QIAN-ME/bird_schema_data huggingface dataset:
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 software_company
【Schema】
# Table: Customers
[
(ID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(SEX:TEXT, Examples: [Male, Female]),
(MARITAL_STATUS:TEXT, Examples: [Never-married, Married-civ-spouse, Divorced]),
(GEOID:INTEGER, Examples: [61, 70, 53]),
(EDUCATIONNUM:INTEGER, Examples: [7, 3, 4]),
(OCCUPATION:TEXT, Examples: [Machine-op-inspct, Handlers-cleaners, Exec-managerial]),
(age:INTEGER, Examples: [62, 78, 69])
]
# Table: Mailings1_2
[
(REFID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(REF_DATE:DATETIME, Examples: [2007-02-01 12:00:00]),
(RESPONSE:TEXT, Examples: [false, true])
]
# Table: mailings3
[
(REFID:INTEGER, Primary Key, Examples: [60000, 60001, 60002]),
(REF_DATE:DATETIME, Examples: [2007-07-01 12:00:00]),
(RESPONSE:TEXT, Examples: [false, true])
]
# Table: Demog
[
(GEOID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(INHABITANTS_K:REAL, Examples: [30.046, 36.25, 47.645]),
(INCOME_K:REAL, Examples: [2631.47, 3012.75, 2192.41]),
(A_VAR1:REAL, Examples: [6.084, 4.604, 4.911]),
(A_VAR2:REAL, Examples: [5.79, 8.309, 8.557]),
(A_VAR3:REAL, Examples: [8.595, 6.007, 5.934]),
(A_VAR4:REAL, Examples: [3.935, 5.938, 6.494]),
(A_VAR5:REAL, Examples: [6.362, 8.773, 9.172]),
(A_VAR6:REAL, Examples: [8.626, 3.579, 3.202]),
(A_VAR7:REAL, Examples: [4.624, 6.349, 6.157]),
(A_VAR8:REAL, Examples: [8.324, 4.694, 4.822]),
(A_VAR9:REAL, Examples: [5.233, 6.884, 7.942]),
(A_VAR10:REAL, Examples: [6.232, 7.062, 7.901]),
(A_VAR11:REAL, Examples: [5.205, 7.319, 7.928]),
(A_VAR12:REAL, Examples: [8.231, 3.72, 2.33]),
(A_VAR13:REAL, Examples: [6.746, 6.405, 6.029]),
(A_VAR14:REAL, Examples: [8.679, 7.202, 6.455]),
(A_VAR15:REAL, Examples: [5.292, 4.932, 4.72]),
(A_VAR16:REAL, Examples: [3.5, 7.969, 8.564]),
(A_VAR17:REAL, Examples: [5.512, 8.15, 8.342]),
(A_VAR18:REAL, Examples: [5.783, 5.633, 4.938])
]
# Table: Sales
[
(EVENTID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(REFID:INTEGER, Examples: [0, 1, 2]),
(EVENT_DATE:DATETIME, Examples: [2006-12-21 12:00:00]),
(AMOUNT:REAL, Examples: [17.907, 17.401, 13.277])
]
【Foreign keys】
Customers.GEOID=Demog.GEOID
Mailings1_2.REFID=Customers.ID
Sales.REFID=Customers.ID

    【Question】
    List the income and number of inhabitants of customers with an age greater than the 80% of average age of all customers? age greater than the 80% of average age refers to age > (AVG(age) * 0.8); income refers to INCOME_K; number of inhabitants refers to INHABITANTS_K;

    【Evidence】
    age greater than the 80% of average age refers to age > (AVG(age) * 0.8); income refers to INCOME_K; number of inhabitants refers to INHABITANTS_K;

