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 easily convert to/from
DataFrame
objects.
Let’s consider a simple example starting from a DataFrame
:
>>> import pandas
>>> import numpy as np
>>> 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
object 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 easily be done with
to_pandas()
:
>>> ts['b'] = [1.2, 3.4, 5.4]
>>> df_new = ts.to_pandas()
>>> df_new
a b
2015-07-04 1 1.2
2015-07-05 2 3.4
2015-07-06 3 5.4