


Accessing Multiple JSON Objects from a Single File
Working with JSON files can be challenging, especially when dealing with multiple JSON objects stored within a single file. To extract specific information from such files, customized solutions are required.
In this scenario, a JSON file contains multiple JSON objects, each representing information of a specific event. The task is to extract the "Timestamp" and "Usefulness" fields from each object and format them into a data frame.
To achieve this, leveraging the jsonstream library is recommended. It provides a specialized approach to handling large JSON files without the need for loading the entire file into memory. The library allows for iteratively decoding JSON objects from a file, one at a time.
The JSONstream library can be employed as follows:
<code class="python">from jsonstream import json with open("input.json", "r") as f: for obj in json.parse(f): # Access and process individual fields from the parsed JSON object timestamp = obj["Timestamp"] usefulness = obj["Usefulness"] # ... (perform any necessary actions with the extracted data)</code>
Alternatively, if direct file access is not possible or preferred, utilizing the JSONDecoder class with the raw_decode method can be an efficient solution. This method enables the decoding of large JSON strings without the requirement of reading the entire file in one operation. It iteratively finds valid JSON objects and keeps track of the last parsing position.
<code class="python">from json import JSONDecoder decoder = JSONDecoder() with open("input.json", "r") as f: for line in f: try: obj, pos = decoder.raw_decode(line, 0) timestamp = obj["Timestamp"] usefulness = obj["Usefulness"] # ... (perform actions with the extracted data) except JSONDecodeError: # Handle any errors encountered during decoding</code>
Both the JSONstream library and the raw_decode method provide efficient ways to extract multiple JSON objects from a single file, making it easier to work with and analyze large JSON data sets.
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