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v0.18.0.txt
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.. _whatsnew_0180:
v0.18.0 (January ??, 2016)
--------------------------
This is a major release from 0.17.1 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
users upgrade to this version.
.. warning::
pandas >= 0.18.0 will no longer support compatibility with Python version 3.3 (:issue:`11273`)
Highlights include:
- Window functions are now methods on ``.groupby`` like objects, see :ref:`here <whatsnew_0180.enhancements.moments>`.
Check the :ref:`API Changes <whatsnew_0180.api>` and :ref:`deprecations <whatsnew_0180.deprecations>` before updating.
.. contents:: What's new in v0.18.0
:local:
:backlinks: none
.. _whatsnew_0180.enhancements:
New features
~~~~~~~~~~~~
.. _whatsnew_0180.enhancements.moments:
Window functions are now methods
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Window functions have been refactored to be methods on ``Series/DataFrame`` objects, rather than top-level functions, which are now deprecated. This allows these window-type functions, to have a similar API to that of ``.groupby``. See the full documentation :ref:`here <stats.moments>` (:issue:`11603`)
.. ipython:: python
np.random.seed(1234)
df = DataFrame({'A' : range(10), 'B' : np.random.randn(10)})
df
Previous Behavior:
.. code-block:: python
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]:
A B
0 NaN NaN
1 NaN NaN
2 1 0.237722
3 2 -0.023640
4 3 0.133155
5 4 -0.048693
6 5 0.342054
7 6 0.370076
8 7 0.079587
9 8 -0.954504
New Behavior:
.. ipython:: python
r = df.rolling(window=3)
These show a descriptive repr
.. ipython:: python
r
with tab-completion of available methods and properties.
.. code-block:: python
In [9]: r.
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
The methods operate on the ``Rolling`` object itself
.. ipython:: python
r.mean()
They provide getitem accessors
.. ipython:: python
r['A'].mean()
And multiple aggregations
.. ipython:: python
r.agg({'A' : ['mean','std'],
'B' : ['mean','std']})
.. _whatsnew_0180.enhancements.other:
Other enhancements
^^^^^^^^^^^^^^^^^^
- Handle truncated floats in SAS xport files (:issue:`11713`)
- Added option to hide index in ``Series.to_string`` (:issue:`11729`)
- ``read_excel`` now supports s3 urls of the format ``s3://bucketname/filename`` (:issue:`11447`)
- A simple version of ``Panel.round()`` is now implemented (:issue:`11763`)
- For Python 3.x, ``round(DataFrame)``, ``round(Series)``, ``round(Panel)`` will work (:issue:`11763`)
- ``DataFrame`` has gained a ``_repr_latex_`` method in order to allow for automatic conversion to latex in a ipython/jupyter notebook using nbconvert. Options ``display.latex.escape`` and ``display.latex.longtable`` have been added to the configuration and are used automatically by the ``to_latex`` method. (:issue:`11778`)
- ``sys.getsizeof(obj)`` returns the memory usage of a pandas object, including the
values it contains (:issue:`11597`)
- ``set_index`` now interprets views of the columns index passed to the keys parameter as lists of existing columns to use as the index (:issue:`10797`)
.. _whatsnew_0180.enhancements.rounding:
Datetimelike rounding
^^^^^^^^^^^^^^^^^^^^^
``DatetimeIndex``, ``Timestamp``, ``TimedeltaIndex``, ``Timedelta`` have gained the ``.round()`` method for datetimelike rounding. (:issue:`4314`)
Naive datetimes
.. ipython:: python
dr = pd.date_range('20130101 09:12:56.1234', periods=3)
dr
dr.round('s')
# Timestamp scalar
dr[0]
dr[0].round('10s')
Tz-aware are rounded in local times
.. ipython:: python
dr = dr.tz_localize('US/Eastern')
dr
dr.round('s')
Timedeltas
.. ipython:: python
t = timedelta_range('1 days 2 hr 13 min 45 us',periods=3,freq='d')
t
t.round('10min')
# Timedelta scalar
t[0]
t[0].round('2h')
In addition, ``.round()`` will be available thru the ``.dt`` accessor of ``Series``.
.. ipython:: python
s = Series(dr)
s
s.dt.round('D')
.. _whatsnew_0180.api:
.. _whatsnew_0180.api_breaking:
Backwards incompatible API changes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- the leading whitespaces have been removed from the output of ``.to_string(index=False)`` method (:issue:`11833`)
- the ``out`` parameter has been removed from the ``Series.round()`` method. (:issue:`11763`)
- ``DataFrame.round()`` leaves non-numeric columns unchanged in its return, rather than raises. (:issue:`11885`)
- ``DataFrame.head(0)`` and ``DataFrame.tail(0)`` return empty frames, rather than ``self``. (:issue:`11937`)
- ``Series.head(0)`` and ``Series.tail(0)`` return empty series, rather than ``self``. (:issue:`11937`)
NaT and Timedelta operations
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
``NaT`` and ``Timedelta`` have expanded arithmetic operations, which are extended to ``Series``
arithmetic where applicable. Operations defined for ``datetime64[ns]`` or ``timedelta64[ns]``
are now also defined for ``NaT`` (:issue:`11564`).
