diffolars 0.3.1

Yet another data diff

diffolars

A small Polars-based toolkit for comparing two versions of a dataframe and generating randomized test data to exercise that comparison.

Ideally used to compare dataloads in the day-to-day of a database analyst.

Installation

uv add diffolars

Generating test data

diffolars.demo generates a random dataframe and a mutated copy of it, useful for testing diff logic without hand-crafting fixtures.

from diffolars.demo import get_df_pair

pair = get_df_pair(
    n_rows=100,
    n_cols=10,
    n_new_rows=5,    # rows added in the mutated copy
    n_new_cols=2,    # columns added in the mutated copy
    coverage=0.1,    # fraction of existing cells randomly changed
    seed=42,
)

original = pair["original"]
mutated = pair["mutated"]

Every generated row gets a record_id UUID column, used to match rows between the original and mutated dataframes. get_random_data and get_mutated_data are also available individually if you want to generate or mutate a dataframe on its own.

Diffing

diffolars.diff provides the comparison API for two dataloads (e.g. a previous load vs. the latest load), and is under active development.

Inputs to these functions can be a list, polars.DataFrame, or polars.LazyFrame.

• column_intercept / column_symmetric_diff — shared vs. exclusive columns between the two tables
• row_intercept / row_symmetric_diff — shared vs. exclusive rows, based on a record ID column
• prune_rows — returns the rows exclusive to each table (i.e. dropped by the join), tagged with which table they came from
• report_prune — summarizes the pruned rows/columns as a single dict entry, suitable for logging
• get_core — joins the two tables down to their shared rows and columns, for doing the field-to-field comparison

Currently only the Windows build is available.

License

MIT — see LICENSE.