Resokit.datasets Tutorial

This tutorial is a guide to use ResoKit.datasets package. ResoKit.datasets provides tools for loading the available datasets:

NASA
EU

In this tutorial we show, step by step, how to get started with the ResoKit.datasets package.

Import ResoKit and the necessary packages

[1]:

import resokit.datasets as datasets

import matplotlib.pyplot as plt

Load the dataset

Let’s start with EU catalogue.

First, download the dataset (if not downloaded yet)

[2]:

datasets.download('eu', overwrite=False, soft=True)

Checking local dataset from which='eu' source...
 File from which='eu' source to check if outdated not found.
Downloading data from https://exoplanet.eu/catalog/csv/...
 Note: Progress is shown at every 0.15 MB
 Data downloaded successfully. (3.39 MB)
Creating the ZIP archive /home/egianuzzi/.resokit_data/exoplanet_eu.zip...
 Written exoplanet_eu.csv to /home/egianuzzi/.resokit_data/exoplanet_eu.zip.
Updated stored index in memory.
Stored dataset in memory.

[2]:

PosixPath('/home/egianuzzi/.resokit_data/exoplanet_eu.zip/exoplanet_eu.csv')

Now, load it.

[3]:

datasets.load('eu')

 Loaded full dataset from memory stored datasets.

[3]:

	name	mass	mass_err_min	mass_err_max	mass_sin_i	mass_sin_i_err_min	mass_sin_i_err_max	radius	radius_err_min	radius_err_max	...	star_dist_err_max	star_mass	star_mass_err_min	star_mass_err_max	star_radius	star_radius_err_min	star_radius_err_max	n	n_err_min	n_err_max
0	109 Psc b	5.743	0.28900	1.01100	6.3830	0.07800	0.07800	1.152	NaN	NaN	...	0.88000	1.13	0.030	0.030	1.79000	0.1700	0.1700	0.005843	7.853982e+00	8.975979e+00
1	112 Psc b	NaN	0.00500	0.00400	0.0330	0.00500	0.00400	NaN	NaN	NaN	...	0.10695	1.10	0.133	0.133	1.80100	0.0725	0.0725	1.427997	3.141593e+04	1.570796e+04
2	112 Psc c	9.866	1.78100	3.19000	NaN	NaN	NaN	NaN	NaN	NaN	...	0.10695	1.10	0.133	0.133	1.80100	0.0725	0.0725	0.000173	6.460195e-04	1.040366e-03
3	11 Com Ab	NaN	1.53491	1.53491	16.1284	1.53491	1.53491	NaN	NaN	NaN	...	10.50000	2.70	0.300	0.300	19.00000	2.0000	2.0000	0.019272	1.963495e+01	1.963495e+01
4	11 UMi b	NaN	1.10000	1.10000	11.0873	1.10000	1.10000	NaN	NaN	NaN	...	6.90000	1.80	0.250	0.250	24.08000	1.8400	1.8400	0.012172	1.933288e+00	1.933288e+00
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7599	ZTF J1406+1222 Ab	50.000	NaN	NaN	NaN	NaN	NaN	0.292	NaN	NaN	...	200.00000	1.40	NaN	NaN	0.00002	NaN	NaN	116.233408	NaN	NaN
7600	ZTF J1622+47 b	61.000	19.00000	19.00000	NaN	NaN	NaN	0.980	0.02	0.02	...	NaN	0.47	NaN	NaN	0.18200	0.0040	0.0040	90.031428	NaN	NaN
7601	ZTF J1637+49 b	23.000	8.00000	8.00000	NaN	NaN	NaN	0.680	0.07	0.07	...	8.00000	0.90	0.050	0.050	0.00900	0.0010	0.0010	146.120589	NaN	NaN
7602	ZTF J1828+2308 b	19.500	0.80000	0.80000	NaN	NaN	NaN	1.020	0.02	0.02	...	5.00000	0.61	0.040	0.040	0.01310	0.0002	0.0002	56.096513	7.222052e+06	7.222052e+06
7603	ZTF J2252-05 b	26.000	8.00000	8.00000	NaN	NaN	NaN	0.490	0.04	0.04	...	82.00000	0.76	0.050	0.050	0.01000	0.0010	0.0010	261.799388	NaN	NaN

Full ResokitDataSet - 7604 rows x 45 columns

Now, the dataset is stored into memory, so it can be accessed faster than reading the file it again.

