numpy_practice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "a = np.arange(15).reshape(3, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 5)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'int32'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.dtype.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<memory at 0x000001BE3AC4A260>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.itemsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.itemsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.array((1,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.array([1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function ndarray.__array__>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.__array__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function ndarray.all>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.all\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function ndarray.min>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = np.array((1,2,3), dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "d=np.array((2, 3, 4), dtype=np.int16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = np.array([[1.5, 2, 3], [4, 5, 6]], dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.22222222, 0.44444444, 0.66666667, 0.88888889,\n",
       "       1.11111111, 1.33333333, 1.55555556, 1.77777778, 2.        ])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,2,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.22222222, 0.44444444, 0.66666667, 0.88888889,\n",
       "       1.11111111, 1.33333333, 1.55555556, 1.77777778, 2.        ])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,2,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.zeros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function numpy.zeros>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.zeros((2,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.arange(24).reshape(2,3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14, 15],\n",
       "        [16, 17, 18, 19],\n",
       "        [20, 21, 22, 23]]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   0    1    2 ...   97   98   99]\n",
      " [ 100  101  102 ...  197  198  199]\n",
      " [ 200  201  202 ...  297  298  299]\n",
      " ...\n",
      " [9700 9701 9702 ... 9797 9798 9799]\n",
      " [9800 9801 9802 ... 9897 9898 9899]\n",
      " [9900 9901 9902 ... 9997 9998 9999]]\n"
     ]
    }
   ],
   "source": [
    "print(np.arange(10000).reshape(100, 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sys' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\HP\\Desktop\\python codes\\numpy_practice.ipynb Cell 31\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/HP/Desktop/python%20codes/numpy_practice.ipynb#X42sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m np\u001b[39m.\u001b[39mset_printoptions(threshold\u001b[39m=\u001b[39msys\u001b[39m.\u001b[39mmaxsize)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'sys' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "np.set_printoptions(threshold=sys.maxsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "np.set_printoptions(threshold=sys.maxsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   0    1    2    3    4    5    6    7    8    9   10   11   12   13\n",
      "    14   15   16   17   18   19   20   21   22   23   24   25   26   27\n",
      "    28   29   30   31   32   33   34   35   36   37   38   39   40   41\n",
      "    42   43   44   45   46   47   48   49   50   51   52   53   54   55\n",
      "    56   57   58   59   60   61   62   63   64   65   66   67   68   69\n",
      "    70   71   72   73   74   75   76   77   78   79   80   81   82   83\n",
      "    84   85   86   87   88   89   90   91   92   93   94   95   96   97\n",
      "    98   99]\n",
      " [ 100  101  102  103  104  105  106  107  108  109  110  111  112  113\n",
      "   114  115  116  117  118  119  120  121  122  123  124  125  126  127\n",
      "   128  129  130  131  132  133  134  135  136  137  138  139  140  141\n",
      "   142  143  144  145  146  147  148  149  150  151  152  153  154  155\n",
      "   156  157  158  159  160  161  162  163  164  165  166  167  168  169\n",
      "   170  171  172  173  174  175  176  177  178  179  180  181  182  183\n",
      "   184  185  186  187  188  189  190  191  192  193  194  195  196  197\n",
      "   198  199]\n",
      " [ 200  201  202  203  204  205  206  207  208  209  210  211  212  213\n",
      "   214  215  216  217  218  219  220  221  222  223  224  225  226  227\n",
      "   228  229  230  231  232  233  234  235  236  237  238  239  240  241\n",
      "   242  243  244  245  246  247  248  249  250  251  252  253  254  255\n",
      "   256  257  258  259  260  261  262  263  264  265  266  267  268  269\n",
      "   270  271  272  273  274  275  276  277  278  279  280  281  282  283\n",
      "   284  285  286  287  288  289  290  291  292  293  294  295  296  297\n",
      "   298  299]\n",
      " [ 300  301  302  303  304  305  306  307  308  309  310  311  312  313\n",
      "   314  315  316  317  318  319  320  321  322  323  324  325  326  327\n",
      "   328  329  330  331  332  333  334  335  336  337  338  339  340  341\n",
      "   342  343  344  345  346  347  348  349  350  351  352  353  354  355\n",
      "   356  357  358  359  360  361  362  363  364  365  366  367  368  369\n",
      "   370  371  372  373  374  375  376  377  378  379  380  381  382  383\n",
      "   384  385  386  387  388  389  390  391  392  393  394  395  396  397\n",
      "   398  399]\n",
      " [ 400  401  402  403  404  405  406  407  408  409  410  411  412  413\n",
      "   414  415  416  417  418  419  420  421  422  423  424  425  426  427\n",
      "   428  429  430  431  432  433  434  435  436  437  438  439  440  441\n",
      "   442  443  444  445  446  447  448  449  450  451  452  453  454  455\n",
      "   456  457  458  459  460  461  462  463  464  465  466  467  468  469\n",
      "   470  471  472  473  474  475  476  477  478  479  480  481  482  483\n",
      "   484  485  486  487  488  489  490  491  492  493  494  495  496  497\n",
      "   498  499]\n",
      " [ 500  501  502  503  504  505  506  507  508  509  510  511  512  513\n",
      "   514  515  516  517  518  519  520  521  522  523  524  525  526  527\n",
      "   528  529  530  531  532  533  534  535  536  537  538  539  540  541\n",
      "   542  543  544  545  546  547  548  549  550  551  552  553  554  555\n",
      "   556  557  558  559  560  561  562  563  564  565  566  567  568  569\n",
      "   570  571  572  573  574  575  576  577  578  579  580  581  582  583\n",
      "   584  585  586  587  588  589  590  591  592  593  594  595  596  597\n",
      "   598  599]\n",
      " [ 600  601  602  603  604  605  606  607  608  609  610  611  612  613\n",
      "   614  615  616  617  618  619  620  621  622  623  624  625  626  627\n",
      "   628  629  630  631  632  633  634  635  636  637  638  639  640  641\n",
      "   642  643  644  645  646  647  648  649  650  651  652  653  654  655\n",
      "   656  657  658  659  660  661  662  663  664  665  666  667  668  669\n",
      "   670  671  672  673  674  675  676  677  678  679  680  681  682  683\n",
      "   684  685  686  687  688  689  690  691  692  693  694  695  696  697\n",
      "   698  699]\n",
      " [ 700  701  702  703  704  705  706  707  708  709  710  711  712  713\n",
      "   714  715  716  717  718  719  720  721  722  723  724  725  726  727\n",
      "   728  729  730  731  732  733  734  735  736  737  738  739  740  741\n",
      "   742  743  744  745  746  747  748  749  750  751  752  753  754  755\n",
      "   756  757  758  759  760  761  762  763  764  765  766  767  768  769\n",
      "   770  771  772  773  774  775  776  777  778  779  780  781  782  783\n",
      "   784  785  786  787  788  789  790  791  792  793  794  795  796  797\n",
      "   798  799]\n",
      " [ 800  801  802  803  804  805  806  807  808  809  810  811  812  813\n",
      "   814  815  816  817  818  819  820  821  822  823  824  825  826  827\n",
      "   828  829  830  831  832  833  834  835  836  837  838  839  840  841\n",
      "   842  843  844  845  846  847  848  849  850  851  852  853  854  855\n",
      "   856  857  858  859  860  861  862  863  864  865  866  867  868  869\n",
      "   870  871  872  873  874  875  876  877  878  879  880  881  882  883\n",
      "   884  885  886  887  888  889  890  891  892  893  894  895  896  897\n",
      "   898  899]\n",
      " [ 900  901  902  903  904  905  906  907  908  909  910  911  912  913\n",
      "   914  915  916  917  918  919  920  921  922  923  924  925  926  927\n",
      "   928  929  930  931  932  933  934  935  936  937  938  939  940  941\n",
      "   942  943  944  945  946  947  948  949  950  951  952  953  954  955\n",
      "   956  957  958  959  960  961  962  963  964  965  966  967  968  969\n",
      "   970  971  972  973  974  975  976  977  978  979  980  981  982  983\n",
      "   984  985  986  987  988  989  990  991  992  993  994  995  996  997\n",
      "   998  999]\n",
      " [1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013\n",
      "  1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027\n",
      "  1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041\n",
      "  1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055\n",
      "  1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069\n",
      "  1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083\n",
      "  1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097\n",
      "  1098 1099]\n",
      " [1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113\n",
      "  1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127\n",
      "  1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141\n",
      "  1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155\n",
      "  1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169\n",
      "  1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183\n",
      "  1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197\n",
      "  1198 1199]\n",
      " [1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213\n",
      "  1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227\n",
      "  1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241\n",
