If I understands your question correctly, I think this will solve it:
with jsonlines.open('yourTextFile', mode='a') as writer:
writer.write(...)
As you mentioned you are overwriting the file, I think this is because you use mode='w' (w = writing) instead of using mode='a' (a = appending)
» pip install jsonlines
Videos
Python: How to write jsonline without overwriting? - Stack Overflow
python - How to load jsonlines file with simple file read - Stack Overflow
python - Loading and parsing a JSON file with multiple JSON objects - Stack Overflow
Reading a large (30.6G) JSONL file
What is the best way to visualize JSONL data?
Can you open JSONL in Excel?
Who uses JSONL files?
import jsonlines
with jsonlines.open('example.jsonl', 'r') as jsonl_f:
lst = [obj for obj in jsonl_f]
The jsonl_f is the reader and can be used directly. It contains the lines in the json file.
Simply:
import jsonlines
with jsonlines.open("json_file.json") as file:
data = list(file.iter())
You have a JSON Lines format text file. You need to parse your file line by line:
import json
data = []
with open('file') as f:
for line in f:
data.append(json.loads(line))
Each line contains valid JSON, but as a whole, it is not a valid JSON value as there is no top-level list or object definition.
Note that because the file contains JSON per line, you are saved the headaches of trying to parse it all in one go or to figure out a streaming JSON parser. You can now opt to process each line separately before moving on to the next, saving memory in the process. You probably don't want to append each result to one list and then process everything if your file is really big.
If you have a file containing individual JSON objects with delimiters in-between, use How do I use the 'json' module to read in one JSON object at a time? to parse out individual objects using a buffered method.
In case you are using pandas and you will be interested in loading the json file as a dataframe, you can use:
import pandas as pd
df = pd.read_json('file.json', lines=True)
And to convert it into a json array, you can use:
df.to_json('new_file.json')
Hi all,
I am working on a project where I have text data stored in a massive (30.6G) json lines file. While I do have 32G of RAM, I would obviously like to avoid loading the entire file into memory.
What is the best way to go about loading a json file like this in without hogging memory?
» pip install json-lines
Hello, As the title, I have thousands to millions json lines spread over multiple JSONL format files.
What would be an efficient approach to process those lines and store in a temporary object that I want to index to some application?
Currently I'm thinking to process N lines per batch, index them and repeat until the last line in last file.