abraxos 0.0.7

Upgrade you Pandas ETL process.

Abraxos

Abraxos is a lightweight Python toolkit for robust, row-aware data processing using Pandas and Pydantic. It helps you:

• Read and clean messy CSVs
• Transform data with fault-tolerant functions
• Validate rows using Pydantic models
• Load data into SQL databases with graceful error recovery

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🚀 Features

• 📄 CSV Ingestion with Bad Line Recovery
  Read CSVs in full or in chunks, and recover malformed lines separately.

• 🔁 Transform DataFrames Resiliently
  Apply transformation functions and isolate rows that fail.

• 🧪 Pydantic-Based Row Validation
  Validate each row using a Pydantic model, separating valid and invalid records.

• 🛢️ SQL Insertion with Error Splitting
  Insert DataFrames into SQL databases with automatic retry and chunking logic.

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📦 Installation

pip install abraxos

Abraxo requires Python 3.8+ and depends on: - pandas - numpy - optionally sqlalchemy for SQL I/O - your own pydantic models for validation

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Documentation

Full documentation is available at:
https://abraxos.readthedocs.io

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🧭 Usage Examples

🔍 Read CSVs with Error Recovery

from abraxos import read_csv

bad_lines, df = read_csv("data.csv")
print("Bad lines:", bad_lines)
print("Clean data:", df.head())

Example Output

Bad lines: [['', 'oops', 'bad', 'row']]
Clean data:
   id    name  age
0   1     Joe   28
1   2   Alice   35
2   3  Marcus   40

🧼 Transform DataFrames with Fault Isolation

from abraxos import transform

def clean_data(df):
    df["name"] = df["name"].str.strip().str.lower()
    return df

result = transform(df, clean_data)
print("Errors:", result.errors)
print("Success:", result.success_df)

Example Output

Errors: []
Success:
   id    name  age
0   1     joe   28
1   2   alice   35
2   3  marcus   40

✅ Validate Rows Using Pydantic

from abraxos import validate
from pydantic import BaseModel

class Person(BaseModel):
    name: str
    age: int

result = validate(df, Person())
print("Valid rows:", result.success_df)
print("Validation errors:", result.errors)

Example Output

Valid rows:
   name  age
0   Joe   28

Validation errors:
[
  ValidationError: 1 validation error for Person
  age
    value is not a valid integer (type=type_error.integer),

  ValidationError: 1 validation error for Person
  name
    none is not an allowed value (type=type_error.none.not_allowed)
]

🗃️ Insert Into SQL With Retry Logic

from abraxos import to_sql
from sqlalchemy import create_engine

engine = create_engine("sqlite:///example.db")
result = to_sql(df, "people", engine)

print("Successful inserts:", result.success_df.shape[0])
print("Failed rows:", result.errored_df)

Example Output

Successful inserts: 2
Failed rows:
   name  age
2  None   40

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🧪 Test Coverage

Abraxo's internal structure is modular and testable. You can run tests via:

pytest tests/

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📄 License

MIT License © 2024 Odos Matthews

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🧙‍♂️ Author

Crafted by Odos Matthews to bring some magic to data workflows.