# 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
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:

```
>>> 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
```