df.to_json() method seems consistently ~1.8x slower in version 0.13.x (and a few older 0.12.x versions in the master branch on git) than in 0.12.0.
Version 0.12.0:
Python 2.7.5+ (default, Sep 17 2013, 15:31:50)
In [1]: import pandas as pd, numpy as np
In [2]: df = pd.DataFrame(np.random.rand(100000,10))
In [3]: %timeit df.to_json(orient='split')
10 loops, best of 3: 96.1 ms per loop
In [4]: pd.__version__, np.__version__
Out[4]: ('0.12.0', '1.7.1')
Version 0.13.0rc1:
Python 2.7.5+ (default, Sep 17 2013, 15:31:50)
In [1]: import pandas as pd, numpy as np
In [2]: df = pd.DataFrame(np.random.rand(100000,10))
In [3]: %timeit df.to_json(orient='split')
10 loops, best of 3: 172 ms per loop
In [4]: pd.__version__, np.__version__
Out[4]: ('0.13.0rc1-119-g2485e09', '1.8.0')
The 1.8x factor seems to hold on my machine across Python versions (2.7.5 vs 3.3.2), dataframe sizes, orient values and dtypes (only tried floats and DatetimeIndex).
Was there some change in to_json() or have I goofed something up in my environment?
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