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.. _timeseries-pandas:
Interfacing with the Pandas Package
***********************************
The `astropy.timeseries` package is not the only package to provide
functionality related to time series. Another notable package is `pandas
<https://pandas.pydata.org/>`_, which provides a :class:`pandas.DataFrame`
class. The main benefits of `astropy.timeseries` in the context of astronomical
research are the following:
* The time column is a |Time| object that supports very high precision
representation of times, and makes it easy to convert between different
time scales and formats (e.g., ISO 8601 timestamps, Julian Dates, and so on).
* The data columns can include |Quantity| objects with units.
* The |BinnedTimeSeries| class includes variable-width time bins.
* There are built-in readers for common time series file formats, as well as
the ability to define custom readers/writers.
Nevertheless, there are cases where using pandas :class:`~pandas.DataFrame`
objects might make sense, so we provide methods to convert to/from
:class:`~pandas.DataFrame` objects.
Example
-------
.. EXAMPLE START: Interfacing between Time Series and the Pandas DataFrame
Consider a concise example starting from a :class:`~pandas.DataFrame`:
.. doctest-requires:: pandas
>>> import pandas
>>> import numpy as np
>>> from astropy.utils.introspection import minversion
>>> df = pandas.DataFrame()
>>> df['a'] = [1, 2, 3]
>>> times = np.array(['2015-07-04', '2015-07-05', '2015-07-06'], dtype=np.datetime64)
>>> df.set_index(pandas.DatetimeIndex(times), inplace=True)
>>> df
a
2015-07-04 1
2015-07-05 2
2015-07-06 3
We can convert this to an ``astropy`` |TimeSeries| using
:meth:`~astropy.timeseries.TimeSeries.from_pandas`:
.. doctest-requires:: pandas
>>> from astropy.timeseries import TimeSeries
>>> ts = TimeSeries.from_pandas(df)
>>> ts
<TimeSeries length=3>
time a
Time int64
----------------------------- -----
2015-07-04T00:00:00.000000000 1
2015-07-05T00:00:00.000000000 2
2015-07-06T00:00:00.000000000 3
Converting to :class:`~pandas.DataFrame` can also be done with
:meth:`~astropy.timeseries.TimeSeries.to_pandas`:
.. doctest-requires:: pandas
>>> ts['b'] = [1.2, 3.4, 5.4]
>>> df_new = ts.to_pandas()
>>> df_new
a b
time
2015-07-04 1 1.2
2015-07-05 2 3.4
2015-07-06 3 5.4
Missing values in the time column are supported and correctly converted to a
pandas' NaT object:
.. doctest-requires:: pandas
>>> ts.time[2] = np.nan
>>> ts
<TimeSeries length=3>
time a b
Time int64 float64
----------------------------- ----- -------
2015-07-04T00:00:00.000000000 1 1.2
2015-07-05T00:00:00.000000000 2 3.4
——— 3 5.4
>>> df_missing = ts.to_pandas()
>>> df_missing
a b
time
2015-07-04 1 1.2
2015-07-05 2 3.4
NaT 3 5.4
.. EXAMPLE END
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