NameGender Python

pip install namegender-client

from namegender import NameGender
client = NameGender("YOUR_API_KEY")
result = client.name("Ayşe", country="TR")
print(result["gender"], result["probability"], result["sample_size"], result["confidence"])

Options and response

name, email, username and bulk accept country, locale, ip, ai_fallback and best_guess as keyword arguments:

result = client.name("Andrea", country="IT", best_guess=True)

When you don't know the country, pass what you do know. locale is a language tag such as "it-IT" or "pt_BR"; its region is used as the country ("en" alone sets none). ip is your end user's IP address; its country is used when there is no country and no regional locale. The API does not store it. country wins over locale, and locale over ip; country_source on the response says which one was used ("country", "locale", "ip" or None); for bulk it is on the top level of the response, next to summary, not on each item in results.

result = client.name("Andrea", locale="it-IT", ip="203.0.113.7")
print(result["country"], result["country_source"])  # IT locale

A result carries query, name, first_name, middle_name, last_name, name_type, gender, country, country_source, probability, sample_size, took_ms, source, confidence and matched_as, alongside credits_charged, credits_remaining, data_version and request_id. Success is the HTTP status: any non-2xx response raises NameGenderError with status and body ({"error", "message", "request_id", "docs"}). Branch on body["error"], not on the message.

Salutation

Returns a ready-to-use letter salutation for a name, with titles parsed out.

result = client.salutation("Dr. Anna Müller", language="de")
print(result["salutation"]["formal"])  # Sehr geehrte Frau Dr. Müller,

result = client.salutation("Ahmet Yılmaz", language="tr")
print(result["salutation"]["formal"])  # Sayın Ahmet Bey,

# First and last name stored separately:
result = client.salutation(first_name="Anna", last_name="Müller", title="Dr.", language="de")

result = client.salutation_bulk(["Dr. Anna Müller", "ACME GmbH"], language="de")
print(result["summary"])  # {"total": 2, "gendered": 1, "neutral": 0, "organization": 1}

Keyword arguments: language (en, en-US, en-GB, de, de-AT, de-CH, fr, es, it, pt, pt-PT, pt-BR, nl, tr, pl, ja; an unsupported one raises NameGenderError with status 422), country, locale, ip, gender ("male", "female" or "neutral"), min_probability (50–100, default 90) and title. One credit per name; salutation_bulk takes up to 100 names and returns results in input order.

salutation carries formal, informal and neutral, and parts the pieces (opening, courtesy, academic, name, each possibly None). When the gender is not certain you get the neutral form: form is "gendered", "neutral" or "organization", and reason says why it is not gendered ("gender_unknown", "below_min_probability", ...). best_guess does not apply to salutations.

Name check

Says whether a name typed into a form looks like a real person's name, with the reasons.

result = client.name_check("asdf qwerty")
print(result["assessment"])  # implausible
print(result["signals"][0]["code"])  # keyboard_pattern

result = client.name_check("Jennifer Null")
print(result["assessment"])  # plausible

# First and last name stored separately:
result = client.name_check(first_name="Jennifer", last_name="Null", country="US")

result = client.name_check_bulk(["asdf qwerty", "Jennifer Null"])
print(result["summary"])  # {"total": 2, "plausible": 1, "suspicious": 0, "implausible": 1}

Keyword arguments: country, locale and ip. One credit per name; name_check_bulk takes up to 100 names and returns results in input order. assessment is "plausible", "suspicious" or "implausible", score is 0–100, and each signal has code, severity, part and value (the last two possibly None).

It never calls a name fake: use it to flag records for a second look, not to reject people automatically. Surnames are judged by their shape only.

