DOC: fix errors/warnings in running code blocks (pandas-dev#26076)

jorisvandenbossche · web-flow · commit aad77394b14f · 2019-04-16T09:26:00.000+02:00
diff --git a/doc/source/user_guide/io.rst b/doc/source/user_guide/io.rst
@@ -3565,13 +3565,6 @@ HDFStore will by default not drop rows that are all missing. This behavior can b
    os.remove('file.h5')
 
 
-.. ipython:: python
-   :suppress:
-
-   os.remove('file.h5')
-
-
-
 .. _io.hdf5-fixed:
 
 Fixed Format
diff --git a/doc/source/user_guide/timedeltas.rst b/doc/source/user_guide/timedeltas.rst
@@ -191,13 +191,12 @@ Operands can also appear in a reversed order (a singular object operated with a
    df.min().idxmax()
    df.min(axis=1).idxmin()
 
-You can fillna on timedeltas. Integers will be interpreted as seconds. You can
-pass a timedelta to get a particular value.
+You can fillna on timedeltas, passing a timedelta to get a particular value.
 
 .. ipython:: python
 
-   y.fillna(0)
-   y.fillna(10)
+   y.fillna(pd.Timedelta(0))
+   y.fillna(pd.Timedelta(10, unit='s'))
    y.fillna(pd.Timedelta('-1 days, 00:00:05'))
 
 You can also negate, multiply and use ``abs`` on ``Timedeltas``:
diff --git a/doc/source/user_guide/timeseries.rst b/doc/source/user_guide/timeseries.rst
@@ -322,13 +322,16 @@ which can be specified. These are computed from the starting point specified by
                    1349720105400, 1349720105500], unit='ms')
 
 Constructing a :class:`Timestamp` or :class:`DatetimeIndex` with an epoch timestamp
-with the ``tz`` argument specified will localize the epoch timestamps to UTC
-first then convert the result to the specified time zone.
+with the ``tz`` argument specified will currently localize the epoch timestamps to UTC
+first then convert the result to the specified time zone. However, this behavior
+is :ref:`deprecated <whatsnew_0240.deprecations.integer_tz>`, and if you have
+epochs in wall time in another timezone, it is recommended to read the epochs
+as timezone-naive timestamps and then localize to the appropriate timezone:
 
 .. ipython:: python
 
-   pd.Timestamp(1262347200000000000, tz='US/Pacific')
-   pd.DatetimeIndex([1262347200000000000], tz='US/Pacific')
+   pd.Timestamp(1262347200000000000).tz_localize('US/Pacific')
+   pd.DatetimeIndex([1262347200000000000]).tz_localize('US/Pacific')
 
 .. note::
 
diff --git a/doc/source/whatsnew/v0.10.0.rst b/doc/source/whatsnew/v0.10.0.rst
@@ -295,79 +295,171 @@ Updated PyTables Support
 
 :ref:`Docs <io.hdf5>` for PyTables ``Table`` format & several enhancements to the api. Here is a taste of what to expect.
 
-.. ipython:: python
-   :suppress:
-   :okexcept:
+.. code-block:: ipython
 
-   import os
+    In [41]: store = pd.HDFStore('store.h5')
 
-   os.remove('store.h5')
+    In [42]: df = pd.DataFrame(np.random.randn(8, 3),
+       ....:                   index=pd.date_range('1/1/2000', periods=8),
+       ....:                   columns=['A', 'B', 'C'])
 
-.. ipython:: python
+    In [43]: df
+    Out[43]:
+                       A         B         C
+    2000-01-01 -2.036047  0.000830 -0.955697
+    2000-01-02 -0.898872 -0.725411  0.059904
+    2000-01-03 -0.449644  1.082900 -1.221265
+    2000-01-04  0.361078  1.330704  0.855932
+    2000-01-05 -1.216718  1.488887  0.018993
+    2000-01-06 -0.877046  0.045976  0.437274
+    2000-01-07 -0.567182 -0.888657 -0.556383
+    2000-01-08  0.655457  1.117949 -2.782376
 
