Skip to main content

A simple DBF file converter for Python3

Project description

A Python3 compatible utility for converting DBF version 5 files to CSV files, Pandas DataFrames, SQL tables, or HDF5 tables. This code was designed to be a very simple, fast and memory efficient conversion tool for legacy DBF files. Therefore, it lacks many features (such as a DBF file writer) that other packages might provide. DBF version 7, the most recent DBF file spec, is not currently supported by this package.

Bug fix and update requests can be filed at the GitHub repo.

This code is derived from an ActiveState DBF example that works with Python2 and is distributed under a PSF license.


This module was tested with the following Python and package versions. Python is the only requirement if you only want to export to CSV. In that case, comment out the other imports in the source code (only one file) to avoid using the optional packages.

  • Python >= 3.4

  • Pandas >= 0.15.2

  • PyTables >= 3.1

  • SQLalchemy >= 0.9


It’s probably easiest to install this package using pip:

$ pip install simpledbf

Or from GitHub:

$ pip install git+

Although this package is only one file, so you can just download it as is in any folder of your choosing.

Example Usage

Load a DBF file

This module currently only defines a single class, Dbf5, which is instantiated with a DBF file name (can contain path info).

In : from simpledbf import Dbf5

In : dbf = Dbf5('fake_file_name.dbf')

The Dbf5 object will initially only read the header information from the file, so you can inspect some of the properties. For example, numrec is the number of records in the DBF file, and fields is a list of tuples with information about the data columns. (See the DBF file spec for info on the column type characters. The “DeletionFlag” column is not exported, but simply checks if a record has been deleted.)

In : dbf.numrec
Out: 10000

In : dbf.fields
Out: [('DeletionFlag', 'C', 1), ('col_1', 'C', 15), ('col_2', 'N', 2)]

The docstring for this object contains a complete listing of attributes and their descriptions.

The mem method gives an approximate memory requirement for processing this DBF file. (~2x the total file size.) In addition, all of the output methods in this object take a chunksize keyword argument, which lets you split up the processing of large files into smaller chunks, to limit the total memory usage of the conversion process. When this keyword argument is passed into mem, the approximate memory footprint of the chunk will also be given, which can be useful when trying to determine the maximum chunksize your memory will allow.

In : dbf.mem()
This total process would require more than 350.2 MB of RAM.

In : dbf.mem(chunksize=1000)
Each chunk will require 4.793 MB of RAM.
This total process would require more than 350.2 MB of RAM.


To export the data to a CSV file, use the to_csv method, which takes the name of a CSV file as an input. The default behavior is to append new data to an existing file, so be careful. If chunksize is passed as a keyword argument, the file buffer will be flushed after processing that many records.

In : dbf = Dbf5('fake_file_name.dbf')

In : dbf.to_csv('junk.csv')

To DataFrame

The to_dataframe method returns the DBF records as a Pandas DataFrame. (Obviously, this method requires that Pandas is installed.) If the size of the DBF file exceeds available memory, then passing the chunksize keyword argument will return a generator function. This generator yields DataFrames of len(<=chunksize) until all of the records have been processed.

In : dbf = Dbf5('fake_file_name.dbf')

In : df = dbf.to_dataframe()
# df is a DataFrame with all records

In : dbf = Dbf5('fake_file_name.dbf')

In : for df in dbf.to_dataframe(chunksize=10000)
....     do_cool_stuff(df)
# Here a generator is returned

To an SQL Table

The to_pandassql method will transfer the DBF entries to an SQL database table of your choice. This method uses a combination of Pandas DataFrames and SQLalchemy, so both of these packages must be installed. This method requires an SQLalchemy engine string, which is used to initialize the database connection. This will be limited to the SQL databases supported by SQLalchemy, see the SQLalchemy docs for more info. (This has been tested with SQLite and Postgresql.)

In : dbf = Dbf5('fake_file_name.dbf')

In : dbf = dbf.to_pandassql('sqlite:///foo.db')

This method takes two optional arguments. table is the name of the table you’d like to use. If this is not passed, your new table will have the same name as the DBF file without file extension. Again, the default here is to append to an existing table. If you want to start fresh, delete the existing table before using this function. The chunksize keyword processes the DBF file in chunks of records no larger than this size.

In : dbf = Dbf5('fake_file_name.dbf')

In : dbf = dbf.to_pandassql('sqlite:///foo.db', table="fake_tbl",
....                        chunksize=100000)

To an HDF5 Table

The to_pandashdf method will transfer the DBF entries to an HDF5 table of your choice. This method uses a combination of Pandas DataFrames and PyTables, so both of these packages must be installed. This method requires a file name string for the HDF file you’d like to use. This file will be created if it does not exist. Again, the default is to append to an existing file of that name, so be careful here. The HDF file will be created using the highest level of compression (9) with the ‘blosc’ compression lib. This saves an enormous amount of disk space, with little degradation of performance.

In : dbf = Dbf5('fake_file_name.dbf')

In : dbf = dbf.to_pandashdf('fake.h5')

This method uses the same optional arguments, and corresponding defaults, as to_pandassql. See above.

In : dbf = Dbf5('fake_file_name.dbf')

In : dbf = dbf.to_pandassql('fake.h5', table="fake_tbl", chunksize=100000)

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

simpledbf-0.1.0.tar.gz (8.0 kB view hashes)

Uploaded source

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page