[SPARK-16772][PYTHON][DOCS] Restore "datatype string" to Python API docstrings

## What changes were proposed in this pull request?

This PR corrects [an error made in an earlier PR](https://github.com/apache/spark/pull/14393/files#r72843069).

## How was this patch tested?

```sh
$ ./dev/lint-python
PEP8 checks passed.
rm -rf _build/*
pydoc checks passed.
```

I also built the docs and confirmed that they looked good in my browser.

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

Closes #14408 from nchammas/SPARK-16772.

python/pyspark/sql/context.py

@@ -226,9 +226,8 @@ class SQLContext(object):
         from ``data``, which should be an RDD of :class:`Row`,
         or :class:`namedtuple`, or :class:`dict`.
 
-        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
+        When ``schema`` is :class:`pyspark.sql.types.DataType` or a datatype string 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.
@@ -239,8 +238,7 @@ class SQLContext(object):
         :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
+        :param schema: a :class:`pyspark.sql.types.DataType` or a datatype string 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
@@ -251,7 +249,7 @@ class SQLContext(object):
 
         .. versionchanged:: 2.0
            The ``schema`` parameter can be a :class:`pyspark.sql.types.DataType` or a
-           :class:`pyspark.sql.types.StringType` after 2.0.
+           datatype string 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.
 

python/pyspark/sql/session.py

@@ -414,9 +414,8 @@ class SparkSession(object):
         from ``data``, which should be an RDD of :class:`Row`,
         or :class:`namedtuple`, or :class:`dict`.
 
-        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
+        When ``schema`` is :class:`pyspark.sql.types.DataType` or a datatype string, 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.
@@ -426,8 +425,7 @@ class SparkSession(object):
 
         :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:`pyspark.sql.types.DataType` or a
-            :class:`pyspark.sql.types.StringType` or a list of
+        :param schema: a :class:`pyspark.sql.types.DataType` or a datatype string 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
@@ -438,7 +436,7 @@ class SparkSession(object):
 
         .. versionchanged:: 2.0
            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
+           datatype string 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.