Pandas: Grabbing values from three different columns based on values in first column
Question:
I have a dataframe that looks like this:
rowid,kepid,kepoi_name,kepler_name,koi_disposition,koi_vet_stat,koi_vet_date,koi_pdisposition,koi_score,koi_fpflag_nt,koi_fpflag_ss,koi_fpflag_co,koi_fpflag_ec,koi_disp_prov,koi_comment,koi_period,koi_time0bk,koi_time0,koi_eccen,koi_longp,koi_impact,koi_duration,koi_ingress,koi_depth,koi_ror,koi_srho,koi_fittype,koi_prad,koi_sma,koi_incl,koi_teq,koi_insol,koi_dor,koi_limbdark_mod,koi_ldm_coeff4,koi_ldm_coeff3,koi_ldm_coeff2,koi_ldm_coeff1,koi_parm_prov,koi_max_sngle_ev,koi_max_mult_ev,koi_model_snr,koi_count,koi_num_transits,koi_tce_plnt_num,koi_tce_delivname,koi_quarters,koi_bin_oedp_sig,koi_trans_mod,koi_model_dof,koi_model_chisq,koi_datalink_dvr,koi_datalink_dvs,koi_steff,koi_slogg,koi_smet,koi_srad,koi_smass,koi_sage,koi_sparprov,ra,dec,koi_kepmag,koi_gmag,koi_rmag,koi_imag,koi_zmag,koi_jmag,koi_hmag,koi_kmag,koi_fwm_stat_sig,koi_fwm_sra,koi_fwm_sdec,koi_fwm_srao,koi_fwm_sdeco,koi_fwm_prao,koi_fwm_pdeco,koi_dicco_mra,koi_dicco_mdec,koi_dicco_msky,koi_dikco_mra,koi_dikco_mdec,koi_dikco_msky
1,10797460,K00752.01,Kepler-227 b,CONFIRMED,Done,2018-08-16,CANDIDATE,1.0000,0,0,0,0,q1_q17_dr25_sup_koi,NO_COMMENT,9.488035570,170.5387500,2455003.539,0,,0.1460,2.95750,,6.158E+02,0.022344,3.20796,LS+MCMC,2.26,0.0853,89.66,793.0,93.59,24.810000,Claret (2011 A&A 529 75) ATLAS LS,0.0000,0.0000,0.2291,0.4603,q1_q17_dr25_koi,5.1358490,28.4708200,35.80,2,142,1,q1_q17_dr25_tce,11111111111111111000000000000000,0.68640,Mandel and Agol (2002 ApJ 580 171),,,010/010797/010797460/dv/kplr010797460-20160209194854_dvr.pdf,010/010797/010797460/dv/kplr010797460-001-20160209194854_dvs.pdf,5455.00,4.467,0.1400,0.9270,0.9190,,q1_q17_dr25_stellar,291.934230,48.141651,15.347,15.890,15.270,15.114,15.006,14.082,13.751,13.648,0.002,19.462294000,48.14191000,0.43000,0.94000,-2E-04,-5.5E-04,-0.0100,0.2000,0.2000,0.0800,0.3100,0.3200
2,10797460,K00752.02,Kepler-227 c,CONFIRMED,Done,2018-08-16,CANDIDATE,0.9690,0,0,0,0,q1_q17_dr25_sup_koi,NO_COMMENT,54.418382700,162.5138400,2454995.514,0,,0.5860,4.50700,,8.748E+02,0.027954,3.02368,LS+MCMC,2.83,0.2734,89.57,443.0,9.11,77.900000,Claret (2011 A&A 529 75) ATLAS LS,0.0000,0.0000,0.2291,0.4603,q1_q17_dr25_koi,7.0276690,20.1095070,25.80,2,25,2,q1_q17_dr25_tce,11111111111111111000000000000000,0.00230,Mandel and Agol (2002 ApJ 580 171),,,010/010797/010797460/dv/kplr010797460-20160209194854_dvr.pdf,010/010797/010797460/dv/kplr010797460-002-20160209194854_dvs.pdf,5455.00,4.467,0.1400,0.9270,0.9190,,q1_q17_dr25_stellar,291.934230,48.141651,15.347,15.890,15.270,15.114,15.006,14.082,13.751,13.648,0.003,19.462265000,48.14199000,-0.63000,1.23000,6.6E-04,-1.05E-03,0.3900,0.0000,0.3900,0.4900,0.1200,0.5000
