revise docstring for several functions and properties

Author: coffeedjimmy

pandas/core/frame.py

@@ -2432,8 +2432,8 @@ def eval(self, expr, inplace=False, **kwargs):
         return _eval(expr, inplace=inplace, **kwargs)
 
     def select_dtypes(self, include=None, exclude=None):
-        """Return a subset of a DataFrame including/excluding columns based on
-        their ``dtype``.
+        """
+        Return a subset of the DataFrame based on the column dtypes.
 
         Parameters
         ----------
@@ -2471,19 +2471,19 @@ def select_dtypes(self, include=None, exclude=None):
 
         Examples
         --------
-        >>> df = pd.DataFrame({'a': np.random.randn(6).astype('f4'),
+        >>> df = pd.DataFrame({'a': [1, 2] * 3,
         ...                    'b': [True, False] * 3,
         ...                    'c': [1.0, 2.0] * 3})
         >>> df
                 a      b  c
-        0  0.3962   True  1.0
-        1  0.1459  False  2.0
-        2  0.2623   True  1.0
-        3  0.0764  False  2.0
-        4 -0.9703   True  1.0
-        5 -1.2094  False  2.0
+        0       1   True  1.0
+        1       2  False  2.0
+        2       1   True  1.0
+        3       2  False  2.0
+        4       1   True  1.0
+        5       2  False  2.0
         >>> df.select_dtypes(include='bool')
-           c
+           b
         0  True
         1  False
         2  True
@@ -2498,14 +2498,14 @@ def select_dtypes(self, include=None, exclude=None):
         3  2.0
         4  1.0
         5  2.0
-        >>> df.select_dtypes(exclude=['floating'])
-               b
-        0   True
-        1  False
-        2   True
-        3  False
-        4   True
-        5  False
+        >>> df.select_dtypes(exclude=['int'])
+               b    c
+        0   True  1.0
+        1  False  2.0
+        2   True  1.0
+        3  False  2.0
+        4   True  1.0
+        5  False  2.0
         """
 
         if not is_list_like(include):

pandas/core/generic.py

@@ -4260,16 +4260,79 @@ def _get_values(self):
         return self.values
 
     def get_values(self):
-        """same as values (but handles sparseness conversions)"""
+        """
+        Return a NumPy representation of the data
+            after converting sparse to dense.
+
+        Returns
+        -------
+        numpy.ndarray
+            Numpy representation of DataFrame
+
+        See Also
+        --------
+        values : Numpy representation of DataFrame
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'a': [1, 2], 'b': [True, False],
+        ...                    'c': [1.0, 2.0]})
+        >>> df.get_values()
+        array([[1, True, 1.0], [2, False, 2.0]], dtype=object)
+        """
         return self.values
 
     def get_dtype_counts(self):
-        """Return the counts of dtypes in this object."""
+        """
+        Return counts of unique dtypes in this object.
+
+        Returns
+        -------
+        dtype : Series
+            Series with the count of columns with each dtype
+
+        See Also
+        --------
+        dtypes : Return the dtypes in this object.
+
+        Examples
+        --------
+        >>> a = [['a', 1, 1.0], ['b', 2, 2.0], ['c', 3, 3.0]]
+        >>> df = pd.DataFrame(a, columns=['str', 'int', 'float'])
+        >>> df.get_dtype_counts()
+        float64    1
+        int64      1
+        object     1
+        dtype: int64
+        """
         from pandas import Series
         return Series(self._data.get_dtype_counts())
 
     def get_ftype_counts(self):
-        """Return the counts of ftypes in this object."""
+        """
+        Return counts of unique ftypes in this object.
+
+        Returns
+        -------
+        dtype : Series
+            Series with the count of columns
+                with each type and sparcity(dense/sparse)
+
+        See Also
+        --------
+        ftypes : Return
+                 ftypes (indication of sparse/dense and dtype) in this object.
+
+        Examples
+        --------
+        >>> a = [['a', 1, 1.0], ['b', 2, 2.0], ['c', 3, 3.0]]
+        >>> df = pd.DataFrame(a, columns=['str', 'int', 'float'])
+        >>> df.get_ftype_counts()
+        float64:dense    1
+        int64:dense      1
+        object:dense     1
+        dtype: int64
+        """
         from pandas import Series
         return Series(self._data.get_ftype_counts())