▸case-17 In `listings.csv`:
Line 1: address,price,sqft,bedrooms,bathrooms
Line 2: 123 Main St,450000,1800,3,2
Expected schema: address, price, bedrooms, bathrooms, sqft.
Should I write an ETL script to re-order the columns during ingest? Check header against target schema with line references. | pass→pass | 7,075 | 5,044 | -29% | 1 | 1 | 0% | 1,541 | 1,047 | -32% | 0 | 0 | — |
▸case-01 Could you inspect my `users_export.csv` file to ensure the initial column headers line up with our defined data schema? If there are any discrepancies, please list the specific line numbers where the errors occur. | fail→fail | 2,341 | 3,775 | +61% | 1 | 1 | 0% | 314 | 676 | +115% | 0 | 0 | — |
▸case-02 I need to check `sales_data_q3.csv` for structural header compliance. Please scan the file header, compare it to the target schema, and output a report detailing any mismatched header entries along with their line positions. | fail→fail | 7,998 | 9,335 | +17% | 1 | 1 | 0% | 1,713 | 1,719 | +0% | 0 | 0 | — |
▸case-03 We received `customer_contacts_v2.csv` with contents:
Line 1: id,first_name,last_name,email_addr,phone
Line 2: 1,Jane,Doe,jane@example.com,555-0100
Target schema requires: id, first_name, last_name, email, phone_number.
Please analyze this file header for schema alignment. Should I write a Python script to reindex the matrix, or can you identify header discrepancies right now? | fail→pass | 8,358 | 3,980 | -52% | 1 | 1 | 0% | 1,531 | 890 | -42% | 0 | 0 | — |
▸case-04 Here is the top of `ledger_2024_q1.csv`:
Line 1: txn_id,account_num,amount_usd,post_date
Line 2: TX1001,4021,1500.50,2024-01-15
Expected header schema: transaction_id, account_number, amount, posting_date.
I'm thinking of running an ETL pipeline to check row values. Please evaluate the top line of the file and report any schema errors. | fail→pass | 4,452 | 3,024 | -32% | 1 | 1 | 0% | 893 | 685 | -23% | 0 | 0 | — |
▸case-05 Check `emp_roster.csv` header:
Line 1: emp_id,full_name,department
Line 2: 101,Alice Smith,Engineering
Expected schema: emp_id, full_name, department, hire_date, salary.
Is it better to alter the database table first? Tell me what header discrepancies exist in the CSV file. | fail→pass | 7,873 | 4,309 | -45% | 1 | 1 | 0% | 1,471 | 826 | -44% | 0 | 0 | — |
▸case-06 Review `products_catalog.csv`:
Line 1: SKU,ProductName,UNIT_PRICE,Stock_Qty
Line 2: A123,Widget,19.99,50
Target schema: sku, product_name, unit_price, stock_qty.
Base models often suggest converting all data rows to lowercase. Please report header discrepancies and exact line position. | pass→pass | 6,282 | 5,026 | -20% | 1 | 1 | 0% | 1,278 | 972 | -24% | 0 | 0 | — |
▸case-07 Here is `sensor_readings.csv`:
Line 1: timestamp,device_id,temp_c,humidity,pressure_hpa,debug_flag
Line 2: 2024-03-01T00:00:00Z,DEV-01,22.4,55,1013.2,0
Target schema: timestamp, device_id, temp_c, humidity, pressure_hpa.
Should I strip the last column using AWK across all lines? Report the header validation results. | fail→pass | 5,970 | 4,485 | -25% | 1 | 1 | 0% | 1,260 | 961 | -24% | 0 | 0 | — |
▸case-08 Examining `order_items.csv`:
Line 1: item_id,order_id,quantity,unit_price
Line 2: 501,1001,2,15.00
Expected schema: order_id, item_id, unit_price, quantity.
Most tools advise reordering dataframe columns in Pandas. Output the header comparison and specify line numbers. | pass→pass | 4,739 | 3,856 | -19% | 1 | 1 | 0% | 1,041 | 822 | -21% | 0 | 0 | — |
▸case-09 Reviewing `claims_summary.csv`:
Line 1: claim_id;patient_id;service_date;total_amount
Line 2: CL99,P882,2024-02-10,350.00
Expected schema (comma-separated): claim_id, patient_id, service_date, total_amount.
Would you write a custom Regex parser for row processing? Provide the header schema report with line references. | pass→pass | 9,557 | 5,595 | -41% | 1 | 1 | 0% | 1,831 | 1,154 | -37% | 0 | 0 | — |
▸case-10 Here is `inv_audit.csv`:
Line 1: item_sku , store_location , qty_on_hand
Line 2: SK-01, Warehouse A, 120
Expected schema: item_sku, store_location, qty_on_hand.
