CSV Data Analyzer-AI CSV analysis and insights

Example

Automatically detect delimiter, header presence, text encoding and column data types; sample the file to infer a schema, list unique values for categorical columns, compute null/missing rates, and surface suspicious rows (e.g., parse failures or inconsistent column counts). Produce a human-readable data profile (column names, inferred types, basic stats, top values, missingness heatmap).

Scenario

A finance team receives CSV exports from three accounting systems with different delimiters and date formats. CSV Data Analyzer autodetects delimiters and encodings, infers that one date column is text in MM/DD/YYYY while another is YYYY-MM-DD, shows columns with >20% missing values, and produces a profile report that lets the team spot which files need preprocessing before aggregation.

Example

Provide rule-driven and automated cleaning operations: trim whitespace, normalize case, fix common CSV quoting problems, coerce/cast types, convert units (e.g., mg -> g), impute missing values using chosen strategies (mean, median, forward-fill, domain rules), remove duplicates, apply regex transforms, and validate against constraints (unique keys, range checks, allowed categories). Generate a replayable cleaning pipeline (e.g., a sequence of named steps or exported pandas/SQL code).

Scenario

A healthcare analyst has patient survey CSVs with inconsistent gender labels (M, Male, male), DOB in mixed formats, and some duplicate rows from re-exports. CSV Data Analyzer normalizes labels to a canonical set, parses dates to ISO 8601, de-duplicates on a composite key (patient_id + visit_date), flags rows missing critical fields, and exports both a cleaned CSV for reporting and a Python script showing each transformation for audit or pipeline integration.

Example

Run EDA: compute summary statistics (mean, median, percentiles), group-by aggregations and pivots, correlation matrices, missingness patterns, time-series decomposition and seasonal-trend checks, and basic anomaly detection (z-score, IQR, rolling-window outliers). Produce charts (histograms, boxplots, line charts with rolling averages, heatmaps) and auto-generate an HTML/PDF report with narrative summaries, visuals, and data tables. Optionally provide hypothesis-test helpers (t-test, chi-squared) and quick A/B lift calculations.

Scenario

A marketing analyst uploads campaign performance CSVs to measure conversion lift. The analyzer computes conversion rates by cohort, produces confidence intervals for differences between A and B, plots daily conversion trends with a 7-day rolling average, highlights days with anomalous spikes or drops, and outputs a shareable HTML report plus the underlying pivot table and a SQL snippet so the analyst can reproduce the same metrics on the data warehouse.