In versions of Pandas > 0.19.0, DataFrame.to_json has a parameter, lines, that will write out JSONL format.

Given that, a more succinct version of your solution might look like this:

import pandas as pd

data = [{'label': 'DRUG', 'pattern': 'aspirin'},
        {'label': 'DRUG', 'pattern': 'trazodone'},
        {'label': 'DRUG', 'pattern': 'citalopram'}]
df = pd.DataFrame(data)

# Wrap pattern column in a dictionary
df["pattern"] = df.pattern.apply(lambda x: {"lower": x})

# Output in JSONL format
print(df.to_json(orient='records', lines=True))

Output:

{"label":"DRUG","pattern":{"lower":"aspirin"}}
{"label":"DRUG","pattern":{"lower":"trazodone"}}
{"label":"DRUG","pattern":{"lower":"citalopram"}}

The pd.read_json() function in the pandas library is used to read JSON data into a DataFrame. When reading a JSON Lines (JSONL) file, where each line represents a separate JSON object, you can use the lines=True parameter to properly parse the file, treating each line in the file as a separate JSON object.

df = pd.read_json("test.jsonl", lines=True)

If the file is large, you can also pass the chunksize to manipulate it in chunks.

In newer versions of pandas (0.20.0+, I believe), this can be done directly:

df.to_json('temp.json', orient='records', lines=True)

Direct compression is also possible:

df.to_json('temp.json.gz', orient='records', lines=True, compression='gzip')