| .. _timeseries-pandas: |
|
|
| Interfacing with the pandas package |
| *********************************** |
|
|
| .. |Time| replace:: :class:`~astropy.time.Time` |
| .. |Quantity| replace:: :class:`~astropy.units.Quantity` |
| .. |TimeSeries| replace:: :class:`~astropy.timeseries.TimeSeries` |
| .. |BinnedTimeSeries| replace:: :class:`~astropy.timeseries.BinnedTimeSeries` |
|
|
| 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 easily convert to/from |
| :class:`~pandas.DataFrame` objects. |
|
|
| Let's consider a simple example starting from a :class:`~pandas.DataFrame`: |
| |
| .. doctest-requires:: pandas |
| |
| >>> 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 |
| :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 |
| 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 :class:`~pandas.DataFrame` can also easily 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 |
| pandas' NaT object: |
|
|
| .. doctest-requires:: pandas |
|
|
| >>> ts.time[2] = np.nan |
| >>> ts |
| <TimeSeries length=3> |
| time a b |
| object 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 |
|
|
