deltascan

deltascan is a Python package that finds and summarizes the differences between two datasets.

Installation

pip install deltascan

Main Features

The DeltaScan class compares any two supported data structures accross one or more dimensions.

Data Structures:

• DataFrame
• Series
• LazyFrame (Polars only)

Dimensions:

• Rows → rows present in one dataset but missing in the other, aligned using join_on.
• Columns → differences in column names and data types.
• Values → mismatched values within matching rows and columns.

Example Usage

Imports

Import the DeltaScan class.

from deltascan import DeltaScan

Create DataFrames

Create two sample DataFrame objects to compare.

import pandas as pd
import polars as pl
import datetime


# February Data
left_data = pd.DataFrame({
    'id': [1, 2, 3, 4],
    'date': [pd.to_datetime('2026-02-28')] * 4,
    'first_name': ['Alice', 'Mike', 'John', 'Sarah'],
    'flag': [True, False, True, False],
    'amount': [10.0, 5.3, 33.7, 99.3],
    })

# January Data
right_data = pl.DataFrame({
    'id': [1, 3, 9],
    'date': [datetime.date(2026, 1, 31)] * 3,
    'first_name': ['Alice', 'Michael', 'Zachary'],
    'color': ['Pink', 'Blue', 'Red'],
    'last_name': ['Jones', 'Smith', 'Einck'],
    'flag': [False, True, False],
    'amount': [10, None, 14],
    })

Compare DataFrames

Create a DeltaScan instance to perform the comparison. See the in-code documentation for a complete list of available arguments.

ds = DeltaScan(
    left_data=left_data,
    right_data=right_data,
    join_on='id',
    left_alias='feb',
    right_alias='jan',
    left_context=['first_name'],
    right_context=None,
    verbose=True,
    )

Comparison Results

Access the comparison results using the summary and differences attributes.

print(ds.summary)

shape: (8, 6)
┌─────────────────────┬───────────┬─────────────┬─────────┬───────┬──────────────┐
│ Comparison          ┆ Dimension ┆ Differences ┆ Matches ┆ Total ┆ Match Rate % │
│ ---                 ┆ ---       ┆ ---         ┆ ---     ┆ ---   ┆ ---          │
│ str                 ┆ str       ┆ i64         ┆ i64     ┆ i64   ┆ f64          │
╞═════════════════════╪═══════════╪═════════════╪═════════╪═══════╪══════════════╡
│ jan cols not in feb ┆ columns   ┆ 2           ┆ 5       ┆ 7     ┆ 0.714286     │
│ data types          ┆ columns   ┆ 2           ┆ 3       ┆ 5     ┆ 0.6          │
│ feb rows not in jan ┆ rows      ┆ 2           ┆ 2       ┆ 4     ┆ 0.5          │
│ jan rows not in feb ┆ rows      ┆ 1           ┆ 2       ┆ 3     ┆ 0.666667     │
│ amount              ┆ values    ┆ 1           ┆ 1       ┆ 2     ┆ 0.5          │
│ date                ┆ values    ┆ 2           ┆ 0       ┆ 2     ┆ 0.0          │
│ first_name          ┆ values    ┆ 1           ┆ 1       ┆ 2     ┆ 0.5          │
│ flag                ┆ values    ┆ 1           ┆ 1       ┆ 2     ┆ 0.5          │
└─────────────────────┴───────────┴─────────────┴─────────┴───────┴──────────────┘

Export to Excel

Export the results to an excel file.

ds.to_excel()