3,10811496,K00753.01,,CANDIDATE,Done,2018-08-16,CANDIDATE,0.0000,0,0,0,0,q1_q17_dr25_sup_koi,DEEP_V_SHAPED,19.899139950,175.8502520,2455008.850,0,,0.9690,1.78220,,1.0829E+04,0.154046,7.29555,LS+MCMC,14.60,0.1419,88.96,638.0,39.30,53.500000,Claret (2011 A&A 529 75) ATLAS LS,0.0000,0.0000,0.2711,0.3858,q1_q17_dr25_koi,37.1597670,187.4491000,76.30,1,56,1,q1_q17_dr25_tce,11111101110111011000000000000000,0.66240,Mandel and Agol (2002 ApJ 580 171),,,010/010811/010811496/dv/kplr010811496-20160209194854_dvr.pdf,010/010811/010811496/dv/kplr010811496-001-20160209194854_dvs.pdf,5853.00,4.544,-0.1800,0.8680,0.9610,,q1_q17_dr25_stellar,297.004820,48.134129,15.436,15.943,15.390,15.220,15.166,14.254,13.900,13.826,0.278,19.800320700,48.13412000,-0.02100,-0.03800,7E-04,6E-04,-0.0250,-0.0340,0.0420,0.0020,-0.0270,0.0270
4,10848459,K00754.01,,FALSE POSITIVE,Done,2018-08-16,FALSE POSITIVE,0.0000,0,1,0,0,q1_q17_dr25_sup_koi,MOD_ODDEVEN_DV---MOD_ODDEVEN_ALT---DEEP_V_SHAPED,1.736952453,170.3075650,2455003.308,0,,1.2760,2.40641,,8.0792E+03,0.387394,0.22080,LS+MCMC,33.46,0.0267,67.09,1395.0,891.96,3.278000,Claret (2011 A&A 529 75) ATLAS LS,0.0000,0.0000,0.2865,0.3556,q1_q17_dr25_koi,39.0665500,541.8951000,505.60,1,621,1,q1_q17_dr25_tce,11111110111011101000000000000000,0.00000,Mandel and Agol (2002 ApJ 580 171),,,010/010848/010848459/dv/kplr010848459-20160209194854_dvr.pdf,010/010848/010848459/dv/kplr010848459-001-20160209194854_dvs.pdf,5805.00,4.564,-0.5200,0.7910,0.8360,,q1_q17_dr25_stellar,285.534610,48.285210,15.597,16.100,15.554,15.382,15.266,14.326,13.911,13.809,0.000,19.035637620,48.28521050,-0.11100,0.00200,3.02E-03,-1.42E-03,-0.2490,0.1470,0.2890,-0.2570,0.0990,0.2760
5,10854555,K00755.01,Kepler-664 b,CONFIRMED,Done,2018-08-16,CANDIDATE,1.0000,0,0,0,0,q1_q17_dr25_sup_koi,NO_COMMENT,2.525591777,171.5955500,2455004.596,0,,0.7010,1.65450,,6.033E+02,0.024064,1.98635,LS+MCMC,2.75,0.0374,85.41,1406.0,926.16,8.750000,Claret (2011 A&A 529 75) ATLAS LS,0.0000,0.0000,0.2844,0.3661,q1_q17_dr25_koi,4.7499450,33.1919000,40.90,1,515,1,q1_q17_dr25_tce,01111111111111111000000000000000,0.30900,Mandel and Agol (2002 ApJ 580 171),,,010/010854/010854555/dv/kplr010854555-20160209194854_dvr.pdf,010/010854/010854555/dv/kplr010854555-001-20160209194854_dvs.pdf,6031.00,4.438,0.0700,1.0460,1.0950,,q1_q17_dr25_stellar,288.754880,48.226200,15.509,16.015,15.468,15.292,15.241,14.366,14.064,13.952,0.733,19.250325800,48.22626000,-0.01000,0.23000,8E-05,-7E-05,0.0300,-0.0900,0.1000,0.0700,0.0200,0.0700
For a particular kepid I would like to grab the koi_impact, koi_duration and koi_depth from the same row. These three values will eventually be used in a training set for a neural network. Is there a convenient way to do this?
