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### What changes were proposed in this pull request?

This PR adds the following options to mypy configuration

```
warn_unused_ignores = True
warn_redundant_casts = True
```

### Why are the changes needed?

This ensures that no unused casts and imports are used to reduce overall noise.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Existing tests.

Closes #35740 from zero323/SPARK-38424.

Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
32 contributors

Users who have contributed to this file

@zero323 @rxin @HyukjinKwon @xinrong-databricks @0x0FFF @jsoref @imback82 @yoda-mon @cloud-fan @WeichenXu123 @viirya @ueshin
#
# 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 sys
import json
import warnings
from typing import (
cast,
overload,
Any,
Callable,
Iterable,
List,
Optional,
Tuple,
TYPE_CHECKING,
Union,
)
from py4j.java_gateway import JavaObject
from pyspark import copy_func
from pyspark.context import SparkContext
from pyspark.sql.types import DataType, StructField, StructType, IntegerType, StringType
if TYPE_CHECKING:
from pyspark.sql._typing import ColumnOrName, LiteralType, DecimalLiteral, DateTimeLiteral
from pyspark.sql.window import WindowSpec
__all__ = ["Column"]
def _create_column_from_literal(literal: Union["LiteralType", "DecimalLiteral"]) -> "Column":
sc = SparkContext._active_spark_context
assert sc is not None and sc._jvm is not None
return sc._jvm.functions.lit(literal)
def _create_column_from_name(name: str) -> "Column":
sc = SparkContext._active_spark_context
assert sc is not None and sc._jvm is not None
return sc._jvm.functions.col(name)
def _to_java_column(col: "ColumnOrName") -> JavaObject:
if isinstance(col, Column):
jcol = col._jc
elif isinstance(col, str):
jcol = _create_column_from_name(col)
else:
raise TypeError(
"Invalid argument, not a string or column: "
"{0} of type {1}. "
"For column literals, use 'lit', 'array', 'struct' or 'create_map' "
"function.".format(col, type(col))
)
return jcol
def _to_seq(
sc: SparkContext,
cols: Iterable["ColumnOrName"],
converter: Optional[Callable[["ColumnOrName"], JavaObject]] = None,
) -> JavaObject:
"""
Convert a list of Column (or names) into a JVM Seq of Column.
An optional `converter` could be used to convert items in `cols`
into JVM Column objects.
"""
if converter:
cols = [converter(c) for c in cols]
assert sc._jvm is not None
return sc._jvm.PythonUtils.toSeq(cols)
def _to_list(
sc: SparkContext,
cols: List["ColumnOrName"],
converter: Optional[Callable[["ColumnOrName"], JavaObject]] = None,
) -> JavaObject:
"""
Convert a list of Column (or names) into a JVM (Scala) List of Column.
An optional `converter` could be used to convert items in `cols`
into JVM Column objects.