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

SQL: SELECT T2.INCOME_K, T2.INHABITANTS_K FROM Customers AS T1 INNER JOIN Demog AS T2 ON T1.GEOID = T2.GEOID GROUP BY T2.INCOME_K, T2.INHABITANTS_K HAVING T1.age > 0.8 * AVG(T1.age)
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 software_company
【Schema】
# Table: Customers
[
(ID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(SEX:TEXT, Examples: [Male, Female]),
(MARITAL_STATUS:TEXT, Examples: [Never-married, Married-civ-spouse, Divorced]),
(GEOID:INTEGER, Examples: [61, 70, 53]),
(EDUCATIONNUM:INTEGER, Examples: [7, 3, 4]),
(OCCUPATION:TEXT, Examples: [Machine-op-inspct, Handlers-cleaners, Exec-managerial]),
(age:INTEGER, Examples: [62, 78, 69])
]
# Table: Mailings1_2
[
(REFID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(REF_DATE:DATETIME, Examples: [2007-02-01 12:00:00]),
(RESPONSE:TEXT, Examples: [false, true])
]
# Table: mailings3
[
(REFID:INTEGER, Primary Key, Examples: [60000, 60001, 60002]),
(REF_DATE:DATETIME, Examples: [2007-07-01 12:00:00]),
(RESPONSE:TEXT, Examples: [false, true])
]
# Table: Demog
[
(GEOID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(INHABITANTS_K:REAL, Examples: [30.046, 36.25, 47.645]),
(INCOME_K:REAL, Examples: [2631.47, 3012.75, 2192.41]),
(A_VAR1:REAL, Examples: [6.084, 4.604, 4.911]),
(A_VAR2:REAL, Examples: [5.79, 8.309, 8.557]),
(A_VAR3:REAL, Examples: [8.595, 6.007, 5.934]),
(A_VAR4:REAL, Examples: [3.935, 5.938, 6.494]),
(A_VAR5:REAL, Examples: [6.362, 8.773, 9.172]),
(A_VAR6:REAL, Examples: [8.626, 3.579, 3.202]),
(A_VAR7:REAL, Examples: [4.624, 6.349, 6.157]),
(A_VAR8:REAL, Examples: [8.324, 4.694, 4.822]),
(A_VAR9:REAL, Examples: [5.233, 6.884, 7.942]),
(A_VAR10:REAL, Examples: [6.232, 7.062, 7.901]),
(A_VAR11:REAL, Examples: [5.205, 7.319, 7.928]),
(A_VAR12:REAL, Examples: [8.231, 3.72, 2.33]),
(A_VAR13:REAL, Examples: [6.746, 6.405, 6.029]),
(A_VAR14:REAL, Examples: [8.679, 7.202, 6.455]),
(A_VAR15:REAL, Examples: [5.292, 4.932, 4.72]),
(A_VAR16:REAL, Examples: [3.5, 7.969, 8.564]),
(A_VAR17:REAL, Examples: [5.512, 8.15, 8.342]),
(A_VAR18:REAL, Examples: [5.783, 5.633, 4.938])
]
# Table: Sales
[
(EVENTID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(REFID:INTEGER, Examples: [0, 1, 2]),
(EVENT_DATE:DATETIME, Examples: [2006-12-21 12:00:00]),
(AMOUNT:REAL, Examples: [17.907, 17.401, 13.277])
]
【Foreign keys】
Customers.GEOID=Demog.GEOID
Mailings1_2.REFID=Customers.ID
Sales.REFID=Customers.ID

    【Question】
    In customers with marital status of never married, what is the percentage of customers with income of 2500 and above? DIVIDE(COUNT(INCOME_K ≥ 2500 where MARITAL_STATUS = 'Never-married'), COUNT(INCOME_K where MARITAL_STATUS = 'Never-married')) as percentage;

    【Evidence】
    DIVIDE(COUNT(INCOME_K ≥ 2500 where MARITAL_STATUS = 'Never-married'), COUNT(INCOME_K where MARITAL_STATUS = 'Never-married')) as percentage;

