diff --git a/doc/source/whatsnew/v0.10.0.rst b/doc/source/whatsnew/v0.10.0.rst
index aa2749c85a232..2f5162dcd4b67 100644
--- a/doc/source/whatsnew/v0.10.0.rst
+++ b/doc/source/whatsnew/v0.10.0.rst
@@ -45,8 +45,7 @@ want to broadcast, we are phasing out this special case (Zen of Python:
 *Special cases aren't special enough to break the rules*). Here's what I'm
 talking about:
 
-.. ipython:: python
-   :okwarning:
+.. code-block:: python
 
    import pandas as pd
 
@@ -180,7 +179,7 @@ labeled the aggregated group with the end of the interval: the next day).
   DataFrame constructor with no columns specified. The v0.9.0 behavior (names
   ``X0``, ``X1``, ...) can be reproduced by specifying ``prefix='X'``:
 
-.. ipython:: python
+.. code-block:: python
 
     import io
 
@@ -197,7 +196,7 @@ labeled the aggregated group with the end of the interval: the next day).
   though this can be controlled by new ``true_values`` and ``false_values``
   arguments:
 
-.. ipython:: python
+.. code-block:: python
 
     print(data)
     pd.read_csv(io.StringIO(data))
@@ -210,7 +209,7 @@ labeled the aggregated group with the end of the interval: the next day).
 - Calling ``fillna`` on Series or DataFrame with no arguments is no longer
   valid code. You must either specify a fill value or an interpolation method:
 
-.. ipython:: python
+.. code-block:: python
 
    s = pd.Series([np.nan, 1.0, 2.0, np.nan, 4])
    s
@@ -219,7 +218,7 @@ labeled the aggregated group with the end of the interval: the next day).
 
 Convenience methods ``ffill`` and  ``bfill`` have been added:
 
-.. ipython:: python
+.. code-block:: python
 
    s.ffill()
 
@@ -228,7 +227,7 @@ Convenience methods ``ffill`` and  ``bfill`` have been added:
   function, that is itself a series, and possibly upcast the result to a
   DataFrame
 
-  .. ipython:: python
+  .. code-block:: python
 
       def f(x):
           return pd.Series([x, x ** 2], index=["x", "x^2"])
@@ -249,7 +248,7 @@ Convenience methods ``ffill`` and  ``bfill`` have been added:
   Note: ``set_printoptions``/ ``reset_printoptions`` are now deprecated (but
   functioning), the print options now live under "display.XYZ". For example:
 
-  .. ipython:: python
+  .. code-block:: python
 
      pd.get_option("display.max_rows")
 
@@ -264,7 +263,7 @@ Wide DataFrame printing
 Instead of printing the summary information, pandas now splits the string
 representation across multiple rows by default:
 
-.. ipython:: python
+.. code-block:: python
 
    wide_frame = pd.DataFrame(np.random.randn(5, 16))
 
@@ -273,14 +272,13 @@ representation across multiple rows by default:
 The old behavior of printing out summary information can be achieved via the
 'expand_frame_repr' print option:
 
-.. ipython:: python
+.. code-block:: python
 
    pd.set_option("expand_frame_repr", False)
 
    wide_frame
 
-.. ipython:: python
-   :suppress:
+.. code-block:: python
 
    pd.reset_option("expand_frame_repr")
 
diff --git a/doc/source/whatsnew/v0.10.1.rst b/doc/source/whatsnew/v0.10.1.rst
index 611ac2021fcec..1bddfb8a85fd8 100644
--- a/doc/source/whatsnew/v0.10.1.rst
+++ b/doc/source/whatsnew/v0.10.1.rst
@@ -39,9 +39,7 @@ You may need to upgrade your existing data files. Please visit the
 **compatibility** section in the main docs.
 
 
-.. ipython:: python
-   :suppress:
-   :okexcept:
+.. code-block:: python
 
    import os
 
@@ -50,7 +48,7 @@ You may need to upgrade your existing data files. Please visit the
 You can designate (and index) certain columns that you want to be able to
 perform queries on a table, by passing a list to ``data_columns``
 
-.. ipython:: python
+.. code-block:: python
 
    store = pd.HDFStore("store.h5")
    df = pd.DataFrame(
@@ -82,7 +80,7 @@ Retrieving unique values in an indexable or data column.
 
 You can now store ``datetime64`` in data columns
 
-.. ipython:: python
+.. code-block:: python
 
     df_mixed = df.copy()
     df_mixed["datetime64"] = pd.Timestamp("20010102")
@@ -97,7 +95,7 @@ You can pass ``columns`` keyword to select to filter a list of the return
 columns, this is equivalent to passing a
 ``Term('columns',list_of_columns_to_filter)``
 
-.. ipython:: python
+.. code-block:: python
 
    store.select("df", columns=["A", "B"])
 
@@ -160,7 +158,7 @@ Multi-table creation via ``append_to_multiple`` and selection via
 ``select_as_multiple`` can create/select from multiple tables and return a
 combined result, by using ``where`` on a selector table.
 
-.. ipython:: python
+.. code-block:: python
 
    df_mt = pd.DataFrame(
        np.random.randn(8, 6),
@@ -184,8 +182,7 @@ combined result, by using ``where`` on a selector table.
        ["df1_mt", "df2_mt"], where=["A>0", "B>0"], selector="df1_mt"
    )
 
-.. ipython:: python
-   :suppress:
+.. code-block:: python
 
    store.close()
    os.remove("store.h5")
diff --git a/doc/source/whatsnew/v0.11.0.rst b/doc/source/whatsnew/v0.11.0.rst
index 0fba784e36661..450ec73b411d9 100644
--- a/doc/source/whatsnew/v0.11.0.rst
+++ b/doc/source/whatsnew/v0.11.0.rst
@@ -72,7 +72,7 @@ Dtypes
 
 Numeric dtypes will propagate and can coexist in DataFrames. If a dtype is passed (either directly via the ``dtype`` keyword, a passed ``ndarray``, or a passed ``Series``, then it will be preserved in DataFrame operations. Furthermore, different numeric dtypes will **NOT** be combined. The following example will give you a taste.
 
-.. ipython:: python
+.. code-block:: python
 
    df1 = pd.DataFrame(np.random.randn(8, 1), columns=['A'], dtype='float32')
    df1
@@ -93,13 +93,13 @@ Dtype conversion
 
 This is lower-common-denominator upcasting, meaning you get the dtype which can accommodate all of the types
 
-.. ipython:: python
+.. code-block:: python
 
    df3.values.dtype
 
 Conversion
 
-.. ipython:: python
+.. code-block:: python
 
    df3.astype('float32').dtypes
 
@@ -288,7 +288,7 @@ in addition to the traditional ``NaT``, or not-a-time. This allows convenient na
 Furthermore ``datetime64[ns]`` columns are created by default, when passed datetimelike objects (*this change was introduced in 0.10.1*)
 (:issue:`2809`, :issue:`2810`)
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame(np.random.randn(6, 2), pd.date_range('20010102', periods=6),
                      columns=['A', ' B'])
@@ -304,7 +304,7 @@ Furthermore ``datetime64[ns]`` columns are created by default, when passed datet
 
 Astype conversion on ``datetime64[ns]`` to ``object``, implicitly converts ``NaT`` to ``np.nan``
 
-.. ipython:: python
+.. code-block:: python
 
    import datetime
    s = pd.Series([datetime.datetime(2001, 1, 2, 0, 0) for i in range(3)])
@@ -344,15 +344,13 @@ Enhancements
 
     - support ``read_hdf/to_hdf`` API similar to ``read_csv/to_csv``
 
-      .. ipython:: python
+      .. code-block:: python
 
           df = pd.DataFrame({'A': range(5), 'B': range(5)})
           df.to_hdf('store.h5', 'table', append=True)
           pd.read_hdf('store.h5', 'table', where=['index > 2'])
 
-      .. ipython:: python
-          :suppress:
-          :okexcept:
+      .. code-block:: python
 
           import os
 
@@ -367,8 +365,7 @@ Enhancements
 
   - You can now select with a string from a DataFrame with a datelike index, in a similar way to a Series (:issue:`3070`)
 
-    .. ipython:: python
-       :okwarning:
+    .. code-block:: python
 
         idx = pd.date_range("2001-10-1", periods=5, freq='M')
         ts = pd.Series(np.random.rand(len(idx)), index=idx)
diff --git a/doc/source/whatsnew/v0.12.0.rst b/doc/source/whatsnew/v0.12.0.rst
index c12adb2f1334f..126d250e9718f 100644
--- a/doc/source/whatsnew/v0.12.0.rst
+++ b/doc/source/whatsnew/v0.12.0.rst
@@ -45,7 +45,7 @@ API changes
     ``np.nan`` or ``np.inf`` as appropriate (:issue:`3590`). This correct a numpy bug that treats ``integer``
     and ``float`` dtypes differently.
 
-    .. ipython:: python
+    .. code-block:: python
 
         p = pd.DataFrame({"first": [4, 5, 8], "second": [0, 0, 3]})
         p % 0
@@ -93,7 +93,7 @@ API changes
     This case is rarely used, and there are plenty of alternatives. This preserves the
     ``iloc`` API to be *purely* positional based.
 
-    .. ipython:: python
+    .. code-block:: python
 
        df = pd.DataFrame(range(5), index=list("ABCDE"), columns=["a"])
        mask = df.a % 2 == 0
@@ -200,8 +200,7 @@ IO enhancements
 
     You can use ``pd.read_html()`` to read the output from ``DataFrame.to_html()`` like so
 
-    .. ipython:: python
-       :okwarning:
+    .. code-block:: python
 
         df = pd.DataFrame({"a": range(3), "b": list("abc")})
         print(df)
@@ -248,7 +247,7 @@ IO enhancements
       with ``df.to_csv(..., index=False``), then any ``names`` on the columns index will
       be *lost*.
 
-      .. ipython:: python
+      .. code-block:: python
 
          from pandas._testing import makeCustomDataframe as mkdf
 
@@ -257,8 +256,7 @@ IO enhancements
          print(open("mi.csv").read())
          pd.read_csv("mi.csv", header=[0, 1, 2, 3], index_col=[0, 1])
 
-      .. ipython:: python
-         :suppress:
+      .. code-block:: python
 
          import os
 
@@ -307,7 +305,7 @@ Other enhancements
 
     For example you can do
 
-    .. ipython:: python
+    .. code-block:: python
 
         df = pd.DataFrame({"a": list("ab.."), "b": [1, 2, 3, 4]})
         df.replace(regex=r"\s*\.\s*", value=np.nan)
@@ -317,7 +315,7 @@ Other enhancements
 
     Regular string replacement still works as expected. For example, you can do
 
-    .. ipython:: python
+    .. code-block:: python
 
         df.replace(".", np.nan)
 
@@ -351,7 +349,7 @@ Other enhancements
     object. Suppose we want to take only elements that belong to groups with a
     group sum greater than 2.
 
-    .. ipython:: python
+    .. code-block:: python
 
        sf = pd.Series([1, 1, 2, 3, 3, 3])
        sf.groupby(sf).filter(lambda x: x.sum() > 2)
@@ -362,7 +360,7 @@ Other enhancements
     Another useful operation is filtering out elements that belong to groups
     with only a couple members.
 
-    .. ipython:: python
+    .. code-block:: python
 
        dff = pd.DataFrame({"A": np.arange(8), "B": list("aabbbbcc")})
        dff.groupby("B").filter(lambda x: len(x) > 2)
@@ -371,7 +369,7 @@ Other enhancements
     like-indexed objects where the groups that do not pass the filter are
     filled with NaNs.
 
-    .. ipython:: python
+    .. code-block:: python
 
        dff.groupby("B").filter(lambda x: len(x) > 2, dropna=False)
 
@@ -398,7 +396,7 @@ Experimental features
        This uses the ``numpy.busdaycalendar`` API introduced in Numpy 1.7 and
        therefore requires Numpy 1.7.0 or newer.
 
-    .. ipython:: python
+    .. code-block:: python
 
       from pandas.tseries.offsets import CustomBusinessDay
       from datetime import datetime
@@ -433,8 +431,7 @@ Bug fixes
     a ``Series`` with either a single character at each index of the original
     ``Series`` or ``NaN``. For example,
 
-    .. ipython:: python
-        :okwarning:
+    .. code-block:: python
 
         strs = "go", "bow", "joe", "slow"
         ds = pd.Series(strs)
diff --git a/doc/source/whatsnew/v0.13.0.rst b/doc/source/whatsnew/v0.13.0.rst
index 3c6b70fb21383..3dc96731cbe36 100644
--- a/doc/source/whatsnew/v0.13.0.rst
+++ b/doc/source/whatsnew/v0.13.0.rst
@@ -153,7 +153,7 @@ API changes
 
   Added the ``.bool()`` method to ``NDFrame`` objects to facilitate evaluating of single-element boolean Series:
 
-  .. ipython:: python
+  .. code-block:: python
 
      pd.Series([True]).bool()
      pd.Series([False]).bool()
@@ -170,15 +170,14 @@ API changes
 - Chained assignment will now by default warn if the user is assigning to a copy. This can be changed
   with the option ``mode.chained_assignment``, allowed options are ``raise/warn/None``. See :ref:`the docs<indexing.view_versus_copy>`.
 
-  .. ipython:: python
+  .. code-block:: python
 
      dfc = pd.DataFrame({'A': ['aaa', 'bbb', 'ccc'], 'B': [1, 2, 3]})
      pd.set_option('chained_assignment', 'warn')
 
   The following warning / exception will show if this is attempted.
 
-  .. ipython:: python
-     :okwarning:
+  .. code-block:: python
 
      dfc.loc[0]['A'] = 1111
 
@@ -192,7 +191,7 @@ API changes
 
   Here is the correct method of assignment.
 
-  .. ipython:: python
+  .. code-block:: python
 
      dfc.loc[0, 'A'] = 11
      dfc
@@ -242,14 +241,14 @@ was not contained in the index of a particular axis. (:issue:`2578`). See :ref:`
 
 In the ``Series`` case this is effectively an appending operation
 
-.. ipython:: python
+.. code-block:: python
 
    s = pd.Series([1, 2, 3])
    s
    s[5] = 5.
    s
 
-.. ipython:: python
+.. code-block:: python
 
    dfi = pd.DataFrame(np.arange(6).reshape(3, 2),
                       columns=['A', 'B'])
@@ -257,14 +256,14 @@ In the ``Series`` case this is effectively an appending operation
 
 This would previously ``KeyError``
 
-.. ipython:: python
+.. code-block:: python
 
    dfi.loc[:, 'C'] = dfi.loc[:, 'A']
    dfi
 
 This is like an ``append`` operation.
 
-.. ipython:: python
+.. code-block:: python
 
    dfi.loc[3] = 5
    dfi
@@ -314,7 +313,7 @@ Float64Index API change
 
   Construction is by default for floating type values.
 
-  .. ipython:: python
+  .. code-block:: python
 
      index = pd.Index([1.5, 2, 3, 4.5, 5])
      index
@@ -323,14 +322,14 @@ Float64Index API change
 
   Scalar selection for ``[],.ix,.loc`` will always be label based. An integer will match an equal float index (e.g. ``3`` is equivalent to ``3.0``)
 
-  .. ipython:: python
+  .. code-block:: python
 
      s[3]
      s.loc[3]
 
   The only positional indexing is via ``iloc``
 
-  .. ipython:: python
+  .. code-block:: python
 
      s.iloc[3]
 
@@ -338,7 +337,7 @@ Float64Index API change
 
   Slicing is ALWAYS on the values of the index, for ``[],ix,loc`` and ALWAYS positional with ``iloc``
 
-  .. ipython:: python
+  .. code-block:: python
 
      s[2:4]
      s.loc[2:4]
@@ -346,7 +345,7 @@ Float64Index API change
 
   In float indexes, slicing using floats are allowed
 
-  .. ipython:: python
+  .. code-block:: python
 
      s[2.1:4.6]
      s.loc[2.1:4.6]
@@ -374,7 +373,7 @@ HDFStore API changes
 
 - Query Format Changes. A much more string-like query format is now supported. See :ref:`the docs<io.hdf5-query>`.
 
-  .. ipython:: python
+  .. code-block:: python
 
      path = 'test.h5'
      dfq = pd.DataFrame(np.random.randn(10, 4),
@@ -384,20 +383,19 @@ HDFStore API changes
 
   Use boolean expressions, with in-line function evaluation.
 
-  .. ipython:: python
+  .. code-block:: python
 
      pd.read_hdf(path, 'dfq',
                  where="index>Timestamp('20130104') & columns=['A', 'B']")
 
   Use an inline column reference
 
-  .. ipython:: python
+  .. code-block:: python
 
      pd.read_hdf(path, 'dfq',
                  where="A>0 or C>0")
 
-  .. ipython:: python
-     :suppress:
+  .. code-block:: python
 
      import os
      os.remove(path)
@@ -406,7 +404,7 @@ HDFStore API changes
   the same defaults as prior < 0.13.0 remain, e.g. ``put`` implies ``fixed`` format and ``append`` implies
   ``table`` format. This default format can be set as an option by setting ``io.hdf.default_format``.
 
-  .. ipython:: python
+  .. code-block:: python
 
      path = 'test.h5'
      df = pd.DataFrame(np.random.randn(10, 2))
@@ -416,8 +414,7 @@ HDFStore API changes
      with pd.HDFStore(path) as store:
          print(store)
 
-  .. ipython:: python
-     :suppress:
+  .. code-block:: python
 
      import os
      os.remove(path)
@@ -435,7 +432,7 @@ HDFStore API changes
   until they themselves are closed. Performing an action on a closed file will raise
   ``ClosedFileError``
 
-  .. ipython:: python
+  .. code-block:: python
 
      path = 'test.h5'
      df = pd.DataFrame(np.random.randn(10, 2))
@@ -451,8 +448,7 @@ HDFStore API changes
      store2.close()
      store2
 
-  .. ipython:: python
-     :suppress:
+  .. code-block:: python
 
      import os
      os.remove(path)
@@ -500,7 +496,7 @@ Enhancements
 
 - ``NaN`` handing in get_dummies (:issue:`4446`) with ``dummy_na``
 
-  .. ipython:: python
+  .. code-block:: python
 
      # previously, nan was erroneously counted as 2 here
      # now it is not counted at all
@@ -519,7 +515,7 @@ Enhancements
   Using the new top-level ``to_timedelta``, you can convert a scalar or array from the standard
   timedelta format (produced by ``to_csv``) into a timedelta type (``np.timedelta64`` in ``nanoseconds``).
 
-  .. ipython:: python
+  .. code-block:: python
 
      pd.to_timedelta('1 days 06:05:01.00003')
      pd.to_timedelta('15.5us')
@@ -531,7 +527,7 @@ Enhancements
   ``timedelta64[ns]`` object, or astyped to yield a ``float64`` dtyped Series. This
   is frequency conversion. See :ref:`the docs<timedeltas.timedeltas_convert>` for the docs.
 
