file-tool
CSV Data Profiler
Inspect CSV columns, inferred data types, missing values, duplicate rows, and descriptive summaries without uploading the file.
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Browser-side CSV utilities for profiling, cleaning, missing values, duplicates, outliers, normalization, encoding, splitting, correlation, and JSON export.
Data science tools prioritize local browser-side CSV processing so users can profile datasets, analyze missing values, find duplicate rows, detect outliers, clean data, normalize numeric columns, encode categorical columns, calculate correlations, create deterministic train-test splits, and convert CSV to JSON without uploading files to a server.
10 tools
Published file tool entries in this category.
Inspect CSV columns, inferred data types, missing values, duplicate rows, and descriptive summaries without uploading the file.
Find missing-value counts and percentages by CSV column, then optionally fill values or drop affected rows after explicit review.
Detect exact duplicate rows or duplicate keys in a loaded CSV file without uploading the dataset.
Find potential outliers in selected numeric CSV columns and export detected rows for review.
Scale numeric CSV columns locally and download a normalized CSV after reviewing before-and-after ranges.
Generate a local browser-side correlation matrix for selected numeric columns with missing-value handling options.
Split a loaded CSV into train, validation, and test downloads using a reproducible browser-local shuffle.
Convert selected categorical columns to model-friendly encoded output after explicit local processing.
Apply explicit CSV cleaning options and download a cleaned CSV without uploading the file.
Convert a loaded CSV file to JSON objects, array rows, or JSON Lines and download the result locally.
Use a consistent delimiter and header row, preserve an untouched source copy, document encodings and missing-value tokens, and inspect duplicate headers or uneven rows.
It reports rows, columns, inferred types, missing values, uniqueness, duplicates, and numeric summaries after the user explicitly loads and analyzes the file.
First identify domain-specific missing tokens and patterns, then choose deletion or imputation only after considering bias, column meaning, and the intended analysis.
A flagged value may be a rare valid event or subgroup rather than bad data, so source verification and domain context should precede removal or replacement.
Choose min-max, z-score, or robust scaling according to the downstream method, distribution, outliers, and whether fitted parameters will be reused consistently.
No. Correlation summarizes pairwise association and can be affected by outliers, nonlinear patterns, missing data, confounding, and sample size.
Split before fitting transformations, keep related or time-ordered records together when required, and apply training-derived parameters to the test set.
No. Supported CSV parsing and transformations run locally in the browser, subject to the documented file-size and export safeguards.
Data Science Tools profile, clean, transform, and summarize user-selected CSV data locally in the browser. They support inspection and preparation rather than a complete analytical pipeline.
Preserve the raw file, confirm delimiter and schema, profile types and missingness, document every cleaning or transformation rule, export a reviewed artifact, and validate it in the downstream analysis environment.
Type inference, missing tokens, encoding, duplicate definitions, outlier rules, and normalization choices depend on domain context. A clean export does not establish sampling quality, causal validity, or model suitability.