data-fingerprint 0.1.9

DataFingerprint is a Python package designed to compare two datasets and generate a detailed report highlighting the differences between them. This tool is particularly useful for data validation, quality assurance, and ensuring data consistency across different sources.

DataFingerprint

DataFingerprint is a Python package designed to compare two datasets and generate a detailed report highlighting the differences between them. This tool is particularly useful for data validation, quality assurance, and ensuring data consistency across different sources.

Features

• Column Name Differences: Identify columns that are present in one dataset but missing in the other.
• Column Data Type Differences: Detect discrepancies in data types between corresponding columns in the two datasets.
• Row Differences: Find rows that are present in one dataset but missing in the other, or rows that have different values in corresponding columns.
• Paired Row Differences: Compare rows that have the same primary key or unique identifier in both datasets and identify differences in their values.
• Data Report: Generate a comprehensive report summarizing all the differences found between the two datasets.

function	purpose	result
data_fingerprint.src.comparator.get_data_report	Get data report object that has all the information about the differences	data_fingerprint.src.models.DataReport
data_fingerprint.src.utils.get_dataframe	Get polars.Dataframe of rows that are different (added source column)	polars.DataFrame
data_fingerprint.src.utils.get_number_of_row_differences	Get the number of different rows	int
data_fingerprint.src.utils.get_number_of_differences_per_source	Get the number of row differences per source	dict[str, int]
data_fingerprint.src.utils.get_ratio_of_differences_per_source	Get the ratio of row differences per source	dict[str, float]
data_fingerprint.src.utils.get_column_difference_ratio	[When grouping is used] Get the distribution of differences per column	dict[str, float]

Installation

To install DataFingerprint, you can use pip:

pip install data-fingerprint

Examples

Here's a basic example of how to use DataFingerprint to compare two datasets:

import polars as pl

from data_fingerprint.src.utils import get_dataframe
from data_fingerprint.src.comparator import get_data_report
from data_fingerprint.src.models import DataReport

# Create two sample datasets
df1 = pl.DataFrame(
    {"id": [1, 2, 3, 4], "name": ["Alice", "Bob", "Charlie", "George"], "age": [25, 30, 35, 26], "height": [170, 180, 175, 160], "weight": [60, 70, 75, 65]}
)
df2 = pl.DataFrame(
    {"id": [1, 2, 3], "name": ["Alice", "Bob", "David"], "age": [25, 30, 35], "weight": ["60", "70", "75"], "married": [True, False, True]}
)
# Generate a data report comparing the two datasets
report: DataReport = get_data_report(df1, df2, "df_0", "df_1", grouping_columns=["id"])
print(report.model_dump_json(indent=4))

Output:

{
    "df0_length": 4,
    "df1_length": 3,
    "df0_name": "df_0",
    "df1_name": "df_1",
    "comparable_columns": [
        "name",
        "id",
        "age"
    ],
    "column_differences": [
        {
            "source": "df_0",
            "column_name": "married",
            "difference_type": "MISSING",
            "more_information": null
        },
        {
            "source": "df_0",
            "column_name": "height",
            "difference_type": "EXTRA",
            "more_information": null
        },
        {
            "source": "df_0",
            "column_name": "weight",
            "difference_type": "DIFFERENT_TYPE",
            "more_information": {
                "df_0": "Int64",
                "df_1": "String"
            }
        }
    ],
    "row_differences": [
        {
            "source": "df_0",
            "row": {
                "age": [
                    26
                ],
                "id": [
                    4
                ],
                "name": [
                    "George"
                ]
            },
            "number_of_occurrences": 1,
            "difference_type": "MISSING_ROW",
            "more_information": null
        },
        {
            "sources": [
                "df_0",
                "df_1"
            ],
            "row": {
                "age": [
                    35,
                    35
                ],
                "id": [
                    3,
                    3
                ],
                "name": [
                    "Charlie",
                    "David"
                ]
            },
            "number_of_occurrences": 2,
            "grouping_columns": [
                "id"
            ],
            "column_differences": [
                "name"
            ],
            "consise_information": {
                "id": [
                    3,
                    3
                ],
                "name": [
                    "Charlie",
                    "David"
                ],
                "source": [
                    "df_0",
                    "df_1"
                ]
            },
            "row_with_source": {
                "age": [
                    35,
                    35
                ],
                "id": [
                    3,
                    3
                ],
                "name": [
                    "Charlie",
                    "David"
                ],
                "source": [
                    "df_0",
                    "df_1"
                ]
            }
        }
    ]
}

