pandascompare 0.1.0

Suite of pandas utilities including a DataFrame comparison report builder.

pandascompare

pandascompare is a library that offers a suite of pandas utilities.

Core Utilities

The flagship feature of pandascompare is the PandasCompare class that compares any two DataFrame objects along the following dimensions:

• Rows: discrepancies with respect to the join key(s) specified via the join_on argument.
• Columns: name differences or missing columns.
• Values: data that differs in terms of value or type.

Example Usage

Please refer to the documentation within the code for more information.

Imports

from pandascompare import PandasCompare
import pandas as pd
import numpy as np

Create DataFrames

First, let's create two sample DataFrame objects to compare.

# February Data
left_df = pd.DataFrame({
    'id': [1, 2, 3],
    'date': [pd.to_datetime('2024-02-29')] * 3,
    'first_name': ['Alice', 'Mike', 'John'],
    'amount': [10.5, 5.3, 33.77],
    })

# January Data
right_df = pd.DataFrame({
    'id': [1, 2, 9],
    'date': [pd.to_datetime('2024-01-31')] * 3,
    'first_name': ['Alice', 'Michael', 'Zachary'],
    'last_name': ['Jones', 'Smith', 'Einck'],
    'amount': [11.1, np.nan, 14],
    })

Compare DataFrames

Next, we will initialize a PandasCompare instance to perform the comparison. Please consult the in-code documentation for a comprehensive list of available arguments.

pc = PandasCompare(
    left=left_df,
    right=right_df,
    left_label='feb',
    right_label='jan',
    join_on='id',
    left_ref=['first_name'],
    include_delta=True,
    verbose=True,
    )

Export to Excel

Finally, let's export the compare report to an Excel file to view the results.

pc.export_to_excel()