-  .. ipython:: python
+  .. code-block:: python
 
      import datetime
      td = pd.Series(pd.date_range('20130101', periods=4)) - pd.Series(
@@ -550,28 +546,28 @@ Enhancements
 
   Dividing or multiplying a ``timedelta64[ns]`` Series by an integer or integer Series
 
-  .. ipython:: python
+  .. code-block:: python
 
      td * -1
      td * pd.Series([1, 2, 3, 4])
 
   Absolute ``DateOffset`` objects can act equivalently to ``timedeltas``
 
-  .. ipython:: python
+  .. code-block:: python
 
      from pandas import offsets
      td + offsets.Minute(5) + offsets.Milli(5)
 
   Fillna is now supported for timedeltas
 
-  .. ipython:: python
+  .. code-block:: python
 
      td.fillna(pd.Timedelta(0))
      td.fillna(datetime.timedelta(days=1, seconds=5))
 
   You can do numeric reduction operations on timedeltas.
 
-  .. ipython:: python
+  .. code-block:: python
 
      td.mean()
      td.quantile(.1)
@@ -586,8 +582,7 @@ Enhancements
 - The new vectorized string method ``extract`` return regular expression
   matches more conveniently.
 
-  .. ipython:: python
-     :okwarning:
+  .. code-block:: python
 
      pd.Series(['a1', 'b2', 'c3']).str.extract('[ab](\\d)')
 
@@ -595,8 +590,7 @@ Enhancements
   with more than one group returns a DataFrame with one column per group.
 
 
-  .. ipython:: python
-     :okwarning:
+  .. code-block:: python
 
      pd.Series(['a1', 'b2', 'c3']).str.extract('([ab])(\\d)')
 
@@ -607,16 +601,14 @@ Enhancements
 
   Named groups like
 
-  .. ipython:: python
-     :okwarning:
+  .. code-block:: python
 
      pd.Series(['a1', 'b2', 'c3']).str.extract(
          '(?P<letter>[ab])(?P<digit>\\d)')
 
   and optional groups can also be used.
 
-  .. ipython:: python
-     :okwarning:
+  .. code-block:: python
 
       pd.Series(['a1', 'b2', '3']).str.extract(
           '(?P<letter>[ab])?(?P<digit>\\d)')
@@ -636,19 +628,19 @@ Enhancements
   Period conversions in the range of seconds and below were reworked and extended
   up to nanoseconds. Periods in the nanosecond range are now available.
 
-  .. ipython:: python
+  .. code-block:: python
 
      pd.date_range('2013-01-01', periods=5, freq='5N')
 
   or with frequency as offset
 
-  .. ipython:: python
+  .. code-block:: python
 
      pd.date_range('2013-01-01', periods=5, freq=pd.offsets.Nano(5))
 
   Timestamps can be modified in the nanosecond range
 
-  .. ipython:: python
+  .. code-block:: python
 
      t = pd.Timestamp('20130101 09:01:02')
      t + pd.tseries.offsets.Nano(123)
@@ -657,7 +649,7 @@ Enhancements
 
   To get the rows where any of the conditions are met:
 
-  .. ipython:: python
+  .. code-block:: python
 
      dfi = pd.DataFrame({'A': [1, 2, 3, 4], 'B': ['a', 'b', 'f', 'n']})
      dfi
@@ -696,7 +688,7 @@ Enhancements
 
 - DataFrame has a new ``interpolate`` method, similar to Series (:issue:`4434`, :issue:`1892`)
 
-  .. ipython:: python
+  .. code-block:: python
 
       df = pd.DataFrame({'A': [1, 2.1, np.nan, 4.7, 5.6, 6.8],
                         'B': [.25, np.nan, np.nan, 4, 12.2, 14.4]})
@@ -711,14 +703,14 @@ Enhancements
   Interpolate now also accepts a ``limit`` keyword argument.
   This works similar to ``fillna``'s limit:
 
-  .. ipython:: python
+  .. code-block:: python
 
     ser = pd.Series([1, 3, np.nan, np.nan, np.nan, 11])
     ser.interpolate(limit=2)
 
 - Added ``wide_to_long`` panel data convenience function. See :ref:`the docs<reshaping.melt>`.
 
-  .. ipython:: python
+  .. code-block:: python
 
     np.random.seed(123)
     df = pd.DataFrame({"A1970" : {0 : "a", 1 : "b", 2 : "c"},
@@ -750,18 +742,18 @@ Experimental
   ``numexpr`` behind the scenes. This results in large speedups for
   complicated expressions involving large DataFrames/Series. For example,
 
-  .. ipython:: python
+  .. code-block:: python
 
      nrows, ncols = 20000, 100
      df1, df2, df3, df4 = [pd.DataFrame(np.random.randn(nrows, ncols))
                            for _ in range(4)]
 
-  .. ipython:: python
+  .. code-block:: python
 
      # eval with NumExpr backend
      %timeit pd.eval('df1 + df2 + df3 + df4')
 
-  .. ipython:: python
+  .. code-block:: python
 
      # pure Python evaluation
      %timeit df1 + df2 + df3 + df4
@@ -772,8 +764,7 @@ Experimental
   ``DataFrame.eval`` method that evaluates an expression in the context of
   the ``DataFrame``. For example,
 
-  .. ipython:: python
-     :suppress:
+  .. code-block:: python
 
      try:
          del a  # noqa: F821
@@ -785,7 +776,7 @@ Experimental
      except NameError:
          pass
 
-  .. ipython:: python
+  .. code-block:: python
 
      df = pd.DataFrame(np.random.randn(10, 2), columns=['a', 'b'])
      df.eval('a + b')
@@ -794,8 +785,7 @@ Experimental
   you to select elements of a ``DataFrame`` using a natural query syntax
   nearly identical to Python syntax. For example,
 
-  .. ipython:: python
-     :suppress:
+  .. code-block:: python
 
      try:
          del a  # noqa: F821
@@ -812,7 +802,7 @@ Experimental
      except NameError:
          pass
 
-  .. ipython:: python
+  .. code-block:: python
 
      n = 20
      df = pd.DataFrame(np.random.randint(n, size=(n, 3)), columns=['a', 'b', 'c'])
@@ -845,9 +835,7 @@ Experimental
      for o in pd.read_msgpack('foo.msg', iterator=True):
          print(o)
 
-  .. ipython:: python
-     :suppress:
-     :okexcept:
+  .. code-block:: python
 
      os.remove('foo.msg')
 
@@ -931,13 +919,13 @@ to unify methods and behaviors. Series formerly subclassed directly from
      as an argument. This seems only to affect ``np.ones_like``, ``np.empty_like``,
      ``np.diff`` and ``np.where``. These now return ``ndarrays``.
 
-     .. ipython:: python
+     .. code-block:: python
 
         s = pd.Series([1, 2, 3, 4])
 
      Numpy Usage
 
-     .. ipython:: python
+     .. code-block:: python
 
         np.ones_like(s)
         np.diff(s)
@@ -945,7 +933,7 @@ to unify methods and behaviors. Series formerly subclassed directly from
 
      Pandonic Usage
 
-     .. ipython:: python
+     .. code-block:: python
 
         pd.Series(1, index=s.index)
         s.diff()
@@ -1021,7 +1009,7 @@ to unify methods and behaviors. Series formerly subclassed directly from
 - Refactor of ``_get_numeric_data/_get_bool_data`` to core/generic.py, allowing Series/Panel functionality
 - ``Series`` (for index) / ``Panel`` (for items) now allow attribute access to its elements  (:issue:`1903`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      s = pd.Series([1, 2, 3], index=list('abc'))
      s.b
diff --git a/doc/source/whatsnew/v0.13.1.rst b/doc/source/whatsnew/v0.13.1.rst
index 249b9555b7fd4..4ccc9318f9e3f 100644
--- a/doc/source/whatsnew/v0.13.1.rst
+++ b/doc/source/whatsnew/v0.13.1.rst
@@ -29,7 +29,7 @@ Highlights include:
 
    This would previously segfault:
 
-   .. ipython:: python
+   .. code-block:: python
 
       df = pd.DataFrame({"A": np.array(["foo", "bar", "bah", "foo", "bar"])})
       df["A"].iloc[0] = np.nan
@@ -37,7 +37,7 @@ Highlights include:
 
    The recommended way to do this type of assignment is:
 
-   .. ipython:: python
+   .. code-block:: python
 
       df = pd.DataFrame({"A": np.array(["foo", "bar", "bah", "foo", "bar"])})
       df.loc[0, "A"] = np.nan
@@ -50,7 +50,7 @@ Output formatting enhancements
 
 - df.info() now honors the option ``max_info_rows``, to disable null counts for large frames (:issue:`5974`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      max_info_rows = pd.get_option("max_info_rows")
 
@@ -63,13 +63,13 @@ Output formatting enhancements
      )
      df.iloc[3:6, [0, 2]] = np.nan
 
-  .. ipython:: python
+  .. code-block:: python
 
      # set to not display the null counts
      pd.set_option("max_info_rows", 0)
      df.info()
 
-  .. ipython:: python
+  .. code-block:: python
 
      # this is the default (same as in 0.13.0)
      pd.set_option("max_info_rows", max_info_rows)
@@ -77,7 +77,7 @@ Output formatting enhancements
 
 - Add ``show_dimensions`` display option for the new DataFrame repr to control whether the dimensions print.
 
-  .. ipython:: python
+  .. code-block:: python
 
       df = pd.DataFrame([[1, 2], [3, 4]])
       pd.set_option("show_dimensions", False)
@@ -99,7 +99,7 @@ Output formatting enhancements
 
   Now the output looks like:
 
-  .. ipython:: python
+  .. code-block:: python
 
      df = pd.DataFrame(
          [pd.Timestamp("20010101"), pd.Timestamp("20040601")], columns=["age"]
@@ -117,7 +117,7 @@ API changes
 - Added ``Series.str.get_dummies`` vectorized string method (:issue:`6021`), to extract
   dummy/indicator variables for separated string columns:
 
-  .. ipython:: python
+  .. code-block:: python
 
       s = pd.Series(["a", "a|b", np.nan, "a|c"])
       s.str.get_dummies(sep="|")
@@ -218,7 +218,7 @@ Enhancements
 - ``MultiIndex.from_product`` convenience function for creating a MultiIndex from
   the cartesian product of a set of iterables (:issue:`6055`):
 
-  .. ipython:: python
+  .. code-block:: python
 
       shades = ["light", "dark"]
       colors = ["red", "green", "blue"]
diff --git a/doc/source/whatsnew/v0.14.0.rst b/doc/source/whatsnew/v0.14.0.rst
index b59938a9b9c9b..278a13f42e559 100644
--- a/doc/source/whatsnew/v0.14.0.rst
+++ b/doc/source/whatsnew/v0.14.0.rst
@@ -57,7 +57,7 @@ API changes
   values. A single indexer that is out-of-bounds and drops the dimensions of the object will still raise
   ``IndexError`` (:issue:`6296`, :issue:`6299`). This could result in an empty axis (e.g. an empty DataFrame being returned)
 
-  .. ipython:: python
+  .. code-block:: python
 
      dfl = pd.DataFrame(np.random.randn(5, 2), columns=list('AB'))
      dfl
@@ -113,7 +113,7 @@ API changes
   as :meth:`Index.delete` and :meth:`Index.drop` methods will no longer change the type of the
   resulting index (:issue:`6440`, :issue:`7040`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      i = pd.Index([1, 2, 3, 'a', 'b', 'c'])
      i[[0, 1, 2]]
@@ -122,15 +122,14 @@ API changes
   Previously, the above operation would return ``Int64Index``.  If you'd like
   to do this manually, use :meth:`Index.astype`
 
-  .. ipython:: python
+  .. code-block:: python
 
      i[[0, 1, 2]].astype(np.int_)
 
 - ``set_index`` no longer converts MultiIndexes to an Index of tuples. For example,
   the old behavior returned an Index in this case (:issue:`6459`):
 
-  .. ipython:: python
-     :suppress:
+  .. code-block:: python
 
      np.random.seed(1234)
      from itertools import product
@@ -140,7 +139,7 @@ API changes
      tuple_ind = pd.Index(tuples, tupleize_cols=False)
      df_multi.index
 
-  .. ipython:: python
+  .. code-block:: python
 
      # Old behavior, casted MultiIndex to an Index
      tuple_ind
@@ -152,7 +151,7 @@ API changes
 
   This also applies when passing multiple indices to ``set_index``:
 
-  .. ipython:: python
+  .. code-block:: python
 
     @suppress
     df_multi.index = tuple_ind
@@ -272,7 +271,7 @@ Display changes
   The default for ``display.show_dimensions`` will now be ``truncate``. This is consistent with
   how Series display length.
 
-  .. ipython:: python
+  .. code-block:: python
 
      dfd = pd.DataFrame(np.arange(25).reshape(-1, 5),
                         index=[0, 1, 2, 3, 4],
@@ -328,7 +327,7 @@ More consistent behavior for some groupby methods:
 
 - groupby ``head`` and ``tail`` now act more like ``filter`` rather than an aggregation:
 
-  .. ipython:: python
+  .. code-block:: python
 
      df = pd.DataFrame([[1, 2], [1, 4], [5, 6]], columns=['A', 'B'])
      g = df.groupby('A')
@@ -338,7 +337,7 @@ More consistent behavior for some groupby methods:
 
 - groupby head and tail respect column selection:
 
-  .. ipython:: python
+  .. code-block:: python
 
      g[['B']].head(1)
 
@@ -347,7 +346,7 @@ More consistent behavior for some groupby methods:
 
   Reducing
 
-  .. ipython:: python
+  .. code-block:: python
 
      df = pd.DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=['A', 'B'])
      g = df.groupby('A')
@@ -361,7 +360,7 @@ More consistent behavior for some groupby methods:
 
   Filtering
 
-  .. ipython:: python
+  .. code-block:: python
 
      gf = df.groupby('A', as_index=False)
      gf.nth(0)
@@ -370,7 +369,7 @@ More consistent behavior for some groupby methods:
 - groupby will now not return the grouped column for non-cython functions (:issue:`5610`, :issue:`5614`, :issue:`6732`),
   as its already the index
 
-  .. ipython:: python
+  .. code-block:: python
 
      df = pd.DataFrame([[1, np.nan], [1, 4], [5, 6], [5, 8]], columns=['A', 'B'])
      g = df.groupby('A')
@@ -379,7 +378,7 @@ More consistent behavior for some groupby methods:
 
 - passing ``as_index`` will leave the grouped column in-place (this is not change in 0.14.0)
 
-  .. ipython:: python
+  .. code-block:: python
 
      df = pd.DataFrame([[1, np.nan], [1, 4], [5, 6], [5, 8]], columns=['A', 'B'])
      g = df.groupby('A', as_index=False)
@@ -426,7 +425,7 @@ To connect with SQLAlchemy you use the :func:`create_engine` function to create
 object from database URI. You only need to create the engine once per database you are
 connecting to. For an in-memory sqlite database:
 
-.. ipython:: python
+.. code-block:: python
 
    from sqlalchemy import create_engine
    # Create your connection.
@@ -434,20 +433,20 @@ connecting to. For an in-memory sqlite database:
 
 This ``engine`` can then be used to write or read data to/from this database:
 
-.. ipython:: python
+.. code-block:: python
 
     df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'c']})
     df.to_sql('db_table', engine, index=False)
 
 You can read data from a database by specifying the table name:
 
-.. ipython:: python
+.. code-block:: python
 
    pd.read_sql_table('db_table', engine)
 
 or by specifying a sql query:
 
-.. ipython:: python
+.. code-block:: python
 
    pd.read_sql_query('SELECT * FROM db_table', engine)
 
@@ -512,7 +511,7 @@ See also issues (:issue:`6134`, :issue:`4036`, :issue:`3057`, :issue:`2598`, :is
 
    You will need to make sure that the selection axes are fully lexsorted!
 
-.. ipython:: python
+.. code-block:: python
 
    def mklbl(prefix, n):
        return ["%s%s" % (prefix, i) for i in range(n)]
@@ -532,13 +531,13 @@ See also issues (:issue:`6134`, :issue:`4036`, :issue:`3057`, :issue:`2598`, :is
 
 Basic MultiIndex slicing using slices, lists, and labels.
 
-.. ipython:: python
+.. code-block:: python
 
    df.loc[(slice('A1', 'A3'), slice(None), ['C1', 'C3']), :]
 
 You can use a ``pd.IndexSlice`` to shortcut the creation of these slices
 
-.. ipython:: python
+.. code-block:: python
 
    idx = pd.IndexSlice
    df.loc[idx[:, :, ['C1', 'C3']], idx[:, 'foo']]
@@ -546,14 +545,14 @@ You can use a ``pd.IndexSlice`` to shortcut the creation of these slices
 It is possible to perform quite complicated selections using this method on multiple
 axes at the same time.
 
-.. ipython:: python
+.. code-block:: python
 
    df.loc['A1', (slice(None), 'foo')]
    df.loc[idx[:, :, ['C1', 'C3']], idx[:, 'foo']]
 
 Using a boolean indexer you can provide selection related to the *values*.
 
-.. ipython:: python
+.. code-block:: python
 
    mask = df[('a', 'foo')] > 200
    df.loc[idx[mask, :, ['C1', 'C3']], idx[:, 'foo']]
@@ -561,13 +560,13 @@ Using a boolean indexer you can provide selection related to the *values*.
 You can also specify the ``axis`` argument to ``.loc`` to interpret the passed
 slicers on a single axis.
 
-.. ipython:: python
+.. code-block:: python
 
    df.loc(axis=0)[:, :, ['C1', 'C3']]
 
 Furthermore you can *set* the values using these methods
 
-.. ipython:: python
+.. code-block:: python
 
    df2 = df.copy()
    df2.loc(axis=0)[:, :, ['C1', 'C3']] = -10
@@ -575,7 +574,7 @@ Furthermore you can *set* the values using these methods
 
 You can use a right-hand-side of an alignable object as well.
 
-.. ipython:: python
+.. code-block:: python
 
    df2 = df.copy()
    df2.loc[idx[:, :, ['C1', 'C3']], :] = df2 * 1000
@@ -744,7 +743,7 @@ Enhancements
 
 - DataFrame and Series will create a MultiIndex object if passed a tuples dict, See :ref:`the docs<basics.dataframe.from_dict_of_tuples>` (:issue:`3323`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      pd.Series({('a', 'b'): 1, ('a', 'a'): 0,
                 ('a', 'c'): 2, ('b', 'a'): 3, ('b', 'b'): 4})
@@ -763,7 +762,7 @@ Enhancements
 
   See :ref:`the docs<merging.join_on_mi>`. Joining MultiIndex DataFrames on both the left and right is not yet supported ATM.
 