"""
if converter:
cols = [converter(c) for c in cols]
assert sc._jvm is not None
return sc._jvm.PythonUtils.toList(cols)
def _unary_op(
name: str,
doc: str = "unary operator",
) -> Callable[["Column"], "Column"]:
"""Create a method for given unary operator"""
def _(self: "Column") -> "Column":
jc = getattr(self._jc, name)()
return Column(jc)
_.__doc__ = doc
return _
def _func_op(name: str, doc: str = "") -> Callable[["Column"], "Column"]:
def _(self: "Column") -> "Column":
sc = SparkContext._active_spark_context
assert sc is not None and sc._jvm is not None
jc = getattr(sc._jvm.functions, name)(self._jc)
return Column(jc)
_.__doc__ = doc
return _
def _bin_func_op(
name: str,
reverse: bool = False,
doc: str = "binary function",
) -> Callable[["Column", Union["Column", "LiteralType", "DecimalLiteral"]], "Column"]:
def _(self: "Column", other: Union["Column", "LiteralType", "DecimalLiteral"]) -> "Column":
sc = SparkContext._active_spark_context
assert sc is not None and sc._jvm is not None
fn = getattr(sc._jvm.functions, name)
jc = other._jc if isinstance(other, Column) else _create_column_from_literal(other)
njc = fn(self._jc, jc) if not reverse else fn(jc, self._jc)
return Column(njc)
_.__doc__ = doc
return _
def _bin_op(
name: str,
doc: str = "binary operator",
) -> Callable[
["Column", Union["Column", "LiteralType", "DecimalLiteral", "DateTimeLiteral"]], "Column"
]:
"""Create a method for given binary operator"""
def _(
self: "Column",
other: Union["Column", "LiteralType", "DecimalLiteral", "DateTimeLiteral"],
) -> "Column":
jc = other._jc if isinstance(other, Column) else other
njc = getattr(self._jc, name)(jc)
return Column(njc)
_.__doc__ = doc
return _
def _reverse_op(
name: str,
doc: str = "binary operator",
) -> Callable[["Column", Union["LiteralType", "DecimalLiteral"]], "Column"]:
"""Create a method for binary operator (this object is on right side)"""
def _(self: "Column", other: Union["LiteralType", "DecimalLiteral"]) -> "Column":
jother = _create_column_from_literal(other)
jc = getattr(jother, name)(self._jc)
return Column(jc)
_.__doc__ = doc
return _
class Column:
"""
A column in a DataFrame.
:class:`Column` instances can be created by::
# 1. Select a column out of a DataFrame
df.colName
df["colName"]
# 2. Create from an expression
df.colName + 1
1 / df.colName
.. versionadded:: 1.3.0
"""
def __init__(self, jc: JavaObject) -> None:
self._jc = jc
# arithmetic operators
__neg__ = _func_op("negate")
__add__ = cast(
Callable[["Column", Union["Column", "LiteralType", "DecimalLiteral"]], "Column"],
_bin_op("plus"),
)
__sub__ = cast(
Callable[["Column", Union["Column", "LiteralType", "DecimalLiteral"]], "Column"],
_bin_op("minus"),
)
__mul__ = cast(
Callable[["Column", Union["Column", "LiteralType", "DecimalLiteral"]], "Column"],
_bin_op("multiply"),
)
__div__ = cast(
Callable[["Column", Union["Column", "LiteralType", "DecimalLiteral"]], "Column"],
_bin_op("divide"),
)
__truediv__ = cast(
Callable[["Column", Union["Column", "LiteralType", "DecimalLiteral"]], "Column"],
_bin_op("divide"),
)
__mod__ = cast(
Callable[["Column", Union["Column", "LiteralType", "DecimalLiteral"]], "Column"],
_bin_op("mod"),
)
__radd__ = cast(
Callable[["Column", Union["LiteralType", "DecimalLiteral"]], "Column"], _bin_op("plus")
)
__rsub__ = _reverse_op("minus")
__rmul__ = cast(
Callable[["Column", Union["LiteralType", "DecimalLiteral"]], "Column"], _bin_op("multiply")
)
__rdiv__ = _reverse_op("divide")
__rtruediv__ = _reverse_op("divide")
__rmod__ = _reverse_op("mod")
__pow__ = _bin_func_op("pow")
__rpow__ = cast(
Callable[["Column", Union["LiteralType", "DecimalLiteral"]], "Column"],
_bin_func_op("pow", reverse=True),
)
# logistic operators
def __eq__( # type: ignore[override]
self,
other: Union["Column", "LiteralType", "DecimalLiteral", "DateTimeLiteral"],
) -> "Column":
"""binary function"""
return _bin_op("equalTo")(self, other)
def __ne__( # type: ignore[override]
self,
other: Any,
) -> "Column":
"""binary function"""
return _bin_op("notEqual")(self, other)
__lt__ = _bin_op("lt")
__le__ = _bin_op("leq")
__ge__ = _bin_op("geq")
__gt__ = _bin_op("gt")
_eqNullSafe_doc = """
Equality test that is safe for null values.