Answers:
value = 10797460
df.loc[df['kepid']==value, ['koi_impact', 'koi_duration', 'koi_depth']]
I have a dataframe that looks like this:
rowid,kepid,kepoi_name,kepler_name,koi_disposition,koi_vet_stat,koi_vet_date,koi_pdisposition,koi_score,koi_fpflag_nt,koi_fpflag_ss,koi_fpflag_co,koi_fpflag_ec,koi_disp_prov,koi_comment,koi_period,koi_time0bk,koi_time0,koi_eccen,koi_longp,koi_impact,koi_duration,koi_ingress,koi_depth,koi_ror,koi_srho,koi_fittype,koi_prad,koi_sma,koi_incl,koi_teq,koi_insol,koi_dor,koi_limbdark_mod,koi_ldm_coeff4,koi_ldm_coeff3,koi_ldm_coeff2,koi_ldm_coeff1,koi_parm_prov,koi_max_sngle_ev,koi_max_mult_ev,koi_model_snr,koi_count,koi_num_transits,koi_tce_plnt_num,koi_tce_delivname,koi_quarters,koi_bin_oedp_sig,koi_trans_mod,koi_model_dof,koi_model_chisq,koi_datalink_dvr,koi_datalink_dvs,koi_steff,koi_slogg,koi_smet,koi_srad,koi_smass,koi_sage,koi_sparprov,ra,dec,koi_kepmag,koi_gmag,koi_rmag,koi_imag,koi_zmag,koi_jmag,koi_hmag,koi_kmag,koi_fwm_stat_sig,koi_fwm_sra,koi_fwm_sdec,koi_fwm_srao,koi_fwm_sdeco,koi_fwm_prao,koi_fwm_pdeco,koi_dicco_mra,koi_dicco_mdec,koi_dicco_msky,koi_dikco_mra,koi_dikco_mdec,koi_dikco_msky
1,10797460,K00752.01,Kepler-227 b,CONFIRMED,Done,2018-08-16,CANDIDATE,1.0000,0,0,0,0,q1_q17_dr25_sup_koi,NO_COMMENT,9.488035570,170.5387500,2455003.539,0,,0.1460,2.95750,,6.158E+02,0.022344,3.20796,LS+MCMC,2.26,0.0853,89.66,793.0,93.59,24.810000,Claret (2011 A&A 529 75) ATLAS LS,0.0000,0.0000,0.2291,0.4603,q1_q17_dr25_koi,5.1358490,28.4708200,35.80,2,142,1,q1_q17_dr25_tce,11111111111111111000000000000000,0.68640,Mandel and Agol (2002 ApJ 580 171),,,010/010797/010797460/dv/kplr010797460-20160209194854_dvr.pdf,010/010797/010797460/dv/kplr010797460-001-20160209194854_dvs.pdf,5455.00,4.467,0.1400,0.9270,0.9190,,q1_q17_dr25_stellar,291.934230,48.141651,15.347,15.890,15.270,15.114,15.006,14.082,13.751,13.648,0.002,19.462294000,48.14191000,0.43000,0.94000,-2E-04,-5.5E-04,-0.0100,0.2000,0.2000,0.0800,0.3100,0.3200
2,10797460,K00752.02,Kepler-227 c,CONFIRMED,Done,2018-08-16,CANDIDATE,0.9690,0,0,0,0,q1_q17_dr25_sup_koi,NO_COMMENT,54.418382700,162.5138400,2454995.514,0,,0.5860,4.50700,,8.748E+02,0.027954,3.02368,LS+MCMC,2.83,0.2734,89.57,443.0,9.11,77.900000,Claret (2011 A&A 529 75) ATLAS LS,0.0000,0.0000,0.2291,0.4603,q1_q17_dr25_koi,7.0276690,20.1095070,25.80,2,25,2,q1_q17_dr25_tce,11111111111111111000000000000000,0.00230,Mandel and Agol (2002 ApJ 580 171),,,010/010797/010797460/dv/kplr010797460-20160209194854_dvr.pdf,010/010797/010797460/dv/kplr010797460-002-20160209194854_dvs.pdf,5455.00,4.467,0.1400,0.9270,0.9190,,q1_q17_dr25_stellar,291.934230,48.141651,15.347,15.890,15.270,15.114,15.006,14.082,13.751,13.648,0.003,19.462265000,48.14199000,-0.63000,1.23000,6.6E-04,-1.05E-03,0.3900,0.0000,0.3900,0.4900,0.1200,0.5000
