DOC: Improved the docstrings under pd.DataFrame

commit after checking docstring

Author: coffeedjimmy

pandas/core/frame.py

@@ -2432,7 +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
+        """
+        Return a subset of a DataFrame including/excluding columns based on
         their ``dtype``.
 
         Parameters
@@ -2483,7 +2484,7 @@ def select_dtypes(self, include=None, exclude=None):
         4 -0.9703   True  1.0
         5 -1.2094  False  2.0
         >>> df.select_dtypes(include='bool')
-           c
+           b
         0  True
         1  False
         2  True

pandas/core/generic.py

@@ -4232,7 +4232,8 @@ def as_matrix(self, columns=None):
 
     @property
     def values(self):
-        """Numpy representation of NDFrame
+        """
+        Return NDFrame as ndarray or ndarray-like depending on the dtype.
 
         Notes
         -----
@@ -4245,6 +4246,16 @@ def values(self):
         float32.  If dtypes are int32 and uint8, dtype will be upcast to
         int32. By numpy.find_common_type convention, mixing int64 and uint64
         will result in a flot64 dtype.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'a': np.random.randn(2).astype('f4'),
+        ...                    'b': [True, False], 'c': [1.0, 2.0]})
+        >>> type(df.values)
+        <class 'numpy.ndarray'>
+        >>> df.values
+        array([[0.25209328532218933, True, 1.0],
+        [0.35383567214012146, False, 2.0]], dtype=object)
         """
         self._consolidate_inplace()
         return self._data.as_array(transpose=self._AXIS_REVERSED)
@@ -4260,16 +4271,76 @@ def _get_values(self):
         return self.values
 
     def get_values(self):
-        """same as values (but handles sparseness conversions)"""
+        """
+        Same as values (but handles sparseness conversions).
+
+        Returns
+        -------
+        numpy.ndaray
+            Numpy representation of NDFrame
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'a': np.random.randn(2).astype('f4'),
+        ...                    'b': [True, False], 'c': [1.0, 2.0]})
+        >>> df.get_values()
+        array([[0.25209328532218933, True, 1.0],
+        [0.35383567214012146, 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   Number of 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['int'].astype(int)
+        >>> df['float'].astype(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   Number of dtype: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['int'].astype(int)
+        >>> df['float'].astype(float)
+        >>> df.get_dtype_counts()
+        float64:dense    1
+        int64:dense      1
+        object:dense     1
+        dtype: int64
+        """
         from pandas import Series
         return Series(self._data.get_ftype_counts())