-  .. ipython:: python
+  .. code-block:: python
 
      household = pd.DataFrame({'household_id': [1, 2, 3],
                                'male': [0, 1, 0],
@@ -822,7 +821,7 @@ Enhancements
 - :meth:`~DataFrame.describe` now accepts an array of percentiles to include in the summary statistics (:issue:`4196`)
 - ``pivot_table`` can now accept ``Grouper`` by ``index`` and ``columns`` keywords (:issue:`6913`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      import datetime
      df = pd.DataFrame({
@@ -853,7 +852,7 @@ Enhancements
 
 - ``PeriodIndex`` fully supports partial string indexing like ``DatetimeIndex`` (:issue:`7043`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      prng = pd.period_range('2013-01-01 09:00', periods=100, freq='H')
      ps = pd.Series(np.random.randn(len(prng)), index=prng)
diff --git a/doc/source/whatsnew/v0.14.1.rst b/doc/source/whatsnew/v0.14.1.rst
index a8f8955c3c1b9..eb686bf4bee71 100644
--- a/doc/source/whatsnew/v0.14.1.rst
+++ b/doc/source/whatsnew/v0.14.1.rst
@@ -64,14 +64,13 @@ API changes
   Starting from 0.14.1 all offsets preserve time by default. The old
   behaviour can be obtained with ``normalize=True``
 
-  .. ipython:: python
-     :suppress:
+  .. code-block:: python
 
      import pandas.tseries.offsets as offsets
 
      d = pd.Timestamp("2014-01-01 09:00")
 
-  .. ipython:: python
+  .. code-block:: python
 
      # new behaviour
      d + offsets.MonthEnd()
@@ -122,7 +121,7 @@ Enhancements
 - Support for dateutil timezones, which can now be used in the same way as
   pytz timezones across pandas. (:issue:`4688`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      rng = pd.date_range(
          "3/6/2012 00:00", periods=10, freq="D", tz="dateutil/Europe/London"
diff --git a/doc/source/whatsnew/v0.15.0.rst b/doc/source/whatsnew/v0.15.0.rst
index fc2b070df4392..2395c0cc19999 100644
--- a/doc/source/whatsnew/v0.15.0.rst
+++ b/doc/source/whatsnew/v0.15.0.rst
@@ -69,8 +69,7 @@ methods to manipulate. Thanks to Jan Schulz for much of this API/implementation.
 For full docs, see the :ref:`categorical introduction <categorical>` and the
 :ref:`API documentation <api.arrays.categorical>`.
 
-.. ipython:: python
-    :okwarning:
+.. code-block:: python
 
     df = pd.DataFrame({"id": [1, 2, 3, 4, 5, 6],
                        "raw_grade": ['a', 'b', 'b', 'a', 'a', 'e']})
@@ -146,7 +145,7 @@ This type is very similar to how ``Timestamp`` works for ``datetimes``. It is a
 
 Construct a scalar
 
-.. ipython:: python
+.. code-block:: python
 
    pd.Timedelta('1 days 06:05:01.00003')
    pd.Timedelta('15.5us')
@@ -161,7 +160,7 @@ Construct a scalar
 
 Access fields for a ``Timedelta``
 
-.. ipython:: python
+.. code-block:: python
 
    td = pd.Timedelta('1 hour 3m 15.5us')
    td.seconds
@@ -170,12 +169,11 @@ Access fields for a ``Timedelta``
 
 Construct a ``TimedeltaIndex``
 
-.. ipython:: python
-   :suppress:
+.. code-block:: python
 
    import datetime
 
-.. ipython:: python
+.. code-block:: python
 
    pd.TimedeltaIndex(['1 days', '1 days, 00:00:05',
                       np.timedelta64(2, 'D'),
@@ -183,14 +181,14 @@ Construct a ``TimedeltaIndex``
 
 Constructing a ``TimedeltaIndex`` with a regular range
 
-.. ipython:: python
+.. code-block:: python
 
    pd.timedelta_range('1 days', periods=5, freq='D')
    pd.timedelta_range(start='1 days', end='2 days', freq='30T')
 
 You can now use a ``TimedeltaIndex`` as the index of a pandas object
 
-.. ipython:: python
+.. code-block:: python
 
    s = pd.Series(np.arange(5),
                  index=pd.timedelta_range('1 days', periods=5, freq='s'))
@@ -198,14 +196,14 @@ You can now use a ``TimedeltaIndex`` as the index of a pandas object
 
 You can select with partial string selections
 
-.. ipython:: python
+.. code-block:: python
 
    s['1 day 00:00:02']
    s['1 day':'1 day 00:00:02']
 
 Finally, the combination of ``TimedeltaIndex`` with ``DatetimeIndex`` allow certain combination operations that are ``NaT`` preserving:
 
-.. ipython:: python
+.. code-block:: python
 
    tdi = pd.TimedeltaIndex(['1 days', pd.NaT, '2 days'])
    tdi.tolist()
@@ -227,7 +225,7 @@ Implemented methods to find memory usage of a DataFrame. See the :ref:`FAQ <df-m
 
 A new display option ``display.memory_usage`` (see :ref:`options`) sets the default behavior of the ``memory_usage`` argument in the ``df.info()`` method. By default ``display.memory_usage`` is ``True``.
 
-.. ipython:: python
+.. code-block:: python
 
     dtypes = ['int64', 'float64', 'datetime64[ns]', 'timedelta64[ns]',
               'complex128', 'object', 'bool']
@@ -240,7 +238,7 @@ A new display option ``display.memory_usage`` (see :ref:`options`) sets the defa
 
 Additionally :meth:`~pandas.DataFrame.memory_usage` is an available method for a dataframe object which returns the memory usage of each column.
 
-.. ipython:: python
+.. code-block:: python
 
     df.memory_usage(index=True)
 
@@ -253,7 +251,7 @@ Series.dt accessor
 ``Series`` has gained an accessor to succinctly return datetime like properties for the *values* of the Series, if its a datetime/period like Series. (:issue:`7207`)
 This will return a Series, indexed like the existing Series. See the :ref:`docs <basics.dt_accessors>`
 
-.. ipython:: python
+.. code-block:: python
 
    # datetime
    s = pd.Series(pd.date_range('20130101 09:10:12', periods=4))
@@ -265,13 +263,13 @@ This will return a Series, indexed like the existing Series. See the :ref:`docs
 
 This enables nice expressions like this:
 
-.. ipython:: python
+.. code-block:: python
 
    s[s.dt.day == 2]
 
 You can easily produce tz aware transformations:
 
-.. ipython:: python
+.. code-block:: python
 
    stz = s.dt.tz_localize('US/Eastern')
    stz
@@ -279,13 +277,13 @@ You can easily produce tz aware transformations:
 
 You can also chain these types of operations:
 
-.. ipython:: python
+.. code-block:: python
 
    s.dt.tz_localize('UTC').dt.tz_convert('US/Eastern')
 
 The ``.dt`` accessor works for period and timedelta dtypes.
 
-.. ipython:: python
+.. code-block:: python
 
    # period
    s = pd.Series(pd.period_range('20130101', periods=4, freq='D'))
@@ -293,7 +291,7 @@ The ``.dt`` accessor works for period and timedelta dtypes.
    s.dt.year
    s.dt.day
 
-.. ipython:: python
+.. code-block:: python
 
    # timedelta
    s = pd.Series(pd.timedelta_range('1 day 00:00:05', periods=4, freq='s'))
@@ -311,7 +309,7 @@ Timezone handling improvements
 - ``tz_localize(None)`` for tz-aware ``Timestamp`` and ``DatetimeIndex`` now removes timezone holding local time,
   previously this resulted in ``Exception`` or ``TypeError`` (:issue:`7812`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      ts = pd.Timestamp('2014-08-01 09:00', tz='US/Eastern')
      ts
@@ -347,7 +345,7 @@ Rolling/expanding moments improvements
 
   Prior to 0.15.0
 
-  .. ipython:: python
+  .. code-block:: python
 
      s = pd.Series([10, 11, 12, 13])
 
@@ -407,7 +405,7 @@ Rolling/expanding moments improvements
   the calculated weighted means (e.g. 'triang', 'gaussian') are distributed about the same means as those
   calculated without weighting (i.e. 'boxcar'). See :ref:`the note on normalization <window.weighted>` for further details. (:issue:`7618`)
 
-  .. ipython:: python
+  .. code-block:: python
 
     s = pd.Series([10.5, 8.8, 11.4, 9.7, 9.3])
 
@@ -456,7 +454,7 @@ Rolling/expanding moments improvements
   Prior behavior (note values start at index ``2``, which is ``min_periods`` after index ``0``
   (the index of the first non-empty value)):
 
-  .. ipython:: python
+  .. code-block:: python
 
     s  = pd.Series([1, None, None, None, 2, 3])
 
@@ -551,7 +549,7 @@ Rolling/expanding moments improvements
   For example, consider the following pre-0.15.0 results for ``ewmvar(..., bias=False)``,
   and the corresponding debiasing factors:
 
-  .. ipython:: python
+  .. code-block:: python
 
      s = pd.Series([1., 2., 0., 4.])
 
@@ -665,7 +663,7 @@ Other notable API changes:
 
 - Consistency when indexing with ``.loc`` and a list-like indexer when no values are found.
 
-  .. ipython:: python
+  .. code-block:: python
 
      df = pd.DataFrame([['a'], ['b']], index=[1, 2])
      df
@@ -727,8 +725,7 @@ Other notable API changes:
 
   Furthermore, ``.loc`` will raise If no values are found in a MultiIndex with a list-like indexer:
 
-  .. ipython:: python
-     :okexcept:
+  .. code-block:: python
 
      s = pd.Series(np.arange(3, dtype='int64'),
                    index=pd.MultiIndex.from_product([['A'],
@@ -747,7 +744,7 @@ Other notable API changes:
   dtype to object (or errored, depending on the call). It now uses
   ``NaN``:
 
-  .. ipython:: python
+  .. code-block:: python
 
      s = pd.Series([1, 2, 3])
      s.loc[0] = None
@@ -758,7 +755,7 @@ Other notable API changes:
   For object containers, we now preserve ``None`` values (previously these
   were converted to ``NaN`` values).
 
-  .. ipython:: python
+  .. code-block:: python
 
      s = pd.Series(["a", "b", "c"])
      s.loc[0] = None
@@ -768,7 +765,7 @@ Other notable API changes:
 
 - In prior versions, updating a pandas object inplace would not reflect in other python references to this object. (:issue:`8511`, :issue:`5104`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      s = pd.Series([1, 2, 3])
      s2 = s
@@ -798,7 +795,7 @@ Other notable API changes:
 
   This is now the correct behavior
 
-  .. ipython:: python
+  .. code-block:: python
 
      # the original object
      s
@@ -820,7 +817,7 @@ Other notable API changes:
   In prior versions this would drop the timezone, now it retains the timezone,
   but gives a column of ``object`` dtype:
 
-  .. ipython:: python
+  .. code-block:: python
 
         i = pd.date_range('1/1/2011', periods=3, freq='10s', tz='US/Eastern')
         i
@@ -841,7 +838,7 @@ Other notable API changes:
 
 - ``SettingWithCopy`` raise/warnings (according to the option ``mode.chained_assignment``) will now be issued when setting a value on a sliced mixed-dtype DataFrame using chained-assignment. (:issue:`7845`, :issue:`7950`)
 
-  .. code-block:: python
+  .. code-block:: ipython
 
      In [1]: df = pd.DataFrame(np.arange(0, 9), columns=['count'])
 
@@ -859,7 +856,7 @@ Other notable API changes:
 
 - Previously an enlargement with a mixed-dtype frame would act unlike ``.append`` which will preserve dtypes (related :issue:`2578`, :issue:`8176`):
 
-  .. ipython:: python
+  .. code-block:: python
 
      df = pd.DataFrame([[True, 1], [False, 2]],
                        columns=["female", "fitness"])
@@ -983,7 +980,7 @@ Other:
 - :func:`describe` on mixed-types DataFrames is more flexible. Type-based column filtering is now possible via the ``include``/``exclude`` arguments.
   See the :ref:`docs <basics.describe>` (:issue:`8164`).
 
-  .. ipython:: python
+  .. code-block:: python
 
     df = pd.DataFrame({'catA': ['foo', 'foo', 'bar'] * 8,
                        'catB': ['a', 'b', 'c', 'd'] * 6,
@@ -994,7 +991,7 @@ Other:
 
   Requesting all columns is possible with the shorthand 'all'
 
-  .. ipython:: python
+  .. code-block:: python
 
     df.describe(include='all')
 
@@ -1006,7 +1003,7 @@ Other:
   categorical columns are encoded as 0's and 1's, while other columns are
   left untouched.
 
-  .. ipython:: python
+  .. code-block:: python
 
     df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['c', 'c', 'b'],
                     'C': [1, 2, 3]})
@@ -1018,7 +1015,7 @@ Other:
 - ``pandas.tseries.holiday.Holiday`` now supports a days_of_week parameter (:issue:`7070`)
 - ``GroupBy.nth()`` now supports selecting multiple nth values (:issue:`7910`)
 
-  .. ipython:: python
+  .. code-block:: python
 
     business_dates = pd.date_range(start='4/1/2014', end='6/30/2014', freq='B')
     df = pd.DataFrame(1, index=business_dates, columns=['a', 'b'])
@@ -1029,7 +1026,7 @@ Other:
 
   If ``Period`` freq is ``D``, ``H``, ``T``, ``S``, ``L``, ``U``, ``N``, ``Timedelta``-like can be added if the result can have same freq. Otherwise, only the same ``offsets`` can be added.
 
-  .. ipython:: python
+  .. code-block:: python
 
      idx = pd.period_range('2014-07-01 09:00', periods=5, freq='H')
      idx
@@ -1055,7 +1052,7 @@ Other:
 
 - :func:`set_names`, :func:`set_labels`, and :func:`set_levels` methods now take an optional ``level`` keyword argument to all modification of specific level(s) of a MultiIndex. Additionally :func:`set_names` now accepts a scalar string value when operating on an ``Index`` or on a specific level of a ``MultiIndex`` (:issue:`7792`)
 
-  .. ipython:: python
+  .. code-block:: python
 
       idx = pd.MultiIndex.from_product([['a'], range(3), list("pqr")],
                                        names=['foo', 'bar', 'baz'])
@@ -1079,7 +1076,7 @@ Other:
 
 - ``Index`` now supports ``duplicated`` and ``drop_duplicates``. (:issue:`4060`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      idx = pd.Index([1, 2, 3, 4, 1, 2])
      idx
diff --git a/doc/source/whatsnew/v0.15.1.rst b/doc/source/whatsnew/v0.15.1.rst
index a1d4f9d14a905..6ff3218ce9fb7 100644
--- a/doc/source/whatsnew/v0.15.1.rst
+++ b/doc/source/whatsnew/v0.15.1.rst
@@ -21,7 +21,7 @@ API changes
 
 - ``s.dt.hour`` and other ``.dt`` accessors will now return ``np.nan`` for missing values (rather than previously -1), (:issue:`8689`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      s = pd.Series(pd.date_range("20130101", periods=5, freq="D"))
      s.iloc[2] = np.nan
@@ -42,14 +42,14 @@ API changes
 
   current behavior:
 
-  .. ipython:: python
+  .. code-block:: python
 
      s.dt.hour
 
 - ``groupby`` with ``as_index=False`` will not add erroneous extra columns to
   result (:issue:`8582`):
 
-  .. ipython:: python
+  .. code-block:: python
 
      np.random.seed(2718281)
      df = pd.DataFrame(np.random.randint(0, 100, (10, 2)), columns=["jim", "joe"])
@@ -70,14 +70,14 @@ API changes
 
   current behavior:
 
-  .. ipython:: python
+  .. code-block:: python
 
     df.groupby(ts, as_index=False).max()
 
 - ``groupby`` will not erroneously exclude columns if the column name conflicts
   with the grouper name (:issue:`8112`):
 
-  .. ipython:: python
+  .. code-block:: python
 
      df = pd.DataFrame({"jim": range(5), "joe": range(5, 10)})
      df
@@ -96,14 +96,14 @@ API changes
 
   current behavior:
 
-  .. ipython:: python
+  .. code-block:: python
 
      gr.apply(sum)
 
 - Support for slicing with monotonic decreasing indexes, even if ``start`` or ``stop`` is
   not found in the index (:issue:`7860`):
 
-  .. ipython:: python
+  .. code-block:: python
 
     s = pd.Series(["a", "b", "c", "d"], [4, 3, 2, 1])
     s
@@ -117,7 +117,7 @@ API changes
 
   current behavior:
 
-  .. ipython:: python
+  .. code-block:: python
 
      s.loc[3.5:1.5]
 
@@ -204,7 +204,7 @@ Enhancements
 - ``concat`` permits a wider variety of iterables of pandas objects to be
   passed as the first parameter (:issue:`8645`):
 
-  .. ipython:: python
+  .. code-block:: python
 
      from collections import deque
 
@@ -220,13 +220,13 @@ Enhancements
 
   current behavior:
 
-  .. ipython:: python
+  .. code-block:: python
 
      pd.concat(deque((df1, df2)))
 
 - Represent ``MultiIndex`` labels with a dtype that utilizes memory based on the level size. In prior versions, the memory usage was a constant 8 bytes per element in each level. In addition, in prior versions, the *reported* memory usage was incorrect as it didn't show the usage for the memory occupied by the underling data array. (:issue:`8456`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      dfi = pd.DataFrame(
          1, index=pd.MultiIndex.from_product([["a"], range(1000)]), columns=["A"]
@@ -246,7 +246,7 @@ Enhancements
 
   current behavior:
 
-  .. ipython:: python
+  .. code-block:: python
 
      dfi.memory_usage(index=True)
 
diff --git a/doc/source/whatsnew/v0.15.2.rst b/doc/source/whatsnew/v0.15.2.rst
index 2dae76dd6b461..b2f77f6e2a286 100644
--- a/doc/source/whatsnew/v0.15.2.rst
+++ b/doc/source/whatsnew/v0.15.2.rst
@@ -24,8 +24,7 @@ API changes
 - Indexing in ``MultiIndex`` beyond lex-sort depth is now supported, though
   a lexically sorted index will have a better performance. (:issue:`2646`)
 
-  .. ipython:: python
-    :okwarning:
+  .. code-block:: python
 
     df = pd.DataFrame({'jim':[0, 0, 1, 1],
                        'joe':['x', 'x', 'z', 'y'],
@@ -61,7 +60,7 @@ API changes
 
   Now, only the categories that do effectively occur in the array are returned:
 
-  .. ipython:: python
+  .. code-block:: python
 
     cat = pd.Categorical(['a', 'b', 'a'], categories=['a', 'b', 'c'])
     cat.unique()
@@ -72,7 +71,7 @@ API changes
 
 - Bug in ``NDFrame``: conflicting attribute/column names now behave consistently between getting and setting. Previously, when both a column and attribute named ``y`` existed, ``data.y`` would return the attribute, while ``data.y = z`` would update the column (:issue:`8994`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      data = pd.DataFrame({'x': [1, 2, 3]})
      data.y = 2
@@ -94,7 +93,7 @@ API changes
 
   New behavior:
 
-  .. ipython:: python
+  .. code-block:: python
 
      data.y
      data['y'].values
@@ -121,7 +120,7 @@ API changes
 
   New behavior:
 
-  .. ipython:: python
+  .. code-block:: python
 
      s = pd.Series(np.arange(3), ['a', 'b', 'c'])
      s.loc['c':'a':-1]
@@ -153,8 +152,7 @@ Other enhancements:
 
 - ``Series.all`` and ``Series.any`` now support the ``level`` and ``skipna`` parameters (:issue:`8302`):
 
-  .. ipython:: python
-     :okwarning:
+  .. code-block:: python
 
      s = pd.Series([False, True, False], index=[0, 0, 1])
      s.any(level=0)
diff --git a/doc/source/whatsnew/v0.16.0.rst b/doc/source/whatsnew/v0.16.0.rst
index 8d0d6854cbf85..4c0b10cb315ec 100644
--- a/doc/source/whatsnew/v0.16.0.rst
+++ b/doc/source/whatsnew/v0.16.0.rst
@@ -51,7 +51,7 @@ to be inserted (for example, a ``Series`` or NumPy array), or a function
 of one argument to be called on the ``DataFrame``. The new values are inserted,
 and the entire DataFrame (with all original and new columns) is returned.
 
