Segmenting users by their activity levels is a powerful way to interpret product engagement and tailor strategies for different user groups. By grouping users into categories such as **"high"**, **"medium"**, and **"low"** activity, you can quickly assess which segments are most engaged, spot opportunities for growth, and present clear, actionable insights on a product dashboard. This approach helps you prioritize feature development, retention efforts, and marketing strategies by focusing on the needs of each segment.

# Example: Categorizing users by session activity

user_activity = [
    {"user_id": 1, "sessions": 22},
    {"user_id": 2, "sessions": 5},
    {"user_id": 3, "sessions": 12},
    {"user_id": 4, "sessions": 30},
    {"user_id": 5, "sessions": 8},
]

def categorize_user(sessions):
    if sessions >= 20:
        return "high"
    elif sessions >= 10:
        return "medium"
    else:
        return "low"

for user in user_activity:
    segment = categorize_user(user["sessions"])
    print(f"User {user['user_id']} is in the '{segment}' activity group.")


import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib

class TestTask(unittest.TestCase):
    def test_returns_dict_with_keys(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.segment_users([])
        _dynamic_test(
            self,
            isinstance(result, dict) and all(k in result for k in ["high", "medium", "low"]),
            "Returns dictionary with all required keys.",
            f"Returned keys: {list(result.keys()) if isinstance(result, dict) else type(result)}"
        )

    def test_high_group_count(self):
        import user_code
        importlib.reload(user_code)
        logs = [
            {"user_id": 1, "sessions": 20},
            {"user_id": 2, "sessions": 21},
            {"user_id": 3, "sessions": 19}
        ]
        result = user_code.segment_users(logs)
        _dynamic_test(
            self,
            result is not None and result.get("high", -1) == 2,
            "Correctly counts high activity users.",
            f"Expected 2 high, got {result.get('high', None) if result else None}"
        )

    def test_medium_group_count(self):
        import user_code
        importlib.reload(user_code)
        logs = [
            {"user_id": 1, "sessions": 10},
            {"user_id": 2, "sessions": 19},
            {"user_id": 3, "sessions": 20}
        ]
        result = user_code.segment_users(logs)
        _dynamic_test(
            self,
            result is not None and result.get("medium", -1) == 2,
            "Correctly counts medium activity users.",
            f"Expected 2 medium, got {result.get('medium', None) if result else None}"
        )

    def test_low_group_count(self):
        import user_code
        importlib.reload(user_code)
        logs = [
            {"user_id": 1, "sessions": 0},
            {"user_id": 2, "sessions": 9},
            {"user_id": 3, "sessions": 10}
        ]
        result = user_code.segment_users(logs)
        _dynamic_test(
            self,
            result is not None and result.get("low", -1) == 2,
            "Correctly counts low activity users.",
            f"Expected 2 low, got {result.get('low', None) if result else None}"
        )

    def test_empty_input(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.segment_users([])
        _dynamic_test(
            self,
            result is not None and result.get("high", -1) == 0 and result.get("medium", -1) == 0 and result.get("low", -1) == 0,
            "Handles empty input with zero counts.",
            f"Expected all counts zero, got {result if result else None}"
        )

def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)

def normalize_text(text):
    text = text.lower()
    text = re.sub(r"\\s{2,}", " ", text)
    text = re.sub(r"\\s*([,:?])\\s*", r"\\1 ", text)
    return text.strip()

def change_var(code: str, var_name: str, value: str) -> str:
    tree = ast.parse(code)
    lines = code.splitlines()
    changed = False
    # Collect all assignment nodes to modify
    assign_nodes = [
        (i, node)
        for i, node in enumerate(tree.body)
        if isinstance(node, ast.Assign)
        and any(isinstance(target, ast.Name) and target.id == var_name for target in node.targets)
    ]

    # If nothing to change, return unmodified code
    if not assign_nodes:
        return code

    # Perform replacements for all matching assignments (from last to first to not break line offsets)
    for i, node in reversed(assign_nodes):
        start_line = node.lineno - 1
        line = lines[start_line]
        indent = ' ' * (len(line) - len(line.lstrip()))
        lines[start_line] = f"{indent}{var_name} = {value}"
        next_line = len(lines)
        for next_node in tree.body[i+1:]:
            if hasattr(next_node, 'lineno'):
                next_line = next_node.lineno - 1
                break
        if next_line > start_line + 1:
            lines[start_line+1:next_line] = []
        changed = True

    return '\\n'.join(lines) if changed else code

if __name__ == "__main__":
    unittest.main()


test_main.py

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Learn how to use Python to explore, summarize, and interpret key product metrics. This section focuses on understanding user data, product usage, and basic analytics relevant to product management.

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Master Python techniques to automate repetitive product management tasks, generate reports, and streamline product operations.

Challenge: Segment Users by Activity

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