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a guest Feb 19th, 2019 69 Never
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  1. Sample  Dog Cat Tarsier
  2. A47 1   7   2
  3. A48 3   3   4
  4. A51 2   1   8
  5. A53 0   0   0
  6. A54 1   7   2
  7. A57 0   0   10
  8.    
  9. Cat   Tarsier
  10. A47    7       2
  11.    
  12. struct row{
  13.     char *name;
  14.     int animal[3]; //id 0 is dog, 1 is cat and 2 is tarsier
  15. }
  16.    
  17. int i;
  18. float sum=0; //we need float to force the result to be float
  19. for (i=0;i<3;i++){
  20.     sum += row.animal[i]; //count the total row population
  21. }
  22. for (i=0;i<3;i++){ //for every animal
  23.     if (row.animal[i]/sum <= 0.1){ //if this animal is equal or less than 10% of the row population
  24.         row.animal[i]=0; //set his population to 0
  25.     }
  26. }
  27.    
  28. import csv
  29.  
  30. def getvals(file):
  31.     """
  32.         gets the val's from a file of whitespace separated values, and
  33.         turns them into easy to use Python var's
  34.     """
  35.     samples = csv.reader(open(file))
  36.     s = []
  37.     n = 0
  38.     for row in samples:
  39.         r = [row[0].split()]
  40.         s += r
  41.         n+=1
  42.     return s
  43.    
  44. [
  45.  ['Sample', 'Dog', 'Cat', 'Tarsier'],
  46.  ['A47', '1', '7', '2'],
  47.  ['A48', '3', '3', '4'],
  48.  ['A51', '2', '1', '8'],
  49.  ['A53', '0', '0', '0'],
  50.  ['A54', '1', '7', '2'],
  51.  ['A57', '0', '0', '10']
  52. ]
  53.    
  54. >>> data = np.genfromtxt('data.txt', delimiter="t", names=True, dtype=None)
  55.    
  56. data = array([('A47', 1, 7, 2), ('A48', 3, 3, 4), ('A51', 2, 1, 8),
  57.        ('A53', 0, 0, 0), ('A54', 1, 7, 2), ('A57', 0, 0, 10)],
  58.       dtype=[('Sample', '|S3'), ('Dog', '<i8'), ('Cat', '<i8'), ('Tarsier', '<i8')])
  59.    
  60. >>> data[["Cat","Tarsier"]]
  61. array([(7, 2), (3, 4), (1, 8), (0, 0), (7, 2), (0, 10)],
  62.   dtype=[('Cat', '<i8'), ('Tarsier', '<i8')])
  63.    
  64. >>> data[[0,2]]
  65. array([('A47', 1, 7, 2), ('A51', 2, 1, 8)],
  66.      dtype=[('Sample', '|S3'), ('Dog', '<i8'), ('Cat', '<i8'), ('Tarsier', '<i8')])
  67.    
  68. >>> data["Dog"].mean()
  69. 1.1666667
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