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.

Just read each line and construct a json object at this time:

with open(file_path) as f:
    for line in f:
        j_content = json.loads(line)

This way, you load proper complete json object (provided there is no \n in a json value somewhere or in the middle of your json object) and you avoid memory issue as each object is created when needed.

There is also this answer.:

https://stackoverflow.com/a/7795029/671543

Note: Line separated json is now supported in read_json (since 0.19.0):

CopyIn [31]: pd.read_json('{"a":1,"b":2}\n{"a":3,"b":4}', lines=True)
Out[31]:
   a  b
0  1  2
1  3  4

or with a file/filepath rather than a json string:

Copypd.read_json(json_file, lines=True)

It's going to depend on the size of you DataFrames which is faster, but another option is to use str.join to smash your multi line "JSON" (Note: it's not valid json), into valid json and use read_json:

CopyIn [11]: '[%s]' % ','.join(test.splitlines())
Out[11]: '[{"a":1,"b":2},{"a":3,"b":4}]'

For this tiny example this is slower, if around 100 it's the similar, signicant gains if it's larger...

CopyIn [21]: %timeit pd.read_json('[%s]' % ','.join(test.splitlines()))
1000 loops, best of 3: 977 µs per loop

In [22]: %timeit l=[ json.loads(l) for l in test.splitlines()]; df = pd.DataFrame(l)
1000 loops, best of 3: 282 µs per loop

In [23]: test_100 = '\n'.join([test] * 100)

In [24]: %timeit pd.read_json('[%s]' % ','.join(test_100.splitlines()))
1000 loops, best of 3: 1.25 ms per loop

In [25]: %timeit l = [json.loads(l) for l in test_100.splitlines()]; df = pd.DataFrame(l)
1000 loops, best of 3: 1.25 ms per loop

In [26]: test_1000 = '\n'.join([test] * 1000)

In [27]: %timeit l = [json.loads(l) for l in test_1000.splitlines()]; df = pd.DataFrame(l)
100 loops, best of 3: 9.78 ms per loop

In [28]: %timeit pd.read_json('[%s]' % ','.join(test_1000.splitlines()))
100 loops, best of 3: 3.36 ms per loop

Note: of that time the join is surprisingly fast.

You can simply use f.read() if you aren't worried about the memory usage in case of large files or else your implementation seems fine, except the part which you are trying to use append() with string. This can be achieved through simple modification

lines = ""
with open("links.jl", "r") as f:
    for line in f:
        lines += line