First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:
In [1]:
# To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
import pandas as pd
# To plot pretty figures
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12
%load_ext autoreload
%autoreload 2
In [3]:
from obiwan.priors import fits2pandas
In [12]:
fn= "~/mydata/ccds/legacysurvey-0070p207-ccds.csv"
band= 'z'
df= pd.read_csv(fn)
In [15]:
df.columns
Out[15]:
Index([u'image_filename', u'image_hdu', u'camera', u'expnum', u'ccdname',
u'object', u'propid', u'filter', u'exptime', u'mjd_obs', u'fwhm',
u'width', u'height', u'ra_bore', u'dec_bore', u'crpix1', u'crpix2',
u'crval1', u'crval2', u'cd1_1', u'cd1_2', u'cd2_1', u'cd2_2', u'ra',
u'dec', u'skyrms', u'ccdzpt', u'zpt', u'ccdraoff', u'ccddecoff',
u'ccdnmatch', u'depth_cut_ok', u'has_zeropoint', u'ccd_cuts', u'ccd_x0',
u'ccd_y0', u'ccd_x1', u'ccd_y1', u'brick_x0', u'brick_x1', u'brick_y0',
u'brick_y1', u'sig1', u'psfnorm', u'galnorm', u'plver', u'skyver',
u'wcsver', u'psfver', u'skyplver', u'wcsplver', u'psfplver'],
dtype='object')
In [16]:
numer_cols=['fwhm','ra','dec','skyrms','ccdzpt','zpt',
'ccdraoff','ccddecoff','ccdnmatch','psfnorm',
'galnorm']
Out[16]:
0 1.131320
1 0.189771
2 1.087559
3 0.179445
4 1.083312
5 0.188582
6 0.192367
7 1.097675
Name: skyrms, dtype: float64
In [28]:
print( df[df['filter'] == band][numer_cols].describe() )
_= df[df['filter'] == band][numer_cols].hist(figsize=(10,10))
fwhm ra dec skyrms ccdzpt zpt \
count 4.000000 4.000000 4.000000 4.000000 4.000000 4.000000
mean 6.033972 7.242293 20.804507 1.099967 24.873505 24.866391
std 0.044937 0.159173 0.157167 0.021753 0.005286 0.011330
min 5.977843 7.034264 20.599379 1.083312 24.869139 24.851252
25% 6.011570 7.159441 20.722348 1.086497 24.870740 24.862169
50% 6.037159 7.283571 20.845391 1.092617 24.871859 24.868032
75% 6.059560 7.366423 20.927550 1.106086 24.874624 24.872254
max 6.083727 7.367765 20.927869 1.131320 24.881163 24.878250
ccdraoff ccddecoff ccdnmatch psfnorm galnorm
count 4.000000 4.000000 4.000000 4.000000 4.000000
mean 0.073364 -0.143668 82.750000 0.092905 0.079992
std 0.006703 0.013458 6.800735 0.000574 0.000329
min 0.065004 -0.152772 76.000000 0.092182 0.079580
25% 0.069910 -0.151327 79.000000 0.092654 0.079849
50% 0.073893 -0.149064 81.500000 0.092938 0.080009
75% 0.077347 -0.141406 85.250000 0.093189 0.080152
max 0.080667 -0.123771 92.000000 0.093561 0.080372
In [38]:
# dev ra dec brick
dev_radec_brick={}
for band in ['g','r','z']:
dr= '/home/kaylan/mydata/ccds/'
dev_radec_brick[band]= np.loadtxt(dr+'ccds_stats_%s.txt' % band,
dtype=str)
In [44]:
df= pd.read_csv( get_ccds_fn('0067p125') )
df
Out[44]:
image_filename
image_hdu
camera
expnum
ccdname
object
propid
filter
exptime
mjd_obs
...
sig1
psfnorm
galnorm
plver
skyver
wcsver
psfver
skyplver
wcsplver
psfplver
0
decam/DECam_CP/CP20160720/c4d_160722_082157_ok...
