My list names are getting repetitive alongside my values-CodePudding

I have been trying to print out the names of the features along with their corresponding values but I am getting repetition of my names along side the values. The values are as follows:

print(importance)
[-5.07573087e-01  1.41361595e 00 -7.62186274e-01 -8.91087742e-02
  2.18873995e-01  5.22420043e-02  1.95510634e 00 -1.14541803e 00
  2.23063723e-01  1.16037309e-02  8.77691010e-01  8.88617771e-01
 -6.84845807e-01  5.26310745e-01 -2.03825113e 00  4.52190443e 00
  5.01364675e-01 -4.68664684e 00 -5.22671627e 00 -1.31013919e 01
 -2.48262965e-01 -3.28450068e-02 -2.05078037e 00 -2.07719831e 00
  3.75180361e 00 -1.22410265e 00  1.27131566e 00 -2.28637429e 00
  4.04486539e-01  1.39317283e 00]

print(list_1)
['steelgrade', 'material_spec', 'hsm_temp_strip_coiling_meas_tail', 'hsm_temp_strip_exit_fm_meas_tail', 'ccm_casting_speed_tail', 'hsm_thickness_reduction_ratio_f1', 'tcm_deformation_total', 'tund_chem_boron', 'tund_chem_chromium', 'tund_chem_cobalt', 'tund_chem_manganese', 'tund_chem_molybdenum', 'tund_chem_titanium', 'tund_chem_vanadium', 'cgl_bridle_speed_3_meas_tail', 'cgl_temp_strip_dff_tail', 'cgl_temp_strip_z2_rc_tail', 'cgl_temp_strip_rtf_tail', 'cgl_temp_strip_sc_tail', 'cgl_temp_strip_soak_tail', 'cgl_elong_spm_tail', 'cgl_elong_tl_tail', 'cgl_thick_exit_meas_tail', 'cgl_wr_force_spm_head', 'cgl_wr_force_spm_body', 'cgl_wr_force_spm_tail', 'cgl_wr_force_spm_spec_head', 'cgl_wr_force_spm_spec_body', 'cgl_wr_force_spm_spec_tail', 'cgl_production_start_year']

The way I am trying to print it as follows:

for name in list_1:
    for i in(importance):
        print(f'Feature:{name},Score: %.5f' % i)

The result I am getting is as follows:

Feature:steelgrade,Score: -0.50757
Feature:steelgrade,Score: 1.41362
Feature:steelgrade,Score: -0.76219
Feature:steelgrade,Score: -0.08911
Feature:steelgrade,Score: 0.21887
Feature:steelgrade,Score: 0.05224
Feature:steelgrade,Score: 1.95511
Feature:steelgrade,Score: -1.14542
Feature:steelgrade,Score: 0.22306
Feature:steelgrade,Score: 0.01160
Feature:steelgrade,Score: 0.87769
Feature:steelgrade,Score: 0.88862
Feature:steelgrade,Score: -0.68485
Feature:steelgrade,Score: 0.52631
Feature:steelgrade,Score: -2.03825
Feature:steelgrade,Score: 4.52190
Feature:steelgrade,Score: 0.50136
Feature:steelgrade,Score: -4.68665
Feature:steelgrade,Score: -5.22672
Feature:steelgrade,Score: -13.10139
Feature:steelgrade,Score: -0.24826
Feature:steelgrade,Score: -0.03285
Feature:steelgrade,Score: -2.05078
Feature:steelgrade,Score: -2.07720
Feature:steelgrade,Score: 3.75180
Feature:steelgrade,Score: -1.22410
Feature:steelgrade,Score: 1.27132
Feature:steelgrade,Score: -2.28637
Feature:steelgrade,Score: 0.40449
Feature:steelgrade,Score: 1.39317
Feature:material_spec,Score: -0.50757
Feature:material_spec,Score: 1.41362
Feature:material_spec,Score: -0.76219
Feature:material_spec,Score: -0.08911
Feature:material_spec,Score: 0.21887
Feature:material_spec,Score: 0.05224
Feature:material_spec,Score: 1.95511
Feature:material_spec,Score: -1.14542
Feature:material_spec,Score: 0.22306
Feature:material_spec,Score: 0.01160
Feature:material_spec,Score: 0.87769
Feature:material_spec,Score: 0.88862
Feature:material_spec,Score: -0.68485
Feature:material_spec,Score: 0.52631
Feature:material_spec,Score: -2.03825
Feature:material_spec,Score: 4.52190
Feature:material_spec,Score: 0.50136
Feature:material_spec,Score: -4.68665
Feature:material_spec,Score: -5.22672
Feature:material_spec,Score: -13.10139
Feature:material_spec,Score: -0.24826
Feature:material_spec,Score: -0.03285
Feature:material_spec,Score: -2.05078
Feature:material_spec,Score: -2.07720
Feature:material_spec,Score: 3.75180
Feature:material_spec,Score: -1.22410
Feature:material_spec,Score: 1.27132
Feature:material_spec,Score: -2.28637
Feature:material_spec,Score: 0.40449
Feature:material_spec,Score: 1.39317
Feature:hsm_temp_strip_coiling_meas_tail,Score: -0.50757
Feature:hsm_temp_strip_coiling_meas_tail,Score: 1.41362
Feature:hsm_temp_strip_coiling_meas_tail,Score: -0.76219
Feature:hsm_temp_strip_coiling_meas_tail,Score: -0.08911
Feature:hsm_temp_strip_coiling_meas_tail,Score: 0.21887
Feature:hsm_temp_strip_coiling_meas_tail,Score: 0.05224
Feature:hsm_temp_strip_coiling_meas_tail,Score: 1.95511
Feature:hsm_temp_strip_coiling_meas_tail,Score: -1.14542
Feature:hsm_temp_strip_coiling_meas_tail,Score: 0.22306
Feature:hsm_temp_strip_coiling_meas_tail,Score: 0.01160
Feature:hsm_temp_strip_coiling_meas_tail,Score: 0.87769
Feature:hsm_temp_strip_coiling_meas_tail,Score: 0.88862
Feature:hsm_temp_strip_coiling_meas_tail,Score: -0.68485
Feature:hsm_temp_strip_coiling_meas_tail,Score: 0.52631
Feature:hsm_temp_strip_coiling_meas_tail,Score: -2.03825
Feature:hsm_temp_strip_coiling_meas_tail,Score: 4.52190
Feature:hsm_temp_strip_coiling_meas_tail,Score: 0.50136
Feature:hsm_temp_strip_coiling_meas_tail,Score: -4.68665
Feature:hsm_temp_strip_coiling_meas_tail,Score: -5.22672
Feature:hsm_temp_strip_coiling_meas_tail,Score: -13.10139
Feature:hsm_temp_strip_coiling_meas_tail,Score: -0.24826
Feature:hsm_temp_strip_coiling_meas_tail,Score: -0.03285
Feature:hsm_temp_strip_coiling_meas_tail,Score: -2.05078
Feature:hsm_temp_strip_coiling_meas_tail,Score: -2.07720
Feature:hsm_temp_strip_coiling_meas_tail,Score: 3.75180
Feature:hsm_temp_strip_coiling_meas_tail,Score: -1.22410
Feature:hsm_temp_strip_coiling_meas_tail,Score: 1.27132
Feature:hsm_temp_strip_coiling_meas_tail,Score: -2.28637
Feature:hsm_temp_strip_coiling_meas_tail,Score: 0.40449
Feature:hsm_temp_strip_coiling_meas_tail,Score: 1.39317
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: -0.50757