[4]:

eu = datasets.load('eu')
eu

 Loaded full dataset from memory stored datasets.

[4]:

	name	mass	mass_err_min	mass_err_max	mass_sin_i	mass_sin_i_err_min	mass_sin_i_err_max	radius	radius_err_min	radius_err_max	...	star_dist_err_max	star_mass	star_mass_err_min	star_mass_err_max	star_radius	star_radius_err_min	star_radius_err_max	n	n_err_min	n_err_max
0	109 Psc b	5.743	0.28900	1.01100	6.3830	0.07800	0.07800	1.152	NaN	NaN	...	0.88000	1.13	0.030	0.030	1.79000	0.1700	0.1700	0.005843	7.853982e+00	8.975979e+00
1	112 Psc b	NaN	0.00500	0.00400	0.0330	0.00500	0.00400	NaN	NaN	NaN	...	0.10695	1.10	0.133	0.133	1.80100	0.0725	0.0725	1.427997	3.141593e+04	1.570796e+04
2	112 Psc c	9.866	1.78100	3.19000	NaN	NaN	NaN	NaN	NaN	NaN	...	0.10695	1.10	0.133	0.133	1.80100	0.0725	0.0725	0.000173	6.460195e-04	1.040366e-03
3	11 Com Ab	NaN	1.53491	1.53491	16.1284	1.53491	1.53491	NaN	NaN	NaN	...	10.50000	2.70	0.300	0.300	19.00000	2.0000	2.0000	0.019272	1.963495e+01	1.963495e+01
4	11 UMi b	NaN	1.10000	1.10000	11.0873	1.10000	1.10000	NaN	NaN	NaN	...	6.90000	1.80	0.250	0.250	24.08000	1.8400	1.8400	0.012172	1.933288e+00	1.933288e+00
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7599	ZTF J1406+1222 Ab	50.000	NaN	NaN	NaN	NaN	NaN	0.292	NaN	NaN	...	200.00000	1.40	NaN	NaN	0.00002	NaN	NaN	116.233408	NaN	NaN
7600	ZTF J1622+47 b	61.000	19.00000	19.00000	NaN	NaN	NaN	0.980	0.02	0.02	...	NaN	0.47	NaN	NaN	0.18200	0.0040	0.0040	90.031428	NaN	NaN
7601	ZTF J1637+49 b	23.000	8.00000	8.00000	NaN	NaN	NaN	0.680	0.07	0.07	...	8.00000	0.90	0.050	0.050	0.00900	0.0010	0.0010	146.120589	NaN	NaN
7602	ZTF J1828+2308 b	19.500	0.80000	0.80000	NaN	NaN	NaN	1.020	0.02	0.02	...	5.00000	0.61	0.040	0.040	0.01310	0.0002	0.0002	56.096513	7.222052e+06	7.222052e+06
7603	ZTF J2252-05 b	26.000	8.00000	8.00000	NaN	NaN	NaN	0.490	0.04	0.04	...	82.00000	0.76	0.050	0.050	0.01000	0.0010	0.0010	261.799388	NaN	NaN

Full ResokitDataSet - 7604 rows x 45 columns

Let’s load NASA dataset too.

[5]:

datasets.download('nasa', overwrite=False, soft=True)
nasa_full = datasets.load("nasa")
nasa_full

Zip file /home/egianuzzi/.resokit_data/nasa_exoplanets.zip already exists. Set overwrite=True to force the download.
 Loading the entire dataset...
  Reading nasa.csv directly from /home/egianuzzi/.resokit_data/nasa_exoplanets.zip...
Updated stored index in memory.
Stored dataset in memory.

