
import numpy as np


a = np.loadtxt("grades2.txt")


print(a)

[[  1.     1.23]
 [  2.     4.56]
 [  3.     7.89]
 [  4.    11.22]
 [  5.    14.55]
 [  6.    17.88]
 [  7.    21.21]
 [  8.    24.54]
 [  9.    27.87]
 [ 10.    31.2 ]
 [ 11.    34.53]
 [ 12.    37.86]
 [ 13.    41.19]
 [ 14.    44.52]
 [ 15.    47.85]
 [ 16.    51.18]
 [ 17.    54.51]
 [ 18.    57.84]
 [ 19.    61.17]
 [ 20.    64.5 ]
 [ 21.    67.83]
 [ 22.    71.16]
 [ 23.    74.49]
 [ 24.    77.82]
 [ 25.    81.15]
 [ 26.    84.48]
 [ 27.    87.81]
 [ 28.    91.14]
 [ 29.    94.47]
 [ 30.    97.8 ]
 [ 31.   101.13]
 [ 32.   104.46]
 [ 33.   107.79]
 [ 34.   111.12]
 [ 35.   114.45]
 [ 36.   117.78]
 [ 37.   121.11]
 [ 38.   124.44]
 [ 39.   127.77]
 [ 40.   131.1 ]
 [ 41.   134.43]
 [ 42.   137.76]
 [ 43.   141.09]
 [ 44.   144.42]
 [ 45.   147.75]
 [ 46.   151.08]
 [ 47.   154.41]
 [ 48.   157.74]
 [ 49.   161.07]
 [ 50.   164.4 ]
 [ 51.   167.73]
 [ 52.   171.06]
 [ 53.   174.39]
 [ 54.   177.72]
 [ 55.   181.05]
 [ 56.   184.38]
 [ 57.   187.71]
 [ 58.   191.04]
 [ 59.   194.37]
 [ 60.   197.7 ]
 [ 61.   201.03]
 [ 62.   204.36]
 [ 63.   207.69]
 [ 64.   211.02]
 [ 65.   214.35]
 [ 66.   217.68]
 [ 67.   221.01]
 [ 68.   224.34]
 [ 69.   227.67]
 [ 70.   231.  ]
 [ 71.   234.33]
 [ 72.   237.66]
 [ 73.   240.99]
 [ 74.   244.32]
 [ 75.   247.65]
 [ 76.   250.98]
 [ 77.   254.31]
 [ 78.   257.64]
 [ 79.   260.97]
 [ 80.   264.3 ]
 [ 81.   267.63]
 [ 82.   270.96]
 [ 83.   274.29]
 [ 84.   277.62]
 [ 85.   280.95]
 [ 86.   284.28]
 [ 87.   287.61]
 [ 88.   290.94]
 [ 89.   294.27]
 [ 90.   297.6 ]
 [ 91.   300.93]
 [ 92.   304.26]
 [ 93.   307.59]
 [ 94.   310.92]
 [ 95.   314.25]
 [ 96.   317.58]
 [ 97.   320.91]
 [ 98.   324.24]
 [ 99.   327.57]
 [100.   330.9 ]]


# i want to just one column from two columns of the above function to do that I can use the follwing way
b1 = a[:,1] #second column
b0 = a[:,0] #first coulmn
print(b1)

[  1.23   4.56   7.89  11.22  14.55  17.88  21.21  24.54  27.87  31.2
  34.53  37.86  41.19  44.52  47.85  51.18  54.51  57.84  61.17  64.5
  67.83  71.16  74.49  77.82  81.15  84.48  87.81  91.14  94.47  97.8
 101.13 104.46 107.79 111.12 114.45 117.78 121.11 124.44 127.77 131.1
 134.43 137.76 141.09 144.42 147.75 151.08 154.41 157.74 161.07 164.4
 167.73 171.06 174.39 177.72 181.05 184.38 187.71 191.04 194.37 197.7
 201.03 204.36 207.69 211.02 214.35 217.68 221.01 224.34 227.67 231.
 234.33 237.66 240.99 244.32 247.65 250.98 254.31 257.64 260.97 264.3
 267.63 270.96 274.29 277.62 280.95 284.28 287.61 290.94 294.27 297.6
 300.93 304.26 307.59 310.92 314.25 317.58 320.91 324.24 327.57 330.9 ]