-.. ipython:: python
+.. code-block:: python
 
    iris = pd.read_csv('data/iris.data')
    iris.head()
@@ -61,7 +61,7 @@ and the entire DataFrame (with all original and new columns) is returned.
 Above was an example of inserting a precomputed value. We can also pass in
 a function to be evaluated.
 
-.. ipython:: python
+.. code-block:: python
 
     iris.assign(sepal_ratio=lambda x: (x['SepalWidth']
                                        / x['SepalLength'])).head()
@@ -70,7 +70,7 @@ The power of ``assign`` comes when used in chains of operations. For example,
 we can limit the DataFrame to just those with a Sepal Length greater than 5,
 calculate the ratio, and plot
 
-.. ipython:: python
+.. code-block:: python
 
    iris = pd.read_csv('data/iris.data')
    (iris.query('SepalLength > 5')
@@ -146,7 +146,7 @@ String methods enhancements
   ``find()``     ``rfind()``    ``ljust()``    ``rjust()``        ``zfill()``
   =============  =============  =============  ===============    ===============
 
-  .. ipython:: python
+  .. code-block:: python
 
      s = pd.Series(['abcd', '3456', 'EFGH'])
      s.str.isalpha()
@@ -154,14 +154,14 @@ String methods enhancements
 
 - :meth:`Series.str.pad` and :meth:`Series.str.center` now accept ``fillchar`` option to specify filling character (:issue:`9352`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      s = pd.Series(['12', '300', '25'])
      s.str.pad(5, fillchar='_')
 
 - Added :meth:`Series.str.slice_replace`, which previously raised ``NotImplementedError`` (:issue:`8888`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      s = pd.Series(['ABCD', 'EFGH', 'IJK'])
      s.str.slice_replace(1, 3, 'X')
@@ -175,7 +175,7 @@ Other enhancements
 
 - Reindex now supports ``method='nearest'`` for frames or series with a monotonic increasing or decreasing index (:issue:`9258`):
 
-  .. ipython:: python
+  .. code-block:: python
 
      df = pd.DataFrame({'x': range(5)})
      df.reindex([0.2, 1.8, 3.5], method='nearest')
@@ -243,7 +243,7 @@ Previous behavior
 
 New behavior
 
-.. ipython:: python
+.. code-block:: python
 
    t = pd.Timedelta('1 day, 10:11:12.100123')
    t.days
@@ -252,7 +252,7 @@ New behavior
 
 Using ``.components`` allows the full component access
 
-.. ipython:: python
+.. code-block:: python
 
    t.components
    t.components.seconds
@@ -266,7 +266,7 @@ The behavior of a small sub-set of edge cases for using ``.loc`` have changed (:
 
 - Slicing with ``.loc`` where the start and/or stop bound is not found in the index is now allowed; this previously would raise a ``KeyError``. This makes the behavior the same as ``.ix`` in this case. This change is only for slicing, not when indexing with a single label.
 
-  .. ipython:: python
+  .. code-block:: python
 
      df = pd.DataFrame(np.random.randn(5, 4),
                        columns=list('ABCD'),
@@ -287,7 +287,7 @@ The behavior of a small sub-set of edge cases for using ``.loc`` have changed (:
 
   New behavior
 
-  .. ipython:: python
+  .. code-block:: python
 
      df.loc['2013-01-02':'2013-01-10']
      s.loc[-10:3]
@@ -367,7 +367,7 @@ Previous behavior
 
 New behavior
 
-.. ipython:: python
+.. code-block:: python
 
    s = pd.Series([0, 1, 2], dtype='category')
    s
@@ -491,7 +491,7 @@ Other API changes
 
   New behavior
 
-  .. ipython:: python
+  .. code-block:: python
 
      p = pd.Series([0, 1])
      p / 0
@@ -511,7 +511,7 @@ Other API changes
 
   Fixed behavior:
 
-  .. ipython:: python
+  .. code-block:: python
 
     pd.to_datetime(['2000-01-31', '2000-02-28']).asof('2000-02')
 
@@ -674,7 +674,7 @@ Bug fixes
 
   The following would previously report a ``SettingWithCopy`` Warning.
 
-  .. ipython:: python
+  .. code-block:: python
 
      df1 = pd.DataFrame({'x': pd.Series(['a', 'b', 'c']),
                          'y': pd.Series(['d', 'e', 'f'])})
diff --git a/doc/source/whatsnew/v0.16.1.rst b/doc/source/whatsnew/v0.16.1.rst
index 269854111373f..74fbc5da37839 100644
--- a/doc/source/whatsnew/v0.16.1.rst
+++ b/doc/source/whatsnew/v0.16.1.rst
@@ -181,7 +181,7 @@ total number or rows or columns. It also has options for sampling with or withou
 for passing in a column for weights for non-uniform sampling, and for setting seed values to
 facilitate replication. (:issue:`2419`)
 
-.. ipython:: python
+.. code-block:: python
 
    example_series = pd.Series([0, 1, 2, 3, 4, 5])
 
@@ -207,7 +207,7 @@ facilitate replication. (:issue:`2419`)
 When applied to a DataFrame, one may pass the name of a column to specify sampling weights
 when sampling from rows.
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame({"col1": [9, 8, 7, 6], "weight_column": [0.5, 0.4, 0.1, 0]})
    df.sample(n=3, weights="weight_column")
@@ -226,7 +226,7 @@ enhancements make string operations easier and more consistent with standard pyt
 
   The ``.str`` accessor is now available for both ``Series`` and ``Index``.
 
-  .. ipython:: python
+  .. code-block:: python
 
      idx = pd.Index([" jack", "jill ", " jesse ", "frank"])
      idx.str.strip()
@@ -235,7 +235,7 @@ enhancements make string operations easier and more consistent with standard pyt
   will return a ``np.array`` instead of a boolean ``Index`` (:issue:`8875`). This enables the following expression
   to work naturally:
 
-  .. ipython:: python
+  .. code-block:: python
 
      idx = pd.Index(["a1", "a2", "b1", "b2"])
      s = pd.Series(range(4), index=idx)
@@ -254,7 +254,7 @@ enhancements make string operations easier and more consistent with standard pyt
 
 - ``split`` now takes ``expand`` keyword to specify whether to expand dimensionality. ``return_type`` is deprecated. (:issue:`9847`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      s = pd.Series(["a,b", "a,c", "b,c"])
 
@@ -283,7 +283,7 @@ Other enhancements
 
 - ``BusinessHour`` offset is now supported, which represents business hours starting from 09:00 - 17:00 on ``BusinessDay`` by default. See :ref:`Here <timeseries.businesshour>` for details. (:issue:`7905`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      pd.Timestamp("2014-08-01 09:00") + pd.tseries.offsets.BusinessHour()
      pd.Timestamp("2014-08-01 07:00") + pd.tseries.offsets.BusinessHour()
@@ -297,7 +297,7 @@ Other enhancements
 
 - ``drop`` function can now accept ``errors`` keyword to suppress ``ValueError`` raised when any of label does not exist in the target data. (:issue:`6736`)
 
-  .. ipython:: python
+  .. code-block:: python
 
     df = pd.DataFrame(np.random.randn(3, 3), columns=["A", "B", "C"])
     df.drop(["A", "X"], axis=1, errors="ignore")
@@ -379,7 +379,7 @@ Previous behavior
 
 New behavior
 
-.. ipython:: python
+.. code-block:: python
 
    pd.set_option("display.width", 80)
    pd.Index(range(4), name="foo")
diff --git a/doc/source/whatsnew/v0.16.2.rst b/doc/source/whatsnew/v0.16.2.rst
index 37e8c64ea9ced..514be342c67c5 100644
--- a/doc/source/whatsnew/v0.16.2.rst
+++ b/doc/source/whatsnew/v0.16.2.rst
@@ -61,7 +61,7 @@ In the example above, the functions ``f``, ``g``, and ``h`` each expected the Da
 When the function you wish to apply takes its data anywhere other than the first argument, pass a tuple
 of ``(function, keyword)`` indicating where the DataFrame should flow. For example:
 
-.. ipython:: python
+.. code-block:: python
 
    import statsmodels.formula.api as sm
 
diff --git a/doc/source/whatsnew/v0.17.0.rst b/doc/source/whatsnew/v0.17.0.rst
index d8f39a7d6e3c0..498663b3f4130 100644
--- a/doc/source/whatsnew/v0.17.0.rst
+++ b/doc/source/whatsnew/v0.17.0.rst
@@ -78,7 +78,7 @@ number rows. See the :ref:`docs <timeseries.timezone_series>` for more details.
 
 The new implementation allows for having a single-timezone across all rows, with operations in a performant manner.
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame(
        {
@@ -90,14 +90,14 @@ The new implementation allows for having a single-timezone across all rows, with
    df
    df.dtypes
 
-.. ipython:: python
+.. code-block:: python
 
    df.B
    df.B.dt.tz_localize(None)
 
 This uses a new-dtype representation as well, that is very similar in look-and-feel to its numpy cousin ``datetime64[ns]``
 
-.. ipython:: python
+.. code-block:: python
 
    df["B"].dtype
    type(df["B"].dtype)
@@ -121,7 +121,7 @@ This uses a new-dtype representation as well, that is very similar in look-and-f
 
    New behavior:
 
-   .. ipython:: python
+   .. code-block:: python
 
       pd.date_range("20130101", periods=3, tz="US/Eastern")
       pd.date_range("20130101", periods=3, tz="US/Eastern").dtype
@@ -161,8 +161,7 @@ The Series and DataFrame ``.plot()`` method allows for customizing :ref:`plot ty
 
 To alleviate this issue, we have added a new, optional plotting interface, which exposes each kind of plot as a method of the ``.plot`` attribute. Instead of writing ``series.plot(kind=<kind>, ...)``, you can now also use ``series.plot.<kind>(...)``:
 
-.. ipython::
-    :verbatim:
+.. code-block:: ipython
 
     In [13]: df = pd.DataFrame(np.random.rand(10, 2), columns=['a', 'b'])
 
@@ -172,8 +171,7 @@ To alleviate this issue, we have added a new, optional plotting interface, which
 
 As a result of this change, these methods are now all discoverable via tab-completion:
 
-.. ipython::
-    :verbatim:
+.. code-block:: ipython
 
     In [15]: df.plot.<TAB>  # noqa: E225, E999
     df.plot.area     df.plot.barh     df.plot.density  df.plot.hist     df.plot.line     df.plot.scatter
@@ -191,14 +189,14 @@ Series.dt.strftime
 
 We are now supporting a ``Series.dt.strftime`` method for datetime-likes to generate a formatted string (:issue:`10110`). Examples:
 
-.. ipython:: python
+.. code-block:: python
 
    # DatetimeIndex
    s = pd.Series(pd.date_range("20130101", periods=4))
    s
    s.dt.strftime("%Y/%m/%d")
 
-.. ipython:: python
+.. code-block:: python
 
    # PeriodIndex
    s = pd.Series(pd.period_range("20130101", periods=4))
@@ -212,7 +210,7 @@ Series.dt.total_seconds
 
 ``pd.Series`` of type ``timedelta64`` has new method ``.dt.total_seconds()`` returning the duration of the timedelta in seconds (:issue:`10817`)
 
-.. ipython:: python
+.. code-block:: python
 
    # TimedeltaIndex
    s = pd.Series(pd.timedelta_range("1 minutes", periods=4))
@@ -228,7 +226,7 @@ Period frequency enhancement
 
 A multiplied freq represents a span of corresponding length. The example below creates a period of 3 days. Addition and subtraction will shift the period by its span.
 
-.. ipython:: python
+.. code-block:: python
 
    p = pd.Period("2015-08-01", freq="3D")
    p
@@ -239,7 +237,7 @@ A multiplied freq represents a span of corresponding length. The example below c
 
 You can use the multiplied freq in ``PeriodIndex`` and ``period_range``.
 
-.. ipython:: python
+.. code-block:: python
 
    idx = pd.period_range("2015-08-01", periods=4, freq="2D")
    idx
@@ -295,7 +293,7 @@ in the ``header`` and ``index_col`` parameters (:issue:`4679`)
 
 See the :ref:`documentation <io.excel>` for more details.
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame(
        [[1, 2, 3, 4], [5, 6, 7, 8]],
@@ -311,8 +309,7 @@ See the :ref:`documentation <io.excel>` for more details.
    df = pd.read_excel("test.xlsx", header=[0, 1], index_col=[0, 1])
    df
 
-.. ipython:: python
-   :suppress:
+.. code-block:: python
 
    import os
 
@@ -360,14 +357,14 @@ Some East Asian countries use Unicode characters its width is corresponding to 2
 - ``display.unicode.east_asian_width``: Whether to use the Unicode East Asian Width to calculate the display text width. (:issue:`2612`)
 - ``display.unicode.ambiguous_as_wide``: Whether to handle Unicode characters belong to Ambiguous as Wide. (:issue:`11102`)
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame({u"国籍": ["UK", u"日本"], u"名前": ["Alice", u"しのぶ"]})
    df;
 
 .. image:: ../_static/option_unicode01.png
 
-.. ipython:: python
+.. code-block:: python
 
    pd.set_option("display.unicode.east_asian_width", True)
    df;
@@ -376,8 +373,7 @@ Some East Asian countries use Unicode characters its width is corresponding to 2
 
 For further details, see :ref:`here <options.east_asian_width>`
 
-.. ipython:: python
-   :suppress:
+.. code-block:: python
 
    pd.set_option("display.unicode.east_asian_width", False)
 
@@ -397,7 +393,7 @@ Other enhancements
   Merge key in both frames              ``both``
   ===================================   ================
 
-  .. ipython:: python
+  .. code-block:: python
 
     df1 = pd.DataFrame({"col1": [0, 1], "col_left": ["a", "b"]})
     df2 = pd.DataFrame({"col1": [1, 2, 2], "col_right": [2, 2, 2]})
@@ -413,7 +409,7 @@ Other enhancements
 
 - ``pd.concat`` will now use existing Series names if provided (:issue:`10698`).
 
-  .. ipython:: python
+  .. code-block:: python
 
      foo = pd.Series([1, 2], name="foo")
      bar = pd.Series([1, 2])
@@ -431,7 +427,7 @@ Other enhancements
 
   New behavior:
 
-  .. ipython:: python
+  .. code-block:: python
 
     pd.concat([foo, bar, baz], 1)
 
@@ -439,14 +435,14 @@ Other enhancements
 
 - Add a ``limit_direction`` keyword argument that works with ``limit`` to enable ``interpolate`` to fill ``NaN`` values forward, backward, or both (:issue:`9218`, :issue:`10420`, :issue:`11115`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      ser = pd.Series([np.nan, np.nan, 5, np.nan, np.nan, np.nan, 13])
      ser.interpolate(limit=1, limit_direction="both")
 
 - Added a ``DataFrame.round`` method to round the values to a variable number of decimal places (:issue:`10568`).
 
-  .. ipython:: python
+  .. code-block:: python
 
      df = pd.DataFrame(
          np.random.random([3, 3]),
@@ -459,7 +455,7 @@ Other enhancements
 
 - ``drop_duplicates`` and ``duplicated`` now accept a ``keep`` keyword to target first, last, and all duplicates. The ``take_last`` keyword is deprecated, see :ref:`here <whatsnew_0170.deprecations>` (:issue:`6511`, :issue:`8505`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      s = pd.Series(["A", "B", "C", "A", "B", "D"])
      s.drop_duplicates()
@@ -468,14 +464,14 @@ Other enhancements
 
 - Reindex now has a ``tolerance`` argument that allows for finer control of :ref:`basics.limits_on_reindex_fill` (:issue:`10411`):
 
-  .. ipython:: python
+  .. code-block:: python
 
      df = pd.DataFrame({"x": range(5), "t": pd.date_range("2000-01-01", periods=5)})
      df.reindex([0.1, 1.9, 3.5], method="nearest", tolerance=0.2)
 
   When used on a ``DatetimeIndex``, ``TimedeltaIndex`` or ``PeriodIndex``, ``tolerance`` will coerced into a ``Timedelta`` if possible. This allows you to specify tolerance with a string:
 
-  .. ipython:: python
+  .. code-block:: python
 
      df = df.set_index("t")
      df.reindex(pd.to_datetime(["1999-12-31"]), method="nearest", tolerance="1 day")
@@ -630,13 +626,13 @@ New behavior:
 
 Of course you can coerce this as well.
 
-.. ipython:: python
+.. code-block:: python
 
    pd.to_datetime(["2009-07-31", "asd"], errors="coerce")
 
 To keep the previous behavior, you can use ``errors='ignore'``:
 
-.. ipython:: python
+.. code-block:: python
 
    pd.to_datetime(["2009-07-31", "asd"], errors="ignore")
 
@@ -670,7 +666,7 @@ v0.17.0 can parse them as below. It works on ``DatetimeIndex`` also.
 
 New behavior:
 
-.. ipython:: python
+.. code-block:: python
 
    pd.Timestamp("2012Q2")
    pd.Timestamp("2014")
@@ -680,7 +676,7 @@ New behavior:
 
    If you want to perform calculations based on today's date, use ``Timestamp.now()`` and ``pandas.tseries.offsets``.
 
-   .. ipython:: python
+   .. code-block:: python
 
       import pandas.tseries.offsets as offsets
 
@@ -724,14 +720,13 @@ New behavior:
 Note that this is different from the ``numpy`` behavior where a comparison can
 be broadcast:
 
-.. ipython:: python
+.. code-block:: python
 
    np.array([1, 2, 3]) == np.array([1])
 
 or it can return False if broadcasting can not be done:
 
-.. ipython:: python
-   :okwarning:
+.. code-block:: python
 
    np.array([1, 2, 3]) == np.array([1, 2])
 
@@ -740,7 +735,7 @@ Changes to boolean comparisons vs. None
 
 Boolean comparisons of a ``Series`` vs ``None`` will now be equivalent to comparing with ``np.nan``, rather than raise ``TypeError``. (:issue:`1079`).
 
-.. ipython:: python
+.. code-block:: python
 
    s = pd.Series(range(3))
    s.iloc[1] = None
@@ -755,13 +750,13 @@ Previous behavior:
 
 New behavior:
 
-.. ipython:: python
+.. code-block:: python
 
    s == None
 
 Usually you simply want to know which values are null.
 
-.. ipython:: python
+.. code-block:: python
 
    s.isnull()
 
@@ -770,7 +765,7 @@ Usually you simply want to know which values are null.
    You generally will want to use ``isnull/notnull`` for these types of comparisons, as ``isnull/notnull`` tells you which elements are null. One has to be
    mindful that ``nan's`` don't compare equal, but ``None's`` do. Note that pandas/numpy uses the fact that ``np.nan != np.nan``, and treats ``None`` like ``np.nan``.
 