.. versionadded:: 2.3.0
Parameters
----------
other
a value or :class:`Column`
Examples
--------
>>> from pyspark.sql import Row
>>> df1 = spark.createDataFrame([
... Row(id=1, value='foo'),
... Row(id=2, value=None)
... ])
>>> df1.select(
... df1['value'] == 'foo',
... df1['value'].eqNullSafe('foo'),
... df1['value'].eqNullSafe(None)
... ).show()
+-------------+---------------+----------------+
|(value = foo)|(value <=> foo)|(value <=> NULL)|
+-------------+---------------+----------------+
| true| true| false|
| null| false| true|
+-------------+---------------+----------------+
>>> df2 = spark.createDataFrame([
... Row(value = 'bar'),
... Row(value = None)
... ])
>>> df1.join(df2, df1["value"] == df2["value"]).count()
0
>>> df1.join(df2, df1["value"].eqNullSafe(df2["value"])).count()
1
>>> df2 = spark.createDataFrame([
... Row(id=1, value=float('NaN')),
... Row(id=2, value=42.0),
... Row(id=3, value=None)
... ])
>>> df2.select(
... df2['value'].eqNullSafe(None),
... df2['value'].eqNullSafe(float('NaN')),
... df2['value'].eqNullSafe(42.0)
... ).show()
+----------------+---------------+----------------+
|(value <=> NULL)|(value <=> NaN)|(value <=> 42.0)|
+----------------+---------------+----------------+
| false| true| false|
| false| false| true|
| true| false| false|
+----------------+---------------+----------------+
Notes
-----
Unlike Pandas, PySpark doesn't consider NaN values to be NULL. See the
`NaN Semantics <https://spark.apache.org/docs/latest/sql-ref-datatypes.html#nan-semantics>`_
for details.
"""
eqNullSafe = _bin_op("eqNullSafe", _eqNullSafe_doc)
# `and`, `or`, `not` cannot be overloaded in Python,
# so use bitwise operators as boolean operators
__and__ = _bin_op("and")
__or__ = _bin_op("or")
__invert__ = _func_op("not")
__rand__ = _bin_op("and")
__ror__ = _bin_op("or")
# container operators
def __contains__(self, item: Any) -> None:
raise ValueError(
"Cannot apply 'in' operator against a column: please use 'contains' "
"in a string column or 'array_contains' function for an array column."
)
# bitwise operators
_bitwiseOR_doc = """
Compute bitwise OR of this expression with another expression.
Parameters
----------
other
a value or :class:`Column` to calculate bitwise or(|) with
this :class:`Column`.
Examples
--------
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([Row(a=170, b=75)])
>>> df.select(df.a.bitwiseOR(df.b)).collect()
[Row((a | b)=235)]
"""
_bitwiseAND_doc = """
Compute bitwise AND of this expression with another expression.
Parameters
----------
other
a value or :class:`Column` to calculate bitwise and(&) with
this :class:`Column`.
Examples
--------
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([Row(a=170, b=75)])
>>> df.select(df.a.bitwiseAND(df.b)).collect()
[Row((a & b)=10)]
"""
_bitwiseXOR_doc = """
Compute bitwise XOR of this expression with another expression.
Parameters
----------
other
a value or :class:`Column` to calculate bitwise xor(^) with
this :class:`Column`.
Examples
--------
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([Row(a=170, b=75)])
>>> df.select(df.a.bitwiseXOR(df.b)).collect()
[Row((a ^ b)=225)]
"""
bitwiseOR = _bin_op("bitwiseOR", _bitwiseOR_doc)
bitwiseAND = _bin_op("bitwiseAND", _bitwiseAND_doc)
bitwiseXOR = _bin_op("bitwiseXOR", _bitwiseXOR_doc)
def getItem(self, key: Any) -> "Column":
"""
An expression that gets an item at position ``ordinal`` out of a list,
or gets an item by key out of a dict.
