fastdbf 0.5.1

A Python package for reading and writing DBF files, powered by a Rust core

fastdbf

fastdbf is a high-performance Python package for reading and writing .dbf files.

Written in Rust (using PyO3), it provides standard Python bindings designed specifically for large datasets, where traditional pure-Python solutions (like the standard dbf package) suffer from significant performance bottlenecks.

Status

fastdbf is fully production-ready for core data exchange workloads.

Supported Features

• High-Performance I/O: Lightning fast reads and writes of standard .dbf files.
• Zero-Copy Bulk Transfers: Native Apache Arrow integration (to_arrow(), extend_arrow()) for high-speed exchange with Pandas/Polars.
• Visual FoxPro Support: Direct handling of VFP .dbf flavors, including mandatory null-flag layouts.
• Memo Fields: Automatic management of companion .fpt memo files for unbounded strings.
• Native Type Mapping: Correct Python/Arrow types for dates, datetimes, and integers — no manual casting needed.
• Strict Typing: Clear data mappings with custom exception classes (DbfFormatError).

Not Implemented yet

• In-place Schema Modification: Dynamic addition/removal of columns on pre-written tables (currently raises UnsupportedDbfTypeError).
• Advanced Engine Tools: Indexing, cross-table relationships, or built-in query paradigms.

Installation

Create or sync the development environment with uv:

uv sync

Install into the current environment with uv:

uv pip install .

Editable install:

uv pip install -e .

Run tests:

uv run pytest

Build a wheel:

uv build

Quick Start

Read an existing DBF file:

import fastdbf

with fastdbf.Table("people.dbf").open("r") as table:
    print(table.kind)
    print(table.field_names)
    print(table.record_count)
    print(table.row(0))

    for field in table.fields():
        print(field["name"], field["type_code"], field["nullable"])

    for row in table:
        print(row)

Create and write a new DBF file:

import fastdbf
from datetime import date, datetime

specs = "name C(25) null; age N(3,0) null; birth D null; created T null; active L null"
with fastdbf.Table("people.dbf", specs, dbf_type="vfp") as table:
    table.append({
        "name": "Alice",       # case-insensitive keys
        "age": 30,
        "birth": date(1994, 5, 20),
        "created": datetime(2024, 1, 1, 12, 0),
        "active": True,
    })

    table.append({
        "NAME": None,
        "AGE": None,
        "BIRTH": None,
        "CREATED": None,
        "ACTIVE": None,
    })

Type Mapping

fastdbf maps DBF field types to native Python and Arrow types, so no manual casting is needed in either direction.

DBF → Python / Arrow

DBF Field	DBF Type Code	Python type	Arrow type
Character	C	str	Utf8
Numeric (integer)	N(n,0)	int	Int64
Numeric (decimal)	N(n,k)	float	Float64
Date	D	datetime.date	Date32
DateTime	T	datetime.datetime	Timestamp(ms)
Logical	L	bool	Boolean
Integer	I	int	Int32
Double	B	float	Float64

Python / Pandas → DBF (writing)

append() and extend_arrow() both accept the types listed above, plus Pandas-native types without any manual casting:

Input type	DBF field written
datetime.date	D (Date)
datetime.datetime	T (DateTime)
pandas.Timestamp	T (DateTime)
numpy.int64 / int	N(n,0) or I
numpy.float64 / float	N(n,k) or B

Note: append(dict) is case-insensitive — "name", "NAME", and "Name" all resolve to the same DBF field.

Field Types

Currently supported field types:

• C Character
• D Date
• L Logical
• N Numeric
• F Float
• I Integer
• B Double
• T / @ DateTime
• Y Currency
• M / G / P as reference values

Nullable fields are supported through null or nullable modifiers in the field specification:

"name C(25) null; amount N(10,2) nullable; created T null"

Nullable fields should be used with dbf_type="vfp" for Visual FoxPro-compatible null flags.