    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(CASE WHEN T2.INCOME_K > 2500 THEN 1.0 ELSE 0 END) AS REAL) * 100 / COUNT(*) FROM Customers AS T1 INNER JOIN Demog AS T2 ON T1.GEOID = T2.GEOID WHERE T1.MARITAL_STATUS = 'Never-married'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 software_company
【Schema】
# Table: Customers
[
(ID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(SEX:TEXT, Examples: [Male, Female]),
(MARITAL_STATUS:TEXT, Examples: [Never-married, Married-civ-spouse, Divorced]),
(GEOID:INTEGER, Examples: [61, 70, 53]),
(EDUCATIONNUM:INTEGER, Examples: [7, 3, 4]),
(OCCUPATION:TEXT, Examples: [Machine-op-inspct, Handlers-cleaners, Exec-managerial]),
(age:INTEGER, Examples: [62, 78, 69])
]
# Table: Mailings1_2
[
(REFID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(REF_DATE:DATETIME, Examples: [2007-02-01 12:00:00]),
(RESPONSE:TEXT, Examples: [false, true])
]
# Table: mailings3
[
(REFID:INTEGER, Primary Key, Examples: [60000, 60001, 60002]),
(REF_DATE:DATETIME, Examples: [2007-07-01 12:00:00]),
(RESPONSE:TEXT, Examples: [false, true])
]
# Table: Demog
[
(GEOID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(INHABITANTS_K:REAL, Examples: [30.046, 36.25, 47.645]),
(INCOME_K:REAL, Examples: [2631.47, 3012.75, 2192.41]),
(A_VAR1:REAL, Examples: [6.084, 4.604, 4.911]),
(A_VAR2:REAL, Examples: [5.79, 8.309, 8.557]),
(A_VAR3:REAL, Examples: [8.595, 6.007, 5.934]),
(A_VAR4:REAL, Examples: [3.935, 5.938, 6.494]),
(A_VAR5:REAL, Examples: [6.362, 8.773, 9.172]),
(A_VAR6:REAL, Examples: [8.626, 3.579, 3.202]),
(A_VAR7:REAL, Examples: [4.624, 6.349, 6.157]),
(A_VAR8:REAL, Examples: [8.324, 4.694, 4.822]),
(A_VAR9:REAL, Examples: [5.233, 6.884, 7.942]),
(A_VAR10:REAL, Examples: [6.232, 7.062, 7.901]),
(A_VAR11:REAL, Examples: [5.205, 7.319, 7.928]),
(A_VAR12:REAL, Examples: [8.231, 3.72, 2.33]),
(A_VAR13:REAL, Examples: [6.746, 6.405, 6.029]),
(A_VAR14:REAL, Examples: [8.679, 7.202, 6.455]),
(A_VAR15:REAL, Examples: [5.292, 4.932, 4.72]),
(A_VAR16:REAL, Examples: [3.5, 7.969, 8.564]),
(A_VAR17:REAL, Examples: [5.512, 8.15, 8.342]),
(A_VAR18:REAL, Examples: [5.783, 5.633, 4.938])
]
# Table: Sales
[
(EVENTID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(REFID:INTEGER, Examples: [0, 1, 2]),
(EVENT_DATE:DATETIME, Examples: [2006-12-21 12:00:00]),
(AMOUNT:REAL, Examples: [17.907, 17.401, 13.277])
]
【Foreign keys】
Customers.GEOID=Demog.GEOID
Mailings1_2.REFID=Customers.ID
Sales.REFID=Customers.ID

    【Question】
    Find and list the id and geographic ID of the elderly customers with an education level below 3. elderly customers with an education level below 3 refer to age > 65 where EDUCATIONNUM < 3; geographic ID refers to GEOID;

    【Evidence】
    elderly customers with an education level below 3 refer to age > 65 where EDUCATIONNUM < 3; geographic ID refers to GEOID;

