Python Pandas或Numpy，搜索、编辑和提取数据

几周前，为了编写一个程序，我开始学习python，
当我试图从文件中提取一些信息时，它看起来是这样的：

mae文件：benopga1.01.mae
&根
igeopt=1
iaccg=5
ip160=4
ifreq=1
ip24=2
nogas=2
iacc=1
&
&兹马特
C1 18.7209160000 18.5628090000 15.2914270000
C2 17.411470000018.8454610000 15.7184360000
C3 17.0539910000 18.6449570000 17.0518080000
C4 18.0032320000 18.1601840000 17.9688000000
C5 19.3024250000 17.879807000 17.5452620000
C6 19.6626680000 18.0820920000 16.2013130000
H7 18.9985910000 18.7195810000 14.2522760000
H8 16.6780090000 19.219890000015.0089150000
H9 16.0417370000 18.8626870000 17.3826630000
H10 17.7239350000 18.0037410000 19.0076080000
H11 20.0369730000 17.5051670000 18.2535010000
H12 20.6751990000 17.8633650000 15.8718730000
&
&猜测basgss=6-311g**+numd=5
1轨道能量-11.241317占1.000000
-0.237515017757232 -0.196647289792153 -0.000012299229451  0.000012283693667
-0.000038832391585 -0.002068759436403  0.000019096186058  0.000035102037517
-0.000178850142376  0.000386718554923  0.000028243495837 -0.000010189167830
0.000009719189111 -0.000083379603107  0.000088703347921 -0.000005323744814
0.000074406344067 -0.000034780134268  0.000042134744869 -0.000025018268285
0.000005796046950  0.000032438198590 -0.000151512252705 -0.198843760947433
-0.164626268788198 -0.000032620645254  0.000011142490285 -0.000007071762982
-0.001751134173474 -0.000117771869441  0.000056795470271 -0.000099889624378
0.000355125910896  0.000016162939390 -0.000000591018861 -0.000018504072314
-0.000043258886307  0.000085387104771 -0.000042128218464  0.000032711718420
0.000062742388095  0.000018426112961 -0.000001087454691 -0.000087393288567
0.000036394574524 -0.000046738022296 -0.189838829586922 -0.157171079929277
-0.000027575467355  0.000006987237905  0.000003880133365 -0.001671553419738
-0.000145911512315  0.000031184688524  0.000049574180168  0.000356303208123
0.000008457559748 -0.000002555314618  0.000000156292819  0.000001781216751
0.000074959304234 -0.000076740520986  0.000034291328716 -0.000015539635327
0.000048978495218  0.000007568809345 -0.000078160169234  0.000017433925123
0.000021965260595 -0.206688115052246 -0.171120694083044 -0.000024613036749
0.000000578071180  0.000028857510870 -0.001816148417123 -0.000060499311777
-0.000017129060142  0.000151731576240  0.000366295601898  0.000027273496960
-0.000008681030719  0.000003505182219 -0.000084713189897  0.000079444547592
0.000005268642305  0.000061929650436 -0.000000945876945  0.000025397622782
-0.000006331576449 -0.000059596946982 -0.000005577539402  0.000100798841928
-0.249803952550950 -0.206823550176572  0.000003194386460 -0.000011257593014
0.000044548734829 -0.002165674347528  0.000119312075990 -0.000063891648135
0.000128577103429  0.000383381470817  0.000015141171971  0.000000035916246
-0.000018789517620 -0.000044930372579  0.000110215751912 -0.000065285379334
0.000039159765842  0.000078659142665  0.000027379751909 -0.000029883760840
0.000089805971123 -0.000055471940289  0.000127658675297 -0.282967668103962
-0.234291216881311  0.000022041761553 -0.000002823143502 -0.000015098289029
-0.002411282844625  0.000189683107712 -0.000041945582722 -0.000057757033880
0.000410957267636 -0.000018584696026  0.000002703022996  0.000011405600689
-0.000005600032267  0.000119091011558 -0.000113490979291  0.000058095889213
-0.000027269439738  0.000076776523871 -0.000039022163445  0.000173859295776
-0.000036395429255 -0.000059944178961 -0.000180764515503  0.000007292236479
-0.000211164361974  0.000002900643801  0.000006787640262 -0.000032874030369
-0.000152282839545  0.000001347403058 -0.000192590475337 -0.000022257991946
0.000010326038114 -0.000016900157341 -0.000145389069678  0.000002334090915
-0.000184253055893 -0.000026241454432  0.000005721872839  0.000008503996611
-0.000157846225852  0.000005719915333 -0.000197522897687 -0.000012294624010
-0.000002547402448  0.000026305673479 -0.000190277091678  0.000012467113833
-0.000218567465104  0.000019225415536 -0.000011030299816  0.000023682450623
-0.000214624610593  0.000020667490110 -0.000229289099730  0.000031900177357
-0.000006901099326 -0.000010189424947
-------
31轨道能量0.152723占0.000000
-0.000001484697707 -0.000002021678521 -0.011269736987441 -0.038830417095759
-0.008858615646339  0.000001192390365 -0.006923508623065 -0.023842815223962
-0.005418954995446  0.000041590787919 -0.067391763573696 -0.232026052460321
-0.052998414149352  0.001793328350150  0.000775609630928 -0.002568937981078
0.003688605910249 -0.001068780674560 -0.006386930085737 -0.000062943108962
0.199687105918283  0.687684976534009  0.156868067056184  0.000000096154362
0.000000657329257 -0.011210137098576 -0.038624144508054 -0.008813875928149
-0.000002083821188 -0.006889998263354 -0.023739788458030 -0.005413447465109
-0.000018259645012 -0.066955664071598 -0.230792413990635 -0.052580672751758
-0.003071293835289  0.004370602232574 -0.001299308397285 -0.005372985400140
-0.002350939654665 -0.002706808510529 -0.000070970422528  0.198318280915318
0.683514266154416  0.155894609381509  0.000000339923235  0.000001176339340
0.000304981657499  0.001042477771998  0.000239760902136 -0.000010467863819
0.000544040658142  0.001845662740944  0.000423422866543  0.000056642800415
0.000984897727369  0.003286180141148  0.000798961685623  0.000314520592607
-0.001550492509766  0.001235971917159  0.00036232885
dt =pandas.read_fwf ("ga1.01.in", header =None, skiprows=11, nrows=12, colspecs=[(0,5), (12,35), (36,61),(62,91)])