.. versionadded:: 1.3.0
Examples
--------
>>> df = spark.createDataFrame([([1, 2], {"key": "value"})], ["l", "d"])
>>> df.select(df.l.getItem(0), df.d.getItem("key")).show()
+----+------+
|l[0]|d[key]|
+----+------+
| 1| value|
+----+------+
"""
if isinstance(key, Column):
warnings.warn(
"A column as 'key' in getItem is deprecated as of Spark 3.0, and will not "
"be supported in the future release. Use `column[key]` or `column.key` syntax "
"instead.",
FutureWarning,
)
return self[key]
def getField(self, name: Any) -> "Column":
"""
An expression that gets a field by name in a :class:`StructType`.
.. versionadded:: 1.3.0
Examples
--------
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([Row(r=Row(a=1, b="b"))])
>>> df.select(df.r.getField("b")).show()
+---+
|r.b|
+---+
| b|
+---+
>>> df.select(df.r.a).show()
+---+
|r.a|
+---+
| 1|
+---+
"""
if isinstance(name, Column):
warnings.warn(
"A column as 'name' in getField is deprecated as of Spark 3.0, and will not "
"be supported in the future release. Use `column[name]` or `column.name` syntax "
"instead.",
FutureWarning,
)
return self[name]
def withField(self, fieldName: str, col: "Column") -> "Column":
"""
An expression that adds/replaces a field in :class:`StructType` by name.
.. versionadded:: 3.1.0
Examples
--------
>>> from pyspark.sql import Row
>>> from pyspark.sql.functions import lit
>>> df = spark.createDataFrame([Row(a=Row(b=1, c=2))])
>>> df.withColumn('a', df['a'].withField('b', lit(3))).select('a.b').show()
+---+
| b|
+---+
| 3|
+---+
>>> df.withColumn('a', df['a'].withField('d', lit(4))).select('a.d').show()
+---+
| d|
+---+
| 4|
+---+
"""
if not isinstance(fieldName, str):
raise TypeError("fieldName should be a string")
if not isinstance(col, Column):
raise TypeError("col should be a Column")
return Column(self._jc.withField(fieldName, col._jc))
def dropFields(self, *fieldNames: str) -> "Column":
"""
An expression that drops fields in :class:`StructType` by name.
This is a no-op if schema doesn't contain field name(s).
.. versionadded:: 3.1.0
Examples
--------
>>> from pyspark.sql import Row
>>> from pyspark.sql.functions import col, lit
>>> df = spark.createDataFrame([
... Row(a=Row(b=1, c=2, d=3, e=Row(f=4, g=5, h=6)))])
>>> df.withColumn('a', df['a'].dropFields('b')).show()
+-----------------+
| a|
+-----------------+
|{2, 3, {4, 5, 6}}|
+-----------------+
>>> df.withColumn('a', df['a'].dropFields('b', 'c')).show()
+--------------+
| a|
+--------------+
|{3, {4, 5, 6}}|
+--------------+
This method supports dropping multiple nested fields directly e.g.
>>> df.withColumn("a", col("a").dropFields("e.g", "e.h")).show()
+--------------+
| a|
+--------------+
|{1, 2, 3, {4}}|
+--------------+
However, if you are going to add/replace multiple nested fields,
it is preferred to extract out the nested struct before
adding/replacing multiple fields e.g.
