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

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: Pasar de Filas a Columnas

Solución