.. currentmodule:: pandas
.. ipython:: python :suppress: from datetime import datetime import numpy as np np.random.seed(123456) from pandas import * randn = np.random.randn randint = np.random.randint np.set_printoptions(precision=4, suppress=True) from dateutil.relativedelta import relativedelta from pandas.tseries.api import * from pandas.tseries.offsets import * import matplotlib.pyplot as plt plt.close('all')
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 pnow('D') # scikits.timeseries.now() Period(year=2007, month=3, day=15, freq='D') p = Period('1984Q3') p p.asfreq('D', 'start') p.asfreq('D', 'end') (p + 3).asfreq('T') + 6 * 60 + 30 rng = period_range('1990', '2010', freq='A') rng rng.asfreq('B', 'end') - 3
scikits.timeseries | pandas | Notes |
---|---|---|
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` |
The scikits.timeseries DateArray
had a number of information
properties. Here are the pandas equivalents:
scikits.timeseries | pandas | Notes |
---|---|---|
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 is implemented using the resample
method on TimeSeries
and DataFrame objects (multiple time series). resample
also works on panels
(3D). Here is some code that resamples daily data to montly with
scikits.timeseries:
.. ipython:: python import scikits.timeseries as ts data = ts.time_series(np.random.randn(50), start_date='Jan-2000', freq='M') data data.convert('A', func=np.mean)
Here is the equivalent pandas code:
.. ipython:: python rng = period_range('Jan-2000', periods=50, freq='M') data = Series(np.random.randn(50), index=rng) data data.resample('A', how=np.mean)
Much of the plotting functionality of scikits.timeseries has been ported and adopted to pandas's data structures. For example:
.. ipython:: python rng = period_range('1987Q2', periods=10, freq='Q-DEC') data = Series(np.random.randn(10), index=rng) @savefig skts_ts_plot.png width=4.5in plt.figure(); data.plot()
Use the to_timestamp
and to_period
instance methods.
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.
resample
has a kind
argument which allows you to resample time series
with a DatetimeIndex to PeriodIndex:
.. ipython:: python rng = date_range('1/1/2000', periods=200, freq='D') data = 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 = period_range('Jan-2000', periods=50, freq='M') data = Series(np.random.randn(50), index=rng) resampled = data.resample('A', kind='timestamp', convention='end') resampled.index