In [0]:
columns = ['r_petro', 'MUMAX', 'FWHM_n', 'A', 'zb', 'PROB_GAL']
for i in range(len(columns)):
pd.DataFrame.hist(data=data, column=columns[i], bins=200, figsize=(7, 5))
from google.colab import drive
drive.mount('/gdrive')
%cd /gdrive
%ls
Drive already mounted at /gdrive; to attempt to forcibly remount, call drive.mount("/gdrive", force_remount=True).
/gdrive
'My Drive'/
import pandas as pd
path = '/gdrive/My Drive/ml_datasets/splus_march2019.cat.gz'
data = pd.read_csv(path, comment='#', delim_whitespace=True, compression='gzip', error_bad_lines=False)
data.describe()
| RA | Dec | X | Y | ISOarea | s2nDet | PhotoFlag | FWHM | FWHM_n | MUMAX | A | B | THETA | FlRadDet | KrRadDet | nDet_auto | nDet_petro | nDet_aper | uJAVA_auto | euJAVA_auto | s2n_uJAVA_auto | uJAVA_petro | euJAVA_petro | s2n_uJAVA_petro | uJAVA_aper | euJAVA_aper | s2n_uJAVA_aper | F378_auto | eF378_auto | s2n_F378_auto | F378_petro | eF378_petro | s2n_F378_petro | F378_aper | eF378_aper | s2n_F378_aper | F395_auto | eF395_auto | s2n_F395_auto | F395_petro | ... | eF660_aper | s2n_F660_aper | i_auto | ei_auto | s2n_i_auto | i_petro | ei_petro | s2n_i_petro | i_aper | ei_aper | s2n_i_aper | F861_auto | eF861_auto | s2n_F861_auto | F861_petro | eF861_petro | s2n_F861_petro | F861_aper | eF861_aper | s2n_F861_aper | z_auto | ez_auto | s2n_z_auto | z_petro | ez_petro | s2n_z_petro | z_aper | ez_aper | s2n_z_aper | zb | zb_Min | zb_Max | Tb | Odds | Chi2 | M_B | Stell_Mass | CLASS | PROB_GAL | PROB_STAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | ... | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 | 3.009731e+06 |
| mean | 2.393182e+02 | 3.729784e-02 | 5.867343e+03 | 5.650478e+03 | 9.931179e+01 | 1.585716e+02 | 7.985667e-01 | 4.094252e+00 | 1.604659e+00 | 1.599394e+01 | 2.341972e+00 | 1.901277e+00 | 2.930669e+00 | 1.524685e+00 | 1.358452e+00 | 1.114667e+01 | 1.101865e+01 | 1.115249e+01 | 2.915357e+01 | 3.859291e+00 | 1.759135e+01 | 3.011932e+01 | 4.174352e+00 | 1.711838e+01 | 2.883871e+01 | 3.781725e+00 | 1.475079e+01 | 3.090859e+01 | 4.026550e+00 | 1.489460e+01 | 3.192898e+01 | 4.341888e+00 | 1.457068e+01 | 3.067491e+01 | 3.974719e+00 | 1.240050e+01 | 3.262448e+01 | 4.494831e+00 | 1.188718e+01 | 3.395459e+01 | ... | 1.318904e-01 | 8.472082e+01 | 1.822576e+01 | 7.833209e-02 | 1.141004e+02 | 1.782993e+01 | 9.192015e-02 | 1.068828e+02 | 1.814205e+01 | 8.101687e-02 | 1.030809e+02 | 1.750218e+01 | 1.662320e-01 | 6.826212e+01 | 1.714567e+01 | 2.055368e-01 | 6.183996e+01 | 1.740764e+01 | 1.697288e-01 | 6.097349e+01 | 1.803180e+01 | 1.039814e-01 | 9.656842e+01 | 1.764750e+01 | 1.293420e-01 | 8.419688e+01 | 1.796767e+01 | 1.136069e-01 | 8.551939e+01 | 2.767456e-01 | 2.108666e-01 | 3.517618e-01 | 7.841791e+00 | 4.321364e-01 | 4.117723e+00 | -2.008038e+01 | 1.033695e+01 | -3.188077e+02 | 3.494156e-01 | 6.473472e-01 |
