##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:
PRIJEM refers to incoming transactions (credits), VYDAJ refers to outgoing transactions (debits). Account district refers to the district where the account was opened, which may differ from the client's residence district.

##Role Access Policy (Column-Level):
Role: DataOperator_1
Accessible Columns: account: date, district_id, frequency; card: ALL COLUMNS; disp: ALL COLUMNS; district: A10, A13, A14, A16, A2, A5, A6, A7, A9, district_id; loan: account_id, date, loan_id, status; order: account_id, account_to, amount, bank_to, k_symbol; trans: ALL COLUMNS

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: For the female client born on January 29, 1976, provide a comprehensive analysis of all her owned accounts including their locations, transaction activity, financial products, and regional economic indicators.
Evidence: PRIJEM refers to incoming transactions (credits), VYDAJ refers to outgoing transactions (debits). Account district refers to the district where the account was opened, which may differ from the client's residence district.
Answer: Sorry, I cannot answer.
Permission: denied (Missing column permissions: district: A11, A3, account_id, disp_id; account: account_id)
Gold SQL: WITH female_client AS (
    SELECT 
        c.client_id, 
        c.birth_date, 
        c.district_id,
        d.A2 AS district_name
    FROM client c
    JOIN district d ON c.district_id = d.district_id
    WHERE c.birth_date = '1976-01-29' AND c.gender = 'F'
),
client_accounts AS (
    SELECT 
        fc.client_id,
        fc.district_name AS residence_district,
        a.account_id,
        a.date AS account_open_date,
        d2.A2 AS account_district,
        d2.A3 AS account_region,
        CASE 
            WHEN fc.district_id = a.district_id THEN 'Same as residence'
            ELSE 'Different from residence'
        END AS district_comparison,
        ROW_NUMBER() OVER (PARTITION BY fc.client_id ORDER BY a.date) AS account_order
    FROM female_client fc
    JOIN disp dp ON fc.client_id = dp.client_id
    JOIN account a ON dp.account_id = a.account_id
    JOIN district d2 ON a.district_id = d2.district_id
    WHERE dp.type = 'OWNER'
),
account_transactions AS (
    SELECT
        ca.client_id,
        ca.account_id,
        ca.account_district,
        COUNT(t.trans_id) AS transaction_count,
        SUM(CASE WHEN t.type = 'PRIJEM' THEN t.amount ELSE 0 END) AS total_income,
        SUM(CASE WHEN t.type = 'VYDAJ' THEN t.amount ELSE 0 END) AS total_expense,
        MAX(t.balance) AS max_balance,
        COUNT(DISTINCT t.k_symbol) AS distinct_transaction_types
    FROM client_accounts ca
    LEFT JOIN trans t ON ca.account_id = t.account_id
    GROUP BY ca.client_id, ca.account_id, ca.account_district
)

SELECT 
    ca.residence_district,
    ca.account_district,
    ca.district_comparison,
    ca.account_open_date,
    at.transaction_count,
    at.total_income,
    at.total_expense,
    at.max_balance,
    (SELECT COUNT(*) FROM loan l WHERE l.account_id = ca.account_id) AS loan_count,
    (SELECT COUNT(*) FROM card c JOIN disp d ON c.disp_id = d.disp_id WHERE d.account_id = ca.account_id) AS card_count,
    d.A3 AS region,
    d.A10 AS urban_ratio,
    d.A11 AS avg_salary,
    d.A14 AS entrepreneurs_per_1000
FROM client_accounts ca
JOIN account_transactions at ON ca.account_id = at.account_id
JOIN district d ON ca.account_district = d.A2
ORDER BY ca.account_open_date;