[SPARK-8060] Improve DataFrame Python test coverage and documentation.

Author: Reynold Xin <rxin@databricks.com>

Closes #6601 from rxin/python-read-write-test-and-doc and squashes the following commits:

baa8ad5 [Reynold Xin] Code review feedback.
f081d47 [Reynold Xin] More documentation updates.
c9902fa [Reynold Xin] [SPARK-8060] Improve DataFrame Python reader/writer interface doc and testing.

1 files changed, 37 insertions, 52 deletions

diff --git a/python/pyspark/sql/context.py b/python/pyspark/sql/context.py
index 22f6257dfe..9fdf43c3e6 100644
--- a/python/pyspark/sql/context.py
+++ b/python/pyspark/sql/context.py
@@ -124,7 +124,10 @@ class SQLContext(object):
     @property
     @since("1.3.1")
     def udf(self):
-        """Returns a :class:`UDFRegistration` for UDF registration."""
+        """Returns a :class:`UDFRegistration` for UDF registration.
+
+        :return: :class:`UDFRegistration`
+        """
         return UDFRegistration(self)
 
     @since(1.4)
@@ -138,7 +141,7 @@ class SQLContext(object):
         :param end: the end value (exclusive)
         :param step: the incremental step (default: 1)
         :param numPartitions: the number of partitions of the DataFrame
-        :return: A new DataFrame
+        :return: :class:`DataFrame`
 
         >>> sqlContext.range(1, 7, 2).collect()
         [Row(id=1), Row(id=3), Row(id=5)]
@@ -195,8 +198,8 @@ class SQLContext(object):
             raise ValueError("The first row in RDD is empty, "
                              "can not infer schema")
         if type(first) is dict:
-            warnings.warn("Using RDD of dict to inferSchema is deprecated,"
-                          "please use pyspark.sql.Row instead")
+            warnings.warn("Using RDD of dict to inferSchema is deprecated. "
+                          "Use pyspark.sql.Row instead")
 
         if samplingRatio is None:
             schema = _infer_schema(first)
@@ -219,7 +222,7 @@ class SQLContext(object):
         """
         .. note:: Deprecated in 1.3, use :func:`createDataFrame` instead.
         """
-        warnings.warn("inferSchema is deprecated, please use createDataFrame instead")
+        warnings.warn("inferSchema is deprecated, please use createDataFrame instead.")
 
         if isinstance(rdd, DataFrame):
             raise TypeError("Cannot apply schema to DataFrame")
@@ -262,6 +265,7 @@ class SQLContext(object):
             :class:`list`, or :class:`pandas.DataFrame`.
         :param schema: a :class:`StructType` or list of column names. default None.
         :param samplingRatio: the sample ratio of rows used for inferring
+        :return: :class:`DataFrame`
 
         >>> l = [('Alice', 1)]
         >>> sqlContext.createDataFrame(l).collect()
@@ -359,18 +363,15 @@ class SQLContext(object):
         else:
             raise ValueError("Can only register DataFrame as table")
 
-    @since(1.0)
     def parquetFile(self, *paths):
         """Loads a Parquet file, returning the result as a :class:`DataFrame`.
 
-        >>> import tempfile, shutil
-        >>> parquetFile = tempfile.mkdtemp()
-        >>> shutil.rmtree(parquetFile)
-        >>> df.saveAsParquetFile(parquetFile)
-        >>> df2 = sqlContext.parquetFile(parquetFile)
-        >>> sorted(df.collect()) == sorted(df2.collect())
-        True
+        .. note:: Deprecated in 1.4, use :func:`DataFrameReader.parquet` instead.
+
+        >>> sqlContext.parquetFile('python/test_support/sql/parquet_partitioned').dtypes
+        [('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')]
         """
+        warnings.warn("parquetFile is deprecated. Use read.parquet() instead.")
         gateway = self._sc._gateway
         jpaths = gateway.new_array(gateway.jvm.java.lang.String, len(paths))
         for i in range(0, len(paths)):
@@ -378,39 +379,15 @@ class SQLContext(object):
         jdf = self._ssql_ctx.parquetFile(jpaths)
         return DataFrame(jdf, self)
 
-    @since(1.0)
     def jsonFile(self, path, schema=None, samplingRatio=1.0):
         """Loads a text file storing one JSON object per line as a :class:`DataFrame`.
 
-        If the schema is provided, applies the given schema to this JSON dataset.
-        Otherwise, it samples the dataset with ratio ``samplingRatio`` to determine the schema.
-
-        >>> import tempfile, shutil
-        >>> jsonFile = tempfile.mkdtemp()
-        >>> shutil.rmtree(jsonFile)
-        >>> with open(jsonFile, 'w') as f:
-        ...     f.writelines(jsonStrings)
-        >>> df1 = sqlContext.jsonFile(jsonFile)
-        >>> df1.printSchema()
-        root
-         |-- field1: long (nullable = true)
-         |-- field2: string (nullable = true)
-         |-- field3: struct (nullable = true)
-         |    |-- field4: long (nullable = true)
+        .. note:: Deprecated in 1.4, use :func:`DataFrameReader.json` instead.
 
