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Fix flake8 issues on v19, v20 and v21.0.rst #24236

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6f4a546
Fix flake8 issues on v19, v20 and v21.0.rst
thoo Dec 11, 2018
78372e4
Fix at L293
thoo Dec 11, 2018
1131f71
Fix code-block
thoo Dec 11, 2018
c529863
Fix code-block indent
thoo Dec 11, 2018
b67a4b5
Fix code-block
thoo Dec 11, 2018
221d35f
Add spaces to align code-lines
thoo Dec 12, 2018
02f5844
fix raw string
thoo Dec 12, 2018
f6314b0
Merge remote-tracking branch 'upstream/master' into flake8_whatsnew21
thoo Dec 12, 2018
fc214e3
code block alignments
thoo Dec 12, 2018
aa8f5b4
Merge remote-tracking branch 'upstream/master' into flake8_whatsnew21
thoo Dec 12, 2018
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200 changes: 99 additions & 101 deletions doc/source/whatsnew/v0.19.0.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -5,12 +5,6 @@ v0.19.0 (October 2, 2016)

{{ header }}

.. ipython:: python
:suppress:

from pandas import * # noqa F401, F403


This is a major release from 0.18.1 and includes number of API changes, several new features,
enhancements, and performance improvements along with a large number of bug fixes. We recommend that all
users upgrade to this version.
Expand Down Expand Up @@ -82,52 +76,51 @@ This also illustrates using the ``by`` parameter to group data before merging.

.. ipython:: python

trades = pd.DataFrame({
'time': pd.to_datetime(['20160525 13:30:00.023',
'20160525 13:30:00.038',
'20160525 13:30:00.048',
'20160525 13:30:00.048',
'20160525 13:30:00.048']),
'ticker': ['MSFT', 'MSFT',
'GOOG', 'GOOG', 'AAPL'],
'price': [51.95, 51.95,
720.77, 720.92, 98.00],
'quantity': [75, 155,
100, 100, 100]},
columns=['time', 'ticker', 'price', 'quantity'])

quotes = pd.DataFrame({
'time': pd.to_datetime(['20160525 13:30:00.023',
'20160525 13:30:00.023',
'20160525 13:30:00.030',
'20160525 13:30:00.041',
'20160525 13:30:00.048',
'20160525 13:30:00.049',
'20160525 13:30:00.072',
'20160525 13:30:00.075']),
'ticker': ['GOOG', 'MSFT', 'MSFT',
'MSFT', 'GOOG', 'AAPL', 'GOOG',
'MSFT'],
'bid': [720.50, 51.95, 51.97, 51.99,
720.50, 97.99, 720.50, 52.01],
'ask': [720.93, 51.96, 51.98, 52.00,
720.93, 98.01, 720.88, 52.03]},
columns=['time', 'ticker', 'bid', 'ask'])
trades = pd.DataFrame({
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is this the new standard to align not on the ipython (the i), but 1 space in? this seems odd

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@datapythonista

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this seems inconsistent even in this file

'time': pd.to_datetime(['20160525 13:30:00.023',
'20160525 13:30:00.038',
'20160525 13:30:00.048',
'20160525 13:30:00.048',
'20160525 13:30:00.048']),
'ticker': ['MSFT', 'MSFT',
'GOOG', 'GOOG', 'AAPL'],
'price': [51.95, 51.95,
720.77, 720.92, 98.00],
'quantity': [75, 155,
100, 100, 100]},
columns=['time', 'ticker', 'price', 'quantity'])

quotes = pd.DataFrame({
'time': pd.to_datetime(['20160525 13:30:00.023',
'20160525 13:30:00.023',
'20160525 13:30:00.030',
'20160525 13:30:00.041',
'20160525 13:30:00.048',
'20160525 13:30:00.049',
'20160525 13:30:00.072',
'20160525 13:30:00.075']),
'ticker': ['GOOG', 'MSFT', 'MSFT', 'MSFT',
'GOOG', 'AAPL', 'GOOG', 'MSFT'],
'bid': [720.50, 51.95, 51.97, 51.99,
720.50, 97.99, 720.50, 52.01],
'ask': [720.93, 51.96, 51.98, 52.00,
720.93, 98.01, 720.88, 52.03]},
columns=['time', 'ticker', 'bid', 'ask'])

.. ipython:: python

trades
quotes
trades
quotes

An asof merge joins on the ``on``, typically a datetimelike field, which is ordered, and
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

pd.merge_asof(trades, quotes,
on='time',
by='ticker')
pd.merge_asof(trades, quotes,
on='time',
by='ticker')

This returns a merged DataFrame with the entries in the same order as the original left
passed DataFrame (``trades`` in this case), with the fields of the ``quotes`` merged.
Expand All @@ -142,16 +135,17 @@ See the full documentation :ref:`here <stats.moments.ts>`.

.. ipython:: python

dft = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
index=pd.date_range('20130101 09:00:00', periods=5, freq='s'))
dft
dft = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
index=pd.date_range('20130101 09:00:00',
periods=5, freq='s'))
dft

This is a regular frequency index. Using an integer window parameter works to roll along the window frequency.