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

SQL: SELECT ID, GEOID FROM Customers WHERE EDUCATIONNUM < 3 AND age > 65
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 software_company
【Schema】
# Table: Customers
[
(ID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(SEX:TEXT, Examples: [Male, Female]),
(MARITAL_STATUS:TEXT, Examples: [Never-married, Married-civ-spouse, Divorced]),
(GEOID:INTEGER, Examples: [61, 70, 53]),
(EDUCATIONNUM:INTEGER, Examples: [7, 3, 4]),
(OCCUPATION:TEXT, Examples: [Machine-op-inspct, Handlers-cleaners, Exec-managerial]),
(age:INTEGER, Examples: [62, 78, 69])
]
# Table: Mailings1_2
[
(REFID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(REF_DATE:DATETIME, Examples: [2007-02-01 12:00:00]),
(RESPONSE:TEXT, Examples: [false, true])
]
# Table: mailings3
[
(REFID:INTEGER, Primary Key, Examples: [60000, 60001, 60002]),
(REF_DATE:DATETIME, Examples: [2007-07-01 12:00:00]),
(RESPONSE:TEXT, Examples: [false, true])
]
# Table: Demog
[
(GEOID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(INHABITANTS_K:REAL, Examples: [30.046, 36.25, 47.645]),
(INCOME_K:REAL, Examples: [2631.47, 3012.75, 2192.41]),
(A_VAR1:REAL, Examples: [6.084, 4.604, 4.911]),
(A_VAR2:REAL, Examples: [5.79, 8.309, 8.557]),
(A_VAR3:REAL, Examples: [8.595, 6.007, 5.934]),
(A_VAR4:REAL, Examples: [3.935, 5.938, 6.494]),
(A_VAR5:REAL, Examples: [6.362, 8.773, 9.172]),
(A_VAR6:REAL, Examples: [8.626, 3.579, 3.202]),
(A_VAR7:REAL, Examples: [4.624, 6.349, 6.157]),
(A_VAR8:REAL, Examples: [8.324, 4.694, 4.822]),
(A_VAR9:REAL, Examples: [5.233, 6.884, 7.942]),
(A_VAR10:REAL, Examples: [6.232, 7.062, 7.901]),
(A_VAR11:REAL, Examples: [5.205, 7.319, 7.928]),
(A_VAR12:REAL, Examples: [8.231, 3.72, 2.33]),
(A_VAR13:REAL, Examples: [6.746, 6.405, 6.029]),
(A_VAR14:REAL, Examples: [8.679, 7.202, 6.455]),
(A_VAR15:REAL, Examples: [5.292, 4.932, 4.72]),
(A_VAR16:REAL, Examples: [3.5, 7.969, 8.564]),
(A_VAR17:REAL, Examples: [5.512, 8.15, 8.342]),
(A_VAR18:REAL, Examples: [5.783, 5.633, 4.938])
]
# Table: Sales
[
(EVENTID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(REFID:INTEGER, Examples: [0, 1, 2]),
(EVENT_DATE:DATETIME, Examples: [2006-12-21 12:00:00]),
(AMOUNT:REAL, Examples: [17.907, 17.401, 13.277])
]
【Foreign keys】
Customers.GEOID=Demog.GEOID
Mailings1_2.REFID=Customers.ID
Sales.REFID=Customers.ID

    【Question】
    List the geographic id of places where the income is above average. geographic ID refers to GEOID; income is above average refers to INCOME_K > DIVIDE(SUM(INCOME_K), COUNT(GEOID));

    【Evidence】
    geographic ID refers to GEOID; income is above average refers to INCOME_K > DIVIDE(SUM(INCOME_K), COUNT(GEOID));

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

SQL: SELECT AVG(INCOME_K) FROM Demog
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 software_company
【Schema】
# Table: Customers
[
(ID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(SEX:TEXT, Examples: [Male, Female]),
(MARITAL_STATUS:TEXT, Examples: [Never-married, Married-civ-spouse, Divorced]),
(GEOID:INTEGER, Examples: [61, 70, 53]),
(EDUCATIONNUM:INTEGER, Examples: [7, 3, 4]),
(OCCUPATION:TEXT, Examples: [Machine-op-inspct, Handlers-cleaners, Exec-managerial]),
(age:INTEGER, Examples: [62, 78, 69])
]
# Table: Mailings1_2
[
(REFID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(REF_DATE:DATETIME, Examples: [2007-02-01 12:00:00]),
(RESPONSE:TEXT, Examples: [false, true])
]
# Table: mailings3
[
(REFID:INTEGER, Primary Key, Examples: [60000, 60001, 60002]),
(REF_DATE:DATETIME, Examples: [2007-07-01 12:00:00]),
(RESPONSE:TEXT, Examples: [false, true])
]
# Table: Demog
[
(GEOID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(INHABITANTS_K:REAL, Examples: [30.046, 36.25, 47.645]),
(INCOME_K:REAL, Examples: [2631.47, 3012.75, 2192.41]),
(A_VAR1:REAL, Examples: [6.084, 4.604, 4.911]),
(A_VAR2:REAL, Examples: [5.79, 8.309, 8.557]),
(A_VAR3:REAL, Examples: [8.595, 6.007, 5.934]),
(A_VAR4:REAL, Examples: [3.935, 5.938, 6.494]),
(A_VAR5:REAL, Examples: [6.362, 8.773, 9.172]),
(A_VAR6:REAL, Examples: [8.626, 3.579, 3.202]),
(A_VAR7:REAL, Examples: [4.624, 6.349, 6.157]),
(A_VAR8:REAL, Examples: [8.324, 4.694, 4.822]),
(A_VAR9:REAL, Examples: [5.233, 6.884, 7.942]),
(A_VAR10:REAL, Examples: [6.232, 7.062, 7.901]),
(A_VAR11:REAL, Examples: [5.205, 7.319, 7.928]),
(A_VAR12:REAL, Examples: [8.231, 3.72, 2.33]),
(A_VAR13:REAL, Examples: [6.746, 6.405, 6.029]),
(A_VAR14:REAL, Examples: [8.679, 7.202, 6.455]),
(A_VAR15:REAL, Examples: [5.292, 4.932, 4.72]),
(A_VAR16:REAL, Examples: [3.5, 7.969, 8.564]),
(A_VAR17:REAL, Examples: [5.512, 8.15, 8.342]),
(A_VAR18:REAL, Examples: [5.783, 5.633, 4.938])
]
# Table: Sales
[
(EVENTID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(REFID:INTEGER, Examples: [0, 1, 2]),
(EVENT_DATE:DATETIME, Examples: [2006-12-21 12:00:00]),
(AMOUNT:REAL, Examples: [17.907, 17.401, 13.277])
]
【Foreign keys】
Customers.GEOID=Demog.GEOID
Mailings1_2.REFID=Customers.ID
Sales.REFID=Customers.ID

