Astropy: Tables

Interact

Objectives

Create tables
Access data in tables
Combining tables
Aggregation
Masking
Reading/writing

Documentation

For more information about the features presented below, you can read the astropy.table docs.

Creating tables

import numpy as np
from astropy.table import Table

# Creating a table from scratch
t1 = Table()
t1['name'] = ['source 1', 'source 2', 'source 3']
t1['flux'] = [1.2, 2.2, 3.1]

# Looking at the table
t1

Table length=3

name	flux
str8	float64
source 1	1.2
source 2	2.2
source 3	3.1

# Adding a column
t1['size'] = [1,5,4]
t1

Table length=3

name	flux	size
str8	float64	int64
source 1	1.2	1
source 2	2.2	5
source 3	3.1	4

# Accessing a column
t1['size']

# Converting to a Numpy array
np.array(t1['size'])

array([1, 5, 4])

# Accessing a cell
t1['size'][0]

# Accessing a row
t1[0]

Row index=0

name	flux	size
str8	float64	int64
source 1	1.2	1

Units in tables

# Set unit on column
t1['size'].unit = 'cm'
t1

Table length=3

name	flux	size
		cm
str8	float64	int64
source 1	1.2	1
source 2	2.2	5
source 3	3.1	4

Some unitful operations will then work:

t1['size'].to('m')

$[0.01,~0.05,~0.04] \; \mathrm{m}$

However, you may run into unexpected behavior, so if you are planning on using table columns as Quantities, we recommend that you use the QTable class:

type(t1['size'])

astropy.table.column.Column

from astropy.table import QTable
qt1 = QTable(t1)
type(qt1['size'])

astropy.units.quantity.Quantity

Challenge

Make a table that contains three columns: spectral type, temperature, and radius, and incude 5 rows with fake data (or real data if you like, for example from here). Try including units on the columns that can have them.
Find the mean temperature and the maximum radius
Try and find out how to add and remove rows
Add a new column which gives the luminosity (using $L=4\pi R^2 \sigma T^4$ )

#1
from astropy import units as u
t = QTable()
t['spectral type'] = ['O5', 'B5', 'A5', 'F5', 'G5']
t['radius'] = [12, 3.9, 1.7, 1.3, 0.92] * u.R_sun
t['temperature'] = [45000, 15000, 8200, 6400, 5700] * u.K
t

QTable length=5

spectral type	radius	temperature
	solRad	K
str2	float64	float64
O5	12.0	45000.0
B5	3.9	15000.0
A5	1.7	8200.0
F5	1.3	6400.0
G5	0.92	5700.0

#2
print('Mean temperature:', np.mean(t['temperature']))
print('Maximum radius:', np.mean(t['radius']))

Mean temperature: 16060.0 K
Maximum radius: 3.964000000000001 solRad

#3
t.add_row({'spectral type': 'K5',
           'temperature': 4300 * u.K,
           'radius': 0.72 * u.R_sun})
t.remove_row(0)
t

QTable length=5

spectral type	radius	temperature
	solRad	K
str2	float64	float64
B5	3.9	15000.0
A5	1.7	8200.0
F5	1.3	6400.0
G5	0.92	5700.0
K5	0.72	4300.0

#4
from numpy import pi
from astropy.constants import sigma_sb
t['luminosity'] = (4 * pi * t['radius'] ** 2 * sigma_sb * t['temperature'] ** 4).to(u.L_sun)
t

QTable length=5

spectral type	radius	temperature	luminosity
	solRad	K	solLum
str2	float64	float64	float64
B5	3.9	15000.0	693.7250235023215
A5	1.7	8200.0	11.7718945281512
F5	1.3	6400.0	2.554463553120115
G5	0.92	5700.0	0.8049486705065919
K5	0.72	4300.0	0.15967316182594046

Iterating over tables

It is possible to iterate over rows or over columns. To iterate over rows, simply iterate over the table itself:

for row in t1:
    print(row)

  name   flux size
               cm 
-------- ---- ----
source 1  1.2    1
  name   flux size
               cm 
-------- ---- ----
source 2  2.2    5
  name   flux size
               cm 
-------- ---- ----
source 3  3.1    4

Rows can act like dictionaries, so you can access specific columns from a row:

for row in t1:
    print(row['name'])

source 1
source 2
source 3

Iterating over columns is also easy:

for colname in t1.columns:
    column = t1[colname]
    print(column)

  name  
--------
source 1
source 2
source 3
flux
----
 1.2
 2.2
 3.1
size
 cm 
----
   1
   5
   4

