delayThreshold or type(delayThreshold) is not str:
raise PySparkTypeError(
error_class="NOT_STR",
message_parameters={
"arg_name": "delayThreshold",
"arg_type": type(delayThreshold).__name__,
},
)
jdf = self._jdf.withWatermark(eventTime, delayThreshold)
return DataFrame(jdf, self.sparkSession)
def hint(
self, name: str, *parameters: Union["PrimitiveType", "Column", List["PrimitiveType"]]
) -> "DataFrame":
"""Specifies some hint on the current :class:`DataFrame`.
.. versionadded:: 2.2.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
name : str
A name of the hint.
parameters : str, list, float or int
Optional parameters.
Returns
-------
:class:`DataFrame`
Hinted DataFrame
Examples
--------
>>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
>>> df2 = spark.createDataFrame([Row(height=80, name="Tom"), Row(height=85, name="Bob")])
>>> df.join(df2, "name").explain() # doctest: +SKIP
== Physical Plan ==
...
... +- SortMergeJoin ...
...
Explicitly trigger the broadcast hashjoin by providing the hint in ``df2``.
>>> df.join(df2.hint("broadcast"), "name").explain()
== Physical Plan ==
...
... +- BroadcastHashJoin ...
...
"""
if len(parameters) == 1 and isinstance(parameters[0], list):
parameters = parameters[0] # type: ignore[assignment]
if not isinstance(name, str):
raise PySparkTypeError(
error_class="NOT_STR",
message_parameters={"arg_name": "name", "arg_type": type(name).__name__},
)
allowed_types = (str, float, int, Column, list)
allowed_primitive_types = (str, float, int)
allowed_types_repr = ", ".join(
[t.__name__ for t in allowed_types[:-1]]
+ ["list[" + t.__name__ + "]" for t in allowed_primitive_types]
)
for p in parameters:
if not isinstance(p, allowed_types):
raise PySparkTypeError(
error_class="DISALLOWED_TYPE_FOR_CONTAINER",
message_parameters={
"arg_name": "parameters",
"arg_type": type(parameters).__name__,
"allowed_types": allowed_types_repr,
"item_type": type(p).__name__,
},
)
if isinstance(p, list):
if not all(isinstance(e, allowed_primitive_types) for e in p):
raise PySparkTypeError(
error_class="DISALLOWED_TYPE_FOR_CONTAINER",
message_parameters={
"arg_name": "parameters",
"arg_type": type(parameters).__name__,
"allowed_types": allowed_types_repr,
"item_type": type(p).__name__ + "[" + type(p[0]).__name__ + "]",
},
)
def _converter(parameter: Union[str, list, float, int, Column]) -> Any:
if isinstance(parameter, Column):
return _to_java_column(parameter)
elif isinstance(parameter, list):
# for list input, we are assuming only one element type exist in the list.
# for empty list, we are converting it into an empty long[] in the JVM side.
gateway = self._sc._gateway
assert gateway is not None
jclass = gateway.jvm.long
if len(parameter) >= 1:
mapping = {
str: gateway.jvm.java.lang.String,
float: gateway.jvm.double,
int: gateway.jvm.long,
}
jclass = mapping[type(parameter[0])]
return toJArray(gateway, jclass, parameter)
else:
return parameter
jdf = self._jdf.hint(name, self._jseq(parameters, _converter))
return DataFrame(jdf, self.sparkSession)
def count(self) -> int:
"""Returns the number of rows in this :class:`DataFrame`.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
int
Number of rows.
Examples
--------
>>> df = spark.createDataFrame(
... [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
Return the number of rows in the :class:`DataFrame`.
>>> df.count()
3
"""
return int(self._jdf.count())
def collect(self) -> List[Row]:
"""Returns all the records in the DataFrame as a list of :class:`Row`.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
list
A list of :class:`Row` objects, each representing a row in the DataFrame.
See Also
--------
DataFrame.take : Returns the first `n` rows.
DataFrame.head : Returns the first `n` rows.
DataFrame.toPandas : Returns the data as a pandas DataFrame.
Notes
-----
This method should only be used if the resulting list is expected to be small,
as all the data is loaded into the driver's memory.
Examples
--------
Example: Collecting all rows of a DataFrame
>>> df = spark.createDataFrame([(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
>>> df.collect()
[Row(age=14, name='Tom'), Row(age=23, name='Alice'), Row(age=16, name='Bob')]
Example: Collecting all rows after filtering
>>> df = spark.createDataFrame([(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
>>> df.filter(df.age > 15).collect()
[Row(age=23, name='Alice'), Row(age=16, name='Bob')]
Example: Collecting all rows after selecting specific columns
>>> df = spark.createDataFrame([(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
>>> df.select("name").collect()
[Row(name='Tom'), Row(name='Alice'), Row(name='Bob')]
Example: Collecting all rows after applying a function to a column
>>> from pyspark.sql.functions import upper
>>> df = spark.createDataFrame([(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
>>> df.select(upper(df.name)).collect()
[Row(upper(name)='TOM'), Row(upper(name)='ALICE'), Row(upper(name)='BOB')]
Example: Collecting all rows from a DataFrame and converting a specific column to a list
>>> df = spark.createDataFrame([(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
>>> rows = df.collect()
>>> [row["name"] for row in rows]
['Tom', 'Alice', 'Bob']
Example: Collecting all rows from a DataFrame and converting to a list of dictionaries
>>> df = spark.createDataFrame([(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
>>> rows = df.collect()
>>> [row.asDict() for row in rows]
[{'age': 14, 'name': 'Tom'}, {'age': 23, 'name': 'Alice'}, {'age': 16, 'name': 'Bob'}]
"""
with SCCallSiteSync(self._sc):
sock_info = self._jdf.collectToPython()
return list(_load_from_socket(sock_info, BatchedSerializer(CPickleSerializer())))
def toLocalIterator(self, prefetchPartitions: bool = False) -> Iterator[Row]:
"""
Returns an iterator that contains all of the rows in this :class:`DataFrame`.
The iterator will consume as much memory as the largest partition in this
:class:`DataFrame`. With prefetch it may consume up to the memory of the 2 largest
partitions.
.. versionadded:: 2.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
prefetchPartitions : bool, optional
If Spark should pre-fetch the next partition before it is needed.
.. versionchanged:: 3.4.0
This argument does not take effect for Spark Connect.
Returns
-------
Iterator
Iterator of rows.
Examples
--------
>>> df = spark.createDataFrame(
... [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
>>> list(df.toLocalIterator())
[Row(age=14, name='Tom'), Row(age=23, name='Alice'), Row(age=16, name='Bob')]
"""
with SCCallSiteSync(self._sc):
sock_info = self._jdf.toPythonIterator(prefetchPartitions)
return _local_iterator_from_socket(sock_info, BatchedSerializer(CPickleSerializer()))
def limit(self, num: int) -> "DataFrame":
"""Limits the result count to the number specified.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
num : int
Number of records to return. Will return this number of records
or all records if the DataFrame contains less than this number of records.
Returns
-------
:class:`DataFrame`
Subset of the records
Examples
--------
>>> df = spark.createDataFrame(
... [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
>>> df.limit(1).show()
+---+----+
|age|name|
+---+----+
| 14| Tom|
+---+----+
>>> df.limit(0).show()
+---+----+
|age|name|
+---+----+
+---+----+
"""
jdf = self._jdf.limit(num)
return DataFrame(jdf, self.sparkSession)
def offset(self, num: int) -> "DataFrame":
"""Returns a new :class: `DataFrame` by skipping the first `n` rows.
.. versionadded:: 3.4.0
.. versionchanged:: 3.5.0
Supports vanilla PySpark.
Parameters
----------
num : int
Number of records to skip.
Returns
-------
:class:`DataFrame`
Subset of the records
Examples
--------
>>> df = spark.createDataFrame(
... [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
>>> df.offset(1).show()
+---+-----+
|age| name|
+---+-----+
| 23|Alice|
| 16| Bob|
+---+-----+
>>> df.offset(10).show()
+---+----+
|age|name|
+---+----+
+---+----+
"""
jdf = self._jdf.offset(num)
return DataFrame(jdf, self.sparkSession)
def take(self, num: int) -> List[Row]:
"""Returns the first ``num`` rows as a :class:`list` of :class:`Row`.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
num : int
Number of records to return. Will return this number of records
or all records if the DataFrame contains less than this number of records..
Returns
-------
list
List of rows
Examples
--------
>>> df = spark.createDataFrame(
... [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
Return the first 2 rows of the :class:`DataFrame`.
>>> df.take(2)
[Row(age=14, name='Tom'), Row(age=23, name='Alice')]
"""
return self.limit(num).collect()
def tail(self, num: int) -> List[Row]:
"""
Returns the last ``num`` rows as a :class:`list` of :class:`Row`.
Running tail requires moving data into the application's driver process, and doing so with
a very large ``num`` can crash the driver process with OutOfMemoryError.
.. versionadded:: 3.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
num : int
Number of records to return. Will return this number of records
or all records if the DataFrame contains less than this number of records.
Returns
-------
list
List of rows
Examples
--------
>>> df = spark.createDataFrame(
... [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
>>> df.tail(2)
[Row(age=23, name='Alice'), Row(age=16, name='Bob')]
"""
with SCCallSiteSync(self._sc):
sock_info = self._jdf.tailToPython(num)
return list(_load_from_socket(sock_info, BatchedSerializer(CPickleSerializer())))
def foreach(self, f: Callable[[Row], None]) -> None:
"""Applies the ``f`` function to all :class:`Row` of this :class:`DataFrame`.
This is a shorthand for ``df.rdd.foreach()``.
.. versionadded:: 1.3.0
.. versionchanged:: 4.0.0
Supports Spark Connect.
Parameters
----------
f : function
A function that accepts one parameter which will
receive each row to process.
Examples
--------
>>> df = spark.createDataFrame(
... [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
>>> def func(person):
... print(person.name)
...
>>> df.foreach(func)
"""
self.rdd.foreach(f)
def foreachPartition(self, f: Callable[[Iterator[Row]], None]) -> None:
"""Applies the ``f`` function to each partition of this :class:`DataFrame`.
This a shorthand for ``df.rdd.foreachPartition()``.
.. versionadded:: 1.3.0
.. versionchanged:: 4.0.0
Supports Spark Connect.
Parameters
----------
f : function
A function that accepts one parameter which will receive
each partition to process.
Examples
--------
>>> df = spark.createDataFrame(
... [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
>>> def func(itr):
... for person in itr:
... print(person.name)
...
>>> df.foreachPartition(func)
"""
self.rdd.foreachPartition(f) # type: ignore[arg-type]
def cache(self) -> "DataFrame":
"""Persists the :class:`DataFrame` with the default storage level (`MEMORY_AND_DISK_DESER`).
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Notes
-----
The default storage level has changed to `MEMORY_AND_DISK_DESER` to match Scala in 3.0.
Returns
-------
:class:`DataFrame`
Cached DataFrame.
Examples
--------
>>> df = spark.range(1)
>>> df.cache()
DataFrame[id: bigint]
>>> df.explain()
== Physical Plan ==
InMemoryTableScan ...
"""
self.is_cached = True
self._jdf.cache()
return self
def persist(
self,
storageLevel: StorageLevel = (StorageLevel.MEMORY_AND_DISK_DESER),
) -> "DataFrame":
"""Sets the storage level to persist the contents of the :class:`DataFrame` across
operations after the first time it is computed. This can only be used to assign
a new storage level if the :class:`DataFrame` does not have a storage level set yet.
If no storage level is specified defaults to (`MEMORY_AND_DISK_DESER`)
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Notes
-----
The default storage level has changed to `MEMORY_AND_DISK_DESER` to match Scala in 3.0.
Parameters
----------
storageLevel : :class:`StorageLevel`
Storage level to set for persistence. Default is MEMORY_AND_DISK_DESER.
Returns
-------
:class:`DataFrame`
Persisted DataFrame.
Examples
--------
>>> df = spark.range(1)
>>> df.persist()
DataFrame[id: bigint]
>>> df.explain()
== Physical Plan ==
InMemoryTableScan ...
Persists the data in the disk by specifying the storage level.
>>> from pyspark.storagelevel import StorageLevel
>>> df.persist(StorageLevel.DISK_ONLY)
DataFrame[id: bigint]
"""
self.is_cached = True
javaStorageLevel = self._sc._getJavaStorageLevel(storageLevel)
self._jdf.persist(javaStorageLevel)
return self
@property
def storageLevel(self) -> StorageLevel:
"""Get the :class:`DataFrame`'s current storage level.
.. versionadded:: 2.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
:class:`StorageLevel`
Currently defined storage level.
Examples
--------
>>> df1 = spark.range(10)
>>> df1.storageLevel
StorageLevel(False, False, False, False, 1)
>>> df1.cache().storageLevel
StorageLevel(True, True, False, True, 1)
>>> df2 = spark.range(5)
>>> df2.persist(StorageLevel.DISK_ONLY_2).storageLevel
StorageLevel(True, False, False, False, 2)
"""
java_storage_level = self._jdf.storageLevel()
storage_level = StorageLevel(
java_storage_level.useDisk(),
java_storage_level.useMemory(),
java_storage_level.useOffHeap(),
java_storage_level.deserialized(),
java_storage_level.replication(),
)
return storage_level
def unpersist(self, blocking: bool = False) -> "DataFrame":
"""Marks the :class:`DataFrame` as non-persistent, and remove all blocks for it from
memory and disk.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Notes
-----
`blocking` default has changed to ``False`` to match Scala in 2.0.
Parameters
----------
blocking : bool
Whether to block until all blocks are deleted.
Returns
-------
:class:`DataFrame`
Unpersisted DataFrame.
Examples
--------
>>> df = spark.range(1)
>>> df.persist()
DataFrame[id: bigint]
>>> df.unpersist()
DataFrame[id: bigint]
>>> df = spark.range(1)
>>> df.unpersist(True)
DataFrame[id: bigint]
"""
self.is_cached = False
self._jdf.unpersist(blocking)
return self
def coalesce(self, numPartitions: int) -> "DataFrame":
"""
Returns a new :class:`DataFrame` that has exactly `numPartitions` partitions.
Similar to coalesce defined on an :class:`RDD`, this operation results in a
narrow dependency, e.g. if you go from 1000 partitions to 100 partitions,
there will not be a shuffle, instead each of the 100 new partitions will
claim 10 of the current partitions. If a larger number of partitions is requested,
it will stay at the current number of partitions.
However, if you're doing a drastic coalesce, e.g. to numPartitions = 1,
this may result in your computation taking place on fewer nodes than
you like (e.g. one node in the case of numPartitions = 1). To avoid this,
you can call repartition(). This will add a shuffle step, but means the
current upstream partitions will be executed in parallel (per whatever
the current partitioning is).
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
numPartitions : int
specify the target number of partitions
Returns
-------
:class:`DataFrame`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> spark.range(0, 10, 1, 3).select(
... sf.spark_partition_id().alias("partition")
... ).distinct().sort("partition").show()
+---------+
|partition|
+---------+
| 0|
| 1|
| 2|
+---------+
>>> from pyspark.sql import functions as sf
>>> spark.range(0, 10, 1, 3).coalesce(1).select(
... sf.spark_partition_id().alias("partition")
... ).distinct().sort("partition").show()
+---------+
|partition|
+---------+
| 0|
+---------+
"""
return DataFrame(self._jdf.coalesce(numPartitions), self.sparkSession)
@overload
def repartition(self, numPartitions: int, *cols: "ColumnOrName") -> "DataFrame":
...
@overload
def repartition(self, *cols: "ColumnOrName") -> "DataFrame":
...
def repartition( # type: ignore[misc]
self, numPartitions: Union[int, "ColumnOrName"], *cols: "ColumnOrName"
) -> "DataFrame":
"""
Returns a new :class:`DataFrame` partitioned by the given partitioning expressions. The
resulting :class:`DataFrame` is hash partitioned.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
numPartitions : int
can be an int to specify the target number of partitions or a Column.
If it is a Column, it will be used as the first partitioning column. If not specified,
the default number of partitions is used.
cols : str or :class:`Column`
partitioning columns.
.. versionchanged:: 1.6.0
Added optional arguments to specify the partitioning columns. Also made numPartitions
optional if partitioning columns are specified.
Returns
-------
:class:`DataFrame`
Repartitioned DataFrame.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.range(0, 64, 1, 9).withColumn(
... "name", sf.concat(sf.lit("name_"), sf.col("id").cast("string"))
... ).withColumn(
... "age", sf.col("id") - 32
... )
>>> df.select(
... sf.spark_partition_id().alias("partition")
... ).distinct().sort("partition").show()
+---------+
|partition|
+---------+
| 0|
| 1|
| 2|
| 3|
| 4|
| 5|
| 6|
| 7|
| 8|
+---------+
Repartition the data into 10 partitions.
>>> df.repartition(10).select(
... sf.spark_partition_id().alias("partition")
... ).distinct().sort("partition").show()
+---------+
|partition|
+---------+
| 0|
| 1|
| 2|
| 3|
| 4|
| 5|
| 6|
| 7|
| 8|
| 9|
+---------+
Repartition the data into 7 partitions by 'age' column.
>>> df.repartition(7, "age").select(
... sf.spark_partition_id().alias("partition")
... ).distinct().sort("partition").show()
+---------+
|partition|
+---------+
| 0|
| 1|
| 2|
| 3|
| 4|
| 5|
| 6|
+---------+
Repartition the data into 3 partitions by 'age' and 'name' columns.
>>> df.repartition(3, "name", "age").select(
... sf.spark_partition_id().alias("partition")
... ).distinct().sort("partition").show()
+---------+
|partition|
+---------+
| 0|
| 1|
| 2|
+---------+
"""
if isinstance(numPartitions, int):
if len(cols) == 0:
return DataFrame(self._jdf.repartition(numPartitions), self.sparkSession)
else:
return DataFrame(
self._jdf.repartition(numPartitions, self._jcols(*cols)),
self.sparkSession,
)
elif isinstance(numPartitions, (str, Column)):
cols = (numPartitions,) + cols
return DataFrame(self._jdf.repartition(self._jcols(*cols)), self.sparkSession)
else:
raise PySparkTypeError(
error_class="NOT_COLUMN_OR_STR",
message_parameters={
"arg_name": "numPartitions",
"arg_type": type(numPartitions).__name__,
},
)
@overload
def repartitionByRange(self, numPartitions: int, *cols: "ColumnOrName") -> "DataFrame":
...
@overload
def repartitionByRange(self, *cols: "ColumnOrName") -> "DataFrame":
...
def repartitionByRange( # type: ignore[misc]
self, numPartitions: Union[int, "ColumnOrName"], *cols: "ColumnOrName"
) -> "DataFrame":
"""
Returns a new :class:`DataFrame` partitioned by the given partitioning expressions. The
resulting :class:`DataFrame` is range partitioned.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
numPartitions : int
can be an int to specify the target number of partitions or a Column.
If it is a Column, it will be used as the first partitioning column. If not specified,
the default number of partitions is used.
cols : str or :class:`Column`
partitioning columns.
Returns
-------
:class:`DataFrame`
Repartitioned DataFrame.
Notes
-----
At least one partition-by expression must be specified.
When no explicit sort order is specified, "ascending nulls first" is assumed.
Due to performance reasons this method uses sampling to estimate the ranges.
Hence, the output may not be consistent, since sampling can return different values.
The sample size can be controlled by the config
`spark.sql.execution.rangeExchange.sampleSizePerPartition`.
