read_json: ValueError: Value is too big
Reopening issue #14530.  The close description is incorrect.  The JSON specification explicitly states that limits are not in the specification.

From https://tools.ietf.org/html/rfc7159#section-6
> This specification allows implementations to set limits on the range and precision of numbers accepted

The standard `json` library in python supports large numbers, meaning the language supports JSON with these values.

```
Python 3.6.8 (default, Apr  7 2019, 21:09:51)
[GCC 5.3.1 20160406 (Red Hat 5.3.1-6)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> j = """{"id": 9253674967913938907}"""
>>> json.loads(j)
{'id': 9253674967913938907}
```

> Loading a json file with large integers (> 2^32), results in "Value is too big". I have tried changing the orient to "records" and also passing in dtype={'id': numpy.dtype('uint64')}. The error is the same.
> 
> ```python
> import pandas
> data = pandas.read_json('''{"id": 10254939386542155531}''')
> print(data.describe())
> ```
> 
> #### Expected Output
> ```
>                           id
> count                      1
> unique                     1
> top     10254939386542155531
> freq                       1
> ```
> #### Actual Output (even with dtype passed in)
> ```
>  File "./parse_dispatch_table.py", line 34, in <module>
>     print(pandas.read_json('''{"id": 10254939386542155531}''', dtype=dtype_conversions).describe())
>   File "/users/XXX/.local/lib/python3.4/site-packages/pandas/io/json.py", line 234, in read_json
>     date_unit).parse()
>   File "/users/XXX/.local/lib/python3.4/site-packages/pandas/io/json.py", line 302, in parse
>     self._parse_no_numpy()
>   File "/users/XXX/.local/lib/python3.4/site-packages/pandas/io/json.py", line 519, in _parse_no_numpy
>     loads(json, precise_float=self.precise_float), dtype=None)
> ValueError: Value is too big
> ```
> No problem using read_csv:
> 
> ```python
> import pandas
> import io
> print(pandas.read_csv(io.StringIO('''id\n10254939386542155531''')).describe())
> ```
> 
> #### Output using read_csv
> ```
>                           id
> count                      1
> unique                     1
> top     10254939386542155531
> freq                       1
> ```
> #### Output of `pd.show_versions()`

