DOC: Prepare to add first docstrings checks to the CI

pandas/core/frame.py

@@ -864,12 +864,17 @@ def iterrows(self):
            data types, the iterator returns a copy and not a view, and writing
            to it will have no effect.
 
-        Returns
-        -------
+        Yields
+        ------
+        index : label or tuple of label
+            The index of the row. A tuple for a `MultiIndex`.
+        data : Series
+            The data of the row as a Series.
+
         it : generator
             A generator that iterates over the rows of the frame.
 
-        See also
+        See Also
         --------
         itertuples : Iterate over DataFrame rows as namedtuples of the values.
         iteritems : Iterate over (column name, Series) pairs.
@@ -3951,6 +3956,10 @@ def set_index(self, keys, drop=True, append=False, inplace=False,
             necessary. Setting to False will improve the performance of this
             method
 
+        Returns
+        -------
+        DataFrame
+
         Examples
         --------
         >>> df = pd.DataFrame({'month': [1, 4, 7, 10],
@@ -3991,10 +4000,6 @@ def set_index(self, keys, drop=True, append=False, inplace=False,
         2  2014  4      40
         3  2013  7      84
         4  2014  10     31
-
-        Returns
-        -------
-        dataframe : DataFrame
         """
         inplace = validate_bool_kwarg(inplace, 'inplace')
         if not isinstance(keys, list):
@@ -6694,6 +6699,15 @@ def round(self, decimals=0, *args, **kwargs):
             of `decimals` which are not columns of the input will be
             ignored.
 
+        Returns
+        -------
+        DataFrame
+
+        See Also
+        --------
+        numpy.around
+        Series.round
+
         Examples
         --------
         >>> df = pd.DataFrame(np.random.random([3, 3]),
@@ -6719,15 +6733,6 @@ def round(self, decimals=0, *args, **kwargs):
         first   0.0  1  0.17
         second  0.0  1  0.58
         third   0.9  0  0.49
-
-        Returns
-        -------
-        DataFrame object
-
-        See Also
-        --------
-        numpy.around
-        Series.round
         """
         from pandas.core.reshape.concat import concat
 
@@ -6793,7 +6798,6 @@ def corr(self, method='pearson', min_periods=1):
 
         Examples
         --------
-        >>> import numpy as np
         >>> histogram_intersection = lambda a, b: np.minimum(a, b
         ... ).sum().round(decimals=1)
         >>> df = pd.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],

pandas/core/generic.py

@@ -9691,8 +9691,7 @@ def nanptp(values, axis=0, skipna=True):
 
         cls.ptp = _make_stat_function(
             cls, 'ptp', name, name2, axis_descr,
-            """
-            Returns the difference between the maximum value and the
+            """Returns the difference between the maximum value and the
             minimum value in the object. This is the equivalent of the
             ``numpy.ndarray`` method ``ptp``.
 

pandas/core/panel.py

@@ -106,14 +106,14 @@ def panel_index(time, panels, names=None):
 
 class Panel(NDFrame):
     """
-    Represents wide format panel data, stored as 3-dimensional array
-
-   .. deprecated:: 0.20.0
-       The recommended way to represent 3-D data are with a MultiIndex on a
-       DataFrame via the :attr:`~Panel.to_frame()` method or with the
-       `xarray package <http://xarray.pydata.org/en/stable/>`__.
-       Pandas provides a :attr:`~Panel.to_xarray()` method to automate this
-       conversion.
+    Represents wide format panel data, stored as 3-dimensional array.
+
+    .. deprecated:: 0.20.0
+        The recommended way to represent 3-D data are with a MultiIndex on a
+        DataFrame via the :attr:`~Panel.to_frame()` method or with the
+        `xarray package <http://xarray.pydata.org/en/stable/>`__.
+        Pandas provides a :attr:`~Panel.to_xarray()` method to automate this
+        conversion.
 
     Parameters
     ----------

pandas/core/series.py

@@ -1913,7 +1913,6 @@ def corr(self, other, method='pearson', min_periods=None):
 
         Examples
         --------
-        >>> import numpy as np
         >>> histogram_intersection = lambda a, b: np.minimum(a, b
         ... ).sum().round(decimals=1)
         >>> s1 = pd.Series([.2, .0, .6, .2])