1
decam
553838
S29
DECaLS_33981_z
2016A-0190
z
173.0
57591.34601
...
0.032135
0.100445
0.086331
V3.9.2
legacypipe
V3.9.2
V3.9.2
V3.9.2
1 rows × 52 columns
In [76]:
for band in ['g','r','z']:
with open('ccd_list_%s.txt' % band,'w') as foo:
for cnt,brick in enumerate( dev_radec_brick[band][:,3] ):
df= pd.read_csv( get_ccds_fn(brick) )
print(brick)
foo.write('%s\n' % brick)
for filt,ccdname,imgfn in zip(df['filter'],df['ccdname'],df['image_filename']):
foo.write('%s %s %s\n' % (filt,ccdname,imgfn))
print(filt,ccdname,imgfn)
0067p125
z S29 decam/DECam_CP/CP20160720/c4d_160722_082157_oki_z_v1.fits.fz
0102p092
z N31 decam/DECam_CP/CP20151028/c4d_151029_030656_oki_z_v1.fits.fz
z N27 decam/DECam_CP/CP20160720/c4d_160722_082956_oki_z_v1.fits.fz
z N28 decam/DECam_CP/CP20151028/c4d_151029_030656_oki_z_v1.fits.fz
g N28 decam/DECam_CP/CP20160801/c4d_160813_080905_oki_g_v1.fits.fz
r N28 decam/DECam_CP/CP20160801/c4d_160813_080644_oki_r_v1.fits.fz
z N28 decam/DECam_CP/CP20160720/c4d_160722_082956_oki_z_v1.fits.fz
0657m042
z S26 decam/NonDECaLS/CP20130829/c4d_130830_083638_ooi_z_v1.fits.fz
z S21 decam/NonDECaLS/CP20130829/c4d_130830_083638_ooi_z_v1.fits.fz
z S29 decam/NonDECaLS/CP20130829/c4d_130830_083638_ooi_z_v1.fits.fz
z S25 decam/NonDECaLS/CP20130829/c4d_130830_083638_ooi_z_v1.fits.fz
0060m120
z S16 decam/DECam_CP/CP20161113/c4d_161115_055748_oki_z_v1.fits.fz
z S17 decam/DECam_CP/CP20161113/c4d_161115_055748_oki_z_v1.fits.fz
z S26 decam/DECam_CP/CP20161113/c4d_161115_055748_oki_z_v1.fits.fz
z S22 decam/DECam_CP/CP20161113/c4d_161115_055748_oki_z_v1.fits.fz
z S27 decam/DECam_CP/CP20161113/c4d_161115_055748_oki_z_v1.fits.fz
0070p207
z N15 decam/DECam_CP/CP20140810_z_v2/c4d_140811_093852_ooi_z_v2.fits.fz
g N25 decam/DECam_CP/CP20160801/c4d_160812_081732_oki_g_v1.fits.fz
z N25 decam/DECam_CP/CP20140810_z_v2/c4d_140811_093852_ooi_z_v2.fits.fz
g N20 decam/DECam_CP/CP20160801/c4d_160812_081732_oki_g_v1.fits.fz
z N20 decam/DECam_CP/CP20140810_z_v2/c4d_140811_093852_ooi_z_v2.fits.fz
g N14 decam/DECam_CP/CP20160801/c4d_160812_081732_oki_g_v1.fits.fz
g N15 decam/DECam_CP/CP20160801/c4d_160812_081732_oki_g_v1.fits.fz
z N14 decam/DECam_CP/CP20140810_z_v2/c4d_140811_093852_ooi_z_v2.fits.fz
0698p012
r S2 decam/NonDECaLS/CP201211/c4d_121120_064342_ooi_r_a1.fits.fz
r S8 decam/NonDECaLS/CP201211/c4d_121120_044006_ooi_r_a1.fits.fz
r S14 decam/NonDECaLS/CP201211/c4d_121120_044006_ooi_r_a1.fits.fz
r S15 decam/NonDECaLS/CP201211/c4d_121120_044006_ooi_r_a1.fits.fz
r S14 decam/NonDECaLS/CP201211/c4d_121120_064342_ooi_r_a1.fits.fz
