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[SPARK-16772] Correct API doc references to PySpark classes + formatting fixes

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## What's Been Changed

The PR corrects several broken or missing class references in the Python API docs. It also correct formatting problems.

For example, you can see [here](http://spark.apache.org/docs/2.0.0/api/python/pyspark.sql.html#pyspark.sql.SQLContext.registerFunction) how Sphinx is not picking up the reference to `DataType`. That's because the reference is relative to the current module, whereas `DataType` is in a different module.

You can also see [here](http://spark.apache.org/docs/2.0.0/api/python/pyspark.sql.html#pyspark.sql.SQLContext.createDataFrame) how the formatting for byte, tinyint, and so on is italic instead of monospace. That's because in ReST single backticks just make things italic, unlike in Markdown.

## Testing

I tested this PR by [building the Python docs](https://github.com/apache/spark/tree/master/docs#generating-the-documentation-html) and reviewing the results locally in my browser. I confirmed that the broken or missing class references were resolved, and that the formatting was corrected.

Author: Nicholas Chammas <nicholas.chammas@gmail.com>

Closes #14393 from nchammas/python-docstring-fixes.
This commit is contained in:
Nicholas Chammas 2016-07-28 14:57:15 -07:00 committed by Reynold Xin
parent 3fd39b87bd
commit 274f3b9ec8
8 changed files with 75 additions and 58 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

2
python/pyspark/sql/catalog.py
View file

@ -193,7 +193,7 @@ class Catalog(object):
:param name: name of the UDF
:param f: python function
:param returnType: a :class:`DataType` object
:param returnType: a :class:`pyspark.sql.types.DataType` object
>>> spark.catalog.registerFunction("stringLengthString", lambda x: len(x))
>>> spark.sql("SELECT stringLengthString('test')").collect()

44
python/pyspark/sql/context.py
View file

@ -152,9 +152,9 @@ class SQLContext(object):
@since(1.4)
def range(self, start, end=None, step=1, numPartitions=None):
"""
Create a :class:`DataFrame` with single LongType column named `id`,
containing elements in a range from `start` to `end` (exclusive) with
step value `step`.
Create a :class:`DataFrame` with single :class:`pyspark.sql.types.LongType` column named
``id``, containing elements in a range from ``start`` to ``end`` (exclusive) with
step value ``step``.
:param start: the start value
:param end: the end value (exclusive)
@ -184,7 +184,7 @@ class SQLContext(object):
:param name: name of the UDF
:param f: python function
:param returnType: a :class:`DataType` object
:param returnType: a :class:`pyspark.sql.types.DataType` object
>>> sqlContext.registerFunction("stringLengthString", lambda x: len(x))
>>> sqlContext.sql("SELECT stringLengthString('test')").collect()
@ -209,7 +209,7 @@ class SQLContext(object):
:param rdd: an RDD of Row or tuple
:param samplingRatio: sampling ratio, or no sampling (default)
:return: StructType
:return: :class:`pyspark.sql.types.StructType`
"""
return self.sparkSession._inferSchema(rdd, samplingRatio)
@ -226,28 +226,34 @@ class SQLContext(object):
from ``data``, which should be an RDD of :class:`Row`,
or :class:`namedtuple`, or :class:`dict`.
When ``schema`` is :class:`DataType` or datatype string, it must match the real data, or
exception will be thrown at runtime. If the given schema is not StructType, it will be
wrapped into a StructType as its only field, and the field name will be "value", each record
will also be wrapped into a tuple, which can be converted to row later.
When ``schema`` is :class:`pyspark.sql.types.DataType` or
:class:`pyspark.sql.types.StringType`, it must match the
real data, or an exception will be thrown at runtime. If the given schema is not
:class:`pyspark.sql.types.StructType`, it will be wrapped into a
:class:`pyspark.sql.types.StructType` as its only field, and the field name will be "value",
each record will also be wrapped into a tuple, which can be converted to row later.
If schema inference is needed, ``samplingRatio`` is used to determined the ratio of
rows used for schema inference. The first row will be used if ``samplingRatio`` is ``None``.
:param data: an RDD of any kind of SQL data representation(e.g. row, tuple, int, boolean,
etc.), or :class:`list`, or :class:`pandas.DataFrame`.
:param schema: a :class:`DataType` or a datatype string or a list of column names, default
is None. The data type string format equals to `DataType.simpleString`, except that
top level struct type can omit the `struct<>` and atomic types use `typeName()` as
their format, e.g. use `byte` instead of `tinyint` for ByteType. We can also use `int`
as a short name for IntegerType.
:param data: an RDD of any kind of SQL data representation(e.g. :class:`Row`,
:class:`tuple`, ``int``, ``boolean``, etc.), or :class:`list`, or
:class:`pandas.DataFrame`.
:param schema: a :class:`pyspark.sql.types.DataType` or a
:class:`pyspark.sql.types.StringType` or a list of
column names, default is None. The data type string format equals to
:class:`pyspark.sql.types.DataType.simpleString`, except that top level struct type can
omit the ``struct<>`` and atomic types use ``typeName()`` as their format, e.g. use
``byte`` instead of ``tinyint`` for :class:`pyspark.sql.types.ByteType`.
We can also use ``int`` as a short name for :class:`pyspark.sql.types.IntegerType`.
:param samplingRatio: the sample ratio of rows used for inferring
:return: :class:`DataFrame`
.. versionchanged:: 2.0
The schema parameter can be a DataType or a datatype string after 2.0. If it's not a
StructType, it will be wrapped into a StructType and each record will also be wrapped
into a tuple.
The ``schema`` parameter can be a :class:`pyspark.sql.types.DataType` or a
:class:`pyspark.sql.types.StringType` after 2.0.
If it's not a :class:`pyspark.sql.types.StructType`, it will be wrapped into a
:class:`pyspark.sql.types.StructType` and each record will also be wrapped into a tuple.
>>> l = [('Alice', 1)]
>>> sqlContext.createDataFrame(l).collect()

