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

class TestTask(unittest.TestCase):
    def setUp(self):
        # Updated to match the exact column names expected by your solution
        self.df = pl.DataFrame({
            "Name": ["Portal 2", "Counter-Strike", "Dota 2"],
            "Positive": [90, 80, 50],
            "Negative": [10, 20, 50]
        })

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

    def test_positive_ratio_values_correct(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, "calculate_positive_ratio", None)
        result = func(self.df)
        
        # Expected ratios: 90/100 = 0.9, 80/100 = 0.8, 50/100 = 0.5
        expected = [0.9, 0.8, 0.5]
        if result is not None and "Positive Ratio" in result.columns:
            actual = result["Positive Ratio"].to_list()
            # Checking with tolerance for floating-point values
            is_correct = all(abs(a - b) < 1e-5 for a, b in zip(actual, expected))
            _dynamic_test(
                self,
                is_correct,
                "'Positive Ratio' column contains correct proportional values.",
                f"'Positive Ratio' values incorrect. Expected: {expected}, Got: {actual}"
            )
        else:
            _dynamic_test(self, False, "", "Result missing 'Positive Ratio' column.")

    def test_context_behavior_preserves_columns(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, "calculate_positive_ratio", None)
        result = func(self.df)
        if result is not None:
            # Enforce that it used with_columns (keeps everything) rather than select (drops unmentioned columns)
            # Updated to match your expected input column names
            for col in ["Name", "Positive", "Negative"]:
                _dynamic_test(
                    self,
                    col in result.columns,
                    f"Context verification passed: Column '{col}' was preserved.",
                    f"Context verification failed: Column '{col}' is missing. Did you accidentally use .select() instead of .with_columns()?"
                )
        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__":
    # verbosity=2 ensures dynamic names are printed to standard output
    unittest.main(verbosity=2)

test_main.py

Un curso práctico de nivel intermedio para desarrolladores de Python que deseen dominar la manipulación de datos con Polars, utilizando conjuntos de datos reales de juegos de Steam. Aprenda el paradigma columnar de Polars, selección, agregación, combinación, reestructuración y operaciones esenciales con cadenas, fechas y datos faltantes.

Explora las diferencias fundamentales entre Polars y los DataFrames tradicionales basados en filas, centrándote en las operaciones columnares, la selección y la lógica condicional utilizando el conjunto de datos de juegos de Steam.

Adquisición de una comprensión sólida de las capacidades de agrupación paralela, agregación, combinación y reestructuración de Polars utilizando los conjuntos de datos de juegos de Steam y de insights de espías.

Explora los aspectos esenciales de la limpieza de datos: normalización de texto, análisis de fechas y manejo robusto de valores faltantes en Polars.

Desafío: Contextos Principales Select vs With_Columns

Solución