-   .. ipython:: python
+   .. code-block:: python
 
       None == None
       np.nan == np.nan
@@ -784,7 +779,7 @@ The default behavior for HDFStore write functions with ``format='table'`` is now
 
 Previous behavior:
 
-.. ipython:: python
+.. code-block:: python
 
    df_with_missing = pd.DataFrame(
        {"col1": [0, np.nan, 2], "col2": [1, np.nan, np.nan]}
@@ -811,14 +806,13 @@ Previous behavior:
 
 New behavior:
 
-.. ipython:: python
+.. code-block:: python
 
    df_with_missing.to_hdf("file.h5", "df_with_missing", format="table", mode="w")
 
    pd.read_hdf("file.h5", "df_with_missing")
 
-.. ipython:: python
-   :suppress:
+.. code-block:: python
 
    import os
 
@@ -851,7 +845,7 @@ did not work for values with standard formatting. It was also out of step with h
 Going forward the value of ``display.precision`` will directly control the number of places after the decimal, for
 regular formatting as well as scientific notation, similar to how numpy's ``precision`` print option works.
 
-.. ipython:: python
+.. code-block:: python
 
   pd.set_option("display.precision", 2)
   pd.DataFrame({"x": [123.456789]})
@@ -859,8 +853,7 @@ regular formatting as well as scientific notation, similar to how numpy's ``prec
 To preserve output behavior with prior versions the default value of ``display.precision`` has been reduced to ``6``
 from ``7``.
 
-.. ipython:: python
-  :suppress:
+.. code-block:: python
 
   pd.set_option("display.precision", 6)
 
@@ -874,7 +867,7 @@ Changes to ``Categorical.unique``
 - unordered category: values and categories are sorted by appearance order.
 - ordered category: values are sorted by appearance order, categories keep existing order.
 
-.. ipython:: python
+.. code-block:: python
 
    cat = pd.Categorical(["C", "A", "B", "C"], categories=["A", "B", "C"], ordered=True)
    cat
@@ -979,7 +972,7 @@ Removal of prior version deprecations/changes
 - Removal of ``colSpace`` parameter from ``DataFrame.to_string()``, in favor of ``col_space``, circa 0.8.0 version.
 - Removal of automatic time-series broadcasting (:issue:`2304`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      np.random.seed(1234)
      df = pd.DataFrame(
@@ -1007,7 +1000,7 @@ Removal of prior version deprecations/changes
 
   Current
 
-  .. ipython:: python
+  .. code-block:: python
 
      df.add(df.A, axis="index")
 
diff --git a/doc/source/whatsnew/v0.17.1.rst b/doc/source/whatsnew/v0.17.1.rst
index 6b0a28ec47568..2db4a9c201e95 100644
--- a/doc/source/whatsnew/v0.17.1.rst
+++ b/doc/source/whatsnew/v0.17.1.rst
@@ -49,7 +49,7 @@ an instance of :class:`~pandas.core.style.Styler` with your data attached.
 
 Here's a quick example:
 
-  .. ipython:: python
+  .. code-block:: python
 
     np.random.seed(123)
     df = pd.DataFrame(np.random.randn(10, 5), columns=list("abcde"))
@@ -78,7 +78,7 @@ Enhancements
 - Added ``axvlines_kwds`` to parallel coordinates plot (:issue:`10709`)
 - Option to ``.info()`` and ``.memory_usage()`` to provide for deep introspection of memory consumption. Note that this can be expensive to compute and therefore is an optional parameter. (:issue:`11595`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      df = pd.DataFrame({"A": ["foo"] * 1000})  # noqa: F821
      df["B"] = df["A"].astype("category")
@@ -91,13 +91,13 @@ Enhancements
 
 - ``Index`` now has a ``fillna`` method (:issue:`10089`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      pd.Index([1, np.nan, 3]).fillna(2)
 
 - Series of type ``category`` now make ``.str.<...>`` and ``.dt.<...>`` accessor methods / properties available, if the categories are of that type. (:issue:`10661`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      s = pd.Series(list("aabb")).astype("category")
      s
diff --git a/doc/source/whatsnew/v0.18.0.rst b/doc/source/whatsnew/v0.18.0.rst
index 829c04dac9f2d..07a078eee18e5 100644
--- a/doc/source/whatsnew/v0.18.0.rst
+++ b/doc/source/whatsnew/v0.18.0.rst
@@ -56,7 +56,7 @@ Window functions are now methods
 Window functions have been refactored to be methods on ``Series/DataFrame`` objects, rather than top-level functions, which are now deprecated. This allows these window-type functions, to have a similar API to that of ``.groupby``. See the full documentation :ref:`here <window.overview>` (:issue:`11603`, :issue:`12373`)
 
 
-.. ipython:: python
+.. code-block:: python
 
    np.random.seed(1234)
    df = pd.DataFrame({'A': range(10), 'B': np.random.randn(10)})
@@ -84,15 +84,16 @@ Previous behavior:
 
 New behavior:
 
-.. ipython:: python
+.. code-block:: python
 
    r = df.rolling(window=3)
 
 These show a descriptive repr
 
-.. ipython:: python
+.. code-block:: python
 
    r
+
 with tab-completion of available methods and properties.
 
 .. code-block:: ipython
@@ -103,19 +104,19 @@ with tab-completion of available methods and properties.
 
 The methods operate on the ``Rolling`` object itself
 
-.. ipython:: python
+.. code-block:: python
 
    r.mean()
 
 They provide getitem accessors
 
-.. ipython:: python
+.. code-block:: python
 
    r['A'].mean()
 
 And multiple aggregations
 
-.. ipython:: python
+.. code-block:: python
 
    r.agg({'A': ['mean', 'std'],
           'B': ['mean', 'std']})
@@ -128,12 +129,12 @@ Changes to rename
 ``Series.rename`` and ``NDFrame.rename_axis`` can now take a scalar or list-like
 argument for altering the Series or axis *name*, in addition to their old behaviors of altering labels. (:issue:`9494`, :issue:`11965`)
 
-.. ipython:: python
+.. code-block:: python
 
    s = pd.Series(np.random.randn(5))
    s.rename('newname')
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame(np.random.randn(5, 2))
    (df.rename_axis("indexname")
@@ -170,7 +171,7 @@ Previous behavior:
 
 New behavior:
 
-.. ipython:: python
+.. code-block:: python
 
    s = pd.Series(range(1000))
    s.index
@@ -209,20 +210,20 @@ Currently the default is ``expand=None`` which gives a ``FutureWarning`` and use
 Extracting a regular expression with one group returns a Series if
 ``expand=False``.
 
-.. ipython:: python
+.. code-block:: python
 
    pd.Series(['a1', 'b2', 'c3']).str.extract(r'[ab](\d)', expand=False)
 
 It returns a ``DataFrame`` with one column if ``expand=True``.
 
-.. ipython:: python
+.. code-block:: python
 
    pd.Series(['a1', 'b2', 'c3']).str.extract(r'[ab](\d)', expand=True)
 
 Calling on an ``Index`` with a regex with exactly one capture group
 returns an ``Index`` if ``expand=False``.
 
-.. ipython:: python
+.. code-block:: python
 
    s = pd.Series(["a1", "b2", "c3"], ["A11", "B22", "C33"])
    s.index
@@ -230,7 +231,7 @@ returns an ``Index`` if ``expand=False``.
 
 It returns a ``DataFrame`` with one column if ``expand=True``.
 
-.. ipython:: python
+.. code-block:: python
 
    s.index.str.extract("(?P<letter>[a-zA-Z])", expand=True)
 
@@ -244,7 +245,7 @@ raises ``ValueError`` if ``expand=False``.
 
 It returns a ``DataFrame`` if ``expand=True``.
 
-.. ipython:: python
+.. code-block:: python
 
    s.index.str.extract("(?P<letter>[a-zA-Z])([0-9]+)", expand=True)
 
@@ -261,7 +262,7 @@ The :ref:`.str.extractall <text.extractall>` method was added
 (:issue:`11386`).  Unlike ``extract``, which returns only the first
 match.
 
-.. ipython:: python
+.. code-block:: python
 
    s = pd.Series(["a1a2", "b1", "c1"], ["A", "B", "C"])
    s
@@ -269,7 +270,7 @@ match.
 
 The ``extractall`` method returns all matches.
 
-.. ipython:: python
+.. code-block:: python
 
    s.str.extractall(r"(?P<letter>[ab])(?P<digit>\d)")
 
@@ -282,7 +283,7 @@ The method ``.str.cat()`` concatenates the members of a ``Series``. Before, if `
 
 A new, friendlier ``ValueError`` is added to protect against the mistake of supplying the ``sep`` as an arg, rather than as a kwarg. (:issue:`11334`).
 
-.. ipython:: python
+.. code-block:: python
 
     pd.Series(['a', 'b', np.nan, 'c']).str.cat(sep=' ')
     pd.Series(['a', 'b', np.nan, 'c']).str.cat(sep=' ', na_rep='?')
@@ -302,7 +303,7 @@ Datetimelike rounding
 
 Naive datetimes
 
-.. ipython:: python
+.. code-block:: python
 
    dr = pd.date_range('20130101 09:12:56.1234', periods=3)
    dr
@@ -314,7 +315,7 @@ Naive datetimes
 
 Tz-aware are rounded, floored and ceiled in local times
 
-.. ipython:: python
+.. code-block:: python
 
    dr = dr.tz_localize('US/Eastern')
    dr
@@ -322,7 +323,7 @@ Tz-aware are rounded, floored and ceiled in local times
 
 Timedeltas
 
-.. ipython:: python
+.. code-block:: python
 
    t = pd.timedelta_range('1 days 2 hr 13 min 45 us', periods=3, freq='d')
    t
@@ -335,7 +336,7 @@ Timedeltas
 
 In addition, ``.round()``, ``.floor()`` and ``.ceil()`` will be available through the ``.dt`` accessor of ``Series``.
 
-.. ipython:: python
+.. code-block:: python
 
    s = pd.Series(dr)
    s
@@ -371,7 +372,7 @@ Previous behavior:
 
 New behavior:
 
-.. ipython:: python
+.. code-block:: python
 
    s = pd.Series([1, 2, 3], index=np.arange(3.))
    s
@@ -408,7 +409,7 @@ Previous behavior:
 
 New behavior:
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame({'a': [0, 1, 1],
                       'b': pd.Series([100, 200, 300], dtype='uint32')})
@@ -445,7 +446,7 @@ Previous behavior:
 
 New behavior:
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame(np.array(range(1,10)).reshape(3,3),
                      columns=list('abc'),
@@ -550,7 +551,7 @@ are now also defined for ``NaT`` (:issue:`11564`).
 
 ``NaT`` now supports arithmetic operations with integers and floats.
 
-.. ipython:: python
+.. code-block:: python
 
    pd.NaT * 1
    pd.NaT * 1.5
@@ -559,7 +560,7 @@ are now also defined for ``NaT`` (:issue:`11564`).
 
 ``NaT`` defines more arithmetic operations with ``datetime64[ns]`` and ``timedelta64[ns]``.
 
-.. ipython:: python
+.. code-block:: python
 
    pd.NaT / pd.NaT
    pd.Timedelta('1s') / pd.NaT
@@ -568,7 +569,7 @@ are now also defined for ``NaT`` (:issue:`11564`).
 Given the ambiguity, it is treated as a ``timedelta64[ns]``, which allows more operations
 to succeed.
 
-.. ipython:: python
+.. code-block:: python
 
    pd.NaT + pd.NaT
 
@@ -591,19 +592,19 @@ the ``dtype`` information is respected.
    TypeError: can only operate on a datetimes for subtraction,
               but the operator [__add__] was passed
 
-.. ipython:: python
+.. code-block:: python
 
    pd.Series([pd.NaT], dtype='<m8[ns]') + pd.Series([pd.NaT], dtype='<m8[ns]')
 
 ``Timedelta`` division by ``floats`` now works.
 
-.. ipython:: python
+.. code-block:: python
 
    pd.Timedelta('1s') / 2.0
 
 Subtraction by ``Timedelta`` in a ``Series`` by a ``Timestamp`` works (:issue:`11925`)
 
-.. ipython:: python
+.. code-block:: python
 
    ser = pd.Series(pd.timedelta_range('1 day', periods=3))
    ser
@@ -667,7 +668,7 @@ Previous signature
 
 New signature
 
-.. ipython:: python
+.. code-block:: python
 
    pd.Series([0,1]).rank(axis=0, method='average', numeric_only=None,
                          na_option='keep', ascending=True, pct=False)
@@ -685,7 +686,7 @@ The general semantics of anchored offsets for ``n=0`` is to not move the date
 when it is an anchor point (e.g., a quarter start date), and otherwise roll
 forward to the next anchor point.
 
-.. ipython:: python
+.. code-block:: python
 
    d = pd.Timestamp('2014-02-01')
    d
@@ -705,7 +706,7 @@ For the ``QuarterBegin`` offset in previous versions, the date would be rolled
 This behavior has been corrected in version 0.18.0, which is consistent with
 other anchored offsets like ``MonthBegin`` and ``YearBegin``.
 
-.. ipython:: python
+.. code-block:: python
 
    d = pd.Timestamp('2014-02-15')
    d + pd.offsets.QuarterBegin(n=0, startingMonth=2)
@@ -717,7 +718,7 @@ Resample API
 
 Like the change in the window functions API :ref:`above <whatsnew_0180.enhancements.moments>`, ``.resample(...)`` is changing to have a more groupby-like API. (:issue:`11732`, :issue:`12702`, :issue:`12202`, :issue:`12332`, :issue:`12334`, :issue:`12348`, :issue:`12448`).
 
-.. ipython:: python
+.. code-block:: python
 
    np.random.seed(1234)
    df = pd.DataFrame(np.random.rand(10,4),
@@ -761,8 +762,7 @@ You could also specify a ``how`` directly
 Now, you can write ``.resample(..)`` as a 2-stage operation like ``.groupby(...)``, which
 yields a ``Resampler``.
 
-.. ipython:: python
-   :okwarning:
+.. code-block:: python
 
    r = df.resample('2s')
    r
@@ -773,29 +773,29 @@ Downsampling
 You can then use this object to perform operations.
 These are downsampling operations (going from a higher frequency to a lower one).
 
-.. ipython:: python
+.. code-block:: python
 
    r.mean()
 
-.. ipython:: python
+.. code-block:: python
 
    r.sum()
 
 Furthermore, resample now supports ``getitem`` operations to perform the resample on specific columns.
 
-.. ipython:: python
+.. code-block:: python
 
    r[['A','C']].mean()
 
 and ``.aggregate`` type operations.
 
-.. ipython:: python
+.. code-block:: python
 
    r.agg({'A' : 'mean', 'B' : 'sum'})
 
 These accessors can of course, be combined
 
-.. ipython:: python
+.. code-block:: python
 
    r[['A','B']].agg(['mean','sum'])
 
@@ -808,7 +808,7 @@ Upsampling operations take you from a lower frequency to a higher frequency. The
 performed with the ``Resampler`` objects with :meth:`~Resampler.backfill`,
 :meth:`~Resampler.ffill`, :meth:`~Resampler.fillna` and :meth:`~Resampler.asfreq` methods.
 
-.. ipython:: python
+.. code-block:: python
 
    s = pd.Series(np.arange(5, dtype='int64'),
                  index=pd.date_range('2010-01-01', periods=5, freq='Q'))
@@ -837,7 +837,7 @@ Previously
 
 New API
 
-.. ipython:: python
+.. code-block:: python
 
    s.resample('M').ffill()
 
@@ -891,7 +891,7 @@ Previous API will work but with deprecations
 
    The new API will:
 
-   .. ipython:: python
+   .. code-block:: python
 
       df.resample('2s').min()
 
@@ -900,7 +900,7 @@ Previous API will work but with deprecations
 
    To replicate the original operation
 
-   .. ipython:: python
+   .. code-block:: python
 
       df.resample('2s').mean().min()
 
@@ -910,13 +910,12 @@ Changes to eval
 In prior versions, new columns assignments in an ``eval`` expression resulted
 in an inplace change to the ``DataFrame``. (:issue:`9297`, :issue:`8664`, :issue:`10486`)
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame({'a': np.linspace(0, 10, 5), 'b': range(5)})
    df
 
-.. ipython:: python
-   :suppress:
+.. code-block:: python
 
    df.eval('c = a + b', inplace=True)
 
@@ -938,7 +937,7 @@ in an inplace change to the ``DataFrame``. (:issue:`9297`, :issue:`8664`, :issue
 In version 0.18.0, a new ``inplace`` keyword was added to choose whether the
 assignment should be done inplace or return a copy.
 
-.. ipython:: python
+.. code-block:: python
 
    df
    df.eval('d = c - b', inplace=False)
@@ -954,7 +953,7 @@ assignment should be done inplace or return a copy.
 
 The ``inplace`` keyword parameter was also added the ``query`` method.
 
-.. ipython:: python
+.. code-block:: python
 
    df.query('a > 5')
    df.query('a > 5', inplace=True)
@@ -969,7 +968,7 @@ The ``inplace`` keyword parameter was also added the ``query`` method.
 assignments.  These expressions will be evaluated one at a time in order.  Only
 assignments are valid for multi-line expressions.
 
-.. ipython:: python
+.. code-block:: python
 
    df
    df.eval("""
@@ -985,7 +984,7 @@ Other API changes
 ^^^^^^^^^^^^^^^^^
 - ``DataFrame.between_time`` and ``Series.between_time`` now only parse a fixed set of time strings. Parsing of date strings is no longer supported and raises a ``ValueError``. (:issue:`11818`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      s = pd.Series(range(10), pd.date_range('2015-01-01', freq='H', periods=10))
      s.between_time("7:00am", "9:00am")
@@ -1067,7 +1066,7 @@ Removal of deprecated float indexers
 In :issue:`4892` indexing with floating point numbers on a non-``Float64Index`` was deprecated (in version 0.14.0).
 In 0.18.0, this deprecation warning is removed and these will now raise a ``TypeError``. (:issue:`12165`, :issue:`12333`)
 
-.. ipython:: python
+.. code-block:: python
 
    s = pd.Series([1, 2, 3], index=[4, 5, 6])
    s
@@ -1115,14 +1114,14 @@ For iloc, getting & setting via a float scalar will always raise.
 
 Other indexers will coerce to a like integer for both getting and setting. The ``FutureWarning`` has been dropped for ``.loc``, ``.ix`` and ``[]``.
 
-.. ipython:: python
+.. code-block:: python
 
    s[5.0]
    s.loc[5.0]
 
 and setting
 
-.. ipython:: python
+.. code-block:: python
 
    s_copy = s.copy()
    s_copy[5.0] = 10
@@ -1146,19 +1145,19 @@ Positional setting with ``.ix`` and a float indexer will ADD this value to the i
 
 Slicing will also coerce integer-like floats to integers for a non-``Float64Index``.
 
-.. ipython:: python
+.. code-block:: python
 
    s.loc[5.0:6]
 
 Note that for floats that are NOT coercible to ints, the label based bounds will be excluded
 
-.. ipython:: python
+.. code-block:: python
 
    s.loc[5.1:6]
 
 Float indexing on a ``Float64Index`` is unchanged.
 