Age from name

How old the living people with a first name are: the median age, the middle half (age_range) and the middle 80% (age_range_80).

result = client.age("Brittany")
print(result["age"], result["age_range"])  # 36 {'low': 32, 'high': 38}
print(result["country_source"])  # default: no hint, so the US series

result = client.age("Kevin", country="FR")
print(result["age"])  # 34 (47 in the US)

result = client.age("Leslie", gender="male")  # 66; women called Leslie: 50

result = client.age_bulk(["Jean", "Léa"], country="FR")  # up to 100

Keyword arguments: gender, country, locale and ip. Covered: the United States (SSA births 1880–2024), France (INSEE 1900–2025) and Norway (SSB 1945–2025); without a hint the US series is used. When age is None, reason says why: "country_not_covered" costs no credit, "not_found" and "insufficient_data" cost one like any other name.

It describes a group, not a person: for most names the middle half spans 10 to 25 years. Use it for audience analysis and research, never to decide anything about one person.

Country distribution

Returns the countries a name is recorded in. This is not a country-of-origin or ethnicity inference, and must not be used as one.

result = client.countries("Mehmet", limit=10)
print(result["registrations"])  # [{"country": "FR", "count": 3775, "share": 58.97, "gender": "male", "probability": 99, "source": "insee"}, ...]
print(result["attested_in"])    # ["AL", "AU", "BE", ..., "TR", "US"]
print(result["basis"]["note"])

The two lists are deliberately kept apart. registrations is measured volume and is comparable only among the seven countries that publish counted birth statistics (US, UK, France, Canada, Spain, Ireland, Norway); share is a percentage across those counts alone. attested_in is presence with no weight attached, which is where countries that publish no counts, such as Turkey, Japan and India, appear. Show basis["note"] next to any percentage you display.

limit (1–100, default 25) caps how many counted countries come back in registrations. One credit per request.

File jobs

Upload a CSV or XLSX file (up to 100 MB and 1,000,000 rows) and get it back with gender columns added. One credit per row, charged only if the job completes.

job = client.batches.create(
    "customers.csv",             # a path, bytes (with filename=) or a binary file object
    name_column="first_name",    # required to start
    country_column="country",    # optional: a country code per row
)

done = client.batches.wait(job["id"], on_progress=lambda j: print(j["progress"]))
if done["status"] == "failed":
    raise RuntimeError(done["error"]["code"])

client.batches.download(done["id"], "customers-gender.csv")

name_column is required to start: a guessed column that turns out to be wrong would spend credits on the wrong data. To see the columns and the cost first, upload with start=False, read job["inspection"], then call client.batches.start(job["id"], name_column=...).

create sends an Idempotency-Key and retries network errors and 502/503/504 with the same key, so a retry never opens a second job. Pass your own idempotency_key to keep that guarantee across your own retries.

wait returns a failed job rather than raising; branch on job["error"]["code"]. cancel returns the credit of a job that has not started, and deletes a finished one. list(limit=, page=) includes jobs started from the dashboard. Up to three jobs can be queued or running at once; a fourth is refused with 429 too_many_batches.

The result appends gender, probability, sample_size, country, source, matched_as, first_name, middle_name, last_name and name_type to every row. A CSV result starts with a UTF-8 byte order mark; read it with encoding="utf-8-sig".

Webhooks

Add an endpoint under Webhooks in the dashboard, and NameGender sends a signed POST to it when a file job completes or fails. webhooks.verify checks the signature and the timestamp, and returns the event.

import os
from flask import Flask, request
from namegender import webhooks, WebhookVerificationError

app = Flask(__name__)

@app.post("/namegender")
def namegender_webhook():
    try:
        event = webhooks.verify(
            request.get_data(),   # the raw bytes, not request.json
            request.headers.get("NameGender-Signature"),
            os.environ["NAMEGENDER_WEBHOOK_SECRET"],
        )
    except WebhookVerificationError:
        return "", 400

    if event["type"] == "batch.completed":
        job = event["data"]["object"]   # the job, as batches.get() returns it
        ...
    return "", 204

Answer quickly and do slow work afterwards. Anything other than a 2xx within 10 seconds is retried, up to 8 attempts over about 45 hours. Use event["id"] (also the NameGender-Event-Id header) to ignore a delivery you have already handled: a retry carries the same id, and order is not guaranteed.