[5]:

	name	star_name	default_set	reference	disc_year	disc_method	P	P_err_min	P_err_max	w_err_min	...	star_radius_err_min	star_radius_err_max	n_stars	n_planets	star_dist	star_dist_err_min	star_dist_err_max	n	n_err_min	n_err_max
0	Kepler-6 b	Kepler-6	0	<a refstr=Q1_Q12_KOI_TABLE href=https://exopla...	2009	Transit	3.234699	1.130000e-07	1.130000e-07	NaN	...	0.093000	0.099000	1	1	587.039	5.071	4.986	1.942433	5.560341e+07	5.560341e+07
1	Kepler-6 b	Kepler-6	1	<a refstr=ESTEVES_ET_AL__2015 href=https://ui....	2009	Transit	3.234700	4.000000e-07	4.000000e-07	NaN	...	0.017000	0.034000	1	1	587.039	5.071	4.986	1.942432	1.570796e+07	1.570796e+07
2	Kepler-6 b	Kepler-6	0	<a refstr=Q1_Q17_DR24_KOI_TABLE href=https://e...	2009	Transit	3.234699	1.130000e-07	1.130000e-07	NaN	...	0.093000	0.099000	1	1	587.039	5.071	4.986	1.942433	5.560341e+07	5.560341e+07
3	Kepler-6 b	Kepler-6	0	<a refstr=EXOFOP_TESS_TOI href=https://exofop....	2009	Transit	3.234694	6.012800e-06	6.012800e-06	NaN	...	0.059593	0.059593	1	1	587.039	5.071	4.986	1.942436	1.044968e+06	1.044968e+06
4	Kepler-6 b	Kepler-6	0	<a refstr=HOLCZER_ET_AL__2016 href=https://ui....	2009	Transit	3.234699	3.000000e-08	3.000000e-08	NaN	...	0.072914	0.046272	1	1	587.039	5.071	4.986	1.942433	2.094395e+08	2.094395e+08
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
38495	Kepler-33 f	Kepler-33	0	<a refstr=HADDEN__AMP__LITHWICK_2016 href=http...	2011	Transit	41.029000	NaN	NaN	NaN	...	0.260000	0.420000	1	5	1209.160	22.195	22.195	0.153140	NaN	NaN
38496	Kepler-33 f	Kepler-33	0	<a refstr=FULTON__AMP__PETIGURA_2018 href=http...	2011	Transit	41.028064	1.385000e-04	1.385000e-04	NaN	...	0.047000	0.045000	1	5	1209.160	22.195	22.195	0.153144	4.536596e+04	4.536596e+04
38497	Kepler-33 f	Kepler-33	0	<a refstr=HOLCZER_ET_AL__2016 href=https://ui....	2011	Transit	41.028139	9.930000e-06	9.930000e-06	NaN	...	NaN	NaN	1	5	1209.160	22.195	22.195	0.153143	6.327478e+05	6.327478e+05
38498	Kepler-33 f	Kepler-33	0	<a refstr=BERGER_ET_AL__2018 href=https://ui.a...	2011	Transit	NaN	NaN	NaN	NaN	...	0.076000	0.073000	1	5	1209.160	22.195	22.195	NaN	NaN	NaN
38499	Kepler-33 f	Kepler-33	0	<a refstr=Q1_Q12_KOI_TABLE href=https://exopla...	2011	Transit	41.028064	1.385000e-04	1.385000e-04	NaN	...	0.399000	0.381000	1	5	1209.160	22.195	22.195	0.153144	4.536596e+04	4.536596e+04

Full ResokitDataSet - 38500 rows x 49 columns

Nasa dataset contains all exoplanet solutions (including non default and controversial), so we filter them here

[6]:

nasa = nasa_full[(nasa_full.controversial == 0) & (nasa_full.default_set == 1)]
nasa

