The name tool starts with five female first-and-last-name examples using the US configuration. Select a country and a quantity from 1 to 100, then generate. Female is fixed for this list: changing country changes the locale while preserving the female given-name selection. Surnames are drawn separately, and the name list adds neither an honorific nor a middle initial. Results already displayed stay unchanged until you generate again. Because the default country is the United States, the first batch shows female names in USA form; each US woman name is a sample from the list, not a popularity ranking, and the page works as a fake female name generator for forms and characters.
Copy one result into a form, use Copy all for one name per line, or download CSV and JSON. Both downloads contain a single name field and only the values currently shown, even if you have changed a selector since generating. The separate full profile tool above supplies a female US identity with address and contact fields. Its country stays US; changing the name list does not change that profile.
In the installed Faker 9.9.0 data, the shared English female given-name list has 500 entries. Exactly 27 also occur in its male list, including Angel, Casey, Courtney, Dana, Jamie and Robin. Selecting female draws directly from the female list; it does not merely increase a probability, exclude shared names or establish a real person's gender. The US, UK and Canadian configurations use this English list as fallback, so they do not represent three independent national name collections.
The table compares source-list entries across six locales, not resident populations or measured popularity. German and French lists also overlap across the two categories; Italian and Spanish lists have no shared entries in this version. That absence says something about the library, not that real names in those languages belong exclusively to one gender. Repeated combinations and chance matches with real people remain possible. For testing, deliberately include ambiguous names and Unicode cases alongside random samples rather than assuming a batch will cover them all.
