6/3/2023 0 Comments Json query python 3There is even a module you can use right out of the box: flatten_json. If you change the original JSON like this you obtain a JSON that can be directly fed into pandas. A common strategy is to flatten the original JSON by doing something very similar like we did here: pull out all nested objects by concatenating all keys and keeping the final inner value. You can verify yourself that the data frame obtained by this approach is identical to the data frame obtained from the previous iterative solution. Isn’t that a beauty! Like often when a recursive approach is more natural to the task at hand the recursive implementation is more readable and often shorter than the iterative approach. Recursive_parser(entry, data_dict, extended_col_name) The first rows of this data frame looks as follows ( df.head(3)): More complex properties like “author” are again nestedīefore I dive deeper in how to parse this nested structure, let me try pandas read_json() method first.The most simple property is an object with just a “label” key and a value.“entry” is a list of objects and each object has a set of properties like “author”, “link” and ,”im:rating”.this root element has only two children, “author” and “entry”, from which I am only interested in “entry”.So the JSON response is structured in the following way: I’ve only shown the first author object of the entry list.
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