Add materials for "Python Performance Optimization: A Practical Guide"

python-performance-optimization-guide/README.md

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+# Python Performance Optimization: A Practical Guide
+
+This folder provides the code examples for the Real Python tutorial Python Performance Optimization: A Practical Guide.

python-performance-optimization-guide/step-1.py

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+import timeit
+
+
+def calculate_order_total(items):
+    total = 0
+    for item in items:
+        total = total + item["price"] * item["quantity"]
+    return total
+
+
+order = [
+    {"price": 19.99, "quantity": 3},
+    {"price": 5.50, "quantity": 10},
+    {"price": 42.00, "quantity": 1},
+] * 100
+
+runs = 500_000
+
+print(f"{calculate_order_total(order):.2f}")
+
+order_time = timeit.timeit(
+    lambda: calculate_order_total(order), number=runs
+)
+print(f"total for {runs:,} runs: {order_time:.4f} seconds")
+print(f"per call: {order_time / runs * 1_000_000:.1f} microseconds")

python-performance-optimization-guide/step-2-fast.py

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+import timeit
+
+numbers_list = list(range(100_000))
+target = 99_999
+
+numbers_set = set(numbers_list)
+
+set_time = timeit.timeit(lambda: target in numbers_set, number=1000)
+
+print(target in numbers_set)
+print(f"set membership:  {set_time:.8f} seconds")

python-performance-optimization-guide/step-2-slow.py

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+import timeit
+
+numbers_list = list(range(100_000))
+
+target = 99_999
+
+list_time = timeit.timeit(lambda: target in numbers_list, number=1000)
+
+print(target in numbers_list)
+print(f"list membership: {list_time:.8f} seconds")

python-performance-optimization-guide/step-3-fast.py

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+from collections import Counter
+import timeit
+
+words = ["apple", "pear", "apple", "cherry", "pear", "apple"] * 1000
+
+
+def counter_count(items):
+    return Counter(items)
+
+
+print(counter_count(words))
+
+counter_time = timeit.timeit(lambda: counter_count(words), number=1000)
+print(f"collections.Counter: {counter_time:.4f} seconds")

python-performance-optimization-guide/step-3-slow.py

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+import timeit
+
+words = ["apple", "pear", "apple", "cherry", "pear", "apple"] * 1000
+
+
+def manual_count(items):
+    counts = {}
+    for item in items:
+        if item in counts:
+            counts[item] += 1
+        else:
+            counts[item] = 1
+    return counts
+
+
+print(manual_count(words))
+
+manual_time = timeit.timeit(lambda: manual_count(words), number=1000)
+print(f"manual loop: {manual_time:.4f} seconds")

python-performance-optimization-guide/step-4-fast.py

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+from functools import cache
+import timeit
+
+numbers = list(range(2_000_000))
+target = 1_999_999
+
+
+@cache
+def find_index_cached(target):
+    for i, value in enumerate(numbers):
+        if value == target:
+            return i
+    return -1
+
+
+print(find_index_cached(target))
+
+fast_time = timeit.timeit(lambda: find_index_cached(target), number=20)
+print(f"with cache: {fast_time:.8f} seconds")

python-performance-optimization-guide/step-4-slow.py

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+import timeit
+
+numbers = list(range(2_000_000))
+
+
+def find_index(target):
+    for i, value in enumerate(numbers):
+        if value == target:
+            return i
+    return -1
+
+
+target = 1_999_999
+
+print(find_index(target))
+
+slow_time = timeit.timeit(lambda: find_index(target), number=20)
+print(f"without cache: {slow_time:.4f} seconds")