Examples
--------
Repartition the data into 2 partitions by range in 'age' column.
For example, the first partition can have ``(14, "Tom")`` and ``(16, "Bob")``,
and the second partition would have ``(23, "Alice")``.
>>> from pyspark.sql import functions as sf
>>> spark.createDataFrame(
... [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"]
... ).repartitionByRange(2, "age").select(
... "age", "name", sf.spark_partition_id()
... ).show()
+---+-----+--------------------+
|age| name|SPARK_PARTITION_ID()|
+---+-----+--------------------+
| 14| Tom| 0|
| 16| Bob| 0|
| 23|Alice| 1|
+---+-----+--------------------+
"""
if isinstance(numPartitions, int):
if len(cols) == 0:
raise PySparkValueError(
error_class="CANNOT_BE_EMPTY",
message_parameters={"item": "partition-by expression"},
)
else:
return DataFrame(
self._jdf.repartitionByRange(numPartitions, self._jcols(*cols)),
self.sparkSession,
)
elif isinstance(numPartitions, (str, Column)):
cols = (numPartitions,) + cols
return DataFrame(self._jdf.repartitionByRange(self._jcols(*cols)), self.sparkSession)
else:
raise PySparkTypeError(
error_class="NOT_COLUMN_OR_INT_OR_STR",
message_parameters={
"arg_name": "numPartitions",
"arg_type": type(numPartitions).__name__,
},
)
def distinct(self) -> "DataFrame":
"""Returns a new :class:`DataFrame` containing the distinct rows in this :class:`DataFrame`.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
:class:`DataFrame`
DataFrame with distinct records.
See Also
--------
DataFrame.dropDuplicates
Examples
--------
Remove duplicate rows from a DataFrame
>>> df = spark.createDataFrame(
... [(14, "Tom"), (23, "Alice"), (23, "Alice")], ["age", "name"])
>>> df.distinct().show()
+---+-----+
|age| name|
+---+-----+
| 14| Tom|
| 23|Alice|
+---+-----+
Count the number of distinct rows in a DataFrame
>>> df.distinct().count()
2
Get distinct rows from a DataFrame with multiple columns
>>> df = spark.createDataFrame(
... [(14, "Tom", "M"), (23, "Alice", "F"), (23, "Alice", "F"), (14, "Tom", "M")],
... ["age", "name", "gender"])
>>> df.distinct().show()
+---+-----+------+
|age| name|gender|
+---+-----+------+
| 14| Tom| M|
| 23|Alice| F|
+---+-----+------+
Get distinct values from a specific column in a DataFrame
>>> df.select("name").distinct().show()
+-----+
| name|
+-----+
| Tom|
|Alice|
+-----+
Count the number of distinct values in a specific column
>>> df.select("name").distinct().count()
2
Get distinct values from multiple columns in DataFrame
>>> df.select("name", "gender").distinct().show()
+-----+------+
| name|gender|
+-----+------+
| Tom| M|
|Alice| F|
+-----+------+
Get distinct rows from a DataFrame with null values
>>> df = spark.createDataFrame(
... [(14, "Tom", "M"), (23, "Alice", "F"), (23, "Alice", "F"), (14, "Tom", None)],
... ["age", "name", "gender"])
>>> df.distinct().show()
+---+-----+------+
|age| name|gender|
+---+-----+------+
| 14| Tom| M|
| 23|Alice| F|
| 14| Tom| NULL|
+---+-----+------+
Get distinct non-null values from a DataFrame
>>> df.distinct().filter(df.gender.isNotNull()).show()
+---+-----+------+
|age| name|gender|
+---+-----+------+
| 14| Tom| M|
| 23|Alice| F|
+---+-----+------+
"""
return DataFrame(self._jdf.distinct(), self.sparkSession)
@overload
def sample(self, fraction: float, seed: Optional[int] = ...) -> "DataFrame":
...
@overload
def sample(
self,
withReplacement: Optional[bool],
fraction: float,
seed: Optional[int] = ...,
) -> "DataFrame":
...
def sample( # type: ignore[misc]
self,
withReplacement: Optional[Union[float, bool]] = None,
fraction: Optional[Union[int, float]] = None,
seed: Optional[int] = None,
) -> "DataFrame":
"""Returns a sampled subset of this :class:`DataFrame`.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
withReplacement : bool, optional
Sample with replacement or not (default ``False``).
fraction : float, optional
Fraction of rows to generate, range [0.0, 1.0].
seed : int, optional
Seed for sampling (default a random seed).
Returns
-------
:class:`DataFrame`
Sampled rows from given DataFrame.
Notes
-----
This is not guaranteed to provide exactly the fraction specified of the total
count of the given :class:`DataFrame`.
`fraction` is required and, `withReplacement` and `seed` are optional.
Examples
--------
>>> df = spark.range(10)
>>> df.sample(0.5, 3).count() # doctest: +SKIP
7
>>> df.sample(fraction=0.5, seed=3).count() # doctest: +SKIP
7
>>> df.sample(withReplacement=True, fraction=0.5, seed=3).count() # doctest: +SKIP
1
>>> df.sample(1.0).count()
10
>>> df.sample(fraction=1.0).count()
10
>>> df.sample(False, fraction=1.0).count()
10
"""
# For the cases below:
# sample(True, 0.5 [, seed])
# sample(True, fraction=0.5 [, seed])
# sample(withReplacement=False, fraction=0.5 [, seed])
is_withReplacement_set = type(withReplacement) == bool and isinstance(fraction, float)
# For the case below:
# sample(faction=0.5 [, seed])
is_withReplacement_omitted_kwargs = withReplacement is None and isinstance(fraction, float)
# For the case below:
# sample(0.5 [, seed])
is_withReplacement_omitted_args = isinstance(withReplacement, float)
if not (
is_withReplacement_set
or is_withReplacement_omitted_kwargs
or is_withReplacement_omitted_args
):
argtypes = [type(arg).__name__ for arg in [withReplacement, fraction, seed]]
raise PySparkTypeError(
error_class="NOT_BOOL_OR_FLOAT_OR_INT",
message_parameters={
"arg_name": "withReplacement (optional), "
+ "fraction (required) and seed (optional)",
"arg_type": ", ".join(argtypes),
},
)
if is_withReplacement_omitted_args:
if fraction is not None:
seed = cast(int, fraction)
fraction = withReplacement
withReplacement = None
seed = int(seed) if seed is not None else None
args = [arg for arg in [withReplacement, fraction, seed] if arg is not None]
jdf = self._jdf.sample(*args)
return DataFrame(jdf, self.sparkSession)
def sampleBy(
self, col: "ColumnOrName", fractions: Dict[Any, float], seed: Optional[int] = None
) -> "DataFrame":
"""
Returns a stratified sample without replacement based on the
fraction given on each stratum.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`Column` or str
column that defines strata
.. versionchanged:: 3.0.0
Added sampling by a column of :class:`Column`
fractions : dict
sampling fraction for each stratum. If a stratum is not
specified, we treat its fraction as zero.
seed : int, optional
random seed
Returns
-------
a new :class:`DataFrame` that represents the stratified sample
Examples
--------
>>> from pyspark.sql.functions import col
>>> dataset = spark.range(0, 100).select((col("id") % 3).alias("key"))
>>> sampled = dataset.sampleBy("key", fractions={0: 0.1, 1: 0.2}, seed=0)
>>> sampled.groupBy("key").count().orderBy("key").show()
+---+-----+
|key|count|
+---+-----+
| 0| 3|
| 1| 6|
+---+-----+
>>> dataset.sampleBy(col("key"), fractions={2: 1.0}, seed=0).count()
33
"""
if isinstance(col, str):
col = Column(col)
elif not isinstance(col, Column):
raise PySparkTypeError(
error_class="NOT_COLUMN_OR_STR",
message_parameters={"arg_name": "col", "arg_type": type(col).__name__},
)
if not isinstance(fractions, dict):
raise PySparkTypeError(
error_class="NOT_DICT",
message_parameters={"arg_name": "fractions", "arg_type": type(fractions).__name__},
)
for k, v in fractions.items():
if not isinstance(k, (float, int, str)):
raise PySparkTypeError(
error_class="DISALLOWED_TYPE_FOR_CONTAINER",
message_parameters={
"arg_name": "fractions",
"arg_type": type(fractions).__name__,
"allowed_types": "float, int, str",
"item_type": type(k).__name__,
},
)
fractions[k] = float(v)
col = col._jc
seed = seed if seed is not None else random.randint(0, sys.maxsize)
return DataFrame(
self._jdf.stat().sampleBy(col, self._jmap(fractions), seed), self.sparkSession
)
def randomSplit(self, weights: List[float], seed: Optional[int] = None) -> List["DataFrame"]:
"""Randomly splits this :class:`DataFrame` with the provided weights.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
weights : list
list of doubles as weights with which to split the :class:`DataFrame`.
Weights will be normalized if they don't sum up to 1.0.
seed : int, optional
The seed for sampling.
Returns
-------
list
List of DataFrames.
Examples
--------
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([
... Row(age=10, height=80, name="Alice"),
... Row(age=5, height=None, name="Bob"),
... Row(age=None, height=None, name="Tom"),
... Row(age=None, height=None, name=None),
... ])
>>> splits = df.randomSplit([1.0, 2.0], 24)
>>> splits[0].count()
2
>>> splits[1].count()
2
"""
for w in weights:
if w < 0.0:
raise PySparkValueError(
error_class="VALUE_NOT_POSITIVE",
message_parameters={"arg_name": "weights", "arg_value": str(w)},
)
seed = seed if seed is not None else random.randint(0, sys.maxsize)
df_array = self._jdf.randomSplit(
_to_list(self.sparkSession._sc, cast(List["ColumnOrName"], weights)), int(seed)
)
return [DataFrame(df, self.sparkSession) for df in df_array]
@property
def dtypes(self) -> List[Tuple[str, str]]:
"""Returns all column names and their data types as a list.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
list
List of columns as tuple pairs.
Examples
--------
>>> df = spark.createDataFrame(
... [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
>>> df.dtypes
[('age', 'bigint'), ('name', 'string')]
"""
return [(str(f.name), f.dataType.simpleString()) for f in self.schema.fields]
@property
def columns(self) -> List[str]:
"""
Retrieves the names of all columns in the :class:`DataFrame` as a list.
The order of the column names in the list reflects their order in the DataFrame.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
list
List of column names in the DataFrame.
Examples
--------
Example 1: Retrieve column names of a DataFrame
>>> df = spark.createDataFrame(
... [(14, "Tom", "CA"), (23, "Alice", "NY"), (16, "Bob", "TX")],
... ["age", "name", "state"]
... )
>>> df.columns
['age', 'name', 'state']
Example 2: Using column names to project specific columns
>>> selected_cols = [col for col in df.columns if col != "age"]
>>> df.select(selected_cols).show()
+-----+-----+
| name|state|
+-----+-----+
| Tom| CA|
|Alice| NY|
| Bob| TX|
+-----+-----+
Example 3: Checking if a specific column exists in a DataFrame
>>> "state" in df.columns
True
>>> "salary" in df.columns
False
Example 4: Iterating over columns to apply a transformation
>>> import pyspark.sql.functions as f
>>> for col_name in df.columns:
... df = df.withColumn(col_name, f.upper(f.col(col_name)))
>>> df.show()
+---+-----+-----+
|age| name|state|
+---+-----+-----+
| 14| TOM| CA|
| 23|ALICE| NY|
| 16| BOB| TX|
+---+-----+-----+
Example 5: Renaming columns and checking the updated column names
>>> df = df.withColumnRenamed("name", "first_name")
>>> df.columns
['age', 'first_name', 'state']
Example 6: Using the `columns` property to ensure two DataFrames have the
same columns before a union
>>> df2 = spark.createDataFrame(
... [(30, "Eve", "FL"), (40, "Sam", "WA")], ["age", "name", "location"])
>>> df.columns == df2.columns
False
"""
return [f.name for f in self.schema.fields]
def colRegex(self, colName: str) -> Column:
"""
Selects column based on the column name specified as a regex and returns it
as :class:`Column`.
.. versionadded:: 2.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
colName : str
string, column name specified as a regex.
Returns
-------
:class:`Column`
Examples
--------
>>> df = spark.createDataFrame([("a", 1), ("b", 2), ("c", 3)], ["Col1", "Col2"])
>>> df.select(df.colRegex("`(Col1)?+.+`")).show()
+----+
|Col2|
+----+
| 1|
| 2|
| 3|
+----+
"""
if not isinstance(colName, str):
raise PySparkTypeError(
error_class="NOT_STR",
message_parameters={"arg_name": "colName", "arg_type": type(colName).__name__},
)
jc = self._jdf.colRegex(colName)
return Column(jc)
def to(self, schema: StructType) -> "DataFrame":
"""
Returns a new :class:`DataFrame` where each row is reconciled to match the specified
schema.
.. versionadded:: 3.4.0
Parameters
----------
schema : :class:`StructType`
Specified schema.
Returns
-------
:class:`DataFrame`
Reconciled DataFrame.
Notes
-----
* Reorder columns and/or inner fields by name to match the specified schema.
* Project away columns and/or inner fields that are not needed by the specified schema.
Missing columns and/or inner fields (present in the specified schema but not input
DataFrame) lead to failures.
* Cast the columns and/or inner fields to match the data types in the specified schema,
if the types are compatible, e.g., numeric to numeric (error if overflows), but
not string to int.
* Carry over the metadata from the specified schema, while the columns and/or inner fields
still keep their own metadata if not overwritten by the specified schema.
* Fail if the nullability is not compatible. For example, the column and/or inner field
is nullable but the specified schema requires them to be not nullable.
Supports Spark Connect.
Examples
--------
>>> from pyspark.sql.types import StructField, StringType
>>> df = spark.createDataFrame([("a", 1)], ["i", "j"])
>>> df.schema
StructType([StructField('i', StringType(), True), StructField('j', LongType(), True)])
>>> schema = StructType([StructField("j", StringType()), StructField("i", StringType())])
>>> df2 = df.to(schema)
>>> df2.schema
StructType([StructField('j', StringType(), True), StructField('i', StringType(), True)])
>>> df2.show()
+---+---+
| j| i|
+---+---+
| 1| a|
+---+---+
"""
assert schema is not None
jschema = self._jdf.sparkSession().parseDataType(schema.json())
return DataFrame(self._jdf.to(jschema), self.sparkSession)
def alias(self, alias: str) -> "DataFrame":
"""Returns a new :class:`DataFrame` with an alias set.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
alias : str
an alias name to be set for the :class:`DataFrame`.
Returns
-------
:class:`DataFrame`
Aliased DataFrame.
Examples
--------
>>> from pyspark.sql.functions import col, desc
>>> df = spark.createDataFrame(
... [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
>>> df_as1 = df.alias("df_as1")
>>> df_as2 = df.alias("df_as2")
>>> joined_df = df_as1.join(df_as2, col("df_as1.name") == col("df_as2.name"), 'inner')
>>> joined_df.select(
... "df_as1.name", "df_as2.name", "df_as2.age").sort(desc("df_as1.name")).show()
+-----+-----+---+
| name| name|age|
+-----+-----+---+
| Tom| Tom| 14|
| Bob| Bob| 16|
|Alice|Alice| 23|
+-----+-----+---+
"""
assert isinstance(alias, str), "alias should be a string"
return DataFrame(getattr(self._jdf, "as")(alias), self.sparkSession)
def crossJoin(self, other: "DataFrame") -> "DataFrame":
"""Returns the cartesian product with another :class:`DataFrame`.
.. versionadded:: 2.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
other : :class:`DataFrame`
Right side of the cartesian product.
Returns
-------
:class:`DataFrame`
Joined DataFrame.
Examples
--------
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame(
... [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
>>> df2 = spark.createDataFrame(
... [Row(height=80, name="Tom"), Row(height=85, name="Bob")])
>>> df.crossJoin(df2.select("height")).select("age", "name", "height").show()
+---+-----+------+
|age| name|height|
+---+-----+------+
| 14| Tom| 80|
| 14| Tom| 85|
| 23|Alice| 80|
| 23|Alice| 85|
| 16| Bob| 80|
| 16| Bob| 85|
+---+-----+------+
"""
jdf = self._jdf.crossJoin(other._jdf)
return DataFrame(jdf, self.sparkSession)
def join(
self,
other: "DataFrame",
on: Optional[Union[str, List[str], Column, List[Column]]] = None,
how: Optional[str] = None,
) -> "DataFrame":
"""
Joins with another :class:`DataFrame`, using the given join expression.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
other : :class:`DataFrame`
Right side of the join
on : str, list or :class:`Column`, optional
a string for the join column name, a list of column names,
a join expression (Column), or a list of Columns.
If `on` is a string or a list of strings indicating the name of the join column(s),
the column(s) must exist on both sides, and this performs an equi-join.
how : str, optional
default ``inner``. Must be one of: ``inner``, ``cross``, ``outer``,
``full``, ``fullouter``, ``full_outer``, ``left``, ``leftouter``, ``left_outer``,
``right``, ``rightouter``, ``right_outer``, ``semi``, ``leftsemi``, ``left_semi``,
``anti``, ``leftanti`` and ``left_anti``.
Returns
-------
:class:`DataFrame`
Joined DataFrame.
Examples
--------
The following examples demonstrate various join types among ``df1``, ``df2``, and ``df3``.
>>> import pyspark.sql.functions as sf
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([Row(name="Alice", age=2), Row(name="Bob", age=5)])
>>> df2 = spark.createDataFrame([Row(name="Tom", height=80), Row(name="Bob", height=85)])
>>> df3 = spark.createDataFrame([
... Row(name="Alice", age=10, height=80),
... Row(name="Bob", age=5, height=None),
... Row(name="Tom", age=None, height=None),
... Row(name=None, age=None, height=None),
... ])
Inner join on columns (default)
>>> df.join(df2, "name").show()
+----+---+------+
|name|age|height|
+----+---+------+
| Bob| 5| 85|
+----+---+------+
>>> df.join(df3, ["name", "age"]).show()
+----+---+------+
|name|age|height|
+----+---+------+
| Bob| 5| NULL|
+----+---+------+
Outer join on a single column with an explicit join condition.
When the join condition is explicited stated: `df.name == df2.name`, this will
produce all records where the names match, as well as those that don't (since
it's an outer join). If there are names in `df2` that are not present in `df`,
they will appear with `NULL` in the `name` column of `df`, and vice versa for `df2`.
>>> joined = df.join(df2, df.name == df2.name, "outer").sort(sf.desc(df.name))
>>> joined.show() # doctest: +SKIP
+-----+----+----+------+
| name| age|name|height|
+-----+----+----+------+
| Bob| 5| Bob| 85|
|Alice| 2|NULL| NULL|
| NULL|NULL| Tom| 80|
+-----+----+----+------+
To unambiguously select output columns, specify the dataframe along with the column name:
>>> joined.select(df.name, df2.height).show() # doctest: +SKIP
+-----+------+
| name|height|
+-----+------+
| Bob| 85|
|Alice| NULL|
| NULL| 80|
+-----+------+
However, in self-joins, direct column references can cause ambiguity:
>>> df.join(df, df.name == df.name, "outer").select(df.name).show() # doctest: +SKIP
Traceback (most recent call last):
...
pyspark.errors.exceptions.captured.AnalysisException: Column name#0 are ambiguous...
