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Dec 18, 2018
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DOC: Fix flake8 issues with whatsnew v0.18.* #24303

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67ff7ae
DOC: Fix flake8 issues with whatsnew v0.18.*
JimJeon Dec 16, 2018
7e4bc37
DOC: Fixes import of StringIO
JimJeon Dec 16, 2018
f898da7
DOC: Fix reviewed issues
JimJeon Dec 17, 2018
2d7d5e5
DOC: Remove pandas import from v0.18.1
JimJeon Dec 17, 2018
c3fd30f
DOC: Rearrange the import block
JimJeon Dec 17, 2018
a17fa1b
DOC: Add a blank line
JimJeon Dec 18, 2018
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35 changes: 18 additions & 17 deletions doc/source/whatsnew/v0.18.0.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -64,14 +64,14 @@ Window functions have been refactored to be methods on ``Series/DataFrame`` obje
.. ipython:: python

np.random.seed(1234)
df = pd.DataFrame({'A' : range(10), 'B' : np.random.randn(10)})
df = pd.DataFrame({'A': range(10), 'B': np.random.randn(10)})
df

Previous Behavior:

.. code-block:: ipython

In [8]: pd.rolling_mean(df,window=3)
In [8]: pd.rolling_mean(df, window=3)
FutureWarning: pd.rolling_mean is deprecated for DataFrame and will be removed in a future version, replace with
DataFrame.rolling(window=3,center=False).mean()
Out[8]:
Expand Down Expand Up @@ -102,7 +102,7 @@ with tab-completion of available methods and properties.

.. code-block:: ipython

In [9]: r.
In [9]: r.<TAB>
r.A r.agg r.apply r.count r.exclusions r.max r.median r.name r.skew r.sum
r.B r.aggregate r.corr r.cov r.kurt r.mean r.min r.quantile r.std r.var

Expand All @@ -122,8 +122,8 @@ And multiple aggregations

.. ipython:: python

r.agg({'A' : ['mean','std'],
'B' : ['mean','std']})
r.agg({'A': ['mean', 'std'],
'B': ['mean', 'std']})

.. _whatsnew_0180.enhancements.rename:

Expand Down Expand Up @@ -201,7 +201,7 @@ Currently the default is ``expand=None`` which gives a ``FutureWarning`` and use

.. code-block:: ipython

In [1]: pd.Series(['a1', 'b2', 'c3']).str.extract('[ab](\d)', expand=None)
In [1]: pd.Series(['a1', 'b2', 'c3']).str.extract(r'[ab](\d)', expand=None)
FutureWarning: currently extract(expand=None) means expand=False (return Index/Series/DataFrame)
but in a future version of pandas this will be changed to expand=True (return DataFrame)

Expand All @@ -216,13 +216,13 @@ Extracting a regular expression with one group returns a Series if

.. ipython:: python

pd.Series(['a1', 'b2', 'c3']).str.extract('[ab](\d)', expand=False)
pd.Series(['a1', 'b2', 'c3']).str.extract(r'[ab](\d)', expand=False)

It returns a ``DataFrame`` with one column if ``expand=True``.

.. ipython:: python

pd.Series(['a1', 'b2', 'c3']).str.extract('[ab](\d)', expand=True)
pd.Series(['a1', 'b2', 'c3']).str.extract(r'[ab](\d)', expand=True)

Calling on an ``Index`` with a regex with exactly one capture group
returns an ``Index`` if ``expand=False``.
Expand Down Expand Up @@ -270,13 +270,13 @@ match.

s = pd.Series(["a1a2", "b1", "c1"], ["A", "B", "C"])
s
s.str.extract("(?P<letter>[ab])(?P<digit>\d)", expand=False)
s.str.extract(r"(?P<letter>[ab])(?P<digit>\d)", expand=False)

The ``extractall`` method returns all matches.

.. ipython:: python

s.str.extractall("(?P<letter>[ab])(?P<digit>\d)")
s.str.extractall(r"(?P<letter>[ab])(?P<digit>\d)")

.. _whatsnew_0180.enhancements.strcat:

Expand All @@ -289,12 +289,12 @@ A new, friendlier ``ValueError`` is added to protect against the mistake of supp

.. ipython:: python

pd.Series(['a','b',np.nan,'c']).str.cat(sep=' ')
pd.Series(['a','b',np.nan,'c']).str.cat(sep=' ', na_rep='?')
pd.Series(['a', 'b', np.nan, 'c']).str.cat(sep=' ')
pd.Series(['a', 'b', np.nan, 'c']).str.cat(sep=' ', na_rep='?')

.. code-block:: ipython

In [2]: pd.Series(['a','b',np.nan,'c']).str.cat(' ')
In [2]: pd.Series(['a', 'b', np.nan, 'c']).str.cat(' ')
ValueError: Did you mean to supply a `sep` keyword?


Expand Down Expand Up @@ -329,7 +329,7 @@ Timedeltas

.. ipython:: python

t = timedelta_range('1 days 2 hr 13 min 45 us',periods=3,freq='d')
t = timedelta_range('1 days 2 hr 13 min 45 us', periods=3, freq='d')
t
t.round('10min')

Expand All @@ -356,7 +356,7 @@ Previous Behavior:

.. code-block:: ipython

In [2]: s = pd.Series([1,2,3], index=np.arange(3.))
In [2]: s = pd.Series([1, 2, 3], index=np.arange(3.))

