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Dec 18, 2018
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DOC: Fix flake 8 errors for whatsnew v0.16* #24338

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67 changes: 32 additions & 35 deletions doc/source/whatsnew/v0.16.0.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -5,11 +5,6 @@ v0.16.0 (March 22, 2015)

{{ header }}

.. ipython:: python
:suppress:

from pandas import * # noqa F401, F403


This is a major release from 0.15.2 and includes a small number of API changes, several new features,
enhancements, and performance improvements along with a large number of bug fixes. We recommend that all
Expand Down Expand Up @@ -58,7 +53,7 @@ and the entire DataFrame (with all original and new columns) is returned.

.. ipython :: python

iris = read_csv('data/iris.data')
iris = pd.read_csv('data/iris.data')
iris.head()

iris.assign(sepal_ratio=iris['SepalWidth'] / iris['SepalLength']).head()
Expand All @@ -77,9 +72,10 @@ calculate the ratio, and plot

.. ipython:: python

iris = pd.read_csv('data/iris.data')
(iris.query('SepalLength > 5')
.assign(SepalRatio = lambda x: x.SepalWidth / x.SepalLength,
PetalRatio = lambda x: x.PetalWidth / x.PetalLength)
.assign(SepalRatio=lambda x: x.SepalWidth / x.SepalLength,
PetalRatio=lambda x: x.PetalWidth / x.PetalLength)
.plot(kind='scatter', x='SepalRatio', y='PetalRatio'))

.. image:: ../_static/whatsnew_assign.png
Expand All @@ -97,15 +93,14 @@ Added :meth:`SparseSeries.to_coo` and :meth:`SparseSeries.from_coo` methods (:is

.. ipython:: python

from numpy import nan
s = Series([3.0, nan, 1.0, 3.0, nan, nan])
s.index = MultiIndex.from_tuples([(1, 2, 'a', 0),
(1, 2, 'a', 1),
(1, 1, 'b', 0),
(1, 1, 'b', 1),
(2, 1, 'b', 0),
(2, 1, 'b', 1)],
names=['A', 'B', 'C', 'D'])
s = pd.Series([3.0, np.nan, 1.0, 3.0, np.nan, np.nan])
s.index = pd.MultiIndex.from_tuples([(1, 2, 'a', 0),
(1, 2, 'a', 1),
(1, 1, 'b', 0),
(1, 1, 'b', 1),
(2, 1, 'b', 0),
(2, 1, 'b', 1)],
names=['A', 'B', 'C', 'D'])

s

Expand All @@ -129,11 +124,11 @@ from a ``scipy.sparse.coo_matrix``:

from scipy import sparse
A = sparse.coo_matrix(([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])),
shape=(3, 4))
shape=(3, 4))
A
A.todense()

ss = SparseSeries.from_coo(A)
ss = pd.SparseSeries.from_coo(A)
ss

.. _whatsnew_0160.enhancements.string:
Expand All @@ -153,22 +148,22 @@ String Methods Enhancements

.. ipython:: python

s = Series(['abcd', '3456', 'EFGH'])
s = pd.Series(['abcd', '3456', 'EFGH'])
s.str.isalpha()
s.str.find('ab')

- :meth:`Series.str.pad` and :meth:`Series.str.center` now accept ``fillchar`` option to specify filling character (:issue:`9352`)

.. ipython:: python

s = Series(['12', '300', '25'])
s = pd.Series(['12', '300', '25'])
s.str.pad(5, fillchar='_')

- Added :meth:`Series.str.slice_replace`, which previously raised ``NotImplementedError`` (:issue:`8888`)

.. ipython:: python

s = Series(['ABCD', 'EFGH', 'IJK'])
s = pd.Series(['ABCD', 'EFGH', 'IJK'])
s.str.slice_replace(1, 3, 'X')
# replaced with empty char
s.str.slice_replace(0, 1)
Expand All @@ -192,7 +187,7 @@ Other enhancements
.. code-block:: python

