I am new to PYTHON.
Can someone give me some sample code for getting database or real time data via API?
for example: getting data from NSRDB i.e. solar radiation database.

in this code some on explain step by step so that it is easy to understand.

#STEP1……………………………………………………………
#I UNDERSTTOD THIS, IMPORT LIB OK
import requests import pandas as pd import urllib.parse import time

#STEP2……………………………………………………………
#DEFINE VARIABLES AND WEB ADDRESS OK

API_KEY = "{{YOUR_API_KEY}}" EMAIL = "[email protected]" BASE_URL = "https://developer.nrel.gov/api/nsrdb/v2/solar/spectral-ondemand-himawari-download.json?" POINTS = [ '324313' ]

#STEP3……………………………………………………………
#DEFINING A FUNCTION CALLED main() AND ITS PARAMETERS OK

`def main():
input_data = {
‘interval’: ’60’,
‘equipment’: ‘fixed_tilt’,
’tilt’: ’45’,
‘angle’: ‘180’,

    'api_key': API_KEY,
    'email': EMAIL,
}

`
#STEP4……………………………………………………………
#FOR LOOP ok, BUT WHAT IS [‘] AND enumerate?

`for name in [']:
    print(f"Processing name: {name}")
    for id, location_ids in enumerate(POINTS):
        input_data['names'] = [name]
        input_data['location_ids'] = location_ids
        print(f'Making request for point group {id + 1} of {len(POINTS)}...')`

#STEP5……………………………………………………………
#I CANNOT UNDERSTAND BELOW STEP, IF CONDITION AND urllib.parse.urlencode(data, True)

    `    if '.csv' in BASE_URL:
            url = BASE_URL + urllib.parse.urlencode(data, True)
            # Note: CSV format is only supported for single point requests
            # Suggest that you might append to a larger data frame
            data = pd.read_csv(url)
            print(f'Response data (you should replace this print statement with your processing): {data}')
            # You can use the following code to write it to a file
            # data.to_csv('SingleBigDataPoint.csv')
        else:
            headers = {
              'x-api-key': API_KEY
            }`

#STEP6……………………………………………………………
#WHAT IS THIS get_response_json_and_handle_errors(requests.post(BASE_URL, input_data, headers=headers))
data = get_response_json_and_handle_errors(requests.post(BASE_URL, input_data, headers=headers)) download_url = data['outputs']['downloadUrl']#I THINK IT IS NESTED DICTIONARY # You can do with what you will the download url print(data['outputs']['message']) print(f"Data can be downloaded from this url when ready: {download_url}")
#STEP6……………………………………………………………
#WHAT IS RATE LIMITING?
# Delay for 1 second to prevent rate limiting time.sleep(1) print(f'Processed')
#STEP7……………………………………………………………
#WHAT IS -> dict: “”” “””

`def get_response_json_and_handle_errors(response: requests.Response) -> dict:
“””Takes the given response and handles any errors, along with providing
the resulting json

Parameters
----------
response : requests.Response
    The response object

Returns
-------
dict
    The resulting json
"""`

#STEP8……………………………………………………………
#200 MEANS SUCCESS OK BUT WHAT IS exit(1)?

`if response.status_code != 200:
    print(f"An error has occurred with the server or the request. The request response code/status: {response.status_code} {response.reason}")
    print(f"The response body: {response.text}")
    exit(1)

`
#STEP9……………………………………………………………
#TRY EXCEPT ELSE FINALLY OK

try: response_json = response.json() except: print(f"The response couldn't be parsed as JSON, likely an issue with the server, here is the text: {response.text}") exit(1)
#STEP10……………………………………………………………
WHAT IS THAT

`if len(response_json['errors']) > 0:
    errors = 'n'.join(response_json['errors'])
    print(f"The request errored out, here are the errors: {errors}")
    exit(1)
return response_json

`
#STEP11……………………………………………………………
#THIS CONDITION OK

if __name__ == "__main__":

#STEP5……………………………………………………………
#CALLINF THE main() FUNCTION OK
your text
main()