Infer types from columns in JSON
npm install type-analyzerInfer data types from CSV columns.
This package provides a single interface for generating the datatype for a given
row-column formatted dataset. We support the following datatypes:
* DATE
* TIME
* DATETIME
* NUMBER
* INT
* FLOAT
* CURRENCY
* PERCENT
* STRING
* ARRAY
* OBJECT
* ZIPCODE
* BOOLEAN
* GEOMETRY
* GEOMETRY_FROM_STRING
* PAIR_GEOMETRY_FROM_STRING
* NONE
npm install type-analyzer
Parameters
- data Array _required_ An array of row object
- rules Array _optional_ An array of custom regex rules
- options Object _optional_ Option object
- options.ignoreDataTypes Array _optional_ Data types to ignore
``js`
var Analyzer = require('type-analyzer').Analyzer;
var data = [
{
"ST_AsText": "MULTIPOLYGON (((30 20, 45 40, 10 40, 30 20)), ((15 5, 40 10, 10 20, 5 10, 15 5)))",
"name": "san_francisco",
"lat": "37.7749295",
"lng": "-122.4194155",
"launch_date": "2010-06-05",
"added_at": "2010-06-05 12:00"
},
{
"ST_AsText": "MULTIPOLYGON (((30 20, 45 40, 10 40, 30 20)), ((15 5, 40 10, 10 20, 5 10, 15 5)))",
"name": "paris",
"lat": "48.856666",
"lng": "2.3509871",
"launch_date": "2011-12-04",
"added_at": "2010-06-05 12:00"
},
]
var colMeta = Analyzer.computeColMeta(data);
-
You can pass in an array of custom rules. For example. if you want to ensure that a column full of ids represented as numbers is identified as a column of strings. Rules can be matched with either exact name of the column, or regex used to match names. Note: Analyzer prefers rules using name over regex since better performance.
`js
var Analyzer = require('type-analyzer').Analyzer;
var colMeta = Analyzer.computeColMeta(data, [{name: 'id', dataType: 'STRING'}]);
// or
var colMeta = Analyzer.computeColMeta(data, [{regex: /id/, dataType: 'STRING'}]);
`
- options.ignoreDataTypes
You can also pass in ignoreDataTypes to ignore certain types. This will improve your type checking performance.
`js
var DATA_TYPES = require('type-analyzer').DATA_TYPES;
var colMeta = Analyzer.computeColMeta(arr, [], {ignoredDataTypes: DATA_TYPES.CURRENCY})[0].type,
``
And it will short cut around the usual analysis system and give
you back the column formatted as you'd expect.
You can import all availale types as a constant.