okwarning option

Author: jseabold

doc/source/user_guide/10min.rst

@@ -687,6 +687,7 @@ Sorting is per order in the categories, not lexical order.
 Grouping by a categorical column also shows empty categories.
 
 .. ipython:: python
+    :okwarning:
 
     df.groupby("grade").size()
 

doc/source/user_guide/advanced.rst

@@ -792,6 +792,7 @@ order is ``cab``).
 Groupby operations on the index will preserve the index nature as well.
 
 .. ipython:: python
+    :okwarning:
 
    df2.groupby(level=0).sum()
    df2.groupby(level=0).sum().index

doc/source/user_guide/categorical.rst

@@ -628,9 +628,12 @@ even if some categories are not present in the data:
                   categories=["c", "a", "b", "d"]))
     s.value_counts()
 
-Groupby will also show "unused" categories:
+Groupby will also show "unused" categories, though this default is deprecated
+and will be changed in a future release. It is recommended to use the `observed`
+keyword explicitly:
 
 .. ipython:: python
+    :okwarning:
 
     cats = pd.Categorical(["a", "b", "b", "b", "c", "c", "c"],
                           categories=["a", "b", "c", "d"])
@@ -647,6 +650,7 @@ Groupby will also show "unused" categories:
 Pivot tables:
 
 .. ipython:: python
+    :okwarning:
 
     raw_cat = pd.Categorical(["a", "a", "b", "b"], categories=["a", "b", "c"])
     df = pd.DataFrame({"A": raw_cat,

doc/source/user_guide/cookbook.rst

@@ -900,6 +900,7 @@ The :ref:`Plotting <visualization>` docs.
 <https://stackoverflow.com/questions/23232989/boxplot-stratified-by-column-in-python-pandas>`__
 
 .. ipython:: python
+    :okwarning:
 
    df = pd.DataFrame(
        {'stratifying_var': np.random.uniform(0, 100, 20),

doc/source/user_guide/groupby.rst

@@ -1223,6 +1223,7 @@ Categorical variables represented as instance of pandas's ``Categorical`` class
 can be used as group keys. If so, the order of the levels will be preserved:
 
 .. ipython:: python
+   :okwarning:
 
    data = pd.Series(np.random.randn(100))
 

doc/source/whatsnew/v0.19.0.rst

@@ -1112,6 +1112,7 @@ An analogous change has been made to ``MultiIndex.from_product``.
 As a consequence, ``groupby`` and ``set_index`` also preserve categorical dtypes in indexes
 
 .. ipython:: python
+    :okwarning:
 
    df = pd.DataFrame({'A': [0, 1], 'B': [10, 11], 'C': cat})
    df_grouped = df.groupby(by=['A', 'C']).first()

doc/source/whatsnew/v0.20.0.rst

@@ -291,6 +291,7 @@ In previous versions, ``.groupby(..., sort=False)`` would fail with a ``ValueErr
 **New behavior**:
 
 .. ipython:: python
+    :okwarning:
 
    df[df.chromosomes != '1'].groupby('chromosomes', sort=False).sum()
 

doc/source/whatsnew/v0.22.0.rst

@@ -118,6 +118,7 @@ instead of ``NaN``.
 *pandas 0.22*
 
 .. ipython:: python
+    :okwarning:
 
    grouper = pd.Categorical(['a', 'a'], categories=['a', 'b'])
    pd.Series([1, 2]).groupby(grouper).sum()