DOC: freeze old whatsnew pandas-dev#6856

Author: fangchenli

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 - Calling ``fillna`` on Series or DataFrame with no arguments is no longer
   valid code.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")
 

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")

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)])
@@ -344,13 +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
+      .. code-block:: python
           :suppress:
           :okexcept:
 
@@ -367,8 +367,7 @@ 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)

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)