Accessing specific rows from a column object can also be done with the item notation:

for colname in t1.columns:
    column = t1[colname]
    print(column[0])

source 1
1.2
1

Joining tables

from astropy.table import join

t2 = Table()
t2['name'] = ['source 1', 'source 3']
t2['flux2'] = [1,9]

t3 = join(t1, t2, join_type='outer')
t3

Table masked=True length=3

name	flux	size	flux2
		cm
str8	float64	int64	int64
source 1	1.2	1	1
source 2	2.2	5	--
source 3	3.1	4	9

np.mean(t3['flux2'])

5.0

Masked tables

t4 = Table(masked=True)
t4['name'] = ['source 1', 'source 2', 'source 3']
t4['flux'] = [1.2, 2.2, 3.1]

t4['flux'].mask = [1,0,1]
t4

Table masked=True length=3

name	flux
str8	float64
source 1	--
source 2	2.2
source 3	--

Slicing

Tables can be sliced like Numpy arrays:

obs = Table.read("""name    obs_date    mag_b  mag_v
                    M31     2012-01-02  17.0   17.5
                    M31     2012-01-02  17.1   17.4
                    M101    2012-01-02  15.1   13.5
                    M82     2012-02-14  16.2   14.5
                    M31     2012-02-14  16.9   17.3
                    M82     2012-02-14  15.2   15.5
                    M101    2012-02-14  15.0   13.6
                    M82     2012-03-26  15.7   16.5
                    M101    2012-03-26  15.1   13.5
                    M101    2012-03-26  14.8   14.3
                    """, format='ascii')

obs[1:4]

Table length=3

name	obs_date	mag_b	mag_v
str4	str10	float64	float64
M31	2012-01-02	17.1	17.4
M101	2012-01-02	15.1	13.5
M82	2012-02-14	16.2	14.5

obs[obs['mag_b'] > 16]

Table length=4

name	obs_date	mag_b	mag_v
str4	str10	float64	float64
M31	2012-01-02	17.0	17.5
M31	2012-01-02	17.1	17.4
M82	2012-02-14	16.2	14.5
M31	2012-02-14	16.9	17.3

obs['mag_b', 'mag_v']

Table length=10

mag_b	mag_v
float64	float64
17.0	17.5
17.1	17.4
15.1	13.5
16.2	14.5
16.9	17.3
15.2	15.5
15.0	13.6
15.7	16.5
15.1	13.5
14.8	14.3

Challenge

Starting from the obs table:

Make a new table that shows every other row, starting with the second row? (that is, the second, fourth, sixth, etc. rows).
Make a new table the only contains rows where name is M31

#1
subset1 = obs[1::2]
subset1

Table length=5

name	obs_date	mag_b	mag_v
str4	str10	float64	float64
M31	2012-01-02	17.1	17.4
M82	2012-02-14	16.2	14.5
M82	2012-02-14	15.2	15.5
M82	2012-03-26	15.7	16.5
M101	2012-03-26	14.8	14.3

#2
subset2 = obs[obs['name'] == 'M31']
subset2

Table length=3

name	obs_date	mag_b	mag_v
str4	str10	float64	float64
M31	2012-01-02	17.0	17.5
M31	2012-01-02	17.1	17.4
M31	2012-02-14	16.9	17.3

Grouping and Aggregation

obs_by_name = obs.group_by('name')

obs_by_name

Table length=10

name	obs_date	mag_b	mag_v
str4	str10	float64	float64
M101	2012-01-02	15.1	13.5
M101	2012-02-14	15.0	13.6
M101	2012-03-26	15.1	13.5
M101	2012-03-26	14.8	14.3
M31	2012-01-02	17.0	17.5
M31	2012-01-02	17.1	17.4
M31	2012-02-14	16.9	17.3
M82	2012-02-14	16.2	14.5
M82	2012-02-14	15.2	15.5
M82	2012-03-26	15.7	16.5

for group in obs_by_name.groups:
    print(group)
    print("")

name  obs_date  mag_b mag_v
---- ---------- ----- -----
M101 2012-01-02  15.1  13.5
M101 2012-02-14  15.0  13.6
M101 2012-03-26  15.1  13.5
M101 2012-03-26  14.8  14.3

name  obs_date  mag_b mag_v
---- ---------- ----- -----
 M31 2012-01-02  17.0  17.5
 M31 2012-01-02  17.1  17.4
 M31 2012-02-14  16.9  17.3

name  obs_date  mag_b mag_v
---- ---------- ----- -----
 M82 2012-02-14  16.2  14.5
 M82 2012-02-14  15.2  15.5
 M82 2012-03-26  15.7  16.5

obs_by_name.groups.aggregate(np.mean)