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: 1.41362
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: -0.76219
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: -0.08911
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: 0.21887
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: 0.05224
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: 1.95511
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: -1.14542
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: 0.22306
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: 0.01160
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: 0.87769
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: 0.88862
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: -0.68485
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: 0.52631
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: -2.03825
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: 4.52190
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: 0.50136
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: -4.68665
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: -5.22672
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: -13.10139
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: -0.24826
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: -0.03285
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: -2.05078
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: -2.07720
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: 3.75180
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: -1.22410
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: 1.27132
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: -2.28637
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: 0.40449
Feature:hsm_temp_strip_exit_fm_meas_tail,Score: 1.39317
Feature:ccm_casting_speed_tail,Score: -0.50757
Feature:ccm_casting_speed_tail,Score: 1.41362
Feature:ccm_casting_speed_tail,Score: -0.76219
Feature:ccm_casting_speed_tail,Score: -0.08911
Feature:ccm_casting_speed_tail,Score: 0.21887
Feature:ccm_casting_speed_tail,Score: 0.05224
Feature:ccm_casting_speed_tail,Score: 1.95511
Feature:ccm_casting_speed_tail,Score: -1.14542
Feature:ccm_casting_speed_tail,Score: 0.22306
Feature:ccm_casting_speed_tail,Score: 0.01160
Feature:ccm_casting_speed_tail,Score: 0.87769
Feature:ccm_casting_speed_tail,Score: 0.88862
Feature:ccm_casting_speed_tail,Score: -0.68485
Feature:ccm_casting_speed_tail,Score: 0.52631
Feature:ccm_casting_speed_tail,Score: -2.03825
Feature:ccm_casting_speed_tail,Score: 4.52190
Feature:ccm_casting_speed_tail,Score: 0.50136
Feature:ccm_casting_speed_tail,Score: -4.68665
Feature:ccm_casting_speed_tail,Score: -5.22672
Feature:ccm_casting_speed_tail,Score: -13.10139
Feature:ccm_casting_speed_tail,Score: -0.24826
Feature:ccm_casting_speed_tail,Score: -0.03285
Feature:ccm_casting_speed_tail,Score: -2.05078
Feature:ccm_casting_speed_tail,Score: -2.07720
Feature:ccm_casting_speed_tail,Score: 3.75180
Feature:ccm_casting_speed_tail,Score: -1.22410
Feature:ccm_casting_speed_tail,Score: 1.27132
Feature:ccm_casting_speed_tail,Score: -2.28637
Feature:ccm_casting_speed_tail,Score: 0.40449
Feature:ccm_casting_speed_tail,Score: 1.39317
Feature:hsm_thickness_reduction_ratio_f1,Score: -0.50757
Feature:hsm_thickness_reduction_ratio_f1,Score: 1.41362
Feature:hsm_thickness_reduction_ratio_f1,Score: -0.76219
Feature:hsm_thickness_reduction_ratio_f1,Score: -0.08911
Feature:hsm_thickness_reduction_ratio_f1,Score: 0.21887
Feature:hsm_thickness_reduction_ratio_f1,Score: 0.05224
Feature:hsm_thickness_reduction_ratio_f1,Score: 1.95511
Feature:hsm_thickness_reduction_ratio_f1,Score: -1.14542
Feature:hsm_thickness_reduction_ratio_f1,Score: 0.22306
Feature:hsm_thickness_reduction_ratio_f1,Score: 0.01160
Feature:hsm_thickness_reduction_ratio_f1,Score: 0.87769
Feature:hsm_thickness_reduction_ratio_f1,Score: 0.88862
