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

class TestTask(unittest.TestCase):
    def setUp(self):
        # Build local mock dataframe using exact un-renamed file schemas
        self.df = pl.DataFrame({
            "Name": ["  cyberpunk 2077  ", "Terraria", "   portal 2 ", "half-life"],
            "Price": [59.99, 9.99, 19.99, 9.99]
        })

    def test_name_column_exists_and_unrenamed(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, "clean_game_titles", None)
        _dynamic_test(
            self,
            callable(func),
            "clean_game_titles function exists and is callable.",
            "clean_game_titles function is missing or not callable."
        )
        result = func(self.df)
        _dynamic_test(
            self,
            result is not None and "Name" in result.columns,
            "Returned DataFrame successfully preserves the raw casing header 'Name'.",
            "Returned DataFrame is missing the required raw column 'Name'. Ensure column names are not altered."
        )

    def test_string_transformations_are_accurate(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, "clean_game_titles", None)
        result = func(self.df)
        
        # Expected outputs: stripped of whitespace and fully converted to uppercase
        expected_names = ["CYBERPUNK 2077", "TERRARIA", "PORTAL 2", "HALF-LIFE"]
        
        if result is not None and "Name" in result.columns:
            actual_names = result["Name"].to_list()
            _dynamic_test(
                self,
                actual_names == expected_names,
                "String engine successfully stripped outer spacing and normalized text variables to uppercase.",
                f"String processing calculations failed. Expected: {expected_names}, Got: {actual_names}"
            )
        else:
            _dynamic_test(self, False, "", "Text cleaning evaluation halted due to missing schema definitions.")

    def test_other_columns_intact(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, "clean_game_titles", None)
        result = func(self.df)
        if result is not None:
            _dynamic_test(
                self,
                "Price" in result.columns,
                "Context validation passed: Non-targeted metric columns like 'Price' remain safe.",
                "Context validation failed: Unrelated columns were dropped during string operations."
            )
        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__":
    unittest.main()

test_main.py

Un corso pratico di livello intermedio per sviluppatori Python per padroneggiare la manipolazione dei dati con Polars, utilizzando dataset reali di giochi Steam. Approfondimento sul paradigma colonnare di Polars, selezione, aggregazione, join, reshaping e operazioni essenziali su stringhe, date e dati mancanti.

Esplora le differenze fondamentali tra Polars e i tradizionali DataFrame basati su righe, concentrandoti sulle operazioni colonnari, sulla selezione e sulla logica condizionale utilizzando il dataset dei giochi Steam.

Acquisizione di una solida comprensione delle capacità di group-by parallelo, aggregazione, join e rimodellamento di Polars utilizzando i dataset di giochi Steam e di analisi delle spie.

Esplora gli elementi essenziali della pulizia dei dati: normalizzazione del testo, parsing delle date e gestione robusta dei valori mancanti in Polars.

Sfida: Pulizia del Testo

Soluzione