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

    【Schema】
    【DB_ID】 financial
【Schema】
# Table: client
[
(client_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(gender:TEXT, Examples: [F, M]),
(birth_date:DATE, Examples: [1970-12-13]),
(district_id:INTEGER, Examples: [18, 1, 5])
]
# Table: card
[
(card_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(disp_id:INTEGER, Examples: [9, 19, 41]),
(type:TEXT, Examples: [gold, classic, junior]),
(issued:DATE, Examples: [1998-10-16])
]
# Table: disp
[
(disp_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(client_id:INTEGER, Examples: [1, 2, 3]),
(account_id:INTEGER, Examples: [1, 2, 3]),
(type:TEXT, Examples: [OWNER, DISPONENT])
]
# Table: account
[
(account_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(district_id:INTEGER, Examples: [18, 1, 5]),
(frequency:TEXT, Examples: [POPLATEK MESICNE, POPLATEK TYDNE, POPLATEK PO OBRATU]),
(date:DATE, Examples: [1995-03-24])
]
# Table: district
[
(district_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(A2:TEXT, Examples: [Hl.m. Praha, Benesov, Beroun]),
(A3:TEXT, Examples: [Prague, central Bohemia, south Bohemia]),
(A4:TEXT, Examples: [1204953, 88884, 75232]),
(A5:TEXT, Examples: [0, 80, 55]),
(A6:TEXT, Examples: [0, 26, 29]),
(A7:TEXT, Examples: [0, 6, 4]),
(A8:INTEGER, Examples: [1, 2, 3]),
(A9:INTEGER, Examples: [1, 5, 6]),
(A10:REAL, Examples: [100.0, 46.7, 41.7]),
(A11:INTEGER, Examples: [12541, 8507, 8980]),
(A12:REAL, Examples: [0.2, 1.6, 1.9]),
(A13:REAL, Examples: [0.43, 1.85, 2.21]),
(A14:INTEGER, Examples: [167, 132, 111]),
(A15:INTEGER, Examples: [85677, 2159, 2824]),
(A16:INTEGER, Examples: [99107, 2674, 2813])
]
# Table: order
[
(order_id:INTEGER, Primary Key, Examples: [29401, 29402, 29403]),
(account_id:INTEGER, Examples: [1, 2, 3]),
(bank_to:TEXT, Examples: [YZ, ST, QR]),
(account_to:INTEGER, Examples: [87144583, 89597016, 13943797]),
(amount:REAL, Examples: [2452.0, 3372.7, 7266.0]),
(k_symbol:TEXT, Examples: [SIPO, UVER, POJISTNE])
]
# Table: loan
[
(loan_id:INTEGER, Primary Key, Examples: [4959, 4961, 4962]),
(account_id:INTEGER, Examples: [2, 19, 25]),
(date:DATE, Examples: [1994-01-05]),
(amount:INTEGER, Examples: [80952, 30276, 318480]),
(duration:INTEGER, Examples: [24, 12, 60]),
(payments:REAL, Examples: [3373.0, 2523.0, 5308.0]),
(status:TEXT, Examples: [A, B, D])
]
# Table: trans
[
(trans_id:INTEGER, Primary Key, Examples: [1, 5, 6]),
(account_id:INTEGER, Examples: [1, 2, 3]),
(date:DATE, Examples: [1995-03-24]),
(type:TEXT, Examples: [PRIJEM, VYDAJ, VYBER]),
(operation:TEXT, Examples: [VKLAD, PREVOD Z UCTU, PREVOD NA UCET]),
(amount:INTEGER, Examples: [1000, 3679, 2452]),
(balance:INTEGER, Examples: [1000, 4679, 20977]),
(k_symbol:TEXT, Examples: [SIPO, SLUZBY, UVER]),
(bank:TEXT, Examples: [AB, YZ, ST]),
(account:INTEGER, Examples: [41403269, 87144583, 66487163])
]
【Foreign keys】
client.district_id=district.district_id
card.disp_id=disp.disp_id
disp.account_id=account.account_id
disp.client_id=client.client_id
account.district_id=district.district_id
order.account_id=account.account_id
loan.account_id=account.account_id
trans.account_id=account.account_id

    【Question】
    What proportion of customers who have accounts at the Prague branch are female? Female refers to gender = 'F'; Percentage of female clients in Prague branch = count[female clients with accounts in Prague branch / count(clients with accounts in Prague branch)] * 100%; A3 may contain information about Prague

    【Evidence】
    Female refers to gender = 'F'; Percentage of female clients in Prague branch = count[female clients with accounts in Prague branch / count(clients with accounts in Prague branch)] * 100%; A3 may contain information about Prague

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

SQL: SELECT CAST(SUM(T2.gender = 'F') AS REAL) / COUNT(T2.client_id) * 100 FROM district AS T1 INNER JOIN client AS T2 ON T1.district_id = T2.district_id WHERE T1.A3 = 'Prague'