-   store = pd.HDFStore('store.h5')
-   df = pd.DataFrame(np.random.randn(8, 3),
-                     index=pd.date_range('1/1/2000', periods=8),
-                     columns=['A', 'B', 'C'])
-   df
+    [8 rows x 3 columns]
 
-   # appending data frames
-   df1 = df[0:4]
-   df2 = df[4:]
-   store.append('df', df1)
-   store.append('df', df2)
-   store
+    # appending data frames
+    In [44]: df1 = df[0:4]
 
-   # selecting the entire store
-   store.select('df')
+    In [45]: df2 = df[4:]
 
-.. ipython:: python
-   :okwarning:
+    In [46]: store.append('df', df1)
 
-   wp = pd.Panel(np.random.randn(2, 5, 4), items=['Item1', 'Item2'],
-                 major_axis=pd.date_range('1/1/2000', periods=5),
-                 minor_axis=['A', 'B', 'C', 'D'])
-   wp
+    In [47]: store.append('df', df2)
 
-   # storing a panel
-   store.append('wp', wp)
+    In [48]: store
+    Out[48]:
+    <class 'pandas.io.pytables.HDFStore'>
+    File path: store.h5
+    /df            frame_table  (typ->appendable,nrows->8,ncols->3,indexers->[index])
 
-   # selecting via A QUERY
-   store.select('wp', "major_axis>20000102 and minor_axis=['A','B']")
+    # selecting the entire store
+    In [49]: store.select('df')
+    Out[49]:
+                       A         B         C
+    2000-01-01 -2.036047  0.000830 -0.955697
+    2000-01-02 -0.898872 -0.725411  0.059904
+    2000-01-03 -0.449644  1.082900 -1.221265
+    2000-01-04  0.361078  1.330704  0.855932
+    2000-01-05 -1.216718  1.488887  0.018993
+    2000-01-06 -0.877046  0.045976  0.437274
+    2000-01-07 -0.567182 -0.888657 -0.556383
+    2000-01-08  0.655457  1.117949 -2.782376
 
-   # removing data from tables
-   store.remove('wp', "major_axis>20000103")
-   store.select('wp')
+    [8 rows x 3 columns]
+
+.. code-block:: ipython
+
+    In [50]: wp = pd.Panel(np.random.randn(2, 5, 4), items=['Item1', 'Item2'],
+       ....:               major_axis=pd.date_range('1/1/2000', periods=5),
+       ....:               minor_axis=['A', 'B', 'C', 'D'])
+
+    In [51]: wp
+    Out[51]:
+    <class 'pandas.core.panel.Panel'>
+    Dimensions: 2 (items) x 5 (major_axis) x 4 (minor_axis)
+    Items axis: Item1 to Item2
+    Major_axis axis: 2000-01-01 00:00:00 to 2000-01-05 00:00:00
+    Minor_axis axis: A to D
+
+    # storing a panel
+    In [52]: store.append('wp', wp)
+
+    # selecting via A QUERY
+    In [53]: store.select('wp', [pd.Term('major_axis>20000102'),
+       ....:                     pd.Term('minor_axis', '=', ['A', 'B'])])
+       ....:
+    Out[53]:
+    <class 'pandas.core.panel.Panel'>
+    Dimensions: 2 (items) x 3 (major_axis) x 2 (minor_axis)
+    Items axis: Item1 to Item2
+    Major_axis axis: 2000-01-03 00:00:00 to 2000-01-05 00:00:00
+    Minor_axis axis: A to B
+
+    # removing data from tables
+    In [54]: store.remove('wp', pd.Term('major_axis>20000103'))
+    Out[54]: 8
+
+    In [55]: store.select('wp')
+    Out[55]:
+    <class 'pandas.core.panel.Panel'>
+    Dimensions: 2 (items) x 3 (major_axis) x 4 (minor_axis)
+    Items axis: Item1 to Item2
+    Major_axis axis: 2000-01-01 00:00:00 to 2000-01-03 00:00:00
+    Minor_axis axis: A to D
+
+    # deleting a store
+    In [56]: del store['df']
+
+    In [57]: store
+    Out[57]:
+    <class 'pandas.io.pytables.HDFStore'>
+    File path: store.h5
+    /wp            wide_table   (typ->appendable,nrows->12,ncols->2,indexers->[major_axis,minor_axis])
 
