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

Get the data


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

All CCDs


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 [ ]: