To analyze legal case data efficiently, you can use Python's built-in `csv` module to load data from a hardcoded CSV string. Begin by reading the string as if it were a file using `io.StringIO`, which allows you to treat strings as file-like objects. After loading the data, you can group cases by the 'Outcome' column to count how many cases resulted in each outcome, such as `Win` or `Loss`. To determine which party has the most wins, filter the data for cases where the outcome is `Win` and then count the number of wins for each party. The party with the highest win count is identified as the top winning party. This process helps you quickly summarize case results and highlight the most successful parties in your data.

import unittest
import importlib
import re

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()

class TestTask(unittest.TestCase):
    def test_outcome_counts_correct(self):
        import user_code
        importlib.reload(user_code)

        func = getattr(user_code, "analyze_case_outcomes", None)
        _dynamic_test(
            self,
            callable(func),
            "Function 'analyze_case_outcomes' is defined.",
            "Function 'analyze_case_outcomes' is not defined."
        )
        if not callable(func):
            return

        csv_data = "Case ID,Party,Outcome\\n1,Smith,Win\\n2,Jones,Loss\\n3,Smith,Win\\n4,Lee,Win\\n5,Jones,Win\\n6,Lee,Loss\\n7,Smith,Loss\\n8,Jones,Win\\n9,Lee,Win\\n10,Smith,Win\\n"
        result_outcome_counts, result_top_party, result_top_wins = func(csv_data)

        expected = {"Win": 7, "Loss": 3}
        _dynamic_test(
            self,
            isinstance(result_outcome_counts, dict) and result_outcome_counts == expected,
            "Correctly counts number of cases for each outcome.",
            f"Expected {expected}, got {result_outcome_counts}",
        )

    def test_top_winning_party_and_count(self):
        import user_code
        importlib.reload(user_code)

        func = getattr(user_code, "analyze_case_outcomes", None)
        if not callable(func):
            _dynamic_test(self, False, "Function exists", "Function missing")
            return

        csv_data = "Case ID,Party,Outcome\\n1,Smith,Win\\n2,Jones,Loss\\n3,Smith,Win\\n4,Lee,Win\\n5,Jones,Win\\n6,Lee,Loss\\n7,Smith,Loss\\n8,Jones,Win\\n9,Lee,Win\\n10,Smith,Win\\n"
        _, result_top_party, result_top_wins = func(csv_data)

        _dynamic_test(
            self,
            result_top_party == "Smith",
            "Correctly identifies party with most wins (Smith).",
            f"Expected 'Smith', got {result_top_party}",
        )
        _dynamic_test(
            self,
            result_top_wins == 3,
            "Correctly counts number of wins for top party (3).",
            f"Expected 3, got {result_top_wins}",
        )

    def test_tie_for_top_party(self):
        import user_code
        importlib.reload(user_code)

        func = getattr(user_code, "analyze_case_outcomes", None)
        if not callable(func):
            _dynamic_test(self, False, "Function exists", "Function missing")
            return

        csv_data = "Case ID,Party,Outcome\\n1,Alice,Win\\n2,Bob,Win\\n3,Alice,Win\\n4,Bob,Win\\n5,Carol,Loss\\n"
        result_outcome_counts, result_top_party, result_top_wins = func(csv_data)

        _dynamic_test(
            self,
            isinstance(result_outcome_counts, dict)
            and result_outcome_counts.get("Win", 0) == 4
            and result_outcome_counts.get("Loss", 0) == 1,
            "Correctly counts outcomes when there is a tie.",
            f"Expected 4 wins and 1 loss, got {result_outcome_counts}",
        )
        _dynamic_test(
            self,
            result_top_wins == 2,
            "Correctly counts max wins in tie situation (2).",
            f"Expected 2, got {result_top_wins}",
        )
        _dynamic_test(
            self,
            result_top_party in ("Alice", "Bob"),
            "Returns one of the top parties in case of a tie.",
            f"Expected 'Alice' or 'Bob', got {result_top_party}",
        )

    def test_varied_party_names(self):
        import user_code
        importlib.reload(user_code)

        func = getattr(user_code, "analyze_case_outcomes", None)
        if not callable(func):
            _dynamic_test(self, False, "Function exists", "Function missing")
            return

        csv_data = "Case ID,Party,Outcome\\n1,Alpha,Win\\n2,Beta,Loss\\n3,Gamma,Loss\\n4,Delta,Win\\n5,Alpha,Win\\n6,Delta,Win\\n"
        result_outcome_counts, result_top_party, result_top_wins = func(csv_data)

        _dynamic_test(
            self,
            isinstance(result_outcome_counts, dict)
            and result_outcome_counts.get("Win", 0) == 4
            and result_outcome_counts.get("Loss", 0) == 2,
            "Correctly counts outcomes with varied party names.",
            f"Expected 4 wins and 2 losses, got {result_outcome_counts}",
        )
        _dynamic_test(
            self,
            result_top_party in ("Alpha", "Delta"),
            "Correctly identifies top party with most wins (Alpha or Delta).",
            f"Expected 'Alpha' or 'Delta', got {result_top_party}",
        )
        _dynamic_test(
            self,
            result_top_wins == 2,
            "Correctly counts number of wins for top party (2).",
            f"Expected 2, got {result_top_wins}",
        )

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


test_main.py

This course empowers legal professionals to harness the power of Python for automating, analyzing, and streamlining legal tasks. Through engaging theory and hands-on challenges, you'll learn to process legal documents, analyze case data, and visualize legal trends using Python's robust ecosystem.

Learn how Python can automate the handling, extraction, and management of legal documents, reducing manual effort and increasing accuracy.

Explore how Python can be used to analyze legal case data, uncover trends, and support legal research.

Master Python techniques for legal research, compliance monitoring, and reporting to enhance legal practice.

Challenge: Case Outcome Analyzer

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