python02():
  Exercises for python02 lab.

  numpy.sqrt ( 20.0 ) =  4.47213595499958

  Data read from file:
[[0. 0. 1. ... 3. 0. 0.]
 [0. 1. 2. ... 1. 0. 1.]
 [0. 1. 1. ... 2. 1. 1.]
 ...
 [0. 1. 1. ... 1. 1. 1.]
 [0. 0. 0. ... 0. 2. 0.]
 [0. 0. 1. ... 1. 1. 0.]]

  type(data) =  <class 'numpy.ndarray'>
  data.dtype =  float64
  data.shape =  (60, 40)
  First entry is   0.0
  Middle entry is  10.0
  Last entry is    0.0
  data[0,0] =  0.0
  x = data[0,0] - 2.0 * data[0,1] + data[0,2] =  1.0
  data[0,0] =  1.0
[ 0.  0.  0.  1.  2.  1.  4.  3.  6.  7.  4.  2. 12.  6. 12.  4. 14.  7.
  8. 14. 13. 19.  6.  9. 12.  6.  4. 13.  6.  7.  2.  3.  6.  5.  4.  2.
  3.  0.  1.  0.]
[ 7.  7. 12. 15.  9.  3.  3. 10. 14.  7. 14.  5.  7.  4.  4. 11. 13.  6.
  4.  9. 12. 12.  9. 14. 15. 11. 12.  5.  7. 13.  7.  8.  5. 15.  4.  3.
  6.  4.  5. 10. 12. 12.  3.  9. 12.  7. 11.  7.  8.  4.  4.  6.  7. 12.
 12.  9. 14. 10.  9. 11.]
[[2. 3. 0. 0.]
 [1. 1. 0. 1.]
 [2. 2. 1. 1.]]

  Statistics for entire table:
  maximum =  20.0
  minimum =  0.0
  average =  6.14875
  stdev =    4.613833197118566

  Statistics for row i = 17
  maximum =  19.0
  minimum =  0.0
  average =  5.7
  stdev =    4.696807426326951

  Statistics for row j = 25
  maximum =  15.0
  minimum =  3.0
  average =  8.666666666666666
  stdev =    3.6086316273931622

  meanrows = np.mean ( data, axis = 1 )
  meanrows =  [5.45  5.425 6.1   5.9   5.55  6.225 5.975 6.65  6.625 6.525 6.775 5.8
 6.225 5.75  5.225 6.3   6.55  5.7   5.85  6.55  5.775 5.825 6.175 6.1
 5.8   6.425 6.05  6.025 6.175 6.55  6.175 6.35  6.725 6.125 7.075 5.725
 5.925 6.15  6.075 5.75  5.975 5.725 6.3   5.9   6.75  5.925 7.225 6.15
 5.95  6.275 5.7   6.1   6.825 5.975 6.725 5.7   6.25  6.4   7.05  5.9  ]
  meanrows.shape =  (60,)
  meanrows[25] =  6.425

  maxcols = np.max ( data, axis = 0 )
  maxcols =  [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.
 18. 19. 20. 19. 18. 17. 16. 15. 14. 13. 12. 11. 10.  9.  8.  7.  6.  5.
  4.  3.  2.  1.]
  maxcols.shape =  (40,)
  maxcols[17] =  17.0

  s1 =  breakfast
  s1[0] =  b
  s1[0:0] =  
  s1[0:2] =  br

  s1 =  breakfast
  s2 =  lunch
  s2[1:] =  unch
  s1[:2] + s2[1:] =  brunch
  s1[5:-1] =  fas
  s[ -3 : -1 ] =  al
  s[ -3 : -1 ] =  aa
  s[ -3 : -1 ] =  m
  s =  buffalo
  s[3:3] =  

  data[3:3,4:4].shape =  (0, 0)
  data[3:3,;].shape =  (0, 40)

  rowvec =  [0 1 2]
  table =  [[ 0  1  2]
 [10 11 12]
 [20 21 22]]
  colvec =  [[ 0]
 [10]
 [20]]

  A =  [[1 2 3]
 [4 5 6]
 [7 8 9]]
  np.hstack( [A,A] ) =  [[1 2 3 1 2 3]
 [4 5 6 4 5 6]
 [7 8 9 7 8 9]]
  np.vstack( [A,A] ) =  [[1 2 3]
 [4 5 6]
 [7 8 9]
 [1 2 3]
 [4 5 6]
 [7 8 9]]

  sample =  [ 4.  2. 12.  6. 12.]
  np.diff ( sample ) =  [-2. 10. -6.  6.]
  np.diff(data) =  [[ 0.  1.  2. ...  1. -3.  0.]
 [ 1.  1. -1. ...  0. -1.  1.]
 [ 1.  0.  2. ...  0. -1.  0.]
 ...
 [ 1.  0.  0. ... -1.  0.  0.]
 [ 0.  0.  1. ... -2.  2. -2.]
 [ 0.  1. -1. ... -2.  0. -1.]]