3,10811496,K00753.01,,CANDIDATE,Done,2018-08-16,CANDIDATE,0.0000,0,0,0,0,q1_q17_dr25_sup_koi,DEEP_V_SHAPED,19.899139950,175.8502520,2455008.850,0,,0.9690,1.78220,,1.0829E+04,0.154046,7.29555,LS+MCMC,14.60,0.1419,88.96,638.0,39.30,53.500000,Claret (2011 A&A 529 75) ATLAS LS,0.0000,0.0000,0.2711,0.3858,q1_q17_dr25_koi,37.1597670,187.4491000,76.30,1,56,1,q1_q17_dr25_tce,11111101110111011000000000000000,0.66240,Mandel and Agol (2002 ApJ 580 171),,,010/010811/010811496/dv/kplr010811496-20160209194854_dvr.pdf,010/010811/010811496/dv/kplr010811496-001-20160209194854_dvs.pdf,5853.00,4.544,-0.1800,0.8680,0.9610,,q1_q17_dr25_stellar,297.004820,48.134129,15.436,15.943,15.390,15.220,15.166,14.254,13.900,13.826,0.278,19.800320700,48.13412000,-0.02100,-0.03800,7E-04,6E-04,-0.0250,-0.0340,0.0420,0.0020,-0.0270,0.0270
4,10848459,K00754.01,,FALSE POSITIVE,Done,2018-08-16,FALSE POSITIVE,0.0000,0,1,0,0,q1_q17_dr25_sup_koi,MOD_ODDEVEN_DV---MOD_ODDEVEN_ALT---DEEP_V_SHAPED,1.736952453,170.3075650,2455003.308,0,,1.2760,2.40641,,8.0792E+03,0.387394,0.22080,LS+MCMC,33.46,0.0267,67.09,1395.0,891.96,3.278000,Claret (2011 A&A 529 75) ATLAS LS,0.0000,0.0000,0.2865,0.3556,q1_q17_dr25_koi,39.0665500,541.8951000,505.60,1,621,1,q1_q17_dr25_tce,11111110111011101000000000000000,0.00000,Mandel and Agol (2002 ApJ 580 171),,,010/010848/010848459/dv/kplr010848459-20160209194854_dvr.pdf,010/010848/010848459/dv/kplr010848459-001-20160209194854_dvs.pdf,5805.00,4.564,-0.5200,0.7910,0.8360,,q1_q17_dr25_stellar,285.534610,48.285210,15.597,16.100,15.554,15.382,15.266,14.326,13.911,13.809,0.000,19.035637620,48.28521050,-0.11100,0.00200,3.02E-03,-1.42E-03,-0.2490,0.1470,0.2890,-0.2570,0.0990,0.2760
5,10854555,K00755.01,Kepler-664 b,CONFIRMED,Done,2018-08-16,CANDIDATE,1.0000,0,0,0,0,q1_q17_dr25_sup_koi,NO_COMMENT,2.525591777,171.5955500,2455004.596,0,,0.7010,1.65450,,6.033E+02,0.024064,1.98635,LS+MCMC,2.75,0.0374,85.41,1406.0,926.16,8.750000,Claret (2011 A&A 529 75) ATLAS LS,0.0000,0.0000,0.2844,0.3661,q1_q17_dr25_koi,4.7499450,33.1919000,40.90,1,515,1,q1_q17_dr25_tce,01111111111111111000000000000000,0.30900,Mandel and Agol (2002 ApJ 580 171),,,010/010854/010854555/dv/kplr010854555-20160209194854_dvr.pdf,010/010854/010854555/dv/kplr010854555-001-20160209194854_dvs.pdf,6031.00,4.438,0.0700,1.0460,1.0950,,q1_q17_dr25_stellar,288.754880,48.226200,15.509,16.015,15.468,15.292,15.241,14.366,14.064,13.952,0.733,19.250325800,48.22626000,-0.01000,0.23000,8E-05,-7E-05,0.0300,-0.0900,0.1000,0.0700,0.0200,0.0700
For a particular kepid I would like to grab the koi_impact, koi_duration and koi_depth from the same row. These three values will eventually be used in a training set for a neural network. Is there a convenient way to do this?
value = 10797460
df.loc[df['kepid']==value, ['koi_impact', 'koi_duration', 'koi_depth']]