csv-validation 0.0.11

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CSV Validation Library

Blazing fast format validations for your CSV files

This is a Python lib with a Rust core that will allow you to validate huge CSV files (GBs) in seconds (or in few minutes for really huge files) using a minimal amount of memory.

Features

• ✨ Validate both plain and gzipped CSV files
• 🔍 Multiple validation types supported:
  • Regular expressions
  • Minimum/Maximum value checks
  • Value set validation (allowed values)
  • Column name and order validation
• 🐍 Python bindings included
• 📝 Detailed validation summaries with sample invalid values
• 🚀 High performance with optimizations like regex pre-compilation
• 📊 Support for large CSV files

Installation

Python

pip install csv_validation

Usage

Python

You can provide a file with the validation rules

from csv_validation import CSVValidator

validator = CSVValidator.from_file("validation_rules.yaml")
is_valid = validator.validate("data.csv")

You can also create a validator from a string

validation_rules = """
columns:
  - name: Name
    regex: ^[A-Za-z\s]{2,50}$
  - name: Age
    format: positive_integer
    max: 120
"""

validator = CSVValidator.from_string(validation_rules)
# Optionally set a custom column separator
validator.set_separator(";")  # Default is comma (,)

# Validate a CSV file
is_valid = validator.validate("data.csv")

Validation Definition Format

Create a small, easy to read YAML file with your validation rules. Example:

columns:
  - name: Name
    regex: ^[A-Za-z\s]{2,50}$  # Letters and spaces, 2-50 characters
  - name: Family Name
    regex: ^[A-Za-z\s'-]{2,50}$  # Letters, spaces, hyphens and apostrophes
  - name: Age
    format: positive_integer  # Using predefined format instead of custom regex
    max: 120
  - name: Salary
    format: integer  # Allows negative integers too
    min: 20000
  - name: Email
    regex: ^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$  # Standard email format
  - name: Phone
    regex: ^\+?[0-9]{10,15}$  # International phone format with optional +
  - name: Status
    values: [active, inactive, pending, suspended]  # Only these values are allowed
  - name: Gender
    values: [M, F, NB, O]  # M: Male, F: Female, NB: Non-Binary, O: Other

Validation Types

1. Regular Expression (regex)

   • Validate fields against custom regex patterns

2. Format (format)

   • Predefined formats for common validations
   • Available formats:
     • integer: Validates any integer number (positive or negative)
     • positive_integer: Validates positive integer numbers
     • non_empty: Validates that field contains at least one character
   • More formats will be added in next versions

3. Minimum Value (min)

   • Check if numeric fields are greater than or equal to a specified value

4. Maximum Value (max)

   • Check if numeric fields are less than or equal to a specified value

5. Value Set (values)

   • Ensure fields only contain values from a predefined set

Global Validations

empty_not_ok

Empty values (empty string '') are considered correct and accepted by default. However, if your data must always have some content, you can add a global empty_not_ok flag at the root level of your YAML definition to automatically add the format: non_empty validation to all columns:

empty_not_ok: true
columns:
  - name: Column1
    # other validations...

Supported Formats

• integer: Any integer number (positive or negative)
• positive integer: Any positive integer number
• decimal: Any decimal number (positive or negative)
• positive decimal point: Any positive decimal number using point as decimal separator
• positive decimal comma: Any positive decimal number using comma as decimal separator
• decimal scientific: Any decimal number in scientific notation
• non_empty: Any non-empty value

Column Separators

By default, the library uses comma (,) as the column separator. You can change this using the set_separator method:

validator = CSVValidator.from_file("rules.yml")
validator.set_separator(";")  # Use semicolon as separator
validator.set_separator("\t")  # Use tab as separator

Error Handling

The library provides detailed validation reports through Python's logging system. When a validation fails, you'll get information about:

• Which columns failed validation
• What type of validation failed
• Sample of invalid values
• Number of rows that failed each validation

Development

Prerequisites

• Rust 2021 edition or later
• Python 3.6+ (for Python bindings)
• Cargo and standard Rust tooling

Building from Source (Rust version)

# Clone the repository
git clone https://github.com/charro/csv_validation
cd csv_validation

# Build the project
cargo build --release

# Run tests
cargo test

Dependencies

• csv: CSV parsing
• flate2: Compression support
• pyo3: Python bindings
• regex: Regular expression support
• yaml-rust2: YAML parsing
• Various utilities for logging and serialization

License

MIT License

Copyright (c) 2024 CSV Validation Contributors

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.