注意:(read_json
自0.19.0开始)现在支持行分隔的json :
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
pd.read_json(json_file, lines=True)
这将取决于您DataFrames的大小,该大小会更快,但是另一种选择是用于str.join
将多行“ JSON”(注意:它不是有效的json)粉碎为有效的json并使用read_json:
In [11]: '[%s]' % ','.join(test.splitlines())
Out[11]: '[{"a":1,"b":2},{"a":3,"b":4}]'
对于这个小例子,它的速度较慢,如果大约100,则更大,这是相似的,可观的收益…
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
注意:那时联接速度非常快。