Table length=3

name	mag_b	mag_v
str4	float64	float64
M101	15.000000000000002	13.725000000000001
M31	17.0	17.400000000000002
M82	15.699999999999998	15.5

Writing data

obs.write('test.fits', overwrite=True)

obs.write('test.vot', format='votable', overwrite=True)

Reading data

t4 = Table.read('2mass.tbl', format='ascii.ipac')

t4

Table masked=True length=929

ra	dec	clon	clat	err_maj	err_min	err_ang	designation	j_m	j_cmsig	j_msigcom	j_snr	h_m	h_cmsig	h_msigcom	h_snr	k_m	k_cmsig	k_msigcom	k_snr	ph_qual	rd_flg	bl_flg	cc_flg	ndet	gal_contam	mp_flg	dist	angle	j_h	h_k	j_k
deg	deg			arcsec	arcsec	deg		mag	mag	mag		mag	mag	mag		mag	mag	mag
float64	float64	str12	str13	float64	float64	int64	str16	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	str3	str3	str3	str3	str6	int64	int64	float64	float64	float64	float64	float64
274.429506	-13.870547	18h17m43.08s	-13d52m13.97s	0.08	0.08	45	18174308-1352139	16.305	0.142	0.143	6.7	14.048	0.107	0.108	13.6	13.257	0.066	0.066	16.5	CAA	222	111	0ss	066655	0	0	975.080151	256.448	2.257	0.791	3.048
274.423821	-13.86974	18h17m41.72s	-13d52m11.06s	0.06	0.06	90	18174171-1352110	14.802	0.058	0.059	26.7	12.635	0.059	0.06	50.1	11.768	0.045	0.046	65.2	AAA	222	111	0ss	666666	0	0	993.752042	256.878	2.167	0.867	3.034
274.424587	-13.739629	18h17m41.90s	-13d44m22.66s	0.08	0.08	45	18174190-1344226	16.328	--	--	--	14.345	0.059	0.06	10.4	13.405	0.046	0.047	14.4	UAA	022	011	0cc	003666	0	0	995.726698	284.113	--	0.94	--
274.433933	-13.769502	18h17m44.14s	-13d46m10.21s	0.08	0.08	45	18174414-1346102	16.281	0.098	0.099	6.8	14.057	0.035	0.036	13.5	12.956	0.032	0.033	21.8	CAA	222	111	000	065566	0	0	942.627418	278.252	2.224	1.101	3.325
274.437013	-13.885698	18h17m44.88s	-13d53m08.51s	0.09	0.09	45	18174488-1353085	15.171	--	--	--	14.412	0.152	0.152	9.8	13.742	0.095	0.095	10.6	UBA	622	022	0cc	005566	0	0	964.105389	252.93	--	0.67	--
274.433996	-13.752446	18h17m44.16s	-13d45m08.81s	0.08	0.08	90	18174415-1345088	16.54	--	--	--	14.519	0.083	0.083	8.8	13.604	0.043	0.044	12.0	UBA	022	011	0cc	005666	0	0	953.230532	281.908	--	0.915	--
274.418138	-13.77215	18h17m40.35s	-13d46m19.74s	0.08	0.08	90	18174035-1346197	17.98	--	--	--	14.61	0.043	0.044	8.1	13.456	0.056	0.057	13.8	UBA	022	011	000	001645	0	0	996.047248	277.25	--	1.154	--
274.433695	-13.899049	18h17m44.09s	-13d53m56.58s	0.06	0.06	90	18174408-1353565	13.011	0.021	0.024	139.0	10.917	0.02	0.021	243.8	10.013	0.017	0.019	328.3	AAA	222	111	000	666666	0	0	990.166399	250.466	2.094	0.904	2.998
274.425482	-13.77149	18h17m42.12s	-13d46m17.36s	0.08	0.08	135	18174211-1346173	16.086	--	--	--	13.709	0.065	0.066	18.6	12.503	0.044	0.045	33.1	UAA	622	012	00c	005555	0	0	970.896919	277.582	--	1.206	--
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
274.81801	-14.001245	18h19m16.32s	-14d00m04.48s	0.18	0.16	1	18191632-1400044	16.24	0.113	0.113	5.6	15.531	0.164	0.164	2.5	15.252	--	--	--	CDU	220	110	000	060600	0	0	809.817146	149.61	0.709	--	--
274.822709	-14.037254	18h19m17.45s	-14d02m14.11s	0.07	0.07	45	18191745-1402141	15.999	0.097	0.098	7.0	14.009	0.032	0.033	10.0	13.077	0.035	0.036	16.4	CAA	222	111	000	062656	0	0	931.339773	152.779	1.99	0.932	2.922
274.880758	-13.99956	18h19m31.38s	-13d59m58.42s	0.06	0.06	90	18193138-1359584	14.163	0.035	0.037	37.8	11.179	0.02	0.021	135.6	9.765	0.017	0.019	347.1	AAA	222	111	000	556666	0	0	935.512452	137.762	2.984	1.414	4.398
274.652526	-14.055106	18h18m36.61s	-14d03m18.38s	0.06	0.06	90	18183660-1403183	15.035	0.052	0.054	19.4	13.099	0.04	0.041	27.5	12.254	0.041	0.041	41.7	AAA	222	111	c00	566666	0	0	908.109808	190.682	1.936	0.845	2.781
274.760586	-13.999927	18h19m02.54s	-13d59m59.74s	0.08	0.08	90	18190254-1359597	16.329	0.122	0.123	5.5	14.488	0.067	0.067	6.4	13.617	0.051	0.052	11.1	CCA	222	111	000	060616	0	0	724.557553	163.227	1.841	0.871	2.712
274.831132	-14.020027	18h19m19.47s	-14d01m12.10s	0.08	0.08	45	18191947-1401120	16.203	--	--	--	13.238	0.02	0.021	20.4	12.016	0.023	0.024	43.6	UAA	022	011	000	006666	0	0	891.347132	149.27	--	1.222	--
274.972435	-13.760374	18h19m53.38s	-13d45m37.35s	0.12	0.11	10	18195338-1345373	17.472	--	--	--	16.755	--	--	--	14.413	0.084	0.084	4.8	UUD	002	001	000	000006	0	0	964.828933	79.963	--	--	--
274.870009	-13.817775	18h19m28.80s	-13d49m03.99s	0.08	0.08	45	18192880-1349039	16.933	--	--	--	14.514	0.064	0.065	6.3	12.957	0.041	0.041	18.4	UCA	022	011	000	002666	0	0	592.998058	93.69	--	1.557	--
274.735323	-13.941575	18h18m56.48s	-13d56m29.67s	0.14	0.14	45	18185647-1356296	16.643	--	--	--	14.88	--	--	--	14.291	0.116	0.117	6.0	UUC	002	001	000	000004	0	0	498.524438	165.968	--	--	--
274.866294	-13.841778	18h19m27.91s	-13d50m30.40s	0.08	0.08	45	18192791-1350304	15.615	--	--	--	13.911	0.075	0.075	10.9	12.765	0.134	0.134	21.9	UAE	022	011	0cc	005545	0	0	591.97725	102.147	--	1.146	--