[6]:

	name	star_name	default_set	reference	disc_year	disc_method	P	P_err_min	P_err_max	w_err_min	...	star_radius_err_min	star_radius_err_max	n_stars	n_planets	star_dist	star_dist_err_min	star_dist_err_max	n	n_err_min	n_err_max
1	Kepler-6 b	Kepler-6	1	<a refstr=ESTEVES_ET_AL__2015 href=https://ui....	2009	Transit	3.234700	4.000000e-07	4.000000e-07	NaN	...	0.017	0.034	1	1	587.039	5.0710	4.9860	1.942432	1.570796e+07	1.570796e+07
25	Kepler-491 b	Kepler-491	1	<a refstr=MORTON_ET_AL__2016 href=https://ui.a...	2016	Transit	4.225385	2.680000e-07	2.680000e-07	NaN	...	0.092	0.059	1	1	630.785	6.4310	6.3050	1.487009	2.344472e+07	2.344472e+07
34	Kepler-257 b	Kepler-257	1	<a refstr=ROWE_ET_AL__2014 href=https://ui.ads...	2014	Transit	2.382667	6.000000e-06	6.000000e-06	NaN	...	0.518	0.518	1	3	780.256	17.5400	16.7990	2.637039	1.047198e+06	1.047198e+06
43	Kepler-216 b	Kepler-216	1	<a refstr=ROWE_ET_AL__2014 href=https://ui.ads...	2014	Transit	7.693641	3.100000e-05	3.100000e-05	NaN	...	0.243	0.243	1	2	1187.470	21.4200	21.4200	0.816673	2.026834e+05	2.026834e+05
67	Kepler-32 c	Kepler-32	1	<a refstr=FABRYCKY_ET_AL__2012 href=https://ui...	2011	Transit	8.752200	3.000000e-04	3.000000e-04	NaN	...	0.040	0.040	1	5	323.847	3.4025	3.4025	0.717898	2.094395e+04	2.094395e+04
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
38444	Kepler-561 c	Kepler-561	1	<a refstr=MORTON_ET_AL__2016 href=https://ui.a...	2016	Transit	5.350162	4.435000e-06	4.435000e-06	NaN	...	0.164	0.082	1	2	621.378	9.3350	9.3350	1.174392	1.416727e+06	1.416727e+06
38458	Kepler-215 b	Kepler-215	1	<a refstr=ROWE_ET_AL__2014 href=https://ui.ads...	2014	Transit	9.360672	4.000000e-05	4.000000e-05	NaN	...	0.236	0.236	1	4	485.997	3.6540	3.6540	0.671232	1.570796e+05	1.570796e+05
38462	Kepler-62 b	Kepler-62	1	<a refstr=BORUCKI_ET_AL__2013 href=https://ui....	2013	Transit	5.714932	9.000000e-06	9.000000e-06	NaN	...	0.020	0.020	1	5	300.874	1.2190	1.2190	1.099433	6.981317e+05	6.981317e+05
38477	Kepler-192 c	Kepler-192	1	<a refstr=ROWE_ET_AL__2014 href=https://ui.ads...	2014	Transit	21.223400	7.600000e-05	7.600000e-05	NaN	...	0.164	0.164	1	3	651.674	6.7435	6.7435	0.296050	8.267349e+04	8.267349e+04
38486	Kepler-33 f	Kepler-33	1	<a refstr=LISSAUER_ET_AL__2012 href=https://ui...	2011	Transit	41.029020	4.200000e-04	4.200000e-04	NaN	...	0.140	0.180	1	5	1209.160	22.1950	22.1950	0.153140	1.495997e+04	1.495997e+04

Partial ResokitDataSet - 5887 rows x 49 columns

We can see both datasets have different amount of exoplanets:

[7]:

print(f"# of planets in NASA dataset: {nasa.shape[0]}")
print(f"# of planets in EU dataset: {eu.shape[0]}")

# of planets in NASA dataset: 5887
# of planets in EU dataset: 7604

Inspection

Checking the columns, we find that both data frames have almost all equal column names

[8]:

print(f"{'Column':^19s} || {'NASA':^4s} | {'EU':^4s}")
print("-" * 50)
for col in nasa.columns.sort_values():
    print(f"{col:>19s} || {'YES':^4s} | {'YES' if col in eu.columns else 'NO':^4s}")

