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我正在尝试将CSV文件读入pandas数据框并选择一列，但不断收到一个键错误
这是我正在处理的输入文件的一个片段。我想跳过前5行，使用带有model_number、star_age等作为标题的sitxh行，并收集数据列：
1 2 3 4 5
version_number initial_mass initial_z burn_min1 burn_min2
10398 8.1000000000000005E-001 1.0000000000000000E-003 5.0000000000000000E+001 1.0000000000000000E+003
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88
model_number star_age star_mass mass_conv_core he_core_mass he_core_radius conv_mx1_top conv_mx1_bot conv_mx2_top conv_mx2_bot conv_mx1_top_r conv_mx1_bot_r conv_mx2_top_r conv_mx2_bot_r cz_bot_radius log_LH log_LHe log_Teff luminosity log_L log_R log_g log_surf_cell_z log_center_T log_center_Rho log_center_P center_mu center_degeneracy center_h1 center_h2 center_he3 center_he4 center_li7 center_be7 center_b8 center_c12 center_c13 center_n13 center_n14 center_n15 center_o14 center_o15 center_o16 center_o17 center_o18 center_f17 center_f18 center_f19 center_ne18 center_ne19 center_ne20 center_ne22 center_mg22 center_mg24 surface_h1 surface_h2 surface_he3 surface_he4 surface_li7 surface_be7 surface_b8 surface_c12 surface_c13 surface_n13 surface_n14 surface_n15 surface_o14 surface_o15 surface_o16 surface_o17 surface_o18 surface_f17 surface_f18 surface_f19 surface_ne18 surface_ne19 surface_ne20 surface_ne22 surface_mg22 surface_mg24 nu_max delta_nu delta_Pg acoustic_cutoff acoustic_radius d_log_L_dT h_exh_core_mass h_exh_core_radius
590 7.4832653674979620E+003 8.0999231594859322E-001 9.3013103578994949E-002 0.0000000000000000E+000 0.0000000000000000E+000 1.1367933255238216E-001 7.8129078318056600E-008 1.0000000000000000E+000 9.9318920202569283E-001 1.1581183159043519E-001 9.6384235196922451E-004 6.9887906688924084E-001 5.6970970716172920E-001 0.0000000000000000E+000 -1.9971124824757927E-001 -4.8201179092980333E+001 3.7897634917269190E+000 6.3290767033577267E-001 -1.9865964099109620E-001 -1.5559796749675942E-001 4.6575661873384373E+000 -3.0000000000000475E+000 7.1159454763339030E+000 1.9980751452335390E+000 1.7261783473862238E+001 5.9749025137280032E-001 -1.3921254545517694E+000 7.4862988071983971E-001 6.4224755686599933E-018 1.1040796520256433E-004 2.5025816673994183E-001 3.6245810211769934E-016 7.0974332935137005E-013 4.6287532383736027E-023 1.6090485487993254E-004 1.9156053663187585E-006 3.4877009372383987E-016 6.0985269793407524E-005 2.2754513772045929E-007 2.9195681915663947E-031 8.7420459328129831E-020 4.6667973725421542E-004 2.0414721029513937E-007 1.1381709511386918E-006 2.2860196785405309E-021 9.1671666641570419E-023 2.0874159744382837E-008 3.7083264706195407E-042 3.9279368604326808E-040 1.0269030813449696E-004 6.7091975862944943E-006 4.2398886865254202E-017 2.0006886383171565E-004 7.4899999942868345E-001 2.7200701027954921E-017 2.6605602791999035E-005 2.4997339496852472E-001 1.6905661394980510E-037 1.7567453744563094E-040 1.0000000000000274E-099 1.7017919585850694E-004 1.9011704267928175E-006 7.2620283197083900E-094 5.0179160420602355E-005 2.2876560599578056E-007 1.0000000000000006E-099 1.0000000000000006E-099 4.6668013273267953E-004 2.0414644161309358E-007 1.1381848144163238E-006 1.0000000000000006E-099 8.4411414882972268E-074 2.0874146844472571E-008 1.0000000000000006E-099 1.0000000000000006E-099 1.0269030813453584E-004 6.7091975862945028E-006 1.1399939856108351E-057 2.0006886383171633E-004 4.9774621436997058E+003 2.1457444791057421E+002 0.0000000000000000E+000 4.5146723037873518E+004 2.3301935755574186E+003 1.0590627364747510E+001 0.0000000000000000E+000 0.0000000000000000E+000