Feature:hsm_thickness_reduction_ratio_f1,Score: -0.68485
Feature:hsm_thickness_reduction_ratio_f1,Score: 0.52631
Feature:hsm_thickness_reduction_ratio_f1,Score: -2.03825
Feature:hsm_thickness_reduction_ratio_f1,Score: 4.52190
Feature:hsm_thickness_reduction_ratio_f1,Score: 0.50136
Feature:hsm_thickness_reduction_ratio_f1,Score: -4.68665
Feature:hsm_thickness_reduction_ratio_f1,Score: -5.22672
Feature:hsm_thickness_reduction_ratio_f1,Score: -13.10139
Feature:hsm_thickness_reduction_ratio_f1,Score: -0.24826
Feature:hsm_thickness_reduction_ratio_f1,Score: -0.03285
Feature:hsm_thickness_reduction_ratio_f1,Score: -2.05078
Feature:hsm_thickness_reduction_ratio_f1,Score: -2.07720
Feature:hsm_thickness_reduction_ratio_f1,Score: 3.75180
Feature:hsm_thickness_reduction_ratio_f1,Score: -1.22410
Feature:hsm_thickness_reduction_ratio_f1,Score: 1.27132
Feature:hsm_thickness_reduction_ratio_f1,Score: -2.28637
Feature:hsm_thickness_reduction_ratio_f1,Score: 0.40449
Feature:hsm_thickness_reduction_ratio_f1,Score: 1.39317
Feature:tcm_deformation_total,Score: -0.50757
Feature:tcm_deformation_total,Score: 1.41362
Feature:tcm_deformation_total,Score: -0.76219
Feature:tcm_deformation_total,Score: -0.08911
Feature:tcm_deformation_total,Score: 0.21887
Feature:tcm_deformation_total,Score: 0.05224
Feature:tcm_deformation_total,Score: 1.95511
Feature:tcm_deformation_total,Score: -1.14542
Feature:tcm_deformation_total,Score: 0.22306
Feature:tcm_deformation_total,Score: 0.01160
Feature:tcm_deformation_total,Score: 0.87769
Feature:tcm_deformation_total,Score: 0.88862
Feature:tcm_deformation_total,Score: -0.68485
Feature:tcm_deformation_total,Score: 0.52631
Feature:tcm_deformation_total,Score: -2.03825
Feature:tcm_deformation_total,Score: 4.52190
Feature:tcm_deformation_total,Score: 0.50136
Feature:tcm_deformation_total,Score: -4.68665
Feature:tcm_deformation_total,Score: -5.22672
Feature:tcm_deformation_total,Score: -13.10139
Feature:tcm_deformation_total,Score: -0.24826
Feature:tcm_deformation_total,Score: -0.03285
Feature:tcm_deformation_total,Score: -2.05078
Feature:tcm_deformation_total,Score: -2.07720
Feature:tcm_deformation_total,Score: 3.75180
Feature:tcm_deformation_total,Score: -1.22410
Feature:tcm_deformation_total,Score: 1.27132
Feature:tcm_deformation_total,Score: -2.28637
Feature:tcm_deformation_total,Score: 0.40449
Feature:tcm_deformation_total,Score: 1.39317
Feature:tund_chem_boron,Score: -0.50757
Feature:tund_chem_boron,Score: 1.41362
Feature:tund_chem_boron,Score: -0.76219
Feature:tund_chem_boron,Score: -0.08911
Feature:tund_chem_boron,Score: 0.21887
Feature:tund_chem_boron,Score: 0.05224
Feature:tund_chem_boron,Score: 1.95511
Feature:tund_chem_boron,Score: -1.14542
Feature:tund_chem_boron,Score: 0.22306
Feature:tund_chem_boron,Score: 0.01160
Feature:tund_chem_boron,Score: 0.87769
Feature:tund_chem_boron,Score: 0.88862
Feature:tund_chem_boron,Score: -0.68485
Feature:tund_chem_boron,Score: 0.52631
Feature:tund_chem_boron,Score: -2.03825
Feature:tund_chem_boron,Score: 4.52190
Feature:tund_chem_boron,Score: 0.50136
Feature:tund_chem_boron,Score: -4.68665
Feature:tund_chem_boron,Score: -5.22672
Feature:tund_chem_boron,Score: -13.10139
Feature:tund_chem_boron,Score: -0.24826
Feature:tund_chem_boron,Score: -0.03285
Feature:tund_chem_boron,Score: -2.05078
Feature:tund_chem_boron,Score: -2.07720
Feature:tund_chem_boron,Score: 3.75180
Feature:tund_chem_boron,Score: -1.22410
Feature:tund_chem_boron,Score: 1.27132
Feature:tund_chem_boron,Score: -2.28637
Feature:tund_chem_boron,Score: 0.40449
Feature:tund_chem_boron,Score: 1.39317
Feature:tund_chem_chromium,Score: -0.50757
Feature:tund_chem_chromium,Score: 1.41362
Feature:tund_chem_chromium,Score: -0.76219
Feature:tund_chem_chromium,Score: -0.08911
Feature:tund_chem_chromium,Score: 0.21887
Feature:tund_chem_chromium,Score: 0.05224
Feature:tund_chem_chromium,Score: 1.95511
Feature:tund_chem_chromium,Score: -1.14542