>>> df.select(col("a").withField(
... "e", col("a.e").dropFields("g", "h")).alias("a")
... ).show()
+--------------+
| a|
+--------------+
|{1, 2, 3, {4}}|
+--------------+
"""
sc = SparkContext._active_spark_context
assert sc is not None
jc = self._jc.dropFields(_to_seq(sc, fieldNames))
return Column(jc)
def __getattr__(self, item: Any) -> "Column":
if item.startswith("__"):
raise AttributeError(item)
return self[item]
def __getitem__(self, k: Any) -> "Column":
if isinstance(k, slice):
if k.step is not None:
raise ValueError("slice with step is not supported.")
return self.substr(k.start, k.stop)
else:
return _bin_op("apply")(self, k)
def __iter__(self) -> None:
raise TypeError("Column is not iterable")
# string methods
_contains_doc = """
Contains the other element. Returns a boolean :class:`Column` based on a string match.
Parameters
----------
other
string in line. A value as a literal or a :class:`Column`.
Examples
--------
>>> df.filter(df.name.contains('o')).collect()
[Row(age=5, name='Bob')]
"""
_rlike_doc = """
SQL RLIKE expression (LIKE with Regex). Returns a boolean :class:`Column` based on a regex
match.
Parameters
----------
other : str
an extended regex expression
Examples
--------
>>> df.filter(df.name.rlike('ice$')).collect()
[Row(age=2, name='Alice')]
"""
_like_doc = """
SQL like expression. Returns a boolean :class:`Column` based on a SQL LIKE match.
Parameters
----------
other : str
a SQL LIKE pattern
See Also
--------
pyspark.sql.Column.rlike
Examples
--------
>>> df.filter(df.name.like('Al%')).collect()
[Row(age=2, name='Alice')]
"""
_ilike_doc = """
SQL ILIKE expression (case insensitive LIKE). Returns a boolean :class:`Column`
based on a case insensitive match.
.. versionadded:: 3.3.0
Parameters
----------
other : str
a SQL LIKE pattern
See Also
--------
pyspark.sql.Column.rlike
Examples
--------
>>> df.filter(df.name.ilike('%Ice')).collect()
[Row(age=2, name='Alice')]
"""
_startswith_doc = """
String starts with. Returns a boolean :class:`Column` based on a string match.
Parameters
----------
other : :class:`Column` or str
string at start of line (do not use a regex `^`)
Examples
--------
>>> df.filter(df.name.startswith('Al')).collect()
[Row(age=2, name='Alice')]
>>> df.filter(df.name.startswith('^Al')).collect()
[]
"""
_endswith_doc = """
String ends with. Returns a boolean :class:`Column` based on a string match.
Parameters
----------
other : :class:`Column` or str
string at end of line (do not use a regex `$`)
Examples
--------
>>> df.filter(df.name.endswith('ice')).collect()
[Row(age=2, name='Alice')]
>>> df.filter(df.name.endswith('ice$')).collect()
[]
"""
contains = _bin_op("contains", _contains_doc)
rlike = _bin_op("rlike", _rlike_doc)
like = _bin_op("like", _like_doc)
ilike = _bin_op("ilike", _ilike_doc)
startswith = _bin_op("startsWith", _startswith_doc)
endswith = _bin_op("endsWith", _endswith_doc)
@overload
def substr(self, startPos: int, length: int) -> "Column":
...
@overload
def substr(self, startPos: "Column", length: "Column") -> "Column":
...
def substr(self, startPos: Union[int, "Column"], length: Union[int, "Column"]) -> "Column":
"""
Return a :class:`Column` which is a substring of the column.
.. versionadded:: 1.3.0
Parameters
----------
startPos : :class:`Column` or int
start position
length : :class:`Column` or int
length of the substring
Examples
--------
>>> df.select(df.name.substr(1, 3).alias("col")).collect()
[Row(col='Ali'), Row(col='Bob')]
"""
if type(startPos) != type(length):
raise TypeError(
"startPos and length must be the same type. "
"Got {startPos_t} and {length_t}, respectively.".format(
startPos_t=type(startPos),
length_t=type(length),
)
)
if isinstance(startPos, int):
jc = self._jc.substr(startPos, length)
elif isinstance(startPos, Column):
jc = self._jc.substr(startPos._jc, cast("Column", length)._jc)
else:
raise TypeError("Unexpected type: %s" % type(startPos))
return Column(jc)
def isin(self, *cols: Any) -> "Column":
"""
A boolean expression that is evaluated to true if the value of this
expression is contained by the evaluated values of the arguments.