      "  1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255\n",
      "  1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269\n",
      "  1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283\n",
      "  1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297\n",
      "  1298 1299]\n",
      " [1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313\n",
      "  1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327\n",
      "  1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341\n",
      "  1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355\n",
      "  1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369\n",
      "  1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383\n",
      "  1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397\n",
      "  1398 1399]\n",
      " [1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413\n",
      "  1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427\n",
      "  1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441\n",
      "  1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455\n",
      "  1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469\n",
      "  1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483\n",
      "  1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497\n",
      "  1498 1499]\n",
      " [1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513\n",
      "  1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527\n",
      "  1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541\n",
      "  1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555\n",
      "  1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569\n",
      "  1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583\n",
      "  1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597\n",
      "  1598 1599]\n",
      " [1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613\n",
      "  1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627\n",
      "  1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641\n",
      "  1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655\n",
      "  1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669\n",
      "  1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683\n",
      "  1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697\n",
      "  1698 1699]\n",
      " [1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713\n",
      "  1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727\n",
      "  1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741\n",
      "  1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755\n",
      "  1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769\n",
      "  1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783\n",
      "  1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797\n",
      "  1798 1799]\n",
      " [1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813\n",
      "  1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827\n",
      "  1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841\n",
      "  1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855\n",
      "  1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869\n",
      "  1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883\n",
      "  1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897\n",
      "  1898 1899]\n",
      " [1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913\n",
      "  1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927\n",
      "  1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941\n",
      "  1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955\n",
      "  1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969\n",
      "  1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983\n",
      "  1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997\n",
      "  1998 1999]\n",
      " [2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013\n",
      "  2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027\n",
      "  2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041\n",
      "  2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055\n",
      "  2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069\n",
      "  2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083\n",
      "  2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097\n",
      "  2098 2099]\n",
      " [2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113\n",
      "  2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127\n",
      "  2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141\n",
      "  2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155\n",
      "  2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169\n",
      "  2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183\n",
      "  2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197\n",
      "  2198 2199]\n",
      " [2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213\n",
      "  2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227\n",
      "  2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241\n",
      "  2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255\n",
      "  2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269\n",
      "  2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283\n",
      "  2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297\n",
      "  2298 2299]\n",
      " [2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313\n",
      "  2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327\n",
      "  2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340 2341\n",
      "  2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355\n",
      "  2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 2368 2369\n",
      "  2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381 2382 2383\n",
      "  2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395 2396 2397\n",
      "  2398 2399]\n",
      " [2400 2401 2402 2403 2404 2405 2406 2407 2408 2409 2410 2411 2412 2413\n",
      "  2414 2415 2416 2417 2418 2419 2420 2421 2422 2423 2424 2425 2426 2427\n",
      "  2428 2429 2430 2431 2432 2433 2434 2435 2436 2437 2438 2439 2440 2441\n",
      "  2442 2443 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455\n",
      "  2456 2457 2458 2459 2460 2461 2462 2463 2464 2465 2466 2467 2468 2469\n",
      "  2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483\n",
      "  2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496 2497\n",
      "  2498 2499]\n",
      " [2500 2501 2502 2503 2504 2505 2506 2507 2508 2509 2510 2511 2512 2513\n",
      "  2514 2515 2516 2517 2518 2519 2520 2521 2522 2523 2524 2525 2526 2527\n",
      "  2528 2529 2530 2531 2532 2533 2534 2535 2536 2537 2538 2539 2540 2541\n",
      "  2542 2543 2544 2545 2546 2547 2548 2549 2550 2551 2552 2553 2554 2555\n",
      "  2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2567 2568 2569\n",
      "  2570 2571 2572 2573 2574 2575 2576 2577 2578 2579 2580 2581 2582 2583\n",
      "  2584 2585 2586 2587 2588 2589 2590 2591 2592 2593 2594 2595 2596 2597\n",
      "  2598 2599]\n",
      " [2600 2601 2602 2603 2604 2605 2606 2607 2608 2609 2610 2611 2612 2613\n",
      "  2614 2615 2616 2617 2618 2619 2620 2621 2622 2623 2624 2625 2626 2627\n",
      "  2628 2629 2630 2631 2632 2633 2634 2635 2636 2637 2638 2639 2640 2641\n",
      "  2642 2643 2644 2645 2646 2647 2648 2649 2650 2651 2652 2653 2654 2655\n",
      "  2656 2657 2658 2659 2660 2661 2662 2663 2664 2665 2666 2667 2668 2669\n",
      "  2670 2671 2672 2673 2674 2675 2676 2677 2678 2679 2680 2681 2682 2683\n",
      "  2684 2685 2686 2687 2688 2689 2690 2691 2692 2693 2694 2695 2696 2697\n",
      "  2698 2699]\n",
      " [2700 2701 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711 2712 2713\n",
      "  2714 2715 2716 2717 2718 2719 2720 2721 2722 2723 2724 2725 2726 2727\n",
      "  2728 2729 2730 2731 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741\n",
      "  2742 2743 2744 2745 2746 2747 2748 2749 2750 2751 2752 2753 2754 2755\n",
      "  2756 2757 2758 2759 2760 2761 2762 2763 2764 2765 2766 2767 2768 2769\n",
      "  2770 2771 2772 2773 2774 2775 2776 2777 2778 2779 2780 2781 2782 2783\n",
      "  2784 2785 2786 2787 2788 2789 2790 2791 2792 2793 2794 2795 2796 2797\n",
      "  2798 2799]\n",
      " [2800 2801 2802 2803 2804 2805 2806 2807 2808 2809 2810 2811 2812 2813\n",
      "  2814 2815 2816 2817 2818 2819 2820 2821 2822 2823 2824 2825 2826 2827\n",
      "  2828 2829 2830 2831 2832 2833 2834 2835 2836 2837 2838 2839 2840 2841\n",
      "  2842 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855\n",
      "  2856 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2869\n",
      "  2870 2871 2872 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883\n",
      "  2884 2885 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895 2896 2897\n",
      "  2898 2899]\n",
      " [2900 2901 2902 2903 2904 2905 2906 2907 2908 2909 2910 2911 2912 2913\n",
      "  2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926 2927\n",
      "  2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 2940 2941\n",
      "  2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2954 2955\n",
      "  2956 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966 2967 2968 2969\n",
      "  2970 2971 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983\n",
      "  2984 2985 2986 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996 2997\n",
      "  2998 2999]\n",
      " [3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013\n",
      "  3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027\n",
      "  3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041\n",
      "  3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055\n",
      "  3056 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068 3069\n",
      "  3070 3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083\n",
      "  3084 3085 3086 3087 3088 3089 3090 3091 3092 3093 3094 3095 3096 3097\n",
      "  3098 3099]\n",
      " [3100 3101 3102 3103 3104 3105 3106 3107 3108 3109 3110 3111 3112 3113\n",
      "  3114 3115 3116 3117 3118 3119 3120 3121 3122 3123 3124 3125 3126 3127\n",
      "  3128 3129 3130 3131 3132 3133 3134 3135 3136 3137 3138 3139 3140 3141\n",
      "  3142 3143 3144 3145 3146 3147 3148 3149 3150 3151 3152 3153 3154 3155\n",