# Returns the 1st and 4th sheet, as a dictionary of DataFrames.
pd.read_excel('path_to_file.xls',sheetname=['Sheet1',3])
pd.read_excel('path_to_file.xls', sheetname=['Sheet1', 3])


- Allow Stata files to be read incrementally with an iterator; support for long strings in Stata files. See the docs :ref:`here<io.stata_reader>` (:issue:`9493`:).
Expand Down Expand Up @@ -273,11 +268,11 @@ The behavior of a small sub-set of edge cases for using ``.loc`` have changed (:

.. ipython:: python

df = DataFrame(np.random.randn(5,4),
columns=list('ABCD'),
index=date_range('20130101',periods=5))
df = pd.DataFrame(np.random.randn(5, 4),
columns=list('ABCD'),
index=pd.date_range('20130101', periods=5))
df
s = Series(range(5),[-2,-1,1,2,3])
s = pd.Series(range(5), [-2, -1, 1, 2, 3])
s

Previous Behavior
Expand Down Expand Up @@ -347,7 +342,7 @@ Previous Behavior

.. code-block:: ipython

In [3]: s = Series([0,1,2], dtype='category')
In [3]: s = pd.Series([0, 1, 2], dtype='category')

In [4]: s
Out[4]:
Expand All @@ -374,23 +369,23 @@ New Behavior

.. ipython:: python

s = Series([0,1,2], dtype='category')
s = pd.Series([0, 1, 2], dtype='category')
s
s.cat.ordered
s = s.cat.as_ordered()
s
s.cat.ordered

# you can set in the constructor of the Categorical
s = Series(Categorical([0,1,2],ordered=True))
s = pd.Series(pd.Categorical([0, 1, 2], ordered=True))
s
s.cat.ordered

For ease of creation of series of categorical data, we have added the ability to pass keywords when calling ``.astype()``. These are passed directly to the constructor.

.. code-block:: python

In [54]: s = Series(["a","b","c","a"]).astype('category',ordered=True)
In [54]: s = pd.Series(["a", "b", "c", "a"]).astype('category', ordered=True)

In [55]: s
Out[55]:
Expand All @@ -401,7 +396,8 @@ For ease of creation of series of categorical data, we have added the ability to
dtype: category
Categories (3, object): [a < b < c]

In [56]: s = Series(["a","b","c","a"]).astype('category',categories=list('abcdef'),ordered=False)
In [56]: s = (pd.Series(["a", "b", "c", "a"])
....: .astype('category', categories=list('abcdef'), ordered=False))

In [57]: s
Out[57]:
Expand Down Expand Up @@ -449,7 +445,7 @@ Other API Changes

.. code-block:: ipython

In [2]: pd.Series([0,1,2,3], list('abcd')) | pd.Series([4,4,4,4], list('abcd'))
In [2]: pd.Series([0, 1, 2, 3], list('abcd')) | pd.Series([4, 4, 4, 4], list('abcd'))
Out[2]:
a True
b True
Expand All @@ -462,7 +458,7 @@ Other API Changes

.. code-block:: ipython

In [2]: pd.Series([0,1,2,3], list('abcd')) | pd.Series([4,4,4,4], list('abcd'))
In [2]: pd.Series([0, 1, 2, 3], list('abcd')) | pd.Series([4, 4, 4, 4], list('abcd'))
Out[2]:
a 4
b 5
Expand Down Expand Up @@ -680,7 +676,8 @@ Bug Fixes

.. ipython:: python

df1 = DataFrame({'x': Series(['a','b','c']), 'y': Series(['d','e','f'])})
df1 = pd.DataFrame({'x': pd.Series(['a', 'b', 'c']),
'y': pd.Series(['d', 'e', 'f'])})
df2 = df1[['x']]
df2['y'] = ['g', 'h', 'i']

