Reading multiple JSON records into a Pandas dataframe

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)

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.

If you are trying to save memory, then reading the file a line at a time will be much more memory efficient:

with open('test.json') as f:
    data = pd.DataFrame(json.loads(line) for line in f)

Also, if you import simplejson as json, the compiled C extensions included with simplejson are much faster than the pure-Python json module.

As of Pandas 0.19, read_json has native support for line-delimited JSON:

pd.read_json(jsonfile, lines=True)

++++++++Update++++++++++++++

As of v0.19, Pandas supports this natively (see https://github.com/pandas-dev/pandas/pull/13351). Just run:

df=pd.read_json('test.json', lines=True)

++++++++Old Answer++++++++++

The existing answers are good, but for a little variety, here is another way to accomplish your goal that requires a simple pre-processing step outside of python so that pd.read_json() can consume the data.

• Install jq https://stedolan.github.io/jq/.
• Create a valid json file with cat test.json | jq -c --slurp . > valid_test.json
• Create dataframe with df=pd.read_json('valid_test.json')

In ipython notebook, you can run the shell command directly from the cell interface with

!cat test.json | jq -c --slurp . > valid_test.json
df=pd.read_json('valid_test.json')