      "  3156 3157 3158 3159 3160 3161 3162 3163 3164 3165 3166 3167 3168 3169\n",
      "  3170 3171 3172 3173 3174 3175 3176 3177 3178 3179 3180 3181 3182 3183\n",
      "  3184 3185 3186 3187 3188 3189 3190 3191 3192 3193 3194 3195 3196 3197\n",
      "  3198 3199]\n",
      " [3200 3201 3202 3203 3204 3205 3206 3207 3208 3209 3210 3211 3212 3213\n",
      "  3214 3215 3216 3217 3218 3219 3220 3221 3222 3223 3224 3225 3226 3227\n",
      "  3228 3229 3230 3231 3232 3233 3234 3235 3236 3237 3238 3239 3240 3241\n",
      "  3242 3243 3244 3245 3246 3247 3248 3249 3250 3251 3252 3253 3254 3255\n",
      "  3256 3257 3258 3259 3260 3261 3262 3263 3264 3265 3266 3267 3268 3269\n",
      "  3270 3271 3272 3273 3274 3275 3276 3277 3278 3279 3280 3281 3282 3283\n",
      "  3284 3285 3286 3287 3288 3289 3290 3291 3292 3293 3294 3295 3296 3297\n",
      "  3298 3299]\n",
      " [3300 3301 3302 3303 3304 3305 3306 3307 3308 3309 3310 3311 3312 3313\n",
      "  3314 3315 3316 3317 3318 3319 3320 3321 3322 3323 3324 3325 3326 3327\n",
      "  3328 3329 3330 3331 3332 3333 3334 3335 3336 3337 3338 3339 3340 3341\n",
      "  3342 3343 3344 3345 3346 3347 3348 3349 3350 3351 3352 3353 3354 3355\n",
      "  3356 3357 3358 3359 3360 3361 3362 3363 3364 3365 3366 3367 3368 3369\n",
      "  3370 3371 3372 3373 3374 3375 3376 3377 3378 3379 3380 3381 3382 3383\n",
      "  3384 3385 3386 3387 3388 3389 3390 3391 3392 3393 3394 3395 3396 3397\n",
      "  3398 3399]\n",
      " [3400 3401 3402 3403 3404 3405 3406 3407 3408 3409 3410 3411 3412 3413\n",
      "  3414 3415 3416 3417 3418 3419 3420 3421 3422 3423 3424 3425 3426 3427\n",
      "  3428 3429 3430 3431 3432 3433 3434 3435 3436 3437 3438 3439 3440 3441\n",
      "  3442 3443 3444 3445 3446 3447 3448 3449 3450 3451 3452 3453 3454 3455\n",
      "  3456 3457 3458 3459 3460 3461 3462 3463 3464 3465 3466 3467 3468 3469\n",
      "  3470 3471 3472 3473 3474 3475 3476 3477 3478 3479 3480 3481 3482 3483\n",
      "  3484 3485 3486 3487 3488 3489 3490 3491 3492 3493 3494 3495 3496 3497\n",
      "  3498 3499]\n",
      " [3500 3501 3502 3503 3504 3505 3506 3507 3508 3509 3510 3511 3512 3513\n",
      "  3514 3515 3516 3517 3518 3519 3520 3521 3522 3523 3524 3525 3526 3527\n",
      "  3528 3529 3530 3531 3532 3533 3534 3535 3536 3537 3538 3539 3540 3541\n",
      "  3542 3543 3544 3545 3546 3547 3548 3549 3550 3551 3552 3553 3554 3555\n",
      "  3556 3557 3558 3559 3560 3561 3562 3563 3564 3565 3566 3567 3568 3569\n",
      "  3570 3571 3572 3573 3574 3575 3576 3577 3578 3579 3580 3581 3582 3583\n",
      "  3584 3585 3586 3587 3588 3589 3590 3591 3592 3593 3594 3595 3596 3597\n",
      "  3598 3599]\n",
      " [3600 3601 3602 3603 3604 3605 3606 3607 3608 3609 3610 3611 3612 3613\n",
      "  3614 3615 3616 3617 3618 3619 3620 3621 3622 3623 3624 3625 3626 3627\n",
      "  3628 3629 3630 3631 3632 3633 3634 3635 3636 3637 3638 3639 3640 3641\n",
      "  3642 3643 3644 3645 3646 3647 3648 3649 3650 3651 3652 3653 3654 3655\n",
      "  3656 3657 3658 3659 3660 3661 3662 3663 3664 3665 3666 3667 3668 3669\n",
      "  3670 3671 3672 3673 3674 3675 3676 3677 3678 3679 3680 3681 3682 3683\n",
      "  3684 3685 3686 3687 3688 3689 3690 3691 3692 3693 3694 3695 3696 3697\n",
      "  3698 3699]\n",
      " [3700 3701 3702 3703 3704 3705 3706 3707 3708 3709 3710 3711 3712 3713\n",
      "  3714 3715 3716 3717 3718 3719 3720 3721 3722 3723 3724 3725 3726 3727\n",
      "  3728 3729 3730 3731 3732 3733 3734 3735 3736 3737 3738 3739 3740 3741\n",
      "  3742 3743 3744 3745 3746 3747 3748 3749 3750 3751 3752 3753 3754 3755\n",
      "  3756 3757 3758 3759 3760 3761 3762 3763 3764 3765 3766 3767 3768 3769\n",
      "  3770 3771 3772 3773 3774 3775 3776 3777 3778 3779 3780 3781 3782 3783\n",
      "  3784 3785 3786 3787 3788 3789 3790 3791 3792 3793 3794 3795 3796 3797\n",
      "  3798 3799]\n",
      " [3800 3801 3802 3803 3804 3805 3806 3807 3808 3809 3810 3811 3812 3813\n",
      "  3814 3815 3816 3817 3818 3819 3820 3821 3822 3823 3824 3825 3826 3827\n",
      "  3828 3829 3830 3831 3832 3833 3834 3835 3836 3837 3838 3839 3840 3841\n",
      "  3842 3843 3844 3845 3846 3847 3848 3849 3850 3851 3852 3853 3854 3855\n",
      "  3856 3857 3858 3859 3860 3861 3862 3863 3864 3865 3866 3867 3868 3869\n",
      "  3870 3871 3872 3873 3874 3875 3876 3877 3878 3879 3880 3881 3882 3883\n",
      "  3884 3885 3886 3887 3888 3889 3890 3891 3892 3893 3894 3895 3896 3897\n",
      "  3898 3899]\n",
      " [3900 3901 3902 3903 3904 3905 3906 3907 3908 3909 3910 3911 3912 3913\n",
      "  3914 3915 3916 3917 3918 3919 3920 3921 3922 3923 3924 3925 3926 3927\n",
      "  3928 3929 3930 3931 3932 3933 3934 3935 3936 3937 3938 3939 3940 3941\n",
      "  3942 3943 3944 3945 3946 3947 3948 3949 3950 3951 3952 3953 3954 3955\n",
      "  3956 3957 3958 3959 3960 3961 3962 3963 3964 3965 3966 3967 3968 3969\n",
      "  3970 3971 3972 3973 3974 3975 3976 3977 3978 3979 3980 3981 3982 3983\n",
      "  3984 3985 3986 3987 3988 3989 3990 3991 3992 3993 3994 3995 3996 3997\n",
      "  3998 3999]\n",
      " [4000 4001 4002 4003 4004 4005 4006 4007 4008 4009 4010 4011 4012 4013\n",
      "  4014 4015 4016 4017 4018 4019 4020 4021 4022 4023 4024 4025 4026 4027\n",
      "  4028 4029 4030 4031 4032 4033 4034 4035 4036 4037 4038 4039 4040 4041\n",
      "  4042 4043 4044 4045 4046 4047 4048 4049 4050 4051 4052 4053 4054 4055\n",
      "  4056 4057 4058 4059 4060 4061 4062 4063 4064 4065 4066 4067 4068 4069\n",
      "  4070 4071 4072 4073 4074 4075 4076 4077 4078 4079 4080 4081 4082 4083\n",
      "  4084 4085 4086 4087 4088 4089 4090 4091 4092 4093 4094 4095 4096 4097\n",
      "  4098 4099]\n",
      " [4100 4101 4102 4103 4104 4105 4106 4107 4108 4109 4110 4111 4112 4113\n",
      "  4114 4115 4116 4117 4118 4119 4120 4121 4122 4123 4124 4125 4126 4127\n",
      "  4128 4129 4130 4131 4132 4133 4134 4135 4136 4137 4138 4139 4140 4141\n",
      "  4142 4143 4144 4145 4146 4147 4148 4149 4150 4151 4152 4153 4154 4155\n",
      "  4156 4157 4158 4159 4160 4161 4162 4163 4164 4165 4166 4167 4168 4169\n",
      "  4170 4171 4172 4173 4174 4175 4176 4177 4178 4179 4180 4181 4182 4183\n",
      "  4184 4185 4186 4187 4188 4189 4190 4191 4192 4193 4194 4195 4196 4197\n",
      "  4198 4199]\n",
      " [4200 4201 4202 4203 4204 4205 4206 4207 4208 4209 4210 4211 4212 4213\n",
      "  4214 4215 4216 4217 4218 4219 4220 4221 4222 4223 4224 4225 4226 4227\n",
      "  4228 4229 4230 4231 4232 4233 4234 4235 4236 4237 4238 4239 4240 4241\n",
      "  4242 4243 4244 4245 4246 4247 4248 4249 4250 4251 4252 4253 4254 4255\n",
      "  4256 4257 4258 4259 4260 4261 4262 4263 4264 4265 4266 4267 4268 4269\n",
      "  4270 4271 4272 4273 4274 4275 4276 4277 4278 4279 4280 4281 4282 4283\n",
      "  4284 4285 4286 4287 4288 4289 4290 4291 4292 4293 4294 4295 4296 4297\n",
      "  4298 4299]\n",
      " [4300 4301 4302 4303 4304 4305 4306 4307 4308 4309 4310 4311 4312 4313\n",
      "  4314 4315 4316 4317 4318 4319 4320 4321 4322 4323 4324 4325 4326 4327\n",
      "  4328 4329 4330 4331 4332 4333 4334 4335 4336 4337 4338 4339 4340 4341\n",
      "  4342 4343 4344 4345 4346 4347 4348 4349 4350 4351 4352 4353 4354 4355\n",
      "  4356 4357 4358 4359 4360 4361 4362 4363 4364 4365 4366 4367 4368 4369\n",
      "  4370 4371 4372 4373 4374 4375 4376 4377 4378 4379 4380 4381 4382 4383\n",
      "  4384 4385 4386 4387 4388 4389 4390 4391 4392 4393 4394 4395 4396 4397\n",
      "  4398 4399]\n",
      " [4400 4401 4402 4403 4404 4405 4406 4407 4408 4409 4410 4411 4412 4413\n",
      "  4414 4415 4416 4417 4418 4419 4420 4421 4422 4423 4424 4425 4426 4427\n",
      "  4428 4429 4430 4431 4432 4433 4434 4435 4436 4437 4438 4439 4440 4441\n",
      "  4442 4443 4444 4445 4446 4447 4448 4449 4450 4451 4452 4453 4454 4455\n",
      "  4456 4457 4458 4459 4460 4461 4462 4463 4464 4465 4466 4467 4468 4469\n",
      "  4470 4471 4472 4473 4474 4475 4476 4477 4478 4479 4480 4481 4482 4483\n",
      "  4484 4485 4486 4487 4488 4489 4490 4491 4492 4493 4494 4495 4496 4497\n",
      "  4498 4499]\n",
      " [4500 4501 4502 4503 4504 4505 4506 4507 4508 4509 4510 4511 4512 4513\n",
      "  4514 4515 4516 4517 4518 4519 4520 4521 4522 4523 4524 4525 4526 4527\n",
      "  4528 4529 4530 4531 4532 4533 4534 4535 4536 4537 4538 4539 4540 4541\n",
      "  4542 4543 4544 4545 4546 4547 4548 4549 4550 4551 4552 4553 4554 4555\n",
      "  4556 4557 4558 4559 4560 4561 4562 4563 4564 4565 4566 4567 4568 4569\n",
      "  4570 4571 4572 4573 4574 4575 4576 4577 4578 4579 4580 4581 4582 4583\n",
      "  4584 4585 4586 4587 4588 4589 4590 4591 4592 4593 4594 4595 4596 4597\n",
      "  4598 4599]\n",
      " [4600 4601 4602 4603 4604 4605 4606 4607 4608 4609 4610 4611 4612 4613\n",
      "  4614 4615 4616 4617 4618 4619 4620 4621 4622 4623 4624 4625 4626 4627\n",
      "  4628 4629 4630 4631 4632 4633 4634 4635 4636 4637 4638 4639 4640 4641\n",
      "  4642 4643 4644 4645 4646 4647 4648 4649 4650 4651 4652 4653 4654 4655\n",
      "  4656 4657 4658 4659 4660 4661 4662 4663 4664 4665 4666 4667 4668 4669\n",
      "  4670 4671 4672 4673 4674 4675 4676 4677 4678 4679 4680 4681 4682 4683\n",
      "  4684 4685 4686 4687 4688 4689 4690 4691 4692 4693 4694 4695 4696 4697\n",
      "  4698 4699]\n",
      " [4700 4701 4702 4703 4704 4705 4706 4707 4708 4709 4710 4711 4712 4713\n",
      "  4714 4715 4716 4717 4718 4719 4720 4721 4722 4723 4724 4725 4726 4727\n",
      "  4728 4729 4730 4731 4732 4733 4734 4735 4736 4737 4738 4739 4740 4741\n",
      "  4742 4743 4744 4745 4746 4747 4748 4749 4750 4751 4752 4753 4754 4755\n",
      "  4756 4757 4758 4759 4760 4761 4762 4763 4764 4765 4766 4767 4768 4769\n",
      "  4770 4771 4772 4773 4774 4775 4776 4777 4778 4779 4780 4781 4782 4783\n",
      "  4784 4785 4786 4787 4788 4789 4790 4791 4792 4793 4794 4795 4796 4797\n",
      "  4798 4799]\n",
      " [4800 4801 4802 4803 4804 4805 4806 4807 4808 4809 4810 4811 4812 4813\n",
      "  4814 4815 4816 4817 4818 4819 4820 4821 4822 4823 4824 4825 4826 4827\n",
      "  4828 4829 4830 4831 4832 4833 4834 4835 4836 4837 4838 4839 4840 4841\n",
      "  4842 4843 4844 4845 4846 4847 4848 4849 4850 4851 4852 4853 4854 4855\n",
      "  4856 4857 4858 4859 4860 4861 4862 4863 4864 4865 4866 4867 4868 4869\n",
      "  4870 4871 4872 4873 4874 4875 4876 4877 4878 4879 4880 4881 4882 4883\n",
      "  4884 4885 4886 4887 4888 4889 4890 4891 4892 4893 4894 4895 4896 4897\n",
