Analyzing Frequencies with `collections.Counter`

from collections import Counter
from typing import List

# Simulate web server access logs or user-generated tags
access_logs = [
    "/api/users", "/api/products", "/api/users", "/dashboard",
    "/api/products", "/api/orders", "/api/users", "/login",
    "/api/products", "/dashboard", "/api/users"
]

# Simulate user-defined tags for blog posts
blog_tags = [
    "python", "webdev", "django", "flask", "python", "api",
    "database", "webdev", "python", "javascript", "frontend",
    "django", "python"
]

def analyze_access_patterns(paths: List[str]):
    path_counts = Counter(paths)
    print("--- Most Common Access Paths ---")
    for path, count in path_counts.most_common(3):
        print(f"Path: {path}, Count: {count}")
    print(f"Total unique paths: {len(path_counts)}")
    print("-" * 30)

def generate_tag_cloud_data(tags: List[str]):
    tag_counts = Counter(tags)
    print("--- Top 5 Blog Tags ---")
    for tag, count in tag_counts.most_common(5):
        print(f"Tag: {tag}, Frequency: {count}")
    print(f"Least common tag: {tag_counts.most_common()[-1][0]} (Count: {tag_counts.most_common()[-1][1]})")
    print("-" * 30)

analyze_access_patterns(access_logs)
generate_tag_cloud_data(blog_tags)

# Incrementing counts manually
error_codes = Counter()
error_codes['404'] += 5
error_codes['500'] += 2
error_codes['200'] += 10
error_codes['404'] += 1
print("--- Error Code Frequencies ---")
print(error_codes)
print("-" * 30)