.. versionadded:: 1.5.0
Examples
--------
>>> df[df.name.isin("Bob", "Mike")].collect()
[Row(age=5, name='Bob')]
>>> df[df.age.isin([1, 2, 3])].collect()
[Row(age=2, name='Alice')]
"""
if len(cols) == 1 and isinstance(cols[0], (list, set)):
cols = cast(Tuple, cols[0])
cols = cast(
Tuple,
[c._jc if isinstance(c, Column) else _create_column_from_literal(c) for c in cols],
)
sc = SparkContext._active_spark_context
assert sc is not None
jc = getattr(self._jc, "isin")(_to_seq(sc, cols))
return Column(jc)
# order
_asc_doc = """
Returns a sort expression based on ascending order of the column.
Examples
--------
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([('Tom', 80), ('Alice', None)], ["name", "height"])
>>> df.select(df.name).orderBy(df.name.asc()).collect()
[Row(name='Alice'), Row(name='Tom')]
"""
_asc_nulls_first_doc = """
Returns a sort expression based on ascending order of the column, and null values
return before non-null values.
.. versionadded:: 2.4.0
Examples
--------
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([('Tom', 80), (None, 60), ('Alice', None)], ["name", "height"])
>>> df.select(df.name).orderBy(df.name.asc_nulls_first()).collect()
[Row(name=None), Row(name='Alice'), Row(name='Tom')]
"""
_asc_nulls_last_doc = """
Returns a sort expression based on ascending order of the column, and null values
appear after non-null values.
.. versionadded:: 2.4.0
Examples
--------
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([('Tom', 80), (None, 60), ('Alice', None)], ["name", "height"])
>>> df.select(df.name).orderBy(df.name.asc_nulls_last()).collect()
[Row(name='Alice'), Row(name='Tom'), Row(name=None)]
"""
_desc_doc = """
Returns a sort expression based on the descending order of the column.
.. versionadded:: 2.4.0
Examples
--------
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([('Tom', 80), ('Alice', None)], ["name", "height"])
>>> df.select(df.name).orderBy(df.name.desc()).collect()
[Row(name='Tom'), Row(name='Alice')]
"""
_desc_nulls_first_doc = """
Returns a sort expression based on the descending order of the column, and null values
appear before non-null values.
.. versionadded:: 2.4.0
Examples
--------
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([('Tom', 80), (None, 60), ('Alice', None)], ["name", "height"])
>>> df.select(df.name).orderBy(df.name.desc_nulls_first()).collect()
[Row(name=None), Row(name='Tom'), Row(name='Alice')]
"""
_desc_nulls_last_doc = """
Returns a sort expression based on the descending order of the column, and null values
appear after non-null values.
.. versionadded:: 2.4.0
Examples
--------
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([('Tom', 80), (None, 60), ('Alice', None)], ["name", "height"])
>>> df.select(df.name).orderBy(df.name.desc_nulls_last()).collect()
[Row(name='Tom'), Row(name='Alice'), Row(name=None)]
"""
asc = _unary_op("asc", _asc_doc)
asc_nulls_first = _unary_op("asc_nulls_first", _asc_nulls_first_doc)
asc_nulls_last = _unary_op("asc_nulls_last", _asc_nulls_last_doc)
desc = _unary_op("desc", _desc_doc)
desc_nulls_first = _unary_op("desc_nulls_first", _desc_nulls_first_doc)
desc_nulls_last = _unary_op("desc_nulls_last", _desc_nulls_last_doc)
_isNull_doc = """
True if the current expression is null.