      "  4898 4899]\n",
      " [4900 4901 4902 4903 4904 4905 4906 4907 4908 4909 4910 4911 4912 4913\n",
      "  4914 4915 4916 4917 4918 4919 4920 4921 4922 4923 4924 4925 4926 4927\n",
      "  4928 4929 4930 4931 4932 4933 4934 4935 4936 4937 4938 4939 4940 4941\n",
      "  4942 4943 4944 4945 4946 4947 4948 4949 4950 4951 4952 4953 4954 4955\n",
      "  4956 4957 4958 4959 4960 4961 4962 4963 4964 4965 4966 4967 4968 4969\n",
      "  4970 4971 4972 4973 4974 4975 4976 4977 4978 4979 4980 4981 4982 4983\n",
      "  4984 4985 4986 4987 4988 4989 4990 4991 4992 4993 4994 4995 4996 4997\n",
      "  4998 4999]\n",
      " [5000 5001 5002 5003 5004 5005 5006 5007 5008 5009 5010 5011 5012 5013\n",
      "  5014 5015 5016 5017 5018 5019 5020 5021 5022 5023 5024 5025 5026 5027\n",
      "  5028 5029 5030 5031 5032 5033 5034 5035 5036 5037 5038 5039 5040 5041\n",
      "  5042 5043 5044 5045 5046 5047 5048 5049 5050 5051 5052 5053 5054 5055\n",
      "  5056 5057 5058 5059 5060 5061 5062 5063 5064 5065 5066 5067 5068 5069\n",
      "  5070 5071 5072 5073 5074 5075 5076 5077 5078 5079 5080 5081 5082 5083\n",
      "  5084 5085 5086 5087 5088 5089 5090 5091 5092 5093 5094 5095 5096 5097\n",
      "  5098 5099]\n",
      " [5100 5101 5102 5103 5104 5105 5106 5107 5108 5109 5110 5111 5112 5113\n",
      "  5114 5115 5116 5117 5118 5119 5120 5121 5122 5123 5124 5125 5126 5127\n",
      "  5128 5129 5130 5131 5132 5133 5134 5135 5136 5137 5138 5139 5140 5141\n",
      "  5142 5143 5144 5145 5146 5147 5148 5149 5150 5151 5152 5153 5154 5155\n",
      "  5156 5157 5158 5159 5160 5161 5162 5163 5164 5165 5166 5167 5168 5169\n",
      "  5170 5171 5172 5173 5174 5175 5176 5177 5178 5179 5180 5181 5182 5183\n",
      "  5184 5185 5186 5187 5188 5189 5190 5191 5192 5193 5194 5195 5196 5197\n",
      "  5198 5199]\n",
      " [5200 5201 5202 5203 5204 5205 5206 5207 5208 5209 5210 5211 5212 5213\n",
      "  5214 5215 5216 5217 5218 5219 5220 5221 5222 5223 5224 5225 5226 5227\n",
      "  5228 5229 5230 5231 5232 5233 5234 5235 5236 5237 5238 5239 5240 5241\n",
      "  5242 5243 5244 5245 5246 5247 5248 5249 5250 5251 5252 5253 5254 5255\n",
      "  5256 5257 5258 5259 5260 5261 5262 5263 5264 5265 5266 5267 5268 5269\n",
      "  5270 5271 5272 5273 5274 5275 5276 5277 5278 5279 5280 5281 5282 5283\n",
      "  5284 5285 5286 5287 5288 5289 5290 5291 5292 5293 5294 5295 5296 5297\n",
      "  5298 5299]\n",
      " [5300 5301 5302 5303 5304 5305 5306 5307 5308 5309 5310 5311 5312 5313\n",
      "  5314 5315 5316 5317 5318 5319 5320 5321 5322 5323 5324 5325 5326 5327\n",
      "  5328 5329 5330 5331 5332 5333 5334 5335 5336 5337 5338 5339 5340 5341\n",
      "  5342 5343 5344 5345 5346 5347 5348 5349 5350 5351 5352 5353 5354 5355\n",
      "  5356 5357 5358 5359 5360 5361 5362 5363 5364 5365 5366 5367 5368 5369\n",
      "  5370 5371 5372 5373 5374 5375 5376 5377 5378 5379 5380 5381 5382 5383\n",
      "  5384 5385 5386 5387 5388 5389 5390 5391 5392 5393 5394 5395 5396 5397\n",
      "  5398 5399]\n",
      " [5400 5401 5402 5403 5404 5405 5406 5407 5408 5409 5410 5411 5412 5413\n",
      "  5414 5415 5416 5417 5418 5419 5420 5421 5422 5423 5424 5425 5426 5427\n",
      "  5428 5429 5430 5431 5432 5433 5434 5435 5436 5437 5438 5439 5440 5441\n",
      "  5442 5443 5444 5445 5446 5447 5448 5449 5450 5451 5452 5453 5454 5455\n",
      "  5456 5457 5458 5459 5460 5461 5462 5463 5464 5465 5466 5467 5468 5469\n",
      "  5470 5471 5472 5473 5474 5475 5476 5477 5478 5479 5480 5481 5482 5483\n",
      "  5484 5485 5486 5487 5488 5489 5490 5491 5492 5493 5494 5495 5496 5497\n",
      "  5498 5499]\n",
      " [5500 5501 5502 5503 5504 5505 5506 5507 5508 5509 5510 5511 5512 5513\n",
      "  5514 5515 5516 5517 5518 5519 5520 5521 5522 5523 5524 5525 5526 5527\n",
      "  5528 5529 5530 5531 5532 5533 5534 5535 5536 5537 5538 5539 5540 5541\n",
      "  5542 5543 5544 5545 5546 5547 5548 5549 5550 5551 5552 5553 5554 5555\n",
      "  5556 5557 5558 5559 5560 5561 5562 5563 5564 5565 5566 5567 5568 5569\n",
      "  5570 5571 5572 5573 5574 5575 5576 5577 5578 5579 5580 5581 5582 5583\n",
      "  5584 5585 5586 5587 5588 5589 5590 5591 5592 5593 5594 5595 5596 5597\n",
      "  5598 5599]\n",
      " [5600 5601 5602 5603 5604 5605 5606 5607 5608 5609 5610 5611 5612 5613\n",
      "  5614 5615 5616 5617 5618 5619 5620 5621 5622 5623 5624 5625 5626 5627\n",
      "  5628 5629 5630 5631 5632 5633 5634 5635 5636 5637 5638 5639 5640 5641\n",
      "  5642 5643 5644 5645 5646 5647 5648 5649 5650 5651 5652 5653 5654 5655\n",
      "  5656 5657 5658 5659 5660 5661 5662 5663 5664 5665 5666 5667 5668 5669\n",
      "  5670 5671 5672 5673 5674 5675 5676 5677 5678 5679 5680 5681 5682 5683\n",
      "  5684 5685 5686 5687 5688 5689 5690 5691 5692 5693 5694 5695 5696 5697\n",
      "  5698 5699]\n",
      " [5700 5701 5702 5703 5704 5705 5706 5707 5708 5709 5710 5711 5712 5713\n",
      "  5714 5715 5716 5717 5718 5719 5720 5721 5722 5723 5724 5725 5726 5727\n",
      "  5728 5729 5730 5731 5732 5733 5734 5735 5736 5737 5738 5739 5740 5741\n",
      "  5742 5743 5744 5745 5746 5747 5748 5749 5750 5751 5752 5753 5754 5755\n",
      "  5756 5757 5758 5759 5760 5761 5762 5763 5764 5765 5766 5767 5768 5769\n",
      "  5770 5771 5772 5773 5774 5775 5776 5777 5778 5779 5780 5781 5782 5783\n",
      "  5784 5785 5786 5787 5788 5789 5790 5791 5792 5793 5794 5795 5796 5797\n",
      "  5798 5799]\n",
      " [5800 5801 5802 5803 5804 5805 5806 5807 5808 5809 5810 5811 5812 5813\n",
      "  5814 5815 5816 5817 5818 5819 5820 5821 5822 5823 5824 5825 5826 5827\n",
      "  5828 5829 5830 5831 5832 5833 5834 5835 5836 5837 5838 5839 5840 5841\n",
      "  5842 5843 5844 5845 5846 5847 5848 5849 5850 5851 5852 5853 5854 5855\n",
      "  5856 5857 5858 5859 5860 5861 5862 5863 5864 5865 5866 5867 5868 5869\n",
      "  5870 5871 5872 5873 5874 5875 5876 5877 5878 5879 5880 5881 5882 5883\n",
      "  5884 5885 5886 5887 5888 5889 5890 5891 5892 5893 5894 5895 5896 5897\n",
      "  5898 5899]\n",
      " [5900 5901 5902 5903 5904 5905 5906 5907 5908 5909 5910 5911 5912 5913\n",
      "  5914 5915 5916 5917 5918 5919 5920 5921 5922 5923 5924 5925 5926 5927\n",
      "  5928 5929 5930 5931 5932 5933 5934 5935 5936 5937 5938 5939 5940 5941\n",
      "  5942 5943 5944 5945 5946 5947 5948 5949 5950 5951 5952 5953 5954 5955\n",
      "  5956 5957 5958 5959 5960 5961 5962 5963 5964 5965 5966 5967 5968 5969\n",
      "  5970 5971 5972 5973 5974 5975 5976 5977 5978 5979 5980 5981 5982 5983\n",
      "  5984 5985 5986 5987 5988 5989 5990 5991 5992 5993 5994 5995 5996 5997\n",
      "  5998 5999]\n",
      " [6000 6001 6002 6003 6004 6005 6006 6007 6008 6009 6010 6011 6012 6013\n",
      "  6014 6015 6016 6017 6018 6019 6020 6021 6022 6023 6024 6025 6026 6027\n",
      "  6028 6029 6030 6031 6032 6033 6034 6035 6036 6037 6038 6039 6040 6041\n",
      "  6042 6043 6044 6045 6046 6047 6048 6049 6050 6051 6052 6053 6054 6055\n",
      "  6056 6057 6058 6059 6060 6061 6062 6063 6064 6065 6066 6067 6068 6069\n",
      "  6070 6071 6072 6073 6074 6075 6076 6077 6078 6079 6080 6081 6082 6083\n",
      "  6084 6085 6086 6087 6088 6089 6090 6091 6092 6093 6094 6095 6096 6097\n",
      "  6098 6099]\n",
      " [6100 6101 6102 6103 6104 6105 6106 6107 6108 6109 6110 6111 6112 6113\n",
      "  6114 6115 6116 6117 6118 6119 6120 6121 6122 6123 6124 6125 6126 6127\n",
      "  6128 6129 6130 6131 6132 6133 6134 6135 6136 6137 6138 6139 6140 6141\n",
      "  6142 6143 6144 6145 6146 6147 6148 6149 6150 6151 6152 6153 6154 6155\n",
      "  6156 6157 6158 6159 6160 6161 6162 6163 6164 6165 6166 6167 6168 6169\n",
      "  6170 6171 6172 6173 6174 6175 6176 6177 6178 6179 6180 6181 6182 6183\n",
      "  6184 6185 6186 6187 6188 6189 6190 6191 6192 6193 6194 6195 6196 6197\n",
      "  6198 6199]\n",
      " [6200 6201 6202 6203 6204 6205 6206 6207 6208 6209 6210 6211 6212 6213\n",
      "  6214 6215 6216 6217 6218 6219 6220 6221 6222 6223 6224 6225 6226 6227\n",
      "  6228 6229 6230 6231 6232 6233 6234 6235 6236 6237 6238 6239 6240 6241\n",
      "  6242 6243 6244 6245 6246 6247 6248 6249 6250 6251 6252 6253 6254 6255\n",
      "  6256 6257 6258 6259 6260 6261 6262 6263 6264 6265 6266 6267 6268 6269\n",
      "  6270 6271 6272 6273 6274 6275 6276 6277 6278 6279 6280 6281 6282 6283\n",
      "  6284 6285 6286 6287 6288 6289 6290 6291 6292 6293 6294 6295 6296 6297\n",
      "  6298 6299]\n",
      " [6300 6301 6302 6303 6304 6305 6306 6307 6308 6309 6310 6311 6312 6313\n",
      "  6314 6315 6316 6317 6318 6319 6320 6321 6322 6323 6324 6325 6326 6327\n",
      "  6328 6329 6330 6331 6332 6333 6334 6335 6336 6337 6338 6339 6340 6341\n",
      "  6342 6343 6344 6345 6346 6347 6348 6349 6350 6351 6352 6353 6354 6355\n",
      "  6356 6357 6358 6359 6360 6361 6362 6363 6364 6365 6366 6367 6368 6369\n",
      "  6370 6371 6372 6373 6374 6375 6376 6377 6378 6379 6380 6381 6382 6383\n",
      "  6384 6385 6386 6387 6388 6389 6390 6391 6392 6393 6394 6395 6396 6397\n",
      "  6398 6399]\n",
      " [6400 6401 6402 6403 6404 6405 6406 6407 6408 6409 6410 6411 6412 6413\n",
      "  6414 6415 6416 6417 6418 6419 6420 6421 6422 6423 6424 6425 6426 6427\n",
      "  6428 6429 6430 6431 6432 6433 6434 6435 6436 6437 6438 6439 6440 6441\n",
      "  6442 6443 6444 6445 6446 6447 6448 6449 6450 6451 6452 6453 6454 6455\n",
      "  6456 6457 6458 6459 6460 6461 6462 6463 6464 6465 6466 6467 6468 6469\n",
      "  6470 6471 6472 6473 6474 6475 6476 6477 6478 6479 6480 6481 6482 6483\n",
      "  6484 6485 6486 6487 6488 6489 6490 6491 6492 6493 6494 6495 6496 6497\n",
      "  6498 6499]\n",
      " [6500 6501 6502 6503 6504 6505 6506 6507 6508 6509 6510 6511 6512 6513\n",
      "  6514 6515 6516 6517 6518 6519 6520 6521 6522 6523 6524 6525 6526 6527\n",
      "  6528 6529 6530 6531 6532 6533 6534 6535 6536 6537 6538 6539 6540 6541\n",
      "  6542 6543 6544 6545 6546 6547 6548 6549 6550 6551 6552 6553 6554 6555\n",
      "  6556 6557 6558 6559 6560 6561 6562 6563 6564 6565 6566 6567 6568 6569\n",
      "  6570 6571 6572 6573 6574 6575 6576 6577 6578 6579 6580 6581 6582 6583\n",
      "  6584 6585 6586 6587 6588 6589 6590 6591 6592 6593 6594 6595 6596 6597\n",
      "  6598 6599]\n",
      " [6600 6601 6602 6603 6604 6605 6606 6607 6608 6609 6610 6611 6612 6613\n",
      "  6614 6615 6616 6617 6618 6619 6620 6621 6622 6623 6624 6625 6626 6627\n",
      "  6628 6629 6630 6631 6632 6633 6634 6635 6636 6637 6638 6639 6640 6641\n",