-   # deleting a store
-   del store['df']
-   store
 
 **Enhancements**
 
 - added ability to hierarchical keys
 
-   .. ipython:: python
+   .. code-block:: ipython
+
+        In [58]: store.put('foo/bar/bah', df)
+
+        In [59]: store.append('food/orange', df)
 
-     store.put('foo/bar/bah', df)
-     store.append('food/orange', df)
-     store.append('food/apple',  df)
-     store
+        In [60]: store.append('food/apple', df)
 
-     # remove all nodes under this level
-     store.remove('food')
-     store
+        In [61]: store
+        Out[61]:
+        <class 'pandas.io.pytables.HDFStore'>
+        File path: store.h5
+        /foo/bar/bah            frame        (shape->[8,3])
+        /food/apple             frame_table  (typ->appendable,nrows->8,ncols->3,indexers->[index])
+        /food/orange            frame_table  (typ->appendable,nrows->8,ncols->3,indexers->[index])
+        /wp                     wide_table   (typ->appendable,nrows->12,ncols->2,indexers->[major_axis,minor_axis])
+
+        # remove all nodes under this level
+        In [62]: store.remove('food')
+
+        In [63]: store
+        Out[63]:
+        <class 'pandas.io.pytables.HDFStore'>
+        File path: store.h5
+        /foo/bar/bah            frame        (shape->[8,3])
+        /wp                     wide_table   (typ->appendable,nrows->12,ncols->2,indexers->[major_axis,minor_axis])
 
 - added mixed-dtype support!
 
    .. ipython:: python
 
-      df['string'] = 'string'
-      df['int'] = 1
-      store.append('df', df)
-      df1 = store.select('df')
-      df1
-      df1.get_dtype_counts()
+        In [64]: df['string'] = 'string'
+
+        In [65]: df['int'] = 1
+
+        In [66]: store.append('df', df)
+
+        In [67]: df1 = store.select('df')
+
+        In [68]: df1
+        Out[68]:
+                           A         B         C  string  int
+        2000-01-01 -2.036047  0.000830 -0.955697  string    1
+        2000-01-02 -0.898872 -0.725411  0.059904  string    1
+        2000-01-03 -0.449644  1.082900 -1.221265  string    1
+        2000-01-04  0.361078  1.330704  0.855932  string    1
+        2000-01-05 -1.216718  1.488887  0.018993  string    1
+        2000-01-06 -0.877046  0.045976  0.437274  string    1
+        2000-01-07 -0.567182 -0.888657 -0.556383  string    1
+        2000-01-08  0.655457  1.117949 -2.782376  string    1
+
+        [8 rows x 5 columns]
+
+        In [69]: df1.get_dtype_counts()
+        Out[69]:
+        float64    3
+        int64      1
+        object     1
+        dtype: int64
 
 - performance improvements on table writing
 - support for arbitrarily indexed dimensions
@@ -392,13 +484,6 @@ Updated PyTables Support
 - minor change to select and remove: require a table ONLY if where is also
   provided (and not None)
 
-.. ipython:: python
-   :suppress:
-
-   store.close()
-   import os
-   os.remove('store.h5')
-
 **Compatibility**
 
 0.10 of ``HDFStore`` is backwards compatible for reading tables created in a prior version of pandas,
diff --git a/doc/source/whatsnew/v0.11.0.rst b/doc/source/whatsnew/v0.11.0.rst
@@ -278,6 +278,7 @@ Enhancements
   - ``Squeeze`` to possibly remove length 1 dimensions from an object.
 
