NameAPI is a web API
to handle people's names
in your software.

News

25.07.2024

Database Update: An Extensive Collection of Croatian Names Available

Naming conventions in Croatia emphasize family heritage, often involving naming children after...


16.07.2024

Albanian Names in Focus

Did you know that Albanian names reflect a blend of ancient Illyrian heritage, religious influences...


25.06.2024

Swedish names: Tracing the Threads of Northern Culture

Originating from Old Norse, Swedish is spoken in Sweden and in regions of Finland, where it enjoys...


07.06.2024

Expanded NameAPI Database for Company Legal Forms

We've expanded our NameAPI database to include a comprehensive list of company legal forms from...


27.05.2024

Software Version 10.3.0 Deployed

We are pleased to announce the deployment of Software Version 10.3.0, bringing significant...


   

Name Genderizer


       
Attempts to detect the person's gender based on the inputs, especially the person's name.
See also the Swagger specification.
                    
POST
       
application/json (you must set the content-type as http header)
       
We have integrated Swagger directly into our API.
Visit https://api.nameapi.org/rest/swagger-ui/.

   

Input

               
See Context.
   
{
  "context" : {
     "priority" : "REALTIME",
     "properties" : [ ]
   },
  "inputPerson" : {
    "type" : "NaturalInputPerson",
    "personName" : {
      "nameFields" : [ {
        "string" : "Andrea",
        "fieldType" : "GIVENNAME"
      }, {
        "string" : "Bocelli",
        "fieldType" : "SURNAME"
      } ]
    },
    "gender" : "UNKNOWN"
  }
}      
   

   

Output

       
Possible values:

The person is clearly 'male'.

The person is clearly 'female'.

Can be either male or female. See malePercent.

No gender could be computed, but better intelligence should be able to tell the gender. An example is a name input of which we have never heard before.

From the given data it is or seems impossible to tell the gender.
For example all terms are gender-inapplicable, or there are no names at all. Thus this differs from NEUTRAL where something is clearly known to be neutral.

There are conflicting genders in the given data.
Example: "Mr Daniela Miller" (salutation vs. given name).
The input data must be manually reviewed. It is impossible and useless to make a guess (garbage in would only cause garbage out).

       
If neutral (otherwise null) then this may be specified (but does not have to be), 0-1, the remaining % are for female.
       
0-1 where 1 is the best.
   
{
  "gender" : "MALE",
  "confidence" : 0.9111111111111111
}