Add storm facts to your own spreadsheet.

import time
import pandas as pd
import requests

URL = "https://data.truefixr.com/v1/data"
DELAY = 1.1  # seconds between calls, about 55 per minute

def lookup(lat, lon, tries=4):
    for n in range(tries):
        try:
            r = requests.get(URL, params={"dataset": "address", "lat": lat, "lon": lon}, timeout=30)
        except (requests.Timeout, requests.ConnectionError):
            time.sleep(2 ** (n + 1))
            continue
        if r.status_code == 200:
            return r.json()
        if r.status_code in (429, 500, 502, 503, 504):
            time.sleep(int(r.headers.get("Retry-After", 2 ** (n + 2))))
            continue
        r.raise_for_status()
    return {}

rows = []
df = pd.read_csv("addresses.csv")
for lat, lon in zip(df["lat"], df["lon"]):
    d = lookup(lat, lon)
    risk = d.get("current_forecast_risk") or {}
    rows.append({
        "storm_event_count": d.get("storm_event_count"),
        "storm_types": ",".join((d.get("storm_types") or {}).keys()),
        "risk_grade": risk.get("grade"),
        "risk_label": risk.get("grade_label"),
    })
    time.sleep(DELAY)

out = pd.concat([df, pd.DataFrame(rows)], axis=1)
out.to_csv("addresses_enriched.csv", index=False)
print(out.to_string(index=False))

 id                   address   lat    lon  storm_event_count      storm_types risk_grade      risk_label
  1         Main St Dallas TX 32.78 -96.80                297       HEAVY_RAIN          C   Elevated risk
  2    Congress Ave Austin TX 30.27 -97.74                107 HEAVY_RAIN,OTHER          B   Moderate risk
  3 Broadway Oklahoma City OK 35.47 -97.52                  0                           A No current risk
  4      Collins Ave Miami FL 25.79 -80.13                 86       HEAVY_RAIN          A No current risk
  5       State St Chicago IL 41.88 -87.63                 54            FLOOD          A No current risk