Profile a tabular data file in one call: the fastest way to understand a dataset.

Reads the file at path (CSV, TSV, Parquet, Excel or JSON/JSONL, detected from the extension) and returns a structured overview:

• file metadata (format, size),

• shape (row and column counts, and whether the profile was sampled),

• total memory footprint,

• a missing-value summary and a duplicate-row count,

• a per-column summary (dtype, inferred type, null %, unique %, sample values, and basic stats for numeric/datetime columns), and

• a list of plain-language data-quality flags.

Use this first whenever a user points you at a data file and wants to know what is in it. max_rows caps how many rows are read (default: up to one million); the result flags when the file was larger and the stats are a head sample. Pass 0 to remove the cap entirely.

Peek at actual rows of a data file.

Returns n rows (capped at 100) as records. mode selects which rows: head (default), tail, or sample (random). Use this to see real example values rather than just statistics, for example to check formatting, encodings, or how a specific column looks in practice.

Deep statistical dive on a single column.

For numeric columns: min/max, mean, std, a full set of percentiles (p1/p5/q1/median/q3/p95/p99), skewness, kurtosis, zero and negative counts, an IQR-based outlier count with bounds, and a 10-bin histogram. For datetime columns: the min and max timestamp. For text/categorical columns: the top values with counts and percentages, plus string-length statistics.

Reach for this after profile_dataset when one column needs closer inspection. Raises an error listing the available columns if column is not found.

Run a focused data-quality audit and return issues grouped by severity.

Detects duplicate rows, all-missing and high-missing columns, constant columns, likely identifier columns, numbers stored as text, dates stored as text, columns mixing numeric and text values, leading/trailing whitespace, and empty (whitespace-only) strings. Each issue carries a column (or null for table-level), an issue code, a severity (high/warning/info), and a plain-language explanation.

Use this when the user cares specifically about cleanliness, is preparing data for modeling, or asks "is anything wrong with this data?".

Recommend more memory-efficient or more-correct column dtypes.

For each column, proposes a better dtype when one exists: text that is fully numeric to a numeric type, low-cardinality text to category, and oversized integer/float columns downcast to smaller types. Reports per-column and total estimated memory savings.

Use this to help a user shrink a DataFrame's memory footprint or fix columns that were loaded with the wrong type.

Diff two tabular files: what changed between version A and version B.

Reports the row-count delta, columns added or removed in B, dtype changes on shared columns, and per-column null-rate (and, for numeric columns, mean) for both files side by side.

Use this to compare two snapshots of the same dataset, validate a data pipeline's output against a baseline, or check what a transformation changed.