.. _whatsnew_080:

v0.8.0 (June 29, 2012)
------------------------

This is a major release from 0.7.3 and includes extensive work on the time
series handling and processing infrastructure as well as a great deal of new
functionality throughout the library. It includes over 700 commits from more
than 20 distinct authors. Most pandas 0.7.3 and earlier users should not
experience any issues upgrading, but due to the migration to the NumPy
datetime64 dtype, there may be a number of bugs and incompatibilities
lurking. Lingering incompatibilities will be fixed ASAP in a 0.8.1 release if
necessary. See the :ref:`full release notes
<release>` or issue tracker
on GitHub for a complete list.

Support for non-unique indexes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

All objects can now work with non-unique indexes. Data alignment / join
operations work according to SQL join semantics (including, if application,
index duplication in many-to-many joins)

NumPy datetime64 dtype and 1.6 dependency
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Time series data are now represented using NumPy's datetime64 dtype; thus,
pandas 0.8.0 now requires at least NumPy 1.6. It has been tested and verified
to work with the development version (1.7+) of NumPy as well which includes
some significant user-facing API changes. NumPy 1.6 also has a number of bugs
having to do with nanosecond resolution data, so I recommend that you steer
clear of NumPy 1.6's datetime64 API functions (though limited as they are) and
only interact with this data using the interface that pandas provides.

See the end of the 0.8.0 section for a "porting" guide listing potential issues
for users migrating legacy codebases from pandas 0.7 or earlier to 0.8.0.

Bug fixes to the 0.7.x series for legacy NumPy < 1.6 users will be provided as
they arise. There will be no more further development in 0.7.x beyond bug
fixes.

Time series changes and improvements
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. note::

    With this release, legacy scikits.timeseries users should be able to port
    their code to use pandas.

.. note::

    See :ref:`documentation <timeseries>` for overview of pandas timeseries API.

- New datetime64 representation **speeds up join operations and data
  alignment**, **reduces memory usage**, and improve serialization /
  deserialization performance significantly over datetime.datetime
- High performance and flexible **resample** method for converting from
  high-to-low and low-to-high frequency. Supports interpolation, user-defined
  aggregation functions, and control over how the intervals and result labeling
  are defined. A suite of high performance Cython/C-based resampling functions
  (including Open-High-Low-Close) have also been implemented.
- Revamp of :ref:`frequency aliases <timeseries.offset_aliases>` and support for
  **frequency shortcuts** like '15min', or '1h30min'
- New :ref:`DatetimeIndex class <timeseries.datetimeindex>` supports both fixed
  frequency and irregular time
  series. Replaces now deprecated DateRange class
- New ``PeriodIndex`` and ``Period`` classes for representing
  :ref:`time spans <timeseries.periods>` and performing **calendar logic**,
  including the `12 fiscal quarterly frequencies <timeseries.quarterly>`.
  This is a partial port of, and a substantial enhancement to,
  elements of the scikits.timeseries codebase. Support for conversion between
  PeriodIndex and DatetimeIndex
- New Timestamp data type subclasses `datetime.datetime`, providing the same
  interface while enabling working with nanosecond-resolution data. Also
  provides :ref:`easy time zone conversions <timeseries.timezone>`.
- Enhanced support for :ref:`time zones <timeseries.timezone>`. Add
  `tz_convert` and ``tz_lcoalize`` methods to TimeSeries and DataFrame. All
  timestamps are stored as UTC; Timestamps from DatetimeIndex objects with time
  zone set will be localized to localtime. Time zone conversions are therefore
  essentially free. User needs to know very little about pytz library now; only
  time zone names as as strings are required. Time zone-aware timestamps are
  equal if and only if their UTC timestamps match. Operations between time
  zone-aware time series with different time zones will result in a UTC-indexed
  time series.
- Time series **string indexing conveniences** / shortcuts: slice years, year
  and month, and index values with strings
- Enhanced time series **plotting**; adaptation of scikits.timeseries
  matplotlib-based plotting code
- New ``date_range``, ``bdate_range``, and ``period_range`` :ref:`factory
  functions <timeseries.daterange>`
- Robust **frequency inference** function `infer_freq` and ``inferred_freq``
  property of DatetimeIndex, with option to infer frequency on construction of
  DatetimeIndex
- to_datetime function efficiently **parses array of strings** to
  DatetimeIndex. DatetimeIndex will parse array or list of strings to
  datetime64
- **Optimized** support for datetime64-dtype data in Series and DataFrame
  columns
- New NaT (Not-a-Time) type to represent **NA** in timestamp arrays
- Optimize Series.asof for looking up **"as of" values** for arrays of
  timestamps
- Milli, Micro, Nano date offset objects
- Can index time series with datetime.time objects to select all data at
  particular **time of day** (``TimeSeries.at_time``) or **between two times**
  (``TimeSeries.between_time``)
- Add :ref:`tshift <timeseries.advanced_datetime>` method for leading/lagging
  using the frequency (if any) of the index, as opposed to a naive lead/lag
  using shift