    【Question】
    Calculate the number of customers who did not respond in February of 2007. did not respond refers to RESPONSE = 'false'; February of 2007 refers to REF_DATE BETWEEN '2007-02-01 12:00:00.0'AND '2007-02-28 12:00:00.0';

    【Evidence】
    did not respond refers to RESPONSE = 'false'; February of 2007 refers to REF_DATE BETWEEN '2007-02-01 12:00:00.0'AND '2007-02-28 12:00:00.0';

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

SQL: SELECT COUNT(REFID) custmoer_number FROM Mailings1_2 WHERE RESPONSE = 'false' AND REF_DATE BETWEEN '2007-02-01' AND '2007-02-28'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 software_company
【Schema】
# Table: Customers
[
(ID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(SEX:TEXT, Examples: [Male, Female]),
(MARITAL_STATUS:TEXT, Examples: [Never-married, Married-civ-spouse, Divorced]),
(GEOID:INTEGER, Examples: [61, 70, 53]),
(EDUCATIONNUM:INTEGER, Examples: [7, 3, 4]),
(OCCUPATION:TEXT, Examples: [Machine-op-inspct, Handlers-cleaners, Exec-managerial]),
(age:INTEGER, Examples: [62, 78, 69])
]
# Table: Mailings1_2
[
(REFID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(REF_DATE:DATETIME, Examples: [2007-02-01 12:00:00]),
(RESPONSE:TEXT, Examples: [false, true])
]
# Table: mailings3
[
(REFID:INTEGER, Primary Key, Examples: [60000, 60001, 60002]),
(REF_DATE:DATETIME, Examples: [2007-07-01 12:00:00]),
(RESPONSE:TEXT, Examples: [false, true])
]
# Table: Demog
[
(GEOID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(INHABITANTS_K:REAL, Examples: [30.046, 36.25, 47.645]),
(INCOME_K:REAL, Examples: [2631.47, 3012.75, 2192.41]),
(A_VAR1:REAL, Examples: [6.084, 4.604, 4.911]),
(A_VAR2:REAL, Examples: [5.79, 8.309, 8.557]),
(A_VAR3:REAL, Examples: [8.595, 6.007, 5.934]),
(A_VAR4:REAL, Examples: [3.935, 5.938, 6.494]),
(A_VAR5:REAL, Examples: [6.362, 8.773, 9.172]),
(A_VAR6:REAL, Examples: [8.626, 3.579, 3.202]),
(A_VAR7:REAL, Examples: [4.624, 6.349, 6.157]),
(A_VAR8:REAL, Examples: [8.324, 4.694, 4.822]),
(A_VAR9:REAL, Examples: [5.233, 6.884, 7.942]),
(A_VAR10:REAL, Examples: [6.232, 7.062, 7.901]),
(A_VAR11:REAL, Examples: [5.205, 7.319, 7.928]),
(A_VAR12:REAL, Examples: [8.231, 3.72, 2.33]),
(A_VAR13:REAL, Examples: [6.746, 6.405, 6.029]),
(A_VAR14:REAL, Examples: [8.679, 7.202, 6.455]),
(A_VAR15:REAL, Examples: [5.292, 4.932, 4.72]),
(A_VAR16:REAL, Examples: [3.5, 7.969, 8.564]),
(A_VAR17:REAL, Examples: [5.512, 8.15, 8.342]),
(A_VAR18:REAL, Examples: [5.783, 5.633, 4.938])
]
# Table: Sales
[
(EVENTID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(REFID:INTEGER, Examples: [0, 1, 2]),
(EVENT_DATE:DATETIME, Examples: [2006-12-21 12:00:00]),
(AMOUNT:REAL, Examples: [17.907, 17.401, 13.277])
]
【Foreign keys】
Customers.GEOID=Demog.GEOID
Mailings1_2.REFID=Customers.ID
Sales.REFID=Customers.ID

