Removed scikit-timeseries migration docs from FAQ

doc/source/faq.rst

@@ -81,149 +81,6 @@ representation; i.e., 1KB = 1024 bytes).
 
 See also :ref:`Categorical Memory Usage <categorical.memory>`.
 
-
-.. _ref-scikits-migration:
-
-Migrating from scikits.timeseries to pandas >= 0.8.0
-----------------------------------------------------
-
-Starting with pandas 0.8.0, users of scikits.timeseries should have all of the
-features that they need to migrate their code to use pandas. Portions of the
-scikits.timeseries codebase for implementing calendar logic and timespan
-frequency conversions (but **not** resampling, that has all been implemented
-from scratch from the ground up) have been ported to the pandas codebase.
-
-The scikits.timeseries notions of ``Date`` and ``DateArray`` are responsible
-for implementing calendar logic:
-
-::
-
-    In [16]: dt = ts.Date('Q', '1984Q3')
-
-    # sic
-    In [17]: dt
-    Out[17]: <Q-DEC : 1984Q1>
-
-    In [18]: dt.asfreq('D', 'start')
-    Out[18]: <D : 01-Jan-1984>
-
-    In [19]: dt.asfreq('D', 'end')
-    Out[19]: <D : 31-Mar-1984>
-
-    In [20]: dt + 3
-    Out[20]: <Q-DEC : 1984Q4>
-
-``Date`` and ``DateArray`` from scikits.timeseries have been reincarnated in
-pandas ``Period`` and ``PeriodIndex``:
-
-.. ipython:: python
-
-   pd.pnow('D')  # scikits.timeseries.now()
-   pd.Period(year=2007, month=3, day=15, freq='D')
-   p = pd.Period('1984Q3')
-   p
-   p.asfreq('D', 'start')
-   p.asfreq('D', 'end')
-   (p + 3).asfreq('T') + 6 * 60 + 30
-   rng = pd.period_range('1990', '2010', freq='A')
-   rng
-   rng.asfreq('B', 'end') - 3
-
-.. csv-table::
-    :header: "scikits.timeseries", "pandas", "Notes"
-    :widths: 20, 20, 60
-
-    Date, Period, "A span of time, from yearly through to secondly"
-    DateArray, PeriodIndex, "An array of timespans"
-    convert, resample, "Frequency conversion in scikits.timeseries"
-    convert_to_annual, pivot_annual, "currently supports up to daily frequency, see :issue:`736`"
-
-
-PeriodIndex / DateArray properties and functions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The scikits.timeseries ``DateArray`` had a number of information
-properties. Here are the pandas equivalents:
-
-.. csv-table::
-    :header: "scikits.timeseries", "pandas", "Notes"
-    :widths: 20, 60, 20
-
-    get_steps, ``np.diff(idx.values)``,
-    has_missing_dates, ``not idx.is_full``,
-    is_full, ``idx.is_full``,
-    is_valid, ``idx.is_monotonic and idx.is_unique``,
-    is_chronological, ``is_monotonic``,
-    ``arr.sort_chronologically()``, ``idx.order()``,
-
-Frequency conversion
-~~~~~~~~~~~~~~~~~~~~
-
-Frequency conversion is implemented using the ``resample`` method on Series
-and DataFrame objects with a DatetimeIndex or PeriodIndex. ``resample`` also
-works on panels (3D). Here is some code that resamples daily data to montly:
-
-.. ipython:: python
-
-   rng = pd.period_range('Jan-2000', periods=50, freq='M')
-   data = pd.Series(np.random.randn(50), index=rng)
-   data
-   data.resample('A', how=np.mean)
-
-Plotting
-~~~~~~~~
-
-Much of the plotting functionality of scikits.timeseries has been ported and
-adopted to pandas's data structures. For example:
-
-.. ipython:: python
-
-   rng = pd.period_range('1987Q2', periods=10, freq='Q-DEC')
-   data = pd.Series(np.random.randn(10), index=rng)
-
-   @savefig skts_ts_plot.png
-   data.plot()
-
-Converting to and from period format
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Use the ``to_timestamp`` and ``to_period`` instance methods.
-
-Treatment of missing data
-~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Unlike scikits.timeseries, pandas data structures are not based on NumPy's
-``MaskedArray`` object. Missing data is represented as ``NaN`` in numerical
-arrays and either as ``None`` or ``NaN`` in non-numerical arrays. Implementing
-a version of pandas's data structures that use MaskedArray is possible but
-would require the involvement of a dedicated maintainer. Active pandas
-developers are not interested in this.
-
-Resampling with timestamps and periods
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-``resample`` has a ``kind`` argument which allows you to resample time series
-with a DatetimeIndex to PeriodIndex:
-
-.. ipython:: python
-
-   rng = pd.date_range('1/1/2000', periods=200, freq='D')
-   data = pd.Series(np.random.randn(200), index=rng)
-   data[:10]
-   data.index
-   data.resample('M', kind='period')
-
-Similarly, resampling from periods to timestamps is possible with an optional
-interval (``'start'`` or ``'end'``) convention:
-
-.. ipython:: python
-
-   rng = pd.period_range('Jan-2000', periods=50, freq='M')
-   data = pd.Series(np.random.randn(50), index=rng)
-   resampled = data.resample('A', kind='timestamp', convention='end')
-   resampled.index
-
-
 Byte-Ordering Issues
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