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# just as you would do with a list but here the file is streamed for jsonline in iterator_over_lines For example, given a pandas d. # do something for each line # the function mapped, json.loads is only call on each iteration # that's why the file must stay opened # you can try to call yourself the next function used by the for loop:

Andrea Anaconda (@theonlyanaconduh) • Instagram photos and videos

I need this format in order to have processed by glue job in aws I couldn't find a good package to do it and tried to implement myself, but it looks a bit ugly and not efficient The glue job only handles it well if data is in this format, which is the case if it is generated by sns topic

When it is generated by rest api, structure is different than the above.

Consider having the following code and a jsonl file, there is a specific reason i don't read file with jsonlines.open() api, so please take this as a fact I couldn't find a method which can write without overwriting the jsonline file So how i can handle such big output Besides, i'm using parallel threads to process result

From multiprocessing.dummy import pool pool(4).map(get_result, file_lst) i do hope to open the json_file, write each result and then release the memory. I have a jsonl file which contains per line both a sentence and the tokens that are found in that sentence I wish to extract the tokens from each line in the json lines file, but my loop only retu. Json lines is supported by some aws services and they use application/jsonlines content type.

I can't figure out how to create jsonl using python3

'b'}] with open (data.json, 'w') as f See json lines json_file = [{u'index' You'll need to complete a few actions and gain 15 reputation points before being able to upvote Upvoting indicates when questions and answers are useful

What's reputation and how do i get it Instead, you can save this post to reference later. I need to convert pandas data frame to jsonl format