![]() ![]() The Json output can then be parsed with the. To do this we call the request.get method with the base URL and the endpoint and store the returned values in the variable first_response. To get the data as Json output you can use the requests package. first_response = requests.get(base_url+facts) response_list=first_response.json() Now we save the base URL and the used endpoint in variables. base_url = "" facts = "/facts/random?animal_type=cat&amount=500" Seaborn and matplotlib are used to visualize the data. Pandas to read the JSON data into a DataFrame and do an explorative analysis. In this tutorial we use requests to access the API via HTTP request. Now let’s start creating the query in Python: import requests import pandas as pd import json import pprint import seaborn as sns import matplotlib.pyplot as pltįirst we have to import the necessary packages. We query this endpoint to retrieve the individual facts, with their ID, the user who uploaded the fact, and the creation date. More information about the Fact endpoint can be found at this URL Here you will also find information about the query parameters and how an API response will look like and how it will be structured. Then we need the specification what type of facts we want to get returned. The CData Python Connector for JSON enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of JSON services. Querying the APIįirst you need to look at the API documentation and find out the URL of the API interface and the endpoints. ![]() The knowledge of what an API is, is assumed. To follow this guide, basic Python knowledge as well as knowledge of the pandas package is required. ![]()
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