Data Cleaning Automation Script

Dataset issues: Names in mixed case, phone numbers in various formats, date column has multiple formats, some duplicate emails.

## Python Data Cleaning Script\n\n```python\nimport pandas as pd\nimport re\nfrom dateutil import parser\n\ndef clean_dataset(df):\n # Standardize names (Title Case)\n df["name"] = df["name"].str.strip().str.title()\n \n # Standardize phone numbers\n def clean_phone(phone):\n if pd.isna(phone):\n return None\n digits = re.sub(r"\D", "", str(phone))\n if len(digits) == 10:\n return f"({digits[:3]}) {digits[3:6]}-{digits[6:]}"\n return phone\n \n df["phone"] = df["phone"].apply(clean_phone)\n \n # Parse mixed date formats\n def parse_date(date_str):\n try:\n return parser.parse(str(date_str))\n except:\n return None\n \n df["date"] = df["date"].apply(parse_date)\n \n # Remove duplicate emails (keep first)\n df = df.drop_duplicates(subset=["email"], keep="first")\n \n return df\n\n# Usage\ndf_clean = clean_dataset(df)\n```\n\n**Notes:**\n- Install: `pip install python-dateutil`\n- Handles most common edge cases\n- Preserves original data if parsing fails