.. ipython:: python

dft.rolling(2).sum()
dft.rolling(2, min_periods=1).sum()
dft.rolling(2).sum()
dft.rolling(2, min_periods=1).sum()

Specifying an offset allows a more intuitive specification of the rolling frequency.

Expand All @@ -164,13 +158,13 @@ Using a non-regular, but still monotonic index, rolling with an integer window d
.. ipython:: python


dft = DataFrame({'B': [0, 1, 2, np.nan, 4]},
index = pd.Index([pd.Timestamp('20130101 09:00:00'),
pd.Timestamp('20130101 09:00:02'),
pd.Timestamp('20130101 09:00:03'),
pd.Timestamp('20130101 09:00:05'),
pd.Timestamp('20130101 09:00:06')],
name='foo'))
dft = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
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here too

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I think I realigned all to overlap with the first letter "i" or "c". Let me know if I miss any block.

index=pd.Index([pd.Timestamp('20130101 09:00:00'),
pd.Timestamp('20130101 09:00:02'),
pd.Timestamp('20130101 09:00:03'),
pd.Timestamp('20130101 09:00:05'),
pd.Timestamp('20130101 09:00:06')],
name='foo'))

dft
dft.rolling(2).sum()
Expand Down Expand Up @@ -289,18 +283,18 @@ Categorical Concatenation
s1 = pd.Series(['a', 'b'], dtype='category')
s2 = pd.Series(['b', 'c'], dtype='category')

**Previous behavior**:
**Previous behavior**:

.. code-block:: ipython
.. code-block:: ipython

In [1]: pd.concat([s1, s2])
ValueError: incompatible categories in categorical concat
In [1]: pd.concat([s1, s2])
ValueError: incompatible categories in categorical concat

**New behavior**:
**New behavior**:

.. ipython:: python
.. ipython:: python

pd.concat([s1, s2])
pd.concat([s1, s2])

.. _whatsnew_0190.enhancements.semi_month_offsets:

Expand All @@ -319,15 +313,15 @@ These provide date offsets anchored (by default) to the 15th and end of month, a

.. ipython:: python

Timestamp('2016-01-01') + SemiMonthEnd()
pd.Timestamp('2016-01-01') + SemiMonthEnd()
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Is it possible to use pd.offsets.SemiMonthEnd()?
I find it more descriptive as the reader doesn't have to scroll around to find where SemiMonthEnd() came from.

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Do you mean pd.tseries.offsets.SemiMonthEnd()? I think SemiMonthEnd() and SemiMonthBegin() are used right after importing from pandas.tseries.offsets and that was the only time they were used. I could be wrong. Let me know if it is the case.

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pd.tseries.offsets is imported as pd.offsets. They are basically the same. But maybe it's better to leave it like pd.tseries.offsets.SemiMonthEnd() as I don't know whether it's been the case in v0.19.0

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The document for v0.19 also uses a single class name like BDay or DataOffset. So I will keep it as it is now for consistency.


pd.date_range('2015-01-01', freq='SM', periods=4)

**SemiMonthBegin**:

.. ipython:: python

Timestamp('2016-01-01') + SemiMonthBegin()
pd.Timestamp('2016-01-01') + SemiMonthBegin()

pd.date_range('2015-01-01', freq='SMS', periods=4)

Expand Down Expand Up @@ -366,18 +360,18 @@ For ``MultiIndex``, values are dropped if any level is missing by default. Speci

.. ipython:: python

midx = pd.MultiIndex.from_arrays([[1, 2, np.nan, 4],
[1, 2, np.nan, np.nan]])
midx
midx.dropna()
midx.dropna(how='all')
midx = pd.MultiIndex.from_arrays([[1, 2, np.nan, 4],
[1, 2, np.nan, np.nan]])
midx
midx.dropna()
midx.dropna(how='all')

``Index`` now supports ``.str.extractall()`` which returns a ``DataFrame``, see the :ref:`docs here <text.extractall>` (:issue:`10008`, :issue:`13156`)

.. ipython:: python

idx = pd.Index(["a1a2", "b1", "c1"])
idx.str.extractall("[ab](?P<digit>\d)")
idx.str.extractall(r"[ab](?P<digit>\d)")

``Index.astype()`` now accepts an optional boolean argument ``copy``, which allows optional copying if the requirements on dtype are satisfied (:issue:`13209`)

Expand Down Expand Up @@ -453,7 +447,7 @@ The following are now part of this API:

import pprint
from pandas.api import types
funcs = [ f for f in dir(types) if not f.startswith('_') ]
funcs = [f for f in dir(types) if not f.startswith('_')]
pprint.pprint(funcs)

.. note::
Expand All @@ -478,15 +472,15 @@ Other enhancements

.. ipython:: python

df = pd.DataFrame({'date': pd.date_range('2015-01-01', freq='W', periods=5),
'a': np.arange(5)},
index=pd.MultiIndex.from_arrays([
[1,2,3,4,5],
pd.date_range('2015-01-01', freq='W', periods=5)],
names=['v','d']))
df
df.resample('M', on='date').sum()
df.resample('M', level='d').sum()
df = pd.DataFrame({'date': pd.date_range('2015-01-01', freq='W', periods=5),
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here its different

