Chapter 6

Timing and Measuring Code Performance in Python

As programs grow in complexity, it becomes increasingly important to ensure that they run efficiently. Performance can be measured in terms of time complexity (how long a program takes to run) and space complexity (how much memory it uses). In Python, several libraries and techniques allow us to measure how our code performs and optimize it where necessary.

In this chapter, we will cover how to measure time complexity using Python’s libraries, introduce space complexity monitoring, and discuss how various factors impact the performance of code.

---

Why Measure Code Performance?

Understanding how efficiently a piece of code performs allows you to:

1. Optimize Code: Identify bottlenecks in the code that slow down execution.

2. Compare Algorithms: Compare different approaches to solving the same problem.

3. Scale Code: Ensure that as the input size grows, the program still runs within acceptable time limits.

4. Identify Resource Usage: Evaluate how much memory and computational resources your code consumes.

---

Measuring Time Complexity in Python

1. Using the time Module

The time module is the simplest way to measure the time it takes for a piece of code to execute. The basic idea is to capture the start and end times and compute the difference.

Example:

import time

def example_function(n):
    total = 0
    for i in range(n):
        total += i
    return total

start_time = time.time()  # Capture start time
example_function(1000000)  # Run the function
end_time = time.time()  # Capture end time

execution_time = end_time - start_time
print(f"Execution time: {execution_time} seconds")

In this example:

• time.time() returns the current time in seconds since the epoch.

• The difference between the end time and start time gives the total time taken for the function to execute.

Limitations:

• The time module measures wall-clock time, which includes everything happening on the machine. If other processes are running, they might interfere with the measurements.

• It does not give detailed information about how time is spent in different parts of the code.

---

2. Using the timeit Module

The timeit module provides a more accurate way to time small code snippets. It runs the code multiple times and returns the average execution time, minimizing the impact of background processes.

Example:

import timeit

code_to_test = """
def example_function(n):
    total = 0
    for i in range(n):
        total += i
    return total

example_function(1000000)
"""

execution_time = timeit.timeit(code_to_test, number=100)
print(f"Average execution time: {execution_time / 100} seconds")

In this example:

• The timeit.timeit() function runs the code snippet 100 times and returns the total time taken.

• By dividing by 100, we get the average time per execution.

Custom Timing Using timeit

You can also time individual functions directly using timeit without writing the code in a string format.

def example_function(n):
    total = 0
    for i in range(n):
        total += i
    return total

execution_time = timeit.timeit(lambda: example_function(1000000), number=100)
print(f"Average execution time: {execution_time / 100} seconds")

Here, we use a lambda function to call example_function() within timeit.timeit().

---

3. Using the cProfile Module

For larger programs or when you want to profile the entire application, cProfile is the go-to module. It provides a detailed breakdown of the time spent in each function.

Example:

import cProfile

def example_function(n):
    total = 0
    for i in range(n):
        total += i
    return total

cProfile.run('example_function(1000000)')

This outputs a detailed report including:

• The number of calls made to each function.

• The total time spent in each function.

• The time per call.

This helps identify which functions are taking up the most time and where optimization is needed.

---

Factors Affecting Execution Time

Several factors influence how long a piece of code takes to execute:

1. Algorithm Complexity:

The most significant factor is the time complexity of the algorithm itself. For example, a function that runs in O(n) time will scale linearly with input size, whereas a function that runs in O(n²) time will slow down significantly as input grows.

Example:

# O(n) complexity
def linear_search(arr, target):
    for i in range(len(arr)):
        if arr[i] == target:
            return i
    return -1

# O(n^2) complexity
def bubble_sort(arr):
    n = len(arr)
    for i in range(n):
        for j in range(0, n-i-1):
            if arr[j] > arr[j+1]:
                arr[j], arr[j+1] = arr[j+1], arr[j]

2. Hardware Limitations:

The CPU speed, available memory, and other system resources can affect the runtime performance. Different machines will yield different results even for the same piece of code.

3. Background Processes:

Programs running in the background can impact the timing of your code. This is especially true when using simple timing methods like time.time().

4. Python Version:

Different Python versions may have slight differences in how efficiently they handle certain operations. Python 3 tends to have more optimized internal functions than Python 2.

---

Measuring Space Complexity in Python

While time complexity focuses on how long a program takes to run, space complexity focuses on how much memory it consumes. The sys and tracemalloc modules are commonly used for measuring space complexity.

1. Using the sys Module

The sys module provides functions to track memory usage of objects.

Example:

import sys

a = [1] * 1000  # List of 1000 elements
b = "Hello, world!"  # String

print(sys.getsizeof(a))  # Memory consumed by list 'a'
print(sys.getsizeof(b))  # Memory consumed by string 'b'

Here, sys.getsizeof() returns the size of the object in bytes.

2. Using the tracemalloc Module

The tracemalloc module allows you to track memory allocation over time, providing a more detailed analysis of space complexity.

Example:

import tracemalloc

def example_function():
    lst = [i for i in range(1000)]  # Create a large list
    return lst

# Start tracking memory allocation
tracemalloc.start()

example_function()

# Get the current and peak memory usage
current, peak = tracemalloc.get_traced_memory()
print(f"Current memory usage: {current / 1024} KB")
print(f"Peak memory usage: {peak / 1024} KB")

# Stop tracking memory allocation
tracemalloc.stop()

In this example, tracemalloc tracks memory allocation from the moment it is started and provides both the current and peak memory usage.

---

Optimizing Performance

Once you have measured the time and space complexity, you can start optimizing the code.

1. Optimizing Time Complexity:

• Choose Efficient Algorithms: Switch from a quadratic algorithm like bubble sort (O(n²)) to a more efficient one like quicksort (O(n log n)).

• Use Built-in Functions: Python’s built-in functions (like sum(), sorted(), etc.) are optimized and run faster than custom implementations.

2. Optimizing Space Complexity:

• Avoid Unnecessary Objects: Reuse variables and avoid creating copies of large data structures when not needed.

• Generators Instead of Lists: Use generators (which yield items one at a time) instead of lists (which store all items in memory at once) when working with large data.

Example:

def generator_function(n):
    for i in range(n):
        yield i

gen = generator_function(1000)  # Only yields one value at a time

lst = [i for i in range(1000)]  # Stores all values in memory

In the case of large data, a generator will save memory compared to storing all elements in a list.

---

Conclusion

Measuring and optimizing the performance of Python code is a crucial part of writing efficient programs. Whether you're working on a small script or a large application, understanding the time and space complexity of your code will help you make better design decisions.

Key Takeaways:

• Use time and timeit for simple time measurements.

• Use cProfile for in-depth performance profiling.

• Use sys and tracemalloc for tracking memory usage.

• Focus on optimizing algorithms and choosing the right data structures for better time and space efficiency.

By timing and profiling your code, you’ll be able to find bottlenecks, improve performance, and ensure that your applications scale efficiently.