def as_json_table_type(x):
if is_integer_dtype(x):<EOL><INDENT>return '<STR_LIT>'<EOL><DEDENT>elif is_bool_dtype(x):<EOL><INDENT>return '<STR_LIT>'<EOL><DEDENT>elif is_numeric_dtype(x):<EOL><INDENT>return '<STR_LIT>'<EOL><DEDENT>elif (is_datetime64_dtype(x) or is_datetime64tz_dtype(x) or<EOL>is_period_dtype(x)):<EOL><INDENT>return '<STR_LIT>'<EOL><DEDENT>elif is_timedelta64_dtype(x):<EOL><INDENT>return '<STR_LIT>'<EOL><DEDENT>elif is_categorical_dtype(x):<EOL><INDENT>return '<STR_LIT>'<EOL><DEDENT>elif is_string_dtype(x):<EOL><INDENT>return '<STR_LIT:string>'<EOL><DEDENT>else:<EOL><INDENT>return '<STR_LIT>'<EOL><DEDENT>
Convert a NumPy / pandas type to its corresponding json_table.

Parameters
----------
x : array or dtype

Returns
-------
t : str
    the Table Schema data types

Notes
-----
This table shows the relationship between NumPy / pandas dtypes,
and Table Schema dtypes.

==============  =================
Pandas type     Table Schema type
==============  =================
int64           integer
float64         number
bool            boolean
datetime64[ns]  datetime
timedelta64[ns] duration
object          str
categorical     any
=============== =================