As You can see the DataReport will have some basic information like:

• df0_length and df1_length which are the lengths of the dataframes
• df0_name and df1_name which are the names of the dataframes
• comparable_columns which are the columns that are comparable between the two dataframes
• colum_differences which are the columns that have differences between the two dataframes and the type of difference
• row_differences which are the rows that have differences between the two dataframes and the type of difference

If you look closely at column_differences you will see that we always refrence the first dataframe as the source. Also You can see that there are different type of differences with more detailed information about the differences.

If you look at row_differences you will see that there are also multiple type of differences. We generally have two types of differences:

• RowDifference which is a difference between two rows that couldn't be grouped or there wan't any grouping
• RowGroupDifference which is a difference between two groups of rows that were grouped by the grouping_columns

When talking about RowDifference we have the following information:

• source which is the source of the row
• row which is original row that is different (keep on mind that it contains only the comparable columns, look at parameter comparable_columns)
• number_of_occurrences which is the number of times this row is present in the source
• difference_type which is the type of difference (MISSING_ROW says that the row is missing in the other dataframe)
• more_information which is more information about the difference (usually None)

When talking about RowGroupDifference we have the following information:

• sources which are the sources of the grouped rows (grouped by grouping_columns)
• row which are rows present in that group (keep on mind that it contains only the comparable columns, look at parameter comparable_columns)
• number_of_occurrences which is the number of times this difference is present in all sources (total)
• grouping_columns which are the columns used to group the rows
• column_differences which are the columns that are different
• consise_information which is a dictionary with more information about the differences containing the grouping columns, source of the row and the column_differences from original data (parameter row)
• row_with_source which is basically the same as the row but with the source

Now when we have differences we can get tabular information about those differences:

shape: (3, 4)
┌─────┬─────┬─────────┬────────┐
│ age ┆ id  ┆ name    ┆ source │
│ --- ┆ --- ┆ ---     ┆ ---    │
│ i64 ┆ i64 ┆ str     ┆ str    │
╞═════╪═════╪═════════╪════════╡
│ 26  ┆ 4   ┆ George  ┆ df_0   │
│ 35  ┆ 3   ┆ Charlie ┆ df_0   │
│ 35  ┆ 3   ┆ David   ┆ df_1   │
└─────┴─────┴─────────┴────────┘

We also have an option to gather more information about the differences:

• Get the number of row differences:

from data_fingerprint.src.utils import get_number_of_row_differences

print(get_number_of_row_differences(report))

Output:

3

• Get the number of differences per source:

from data_fingerprint.src.utils import get_number_of_differences_per_source

print(get_number_of_differences_per_source(report))

Output:

{'df_0': 2, 'df_1': 1}

• Get the ratio of differences per source:

from data_fingerprint.src.utils import get_ratio_of_differences_per_source

print(get_ratio_of_differences_per_source(report))

Output:

{'df_0': 0.6666666666666666, 'df_1': 0.3333333333333333}

• Get column difference ratio:

from data_fingerprint.src.utils import get_column_difference_ratio

print(get_column_difference_ratio(report))

Output:

{'age': 0.25, 'name': 0.5, 'id': 0.25}

License

This project is licensed under the GPLv3 License. See the LICENSE file for details.

Contributing

Contributions are welcome! Please open an issue or submit a pull request on GitHub.

Contact

For any questions or feedback, please contact [your email].