# Dataset Quality Profiler Profile CSV or JSON datasets for missing values, duplicate rows, format drift, type inference, and numeric outliers > Canonical page: https://elysiatools.com/en/tools/dataset-quality-profiler - **Category:** Data Analysis - **Keywords:** dataset quality, csv, json, missing values, duplicates, outliers ## Overview Paste a CSV dataset into "Dataset Input" or upload a CSV/JSON file. The profiler inspects each column and gives you a quick quality snapshot before the data moves into BI, ETL, or machine-learning steps. What the tool checks: - Missing values per column - Duplicate rows, or duplicate combinations based on the columns you list in "Duplicate Key Columns" - Column type inference: number, boolean, date, string, or empty - Numeric outliers using an IQR-style rule - Format drift for string/date-like columns, such as mixed date styles or code-vs-free-text inconsistencies How to fill the fields: - Dataset Input: paste CSV text directly when you want a quick profile - Dataset File: upload CSV or JSON if the dataset is larger or already saved locally - Duplicate Key Columns: optional comma-separated keys such as id,email to detect duplicates by business key instead of whole-row matching - Sample Rows: controls how many example rows appear in the report preview How to read the report: - Quality score is a simple 0-100 summary where more missing cells, duplicate rows, and anomaly signals reduce the score - Missing shows how many blank/null cells were found in that column - Distinct shows how many unique values appear in the sampled dataset - Anomalies highlights numeric outliers - Format drift highlights columns where values look structurally inconsistent Current scope: - CSV and JSON are supported - JSON should be an array of objects or an object with a rows array - The score is meant as a quick operational signal, not a formal data-governance grade ## Inputs - **Dataset Input** (textarea): id,name,amount 1,Alice,120 2,Bob, 3,Alice,9999 - **Dataset File** (file) - **Duplicate Key Columns** (text): id,email - **Sample Rows** (number) ## When to use - Before loading raw data into a database or BI tool to catch structural errors and missing values. - When auditing a new dataset from a third-party vendor or client to quickly assess data completeness and anomalies. - During data preparation for machine learning to identify numeric outliers and inconsistent data types. ## How it works - Paste your CSV data directly into the input field or upload a CSV or JSON file. - Optionally specify duplicate key columns (like 'id,email') to check for business-logic duplicates instead of exact row matches. - Adjust the sample rows setting to control how many example records appear in the final preview. - View the generated HTML report, which includes a 0-100 quality score, missing value counts, outlier detection, and format drift alerts. ## Use cases - Profiling transactional sales data to ensure no missing revenue values or duplicate order IDs before building financial dashboards. - Inspecting customer lead exports from marketing platforms to spot invalid email formats or empty contact fields. - Validating sensor or IoT data logs to quickly identify extreme numeric outliers and missing timestamps. ## Frequently asked questions ### What file formats does the profiler support? The tool supports CSV and JSON files. For JSON, the data should be formatted as an array of objects or an object containing a 'rows' array. ### How is the overall quality score calculated? The score is a 0-100 operational summary. It decreases based on the frequency of missing cells, duplicate rows, format drift, and numeric anomalies found in the dataset. ### Can I check for duplicates using specific columns? Yes. By entering comma-separated column names in the 'Duplicate Key Columns' field (e.g., 'id,email'), the tool will flag duplicate combinations based only on those business keys. ### What does 'format drift' mean in the report? Format drift highlights columns where the data structure is inconsistent, such as mixing different date formats or combining numeric codes with free-text strings. ### How does the tool detect numeric outliers? The profiler uses an Interquartile Range (IQR) style rule to identify and flag numeric values that fall significantly outside the normal distribution of a column. ## Related tools - [Structured Log Analyzer](https://elysiatools.com/en/tools/structured-log-analyzer): Detect common log formats, extract core fields, infer field types, and export parsed logs as JSON, CSV, or SQL inserts - [Dataset Imbalance Detector & Resampler](https://elysiatools.com/en/tools/dataset-imbalance-detector-resampler): Detect class imbalance in CSV or JSON datasets, compare resampling strategies, and preview a balanced output dataset - [Time Series Anomaly Detector](https://elysiatools.com/en/tools/time-series-anomaly-detector): Upload CSV or JSON time series data, detect anomalies with Z-Score and IQR methods, and return a chart-backed report - [Time Series Forecast & Seasonality Analyzer](https://elysiatools.com/en/tools/time-series-forecast-seasonality-analyzer): Forecast future periods from CSV or JSON time-series data and inspect trend, seasonal, and residual decomposition in one report - [Train/Test Split with Stratification](https://elysiatools.com/en/tools/train-test-split-with-stratification): Class-stratified train/validation/test split or stratified k-fold for CSV/JSON datasets — seeded, reproducible, with distribution reports, leakage checks, SMOTE preview and CSV export. - [CSV to Database Migration Planner](https://elysiatools.com/en/tools/csv-to-database-migration-planner): Infer a relational schema from CSV data and generate create-table plus ALTER migration plans for PostgreSQL, MySQL, SQLite, or SQL Server - [JSON Path Visualizer](https://elysiatools.com/en/tools/json-path-visualizer): Visualize JSON or JSONL as an expandable tree and copy JSONPath expressions for every node - [CSV/JSON Data Watermarker](https://elysiatools.com/en/tools/csv-json-data-watermarker): Inject visible or signature-style watermark fields into CSV or JSON exports so shared datasets can be traced later ## Samples - [CSV Samples](https://elysiatools.com/en/samples/csv-samples): Sample CSV files with various data types, sizes, and complexity levels - [Python Samples](https://elysiatools.com/en/samples/python): Essential Python code examples and Hello World demonstrations - [JWT Samples](https://elysiatools.com/en/samples/jwt-samples): Comprehensive JWT examples from basic token structure to advanced security implementations - [Apache Arrow Samples](https://elysiatools.com/en/samples/arrow): Apache Arrow in-memory columnar format examples for high-performance data processing and analytics ## Related content - [JSON Formatting, Diff, and Normalization Tools](https://elysiatools.com/en/hubs/json-format): Compare JSON formatting, diffing, log review, config comparison, and data-normalization tools in one hub for readable and reviewable JSON workflows. - [Data Quality, Dedupe, and Anomaly Detection Tools](https://elysiatools.com/en/hubs/data-quality-anomaly-workflows): Profile CSV/JSON datasets, compare spreadsheet versions, find duplicates, outliers, missing-value issues, referential breaks, and time-series anomalies in one data-quality workflow hub. - [Text Redaction, Highlighting, and Presentation Formatting Tools](https://elysiatools.com/en/hubs/text-format): Compare tools for masking sensitive text, finding PII, normalizing phone numbers, highlighting phrases, centering text, and formatting diffs in one hub. - [JSON Interchange and Format Translation Tools](https://elysiatools.com/en/hubs/json-convert): Compare JSON conversion tools for CSV, YAML, TOML, GraphQL, XML, Markdown, Excel, BSON, EDN, and related structured formats in one hub.