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saurav-chakravortyTomAugspurger
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DOC: Fix flake 8 errors for whatsnew v0.16* (pandas-dev#24338)
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‎doc/source/whatsnew/v0.16.0.rst

+32-35
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@@ -5,11 +5,6 @@ v0.16.0 (March 22, 2015)
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{{ header }}
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.. ipython:: python
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:suppress:
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from pandas import * # noqa F401, F403
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This is a major release from 0.15.2 and includes a small number of API changes, several new features,
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enhancements, and performance improvements along with a large number of bug fixes. We recommend that all
@@ -58,7 +53,7 @@ and the entire DataFrame (with all original and new columns) is returned.
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.. ipython :: python
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iris = read_csv('data/iris.data')
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iris = pd.read_csv('data/iris.data')
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iris.head()
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iris.assign(sepal_ratio=iris['SepalWidth'] / iris['SepalLength']).head()
@@ -77,9 +72,10 @@ calculate the ratio, and plot
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.. ipython:: python
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iris = pd.read_csv('data/iris.data')
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(iris.query('SepalLength > 5')
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.assign(SepalRatio = lambda x: x.SepalWidth / x.SepalLength,
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PetalRatio = lambda x: x.PetalWidth / x.PetalLength)
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.assign(SepalRatio=lambda x: x.SepalWidth / x.SepalLength,
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PetalRatio=lambda x: x.PetalWidth / x.PetalLength)
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.plot(kind='scatter', x='SepalRatio', y='PetalRatio'))
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.. image:: ../_static/whatsnew_assign.png
@@ -97,15 +93,14 @@ Added :meth:`SparseSeries.to_coo` and :meth:`SparseSeries.from_coo` methods (:is
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.. ipython:: python
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from numpy import nan
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s = Series([3.0, nan, 1.0, 3.0, nan, nan])
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s.index = MultiIndex.from_tuples([(1, 2, 'a', 0),
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(1, 2, 'a', 1),
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(1, 1, 'b', 0),
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(1, 1, 'b', 1),
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(2, 1, 'b', 0),
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(2, 1, 'b', 1)],
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names=['A', 'B', 'C', 'D'])
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s = pd.Series([3.0, np.nan, 1.0, 3.0, np.nan, np.nan])
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s.index = pd.MultiIndex.from_tuples([(1, 2, 'a', 0),
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(1, 2, 'a', 1),
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(1, 1, 'b', 0),
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(1, 1, 'b', 1),
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(2, 1, 'b', 0),
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(2, 1, 'b', 1)],
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names=['A', 'B', 'C', 'D'])
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s
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@@ -129,11 +124,11 @@ from a ``scipy.sparse.coo_matrix``:
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from scipy import sparse
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A = sparse.coo_matrix(([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])),
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shape=(3, 4))
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shape=(3, 4))
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A
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A.todense()
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ss = SparseSeries.from_coo(A)
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ss = pd.SparseSeries.from_coo(A)
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ss
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.. _whatsnew_0160.enhancements.string:
@@ -153,22 +148,22 @@ String Methods Enhancements
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.. ipython:: python
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s = Series(['abcd', '3456', 'EFGH'])
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s = pd.Series(['abcd', '3456', 'EFGH'])
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s.str.isalpha()
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s.str.find('ab')
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- :meth:`Series.str.pad` and :meth:`Series.str.center` now accept ``fillchar`` option to specify filling character (:issue:`9352`)
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.. ipython:: python
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s = Series(['12', '300', '25'])
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s = pd.Series(['12', '300', '25'])
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s.str.pad(5, fillchar='_')
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- Added :meth:`Series.str.slice_replace`, which previously raised ``NotImplementedError`` (:issue:`8888`)
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.. ipython:: python
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s = Series(['ABCD', 'EFGH', 'IJK'])
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s = pd.Series(['ABCD', 'EFGH', 'IJK'])
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s.str.slice_replace(1, 3, 'X')
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# replaced with empty char
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s.str.slice_replace(0, 1)
@@ -192,7 +187,7 @@ Other enhancements
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.. code-block:: python
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# Returns the 1st and 4th sheet, as a dictionary of DataFrames.
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pd.read_excel('path_to_file.xls',sheetname=['Sheet1',3])
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pd.read_excel('path_to_file.xls', sheetname=['Sheet1', 3])
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- 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`:).
