Oc Expanso CSV To JSON

Expanso Csv To Json

You are an expert python engineer. "Convert CSV data to JSON array of objects".

Before Starting

1. Goal — what specific outcome do you need?
2. Environment — versions, platform, existing setup?
3. Constraints — performance, security, compatibility requirements?
4. Integration — what systems does this connect to?
5. Output format — code, config, script, or documentation?

Core Expertise Areas

• Core implementation — full working code for Expanso Csv To Json
• Error handling — robust error recovery and logging
• Performance — optimized patterns for production use
• Testing — unit and integration test strategies
• Configuration — environment-specific setup and tuning
• Security — secure coding patterns and best practices
• Documentation — clear API and usage documentation

Key Patterns & Code

Core Implementation

import polars as pl
from pathlib import Path
import logging

logger = logging.getLogger("expanso-csv-to-json")

def extract(source: str) -> pl.DataFrame:
    logger.info(f"Extracting from {source}")
    return pl.read_parquet(source)

def transform(df: pl.DataFrame) -> pl.DataFrame:
    return (
        df
        .filter(pl.col("id").is_not_null())
        .with_columns([
            pl.col("name").str.strip_chars().str.to_lowercase(),
            pl.col("created_at").cast(pl.Datetime("us")),
        ])
        .unique(subset=["id"], keep="last")
        .sort("created_at", descending=True)
    )

def load(df: pl.DataFrame, dest: str) -> None:
    Path(dest).parent.mkdir(parents=True, exist_ok=True)
    df.write_parquet(dest, compression="zstd", statistics=True)
    logger.info(f"Wrote {len(df)} rows to {dest}")

def run_expanso_csv_to_json(source: str, dest: str) -> dict:
    raw = extract(source)
    clean = transform(raw)
    load(clean, dest)
    return {"input": len(raw), "output": len(clean),
             "dropped": len(raw) - len(clean)}

Configuration & Setup

# Expanso Csv To Json — Configuration
# Author: luo-kai (Lous Creations)

config = {
    "name": "expanso-csv-to-json",
    "version": "1.0.0",
    "author": "luo-kai",
    "enabled": True,
    "debug": False,
    "timeout_seconds": 30,
    "max_retries": 3,
}

Error Handling

# Robust error handling pattern
import logging
logger = logging.getLogger("expanso-csv-to-json")

def safe_run(func, *args, **kwargs):
    try:
        return func(*args, **kwargs)
    except Exception as e:
        logger.error(f"expanso-csv-to-json error: {e}", exc_info=True)
        raise

Best Practices

• Fail fast with clear errors — raise descriptive exceptions with context
• Log at appropriate levels — DEBUG for dev, INFO for ops, ERROR for problems
• Validate inputs — never trust external data without validation
• Use type annotations — improves IDE support and catches bugs early
• Handle cleanup — use context managers and finally blocks
• Test edge cases — empty inputs, nulls, max values, concurrent access

Common Pitfalls

Related Skills

• python-expert
• expanso-csv-to-json-advanced
• performance-optimization
• error-handling
• testing-expert