This video provides a walkthrough of the challenge requirements for preparing a dataset for cohort analysis. You will see how to preprocess a sample transactional dataset, assign users to cohorts based on their first purchase month, and engineer key features such as cohort index and order counts. The video covers strategies for handling raw data, creating cohort labels, and generating new columns to support downstream cohort analysis.

Vous allez appliquer vos connaissances des chapitres précédents sur l’attribution des cohortes et l’ingénierie des caractéristiques. Votre tâche consiste à préparer un jeu de données transactionnelles d’exemple pour l’analyse de cohortes.




import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import pandas as pd
import importlib

class TestTask(unittest.TestCase):
    def setUp(self):
        self.data = {
            'user_id': [1, 1, 2, 2, 2, 3],
            'order_id': [101, 102, 201, 202, 203, 301],
            'order_date': [
                '2024-01-15', '2024-02-10',
                '2024-01-20', '2024-03-05', '2024-03-25',
                '2024-02-01'
            ]
        }
        self.df = pd.DataFrame(self.data)

    def test_cohort_month_column(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.prepare_cohort_data(self.df.copy())
        _dynamic_test(
            self,
            'cohort_month' in result.columns,
            "DataFrame includes 'cohort_month' column.",
            "Missing 'cohort_month' column in DataFrame."
        )
        # Check correct cohort_month values for each user
        for user in result['user_id'].unique():
            user_rows = result[result['user_id'] == user]
            cohort_months = user_rows['cohort_month'].unique()
            _dynamic_test(
                self,
                len(cohort_months) == 1,
                f"All transactions for user {user} have same cohort_month.",
                f"User {user} has multiple cohort_month values: {cohort_months}"
            )
            first_order = pd.to_datetime(user_rows['order_date']).min()
            expected = first_order.strftime('%Y-%m')
            _dynamic_test(
                self,
                cohort_months[0] == expected,
                f"cohort_month for user {user} is correct.",
                f"Expected cohort_month {expected} for user {user}, got {cohort_months[0]}"
            )

    def test_cohort_index_column(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.prepare_cohort_data(self.df.copy())
        _dynamic_test(
            self,
            'cohort_index' in result.columns,
            "DataFrame includes 'cohort_index' column.",
            "Missing 'cohort_index' column in DataFrame."
        )
        # cohort_index for first order of each user must be 0
        for user in result['user_id'].unique():
            user_rows = result[result['user_id'] == user].sort_values('order_date')
            cohort_index_first = user_rows.iloc[0]['cohort_index']
            _dynamic_test(
                self,
                cohort_index_first == 0,
                f"cohort_index for first order of user {user} is 0.",
                f"Expected cohort_index 0 for first order of user {user}, got {cohort_index_first}"
            )
            # Check cohort_index increments by 1 per month
            cohort_month = pd.to_datetime(user_rows.iloc[0]['order_date']).to_period('M')
            for idx, row in user_rows.iterrows():
                order_month = pd.to_datetime(row['order_date']).to_period('M')
                expected_index = (order_month - cohort_month).n
                _dynamic_test(
                    self,
                    row['cohort_index'] == expected_index,
                    f"cohort_index for user {user} order {row['order_id']} is correct.",
                    f"Expected cohort_index {expected_index} for user {user}, got {row['cohort_index']}"
                )

    def test_total_orders_column(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.prepare_cohort_data(self.df.copy())
        _dynamic_test(
            self,
            'total_orders' in result.columns,
            "DataFrame includes 'total_orders' column.",
            "Missing 'total_orders' column in DataFrame."
        )
        for user in result['user_id'].unique():
            user_rows = result[result['user_id'] == user].sort_values('order_date')
            for idx, (i, row) in enumerate(user_rows.iterrows()):
                expected = idx + 1
                _dynamic_test(
                    self,
                    row['total_orders'] == expected,
                    f"total_orders for user {user} order {row['order_id']} is correct.",
                    f"Expected total_orders {expected} for user {user}, got {row['total_orders']}"
                )

    def test_columns_and_shape(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.prepare_cohort_data(self.df.copy())
        expected_cols = set(list(self.df.columns) + ['cohort_month', 'cohort_index', 'total_orders'])
        _dynamic_test(
            self,
            set(result.columns) == expected_cols,
            "Returned DataFrame has all required columns.",
            f"Expected columns {expected_cols}, got {set(result.columns)}"
        )
        _dynamic_test(
            self,
            len(result) == len(self.df),
            "Returned DataFrame has same number of rows as input.",
            f"Expected {len(self.df)} rows, got {len(result)}"
        )

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
    # Collect all assignment nodes to modify
    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 nothing to change, return unmodified code
    if not assign_nodes:
        return code

    # Perform replacements for all matching assignments (from last to first to not break line offsets)
    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

Maîtriser les techniques d'analyse de cohortes avec Python. Ce cours couvre la structuration des données de cohortes, la segmentation avancée, l'analyse de la rétention et des insights business exploitables, avec des exercices pratiques dans chaque section.

Apprenez à structurer, prétraiter et préparer les données pour une analyse avancée des cohortes, y compris l'attribution des cohortes et l'ingénierie des caractéristiques.

Plongez dans des stratégies de segmentation avancées et des calculs de métriques de rétention pour découvrir des analyses plus approfondies à partir des données de cohortes.

Visualiser les données de cohortes et extraire des informations commerciales exploitables en utilisant des techniques avancées de visualisation et d'interprétation.

Challenge: Préparation des Données de Cohorte

Solution