You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 synthea
【Schema】
# Table: allergies
[
(START:TEXT, Examples: [3/11/95, 6/15/58, 11/14/12]),
(STOP:TEXT, Examples: [12/22/14, 4/21/10, 10/15/11]),
(PATIENT:TEXT, Primary Key, Examples: [00341a88-1cc1-4b39-b0f9-05b0531991a0]),
(ENCOUNTER:TEXT, Primary Key, Examples: [ddc8a625-07e6-4a02-a616-e0ce07b5f305]),
(CODE:INTEGER, Primary Key, Examples: [232347008, 300916003, 419474003]),
(DESCRIPTION:TEXT, Examples: [Allergy to dairy product])
]
# Table: encounters
[
(ID:TEXT, Primary Key, Examples: [000c20ea-5c3d-43a3-9608-bd37c22f13c8]),
(DATE:DATE, Examples: [2008-03-11]),
(PATIENT:TEXT, Examples: [71949668-1c2e-43ae-ab0a-64654608defb]),
(CODE:INTEGER, Examples: [185349003, 185345009, 698314001]),
(DESCRIPTION:TEXT, Examples: [Outpatient Encounter]),
(REASONCODE:INTEGER, Examples: [10509002, 38822007, 72892002]),
(REASONDESCRIPTION:TEXT, Examples: [Acute bronchitis (disorder)])
]
# Table: all_prevalences
[
(ITEM:TEXT, Primary Key),
(POPULATION TYPE:TEXT, Examples: [LIVING]),
(OCCURRENCES:INTEGER, Examples: [868, 487, 117]),
(POPULATION COUNT:INTEGER, Examples: [1000]),
(PREVALENCE RATE:REAL, Examples: [0.868, 0.487, 0.117]),
(PREVALENCE PERCENTAGE:REAL, Examples: [86.8, 48.7, 11.7])
]
# Table: patients
[
(patient:TEXT, Primary Key, Examples: [00269bb7-e3ab-43a9-9cdf-cdf9b6e3b2b3]),
(birthdate:DATE, Examples: [1929-04-08]),
(deathdate:DATE, Examples: [2029-11-11]),
(ssn:TEXT, Examples: [999-78-5976, 999-59-9186, 999-43-3780]),
(drivers:TEXT, Examples: [S99992928, S99975961, S99997003]),
(passport:TEXT, Examples: [FALSE, X98167138X, X65866752X]),
(prefix:TEXT, Examples: [Mr., Mrs., Ms.]),
(first:TEXT, Examples: [Rosamaria, Loan, Angelo]),
(last:TEXT, Examples: [Pfannerstill, Bashirian, Buckridge]),
(suffix:TEXT, Examples: [PhD, JD, MD]),
(maiden:TEXT, Examples: [Schuster, Schaden, Brekke]),
(marital:TEXT, Examples: [S, M]),
(race:TEXT, Examples: [black, white, hispanic]),
(ethnicity:TEXT, Examples: [dominican, american, african]),
(gender:TEXT, Examples: [F, M]),
(birthplace:TEXT, Examples: [Pittsfield MA US, Medford MA US, Framingham MA US]),
(address:TEXT)
]
# Table: medications
[
(START:DATE, Primary Key, Examples: [1918-04-17]),
(STOP:DATE, Examples: [2008-06-04]),
(PATIENT:TEXT, Primary Key, Examples: [ce11bcba-c83c-43ae-802d-b20ee8715afe]),
(ENCOUNTER:TEXT, Primary Key, Examples: [6334071d-e6b0-42c1-a082-2a08e123de4e]),
(CODE:INTEGER, Primary Key, Examples: [834060, 745679, 895994]),
(DESCRIPTION:TEXT),
(REASONCODE:INTEGER, Examples: [43878008, 10509002, 38822007]),
(REASONDESCRIPTION:TEXT, Examples: [Streptococcal sore throat (disorder)])
]
# Table: procedures
[
(DATE:DATE, Examples: [2013-02-09]),
(PATIENT:TEXT, Examples: [71949668-1c2e-43ae-ab0a-64654608defb]),
(ENCOUNTER:TEXT, Examples: [6f2e3935-b203-493e-a9c0-f23e847b9798]),
(CODE:INTEGER, Examples: [23426006, 252160004, 237001001]),
(DESCRIPTION:TEXT, Examples: [Measurement of respiratory function (procedure)]),
(REASONCODE:INTEGER, Examples: [10509002, 72892002, 33737001]),
(REASONDESCRIPTION:TEXT, Examples: [Acute bronchitis (disorder)])
]
# Table: claims
[
(ID:TEXT, Primary Key, Examples: [0004caaf-fd7a-4f64-bda6-dce5ebd1196c]),
