A network administrator uses a Python script to analyze firewall logs. The script reads a CSV file with columns 'src_ip', 'dst_ip', 'action', 'time'. It needs to build a list of source IPs that have been blocked more than 3 times. The current code:

blocked_count = {}
blocked_ips = []
for row in logs:
if row['action'] == 'block':
if row['src_ip'] in blocked_count:
blocked_count[row['src_ip']] += 1
else:
blocked_count[row['src_ip']] = 1
for ip, count in blocked_count.items():
if count 3:
blocked_ips.append(ip)

The script runs correctly but slowly on large logs. The administrator wants to optimize it. Which change would most improve performance?

Question

A network administrator uses a Python script to analyze firewall logs. The script reads a CSV file with columns 'src_ip', 'dst_ip', 'action', 'time'. It needs to build a list of source IPs that have been blocked more than 3 times. The current code:

blocked_count = {}
blocked_ips = []
for row in logs:
    if row['action'] == 'block':
        if row['src_ip'] in blocked_count:
            blocked_count[row['src_ip']] += 1
        else:
            blocked_count[row['src_ip']] = 1
for ip, count in blocked_count.items():
    if count > 3:
        blocked_ips.append(ip)

The script runs correctly but slowly on large logs. The administrator wants to optimize it. Which change would most improve performance?

Accepted Answer

Use a counter from collections module. Using `collections.Counter` replaces the manual dictionary increment logic with a single optimized C-level operation, reducing Python bytecode execution overhead. The Counter's `most_common()` method or direct iteration over items still requires a second loop, but the first loop's increment is significantly faster due to internal C implementation, which is the primary bottleneck in large log processing.

Answer

Use a list comprehension for the second loop

Answer

Pre-allocate the blocked_ips list

Answer

Use a set for blocked_ips to avoid duplicates

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