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Note: Line separated json is now supported in <a href="http://pandas-docs.github.io/pandas-docs-travis/io.html#io-jsonl" rel="nofollow noreferrer"><code>read_json</code></a> (since 0.19.0): <pre><code>In [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 </code></pre> or with a file/filepath rather than a json string: <pre><code>pd.read_json(json_file) </code></pre> <hr> It's going to depend on the size of you DataFrames which is faster, but another option is to use <code>str.join</code> to smash your multi line "JSON" (Note: it's not valid json), into valid json and use read_json: <pre><code>In [11]: '[%s]' % ','.join(test.splitlines()) Out[11]: '[{"a":1,"b":2},{"a":3,"b":4}]' </code></pre> For this tiny example this is slower, if around 100 it's the similar, signicant gains if it's larger... <pre><code>In [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 </code></pre> Note: of that time the join is surprisingly fast.
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1. CONot including time to read in the string (which both solutions use), I wonder if there's a trick here...
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