-.. ipython:: python
+.. code-block:: python
 
    s = pd.Series([1, 2, 3], index=np.arange(3.))
    s[1.0]
diff --git a/doc/source/whatsnew/v0.18.1.rst b/doc/source/whatsnew/v0.18.1.rst
index 3db00f686d62c..0d7783d24ae80 100644
--- a/doc/source/whatsnew/v0.18.1.rst
+++ b/doc/source/whatsnew/v0.18.1.rst
@@ -38,7 +38,7 @@ The ``CustomBusinessHour`` is a mixture of ``BusinessHour`` and ``CustomBusiness
 allows you to specify arbitrary holidays. For details,
 see :ref:`Custom Business Hour <timeseries.custombusinesshour>` (:issue:`11514`)
 
-.. ipython:: python
+.. code-block:: python
 
     from pandas.tseries.offsets import CustomBusinessHour
     from pandas.tseries.holiday import USFederalHolidayCalendar
@@ -47,7 +47,7 @@ see :ref:`Custom Business Hour <timeseries.custombusinesshour>` (:issue:`11514`)
 
 Friday before MLK Day
 
-.. ipython:: python
+.. code-block:: python
 
     import datetime
 
@@ -57,7 +57,7 @@ Friday before MLK Day
 
 Tuesday after MLK Day (Monday is skipped because it's a holiday)
 
-.. ipython:: python
+.. code-block:: python
 
     dt + bhour_us * 2
 
@@ -72,24 +72,24 @@ You can now use ``.rolling(..)`` and ``.expanding(..)`` as methods on groupbys.
 
 Previously you would have to do this to get a rolling window mean per-group:
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame({"A": [1] * 20 + [2] * 12 + [3] * 8, "B": np.arange(40)})
    df
 
-.. ipython:: python
+.. code-block:: python
 
    df.groupby("A").apply(lambda x: x.rolling(4).B.mean())
 
 Now you can do:
 
-.. ipython:: python
+.. code-block:: python
 
    df.groupby("A").rolling(4).B.mean()
 
 For ``.resample(..)`` type of operations, previously you would have to:
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame(
        {
@@ -101,13 +101,13 @@ For ``.resample(..)`` type of operations, previously you would have to:
 
    df
 
-.. ipython:: python
+.. code-block:: python
 
    df.groupby("group").apply(lambda x: x.resample("1D").ffill())
 
 Now you can do:
 
-.. ipython:: python
+.. code-block:: python
 
    df.groupby("group").resample("1D").ffill()
 
@@ -130,7 +130,7 @@ Methods ``.where()`` and ``.mask()``
 These can accept a callable for the condition and ``other``
 arguments.
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
    df.where(lambda x: x > 4, lambda x: x + 10)
@@ -141,7 +141,7 @@ Methods ``.loc[]``, ``.iloc[]``, ``.ix[]``
 These can accept a callable, and a tuple of callable as a slicer. The callable
 can return a valid boolean indexer or anything which is valid for these indexer's input.
 
-.. ipython:: python
+.. code-block:: python
 
    # callable returns bool indexer
    df.loc[lambda x: x.A >= 2, lambda x: x.sum() > 10]
@@ -156,14 +156,14 @@ Finally, you can use a callable in ``[]`` indexing of Series, DataFrame and Pane
 The callable must return a valid input for ``[]`` indexing depending on its
 class and index type.
 
-.. ipython:: python
+.. code-block:: python
 
    df[lambda x: "A"]
 
 Using these methods / indexers, you can chain data selection operations
 without using temporary variable.
 
-.. ipython:: python
+.. code-block:: python
 
    bb = pd.read_csv("data/baseball.csv", index_col="id")
    (bb.groupby(["year", "team"]).sum().loc[lambda df: df.r > 100])
@@ -175,7 +175,7 @@ Partial string indexing on ``DatetimeIndex`` when part of a ``MultiIndex``
 
 Partial string indexing now matches on ``DateTimeIndex`` when part of a ``MultiIndex`` (:issue:`10331`)
 
-.. ipython:: python
+.. code-block:: python
 
    dft2 = pd.DataFrame(
        np.random.randn(20, 1),
@@ -189,7 +189,7 @@ Partial string indexing now matches on ``DateTimeIndex`` when part of a ``MultiI
 
 On other levels
 
-.. ipython:: python
+.. code-block:: python
 
    idx = pd.IndexSlice
    dft2 = dft2.swaplevel(0, 1).sort_index()
@@ -203,7 +203,7 @@ Assembling datetimes
 
 ``pd.to_datetime()`` has gained the ability to assemble datetimes from a passed in ``DataFrame`` or a dict. (:issue:`8158`).
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame(
        {"year": [2015, 2016], "month": [2, 3], "day": [4, 5], "hour": [2, 3]}
@@ -212,13 +212,13 @@ Assembling datetimes
 
 Assembling using the passed frame.
 
-.. ipython:: python
+.. code-block:: python
 
    pd.to_datetime(df)
 
 You can pass only the columns that you need to assemble.
 
-.. ipython:: python
+.. code-block:: python
 
    pd.to_datetime(df[["year", "month", "day"]])
 
@@ -239,7 +239,7 @@ Other enhancements
 
 - ``Index.take`` now handles ``allow_fill`` and ``fill_value`` consistently (:issue:`12631`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      idx = pd.Index([1.0, 2.0, 3.0, 4.0], dtype="float")
 
@@ -249,7 +249,7 @@ Other enhancements
 
 - ``Index`` now supports ``.str.get_dummies()`` which returns ``MultiIndex``, see :ref:`Creating Indicator Variables <text.indicator>` (:issue:`10008`, :issue:`10103`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      idx = pd.Index(["a|b", "a|c", "b|c"])
      idx.str.get_dummies("|")
@@ -309,7 +309,7 @@ Method ``.groupby(..).nth()`` changes
 
 The index in ``.groupby(..).nth()`` output is now more consistent when the ``as_index`` argument is passed (:issue:`11039`):
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame({"A": ["a", "b", "a"], "B": [1, 2, 3]})
    df
@@ -332,14 +332,14 @@ Previous behavior:
 
 New behavior:
 
-.. ipython:: python
+.. code-block:: python
 
     df.groupby("A", as_index=True)["B"].nth(0)
     df.groupby("A", as_index=False)["B"].nth(0)
 
 Furthermore, previously, a ``.groupby`` would always sort, regardless if ``sort=False`` was passed with ``.nth()``.
 
-.. ipython:: python
+.. code-block:: python
 
    np.random.seed(1234)
    df = pd.DataFrame(np.random.randn(100, 2), columns=["a", "b"])
@@ -369,7 +369,7 @@ Previous behavior:
 
 New behavior:
 
-.. ipython:: python
+.. code-block:: python
 
    df.groupby("c", sort=True).nth(1)
    df.groupby("c", sort=False).nth(1)
@@ -416,7 +416,7 @@ Using ``.apply`` on GroupBy resampling
 
 Using ``apply`` on resampling groupby operations (using a ``pd.TimeGrouper``) now has the same output types as similar ``apply`` calls on other groupby operations. (:issue:`11742`).
 
-.. ipython:: python
+.. code-block:: python
 
     df = pd.DataFrame(
         {"date": pd.to_datetime(["10/10/2000", "11/10/2000"]), "value": [10, 13]}
diff --git a/doc/source/whatsnew/v0.19.0.rst b/doc/source/whatsnew/v0.19.0.rst
index 340e1ce9ee1ef..29abc32f16bae 100644
--- a/doc/source/whatsnew/v0.19.0.rst
+++ b/doc/source/whatsnew/v0.19.0.rst
@@ -47,7 +47,7 @@ support asof style joining of time-series (:issue:`1870`, :issue:`13695`, :issue
 The :func:`merge_asof` performs an asof merge, which is similar to a left-join
 except that we match on nearest key rather than equal keys.
 
-.. ipython:: python
+.. code-block:: python
 
    left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
    right = pd.DataFrame({"a": [1, 2, 3, 6, 7], "right_val": [1, 2, 3, 6, 7]})
@@ -58,13 +58,13 @@ except that we match on nearest key rather than equal keys.
 We typically want to match exactly when possible, and use the most
 recent value otherwise.
 
-.. ipython:: python
+.. code-block:: python
 
    pd.merge_asof(left, right, on="a")
 
 We can also match rows ONLY with prior data, and not an exact match.
 
-.. ipython:: python
+.. code-block:: python
 
    pd.merge_asof(left, right, on="a", allow_exact_matches=False)
 
@@ -72,7 +72,7 @@ We can also match rows ONLY with prior data, and not an exact match.
 In a typical time-series example, we have ``trades`` and ``quotes`` and we want to ``asof-join`` them.
 This also illustrates using the ``by`` parameter to group data before merging.
 
-.. ipython:: python
+.. code-block:: python
 
    trades = pd.DataFrame(
        {
@@ -113,7 +113,7 @@ This also illustrates using the ``by`` parameter to group data before merging.
        columns=["time", "ticker", "bid", "ask"],
    )
 
-.. ipython:: python
+.. code-block:: python
 
    trades
    quotes
@@ -122,7 +122,7 @@ An asof merge joins on the ``on``, typically a datetimelike field, which is orde
 in this case we are using a grouper in the ``by`` field. This is like a left-outer join, except
 that forward filling happens automatically taking the most recent non-NaN value.
 
-.. ipython:: python
+.. code-block:: python
 
    pd.merge_asof(trades, quotes, on="time", by="ticker")
 
@@ -137,7 +137,7 @@ Method ``.rolling()`` is now time-series aware
 ``.rolling()`` objects are now time-series aware and can accept a time-series offset (or convertible) for the ``window`` argument (:issue:`13327`, :issue:`12995`).
 See the full documentation :ref:`here <window.generic>`.
 
-.. ipython:: python
+.. code-block:: python
 
    dft = pd.DataFrame(
        {"B": [0, 1, 2, np.nan, 4]},
@@ -147,20 +147,20 @@ See the full documentation :ref:`here <window.generic>`.
 
 This is a regular frequency index. Using an integer window parameter works to roll along the window frequency.
 
-.. ipython:: python
+.. code-block:: python
 
    dft.rolling(2).sum()
    dft.rolling(2, min_periods=1).sum()
 
 Specifying an offset allows a more intuitive specification of the rolling frequency.
 
-.. ipython:: python
+.. code-block:: python
 
    dft.rolling("2s").sum()
 
 Using a non-regular, but still monotonic index, rolling with an integer window does not impart any special calculation.
 
-.. ipython:: python
+.. code-block:: python
 
 
    dft = pd.DataFrame(
@@ -182,14 +182,14 @@ Using a non-regular, but still monotonic index, rolling with an integer window d
 
 Using the time-specification generates variable windows for this sparse data.
 
-.. ipython:: python
+.. code-block:: python
 
    dft.rolling("2s").sum()
 
 Furthermore, we now allow an optional ``on`` parameter to specify a column (rather than the
 default of the index) in a DataFrame.
 
-.. ipython:: python
+.. code-block:: python
 
    dft = dft.reset_index()
    dft
@@ -200,15 +200,14 @@ default of the index) in a DataFrame.
 Method ``read_csv`` has improved support for duplicate column names
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. ipython:: python
-   :suppress:
+.. code-block:: python
 
    from io import StringIO
 
 :ref:`Duplicate column names <io.dupe_names>` are now supported in :func:`read_csv` whether
 they are in the file or passed in as the ``names`` parameter (:issue:`7160`, :issue:`9424`)
 
-.. ipython:: python
+.. code-block:: python
 
    data = "0,1,2\n3,4,5"
    names = ["a", "b", "a"]
@@ -228,8 +227,7 @@ contained the values ``[0, 3]``.
 
 **New behavior**:
 
-.. ipython:: python
-   :okexcept:
+.. code-block:: python
 
    pd.read_csv(StringIO(data), names=names)
 
@@ -244,7 +242,7 @@ specified as a dtype (:issue:`10153`).  Depending on the structure of the data,
 this can result in a faster parse time and lower memory usage compared to
 converting to ``Categorical`` after parsing.  See the io :ref:`docs here <io.categorical>`.
 
-.. ipython:: python
+.. code-block:: python
 
    data = """
    col1,col2,col3
@@ -259,7 +257,7 @@ converting to ``Categorical`` after parsing.  See the io :ref:`docs here <io.cat
 
 Individual columns can be parsed as a ``Categorical`` using a dict specification
 
-.. ipython:: python
+.. code-block:: python
 
    pd.read_csv(StringIO(data), dtype={"col1": "category"}).dtypes
 
@@ -270,7 +268,7 @@ Individual columns can be parsed as a ``Categorical`` using a dict specification
    :func:`to_numeric` function, or as appropriate, another converter
    such as :func:`to_datetime`.
 
-   .. ipython:: python
+   .. code-block:: python
 
       df = pd.read_csv(StringIO(data), dtype="category")
       df.dtypes
@@ -285,7 +283,7 @@ Categorical concatenation
 
 - A function :func:`union_categoricals` has been added for combining categoricals, see :ref:`Unioning Categoricals<categorical.union>` (:issue:`13361`, :issue:`13763`, :issue:`13846`, :issue:`14173`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      from pandas.api.types import union_categoricals
 
@@ -295,7 +293,7 @@ Categorical concatenation
 
 - ``concat`` and ``append`` now can concat ``category`` dtypes with different ``categories`` as ``object`` dtype (:issue:`13524`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      s1 = pd.Series(["a", "b"], dtype="category")
      s2 = pd.Series(["b", "c"], dtype="category")
@@ -309,7 +307,7 @@ Categorical concatenation
 
 **New behavior**:
 
-.. ipython:: python
+.. code-block:: python
 
    pd.concat([s1, s2])
 
@@ -322,13 +320,13 @@ pandas has gained new frequency offsets, ``SemiMonthEnd`` ('SM') and ``SemiMonth
 These provide date offsets anchored (by default) to the 15th and end of month, and 15th and 1st of month respectively.
 (:issue:`1543`)
 
-.. ipython:: python
+.. code-block:: python
 
    from pandas.tseries.offsets import SemiMonthEnd, SemiMonthBegin
 
 **SemiMonthEnd**:
 
-.. ipython:: python
+.. code-block:: python
 
    pd.Timestamp("2016-01-01") + SemiMonthEnd()
 
@@ -336,7 +334,7 @@ These provide date offsets anchored (by default) to the 15th and end of month, a
 
 **SemiMonthBegin**:
 
-.. ipython:: python
+.. code-block:: python
 
    pd.Timestamp("2016-01-01") + SemiMonthBegin()
 
@@ -344,7 +342,7 @@ These provide date offsets anchored (by default) to the 15th and end of month, a
 
 Using the anchoring suffix, you can also specify the day of month to use instead of the 15th.
 
-.. ipython:: python
+.. code-block:: python
 
    pd.date_range("2015-01-01", freq="SMS-16", periods=4)
 
@@ -359,7 +357,7 @@ The following methods and options are added to ``Index``, to be more consistent
 
 ``Index`` now supports the ``.where()`` function for same shape indexing (:issue:`13170`)
 
-.. ipython:: python
+.. code-block:: python
 
    idx = pd.Index(["a", "b", "c"])
    idx.where([True, False, True])
@@ -367,7 +365,7 @@ The following methods and options are added to ``Index``, to be more consistent
 
 ``Index`` now supports ``.dropna()`` to exclude missing values (:issue:`6194`)
 
-.. ipython:: python
+.. code-block:: python
 
    idx = pd.Index([1, 2, np.nan, 4])
    idx.dropna()
@@ -375,7 +373,7 @@ The following methods and options are added to ``Index``, to be more consistent
 For ``MultiIndex``, values are dropped if any level is missing by default. Specifying
 ``how='all'`` only drops values where all levels are missing.
 
-.. ipython:: python
+.. code-block:: python
 
    midx = pd.MultiIndex.from_arrays([[1, 2, np.nan, 4], [1, 2, np.nan, np.nan]])
    midx
@@ -384,7 +382,7 @@ For ``MultiIndex``, values are dropped if any level is missing by default. Speci
 
 ``Index`` now supports ``.str.extractall()`` which returns a ``DataFrame``, see the :ref:`docs here <text.extractall>` (:issue:`10008`, :issue:`13156`)
 
-.. ipython:: python
+.. code-block:: python
 
    idx = pd.Index(["a1a2", "b1", "c1"])
    idx.str.extractall(r"[ab](?P<digit>\d)")
@@ -429,7 +427,7 @@ The ``pd.get_dummies`` function now returns dummy-encoded columns as small integ
 
 **New behavior**:
 
-.. ipython:: python
+.. code-block:: python
 
    pd.get_dummies(["a", "b", "a", "c"]).dtypes
 
@@ -441,7 +439,7 @@ Downcast values to smallest possible dtype in ``to_numeric``
 
 ``pd.to_numeric()`` now accepts a ``downcast`` parameter, which will downcast the data if possible to smallest specified numerical dtype (:issue:`13352`)
 
-.. ipython:: python
+.. code-block:: python
 
    s = ["1", 2, 3]
    pd.to_numeric(s, downcast="unsigned")
@@ -459,7 +457,7 @@ will be published in future versions of pandas (:issue:`13147`, :issue:`13634`)
 
 The following are now part of this API:
 
-.. ipython:: python
+.. code-block:: python
 
    import pprint
    from pandas.api import types
@@ -479,7 +477,7 @@ Other enhancements
 
 - ``Timestamp`` can now accept positional and keyword parameters similar to :func:`datetime.datetime` (:issue:`10758`, :issue:`11630`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      pd.Timestamp(2012, 1, 1)
 
@@ -487,7 +485,7 @@ Other enhancements
 
 - The ``.resample()`` function now accepts a ``on=`` or ``level=`` parameter for resampling on a datetimelike column or ``MultiIndex`` level (:issue:`13500`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      df = pd.DataFrame(
          {"date": pd.date_range("2015-01-01", freq="W", periods=5), "a": np.arange(5)},
@@ -522,7 +520,7 @@ Other enhancements
 - ``DataFrame`` has gained support to re-order the columns based on the values
   in a row using ``df.sort_values(by='...', axis=1)`` (:issue:`10806`)
 
-  .. ipython:: python
+  .. code-block:: python
 
      df = pd.DataFrame({"A": [2, 7], "B": [3, 5], "C": [4, 8]}, index=["row1", "row2"])
      df
@@ -556,7 +554,7 @@ API changes
 ``Series.tolist()`` will now return Python types in the output, mimicking NumPy ``.tolist()`` behavior (:issue:`10904`)
 
 
-.. ipython:: python
+.. code-block:: python
 
    s = pd.Series([1, 2, 3])
 
@@ -570,7 +568,7 @@ API changes
 
 **New behavior**:
 
-.. ipython:: python
+.. code-block:: python
 
    type(s.tolist()[0])
 
@@ -597,7 +595,7 @@ Arithmetic operators
 
 Arithmetic operators align both ``index`` (no changes).
 
-.. ipython:: python
+.. code-block:: python
 
    s1 = pd.Series([1, 2, 3], index=list("ABC"))
    s2 = pd.Series([2, 2, 2], index=list("ABD"))
@@ -637,13 +635,13 @@ Comparison operators raise ``ValueError`` when ``.index`` are different.
 
    To achieve the same result as previous versions (compare values based on locations ignoring ``.index``), compare both ``.values``.
 
-   .. ipython:: python
+   .. code-block:: python
 
       s1.values == s2.values
 
    If you want to compare ``Series`` aligning its ``.index``, see flexible comparison methods section below:
 
-   .. ipython:: python
+   .. code-block:: python
 
       s1.eq(s2)
 
@@ -675,7 +673,7 @@ Logical operators align both ``.index`` of left and right hand side.
 