@@ -273,11 +268,11 @@ The behavior of a small sub-set of edge cases for using ``.loc`` have changed (:
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.. ipython:: python
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df = DataFrame(np.random.randn(5,4),
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columns=list('ABCD'),
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index=date_range('20130101',periods=5))
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df = pd.DataFrame(np.random.randn(5, 4),
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columns=list('ABCD'),
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index=pd.date_range('20130101', periods=5))
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df
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s = Series(range(5),[-2,-1,1,2,3])
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s = pd.Series(range(5), [-2, -1, 1, 2, 3])
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s
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Previous Behavior
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.. code-block:: ipython
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In [3]: s = Series([0,1,2], dtype='category')
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In [3]: s = pd.Series([0, 1, 2], dtype='category')
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In [4]: s
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Out[4]:
@@ -374,23 +369,23 @@ New Behavior
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.. ipython:: python
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s = Series([0,1,2], dtype='category')
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s = pd.Series([0, 1, 2], dtype='category')
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s
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s.cat.ordered
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s = s.cat.as_ordered()
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s
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s.cat.ordered
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# you can set in the constructor of the Categorical
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s = Series(Categorical([0,1,2],ordered=True))
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s = pd.Series(pd.Categorical([0, 1, 2], ordered=True))
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s
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s.cat.ordered
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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.
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.. code-block:: python
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In [54]: s = Series(["a","b","c","a"]).astype('category',ordered=True)
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In [54]: s = pd.Series(["a", "b", "c", "a"]).astype('category', ordered=True)
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In [55]: s
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Out[55]:
@@ -401,7 +396,8 @@ For ease of creation of series of categorical data, we have added the ability to
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dtype: category
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Categories (3, object): [a < b < c]
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In [56]: s = Series(["a","b","c","a"]).astype('category',categories=list('abcdef'),ordered=False)
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In [56]: s = (pd.Series(["a", "b", "c", "a"])
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....: .astype('category', categories=list('abcdef'), ordered=False))
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In [57]: s
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Out[57]:
@@ -449,7 +445,7 @@ Other API Changes
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.. code-block:: ipython
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In [2]: pd.Series([0,1,2,3], list('abcd')) | pd.Series([4,4,4,4], list('abcd'))
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In [2]: pd.Series([0, 1, 2, 3], list('abcd')) | pd.Series([4, 4, 4, 4], list('abcd'))
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Out[2]:
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a True
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b True
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.. code-block:: ipython
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In [2]: pd.Series([0,1,2,3], list('abcd')) | pd.Series([4,4,4,4], list('abcd'))
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In [2]: pd.Series([0, 1, 2, 3], list('abcd')) | pd.Series([4, 4, 4, 4], list('abcd'))
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Out[2]:
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a 4
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@@ -680,7 +676,8 @@ Bug Fixes
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.. ipython:: python
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df1 = DataFrame({'x': Series(['a','b','c']), 'y': Series(['d','e','f'])})
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df1 = pd.DataFrame({'x': pd.Series(['a', 'b', 'c']),
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'y': pd.Series(['d', 'e', 'f'])})
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df2 = df1[['x']]
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df2['y'] = ['g', 'h', 'i']
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‎doc/source/whatsnew/v0.16.1.rst

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@@ -5,11 +5,6 @@ v0.16.1 (May 11, 2015)
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{{ header }}
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.. ipython:: python
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:suppress:
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from pandas import * # noqa F401, F403
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This is a minor bug-fix release from 0.16.0 and includes a a large number of
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bug fixes along several new features, enhancements, and performance improvements.
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.. code-block:: ipython
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In [1]: df = DataFrame({'A' : np.arange(6),
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...: 'B' : Series(list('aabbca')).astype('category',
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...: categories=list('cab'))
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...: })
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In [1]: df = pd.DataFrame({'A': np.arange(6),
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...: 'B': pd.Series(list('aabbca'))
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...: .astype('category', categories=list('cab'))
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...: })
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...:
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In [2]: df
@@ -146,7 +141,7 @@ values NOT in the categories, similarly to how you can reindex ANY pandas index.