(PATIENT:TEXT, Examples: [71949668-1c2e-43ae-ab0a-64654608defb]),
(BILLABLEPERIOD:DATE, Examples: [2008-03-11]),
(ORGANIZATION:TEXT, Examples: [temp organization]),
(ENCOUNTER:TEXT, Examples: [71949668-1c2e-43ae-ab0a-64654608defb]),
(DIAGNOSIS:TEXT),
(TOTAL:INTEGER, Examples: [100])
]
# Table: observations
[
(DATE:DATE, Examples: [2008-03-11]),
(PATIENT:TEXT, Examples: [71949668-1c2e-43ae-ab0a-64654608defb]),
(ENCOUNTER:TEXT, Examples: [5114a5b4-64b8-47b2-82a6-0ce24aae0943]),
(CODE:TEXT, Examples: [8302-2, 29463-7, 39156-5]),
(DESCRIPTION:TEXT, Examples: [Body Height, Body Weight, Body Mass Index]),
(VALUE:REAL, Examples: [166.03, 54.42, 19.74]),
(UNITS:TEXT, Examples: [cm, kg, kg/m2])
]
# Table: careplans
[
(ID:TEXT, Examples: [e031962d-d13d-4ede-a449-040417d5e4fb]),
(START:DATE, Examples: [2009-01-11]),
(STOP:DATE, Examples: [2009-04-07]),
(PATIENT:TEXT, Examples: [71949668-1c2e-43ae-ab0a-64654608defb]),
(ENCOUNTER:TEXT, Examples: [4d451e22-a354-40c9-8b33-b6126158666d]),
(CODE:REAL, Examples: [53950000.0, 304510005.0, 371605008.0]),
(DESCRIPTION:TEXT, Examples: [Respiratory therapy]),
(REASONCODE:INTEGER, Examples: [10509002, 38822007, 72892002]),
(REASONDESCRIPTION:TEXT, Examples: [Acute bronchitis (disorder)])
]
# Table: immunizations
[
(DATE:DATE, Primary Key, Examples: [2007-11-02]),
(PATIENT:TEXT, Primary Key, Examples: [70cbc79e-dd9d-4e3b-8158-a92d85951f94]),
(ENCOUNTER:TEXT, Primary Key, Examples: [898ab2de-8a71-4fb3-b239-b768b915284a]),
(CODE:INTEGER, Primary Key, Examples: [8, 62, 140]),
(DESCRIPTION:TEXT, Examples: [Influenza  seasonal  injectable  preservative free])
]
# Table: conditions
[
(START:DATE, Examples: [2009-01-08]),
(STOP:DATE, Examples: [2009-01-21]),
(PATIENT:TEXT, Examples: [71949668-1c2e-43ae-ab0a-64654608defb]),
(ENCOUNTER:TEXT, Examples: [4d451e22-a354-40c9-8b33-b6126158666d]),
(CODE:INTEGER, Examples: [10509002, 38822007, 72892002]),
(DESCRIPTION:TEXT, Examples: [Acute bronchitis (disorder)])
]
【Foreign keys】
allergies.ENCOUNTER=encounters.ID
allergies.PATIENT=patients.patient
encounters.PATIENT=patients.patient
medications.ENCOUNTER=encounters.ID
medications.PATIENT=patients.patient
procedures.ENCOUNTER=encounters.ID
procedures.PATIENT=patients.patient
claims.ENCOUNTER=encounters.ID
claims.PATIENT=patients.patient
observations.ENCOUNTER=encounters.ID
observations.PATIENT=patients.patient
careplans.ENCOUNTER=encounters.ID
careplans.PATIENT=patients.patient
immunizations.ENCOUNTER=encounters.ID
immunizations.PATIENT=patients.patient
conditions.ENCOUNTER=encounters.ID
conditions.PATIENT=patients.patient
conditions.DESCRIPTION=all_prevalences.ITEM

    【Question】
    Among the patients with prediabetes, how many are female? patients with prediabetes refer to PATIENT from conditions where DESCRIPTION = 'Prediabetes'; female refers to gender = 'F';

    【Evidence】
    patients with prediabetes refer to PATIENT from conditions where DESCRIPTION = 'Prediabetes'; female refers to gender = 'F';

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT COUNT(DISTINCT T2.patient) FROM conditions AS T1 INNER JOIN patients AS T2 ON T1.PATIENT = T2.patient WHERE T2.gender = 'F' AND T1.DESCRIPTION = 'Prediabetes'