How to Efficiently Parse JSON Data with Multiple Embedded Objects in Python?

JSON Parsing Challenges with Multiple Embedded Objects

This article addresses the challenge of extracting data from a JSON file containing multiple nested JSON objects. Such files often pose challenges when dealing with large datasets.

Problem Statement

Consider a JSON file with multiple JSON objects as follows:

<code class="json">{"ID":"12345","Timestamp":"20140101", "Usefulness":"Yes",
 "Code":[{"event1":"A","result":"1"},…]}
{"ID":"1A35B","Timestamp":"20140102", "Usefulness":"No",
 "Code":[{"event1":"B","result":"1"},…]}
{"ID":"AA356","Timestamp":"20140103", "Usefulness":"No",
 "Code":[{"event1":"B","result":"0"},…]}
…</code>

The task is to extract the "Timestamp" and "Usefulness" values from each object into a data frame:

Timestamp	Usefulness
20140101	Yes
20140102	No
20140103	No
...	...

Solution Overview

To address this challenge, we employ the json.JSONDecoder.raw_decode method in Python. This method allows for the decoding of large strings of "stacked" JSON objects. It returns the last position of the parsed object and a valid object. By passing the returned position back to raw_decode, we can resume parsing from that point.

Implementation

<code class="python">from json import JSONDecoder, JSONDecodeError
import re

NOT_WHITESPACE = re.compile(r'\S')

def decode_stacked(document, pos=0, decoder=JSONDecoder()):
    while True:
        match = NOT_WHITESPACE.search(document, pos)
        if not match:
            return
        pos = match.start()
        
        try:
            obj, pos = decoder.raw_decode(document, pos)
        except JSONDecodeError:
            # Handle errors appropriately
            raise
        yield obj

s = """

{“a”: 1}  


[
1
,   
2
]


"""

for obj in decode_stacked(s):
    print(obj)</code>

This code iterates through the JSON objects in the string s and prints each object:

{'a': 1}
[1, 2]

Conclusion

The provided solution effectively addresses the challenge of extracting data from multiple nested JSON objects embedded in a single file. By utilizing the json.JSONDecoder.raw_decode method and handling potential errors, we can process large datasets efficiently. The decode_stacked function can be used as a reusable tool for handling such file formats.

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