A better approach is to assign aliases to the dataframes, and then reference
the ouptut columns from the join operation using these aliases:
>>> df.alias("a").join(
... df.alias("b"), sf.col("a.name") == sf.col("b.name"), "outer"
... ).sort(sf.desc("a.name")).select("a.name", "b.age").show()
+-----+---+
| name|age|
+-----+---+
| Bob| 5|
|Alice| 2|
+-----+---+
Outer join on a single column with implicit join condition using column name
When you provide the column name directly as the join condition, Spark will treat
both name columns as one, and will not produce separate columns for `df.name` and
`df2.name`. This avoids having duplicate columns in the output.
>>> df.join(df2, "name", "outer").sort(sf.desc("name")).show()
+-----+----+------+
| name| age|height|
+-----+----+------+
| Tom|NULL| 80|
| Bob| 5| 85|
|Alice| 2| NULL|
+-----+----+------+
Outer join on multiple columns
>>> df.join(df3, ["name", "age"], "outer").show()
+-----+----+------+
| name| age|height|
+-----+----+------+
| NULL|NULL| NULL|
|Alice| 2| NULL|
|Alice| 10| 80|
| Bob| 5| NULL|
| Tom|NULL| NULL|
+-----+----+------+
Left outer join on columns
>>> df.join(df2, "name", "left_outer").show()
+-----+---+------+
| name|age|height|
+-----+---+------+
|Alice| 2| NULL|
| Bob| 5| 85|
+-----+---+------+
Right outer join on columns
>>> df.join(df2, "name", "right_outer").show()
+----+----+------+
|name| age|height|
+----+----+------+
| Tom|NULL| 80|
| Bob| 5| 85|
+----+----+------+
Left semi join on columns
>>> df.join(df2, "name", "left_semi").show()
+----+---+
|name|age|
+----+---+
| Bob| 5|
+----+---+
Left anti join on columns
>>> df.join(df2, "name", "left_anti").show()
+-----+---+
| name|age|
+-----+---+
|Alice| 2|
+-----+---+
"""
if on is not None and not isinstance(on, list):
on = [on] # type: ignore[assignment]
if on is not None:
if isinstance(on[0], str):
on = self._jseq(cast(List[str], on))
else:
assert isinstance(on[0], Column), "on should be Column or list of Column"
on = reduce(lambda x, y: x.__and__(y), cast(List[Column], on))
on = on._jc
if on is None and how is None:
jdf = self._jdf.join(other._jdf)
else:
if how is None:
how = "inner"
if on is None:
on = self._jseq([])
assert isinstance(how, str), "how should be a string"
jdf = self._jdf.join(other._jdf, on, how)
return DataFrame(jdf, self.sparkSession)
# TODO(SPARK-22947): Fix the DataFrame API.
def _joinAsOf(
self,
other: "DataFrame",
leftAsOfColumn: Union[str, Column],
rightAsOfColumn: Union[str, Column],
on: Optional[Union[str, List[str], Column, List[Column]]] = None,
how: Optional[str] = None,
*,
tolerance: Optional[Column] = None,
allowExactMatches: bool = True,
direction: str = "backward",
) -> "DataFrame":
"""
Perform an as-of join.
This is similar to a left-join except that we match on the nearest
key rather than equal keys.
.. versionchanged:: 4.0.0
Supports Spark Connect.
Parameters
----------
other : :class:`DataFrame`
Right side of the join
leftAsOfColumn : str or :class:`Column`
a string for the as-of join column name, or a Column
rightAsOfColumn : str or :class:`Column`
a string for the as-of join column name, or a Column
on : str, list or :class:`Column`, optional
a string for the join column name, a list of column names,
a join expression (Column), or a list of Columns.
If `on` is a string or a list of strings indicating the name of the join column(s),
the column(s) must exist on both sides, and this performs an equi-join.
how : str, optional
default ``inner``. Must be one of: ``inner`` and ``left``.
tolerance : :class:`Column`, optional
an asof tolerance within this range; must be compatible
with the merge index.
allowExactMatches : bool, optional
default ``True``.
direction : str, optional
default ``backward``. Must be one of: ``backward``, ``forward``, and ``nearest``.
Examples
--------
The following performs an as-of join between ``left`` and ``right``.
>>> left = spark.createDataFrame([(1, "a"), (5, "b"), (10, "c")], ["a", "left_val"])
>>> right = spark.createDataFrame([(1, 1), (2, 2), (3, 3), (6, 6), (7, 7)],
... ["a", "right_val"])
>>> left._joinAsOf(
... right, leftAsOfColumn="a", rightAsOfColumn="a"
... ).select(left.a, 'left_val', 'right_val').sort("a").collect()
[Row(a=1, left_val='a', right_val=1),
Row(a=5, left_val='b', right_val=3),
Row(a=10, left_val='c', right_val=7)]
>>> from pyspark.sql import functions as sf
>>> left._joinAsOf(
... right, leftAsOfColumn="a", rightAsOfColumn="a", tolerance=sf.lit(1)
... ).select(left.a, 'left_val', 'right_val').sort("a").collect()
[Row(a=1, left_val='a', right_val=1)]
>>> left._joinAsOf(
... right, leftAsOfColumn="a", rightAsOfColumn="a", how="left", tolerance=sf.lit(1)
... ).select(left.a, 'left_val', 'right_val').sort("a").collect()
[Row(a=1, left_val='a', right_val=1),
Row(a=5, left_val='b', right_val=None),
Row(a=10, left_val='c', right_val=None)]
>>> left._joinAsOf(
... right, leftAsOfColumn="a", rightAsOfColumn="a", allowExactMatches=False
... ).select(left.a, 'left_val', 'right_val').sort("a").collect()
[Row(a=5, left_val='b', right_val=3),
Row(a=10, left_val='c', right_val=7)]
>>> left._joinAsOf(
... right, leftAsOfColumn="a", rightAsOfColumn="a", direction="forward"
... ).select(left.a, 'left_val', 'right_val').sort("a").collect()
[Row(a=1, left_val='a', right_val=1),
Row(a=5, left_val='b', right_val=6)]
"""
if isinstance(leftAsOfColumn, str):
leftAsOfColumn = self[leftAsOfColumn]
left_as_of_jcol = leftAsOfColumn._jc
if isinstance(rightAsOfColumn, str):
rightAsOfColumn = other[rightAsOfColumn]
right_as_of_jcol = rightAsOfColumn._jc
if on is not None and not isinstance(on, list):
on = [on] # type: ignore[assignment]
if on is not None:
if isinstance(on[0], str):
on = self._jseq(cast(List[str], on))
else:
assert isinstance(on[0], Column), "on should be Column or list of Column"
on = reduce(lambda x, y: x.__and__(y), cast(List[Column], on))
on = on._jc
if how is None:
how = "inner"
assert isinstance(how, str), "how should be a string"
if tolerance is not None:
assert isinstance(tolerance, Column), "tolerance should be Column"
tolerance = tolerance._jc
jdf = self._jdf.joinAsOf(
other._jdf,
left_as_of_jcol,
right_as_of_jcol,
on,
how,
tolerance,
allowExactMatches,
direction,
)
return DataFrame(jdf, self.sparkSession)
def sortWithinPartitions(
self,
*cols: Union[int, str, Column, List[Union[int, str, Column]]],
**kwargs: Any,
) -> "DataFrame":
"""Returns a new :class:`DataFrame` with each partition sorted by the specified column(s).
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
cols : int, str, list or :class:`Column`, optional
list of :class:`Column` or column names or column ordinals to sort by.
.. versionchanged:: 4.0.0
Supports column ordinal.
Other Parameters
----------------
ascending : bool or list, optional, default True
boolean or list of boolean.
Sort ascending vs. descending. Specify list for multiple sort orders.
If a list is specified, the length of the list must equal the length of the `cols`.
Returns
-------
:class:`DataFrame`
DataFrame sorted by partitions.
Notes
-----
A column ordinal starts from 1, which is different from the
0-based :meth:`__getitem__`.
If a column ordinal is negative, it means sort descending.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
>>> df.sortWithinPartitions("age", ascending=False)
DataFrame[age: bigint, name: string]
>>> df.coalesce(1).sortWithinPartitions(1).show()
+---+-----+
|age| name|
+---+-----+
| 2|Alice|
| 5| Bob|
+---+-----+
>>> df.coalesce(1).sortWithinPartitions(-1).show()
+---+-----+
|age| name|
+---+-----+
| 5| Bob|
| 2|Alice|
+---+-----+
"""
jdf = self._jdf.sortWithinPartitions(self._sort_cols(cols, kwargs))
return DataFrame(jdf, self.sparkSession)
def sort(
self,
*cols: Union[int, str, Column, List[Union[int, str, Column]]],
**kwargs: Any,
) -> "DataFrame":
"""Returns a new :class:`DataFrame` sorted by the specified column(s).
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
cols : int, str, list, or :class:`Column`, optional
list of :class:`Column` or column names or column ordinals to sort by.
.. versionchanged:: 4.0.0
Supports column ordinal.
Other Parameters
----------------
ascending : bool or list, optional, default True
boolean or list of boolean.
Sort ascending vs. descending. Specify list for multiple sort orders.
If a list is specified, the length of the list must equal the length of the `cols`.
Returns
-------
:class:`DataFrame`
Sorted DataFrame.
Notes
-----
A column ordinal starts from 1, which is different from the
0-based :meth:`__getitem__`.
If a column ordinal is negative, it means sort descending.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (2, "Alice"), (5, "Bob")], schema=["age", "name"])
Sort the DataFrame in ascending order.
>>> df.sort(sf.asc("age")).show()
+---+-----+
|age| name|
+---+-----+
| 2|Alice|
| 5| Bob|
+---+-----+
>>> df.sort(1).show()
+---+-----+
|age| name|
+---+-----+
| 2|Alice|
| 5| Bob|
+---+-----+
Sort the DataFrame in descending order.
>>> df.sort(df.age.desc()).show()
+---+-----+
|age| name|
+---+-----+
| 5| Bob|
| 2|Alice|
+---+-----+
>>> df.orderBy(df.age.desc()).show()
+---+-----+
|age| name|
+---+-----+
| 5| Bob|
| 2|Alice|
+---+-----+
>>> df.sort("age", ascending=False).show()
+---+-----+
|age| name|
+---+-----+
| 5| Bob|
| 2|Alice|
+---+-----+
>>> df.sort(-1).show()
+---+-----+
|age| name|
+---+-----+
| 5| Bob|
| 2|Alice|
+---+-----+
Specify multiple columns
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (2, "Alice"), (2, "Bob"), (5, "Bob")], schema=["age", "name"])
>>> df.orderBy(sf.desc("age"), "name").show()
+---+-----+
|age| name|
+---+-----+
| 5| Bob|
| 2|Alice|
| 2| Bob|
+---+-----+
>>> df.orderBy(-1, "name").show()
+---+-----+
|age| name|
+---+-----+
| 5| Bob|
| 2|Alice|
| 2| Bob|
+---+-----+
>>> df.orderBy(-1, 2).show()
+---+-----+
|age| name|
+---+-----+
| 5| Bob|
| 2|Alice|
| 2| Bob|
+---+-----+
Specify multiple columns for sorting order at `ascending`.
>>> df.orderBy(["age", "name"], ascending=[False, False]).show()
+---+-----+
|age| name|
+---+-----+
| 5| Bob|
| 2| Bob|
| 2|Alice|
+---+-----+
>>> df.orderBy([1, "name"], ascending=[False, False]).show()
+---+-----+
|age| name|
+---+-----+
| 5| Bob|
| 2| Bob|
| 2|Alice|
+---+-----+
>>> df.orderBy([1, 2], ascending=[False, False]).show()
+---+-----+
|age| name|
+---+-----+
| 5| Bob|
| 2| Bob|
| 2|Alice|
+---+-----+
"""
jdf = self._jdf.sort(self._sort_cols(cols, kwargs))
return DataFrame(jdf, self.sparkSession)
orderBy = sort
def _jseq(
self,
cols: Sequence,
converter: Optional[Callable[..., Union["PrimitiveType", JavaObject]]] = None,
) -> JavaObject:
"""Return a JVM Seq of Columns from a list of Column or names"""
return _to_seq(self.sparkSession._sc, cols, converter)
def _jmap(self, jm: Dict) -> JavaObject:
"""Return a JVM Scala Map from a dict"""
return _to_scala_map(self.sparkSession._sc, jm)
def _jcols(self, *cols: "ColumnOrName") -> JavaObject:
"""Return a JVM Seq of Columns from a list of Column or column names
If `cols` has only one list in it, cols[0] will be used as the list.
"""
if len(cols) == 1 and isinstance(cols[0], list):
cols = cols[0]
return self._jseq(cols, _to_java_column)
def _jcols_ordinal(self, *cols: "ColumnOrNameOrOrdinal") -> JavaObject:
"""Return a JVM Seq of Columns from a list of Column or column names or column ordinals.
If `cols` has only one list in it, cols[0] will be used as the list.
"""
if len(cols) == 1 and isinstance(cols[0], list):
cols = cols[0]
_cols = []
for c in cols:
if isinstance(c, int) and not isinstance(c, bool):
if c < 1:
raise PySparkIndexError(
error_class="INDEX_NOT_POSITIVE", message_parameters={"index": str(c)}
)
# ordinal is 1-based
_cols.append(self[c - 1])
else:
_cols.append(c) # type: ignore[arg-type]
return self._jseq(_cols, _to_java_column)
def _sort_cols(
self,
cols: Sequence[Union[int, str, Column, List[Union[int, str, Column]]]],
kwargs: Dict[str, Any],
) -> JavaObject:
"""Return a JVM Seq of Columns that describes the sort order"""
if not cols:
raise PySparkValueError(
error_class="CANNOT_BE_EMPTY",
message_parameters={"item": "column"},
)
if len(cols) == 1 and isinstance(cols[0], list):
cols = cols[0]
jcols = []
for c in cols:
if isinstance(c, int) and not isinstance(c, bool):
# TODO: should introduce dedicated error class
# ordinal is 1-based
if c > 0:
_c = self[c - 1]
# negative ordinal means sort by desc
elif c < 0:
_c = self[-c - 1].desc()
else:
raise PySparkIndexError(
error_class="INDEX_NOT_POSITIVE", message_parameters={"index": str(c)}
)
else:
_c = c # type: ignore[assignment]
jcols.append(_to_java_column(cast("ColumnOrName", _c)))
ascending = kwargs.get("ascending", True)
if isinstance(ascending, (bool, int)):
if not ascending:
jcols = [jc.desc() for jc in jcols]
elif isinstance(ascending, list):
jcols = [jc if asc else jc.desc() for asc, jc in zip(ascending, jcols)]
else:
raise PySparkTypeError(
error_class="NOT_BOOL_OR_LIST",
message_parameters={"arg_name": "ascending", "arg_type": type(ascending).__name__},
)
return self._jseq(jcols)
def describe(self, *cols: Union[str, List[str]]) -> "DataFrame":
"""Computes basic statistics for numeric and string columns.
.. versionadded:: 1.3.1
.. versionchanged:: 3.4.0
Supports Spark Connect.
This includes count, mean, stddev, min, and max. If no columns are
given, this function computes statistics for all numerical or string columns.
Notes
-----
This function is meant for exploratory data analysis, as we make no
guarantee about the backward compatibility of the schema of the resulting
:class:`DataFrame`.
Use summary for expanded statistics and control over which statistics to compute.
Parameters
----------
cols : str, list, optional
Column name or list of column names to describe by (default All columns).
Returns
-------
:class:`DataFrame`
A new DataFrame that describes (provides statistics) given DataFrame.
Examples
--------
>>> df = spark.createDataFrame(
... [("Bob", 13, 40.3, 150.5), ("Alice", 12, 37.8, 142.3), ("Tom", 11, 44.1, 142.2)],
... ["name", "age", "weight", "height"],
... )
>>> df.describe(['age']).show()
+-------+----+
|summary| age|
+-------+----+
| count| 3|
| mean|12.0|
| stddev| 1.0|
| min| 11|
| max| 13|
+-------+----+
>>> df.describe(['age', 'weight', 'height']).show()
+-------+----+------------------+-----------------+
|summary| age| weight| height|
+-------+----+------------------+-----------------+
| count| 3| 3| 3|
| mean|12.0| 40.73333333333333| 145.0|
| stddev| 1.0|3.1722757341273704|4.763402145525822|
| min| 11| 37.8| 142.2|
| max| 13| 44.1| 150.5|
+-------+----+------------------+-----------------+
See Also
--------
DataFrame.summary
"""
if len(cols) == 1 and isinstance(cols[0], list):
cols = cols[0] # type: ignore[assignment]
jdf = self._jdf.describe(self._jseq(cols))
return DataFrame(jdf, self.sparkSession)
def summary(self, *statistics: str) -> "DataFrame":
"""Computes specified statistics for numeric and string columns. Available statistics are:
- count
- mean
- stddev
- min
- max
- arbitrary approximate percentiles specified as a percentage (e.g., 75%)
If no statistics are given, this function computes count, mean, stddev, min,
approximate quartiles (percentiles at 25%, 50%, and 75%), and max.
.. versionadded:: 2.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
statistics : str, optional
Column names to calculate statistics by (default All columns).
Returns
-------
:class:`DataFrame`
A new DataFrame that provides statistics for the given DataFrame.
Notes
-----
This function is meant for exploratory data analysis, as we make no
guarantee about the backward compatibility of the schema of the resulting
:class:`DataFrame`.
Examples
--------
>>> df = spark.createDataFrame(
... [("Bob", 13, 40.3, 150.5), ("Alice", 12, 37.8, 142.3), ("Tom", 11, 44.1, 142.2)],
... ["name", "age", "weight", "height"],
... )
>>> df.select("age", "weight", "height").summary().show()
+-------+----+------------------+-----------------+
|summary| age| weight| height|
+-------+----+------------------+-----------------+
| count| 3| 3| 3|
| mean|12.0| 40.73333333333333| 145.0|
| stddev| 1.0|3.1722757341273704|4.763402145525822|
| min| 11| 37.8| 142.2|
| 25%| 11| 37.8| 142.2|
| 50%| 12| 40.3| 142.3|
| 75%| 13| 44.1| 150.5|
| max| 13| 44.1| 150.5|
+-------+----+------------------+-----------------+
>>> df.select("age", "weight", "height").summary("count", "min", "25%", "75%", "max").show()
+-------+---+------+------+
|summary|age|weight|height|
+-------+---+------+------+
| count| 3| 3| 3|
| min| 11| 37.8| 142.2|
| 25%| 11| 37.8| 142.2|
| 75%| 13| 44.1| 150.5|
| max| 13| 44.1| 150.5|
+-------+---+------+------+
See Also
--------
DataFrame.display
"""
if len(statistics) == 1 and isinstance(statistics[0], list):
statistics = statistics[0]
jdf = self._jdf.summary(self._jseq(statistics))
return DataFrame(jdf, self.sparkSession)
@overload
def head(self) -> Optional[Row]:
...
@overload
def head(self, n: int) -> List[Row]:
...
def head(self, n: Optional[int] = None) -> Union[Optional[Row], List[Row]]:
"""Returns the first ``n`` rows.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Notes
-----
This method should only be used if the resulting array is expected
to be small, as all the data is loaded into the driver's memory.
Parameters
----------
n : int, optional
default 1. Number of rows to return.
Returns
-------
If n is supplied, return a list of :class:`Row` of length n
or less if the DataFrame has fewer elements.
If n is missing, return a single Row.