      "  6642 6643 6644 6645 6646 6647 6648 6649 6650 6651 6652 6653 6654 6655\n",
      "  6656 6657 6658 6659 6660 6661 6662 6663 6664 6665 6666 6667 6668 6669\n",
      "  6670 6671 6672 6673 6674 6675 6676 6677 6678 6679 6680 6681 6682 6683\n",
      "  6684 6685 6686 6687 6688 6689 6690 6691 6692 6693 6694 6695 6696 6697\n",
      "  6698 6699]\n",
      " [6700 6701 6702 6703 6704 6705 6706 6707 6708 6709 6710 6711 6712 6713\n",
      "  6714 6715 6716 6717 6718 6719 6720 6721 6722 6723 6724 6725 6726 6727\n",
      "  6728 6729 6730 6731 6732 6733 6734 6735 6736 6737 6738 6739 6740 6741\n",
      "  6742 6743 6744 6745 6746 6747 6748 6749 6750 6751 6752 6753 6754 6755\n",
      "  6756 6757 6758 6759 6760 6761 6762 6763 6764 6765 6766 6767 6768 6769\n",
      "  6770 6771 6772 6773 6774 6775 6776 6777 6778 6779 6780 6781 6782 6783\n",
      "  6784 6785 6786 6787 6788 6789 6790 6791 6792 6793 6794 6795 6796 6797\n",
      "  6798 6799]\n",
      " [6800 6801 6802 6803 6804 6805 6806 6807 6808 6809 6810 6811 6812 6813\n",
      "  6814 6815 6816 6817 6818 6819 6820 6821 6822 6823 6824 6825 6826 6827\n",
      "  6828 6829 6830 6831 6832 6833 6834 6835 6836 6837 6838 6839 6840 6841\n",
      "  6842 6843 6844 6845 6846 6847 6848 6849 6850 6851 6852 6853 6854 6855\n",
      "  6856 6857 6858 6859 6860 6861 6862 6863 6864 6865 6866 6867 6868 6869\n",
      "  6870 6871 6872 6873 6874 6875 6876 6877 6878 6879 6880 6881 6882 6883\n",
      "  6884 6885 6886 6887 6888 6889 6890 6891 6892 6893 6894 6895 6896 6897\n",
      "  6898 6899]\n",
      " [6900 6901 6902 6903 6904 6905 6906 6907 6908 6909 6910 6911 6912 6913\n",
      "  6914 6915 6916 6917 6918 6919 6920 6921 6922 6923 6924 6925 6926 6927\n",
      "  6928 6929 6930 6931 6932 6933 6934 6935 6936 6937 6938 6939 6940 6941\n",
      "  6942 6943 6944 6945 6946 6947 6948 6949 6950 6951 6952 6953 6954 6955\n",
      "  6956 6957 6958 6959 6960 6961 6962 6963 6964 6965 6966 6967 6968 6969\n",
      "  6970 6971 6972 6973 6974 6975 6976 6977 6978 6979 6980 6981 6982 6983\n",
      "  6984 6985 6986 6987 6988 6989 6990 6991 6992 6993 6994 6995 6996 6997\n",
      "  6998 6999]\n",
      " [7000 7001 7002 7003 7004 7005 7006 7007 7008 7009 7010 7011 7012 7013\n",
      "  7014 7015 7016 7017 7018 7019 7020 7021 7022 7023 7024 7025 7026 7027\n",
      "  7028 7029 7030 7031 7032 7033 7034 7035 7036 7037 7038 7039 7040 7041\n",
      "  7042 7043 7044 7045 7046 7047 7048 7049 7050 7051 7052 7053 7054 7055\n",
      "  7056 7057 7058 7059 7060 7061 7062 7063 7064 7065 7066 7067 7068 7069\n",
      "  7070 7071 7072 7073 7074 7075 7076 7077 7078 7079 7080 7081 7082 7083\n",
      "  7084 7085 7086 7087 7088 7089 7090 7091 7092 7093 7094 7095 7096 7097\n",
      "  7098 7099]\n",
      " [7100 7101 7102 7103 7104 7105 7106 7107 7108 7109 7110 7111 7112 7113\n",
      "  7114 7115 7116 7117 7118 7119 7120 7121 7122 7123 7124 7125 7126 7127\n",
      "  7128 7129 7130 7131 7132 7133 7134 7135 7136 7137 7138 7139 7140 7141\n",
      "  7142 7143 7144 7145 7146 7147 7148 7149 7150 7151 7152 7153 7154 7155\n",
      "  7156 7157 7158 7159 7160 7161 7162 7163 7164 7165 7166 7167 7168 7169\n",
      "  7170 7171 7172 7173 7174 7175 7176 7177 7178 7179 7180 7181 7182 7183\n",
      "  7184 7185 7186 7187 7188 7189 7190 7191 7192 7193 7194 7195 7196 7197\n",
      "  7198 7199]\n",
      " [7200 7201 7202 7203 7204 7205 7206 7207 7208 7209 7210 7211 7212 7213\n",
      "  7214 7215 7216 7217 7218 7219 7220 7221 7222 7223 7224 7225 7226 7227\n",
      "  7228 7229 7230 7231 7232 7233 7234 7235 7236 7237 7238 7239 7240 7241\n",
      "  7242 7243 7244 7245 7246 7247 7248 7249 7250 7251 7252 7253 7254 7255\n",
      "  7256 7257 7258 7259 7260 7261 7262 7263 7264 7265 7266 7267 7268 7269\n",
      "  7270 7271 7272 7273 7274 7275 7276 7277 7278 7279 7280 7281 7282 7283\n",
      "  7284 7285 7286 7287 7288 7289 7290 7291 7292 7293 7294 7295 7296 7297\n",
      "  7298 7299]\n",
      " [7300 7301 7302 7303 7304 7305 7306 7307 7308 7309 7310 7311 7312 7313\n",
      "  7314 7315 7316 7317 7318 7319 7320 7321 7322 7323 7324 7325 7326 7327\n",
      "  7328 7329 7330 7331 7332 7333 7334 7335 7336 7337 7338 7339 7340 7341\n",
      "  7342 7343 7344 7345 7346 7347 7348 7349 7350 7351 7352 7353 7354 7355\n",
      "  7356 7357 7358 7359 7360 7361 7362 7363 7364 7365 7366 7367 7368 7369\n",
      "  7370 7371 7372 7373 7374 7375 7376 7377 7378 7379 7380 7381 7382 7383\n",
      "  7384 7385 7386 7387 7388 7389 7390 7391 7392 7393 7394 7395 7396 7397\n",
      "  7398 7399]\n",
      " [7400 7401 7402 7403 7404 7405 7406 7407 7408 7409 7410 7411 7412 7413\n",
      "  7414 7415 7416 7417 7418 7419 7420 7421 7422 7423 7424 7425 7426 7427\n",
      "  7428 7429 7430 7431 7432 7433 7434 7435 7436 7437 7438 7439 7440 7441\n",
      "  7442 7443 7444 7445 7446 7447 7448 7449 7450 7451 7452 7453 7454 7455\n",
      "  7456 7457 7458 7459 7460 7461 7462 7463 7464 7465 7466 7467 7468 7469\n",
      "  7470 7471 7472 7473 7474 7475 7476 7477 7478 7479 7480 7481 7482 7483\n",
      "  7484 7485 7486 7487 7488 7489 7490 7491 7492 7493 7494 7495 7496 7497\n",
      "  7498 7499]\n",
      " [7500 7501 7502 7503 7504 7505 7506 7507 7508 7509 7510 7511 7512 7513\n",
      "  7514 7515 7516 7517 7518 7519 7520 7521 7522 7523 7524 7525 7526 7527\n",
      "  7528 7529 7530 7531 7532 7533 7534 7535 7536 7537 7538 7539 7540 7541\n",
      "  7542 7543 7544 7545 7546 7547 7548 7549 7550 7551 7552 7553 7554 7555\n",
      "  7556 7557 7558 7559 7560 7561 7562 7563 7564 7565 7566 7567 7568 7569\n",
      "  7570 7571 7572 7573 7574 7575 7576 7577 7578 7579 7580 7581 7582 7583\n",
      "  7584 7585 7586 7587 7588 7589 7590 7591 7592 7593 7594 7595 7596 7597\n",
      "  7598 7599]\n",
      " [7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 7610 7611 7612 7613\n",
      "  7614 7615 7616 7617 7618 7619 7620 7621 7622 7623 7624 7625 7626 7627\n",
      "  7628 7629 7630 7631 7632 7633 7634 7635 7636 7637 7638 7639 7640 7641\n",
      "  7642 7643 7644 7645 7646 7647 7648 7649 7650 7651 7652 7653 7654 7655\n",
      "  7656 7657 7658 7659 7660 7661 7662 7663 7664 7665 7666 7667 7668 7669\n",
      "  7670 7671 7672 7673 7674 7675 7676 7677 7678 7679 7680 7681 7682 7683\n",
      "  7684 7685 7686 7687 7688 7689 7690 7691 7692 7693 7694 7695 7696 7697\n",
      "  7698 7699]\n",
      " [7700 7701 7702 7703 7704 7705 7706 7707 7708 7709 7710 7711 7712 7713\n",
      "  7714 7715 7716 7717 7718 7719 7720 7721 7722 7723 7724 7725 7726 7727\n",
      "  7728 7729 7730 7731 7732 7733 7734 7735 7736 7737 7738 7739 7740 7741\n",
      "  7742 7743 7744 7745 7746 7747 7748 7749 7750 7751 7752 7753 7754 7755\n",
      "  7756 7757 7758 7759 7760 7761 7762 7763 7764 7765 7766 7767 7768 7769\n",
      "  7770 7771 7772 7773 7774 7775 7776 7777 7778 7779 7780 7781 7782 7783\n",
      "  7784 7785 7786 7787 7788 7789 7790 7791 7792 7793 7794 7795 7796 7797\n",
      "  7798 7799]\n",
      " [7800 7801 7802 7803 7804 7805 7806 7807 7808 7809 7810 7811 7812 7813\n",
      "  7814 7815 7816 7817 7818 7819 7820 7821 7822 7823 7824 7825 7826 7827\n",
      "  7828 7829 7830 7831 7832 7833 7834 7835 7836 7837 7838 7839 7840 7841\n",
      "  7842 7843 7844 7845 7846 7847 7848 7849 7850 7851 7852 7853 7854 7855\n",
      "  7856 7857 7858 7859 7860 7861 7862 7863 7864 7865 7866 7867 7868 7869\n",
      "  7870 7871 7872 7873 7874 7875 7876 7877 7878 7879 7880 7881 7882 7883\n",
      "  7884 7885 7886 7887 7888 7889 7890 7891 7892 7893 7894 7895 7896 7897\n",
      "  7898 7899]\n",
      " [7900 7901 7902 7903 7904 7905 7906 7907 7908 7909 7910 7911 7912 7913\n",
      "  7914 7915 7916 7917 7918 7919 7920 7921 7922 7923 7924 7925 7926 7927\n",
      "  7928 7929 7930 7931 7932 7933 7934 7935 7936 7937 7938 7939 7940 7941\n",
      "  7942 7943 7944 7945 7946 7947 7948 7949 7950 7951 7952 7953 7954 7955\n",
      "  7956 7957 7958 7959 7960 7961 7962 7963 7964 7965 7966 7967 7968 7969\n",
      "  7970 7971 7972 7973 7974 7975 7976 7977 7978 7979 7980 7981 7982 7983\n",
      "  7984 7985 7986 7987 7988 7989 7990 7991 7992 7993 7994 7995 7996 7997\n",
      "  7998 7999]\n",
      " [8000 8001 8002 8003 8004 8005 8006 8007 8008 8009 8010 8011 8012 8013\n",
      "  8014 8015 8016 8017 8018 8019 8020 8021 8022 8023 8024 8025 8026 8027\n",
      "  8028 8029 8030 8031 8032 8033 8034 8035 8036 8037 8038 8039 8040 8041\n",
      "  8042 8043 8044 8045 8046 8047 8048 8049 8050 8051 8052 8053 8054 8055\n",
      "  8056 8057 8058 8059 8060 8061 8062 8063 8064 8065 8066 8067 8068 8069\n",
      "  8070 8071 8072 8073 8074 8075 8076 8077 8078 8079 8080 8081 8082 8083\n",
      "  8084 8085 8086 8087 8088 8089 8090 8091 8092 8093 8094 8095 8096 8097\n",
      "  8098 8099]\n",
      " [8100 8101 8102 8103 8104 8105 8106 8107 8108 8109 8110 8111 8112 8113\n",
      "  8114 8115 8116 8117 8118 8119 8120 8121 8122 8123 8124 8125 8126 8127\n",
      "  8128 8129 8130 8131 8132 8133 8134 8135 8136 8137 8138 8139 8140 8141\n",
      "  8142 8143 8144 8145 8146 8147 8148 8149 8150 8151 8152 8153 8154 8155\n",
      "  8156 8157 8158 8159 8160 8161 8162 8163 8164 8165 8166 8167 8168 8169\n",
      "  8170 8171 8172 8173 8174 8175 8176 8177 8178 8179 8180 8181 8182 8183\n",
      "  8184 8185 8186 8187 8188 8189 8190 8191 8192 8193 8194 8195 8196 8197\n",
      "  8198 8199]\n",
      " [8200 8201 8202 8203 8204 8205 8206 8207 8208 8209 8210 8211 8212 8213\n",
      "  8214 8215 8216 8217 8218 8219 8220 8221 8222 8223 8224 8225 8226 8227\n",
      "  8228 8229 8230 8231 8232 8233 8234 8235 8236 8237 8238 8239 8240 8241\n",
      "  8242 8243 8244 8245 8246 8247 8248 8249 8250 8251 8252 8253 8254 8255\n",
      "  8256 8257 8258 8259 8260 8261 8262 8263 8264 8265 8266 8267 8268 8269\n",
      "  8270 8271 8272 8273 8274 8275 8276 8277 8278 8279 8280 8281 8282 8283\n",
      "  8284 8285 8286 8287 8288 8289 8290 8291 8292 8293 8294 8295 8296 8297\n",
      "  8298 8299]\n",
      " [8300 8301 8302 8303 8304 8305 8306 8307 8308 8309 8310 8311 8312 8313\n",
      "  8314 8315 8316 8317 8318 8319 8320 8321 8322 8323 8324 8325 8326 8327\n",
      "  8328 8329 8330 8331 8332 8333 8334 8335 8336 8337 8338 8339 8340 8341\n",
      "  8342 8343 8344 8345 8346 8347 8348 8349 8350 8351 8352 8353 8354 8355\n",
      "  8356 8357 8358 8359 8360 8361 8362 8363 8364 8365 8366 8367 8368 8369\n",
      "  8370 8371 8372 8373 8374 8375 8376 8377 8378 8379 8380 8381 8382 8383\n",