      Column        || NASA |  EU
--------------------------------------------------
                  P || YES  | YES
          P_err_max || YES  | YES
          P_err_min || YES  | YES
                  a || YES  | YES
          a_err_max || YES  | YES
          a_err_min || YES  | YES
      controversial || YES  |  NO
        default_set || YES  |  NO
        disc_method || YES  | YES
          disc_year || YES  | YES
                  e || YES  | YES
          e_err_max || YES  | YES
          e_err_min || YES  | YES
                inc || YES  | YES
        inc_err_max || YES  | YES
        inc_err_min || YES  | YES
               mass || YES  | YES
       mass_err_max || YES  | YES
       mass_err_min || YES  | YES
         mass_sin_i || YES  | YES
 mass_sin_i_err_max || YES  | YES
 mass_sin_i_err_min || YES  | YES
                  n || YES  | YES
          n_err_max || YES  | YES
          n_err_min || YES  | YES
          n_planets || YES  |  NO
            n_stars || YES  |  NO
               name || YES  | YES
             radius || YES  | YES
     radius_err_max || YES  | YES
     radius_err_min || YES  | YES
          reference || YES  | YES
          rowupdate || YES  | YES
          star_dist || YES  | YES
  star_dist_err_max || YES  | YES
  star_dist_err_min || YES  | YES
          star_mass || YES  | YES
  star_mass_err_max || YES  | YES
  star_mass_err_min || YES  | YES
          star_name || YES  | YES
        star_radius || YES  | YES
star_radius_err_max || YES  | YES
star_radius_err_min || YES  | YES
              tperi || YES  | YES
      tperi_err_max || YES  | YES
      tperi_err_min || YES  | YES
                  w || YES  | YES
          w_err_max || YES  | YES
          w_err_min || YES  | YES

The only differences are:

controversial : The exoplanet is confirmed (0) or not yet (1).
default_set : The solution is the default set (1) or not (0).
n_planets : Amount of planets in that system.
n_stars : Amount of stars in that system.

We can see both datasets are similar, but not equal.

[9]:

plt.figure(figsize=(5,4), dpi=130)
plt.plot(eu.mass, eu.radius, ".", ms=10, label="EU")
plt.plot(nasa.mass, nasa.radius, "x", ms=3, label="NASA")
plt.semilogx()
plt.semilogy()
plt.xlabel("Planet mass [M$_\\mathrm{J}$]")
plt.ylabel("Planet radius [R$_\\mathrm{J}$]")
plt.suptitle("Mass vs Radius comparison")
plt.xlim(1e-4, 1e2)
plt.ylim(bottom=1e-2)
plt.legend()
plt.tight_layout()
plt.show()

[10]:

plt.figure(figsize=(5,4), dpi=130)
plt.plot(eu.a, eu.e, ".", ms=10, label="EU")
plt.plot(nasa.a, nasa.e, "x", ms=3, label="NASA")
plt.semilogx()
plt.semilogy()
plt.xlabel("Planet semimajor-axis [AU]")
plt.ylabel("Planet eccentricity")
plt.suptitle("$a$ vs $e$ comparison")
plt.ylim(bottom=1e-5)
plt.legend()
plt.tight_layout()
plt.show()

Slicing

In case we need just an specific group of systems / exoplanets, we can access the ResokitDataSet as is were a pandas DataFrame; and slice it.

For example, if we want to gete just the Hot Jupiter like exoplanets (\(m \in [0.36,\, 11.8]\,M_J\) and \(P \in [1.3,\, 111]\,days\)), we simply do:

[11]:

hot_jup = eu[(0.36 < eu.mass) & (eu.mass < 11.8) & (1.3 < eu.P) & (eu.P < 111)]
hot_jup

[11]:

