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

News

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...


17.05.2024

Sinhala Names: A Window into Sri Lanka’s Cultural Identity

Sri Lanka, an island nation nestled in the Indian Ocean, captivates with its diverse landscapes,...


26.04.2024

Enhanced NameAPI Database

We've updated our NameAPI database to better handle names that include professions. We have added...


12.04.2024

Bosnian names: Echoes of Diversity and Heritage

Bosnia and Herzegovina stands out in Europe for its remarkable diversity, being a country where...


   

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
}