r S15 decam/NonDECaLS/CP201211/c4d_121120_064342_ooi_r_a1.fits.fz
0153m002
g N4 decam/NonDECaLS/CP20131004/c4d_131005_020555_ooi_g_v1.fits.fz
r N4 decam/NonDECaLS/CP20131004/c4d_131005_014646_ooi_r_v1.fits.fz
g N4 decam/NonDECaLS/CP20131004/c4d_131005_020945_ooi_g_v1.fits.fz
g N16 decam/NonDECaLS/CP20131004/c4d_131005_020555_ooi_g_v1.fits.fz
g S12 decam/NonDECaLS/CPHETDEX/c4d_130912_082313_ooi_g_v1.fits.fz
z N15 decam/NonDECaLS/CP20141115/c4d_141121_023755_ooi_z_a1.fits.fz
r N11 decam/NonDECaLS/CP20131005/c4d_131006_080412_ooi_r_v1.fits.fz
r N11 decam/NonDECaLS/CP20131005/c4d_131006_013705_ooi_r_v1.fits.fz
z N31 decam/NonDECaLS/CP20141115/c4d_141117_015057_ooi_z_a1.fits.fz
g N31 decam/NonDECaLS/CPHETDEX/c4d_131011_051322_ooi_g_v1.fits.fz
r N31 decam/NonDECaLS/CP20141015/c4d_141002_070902_ooi_r_a1.fits.fz
g N10 decam/NonDECaLS/CP20131004/c4d_131005_020555_ooi_g_v1.fits.fz
r N4 decam/NonDECaLS/CP20131005/c4d_131006_080412_ooi_r_v1.fits.fz
r N2 decam/NonDECaLS/CP20131115/c4d_131105_041307_ooi_r_a1.fits.fz
z N4 decam/NonDECaLS/CP20131004/c4d_131005_012449_ooi_z_v1.fits.fz
g N28 decam/NonDECaLS/CPHETDEX/c4d_131011_051322_ooi_g_v1.fits.fz
g N4 decam/NonDECaLS/CP20131005/c4d_131006_014441_ooi_g_v1.fits.fz
r N28 decam/NonDECaLS/CP20141015/c4d_141002_070902_ooi_r_a1.fits.fz
z N4 decam/NonDECaLS/CP20131004/c4d_131005_013341_ooi_z_v1.fits.fz
g N5 decam/NonDECaLS/CP20131004/c4d_131005_020945_ooi_g_v1.fits.fz
z N5 decam/NonDECaLS/CP20131004/c4d_131005_021424_ooi_z_v1.fits.fz
z N29 decam/NonDECaLS/CPHETDEX/c4d_131122_035451_ooi_z_v2.fits.fz
g N5 decam/NonDECaLS/CP20131005/c4d_131006_014441_ooi_g_v1.fits.fz
z N5 decam/NonDECaLS/CP20131004/c4d_131005_013341_ooi_z_v1.fits.fz
g N5 decam/NonDECaLS/CP20131004/c4d_131005_020555_ooi_g_v1.fits.fz
g N18 decam/NonDECaLS/CP20131004/c4d_131005_020945_ooi_g_v1.fits.fz
g N12 decam/NonDECaLS/CP20131005/c4d_131006_014053_ooi_g_v1.fits.fz
r N12 decam/NonDECaLS/CP20131005/c4d_131006_080412_ooi_r_v1.fits.fz
r N12 decam/NonDECaLS/CP20131005/c4d_131006_013705_ooi_r_v1.fits.fz
g N12 decam/NonDECaLS/CP20131004/c4d_131005_020945_ooi_g_v1.fits.fz
g N17 decam/NonDECaLS/CP20131004/c4d_131005_020555_ooi_g_v1.fits.fz
r N17 decam/NonDECaLS/CP20131005/c4d_131006_013316_ooi_r_v1.fits.fz
g N17 decam/NonDECaLS/CP20131004/c4d_131005_020945_ooi_g_v1.fits.fz
g N16 decam/NonDECaLS/CPHETDEX/c4d_131003_024115_ooi_g_v1.fits.fz
g N11 decam/NonDECaLS/CP20131005/c4d_131006_014053_ooi_g_v1.fits.fz
r N9 decam/NonDECaLS/CP20131115/c4d_131105_041307_ooi_r_a1.fits.fz
z N11 decam/NonDECaLS/CP20131004/c4d_131005_012449_ooi_z_v1.fits.fz
g N11 decam/NonDECaLS/CP20131005/c4d_131006_014441_ooi_g_v1.fits.fz
