68 lines
1.7 KiB
Python
68 lines
1.7 KiB
Python
"""
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analyze_me.py – A data-processing script used in Exercise 2
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==============================================================
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This file contains several realistic bugs and style issues.
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Do NOT fix them manually — in Exercise 2 the LLM will help you find them!
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Can you spot the issues yourself before asking the LLM?
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"""
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def calculate_statistics(numbers):
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total = 0
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for n in numbers:
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total = total + n
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average = total / len(numbers)
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min_val = numbers[0]
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max_val = numbers[0]
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for n in numbers:
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if n < min_val:
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min_val = n
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if n > max_val:
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max_val = n
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variance = 0
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for n in numbers:
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variance = variance + (n - average) ** 2
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variance = variance / len(numbers)
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return {
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"count": len(numbers),
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"sum": total,
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"average": average,
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"min": min_val,
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"max": max_val,
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"variance": variance,
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}
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def process_data(filename):
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numbers = []
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f = open(filename)
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for line in f:
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numbers.append(int(line.strip()))
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f.close()
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result = calculate_statistics(numbers)
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print("Statistics:", result)
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return result
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def normalize(numbers, method="minmax"):
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if method == "minmax":
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mn = min(numbers)
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mx = max(numbers)
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return [(x - mn) / mx - mn for x in numbers]
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elif method == "zscore":
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stats = calculate_statistics(numbers)
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std = stats["variance"] ** 0.5
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return [(x - stats["average"]) / std for x in numbers]
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else:
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print("Unknown normalisation method")
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if __name__ == "__main__":
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sample = [4, 8, 15, 16, 23, 42]
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print(calculate_statistics(sample))
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