datax-validator 0.1.3

A simple data quality checking package

Validata: A tool for data quality checks

A Python package to validate data quality for pandas DataFrames with flexible, configurable rules.

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Features

• Validate columns for numeric, date, string length, and outlier checks
• Automatically generate a data quality report with examples of failures
• Export results to CSV
• Plug-and-play rule configuration for each column
• Support for custom rules
• Includes fuzz testing with Faker for robust testing

Rules

• check for null (missing) values in a column
• ensure the percentage of null (missing) values does not exceed a given threshold
• ensure values in a column are unique
• ensure values fall within a specified range
• ensure all values are positive
• ensure values match a given regex pattern
• ensure column values are in a predefined set
• ensure each value meets a minimum length
• ensure each value does not exceed a maximum length
• ensure valid email format
• ensure that each value is numeric
• ensure that value is among the specified values.
• ensure that none of the values are in the disallowed list.
• check if there are duplicate rows in the dataframe
• check if any value contains leading or trailing spaces
• check if values match a specific case format
• check if a column contain only one unique value
• check if values contain any special characters
• check if a value contains only blank spaces
• check for non-numeric values in a numeric column
• Validates a dependency between two columns using a custom condition
• check if DataFrame has exactly the expected column names
• validate if columns match the expected data types.
• validate if all values are alphabetic
• validate if column values contain only alphanumeric characters

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Installation

Clone this repository:

git clone https://github.com/your-username/validata.git
cd validata

Install dependencies:

pip install -r requirements.txt

(Optional) Install the package locally in editable mode:

pip install -e .

Project Structure

validata/
├── LICENSE
├── pyproject.toml
├── requirements.txt
├── README.md
├── src/
│   └── validata/
│       ├── __init__.py
│       ├── checker.py
│       └── rules.py
└── tests/
    ├── test_checker.py
    ├── test_rules.py
    └── test_faker.py

Usage

Import the package

import pandas as pd
from src.validata.checker import DataQualityChecker
from src.validata.rules import check_min_length, check_numeric, check_date_format, check_outliers_zscore

Prepare your dataframe

df = pd.DataFrame({
    "name": ["Alice", "Bo", "Charles", ""],
    "age": [25, "NaN", 40, 200],
    "dob": ["1990-01-01", "not-a-date", "1985-05-12", "2000-13-01"]
})

Define rules

rules = {
    "name": [check_min_length(3)],
    "age": [check_numeric(), check_outliers_zscore(2)],
    "dob": [check_date_format("%Y-%m-%d")]
}

Run the Checker

checker = DataQualityChecker(df)
results = checker.run(rules)

print(results)

Generate a Report

print(checker.generate_report())

Example Report

=== DATA QUALITY REPORT ===

Column: name
  - Min Length (3): FAIL | 2 values shorter than 3
    Examples of failures:
      Row 1: name = Bo
      Row 3: name = 

Column: age
  - Numeric Check: FAIL | 1 non-numeric value
    Examples of failures:
      Row 1: age = NaN
  - Outlier Z-Score (2): FAIL | 1 outlier detected
    Examples of failures:
      Row 3: age = 200

Column: dob
  - Date Format (%Y-%m-%d): FAIL | 2 invalid dates
    Examples of failures:
      Row 1: dob = not-a-date
      Row 3: dob = 2000-13-01

checker.export_report("validata_report.csv")

checker.export_report("validata_report.csv")

Testing

Run the full test suite

pytest -v

Run only rule test

pytest tests/test_rules.py -v or python -m pytest tests/test_rules.py -v


pytest tests/test_faker.py -v or python -m pytest tests/test_faker.py

Advanced Usage

Add a custom rule You can easily define a custom rule:

def check_no_nulls():
    def _check(series):
        failed = series[series.isnull()].index.tolist()
        return {
            "name": "No Nulls",
            "passed": len(failed) == 0,
            "message": f"{len(failed)} null values" if failed else "No nulls found",
            "failed_rows": failed
        }
    return _check

rules = {
    "name": [check_no_nulls()]
}

Development

Update dependencies in requirements.txt.

Add new rules in src/validata/rules.py.

Add tests in tests/.

Update README.md with new features.

Quick Start: ETL Pipeline Integration:

You can easily integrate DataqualityChecker into an ETL (Extract-Transform-Load) pipeline:

import pandas as pd
from src.validata.checker import DataQualityChecker
from src.validata.rules import check_min_length, check_numeric, check_date_format

def extract_data():
    # Example: Load from CSV
    return pd.read_csv("data/raw/customers.csv")

def transform_data(df):
    # Example: Clean column names and trim spaces
    df.columns = [c.strip().lower() for c in df.columns]
    return df

def validate_data(df):
    rules = {
        "name": [check_min_length(3)],
        "age": [check_numeric()],
        "dob": [check_date_format("%Y-%m-%d")]
    }

    checker = DataQualityChecker(df)
    checker.run(rules)

    print(checker.generate_report())

    # If any check fails, raise an error to stop the pipeline
    if any(not r["passed"] for results in checker.results.values() for r in results):
        raise ValueError("Data quality checks failed. See report above.")

    return df

def load_data(df):
    # Example: Save to processed folder
    df.to_csv("data/processed/customers_clean.csv", index=False)

if __name__ == "__main__":
    df = extract_data()
    df = transform_data(df)
    df = validate_data(df)
    load_data(df)

This structure allows:

Automatic validation before loading data

Integration with CI/CD pipelines

Failing fast if data quality issues are detected

Bad Data Example:

df = pd.DataFrame({
    "name": ["", "A", None],
    "age": ["NaN", "invalid", 9999],
    "dob": ["31-12-2020", "bad-date", None]
})

rules = {
    "name": [check_min_length(3)],
    "age": [check_numeric(), check_outliers_zscore(2)],
    "dob": [check_date_format("%Y-%m-%d")]
}

checker = DataQualityChecker(df)
checker.run(rules)
print(checker.generate_report())

if any(not r["passed"] for results in checker.results.values() for r in results):
    raise ValueError("Data quality validation failed.")

Output

=== DATA QUALITY REPORT ===

Column: name

• Min Length (3): FAIL | 3 values shorter than 3 Examples of failures: Row 0: name = Row 1: name = A Row 2: name = None

Column: age

• Numeric Check: FAIL | 2 non-numeric values Examples of failures: Row 0: age = NaN Row 1: age = invalid
• Outlier Z-Score (2): FAIL | 1 outlier detected Examples of failures: Row 2: age = 9999

Column: dob

• Date Format (%Y-%m-%d): FAIL | 3 invalid dates Examples of failures: Row 0: dob = 31-12-2020 Row 1: dob = bad-date Row 2: dob = None

ETL pipeline terminated due to data quality errors.