Examples
--------
>>> df = spark.createDataFrame([
... (2, "Alice"), (5, "Bob")], schema=["age", "name"])
>>> df.head()
Row(age=2, name='Alice')
>>> df.head(1)
[Row(age=2, name='Alice')]
>>> df.head(0)
[]
"""
if n is None:
rs = self.head(1)
return rs[0] if rs else None
return self.take(n)
def first(self) -> Optional[Row]:
"""Returns the first row as a :class:`Row`.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
:class:`Row`
First row if :class:`DataFrame` is not empty, otherwise ``None``.
Examples
--------
>>> df = spark.createDataFrame([
... (2, "Alice"), (5, "Bob")], schema=["age", "name"])
>>> df.first()
Row(age=2, name='Alice')
"""
return self.head()
@overload
def __getitem__(self, item: Union[int, str]) -> Column:
...
@overload
def __getitem__(self, item: Union[Column, List, Tuple]) -> "DataFrame":
...
def __getitem__(self, item: Union[int, str, Column, List, Tuple]) -> Union[Column, "DataFrame"]:
"""Returns the column as a :class:`Column`.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
item : int, str, :class:`Column`, list or tuple
column index, column name, column, or a list or tuple of columns
Returns
-------
:class:`Column` or :class:`DataFrame`
a specified column, or a filtered or projected dataframe.
* If the input `item` is an int or str, the output is a :class:`Column`.
* If the input `item` is a :class:`Column`, the output is a :class:`DataFrame`
filtered by this given :class:`Column`.
* If the input `item` is a list or tuple, the output is a :class:`DataFrame`
projected by this given list or tuple.
Examples
--------
>>> df = spark.createDataFrame([
... (2, "Alice"), (5, "Bob")], schema=["age", "name"])
Retrieve a column instance.
>>> df.select(df['age']).show()
+---+
|age|
+---+
| 2|
| 5|
+---+
>>> df.select(df[1]).show()
+-----+
| name|
+-----+
|Alice|
| Bob|
+-----+
Select multiple string columns as index.
>>> df[["name", "age"]].show()
+-----+---+
| name|age|
+-----+---+
|Alice| 2|
| Bob| 5|
+-----+---+
>>> df[df.age > 3].show()
+---+----+
|age|name|
+---+----+
| 5| Bob|
+---+----+
>>> df[df[0] > 3].show()
+---+----+
|age|name|
+---+----+
| 5| Bob|
+---+----+
"""
if isinstance(item, str):
jc = self._jdf.apply(item)
return Column(jc)
elif isinstance(item, Column):
return self.filter(item)
elif isinstance(item, (list, tuple)):
return self.select(*item)
elif isinstance(item, int):
jc = self._jdf.apply(self.columns[item])
return Column(jc)
else:
raise PySparkTypeError(
error_class="NOT_COLUMN_OR_FLOAT_OR_INT_OR_LIST_OR_STR",
message_parameters={"arg_name": "item", "arg_type": type(item).__name__},
)
def __getattr__(self, name: str) -> Column:
"""Returns the :class:`Column` denoted by ``name``.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
name : str
Column name to return as :class:`Column`.
Returns
-------
:class:`Column`
Requested column.
Examples
--------
>>> df = spark.createDataFrame([
... (2, "Alice"), (5, "Bob")], schema=["age", "name"])
Retrieve a column instance.
>>> df.select(df.age).show()
+---+
|age|
+---+
| 2|
| 5|
+---+
"""
if name not in self.columns:
raise PySparkAttributeError(
error_class="ATTRIBUTE_NOT_SUPPORTED", message_parameters={"attr_name": name}
)
jc = self._jdf.apply(name)
return Column(jc)
def __dir__(self) -> List[str]:
"""
Examples
--------
>>> from pyspark.sql.functions import lit
Create a dataframe with a column named 'id'.
>>> df = spark.range(3)
>>> [attr for attr in dir(df) if attr[0] == 'i'][:7] # Includes column id
['id', 'inputFiles', 'intersect', 'intersectAll', 'isEmpty', 'isLocal', 'isStreaming']
Add a column named 'i_like_pancakes'.
>>> df = df.withColumn('i_like_pancakes', lit(1))
>>> [attr for attr in dir(df) if attr[0] == 'i'][:7] # Includes columns i_like_pancakes, id
['i_like_pancakes', 'id', 'inputFiles', 'intersect', 'intersectAll', 'isEmpty', 'isLocal']
Try to add an existed column 'inputFiles'.
>>> df = df.withColumn('inputFiles', lit(2))
>>> [attr for attr in dir(df) if attr[0] == 'i'][:7] # Doesn't duplicate inputFiles
['i_like_pancakes', 'id', 'inputFiles', 'intersect', 'intersectAll', 'isEmpty', 'isLocal']
Try to add a column named 'id2'.
>>> df = df.withColumn('id2', lit(3))
>>> [attr for attr in dir(df) if attr[0] == 'i'][:7] # result includes id2 and sorted
['i_like_pancakes', 'id', 'id2', 'inputFiles', 'intersect', 'intersectAll', 'isEmpty']
Don't include columns that are not valid python identifiers.
>>> df = df.withColumn('1', lit(4))
>>> df = df.withColumn('name 1', lit(5))
>>> [attr for attr in dir(df) if attr[0] == 'i'][:7] # Doesn't include 1 or name 1
['i_like_pancakes', 'id', 'id2', 'inputFiles', 'intersect', 'intersectAll', 'isEmpty']
"""
attrs = set(super().__dir__())
attrs.update(filter(lambda s: s.isidentifier(), self.columns))
return sorted(attrs)
@overload
def select(self, *cols: "ColumnOrName") -> "DataFrame":
...
@overload
def select(self, __cols: Union[List[Column], List[str]]) -> "DataFrame":
...
def select(self, *cols: "ColumnOrName") -> "DataFrame": # type: ignore[misc]
"""Projects a set of expressions and returns a new :class:`DataFrame`.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
cols : str, :class:`Column`, or list
column names (string) or expressions (:class:`Column`).
If one of the column names is '*', that column is expanded to include all columns
in the current :class:`DataFrame`.
Returns
-------
:class:`DataFrame`
A DataFrame with subset (or all) of columns.
Examples
--------
>>> df = spark.createDataFrame([
... (2, "Alice"), (5, "Bob")], schema=["age", "name"])
Select all columns in the DataFrame.
>>> df.select('*').show()
+---+-----+
|age| name|
+---+-----+
| 2|Alice|
| 5| Bob|
+---+-----+
Select a column with other expressions in the DataFrame.
>>> df.select(df.name, (df.age + 10).alias('age')).show()
+-----+---+
| name|age|
+-----+---+
|Alice| 12|
| Bob| 15|
+-----+---+
"""
jdf = self._jdf.select(self._jcols(*cols))
return DataFrame(jdf, self.sparkSession)
@overload
def selectExpr(self, *expr: str) -> "DataFrame":
...
@overload
def selectExpr(self, *expr: List[str]) -> "DataFrame":
...
def selectExpr(self, *expr: Union[str, List[str]]) -> "DataFrame":
"""Projects a set of SQL expressions and returns a new :class:`DataFrame`.
This is a variant of :func:`select` that accepts SQL expressions.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
:class:`DataFrame`
A DataFrame with new/old columns transformed by expressions.
Examples
--------
>>> df = spark.createDataFrame([
... (2, "Alice"), (5, "Bob")], schema=["age", "name"])
>>> df.selectExpr("age * 2", "abs(age)").show()
+---------+--------+
|(age * 2)|abs(age)|
+---------+--------+
| 4| 2|
| 10| 5|
+---------+--------+
"""
if len(expr) == 1 and isinstance(expr[0], list):
expr = expr[0] # type: ignore[assignment]
jdf = self._jdf.selectExpr(self._jseq(expr))
return DataFrame(jdf, self.sparkSession)
def filter(self, condition: "ColumnOrName") -> "DataFrame":
"""Filters rows using the given condition.
:func:`where` is an alias for :func:`filter`.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
condition : :class:`Column` or str
A :class:`Column` of :class:`types.BooleanType`
or a string of SQL expressions.
Returns
-------
:class:`DataFrame`
A new DataFrame with rows that satisfy the condition.
Examples
--------
>>> df = spark.createDataFrame([
... (2, "Alice", "Math"), (5, "Bob", "Physics"), (7, "Charlie", "Chemistry")],
... schema=["age", "name", "subject"])
Filter by :class:`Column` instances.
>>> df.filter(df.age > 3).show()
+---+-------+---------+
|age| name| subject|
+---+-------+---------+
| 5| Bob| Physics|
| 7|Charlie|Chemistry|
+---+-------+---------+
>>> df.where(df.age == 2).show()
+---+-----+-------+
|age| name|subject|
+---+-----+-------+
| 2|Alice| Math|
+---+-----+-------+
Filter by SQL expression in a string.
>>> df.filter("age > 3").show()
+---+-------+---------+
|age| name| subject|
+---+-------+---------+
| 5| Bob| Physics|
| 7|Charlie|Chemistry|
+---+-------+---------+
>>> df.where("age = 2").show()
+---+-----+-------+
|age| name|subject|
+---+-----+-------+
| 2|Alice| Math|
+---+-----+-------+
Filter by multiple conditions.
>>> df.filter((df.age > 3) & (df.subject == "Physics")).show()
+---+----+-------+
|age|name|subject|
+---+----+-------+
| 5| Bob|Physics|
+---+----+-------+
>>> df.filter((df.age == 2) | (df.subject == "Chemistry")).show()
+---+-------+---------+
|age| name| subject|
+---+-------+---------+
| 2| Alice| Math|
| 7|Charlie|Chemistry|
+---+-------+---------+
Filter by multiple conditions using SQL expression.
>>> df.filter("age > 3 AND name = 'Bob'").show()
+---+----+-------+
|age|name|subject|
+---+----+-------+
| 5| Bob|Physics|
+---+----+-------+
Filter using the :func:`Column.isin` function.
>>> df.filter(df.name.isin("Alice", "Bob")).show()
+---+-----+-------+
|age| name|subject|
+---+-----+-------+
| 2|Alice| Math|
| 5| Bob|Physics|
+---+-----+-------+
Filter by a list of values using the :func:`Column.isin` function.
>>> df.filter(df.subject.isin(["Math", "Physics"])).show()
+---+-----+-------+
|age| name|subject|
+---+-----+-------+
| 2|Alice| Math|
| 5| Bob|Physics|
+---+-----+-------+
Filter using the `~` operator to exclude certain values.
>>> df.filter(~df.name.isin(["Alice", "Charlie"])).show()
+---+----+-------+
|age|name|subject|
+---+----+-------+
| 5| Bob|Physics|
+---+----+-------+
Filter using the :func:`Column.isNotNull` function.
>>> df.filter(df.name.isNotNull()).show()
+---+-------+---------+
|age| name| subject|
+---+-------+---------+
| 2| Alice| Math|
| 5| Bob| Physics|
| 7|Charlie|Chemistry|
+---+-------+---------+
Filter using the :func:`Column.like` function.
>>> df.filter(df.name.like("Al%")).show()
+---+-----+-------+
|age| name|subject|
+---+-----+-------+
| 2|Alice| Math|
+---+-----+-------+
Filter using the :func:`Column.contains` function.
>>> df.filter(df.name.contains("i")).show()
+---+-------+---------+
|age| name| subject|
+---+-------+---------+
| 2| Alice| Math|
| 7|Charlie|Chemistry|
+---+-------+---------+
Filter using the :func:`Column.between` function.
>>> df.filter(df.age.between(2, 5)).show()
+---+-----+-------+
|age| name|subject|
+---+-----+-------+
| 2|Alice| Math|
| 5| Bob|Physics|
+---+-----+-------+
"""
if isinstance(condition, str):
jdf = self._jdf.filter(condition)
elif isinstance(condition, Column):
jdf = self._jdf.filter(condition._jc)
else:
raise PySparkTypeError(
error_class="NOT_COLUMN_OR_STR",
message_parameters={"arg_name": "condition", "arg_type": type(condition).__name__},
)
return DataFrame(jdf, self.sparkSession)
@overload
def groupBy(self, *cols: "ColumnOrNameOrOrdinal") -> "GroupedData":
...
@overload
def groupBy(self, __cols: Union[List[Column], List[str], List[int]]) -> "GroupedData":
...
def groupBy(self, *cols: "ColumnOrNameOrOrdinal") -> "GroupedData": # type: ignore[misc]
"""
Groups the :class:`DataFrame` by the specified columns so that aggregation
can be performed on them.
See :class:`GroupedData` for all the available aggregate functions.
:func:`groupby` is an alias for :func:`groupBy`.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
cols : list, str, int or :class:`Column`
The columns to group by.
Each element can be a column name (string) or an expression (:class:`Column`)
or a column ordinal (int, 1-based) or list of them.
.. versionchanged:: 4.0.0
Supports column ordinal.
Returns
-------
:class:`GroupedData`
A :class:`GroupedData` object representing the grouped data by the specified columns.
Notes
-----
A column ordinal starts from 1, which is different from the
0-based :meth:`__getitem__`.
Examples
--------
>>> df = spark.createDataFrame([
... ("Alice", 2), ("Bob", 2), ("Bob", 2), ("Bob", 5)], schema=["name", "age"])
Example 1: Empty grouping columns triggers a global aggregation.
>>> df.groupBy().avg().show()
+--------+
|avg(age)|
+--------+
| 2.75|
+--------+
Example 2: Group-by 'name', and specify a dictionary to calculate the summation of 'age'.
>>> df.groupBy("name").agg({"age": "sum"}).sort("name").show()
+-----+--------+
| name|sum(age)|
+-----+--------+
|Alice| 2|
| Bob| 9|
+-----+--------+
Example 3: Group-by 'name', and calculate maximum values.
>>> df.groupBy(df.name).max().sort("name").show()
+-----+--------+
| name|max(age)|
+-----+--------+
|Alice| 2|
| Bob| 5|
+-----+--------+
Example 4: Also group-by 'name', but using the column ordinal.
>>> df.groupBy(1).max().sort("name").show()
+-----+--------+
| name|max(age)|
+-----+--------+
|Alice| 2|
| Bob| 5|
+-----+--------+
Example 5: Group-by 'name' and 'age', and calculate the number of rows in each group.
>>> df.groupBy(["name", df.age]).count().sort("name", "age").show()
+-----+---+-----+
| name|age|count|
+-----+---+-----+
|Alice| 2| 1|
| Bob| 2| 2|
| Bob| 5| 1|
+-----+---+-----+
Example 6: Also Group-by 'name' and 'age', but using the column ordinal.
>>> df.groupBy([df.name, 2]).count().sort("name", "age").show()
+-----+---+-----+
| name|age|count|
+-----+---+-----+
|Alice| 2| 1|
| Bob| 2| 2|
| Bob| 5| 1|
+-----+---+-----+
"""
jgd = self._jdf.groupBy(self._jcols_ordinal(*cols))
from pyspark.sql.group import GroupedData
return GroupedData(jgd, self)
@overload
def rollup(self, *cols: "ColumnOrName") -> "GroupedData":
...
@overload
def rollup(self, __cols: Union[List[Column], List[str]]) -> "GroupedData":
...
def rollup(self, *cols: "ColumnOrNameOrOrdinal") -> "GroupedData": # type: ignore[misc]
"""
Create a multi-dimensional rollup for the current :class:`DataFrame` using
the specified columns, allowing for aggregation on them.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
cols : list, str, int or :class:`Column`
The columns to roll-up by.
Each element should be a column name (string) or an expression (:class:`Column`)
or a column ordinal (int, 1-based) or list of them.
.. versionchanged:: 4.0.0
Supports column ordinal.
Returns
-------
:class:`GroupedData`
Rolled-up data based on the specified columns.
Notes
-----
A column ordinal starts from 1, which is different from the
0-based :meth:`__getitem__`.
Examples
--------
>>> df = spark.createDataFrame([("Alice", 2), ("Bob", 5)], schema=["name", "age"])
Example 1: Rollup-by 'name', and calculate the number of rows in each dimensional.
>>> df.rollup("name").count().orderBy("name").show()
+-----+-----+
| name|count|
+-----+-----+
| NULL| 2|
|Alice| 1|
| Bob| 1|
+-----+-----+
Example 2: Rollup-by 'name' and 'age',
and calculate the number of rows in each dimensional.
>>> df.rollup("name", df.age).count().orderBy("name", "age").show()
+-----+----+-----+
| name| age|count|
+-----+----+-----+
| NULL|NULL| 2|
|Alice|NULL| 1|
|Alice| 2| 1|
| Bob|NULL| 1|
| Bob| 5| 1|
+-----+----+-----+
Example 3: Also Rollup-by 'name' and 'age', but using the column ordinal.
>>> df.rollup(1, 2).count().orderBy(1, 2).show()
+-----+----+-----+
| name| age|count|
+-----+----+-----+
| NULL|NULL| 2|
|Alice|NULL| 1|
|Alice| 2| 1|
| Bob|NULL| 1|
| Bob| 5| 1|
+-----+----+-----+
"""
jgd = self._jdf.rollup(self._jcols_ordinal(*cols))
from pyspark.sql.group import GroupedData
return GroupedData(jgd, self)
@overload
def cube(self, *cols: "ColumnOrName") -> "GroupedData":
...
@overload
def cube(self, __cols: Union[List[Column], List[str]]) -> "GroupedData":
...
def cube(self, *cols: "ColumnOrName") -> "GroupedData": # type: ignore[misc]
"""
Create a multi-dimensional cube for the current :class:`DataFrame` using
the specified columns, allowing aggregations to be performed on them.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
cols : list, str, int or :class:`Column`
The columns to cube by.
Each element should be a column name (string) or an expression (:class:`Column`)
or a column ordinal (int, 1-based) or list of them.
.. versionchanged:: 4.0.0
Supports column ordinal.
Returns
-------
:class:`GroupedData`
Cube of the data based on the specified columns.
Notes
-----
A column ordinal starts from 1, which is different from the
0-based :meth:`__getitem__`.
Examples
--------
>>> df = spark.createDataFrame([("Alice", 2), ("Bob", 5)], schema=["name", "age"])
Example 1: Creating a cube on 'name',
and calculate the number of rows in each dimensional.
>>> df.cube("name").count().orderBy("name").show()
+-----+-----+
| name|count|
+-----+-----+
| NULL| 2|
|Alice| 1|
| Bob| 1|
+-----+-----+
Example 2: Creating a cube on 'name' and 'age',
and calculate the number of rows in each dimensional.
>>> df.cube("name", df.age).count().orderBy("name", "age").show()
+-----+----+-----+
| name| age|count|
+-----+----+-----+
| NULL|NULL| 2|
| NULL| 2| 1|
| NULL| 5| 1|
|Alice|NULL| 1|
|Alice| 2| 1|
| Bob|NULL| 1|
| Bob| 5| 1|
+-----+----+-----+
Example 3: Also creating a cube on 'name' and 'age', but using the column ordinal.
>>> df.cube(1, 2).count().orderBy(1, 2).show()
+-----+----+-----+
| name| age|count|
+-----+----+-----+
| NULL|NULL| 2|
| NULL| 2| 1|
| NULL| 5| 1|
|Alice|NULL| 1|
|Alice| 2| 1|
| Bob|NULL| 1|
| Bob| 5| 1|
+-----+----+-----+
"""
jgd = self._jdf.cube(self._jcols_ordinal(*cols))
from pyspark.sql.group import GroupedData
return GroupedData(jgd, self)
def groupingSets(
self, groupingSets: Sequence[Sequence["ColumnOrName"]], *cols: "ColumnOrName"
) -> "GroupedData":
"""
Create multi-dimensional aggregation for the current `class`:DataFrame using the specified
grouping sets, so we can run aggregation on them.
