snavro 0.1.5

A unified file reader for Parquet and Avro files with automatic format detection

Snavro

A unified file reader for Parquet and Avro files with automatic format detection.

Features

• 🚀 Automatic format detection - Just point to a file and Snavro figures out the format
• 📊 Multiple format support - Handles .parquet, .parquet.snappy, and .avro files
• 🖥️ CLI and Python API - Use from command line or import in your Python code
• 📈 Smart data preview - Shows file info, shape, columns, and sample data
• 🔧 Type hints - Full type annotation support for better IDE experience

Supported Formats

• Parquet files (.parquet)
• Snappy-compressed Parquet files (.parquet.snappy)
• Avro files (.avro)

Installation

From PyPI (when published)

pip install snavro

From source

git clone https://github.com/yourusername/snavro.git
cd snavro
pip install -e .

Usage

Command Line Interface

Interactive mode

snavro

This will show you all supported files in the current directory and let you choose which one to examine.

Direct file access

# Show first 5 rows (default)
snavro myfile.parquet

# Show first 10 rows
snavro myfile.avro 10

# Works with snappy-compressed files too
snavro data.parquet.snappy 3

Python API

Basic usage

import snavro

# Simple function to read and display file info
snavro.read_and_display_file("myfile.parquet", num_rows=5)

Using the FileReader class

from snavro import FileReader

reader = FileReader()

# Check if a file is supported
if reader.is_supported_file("myfile.parquet"):
    # Read the file (returns pandas DataFrame for Parquet, list of dicts for Avro)
    data = reader.read_file("myfile.parquet")
    
    # Or display file info with preview
    reader.display_file_info("myfile.parquet", num_rows=10)

Working with the data

from snavro import FileReader

reader = FileReader()

# For Parquet files - returns pandas DataFrame
df = reader.read_parquet("data.parquet")
print(f"Shape: {df.shape}")
print(df.head())

# For Avro files - returns list of dictionaries
records = reader.read_avro("data.avro")
print(f"Number of records: {len(records)}")
print(records[0])  # First record

Utility functions

from snavro import get_supported_files

# Get all supported files in current directory
files = get_supported_files()
print(files)

# Get supported files in specific directory
files = get_supported_files("/path/to/data")

Example Output

Parquet File

Reading Parquet file: sales_data.parquet
--------------------------------------------------
Shape: (1000, 5)
Columns: ['date', 'product', 'sales', 'region', 'msg']

First few rows:
        date product  sales region                    msg
0 2023-01-01   Widget    100   North  {"status": "active"}
1 2023-01-02   Gadget    150   South  {"status": "pending"}
2 2023-01-03   Widget     75    East  {"status": "active"}

First 'msg' value:
{"status": "active"}

Avro File

Reading Avro file: events.avro
--------------------------------------------------
Total records: 500

First 3 records:
Record 1:
{'timestamp': '2023-01-01T10:00:00Z', 'event': 'login', 'user_id': 12345}

Record 2:
{'timestamp': '2023-01-01T10:05:00Z', 'event': 'page_view', 'user_id': 12345}

Record 3:
{'timestamp': '2023-01-01T10:10:00Z', 'event': 'logout', 'user_id': 12345}

Development

Setup development environment

git clone https://github.com/yourusername/snavro.git
cd snavro
pip install -e ".[dev]"

Run tests

pytest

Code formatting

black snavro/

Type checking

mypy snavro/

Dependencies

• pandas >= 2.0.0 - For Parquet file handling
• pyarrow >= 10.0.0 - Parquet engine with compression support
• fastavro >= 1.8.0 - Fast Avro file reading

License

MIT License - see LICENSE file for details.

Contributing

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

Development Setup

git clone https://github.com/SkinnyPigeon/snavro.git
cd snavro
pip install -e ".[dev]"

Running Tests

pytest tests/ -v

Code Quality

black snavro/ tests/
flake8 snavro/ tests/
mypy snavro/

Releases

This project uses automated releases via GitHub Actions. See RELEASE.md for details.

Changelog

0.1.0

• Initial release
• Support for Parquet and Avro files
• CLI and Python API
• Automatic format detection