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 cours pratique de niveau intermédiaire destiné aux développeurs Python pour maîtriser la manipulation de données avec Polars, en utilisant des jeux de données réels issus de Steam. Approfondissement du paradigme en colonnes de Polars, de la sélection, de l’agrégation, des jointures, de la restructuration, ainsi que des opérations essentielles sur les chaînes de caractères, les dates et les données manquantes.

Explorez les principales différences entre Polars et les DataFrames traditionnels basés sur les lignes, en mettant l'accent sur les opérations en colonnes, la sélection et la logique conditionnelle à l'aide du jeu de données Steam games.

Acquérir une compréhension approfondie des capacités de groupement parallèle, d'agrégation, de jointure et de restructuration de Polars en utilisant les jeux de données Steam games et spy insights.

Explorez les éléments essentiels du nettoyage des données : normalisation du texte, analyse des dates et gestion robuste des valeurs manquantes dans Polars.

Défi : Fusion de Données

Solution