Feature request: Add `fenn.tabular` module for data exploration

[blkdmr]

Introduce a new fenn.tabular module that provides a small, composable toolbox for tabular data exploration built on top of pandas and NumPy. This should focus on ergonomic helpers for common Exploratory Data Analysis (EDA) patterns rather than re-implementing core pandas/NumPy features.

Goal

Create an initial version of fenn.tabular with a clear, well-documented API to simplify early-stage data exploration workflows. The module should make it easy to quickly inspect, summarize, and sanity-check datasets represented as pandas.DataFrame or numpy.ndarray objects.

Proposed features

Ideas for a first iteration (not all are mandatory for a single PR):

• summary (dataframe): one-shot overview combining shape, dtypes, basic stats, missing values counts, and simple cardinality info for categorical-like columns.
• quick_sample (dataframe): convenience wrapper around head/random sampling with optional column subset and seed.
• missing_report (dataframe): compact report of missing values per column, percentage, and flags for all-null or almost-all-null columns.
• unique_report(dataframe): show number of unique values per column and, for low-cardinality columns, a small frequency table.
• numeric_profile(dataframe): describe numeric columns only (min, max, mean, std, quantiles) with optional clipping of extreme quantiles.
• corr_overview(dataframe): compute correlations between numeric columns and return the strongest pairs as a tidy table.
• array_summary(ndarray): NumPy-oriented helper for shape, dtype, basic stats, and NaN checks on ndarray.

These functions should be pure utilities (optional plotting?) and should not alter the input objects in place.

Tasks

• Create the fenn/tabular/__init__.py module and basic package structure.
• Implement a first subset of utilities (for example: summary, missing_report, array_summary).
• Add type hints and docstrings with small usage examples.
• Add unit tests with small, synthetic DataFrames/arrays.
• Integrate the new module into the public API (if applicable) and update the documentation/README with a short usage section.

Contributing

If you want to work on this:

• Please read the CONTRIBUTING guide before starting.
• Comment on this issue to claim a part of it and join the discord server to discuss which subset of functions you plan to implement first.

[K0MPLEXWorksTogether]

I would like to take up this issue. I think I can get up and running with the basic project structure and the summary, missing_report, and array_summary quite easily.

[blkdmr]

This issue has been assigned to you. Thank you very much for your support!

[K0MPLEXWorksTogether]

I appreciate it!