# SWEET-Cat and exoplanet.eu from Python

Two more Python packages, this time to access the SWEET-Cat and exoplanet.eu databases.
Both packages are quite simple, easy to install from PyPI, and come without any dependencies.

SWEET-Cat is a catalogue of stellar parameters for stars with planets. It compiles sets of atmospheric parameters previously published in the literature (Teff, logg, and [Fe/H]) and, whenever possible, derived using the same uniform methodology (see Santos et al. 2004; Sousa et al. 2008). The catalogue is described in Santos et al. 2013.

exoplanet.eu is an online database for extrasolar planet candidates, which is updated regularly (daily) as new planet detections become available. It contains a number of parameters for each exoplanet, including orbital parameters and derived quantities, as well as some metadata about the discovery.

The pySWEETCat and pyExoplaneteu packages allow for a Python-only, no-frills interface to the two catalogues. They can be easily installed with

pip install pySWEETCat
pip install pyExoplaneteu


To use them from within Python just run

import pysweetcat
import pyexoplaneteu


In a deliberate quest for simplicity, the two packages provide only one function, called get_data(), which downloads the data from the online archive and returns it in a dictionary.

dataSC = pysweetcat.get_data()
dataEU = pyexoplaneteu.get_data()


This function will start by checking if you downloaded the data before. If not, or if the data is too old (more than 5 days old), it will download it again.

The two outputs, dataSC and dataEU are (almost) Python dictionaries, with each column of the catalogues as keys. They are almost dictionaries because they both have a few of extra methods and properties which make working with the data even simpler. For example

>>> dataSC.size
2693
>>> dataEU.size
3869


returns the number of values in each column. That is, the number of stars with parameters in SWEET-Cat, and the number of exoplanets in exoplanet.eu.

The columns() method will print (not return!) the available columns.

>>> dataSC.columns()
['name', 'HD', 'ra', 'dec', 'vmag', 'σ_vmag', 'π', 'σ_π', 'source_π', 'teff',
'σ_teff', 'logg', 'σ_logg', 'LC_logg', 'σ_LC_logg', 'vt', 'σ_vt', 'feh',
'σ_feh', 'mass', 'σ_mass', 'reference', 'homogeneity', 'last_update',

>>> dataEU.columns()
['name', 'planet_status', 'mass', 'mass_error_min', 'mass_error_max',
...


The columns can be accessed as in a normal dictionary, with

>>> dataSC['vmag']  # the star's magnitude
>>> dataSC['teff']  # the star's effective temperature

>>> dataEU['name']  # the name of the planet
>>> dataEU['mass']  # the mass of the planet


Also, to drop all the NaN values in a column (for some columns there will be quite a few), we can append _nonan to the name of any column

>>> dataEU['mass_nonan']

# import numpy as np
>>> np.isnan(dataEU['mass']).any()         # True
>>> np.isnan(dataEU['mass_nonan']).any()   # False


which allows us to more easily do histograms of the values.

Finally, it is worth mentioning the .to_numpy(inplace=True) method, which converts all the columns (which are lists) to numpy arrays, either in place or not. This is the only function in both pySWEETCat and pyExoplaneteu that requires numpy.

## wrap up

Two very simple Python packages to access data from SWEET-Cat and exoplanet.eu.
Let me know in the comments if you find them useful.