'a': np.arange(5)},
index=pd.MultiIndex.from_arrays([[1, 2, 3, 4, 5],
pd.date_range('2015-01-01', freq='W', periods=5)],
names=['v', 'd'])
)
df
df.resample('M', on='date').sum()
df.resample('M', level='d').sum()

- The ``.get_credentials()`` method of ``GbqConnector`` can now first try to fetch `the application default credentials <https://developers.google.com/identity/protocols/application-default-credentials>`__. See the docs for more details (:issue:`13577`).
- The ``.tz_localize()`` method of ``DatetimeIndex`` and ``Timestamp`` has gained the ``errors`` keyword, so you can potentially coerce nonexistent timestamps to ``NaT``. The default behavior remains to raising a ``NonExistentTimeError`` (:issue:`13057`)
Expand Down Expand Up @@ -547,7 +541,7 @@ API changes

.. ipython:: python

s = pd.Series([1,2,3])
s = pd.Series([1, 2, 3])

**Previous behavior**:

Expand Down Expand Up @@ -953,7 +947,7 @@ of integers (:issue:`13988`).

In [6]: pi = pd.PeriodIndex(['2011-01', '2011-02'], freq='M')
In [7]: pi.values
array([492, 493])
Out[7]: array([492, 493])

**New behavior**:

Expand Down Expand Up @@ -1006,15 +1000,17 @@ DatetimeIndex objects resulting in a TimedeltaIndex:

.. code-block:: ipython

In [1]: pd.DatetimeIndex(['2016-01-01', '2016-01-02']) - pd.DatetimeIndex(['2016-01-02', '2016-01-03'])
In [1]: (pd.DatetimeIndex(['2016-01-01', '2016-01-02'])
...: - pd.DatetimeIndex(['2016-01-02', '2016-01-03']))
FutureWarning: using '-' to provide set differences with datetimelike Indexes is deprecated, use .difference()
Out[1]: DatetimeIndex(['2016-01-01'], dtype='datetime64[ns]', freq=None)

**New behavior**:

.. ipython:: python

pd.DatetimeIndex(['2016-01-01', '2016-01-02']) - pd.DatetimeIndex(['2016-01-02', '2016-01-03'])
(pd.DatetimeIndex(['2016-01-01', '2016-01-02'])
- pd.DatetimeIndex(['2016-01-02', '2016-01-03']))


.. _whatsnew_0190.api.difference:
Expand Down Expand Up @@ -1060,21 +1056,23 @@ Previously, most ``Index`` classes returned ``np.ndarray``, and ``DatetimeIndex`

.. code-block:: ipython

In [1]: pd.Index([1, 2, 3]).unique()
Out[1]: array([1, 2, 3])
In [1]: pd.Index([1, 2, 3]).unique()
Out[1]: array([1, 2, 3])

In [2]: pd.DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03'], tz='Asia/Tokyo').unique()
Out[2]:
DatetimeIndex(['2011-01-01 00:00:00+09:00', '2011-01-02 00:00:00+09:00',
'2011-01-03 00:00:00+09:00'],
dtype='datetime64[ns, Asia/Tokyo]', freq=None)
In [2]: pd.DatetimeIndex(['2011-01-01', '2011-01-02',
...: '2011-01-03'], tz='Asia/Tokyo').unique()
Out[2]:
DatetimeIndex(['2011-01-01 00:00:00+09:00', '2011-01-02 00:00:00+09:00',
'2011-01-03 00:00:00+09:00'],
dtype='datetime64[ns, Asia/Tokyo]', freq=None)

**New behavior**:

.. ipython:: python

pd.Index([1, 2, 3]).unique()
pd.DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03'], tz='Asia/Tokyo').unique()
pd.Index([1, 2, 3]).unique()
pd.DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03'],
tz='Asia/Tokyo').unique()

.. _whatsnew_0190.api.multiindex:

Expand Down Expand Up @@ -1236,25 +1234,25 @@ Operators now preserve dtypes

- Sparse data structure now can preserve ``dtype`` after arithmetic ops (:issue:`13848`)

.. ipython:: python
.. ipython:: python

s = pd.SparseSeries([0, 2, 0, 1], fill_value=0, dtype=np.int64)
s.dtype
s = pd.SparseSeries([0, 2, 0, 1], fill_value=0, dtype=np.int64)
s.dtype

s + 1
s + 1

- Sparse data structure now support ``astype`` to convert internal ``dtype`` (:issue:`13900`)

.. ipython:: python
.. ipython:: python

s = pd.SparseSeries([1., 0., 2., 0.], fill_value=0)
s
s.astype(np.int64)
s = pd.SparseSeries([1., 0., 2., 0.], fill_value=0)
s
s.astype(np.int64)

``astype`` fails if data contains values which cannot be converted to specified ``dtype``.
Note that the limitation is applied to ``fill_value`` which default is ``np.nan``.

.. code-block:: ipython
.. code-block:: ipython

In [7]: pd.SparseSeries([1., np.nan, 2., np.nan], fill_value=np.nan).astype(np.int64)
Out[7]:
Expand Down
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