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.. code-block:: ipython
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In [12]: df2.reindex(['a','e'])
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In [12]: df2.reindex(['a', 'e'])
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Out[12]:
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A
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B
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a 5.0
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e NaN
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In [13]: df2.reindex(['a','e']).index
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Out[13]: Index(['a', 'a', 'a', 'e'], dtype='object', name='B')
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In [13]: df2.reindex(['a', 'e']).index
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Out[13]: pd.Index(['a', 'a', 'a', 'e'], dtype='object', name='B')
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In [14]: df2.reindex(pd.Categorical(['a','e'],categories=list('abcde')))
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In [14]: df2.reindex(pd.Categorical(['a', 'e'], categories=list('abcde')))
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Out[14]:
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e NaN
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In [15]: df2.reindex(pd.Categorical(['a','e'],categories=list('abcde'))).index
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Out[15]: CategoricalIndex(['a', 'a', 'a', 'e'], categories=['a', 'b', 'c', 'd', 'e'], ordered=False, name='B', dtype='category')
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In [15]: df2.reindex(pd.Categorical(['a', 'e'], categories=list('abcde'))).index
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Out[15]: pd.CategoricalIndex(['a', 'a', 'a', 'e'],
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categories=['a', 'b', 'c', 'd', 'e'],
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ordered=False, name='B',
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dtype='category')
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See the :ref:`documentation <indexing.categoricalindex>` for more. (:issue:`7629`, :issue:`10038`, :issue:`10039`)
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.. ipython:: python
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idx = Index([' jack', 'jill ', ' jesse ', 'frank'])
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idx = pd.Index([' jack', 'jill ', ' jesse ', 'frank'])
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idx.str.strip()
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One special case for the `.str` accessor on ``Index`` is that if a string method returns ``bool``, the ``.str`` accessor
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idx = Index(['a1', 'a2', 'b1', 'b2'])
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s = Series(range(4), index=idx)
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idx = pd.Index(['a1', 'a2', 'b1', 'b2'])
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s = pd.Series(range(4), index=idx)
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s
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idx.str.startswith('a')
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s[s.index.str.startswith('a')]
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s = Series(['a,b', 'a,c', 'b,c'])
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s = pd.Series(['a,b', 'a,c', 'b,c'])
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# return Series
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s.str.split(',')
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# return DataFrame
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s.str.split(',', expand=True)
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idx = Index(['a,b', 'a,c', 'b,c'])
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idx = pd.Index(['a,b', 'a,c', 'b,c'])
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# return Index
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idx.str.split(',')
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from pandas.tseries.offsets import BusinessHour
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Timestamp('2014-08-01 09:00') + BusinessHour()
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Timestamp('2014-08-01 07:00') + BusinessHour()
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Timestamp('2014-08-01 16:30') + BusinessHour()
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pd.Timestamp('2014-08-01 09:00') + pd.tseries.offsets.BusinessHour()
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pd.Timestamp('2014-08-01 07:00') + pd.tseries.offsets.BusinessHour()
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pd.Timestamp('2014-08-01 16:30') + pd.tseries.offsets.BusinessHour()
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- ``DataFrame.diff`` now takes an ``axis`` parameter that determines the direction of differencing (:issue:`9727`)
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.. ipython:: python
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df = DataFrame(np.random.randn(3, 3), columns=['A', 'B', 'C'])
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df = pd.DataFrame(np.random.randn(3, 3), columns=['A', 'B', 'C'])
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df.drop(['A', 'X'], axis=1, errors='ignore')
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- Add support for separating years and quarters using dashes, for
@@ -362,19 +359,19 @@ Previous Behavior
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.. code-block:: ipython
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In [2]: pd.Index(range(4),name='foo')
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In [2]: pd.Index(range(4), name='foo')
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Out[2]: Int64Index([0, 1, 2, 3], dtype='int64')
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In [3]: pd.Index(range(104),name='foo')
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In [3]: pd.Index(range(104), name='foo')
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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')
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In [4]: pd.date_range('20130101',periods=4,name='foo',tz='US/Eastern')
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In [4]: pd.date_range('20130101', periods=4, name='foo', tz='US/Eastern')
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Out[4]:
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<class 'pandas.tseries.index.DatetimeIndex'>
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[2013-01-01 00:00:00-05:00, ..., 2013-01-04 00:00:00-05:00]
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Length: 4, Freq: D, Timezone: US/Eastern
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In [5]: pd.date_range('20130101',periods=104,name='foo',tz='US/Eastern')
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In [5]: pd.date_range('20130101', periods=104, name='foo', tz='US/Eastern')
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Out[5]:
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<class 'pandas.tseries.index.DatetimeIndex'>
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[2013-01-01 00:00:00-05:00, ..., 2013-04-14 00:00:00-04:00]
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pd.Index(range(4), name='foo')
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pd.Index(range(30), name='foo')
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pd.Index(range(104), name='foo')
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pd.CategoricalIndex(['a','bb','ccc','dddd'], ordered=True, name='foobar')
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pd.CategoricalIndex(['a','bb','ccc','dddd']*10, ordered=True, name='foobar')
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pd.CategoricalIndex(['a','bb','ccc','dddd']*100, ordered=True, name='foobar')
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pd.date_range('20130101',periods=4, name='foo', tz='US/Eastern')
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pd.date_range('20130101',periods=25, freq='D')
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pd.date_range('20130101',periods=104, name='foo', tz='US/Eastern')
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pd.CategoricalIndex(['a', 'bb', 'ccc', 'dddd'],
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ordered=True, name='foobar')
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pd.CategoricalIndex(['a', 'bb', 'ccc', 'dddd'] * 10,
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ordered=True, name='foobar')
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pd.CategoricalIndex(['a', 'bb', 'ccc', 'dddd'] * 100,
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ordered=True, name='foobar')
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pd.date_range('20130101', periods=4, name='foo', tz='US/Eastern')
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pd.date_range('20130101', periods=25, freq='D')
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pd.date_range('20130101', periods=104, name='foo', tz='US/Eastern')
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.. _whatsnew_0161.performance:

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