z N11 decam/NonDECaLS/CP20131004/c4d_131005_013341_ooi_z_v1.fits.fz
g N11 decam/NonDECaLS/CP20131004/c4d_131005_020555_ooi_g_v1.fits.fz
r N11 decam/NonDECaLS/CP20131004/c4d_131005_014646_ooi_r_v1.fits.fz
g N11 decam/NonDECaLS/CP20131004/c4d_131005_020945_ooi_g_v1.fits.fz
z N11 decam/NonDECaLS/CP20131004/c4d_131005_021424_ooi_z_v1.fits.fz
z N10 decam/NonDECaLS/CP20141115/c4d_141121_023755_ooi_z_a1.fits.fz
g N5 decam/NonDECaLS/CP20131005/c4d_131006_014053_ooi_g_v1.fits.fz
r N5 decam/NonDECaLS/CP20131005/c4d_131006_080412_ooi_r_v1.fits.fz
z S8 decam/NonDECaLS/CP20141115/c4d_141118_030028_ooi_z_a1.fits.fz
r N5 decam/NonDECaLS/CP20131005/c4d_131006_013705_ooi_r_v1.fits.fz
r N3 decam/NonDECaLS/CP20131115/c4d_131105_041307_ooi_r_a1.fits.fz
z N5 decam/NonDECaLS/CP20131004/c4d_131005_012449_ooi_z_v1.fits.fz
In [57]:
def get_ccds_fn(brick):
return "/home/kaylan/mydata/ccds/legacysurvey-%s-ccds.csv" % brick
#start=True
df_list=[]
for band in ['g','r','z']:
for cnt,brick in enumerate( dev_radec_brick[band][:,3] ):
df= pd.read_csv( get_ccds_fn(brick) )
df_list.append( df )
#if start:
# df_all= df.copy()
# start=False
#else:
# df_all= pd.concat([df_all,df])
print('band=%s, brick=%s, mmag=%s' % \
(band,brick,dev_radec_brick[band][cnt,0]) )
#display( df[df['filter'] == band][numer_cols].describe() )
#_= df[df['filter'] == band][numer_cols].hist(figsize=(10,10))
display( df[numer_cols].describe() )
_= df[numer_cols].hist(figsize=(12,12))
plt.show()
band=g, brick=0067p125, mmag=2.88
fwhm
ra
dec
skyrms
ccdzpt
zpt
ccdraoff
ccddecoff
ccdnmatch
psfnorm
galnorm
count
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.0
1.000000
1.000000
mean
5.525949
6.898787
12.321657
0.996307
24.728947
24.755878
-0.034337
-0.063092
62.0
0.100445
0.086331
std
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
min
5.525949
6.898787
12.321657
0.996307
24.728947
24.755878
-0.034337
-0.063092
62.0
0.100445
0.086331
25%
5.525949
6.898787
12.321657
0.996307
24.728947
24.755878
-0.034337
-0.063092
62.0
0.100445
0.086331
50%
5.525949
6.898787
12.321657
0.996307
24.728947
24.755878
-0.034337
-0.063092
62.0
0.100445
0.086331
75%
5.525949
6.898787
12.321657
0.996307
24.728947
24.755878
-0.034337
-0.063092
62.0
0.100445
0.086331
max
5.525949
6.898787
12.321657
0.996307
24.728947
24.755878
-0.034337
-0.063092
62.0
0.100445
0.086331
band=g, brick=0102p092, mmag=0.61
fwhm
ra
dec
skyrms
ccdzpt
zpt
ccdraoff
ccddecoff
ccdnmatch
psfnorm
galnorm
count
6.000000
6.000000
6.000000
6.000000
6.000000
6.000000
6.000000
6.000000
6.000000
6.000000
6.000000
mean