.. versionadded:: 4.0.0
Parameters
----------
groupingSets : sequence of sequence of columns or str
Individual set of columns to group on.
cols : :class:`Column` or str
Addional grouping columns specified by users.
Those columns are shown as the output columns after aggregation.
Returns
-------
:class:`GroupedData`
Grouping sets of the data based on the specified columns.
Examples
--------
Example 1: Group by city and car_model, city, and all, and calculate the sum of quantity.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (100, 'Fremont', 'Honda Civic', 10),
... (100, 'Fremont', 'Honda Accord', 15),
... (100, 'Fremont', 'Honda CRV', 7),
... (200, 'Dublin', 'Honda Civic', 20),
... (200, 'Dublin', 'Honda Accord', 10),
... (200, 'Dublin', 'Honda CRV', 3),
... (300, 'San Jose', 'Honda Civic', 5),
... (300, 'San Jose', 'Honda Accord', 8)
... ], schema="id INT, city STRING, car_model STRING, quantity INT")
>>> df.groupingSets(
... [("city", "car_model"), ("city",), ()],
... "city", "car_model"
... ).agg(sf.sum(sf.col("quantity")).alias("sum")).sort("city", "car_model").show()
+--------+------------+---+
| city| car_model|sum|
+--------+------------+---+
| NULL| NULL| 78|
| Dublin| NULL| 33|
| Dublin|Honda Accord| 10|
| Dublin| Honda CRV| 3|
| Dublin| Honda Civic| 20|
| Fremont| NULL| 32|
| Fremont|Honda Accord| 15|
| Fremont| Honda CRV| 7|
| Fremont| Honda Civic| 10|
|San Jose| NULL| 13|
|San Jose|Honda Accord| 8|
|San Jose| Honda Civic| 5|
+--------+------------+---+
Example 2: Group by multiple columns and calculate both average and sum.
>>> df.groupingSets(
... [("city", "car_model"), ("city",), ()],
... "city", "car_model"
... ).agg(
... sf.avg(sf.col("quantity")).alias("avg_quantity"),
... sf.sum(sf.col("quantity")).alias("sum_quantity")
... ).sort("city", "car_model").show()
+--------+------------+------------------+------------+
| city| car_model| avg_quantity|sum_quantity|
+--------+------------+------------------+------------+
| NULL| NULL| 9.75| 78|
| Dublin| NULL| 11.0| 33|
| Dublin|Honda Accord| 10.0| 10|
| Dublin| Honda CRV| 3.0| 3|
| Dublin| Honda Civic| 20.0| 20|
| Fremont| NULL|10.666666666666666| 32|
| Fremont|Honda Accord| 15.0| 15|
| Fremont| Honda CRV| 7.0| 7|
| Fremont| Honda Civic| 10.0| 10|
|San Jose| NULL| 6.5| 13|
|San Jose|Honda Accord| 8.0| 8|
|San Jose| Honda Civic| 5.0| 5|
+--------+------------+------------------+------------+
See Also
--------
GroupedData
"""
from pyspark.sql.group import GroupedData
jgrouping_sets = _to_seq(self._sc, [self._jcols(*inner) for inner in groupingSets])
jgd = self._jdf.groupingSets(jgrouping_sets, self._jcols(*cols))
return GroupedData(jgd, self)
def unpivot(
self,
ids: Union["ColumnOrName", List["ColumnOrName"], Tuple["ColumnOrName", ...]],
values: Optional[Union["ColumnOrName", List["ColumnOrName"], Tuple["ColumnOrName", ...]]],
variableColumnName: str,
valueColumnName: str,
) -> "DataFrame":
"""
Unpivot a DataFrame from wide format to long format, optionally leaving
identifier columns set. This is the reverse to `groupBy(...).pivot(...).agg(...)`,
except for the aggregation, which cannot be reversed.
This function is useful to massage a DataFrame into a format where some
columns are identifier columns ("ids"), while all other columns ("values")
are "unpivoted" to the rows, leaving just two non-id columns, named as given
by `variableColumnName` and `valueColumnName`.
When no "id" columns are given, the unpivoted DataFrame consists of only the
"variable" and "value" columns.
The `values` columns must not be empty so at least one value must be given to be unpivoted.
When `values` is `None`, all non-id columns will be unpivoted.
All "value" columns must share a least common data type. Unless they are the same data type,
all "value" columns are cast to the nearest common data type. For instance, types
`IntegerType` and `LongType` are cast to `LongType`, while `IntegerType` and `StringType`
do not have a common data type and `unpivot` fails.
.. versionadded:: 3.4.0
Parameters
----------
ids : str, Column, tuple, list
Column(s) to use as identifiers. Can be a single column or column name,
or a list or tuple for multiple columns.
values : str, Column, tuple, list, optional
Column(s) to unpivot. Can be a single column or column name, or a list or tuple
for multiple columns. If specified, must not be empty. If not specified, uses all
columns that are not set as `ids`.
variableColumnName : str
Name of the variable column.
valueColumnName : str
Name of the value column.
Returns
-------
:class:`DataFrame`
Unpivoted DataFrame.
Notes
-----
Supports Spark Connect.
Examples
--------
>>> df = spark.createDataFrame(
... [(1, 11, 1.1), (2, 12, 1.2)],
... ["id", "int", "double"],
... )
>>> df.show()
+---+---+------+
| id|int|double|
+---+---+------+
| 1| 11| 1.1|
| 2| 12| 1.2|
+---+---+------+
>>> df.unpivot("id", ["int", "double"], "var", "val").show()
+---+------+----+
| id| var| val|
+---+------+----+
| 1| int|11.0|
| 1|double| 1.1|
| 2| int|12.0|
| 2|double| 1.2|
+---+------+----+
See Also
--------
DataFrame.melt
"""
assert ids is not None, "ids must not be None"
def to_jcols(
cols: Union["ColumnOrName", List["ColumnOrName"], Tuple["ColumnOrName", ...]]
) -> JavaObject:
if isinstance(cols, list):
return self._jcols(*cols)
if isinstance(cols, tuple):
return self._jcols(*list(cols))
return self._jcols(cols)
jids = to_jcols(ids)
if values is None:
jdf = self._jdf.unpivotWithSeq(jids, variableColumnName, valueColumnName)
else:
jvals = to_jcols(values)
jdf = self._jdf.unpivotWithSeq(jids, jvals, variableColumnName, valueColumnName)
return DataFrame(jdf, self.sparkSession)
def melt(
self,
ids: Union["ColumnOrName", List["ColumnOrName"], Tuple["ColumnOrName", ...]],
values: Optional[Union["ColumnOrName", List["ColumnOrName"], Tuple["ColumnOrName", ...]]],
variableColumnName: str,
valueColumnName: str,
) -> "DataFrame":
"""
Unpivot a DataFrame from wide format to long format, optionally leaving
identifier columns set. This is the reverse to `groupBy(...).pivot(...).agg(...)`,
except for the aggregation, which cannot be reversed.
:func:`melt` is an alias for :func:`unpivot`.
.. versionadded:: 3.4.0
Parameters
----------
ids : str, Column, tuple, list, optional
Column(s) to use as identifiers. Can be a single column or column name,
or a list or tuple for multiple columns.
values : str, Column, tuple, list, optional
Column(s) to unpivot. Can be a single column or column name, or a list or tuple
for multiple columns. If not specified or empty, use all columns that
are not set as `ids`.
variableColumnName : str
Name of the variable column.
valueColumnName : str
Name of the value column.
Returns
-------
:class:`DataFrame`
Unpivoted DataFrame.
See Also
--------
DataFrame.unpivot
Notes
-----
Supports Spark Connect.
"""
return self.unpivot(ids, values, variableColumnName, valueColumnName)
def agg(self, *exprs: Union[Column, Dict[str, str]]) -> "DataFrame":
"""Aggregate on the entire :class:`DataFrame` without groups
(shorthand for ``df.groupBy().agg()``).
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
exprs : :class:`Column` or dict of key and value strings
Columns or expressions to aggregate DataFrame by.
Returns
-------
:class:`DataFrame`
Aggregated DataFrame.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
>>> df.agg({"age": "max"}).show()
+--------+
|max(age)|
+--------+
| 5|
+--------+
>>> df.agg(sf.min(df.age)).show()
+--------+
|min(age)|
+--------+
| 2|
+--------+
"""
return self.groupBy().agg(*exprs) # type: ignore[arg-type]
def observe(
self,
observation: Union["Observation", str],
*exprs: Column,
) -> "DataFrame":
"""Define (named) metrics to observe on the DataFrame. This method returns an 'observed'
DataFrame that returns the same result as the input, with the following guarantees:
* It will compute the defined aggregates (metrics) on all the data that is flowing through
the Dataset at that point.
* It will report the value of the defined aggregate columns as soon as we reach a completion
point. A completion point is either the end of a query (batch mode) or the end of a
streaming epoch. The value of the aggregates only reflects the data processed since
the previous completion point.
The metrics columns must either contain a literal (e.g. lit(42)), or should contain one or
more aggregate functions (e.g. sum(a) or sum(a + b) + avg(c) - lit(1)). Expressions that
contain references to the input Dataset's columns must always be wrapped in an aggregate
function.
A user can observe these metrics by adding
Python's :class:`~pyspark.sql.streaming.StreamingQueryListener`,
Scala/Java's ``org.apache.spark.sql.streaming.StreamingQueryListener`` or Scala/Java's
``org.apache.spark.sql.util.QueryExecutionListener`` to the spark session.
.. versionadded:: 3.3.0
.. versionchanged:: 3.5.0
Supports Spark Connect.
Parameters
----------
observation : :class:`Observation` or str
`str` to specify the name, or an :class:`Observation` instance to obtain the metric.
.. versionchanged:: 3.4.0
Added support for `str` in this parameter.
exprs : :class:`Column`
column expressions (:class:`Column`).
Returns
-------
:class:`DataFrame`
the observed :class:`DataFrame`.
Notes
-----
When ``observation`` is :class:`Observation`, this method only supports batch queries.
When ``observation`` is a string, this method works for both batch and streaming queries.
Continuous execution is currently not supported yet.
Examples
--------
When ``observation`` is :class:`Observation`, only batch queries work as below.
>>> from pyspark.sql.functions import col, count, lit, max
>>> from pyspark.sql import Observation
>>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
>>> observation = Observation("my metrics")
>>> observed_df = df.observe(observation, count(lit(1)).alias("count"), max(col("age")))
>>> observed_df.count()
2
>>> observation.get
{'count': 2, 'max(age)': 5}
When ``observation`` is a string, streaming queries also work as below.
>>> from pyspark.sql.streaming import StreamingQueryListener
>>> class MyErrorListener(StreamingQueryListener):
... def onQueryStarted(self, event):
... pass
...
... def onQueryProgress(self, event):
... row = event.progress.observedMetrics.get("my_event")
... # Trigger if the number of errors exceeds 5 percent
... num_rows = row.rc
... num_error_rows = row.erc
... ratio = num_error_rows / num_rows
... if ratio > 0.05:
... # Trigger alert
... pass
...
... def onQueryIdle(self, event):
... pass
...
... def onQueryTerminated(self, event):
... pass
...
>>> spark.streams.addListener(MyErrorListener())
>>> # Observe row count (rc) and error row count (erc) in the streaming Dataset
... observed_ds = df.observe(
... "my_event",
... count(lit(1)).alias("rc"),
... count(col("error")).alias("erc")) # doctest: +SKIP
>>> observed_ds.writeStream.format("console").start() # doctest: +SKIP
"""
from pyspark.sql import Observation
if len(exprs) == 0:
raise PySparkValueError(
error_class="CANNOT_BE_EMPTY",
message_parameters={"item": "exprs"},
)
if not all(isinstance(c, Column) for c in exprs):
raise PySparkTypeError(
error_class="NOT_LIST_OF_COLUMN",
message_parameters={"arg_name": "exprs"},
)
if isinstance(observation, Observation):
return observation._on(self, *exprs)
elif isinstance(observation, str):
return DataFrame(
self._jdf.observe(
observation, exprs[0]._jc, _to_seq(self._sc, [c._jc for c in exprs[1:]])
),
self.sparkSession,
)
else:
raise PySparkTypeError(
error_class="NOT_LIST_OF_COLUMN",
message_parameters={
"arg_name": "observation",
"arg_type": type(observation).__name__,
},
)
def union(self, other: "DataFrame") -> "DataFrame":
"""Return a new :class:`DataFrame` containing the union of rows in this and another
:class:`DataFrame`.
.. versionadded:: 2.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
other : :class:`DataFrame`
Another :class:`DataFrame` that needs to be unioned.
Returns
-------
:class:`DataFrame`
A new :class:`DataFrame` containing the combined rows with corresponding columns.
See Also
--------
DataFrame.unionAll
Notes
-----
This method performs a SQL-style set union of the rows from both `DataFrame` objects,
with no automatic deduplication of elements.
Use the `distinct()` method to perform deduplication of rows.
The method resolves columns by position (not by name), following the standard behavior
in SQL.
Examples
--------
Example 1: Combining two DataFrames with the same schema
>>> df1 = spark.createDataFrame([(1, 'A'), (2, 'B')], ['id', 'value'])
>>> df2 = spark.createDataFrame([(3, 'C'), (4, 'D')], ['id', 'value'])
>>> df3 = df1.union(df2)
>>> df3.show()
+---+-----+
| id|value|
+---+-----+
| 1| A|
| 2| B|
| 3| C|
| 4| D|
+---+-----+
Example 2: Combining two DataFrames with different schemas
>>> from pyspark.sql.functions import lit
>>> df1 = spark.createDataFrame([(100001, 1), (100002, 2)], schema="id LONG, money INT")
>>> df2 = spark.createDataFrame([(3, 100003), (4, 100003)], schema="money INT, id LONG")
>>> df1 = df1.withColumn("age", lit(30))
>>> df2 = df2.withColumn("age", lit(40))
>>> df3 = df1.union(df2)
>>> df3.show()
+------+------+---+
| id| money|age|
+------+------+---+
|100001| 1| 30|
|100002| 2| 30|
| 3|100003| 40|
| 4|100003| 40|
+------+------+---+
Example 3: Combining two DataFrames with mismatched columns
>>> df1 = spark.createDataFrame([(1, 2)], ["A", "B"])
>>> df2 = spark.createDataFrame([(3, 4)], ["C", "D"])
>>> df3 = df1.union(df2)
>>> df3.show()
+---+---+
| A| B|
+---+---+
| 1| 2|
| 3| 4|
+---+---+
Example 4: Combining duplicate rows from two different DataFrames
>>> df1 = spark.createDataFrame([(1, 'A'), (2, 'B'), (3, 'C')], ['id', 'value'])
>>> df2 = spark.createDataFrame([(3, 'C'), (4, 'D')], ['id', 'value'])
>>> df3 = df1.union(df2).distinct().sort("id")
>>> df3.show()
+---+-----+
| id|value|
+---+-----+
| 1| A|
| 2| B|
| 3| C|
| 4| D|
+---+-----+
"""
return DataFrame(self._jdf.union(other._jdf), self.sparkSession)
def unionAll(self, other: "DataFrame") -> "DataFrame":
"""Return a new :class:`DataFrame` containing the union of rows in this and another
:class:`DataFrame`.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
other : :class:`DataFrame`
Another :class:`DataFrame` that needs to be combined
Returns
-------
:class:`DataFrame`
A new :class:`DataFrame` containing combined rows from both dataframes.
Notes
-----
This method combines all rows from both `DataFrame` objects with no automatic
deduplication of elements.
Use the `distinct()` method to perform deduplication of rows.
:func:`unionAll` is an alias to :func:`union`
See Also
--------
DataFrame.union
"""
return self.union(other)
def unionByName(self, other: "DataFrame", allowMissingColumns: bool = False) -> "DataFrame":
"""Returns a new :class:`DataFrame` containing union of rows in this and another
:class:`DataFrame`.
This method performs a union operation on both input DataFrames, resolving columns by
name (rather than position). When `allowMissingColumns` is True, missing columns will
be filled with null.
.. versionadded:: 2.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
other : :class:`DataFrame`
Another :class:`DataFrame` that needs to be combined.
allowMissingColumns : bool, optional, default False
Specify whether to allow missing columns.
.. versionadded:: 3.1.0
Returns
-------
:class:`DataFrame`
A new :class:`DataFrame` containing the combined rows with corresponding
columns of the two given DataFrames.
Examples
--------
Example 1: Union of two DataFrames with same columns in different order.
>>> df1 = spark.createDataFrame([[1, 2, 3]], ["col0", "col1", "col2"])
>>> df2 = spark.createDataFrame([[4, 5, 6]], ["col1", "col2", "col0"])
>>> df1.unionByName(df2).show()
+----+----+----+
|col0|col1|col2|
+----+----+----+
| 1| 2| 3|
| 6| 4| 5|
+----+----+----+
Example 2: Union with missing columns and setting `allowMissingColumns=True`.
>>> df1 = spark.createDataFrame([[1, 2, 3]], ["col0", "col1", "col2"])
>>> df2 = spark.createDataFrame([[4, 5, 6]], ["col1", "col2", "col3"])
>>> df1.unionByName(df2, allowMissingColumns=True).show()
+----+----+----+----+
|col0|col1|col2|col3|
+----+----+----+----+
| 1| 2| 3|NULL|
|NULL| 4| 5| 6|
+----+----+----+----+
Example 3: Union of two DataFrames with few common columns.
>>> df1 = spark.createDataFrame([[1, 2, 3]], ["col0", "col1", "col2"])
>>> df2 = spark.createDataFrame([[4, 5, 6, 7]], ["col1", "col2", "col3", "col4"])
>>> df1.unionByName(df2, allowMissingColumns=True).show()
+----+----+----+----+----+
|col0|col1|col2|col3|col4|
+----+----+----+----+----+
| 1| 2| 3|NULL|NULL|
|NULL| 4| 5| 6| 7|
+----+----+----+----+----+
Example 4: Union of two DataFrames with completely different columns.
>>> df1 = spark.createDataFrame([[0, 1, 2]], ["col0", "col1", "col2"])
>>> df2 = spark.createDataFrame([[3, 4, 5]], ["col3", "col4", "col5"])
>>> df1.unionByName(df2, allowMissingColumns=True).show()
+----+----+----+----+----+----+
|col0|col1|col2|col3|col4|col5|
+----+----+----+----+----+----+
| 0| 1| 2|NULL|NULL|NULL|
|NULL|NULL|NULL| 3| 4| 5|
+----+----+----+----+----+----+
"""
return DataFrame(self._jdf.unionByName(other._jdf, allowMissingColumns), self.sparkSession)
def intersect(self, other: "DataFrame") -> "DataFrame":
"""Return a new :class:`DataFrame` containing rows only in
both this :class:`DataFrame` and another :class:`DataFrame`.
Note that any duplicates are removed. To preserve duplicates
use :func:`intersectAll`.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
other : :class:`DataFrame`
Another :class:`DataFrame` that needs to be combined.
Returns
-------
:class:`DataFrame`
Combined DataFrame.
Notes
-----
This is equivalent to `INTERSECT` in SQL.
