Soring, searching, and counting



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import numpy as np



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np.__version__









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'1.11.2'



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author = 'kyubyong. longinglove@nate.com'

Sorting

Q1. Sort x along the second axis.



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x = np.array([[1,4],[3,1]])
out = np.sort(x, axis=1)
x.sort(axis=1)
assert np.array_equal(out, x)
print out









    



[[1 4]
 [1 3]]

Q2. Sort pairs of surnames and first names and return their indices. (first by surname, then by name).



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surnames =    ('Hertz',    'Galilei', 'Hertz')
first_names = ('Heinrich', 'Galileo', 'Gustav')
print np.lexsort((first_names, surnames))

Q3. Get the indices that would sort x along the second axis.



In [17]:

    
x = np.array([[1,4],[3,1]])
out = np.argsort(x, axis=1)
print out









    



[[0 1]
 [1 0]]

Q4. Create an array such that its fifth element would be the same as the element of sorted x, and it divide other elements by their value.



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x = np.random.permutation(10)
print "x =", x
print "\nCheck the fifth element of this new array is 5, the first four elements are all smaller than 5, and 6th through the end are bigger than 5\n", 
out = np.partition(x, 5)
x.partition(5) # in-place equivalent
assert np.array_equal(x, out)
print out









    



x = [5 1 6 3 9 8 2 7 4 0]

Check the fifth element of this new array is 5, the first four elements are all smaller than 5, and 6th through the end are bigger than 5
[2 0 4 3 1 5 8 7 6 9]

Q5. Create the indices of an array such that its third element would be the same as the element of sorted x, and it divide other elements by their value.



In [56]:

    
x = np.random.permutation(10)
print "x =", x
partitioned = np.partition(x, 3)
indices = np.argpartition(x, 3)
print "partitioned =", partitioned
print "indices =", partitioned
assert np.array_equiv(x[indices], partitioned)









    



x = [2 8 3 7 5 6 4 0 9 1]
partitioned = [0 1 2 3 4 5 8 6 9 7]
indices = [0 1 2 3 4 5 8 6 9 7]

Searching

Q6. Get the maximum and minimum values and their indices of x along the second axis.



In [78]:

    
x = np.random.permutation(10).reshape(2, 5)
print "x =", x
print "maximum values =", np.max(x, 1)
print "max indices =", np.argmax(x, 1)
print "minimum values =", np.min(x, 1)
print "min indices =", np.argmin(x, 1)









    



x = [[0 5 9 8 2]
 [3 7 4 1 6]]
maximum values = [9 7]
max indices = [2 1]
minimum values = [0 1]
min indices = [0 3]

Q7. Get the maximum and minimum values and their indices of x along the second axis, ignoring NaNs.



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x = np.array([[np.nan, 4], [3, 2]])
print "maximum values ignoring NaNs =", np.nanmax(x, 1)
print "max indices =", np.nanargmax(x, 1)
print "minimum values ignoring NaNs =", np.nanmin(x, 1)
print "min indices =", np.nanargmin(x, 1)









    



maximum values ignoring NaNs = [ 4.  3.]
max indices = [1 0]
minimum values ignoring NaNs = [ 4.  2.]
min indices = [1 1]

Q8. Get the values and indices of the elements that are bigger than 2 in x.



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x = np.array([[1, 2, 3], [1, 3, 5]])
print "Values bigger than 2 =", x[x>2]
print "Their indices are ", np.nonzero(x > 2)
assert np.array_equiv(x[x>2], x[np.nonzero(x > 2)])
assert np.array_equiv(x[x>2], np.extract(x > 2, x))









    



Values bigger than 2 = [3 3 5]
Their indices are  (array([0, 1, 1], dtype=int64), array([2, 1, 2], dtype=int64))

Q9. Get the indices of the elements that are bigger than 2 in the flattend x.



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x = np.array([[1, 2, 3], [1, 3, 5]])
print np.flatnonzero(x>2)
assert np.array_equiv(np.flatnonzero(x), x.ravel().nonzero())

Q10. Check the elements of x and return 0 if it is less than 0, otherwise the element itself.



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x = np.arange(-5, 4).reshape(3, 3)
print np.where(x <0, 0, x)









    



[[0 0 0]
 [0 0 0]
 [1 2 3]]

Q11. Get the indices where elements of y should be inserted to x to maintain order.



In [109]:

    
x = [1, 3, 5, 7, 9]
y = [0, 4, 2, 6]
np.searchsorted(x, y)









    Out[109]:





array([0, 2, 1, 3], dtype=int64)

Counting

Q12. Get the number of nonzero elements in x.



In [120]:

    
x = [[0,1,7,0,0],[3,0,0,2,19]]
print np.count_nonzero(x)
assert np.count_nonzero(x) == len(x[x!=0])



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