Feature:tund_chem_chromium,Score: 0.22306
Feature:tund_chem_chromium,Score: 0.01160
Feature:tund_chem_chromium,Score: 0.87769
Feature:tund_chem_chromium,Score: 0.88862
Feature:tund_chem_chromium,Score: -0.68485
Feature:tund_chem_chromium,Score: 0.52631
Feature:tund_chem_chromium,Score: -2.03825
Feature:tund_chem_chromium,Score: 4.52190
Feature:tund_chem_chromium,Score: 0.50136
Feature:tund_chem_chromium,Score: -4.68665
Feature:tund_chem_chromium,Score: -5.22672
Feature:tund_chem_chromium,Score: -13.10139
Feature:tund_chem_chromium,Score: -0.24826
Feature:tund_chem_chromium,Score: -0.03285
Feature:tund_chem_chromium,Score: -2.05078
Feature:tund_chem_chromium,Score: -2.07720
Feature:tund_chem_chromium,Score: 3.75180
Feature:tund_chem_chromium,Score: -1.22410
Feature:tund_chem_chromium,Score: 1.27132
Feature:tund_chem_chromium,Score: -2.28637
Feature:tund_chem_chromium,Score: 0.40449
Feature:tund_chem_chromium,Score: 1.39317
Feature:tund_chem_cobalt,Score: -0.50757
Feature:tund_chem_cobalt,Score: 1.41362
Feature:tund_chem_cobalt,Score: -0.76219
Feature:tund_chem_cobalt,Score: -0.08911
Feature:tund_chem_cobalt,Score: 0.21887
Feature:tund_chem_cobalt,Score: 0.05224
Feature:tund_chem_cobalt,Score: 1.95511
Feature:tund_chem_cobalt,Score: -1.14542
Feature:tund_chem_cobalt,Score: 0.22306
Feature:tund_chem_cobalt,Score: 0.01160
Feature:tund_chem_cobalt,Score: 0.87769
Feature:tund_chem_cobalt,Score: 0.88862
Feature:tund_chem_cobalt,Score: -0.68485
Feature:tund_chem_cobalt,Score: 0.52631
Feature:tund_chem_cobalt,Score: -2.03825
Feature:tund_chem_cobalt,Score: 4.52190
Feature:tund_chem_cobalt,Score: 0.50136
Feature:tund_chem_cobalt,Score: -4.68665
Feature:tund_chem_cobalt,Score: -5.22672
Feature:tund_chem_cobalt,Score: -13.10139
Feature:tund_chem_cobalt,Score: -0.24826
Feature:tund_chem_cobalt,Score: -0.03285
Feature:tund_chem_cobalt,Score: -2.05078
Feature:tund_chem_cobalt,Score: -2.07720
Feature:tund_chem_cobalt,Score: 3.75180
Feature:tund_chem_cobalt,Score: -1.22410
Feature:tund_chem_cobalt,Score: 1.27132
Feature:tund_chem_cobalt,Score: -2.28637
Feature:tund_chem_cobalt,Score: 0.40449
Feature:tund_chem_cobalt,Score: 1.39317
Feature:tund_chem_manganese,Score: -0.50757
Feature:tund_chem_manganese,Score: 1.41362
Feature:tund_chem_manganese,Score: -0.76219
Feature:tund_chem_manganese,Score: -0.08911
Feature:tund_chem_manganese,Score: 0.21887
Feature:tund_chem_manganese,Score: 0.05224
Feature:tund_chem_manganese,Score: 1.95511
Feature:tund_chem_manganese,Score: -1.14542
Feature:tund_chem_manganese,Score: 0.22306
Feature:tund_chem_manganese,Score: 0.01160
Feature:tund_chem_manganese,Score: 0.87769
Feature:tund_chem_manganese,Score: 0.88862
Feature:tund_chem_manganese,Score: -0.68485
Feature:tund_chem_manganese,Score: 0.52631
Feature:tund_chem_manganese,Score: -2.03825
Feature:tund_chem_manganese,Score: 4.52190
Feature:tund_chem_manganese,Score: 0.50136
Feature:tund_chem_manganese,Score: -4.68665
Feature:tund_chem_manganese,Score: -5.22672
Feature:tund_chem_manganese,Score: -13.10139
Feature:tund_chem_manganese,Score: -0.24826
Feature:tund_chem_manganese,Score: -0.03285

The result is as above and the names of the features are getting repeated for each value. However, what I want is to make the name of the feature appear only once in the sequence for their corresponding value.

CodePudding user response：

This is happening because you're nesting the loop. So it first runs the second loop for each feature. You could try the following: for i, name in enumerate(list_1): print(f'Feature:{name},Score: %.5f' % importance[i])

CodePudding user response：

So I assume that the list importance and list_1 are of equal length and that every nth item in the list importance maps to the nth element in the list list_1.

You can easiliy loop through two arrays at the same time using the zip operator

for (value,property) in zip(importance, list_1):
    print(property   value)