Microsoft Azure Machine Learning Studio offers a wide array of functionality for manipulating and analyzing data. Sometimes it is necessary to go line by line and add contents from a remote source. Here is an example of how to do it in a Python Executable module.
import pandas as pd # import the libraries for the web request import urllib.request, json # The entry point function can contain up to two input arguments: # Param
: a pandas.DataFrame # Param : a pandas.DataFrame def azureml_main(dataframe1 = None, dataframe2 = None): # Define the columns you are about to add. # It helps to initialize them with the right type dataframe1['code'] = "" dataframe1['popularity'] = 0 for index, row in dataframe1.iterrows(): # Since we are performing a web query, surround with try-except try: # the name must have its spaces replaced param = row['name'].replace(" ", "%20") # Use the current name to find the airport_code_iata with urllib.request.urlopen("http://www.prokerala.com/travel/flight-time/search.unity.php?mode=airport&q=" + param) as url: data = json.loads(url.read().decode()) # Set the data based on row, column dataframe1.loc[index,'code'] = data['airport_code_iata'] dataframe1.loc[index,'popularity'] = data['popularity'] except: print('An error occured.') # Return value must be of a sequence of pandas.DataFrame return dataframe1,