| std | 1.293513e+02 | 8.079389e-01 | 2.685852e+03 | 2.673294e+03 | 5.549305e+02 | 3.452500e+02 | 1.358508e+00 | 6.615653e+00 | 2.697250e+00 | 1.836872e+00 | 2.987684e+00 | 1.241987e+00 | 5.526994e+01 | 3.653140e+00 | 3.758927e-01 | 1.258756e+00 | 1.332265e+00 | 1.259976e+00 | 3.574369e+01 | 7.661409e+00 | 1.190509e+02 | 3.718823e+01 | 7.756358e+00 | 1.609185e+02 | 3.557811e+01 | 7.527450e+00 | 5.129370e+01 | 3.372252e+01 | 7.654317e+00 | 1.032921e+02 | 3.521588e+01 | 7.697657e+00 | 1.426252e+02 | 3.362306e+01 | 7.539354e+00 | 4.319714e+01 | 3.492037e+01 | 7.872050e+00 | 8.793618e+01 | 3.650994e+01 | ... | 1.200194e+00 | 1.830807e+02 | 9.322159e+00 | 8.179810e-01 | 3.635718e+02 | 9.326214e+00 | 8.294465e-01 | 4.885335e+02 | 9.310449e+00 | 8.117270e-01 | 2.173793e+02 | 1.358604e+01 | 1.228227e+00 | 2.446330e+02 | 1.370486e+01 | 1.294263e+00 | 3.263182e+02 | 1.357769e+01 | 1.217056e+00 | 1.401647e+02 | 9.386173e+00 | 8.724107e-01 | 3.386298e+02 | 9.421142e+00 | 8.961376e-01 | 4.494411e+02 | 9.432905e+00 | 9.087035e-01 | 1.983444e+02 | 1.968703e-01 | 1.718025e-01 | 2.221509e-01 | 3.767831e+00 | 2.872937e-01 | 9.248943e+00 | 2.129668e+00 | 1.021832e+00 | 5.680617e+03 | 4.383151e-01 | 4.394139e-01 |
| min | 0.000000e+00 | -1.413900e+00 | 8.543660e+02 | 8.271870e+02 | 0.000000e+00 | -1.000000e+00 | 0.000000e+00 | -2.160000e+01 | -8.930000e+00 | 9.140000e+00 | 5.000000e-01 | 4.000000e-02 | -9.000000e+01 | -1.897412e+03 | 0.000000e+00 | 3.000000e+00 | 0.000000e+00 | 1.000000e+00 | -9.900000e+01 | 0.000000e+00 | -1.000000e+00 | -9.900000e+01 | 0.000000e+00 | -1.000000e+00 | -9.900000e+01 | 0.000000e+00 | -1.000000e+00 | -9.900000e+01 | 0.000000e+00 | -1.000000e+00 | -9.900000e+01 | 0.000000e+00 | -1.000000e+00 | -9.900000e+01 | 0.000000e+00 | -1.000000e+00 | -9.900000e+01 | 0.000000e+00 | -1.000000e+00 | -9.900000e+01 | ... | 0.000000e+00 | -1.000000e+00 | -9.900000e+01 | 0.000000e+00 | -1.000000e+00 | -9.900000e+01 | 0.000000e+00 | -1.000000e+00 | -9.900000e+01 | 0.000000e+00 | -1.000000e+00 | -9.900000e+01 | 0.000000e+00 | -1.000000e+00 | -9.900000e+01 | 0.000000e+00 | -1.000000e+00 | -9.900000e+01 | 0.000000e+00 | -1.000000e+00 | -9.900000e+01 | 0.000000e+00 | -1.000000e+00 | -9.900000e+01 | 0.000000e+00 | -1.000000e+00 | -9.900000e+01 | 0.000000e+00 | -1.000000e+00 | -1.676000e-01 | -4.590000e-01 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | -3.767330e+01 | 0.000000e+00 | -9.999900e+04 | 0.000000e+00 | 0.000000e+00 |