      "  8384 8385 8386 8387 8388 8389 8390 8391 8392 8393 8394 8395 8396 8397\n",
      "  8398 8399]\n",
      " [8400 8401 8402 8403 8404 8405 8406 8407 8408 8409 8410 8411 8412 8413\n",
      "  8414 8415 8416 8417 8418 8419 8420 8421 8422 8423 8424 8425 8426 8427\n",
      "  8428 8429 8430 8431 8432 8433 8434 8435 8436 8437 8438 8439 8440 8441\n",
      "  8442 8443 8444 8445 8446 8447 8448 8449 8450 8451 8452 8453 8454 8455\n",
      "  8456 8457 8458 8459 8460 8461 8462 8463 8464 8465 8466 8467 8468 8469\n",
      "  8470 8471 8472 8473 8474 8475 8476 8477 8478 8479 8480 8481 8482 8483\n",
      "  8484 8485 8486 8487 8488 8489 8490 8491 8492 8493 8494 8495 8496 8497\n",
      "  8498 8499]\n",
      " [8500 8501 8502 8503 8504 8505 8506 8507 8508 8509 8510 8511 8512 8513\n",
      "  8514 8515 8516 8517 8518 8519 8520 8521 8522 8523 8524 8525 8526 8527\n",
      "  8528 8529 8530 8531 8532 8533 8534 8535 8536 8537 8538 8539 8540 8541\n",
      "  8542 8543 8544 8545 8546 8547 8548 8549 8550 8551 8552 8553 8554 8555\n",
      "  8556 8557 8558 8559 8560 8561 8562 8563 8564 8565 8566 8567 8568 8569\n",
      "  8570 8571 8572 8573 8574 8575 8576 8577 8578 8579 8580 8581 8582 8583\n",
      "  8584 8585 8586 8587 8588 8589 8590 8591 8592 8593 8594 8595 8596 8597\n",
      "  8598 8599]\n",
      " [8600 8601 8602 8603 8604 8605 8606 8607 8608 8609 8610 8611 8612 8613\n",
      "  8614 8615 8616 8617 8618 8619 8620 8621 8622 8623 8624 8625 8626 8627\n",
      "  8628 8629 8630 8631 8632 8633 8634 8635 8636 8637 8638 8639 8640 8641\n",
      "  8642 8643 8644 8645 8646 8647 8648 8649 8650 8651 8652 8653 8654 8655\n",
      "  8656 8657 8658 8659 8660 8661 8662 8663 8664 8665 8666 8667 8668 8669\n",
      "  8670 8671 8672 8673 8674 8675 8676 8677 8678 8679 8680 8681 8682 8683\n",
      "  8684 8685 8686 8687 8688 8689 8690 8691 8692 8693 8694 8695 8696 8697\n",
      "  8698 8699]\n",
      " [8700 8701 8702 8703 8704 8705 8706 8707 8708 8709 8710 8711 8712 8713\n",
      "  8714 8715 8716 8717 8718 8719 8720 8721 8722 8723 8724 8725 8726 8727\n",
      "  8728 8729 8730 8731 8732 8733 8734 8735 8736 8737 8738 8739 8740 8741\n",
      "  8742 8743 8744 8745 8746 8747 8748 8749 8750 8751 8752 8753 8754 8755\n",
      "  8756 8757 8758 8759 8760 8761 8762 8763 8764 8765 8766 8767 8768 8769\n",
      "  8770 8771 8772 8773 8774 8775 8776 8777 8778 8779 8780 8781 8782 8783\n",
      "  8784 8785 8786 8787 8788 8789 8790 8791 8792 8793 8794 8795 8796 8797\n",
      "  8798 8799]\n",
      " [8800 8801 8802 8803 8804 8805 8806 8807 8808 8809 8810 8811 8812 8813\n",
      "  8814 8815 8816 8817 8818 8819 8820 8821 8822 8823 8824 8825 8826 8827\n",
      "  8828 8829 8830 8831 8832 8833 8834 8835 8836 8837 8838 8839 8840 8841\n",
      "  8842 8843 8844 8845 8846 8847 8848 8849 8850 8851 8852 8853 8854 8855\n",
      "  8856 8857 8858 8859 8860 8861 8862 8863 8864 8865 8866 8867 8868 8869\n",
      "  8870 8871 8872 8873 8874 8875 8876 8877 8878 8879 8880 8881 8882 8883\n",
      "  8884 8885 8886 8887 8888 8889 8890 8891 8892 8893 8894 8895 8896 8897\n",
      "  8898 8899]\n",
      " [8900 8901 8902 8903 8904 8905 8906 8907 8908 8909 8910 8911 8912 8913\n",
      "  8914 8915 8916 8917 8918 8919 8920 8921 8922 8923 8924 8925 8926 8927\n",
      "  8928 8929 8930 8931 8932 8933 8934 8935 8936 8937 8938 8939 8940 8941\n",
      "  8942 8943 8944 8945 8946 8947 8948 8949 8950 8951 8952 8953 8954 8955\n",
      "  8956 8957 8958 8959 8960 8961 8962 8963 8964 8965 8966 8967 8968 8969\n",
      "  8970 8971 8972 8973 8974 8975 8976 8977 8978 8979 8980 8981 8982 8983\n",
      "  8984 8985 8986 8987 8988 8989 8990 8991 8992 8993 8994 8995 8996 8997\n",
      "  8998 8999]\n",
      " [9000 9001 9002 9003 9004 9005 9006 9007 9008 9009 9010 9011 9012 9013\n",
      "  9014 9015 9016 9017 9018 9019 9020 9021 9022 9023 9024 9025 9026 9027\n",
      "  9028 9029 9030 9031 9032 9033 9034 9035 9036 9037 9038 9039 9040 9041\n",
      "  9042 9043 9044 9045 9046 9047 9048 9049 9050 9051 9052 9053 9054 9055\n",
      "  9056 9057 9058 9059 9060 9061 9062 9063 9064 9065 9066 9067 9068 9069\n",
      "  9070 9071 9072 9073 9074 9075 9076 9077 9078 9079 9080 9081 9082 9083\n",
      "  9084 9085 9086 9087 9088 9089 9090 9091 9092 9093 9094 9095 9096 9097\n",
      "  9098 9099]\n",
      " [9100 9101 9102 9103 9104 9105 9106 9107 9108 9109 9110 9111 9112 9113\n",
      "  9114 9115 9116 9117 9118 9119 9120 9121 9122 9123 9124 9125 9126 9127\n",
      "  9128 9129 9130 9131 9132 9133 9134 9135 9136 9137 9138 9139 9140 9141\n",
      "  9142 9143 9144 9145 9146 9147 9148 9149 9150 9151 9152 9153 9154 9155\n",
      "  9156 9157 9158 9159 9160 9161 9162 9163 9164 9165 9166 9167 9168 9169\n",
      "  9170 9171 9172 9173 9174 9175 9176 9177 9178 9179 9180 9181 9182 9183\n",
      "  9184 9185 9186 9187 9188 9189 9190 9191 9192 9193 9194 9195 9196 9197\n",
      "  9198 9199]\n",
      " [9200 9201 9202 9203 9204 9205 9206 9207 9208 9209 9210 9211 9212 9213\n",
      "  9214 9215 9216 9217 9218 9219 9220 9221 9222 9223 9224 9225 9226 9227\n",
      "  9228 9229 9230 9231 9232 9233 9234 9235 9236 9237 9238 9239 9240 9241\n",
      "  9242 9243 9244 9245 9246 9247 9248 9249 9250 9251 9252 9253 9254 9255\n",
      "  9256 9257 9258 9259 9260 9261 9262 9263 9264 9265 9266 9267 9268 9269\n",
      "  9270 9271 9272 9273 9274 9275 9276 9277 9278 9279 9280 9281 9282 9283\n",
      "  9284 9285 9286 9287 9288 9289 9290 9291 9292 9293 9294 9295 9296 9297\n",
      "  9298 9299]\n",
      " [9300 9301 9302 9303 9304 9305 9306 9307 9308 9309 9310 9311 9312 9313\n",
      "  9314 9315 9316 9317 9318 9319 9320 9321 9322 9323 9324 9325 9326 9327\n",
      "  9328 9329 9330 9331 9332 9333 9334 9335 9336 9337 9338 9339 9340 9341\n",
      "  9342 9343 9344 9345 9346 9347 9348 9349 9350 9351 9352 9353 9354 9355\n",
      "  9356 9357 9358 9359 9360 9361 9362 9363 9364 9365 9366 9367 9368 9369\n",
      "  9370 9371 9372 9373 9374 9375 9376 9377 9378 9379 9380 9381 9382 9383\n",
      "  9384 9385 9386 9387 9388 9389 9390 9391 9392 9393 9394 9395 9396 9397\n",
      "  9398 9399]\n",
      " [9400 9401 9402 9403 9404 9405 9406 9407 9408 9409 9410 9411 9412 9413\n",
      "  9414 9415 9416 9417 9418 9419 9420 9421 9422 9423 9424 9425 9426 9427\n",
      "  9428 9429 9430 9431 9432 9433 9434 9435 9436 9437 9438 9439 9440 9441\n",
      "  9442 9443 9444 9445 9446 9447 9448 9449 9450 9451 9452 9453 9454 9455\n",
      "  9456 9457 9458 9459 9460 9461 9462 9463 9464 9465 9466 9467 9468 9469\n",
      "  9470 9471 9472 9473 9474 9475 9476 9477 9478 9479 9480 9481 9482 9483\n",
      "  9484 9485 9486 9487 9488 9489 9490 9491 9492 9493 9494 9495 9496 9497\n",
      "  9498 9499]\n",
      " [9500 9501 9502 9503 9504 9505 9506 9507 9508 9509 9510 9511 9512 9513\n",
      "  9514 9515 9516 9517 9518 9519 9520 9521 9522 9523 9524 9525 9526 9527\n",
      "  9528 9529 9530 9531 9532 9533 9534 9535 9536 9537 9538 9539 9540 9541\n",
      "  9542 9543 9544 9545 9546 9547 9548 9549 9550 9551 9552 9553 9554 9555\n",
      "  9556 9557 9558 9559 9560 9561 9562 9563 9564 9565 9566 9567 9568 9569\n",
      "  9570 9571 9572 9573 9574 9575 9576 9577 9578 9579 9580 9581 9582 9583\n",
      "  9584 9585 9586 9587 9588 9589 9590 9591 9592 9593 9594 9595 9596 9597\n",
      "  9598 9599]\n",
      " [9600 9601 9602 9603 9604 9605 9606 9607 9608 9609 9610 9611 9612 9613\n",
      "  9614 9615 9616 9617 9618 9619 9620 9621 9622 9623 9624 9625 9626 9627\n",
      "  9628 9629 9630 9631 9632 9633 9634 9635 9636 9637 9638 9639 9640 9641\n",
      "  9642 9643 9644 9645 9646 9647 9648 9649 9650 9651 9652 9653 9654 9655\n",
      "  9656 9657 9658 9659 9660 9661 9662 9663 9664 9665 9666 9667 9668 9669\n",
      "  9670 9671 9672 9673 9674 9675 9676 9677 9678 9679 9680 9681 9682 9683\n",
      "  9684 9685 9686 9687 9688 9689 9690 9691 9692 9693 9694 9695 9696 9697\n",
      "  9698 9699]\n",
      " [9700 9701 9702 9703 9704 9705 9706 9707 9708 9709 9710 9711 9712 9713\n",
      "  9714 9715 9716 9717 9718 9719 9720 9721 9722 9723 9724 9725 9726 9727\n",
      "  9728 9729 9730 9731 9732 9733 9734 9735 9736 9737 9738 9739 9740 9741\n",
      "  9742 9743 9744 9745 9746 9747 9748 9749 9750 9751 9752 9753 9754 9755\n",
      "  9756 9757 9758 9759 9760 9761 9762 9763 9764 9765 9766 9767 9768 9769\n",
      "  9770 9771 9772 9773 9774 9775 9776 9777 9778 9779 9780 9781 9782 9783\n",
      "  9784 9785 9786 9787 9788 9789 9790 9791 9792 9793 9794 9795 9796 9797\n",
      "  9798 9799]\n",
      " [9800 9801 9802 9803 9804 9805 9806 9807 9808 9809 9810 9811 9812 9813\n",
      "  9814 9815 9816 9817 9818 9819 9820 9821 9822 9823 9824 9825 9826 9827\n",
      "  9828 9829 9830 9831 9832 9833 9834 9835 9836 9837 9838 9839 9840 9841\n",
      "  9842 9843 9844 9845 9846 9847 9848 9849 9850 9851 9852 9853 9854 9855\n",
      "  9856 9857 9858 9859 9860 9861 9862 9863 9864 9865 9866 9867 9868 9869\n",
      "  9870 9871 9872 9873 9874 9875 9876 9877 9878 9879 9880 9881 9882 9883\n",
      "  9884 9885 9886 9887 9888 9889 9890 9891 9892 9893 9894 9895 9896 9897\n",
      "  9898 9899]\n",
      " [9900 9901 9902 9903 9904 9905 9906 9907 9908 9909 9910 9911 9912 9913\n",
      "  9914 9915 9916 9917 9918 9919 9920 9921 9922 9923 9924 9925 9926 9927\n",
      "  9928 9929 9930 9931 9932 9933 9934 9935 9936 9937 9938 9939 9940 9941\n",
      "  9942 9943 9944 9945 9946 9947 9948 9949 9950 9951 9952 9953 9954 9955\n",
      "  9956 9957 9958 9959 9960 9961 9962 9963 9964 9965 9966 9967 9968 9969\n",
      "  9970 9971 9972 9973 9974 9975 9976 9977 9978 9979 9980 9981 9982 9983\n",
      "  9984 9985 9986 9987 9988 9989 9990 9991 9992 9993 9994 9995 9996 9997\n",
      "  9998 9999]]\n"
     ]
    }
   ],
   "source": [
    "print(np.arange(10000).reshape(100, 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pi' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\HP\\Desktop\\python codes\\numpy_practice.ipynb Cell 35\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/HP/Desktop/python%20codes/numpy_practice.ipynb#X46sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m x \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mlinspace(\u001b[39m0\u001b[39m, \u001b[39m2\u001b[39m \u001b[39m*\u001b[39m pi, \u001b[39m100\u001b[39m)        \u001b[39m# useful to evaluate function at lots of points\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/HP/Desktop/python%20codes/numpy_practice.ipynb#X46sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m f \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39msin(x)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'pi' is not defined"