Expand Down
70 changes: 35 additions & 35 deletions doc/source/whatsnew/v0.16.1.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,11 +5,6 @@ v0.16.1 (May 11, 2015)

{{ header }}

.. ipython:: python
:suppress:

from pandas import * # noqa F401, F403


This is a minor bug-fix release from 0.16.0 and includes a a large number of
bug fixes along several new features, enhancements, and performance improvements.
Expand Down Expand Up @@ -51,10 +46,10 @@ setting the index of a ``DataFrame/Series`` with a ``category`` dtype would conv

.. code-block:: ipython

In [1]: df = DataFrame({'A' : np.arange(6),
...: 'B' : Series(list('aabbca')).astype('category',
...: categories=list('cab'))
...: })
In [1]: df = pd.DataFrame({'A': np.arange(6),
...: 'B': pd.Series(list('aabbca'))
...: .astype('category', categories=list('cab'))
...: })
...:

In [2]: df
Expand Down Expand Up @@ -146,7 +141,7 @@ values NOT in the categories, similarly to how you can reindex ANY pandas index.

.. code-block:: ipython

In [12]: df2.reindex(['a','e'])
In [12]: df2.reindex(['a', 'e'])
Out[12]:
A
B
Expand All @@ -155,10 +150,10 @@ values NOT in the categories, similarly to how you can reindex ANY pandas index.
a 5.0
e NaN

In [13]: df2.reindex(['a','e']).index
Out[13]: Index(['a', 'a', 'a', 'e'], dtype='object', name='B')
In [13]: df2.reindex(['a', 'e']).index
Out[13]: pd.Index(['a', 'a', 'a', 'e'], dtype='object', name='B')

In [14]: df2.reindex(pd.Categorical(['a','e'],categories=list('abcde')))
In [14]: df2.reindex(pd.Categorical(['a', 'e'], categories=list('abcde')))
Out[14]:
A
B
Expand All @@ -167,8 +162,11 @@ values NOT in the categories, similarly to how you can reindex ANY pandas index.
a 5.0
e NaN

In [15]: df2.reindex(pd.Categorical(['a','e'],categories=list('abcde'))).index
Out[15]: CategoricalIndex(['a', 'a', 'a', 'e'], categories=['a', 'b', 'c', 'd', 'e'], ordered=False, name='B', dtype='category')
In [15]: df2.reindex(pd.Categorical(['a', 'e'], categories=list('abcde'))).index
Out[15]: pd.CategoricalIndex(['a', 'a', 'a', 'e'],
categories=['a', 'b', 'c', 'd', 'e'],
ordered=False, name='B',
dtype='category')

See the :ref:`documentation <indexing.categoricalindex>` for more. (:issue:`7629`, :issue:`10038`, :issue:`10039`)

Expand Down Expand Up @@ -230,7 +228,7 @@ enhancements make string operations easier and more consistent with standard pyt

.. ipython:: python

idx = Index([' jack', 'jill ', ' jesse ', 'frank'])
idx = pd.Index([' jack', 'jill ', ' jesse ', 'frank'])
idx.str.strip()

One special case for the `.str` accessor on ``Index`` is that if a string method returns ``bool``, the ``.str`` accessor
Expand All @@ -239,8 +237,8 @@ enhancements make string operations easier and more consistent with standard pyt

.. ipython:: python

idx = Index(['a1', 'a2', 'b1', 'b2'])
s = Series(range(4), index=idx)
idx = pd.Index(['a1', 'a2', 'b1', 'b2'])
s = pd.Series(range(4), index=idx)
s
idx.str.startswith('a')
s[s.index.str.startswith('a')]
Expand All @@ -258,15 +256,15 @@ enhancements make string operations easier and more consistent with standard pyt

.. ipython:: python

s = Series(['a,b', 'a,c', 'b,c'])
s = pd.Series(['a,b', 'a,c', 'b,c'])

