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

class TestTask(unittest.TestCase):
    def setUp(self):
        # Setup mock primary dataframe matching raw specifications
        self.df = pl.DataFrame({
            "Name": ["Portal", "Half-Life", "Left 4 Dead"],
            "Price": [9.99, 9.99, 19.99]
        })
        
        # Setup mock insights dataframe using string keys
        self.insights_df = pl.DataFrame({
            "Name": ["Portal", "Left 4 Dead", "Dota 2"], # Half-Life is missing, Dota 2 is unmapped
            "Marketing_Tier": ["Gold", "Silver", "Bronze"],
            "Ad_Budget": [5000, 2000, 1500]
        })

    def test_join_columns_exist_and_unaltered(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, "merge_marketing_by_name", None)
        _dynamic_test(
            self,
            callable(func),
            "merge_marketing_by_name function exists and is callable.",
            "merge_marketing_by_name function is missing or not callable."
        )
        result = func(self.df, self.insights_df)
        
        _dynamic_test(
            self,
            result is not None and "Name" in result.columns,
            "Returned DataFrame retains original key tracking column 'Name'.",
            "Returned DataFrame is missing 'Name'. Ensure exact casing is used."
        )
        for col in ["Price", "Marketing_Tier", "Ad_Budget"]:
            _dynamic_test(
                self,
                col in result.columns,
                f"Merged column layout includes variable '{col}'.",
                f"Missing expected attribute column '{col}' after running the join."
            )

    def test_left_join_logic_and_null_fill(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, "merge_marketing_by_name", None)
        result = func(self.df, self.insights_df)
        
        if result is not None and "Marketing_Tier" in result.columns:
            _dynamic_test(
                self,
                len(result) == 3,
                "Left join operation successfully maintained primary dataset row lengths.",
                f"Row counts altered. Expected 3 rows, but received {len(result)} rows."
            )
            
            # Aligned key evaluation (Name: "Portal")
            portal_row = result.filter(pl.col("Name") == "Portal")
            _dynamic_test(
                self,
                portal_row["Marketing_Tier"][0] == "Gold",
                "Successfully matched and merged attributes using string keys.",
                f"Incorrect tier mapping for Portal. Expected 'Gold', Got: {portal_row['Marketing_Tier'][0]}"
            )
            
            # Unaligned key verification (Name: "Half-Life" -> missing from right table)
            halflife_row = result.filter(pl.col("Name") == "Half-Life")
            _dynamic_test(
                self,
                halflife_row["Marketing_Tier"][0] is None and halflife_row["Ad_Budget"][0] is None,
                "Left join rules validated: missing right values successfully defaulted to Null.",
                f"Error on missing key values. Expected Null assignments, Got: {halflife_row['Marketing_Tier'][0]}"
            )
        else:
            _dynamic_test(self, False, "", "Join verification halted due to schema structural sorting errors.")

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: Combinación de Datos

Solución