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.

Correlations between numeric columns, ranked by strength.

Computes pairwise correlations across all numeric columns (pearson by default; spearman or kendall for rank-based relationships) and returns pairs ranked by absolute correlation, a high_correlation_pairs list (|r| >= 0.9, a multicollinearity signal when preparing features for modeling), and the full matrix when there are 15 or fewer numeric columns. Pass column to instead rank how strongly every other numeric column correlates with that one, for example a target variable. Constant columns are excluded and listed.

Use this when the user is selecting features for a model, hunting redundant columns, or asking what moves together with a numeric outcome.