Other new features
~~~~~~~~~~~~~~~~~~

- New :ref:`cut <reshaping.tile.cut>` and ``qcut`` functions (like R's cut
  function) for computing a categorical variable from a continuous variable by
  binning values either into value-based (``cut``) or quantile-based (``qcut``)
  bins
- Rename ``Factor`` to ``Categorical`` and add a number of usability features
- Add :ref:`limit <missing_data.fillna.limit>` argument to fillna/reindex
- More flexible multiple function application in GroupBy, and can pass list
  (name, function) tuples to get result in particular order with given names
- Add flexible :ref:`replace <missing_data.replace>` method for efficiently
  substituting values
- Enhanced :ref:`read_csv/read_table <io.parse_dates>` for reading time series
  data and converting multiple columns to dates
- Add :ref:`comments <io.comments>` option to parser functions: read_csv, etc.
- Add :ref`dayfirst <io.dayfirst>` option to parser functions for parsing
  international DD/MM/YYYY dates
- Allow the user to specify the CSV reader :ref:`dialect <io.dialect>` to
  control quoting etc.
- Handling :ref:`thousands <io.thousands>` separators in read_csv to improve
  integer parsing.
- Enable unstacking of multiple levels in one shot. Alleviate ``pivot_table``
  bugs (empty columns being introduced)
- Move to klib-based hash tables for indexing; better performance and less
  memory usage than Python's dict
- Add first, last, min, max, and prod optimized GroupBy functions
- New :ref:`ordered_merge <merging.merge_ordered>` function
- Add flexible :ref:`comparison <basics.binop>` instance methods eq, ne, lt,
  gt, etc. to DataFrame, Series
- Improve :ref:`scatter_matrix <visualization.scatter_matrix>` plotting
  function and add histogram or kernel density estimates to diagonal
- Add :ref:`'kde' <visualization.kde>` plot option for density plots
- Support for converting DataFrame to R data.frame through rpy2
- Improved support for complex numbers in Series and DataFrame
- Add :ref:`pct_change <computation.pct_change>` method to all data structures
- Add max_colwidth configuration option for DataFrame console output
- :ref:`Interpolate <missing_data.interpolate>` Series values using index values
- Can select multiple columns from GroupBy
- Add :ref:`update <merging.combine_first.update>` methods to Series/DataFrame
  for updating values in place
- Add ``any`` and ``all`` method to DataFrame

New plotting methods
~~~~~~~~~~~~~~~~~~~~

.. ipython:: python
   :suppress:

   import pandas as pd
   fx = pd.read_pickle('data/fx_prices')
   import matplotlib.pyplot as plt

``Series.plot`` now supports a ``secondary_y`` option:

.. ipython:: python

   plt.figure()

   fx['FR'].plot(style='g')

   @savefig whatsnew_secondary_y.png
   fx['IT'].plot(style='k--', secondary_y=True)

Vytautas Jancauskas, the 2012 GSOC participant, has added many new plot
types. For example, ``'kde'`` is a new option:

.. ipython:: python

   s = Series(np.concatenate((np.random.randn(1000),
                              np.random.randn(1000) * 0.5 + 3)))
   plt.figure()
   s.hist(normed=True, alpha=0.2)
   @savefig whatsnew_kde.png
   s.plot(kind='kde')

See :ref:`the plotting page <visualization.other>` for much more.

