ENH: ujson better handling of very large and very small numbers, throw ValueError for bad double_precision arg #4042
closes #4042

This makes ujson handle very big and very small numbers a bit better, it doesn't help with precision but it should at least be able to handle very small and large exponentials now:

```
In [4]: from pandas.json import dumps

In [5]: dumps(1e-5)
Out[5]: '0.00001'

In [6]: dumps(1e-6)
Out[6]: '0.000001'

In [7]: dumps(1e-7)
Out[7]: '0.0000001'

In [8]: dumps(1e-8)
Out[8]: '0.00000001'

In [9]: dumps(1e-9)
Out[9]: '0.000000001'

In [10]: dumps(1e-10)
Out[10]: '0.0000000001'

In [11]: dumps(1e-11)
Out[11]: '0.0'

In [12]: dumps(1e-11, double_precision=15)
Out[12]: '0.00000000001'

In [13]: dumps(1e-12, double_precision=15)
Out[13]: '0.000000000001'

In [14]: dumps(1e-13, double_precision=15)
Out[14]: '0.0000000000001'

In [15]: dumps(1e-14, double_precision=15)
Out[15]: '0.00000000000001'

In [16]: dumps(1e-15, double_precision=15)
Out[16]: '0.000000000000001'

In [17]: dumps(1e-16, double_precision=15)
Out[17]: '1e-16'

In [18]: dumps(1e-16)
Out[18]: '1e-16'

In [19]: dumps(1e-17)
Out[19]: '1e-17'

In [20]: dumps(1e-40)
Out[20]: '1e-40'

In [21]: dumps(1e-100)
Out[21]: '1e-100'

In [22]: dumps(1e-400)
Out[22]: '0.0'

In [28]: dumps(1e40)
Out[28]: '1e+40'

In [29]: dumps(1e100)
Out[29]: '1e+100'

In [30]: dumps(1e400)
Out[30]: 'null'

In [31]: from pandas.json import loads

In [32]: loads(dumps(1e100))
Out[32]: 1e+100

In [33]: loads(dumps(1e40))
Out[33]: 1e+40

In [34]: loads(dumps(1e-40))
Out[34]: 1e-40
```

I have also modified it to throw a `ValueError` when a bad value is given for `double_precision`:

```
In [25]: dumps(1e-400, double_precision=-1)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-25-e15fa4642646> in <module>()
----> 1 dumps(1e-400, double_precision=-1)

ValueError: Invalid value '-1' for option 'double_precision', max is '15'

In [26]: dumps(1e-400, double_precision=16)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-26-ab74b2f14c7f> in <module>()
----> 1 dumps(1e-400, double_precision=16)

ValueError: Invalid value '16' for option 'double_precision', max is '15'
```

Tested on Python 2.7 on Arch-64. T'would be great if someone could test this out on windows.

How about for numbers that make full use of double precision? E.g. 1.234567890123456e-40 

Also, I trust @njsmith 's opinion over my own on this ... :)
 It seems to work ok

```
In [1]: from pandas.json import dumps

In [2]: dumps(1.234567890123456e-40)
Out[2]: '1.23456789e-40'

In [3]: dumps(1.234567890123456e-40, double_precision=15)
Out[3]: '1.23456789012346e-40'

In [4]: dumps(1.234567890123456e-40, double_precision=16)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-4-02b1ef3af2eb> in <module>()
----> 1 dumps(1.234567890123456e-40, double_precision=16)

ValueError: Invalid value '16' for option 'double_precision', max is '15'

```

And at least it throws an error for an invalid precision setting now rather than silently capping it at 15.
 I'm a little concerned about the rounding up of 5 to 6, here:

```
In [3]: dumps(1.234567890123456e-40, double_precision=15)
Out[3]: '1.23456789012346e-40' 
```
 That'll be sprintf rounding things which is standard behaviour for it when given a precision.

I'm investigating the use of `PyOS_double_to_string` (thanks @njsmith) which looks like it should reproduce what simplejson does.
 Although simplejson has the same rounding behaviour, just to one more decimal place, so I'm inclined to stick with the changes above (i.e. sprintf) now:

```
In [9]: import json  # simplejson

In [10]: json.dumps(1.234567890123456e-40)
Out[10]: '1.234567890123456e-40'

In [11]: json.dumps(1.234567890123456789e-40)
Out[11]: '1.2345678901234568e-40'

```
 @Komnomnomnom merge?
 I'm happy with it, and I don't have any other commits in the pipeline. There's probably scope for more improvements, but not in this PR.
 thank you sir!
