Skip to content

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

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 10 commits into from
Dec 13, 2018
Merged

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

Show file tree
Hide file tree
Changes from 6 commits
Commits
Show all changes
10 commits
Select commit Hold shift + click to select a range
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
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
200 changes: 99 additions & 101 deletions doc/source/whatsnew/v0.19.0.rst
View file
Open in desktop
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({
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

is this the new standard to align not on the ipython (the i), but 1 space in? this seems odd

Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

@datapythonista

Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

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]},
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

here too

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

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()
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

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.

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

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.

Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

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

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

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),
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

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
Loading