##Instruction:
Database: financial
Total Tables: 8
Total Columns: 55

Table Schemas:

Database: financial
Total Tables: 8
Total Columns: 55

Table Schemas:

Table: account (4 columns)
--------------------------
• account_id (integer): the id of the account
• district_id (integer): location of branch
• frequency (text): frequency of the acount
• date (date): the creation date of the account
  Values: in the form YYMMDD

Table: card (4 columns)
-----------------------
• card_id (integer): id number of credit card
• disp_id (integer): disposition id
• type (text): type of credit card
  Values: "junior": junior class of credit card; 
"classic": standard class of credit card; 
"gold": high-leve...
• issued (date): the date when the credit card issued
  Values: in the form YYMMDD

Table: client (4 columns)
-------------------------
• client_id (integer): the unique number
• gender (text)
  Values: F：female 
M：male
• birth_date (date): birth date
• district_id (integer): location of branch

Table: disp (4 columns)
-----------------------
• disp_id (integer): unique number of identifying this row of record
• client_id (integer): id number of client
• account_id (integer): id number of account
• type (text): type of disposition
  Values: "OWNER" : "USER" : "DISPONENT"
commonsense evidence:
the account can only have the right to issue pe...

Table: district (16 columns)
----------------------------
• district_id (integer): location of branch
• A2 (text): district_name
• A3 (text): region
• A4 (text)
• A5 (text): municipality < district < region
• A6 (text): municipality < district < region
• A7 (text): municipality < district < region
• A8 (integer): municipality < district < region
• A9 (integer)
  Values: not useful
• A10 (real): ratio of urban inhabitants
• A11 (integer): average salary
• A12 (real): unemployment rate 1995
• A13 (real): unemployment rate 1996
• A14 (integer): no. of entrepreneurs per 1000 inhabitants
• A15 (integer): no. of committed crimes 1995
• A16 (integer): no. of committed crimes 1996

Table: loan (7 columns)
-----------------------
• loan_id (integer): the id number identifying the loan data
• account_id (integer): the id number identifying the account
• date (date): the date when the loan is approved
• amount (integer): approved amount
  Values: unit：US dollar
• duration (integer): loan duration
  Values: unit：month
• payments (real): monthly payments
  Values: unit：month
• status (text): repayment status
  Values: 'A' stands for contract finished, no problems;
'B' stands for contract finished, loan not paid;
'C' ...

Table: order (6 columns)
------------------------
• order_id (integer): identifying the unique order
• account_id (integer): id number of account
• bank_to (text): bank of the recipient
• account_to (integer): account of the recipient
  Values: each bank has unique two-letter code
• amount (real): debited amount
• k_symbol (text): purpose of the payment
  Values: "POJISTNE" stands for insurance payment
"SIPO" stands for household payment
"LEASING" stands for lea...

Table: trans (10 columns)
-------------------------
• trans_id (integer): transaction id
• account_id (integer)
• date (date): date of transaction
• type (text): +/- transaction
  Values: "PRIJEM" stands for credit
"VYDAJ" stands for withdrawal
• operation (text): mode of transaction
  Values: "VYBER KARTOU": credit card withdrawal
"VKLAD": credit in cash
"PREVOD Z UCTU" :collection from anot...
• amount (integer): amount of money
  Values: Unit：USD
• balance (integer): balance after transaction
  Values: Unit：USD
• k_symbol (text)
  Values: "POJISTNE": stands for insurrance payment
"SLUZBY": stands for payment for statement
"UROK": stands ...
• bank (text)
  Values: each bank has unique two-letter code
• account (integer)

##Evidence:
Percentage of male clients = (number of male clients / total number of clients) * 100. Male refers to gender = 'M'. A3 is the region name, A2 is the district name, and A4 contains the number of inhabitants.

##Role Access Policy (Column-Level):
Role: ClientServices
Accessible Columns: client: client_id, gender, birth_date, district_id; account: account_id, frequency, date; card: card_id, type, issued; disp: disp_id, account_id, type; district: district_id, A2, A3, A4

Based on the above schema and your role's column-level access policy, generate a SQL query to answer the question. If your role does not have access to the required columns, respond with: "Sorry, I cannot answer."


Question: What is the district name, number of inhabitants, total number of clients, number of male clients, and percentage of male clients for the most populated district in the south Bohemia region?
Evidence: Percentage of male clients = (number of male clients / total number of clients) * 100. Male refers to gender = 'M'. A3 is the region name, A2 is the district name, and A4 contains the number of inhabitants.
Answer: WITH RegionStats AS (
    SELECT 
        d.district_id,
        d.A3 AS region,
        CAST(d.A4 AS INTEGER) AS inhabitants,
        COUNT(DISTINCT c.client_id) AS total_clients,
        SUM(CASE WHEN c.gender = 'M' THEN 1 ELSE 0 END) AS male_clients,
        RANK() OVER (PARTITION BY d.A3 ORDER BY CAST(d.A4 AS INTEGER) DESC) AS population_rank
    FROM 
        district d
    LEFT JOIN 
        client c ON d.district_id = c.district_id
    WHERE 
        d.A3 = 'south Bohemia'
    GROUP BY 
        d.district_id, d.A3, d.A4
)
SELECT 
    rs.district_id,
    d.A2 AS district_name,
    rs.region,
    rs.inhabitants,
    rs.total_clients,
    rs.male_clients,
    CASE 
        WHEN rs.total_clients > 0 THEN ROUND((rs.male_clients * 100.0 / rs.total_clients), 2)
        ELSE 0 
    END AS pct_male
FROM 
    RegionStats rs
JOIN 
    district d ON rs.district_id = d.district_id
WHERE 
    rs.population_rank = 1
ORDER BY 
    rs.inhabitants DESC, rs.district_id
LIMIT 1;
Permission: allowed (All required columns are permitted)
Gold SQL: WITH RegionStats AS (
    SELECT 
        d.district_id,
        d.A3 AS region,
        CAST(d.A4 AS INTEGER) AS inhabitants,
        COUNT(DISTINCT c.client_id) AS total_clients,
        SUM(CASE WHEN c.gender = 'M' THEN 1 ELSE 0 END) AS male_clients,
        RANK() OVER (PARTITION BY d.A3 ORDER BY CAST(d.A4 AS INTEGER) DESC) AS population_rank
    FROM 
        district d
    LEFT JOIN 
        client c ON d.district_id = c.district_id
    WHERE 
        d.A3 = 'south Bohemia'
    GROUP BY 
        d.district_id, d.A3, d.A4
)
SELECT 
    rs.district_id,
    d.A2 AS district_name,
    rs.region,
    rs.inhabitants,
    rs.total_clients,
    rs.male_clients,
    CASE 
        WHEN rs.total_clients > 0 THEN ROUND((rs.male_clients * 100.0 / rs.total_clients), 2)
        ELSE 0 
    END AS pct_male
FROM 
    RegionStats rs
JOIN 
    district d ON rs.district_id = d.district_id
WHERE 
    rs.population_rank = 1
ORDER BY 
    rs.inhabitants DESC, rs.district_id
LIMIT 1;