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DOC: fix errors/warnings in running code blocks #26076

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DOC: fix errors/warnings in running code blocks
jorisvandenbossche Apr 13, 2019
db11014
linter
jorisvandenbossche Apr 13, 2019
ec8396d
PEP8 in new code block
jorisvandenbossche Apr 15, 2019
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7 changes: 0 additions & 7 deletions doc/source/user_guide/io.rst
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Expand Up @@ -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
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7 changes: 3 additions & 4 deletions doc/source/user_guide/timedeltas.rst
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Expand Up @@ -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``:
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11 changes: 7 additions & 4 deletions doc/source/user_guide/timeseries.rst
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Expand Up @@ -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::

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197 changes: 141 additions & 56 deletions doc/source/whatsnew/v0.10.0.rst
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Expand Up @@ -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
Expand All @@ -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,
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v0.11.0.rst
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Expand Up @@ -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),
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26 changes: 14 additions & 12 deletions doc/source/whatsnew/v0.12.0.rst
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Expand Up @@ -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
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3 changes: 2 additions & 1 deletion doc/source/whatsnew/v0.13.0.rst
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Expand Up @@ -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'],
Expand Down Expand Up @@ -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.
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v0.15.0.rst
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Expand Up @@ -702,6 +702,7 @@ Other notable API changes:
This can also be seen in multi-axis indexing with a ``Panel``.

.. ipython:: python
:okwarning:
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same


p = pd.Panel(np.arange(2 * 3 * 4).reshape(2, 3, 4),
items=['ItemA', 'ItemB'],
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v0.15.2.rst
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Expand Up @@ -161,6 +161,7 @@ Other enhancements:
- ``Panel`` now supports the ``all`` and ``any`` aggregation functions. (:issue:`8302`):

.. ipython:: python
:okwarning:
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prob should change this to a code-block (good for followup though)


p = pd.Panel(np.random.rand(2, 5, 4) > 0.1)
p.all()
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2 changes: 1 addition & 1 deletion doc/source/whatsnew/v0.24.0.rst
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Expand Up @@ -567,7 +567,7 @@ missing indicator, ``np.nan``. (:issue:`20377`)
.. ipython:: python
:suppress:

from pandas.io import StringIO
from io import StringIO

*Previous Behavior*:

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