``NaT`` now supports arithmetic operations with integers and floats.
.. ipython:: python
pd.NaT * 1
pd.NaT * 1.5
pd.NaT / 2
pd.NaT * np.nan
``NaT`` defines more arithmetic operations with ``datetime64[ns]`` and ``timedelta64[ns]``.
.. ipython:: python
pd.NaT / pd.NaT
pd.Timedelta('1s') / pd.NaT
``NaT`` may represent either a ``datetime64[ns]`` null or a ``timedelta64[ns]`` null.
Given the ambiguity, it is treated as a ``timedelta64[ns]``, which allows more operations
to succeed.
.. ipython:: python
pd.NaT + pd.NaT
# same as
pd.Timedelta('1s') + pd.Timedelta('1s')
as opposed to
.. code-block:: python
In [3]: pd.Timestamp('1990315') + pd.Timestamp('19900315')
ValueError:
However, when wrapped in a ``Series`` whose ``dtype`` is ``datetime64[ns]`` or ``timedelta64[ns]``,
the ``dtype`` information is respected.
.. code-block:: python
In [1]: pd.Series([pd.NaT], dtype='<M8[ns]') + pd.Series([pd.NaT], dtype='<M8[ns]')
TypeError: can only operate on a datetimes for subtraction, but the operator [__add__] was passed
.. ipython:: python
pd.Series([pd.NaT], dtype='<m8[ns]') + pd.Series([pd.NaT], dtype='<m8[ns]')
``Timedelta`` division by ``floats`` now works.
.. ipython:: python
pd.Timedelta('1s') / 2.0
Subtraction by ``Timedelta`` in a ``Series`` by a ``Timestamp`` works (:issue:`11925`)
.. ipython:: python
ser = pd.Series(pd.timedelta_range('1 day', periods=3))
ser
pd.Timestamp('2012-01-01') - ser
Bug in QuarterBegin with n=0
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In previous versions, the behavior of the QuarterBegin offset was inconsistent
depending on the date when the ``n`` parameter was 0. (:issue:`11406`)
The general semantics of anchored offsets for ``n=0`` is to not move the date
when it is an anchor point (e.g., a quarter start date), and otherwise roll
forward to the next anchor point.
.. ipython:: python
d = pd.Timestamp('2014-02-01')
d
d + pd.offsets.QuarterBegin(n=0, startingMonth=2)
d + pd.offsets.QuarterBegin(n=0, startingMonth=1)
For the ``QuarterBegin`` offset in previous versions, the date would be rolled
*backwards* if date was in the same month as the quarter start date.
.. code-block:: python
In [3]: d = pd.Timestamp('2014-02-15')
In [4]: d + pd.offsets.QuarterBegin(n=0, startingMonth=2)
Out[4]: Timestamp('2014-02-01 00:00:00')
This behavior has been corrected in version 0.18.0, which is consistent with
other anchored offsets like ``MonthBegin`` and ``YearBegin``.
.. ipython:: python
d = pd.Timestamp('2014-02-15')
d + pd.offsets.QuarterBegin(n=0, startingMonth=2)
Other API Changes
^^^^^^^^^^^^^^^^^
- ``DataFrame.between_time`` and ``Series.between_time`` now only parse a fixed set of time strings. Parsing of
date strings is no longer supported and raises a ``ValueError``. (:issue:`11818`)
.. ipython:: python
s = pd.Series(range(10), pd.date_range('2015-01-01', freq='H', periods=10))
s.between_time("7:00am", "9:00am")
This will now raise.
.. code-block:: python
In [2]: s.between_time('20150101 07:00:00','20150101 09:00:00')
ValueError: Cannot convert arg ['20150101 07:00:00'] to a time.
- ``.memory_usage`` now includes values in the index, as does memory_usage in ``.info`` (:issue:`11597`)
.. _whatsnew_0180.deprecations:
Deprecations
^^^^^^^^^^^^
.. _whatsnew_0180.window_deprecations:
- The functions ``pd.rolling_*``, ``pd.expanding_*``, and ``pd.ewm*`` are deprecated and replaced by the corresponding method call. Note that
the new suggested syntax includes all of the arguments (even if default) (:issue:`11603`)
.. code-block:: python
In [1]: s = Series(range(3))
In [2]: pd.rolling_mean(s,window=2,min_periods=1)
FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with
Series.rolling(min_periods=1,window=2,center=False).mean()
Out[2]:
0 0.0
1 0.5
2 1.5
dtype: float64
In [3]: pd.rolling_cov(s, s, window=2)
FutureWarning: pd.rolling_cov is deprecated for Series and will be removed in a future version, replace with
Series.rolling(window=2).cov(other=<Series>)
Out[3]:
0 NaN
1 0.5
2 0.5
dtype: float64
- The the ``freq`` and ``how`` arguments to the ``.rolling``, ``.expanding``, and ``.ewm`` (new) functions are deprecated, and will be removed in a future version. You can simply resample the input prior to creating a window function. (:issue:`11603`).