591 1.6463183808495516E+004 8.0999231496783064E-001 9.2873327284872409E-002 0.0000000000000000E+000 0.0000000000000000E+000 1.1367933269002861E-001 7.8129078429078902E-008 1.0000000000000000E+000 9.9318494262289947E-001 1.1581293977069999E-001 9.6385193654037807E-004 6.9879898141115959E-001 5.6967302923999130E-001 0.0000000000000000E+000 -1.9978213060522462E-001 -4.8201281132346992E+001 3.7897483582980382E+000 6.3267443633058140E-001 -1.9881971316983504E-001 -1.5564773672836699E-001 4.6576657252757956E+000 -3.0000000000000475E+000 7.1159439547711134E+000 1.9980621893502508E+000 1.7261768696422475E+001 5.9749042822746268E-001 -1.3921513883392005E+000 7.4862947570235450E-001 6.4224852088404529E-018 1.1037767369519526E-004 2.5025860081630319E-001 3.6293040823707671E-016 7.1091046311060774E-013 4.6359908361904593E-023 1.6088811945050166E-004 1.9343478788613242E-006 3.4876029004368768E-016 6.0986182006780295E-005 2.2587329315472058E-007 2.9193215138718783E-031 8.7416768418526394E-020 4.6667973812484934E-004 2.0414813320371832E-007 1.1381543204624337E-006 2.2858303888862015E-021 9.1791850761956234E-023 2.0874175222493570E-008 3.7075984662615788E-042 3.9325993844270721E-040 1.0269030813445076E-004 6.7091975862944951E-006 9.3271194401577135E-017 2.0006886383171565E-004 7.4899999942868334E-001 3.4436613497022586E-017 2.6605602792180270E-005 2.4997339496852464E-001 3.7109055189055696E-037 1.7477951709463041E-040 1.0000000000000597E-099 1.7017919585850675E-004 1.9011704267928160E-006 7.2698748944159613E-094 5.0179160420602321E-005 2.2876560599578056E-007 1.0000000000000004E-099 1.0000000000000004E-099 4.6668013273267904E-004 2.0414644161309340E-007 1.1381848144163240E-006 1.0000000000000004E-099 8.4443007282823002E-074 2.0874146844472544E-008 1.0000000000000004E-099 1.0000000000000004E-099 1.0269030813453577E-004 6.7091975862945011E-006 2.4972413232265062E-057 2.0006886383171628E-004 4.9786898247189956E+003 2.1461143575428008E+002 0.0000000000000000E+000 4.5157989697983539E+004 2.3297919714421755E+003 1.0577389962355879E+001 0.0000000000000000E+000 0.0000000000000000E+000
592 2.7239085937692584E+004 8.0999231379148418E-001 9.2768956348480064E-002 0.0000000000000000E+000 0.0000000000000000E+000 1.1367933285512455E-001 7.8129078429078902E-008 1.0000000000000000E+000 9.9317986516242851E-001 1.1581426267158637E-001 9.6386337090931164E-004 6.9870341901610078E-001 5.6962931836955688E-001 0.0000000000000000E+000 -1.9986631841832908E-001 -4.8201405210184461E+001 3.7897302509749204E+000 6.3239593236503233E-001 -1.9901093215713114E-001 -1.5570713157577978E-001 4.6577845143398982E+000 -3.0000000000000475E+000 7.1159420949650363E+000 1.9980467324434472E+000 1.7261751021496853E+001 5.9749064052195178E-001 -1.3921821623723432E+000 7.4862898953216450E-001 6.4224972602860048E-018 1.1034185970759304E-004 2.5025912131718681E-001 3.6281941590277494E-016 7.1065810408343176E-013 4.6338906148233168E-023 1.6086802751266569E-004 1.9568213315500052E-006 3.4867545924503404E-016 6.0987289529204137E-005 2.2388535189517994E-007 2.9182327486870975E-031 8.7407027840248354E-020 4.6667973915111283E-004 2.0414924054833912E-007 1.1381343663647126E-006 2.2855131115230559E-021 9.1781576961675115E-023 2.0874193793734392E-008 3.7065645159884602E-042 3.9314864173782023E-040 1.0269030813439529E-004 6.7091975862944926E-006 1.5430912131334866E-016 2.0006886383171560E-004 7.4899999942868323E-001 3.6108155599347527E-017 2.6605602792408040E-005 2.4997339496852472E-001 6.1203516138300124E-037 1.7349513354145605E-040 1.0000000000000993E-099 1.7017919585850678E-004 1.9011704267928163E-006 7.2796725886830642E-094 5.0179160420602334E-005 2.2876560599578053E-007 1.0000000000000006E-099 1.0000000000000006E-099 4.6668013273267893E-004 2.0414644161309337E-007 1.1381848144163238E-006 1.0000000000000006E-099 8.4421321164489613E-074 2.0874146844472544E-008 1.0000000000000006E-099 1.0000000000000006E-099 1.0269030813453577E-004 6.7091975862945019E-006 4.1106152142620843E-057 2.0006886383171622E-004 4.9801556111713053E+003 2.1465559040371008E+002 0.0000000000000000E+000 4.5171441672397355E+004 2.3293127332935192E+003 1.0560312314089561E+001 0.0000000000000000E+000 0.0000000000000000E+000