 **New behavior** (``Series``):
 
-.. ipython:: python
+.. code-block:: python
 
    s1 = pd.Series([True, False, True], index=list("ABC"))
    s2 = pd.Series([True, True, True], index=list("ABD"))
@@ -687,13 +685,13 @@ Logical operators align both ``.index`` of left and right hand side.
 .. note::
    To achieve the same result as previous versions (compare values based on only left hand side index), you can use ``reindex_like``:
 
-   .. ipython:: python
+   .. code-block:: python
 
       s1 & s2.reindex_like(s1)
 
 **Current behavior** (``DataFrame``, no change):
 
-.. ipython:: python
+.. code-block:: python
 
    df1 = pd.DataFrame([True, False, True], index=list("ABC"))
    df2 = pd.DataFrame([True, True, True], index=list("ABD"))
@@ -705,7 +703,7 @@ Flexible comparison methods
 ``Series`` flexible comparison methods like ``eq``, ``ne``, ``le``, ``lt``, ``ge`` and ``gt`` now align both ``index``. Use these operators if you want to compare two ``Series``
 which has the different ``index``.
 
-.. ipython:: python
+.. code-block:: python
 
    s1 = pd.Series([1, 2, 3], index=["a", "b", "c"])
    s2 = pd.Series([2, 2, 2], index=["b", "c", "d"])
@@ -722,8 +720,7 @@ Previously, this worked the same as comparison operators (see above).
 A ``Series`` will now correctly promote its dtype for assignment with incompat values to the current dtype (:issue:`13234`)
 
 
-.. ipython:: python
-   :okwarning:
+.. code-block:: python
 
    s = pd.Series()
 
@@ -738,7 +735,7 @@ A ``Series`` will now correctly promote its dtype for assignment with incompat v
 
 **New behavior**:
 
-.. ipython:: python
+.. code-block:: python
 
    s["a"] = pd.Timestamp("2016-01-01")
    s["b"] = 3.0
@@ -763,7 +760,7 @@ Previously if ``.to_datetime()`` encountered mixed integers/floats and strings,
 
 This will now convert integers/floats with the default unit of ``ns``.
 
-.. ipython:: python
+.. code-block:: python
 
    pd.to_datetime([1, "foo"], errors="coerce")
 
@@ -782,7 +779,7 @@ Merging changes
 
 Merging will now preserve the dtype of the join keys (:issue:`8596`)
 
-.. ipython:: python
+.. code-block:: python
 
    df1 = pd.DataFrame({"key": [1], "v1": [10]})
    df1
@@ -810,7 +807,7 @@ Merging will now preserve the dtype of the join keys (:issue:`8596`)
 
 We are able to preserve the join keys
 
-.. ipython:: python
+.. code-block:: python
 
    pd.merge(df1, df2, how="outer")
    pd.merge(df1, df2, how="outer").dtypes
@@ -818,7 +815,7 @@ We are able to preserve the join keys
 Of course if you have missing values that are introduced, then the
 resulting dtype will be upcast, which is unchanged from previous.
 
-.. ipython:: python
+.. code-block:: python
 
    pd.merge(df1, df2, how="outer", on="key")
    pd.merge(df1, df2, how="outer", on="key").dtypes
@@ -830,7 +827,7 @@ Method ``.describe()`` changes
 
 Percentile identifiers in the index of a ``.describe()`` output will now be rounded to the least precision that keeps them distinct (:issue:`13104`)
 
-.. ipython:: python
+.. code-block:: python
 
    s = pd.Series([0, 1, 2, 3, 4])
    df = pd.DataFrame([0, 1, 2, 3, 4])
@@ -864,7 +861,7 @@ The percentiles were rounded to at most one decimal place, which could raise ``V
 
 **New behavior**:
 
-.. ipython:: python
+.. code-block:: python
 
    s.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995, 0.9999])
    df.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995, 0.9999])
@@ -903,7 +900,7 @@ As a consequence of this change, ``PeriodIndex`` no longer has an integer dtype:
 
 **New behavior**:
 
-.. ipython:: python
+.. code-block:: python
 
    pi = pd.PeriodIndex(["2016-08-01"], freq="D")
    pi
@@ -930,7 +927,7 @@ Previously, ``Period`` has its own ``Period('NaT')`` representation different fr
 
 These result in ``pd.NaT`` without providing ``freq`` option.
 
-.. ipython:: python
+.. code-block:: python
 
    pd.Period("NaT")
    pd.Period(None)
@@ -948,7 +945,7 @@ To be compatible with ``Period`` addition and subtraction, ``pd.NaT`` now suppor
 
 **New behavior**:
 
-.. ipython:: python
+.. code-block:: python
 
    pd.NaT + 1
    pd.NaT - 1
@@ -969,7 +966,7 @@ of integers (:issue:`13988`).
 
 **New behavior**:
 
-.. ipython:: python
+.. code-block:: python
 
    pi = pd.PeriodIndex(["2011-01", "2011-02"], freq="M")
    pi.values
@@ -999,7 +996,7 @@ Previous behavior:
 
 **New behavior**: the same operation will now perform element-wise addition:
 
-.. ipython:: python
+.. code-block:: python
 
    pd.Index(["a", "b"]) + pd.Index(["a", "c"])
 
@@ -1007,7 +1004,7 @@ Note that numeric Index objects already performed element-wise operations.
 For example, the behavior of adding two integer Indexes is unchanged.
 The base ``Index`` is now made consistent with this behavior.
 
-.. ipython:: python
+.. code-block:: python
 
    pd.Index([1, 2, 3]) + pd.Index([2, 3, 4])
 
@@ -1025,7 +1022,7 @@ DatetimeIndex objects resulting in a TimedeltaIndex:
 
 **New behavior**:
 
-.. ipython:: python
+.. code-block:: python
 
     (
         pd.DatetimeIndex(["2016-01-01", "2016-01-02"])
@@ -1040,7 +1037,7 @@ DatetimeIndex objects resulting in a TimedeltaIndex:
 
 ``Index.difference`` and ``Index.symmetric_difference`` will now, more consistently, treat ``NaN`` values as any other values. (:issue:`13514`)
 
-.. ipython:: python
+.. code-block:: python
 
    idx1 = pd.Index([1, 2, 3, np.nan])
    idx2 = pd.Index([0, 1, np.nan])
@@ -1057,7 +1054,7 @@ DatetimeIndex objects resulting in a TimedeltaIndex:
 
 **New behavior**:
 
-.. ipython:: python
+.. code-block:: python
 
    idx1.difference(idx2)
    idx1.symmetric_difference(idx2)
@@ -1088,7 +1085,7 @@ Previously, most ``Index`` classes returned ``np.ndarray``, and ``DatetimeIndex`
 
 **New behavior**:
 
-.. ipython:: python
+.. code-block:: python
 
    pd.Index([1, 2, 3]).unique()
    pd.DatetimeIndex(
@@ -1103,7 +1100,7 @@ Previously, most ``Index`` classes returned ``np.ndarray``, and ``DatetimeIndex`
 ``MultiIndex.from_arrays`` and ``MultiIndex.from_product`` will now preserve categorical dtype
 in ``MultiIndex`` levels (:issue:`13743`, :issue:`13854`).
 
-.. ipython:: python
+.. code-block:: python
 
    cat = pd.Categorical(["a", "b"], categories=list("bac"))
    lvl1 = ["foo", "bar"]
@@ -1122,7 +1119,7 @@ in ``MultiIndex`` levels (:issue:`13743`, :issue:`13854`).
 
 **New behavior**: the single level is now a ``CategoricalIndex``:
 
-.. ipython:: python
+.. code-block:: python
 
    midx.levels[0]
    midx.get_level_values(0)
@@ -1130,7 +1127,7 @@ in ``MultiIndex`` levels (:issue:`13743`, :issue:`13854`).
 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
+.. code-block:: python
 
    df = pd.DataFrame({"A": [0, 1], "B": [10, 11], "C": cat})
    df_grouped = df.groupby(by=["A", "C"]).first()
@@ -1160,7 +1157,7 @@ As a consequence, ``groupby`` and ``set_index`` also preserve categorical dtypes
 
 **New behavior**:
 
-.. ipython:: python
+.. code-block:: python
 
    df_grouped.index.levels[1]
    df_grouped.reset_index().dtypes
@@ -1180,7 +1177,7 @@ from ``n`` for the second, and so on, so that, when concatenated, they are ident
 the result of calling :func:`read_csv` without the ``chunksize=`` argument
 (:issue:`12185`).
 
-.. ipython:: python
+.. code-block:: python
 
    data = "A,B\n0,1\n2,3\n4,5\n6,7"
 
@@ -1198,7 +1195,7 @@ the result of calling :func:`read_csv` without the ``chunksize=`` argument
 
 **New behavior**:
 
-.. ipython:: python
+.. code-block:: python
 
    pd.concat(pd.read_csv(StringIO(data), chunksize=2))
 
@@ -1243,8 +1240,7 @@ Previously, sparse data were ``float64`` dtype by default, even if all inputs we
 
 As of v0.19.0, sparse data keeps the input dtype, and uses more appropriate ``fill_value`` defaults (``0`` for ``int64`` dtype, ``False`` for ``bool`` dtype).
 
-.. ipython:: python
-   :okwarning:
+.. code-block:: python
 
    pd.SparseArray([1, 2, 0, 0], dtype=np.int64)
    pd.SparseArray([True, False, False, False])
diff --git a/doc/source/whatsnew/v0.19.1.rst b/doc/source/whatsnew/v0.19.1.rst
index 6ff3fb6900a99..693819d666a12 100644
--- a/doc/source/whatsnew/v0.19.1.rst
+++ b/doc/source/whatsnew/v0.19.1.rst
@@ -5,8 +5,7 @@ Version 0.19.1 (November 3, 2016)
 
 {{ header }}
 
-.. ipython:: python
-   :suppress:
+.. code-block:: python
 
    from pandas import *  # noqa F401, F403
 
diff --git a/doc/source/whatsnew/v0.19.2.rst b/doc/source/whatsnew/v0.19.2.rst
index bba89d78be869..a7b80605e6241 100644
--- a/doc/source/whatsnew/v0.19.2.rst
+++ b/doc/source/whatsnew/v0.19.2.rst
@@ -5,8 +5,7 @@ Version 0.19.2 (December 24, 2016)
 
 {{ header }}
 
-.. ipython:: python
-   :suppress:
+.. code-block:: python
 
    from pandas import *  # noqa F401, F403
 
diff --git a/doc/source/whatsnew/v0.20.0.rst b/doc/source/whatsnew/v0.20.0.rst
index 733995cc718dd..f9c47fd0c83d8 100644
--- a/doc/source/whatsnew/v0.20.0.rst
+++ b/doc/source/whatsnew/v0.20.0.rst
@@ -57,7 +57,7 @@ is :ref:`here <basics.aggregate>` (:issue:`1623`).
 
 Here is a sample
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'],
                      index=pd.date_range('1/1/2000', periods=10))
@@ -68,13 +68,13 @@ One can operate using string function names, callables, lists, or dictionaries o
 
 Using a single function is equivalent to ``.apply``.
 
-.. ipython:: python
+.. code-block:: python
 
    df.agg('sum')
 
 Multiple aggregations with a list of functions.
 
-.. ipython:: python
+.. code-block:: python
 
    df.agg(['sum', 'min'])
 
@@ -82,21 +82,20 @@ Using a dict provides the ability to apply specific aggregations per column.
 You will get a matrix-like output of all of the aggregators. The output has one column
 per unique function. Those functions applied to a particular column will be ``NaN``:
 
-.. ipython:: python
+.. code-block:: python
 
    df.agg({'A': ['sum', 'min'], 'B': ['min', 'max']})
 
 The API also supports a ``.transform()`` function for broadcasting results.
 
-.. ipython:: python
-   :okwarning:
+.. code-block:: python
 
    df.transform(['abs', lambda x: x - x.min()])
 
 When presented with mixed dtypes that cannot be aggregated, ``.agg()`` will only take the valid
 aggregations. This is similar to how groupby ``.agg()`` works. (:issue:`15015`)
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame({'A': [1, 2, 3],
                       'B': [1., 2., 3.],
@@ -104,7 +103,7 @@ aggregations. This is similar to how groupby ``.agg()`` works. (:issue:`15015`)
                       'D': pd.date_range('20130101', periods=3)})
    df.dtypes
 
-.. ipython:: python
+.. code-block:: python
 
    df.agg(['min', 'sum'])
 
@@ -116,12 +115,11 @@ Keyword argument ``dtype`` for data IO
 The ``'python'`` engine for :func:`read_csv`, as well as the :func:`read_fwf` function for parsing
 fixed-width text files and :func:`read_excel` for parsing Excel files, now accept the ``dtype`` keyword argument for specifying the types of specific columns (:issue:`14295`). See the :ref:`io docs <io.dtypes>` for more information.
 
-.. ipython:: python
-   :suppress:
+.. code-block:: python
 
    from io import StringIO
 
-.. ipython:: python
+.. code-block:: python
 
    data = "a  b\n1  2\n3  4"
    pd.read_fwf(StringIO(data)).dtypes
@@ -137,14 +135,14 @@ from where to compute the resulting timestamps when parsing numerical values wit
 
 For example, with 1960-01-01 as the starting date:
 
-.. ipython:: python
+.. code-block:: python
 
    pd.to_datetime([1, 2, 3], unit='D', origin=pd.Timestamp('1960-01-01'))
 
 The default is set at ``origin='unix'``, which defaults to ``1970-01-01 00:00:00``, which is
 commonly called 'unix epoch' or POSIX time. This was the previous default, so this is a backward compatible change.
 
-.. ipython:: python
+.. code-block:: python
 
    pd.to_datetime([1, 2, 3], unit='D')
 
@@ -156,7 +154,7 @@ GroupBy enhancements
 
 Strings passed to ``DataFrame.groupby()`` as the ``by`` parameter may now reference either column names or index level names. Previously, only column names could be referenced. This allows to easily group by a column and index level at the same time. (:issue:`5677`)
 
-.. ipython:: python
+.. code-block:: python
 
    arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
              ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
@@ -183,7 +181,7 @@ Previously, only ``gzip`` compression was supported. By default, compression of
 URLs and paths are now inferred using their file extensions. Additionally,
 support for bz2 compression in the python 2 C-engine improved (:issue:`14874`).
 
-.. ipython:: python
+.. code-block:: python
 
    url = ('https://github.com/{repo}/raw/{branch}/{path}'
           .format(repo='pandas-dev/pandas',
@@ -205,7 +203,7 @@ can now read from and write to compressed pickle files. Compression methods
 can be an explicit parameter or be inferred from the file extension.
 See :ref:`the docs here. <io.pickle.compression>`
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame({'A': np.random.randn(1000),
                       'B': 'foo',
@@ -213,7 +211,7 @@ See :ref:`the docs here. <io.pickle.compression>`
 
 Using an explicit compression type
 
-.. ipython:: python
+.. code-block:: python
 
    df.to_pickle("data.pkl.compress", compression="gzip")
    rt = pd.read_pickle("data.pkl.compress", compression="gzip")
@@ -221,7 +219,7 @@ Using an explicit compression type
 
 The default is to infer the compression type from the extension (``compression='infer'``):
 
-.. ipython:: python
+.. code-block:: python
 
    df.to_pickle("data.pkl.gz")
    rt = pd.read_pickle("data.pkl.gz")
@@ -230,8 +228,7 @@ The default is to infer the compression type from the extension (``compression='
    rt = pd.read_pickle("s1.pkl.bz2")
    rt.head()
 
-.. ipython:: python
-   :suppress:
+.. code-block:: python
 
    import os
    os.remove("data.pkl.compress")
@@ -248,7 +245,7 @@ or purely non-negative, integers. Previously, handling these integers would
 result in improper rounding or data-type casting, leading to incorrect results.
 Notably, a new numerical index, ``UInt64Index``, has been created (:issue:`14937`)
 
-.. ipython:: python
+.. code-block:: python
 
    idx = pd.UInt64Index([1, 2, 3])
    df = pd.DataFrame({'A': ['a', 'b', 'c']}, index=idx)
@@ -268,7 +265,7 @@ GroupBy on categoricals
 
 In previous versions, ``.groupby(..., sort=False)`` would fail with a ``ValueError`` when grouping on a categorical series with some categories not appearing in the data. (:issue:`13179`)
 
-.. ipython:: python
+.. code-block:: python
 
    chromosomes = np.r_[np.arange(1, 23).astype(str), ['X', 'Y']]
    df = pd.DataFrame({
@@ -290,7 +287,7 @@ In previous versions, ``.groupby(..., sort=False)`` would fail with a ``ValueErr
 
 **New behavior**:
 
-.. ipython:: python
+.. code-block:: python
 
    df[df.chromosomes != '1'].groupby('chromosomes', sort=False).sum()
 
@@ -303,7 +300,7 @@ The new orient ``'table'`` for :meth:`DataFrame.to_json`
 will generate a `Table Schema`_ compatible string representation of
 the data.
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame(
        {'A': [1, 2, 3],
@@ -363,8 +360,7 @@ Experimental support has been added to export ``DataFrame.style`` formats to Exc
 
 For example, after running the following, ``styled.xlsx`` renders as below:
 
-.. ipython:: python
-   :okwarning:
+.. code-block:: python
 
    np.random.seed(24)
    df = pd.DataFrame({'A': np.linspace(1, 10, 10)})
@@ -380,8 +376,7 @@ For example, after running the following, ``styled.xlsx`` renders as below:
 
 .. image:: ../_static/style-excel.png
 
-.. ipython:: python
-   :suppress:
+.. code-block:: python
 
    import os
    os.remove('styled.xlsx')
@@ -420,7 +415,7 @@ The returned categories were strings, representing Intervals
 
 New behavior:
 
-.. ipython:: python
+.. code-block:: python
 
    c = pd.cut(range(4), bins=2)
    c
@@ -429,13 +424,13 @@ New behavior:
 Furthermore, this allows one to bin *other* data with these same bins, with ``NaN`` representing a missing
 value similar to other dtypes.
 
-.. ipython:: python
+.. code-block:: python
 
    pd.cut([0, 3, 5, 1], bins=c.categories)
 
 An ``IntervalIndex`` can also be used in ``Series`` and ``DataFrame`` as the index.
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame({'A': range(4),
                       'B': pd.cut([0, 3, 1, 1], bins=c.categories)
@@ -444,13 +439,13 @@ An ``IntervalIndex`` can also be used in ``Series`` and ``DataFrame`` as the ind
 
 Selecting via a specific interval:
 
-.. ipython:: python
+.. code-block:: python
 
    df.loc[pd.Interval(1.5, 3.0)]
 
 Selecting via a scalar value that is contained *in* the intervals.
 