Examples
--------
Example 1: Intersecting two DataFrames with the same schema
>>> df1 = spark.createDataFrame([("a", 1), ("a", 1), ("b", 3), ("c", 4)], ["C1", "C2"])
>>> df2 = spark.createDataFrame([("a", 1), ("a", 1), ("b", 3)], ["C1", "C2"])
>>> result_df = df1.intersect(df2).sort("C1", "C2")
>>> result_df.show()
+---+---+
| C1| C2|
+---+---+
| a| 1|
| b| 3|
+---+---+
Example 2: Intersecting two DataFrames with different schemas
>>> df1 = spark.createDataFrame([(1, "A"), (2, "B")], ["id", "value"])
>>> df2 = spark.createDataFrame([(2, "B"), (3, "C")], ["id", "value"])
>>> result_df = df1.intersect(df2).sort("id", "value")
>>> result_df.show()
+---+-----+
| id|value|
+---+-----+
| 2| B|
+---+-----+
Example 3: Intersecting all rows from two DataFrames with mismatched columns
>>> df1 = spark.createDataFrame([(1, 2), (1, 2), (3, 4)], ["A", "B"])
>>> df2 = spark.createDataFrame([(1, 2), (1, 2)], ["C", "D"])
>>> result_df = df1.intersect(df2).sort("A", "B")
>>> result_df.show()
+---+---+
| A| B|
+---+---+
| 1| 2|
+---+---+
"""
return DataFrame(self._jdf.intersect(other._jdf), self.sparkSession)
def intersectAll(self, other: "DataFrame") -> "DataFrame":
"""Return a new :class:`DataFrame` containing rows in both this :class:`DataFrame`
and another :class:`DataFrame` while preserving duplicates.
This is equivalent to `INTERSECT 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`
Another :class:`DataFrame` that needs to be combined.
Returns
-------
:class:`DataFrame`
Combined DataFrame.
Examples
--------
Example 1: Intersecting two DataFrames with the same schema
>>> df1 = spark.createDataFrame([("a", 1), ("a", 1), ("b", 3), ("c", 4)], ["C1", "C2"])
>>> df2 = spark.createDataFrame([("a", 1), ("a", 1), ("b", 3)], ["C1", "C2"])
>>> result_df = df1.intersectAll(df2).sort("C1", "C2")
>>> result_df.show()
+---+---+
| C1| C2|
+---+---+
| a| 1|
| a| 1|
| b| 3|
+---+---+
Example 2: Intersecting two DataFrames with different schemas
>>> df1 = spark.createDataFrame([(1, "A"), (2, "B")], ["id", "value"])
>>> df2 = spark.createDataFrame([(2, "B"), (3, "C")], ["id", "value"])
>>> result_df = df1.intersectAll(df2).sort("id", "value")
>>> result_df.show()
+---+-----+
| id|value|
+---+-----+
| 2| B|
+---+-----+
Example 3: Intersecting all rows from two DataFrames with mismatched columns
>>> df1 = spark.createDataFrame([(1, 2), (1, 2), (3, 4)], ["A", "B"])
>>> df2 = spark.createDataFrame([(1, 2), (1, 2)], ["C", "D"])
>>> result_df = df1.intersectAll(df2).sort("A", "B")
>>> result_df.show()
+---+---+
| A| B|
+---+---+
| 1| 2|
| 1| 2|
+---+---+
"""
return DataFrame(self._jdf.intersectAll(other._jdf), self.sparkSession)
def subtract(self, other: "DataFrame") -> "DataFrame":
"""Return a new :class:`DataFrame` containing rows in this :class:`DataFrame`
but not in another :class:`DataFrame`.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
other : :class:`DataFrame`
Another :class:`DataFrame` that needs to be subtracted.
Returns
-------
:class:`DataFrame`
Subtracted DataFrame.
Notes
-----
This is equivalent to `EXCEPT DISTINCT` in SQL.
Examples
--------
Example 1: Subtracting two DataFrames with the same schema
>>> df1 = spark.createDataFrame([("a", 1), ("a", 1), ("b", 3), ("c", 4)], ["C1", "C2"])
>>> df2 = spark.createDataFrame([("a", 1), ("a", 1), ("b", 3)], ["C1", "C2"])
>>> result_df = df1.subtract(df2)
>>> result_df.show()
+---+---+
| C1| C2|
+---+---+
| c| 4|
+---+---+
Example 2: Subtracting two DataFrames with different schemas
>>> df1 = spark.createDataFrame([(1, "A"), (2, "B")], ["id", "value"])
>>> df2 = spark.createDataFrame([(2, "B"), (3, "C")], ["id", "value"])
>>> result_df = df1.subtract(df2)
>>> result_df.show()
+---+-----+
| id|value|
+---+-----+
| 1| A|
+---+-----+
Example 3: Subtracting two DataFrames with mismatched columns
>>> df1 = spark.createDataFrame([(1, 2)], ["A", "B"])
>>> df2 = spark.createDataFrame([(1, 2)], ["C", "D"])
>>> result_df = df1.subtract(df2)
>>> result_df.show()
+---+---+
| A| B|
+---+---+
+---+---+
"""
return DataFrame(getattr(self._jdf, "except")(other._jdf), self.sparkSession)
def dropDuplicates(self, subset: Optional[List[str]] = None) -> "DataFrame":
"""Return a new :class:`DataFrame` with duplicate rows removed,
optionally only considering certain columns.
For a static batch :class:`DataFrame`, it just drops duplicate rows. For a streaming
:class:`DataFrame`, it will keep all data across triggers as intermediate state to drop
duplicates rows. You can use :func:`withWatermark` to limit how late the duplicate data can
be and the system will accordingly limit the state. In addition, data older than
watermark will be dropped to avoid any possibility of duplicates.
:func:`drop_duplicates` is an alias for :func:`dropDuplicates`.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
subset : list of column names, optional
List of columns to use for duplicate comparison (default All columns).
Returns
-------
:class:`DataFrame`
DataFrame without duplicates.
Examples
--------
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([
... Row(name='Alice', age=5, height=80),
... Row(name='Alice', age=5, height=80),
... Row(name='Alice', age=10, height=80)
... ])
Deduplicate the same rows.
>>> df.dropDuplicates().show()
+-----+---+------+
| name|age|height|
+-----+---+------+
|Alice| 5| 80|
|Alice| 10| 80|
+-----+---+------+
Deduplicate values on 'name' and 'height' columns.
>>> df.dropDuplicates(['name', 'height']).show()
+-----+---+------+
| name|age|height|
+-----+---+------+
|Alice| 5| 80|
+-----+---+------+
"""
if subset is not None and (not isinstance(subset, Iterable) or isinstance(subset, str)):
raise PySparkTypeError(
error_class="NOT_LIST_OR_TUPLE",
message_parameters={"arg_name": "subset", "arg_type": type(subset).__name__},
)
if subset is None:
jdf = self._jdf.dropDuplicates()
else:
jdf = self._jdf.dropDuplicates(self._jseq(subset))
return DataFrame(jdf, self.sparkSession)
def dropDuplicatesWithinWatermark(self, subset: Optional[List[str]] = None) -> "DataFrame":
"""Return a new :class:`DataFrame` with duplicate rows removed,
optionally only considering certain columns, within watermark.
This only works with streaming :class:`DataFrame`, and watermark for the input
:class:`DataFrame` must be set via :func:`withWatermark`.
For a streaming :class:`DataFrame`, this will keep all data across triggers as intermediate
state to drop duplicated rows. The state will be kept to guarantee the semantic, "Events
are deduplicated as long as the time distance of earliest and latest events are smaller
than the delay threshold of watermark." Users are encouraged to set the delay threshold of
watermark longer than max timestamp differences among duplicated events.
Note: too late data older than watermark will be dropped.
.. versionadded:: 3.5.0
Parameters
----------
subset : List of column names, optional
List of columns to use for duplicate comparison (default All columns).
Returns
-------
:class:`DataFrame`
DataFrame without duplicates.
Notes
-----
Supports Spark Connect.
Examples
--------
>>> from pyspark.sql import Row
>>> from pyspark.sql.functions import timestamp_seconds
>>> df = spark.readStream.format("rate").load().selectExpr(
... "value % 5 AS value", "timestamp")
>>> df.select("value", df.timestamp.alias("time")).withWatermark("time", '10 minutes')
DataFrame[value: bigint, time: timestamp]
Deduplicate the same rows.
>>> df.dropDuplicatesWithinWatermark() # doctest: +SKIP
Deduplicate values on 'value' columns.
>>> df.dropDuplicatesWithinWatermark(['value']) # doctest: +SKIP
"""
if subset is not None and (not isinstance(subset, Iterable) or isinstance(subset, str)):
raise PySparkTypeError(
error_class="NOT_LIST_OR_TUPLE",
message_parameters={"arg_name": "subset", "arg_type": type(subset).__name__},
)
if subset is None:
jdf = self._jdf.dropDuplicatesWithinWatermark()
else:
jdf = self._jdf.dropDuplicatesWithinWatermark(self._jseq(subset))
return DataFrame(jdf, self.sparkSession)
def dropna(
self,
how: str = "any",
thresh: Optional[int] = None,
subset: Optional[Union[str, Tuple[str, ...], List[str]]] = None,
) -> "DataFrame":
"""Returns a new :class:`DataFrame` omitting rows with null values.
:func:`DataFrame.dropna` and :func:`DataFrameNaFunctions.drop` are
aliases of each other.
.. versionadded:: 1.3.1
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
how : str, optional, the values that can be 'any' or 'all', default 'any'.
If 'any', drop a row if it contains any nulls.
If 'all', drop a row only if all its values are null.
thresh: int, optional, default None.
If specified, drop rows that have less than `thresh` non-null values.
This overwrites the `how` parameter.
subset : str, tuple or list, optional
optional list of column names to consider.
Returns
-------
:class:`DataFrame`
DataFrame with null only rows excluded.
Examples
--------
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([
... Row(age=10, height=80, name="Alice"),
... Row(age=5, height=None, name="Bob"),
... Row(age=None, height=None, name="Tom"),
... Row(age=None, height=None, name=None),
... ])
Example 1: Drop the row if it contains any nulls.
>>> df.na.drop().show()
+---+------+-----+
|age|height| name|
+---+------+-----+
| 10| 80|Alice|
+---+------+-----+
Example 2: Drop the row only if all its values are null.
>>> df.na.drop(how='all').show()
+----+------+-----+
| age|height| name|
+----+------+-----+
| 10| 80|Alice|
| 5| NULL| Bob|
|NULL| NULL| Tom|
+----+------+-----+
Example 3: Drop rows that have less than `thresh` non-null values.
>>> df.na.drop(thresh=2).show()
+---+------+-----+
|age|height| name|
+---+------+-----+
| 10| 80|Alice|
| 5| NULL| Bob|
+---+------+-----+
Example 4: Drop rows with non-null values in the specified columns.
>>> df.na.drop(subset=['age', 'name']).show()
+---+------+-----+
|age|height| name|
+---+------+-----+
| 10| 80|Alice|
| 5| NULL| Bob|
+---+------+-----+
"""
if how is not None and how not in ["any", "all"]:
raise PySparkValueError(
error_class="VALUE_NOT_ANY_OR_ALL",
message_parameters={"arg_name": "how", "arg_type": how},
)
if subset is None:
subset = self.columns
elif isinstance(subset, str):
subset = [subset]
elif not isinstance(subset, (list, tuple)):
raise PySparkTypeError(
error_class="NOT_LIST_OR_STR_OR_TUPLE",
message_parameters={"arg_name": "subset", "arg_type": type(subset).__name__},
)
if thresh is None:
thresh = len(subset) if how == "any" else 1
return DataFrame(self._jdf.na().drop(thresh, self._jseq(subset)), self.sparkSession)
@overload
def fillna(
self,
value: "LiteralType",
subset: Optional[Union[str, Tuple[str, ...], List[str]]] = ...,
) -> "DataFrame":
...
@overload
def fillna(self, value: Dict[str, "LiteralType"]) -> "DataFrame":
...
def fillna(
self,
value: Union["LiteralType", Dict[str, "LiteralType"]],
subset: Optional[Union[str, Tuple[str, ...], List[str]]] = None,
) -> "DataFrame":
"""Returns a new :class:`DataFrame` which null values are filled with new value.
:func:`DataFrame.fillna` and :func:`DataFrameNaFunctions.fill` are
aliases of each other.
.. versionadded:: 1.3.1
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
value : int, float, string, bool or dict, the value to replace null values with.
If the value is a dict, then `subset` is ignored and `value` must be a mapping
from column name (string) to replacement value. The replacement value must be
an int, float, boolean, or string.
subset : str, tuple or list, optional
optional list of column names to consider.
Columns specified in subset that do not have matching data types are ignored.
For example, if `value` is a string, and subset contains a non-string column,
then the non-string column is simply ignored.
Returns
-------
:class:`DataFrame`
DataFrame with replaced null values.
Examples
--------
>>> df = spark.createDataFrame([
... (10, 80.5, "Alice", None),
... (5, None, "Bob", None),
... (None, None, "Tom", None),
... (None, None, None, True)],
... schema=["age", "height", "name", "bool"])
Example 1: Fill all null values with 50 for numeric columns.
>>> df.na.fill(50).show()
+---+------+-----+----+
|age|height| name|bool|
+---+------+-----+----+
| 10| 80.5|Alice|NULL|
| 5| 50.0| Bob|NULL|
| 50| 50.0| Tom|NULL|
| 50| 50.0| NULL|true|
+---+------+-----+----+
Example 2: Fill all null values with ``False`` for boolean columns.
>>> df.na.fill(False).show()
+----+------+-----+-----+
| age|height| name| bool|
+----+------+-----+-----+
| 10| 80.5|Alice|false|
| 5| NULL| Bob|false|
|NULL| NULL| Tom|false|
|NULL| NULL| NULL| true|
+----+------+-----+-----+
Example 3: Fill all null values with to 50 and "unknown" for
'age' and 'name' column respectively.
>>> df.na.fill({'age': 50, 'name': 'unknown'}).show()
+---+------+-------+----+
|age|height| name|bool|
+---+------+-------+----+
| 10| 80.5| Alice|NULL|
| 5| NULL| Bob|NULL|
| 50| NULL| Tom|NULL|
| 50| NULL|unknown|true|
+---+------+-------+----+
Example 4: Fill all null values with "Spark" for 'name' column.
>>> df.na.fill(value = 'Spark', subset = 'name').show()
+----+------+-----+----+
| age|height| name|bool|
+----+------+-----+----+
| 10| 80.5|Alice|NULL|
| 5| NULL| Bob|NULL|
|NULL| NULL| Tom|NULL|
|NULL| NULL|Spark|true|
+----+------+-----+----+
"""
if not isinstance(value, (float, int, str, bool, dict)):
raise PySparkTypeError(
error_class="NOT_BOOL_OR_DICT_OR_FLOAT_OR_INT_OR_STR",
message_parameters={"arg_name": "value", "arg_type": type(value).__name__},
)
# Note that bool validates isinstance(int), but we don't want to
# convert bools to floats
if not isinstance(value, bool) and isinstance(value, int):
value = float(value)
if isinstance(value, dict):
return DataFrame(self._jdf.na().fill(value), self.sparkSession)
elif subset is None:
return DataFrame(self._jdf.na().fill(value), self.sparkSession)
else:
if isinstance(subset, str):
subset = [subset]
elif not isinstance(subset, (list, tuple)):
raise PySparkTypeError(
error_class="NOT_LIST_OR_TUPLE",
message_parameters={"arg_name": "subset", "arg_type": type(subset).__name__},
)
return DataFrame(self._jdf.na().fill(value, self._jseq(subset)), self.sparkSession)
@overload
def replace(
self,
to_replace: "LiteralType",
value: "OptionalPrimitiveType",
subset: Optional[List[str]] = ...,
) -> "DataFrame":
...
@overload
def replace(
self,
to_replace: List["LiteralType"],
value: List["OptionalPrimitiveType"],
subset: Optional[List[str]] = ...,
) -> "DataFrame":
...
@overload
def replace(
self,
to_replace: Dict["LiteralType", "OptionalPrimitiveType"],
subset: Optional[List[str]] = ...,
) -> "DataFrame":
...
@overload
def replace(
self,
to_replace: List["LiteralType"],
value: "OptionalPrimitiveType",
subset: Optional[List[str]] = ...,
) -> "DataFrame":
...
def replace( # type: ignore[misc]
self,
to_replace: Union[
"LiteralType", List["LiteralType"], Dict["LiteralType", "OptionalPrimitiveType"]
],
value: Optional[
Union["OptionalPrimitiveType", List["OptionalPrimitiveType"], _NoValueType]
] = _NoValue,
subset: Optional[List[str]] = None,
) -> "DataFrame":
"""Returns a new :class:`DataFrame` replacing a value with another value.
:func:`DataFrame.replace` and :func:`DataFrameNaFunctions.replace` are
aliases of each other.
Values to_replace and value must have the same type and can only be numerics, booleans,
or strings. Value can have None. When replacing, the new value will be cast
to the type of the existing column.
For numeric replacements all values to be replaced should have unique
floating point representation. In case of conflicts (for example with `{42: -1, 42.0: 1}`)
and arbitrary replacement will be used.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
to_replace : bool, int, float, string, list or dict, the value to be replaced.
If the value is a dict, then `value` is ignored or can be omitted, and `to_replace`
must be a mapping between a value and a replacement.
value : bool, int, float, string or None, optional
The replacement value must be a bool, int, float, string or None. If `value` is a
list, `value` should be of the same length and type as `to_replace`.
If `value` is a scalar and `to_replace` is a sequence, then `value` is
used as a replacement for each item in `to_replace`.
subset : list, optional
optional list of column names to consider.
Columns specified in subset that do not have matching data types are ignored.
For example, if `value` is a string, and subset contains a non-string column,
then the non-string column is simply ignored.
Returns
-------
:class:`DataFrame`
DataFrame with replaced values.
Examples
--------
>>> df = spark.createDataFrame([
... (10, 80, "Alice"),
... (5, None, "Bob"),
... (None, 10, "Tom"),
... (None, None, None)],
... schema=["age", "height", "name"])
Example 1: Replace 10 to 20 in all columns.
>>> df.na.replace(10, 20).show()
+----+------+-----+
| age|height| name|
+----+------+-----+
| 20| 80|Alice|
| 5| NULL| Bob|
|NULL| 20| Tom|
|NULL| NULL| NULL|
+----+------+-----+
Example 2: Replace 'Alice' to null in all columns.
>>> df.na.replace('Alice', None).show()
+----+------+----+
| age|height|name|
+----+------+----+
| 10| 80|NULL|
| 5| NULL| Bob|
|NULL| 10| Tom|
|NULL| NULL|NULL|
+----+------+----+
Example 3: Replace 'Alice' to 'A', and 'Bob' to 'B' in the 'name' column.
>>> df.na.replace(['Alice', 'Bob'], ['A', 'B'], 'name').show()
+----+------+----+
| age|height|name|
+----+------+----+
| 10| 80| A|
| 5| NULL| B|
|NULL| 10| Tom|
|NULL| NULL|NULL|
+----+------+----+
Example 4: Replace 10 to 20 in the 'name' column.