In [3]: s
Out[3]:
Expand All @@ -378,7 +378,7 @@ New Behavior:

.. ipython:: python

s = pd.Series([1,2,3], index=np.arange(3.))
s = pd.Series([1, 2, 3], index=np.arange(3.))
s
s.index
print(s.to_csv(path_or_buf=None, header=False))
Expand Down Expand Up @@ -727,7 +727,8 @@ Like the change in the window functions API :ref:`above <whatsnew_0180.enhanceme
np.random.seed(1234)
df = pd.DataFrame(np.random.rand(10,4),
columns=list('ABCD'),
index=pd.date_range('2010-01-01 09:00:00', periods=10, freq='s'))
index=pd.date_range('2010-01-01 09:00:00',
periods=10, freq='s'))
df


Expand Down
34 changes: 22 additions & 12 deletions doc/source/whatsnew/v0.18.1.rst
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Expand Up @@ -53,6 +53,7 @@ Friday before MLK Day

.. ipython:: python

from datetime import datetime
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can you use import datetime instead, and use datetime.datetime(...) below

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Sure. May I ask why you prefer that?
Is that because it can cause some namespace issues?

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There are many reasons, and some are not trivial to explain.

The main one is that if I see a bare datetime in Python code, I need to guess whether it's the main module, or the datetime.datetime module. While there is no ambiguity in reading datetime.datetime.

Then, with a from datetime import datetime I load the whole datetime module in sys.modules, but I only have access in the namespace to the datetime.datetime submodule (with the name datetime). This won't let me perform operations like imp.reload(datetime), as I don't have anything in the namespace pointing to the main datetime module.

dt = datetime(2014, 1, 17, 15)

dt + bhour_us
Expand Down Expand Up @@ -171,8 +172,7 @@ without using temporary variable.
bb = pd.read_csv('data/baseball.csv', index_col='id')
(bb.groupby(['year', 'team'])
.sum()
.loc[lambda df: df.r > 100]
)
.loc[lambda df: df.r > 100])

.. _whatsnew_0181.partial_string_indexing:

Expand All @@ -185,10 +185,11 @@ Partial string indexing now matches on ``DateTimeIndex`` when part of a ``MultiI

dft2 = pd.DataFrame(np.random.randn(20, 1),
columns=['A'],
index=pd.MultiIndex.from_product([pd.date_range('20130101',
periods=10,
freq='12H'),
['a', 'b']]))
index=pd.MultiIndex.
from_product([pd.date_range('20130101',
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I found this indentation confusing. If the lines are too long, can we use something like:

dft2 = pd.DataFrame(
    np.random.randn(20, 1),
    ...

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Thanks for the comment. I wasn't flexible with that. I'll fix it.

periods=10,
freq='12H'),
['a', 'b']]))
dft2
dft2.loc['2013-01-05']

Expand Down Expand Up @@ -317,8 +318,8 @@ The index in ``.groupby(..).nth()`` output is now more consistent when the ``as_

.. ipython:: python

df = DataFrame({'A' : ['a', 'b', 'a'],
'B' : [1, 2, 3]})
df = pd.DataFrame({'A': ['a', 'b', 'a'],
'B': [1, 2, 3]})
df

Previous Behavior:
Expand Down Expand Up @@ -433,13 +434,15 @@ Previous behavior:

.. code-block:: ipython

In [1]: df.groupby(pd.TimeGrouper(key='date', freq='M')).apply(lambda x: x.value.sum())
In [1]: df.groupby(pd.TimeGrouper(key='date',
...: freq='M')).apply(lambda x: x.value.sum())
Out[1]:
...
TypeError: cannot concatenate a non-NDFrame object

# Output is a Series
In [2]: df.groupby(pd.TimeGrouper(key='date', freq='M')).apply(lambda x: x[['value']].sum())
In [2]: df.groupby(pd.TimeGrouper(key='date',
...: freq='M')).apply(lambda x: x[['value']].sum())
Out[2]:
date
2000-10-31 value 10
Expand All @@ -451,15 +454,17 @@ New Behavior:
.. code-block:: python
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Suggested change
.. code-block:: python
.. code-block:: ipython


# Output is a Series
In [55]: df.groupby(pd.TimeGrouper(key='date', freq='M')).apply(lambda x: x.value.sum())
In [55]: df.groupby(pd.TimeGrouper(key='date',
...: freq='M')).apply(lambda x: x.value.sum())
Out[55]:
date
2000-10-31 10
2000-11-30 13
Freq: M, dtype: int64

# Output is a DataFrame
In [56]: df.groupby(pd.TimeGrouper(key='date', freq='M')).apply(lambda x: x[['value']].sum())
In [56]: df.groupby(pd.TimeGrouper(key='date',
...: freq='M')).apply(lambda x: x[['value']].sum())
Out[56]:
value
date
Expand All @@ -471,6 +476,11 @@ New Behavior:
Changes in ``read_csv`` exceptions
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. ipython:: python
:suppress:

from io import StringIO
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can you use import io instead, and have the import visible in the next block, where it's used.

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Sure. I'll fix it.


In order to standardize the ``read_csv`` API for both the ``c`` and ``python`` engines, both will now raise an
``EmptyDataError``, a subclass of ``ValueError``, in response to empty columns or header (:issue:`12493`, :issue:`12506`)

Expand Down
2 changes: 0 additions & 2 deletions setup.cfg
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Expand Up @@ -56,8 +56,6 @@ exclude =
doc/source/whatsnew/v0.16.2.rst
doc/source/whatsnew/v0.17.0.rst
doc/source/whatsnew/v0.17.1.rst
doc/source/whatsnew/v0.18.0.rst
doc/source/whatsnew/v0.18.1.rst
doc/source/basics.rst
doc/source/contributing_docstring.rst
doc/source/enhancingperf.rst
Expand Down