JSON is a text format for structured data. An API can return a JSON response containing named fields, nested objects, and arrays. In Python, distinguish the response text from the values you get after parsing it: `response.text` holds text, while `response.json()` returns Python data you can work with. section 22.2
response = requests.get("https://api.open-meteo.com/v1/forecast?
latitude=41.882&longitude=-87.623¤t=temperature_2m,
wind_speed_10m")
weather = response.text
weather
{"latitude":41.879482,"longitude":-87.64975,"generationtime_ms":0.026941299
438476562,"utc_offset_seconds":0,"timezone":"GMT","timezone_abbreviation":
"GMT","elevation":185.0,"current_units":{"time":"iso8601","interval":
"seconds","temperature_2m":"°C","wind_speed_10m":"km/h"},"current":{"time":
"2024-09-02T02:00","interval":900,"temperature_2m":16.9,
"wind_speed_10m":13.3}}
The weather response has top-level fields such as `latitude` and nested objects such as `current`, which groups the reported time, temperature, and wind speed. section 22.2
Read an API result
A Chicago crime-data request uses a URL query to select a time range. Calling `result.json()` produces a Python list of records; each record has fields such as `date` and `primary_type`, Boolean fields such as `arrest`, and a nested `location` object. section 22.2
JSON-shaped data also appears in document storage, but the format matters. MongoDB stores documents as BSON, or binary JSON. Its documents resemble JSON objects or Python dictionaries, while examples include values such as dates and object IDs. Do not assume a MongoDB document is simply the JSON text returned by an API. section 23.7
