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Convert DBF files to CSV, DataFrames, HDF5 tables, and SQL tables. Python3 compatible.

Project description


A Python3 utility for converting simple DBF files (see Limitations) to CSV files, Pandas DataFrames, SQL tables, or HDF5 tables. (There is almost complete Python2 support as well.) This code was designed to be very simple, fast and memory efficient; therefore, it lacks many features (such as writing DBF files) that other packages might provide. The conversion to CSV and SQL is entirely written in Python, so no additional dependencies are necessary. For other export formats, see Requirements.

Bug fixes, questions, and update requests are encouraged and 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.

DBF File Limitations

This package currently supports a subset of dBase III through 5 DBF files. In particular, support is missing for linked memo files (DBT files). This is mostly due to a limitation in of the types of files available to the author. Feel free to request an update if you can supply a DBF file with an associated DBT file. DBF version 7, the most recent DBF file spec, is not currently supported by this package.

Python 2 Support

Except for HDF file export, this code should work fine with Python2. HDF files created using simpledbf in Python3 are compatible with all Python2 HDF packages, so in principle, you could make any HDF files in a temporary Python3 environment. If you are using the Anaconda Python distribution (recommended), then you can make a small Python3 working environment as follows:

$ conda create -n dbf python=3 pip pandas pytables sqlalchemy
# Lots of output...

$ source activate dbf

dbf>$ pip install simpledbf

dbf>$ python
# This is using Python3

dbf>$ source deactivate

$ python
# This is using Python2 again

The HDF file export is currently broken in Python2 due to a limitation in Pandas HDF export with unicode. This issue may be fixed future versions of Pandas.


This module was tested with the following 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, >=2.7.9 (no HDF export)

  • Pandas >= 0.15.2 (Required for DataFrame)

  • PyTables >= 3.1 (with Pandas required for HDF tables)

  • SQLalchemy >= 0.9 (with Pandas required for DataFrame-SQL tables)


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

$ pip install simpledbf

Or from GitHub:

$ pip install git+

However, this package is currently a single file, so you can just download it into 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). An optional ‘codec’ keyword argument that controls the codec for reading/writing files. The default is ‘utf-8’. See the documentation for Python’s codec standard library module for more codec options.

In : from simpledbf import Dbf5

In : dbf = Dbf5('fake_file_name.dbf', codec='utf-8')

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, which could be wildly inaccurate.) 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.

Export the Data

For all export methods, once the dbf file has been exported, the internal file object will be exhausted, so you will not be able to re-export the data. This is the same behavior as a standard file object. To re-export data, first recreate a new Dbf5 instance using the same file name, which is the procedure followed in the documentation below.

Note on Empty/Bad Data

This package attempts to convert blank strings and poorly formatted values to an empty value of your choosing (almost, see below). This is controlled by the na keyword argument to all export functions. The default for CSV is an empty string (‘’), and for all other exports, it is ‘nan’ which is converted to float('nan'). NOTE The exception here is that float/int columns always use float('nan') for all missing values for DBF->SQL->DF conversion purposes. Pandas has very powerful methods and algorithms for working with missing data, including converting NaN to other values (e.g. empty strings).


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 the file already exists. If chunksize is passed as a keyword argument, the file buffer will be flushed after processing that many records. (May not be necessary.) The na keyword changes the value used for missing/bad entries (default is ‘’). The keyword header is a boolean that controls writing of the column names as the first row of the CSV file. The encoding of the resulting CSV file is determined by the codec that is set when opening the DBF file, see Loading.

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

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

If you are unhappy with the default CSV output of this module, Pandas also has very powerful CSV export capabilities for DataFrames.

To SQL (CSV based)

Most SQL databases can import CSV files directly into an available table. The pure-Python to_textsql method creates a SQL file containing the appropriate table creation SQL and the SQL-variant command needed for loading the file. In addition, the header-less CSV file is also created. (It is up to you to load run the SQL file. See below.) This function takes two mandatory arguments. First, the name of of the SQL text file that you’d like to create, and second, the name of the CSV file you’d like to create. In addition, there are a number of optional keyword arguments as well. sqltype controls the output dialect. The default is ‘sqlite’, but ‘postgres’ is also accepted. table can be used to set the name of the SQL table that will be created. By default, this will be the name of the DBF file without the file extension. You should escape quote characters (”) in the CSV file. This is controlled with the escapeqoute keyword, which defaults to '"'. (This changes ‘”’ in text strings to ‘””’, which the SQL server should ignore.) The chunksize, na, and header keywords are used to control the CSV file. See above.

Here’s an example for SQLite:

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

In : dbf.to_textsql('junk.sql', 'junk.csv')

# Exit Python
$ sqlite3 junk.db < junk.sql

Here’s an example for Postgresql:

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

In : dbf.to_textsql('junk.sql', 'junk.csv', sqltype='postgres')

# Exit Python
$ psql -U username -f junk.sql db_name

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. The na keyword changes the value used for missing/bad entries (default is ‘nan’ which inserts float('nan')).

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

Issue with DataFrame Chunksize

When a DataFrame is constructed, it attempts to determine the dtype of each column. If you chunk the DataFrame output, it turns out that the dtype for a column can change. For example, if one chunk has a column with all strings, the dtype will be np.objec; however, if that column is full of float('nan') in the next chunk, then the dtype will be float. This has some consequences for writing to SQL and HDF tables as well. In principle, this could be changed, but it is currently non-trivial to set the dtypes for DataFrame columns on construction. Please file a PR through GitHub if this is a big problem.

To an SQL Table using Pandas

The to_pandassql method will transfer the DBF entries to an SQL database table of your choice using a combination of Pandas DataFrames and SQLalchemy. A valid SQLalchemy engine string argument is required to connect with the database. Database support will be limited to those supported by SQLalchemy. (This has been tested with SQLite and Postgresql.) Note, if you are transferring a large amount of data, this method will be very slow. If you have direct access to the SQL server, you might want to use the text-based SQL export instead.

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

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

This method takes three 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. The na keyword changes the value used for missing/bad entries (default is ‘nan’ which inserts float('nan')).

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)

See the chunksize issue for DataFrame export for information on a potential problem you may encounter with chunksize.

Export all DBF Files to Same HDF File

Because HDF export use the original file name as the stored table name, it is trivial to process a group of files into a single HDF file. Below is an example for HDF export.

In : import os

In : from simpledbf import Dbf5

In : files = os.listdir('.')

In : for f in files:
....     if f[-3:].lower() == 'dbf':
....         dbf = Dbf5(f)
....         dbf.to_pandashdf('all_data.h5')

The process is very similar for to_textsql or to_pandassql.

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