>>> df.na.replace(10, 18, 'age').show()
+----+------+-----+
| age|height| name|
+----+------+-----+
| 18| 80|Alice|
| 5| NULL| Bob|
|NULL| 10| Tom|
|NULL| NULL| NULL|
+----+------+-----+
"""
if value is _NoValue:
if isinstance(to_replace, dict):
value = None
else:
raise PySparkTypeError(
error_class="ARGUMENT_REQUIRED",
message_parameters={"arg_name": "value", "condition": "`to_replace` is dict"},
)
# Helper functions
def all_of(types: Union[Type, Tuple[Type, ...]]) -> Callable[[Iterable], bool]:
"""Given a type or tuple of types and a sequence of xs
check if each x is instance of type(s)
>>> all_of(bool)([True, False])
True
>>> all_of(str)(["a", 1])
False
"""
def all_of_(xs: Iterable) -> bool:
return all(isinstance(x, types) for x in xs)
return all_of_
all_of_bool = all_of(bool)
all_of_str = all_of(str)
all_of_numeric = all_of((float, int))
# Validate input types
valid_types = (bool, float, int, str, list, tuple)
if not isinstance(to_replace, valid_types + (dict,)):
raise PySparkTypeError(
error_class="NOT_BOOL_OR_DICT_OR_FLOAT_OR_INT_OR_LIST_OR_STR_OR_TUPLE",
message_parameters={
"arg_name": "to_replace",
"arg_type": type(to_replace).__name__,
},
)
if (
not isinstance(value, valid_types)
and value is not None
and not isinstance(to_replace, dict)
):
raise PySparkTypeError(
error_class="NOT_BOOL_OR_FLOAT_OR_INT_OR_LIST_OR_NONE_OR_STR_OR_TUPLE",
message_parameters={
"arg_name": "value",
"arg_type": type(value).__name__,
},
)
if isinstance(to_replace, (list, tuple)) and isinstance(value, (list, tuple)):
if len(to_replace) != len(value):
raise PySparkValueError(
error_class="LENGTH_SHOULD_BE_THE_SAME",
message_parameters={
"arg1": "to_replace",
"arg2": "value",
"arg1_length": str(len(to_replace)),
"arg2_length": str(len(value)),
},
)
if not (subset is None or isinstance(subset, (list, tuple, str))):
raise PySparkTypeError(
error_class="NOT_LIST_OR_STR_OR_TUPLE",
message_parameters={"arg_name": "subset", "arg_type": type(subset).__name__},
)
# Reshape input arguments if necessary
if isinstance(to_replace, (float, int, str)):
to_replace = [to_replace]
if isinstance(to_replace, dict):
rep_dict = to_replace
if value is not None:
warnings.warn("to_replace is a dict and value is not None. value will be ignored.")
else:
if isinstance(value, (float, int, str)) or value is None:
value = [value for _ in range(len(to_replace))]
rep_dict = dict(zip(to_replace, cast("Iterable[Optional[Union[float, str]]]", value)))
if isinstance(subset, str):
subset = [subset]
# Verify we were not passed in mixed type generics.
if not any(
all_of_type(rep_dict.keys())
and all_of_type(x for x in rep_dict.values() if x is not None)
for all_of_type in [all_of_bool, all_of_str, all_of_numeric]
):
raise PySparkValueError(
error_class="MIXED_TYPE_REPLACEMENT",
message_parameters={},
)
if subset is None:
return DataFrame(self._jdf.na().replace("*", rep_dict), self.sparkSession)
else:
return DataFrame(
self._jdf.na().replace(self._jseq(subset), self._jmap(rep_dict)),
self.sparkSession,
)
@overload
def approxQuantile(
self,
col: str,
probabilities: Union[List[float], Tuple[float]],
relativeError: float,
) -> List[float]:
...
@overload
def approxQuantile(
self,
col: Union[List[str], Tuple[str]],
probabilities: Union[List[float], Tuple[float]],
relativeError: float,
) -> List[List[float]]:
...
def approxQuantile(
self,
col: Union[str, List[str], Tuple[str]],
probabilities: Union[List[float], Tuple[float]],
relativeError: float,
) -> Union[List[float], List[List[float]]]:
"""
Calculates the approximate quantiles of numerical columns of a
:class:`DataFrame`.
The result of this algorithm has the following deterministic bound:
If the :class:`DataFrame` has N elements and if we request the quantile at
probability `p` up to error `err`, then the algorithm will return
a sample `x` from the :class:`DataFrame` so that the *exact* rank of `x` is
close to (p * N). More precisely,
floor((p - err) * N) <= rank(x) <= ceil((p + err) * N).
This method implements a variation of the Greenwald-Khanna
algorithm (with some speed optimizations). The algorithm was first
present in [[https://doi.org/10.1145/375663.375670
Space-efficient Online Computation of Quantile Summaries]]
by Greenwald and Khanna.
.. versionadded:: 2.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col: str, tuple or list
Can be a single column name, or a list of names for multiple columns.
.. versionchanged:: 2.2.0
Added support for multiple columns.
probabilities : list or tuple of floats
a list of quantile probabilities
Each number must be a float in the range [0, 1].
For example 0.0 is the minimum, 0.5 is the median, 1.0 is the maximum.
relativeError : float
The relative target precision to achieve
(>= 0). If set to zero, the exact quantiles are computed, which
could be very expensive. Note that values greater than 1 are
accepted but gives the same result as 1.
Returns
-------
list
the approximate quantiles at the given probabilities.
* If the input `col` is a string, the output is a list of floats.
* If the input `col` is a list or tuple of strings, the output is also a
list, but each element in it is a list of floats, i.e., the output
is a list of list of floats.
Notes
-----
Null values will be ignored in numerical columns before calculation.
For columns only containing null values, an empty list is returned.
Examples
--------
Example 1: Calculating quantiles for a single column
>>> data = [(1,), (2,), (3,), (4,), (5,)]
>>> df = spark.createDataFrame(data, ["values"])
>>> quantiles = df.approxQuantile("values", [0.0, 0.5, 1.0], 0.05)
>>> quantiles
[1.0, 3.0, 5.0]
Example 2: Calculating quantiles for multiple columns
>>> data = [(1, 10), (2, 20), (3, 30), (4, 40), (5, 50)]
>>> df = spark.createDataFrame(data, ["col1", "col2"])
>>> quantiles = df.approxQuantile(["col1", "col2"], [0.0, 0.5, 1.0], 0.05)
>>> quantiles
[[1.0, 3.0, 5.0], [10.0, 30.0, 50.0]]
Example 3: Handling null values
>>> data = [(1,), (None,), (3,), (4,), (None,)]
>>> df = spark.createDataFrame(data, ["values"])
>>> quantiles = df.approxQuantile("values", [0.0, 0.5, 1.0], 0.05)
>>> quantiles
[1.0, 3.0, 4.0]
Example 4: Calculating quantiles with low precision
>>> data = [(1,), (2,), (3,), (4,), (5,)]
>>> df = spark.createDataFrame(data, ["values"])
>>> quantiles = df.approxQuantile("values", [0.0, 0.2, 1.0], 0.1)
>>> quantiles
[1.0, 1.0, 5.0]
"""
if not isinstance(col, (str, list, tuple)):
raise PySparkTypeError(
error_class="NOT_LIST_OR_STR_OR_TUPLE",
message_parameters={"arg_name": "col", "arg_type": type(col).__name__},
)
isStr = isinstance(col, str)
if isinstance(col, tuple):
col = list(col)
elif isStr:
col = [cast(str, col)]
for c in col:
if not isinstance(c, str):
raise PySparkTypeError(
error_class="DISALLOWED_TYPE_FOR_CONTAINER",
message_parameters={
"arg_name": "col",
"arg_type": type(col).__name__,
"allowed_types": "str",
"item_type": type(c).__name__,
},
)
col = _to_list(self._sc, cast(List["ColumnOrName"], col))
if not isinstance(probabilities, (list, tuple)):
raise PySparkTypeError(
error_class="NOT_LIST_OR_TUPLE",
message_parameters={
"arg_name": "probabilities",
"arg_type": type(probabilities).__name__,
},
)
if isinstance(probabilities, tuple):
probabilities = list(probabilities)
for p in probabilities:
if not isinstance(p, (float, int)) or p < 0 or p > 1:
raise PySparkTypeError(
error_class="NOT_LIST_OF_FLOAT_OR_INT",
message_parameters={
"arg_name": "probabilities",
"arg_type": type(p).__name__,
},
)
probabilities = _to_list(self._sc, cast(List["ColumnOrName"], probabilities))
if not isinstance(relativeError, (float, int)):
raise PySparkTypeError(
error_class="NOT_FLOAT_OR_INT",
message_parameters={
"arg_name": "relativeError",
"arg_type": type(relativeError).__name__,
},
)
if relativeError < 0:
raise PySparkValueError(
error_class="NEGATIVE_VALUE",
message_parameters={
"arg_name": "relativeError",
"arg_value": str(relativeError),
},
)
relativeError = float(relativeError)
jaq = self._jdf.stat().approxQuantile(col, probabilities, relativeError)
jaq_list = [list(j) for j in jaq]
return jaq_list[0] if isStr else jaq_list
def corr(self, col1: str, col2: str, method: Optional[str] = None) -> float:
"""
Calculates the correlation of two columns of a :class:`DataFrame` as a double value.
Currently only supports the Pearson Correlation Coefficient.
:func:`DataFrame.corr` and :func:`DataFrameStatFunctions.corr` are aliases of each other.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col1 : str
The name of the first column
col2 : str
The name of the second column
method : str, optional
The correlation method. Currently only supports "pearson"
Returns
-------
float
Pearson Correlation Coefficient of two columns.
Examples
--------
>>> df = spark.createDataFrame([(1, 12), (10, 1), (19, 8)], ["c1", "c2"])
>>> df.corr("c1", "c2")
-0.3592106040535498
>>> df = spark.createDataFrame([(11, 12), (10, 11), (9, 10)], ["small", "bigger"])
>>> df.corr("small", "bigger")
1.0
"""
if not isinstance(col1, str):
raise PySparkTypeError(
error_class="NOT_STR",
message_parameters={"arg_name": "col1", "arg_type": type(col1).__name__},
)
if not isinstance(col2, str):
raise PySparkTypeError(
error_class="NOT_STR",
message_parameters={"arg_name": "col2", "arg_type": type(col2).__name__},
)
if not method:
method = "pearson"
if not method == "pearson":
raise PySparkValueError(
error_class="VALUE_NOT_PEARSON",
message_parameters={"arg_name": "method", "arg_value": method},
)
return self._jdf.stat().corr(col1, col2, method)
def cov(self, col1: str, col2: str) -> float:
"""
Calculate the sample covariance for the given columns, specified by their names, as a
double value. :func:`DataFrame.cov` and :func:`DataFrameStatFunctions.cov` are aliases.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col1 : str
The name of the first column
col2 : str
The name of the second column
Returns
-------
float
Covariance of two columns.
Examples
--------
>>> df = spark.createDataFrame([(1, 12), (10, 1), (19, 8)], ["c1", "c2"])
>>> df.cov("c1", "c2")
-18.0
>>> df = spark.createDataFrame([(11, 12), (10, 11), (9, 10)], ["small", "bigger"])
>>> df.cov("small", "bigger")
1.0
"""
if not isinstance(col1, str):
raise PySparkTypeError(
error_class="NOT_STR",
message_parameters={"arg_name": "col1", "arg_type": type(col1).__name__},
)
if not isinstance(col2, str):
raise PySparkTypeError(
error_class="NOT_STR",
message_parameters={"arg_name": "col2", "arg_type": type(col2).__name__},
)
return self._jdf.stat().cov(col1, col2)
def crosstab(self, col1: str, col2: str) -> "DataFrame":
"""
Computes a pair-wise frequency table of the given columns. Also known as a contingency
table.
The first column of each row will be the distinct values of `col1` and the column names
will be the distinct values of `col2`. The name of the first column will be `$col1_$col2`.
Pairs that have no occurrences will have zero as their counts.
:func:`DataFrame.crosstab` and :func:`DataFrameStatFunctions.crosstab` are aliases.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col1 : str
The name of the first column. Distinct items will make the first item of
each row.
col2 : str
The name of the second column. Distinct items will make the column names
of the :class:`DataFrame`.
Returns
-------
:class:`DataFrame`
Frequency matrix of two columns.
Examples
--------
>>> df = spark.createDataFrame([(1, 11), (1, 11), (3, 10), (4, 8), (4, 8)], ["c1", "c2"])
>>> df.crosstab("c1", "c2").sort("c1_c2").show()
+-----+---+---+---+
|c1_c2| 10| 11| 8|
+-----+---+---+---+
| 1| 0| 2| 0|
| 3| 1| 0| 0|
| 4| 0| 0| 2|
+-----+---+---+---+
"""
if not isinstance(col1, str):
raise PySparkTypeError(
error_class="NOT_STR",
message_parameters={"arg_name": "col1", "arg_type": type(col1).__name__},
)
if not isinstance(col2, str):
raise PySparkTypeError(
error_class="NOT_STR",
message_parameters={"arg_name": "col2", "arg_type": type(col2).__name__},
)
return DataFrame(self._jdf.stat().crosstab(col1, col2), self.sparkSession)
def freqItems(
self, cols: Union[List[str], Tuple[str]], support: Optional[float] = None
) -> "DataFrame":
"""
Finding frequent items for columns, possibly with false positives. Using the
frequent element count algorithm described in
"https://doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou".
:func:`DataFrame.freqItems` and :func:`DataFrameStatFunctions.freqItems` are aliases.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
cols : list or tuple
Names of the columns to calculate frequent items for as a list or tuple of
strings.
support : float, optional
The frequency with which to consider an item 'frequent'. Default is 1%.
The support must be greater than 1e-4.
Returns
-------
:class:`DataFrame`
DataFrame with frequent items.
Notes
-----
This function is meant for exploratory data analysis, as we make no
guarantee about the backward compatibility of the schema of the resulting
:class:`DataFrame`.
Examples
--------
>>> df = spark.createDataFrame([(1, 11), (1, 11), (3, 10), (4, 8), (4, 8)], ["c1", "c2"])
>>> df.freqItems(["c1", "c2"]).show() # doctest: +SKIP
+------------+------------+
|c1_freqItems|c2_freqItems|
+------------+------------+
| [4, 1, 3]| [8, 11, 10]|
+------------+------------+
"""
if isinstance(cols, tuple):
cols = list(cols)
if not isinstance(cols, list):
raise PySparkTypeError(
error_class="NOT_LIST_OR_TUPLE",
message_parameters={"arg_name": "cols", "arg_type": type(cols).__name__},
)
if not support:
support = 0.01
return DataFrame(
self._jdf.stat().freqItems(_to_seq(self._sc, cols), support), self.sparkSession
)
def _ipython_key_completions_(self) -> List[str]:
"""Returns the names of columns in this :class:`DataFrame`.
Examples
--------
>>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], ["age", "name"])
>>> df._ipython_key_completions_()
['age', 'name']
Would return illegal identifiers.
>>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], ["age 1", "name?1"])
>>> df._ipython_key_completions_()
['age 1', 'name?1']
"""
return self.columns
def withColumns(self, *colsMap: Dict[str, Column]) -> "DataFrame":
"""
Returns a new :class:`DataFrame` by adding multiple columns or replacing the
existing columns that have the same names.
The colsMap is a map of column name and column, the column must only refer to attributes
supplied by this Dataset. It is an error to add columns that refer to some other Dataset.
.. versionadded:: 3.3.0
Added support for multiple columns adding
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
colsMap : dict
a dict of column name and :class:`Column`. Currently, only a single map is supported.
Returns
-------
:class:`DataFrame`
DataFrame with new or replaced columns.
Examples
--------
>>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
>>> df.withColumns({'age2': df.age + 2, 'age3': df.age + 3}).show()
+---+-----+----+----+
|age| name|age2|age3|
+---+-----+----+----+
| 2|Alice| 4| 5|
| 5| Bob| 7| 8|
+---+-----+----+----+
"""
# Below code is to help enable kwargs in future.
assert len(colsMap) == 1
colsMap = colsMap[0] # type: ignore[assignment]
if not isinstance(colsMap, dict):
raise PySparkTypeError(
error_class="NOT_DICT",
message_parameters={"arg_name": "colsMap", "arg_type": type(colsMap).__name__},
)
col_names = list(colsMap.keys())
cols = list(colsMap.values())
return DataFrame(
self._jdf.withColumns(_to_seq(self._sc, col_names), self._jcols(*cols)),
self.sparkSession,
)
def withColumn(self, colName: str, col: Column) -> "DataFrame":
"""
Returns a new :class:`DataFrame` by adding a column or replacing the
existing column that has the same name.
The column expression must be an expression over this :class:`DataFrame`; attempting to add
a column from some other :class:`DataFrame` will raise an error.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
colName : str
string, name of the new column.
col : :class:`Column`
a :class:`Column` expression for the new column.
Returns
-------
:class:`DataFrame`
DataFrame with new or replaced column.
Notes
-----
This method introduces a projection internally. Therefore, calling it multiple
times, for instance, via loops in order to add multiple columns can generate big
plans which can cause performance issues and even `StackOverflowException`.
To avoid this, use :func:`select` with multiple columns at once.
Examples
--------
>>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
>>> df.withColumn('age2', df.age + 2).show()
+---+-----+----+
|age| name|age2|
+---+-----+----+
| 2|Alice| 4|
| 5| Bob| 7|
+---+-----+----+
"""
if not isinstance(col, Column):
raise PySparkTypeError(
error_class="NOT_COLUMN",
message_parameters={"arg_name": "col", "arg_type": type(col).__name__},
)
return DataFrame(self._jdf.withColumn(colName, col._jc), self.sparkSession)
def withColumnRenamed(self, existing: str, new: str) -> "DataFrame":
"""
Returns a new :class:`DataFrame` by renaming an existing column.
This is a no-op if the schema doesn't contain the given column name.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
existing : str
The name of the existing column to be renamed.
new : str
The new name to be assigned to the column.
Returns
-------
:class:`DataFrame`
A new DataFrame with renamed column.
See Also
--------
:meth:`withColumnsRenamed`
Examples
--------
>>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
Example 1: Rename a single column
>>> df.withColumnRenamed("age", "age2").show()
+----+-----+
|age2| name|
+----+-----+
| 2|Alice|
| 5| Bob|
+----+-----+
Example 2: Rename a column that does not exist (no-op)
>>> df.withColumnRenamed("non_existing", "new_name").show()
+---+-----+
|age| name|
+---+-----+
| 2|Alice|
| 5| Bob|
+---+-----+
Example 3: Rename multiple columns
>>> df.withColumnRenamed("age", "age2").withColumnRenamed("name", "name2").show()
+----+-----+
|age2|name2|
+----+-----+
| 2|Alice|
| 5| Bob|
+----+-----+
"""
return DataFrame(self._jdf.withColumnRenamed(existing, new), self.sparkSession)
def withColumnsRenamed(self, colsMap: Dict[str, str]) -> "DataFrame":
"""
Returns a new :class:`DataFrame` by renaming multiple columns.
This is a no-op if the schema doesn't contain the given column names.
.. versionadded:: 3.4.0
Added support for multiple columns renaming
Parameters
----------
colsMap : dict
A dict of existing column names and corresponding desired column names.
Currently, only a single map is supported.
Returns
-------
:class:`DataFrame`
DataFrame with renamed columns.