Examples
--------
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([Row(name='Tom', height=80), Row(name='Alice', height=None)])
>>> df.filter(df.height.isNull()).collect()
[Row(name='Alice', height=None)]
"""
_isNotNull_doc = """
True if the current expression is NOT null.
Examples
--------
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([Row(name='Tom', height=80), Row(name='Alice', height=None)])
>>> df.filter(df.height.isNotNull()).collect()
[Row(name='Tom', height=80)]
"""
isNull = _unary_op("isNull", _isNull_doc)
isNotNull = _unary_op("isNotNull", _isNotNull_doc)
def alias(self, *alias: str, **kwargs: Any) -> "Column":
"""
Returns this column aliased with a new name or names (in the case of expressions that
return more than one column, such as explode).
.. versionadded:: 1.3.0
Parameters
----------
alias : str
desired column names (collects all positional arguments passed)
Other Parameters
----------------
metadata: dict
a dict of information to be stored in ``metadata`` attribute of the
corresponding :class:`StructField <pyspark.sql.types.StructField>` (optional, keyword
only argument)
.. versionchanged:: 2.2.0
Added optional ``metadata`` argument.
Examples
--------
>>> df.select(df.age.alias("age2")).collect()
[Row(age2=2), Row(age2=5)]
>>> df.select(df.age.alias("age3", metadata={'max': 99})).schema['age3'].metadata['max']
99
"""
metadata = kwargs.pop("metadata", None)
assert not kwargs, "Unexpected kwargs where passed: %s" % kwargs
sc = SparkContext._active_spark_context
assert sc is not None
if len(alias) == 1:
if metadata:
assert sc._jvm is not None
jmeta = sc._jvm.org.apache.spark.sql.types.Metadata.fromJson(json.dumps(metadata))
return Column(getattr(self._jc, "as")(alias[0], jmeta))
else:
return Column(getattr(self._jc, "as")(alias[0]))
else:
if metadata:
raise ValueError("metadata can only be provided for a single column")
return Column(getattr(self._jc, "as")(_to_seq(sc, list(alias))))
name = copy_func(alias, sinceversion=2.0, doc=":func:`name` is an alias for :func:`alias`.")
def cast(self, dataType: Union[DataType, str]) -> "Column":
"""
Casts the column into type ``dataType``.
.. versionadded:: 1.3.0
Examples
--------
>>> df.select(df.age.cast("string").alias('ages')).collect()
[Row(ages='2'), Row(ages='5')]
>>> df.select(df.age.cast(StringType()).alias('ages')).collect()
[Row(ages='2'), Row(ages='5')]
"""
if isinstance(dataType, str):
jc = self._jc.cast(dataType)
elif isinstance(dataType, DataType):
from pyspark.sql import SparkSession
spark = SparkSession._getActiveSessionOrCreate()
jdt = spark._jsparkSession.parseDataType(dataType.json())
jc = self._jc.cast(jdt)
else:
raise TypeError("unexpected type: %s" % type(dataType))
return Column(jc)
astype = copy_func(cast, sinceversion=1.4, doc=":func:`astype` is an alias for :func:`cast`.")
def between(
self,
lowerBound: Union["Column", "LiteralType", "DateTimeLiteral", "DecimalLiteral"],
upperBound: Union["Column", "LiteralType", "DateTimeLiteral", "DecimalLiteral"],
) -> "Column":
"""
True if the current column is between the lower bound and upper bound, inclusive.
.. versionadded:: 1.3.0
Examples
--------
>>> df.select(df.name, df.age.between(2, 4)).show()
+-----+---------------------------+
| name|((age >= 2) AND (age <= 4))|
+-----+---------------------------+
|Alice| true|
| Bob| false|
+-----+---------------------------+
"""
return (self >= lowerBound) & (self <= upperBound)
def when(self, condition: "Column", value: Any) -> "Column":
"""
Evaluates a list of conditions and returns one of multiple possible result expressions.
If :func:`Column.otherwise` is not invoked, None is returned for unmatched conditions.
.. versionadded:: 1.4.0
Parameters
----------
condition : :class:`Column`
a boolean :class:`Column` expression.
value
a literal value, or a :class:`Column` expression.
Examples
--------
>>> from pyspark.sql import functions as F
>>> df.select(df.name, F.when(df.age > 4, 1).when(df.age < 3, -1).otherwise(0)).show()
+-----+------------------------------------------------------------+
| name|CASE WHEN (age > 4) THEN 1 WHEN (age < 3) THEN -1 ELSE 0 END|
+-----+------------------------------------------------------------+
|Alice| -1|
| Bob| 1|
+-----+------------------------------------------------------------+
See Also
--------
pyspark.sql.functions.when
"""
if not isinstance(condition, Column):
raise TypeError("condition should be a Column")
v = value._jc if isinstance(value, Column) else value
jc = self._jc.when(condition._jc, v)
return Column(jc)
def otherwise(self, value: Any) -> "Column":
"""
Evaluates a list of conditions and returns one of multiple possible result expressions.
If :func:`Column.otherwise` is not invoked, None is returned for unmatched conditions.
.. versionadded:: 1.4.0
Parameters
----------
value
a literal value, or a :class:`Column` expression.
Examples
--------
>>> from pyspark.sql import functions as F
>>> df.select(df.name, F.when(df.age > 3, 1).otherwise(0)).show()
+-----+-------------------------------------+
| name|CASE WHEN (age > 3) THEN 1 ELSE 0 END|
+-----+-------------------------------------+
|Alice| 0|
| Bob| 1|
+-----+-------------------------------------+
See Also
--------
pyspark.sql.functions.when
"""
v = value._jc if isinstance(value, Column) else value
jc = self._jc.otherwise(v)
return Column(jc)
def over(self, window: "WindowSpec") -> "Column":
"""
Define a windowing column.
.. versionadded:: 1.4.0
Parameters
----------
window : :class:`WindowSpec`
Returns
-------
:class:`Column`
Examples
--------
>>> from pyspark.sql import Window
>>> window = Window.partitionBy("name").orderBy("age") \
.rowsBetween(Window.unboundedPreceding, Window.currentRow)
>>> from pyspark.sql.functions import rank, min
>>> from pyspark.sql.functions import desc
>>> df.withColumn("rank", rank().over(window)) \
.withColumn("min", min('age').over(window)).sort(desc("age")).show()
+---+-----+----+---+
|age| name|rank|min|
+---+-----+----+---+
| 5| Bob| 1| 5|
| 2|Alice| 1| 2|
+---+-----+----+---+
"""
from pyspark.sql.window import WindowSpec
if not isinstance(window, WindowSpec):
raise TypeError("window should be WindowSpec")
jc = self._jc.over(window._jspec)
return Column(jc)
def __nonzero__(self) -> None:
raise ValueError(
"Cannot convert column into bool: please use '&' for 'and', '|' for 'or', "
"'~' for 'not' when building DataFrame boolean expressions."
)
__bool__ = __nonzero__
def __repr__(self) -> str:
return "Column<'%s'>" % self._jc.toString()
def _test() -> None:
import doctest
from pyspark.sql import SparkSession
import pyspark.sql.column
globs = pyspark.sql.column.__dict__.copy()
spark = SparkSession.builder.master("local[4]").appName("sql.column tests").getOrCreate()
sc = spark.sparkContext
globs["spark"] = spark
globs["df"] = sc.parallelize([(2, "Alice"), (5, "Bob")]).toDF(
StructType([StructField("age", IntegerType()), StructField("name", StringType())])
)
(failure_count, test_count) = doctest.testmod(
pyspark.sql.column,
globs=globs,
optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE | doctest.REPORT_NDIFF,
)
spark.stop()
if failure_count:
sys.exit(-1)
if __name__ == "__main__":
_test()