Question 1: How to make a python function that handles scalars to work on numpy arrays?

Note: Convert the function maxx that works on two scalars, to work on two arrays.

	
	Input:

def maxx(x, y):
    """Get the maximum of two items"""
    if x >= y:
        return x
    else:
        return y

maxx(1, 5)
#> 5

Desired Output:

a = np.array([5, 7, 9, 8, 6, 4, 5])
b = np.array([6, 3, 4, 8, 9, 7, 1])
pair_max(a, b)
#> array([ 6.,  7.,  9.,  8.,  9.,  7.,  5.])
   
   

    
        
Solution
        def maxx(x, y):
    """Get the maximum of two items"""
    if x >= y:
        return x
    else:
        return y

pair_max = np.vectorize(maxx, otypes=[float])

a = np.array([5, 7, 9, 8, 6, 4, 5])
b = np.array([6, 3, 4, 8, 9, 7, 1])

pair_max(a, b)
#> array([ 6.,  7.,  9.,  8.,  9.,  7.,  5.])
    

Question 2: How to swap two columns in a 2d numpy array?

Note: Swap columns 1 and 2 in the array arr.

arr = np.arange(9).reshape(3,3)
arr

Solution
# Input
arr = np.arange(9).reshape(3,3)
arr

# Output
arr[:, [1,0,2]]
#> array([[1, 0, 2],
#>        [4, 3, 5],
#>        [7, 6, 8]])

Question 3: How to swap two rows in a 2d numpy array?

Note: Swap rows 1 and 2 in the array arr:

arr = np.arange(9).reshape(3,3)
arr

Solution
# Input
arr = np.arange(9).reshape(3,3)

# Output
arr[[1,0,2], :]
#> array([[3, 4, 5],
#>        [0, 1, 2],
#>        [6, 7, 8]])

Question 4: How to reverse the rows of a 2D array?

Note: Reverse the rows of a 2D array arr.

# Input
arr = np.arange(9).reshape(3,3)

    
        
Solution
       # Input
arr = np.arange(9).reshape(3,3)

# Solution
arr[::-1]

£ Output
array([[6, 7, 8],
       [3, 4, 5],
       [0, 1, 2]])
    

Question 5: How to reverse the columns of a 2D array?

Note: Reverse the columns of a 2D array arr.

# Input
arr = np.arange(9).reshape(3,3)

Solution
# Input
arr = np.arange(9).reshape(3,3)

# Solution
arr[:, ::-1]
#> array([[2, 1, 0],
#>        [5, 4, 3],
#>        [8, 7, 6]])

Question 6: How to create a 2D array containing random floats between 5 and 10?

Note: Create a 2D array of shape 5x3 to contain random decimal numbers between 5 and 10.

    
        
Solution
# Input
arr = np.arange(9).reshape(3,3)

# Solution Method 1:
rand_arr = np.random.randint(low=5, high=10, size=(5,3)) + np.random.random((5,3))
# print(rand_arr)

# Solution Method 2:
rand_arr = np.random.uniform(5,10, size=(5,3))
print(rand_arr)
#> [[ 8.50061025  9.10531502  6.85867783]
#>  [ 9.76262069  9.87717411  7.13466701]
#>  [ 7.48966403  8.33409158  6.16808631]
#>  [ 7.75010551  9.94535696  5.27373226]
#>  [ 8.0850361   5.56165518  7.31244004]]

Question 7: How to print only 3 decimal places in python numpy array?

Print or show only 3 decimal places of the numpy array rand_arr.

Input:

rand_arr = np.random.random((5,3))

Solution
# Input
rand_arr = np.random.random((5,3))

# Create the random array
rand_arr = np.random.random([5,3])

# Limit to 3 decimal places
np.set_printoptions(precision=3)
rand_arr[:4]
#> array([[ 0.443,  0.109,  0.97 ],
#>        [ 0.388,  0.447,  0.191],
#>        [ 0.891,  0.474,  0.212],
#>        [ 0.609,  0.518,  0.403]])

Question 8: How to pretty print a numpy array by suppressing the scientific notation (like 1e10)?

Note: Pretty print rand_arr by suppressing the scientific notation (like 1e10)

Input:

# Create the random array
np.random.seed(100)
rand_arr = np.random.random([3,3])/1e3
rand_arr

#> array([[  5.434049e-04,   2.783694e-04,   4.245176e-04],
#>        [  8.447761e-04,   4.718856e-06,   1.215691e-04],
#>        [  6.707491e-04,   8.258528e-04,   1.367066e-04]])
Desired Output:

#> array([[ 0.000543,  0.000278,  0.000425],
#>        [ 0.000845,  0.000005,  0.000122],
#>        [ 0.000671,  0.000826,  0.000137]])

Solution
# Reset printoptions to default
np.set_printoptions(suppress=False)

# Create the random array
np.random.seed(100)
rand_arr = np.random.random([3,3])/1e3
rand_arr
#> array([[  5.434049e-04,   2.783694e-04,   4.245176e-04],
#>        [  8.447761e-04,   4.718856e-06,   1.215691e-04],
#>        [  6.707491e-04,   8.258528e-04,   1.367066e-04]])

Output
np.set_printoptions(suppress=True, precision=6)  # precision is optional
rand_arr
#> array([[ 0.000543,  0.000278,  0.000425],
#>        [ 0.000845,  0.000005,  0.000122],
#>        [ 0.000671,  0.000826,  0.000137]])

Question 9: How to limit the number of items printed in output of numpy array?

Note: Limit the number of items printed in python numpy array a to a maximum of 6 elements.

Input:

a = np.arange(15)
#> array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14])

Desired Output:

#> array([ 0,  1,  2, ..., 12, 13, 14])

Solution
np.set_printoptions(threshold=6)
a = np.arange(15)
a
#> array([ 0,  1,  2, ..., 12, 13, 14])

Question 10: How to print the full numpy array without truncating?

Note: Print the full numpy array a without truncating.

Input:

np.set_printoptions(threshold=6)
a = np.arange(15)
a
#> array([ 0,  1,  2, ..., 12, 13, 14])

Desired Output:

a
#> array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14])

Solution
# Input
np.set_printoptions(threshold=6)
a = np.arange(15)

# Solution
np.set_printoptions(threshold=np.nan)
a
#> array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14])