4.755103
10.175370
9.331566
0.615032
24.960436
24.977244
0.047773
-0.056134
60.666667
0.125756
0.096589
std
1.214302
0.148063
0.077793
0.214913
0.182225
0.184663
0.012374
0.027636
4.366539
0.038852
0.018267
min
3.141538
9.997969
9.180059
0.218873
24.839569
24.795523
0.032927
-0.097743
56.000000
0.097272
0.083556
25%
3.776372
10.140442
9.342237
0.566404
24.846509
24.861361
0.043803
-0.073766
58.000000
0.100515
0.084422
50%
5.408245
10.155966
9.343193
0.687063
24.868576
24.922237
0.045634
-0.048702
59.500000
0.102827
0.085802
75%
5.583384
10.156742
9.378679
0.748473
25.006124
25.041666
0.048004
-0.037286
62.500000
0.156517
0.111298
max
5.698408
10.450049
9.390526
0.802069
25.294767
25.300926
0.070458
-0.026056
68.000000
0.177452
0.120618
band=r, brick=0657m042, mmag=1.25
fwhm
ra
dec
skyrms
ccdzpt
zpt
ccdraoff
ccddecoff
ccdnmatch
psfnorm
galnorm
count
4.000000
4.000000
4.000000
4.000000
4.000000
4.000000
4.000000
4.000000
4.000000
4.000000
4.000000
mean
5.024375
65.733609
-4.282045
1.932509
24.951709
24.940013
0.135218
-0.076404
103.500000
0.112167
0.091146
std
0.065098
0.127140
0.133879
0.083164
0.002778
0.040651
0.026820
0.023083
6.608076
0.001867
0.000874
min
4.964282
65.577934
-4.446069
1.841879
24.947813
24.904900
0.101780
-0.098276
95.000000
0.109979
0.090108
25%
4.990993
65.694459
-4.323135
1.872015
24.951102
24.905180
0.122289
-0.093228
101.000000
0.110975
0.090597
50%
5.008463
65.733570
-4.281988
1.940357
24.952314
24.936986
0.136793
-0.079795
104.000000
0.112309
0.091228
75%
5.041845
65.772721
-4.240899
2.000850
24.952921
24.971820
0.149722
-0.062971
106.500000
0.113501
0.091777
max
5.116294
65.889362
-4.118135
2.007443
24.954395
24.981180
0.165504
-0.047751
111.000000
0.114069
0.092021
band=r, brick=0060m120, mmag=0.51
fwhm
ra
dec
skyrms
ccdzpt
zpt
ccdraoff
ccddecoff
ccdnmatch
psfnorm
galnorm
count
5.000000
5.000000
5.000000
5.000000
5.000000
5.000000
5.000000
5.000000
5.000000
5.000000
5.000000
mean
4.772748
6.005012
-11.992407
0.968531
24.730730
24.739353
0.124322
-0.156862
43.000000
0.111262
0.091554
std
0.061647
0.159033
0.164272
0.029901
0.006090
0.028409
0.035217
0.019051
4.472136
0.001375
0.000743
min
4.676562
5.845627
-12.157023
0.942349
24.725286
24.690075
0.087270
-0.175146
37.000000
0.109799
0.090679
25%
4.774456
5.846332
-12.156358
0.946581
24.725670
24.744635
0.094713
-0.170361
41.000000
0.110381
0.090998
50%
4.777296
6.005010
-11.992358
0.967394
24.729090
24.746281
0.118288
-0.158759
43.000000
0.110831
0.091484
75%
4.787035
6.163990
-11.828391
0.968729
24.733915
24.753584
0.156620
-0.153518
45.000000