	name	mass	mass_err_min	mass_err_max	mass_sin_i	mass_sin_i_err_min	mass_sin_i_err_max	radius	radius_err_min	radius_err_max	...	star_dist_err_max	star_mass	star_mass_err_min	star_mass_err_max	star_radius	star_radius_err_min	star_radius_err_max	n	n_err_min	n_err_max
314	51 Peg b	0.470	0.070	0.030	0.460	0.060	0.010	1.900	0.300	0.300	...	0.02970	1.030	0.126	0.126	1.176	0.0510	0.0510	1.485106	1.684929e+05	1.684929e+05
318	55 Cnc Ab	0.840	0.031	0.230	0.840	0.031	0.131	NaN	NaN	NaN	...	0.01235	0.900	0.115	0.115	0.963	0.0654	0.0654	0.428794	6.981317e+03	6.613879e+03
488	CoRoT-10 b	2.750	0.140	0.140	NaN	NaN	NaN	0.970	0.050	0.050	...	50.00000	0.890	0.050	0.050	0.790	0.0500	0.0500	0.474539	3.141593e+04	3.141593e+04
489	CoRoT-11 b	2.330	0.270	0.270	NaN	NaN	NaN	1.430	0.033	0.033	...	30.00000	1.270	0.050	0.050	1.360	0.1300	0.1300	2.098365	2.991993e+05	2.991993e+05
490	CoRoT-12 b	0.917	0.065	0.070	NaN	NaN	NaN	1.440	0.130	0.130	...	85.00000	1.078	0.072	0.072	1.116	0.0920	0.0920	2.221736	5.235988e+06	5.235988e+06
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7578	XO-3 Ab	11.790	0.590	0.590	NaN	NaN	NaN	1.217	0.073	0.073	...	23.00000	1.410	0.080	0.080	1.490	0.0800	0.0800	1.968710	2.731820e+04	2.731820e+04
7579	XO-4 b	1.616	0.100	0.101	1.615	0.100	0.100	1.317	0.029	0.040	...	19.00000	1.320	0.020	0.020	1.550	0.0500	0.0500	1.523296	1.030030e+04	1.336848e+04
7580	XO-5 b	1.077	0.037	0.037	NaN	NaN	NaN	1.030	0.050	0.050	...	13.00000	0.880	0.030	0.030	1.060	0.0500	0.0500	1.500371	3.695991e+06	3.695991e+06
7581	XO-6 b	4.470	0.120	0.120	NaN	NaN	NaN	2.170	0.200	0.200	...	79.00000	1.470	0.060	0.060	1.930	0.1800	0.1800	1.668844	1.396263e+07	1.396263e+07
7582	XO-7 b	0.726	0.038	0.038	NaN	NaN	NaN	1.346	0.020	0.020	...	1.20000	1.405	0.059	0.059	1.480	0.0220	0.0220	2.193748	1.142397e+07	1.142397e+07

Partial ResokitDataSet - 678 rows x 45 columns

[12]:

plt.figure(figsize=(5,4), dpi=130)
plt.plot(hot_jup.mass, hot_jup.radius, ".", ms=10)
plt.semilogx()
# plt.semilogy()
plt.xlabel("Mass [M$_J$]")
plt.ylabel("Radius [R$_J$]")
plt.suptitle("Hot Jupiters $mass$ vs $radius$ comparison")
plt.tight_layout()
plt.show()

Load full dataset

Some planet and star data are not shown because the full data has been trimmed, when using to_resokit=True in load function.

All data can be obtained using to_resokit=False. Moreover, if we wanted a pandas Dataframe, also use to_df=True. Nevertheless, in this case the column names will be the original from each database, instead of being mapped into more comprehensive ones.

[13]:

nasa_original = datasets.load('nasa', to_resokit=False)
nasa_original

 Loaded full dataset from memory stored datasets.

[13]:

