Data Processing and Transformation Questions

import json
import pandas as pd
from typing import List, Dict, Iterable

def normalize_profiles(profiles: Iterable[Dict]) -> pd.DataFrame:
    """
    profiles: iterable of dicts (can be list or generator)
    Returns DataFrame with columns: user_id, email, first_name, last_name, city, signup_date
    """
    # json_normalize handles nested keys using dot notation
    df = pd.json_normalize(profiles)
    
    # extract/rename columns, using .get-like access with fallback to NaN
    out = pd.DataFrame({
        "user_id": df.get("user_id"),
        "email": df.get("contact.email"),
        "first_name": df.get("name.first"),
        "last_name": df.get("name.last"),
        "city": df.get("address.city"),
        "signup_date": df.get("signup")
    })
    
    # parse dates robustly; invalid parsing -> NaT
    out["signup_date"] = pd.to_datetime(out["signup_date"], utc=True, errors="coerce")
    
    # Optional: enforce types, handle missing user_id as NaN or drop
    # out["user_id"] = pd.to_numeric(out["user_id"], errors="coerce")
    
    return out[["user_id","email","first_name","last_name","city","signup_date"]]