Measuring Execution Time and Memory Usage in Python

When developing Python code, it is often useful to know which parts consume the most time and memory. This article summarizes four methods to monitor Python execution time and memory usage.

1. time module

The simplest way to measure elapsed time is to record timestamps before and after the code block using the time module.

<code>import time

start_time = time.time()
result = 5+2
end_time = time.time()

print('Time taken = {} sec'.format(end_time - start_time))</code>

An example comparing a list comprehension with a for‑loop demonstrates the time difference.

<code>import time

# for loop vs. list comp
list_comp_start_time = time.time()
result = [i for i in range(0,1000000)]
list_comp_end_time = time.time()
print('Time taken for list comp = {} sec'.format(list_comp_end_time - list_comp_start_time))

result = []
for_loop_start_time = time.time()
for i in range(0,1000000):
    result.append(i)
for_loop_end_time = time.time()
print('Time taken for for-loop = {} sec'.format(for_loop_end_time - for_loop_start_time))

list_comp_time = list_comp_end_time - list_comp_start_time
for_loop_time = for_loop_end_time - for_loop_start_time
print('Difference = {} %'.format((for_loop_time - list_comp_time)/list_comp_time * 100))</code>

2. %%time magic command

The %%time cell magic in IPython/Jupyter reports the time taken to execute the whole cell.

<code>%%time

def convert_cms(cm, unit='m'):
    '''
    Function to convert cm to m or feet
    '''
    if unit == 'm':
        return cm/100
    return cm/30.48

convert_cms(1000)</code>

The output shows CPU times (user, sys) and wall time.

3. line_profiler

For per‑line timing, install line_profiler and use the %lprun magic.

<code>import line_profiler

def convert_cms(cm, unit='m'):
    '''
    Function to convert cm to m or feet
    '''
    if unit == 'm':
        return cm/100
    return cm/30.48

%load_ext line_profiler
%lprun -f convert_cms convert_cms(1000, 'f')</code>

The profiler prints a table with hits, time per hit, and % time for each line.

4. memory_profiler

To track memory usage per line, install memory_profiler and use %mprun .

<code>from conversions import convert_cms_f
import memory_profiler

%load_ext memory_profiler
%mprun -f convert_cms_f convert_cms_f(1000, 'f')</code>

The output lists memory usage (MiB) and increments for each line of the function.

Although Python is not known for high execution efficiency, these tools provide valuable insight for optimizing code in special cases.