0.112097
0.092293
max
4.848392
6.164102
-11.827903
1.017600
24.739691
24.762192
0.164718
-0.126524
49.000000
0.113202
0.092318
band=z, brick=0070p207, mmag=0.41
fwhm
ra
dec
skyrms
ccdzpt
zpt
ccdraoff
ccddecoff
ccdnmatch
psfnorm
galnorm
count
8.000000
8.000000
8.000000
8.000000
8.000000
8.000000
8.000000
8.000000
8.000000
8.000000
8.000000
mean
5.690160
7.244622
20.802371
0.643754
24.934384
24.924085
0.082107
-0.142085
86.875000
0.099364
0.083850
std
0.375498
0.147375
0.145525
0.487934
0.065355
0.062562
0.011063
0.011659
7.567553
0.006920
0.004135
min
5.244228
7.034264
20.595113
0.179445
24.869139
24.851252
0.065004
-0.152772
76.000000
0.092182
0.079580
25%
5.321934
7.160617
20.719141
0.189474
24.872151
24.869143
0.075067
-0.148598
82.250000
0.093001
0.080044
50%
5.738784
7.285893
20.843244
0.637840
24.934244
24.922445
0.082959
-0.146393
86.000000
0.099468
0.083853
75%
6.029986
7.368494
20.924573
1.090088
24.993070
24.982306
0.088313
-0.139784
92.500000
0.105728
0.087633
max
6.083727
7.372346
20.927869
1.131320
25.003841
24.993637
0.098521
-0.123537
98.000000
0.106282
0.088170
band=z, brick=0698p012, mmag=0.18
fwhm
ra
dec
skyrms
ccdzpt
zpt
ccdraoff
ccddecoff
ccdnmatch
psfnorm
galnorm
count
6.000000
6.000000
6.000000
6.000000
6.000000
6.000000
6.000000
6.000000
6.000000
6.000000
6.000000
mean
6.090366
69.854937
1.313769
0.230735
25.312616
25.324898
-0.127446
-0.227925
61.833333
0.119240
0.093390
std
2.208709
0.158619
0.135257
0.011568
0.009775
0.016878
0.185897
0.143708
43.595489
0.033549
0.019282
min
4.056291
69.703841
1.079533
0.212106
25.301605
25.300192
-0.322101
-0.385025
20.000000
0.088068
0.075470
25%
4.080269
69.714848
1.260164
0.225790
25.304468
25.316217
-0.289148
-0.347364
22.750000
0.088886
0.075904
50%
6.065977
69.825130
1.383598
0.232646
25.312907
25.323971
-0.122056
-0.229402
59.000000
0.118344
0.093103
75%
8.075028
69.986034
1.401967
0.236601
25.321603
25.337684
0.032317
-0.099394
99.750000
0.149654
0.111070
max
8.190875
70.057762
1.408775
0.245586
25.322123
25.345364
0.062608
-0.080980
109.000000
0.151526
0.111435
band=z, brick=0153m002, mmag=0.50
fwhm
ra
dec
skyrms
ccdzpt
zpt
ccdraoff
ccddecoff
ccdnmatch
psfnorm
galnorm
count
50.000000
50.000000
50.000000
50.000000
50.000000
50.000000
50.000000
50.000000
50.000000
50.000000
50.000000
mean
3.855454
15.379833
-0.218507
0.396301
25.122296
25.130537
0.088240
-0.082820
45.940000
0.145360
0.108117
std
0.660329
0.166023
0.110005
0.273044
0.143031
0.142253
0.052060
0.033187
12.966849
0.019287
0.009294
min
3.164245
15.095386
-0.410240
0.199663
24.840649
24.889643
0.008390