Other API changes
~~~~~~~~~~~~~~~~~

- Deprecation of ``offset``, ``time_rule``, and ``timeRule`` arguments names in
  time series functions. Warnings will be printed until pandas 0.9 or 1.0.

Potential porting issues for pandas <= 0.7.3 users
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The major change that may affect you in pandas 0.8.0 is that time series
indexes use NumPy's ``datetime64`` data type instead of ``dtype=object`` arrays
of Python's built-in ``datetime.datetime`` objects. ``DateRange`` has been
replaced by ``DatetimeIndex`` but otherwise behaved identically. But, if you
have code that converts ``DateRange`` or ``Index`` objects that used to contain
``datetime.datetime`` values to plain NumPy arrays, you may have bugs lurking
with code using scalar values because you are handing control over to NumPy:

.. ipython:: python

   import datetime
   rng = date_range('1/1/2000', periods=10)
   rng[5]
   isinstance(rng[5], datetime.datetime)
   rng_asarray = np.asarray(rng)
   scalar_val = rng_asarray[5]
   type(scalar_val)

pandas's ``Timestamp`` object is a subclass of ``datetime.datetime`` that has
nanosecond support (the ``nanosecond`` field store the nanosecond value between
0 and 999). It should substitute directly into any code that used
``datetime.datetime`` values before. Thus, I recommend not casting
``DatetimeIndex`` to regular NumPy arrays.

If you have code that requires an array of ``datetime.datetime`` objects, you
have a couple of options. First, the ``asobject`` property of ``DatetimeIndex``
produces an array of ``Timestamp`` objects:

.. ipython:: python

   stamp_array = rng.asobject
   stamp_array
   stamp_array[5]

To get an array of proper ``datetime.datetime`` objects, use the
``to_pydatetime`` method:

.. ipython:: python

   dt_array = rng.to_pydatetime()
   dt_array
   dt_array[5]

matplotlib knows how to handle ``datetime.datetime`` but not Timestamp
objects. While I recommend that you plot time series using ``TimeSeries.plot``,
you can either use ``to_pydatetime`` or register a converter for the Timestamp
type. See `matplotlib documentation
<http://matplotlib.org/api/units_api.html>`__ for more on this.

.. warning::

    There are bugs in the user-facing API with the nanosecond datetime64 unit
    in NumPy 1.6. In particular, the string version of the array shows garbage
    values, and conversion to ``dtype=object`` is similarly broken.

    .. ipython:: python

       rng = date_range('1/1/2000', periods=10)
       rng
       np.asarray(rng)
       converted = np.asarray(rng, dtype=object)
       converted[5]

    **Trust me: don't panic**. If you are using NumPy 1.6 and restrict your
    interaction with ``datetime64`` values to pandas's API you will be just
    fine. There is nothing wrong with the data-type (a 64-bit integer
    internally); all of the important data processing happens in pandas and is
    heavily tested. I strongly recommend that you **do not work directly with
    datetime64 arrays in NumPy 1.6** and only use the pandas API.


**Support for non-unique indexes**: In the latter case, you may have code
inside a ``try:... catch:`` block that failed due to the index not being
unique. In many cases it will no longer fail (some method like ``append`` still
check for uniqueness unless disabled). However, all is not lost: you can
inspect ``index.is_unique`` and raise an exception explicitly if it is
``False`` or go to a different code branch.