593 4.0170168492729063E+004 8.0999231238068270E-001 9.2657535069124067E-002 0.0000000000000000E+000 0.0000000000000000E+000 1.1367933305312516E-001 7.8129078651123507E-008 1.0000000000000000E+000 9.9317381688473827E-001 1.1581583973990918E-001 9.6387699231684568E-004 6.9858951468809860E-001 5.6957730101692139E-001 0.0000000000000000E+000 -1.9996575097436850E-001 -4.8201556735701352E+001 3.7897085953642202E+000 6.3206367855156909E-001 -1.9923916562517330E-001 -1.5577793708840007E-001 4.6579261246087080E+000 -3.0000000000000475E+000 7.1159398087311061E+000 1.9980283205803717E+000 1.7261729896286983E+001 5.9749089537331979E-001 -1.3922185550828545E+000 7.4862840591704072E-001 6.4225121615288567E-018 1.1029964301003699E-004 2.5025974536316059E-001 3.6268773618728560E-016 7.1035719815415304E-013 4.6313771343818876E-023 1.6084390390354043E-004 1.9837641461756074E-006 3.4857298043539306E-016 6.0988637058530873E-005 2.2152584991130176E-007 2.9169145056016640E-031 8.7395177948976034E-020 4.6667974035628692E-004 2.0415056914861469E-007 1.1381104254089161E-006 2.2851269496277786E-021 9.1769014090128329E-023 2.0874216075586247E-008 3.7053063551717499E-042 3.9301281231132292E-040 1.0269030813432880E-004 6.7091975862944951E-006 2.2754168226061389E-016 2.0006886383171563E-004 7.4899999942868323E-001 3.6446705264203970E-017 2.6605602792683519E-005 2.4997339496852472E-001 8.9903274053503869E-037 1.7196986704942170E-040 1.0000000000001490E-099 1.7017919585850689E-004 1.9011704267928175E-006 7.2888696099299523E-094 5.0179160420602334E-005 2.2876560599578053E-007 1.0000000000000004E-099 1.0000000000000004E-099 4.6668013273267893E-004 2.0414644161309345E-007 1.1381848144163238E-006 1.0000000000000004E-099 8.4345403543658127E-074 2.0874146844472561E-008 1.0000000000000004E-099 1.0000000000000004E-099 1.0269030813453578E-004 6.7091975862945019E-006 6.0248713184901274E-057 2.0006886383171625E-004 4.9819039607629957E+003 2.1470824703177900E+002 0.0000000000000000E+000 4.5187487085895678E+004 2.3287414755242025E+003 1.0539230280853543E+001 0.0000000000000000E+000 0.0000000000000000E+000
下面是我的代码的样子：
import matplotlib.pyplot as plt
import mesa_reader as mr
import pandas as pd
# load and plot data
#h = mr.MesaData('history.data')
h = pd.read_csv('history.data', header=4)
#plt.plot(10**h.log_Teff, h.luminosity)
#plt.plot(10**h["log_Teff"], h["luminosity"])
#print("The column headers :")
#print(list(h.columns.values))
print(h['model_number'])
以及错误：
Traceback (most recent call last):
File "/home/nick/.local/lib/python3.6/site-packages/pandas/core/indexes/base.py", line 2898, in get_loc
return self._engine.get_loc(casted_key)
File "pandas/_libs/index.pyx", line 70, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 101, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 1675, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1683, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'model_number'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "HR_plotter.py", line 12, in <module>
print(h['model_number'])
File "/home/nick/.local/lib/python3.6/site-packages/pandas/core/frame.py", line 2906, in __getitem__
indexer = self.columns.get_loc(key)
File "/home/nick/.local/lib/python3.6/site-packages/pandas/core/indexes/base.py", line 2900, in get_loc
raise KeyError(key) from err
KeyError: 'model_number'
Tags： incorelognumberpandasgetmodelline
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此数据为固定宽度格式，因此使用^{} 和
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