In this example we will show how to create huge data sets in Python.
Source Code
import numpy
a = numpy.random.uniform(0.0, 10.0, 500)
print(a)
Output:
[0.49045755 4.04077808 2.99087707 0.61208928 3.18595275 2.15808885
5.4578181 0.69516512 9.28580191 6.56211042 0.09110887 3.24914326
2.96993361 0.33851967 0.50808232 5.97684734 9.46822632 9.16046096
9.00518288 7.79285203 0.9481733 4.01827189 8.81685521 9.05030006
4.11104388 3.58395177 8.25523492 7.94446796 6.11258316 7.8230953
0.77020416 5.08288487 6.5024108 1.54020122 8.93786169 0.5622971
6.06992442 8.16218416 3.59438875 9.38207308 6.65519216 4.81283697
2.64618512 1.19486718 2.44272869 4.23249429 7.16541947 4.20254406
0.34276773 2.73932866 2.82266194 3.33764083 2.6672006 9.25535364
9.68034106 3.40384612 2.55479436 1.77634397 3.84825458 0.26844113
4.0126251 1.63741342 2.88677212 0.34508138 4.59479155 0.62545402
4.43924971 1.57084926 6.81531342 6.55830505 1.59124473 5.17354456
0.81606448 4.22106931 3.32570052 3.60834331 6.11429834 2.72290964
3.80663145 2.28352176 0.55148397 2.73691157 5.93389177 7.91989628
2.33368904 9.14399985 2.51454269 6.71392589 7.59484225 8.96772695
6.82904134 7.03517275 9.49186989 6.78392469 9.2608395 3.82691951
3.22290599 1.97806793 1.83120583 9.25043138 2.9002708 8.3552442
9.62923481 7.62896586 3.53357675 1.82732376 0.27410392 6.50570955
0.43893834 4.89367806 6.48858697 2.3139695 4.38845499 8.61393812
5.11391591 2.96979738 6.94352335 8.85782484 2.16640026 5.67658121
6.53153 1.00411418 5.31652335 0.4861622 0.85244669 0.71499805
9.28759344 2.05235107 3.02886374 9.65748255 7.4957515 3.93135897
0.40007585 8.53566213 5.96224858 4.12878008 7.06652161 1.45374425
6.71106567 8.0440528 7.38226797 0.60105179 8.69710938 9.74970401
3.58906527 4.24449059 5.47484833 9.89138548 3.37342445 5.6873682
1.85909315 3.32150949 9.11330557 8.45181538 6.82168716 0.06691846
7.61972774 8.112106 5.74796213 7.08585992 0.94361151 5.83387627
9.93710773 3.8429354 4.8794135 2.59916806 8.70058216 4.65171813
2.61802476 1.12157293 8.34883278 6.32341443 8.63386134 1.31091659
9.51599956 0.6175686 6.34554641 3.72334809 1.216399 6.00864298
6.51506567 8.36774772 2.24549723 2.36098075 1.55262264 6.61078173
4.39347786 2.14119982 2.49840351 0.11410231 8.03851058 7.53125495
1.24520709 2.92827605 0.70136139 4.40568271 3.51570883 0.56627677
6.32088124 1.65491063 4.68262063 1.95566904 4.54177389 5.65626112
3.27838731 0.07978899 1.08830714 9.87701902 4.77584322 3.64321788
2.28733738 4.19182663 9.9150518 8.61145254 4.94057408 0.8829268
0.15461052 5.72742964 3.3768469 6.17468255 1.20517476 7.52171661
8.25772575 8.78044186 0.0359578 7.7193723 7.50984227 0.95574187
9.53827639 1.29582906 6.76565398 4.48144483 6.89450743 4.52848881
4.11619381 7.49784577 0.58244297 7.98128002 2.23674279 1.32038785
0.26825532 4.22722184 9.62226404 8.59043071 4.38928214 0.97146045
4.13689686 8.91702967 7.4724879 4.00440565 7.06849427 5.55040822
0.2726553 9.8921518 2.70915311 6.13626906 3.00311499 3.79037513
7.5782631 8.40805178 3.39751955 3.55868244 7.78516514 0.26591465
1.84264249 7.87762459 8.38435064 1.21276115 7.54125996 0.97744051
1.10471837 9.19641103 3.209219 5.10209567 5.01909016 7.5461263
7.18555898 8.53886872 5.22779816 5.72144591 4.12725988 8.48765832
4.19139584 6.82364208 8.59155835 3.70607909 8.88985229 0.45934264
7.1061348 8.97011374 7.01920882 7.2441193 1.69909804 2.80170103
4.1948026 4.88117255 7.18210349 1.09231353 8.62106568 2.52499063
5.95923894 3.04425711 1.37803203 0.83479366 2.90219507 4.00379572
1.51476805 5.11960576 1.86244588 2.19186422 3.1433037 7.14835811
3.48956099 4.45302147 3.07471648 7.84226973 6.2741162 2.35428821
1.8923253 4.86113996 6.89160879 1.96799107 7.84709166 1.10733317
5.19522016 4.09031746 8.0618142 3.64887147 1.74602397 0.87989134
7.89630584 4.57006349 1.8063958 5.59185787 9.01552468 1.12852875
6.36843975 3.00892453 8.27748789 0.24847574 0.3716227 6.29862516
5.3372678 9.91236454 0.15082024 7.17657415 3.29750846 5.69095847
0.50097779 5.8995617 9.20425357 8.18669697 7.51289867 0.77633701
5.22370632 8.02348504 3.10620783 4.50911188 0.01677837 4.29020869
2.47458273 4.59944375 2.32794717 7.76783441 2.18485009 4.79465437
9.06988599 7.02489158 2.57786026 9.08689389 7.54999818 8.1632262
9.24141584 0.4014722 6.69665236 1.67291483 0.56865032 0.15067322
8.63301779 7.55872601 4.20077305 4.5508411 2.73859893 8.11295641
3.26488719 7.11464674 7.00902876 7.06897409 2.23703403 2.12909696
8.4394103 2.46631236 9.56685739 3.4059536 2.57192738 7.25356881
8.84529782 9.35180017 4.88133573 9.80074478 5.37241596 4.91519104
1.27976891 7.92966105 0.02089237 2.58393067 2.2184047 5.31683423
3.00874522 4.69279961 4.94325159 1.73588127 8.05271259 6.66317936
0.99247504 0.52136734 5.62642337 8.46054262 3.18536183 1.9144428
1.18350071 6.6055233 0.79953417 9.9601572 8.07639285 7.02728711
8.93801657 9.88975206 7.36817485 3.52413605 4.85140823 4.0499134
5.43561209 5.48704287 1.89161071 8.44119513 3.16212224 5.1057636
3.70315271 8.37683195 8.44688107 4.57988805 2.73307076 0.85496085
6.48645284 4.61614314 5.6306447 3.58105087 3.73692887 8.32904961
3.00493153 3.80399701 2.59040446 3.087497 2.96255762 3.79952274
8.95012918 9.33243778 7.71142874 7.42242426 1.58890245 3.80558054
7.7736247 9.72464847 9.37916718 3.97697488 8.23174979 2.89592717
1.90069974 0.70178077 6.1875811 7.70939524 6.12214354 6.36459053
4.94011186 2.65621026 5.38846976 9.93813254 1.82723057 1.67466308
6.95232998 0.66026818 2.7554765 9.09664387 0.26687859 8.09636038
6.58860691 9.16494241 0.6200577 9.82263353 2.51216439 1.28405343
5.10452447 6.62629965 5.65130823 8.26557798 3.81395741 4.29770279
3.52888354 3.10901212]