2
python/pyspark/sql/dataframe.py
View file

@ -196,7 +196,7 @@ class DataFrame(object):
@property
@since(1.3)
def schema(self):
"""Returns the schema of this :class:`DataFrame` as a :class:`types.StructType`.
"""Returns the schema of this :class:`DataFrame` as a :class:`pyspark.sql.types.StructType`.
>>> df.schema
StructType(List(StructField(age,IntegerType,true),StructField(name,StringType,true)))

21
python/pyspark/sql/functions.py
View file

@ -142,7 +142,7 @@ _functions_1_6 = {
_binary_mathfunctions = {
'atan2': 'Returns the angle theta from the conversion of rectangular coordinates (x, y) to' +
'polar coordinates (r, theta).',
'hypot': 'Computes `sqrt(a^2 + b^2)` without intermediate overflow or underflow.',
'hypot': 'Computes ``sqrt(a^2 + b^2)`` without intermediate overflow or underflow.',
'pow': 'Returns the value of the first argument raised to the power of the second argument.',
}
@ -958,7 +958,8 @@ def months_between(date1, date2):
@since(1.5)
def to_date(col):
"""
Converts the column of StringType or TimestampType into DateType.
Converts the column of :class:`pyspark.sql.types.StringType` or
:class:`pyspark.sql.types.TimestampType` into :class:`pyspark.sql.types.DateType`.
>>> df = spark.createDataFrame([('1997-02-28 10:30:00',)], ['t'])
>>> df.select(to_date(df.t).alias('date')).collect()
@ -1074,18 +1075,18 @@ def window(timeColumn, windowDuration, slideDuration=None, startTime=None):
[12:05,12:10) but not in [12:00,12:05). Windows can support microsecond precision. Windows in
the order of months are not supported.
The time column must be of TimestampType.
The time column must be of :class:`pyspark.sql.types.TimestampType`.
Durations are provided as strings, e.g. '1 second', '1 day 12 hours', '2 minutes'. Valid
interval strings are 'week', 'day', 'hour', 'minute', 'second', 'millisecond', 'microsecond'.
If the `slideDuration` is not provided, the windows will be tumbling windows.
If the ``slideDuration`` is not provided, the windows will be tumbling windows.
The startTime is the offset with respect to 1970-01-01 00:00:00 UTC with which to start
window intervals. For example, in order to have hourly tumbling windows that start 15 minutes
past the hour, e.g. 12:15-13:15, 13:15-14:15... provide `startTime` as `15 minutes`.
The output column will be a struct called 'window' by default with the nested columns 'start'
and 'end', where 'start' and 'end' will be of `TimestampType`.
and 'end', where 'start' and 'end' will be of :class:`pyspark.sql.types.TimestampType`.
>>> df = spark.createDataFrame([("2016-03-11 09:00:07", 1)]).toDF("date", "val")
>>> w = df.groupBy(window("date", "5 seconds")).agg(sum("val").alias("sum"))
@ -1367,7 +1368,7 @@ def locate(substr, str, pos=1):
could not be found in str.
:param substr: a string
:param str: a Column of StringType
:param str: a Column of :class:`pyspark.sql.types.StringType`
:param pos: start position (zero based)
>>> df = spark.createDataFrame([('abcd',)], ['s',])
@ -1506,8 +1507,9 @@ def bin(col):
@ignore_unicode_prefix
@since(1.5)
def hex(col):
"""Computes hex value of the given column, which could be StringType,
BinaryType, IntegerType or LongType.
"""Computes hex value of the given column, which could be :class:`pyspark.sql.types.StringType`,
:class:`pyspark.sql.types.BinaryType`, :class:`pyspark.sql.types.IntegerType` or
:class:`pyspark.sql.types.LongType`.
>>> spark.createDataFrame([('ABC', 3)], ['a', 'b']).select(hex('a'), hex('b')).collect()
[Row(hex(a)=u'414243', hex(b)=u'3')]
@ -1781,6 +1783,9 @@ def udf(f, returnType=StringType()):
duplicate invocations may be eliminated or the function may even be invoked more times than
it is present in the query.
:param f: python function
:param returnType: a :class:`pyspark.sql.types.DataType` object
>>> from pyspark.sql.types import IntegerType
>>> slen = udf(lambda s: len(s), IntegerType())
>>> df.select(slen(df.name).alias('slen')).collect()

