# First parse all entries to get the complete fieldname listįieldnames.update(get_leaves(entry).keys()) Here is the code that should work apart from not being able to have a CSV friendly dict.
Notice how the first team did not have the “Venue.ID” which makes things even more difficult. Id, name, link, venue_id, venue_name, venue_link…ġ, New Jersey Devils, /api/v1/teams/1, Prudential Center, /api/v1/venues/nullĢ, New York Islanders, /api/v1/teams/2, 5026, Barclays Center, /api/v1/venues/5026 The desired result would be to like this (this is a shortened version): “copyright” (Cell A2) and “teams” (A3) so it doesnt flatten the dict as I want it to. However, the CSV that I get only has two cells. I have Googled this quite a bit and the code for flattening the json/dict is from this post. However, I got stuck trying to get the first JSON response to a CSV for further analysis. On the other hand, JSON files can have much more complex structures than CSV files, so a direct conversion is not always possible and will require us to rework our structure of the file concerned.Since the NHL season is about to start I am planning on downloading some stats and team info through the NHL API. SummaryĪs we have seen, it may be easy to convert a Json file to a CSV file. Of course it’s possible to get all the JSON file data.
To retrieve the header we need to use the keys() function which allows us to get the keys of each “ Name” element of our JSON file. We were able to export the different names of the Pokémon in the CSV. Here is an example with the pokedex.json file :Ĭsvwriter.writerow(data.keys()) To read a JSON file we can use the read_json function. Indeed a lot of python API returns as a result of JSON and with pandas it is very easy to exploit this data directly. Pandas is a python library that allows to easily manipulate data to be analyzed.
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