	pl_name	pl_letter	hostname	hd_name	hip_name	tic_id	gaia_id	default_flag	pl_refname	sy_refname	...	sy_jmagerr1	sy_jmagerr2	sy_jmagstr	sy_hmag	sy_hmagerr1	sy_hmagerr2	sy_hmagstr	sy_kmag	sy_kmagerr1	sy_kmagerr2
0	Kepler-6 b	b	Kepler-6	NaN	NaN	TIC 27916356	Gaia DR2 2086636884980514304	0	<a refstr=Q1_Q12_KOI_TABLE href=https://exopla...	<a refstr=STASSUN_ET_AL__2019 href=https://ui....	...	0.021	-0.021	12.001±0.021	11.706	0.019	-0.019	11.706±0.019	11.634	0.019	-0.019
1	Kepler-6 b	b	Kepler-6	NaN	NaN	TIC 27916356	Gaia DR2 2086636884980514304	1	<a refstr=ESTEVES_ET_AL__2015 href=https://ui....	<a refstr=STASSUN_ET_AL__2019 href=https://ui....	...	0.021	-0.021	12.001±0.021	11.706	0.019	-0.019	11.706±0.019	11.634	0.019	-0.019
2	Kepler-6 b	b	Kepler-6	NaN	NaN	TIC 27916356	Gaia DR2 2086636884980514304	0	<a refstr=Q1_Q17_DR24_KOI_TABLE href=https://e...	<a refstr=STASSUN_ET_AL__2019 href=https://ui....	...	0.021	-0.021	12.001±0.021	11.706	0.019	-0.019	11.706±0.019	11.634	0.019	-0.019
3	Kepler-6 b	b	Kepler-6	NaN	NaN	TIC 27916356	Gaia DR2 2086636884980514304	0	<a refstr=EXOFOP_TESS_TOI href=https://exofop....	<a refstr=STASSUN_ET_AL__2019 href=https://ui....	...	0.021	-0.021	12.001±0.021	11.706	0.019	-0.019	11.706±0.019	11.634	0.019	-0.019
4	Kepler-6 b	b	Kepler-6	NaN	NaN	TIC 27916356	Gaia DR2 2086636884980514304	0	<a refstr=HOLCZER_ET_AL__2016 href=https://ui....	<a refstr=STASSUN_ET_AL__2019 href=https://ui....	...	0.021	-0.021	12.001±0.021	11.706	0.019	-0.019	11.706±0.019	11.634	0.019	-0.019
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
38495	Kepler-33 f	f	Kepler-33	NaN	NaN	TIC 158935283	Gaia DR2 2127355923723254272	0	<a refstr=HADDEN__AMP__LITHWICK_2016 href=http...	<a refstr=STASSUN_ET_AL__2019 href=https://ui....	...	0.020	-0.020	12.871±0.020	12.620	0.021	-0.021	12.620±0.021	12.591	0.022	-0.022
38496	Kepler-33 f	f	Kepler-33	NaN	NaN	TIC 158935283	Gaia DR2 2127355923723254272	0	<a refstr=FULTON__AMP__PETIGURA_2018 href=http...	<a refstr=STASSUN_ET_AL__2019 href=https://ui....	...	0.020	-0.020	12.871±0.020	12.620	0.021	-0.021	12.620±0.021	12.591	0.022	-0.022
38497	Kepler-33 f	f	Kepler-33	NaN	NaN	TIC 158935283	Gaia DR2 2127355923723254272	0	<a refstr=HOLCZER_ET_AL__2016 href=https://ui....	<a refstr=STASSUN_ET_AL__2019 href=https://ui....	...	0.020	-0.020	12.871±0.020	12.620	0.021	-0.021	12.620±0.021	12.591	0.022	-0.022
38498	Kepler-33 f	f	Kepler-33	NaN	NaN	TIC 158935283	Gaia DR2 2127355923723254272	0	<a refstr=BERGER_ET_AL__2018 href=https://ui.a...	<a refstr=STASSUN_ET_AL__2019 href=https://ui....	...	0.020	-0.020	12.871±0.020	12.620	0.021	-0.021	12.620±0.021	12.591	0.022	-0.022
38499	Kepler-33 f	f	Kepler-33	NaN	NaN	TIC 158935283	Gaia DR2 2127355923723254272	0	<a refstr=Q1_Q12_KOI_TABLE href=https://exopla...	<a refstr=STASSUN_ET_AL__2019 href=https://ui....	...	0.020	-0.020	12.871±0.020	12.620	0.021	-0.021	12.620±0.021	12.591	0.022	-0.022

Full ResokitDataSet - 38500 rows x 354 columns

We see that the amount of columns is larger than before

[14]:

print("# of columns in nasa (to_resokit=True):", nasa.shape[1])
print("# of columns in nasa (to_resokit=False):", nasa_original.shape[1])

# of columns in nasa (to_resokit=True): 49
# of columns in nasa (to_resokit=False): 354

Also, we can see that mass or radius are not in the dataframe, but it has pl_massj and pl_radj instead.

