Type annotations to Koalas accessors and Spark accessors

databricks/koalas/accessors.py

@@ -18,7 +18,7 @@
 """
 import inspect
 from distutils.version import LooseVersion
-from typing import Any, Tuple, Union, TYPE_CHECKING
+from typing import Any, Tuple, Union, TYPE_CHECKING, cast
 import types
 
 import numpy as np  # noqa: F401
@@ -185,7 +185,7 @@ def attach_id_column(self, id_type: str, column: Union[Any, Tuple]) -> "DataFram
             ).resolved_copy
         )
 
-    def apply_batch(self, func, args=(), **kwds):
+    def apply_batch(self, func, args=(), **kwds) -> "DataFrame":
         """
         Apply a function that takes pandas DataFrame and outputs pandas DataFrame. The pandas
         DataFrame given to the function is of a batch used internally.
@@ -330,7 +330,7 @@ def apply_batch(self, func, args=(), **kwds):
         original_func = func
         func = lambda o: original_func(o, *args, **kwds)
 
-        self_applied = DataFrame(self._kdf._internal.resolved_copy)
+        self_applied = DataFrame(self._kdf._internal.resolved_copy)  # type: DataFrame
 
         if should_infer_schema:
             # Here we execute with the first 1000 to get the return type.
@@ -343,7 +343,7 @@ def apply_batch(self, func, args=(), **kwds):
                     "The given function should return a frame; however, "
                     "the return type was %s." % type(applied)
                 )
-            kdf = ks.DataFrame(applied)
+            kdf = ks.DataFrame(applied)  # type: DataFrame
             if len(pdf) <= limit:
                 return kdf
 
@@ -389,7 +389,7 @@ def apply_batch(self, func, args=(), **kwds):
 
         return DataFrame(internal)
 
-    def transform_batch(self, func, *args, **kwargs):
+    def transform_batch(self, func, *args, **kwargs) -> Union["DataFrame", "Series"]:
         """
         Transform chunks with a function that takes pandas DataFrame and outputs pandas DataFrame.
         The pandas DataFrame given to the function is of a batch used internally. The length of
@@ -450,7 +450,7 @@ def transform_batch(self, func, *args, **kwargs):
 
         Returns
         -------
-        DataFrame
+        DataFrame or Series
 
         See Also
         --------
@@ -594,12 +594,12 @@ def pandas_frame_func(f):
                 if len(pdf) <= limit:
                     # only do the short cut when it returns a frame to avoid
                     # operations on different dataframes in case of series.
-                    return kdf
+                    return cast(ks.DataFrame, kdf)
 
                 # Force nullability.
                 return_schema = as_nullable_spark_type(kdf._internal.to_internal_spark_frame.schema)
 
-                self_applied = DataFrame(self._kdf._internal.resolved_copy)
+                self_applied = DataFrame(self._kdf._internal.resolved_copy)  # type: DataFrame
 
                 output_func = GroupBy._make_pandas_df_builder_func(
                     self_applied, func, return_schema, retain_index=True

databricks/koalas/spark/accessors.py

@@ -19,7 +19,7 @@
 but Spark has it.
 """
 from distutils.version import LooseVersion
-from typing import TYPE_CHECKING, Optional, Union, List
+from typing import TYPE_CHECKING, Optional, Union, List, cast
 
 import pyspark
 from pyspark import StorageLevel
@@ -59,7 +59,7 @@ def column(self) -> Column:
         """
         return self._data._internal.spark_column_for(self._data._column_label)
 
-    def transform(self, func):
+    def transform(self, func) -> Union["ks.Series", "ks.Index"]:
         """
         Applies a function that takes and returns a Spark column. It allows to natively
         apply a Spark function and column APIs with the Spark column internally used
@@ -126,7 +126,7 @@ def transform(self, func):
 
 class SparkSeriesMethods(SparkIndexOpsMethods):
     def transform(self, func) -> "ks.Series":
-        return super().transform(func)
+        return cast("ks.Series", super().transform(func))
 
     transform.__doc__ = SparkIndexOpsMethods.transform.__doc__
 
@@ -252,7 +252,7 @@ def analyzed(self) -> "ks.Series":
 
 class SparkIndexMethods(SparkIndexOpsMethods):
     def transform(self, func) -> "ks.Index":
-        return super().transform(func)
+        return cast("ks.Index", super().transform(func))
 
     transform.__doc__ = SparkIndexOpsMethods.transform.__doc__
 
@@ -295,7 +295,7 @@ def schema(self, index_col: Optional[Union[str, List[str]]] = None) -> StructTyp
         """
         return self.frame(index_col).schema
 
-    def print_schema(self, index_col: Optional[Union[str, List[str]]] = None):
+    def print_schema(self, index_col: Optional[Union[str, List[str]]] = None) -> None:
         """
         Prints out the underlying Spark schema in the tree format.
 
@@ -305,6 +305,10 @@ def print_schema(self, index_col: Optional[Union[str, List[str]]] = None):
             Column names to be used in Spark to represent Koalas' index. The index name
             in Koalas is ignored. By default, the index is always lost.
 
+        Returns
+        -------
+        None
+
         Examples
         --------
         >>> df = ks.DataFrame({'a': list('abc'),
@@ -634,7 +638,7 @@ def to_table(
         partition_cols: Optional[Union[str, List[str]]] = None,
         index_col: Optional[Union[str, List[str]]] = None,
         **options
-    ):
+    ) -> None:
         """
         Write the DataFrame into a Spark table. :meth:`DataFrame.spark.to_table`
         is an alias of :meth:`DataFrame.to_table`.
@@ -669,6 +673,10 @@ def to_table(
         options
             Additional options passed directly to Spark.
 
+        Returns
+        -------
+        None
+
         See Also
         --------
         read_table
@@ -705,7 +713,7 @@ def to_spark_io(
         partition_cols: Optional[Union[str, List[str]]] = None,
         index_col: Optional[Union[str, List[str]]] = None,
         **options
-    ):
+    ) -> None:
         """Write the DataFrame out to a Spark data source. :meth:`DataFrame.spark.to_spark_io`
         is an alias of :meth:`DataFrame.to_spark_io`.
 
@@ -736,6 +744,10 @@ def to_spark_io(
         options : dict
             All other options passed directly into Spark's data source.
 
+        Returns
+        -------
+        None
+
         See Also
         --------
         read_spark_io
@@ -766,7 +778,7 @@ def to_spark_io(
             path=path, format=format, mode=mode, partitionBy=partition_cols, **options
         )
 
-    def explain(self, extended: Optional[bool] = None, mode: Optional[str] = None):
+    def explain(self, extended: Optional[bool] = None, mode: Optional[str] = None) -> None:
         """
         Prints the underlying (logical and physical) Spark plans to the console for debugging
         purpose.
@@ -778,6 +790,10 @@ def explain(self, extended: Optional[bool] = None, mode: Optional[str] = None):
         mode : string, default ``None``.
             The expected output format of plans.
 
+        Returns
+        -------
+        None
+
         Examples
         --------
         >>> df = ks.DataFrame({'id': range(10)})
@@ -1164,11 +1180,15 @@ def storage_level(self) -> StorageLevel:
         """
         return self._kdf._cached.storageLevel
 
-    def unpersist(self):
+    def unpersist(self) -> None:
         """
         The `unpersist` function is used to uncache the Koalas DataFrame when it
         is not used with `with` statement.
 
+        Returns
+        -------
+        None
+
         Examples
         --------
         >>> df = ks.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],