Practice with NumPy arrays
Let's practice slicing numpy
arrays and using NumPy's broadcasting concept. Remember, broadcasting refers to a numpy
array's ability to vectorize operations, so they are performed on all elements of an object at once.
A two-dimensional numpy
array has been loaded into your session (called nums
) and printed into the console for your convenience. numpy
has been imported into your session as np
.
This exercise is part of the course
Writing Efficient Python Code
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Print second row of nums
print(nums[____,____])
# Print all elements of nums that are greater than six
print(____[____ > ____])
# Double every element of nums
nums_dbl = ____ * ____
print(nums_dbl)
# Replace the third column of nums
nums[____,____] = ____[____,____] + ____
print(nums)