Refine Frame._reduce_for_stat_function. (#1975)

Refines `DataFrame/Series._reduce_for_stat_function` to avoid special handling based on a specific function.

Also:
- Consolidates the implementations of `count` and support `numeric_only` parameter.
- Adds argument type annotations.

databricks/koalas/frame.py

@@ -626,9 +626,6 @@ def _reduce_for_stat_function(self, sfun, name, axis=None, numeric_only=True):
         from databricks.koalas import Series
         from databricks.koalas.series import first_series
 
-        if name not in ("count", "min", "max") and not numeric_only:
-            raise ValueError("Disabling 'numeric_only' parameter is not supported.")
-
         axis = validate_axis(axis)
         if axis == 0:
             exprs = [F.lit(None).cast(StringType()).alias(SPARK_DEFAULT_INDEX_NAME)]
@@ -639,15 +636,9 @@ def _reduce_for_stat_function(self, sfun, name, axis=None, numeric_only=True):
                 col_type = self._internal.spark_type_for(label)
 
                 is_numeric_or_boolean = isinstance(col_type, (NumericType, BooleanType))
-                min_or_max = name in ("min", "max")
-                keep_column = not numeric_only or is_numeric_or_boolean or min_or_max
+                keep_column = not numeric_only or is_numeric_or_boolean
 
                 if keep_column:
-                    if isinstance(col_type, BooleanType) and not min_or_max:
-                        # Stat functions cannot be used with boolean values by default
-                        # Thus, cast to integer (true to 1 and false to 0)
-                        # Exclude the min and max methods though since those work with booleans
-                        col_sdf = col_sdf.cast("integer")
                     if num_args == 1:
                         # Only pass in the column if sfun accepts only one arg
                         col_sdf = sfun(col_sdf)
@@ -6025,67 +6016,6 @@ def select_dtypes(self, include=None, exclude=None) -> "DataFrame":
             self._internal.with_new_columns(data_spark_columns, column_labels=column_labels)
         )
 
-    def count(self, axis=None) -> pd.Series:
-        """
-        Count non-NA cells for each column.
-
-        The values `None`, `NaN` are considered NA.
-
-        Parameters
-        ----------
-        axis : {0 or ‘index’, 1 or ‘columns’}, default 0
-            If 0 or ‘index’ counts are generated for each column. If 1 or ‘columns’ counts are
-            generated for each row.
-
-        Returns
-        -------
-        pandas.Series
-
-        See Also
-        --------
-        Series.count: Number of non-NA elements in a Series.
-        DataFrame.shape: Number of DataFrame rows and columns (including NA
-            elements).
-        DataFrame.isna: Boolean same-sized DataFrame showing places of NA
-            elements.
-
-        Examples
-        --------
-        Constructing DataFrame from a dictionary:
-
-        >>> df = ks.DataFrame({"Person":
-        ...                    ["John", "Myla", "Lewis", "John", "Myla"],
-        ...                    "Age": [24., np.nan, 21., 33, 26],
-        ...                    "Single": [False, True, True, True, False]},
-        ...                   columns=["Person", "Age", "Single"])
-        >>> df
-          Person   Age  Single
-        0   John  24.0   False
-        1   Myla   NaN    True
-        2  Lewis  21.0    True
-        3   John  33.0    True
-        4   Myla  26.0   False
-
-        Notice the uncounted NA values:
-
-        >>> df.count()
-        Person    5
-        Age       4
-        Single    5
-        dtype: int64
-
-        >>> df.count(axis=1)
-        0    3
-        1    2
-        2    3
-        3    3
-        4    3
-        dtype: int64
-        """
-        return self._reduce_for_stat_function(
-            Frame._count_expr, name="count", axis=axis, numeric_only=False
-        )
-
     def droplevel(self, level, axis=0) -> "DataFrame":
         """
         Return DataFrame with requested index / column level(s) removed.