     ]
    }
   ],
   "source": [
    "x = np.linspace(0, 2 * pi, 100)        # useful to evaluate function at lots of points\n",
    "f = np.sin(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00000000e+00,  6.34239197e-02,  1.26592454e-01,  1.89251244e-01,\n",
       "        2.51147987e-01,  3.12033446e-01,  3.71662456e-01,  4.29794912e-01,\n",
       "        4.86196736e-01,  5.40640817e-01,  5.92907929e-01,  6.42787610e-01,\n",
       "        6.90079011e-01,  7.34591709e-01,  7.76146464e-01,  8.14575952e-01,\n",
       "        8.49725430e-01,  8.81453363e-01,  9.09631995e-01,  9.34147860e-01,\n",
       "        9.54902241e-01,  9.71811568e-01,  9.84807753e-01,  9.93838464e-01,\n",
       "        9.98867339e-01,  9.99874128e-01,  9.96854776e-01,  9.89821442e-01,\n",
       "        9.78802446e-01,  9.63842159e-01,  9.45000819e-01,  9.22354294e-01,\n",
       "        8.95993774e-01,  8.66025404e-01,  8.32569855e-01,  7.95761841e-01,\n",
       "        7.55749574e-01,  7.12694171e-01,  6.66769001e-01,  6.18158986e-01,\n",
       "        5.67059864e-01,  5.13677392e-01,  4.58226522e-01,  4.00930535e-01,\n",
       "        3.42020143e-01,  2.81732557e-01,  2.20310533e-01,  1.58001396e-01,\n",
       "        9.50560433e-02,  3.17279335e-02, -3.17279335e-02, -9.50560433e-02,\n",
       "       -1.58001396e-01, -2.20310533e-01, -2.81732557e-01, -3.42020143e-01,\n",
       "       -4.00930535e-01, -4.58226522e-01, -5.13677392e-01, -5.67059864e-01,\n",
       "       -6.18158986e-01, -6.66769001e-01, -7.12694171e-01, -7.55749574e-01,\n",
       "       -7.95761841e-01, -8.32569855e-01, -8.66025404e-01, -8.95993774e-01,\n",
       "       -9.22354294e-01, -9.45000819e-01, -9.63842159e-01, -9.78802446e-01,\n",
       "       -9.89821442e-01, -9.96854776e-01, -9.99874128e-01, -9.98867339e-01,\n",
       "       -9.93838464e-01, -9.84807753e-01, -9.71811568e-01, -9.54902241e-01,\n",
       "       -9.34147860e-01, -9.09631995e-01, -8.81453363e-01, -8.49725430e-01,\n",
       "       -8.14575952e-01, -7.76146464e-01, -7.34591709e-01, -6.90079011e-01,\n",
       "       -6.42787610e-01, -5.92907929e-01, -5.40640817e-01, -4.86196736e-01,\n",
       "       -4.29794912e-01, -3.71662456e-01, -3.12033446e-01, -2.51147987e-01,\n",
       "       -1.89251244e-01, -1.26592454e-01, -6.34239197e-02, -2.44929360e-16])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.06346652, 0.12693304, 0.19039955, 0.25386607,\n",
       "       0.31733259, 0.38079911, 0.44426563, 0.50773215, 0.57119866,\n",
       "       0.63466518, 0.6981317 , 0.76159822, 0.82506474, 0.88853126,\n",
       "       0.95199777, 1.01546429, 1.07893081, 1.14239733, 1.20586385,\n",
       "       1.26933037, 1.33279688, 1.3962634 , 1.45972992, 1.52319644,\n",
       "       1.58666296, 1.65012947, 1.71359599, 1.77706251, 1.84052903,\n",
       "       1.90399555, 1.96746207, 2.03092858, 2.0943951 , 2.15786162,\n",
       "       2.22132814, 2.28479466, 2.34826118, 2.41172769, 2.47519421,\n",
       "       2.53866073, 2.60212725, 2.66559377, 2.72906028, 2.7925268 ,\n",
       "       2.85599332, 2.91945984, 2.98292636, 3.04639288, 3.10985939,\n",
       "       3.17332591, 3.23679243, 3.30025895, 3.36372547, 3.42719199,\n",
       "       3.4906585 , 3.55412502, 3.61759154, 3.68105806, 3.74452458,\n",
       "       3.8079911 , 3.87145761, 3.93492413, 3.99839065, 4.06185717,\n",
       "       4.12532369, 4.1887902 , 4.25225672, 4.31572324, 4.37918976,\n",
       "       4.44265628, 4.5061228 , 4.56958931, 4.63305583, 4.69652235,\n",
       "       4.75998887, 4.82345539, 4.88692191, 4.95038842, 5.01385494,\n",
       "       5.07732146, 5.14078798, 5.2042545 , 5.26772102, 5.33118753,\n",
       "       5.39465405, 5.45812057, 5.52158709, 5.58505361, 5.64852012,\n",
       "       5.71198664, 5.77545316, 5.83891968, 5.9023862 , 5.96585272,\n",
       "       6.02931923, 6.09278575, 6.15625227, 6.21971879, 6.28318531])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.array([0,30,45,60,90])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0, 30, 45, 60, 90])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "b=np.sin(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        , -0.98803162,  0.85090352, -0.30481062,  0.89399666])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.array([0,30,45,60,np.pi/2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "b=np.sin(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        , -0.98803162,  0.85090352, -0.30481062,  1.        ])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.array((1,2,3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.array([1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.array(((1,2),(3,4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.ones((2, 3), dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = np.random.random((1,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.73953911, 0.40494472]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "dtype() missing required argument 'dtype' (pos 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mUntitled-1.ipynb Cell 52\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:Untitled-1.ipynb?jupyter-notebook#Y103sdW50aXRsZWQ%3D?line=0'>1</a>\u001b[0m b\u001b[39m=\u001b[39mnp\u001b[39m.\u001b[39mdtype()\n",
      "\u001b[1;31mTypeError\u001b[0m: dtype() missing required argument 'dtype' (pos 1)"
     ]
    }
   ],
   "source": [
    "b=np.dtype()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'numpy.dtype[int32]' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mUntitled-1.ipynb Cell 56\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:Untitled-1.ipynb?jupyter-notebook#Y110sdW50aXRsZWQ%3D?line=0'>1</a>\u001b[0m a\u001b[39m.\u001b[39mdtype()\n",
      "\u001b[1;31mTypeError\u001b[0m: 'numpy.dtype[int32]' object is not callable"
     ]
    }
   ],
   "source": [
    "a.dtype()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.array([1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function ndarray.sum>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = np.arange(24).reshape(2,3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14, 15],\n",
       "        [16, 17, 18, 19],\n",
       "        [20, 21, 22, 23]]])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.min(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10*3+4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(x, y):\n",
    "    return 10*x+y\n",
    "\n",
    "b = np.fromfunction(f, (5,4), dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [10, 11, 12, 13],\n",
       "       [20, 21, 22, 23],\n",
       "       [30, 31, 32, 33],\n",
       "       [40, 41, 42, 43]])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[2,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.array((2,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a= np.arange(4).reshape(2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1],\n",
       "       [2, 3]])"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.  3. -4.  4. -6.]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.array([1.5, 3., -3.2, 4.0, -5.8])\n",
    "\n",
    "floored_arr = np.floor(arr)\n",
    "\n",
    "print(floored_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a= np.random.random((3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.26299447, 0.18710908, 0.45800225, 0.23620538],\n",
       "       [0.58289804, 0.72292687, 0.9357259 , 0.59803317],\n",
       "       [0.77960545, 0.6179705 , 0.00772245, 0.97382251]])"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a= np.random.random((3,4))\n",
    "np.floor(a)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ufunc 'floor'>"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import newaxis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([4., 2.])\n",
    "b = np.array([3., 8.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.],\n",
       "       [2.]])"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:2, newaxis] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.column_stack is np.hstack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.floor(10 * rg.random((2, 12)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3., 7., 9., 3., 4., 4., 4., 6., 8., 5., 3., 5.],\n",
       "       [9., 2., 4., 7., 4., 0., 3., 3., 5., 4., 6., 6.]])"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[3., 7., 9., 3.],\n",
       "        [9., 2., 4., 7.]]),\n",
       " array([[4., 4., 4., 6.],\n",
       "        [4., 0., 3., 3.]]),\n",
       " array([[8., 5., 3., 5.],\n",
       "        [5., 4., 6., 6.]])]"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.hsplit(a, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[3., 7., 9.],\n",
       "        [9., 2., 4.]]),\n",
       " array([[3.],\n",
       "        [7.]]),\n",
       " array([[4., 4., 4., 6., 8., 5., 3., 5.],\n",
       "        [4., 0., 3., 3., 5., 4., 6., 6.]])]"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.hsplit(a, (3, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([[ 0,  1,  2,  3],\n",
    "              [ 4,  5,  6,  7],\n",
    "              [ 8,  9, 10, 11]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "b=a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b is a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "a = np.arange(10)\n",
    "b = a.view()\n",
    "b = b.reshape(2, 5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3, 4],\n",
       "       [5, 6, 7, 8, 9]])"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a is b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b is a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c= a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3, 4],\n",
       "       [5, 6, 7, 8, 9]])"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.reshape(2,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3, 4],\n",
       "       [5, 6, 7, 8, 9]])"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.array(((1,2),\n",
    "           (3,4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "b=a.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "b[1:]=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [5, 5]])"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.arange(12)**2  # the first 12 square numbers\n",