    【Question】
    How many teenagers are working as Machine-op-inspct? teenager is a person aged between 13 and 19 years; OCCUPATION = 'Machine-op-inspct';

    【Evidence】
    teenager is a person aged between 13 and 19 years; OCCUPATION = 'Machine-op-inspct';

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

SQL: SELECT COUNT(ID) teenager_number FROM Customers WHERE OCCUPATION = 'Machine-op-inspct' AND age >= 13 AND age <= 19
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 software_company
【Schema】
# Table: Customers
[
(ID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(SEX:TEXT, Examples: [Male, Female]),
(MARITAL_STATUS:TEXT, Examples: [Never-married, Married-civ-spouse, Divorced]),
(GEOID:INTEGER, Examples: [61, 70, 53]),
(EDUCATIONNUM:INTEGER, Examples: [7, 3, 4]),
(OCCUPATION:TEXT, Examples: [Machine-op-inspct, Handlers-cleaners, Exec-managerial]),
(age:INTEGER, Examples: [62, 78, 69])
]
# Table: Mailings1_2
[
(REFID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(REF_DATE:DATETIME, Examples: [2007-02-01 12:00:00]),
(RESPONSE:TEXT, Examples: [false, true])
]
# Table: mailings3
[
(REFID:INTEGER, Primary Key, Examples: [60000, 60001, 60002]),
(REF_DATE:DATETIME, Examples: [2007-07-01 12:00:00]),
(RESPONSE:TEXT, Examples: [false, true])
]
# Table: Demog
[
(GEOID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(INHABITANTS_K:REAL, Examples: [30.046, 36.25, 47.645]),
(INCOME_K:REAL, Examples: [2631.47, 3012.75, 2192.41]),
(A_VAR1:REAL, Examples: [6.084, 4.604, 4.911]),
(A_VAR2:REAL, Examples: [5.79, 8.309, 8.557]),
(A_VAR3:REAL, Examples: [8.595, 6.007, 5.934]),
(A_VAR4:REAL, Examples: [3.935, 5.938, 6.494]),
(A_VAR5:REAL, Examples: [6.362, 8.773, 9.172]),
(A_VAR6:REAL, Examples: [8.626, 3.579, 3.202]),
(A_VAR7:REAL, Examples: [4.624, 6.349, 6.157]),
(A_VAR8:REAL, Examples: [8.324, 4.694, 4.822]),
(A_VAR9:REAL, Examples: [5.233, 6.884, 7.942]),
(A_VAR10:REAL, Examples: [6.232, 7.062, 7.901]),
(A_VAR11:REAL, Examples: [5.205, 7.319, 7.928]),
(A_VAR12:REAL, Examples: [8.231, 3.72, 2.33]),
(A_VAR13:REAL, Examples: [6.746, 6.405, 6.029]),
(A_VAR14:REAL, Examples: [8.679, 7.202, 6.455]),
(A_VAR15:REAL, Examples: [5.292, 4.932, 4.72]),
(A_VAR16:REAL, Examples: [3.5, 7.969, 8.564]),
(A_VAR17:REAL, Examples: [5.512, 8.15, 8.342]),
(A_VAR18:REAL, Examples: [5.783, 5.633, 4.938])
]
# Table: Sales
[
(EVENTID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(REFID:INTEGER, Examples: [0, 1, 2]),
(EVENT_DATE:DATETIME, Examples: [2006-12-21 12:00:00]),
(AMOUNT:REAL, Examples: [17.907, 17.401, 13.277])
]
【Foreign keys】
Customers.GEOID=Demog.GEOID
Mailings1_2.REFID=Customers.ID
Sales.REFID=Customers.ID

    【Question】
    Of customers who provide other services, how many are from places where inhabitants are more than 20000? OCCUPATION = 'Other-service'; inhabitants are more than 20000 refer to INHABITANTS_K > 20;

