import pandas as pd

import numpy as np

def flag_cookie_anomalies(df, threshold=3):

"""

Flags potential cookie anomalies in a DataFrame.

Args:

df (pd.DataFrame): DataFrame containing cookie data. Expected columns: 'cookie_name', 'cookie_value', 'timestamp'.

threshold (int): Number of standard deviations from the mean to consider a value anomalous.

Returns:

pd.DataFrame: DataFrame with an added 'is_anomalous' column, indicating if a cookie value is anomalous.

"""

# Input validation: Check if DataFrame is valid and contains required columns

if not isinstance(df, pd.DataFrame):

raise TypeError("Input must be a pandas DataFrame.")

required_columns = ['cookie_name', 'cookie_value', 'timestamp']

if not all(col in df.columns for col in required_columns):

raise ValueError(f"DataFrame must contain columns: {required_columns}")

# Convert cookie_value to numeric, handling potential errors

try:

df['cookie_value'] = pd.to_numeric(df['cookie_value'])

except ValueError:

raise ValueError("The 'cookie_value' column must contain numeric values.")

# Calculate mean and standard deviation of cookie values

mean = df['cookie_value'].mean()

std = df['cookie_value'].std()

# Identify anomalous cookie values

df['is_anomalous'] = (df['cookie_value'] > (mean + threshold * std)) | (df['cookie_value'] < (mean - threshold * std))

return df

if __name__ == '__main__':

#Example Usage

data = {'cookie_name': ['session_id', 'user_id', 'preferences', 'login_status', 'cart_items'],

'cookie_value': [12345, 67890, 10, 'true', 5],

'timestamp': ['2024-01-01 10:00:00', '2024-01-01 10:01:00', '2024-01-01 10:02:00', '2024-01-01 10:03:00', '2024-01-01 10:04:00']}

df = pd.DataFrame(data)

try:

anomalies_df = flag_cookie_anomalies(df)

print(anomalies_df)

except (TypeError, ValueError) as e:

print(f"Error: {e}")