Reading multiple JSON records into a Pandas dataframe

Question

I'd like to know if there is a memory efficient way of reading multi record JSON file ( each line is a JSON dict) into a pandas dataframe. Below is a 2 line example with working solution, I need it for potentially very large number of records. Example use would be to process output from Hadoop Pig JSonStorage function.

import json import pandas as pd  test='''{"a":1,"b":2} {"a":3,"b":4}''' #df=pd.read_json(test,orient='records') doesn't work, expects []  l=[ json.loads(l) for l in test.splitlines()] df=pd.DataFrame(l) 

Answer 1

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

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 

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

pd.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:

In [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...

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 

Note: of that time the join is surprisingly fast.