movementjas.blogg.se - Python nested json to csv

Ids.append(all_pet_data)Ĭsv_data.append(, all_pet_data,Īll_pet_data]) With open(today_date+".csv", "w", newline="", encoding="utf-8") as f: The following code is how I am successfully parsing the data: import csv The result I get is a list of lists, with each inner list the JSON of one page. Return resp.I am fetching responses from thousands of API calls, each of which is a new JSON, as the api is paginated.

Resp = requests.get(endpoint, params=params) """Fetch and extract JSON data from Google Maps."""

units (Optional): Whether to return values as metric or imperial units.

transit_mode (Optional): Method of transportation for the trip (informs trip duration).

destinations: Equivalent of the origins parameter for trip destination(s).

This value can be passed as a city name, address, or other formats essentially what you'd expect from using the Google Maps app.

origins: Physical place (or places) representing where our trip begins.

Below we see how such a request would be made via Python's requests library. We're going to use the Google Maps API to get the distance between two locations, as well as the estimated duration to complete the trip.

It's a great full-featured API, but as you might imagine the resulting JSON for calculating commute time between where you stand and every location in the conceivable universe makes an awfully complex JSON structure. The idea is that with a single API call, a user can calculate the distance and time traveled between an origin and an infinite number of destinations. While Google Maps is a collection of APIs, the Google Maps Distance Matrix. The Google Maps API is a good candidate to fit the bill here. To visualize the problem, let's take an example somebody might want to use. It felt like a rallying call at the time).

Luckily, we code in Python! (okay fine, language doesn't make much of a difference here. Nobody feels like much of a "scientist" or an "engineer" when half their day deals with key value errors. Still, there's just one problem: the complexity of nested JSON objects is endless, and suddenly the job you love needs to be put on hold to painstakingly retrieve the data you want, and it's 5 levels deep in a nested JSON hell. It's easy to pass requests, and responses contain all the information you've been looking for. It happens 1 to 100 times daily: You begin working with a new REST API, and it suits your needs perfectly. We're all data people here, so you already know the scenario.