okwarning option

Author: jseabold

doc/source/user_guide/10min.rst

@@ -705,6 +705,7 @@ 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

@@ -808,6 +808,7 @@ 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

@@ -615,16 +615,20 @@ Apart from :meth:`Series.min`, :meth:`Series.max` and :meth:`Series.mode`, the
 following operations are possible with categorical data:
 
 ``Series`` methods like :meth:`Series.value_counts` will use all categories,
-even if some categories are not present in the data:
+even if some categories are not present in the data, 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:
 
     s = pd.Series(pd.Categorical(["a", "b", "c", "c"], categories=["c", "a", "b", "d"]))
     s.value_counts()
 
 ``DataFrame`` methods like :meth:`DataFrame.sum` also show "unused" categories.
 
 .. ipython:: python
+    :okwarning:
 
     columns = pd.Categorical(
         ["One", "One", "Two"], categories=["One", "Two", "Three"], ordered=True
@@ -638,6 +642,7 @@ even if some categories are not present in the data:
 Groupby will also show "unused" categories:
 
 .. ipython:: python
+    :okwarning:
 
     cats = pd.Categorical(
         ["a", "b", "b", "b", "c", "c", "c"], categories=["a", "b", "c", "d"]
@@ -659,6 +664,7 @@ 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, "B": ["c", "d", "c", "d"], "values": [1, 2, 3, 4]})
@@ -676,6 +682,7 @@ Getting
 
 If the slicing operation returns either a ``DataFrame`` or a column of type
 ``Series``, the ``category`` dtype is preserved.
+    :okwarning:
 
 .. ipython:: python
 

doc/source/user_guide/cookbook.rst

@@ -1035,6 +1035,7 @@ The :ref:`Plotting <visualization>` docs.
 <https://stackoverflow.com/questions/23232989/boxplot-stratified-by-column-in-python-pandas>`__
 
 .. ipython:: python
+    :okwarning:
 
    df = pd.DataFrame(
        {

doc/source/user_guide/groupby.rst

@@ -1255,6 +1255,7 @@ 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

@@ -1131,6 +1131,7 @@ 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()