DOC/CI: Failing documentation build on warnings

azure-pipelines.yml

@@ -5,6 +5,7 @@ jobs:
   parameters:
     name: macOS
     vmImage: xcode9-macos10.13
+
 - template: ci/azure/posix.yml
   parameters:
     name: Linux
@@ -134,7 +135,10 @@ jobs:
   - script: |
       export PATH=$HOME/miniconda3/bin:$PATH
       source activate pandas-dev
-      doc/make.py
+      # Next we should simply have `doc/make.py --warnings-are-errors`, everything else is required because the ipython directive doesn't fail the build on errors (https://github.com/ipython/ipython/issues/11547)
+      doc/make.py --warnings-are-errors | tee sphinx.log ; SPHINX_RET=${PIPESTATUS[0]}
+      grep -B1 "^<<<-------------------------------------------------------------------------$" sphinx.log ; IPY_RET=$(( $? != 1 ))
+      exit $(( $SPHINX_RET + $IPY_RET ))
     displayName: 'Build documentation'
 
   - script: |

doc/source/getting_started/10min.rst

@@ -712,6 +712,7 @@ See the :ref:`Plotting <visualization>` docs.
    plt.close('all')
 
 .. ipython:: python
+   :okwarning:
 
    ts = pd.Series(np.random.randn(1000),
                   index=pd.date_range('1/1/2000', periods=1000))

doc/source/user_guide/io.rst

@@ -3249,24 +3249,39 @@ And then import the data directly to a ``DataFrame`` by calling:
 
 .. code-block:: python
 
-   clipdf = pd.read_clipboard()
-
-.. ipython:: python
-
-   clipdf
-
+    >>> clipdf = pd.read_clipboard()
+    >>> clipdf
+      A B C
+    x 1 4 p
+    y 2 5 q
+    z 3 6 r
 
 The ``to_clipboard`` method can be used to write the contents of a ``DataFrame`` to
 the clipboard. Following which you can paste the clipboard contents into other
 applications (CTRL-V on many operating systems). Here we illustrate writing a
 ``DataFrame`` into clipboard and reading it back.
 
-.. ipython:: python
+.. code-block:: python
 
     df = pd.DataFrame(np.random.randn(5, 3))
     df
     df.to_clipboard()
     pd.read_clipboard()
+    >>> df = pd.DataFrame({'A': [1, 2, 3],
+    ...                    'B': [4, 5, 6],
+    ...                    'C': ['p', 'q', 'r']},
+    ...                   index=['x', 'y', 'z'])
+    >>> df
+      A B C
+    x 1 4 p
+    y 2 5 q
+    z 3 6 r
+    >>> df.to_clipboard()
+    >>> pd.read_clipboard()
+      A B C
+    x 1 4 p
+    y 2 5 q
+    z 3 6 r
 
 We can see that we got the same content back, which we had earlier written to the clipboard.
 

doc/sphinxext/contributors.py

@@ -21,12 +21,15 @@ class ContributorsDirective(Directive):
 
     def run(self):
         range_ = self.arguments[0]
+        if range_.endswith('x..HEAD'):
+            return [nodes.paragraph(), nodes.bullet_list()]
         try:
             components = build_components(range_)
-        except git.GitCommandError:
+        except git.GitCommandError as exc:
             return [
                 self.state.document.reporter.warning(
-                    "Cannot find contributors for range '{}'".format(range_),
+                    "Cannot find contributors for range '{}': {}".format(
+                        range_, exc),
                     line=self.lineno)
             ]
         else:

pandas/core/dtypes/base.py

@@ -68,11 +68,6 @@ class property**.
     ``pandas.errors.AbstractMethodError`` and no ``register`` method is
     provided for registering virtual subclasses.
     """
-    # na_value is the default NA value to use for this type. This is used in
-    # e.g. ExtensionArray.take. This should be the user-facing "boxed" version
-    # of the NA value, not the physical NA value for storage.
-    # e.g. for JSONArray, this is an empty dictionary.
-    na_value = np.nan
     _metadata = ()  # type: Tuple[str, ...]
 
     def __str__(self):
@@ -114,6 +109,17 @@ def __hash__(self):
     def __ne__(self, other):
         return not self.__eq__(other)
 
+    @property
+    def na_value(self):
+        """
+        Default NA value to use for this type.
+
+        This is used in e.g. ExtensionArray.take. This should be the
+        user-facing "boxed" version of the NA value, not the physical NA value
+        for storage.  e.g. for JSONArray, this is an empty dictionary.
+        """
+        return np.nan
+
     @property
     def type(self) -> Type:
         """

pandas/core/indexes/base.py

@@ -229,7 +229,6 @@ def _outer_indexer(self, left, right):
     _data = None
     _id = None
     name = None
-    asi8 = None
     _comparables = ['name']
     _attributes = ['name']
     _is_numeric_dtype = False
@@ -501,6 +500,18 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None,
     See each method's docstring.
     """
 
+    @property
+    def asi8(self):
+        """
+        Integer representation of the values.
+
+        Returns
+        -------
+        ndarray
+            An ndarray with int64 dtype.
+        """
+        return None
+
     @classmethod
     def _simple_new(cls, values, name=None, dtype=None, **kwargs):
         """