-0.169125
20.000000
0.090568
0.078002
25%
3.407987
15.254663
-0.311888
0.214939
24.989496
25.000168
0.057257
-0.100412
36.500000
0.131249
0.101261
50%
3.632657
15.371645
-0.223570
0.317779
25.123401
25.124396
0.069731
-0.081086
46.500000
0.154795
0.112586
75%
4.331309
15.499550
-0.123290
0.442865
25.248637
25.255116
0.107678
-0.058980
54.750000
0.158991
0.114885
max
6.290635
15.651850
-0.058631
1.340007
25.364811
25.400738
0.248594
-0.021462
72.000000
0.177985
0.121680
In [68]:
df_all = pd.concat(df_list)
df_all.describe()
Out[68]:
image_hdu
expnum
exptime
mjd_obs
fwhm
width
height
ra_bore
dec_bore
crpix1
...
ccd_y0
ccd_x1
ccd_y1
brick_x0
brick_x1
brick_y0
brick_y1
sig1
psfnorm
galnorm
count
80.000000
80.000000
80.000000
80.000000
80.000000
80.0
80.0
80.000000
80.000000
80.000000
...
80.000000
80.00000
80.000000
80.000000
80.000000
80.000000
80.000000
80.000000
80.000000
80.000000
mean
37.287500
313940.600000
219.312500
56797.680512
4.410675
2046.0
4094.0
20.114611
2.099301
-1259.580024
...
988.812500
1798.30000
2907.000000
742.250000
2670.837500
1467.362500
2711.587500
0.012913
0.132979
0.101565
std
16.249007
134640.853925
126.437215
436.492471
1.145720
0.0
0.0
18.777935
7.901205
6202.453015
...
1265.299715
517.64717
1425.154409
1085.580665
1245.360376
1283.776897
1099.209033
0.012139
0.026399
0.013434
min
1.000000
153287.000000
30.000000
56251.194514
3.141538
2046.0
4094.0
6.004458
-12.568583
-11376.200195
...
0.000000
133.00000
56.000000
-11.000000
54.000000
-5.000000
132.000000
0.003487
0.088068
0.075470
25%
34.750000
241424.750000
130.000000
56570.084120
3.585592
2046.0
4094.0
13.593937
-0.064472
-4613.000000
...
1.000000
1948.50000
1645.750000
-1.250000
1654.250000
0.000000
1957.750000
0.004137
0.109934
0.090740
50%
42.000000
241622.000000
200.000000
56571.070069
4.083773
2046.0
4094.0
15.185375
0.015403
-2358.600098
...
100.000000
2046.00000
3604.000000
0.000000
3600.000000
1136.000000
3193.500000
0.006085
0.133321
0.102187
75%
47.250000
368154.250000
250.000000
56943.741103
5.004180
2046.0
4094.0
15.278333
0.972472
-104.200104
...
1870.500000
2046.00000
4094.000000
1366.250000
3602.000000
2607.500000
3601.000000
0.021844
0.158108
0.114123
max
61.000000
592870.000000
500.000000
57707.244982
8.190875
2046.0
4094.0
70.524542
21.339667
13422.200195
...
4073.000000
2046.00000
4094.000000
3581.000000
3608.000000
3400.000000
3610.000000
0.043115
0.177985
0.121680
8 rows × 39 columns
In [77]:
_ =df_all[numer_cols].hist(figsize=(12,12))
In [ ]:
Content source: legacysurvey/obiwan
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