See Also
--------
:meth:`withColumnRenamed`
Examples
--------
>>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
Example 1: Rename a single column
>>> df.withColumnsRenamed({"age": "age2"}).show()
+----+-----+
|age2| name|
+----+-----+
| 2|Alice|
| 5| Bob|
+----+-----+
Example 2: Rename multiple columns
>>> df.withColumnsRenamed({"age": "age2", "name": "name2"}).show()
+----+-----+
|age2|name2|
+----+-----+
| 2|Alice|
| 5| Bob|
+----+-----+
Example 3: Rename non-existing column (no-op)
>>> df.withColumnsRenamed({"non_existing": "new_name"}).show()
+---+-----+
|age| name|
+---+-----+
| 2|Alice|
| 5| Bob|
+---+-----+
Example 4: Rename with an empty dictionary (no-op)
>>> df.withColumnsRenamed({}).show()
+---+-----+
|age| name|
+---+-----+
| 2|Alice|
| 5| Bob|
+---+-----+
"""
if not isinstance(colsMap, dict):
raise PySparkTypeError(
error_class="NOT_DICT",
message_parameters={"arg_name": "colsMap", "arg_type": type(colsMap).__name__},
)
col_names: List[str] = []
new_col_names: List[str] = []
for k, v in colsMap.items():
col_names.append(k)
new_col_names.append(v)
return DataFrame(
self._jdf.withColumnsRenamed(
_to_seq(self._sc, col_names), _to_seq(self._sc, new_col_names)
),
self.sparkSession,
)
def withMetadata(self, columnName: str, metadata: Dict[str, Any]) -> "DataFrame":
"""Returns a new :class:`DataFrame` by updating an existing column with metadata.
.. versionadded:: 3.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
columnName : str
string, name of the existing column to update the metadata.
metadata : dict
dict, new metadata to be assigned to df.schema[columnName].metadata
Returns
-------
:class:`DataFrame`
DataFrame with updated metadata column.
Examples
--------
>>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
>>> df_meta = df.withMetadata('age', {'foo': 'bar'})
>>> df_meta.schema['age'].metadata
{'foo': 'bar'}
"""
if not isinstance(metadata, dict):
raise PySparkTypeError(
error_class="NOT_DICT",
message_parameters={"arg_name": "metadata", "arg_type": type(metadata).__name__},
)
sc = get_active_spark_context()
jmeta = cast(JVMView, sc._jvm).org.apache.spark.sql.types.Metadata.fromJson(
json.dumps(metadata)
)
return DataFrame(self._jdf.withMetadata(columnName, jmeta), self.sparkSession)
@overload
def drop(self, cols: "ColumnOrName") -> "DataFrame":
...
@overload
def drop(self, *cols: str) -> "DataFrame":
...
def drop(self, *cols: "ColumnOrName") -> "DataFrame": # type: ignore[misc]
"""
Returns a new :class:`DataFrame` without specified columns.
This is a no-op if the schema doesn't contain the given column name(s).
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
cols: str or :class:`Column`
A name of the column, or the :class:`Column` to be dropped.
Returns
-------
:class:`DataFrame`
A new :class:`DataFrame` without the specified columns.
Notes
-----
- When an input is a column name, it is treated literally without further interpretation.
Otherwise, it will try to match the equivalent expression.
So dropping a column by its name `drop(colName)` has a different semantic
with directly dropping the column `drop(col(colName))`.
Examples
--------
Example 1: Drop a column by name.
>>> df = spark.createDataFrame(
... [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
>>> df.drop('age').show()
+-----+
| name|
+-----+
| Tom|
|Alice|
| Bob|
+-----+
Example 2: Drop a column by :class:`Column` object.
>>> df.drop(df.age).show()
+-----+
| name|
+-----+
| Tom|
|Alice|
| Bob|
+-----+
Example 3: Drop the column that joined both DataFrames on.
>>> df2 = spark.createDataFrame([(80, "Tom"), (85, "Bob")], ["height", "name"])
>>> df.join(df2, df.name == df2.name).drop('name').sort('age').show()
+---+------+
|age|height|
+---+------+
| 14| 80|
| 16| 85|
+---+------+
>>> df3 = df.join(df2)
>>> df3.show()
+---+-----+------+----+
|age| name|height|name|
+---+-----+------+----+
| 14| Tom| 80| Tom|
| 14| Tom| 85| Bob|
| 23|Alice| 80| Tom|
| 23|Alice| 85| Bob|
| 16| Bob| 80| Tom|
| 16| Bob| 85| Bob|
+---+-----+------+----+
Example 4: Drop two column by the same name.
>>> df3.drop("name").show()
+---+------+
|age|height|
+---+------+
| 14| 80|
| 14| 85|
| 23| 80|
| 23| 85|
| 16| 80|
| 16| 85|
+---+------+
Example 5: Can not drop col('name') due to ambiguous reference.
>>> from pyspark.sql import functions as sf
>>> df3.drop(sf.col("name")).show()
Traceback (most recent call last):
...
pyspark.errors.exceptions.captured.AnalysisException: [AMBIGUOUS_REFERENCE] Reference...
Example 6: Can not find a column matching the expression "a.b.c".
>>> from pyspark.sql import functions as sf
>>> df4 = df.withColumn("a.b.c", sf.lit(1))
>>> df4.show()
+---+-----+-----+
|age| name|a.b.c|
+---+-----+-----+
| 14| Tom| 1|
| 23|Alice| 1|
| 16| Bob| 1|
+---+-----+-----+
>>> df4.drop("a.b.c").show()
+---+-----+
|age| name|
+---+-----+
| 14| Tom|
| 23|Alice|
| 16| Bob|
+---+-----+
>>> df4.drop(sf.col("a.b.c")).show()
+---+-----+-----+
|age| name|a.b.c|
+---+-----+-----+
| 14| Tom| 1|
| 23|Alice| 1|
| 16| Bob| 1|
+---+-----+-----+
"""
column_names: List[str] = []
java_columns: List[JavaObject] = []
for c in cols:
if isinstance(c, str):
column_names.append(c)
elif isinstance(c, Column):
java_columns.append(c._jc)
else:
raise PySparkTypeError(
error_class="NOT_COLUMN_OR_STR",
message_parameters={"arg_name": "col", "arg_type": type(c).__name__},
)
jdf = self._jdf
if len(java_columns) > 0:
first_column, *remaining_columns = java_columns
jdf = jdf.drop(first_column, self._jseq(remaining_columns))
if len(column_names) > 0:
jdf = jdf.drop(self._jseq(column_names))
return DataFrame(jdf, self.sparkSession)
def toDF(self, *cols: str) -> "DataFrame":
"""Returns a new :class:`DataFrame` that with new specified column names
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
*cols : tuple
a tuple of string new column name. The length of the
list needs to be the same as the number of columns in the initial
:class:`DataFrame`
Returns
-------
:class:`DataFrame`
DataFrame with new column names.
Examples
--------
>>> df = spark.createDataFrame([(14, "Tom"), (23, "Alice"),
... (16, "Bob")], ["age", "name"])
>>> df.toDF('f1', 'f2').show()
+---+-----+
| f1| f2|
+---+-----+
| 14| Tom|
| 23|Alice|
| 16| Bob|
+---+-----+
"""
for col in cols:
if not isinstance(col, str):
raise PySparkTypeError(
error_class="NOT_LIST_OF_STR",
message_parameters={"arg_name": "cols", "arg_type": type(col).__name__},
)
jdf = self._jdf.toDF(self._jseq(cols))
return DataFrame(jdf, self.sparkSession)
def transform(self, func: Callable[..., "DataFrame"], *args: Any, **kwargs: Any) -> "DataFrame":
"""Returns a new :class:`DataFrame`. Concise syntax for chaining custom transformations.
.. versionadded:: 3.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
func : function
a function that takes and returns a :class:`DataFrame`.
*args
Positional arguments to pass to func.
.. versionadded:: 3.3.0
**kwargs
Keyword arguments to pass to func.
.. versionadded:: 3.3.0
Returns
-------
:class:`DataFrame`
Transformed DataFrame.
Examples
--------
>>> from pyspark.sql.functions import col
>>> df = spark.createDataFrame([(1, 1.0), (2, 2.0)], ["int", "float"])
>>> def cast_all_to_int(input_df):
... return input_df.select([col(col_name).cast("int") for col_name in input_df.columns])
...
>>> def sort_columns_asc(input_df):
... return input_df.select(*sorted(input_df.columns))
...
>>> df.transform(cast_all_to_int).transform(sort_columns_asc).show()
+-----+---+
|float|int|
+-----+---+
| 1| 1|
| 2| 2|
+-----+---+
>>> def add_n(input_df, n):
... return input_df.select([(col(col_name) + n).alias(col_name)
... for col_name in input_df.columns])
>>> df.transform(add_n, 1).transform(add_n, n=10).show()
+---+-----+
|int|float|
+---+-----+
| 12| 12.0|
| 13| 13.0|
+---+-----+
"""
result = func(self, *args, **kwargs)
assert isinstance(
result, DataFrame
), "Func returned an instance of type [%s], " "should have been DataFrame." % type(result)
return result
def sameSemantics(self, other: "DataFrame") -> bool:
"""
Returns `True` when the logical query plans inside both :class:`DataFrame`\\s are equal and
therefore return the same results.
.. versionadded:: 3.1.0
.. versionchanged:: 3.5.0
Supports Spark Connect.
Notes
-----
The equality comparison here is simplified by tolerating the cosmetic differences
such as attribute names.
This API can compare both :class:`DataFrame`\\s very fast but can still return
`False` on the :class:`DataFrame` that return the same results, for instance, from
different plans. Such false negative semantic can be useful when caching as an example.
This API is a developer API.
Parameters
----------
other : :class:`DataFrame`
The other DataFrame to compare against.
Returns
-------
bool
Whether these two DataFrames are similar.
Examples
--------
>>> df1 = spark.range(10)
>>> df2 = spark.range(10)
>>> df1.withColumn("col1", df1.id * 2).sameSemantics(df2.withColumn("col1", df2.id * 2))
True
>>> df1.withColumn("col1", df1.id * 2).sameSemantics(df2.withColumn("col1", df2.id + 2))
False
>>> df1.withColumn("col1", df1.id * 2).sameSemantics(df2.withColumn("col0", df2.id * 2))
True
"""
if not isinstance(other, DataFrame):
raise PySparkTypeError(
error_class="NOT_STR",
message_parameters={"arg_name": "other", "arg_type": type(other).__name__},
)
return self._jdf.sameSemantics(other._jdf)
def semanticHash(self) -> int:
"""
Returns a hash code of the logical query plan against this :class:`DataFrame`.
.. versionadded:: 3.1.0
.. versionchanged:: 3.5.0
Supports Spark Connect.
Notes
-----
Unlike the standard hash code, the hash is calculated against the query plan
simplified by tolerating the cosmetic differences such as attribute names.
This API is a developer API.
Returns
-------
int
Hash value.
Examples
--------
>>> spark.range(10).selectExpr("id as col0").semanticHash() # doctest: +SKIP
1855039936
>>> spark.range(10).selectExpr("id as col1").semanticHash() # doctest: +SKIP
1855039936
"""
return self._jdf.semanticHash()
def inputFiles(self) -> List[str]:
"""
Returns a best-effort snapshot of the files that compose this :class:`DataFrame`.
This method simply asks each constituent BaseRelation for its respective files and
takes the union of all results. Depending on the source relations, this may not find
all input files. Duplicates are removed.
.. versionadded:: 3.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
list
List of file paths.
Examples
--------
>>> import tempfile
>>> with tempfile.TemporaryDirectory() as d:
... # Write a single-row DataFrame into a JSON file
... spark.createDataFrame(
... [{"age": 100, "name": "Hyukjin Kwon"}]
... ).repartition(1).write.json(d, mode="overwrite")
...
... # Read the JSON file as a DataFrame.
... df = spark.read.format("json").load(d)
...
... # Returns the number of input files.
... len(df.inputFiles())
1
"""
return list(self._jdf.inputFiles())
def where(self, condition: "ColumnOrName") -> "DataFrame":
"""
:func:`where` is an alias for :func:`filter`.
.. versionadded:: 1.3.0
"""
return self.filter(condition)
# Two aliases below were added for pandas compatibility many years ago.
# There are too many differences compared to pandas and we cannot just
# make it "compatible" by adding aliases. Therefore, we stop adding such
# aliases as of Spark 3.0. Two methods below remain just
# for legacy users currently.
@overload
def groupby(self, *cols: "ColumnOrNameOrOrdinal") -> "GroupedData":
...
@overload
def groupby(self, __cols: Union[List[Column], List[str], List[int]]) -> "GroupedData":
...
def groupby(self, *cols: "ColumnOrNameOrOrdinal") -> "GroupedData": # type: ignore[misc]
"""
:func:`groupby` is an alias for :func:`groupBy`.
.. versionadded:: 1.4.0
"""
return self.groupBy(*cols)
def drop_duplicates(self, subset: Optional[List[str]] = None) -> "DataFrame":
"""
:func:`drop_duplicates` is an alias for :func:`dropDuplicates`.
.. versionadded:: 1.4.0
"""
return self.dropDuplicates(subset)
def writeTo(self, table: str) -> DataFrameWriterV2:
"""
Create a write configuration builder for v2 sources.
This builder is used to configure and execute write operations.
For example, to append or create or replace existing tables.
.. versionadded:: 3.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
table : str
Target table name to write to.
Returns
-------
:class:`DataFrameWriterV2`
DataFrameWriterV2 to use further to specify how to save the data
Examples
--------
>>> df = spark.createDataFrame(
... [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
>>> df.writeTo("catalog.db.table").append() # doctest: +SKIP
>>> df.writeTo( # doctest: +SKIP
... "catalog.db.table"
... ).partitionedBy("col").createOrReplace()
"""
return DataFrameWriterV2(self, table)
def pandas_api(
self, index_col: Optional[Union[str, List[str]]] = None
) -> "PandasOnSparkDataFrame":
"""
Converts the existing DataFrame into a pandas-on-Spark DataFrame.
.. versionadded:: 3.2.0
.. versionchanged:: 3.5.0
Supports Spark Connect.
If a pandas-on-Spark DataFrame is converted to a Spark DataFrame and then back
to pandas-on-Spark, it will lose the index information and the original index
will be turned into a normal column.
This is only available if Pandas is installed and available.
Parameters
----------
index_col: str or list of str, optional
Index column of table in Spark.
Returns
-------
:class:`PandasOnSparkDataFrame`
See Also
--------
pyspark.pandas.frame.DataFrame.to_spark
Examples
--------
>>> df = spark.createDataFrame(
... [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
>>> df.pandas_api() # doctest: +SKIP
age name
0 14 Tom
1 23 Alice
2 16 Bob
We can specify the index columns.
>>> df.pandas_api(index_col="age") # doctest: +SKIP
name
age
14 Tom
23 Alice
16 Bob
"""
from pyspark.pandas.namespace import _get_index_map
from pyspark.pandas.frame import DataFrame as PandasOnSparkDataFrame
from pyspark.pandas.internal import InternalFrame
index_spark_columns, index_names = _get_index_map(self, index_col)
internal = InternalFrame(
spark_frame=self,
index_spark_columns=index_spark_columns,
index_names=index_names, # type: ignore[arg-type]
)
return PandasOnSparkDataFrame(internal)
def _to_scala_map(sc: SparkContext, jm: Dict) -> JavaObject:
"""
Convert a dict into a JVM Map.
"""
assert sc._jvm is not None
return sc._jvm.PythonUtils.toScalaMap(jm)
class DataFrameNaFunctions:
"""Functionality for working with missing data in :class:`DataFrame`.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
"""
def __init__(self, df: DataFrame):
self.df = df
def drop(
self,
how: str = "any",
thresh: Optional[int] = None,
subset: Optional[Union[str, Tuple[str, ...], List[str]]] = None,
) -> DataFrame:
return self.df.dropna(how=how, thresh=thresh, subset=subset)
drop.__doc__ = DataFrame.dropna.__doc__
@overload
def fill(self, value: "LiteralType", subset: Optional[List[str]] = ...) -> DataFrame:
...
@overload
def fill(self, value: Dict[str, "LiteralType"]) -> DataFrame:
...
def fill(
self,
value: Union["LiteralType", Dict[str, "LiteralType"]],
subset: Optional[List[str]] = None,
) -> DataFrame:
return self.df.fillna(value=value, subset=subset) # type: ignore[arg-type]
fill.__doc__ = DataFrame.fillna.__doc__
@overload
def replace(
self,
to_replace: List["LiteralType"],
value: List["OptionalPrimitiveType"],
subset: Optional[List[str]] = ...,
) -> DataFrame:
...
@overload
def replace(
self,
to_replace: Dict["LiteralType", "OptionalPrimitiveType"],
subset: Optional[List[str]] = ...,
) -> DataFrame:
...
@overload
def replace(
self,
to_replace: List["LiteralType"],
value: "OptionalPrimitiveType",
subset: Optional[List[str]] = ...,
) -> DataFrame:
...
def replace( # type: ignore[misc]
self,
to_replace: Union[List["LiteralType"], Dict["LiteralType", "OptionalPrimitiveType"]],
value: Optional[
Union["OptionalPrimitiveType", List["OptionalPrimitiveType"], _NoValueType]
] = _NoValue,
subset: Optional[List[str]] = None,
) -> DataFrame:
return self.df.replace(to_replace, value, subset) # type: ignore[arg-type]
replace.__doc__ = DataFrame.replace.__doc__
class DataFrameStatFunctions:
"""Functionality for statistic functions with :class:`DataFrame`.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
"""
def __init__(self, df: DataFrame):
self.df = df
@overload
def approxQuantile(
self,
col: str,
probabilities: Union[List[float], Tuple[float]],
relativeError: float,
) -> List[float]:
...
@overload
def approxQuantile(
self,
col: Union[List[str], Tuple[str]],
probabilities: Union[List[float], Tuple[float]],
relativeError: float,
) -> List[List[float]]:
...
def approxQuantile(
self,
col: Union[str, List[str], Tuple[str]],
probabilities: Union[List[float], Tuple[float]],
relativeError: float,
) -> Union[List[float], List[List[float]]]:
return self.df.approxQuantile(col, probabilities, relativeError)
approxQuantile.__doc__ = DataFrame.approxQuantile.__doc__
def corr(self, col1: str, col2: str, method: Optional[str] = None) -> float:
return self.df.corr(col1, col2, method)
corr.__doc__ = DataFrame.corr.__doc__
def cov(self, col1: str, col2: str) -> float:
return self.df.cov(col1, col2)
cov.__doc__ = DataFrame.cov.__doc__
def crosstab(self, col1: str, col2: str) -> DataFrame:
return self.df.crosstab(col1, col2)
crosstab.__doc__ = DataFrame.crosstab.__doc__
def freqItems(self, cols: List[str], support: Optional[float] = None) -> DataFrame:
return self.df.freqItems(cols, support)
freqItems.__doc__ = DataFrame.freqItems.__doc__
def sampleBy(
self, col: str, fractions: Dict[Any, float], seed: Optional[int] = None
) -> DataFrame:
return self.df.sampleBy(col, fractions, seed)
sampleBy.__doc__ = DataFrame.sampleBy.__doc__
def _test() -> None:
import doctest
from pyspark.sql import SparkSession
import pyspark.sql.dataframe
globs = pyspark.sql.dataframe.__dict__.copy()
spark = SparkSession.builder.master("local[4]").appName("sql.dataframe tests").getOrCreate()
globs["spark"] = spark
(failure_count, test_count) = doctest.testmod(
pyspark.sql.dataframe,
globs=globs,
optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE | doctest.REPORT_NDIFF,
)
spark.stop()
if failure_count:
sys.exit(-1)
if __name__ == "__main__":
_test()