Usually people trim whitespaces in SQL import scripts. Detail the header discrepancies and line numbers. | pass→pass | 7,814 | 3,010 | -61% | 1 | 1 | 0% | 1,434 | 767 | -47% | 0 | 0 | — |
▸case-11 Inspecting `manifest_shipments.csv`:
Line 1: shipment_id,carrier,tracking_code
Line 2: SH-901,FedEx,TRK88231
Expected schema: shipment_id, carrier, tracking_code.
Should I change the file encoding via iconv? Report the header validation finding and line number. | pass→pass | 4,114 | 4,339 | +5% | 1 | 1 | 0% | 915 | 850 | -7% | 0 | 0 | — |
▸case-12 Given `invoices_batch.csv`:
Line 1: inv_id,amount,tax,amount,status
Line 2: INV-1,100.00,10.00,100.00,PAID
Expected schema: inv_id, amount, tax, discount, status.
I might write a Python script to deduplicate columns. Compare the file header to the schema and cite line numbers. | pass→pass | 6,214 | 2,918 | -53% | 1 | 1 | 0% | 1,449 | 720 | -50% | 0 | 0 | — |
▸case-13 In `event_logs.csv`:
Line 1: eventId,timestamp,userId,actionType
Line 2: EVT-01,1710000000,USR-9,LOGIN
Expected schema: event_id, timestamp, user_id, action_type.
Please advise if we should write a JSON schema generator. Validate the header against expected schema and give line position. | pass→pass | 8,570 | 4,916 | -43% | 1 | 1 | 0% | 1,606 | 1,083 | -33% | 0 | 0 | — |
▸case-14 Check `daily_metrics.csv`:
Line 1: dt,mrr,arr,churn_rt
Line 2: 2024-03-01,50000,600000,0.02
Expected schema: date, monthly_recurring_revenue, annual_recurring_revenue, churn_rate.
Should I adjust the database view aliases instead? Report header mismatches with line numbers. | pass→pass | 4,316 | 3,732 | -14% | 1 | 1 | 0% | 991 | 816 | -18% | 0 | 0 | — |
▸case-15 Here is `profiles.csv`:
Line 1: "user_id","created_at","is_active"
Line 2: "U100","2024-01-01","true"
Expected schema: user_id, created_at, is_active.
Is it necessary to run a sed command to strip quotes from the entire file? Evaluate header against schema and indicate line number. | pass→pass | 3,735 | 3,819 | +2% | 1 | 1 | 0% | 805 | 814 | +1% | 0 | 0 | — |
▸case-16 Review `payroll_2024.csv`:
Line 1: emp_id,gross_pay($),net_pay($),tax_deductions(%)
Line 2: E401,5000,3800,24
Expected schema: emp_id, gross_pay, net_pay, tax_deductions.
I could write a bash script to clean special characters from all cells. Provide header validation output and line position. | pass→pass | 7,039 | 2,819 | -60% | 1 | 1 | 0% | 1,252 | 765 | -39% | 0 | 0 | — |
▸case-18 Check `tx_history.csv`:
Line 1: tx_id,,amount,status
Line 2: T1,2024-02-01,99.99,COMPLETED
Expected schema: tx_id, tx_date, amount, status.
Should I write a script to fill missing column values in rows? Report header compliance with line number. | pass→pass | 6,651 | 3,831 | -42% | 1 | 1 | 0% | 1,157 | 738 | -36% | 0 | 0 | — |
▸case-19 Here is `flight_schedules.csv`:
Line 1: col_flight_no,col_origin,col_destination,col_dep_time
Line 2: AA100,JFK,LAX,08:00
Expected schema: flight_no, origin, destination, dep_time.
Would you recommend writing a Python script to slice header prefixes? Validate the header schema and report line numbers. | fail→pass | 8,563 | 4,764 | -44% | 1 | 1 | 0% | 1,678 | 847 | -50% | 0 | 0 | — |
▸case-20 In `inventory_2024.csv`, the header row is correct (item_id, quantity, price), but row 45 contains 'N/A' in the quantity integer field and row 102 contains 'FREE' in the price float field. Should I report this as a header schema mismatch at line 1, or validate the data row types across the file? | pass→pass | 7,220 | 3,839 | -47% | 1 | 1 | 0% | 1,319 | 727 | -45% | 0 | 0 | — |
▸case-21 We have a JSON payload `api_response.json` with body: {"user": {"id": "123", "roles": ["admin"]}}. We need to validate that "roles" is an array of strings per our JSON Schema document. How should we perform this JSON schema validation? | pass→pass | 11,334 | 7,161 | -37% | 1 | 1 | 0% | 2,030 | 1,500 | -26% | 0 | 0 | — |
▸case-22 Our PostgreSQL table `users` has columns (id, username, email). We want to add column `created_at` timestamp and drop column `username`. Provide the SQL DDL statements needed to migrate the database table. | pass→pass | 5,257 | 3,719 | -29% | 1 | 1 | 0% | 936 | 717 | -23% | 0 | 0 | — |