-        >>> from pyspark.sql.types import *
-        >>> schema = StructType([
-        ...     StructField("field2", StringType()),
-        ...     StructField("field3",
-        ...         StructType([StructField("field5", ArrayType(IntegerType()))]))])
-        >>> df2 = sqlContext.jsonFile(jsonFile, schema)
-        >>> df2.printSchema()
-        root
-         |-- field2: string (nullable = true)
-         |-- field3: struct (nullable = true)
-         |    |-- field5: array (nullable = true)
-         |    |    |-- element: integer (containsNull = true)
+        >>> sqlContext.jsonFile('python/test_support/sql/people.json').dtypes
+        [('age', 'bigint'), ('name', 'string')]
         """
+        warnings.warn("jsonFile is deprecated. Use read.json() instead.")
         if schema is None:
             df = self._ssql_ctx.jsonFile(path, samplingRatio)
         else:
@@ -462,21 +439,16 @@ class SQLContext(object):
             df = self._ssql_ctx.jsonRDD(jrdd.rdd(), scala_datatype)
         return DataFrame(df, self)
 
-    @since(1.3)
     def load(self, path=None, source=None, schema=None, **options):
         """Returns the dataset in a data source as a :class:`DataFrame`.
 
-        The data source is specified by the ``source`` and a set of ``options``.
-        If ``source`` is not specified, the default data source configured by
-        ``spark.sql.sources.default`` will be used.
-
-        Optionally, a schema can be provided as the schema of the returned DataFrame.
+        .. note:: Deprecated in 1.4, use :func:`DataFrameReader.load` instead.
         """
+        warnings.warn("load is deprecated. Use read.load() instead.")
         return self.read.load(path, source, schema, **options)
 
     @since(1.3)
-    def createExternalTable(self, tableName, path=None, source=None,
-                            schema=None, **options):
+    def createExternalTable(self, tableName, path=None, source=None, schema=None, **options):
         """Creates an external table based on the dataset in a data source.
 
         It returns the DataFrame associated with the external table.
@@ -487,6 +459,8 @@ class SQLContext(object):
 
         Optionally, a schema can be provided as the schema of the returned :class:`DataFrame` and
         created external table.
+
+        :return: :class:`DataFrame`
         """
         if path is not None:
             options["path"] = path
@@ -508,6 +482,8 @@ class SQLContext(object):
     def sql(self, sqlQuery):
         """Returns a :class:`DataFrame` representing the result of the given query.
 
+        :return: :class:`DataFrame`
+
         >>> sqlContext.registerDataFrameAsTable(df, "table1")
         >>> df2 = sqlContext.sql("SELECT field1 AS f1, field2 as f2 from table1")
         >>> df2.collect()
@@ -519,6 +495,8 @@ class SQLContext(object):
     def table(self, tableName):
         """Returns the specified table as a :class:`DataFrame`.
 
+        :return: :class:`DataFrame`
+
         >>> sqlContext.registerDataFrameAsTable(df, "table1")
         >>> df2 = sqlContext.table("table1")
         >>> sorted(df.collect()) == sorted(df2.collect())
@@ -536,6 +514,9 @@ class SQLContext(object):
         The returned DataFrame has two columns: ``tableName`` and ``isTemporary``
         (a column with :class:`BooleanType` indicating if a table is a temporary one or not).
 
+        :param dbName: string, name of the database to use.
+        :return: :class:`DataFrame`
+
         >>> sqlContext.registerDataFrameAsTable(df, "table1")
         >>> df2 = sqlContext.tables()
         >>> df2.filter("tableName = 'table1'").first()
@@ -550,7 +531,8 @@ class SQLContext(object):
     def tableNames(self, dbName=None):
         """Returns a list of names of tables in the database ``dbName``.
 
-        If ``dbName`` is not specified, the current database will be used.
+        :param dbName: string, name of the database to use. Default to the current database.
+        :return: list of table names, in string
 
         >>> sqlContext.registerDataFrameAsTable(df, "table1")
         >>> "table1" in sqlContext.tableNames()
@@ -585,8 +567,7 @@ class SQLContext(object):
         Returns a :class:`DataFrameReader` that can be used to read data
         in as a :class:`DataFrame`.
 
-        >>> sqlContext.read
-        <pyspark.sql.readwriter.DataFrameReader object at ...>
+        :return: :class:`DataFrameReader`
         """
         return DataFrameReader(self)
 
@@ -644,10 +625,14 @@ class UDFRegistration(object):
 
 
 def _test():
+    import os
     import doctest
     from pyspark.context import SparkContext
     from pyspark.sql import Row, SQLContext
     import pyspark.sql.context
+
+    os.chdir(os.environ["SPARK_HOME"])
+
     globs = pyspark.sql.context.__dict__.copy()
     sc = SparkContext('local[4]', 'PythonTest')
     globs['sc'] = sc