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, which provides a 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 DataFrame objects might make sense, so we provide methods to convert to/from DataFrame objects.

Example

Consider a concise example starting from a DataFrame:

>>> 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 from_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 DataFrame can also be done with to_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:

>>> 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