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        content="Chapter 19 of Python Zero to Hero: learn how to benchmark and profile your code with timeit, cProfile, memory_profiler and optimize hotspots."/>
  <meta name="keywords"
        content="Python, profiling, performance, timeit, cProfile, pstats, line_profiler, memory_profiler, optimization"/>
  <meta name="author" content="Luca Bocaletto"/>
  <title>Chapter 19 – Profiling & Performance Optimization | Python Zero to Hero</title>

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    <header class="my-4">
      <h1 class="display-6">Chapter 19: Profiling &amp; Performance Optimization</h1>
      <p class="text-muted">
        Benchmark your code, identify hotspots and apply optimizations with built-in and third-party tools.
      </p>
      <a href="src/chapter19.py" download class="btn btn-outline-primary btn-sm btn-py">
        Download <code>chapter19.py</code>
      </a>
      <hr>
    </header>

    <!-- Objectives -->
    <section id="objectives" class="mb-5">
      <h2>Objectives</h2>
      <ul>
        <li>Measure execution time with <code>timeit</code>.</li>
        <li>Profile functions using <code>cProfile</code> and <code>pstats</code>.</li>
        <li>Inspect line-by-line timings with <code>line_profiler</code>.</li>
        <li>Analyze memory usage via <code>memory_profiler</code>.</li>
        <li>Apply simple code-level optimizations and built-in enhancements.</li>
      </ul>
    </section>

    <!-- 1. timeit -->
    <section id="timeit" class="mb-5">
      <h2>1. Measuring Time with <code>timeit</code></h2>
      <p>Use the <code>timeit</code> module for micro-benchmarks:</p>
      <pre><code class="python">import timeit

# one-liner
print(timeit.timeit("sum(range(1000))", number=10000))

# using Timer object
timer = timeit.Timer("x*x for x in range(1000)")
print(timer.timeit(number=5000))</code></pre>
    </section>

    <!-- 2. cProfile -->
    <section id="cprofile" class="mb-5">
      <h2>2. Profiling with <code>cProfile</code></h2>
      <p>Collect function-level statistics:</p>
      <pre><code class="python">import cProfile, pstats

def work():
    total = 0
    for i in range(100000):
        total += i
    return total

# run profiler
prof = cProfile.Profile()
prof.enable()
work()
prof.disable()

# print sorted stats
stats = pstats.Stats(prof)
stats.sort_stats("cumtime").print_stats(10)</code></pre>
    </section>

    <!-- 3. line_profiler -->
    <section id="line-profiler" class="mb-5">
      <h2>3. Line-by-Line Profiling</h2>
      <p>Install <code>pip install line_profiler</code> and decorate:</p>
      <pre><code class="python">@profile
def compute():
    total = 0
    for i in range(100000):
        total += i*i
    return total

if __name__ == "__main__":
    compute()</code></pre>
      <p>Then run <code>kernprof -l -v chapter19.py</code> to see line timings.</p>
    </section>

    <!-- 4. memory_profiler -->
    <section id="memory-profiler" class="mb-5">
      <h2>4. Memory Profiling</h2>
      <p>Install <code>pip install memory_profiler</code> and use:</p>
      <pre><code class="python">from memory_profiler import profile

@profile
def load_data():
    data = [i for i in range(1000000)]
    return data

if __name__ == "__main__":
    load_data()</code></pre>
      <p>Run with <code>python -m memory_profiler chapter19.py</code>.</p>
    </section>

    <!-- 5. Optimization Tips -->
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      <h2>5. Simple Optimization Tips</h2>
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        <li>Prefer built-ins like <code>sum()</code>, <code>map()</code> and comprehensions over manual loops.</li>
        <li>Cache expensive calls with <code>functools.lru_cache</code>.</li>
        <li>Use local variables inside loops for speed.</li>
        <li>Avoid global lookups: assign frequently used functions to locals.</li>
        <li>Consider <code>numpy</code> or C extensions for numeric heavy loops.</li>
      </ul>
    </section>

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    <section id="exercises" class="mb-5">
      <h2>Exercises</h2>
      <ol>
        <li>Benchmark two implementations of Fibonacci using <code>timeit</code>.</li>
        <li>Profile a data transformation script with <code>cProfile</code> and optimize the top hotspot.</li>
        <li>Use <code>line_profiler</code> on a nested-loop function and speed up its hottest line.</li>
        <li>Measure memory usage of building a large list vs generator expression with <code>memory_profiler</code>.</li>
      </ol>
    </section>

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