DBF Data Analysis

Toolkit for analyzing Thai legacy DBF accounting databases using roonpoo, Parquet, and DuckDB.

Overview

This skill enables analysis of legacy Thai accounting systems that use DBF (dBase) files with TIS-620/CP874 encoding. The workflow involves:

1. Reading DBF - Use roonpoo library to read DBF files with proper Thai encoding
2. Converting to Parquet - Transform DBF to columnar Parquet format for efficient querying
3. Querying with DuckDB - Run SQL analytics on Parquet files

Environment Setup

Before starting analysis, ensure the roonpoo library is available:

cd libs/python
uv sync

For Parquet conversion and DuckDB queries:

uv pip install pyarrow duckdb

Reading DBF Files

Basic Usage with roonpoo

from roonpoo import DBF

# Stream records (memory-efficient)
for record in DBF('path/to/file.DBF', encoding='tis-620'):
    print(record)

# Preload all records
table = DBF('path/to/file.DBF', encoding='tis-620', preload=True)
print(table.records[0])

Key Parameters

Parameter	Description
encoding	Use 'tis-620' or 'cp874' for Thai text
preload	Load all records into memory
ignore_missing_memo	Skip if .FPT/.DBT memo file missing
char_decode_errors	'strict', 'ignore', or 'replace'

Inspect Table Structure

table = DBF('file.DBF', encoding='tis-620')

# Metadata
print(f"Version: {table.dbversion}")
print(f"Last modified: {table.date}")
print(f"Records: {table.header.numrecords}")

# Fields
for field in table.fields:
    print(f"{field.name}: type={field.type}, len={field.length}")

Field Types

Type	Description
C	Character (string)
N	Numeric
D	Date
L	Logical (boolean)
M	Memo (requires .FPT/.DBT)

Converting DBF to Parquet

Use the conversion script at scripts/dbf_to_parquet.py:

uv run python scripts/dbf_to_parquet.py /path/to/DATA/*.DBF -o /path/to/output/

Or inline:

from roonpoo import DBF
import pyarrow as pa
import pyarrow.parquet as pq
from decimal import Decimal

def convert_dbf_to_parquet(dbf_path, output_path):
    table = DBF(dbf_path, encoding='tis-620', char_decode_errors='replace')
    records = list(table)

    columns = {f.name: [] for f in table.fields}
    for rec in records:
        for field in table.fields:
            val = rec.get(field.name)
            if isinstance(val, Decimal):
                val = float(val)
            columns[field.name].append(val)

    arrow_table = pa.table(columns)
    pq.write_table(arrow_table, output_path)
    return len(records)

Querying with DuckDB

Setup

import duckdb
con = duckdb.connect()
parquet_dir = 'path/to/parquet/files'

Common Query Patterns

Query single file:

SELECT * FROM 'asParquet/ARMST.parquet' LIMIT 10

List all tables with row counts:

SELECT
    replace(filename, 'path/', '') as file,
    count(*) as rows
FROM parquet_scan('asParquet/*.parquet', filename=true)
GROUP BY filename
ORDER BY rows DESC

Cross-table JOIN:

SELECT
    a.ACCID,
    m.COMP,
    COUNT(*) as txn_count
FROM 'asParquet/ARTR.parquet' a
JOIN 'asParquet/ARMST.parquet' m ON a.ACCID = m.ACCID
GROUP BY a.ACCID, m.COMP
ORDER BY txn_count DESC

Schema inspection:

DESCRIBE SELECT * FROM 'asParquet/ARTR.parquet'

Common Thai Accounting Tables

Table	Description	Key Fields
ARMST	Customer master	ACCID, COMP, NAME, TEL
APMST	Vendor master	ACCID, COMP, NAME
ARTR	AR transactions	DOCNO, DATEDOC, ACCID, AMOUNT
APTR	AP transactions	DOCNO, DATEDOC, ACCID, AMOUNT
GLTR	GL transactions	GLID, DEBIT, CREDIT
GLTRHD	GL headers	DOCNO, DATEDOC
INVLOC	Inventory location	PCODE, LOCID, QTY

Workflow Example

Complete analysis workflow:

from roonpoo import DBF
from pathlib import Path
import duckdb

# 1. Explore DBF structure
data_dir = Path('sample_company/ALLDATA/DATA2011')
for dbf_file in sorted(data_dir.glob('*.DBF'))[:5]:
    table = DBF(dbf_file, encoding='tis-620')
    print(f"{dbf_file.name}: {table.header.numrecords} records")

# 2. Convert key tables to Parquet
# (use scripts/dbf_to_parquet.py)

# 3. Query with DuckDB
con = duckdb.connect()
result = con.execute("""
    SELECT GLID, SUM(DEBIT) as total_debit, SUM(CREDIT) as total_credit
    FROM 'asParquet/GLTR.parquet'
    GROUP BY GLID
    ORDER BY total_debit DESC
    LIMIT 10
""").fetchdf()
print(result)

Handling Encoding Issues

For files with encoding problems:

# Replace invalid characters
table = DBF('file.DBF', encoding='tis-620', char_decode_errors='replace')

# Or ignore them
table = DBF('file.DBF', encoding='tis-620', char_decode_errors='ignore')

Additional Resources

Scripts

• scripts/dbf_to_parquet.py - Batch convert DBF files to Parquet
• scripts/inspect_dbf.py - Inspect DBF structure and sample data

References

• references/table-schemas.md - Common Thai accounting table schemas
• references/query-patterns.md - Advanced DuckDB query patterns