[15]:

print("'mass' in nasa_original:", "mass" in nasa_original.columns)
print("'radius' in nasa_original:", "radius" in nasa_original.columns)

print("'pl_massj' in nasa_original:", "pl_massj" in nasa_original.columns)
print("'pl_radj' in nasa_original:", "pl_radj" in nasa_original.columns)

'mass' in nasa_original: False
'radius' in nasa_original: False
'pl_massj' in nasa_original: True
'pl_radj' in nasa_original: True

Default units

More information about the units can be found at:

but the basics are:

Planet:
- mass: Jupiter masses
- radius: Jupiter radii
- semimajor-axes: au
- period (time): days
Star:
- mass: Solar masses
- radius: Solar radii
- distance: parsecs
- period (time): days

Update

When loading a dataset, we can check_outdated to get info about when the dataset was updated on our local machine.

[16]:

datasets.check_outdated('both')

Checking local dataset from which='eu' source...
 Last modified: 0 days ago.
 Number of planets in stored dataset: 7604
Checking online dataset from eu...
 Number of planets in online dataset: 7604
 Last online update: 2025-08-20 00:00:00 (0 days ago)
Dataset is already up-to-date.

Checking local dataset from which='nasa' source...
 Last modified: 57 days ago.
 Number of planets in stored dataset: 5921
  (Including only default parameters sets.)
Checking online dataset from nasa...
 Number of planets in online dataset: 5983
 Last online update: 2025-08-14 00:00:00 (6 days ago)
The online dataset has more rows than the stored dataset.
The dataset is outdated.

[16]:

(False, True)

To perform a whole dataset update, we can use the function download, with overwrite=True. If argument check_outd=True, another check will be performed.

[17]:

datasets.download('eu', overwrite=True)

Checking local dataset from which='eu' source...
 Last modified: 0 days ago.
 Number of planets in stored dataset: 7604
Checking online dataset from eu...
 Number of planets in online dataset: 7604
 Last online update: 2025-08-20 00:00:00 (0 days ago)
Dataset is already up-to-date.
No need to download the dataset. Set check_outd=False to really force it.

To store into a file the ‘in memory’ dataset, we can use the method to_file, or even pandas method to_csv.

[18]:

eu.to_file("my_eu.csv")

Dataset saved to my_eu.csv.

Another possibility is to use the function update. This is a wrapper for download, but making a query of only the updated rows.

NOTE: This feature is not available with EU dataset (yet).

Let’s show it with NASA dataset.

[19]:

datasets.update("nasa", overwrite=True)

 Loaded full dataset from memory stored datasets.
Checking local dataset from which='nasa' source...
 Last modified: 57 days ago.
 Number of planets in stored dataset: 5921
  (Including only default parameters sets.)
Checking online dataset from nasa...
 Number of planets in online dataset: 5983
 Last online update: 2025-08-14 00:00:00 (6 days ago)
The online dataset has more rows than the stored dataset.
The dataset is outdated.
Latest row update in local dataset: 2025-06-11
Querying online rows update after that date.
Executing the query...
Query executed successfully.
Amount of rows downloaded: 280
 Rows in only old | both | only new: 38428 | 72 | 208
Removed files: nasa.csv from /home/egianuzzi/.resokit_data/nasa_exoplanets.zip
 Written nasa.csv to /home/egianuzzi/.resokit_data/nasa_exoplanets.zip.
Updated stored index in memory.
Stored dataset in memory.

[19]:

PosixPath('/home/egianuzzi/.resokit_data/nasa_exoplanets.zip/nasa.csv')

If an update is requested and check_outdis True, then it will not download new rows if not needed to.

[20]:

datasets.update("nasa", overwrite=True)

 Loaded full dataset from memory stored datasets.
Checking local dataset from which='nasa' source...
 Last modified: 0 days ago.
 Number of planets in stored dataset: 5998
  (Including only default parameters sets.)
Checking online dataset from nasa...
 Number of planets in online dataset: 5983
 Last online update: 2025-08-14 00:00:00 (6 days ago)
The online dataset has less rows than the stored dataset.
 This could be the result of some online row(s) deleted.
 Although this is usually not a problem, running
`resokit.datasets.download(which='nasa')` could solve it if needed.
No need to download the dataset. Set check_outd=False to really force it.

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