How can I programmatically retrieve all the hourly prices for a specific virtual machine SKU?

Question

I'm attempting to write a python program to extract all the retail prices for a specific SKU using the Azure retail APIhttps://learn.microsoft.com/en-us/rest/api/cost-management/retail-prices/azure-retail-prices. The sample code provided in this link for which I copied below works quite well to retrieve all the SKUs matching a virtual machine critieria, but all of the retail prices are for 1 hourly unit. I want to retrieve all the retail prices for hourly unit ranges 1 - 300 hours in a month for a single SKU. I'm not seeing a search criteria to specify the hour and was curious if anyone else knows how to do this?

    main()/usr/bin/env python3
import requests
import json
from tabulate import tabulate 


def build_pricing_table(json_data, table_data):
    for item in json_data['Items']:
        meter = item['meterName']
        table_data.append([item['armSkuName'], item['retailPrice'], item['unitOfMeasure'], item['armRegionName'], meter, item['productName']])

Answer 1

The retail API is more of a price sheet than a calculator. So, in order to determine retail prices based on an hourly range, you can adjust your code to include a computed column that multiplies the hourly rate by the unit hour.

def build_pricing_table(json_data, df):
    for item in json_data['Items']:
        meter = item['meterName']
        for hours in range(1, 301):
            computed_value = hours * item['unitOfMeasure'] * item['retailPrice']
            df = df.append({
                'SKU': item['armSkuName'],
                'Retail Price': item['retailPrice'],
                'Unit of Measure': item['unitOfMeasure'],
                'Region': item['armRegionName'],
                'Meter': meter,
                'Product Name': item['productName'],
                'Computed Value': computed_value
            }, ignore_index=True)
    return df

The above code block utilizes a DataFrame rather than table_data but the principles are the same.