Challenge

Using the t4 table above:

Make a plot that shows j_m-h_m on the x-axis, and h_m-k_m on the y-axis
Make a new table that contains the subset of rows where the j_snr, h_snr, and k_snr columns, which give the signal-to-noise-ratio in the J, H, and K band, are greater than 10, and try and show these points in red in the plot you just made.
Make a new table (based on the full table) that contains only the RA, Dec, and the j_m, h_m and k_m columns, then try and write out this catalog into a format that you can read into another software package. For example, try and write out the catalog into CSV format, then read it into a spreadsheet software package (e.g. Excel, Google Docs, Numbers, OpenOffice). You may run into an issue at this point - if so, take a look at https://github.com/astropy/astropy/issues/7357 to see how to fix it.

#1
import matplotlib.pyplot as plt
plt.scatter(t4['j_m'] - t4['h_m'], t4['h_m'] - t4['k_m'], )

<matplotlib.collections.PathCollection at 0x7f696f91a630>

#2
subset = t4[(t4['j_snr'] > 10) & (t4['h_snr'] > 10) & (t4['k_snr'] > 10)]
subset

Table masked=True length=264

ra	dec	clon	clat	err_maj	err_min	err_ang	designation	j_m	j_cmsig	j_msigcom	j_snr	h_m	h_cmsig	h_msigcom	h_snr	k_m	k_cmsig	k_msigcom	k_snr	ph_qual	rd_flg	bl_flg	cc_flg	ndet	gal_contam	mp_flg	dist	angle	j_h	h_k	j_k
deg	deg			arcsec	arcsec	deg		mag	mag	mag		mag	mag	mag		mag	mag	mag
float64	float64	str12	str13	float64	float64	int64	str16	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	str3	str3	str3	str3	str6	int64	int64	float64	float64	float64	float64	float64
274.423821	-13.86974	18h17m41.72s	-13d52m11.06s	0.06	0.06	90	18174171-1352110	14.802	0.058	0.059	26.7	12.635	0.059	0.06	50.1	11.768	0.045	0.046	65.2	AAA	222	111	0ss	666666	0	0	993.752042	256.878	2.167	0.867	3.034
274.433695	-13.899049	18h17m44.09s	-13d53m56.58s	0.06	0.06	90	18174408-1353565	13.011	0.021	0.024	139.0	10.917	0.02	0.021	243.8	10.013	0.017	0.019	328.3	AAA	222	111	000	666666	0	0	990.166399	250.466	2.094	0.904	2.998
274.431606	-13.781877	18h17m43.59s	-13d46m54.76s	0.06	0.06	45	18174358-1346547	13.87	0.032	0.034	63.0	13.406	0.06	0.061	24.6	13.365	0.087	0.088	15.0	AAA	222	111	ccc	666666	0	0	945.318343	275.508	0.464	0.041	0.505
274.433361	-13.892246	18h17m44.01s	-13d53m32.09s	0.06	0.06	45	18174400-1353320	15.151	0.059	0.06	19.4	13.37	0.064	0.065	25.5	12.599	0.048	0.049	30.3	AAA	222	111	000	566666	0	0	983.384329	251.834	1.781	0.771	2.552
274.427483	-13.768612	18h17m42.60s	-13d46m07.00s	0.06	0.06	90	18174259-1346070	14.423	0.041	0.043	37.9	13.926	0.064	0.065	15.3	13.744	0.052	0.053	10.6	AAA	222	222	ccc	666666	0	0	965.406417	278.247	0.497	0.182	0.679
274.43155	-13.883111	18h17m43.57s	-13d52m59.20s	0.06	0.06	90	18174357-1352591	15.702	0.084	0.085	11.7	13.725	0.091	0.091	18.4	12.908	0.069	0.07	22.8	AAA	222	222	ccc	266666	0	0	979.746164	253.778	1.977	0.817	2.794
