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 a Polars DataFrame matching the solution schema
        self.df = pl.DataFrame({
            "Publishers": ["Valve", "Valve", "IndieCo", "Valve"],
            "Price": [9.99, 0.0, 4.99, 19.99],
            "Positive": [1000, 500, 100, 2000]
        })

    def test_aggregation_columns_exist(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, "aggregate_publishers_stats", None)
        _dynamic_test(
            self,
            callable(func),
            "aggregate_publishers_stats function exists and is callable.",
            "aggregate_publishers_stats function is missing or not callable."
        )
        result = func(self.df)
        _dynamic_test(
            self,
            result is not None and "Publishers" in result.columns,
            "Returned DataFrame contains grouping column 'Publishers'.",
            "Returned DataFrame is missing grouping column 'Publishers'."
        )
        _dynamic_test(
            self,
            "Average Price" in result.columns,
            "Returned DataFrame contains 'Average Price' metric column.",
            "Returned DataFrame is missing 'Average Price' metric column."
        )
        _dynamic_test(
            self,
            "Total Positive Reviews" in result.columns,
            "Returned DataFrame contains 'Total Positive Reviews' metric column.",
            "Returned DataFrame is missing 'Total Positive Reviews' metric column."
        )

    def test_aggregation_values_correct(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, "aggregate_publishers_stats", None)
        result = func(self.df)
        
        if result is not None and "Average Price" in result.columns and "Total Positive Reviews" in result.columns:
            # Extract Valve records for validation
            valve_data = result.filter(pl.col("Publishers") == "Valve")
            
            _dynamic_test(
                self,
                len(valve_data) == 1,
                "Grouping strategy correctly combined duplicate records.",
                f"Expected unique row for 'Valve', found {len(valve_data)} rows."
            )
            
            # Valve math: 
            # Average Price = (9.99 + 0.0 + 19.99) / 3 = 9.99333...
            # Total Positive Reviews = 1000 + 500 + 2000 = 3500
            actual_price = valve_data["Average Price"][0]
            actual_reviews = valve_data["Total Positive Reviews"][0]
            
            _dynamic_test(
                self,
                abs(actual_price - 9.993333) < 1e-4,
                "Mean aggregation engine evaluated correct 'Average Price'.",
                f"Incorrect 'Average Price' calculation. Expected near 9.9933, Got: {actual_price}"
            )
            _dynamic_test(
                self,
                actual_reviews == 3500,
                "Sum aggregation engine evaluated correct 'Total Positive Reviews'.",
                f"Incorrect 'Total Positive Reviews' calculation. Expected 3500, Got: {actual_reviews}"
            )
            
            # IndieCo records validation
            indie_data = result.filter(pl.col("Publishers") == "IndieCo")
            actual_indie_price = indie_data["Average Price"][0]
            actual_indie_reviews = indie_data["Total Positive Reviews"][0]
            
            _dynamic_test(
                self,
                actual_indie_price == 4.99 and actual_indie_reviews == 100,
                "Single-instance group records aggregation validated cleanly.",
                f"Aggregation failed on single items. Got Price: {actual_indie_price}, Reviews: {actual_indie_reviews}"
            )
        else:
            _dynamic_test(self, False, "", "Metrics execution evaluation halted due to missing target columns.")

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__":
    unittest.main()

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: Group_by y Agregaciones

Solución