| 25% | 5.491730e+01 | -6.595000e-01 | 3.552267e+03 | 3.336789e+03 | 1.800000e+01 | 2.486000e+01 | 0.000000e+00 | 2.390000e+00 | 9.900000e-01 | 1.504000e+01 | 1.630000e+00 | 1.460000e+00 | -4.820000e+01 | 1.097000e+00 | 1.060000e+00 | 1.100000e+01 | 1.000000e+01 | 1.100000e+01 | 2.067000e+01 | 1.200000e-01 | 5.000000e-01 | 2.007000e+01 | 1.500000e-01 | 4.000000e-01 | 2.065000e+01 | 1.200000e-01 | 5.000000e-01 | 2.047000e+01 | 1.500000e-01 | 5.000000e-01 | 1.988000e+01 | 2.000000e-01 | 3.000000e-01 | 2.045000e+01 | 1.600000e-01 | 5.000000e-01 | 2.027000e+01 | 2.000000e-01 | 3.000000e-01 | 1.969000e+01 | ... | 2.000000e-02 | 1.400000e+01 | 1.783000e+01 | 2.000000e-02 | 1.760000e+01 | 1.748000e+01 | 2.000000e-02 | 1.330000e+01 | 1.782000e+01 | 2.000000e-02 | 1.670000e+01 | 1.763000e+01 | 2.000000e-02 | 9.100000e+00 | 1.727000e+01 | 3.000000e-02 | 6.900000e+00 | 1.760000e+01 | 2.000000e-02 | 8.700000e+00 | 1.761000e+01 | 2.000000e-02 | 1.220000e+01 | 1.726000e+01 | 2.000000e-02 | 8.900000e+00 | 1.759000e+01 | 2.000000e-02 | 1.130000e+01 | 1.070000e-01 | 7.900000e-02 | 1.640000e-01 | 5.240000e+00 | 1.850110e-01 | 4.560000e-01 | -2.106930e+01 | 9.745800e+00 | 3.000000e+00 | 0.000000e+00 | 7.000000e-02 |
| 50% | 3.058972e+02 | 4.090000e-02 | 5.884216e+03 | 5.659141e+03 | 3.100000e+01 | 4.871000e+01 | 0.000000e+00 | 2.980000e+00 | 1.070000e+00 | 1.642000e+01 | 1.920000e+00 | 1.680000e+00 | 6.400000e+00 | 1.287000e+00 | 1.240000e+00 | 1.200000e+01 | 1.200000e+01 | 1.200000e+01 | 2.227000e+01 | 4.400000e-01 | 2.200000e+00 | 2.154000e+01 | 4.800000e-01 | 2.000000e+00 | 2.210000e+01 | 4.500000e-01 | 2.200000e+00 | 2.198000e+01 | 5.100000e-01 | 2.000000e+00 | 2.129000e+01 | 5.700000e-01 | 1.800000e+00 | 2.183000e+01 | 5.200000e-01 | 1.900000e+00 | 2.173000e+01 | 6.100000e-01 | 1.700000e+00 | 2.105000e+01 | ... | 4.000000e-02 | 2.670000e+01 | 1.917000e+01 | 3.000000e-02 | 3.530000e+01 | 1.878000e+01 | 4.000000e-02 | 2.510000e+01 | 1.911000e+01 | 3.000000e-02 | 3.310000e+01 | 1.894000e+01 | 5.000000e-02 | 1.940000e+01 | 1.855000e+01 | 8.000000e-02 | 1.360000e+01 | 1.887000e+01 | 6.000000e-02 | 1.820000e+01 | 1.889000e+01 | 4.000000e-02 | 2.600000e+01 | 1.851000e+01 | 6.000000e-02 | 1.760000e+01 | 1.884000e+01 | 4.000000e-02 | 2.400000e+01 | 2.438000e-01 | 1.730000e-01 | 3.380000e-01 | 7.600000e+00 | 3.551670e-01 | 9.745000e-01 | -2.017310e+01 | 1.033700e+01 | 6.000000e+00 | 1.000000e-02 | 9.900000e-01 |