274.432195	-13.723433	18h17m43.73s	-13d43m24.36s	0.06	0.06	90	18174372-1343243	13.843	0.019	0.023	64.6	11.363	0.02	0.021	161.7	10.243	0.019	0.021	265.6	AAA	222	111	000	666666	0	0	986.230333	287.779	2.48	1.12	3.6
274.429849	-13.838672	18h17m43.16s	-13d50m19.22s	0.06	0.06	45	18174316-1350192	15.726	0.069	0.07	11.4	13.911	0.053	0.053	15.5	13.025	0.069	0.07	20.5	AAA	222	111	c00	166666	0	0	953.670451	263.151	1.815	0.886	2.701
274.427993	-13.829856	18h17m42.72s	-13d49m47.48s	0.06	0.06	90	18174271-1349474	13.307	0.029	0.032	105.8	12.919	0.063	0.063	38.6	12.795	0.065	0.065	25.3	AEA	222	212	c0c	665566	0	0	956.908469	265.084	0.388	0.124	0.512
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
274.788401	-13.953847	18h19m09.22s	-13d57m13.85s	0.06	0.06	90	18190921-1357138	14.991	0.041	0.043	18.9	12.716	0.036	0.037	32.9	11.688	0.024	0.026	65.5	AAA	222	111	ccc	666655	0	0	610.307615	149.875	2.275	1.028	3.303
274.797491	-14.066624	18h19m11.40s	-14d03m59.85s	0.06	0.06	90	18191139-1403598	13.965	0.036	0.038	48.6	13.019	0.053	0.053	24.9	12.61	0.042	0.043	28.0	AAA	222	111	000	555566	0	0	993.135358	160.109	0.946	0.409	1.355
274.804877	-13.91268	18h19m13.17s	-13d54m45.65s	0.06	0.06	45	18191317-1354456	14.356	0.035	0.037	33.9	13.504	0.055	0.056	15.9	13.273	0.049	0.05	15.2	AAA	222	111	000	666656	0	0	525.943465	136.214	0.852	0.231	1.083
274.900945	-13.903238	18h19m36.23s	-13d54m11.66s	0.06	0.06	90	18193622-1354116	12.454	0.028	0.03	182.3	11.81	0.03	0.031	75.8	11.36	0.024	0.026	79.9	AAA	222	111	000	556655	0	0	780.501247	116.308	0.644	0.45	1.094
274.836101	-14.037536	18h19m20.66s	-14d02m15.13s	0.06	0.06	45	18192066-1402151	15.217	0.061	0.062	14.3	13.358	0.062	0.062	18.2	12.489	0.051	0.052	28.2	AAA	222	111	000	266666	0	0	954.544823	150.31	1.859	0.869	2.728
274.862392	-13.84573	18h19m26.97s	-13d50m44.63s	0.06	0.06	90	18192697-1350446	14.369	0.041	0.043	31.2	12.46	0.03	0.031	41.7	11.561	0.026	0.027	66.4	AAA	222	111	000	666633	0	0	581.867714	103.799	1.909	0.899	2.808
274.858201	-13.918294	18h19m25.97s	-13d55m05.86s	0.06	0.06	45	18192596-1355058	14.156	0.052	0.054	38.0	12.13	0.051	0.052	56.5	11.215	0.037	0.038	91.3	AAA	222	111	ccc	666666	0	0	680.283673	126.015	2.026	0.915	2.941
274.611341	-14.056347	18h18m26.72s	-14d03m22.85s	0.06	0.06	90	18182672-1403228	15.066	0.07	0.071	18.9	13.367	0.091	0.091	21.5	12.634	0.065	0.065	29.4	AAA	222	111	ccc	666666	0	0	949.652829	199.19	1.699	0.733	2.432
274.880758	-13.99956	18h19m31.38s	-13d59m58.42s	0.06	0.06	90	18193138-1359584	14.163	0.035	0.037	37.8	11.179	0.02	0.021	135.6	9.765	0.017	0.019	347.1	AAA	222	111	000	556666	0	0	935.512452	137.762	2.984	1.414	4.398
274.652526	-14.055106	18h18m36.61s	-14d03m18.38s	0.06	0.06	90	18183660-1403183	15.035	0.052	0.054	19.4	13.099	0.04	0.041	27.5	12.254	0.041	0.041	41.7	AAA	222	111	c00	566666	0	0	908.109808	190.682	1.936	0.845	2.781