8
python/pyspark/sql/readwriter.py
View file

@ -96,7 +96,7 @@ class DataFrameReader(OptionUtils):
By specifying the schema here, the underlying data source can skip the schema
inference step, and thus speed up data loading.
:param schema: a StructType object
:param schema: a :class:`pyspark.sql.types.StructType` object
"""
if not isinstance(schema, StructType):
raise TypeError("schema should be StructType")
@ -125,7 +125,7 @@ class DataFrameReader(OptionUtils):
:param path: optional string or a list of string for file-system backed data sources.
:param format: optional string for format of the data source. Default to 'parquet'.
:param schema: optional :class:`StructType` for the input schema.
:param schema: optional :class:`pyspark.sql.types.StructType` for the input schema.
:param options: all other string options
>>> df = spark.read.load('python/test_support/sql/parquet_partitioned', opt1=True,
@ -166,7 +166,7 @@ class DataFrameReader(OptionUtils):
:param path: string represents path to the JSON dataset,
or RDD of Strings storing JSON objects.
:param schema: an optional :class:`StructType` for the input schema.
:param schema: an optional :class:`pyspark.sql.types.StructType` for the input schema.
:param primitivesAsString: infers all primitive values as a string type. If None is set,
it uses the default value, ``false``.
:param prefersDecimal: infers all floating-point values as a decimal type. If the values
@ -294,7 +294,7 @@ class DataFrameReader(OptionUtils):
``inferSchema`` option or specify the schema explicitly using ``schema``.
:param path: string, or list of strings, for input path(s).
:param schema: an optional :class:`StructType` for the input schema.
:param schema: an optional :class:`pyspark.sql.types.StructType` for the input schema.
:param sep: sets the single character as a separator for each field and value.
If None is set, it uses the default value, ``,``.
:param encoding: decodes the CSV files by the given encoding type. If None is set,

