A guide to accepting table imports through an explicit contract, profile, missingness pattern, and unit conversion rather than appearance.
Turn the guide into a safe trial
Complete the steps with a synthetic example before using real data. Checkmarks live only in this tab.
Write the data contract before opening the file
Define expected headers, types, required status, units, date format, decimal separator, and allowed values in a small dictionary. A correct formula still produces a meaningless result when units or blanks are misread.
Carry the contract version and producer system with the file. Added or renamed columns require explicit review instead of silent inference.
- List headers and types.
- State units and timezone.
- Write the missing-value policy.
Verify delimiter and quoting with a sample
Comma, semicolon, and tab conventions vary by locale. Delimiters, line breaks, or escaped quotes inside quoted cells defeat naive splitting.
A correct first row is not enough. Sample the beginning, middle, and end; unclosed quotes and changing column counts must stop with an explicit error.
Read profiling and missingness together
Compare inferred type, uniqueness, range, and blank rate with the column's intended role. A repeated identifier and a repeated category do not carry the same risk.
Missingness may cluster by row group, source system, or date. Investigate the mechanism and decision impact before replacing blanks with zero.
Accept unit conversion through an independent calculation
Record source unit, target unit, factor, rounding, and display precision. Offset conversions such as temperature are not simple multiplication; currency needs a current rate source and sits outside a static unit tool.
Recalculate at least one row independently, test a boundary, and compare preserved totals. Store the accepted result with contract version and fixture, not the sensitive source table.
Tools and responsibilities in this workflow
Each tool contributes different evidence; no single output approves the whole workflow. Start with synthetic data, apply the acceptance check, and stop when a boundary is exceeded.
- CSV Delimiter Detector
- CSV Column Profiler
- CSV Missing-Data Pattern Analyzer
- Measurement / Unit Converter
Turn the guide into a repeatable review
Use this 4-tool review plan for “Build a CSV Import Contract Before You Calculate”. Goal: A guide to accepting table imports through an explicit contract, profile, missingness pattern, and unit conversion rather than appearance. Start with a safe example instead of real data, then record each expected result and acceptance decision.
CSV Delimiter Detector
- Prepare
- Load the safe example or enter your own data. Expected format for CSV Delimiter Detector: For CSV Delimiter Detector, provide cSV, TSV, tabular, or delimited records with a consistent header and row shape. The requested outcome is to score comma, semicolon, tab, and pipe by row consistency..
- Apply
- Run it on-device and inspect errors, warnings, and metrics. CSV Delimiter Detector applies this method: CSV Delimiter Detector uses this disclosed method to score comma, semicolon, tab, and pipe by row consistency: delimiter, quoting, row, and column boundaries are inspected separately.
- Acceptance check
- Validate the output in the target environment and with edge cases. Acceptance check for CSV Delimiter Detector: Before accepting a CSV Delimiter Detector result, complete header count, row width, quote escaping, and representative records opened in the target table; the evidence should support the goal to score comma, semicolon, tab, and pipe by row consistency..
- Expected output
- When CSV Delimiter Detector finishes, it returns row and column totals, normalized records, and locations of problematic cells, organised around the goal to score comma, semicolon, tab, and pipe by row consistency.. Score comma, semicolon, tab, and pipe by row consistency.
CSV Column Profiler
- Prepare
- Paste data or load the safe example. Expected format for CSV Column Profiler: For CSV Column Profiler, provide cSV, TSV, tabular, or delimited records with a consistent header and row shape. The requested outcome is to summarize column types, missing values, uniqueness, ranges, and sample distributions locally..
- Apply
- Run the transformation and inspect warnings. CSV Column Profiler applies this method: CSV Column Profiler uses this disclosed method to summarize column types, missing values, uniqueness, ranges, and sample distributions locally: delimiter, quoting, row, and column boundaries are inspected separately.
- Acceptance check
- Validate the result in the target system. Acceptance check for CSV Column Profiler: Before accepting a CSV Column Profiler result, complete header count, row width, quote escaping, and representative records opened in the target table; the evidence should support the goal to summarize column types, missing values, uniqueness, ranges, and sample distributions locally..
- Expected output
- When CSV Column Profiler finishes, it returns row and column totals, normalized records, and locations of problematic cells, organised around the goal to summarize column types, missing values, uniqueness, ranges, and sample distributions locally.. Summarize column types, missing values, uniqueness, ranges, and sample distributions locally.
CSV Missing-Data Pattern Analyzer
- Prepare
- Add CSV data with a header row. Expected format for CSV Missing-Data Pattern Analyzer: For CSV Missing-Data Pattern Analyzer, provide a pattern, flags, and representative matching and non-matching text. The requested outcome is to inspect column and row missingness patterns instead of only counting empty values..
- Apply
- Review missing-value tokens and patterns. CSV Missing-Data Pattern Analyzer applies this method: CSV Missing-Data Pattern Analyzer uses this disclosed method to inspect column and row missingness patterns instead of only counting empty values: the pattern runs against bounded input while matches and risk signals remain visible.
- Acceptance check
- Decide with source-system, sampling, and business-rule context. Acceptance check for CSV Missing-Data Pattern Analyzer: Before accepting a CSV Missing-Data Pattern Analyzer result, complete retesting with positive, negative, empty, long, and adversarial boundary cases; the evidence should support the goal to inspect column and row missingness patterns instead of only counting empty values..
- Expected output
- When CSV Missing-Data Pattern Analyzer finishes, it returns match locations, capture groups, and complexity signals that need review, organised around the goal to inspect column and row missingness patterns instead of only counting empty values.. Inspect column and row missingness patterns instead of only counting empty values.
Measurement / Unit Converter
- Prepare
- Choose a measurement category and source unit. Expected format for Measurement / Unit Converter: For Measurement / Unit Converter, provide numeric values with explicit units, periods, and inclusion assumptions. The requested outcome is to convert length, mass, temperature, volume, and data-size units instantly..
- Apply
- Enter a value and select the target unit. Measurement / Unit Converter applies this method: Measurement / Unit Converter uses this disclosed method to convert length, mass, temperature, volume, and data-size units instantly: the formula, intermediate values, rounding, and divide-by-zero boundaries remain visible.
- Acceptance check
- Review the result, factor, and rounding. Acceptance check for Measurement / Unit Converter: Before accepting a Measurement / Unit Converter result, complete a hand-worked example, zero, negative, and extreme values, unit conversion, and comparison with the authoritative rule; the evidence should support the goal to convert length, mass, temperature, volume, and data-size units instantly..
- Expected output
- When Measurement / Unit Converter finishes, it returns the calculated value, formula, units, and scenario assumptions, organised around the goal to convert length, mass, temperature, volume, and data-size units instantly.. Convert length, mass, temperature, volume, and data-size units instantly.
Apply this boundary to CSV Delimiter Detector: CSV Delimiter Detector limitation: Verify schema, encoding, and data-loss assumptions in the target system. If that condition is not met, do not pass the output to the next workflow step.
For “Build a CSV Import Contract Before You Calculate”, record the tool, selected setting, browser version, and acceptance or rejection reason for “Auditable pre-publication quality control: local analysis with CSV Delimiter Detector”—not the sensitive content. This keeps the review repeatable without copying real data.
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