#2 (continued)
import matplotlib.pyplot as plt
plt.scatter(t4['j_m'] - t4['h_m'],
            t4['h_m'] - t4['k_m'],
            s=5, color='black')
plt.scatter(subset['j_m'] - subset['h_m'],
            subset['h_m'] - subset['k_m'],
            s=30, color='red', alpha=0.5)

<matplotlib.collections.PathCollection at 0x7f696f80ac18>

png

#3
simple = t4['ra', 'dec', 'j_m', 'h_m', 'k_m']
simple

Table masked=True length=929

ra	dec	j_m	h_m	k_m
deg	deg	mag	mag	mag
float64	float64	float64	float64	float64
274.429506	-13.870547	16.305	14.048	13.257
274.423821	-13.86974	14.802	12.635	11.768
274.424587	-13.739629	16.328	14.345	13.405
274.433933	-13.769502	16.281	14.057	12.956
274.437013	-13.885698	15.171	14.412	13.742
274.433996	-13.752446	16.54	14.519	13.604
274.418138	-13.77215	17.98	14.61	13.456
274.433695	-13.899049	13.011	10.917	10.013
274.425482	-13.77149	16.086	13.709	12.503
...	...	...	...	...
274.81801	-14.001245	16.24	15.531	15.252
274.822709	-14.037254	15.999	14.009	13.077
274.880758	-13.99956	14.163	11.179	9.765
274.652526	-14.055106	15.035	13.099	12.254
274.760586	-13.999927	16.329	14.488	13.617
274.831132	-14.020027	16.203	13.238	12.016
274.972435	-13.760374	17.472	16.755	14.413
274.870009	-13.817775	16.933	14.514	12.957
274.735323	-13.941575	16.643	14.88	14.291
274.866294	-13.841778	15.615	13.911	12.765

#3 (continued)
simple.write('2mass_subset.csv', format='ascii.csv', overwrite=True, comment='#')