thai_instruction
stringlengths 22
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| eng_instruction
stringlengths 33
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| table
stringclasses 1
value | sql
float64 | pandas
stringlengths 13
130
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stringclasses 1
value |
|---|---|---|---|---|---|
ผู้ที่ซื้อ Canon EOS เป็นผู้ดำเนินการตั๋วจำนวนเท่าใด | How many ticket ID were conducted by the person who purchased Canon EOS? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | df[df['Product_Purchased'] == 'Canon EOS'].shape[0] | customer |
บุคคลที่ซื้อ HP Pavilion เป็นผู้ดำเนินการตั๋วจำนวนเท่าใด | How many ticket ID were conducted by the person who purchased HP Pavilion? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | df[df['Product_Purchased'] == 'HP Pavilion'].shape[0] | customer |
รหัสตั๋วมีลำดับความสำคัญปานกลางกี่รหัส | How many ticket ID were in Medium priority? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | df[df['Ticket_Priority'] == 'Medium'].shape[0] | customer |
มี Ticket ID กี่ใบที่อยู่ในลำดับความสำคัญวิกฤต | How many ticket ID were in Critical priority? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | df[df['Ticket_Priority'] == 'Critical'].shape[0] | customer |
รหัสตั๋วมีลำดับความสำคัญสูงจำนวนเท่าใด | How many ticket ID were in High priority? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | df[df['Ticket_Priority'] == 'High'].shape[0] | customer |
รหัสตั๋วที่มีลำดับความสำคัญต่ำมีกี่รหัส | How many ticket ID were in Low priority? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | df[df['Ticket_Priority'] == 'Low'].shape[0] | customer |
Ticket ID กี่ใบที่เป็นปัญหาทางเทคนิค | How many ticket ID that is Technical issue? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | df[df['Ticket_Type'] == 'Technical issue'].shape[0] | customer |
รหัสตั๋วกี่ใบที่สามารถขอคืนเงินได้? | How many ticket ID that is Refund request? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | df[df['Ticket_Type'] == 'Refund request'].shape[0] | customer |
รหัสตั๋วกี่ใบที่เป็นคำขอยกเลิก? | How many ticket ID that is Cancellation request? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | df[df['Ticket_Type'] == 'Cancellation request'].shape[0] | customer |
มี Ticket ID กี่ใบที่สอบถาม Billing? | How many ticket ID that is Billing inquiry? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | df[df['Ticket_Type'] == 'Billing inquiry'].shape[0] | customer |
รหัสตั๋วกี่ใบที่สอบถามผลิตภัณฑ์? | How many ticket ID that is Product inquiry? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | df[df['Ticket_Type'] == 'Product inquiry'].shape[0] | customer |
รหัสตั๋วที่มาจากลูกค้ารายอื่นมีกี่รหัส? | How many ticket ID that come from Other customer? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | df[df['Customer_Gender'] == 'Other'].shape[0] | customer |
รหัสตั๋วที่มาจากลูกค้าชายมีกี่ใบ? | How many ticket ID that come from Male customer? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | df[df['Customer_Gender'] == 'Male'].shape[0] | customer |
Ticket ID ที่มาจากลูกค้าผู้หญิงมีกี่ใบ? | How many ticket ID that come from Female customer? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | df[df['Customer_Gender'] == 'Female'].shape[0] | customer |