How to get historical soil temperature data in Python using pandas and OpenMeteo

import openmeteo_requests

import requests_cache
import pandas as pd
from retry_requests import retry

# Setup the Open-Meteo API client with cache and retry on error
cache_session = requests_cache.CachedSession('.cache', expire_after = 3600)
retry_session = retry(cache_session, retries = 5, backoff_factor = 0.2)
openmeteo = openmeteo_requests.Client(session = retry_session)

# Make sure all required weather variables are listed here
# The order of variables in hourly or daily is important to assign them correctly below
url = "https://archive-api.open-meteo.com/v1/era5"
params = {
        'latitude': 48.1351,   # Latitude of Munich
        'longitude': 11.5820,  # Longitude of Munich
    'start_date': '2023-04-01',
    'end_date': '2024-05-16',
    "hourly": "soil_temperature_7_to_28cm"
}
responses = openmeteo.weather_api(url, params=params)

# Process first location. Add a for-loop for multiple locations or weather models
response = responses[0]
print(f"Coordinates {response.Latitude()}°N {response.Longitude()}°E")
print(f"Elevation {response.Elevation()} m asl")
print(f"Timezone {response.Timezone()} {response.TimezoneAbbreviation()}")
print(f"Timezone difference to GMT+0 {response.UtcOffsetSeconds()} s")

# Process hourly data. The order of variables needs to be the same as requested.
hourly = response.Hourly()
hourly_soil_temperature = hourly.Variables(0).ValuesAsNumpy()

hourly_data = {"date": pd.date_range(
    start = pd.to_datetime(hourly.Time(), unit = "s", utc = True),
    end = pd.to_datetime(hourly.TimeEnd(), unit = "s", utc = True),
    freq = pd.Timedelta(seconds = hourly.Interval()),
    inclusive = "left"
)}
hourly_data["soil_temperature"] = hourly_soil_temperature

hourly_dataframe = pd.DataFrame(data = hourly_data)
print(hourly_dataframe)
hourly_dataframe.set_index("date", inplace = True)

hourly_dataframe.plot()