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 exact un-renamed raw file schemas
        # FIX: Changed pl.F64 to pl.Float64 to match proper Polars type syntax
        self.df = pl.DataFrame({
            "Name": ["Half-Life 2", "Unreleased Indie Game", "Portal", "Free Beta Test"],
            "Price": [9.99, None, 19.99, None]  # None acts as Null in Polars constructors
        }, schema={"Name": pl.String, "Price": pl.Float64})

    def test_price_column_exists_and_unnamed(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, "handle_missing_prices", None)
        _dynamic_test(
            self,
            callable(func),
            "handle_missing_prices function exists and is callable.",
            "handle_missing_prices function is missing or not callable."
        )
        result = func(self.df)
        _dynamic_test(
            self,
            result is not None and "Price" in result.columns,
            "Returned DataFrame retains original raw column name 'Price'.",
            "Returned DataFrame is missing the required raw column 'Price'. Did you accidentally rename it?"
        )

    def test_null_values_filled_correctly(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, "handle_missing_prices", None)
        result = func(self.df)
        
        # Expected outputs: None values should map to 0.0, actual numbers must remain unaltered
        expected_prices = [9.99, 0.0, 19.99, 0.0]
        
        if result is not None and "Price" in result.columns:
            actual_prices = result["Price"].to_list()
            _dynamic_test(
                self,
                actual_prices == expected_prices,
                "Null values successfully targeted and replaced with fallback float values.",
                f"Null replacement logic failed. Expected: {expected_prices}, Got: {actual_prices}"
            )
        else:
            _dynamic_test(self, False, "", "Sanitation evaluation halted due to column tracking schema faults.")

    def test_other_attributes_preserved(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, "handle_missing_prices", None)
        result = func(self.df)
        if result is not None:
            _dynamic_test(
                self,
                "Name" in result.columns,
                "Context validation passed: Text identifier column 'Name' remains unchanged.",
                "Context validation failed: Primary data structural columns were altered or dropped."
            )
        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: Gestione dei Valori Nulli

Soluzione