41
python/pyspark/sql/session.py
View file

@ -47,7 +47,7 @@ def _monkey_patch_RDD(sparkSession):
This is a shorthand for ``spark.createDataFrame(rdd, schema, sampleRatio)``
:param schema: a StructType or list of names of columns
:param schema: a :class:`pyspark.sql.types.StructType` or list of names of columns
:param samplingRatio: the sample ratio of rows used for inferring
:return: a DataFrame
@ -274,9 +274,9 @@ class SparkSession(object):
@since(2.0)
def range(self, start, end=None, step=1, numPartitions=None):
"""
Create a :class:`DataFrame` with single LongType column named `id`,
containing elements in a range from `start` to `end` (exclusive) with
step value `step`.
Create a :class:`DataFrame` with single :class:`pyspark.sql.types.LongType` column named
``id``, containing elements in a range from ``start`` to ``end`` (exclusive) with
step value ``step``.
:param start: the start value
:param end: the end value (exclusive)
@ -307,7 +307,7 @@ class SparkSession(object):
Infer schema from list of Row or tuple.
:param data: list of Row or tuple
:return: StructType
:return: :class:`pyspark.sql.types.StructType`
"""
if not data:
raise ValueError("can not infer schema from empty dataset")
@ -326,7 +326,7 @@ class SparkSession(object):
:param rdd: an RDD of Row or tuple
:param samplingRatio: sampling ratio, or no sampling (default)
:return: StructType
:return: :class:`pyspark.sql.types.StructType`
"""
first = rdd.first()
if not first:
@ -414,28 +414,33 @@ class SparkSession(object):
from ``data``, which should be an RDD of :class:`Row`,
or :class:`namedtuple`, or :class:`dict`.
When ``schema`` is :class:`DataType` or datatype string, it must match the real data, or
exception will be thrown at runtime. If the given schema is not StructType, it will be
wrapped into a StructType as its only field, and the field name will be "value", each record
will also be wrapped into a tuple, which can be converted to row later.
When ``schema`` is :class:`pyspark.sql.types.DataType` or
:class:`pyspark.sql.types.StringType`, it must match the
real data, or an exception will be thrown at runtime. If the given schema is not
:class:`pyspark.sql.types.StructType`, it will be wrapped into a
:class:`pyspark.sql.types.StructType` as its only field, and the field name will be "value",
each record will also be wrapped into a tuple, which can be converted to row later.
If schema inference is needed, ``samplingRatio`` is used to determined the ratio of
rows used for schema inference. The first row will be used if ``samplingRatio`` is ``None``.
:param data: an RDD of any kind of SQL data representation(e.g. row, tuple, int, boolean,
etc.), or :class:`list`, or :class:`pandas.DataFrame`.
:param schema: a :class:`DataType` or a datatype string or a list of column names, default
is None. The data type string format equals to `DataType.simpleString`, except that
top level struct type can omit the `struct<>` and atomic types use `typeName()` as
their format, e.g. use `byte` instead of `tinyint` for ByteType. We can also use `int`
as a short name for IntegerType.
:param schema: a :class:`pyspark.sql.types.DataType` or a
:class:`pyspark.sql.types.StringType` or a list of
column names, default is ``None``. The data type string format equals to
:class:`pyspark.sql.types.DataType.simpleString`, except that top level struct type can
omit the ``struct<>`` and atomic types use ``typeName()`` as their format, e.g. use
``byte`` instead of ``tinyint`` for :class:`pyspark.sql.types.ByteType`. We can also use
``int`` as a short name for ``IntegerType``.
:param samplingRatio: the sample ratio of rows used for inferring
:return: :class:`DataFrame`
.. versionchanged:: 2.0
The schema parameter can be a DataType or a datatype string after 2.0. If it's not a
StructType, it will be wrapped into a StructType and each record will also be wrapped
into a tuple.
The ``schema`` parameter can be a :class:`pyspark.sql.types.DataType` or a
:class:`pyspark.sql.types.StringType` after 2.0. If it's not a
:class:`pyspark.sql.types.StructType`, it will be wrapped into a
:class:`pyspark.sql.types.StructType` and each record will also be wrapped into a tuple.
>>> l = [('Alice', 1)]
>>> spark.createDataFrame(l).collect()

8
python/pyspark/sql/streaming.py
View file

@ -269,7 +269,7 @@ class DataStreamReader(OptionUtils):
.. note:: Experimental.
:param schema: a StructType object
:param schema: a :class:`pyspark.sql.types.StructType` object
>>> s = spark.readStream.schema(sdf_schema)
"""
@ -310,7 +310,7 @@ class DataStreamReader(OptionUtils):
:param path: optional string for file-system backed data sources.
:param format: optional string for format of the data source. Default to 'parquet'.
:param schema: optional :class:`StructType` for the input schema.
:param schema: optional :class:`pyspark.sql.types.StructType` for the input schema.
:param options: all other string options
>>> json_sdf = spark.readStream.format("json")\
@ -349,7 +349,7 @@ class DataStreamReader(OptionUtils):
:param path: string represents path to the JSON dataset,
or RDD of Strings storing JSON objects.
:param schema: an optional :class:`StructType` for the input schema.
:param schema: an optional :class:`pyspark.sql.types.StructType` for the input schema.
:param primitivesAsString: infers all primitive values as a string type. If None is set,
it uses the default value, ``false``.
:param prefersDecimal: infers all floating-point values as a decimal type. If the values
@ -461,7 +461,7 @@ class DataStreamReader(OptionUtils):
.. note:: Experimental.
:param path: string, or list of strings, for input path(s).
:param schema: an optional :class:`StructType` for the input schema.
:param schema: an optional :class:`pyspark.sql.types.StructType` for the input schema.
:param sep: sets the single character as a separator for each field and value.
If None is set, it uses the default value, ``,``.
:param encoding: decodes the CSV files by the given encoding type. If None is set,

7
python/pyspark/sql/types.py
View file

@ -786,9 +786,10 @@ def _parse_struct_fields_string(s):
def _parse_datatype_string(s):
"""
Parses the given data type string to a :class:`DataType`. The data type string format equals
to `DataType.simpleString`, except that top level struct type can omit the `struct<>` and
atomic types use `typeName()` as their format, e.g. use `byte` instead of `tinyint` for
ByteType. We can also use `int` as a short name for IntegerType.
to :class:`DataType.simpleString`, except that top level struct type can omit
the ``struct<>`` and atomic types use ``typeName()`` as their format, e.g. use ``byte`` instead
of ``tinyint`` for :class:`ByteType`. We can also use ``int`` as a short name
for :class:`IntegerType`.
>>> _parse_datatype_string("int ")
IntegerType