# return Series
s.str.split(',')

# return DataFrame
s.str.split(',', expand=True)

idx = Index(['a,b', 'a,c', 'b,c'])
idx = pd.Index(['a,b', 'a,c', 'b,c'])

# return Index
idx.str.split(',')
Expand All @@ -287,10 +285,9 @@ Other Enhancements

.. ipython:: python

from pandas.tseries.offsets import BusinessHour
Timestamp('2014-08-01 09:00') + BusinessHour()
Timestamp('2014-08-01 07:00') + BusinessHour()
Timestamp('2014-08-01 16:30') + BusinessHour()
pd.Timestamp('2014-08-01 09:00') + pd.tseries.offsets.BusinessHour()
pd.Timestamp('2014-08-01 07:00') + pd.tseries.offsets.BusinessHour()
pd.Timestamp('2014-08-01 16:30') + pd.tseries.offsets.BusinessHour()

- ``DataFrame.diff`` now takes an ``axis`` parameter that determines the direction of differencing (:issue:`9727`)

Expand All @@ -302,7 +299,7 @@ Other Enhancements

.. ipython:: python

df = DataFrame(np.random.randn(3, 3), columns=['A', 'B', 'C'])
df = pd.DataFrame(np.random.randn(3, 3), columns=['A', 'B', 'C'])
df.drop(['A', 'X'], axis=1, errors='ignore')

- Add support for separating years and quarters using dashes, for
Expand Down Expand Up @@ -362,19 +359,19 @@ Previous Behavior

.. code-block:: ipython

In [2]: pd.Index(range(4),name='foo')
In [2]: pd.Index(range(4), name='foo')
Out[2]: Int64Index([0, 1, 2, 3], dtype='int64')

In [3]: pd.Index(range(104),name='foo')
In [3]: pd.Index(range(104), name='foo')
Out[3]: Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...], dtype='int64')

In [4]: pd.date_range('20130101',periods=4,name='foo',tz='US/Eastern')
In [4]: pd.date_range('20130101', periods=4, name='foo', tz='US/Eastern')
Out[4]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-01-01 00:00:00-05:00, ..., 2013-01-04 00:00:00-05:00]
Length: 4, Freq: D, Timezone: US/Eastern

In [5]: pd.date_range('20130101',periods=104,name='foo',tz='US/Eastern')
In [5]: pd.date_range('20130101', periods=104, name='foo', tz='US/Eastern')
Out[5]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-01-01 00:00:00-05:00, ..., 2013-04-14 00:00:00-04:00]
Expand All @@ -388,12 +385,15 @@ New Behavior
pd.Index(range(4), name='foo')
pd.Index(range(30), name='foo')
pd.Index(range(104), name='foo')
pd.CategoricalIndex(['a','bb','ccc','dddd'], ordered=True, name='foobar')
pd.CategoricalIndex(['a','bb','ccc','dddd']*10, ordered=True, name='foobar')
pd.CategoricalIndex(['a','bb','ccc','dddd']*100, ordered=True, name='foobar')
pd.date_range('20130101',periods=4, name='foo', tz='US/Eastern')
pd.date_range('20130101',periods=25, freq='D')
pd.date_range('20130101',periods=104, name='foo', tz='US/Eastern')
pd.CategoricalIndex(['a', 'bb', 'ccc', 'dddd'],
ordered=True, name='foobar')
pd.CategoricalIndex(['a', 'bb', 'ccc', 'dddd'] * 10,
ordered=True, name='foobar')
pd.CategoricalIndex(['a', 'bb', 'ccc', 'dddd'] * 100,
ordered=True, name='foobar')
pd.date_range('20130101', periods=4, name='foo', tz='US/Eastern')
pd.date_range('20130101', periods=25, freq='D')
pd.date_range('20130101', periods=104, name='foo', tz='US/Eastern')


.. _whatsnew_0161.performance:
Expand Down
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