For example, instead of ``s.rolling(window=5,freq='D').max()`` to get the max value on a rolling 5 Day window, one could use ``s.resample('D',how='max').rolling(window=5).max()``, which first resamples the data to daily data, then provides a rolling 5 day window.
- ``pd.tseries.frequencies.get_offset_name`` function is deprecated. Use offset's ``.freqstr`` property as alternative (:issue:`11192`)
- ``pandas.stats.fama_macbeth`` routines are deprecated and will be removed in a future version (:issue:`6077`)
- ``pandas.stats.ols``, ``pandas.stats.plm`` and ``pandas.stats.var`` routines are deprecated and will be removed in a future version (:issue:`6077`)
.. _whatsnew_0180.prior_deprecations:
Removal of prior version deprecations/changes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
- Removal of ``rolling_corr_pairwise`` in favor of ``.rolling().corr(pairwise=True)`` (:issue:`4950`)
- Removal of ``expanding_corr_pairwise`` in favor of ``.expanding().corr(pairwise=True)`` (:issue:`4950`)
.. _whatsnew_0180.performance:
Performance Improvements
~~~~~~~~~~~~~~~~~~~~~~~~
- Improved performance of ``andrews_curves`` (:issue:`11534`)
- Improved huge ``DatetimeIndex``, ``PeriodIndex`` and ``TimedeltaIndex``'s ops performance including ``NaT`` (:issue:`10277`)
.. _whatsnew_0180.bug_fixes:
Bug Fixes
~~~~~~~~~
- Bug in ``GroupBy.size`` when data-frame is empty. (:issue:`11699`)
- Bug in ``Period.end_time`` when a multiple of time period is requested (:issue:`11738`)
- Regression in ``.clip`` with tz-aware datetimes (:issue:`11838`)
- Bug in ``date_range`` when the boundaries fell on the frequency (:issue:`11804`)
- Bug in consistency of passing nested dicts to ``.groupby(...).agg(...)`` (:issue:`9052`)
- Bug in vectorized ``DateOffset`` when ``n`` parameter is ``0`` (:issue:`11370`)
- Bug in ``Timedelta.round`` with negative values (:issue:`11690`)
- Bug in ``.loc`` against ``CategoricalIndex`` may result in normal ``Index`` (:issue:`11586`)
- Bug in ``DataFrame.info`` when duplicated column names exist (:issue:`11761`)
- Bug in ``.copy`` of datetime tz-aware objects (:issue:`11794`)
- Bug in ``Series.apply`` and ``Series.map`` where ``timedelta64`` was not boxed (:issue:`11349`)
- Bug in subclasses of `DataFrame` where `AttributeError` did not propagate (:issue:`11808`)
- Bug groupby on tz-aware data where selection not returning ``Timestamp`` (:issue:`11616`)
- Bug in ``pd.read_clipboard`` and ``pd.to_clipboard`` functions not supporting Unicode; upgrade included ``pyperclip`` to v1.5.15 (:issue:`9263`)
- Bug in timezone info lost when broadcasting scalar datetime to ``DataFrame`` (:issue:`11682`)
- Bug in ``Index`` creation from ``Timestamp`` with mixed tz coerces to UTC (:issue:`11488`)
- Bug in ``to_numeric`` where it does not raise if input is more than one dimension (:issue:`11776`)
- Bug in parsing timezone offset strings with non-zero minutes (:issue:`11708`)
- Bug in ``df.plot`` using incorrect colors for bar plots under matplotlib 1.5+ (:issue:`11614`)
- Bug in the ``groupby`` ``plot`` method when using keyword arguments (:issue:`11805`).
- Bug in ``.loc`` result with duplicated key may have ``Index`` with incorrect dtype (:issue:`11497`)
- Bug in ``pd.rolling_median`` where memory allocation failed even with sufficient memory (:issue:`11696`)
- Bug in ``.style.bar`` may not rendered properly using specific browser (:issue:`11678`)
- Bug in ``df.replace`` while replacing value in mixed dtype ``Dataframe`` (:issue:`11698`)
- Bug in ``Index`` prevents copying name of passed ``Index``, when a new name is not provided (:issue:`11193`)
- Bug in ``read_excel`` failing to read any non-empty sheets when empty sheets exist and ``sheetname=None`` (:issue:`11711`)
- Bug in ``read_excel`` failing to raise ``NotImplemented`` error when keywords `parse_dates` and `date_parser` are provided (:issue:`11544`)
- Bug in ``read_sql`` with pymysql connections failing to return chunked data (:issue:`11522`)
- Bug in ``.to_csv`` ignoring formatting parameters ``decimal``, ``na_rep``, ``float_format`` for float indexes (:issue:`11553`)
- Bug in ``DataFrame`` when masking an empty ``DataFrame`` (:issue:`11859`)