    【Evidence】
    OCCUPATION = 'Other-service'; inhabitants are more than 20000 refer to INHABITANTS_K > 20;

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

SQL: SELECT COUNT(T2.GEOID) FROM Customers AS T1 INNER JOIN Demog AS T2 ON T1.GEOID = T2.GEOID WHERE T1.OCCUPATION = 'Other-service' AND T2.INHABITANTS_K > 20
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 software_company
【Schema】
# Table: Customers
[
(ID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(SEX:TEXT, Examples: [Male, Female]),
(MARITAL_STATUS:TEXT, Examples: [Never-married, Married-civ-spouse, Divorced]),
(GEOID:INTEGER, Examples: [61, 70, 53]),
(EDUCATIONNUM:INTEGER, Examples: [7, 3, 4]),
(OCCUPATION:TEXT, Examples: [Machine-op-inspct, Handlers-cleaners, Exec-managerial]),
(age:INTEGER, Examples: [62, 78, 69])
]
# Table: Mailings1_2
[
(REFID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(REF_DATE:DATETIME, Examples: [2007-02-01 12:00:00]),
(RESPONSE:TEXT, Examples: [false, true])
]
# Table: mailings3
[
(REFID:INTEGER, Primary Key, Examples: [60000, 60001, 60002]),
(REF_DATE:DATETIME, Examples: [2007-07-01 12:00:00]),
(RESPONSE:TEXT, Examples: [false, true])
]
# Table: Demog
[
(GEOID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(INHABITANTS_K:REAL, Examples: [30.046, 36.25, 47.645]),
(INCOME_K:REAL, Examples: [2631.47, 3012.75, 2192.41]),
(A_VAR1:REAL, Examples: [6.084, 4.604, 4.911]),
(A_VAR2:REAL, Examples: [5.79, 8.309, 8.557]),
(A_VAR3:REAL, Examples: [8.595, 6.007, 5.934]),
(A_VAR4:REAL, Examples: [3.935, 5.938, 6.494]),
(A_VAR5:REAL, Examples: [6.362, 8.773, 9.172]),
(A_VAR6:REAL, Examples: [8.626, 3.579, 3.202]),
(A_VAR7:REAL, Examples: [4.624, 6.349, 6.157]),
(A_VAR8:REAL, Examples: [8.324, 4.694, 4.822]),
(A_VAR9:REAL, Examples: [5.233, 6.884, 7.942]),
(A_VAR10:REAL, Examples: [6.232, 7.062, 7.901]),
(A_VAR11:REAL, Examples: [5.205, 7.319, 7.928]),
(A_VAR12:REAL, Examples: [8.231, 3.72, 2.33]),
(A_VAR13:REAL, Examples: [6.746, 6.405, 6.029]),
(A_VAR14:REAL, Examples: [8.679, 7.202, 6.455]),
(A_VAR15:REAL, Examples: [5.292, 4.932, 4.72]),
(A_VAR16:REAL, Examples: [3.5, 7.969, 8.564]),
(A_VAR17:REAL, Examples: [5.512, 8.15, 8.342]),
(A_VAR18:REAL, Examples: [5.783, 5.633, 4.938])
]
# Table: Sales
[
(EVENTID:INTEGER, Primary Key, Examples: [0, 1, 2]),
(REFID:INTEGER, Examples: [0, 1, 2]),
(EVENT_DATE:DATETIME, Examples: [2006-12-21 12:00:00]),
(AMOUNT:REAL, Examples: [17.907, 17.401, 13.277])
]
【Foreign keys】
Customers.GEOID=Demog.GEOID
Mailings1_2.REFID=Customers.ID
Sales.REFID=Customers.ID

    【Question】
    Among the male customer in their twenties, how many are from places where the average income is more than 3000? male customer in their twenties refer to SEX = 'Male' where age BETWEEN 20 AND 29; average income is more than 3000 refers to INCOME_K > 3000;

    【Evidence】
    male customer in their twenties refer to SEX = 'Male' where age BETWEEN 20 AND 29; average income is more than 3000 refers to INCOME_K > 3000;

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

SQL: SELECT COUNT(T2.GEOID) FROM Customers AS T1 INNER JOIN Demog AS T2 ON T1.GEOID = T2.GEOID WHERE T1.SEX = 'Male' AND T2.INCOME_K > 3000 AND T1.age >= 20 AND T1.age <= 29