Session Ready
Exercise

Using %mprun: Hero BMI 2.0

Let's see if using a different approach to calculate the BMIs can save some memory. If you remember, each hero's height and weight is stored in a numpy array. That means you can use NumPy's handy array indexing capabilities and broadcasting to perform your calculations. A function named calc_bmi_arrays has been created and saved to a file titled bmi_arrays.py. For convenience, it is displayed below:

def calc_bmi_arrays(sample_indices, hts, wts):

    # Gather sample heights and weights as arrays
    s_hts = hts[sample_indices]
    s_wts = wts[sample_indices]

    # Convert heights from cm to m and square with broadcasting
    s_hts_m_sqr = (s_hts / 100) ** 2

    # Calculate BMIs as an array using broadcasting
    bmis = s_wts / s_hts_m_sqr

    return bmis

Notice that this function performs all necessary calculations using arrays.

Let's see if this updated array approach decreases your memory footprint:

  • Load the memory_profiler package into your IPython session.
  • Import calc_bmi_arrays from bmi_arrays.
  • Once you've completed the above steps, use %mprun to profile the calc_bmi_arrays() function acting on your superheroes data. The sample_indices array, hts array, and wts array have been loaded into your session.

After you've finished coding, answer the following question:

How much memory do the array indexing and broadcasting lines of code consume in the calc_bmi_array() function? (i.e., what is the total sum of the Increment column for these four lines of code?)

Instructions
50 XP
Possible Answers