Pandas / Arrow Integration

Reading a DBF into Pandas (recommended)

Using the Arrow interface gives correct types directly — no extra dtype conversion needed:

import fastdbf
import pyarrow as pa

with fastdbf.Table("data.dbf").open("r") as table:
    df = pa.record_batch(table.to_arrow()).to_pandas()

# Result:
# - Date fields    → datetime64 (via object column of datetime.date)
# - DateTime fields → datetime64[ms]
# - Numeric(N,0)   → Int64 (no silent float coercion!)
# - Logical        → bool

Writing a Pandas DataFrame to DBF

Method A: Arrow (fastest, recommended for large DataFrames)

import fastdbf
import pyarrow as pa

batch = pa.RecordBatch.from_pandas(df)

with fastdbf.Table("output.dbf", field_specs="NAME C(20); AGE N(10,0); BIRTH D; CREATED T") as table:
    table.extend_arrow(batch)

Method B: Row-by-row append (no dependencies beyond fastdbf)

import fastdbf

with fastdbf.Table("output.dbf", field_specs="NAME C(20); AGE N(10,0); BIRTH D; CREATED T") as table:
    for _, row in df.iterrows():
        table.append(row.to_dict())   # pandas.Timestamp, numpy types accepted natively

The _deleted column

All read methods expose a _deleted: bool column. This reflects the DBF soft-delete flag — a standard DBF concept where records are logically marked as deleted (with a * marker byte) but remain physically in the file until a PACK operation removes them.

# Skip deleted records when reading:
df = df[~df["_deleted"]]

# Mark a record as deleted:
with table.record(0) as rec:
    rec.set_deleted(True)

# Physically remove all deleted records:
table.pack()

Performance & Columnar I/O (Arrow)

For maximum performance, especially with large datasets, fastdbf provides columnar read/write interfaces that avoid the high overhead of Python object allocation.

1. Apache Arrow Interface (Zero-Copy) — Fastest

Leverages the Arrow PyCapsule Interface to exchange data directly between Rust and Pandas / Polars / PyArrow without copying.

Read into Pandas via Arrow:

import fastdbf
import pyarrow as pa

with fastdbf.Table("data.dbf").open("r") as table:
    # Arrow Batch -> Pandas DataFrame
    df = pa.record_batch(table.to_arrow()).to_pandas()

Write from Pandas via Arrow:

import fastdbf
import pyarrow as pa

# Create Arrow batch from DataFrame
batch = pa.RecordBatch.from_pandas(df)

with fastdbf.Table("output.dbf", field_specs="NAME C(20); AGE N(10,2)") as table:
    table.extend_arrow(batch)

2. Columnar Interface (to_columns, extend_columns)

Reads/writes data as a dictionary of lists (one list per column). Faster than row-by-row processing but still bound by GIL limits.

Bulk Columnar Read:

import pandas as pd
import fastdbf

with fastdbf.Table("data.dbf").open("r") as table:
    cols = table.to_columns()
    df = pd.DataFrame(cols)

Bulk Columnar Write:

import pandas as pd
import fastdbf

# Drop internal meta-columns like '_deleted' if present
clean_data = {col: df[col].tolist() for col in df.columns if col != "_deleted"}

with fastdbf.Table("output.dbf", field_specs="NAME C(20); AGE N(10,2)") as table:
    table.extend_columns(clean_data)

---

Overview: Read/Write Methods compared

Method	Implementation	Pros
Row-by-Row	table.row(), table.append()	Easiest to use
Bulk Columnar	to_columns(), extend_columns()	No heavy dependencies
Zero-Copy Arrow	to_arrow(), extend_arrow()	Direct memory exchange

Documentation

Full Python API documentation:

• docs/PYTHON_API.md

Changelog:

• CHANGELOG.md

Rust Example

use fastdbf::{Date, Table, Value};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut table = Table::new("name C(25); age N(3,0); birth D; qualified L")?;

    let mut record = table.new_record();
    record.insert(table.fields(), "name", Value::Character("Spunky".into()))?;
    record.insert(table.fields(), "age", Value::Numeric(23.0))?;
    record.insert(table.fields(), "birth", Value::Date(Some(Date::new(1989, 7, 23))))?;
    record.insert(table.fields(), "qualified", Value::Logical(Some(true)))?;
    table.push_record(record)?;

    table.write_to_path("example.dbf")?;
    Ok(())
}

License

This project is licensed under the Apache License 2.0.