     .. ipython:: python
+       :okwarning:
 
        p = pd.Panel(np.random.randn(3, 4, 4), items=['ItemA', 'ItemB', 'ItemC'],
                     major_axis=pd.date_range('20010102', periods=4),
diff --git a/doc/source/whatsnew/v0.12.0.rst b/doc/source/whatsnew/v0.12.0.rst
@@ -317,22 +317,24 @@ Other Enhancements
 
   - ``pd.set_option()`` now allows N option, value pairs (:issue:`3667`).
 
-      Let's say that we had an option ``'a.b'`` and another option ``'b.c'``.
-      We can set them at the same time:
+    Let's say that we had an option ``'a.b'`` and another option ``'b.c'``.
+    We can set them at the same time:
 
-      .. ipython:: python
-         :suppress:
+    .. ipython:: python
 
-         pd.core.config.register_option('a.b', 2, 'ay dot bee')
-         pd.core.config.register_option('b.c', 3, 'bee dot cee')
+        In [31]: pd.get_option('a.b')
+        Out[31]: 2
 
-      .. ipython:: python
+        In [32]: pd.get_option('b.c')
+        Out[32]: 3
+
+        In [33]: pd.set_option('a.b', 1, 'b.c', 4)
+
+        In [34]: pd.get_option('a.b')
+        Out[34]: 1
 
-         pd.get_option('a.b')
-         pd.get_option('b.c')
-         pd.set_option('a.b', 1, 'b.c', 4)
-         pd.get_option('a.b')
-         pd.get_option('b.c')
+        In [35]: pd.get_option('b.c')
+        Out[35]: 4
 
   - The ``filter`` method for group objects returns a subset of the original
     object. Suppose we want to take only elements that belong to groups with a
diff --git a/doc/source/whatsnew/v0.13.0.rst b/doc/source/whatsnew/v0.13.0.rst
@@ -272,6 +272,7 @@ This is like an ``append`` operation.
 A Panel setting operation on an arbitrary axis aligns the input to the Panel
 
 .. ipython:: python
+   :okwarning:
 
    p = pd.Panel(np.arange(16).reshape(2, 4, 2),
                 items=['Item1', 'Item2'],
@@ -543,7 +544,7 @@ Enhancements
 
   .. ipython:: python
 
-     td.fillna(0)
+     td.fillna(pd.Timedelta(0))
      td.fillna(datetime.timedelta(days=1, seconds=5))
 
   You can do numeric reduction operations on timedeltas.
diff --git a/doc/source/whatsnew/v0.15.0.rst b/doc/source/whatsnew/v0.15.0.rst
@@ -702,6 +702,7 @@ Other notable API changes:
   This can also be seen in multi-axis indexing with a ``Panel``.
 
   .. ipython:: python
+     :okwarning:
 
      p = pd.Panel(np.arange(2 * 3 * 4).reshape(2, 3, 4),
                   items=['ItemA', 'ItemB'],
diff --git a/doc/source/whatsnew/v0.15.2.rst b/doc/source/whatsnew/v0.15.2.rst
@@ -161,6 +161,7 @@ Other enhancements:
 - ``Panel`` now supports the ``all`` and ``any`` aggregation functions. (:issue:`8302`):
 
   .. ipython:: python
+     :okwarning:
 
      p = pd.Panel(np.random.rand(2, 5, 4) > 0.1)
      p.all()
diff --git a/doc/source/whatsnew/v0.24.0.rst b/doc/source/whatsnew/v0.24.0.rst
@@ -567,7 +567,7 @@ missing indicator, ``np.nan``. (:issue:`20377`)
 .. ipython:: python
    :suppress:
 
-   from pandas.io import StringIO
+   from io import StringIO
 
 *Previous Behavior*:
 
