Suppress warnings in docs

Author: kianelbo

doc/source/user_guide/reshaping.rst

@@ -608,6 +608,7 @@ values, can derive a :class:`DataFrame` containing ``k`` columns of 1s and 0s us
 :func:`~pandas.get_dummies`:
 
 .. ipython:: python
+   :okwarning:
 
    df = pd.DataFrame({"key": list("bbacab"), "data1": range(6)})
 
@@ -617,6 +618,7 @@ Sometimes it's useful to prefix the column names, for example when merging the r
 with the original :class:`DataFrame`:
 
 .. ipython:: python
+   :okwarning:
 
    dummies = pd.get_dummies(df["key"], prefix="key")
    dummies
@@ -626,6 +628,7 @@ with the original :class:`DataFrame`:
 This function is often used along with discretization functions like :func:`~pandas.cut`:
 
 .. ipython:: python
+   :okwarning:
 
    values = np.random.randn(10)
    values
@@ -642,6 +645,7 @@ variables (categorical in the statistical sense, those with ``object`` or
 
 
 .. ipython:: python
+    :okwarning:
 
     df = pd.DataFrame({"A": ["a", "b", "a"], "B": ["c", "c", "b"], "C": [1, 2, 3]})
     pd.get_dummies(df)
@@ -650,6 +654,7 @@ All non-object columns are included untouched in the output. You can control
 the columns that are encoded with the ``columns`` keyword.
 
 .. ipython:: python
+    :okwarning:
 
     pd.get_dummies(df, columns=["A"])
 
@@ -667,6 +672,7 @@ the prefix separator. You can specify ``prefix`` and ``prefix_sep`` in 3 ways:
 * dict: Mapping column name to prefix.
 
 .. ipython:: python
+    :okwarning:
 
     simple = pd.get_dummies(df, prefix="new_prefix")
     simple
@@ -680,6 +686,7 @@ variable to avoid collinearity when feeding the result to statistical models.
 You can switch to this mode by turn on ``drop_first``.
 
 .. ipython:: python
+    :okwarning:
 
     s = pd.Series(list("abcaa"))
 
@@ -690,6 +697,7 @@ You can switch to this mode by turn on ``drop_first``.
 When a column contains only one level, it will be omitted in the result.
 
 .. ipython:: python
+    :okwarning:
 
     df = pd.DataFrame({"A": list("aaaaa"), "B": list("ababc")})
 

doc/source/whatsnew/v0.13.0.rst

@@ -501,6 +501,7 @@ Enhancements
 - ``NaN`` handing in get_dummies (:issue:`4446`) with ``dummy_na``
 
   .. ipython:: python
+     :okwarning:
 
      # previously, nan was erroneously counted as 2 here
      # now it is not counted at all

doc/source/whatsnew/v0.15.0.rst

@@ -1007,6 +1007,7 @@ Other:
   left untouched.
 
   .. ipython:: python
+    :okwarning:
 
     df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['c', 'c', 'b'],
                     'C': [1, 2, 3]})

doc/source/whatsnew/v0.19.0.rst

@@ -431,6 +431,7 @@ The ``pd.get_dummies`` function now returns dummy-encoded columns as small integ
 **New behavior**:
 
 .. ipython:: python
+   :okwarning:
 
    pd.get_dummies(["a", "b", "a", "c"]).dtypes
 

doc/source/whatsnew/v0.23.0.rst

@@ -366,6 +366,7 @@ Function ``get_dummies`` now supports ``dtype`` argument
 The :func:`get_dummies` now accepts a ``dtype`` argument, which specifies a dtype for the new columns. The default remains uint8. (:issue:`18330`)
 
 .. ipython:: python
+   :okwarning:
 
    df = pd.DataFrame({'a': [1, 2], 'b': [3, 4], 'c': [5, 6]})
    pd.get_dummies(df, columns=['c']).dtypes

doc/source/whatsnew/v0.24.0.rst

@@ -833,6 +833,7 @@ then all the columns are dummy-encoded, and a :class:`SparseDataFrame` was retur
 Now, the return type is consistently a :class:`DataFrame`.
 
 .. ipython:: python
+   :okwarning:
 
    type(pd.get_dummies(df, sparse=True))
    type(pd.get_dummies(df[['B', 'C']], sparse=True))