-.. ipython:: python
+.. code-block:: python
 
    df.loc[0]
 
@@ -572,7 +567,7 @@ Map on Index types now return other Index types
 
 ``map`` on an ``Index`` now returns an ``Index``, not a numpy array (:issue:`12766`)
 
-.. ipython:: python
+.. code-block:: python
 
    idx = pd.Index([1, 2])
    idx
@@ -597,7 +592,7 @@ Previous behavior:
 
 New behavior:
 
-.. ipython:: python
+.. code-block:: python
 
    idx.map(lambda x: x * 2)
    idx.map(lambda x: (x, x * 2))
@@ -609,7 +604,7 @@ New behavior:
 
 ``map`` on a ``Series`` with ``datetime64`` values may return ``int64`` dtypes rather than ``int32``
 
-.. ipython:: python
+.. code-block:: python
 
    s = pd.Series(pd.date_range('2011-01-02T00:00', '2011-01-02T02:00', freq='H')
                  .tz_localize('Asia/Tokyo'))
@@ -628,7 +623,7 @@ Previous behavior:
 
 New behavior:
 
-.. ipython:: python
+.. code-block:: python
 
    s.map(lambda x: x.hour)
 
@@ -654,7 +649,7 @@ Previous behaviour:
 
 New behavior:
 
-.. ipython:: python
+.. code-block:: python
 
    idx = pd.date_range("2015-01-01", periods=5, freq='10H')
    idx.hour
@@ -698,7 +693,7 @@ data-types would yield different return types. These are now made consistent. (:
 
   New behavior:
 
-  .. ipython:: python
+  .. code-block:: python
 
      # Series, returns an array of Timestamp tz-aware
      pd.Series([pd.Timestamp(r'20160101', tz=r'US/Eastern'),
@@ -728,7 +723,7 @@ data-types would yield different return types. These are now made consistent. (:
 
   New behavior:
 
-  .. ipython:: python
+  .. code-block:: python
 
      # returns a Categorical
      pd.Series(list('baabc'), dtype='category').unique()
@@ -750,11 +745,12 @@ Partial string indexing changes
 
 :ref:`DatetimeIndex Partial String Indexing <timeseries.partialindexing>` now works as an exact match, provided that string resolution coincides with index resolution, including a case when both are seconds (:issue:`14826`). See :ref:`Slice vs. Exact Match <timeseries.slice_vs_exact_match>` for details.
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame({'a': [1, 2, 3]}, pd.DatetimeIndex(['2011-12-31 23:59:59',
                                                          '2012-01-01 00:00:00',
                                                          '2012-01-01 00:00:01']))
+
 Previous behavior:
 
 .. code-block:: ipython
@@ -788,7 +784,7 @@ Concat of different float dtypes will not automatically upcast
 Previously, ``concat`` of multiple objects with different ``float`` dtypes would automatically upcast results to a dtype of ``float64``.
 Now the smallest acceptable dtype will be used (:issue:`13247`)
 
-.. ipython:: python
+.. code-block:: python
 
    df1 = pd.DataFrame(np.array([1.0], dtype=np.float32, ndmin=2))
    df1.dtypes
@@ -807,7 +803,7 @@ Previous behavior:
 
 New behavior:
 
-.. ipython:: python
+.. code-block:: python
 
    pd.concat([df1, df2]).dtypes
 
@@ -867,7 +863,7 @@ This would happen with a ``lexsorted``, but non-monotonic levels. (:issue:`15622
 
 This is *unchanged* from prior versions, but shown for illustration purposes:
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame(np.arange(6), columns=['value'],
                      index=pd.MultiIndex.from_product([list('BA'), range(3)]))
@@ -883,7 +879,7 @@ This is *unchanged* from prior versions, but shown for illustration purposes:
 
 Sorting works as expected
 
-.. ipython:: python
+.. code-block:: python
 
    df.sort_index()
 
@@ -898,7 +894,7 @@ Sorting works as expected
 However, this example, which has a non-monotonic 2nd level,
 doesn't behave as desired.
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame({'value': [1, 2, 3, 4]},
                      index=pd.MultiIndex([['a', 'b'], ['bb', 'aa']],
@@ -989,7 +985,7 @@ Previous behavior:
 
 New behavior:
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame({'A': [1, 1, 2, 2], 'B': [1, 2, 3, 4]})
 
@@ -1008,7 +1004,7 @@ see :ref:`here <whatsnew_0200.api_breaking.deprecate_panel>`. These are equivale
 but a MultiIndexed ``DataFrame`` enjoys more support in pandas.
 See the section on :ref:`Windowed Binary Operations <window.cov_corr>` for more information. (:issue:`15677`)
 
-.. ipython:: python
+.. code-block:: python
 
    np.random.seed(1234)
    df = pd.DataFrame(np.random.rand(100, 2),
@@ -1031,14 +1027,14 @@ Previous behavior:
 
 New behavior:
 
-.. ipython:: python
+.. code-block:: python
 
    res = df.rolling(12).corr()
    res.tail()
 
 Retrieving a correlation matrix for a cross-section
 
-.. ipython:: python
+.. code-block:: python
 
    df.rolling(12).corr().loc['2016-04-07']
 
@@ -1051,7 +1047,7 @@ In previous versions most types could be compared to string column in a ``HDFSto
 usually resulting in an invalid comparison, returning an empty result frame. These comparisons will now raise a
 ``TypeError`` (:issue:`15492`)
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame({'unparsed_date': ['2014-01-01', '2014-01-01']})
    df.to_hdf('store.h5', 'key', format='table', data_columns=True)
@@ -1077,8 +1073,7 @@ New behavior:
    TypeError: Cannot compare 2014-01-01 00:00:00 of
    type <class 'pandas.tslib.Timestamp'> to string column
 
-.. ipython:: python
-   :suppress:
+.. code-block:: python
 
    import os
    os.remove('store.h5')
@@ -1094,7 +1089,7 @@ joins, :meth:`DataFrame.join` and :func:`merge`, and the ``.align`` method.
 
 - ``Index.intersection``
 
-  .. ipython:: python
+  .. code-block:: python
 
      left = pd.Index([2, 1, 0])
      left
@@ -1110,13 +1105,13 @@ joins, :meth:`DataFrame.join` and :func:`merge`, and the ``.align`` method.
 
   New behavior:
 
-  .. ipython:: python
+  .. code-block:: python
 
      left.intersection(right)
 
 - ``DataFrame.join`` and ``pd.merge``
 
-  .. ipython:: python
+  .. code-block:: python
 
      left = pd.DataFrame({'a': [20, 10, 0]}, index=[2, 1, 0])
      left
@@ -1135,7 +1130,7 @@ joins, :meth:`DataFrame.join` and :func:`merge`, and the ``.align`` method.
 
   New behavior:
 
-  .. ipython:: python
+  .. code-block:: python
 
      left.join(right, how='inner')
 
@@ -1147,7 +1142,7 @@ Pivot table always returns a DataFrame
 The documentation for :meth:`pivot_table` states that a ``DataFrame`` is *always* returned. Here a bug
 is fixed that allowed this to return a ``Series`` under certain circumstance. (:issue:`4386`)
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame({'col1': [3, 4, 5],
                       'col2': ['C', 'D', 'E'],
@@ -1168,7 +1163,7 @@ Previous behavior:
 
 New behavior:
 
-.. ipython:: python
+.. code-block:: python
 
    df.pivot_table('col1', index=['col3', 'col2'], aggfunc=np.sum)
 
@@ -1336,7 +1331,7 @@ The recommended methods of indexing are:
 Using ``.ix`` will now show a ``DeprecationWarning`` with a link to some examples of how to convert code `here <https://pandas.pydata.org/pandas-docs/version/1.0/user_guide/indexing.html#ix-indexer-is-deprecated>`__.
 
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame({'A': [1, 2, 3],
                       'B': [4, 5, 6]},
@@ -1356,13 +1351,13 @@ Previous behavior, where you wish to get the 0th and the 2nd elements from the i
 
 Using ``.loc``. Here we will select the appropriate indexes from the index, then use *label* indexing.
 
-.. ipython:: python
+.. code-block:: python
 
    df.loc[df.index[[0, 2]], 'A']
 
 Using ``.iloc``. Here we will get the location of the 'A' column, then use *positional* indexing to select things.
 
-.. ipython:: python
+.. code-block:: python
 
    df.iloc[[0, 2], df.columns.get_loc('A')]
 
@@ -1455,7 +1450,7 @@ between ``Series`` and ``DataFrame``. We are deprecating this 'renaming' functio
 
 This is an illustrative example:
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame({'A': [1, 1, 1, 2, 2],
                       'B': range(5),
@@ -1466,7 +1461,7 @@ Here is a typical useful syntax for computing different aggregations for differe
 is a natural, and useful syntax. We aggregate from the dict-to-list by taking the specified
 columns and applying the list of functions. This returns a ``MultiIndex`` for the columns (this is *not* deprecated).
 
-.. ipython:: python
+.. code-block:: python
 
    df.groupby('A').agg({'B': 'sum', 'C': 'min'})
 
@@ -1487,7 +1482,7 @@ is a combination aggregation & renaming:
 
 You can accomplish the same operation, more idiomatically by:
 
-.. ipython:: python
+.. code-block:: python
 
    df.groupby('A').B.agg(['count']).rename(columns={'count': 'foo'})
 
@@ -1512,7 +1507,7 @@ Here's an example of the second deprecation, passing a dict-of-dict to a grouped
 
 You can accomplish nearly the same by:
 
-.. ipython:: python
+.. code-block:: python
 
    (df.groupby('A')
       .agg({'B': 'sum', 'C': 'min'})
diff --git a/doc/source/whatsnew/v0.20.2.rst b/doc/source/whatsnew/v0.20.2.rst
index 430a39d2d2e97..fd27887e3dd91 100644
--- a/doc/source/whatsnew/v0.20.2.rst
+++ b/doc/source/whatsnew/v0.20.2.rst
@@ -5,8 +5,7 @@ Version 0.20.2 (June 4, 2017)
 
 {{ header }}
 
-.. ipython:: python
-   :suppress:
+.. code-block:: python
 
    from pandas import *  # noqa F401, F403
 
diff --git a/doc/source/whatsnew/v0.20.3.rst b/doc/source/whatsnew/v0.20.3.rst
index ff28f6830783e..647da61051ed8 100644
--- a/doc/source/whatsnew/v0.20.3.rst
+++ b/doc/source/whatsnew/v0.20.3.rst
@@ -5,8 +5,7 @@ Version 0.20.3 (July 7, 2017)
 
 {{ header }}
 
-.. ipython:: python
-   :suppress:
+.. code-block:: python
 
    from pandas import *  # noqa F401, F403
 
diff --git a/doc/source/whatsnew/v0.5.0.rst b/doc/source/whatsnew/v0.5.0.rst
index 7447a10fa1d6b..7b2af577a24d5 100644
--- a/doc/source/whatsnew/v0.5.0.rst
+++ b/doc/source/whatsnew/v0.5.0.rst
@@ -6,8 +6,7 @@ Version 0.5.0 (October 24, 2011)
 
 {{ header }}
 
-.. ipython:: python
-   :suppress:
+.. code-block:: python
 
    from pandas import *  # noqa F401, F403
 
diff --git a/doc/source/whatsnew/v0.6.0.rst b/doc/source/whatsnew/v0.6.0.rst
index 253ca4d4188e5..a3c28a23e07b4 100644
--- a/doc/source/whatsnew/v0.6.0.rst
+++ b/doc/source/whatsnew/v0.6.0.rst
@@ -5,8 +5,7 @@ Version 0.6.0 (November 25, 2011)
 
 {{ header }}
 
-.. ipython:: python
-   :suppress:
+.. code-block:: python
 
    from pandas import *  # noqa F401, F403
 
diff --git a/doc/source/whatsnew/v0.7.0.rst b/doc/source/whatsnew/v0.7.0.rst
index 2fe686d8858a2..71aeb2070e8d4 100644
--- a/doc/source/whatsnew/v0.7.0.rst
+++ b/doc/source/whatsnew/v0.7.0.rst
@@ -31,7 +31,7 @@ New features
 
 - Handle differently-indexed output values in ``DataFrame.apply`` (:issue:`498`)
 
-.. ipython:: python
+.. code-block:: python
 
    df = pd.DataFrame(np.random.randn(10, 4))
    df.apply(lambda x: x.describe())
@@ -116,7 +116,7 @@ One of the potentially riskiest API changes in 0.7.0, but also one of the most
 important, was a complete review of how **integer indexes** are handled with
 regard to label-based indexing. Here is an example:
 
-.. ipython:: python
+.. code-block:: python
 
     s = pd.Series(np.random.randn(10), index=range(0, 20, 2))
     s
@@ -235,7 +235,7 @@ slice to a Series when getting and setting values via ``[]`` (i.e. the
 ``__getitem__`` and ``__setitem__`` methods). The behavior will be the same as
 passing similar input to ``ix`` **except in the case of integer indexing**:
 
-.. ipython:: python
+.. code-block:: python
 
    s = pd.Series(np.random.randn(6), index=list('acegkm'))
    s
@@ -246,7 +246,7 @@ passing similar input to ``ix`` **except in the case of integer indexing**:
 In the case of integer indexes, the behavior will be exactly as before
 (shadowing ``ndarray``):
 
-.. ipython:: python
+.. code-block:: python
 
    s = pd.Series(np.random.randn(6), index=range(0, 12, 2))
    s[[4, 0, 2]]
diff --git a/doc/source/whatsnew/v0.7.3.rst b/doc/source/whatsnew/v0.7.3.rst
index 4ca31baf560bb..b52581c53d54b 100644
--- a/doc/source/whatsnew/v0.7.3.rst
+++ b/doc/source/whatsnew/v0.7.3.rst
@@ -51,7 +51,7 @@ NA boolean comparison API change
 Reverted some changes to how NA values (represented typically as ``NaN`` or
 ``None``) are handled in non-numeric Series:
 
-.. ipython:: python
+.. code-block:: python
 
    series = pd.Series(["Steve", np.nan, "Joe"])
    series == "Steve"
@@ -62,7 +62,7 @@ In comparisons, NA / NaN will always come through as ``False`` except with
 negation, in the presence of NA data. You may wish to add an explicit NA
 filter into boolean array operations if you are worried about this:
 
-.. ipython:: python
+.. code-block:: python
 
    mask = series == "Steve"
    series[mask & series.notnull()]
@@ -80,8 +80,7 @@ Other API changes
 When calling ``apply`` on a grouped Series, the return value will also be a
 Series, to be more consistent with the ``groupby`` behavior with DataFrame:
 
-.. ipython:: python
-    :okwarning:
+.. code-block:: python
 
     df = pd.DataFrame(
         {
diff --git a/doc/source/whatsnew/v0.8.0.rst b/doc/source/whatsnew/v0.8.0.rst
index 490175914cef1..0ae7347f5ded2 100644
--- a/doc/source/whatsnew/v0.8.0.rst
+++ b/doc/source/whatsnew/v0.8.0.rst
@@ -204,7 +204,7 @@ have code that converts ``DateRange`` or ``Index`` objects that used to contain
 ``datetime.datetime`` values to plain NumPy arrays, you may have bugs lurking
 with code using scalar values because you are handing control over to NumPy:
 
-.. ipython:: python
+.. code-block:: python
 
    import datetime
 
@@ -225,7 +225,7 @@ If you have code that requires an array of ``datetime.datetime`` objects, you
 have a couple of options. First, the ``astype(object)`` method of ``DatetimeIndex``
 produces an array of ``Timestamp`` objects:
 
-.. ipython:: python
+.. code-block:: python
 
    stamp_array = rng.astype(object)
    stamp_array
@@ -234,7 +234,7 @@ produces an array of ``Timestamp`` objects:
 To get an array of proper ``datetime.datetime`` objects, use the
 ``to_pydatetime`` method:
 
-.. ipython:: python
+.. code-block:: python
 
    dt_array = rng.to_pydatetime()
    dt_array
@@ -252,7 +252,7 @@ type. See `matplotlib documentation
     in NumPy 1.6. In particular, the string version of the array shows garbage
     values, and conversion to ``dtype=object`` is similarly broken.
 
-    .. ipython:: python
+    .. code-block:: python
 
        rng = pd.date_range("1/1/2000", periods=10)
        rng
diff --git a/doc/source/whatsnew/v0.9.0.rst b/doc/source/whatsnew/v0.9.0.rst
index 44ded51e31fda..d7e5698f1868e 100644
--- a/doc/source/whatsnew/v0.9.0.rst
+++ b/doc/source/whatsnew/v0.9.0.rst
@@ -37,7 +37,7 @@ API changes
     functions like ``read_csv`` has changed to be more Pythonic and amenable to
     attribute access:
 
-.. ipython:: python
+.. code-block:: python
 
    import io
 
@@ -56,7 +56,7 @@ API changes
   "by accident" (this was never intended) will lead to all NA Series in some
   cases. To be perfectly clear:
 
-.. ipython:: python
+.. code-block:: python
 
    s1 = pd.Series([1, 2, 3])
    s1
diff --git a/doc/source/whatsnew/v0.9.1.rst b/doc/source/whatsnew/v0.9.1.rst
index 6b05e5bcded7e..03a2e3f408f56 100644
--- a/doc/source/whatsnew/v0.9.1.rst
+++ b/doc/source/whatsnew/v0.9.1.rst
@@ -38,7 +38,7 @@ New features
     ``na_option`` parameter so missing values can be assigned either the largest
     or the smallest rank (:issue:`1508`, :issue:`2159`)
 
-    .. ipython:: python
+    .. code-block:: python
 
         df = pd.DataFrame(np.random.randn(6, 3), columns=['A', 'B', 'C'])
 
@@ -57,7 +57,7 @@ New features
 	DataFrame currently supports slicing via a boolean vector the same length as the DataFrame (inside the ``[]``).
 	The returned DataFrame has the same number of columns as the original, but is sliced on its index.
 
-        .. ipython:: python
+        .. code-block:: python
 
     	    df = DataFrame(np.random.randn(5, 3), columns = ['A','B','C'])
 
@@ -70,7 +70,7 @@ New features
 	elements that do not meet the boolean condition as ``NaN``. This is accomplished via
 	the new method ``DataFrame.where``. In addition, ``where`` takes an optional ``other`` argument for replacement.
 
-	.. ipython:: python
+	.. code-block:: python
 
 	   df[df>0]
 
@@ -81,7 +81,7 @@ New features
 	Furthermore, ``where`` now aligns the input boolean condition (ndarray or DataFrame), such that partial selection
 	with setting is possible. This is analogous to partial setting via ``.ix`` (but on the contents rather than the axis labels)
 
-	.. ipython:: python
+	.. code-block:: python
 
 	   df2 = df.copy()
    	   df2[ df2[1:4] > 0 ] = 3
@@ -89,13 +89,13 @@ New features
 
 	``DataFrame.mask`` is the inverse boolean operation of ``where``.
 
-	.. ipython:: python
+	.. code-block:: python
 
 	   df.mask(df<=0)
 
   - Enable referencing of Excel columns by their column names (:issue:`1936`)
 
-    .. ipython:: python
+    .. code-block:: python
 
         xl = pd.ExcelFile('data/test.xls')
         xl.parse('Sheet1', index_col=0, parse_dates=True,
@@ -137,7 +137,7 @@ API changes
   - Period.end_time now returns the last nanosecond in the time interval
     (:issue:`2124`, :issue:`2125`, :issue:`1764`)
 
-    .. ipython:: python
+    .. code-block:: python
 
         p = pd.Period('2012')
 
@@ -147,7 +147,7 @@ API changes
   - File parsers no longer coerce to float or bool for columns that have custom
     converters specified (:issue:`2184`)
 
-    .. ipython:: python
+    .. code-block:: python
 
         import io