    "i = np.array([1, 1, 3, 8, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  1,  9, 64, 25])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,   4,   9,  16,  25,  36,  49,  64,  81, 100, 121])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c=np.floor(10*np.random.random((1,7)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8., 7., 5., 1., 0., 5., 5.]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "i=np.array((1,3,5,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8., 7., 5., 1., 0., 5., 5.])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 1 is out of bounds for axis 0 with size 1",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\HP\\Desktop\\python codes\\numpy_practice.ipynb Cell 118\u001b[0m line \u001b[0;36m6\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/HP/Desktop/python%20codes/numpy_practice.ipynb#Y225sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m c \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mfloor(\u001b[39m10\u001b[39m \u001b[39m*\u001b[39m np\u001b[39m.\u001b[39mrandom\u001b[39m.\u001b[39mrandom((\u001b[39m1\u001b[39m, \u001b[39m7\u001b[39m)))\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/HP/Desktop/python%20codes/numpy_practice.ipynb#Y225sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m i \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([\u001b[39m1\u001b[39m, \u001b[39m3\u001b[39m, \u001b[39m5\u001b[39m, \u001b[39m6\u001b[39m])\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/HP/Desktop/python%20codes/numpy_practice.ipynb#Y225sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m result \u001b[39m=\u001b[39m c[i]\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/HP/Desktop/python%20codes/numpy_practice.ipynb#Y225sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m \u001b[39mprint\u001b[39m(result)\n",
      "\u001b[1;31mIndexError\u001b[0m: index 1 is out of bounds for axis 0 with size 1"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "c = np.floor(10 * np.random.random((1, 7)))\n",
    "i = np.array([1, 3, 5, 6])\n",
    "\n",
    "result = c[i]\n",
    "\n",
    "print(result)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = np.arange(12*5+5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = np.array([1, 3, 5, 6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 5, 6])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "       51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.arange(12).reshape(3, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = np.array([[0, 1],  # indices for the first dim of `a`\n",
    "              [1, 2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7]],\n",
       "\n",
       "       [[ 4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11]]])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.arange(12).reshape(3, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = np.array([[0, 2],  # indices for the first dim of `a`\n",
    "              [1, 2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "j = np.array([[2, 1],  # indices for the second dim\n",
    "              [3, 3]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[2, 1],\n",
       "        [3, 3]],\n",
       "\n",
       "       [[6, 5],\n",
       "        [7, 7]]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0:2,j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 8,  9, 10, 11]],\n",
       "\n",
       "       [[ 4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11]]])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 3 is out of bounds for axis 0 with size 3",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\HP\\Desktop\\python codes\\numpy_practice.ipynb Cell 132\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/HP/Desktop/python%20codes/numpy_practice.ipynb#Y245sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m a[j]\n",
      "\u001b[1;31mIndexError\u001b[0m: index 3 is out of bounds for axis 0 with size 3"
     ]
    }
   ],
   "source": [
    "a[j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.arange(12).reshape(3, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "b=a>4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, False],\n",
       "       [False,  True,  True,  True],\n",
       "       [ True,  True,  True,  True]])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5,  6,  7,  8,  9, 10, 11])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[b]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = np.where(b, a, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0,  0,  0],\n",
       "       [ 0,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = np.linspace(0, 1, 5)  # Create an array of X coordinates\n",
    "y = np.linspace(0, 1, 3)  # Create an array of Y coordinates\n",
    "\n",
    "X, Y = np.meshgrid(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.   0.25 0.5  0.75 1.  ]\n",
      " [0.   0.25 0.5  0.75 1.  ]\n",
      " [0.   0.25 0.5  0.75 1.  ]]\n"
     ]
    }
   ],
   "source": [
    "print(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.arange(12).reshape(3, 4)\n",
    "b1 = np.array([False, True, True])         # first dim selection\n",
    "b2 = np.array([True, False, True, False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[b1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[b1, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  2],\n",
       "       [ 4,  6],\n",
       "       [ 8, 10]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[ : , b2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([2, 3, 4])\n",
    "b = np.array([8, 5, 4])\n",
    "c = np.array([5, 4, 6,])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([42, 23, 28])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a+b*c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax, bx, cx = np.ix_(a, b, c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[42, 34, 50],\n",
       "        [27, 22, 32],\n",
       "        [22, 18, 26]],\n",
       "\n",
       "       [[43, 35, 51],\n",
       "        [28, 23, 33],\n",
       "        [23, 19, 27]],\n",
       "\n",
       "       [[44, 36, 52],\n",
       "        [29, 24, 34],\n",
       "        [24, 20, 28]]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax+bx*cx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([11, 11, 13, 14, 15, 16, 17, 12, 13, 11, 14, 18, 19, 20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11 12 13 14 15 16 17 18 19 20]\n"
     ]
    }
   ],
   "source": [
    "b=np.unique(a)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([1, 2, 3, 4, 5, 6, 7, 8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8, 7, 6, 5, 4, 3, 2, 1])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[ : : -1 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array((1,2,3,4,5,6,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('saving_arr.csv',a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "b= np.loadtxt('saving_arr.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2., 3., 4., 5., 6., 7.])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([[-2.58289208,  0.43014843, -1.24082018, 1.59572603],\n",
    "              [ 0.99027828, 1.17150989,  0.94125714, -0.14692469],\n",
    "              [ 0.76989341,  0.81299683, -0.95068423, 0.11769564],\n",
    "              [ 0.20484034,  0.34784527,  1.96979195, 0.51992837]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0         1         2         3\n",
      "0 -2.582892  0.430148 -1.240820  1.595726\n",
      "1  0.990278  1.171510  0.941257 -0.146925\n",
      "2  0.769893  0.812997 -0.950684  0.117696\n",
      "3  0.204840  0.347845  1.969792  0.519928\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('frame.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('frame.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-2.582892</td>\n",
       "      <td>0.430148</td>\n",
       "      <td>-1.240820</td>\n",
       "      <td>1.595726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.990278</td>\n",
       "      <td>1.171510</td>\n",
       "      <td>0.941257</td>\n",
       "      <td>-0.146925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.769893</td>\n",
       "      <td>0.812997</td>\n",
       "      <td>-0.950684</td>\n",
       "      <td>0.117696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.204840</td>\n",
       "      <td>0.347845</td>\n",
       "      <td>1.969792</td>\n",
       "      <td>0.519928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0         0         1         2         3\n",
       "0           0 -2.582892  0.430148 -1.240820  1.595726\n",
       "1           1  0.990278  1.171510  0.941257 -0.146925\n",
       "2           2  0.769893  0.812997 -0.950684  0.117696\n",
       "3           3  0.204840  0.347845  1.969792  0.519928"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([2, 1, 5, 7, 4, 6, 8, 14, 10, 9, 18, 20, 22])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# If you're using Jupyter Notebook, you may also want to run the following\n",
    "# line of code to display your code in the notebook:\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1be40d98d90>]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1be40e0a690>]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 5, 20)\n",
    "y = np.linspace(0, 10, 20)\n",
    "plt.plot(x, y, 'purple') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1be40ea3550>]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, 'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0, 5, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.26315789, 0.52631579, 0.78947368, 1.05263158,\n",
       "       1.31578947, 1.57894737, 1.84210526, 2.10526316, 2.36842105,\n",
       "       2.63157895, 2.89473684, 3.15789474, 3.42105263, 3.68421053,\n",
       "       3.94736842, 4.21052632, 4.47368421, 4.73684211, 5.        ])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.linspace(-5, 5.15, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.        , -3.87222222, -2.74444444, -1.61666667, -0.48888889,\n",
       "        0.63888889,  1.76666667,  2.89444444,  4.02222222,  5.15      ])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x1be42b43e90>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(projection='3d')\n",
    "X = np.arange(-5, 5, 0.15)\n",
    "Y = np.arange(-5, 5, 0.15)\n",
    "X, Y = np.meshgrid(X, Y)\n",
    "R = np.sqrt(X**2 + Y**2)\n",
    "Z = np.sin(R)\n",
    "\n",
    "ax.plot_surface(X, Y, Z, rstride=2, cstride=2, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}