import unittest
import user_code
import ast
import re   
import importlib
import csv
import polars as pl

class TestTask(unittest.TestCase):
    def setUp(self):
        # Initializing local mock dataset using Polars DataFrame to match your solution
        self.df = pl.DataFrame({
            "Name": ["Portal", "Half-Life", "Left 4 Dead"],
            "Positive": [1500, 2000, 1800],
            "Negative": [100, 150, 120]
        })

    def test_total_reviews_column_exists(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, "add_total_reviews", None)
        _dynamic_test(
            self,
            callable(func),
            "add_total_reviews function exists and is callable.",
            "add_total_reviews function is missing or not callable."
        )
        result = func(self.df)
        _dynamic_test(
            self,
            result is not None and "Total Reviews" in result.columns,
            "Returned DataFrame contains 'Total Reviews' column.",
            "Returned DataFrame is missing 'Total Reviews' column."
        )

    def test_total_reviews_values_correct(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, "add_total_reviews", None)
        result = func(self.df)
        expected = [1600, 2150, 1920]
        
        if result is not None and "Total Reviews" in result.columns:
            actual = result["Total Reviews"].to_list()
            _dynamic_test(
                self,
                actual == expected,
                "'Total Reviews' column contains correct sums.",
                f"'Total Reviews' values incorrect. Expected: {expected}, Got: {actual}"
            )
        else:
            _dynamic_test(self, False, "", "Result missing 'Total Reviews' column.")

    def test_original_columns_unchanged(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, "add_total_reviews", None)
        result = func(self.df)
        if result is not None:
            # Updated to match the columns your code actually uses
            for col in ["Name", "Positive", "Negative"]:
                _dynamic_test(
                    self,
                    col in result.columns,
                    f"Column '{col}' is present in the returned DataFrame.",
                    f"Column '{col}' is missing in the returned DataFrame."
                )
        else:
            _dynamic_test(self, False, "", "Returned DataFrame is 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
    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 not assign_nodes:
        return code

    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__":
    # Added verbosity=2 so standard output clearly shows the dynamic naming
    unittest.main(verbosity=2)

test_main.py

Um curso prático de nível intermediário para desenvolvedores Python que desejam dominar a manipulação de dados com Polars, utilizando conjuntos de dados reais de jogos da Steam. Aprenda o paradigma colunar do Polars, seleção, agregação, junção, remodelação e operações essenciais com strings, datas e dados ausentes.

Explore as principais diferenças entre Polars e DataFrames tradicionais baseados em linhas, com foco em operações colunares, seleção e lógica condicional utilizando o conjunto de dados de jogos da Steam.

Adquira uma compreensão sólida das capacidades de agrupamento paralelo, agregação, junção e remodelação do Polars utilizando os conjuntos de dados de jogos da Steam e de insights de espionagem.

Explore os conceitos essenciais de limpeza de dados: normalização de texto, análise de datas e tratamento robusto de valores ausentes no Polars.

Desafio: Migrando de Linhas para Colunas

Solução