| 75% | 3.205500e+02 | 7.371000e-01 | 8.195495e+03 | 7.967101e+03 | 6.700000e+01 | 1.332800e+02 | 2.000000e+00 | 4.090000e+00 | 1.560000e+00 | 1.734000e+01 | 2.400000e+00 | 2.040000e+00 | 5.330000e+01 | 1.589000e+00 | 1.550000e+00 | 1.200000e+01 | 1.200000e+01 | 1.200000e+01 | 2.380000e+01 | 1.530000e+00 | 7.100000e+00 | 2.311000e+01 | 1.870000e+00 | 5.700000e+00 | 2.345000e+01 | 1.520000e+00 | 6.900000e+00 | 2.354000e+01 | 1.810000e+00 | 6.100000e+00 | 2.292000e+01 | 2.350000e+00 | 4.800000e+00 | 2.322000e+01 | 1.830000e+00 | 5.900000e+00 | 2.345000e+01 | 2.600000e+00 | 4.800000e+00 | 2.302000e+01 | ... | 7.000000e-02 | 7.280000e+01 | 2.004000e+01 | 6.000000e-02 | 9.190000e+01 | 1.961000e+01 | 8.000000e-02 | 6.670000e+01 | 1.992000e+01 | 6.000000e-02 | 8.870000e+01 | 1.985000e+01 | 1.100000e-01 | 5.240000e+01 | 1.941000e+01 | 1.500000e-01 | 3.600000e+01 | 1.971000e+01 | 1.200000e-01 | 5.000000e+01 | 1.980000e+01 | 9.000000e-02 | 7.220000e+01 | 1.937000e+01 | 1.200000e-01 | 4.730000e+01 | 1.968000e+01 | 9.000000e-02 | 6.760000e+01 | 3.840000e-01 | 2.940000e-01 | 5.050000e-01 | 1.182000e+01 | 6.529170e-01 | 3.652500e+00 | -1.913130e+01 | 1.080050e+01 | 6.000000e+00 | 9.230000e-01 | 1.000000e+00 |
| max | 3.600000e+02 | 1.450300e+00 | 1.069428e+04 | 1.041314e+04 | 2.391890e+05 | 1.233978e+04 | 2.300000e+01 | 1.675520e+03 | 7.169500e+02 | 9.900000e+01 | 1.135900e+03 | 1.918400e+02 | 9.000000e+01 | 3.850887e+03 | 8.420000e+00 | 1.200000e+01 | 1.200000e+01 | 1.200000e+01 | 9.900000e+01 | 2.342000e+01 | 3.849950e+04 | 9.900000e+01 | 2.277000e+01 | 4.411000e+04 | 9.900000e+01 | 2.319000e+01 | 4.844500e+03 | 9.900000e+01 | 2.273000e+01 | 3.509900e+04 | 9.900000e+01 | 2.206000e+01 | 3.998180e+04 | 9.900000e+01 | 2.268000e+01 | 4.437800e+03 | 9.900000e+01 | 2.232000e+01 | 3.074720e+04 | 9.900000e+01 | ... | 2.323000e+01 | 7.132300e+03 | 9.900000e+01 | 2.371000e+01 | 4.952940e+04 | 9.900000e+01 | 2.300000e+01 | 7.318880e+04 | 9.900000e+01 | 2.313000e+01 | 7.169100e+03 | 9.900000e+01 | 2.244000e+01 | 3.706960e+04 | 9.900000e+01 | 2.154000e+01 | 5.264410e+04 | 9.900000e+01 | 2.197000e+01 | 6.929100e+03 | 9.900000e+01 | 2.292000e+01 | 4.157280e+04 | 9.900000e+01 | 2.233000e+01 | 9.353040e+04 | 9.900000e+01 | 2.249000e+01 | 6.881700e+03 | 1.000000e+00 | 9.870000e-01 | 1.000000e+00 | 1.600000e+01 | 9.999930e-01 | 1.353527e+02 | 0.000000e+00 | 1.794830e+01 | 6.000000e+00 | 1.000000e+00 | 1.000000e+00 |
8 rows × 137 columns
columns = ['r_petro', 'MUMAX', 'FWHM_n', 'A', 'zb', 'PROB_GAL']
for i in range(len(columns)):
pd.DataFrame.hist(data=data, column=columns[i], bins=200, figsize=(7, 5))
import matplotlib.pyplot as plt
plt.ylim(0, 26)
plt.xlim(0, 28)
plt.ylabel('MUMAX')
plt.xlabel('r_petro')
plt.title('r_petro x MUMAX')
plt.scatter(data['r_petro'], data['MUMAX'])
plt.show()
plt.ylim(-50, 500)
plt.xlim(0, 35)
plt.ylabel('FWHM_n')
plt.xlabel('r_petro')
plt.title('r_petro x FWHM_n')
plt.scatter('r_petro', 'FWHM_n', data=data)
plt.show()
