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https://github.com/rnelsonchem/simpledbf

A simple DBF file converter for Python3
https://github.com/rnelsonchem/simpledbf

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A simple DBF file converter for Python3

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README 

        
simpledbf

#########



*simpledbf* is a Python library for converting basic DBF files (see

`Limitations`_) to CSV files, Pandas DataFrames, SQL tables, or HDF5 tables.

This package is fully compatible with Python >=3.4, with almost complete

`Python 2.7 support`_ as well. The conversion to CSV and SQL (see

``to_textsql`` below) is entirely written in Python, so no additional

dependencies are necessary. For other export formats, see `Optional

Requirements`_.  This code was designed to be very simple, fast and memory

efficient for convenient interactive or batch file processing; therefore, it

lacks many features, such as the ability to write DBF files, that other

packages might provide. 



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.



.. _Optional Requirements:



Optional Requirements

---------------------



* 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)



Installation

------------



The most recent release of *simpledbf* can be installed using ``pip`` or

``conda``, if you happen to be using the `Anaconda Python distribution`_.



Using ``conda``::



    $ conda install -c https://conda.binstar.org/rnelsonchem simpledbf



Using ``pip``::



    $ pip install simpledbf



The development version can be installed from GitHub::



    $ pip install git+https://github.com/rnelsonchem/simpledbf.git



As an alternative, this package only contains a single file, so in principle,

you could download the ``simpledbf.py`` file from Github and put it in any

folder of your choosing.



.. _Limitations:



DBF File Limitations

--------------------



This package currently supports a subset of `dBase III through 5`_ DBF files.

In particular, support is missing for linked memo (i.e. DBT) files. This is

mostly due to limitations in the types of files available to the author.  Feel

free to request an update if you can supply a DBF file with an associated memo

file. `DBF version 7`_, the most recent DBF file spec, is not currently

supported by this package.



.. _Python 2.7 support:



Python 2 Support 

----------------



Except for HDF file export, this code should work fine with Python >=2.7.

However, HDF files created 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:



.. code::



    $ conda create -n dbf python=3 pandas pytables sqlalchemy

    # Lots of output...

    

    $ source activate dbf



    dbf>$ conda install -c https://conda.binstar.org/rnelsonchem simpledbf



    dbf>$ python my_py3_hdf_creation_script.py

    # This is using Python3



    dbf>$ source deactivate



    $ python my_py2_stuff_with_hdf.py

    # This is using Python2 again



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/PyTables.



Example Usage

#############



.. _Loading:



Load a DBF file

---------------



This module currently only defines a single class, ``Dbf5``, which is

instantiated with a DBF file name, which can contain path info as well. An

optional 'codec' keyword argument that controls the codec used for

reading/writing files. The default is 'utf-8'. See the documentation for

Python's `codec standard library module`_ for more codec options.



.. code::



    In : from simpledbf import Dbf5



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



The ``Dbf5`` object initially only reads 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 always present as a check

for deleted records; however, it is never exported during conversion.



.. code::



    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.



.. code::



    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

---------------



The ``Ddb5`` object behaves like Python's file object in that it will be

"exhausted" after export. To re-export the DBF data to a different format,

first create a new ``Dbf5`` instance using the same file name. This procedure

is followed in the documentation below.



    

Note on Empty/Bad Data

++++++++++++++++++++++



This package attempts to convert most blank strings and poorly formatted

values to an empty value of your choosing. 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 converts empty/bad

values to ``float('nan')``. *NOTE* The exception here is that float/int

columns always use ``float('nan')`` for all missing values for

DBF->SQL->DataFrame conversion purposes. Pandas has very powerful functions

for `working with missing data`_, including converting NaN to other values

(e.g.  empty strings). 



        

To CSV

++++++



Use the ``to_csv`` method to export the data to a CSV file. This method

requires 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. The

``chunksize`` keyword argument controls the frequency that  the file buffer

will be flushed, which 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`_. 



.. code::



    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 create tables directly from local CSV files. The

pure-Python ``to_textsql`` method creates two files: 1) a header-less CSV file

containing the DBF contents, and 2) a SQL file containing the appropriate

table creation and CSV import code. It is up to you to run the SQL file as a

separate step. This function takes two mandatory arguments, which are simply

the names of the SQL and CSV files, respectively. 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``

sets 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:



.. code::



    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:



.. code::



    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.  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')``).



.. code::



    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



.. _chunksize issue:



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.object``; however, if in the next chunk that same

column is full of ``float('nan')``, the resulting dtype will be set as

``float``. This has some consequences for writing to SQL and HDF tables as

well. In principle, this behavior 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.



.. code::



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



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



This method accepts 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')``).



.. code::



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



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

    ....                    chunksize=100000)

    



To an HDF5 Table

++++++++++++++++



The ``to_pandashdf`` method transfers 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, which will be created if it does not exist.  Again,

the default behavior 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; however, this

compression library is non-standard, which can cause problems with other HDF

libraries. Compression options are controlled use the ``complib`` and

``complevel`` keyword arguments, which are identical to the ones described in

the `Pandas HDF compression docs`_.



.. code::



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



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



This method uses the same optional arguments, and corresponding defaults, as

``to_pandassql`` (see above). A example with ``chunksize`` is shown below. In

addition, a ``data_columns`` keyword argument is also available, which sets

the columns that will be used as data columns in the HDF table. Data columns

can be used for advanced searching and selection; however, there is some

degredation of preformance for large numbers of data columns. See the `Pandas

data columns docs`_ for a more detailed explanation.



.. code::



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



    In : dbf.to_pandashdf('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.



Batch Export

++++++++++++



Batch file export is trivial using *simpledbf*. For example, the following

code processes all DBF files in the current directory into separate tables in

a single HDF file.



.. code:: 



    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')



   

.. External Hyperlinks



.. _ActiveState DBF example: http://code.activestate.com/recipes/

        362715-dbf-reader-and-writer/

.. _GitHub repo: https://github.com/rnelsonchem/simpledbf

.. _dBase III through 5: http://ulisse.elettra.trieste.it/services/doc/

        dbase/DBFstruct.htm

.. _DBF version 7: http://www.dbase.com/KnowledgeBase/int/db7_file_fmt.htm

.. _Anaconda Python distribution: http://continuum.io/downloads

.. _limitation in Pandas HDF export with unicode: http://pandas.pydata.org/

        pandas-docs/stable/io.html#datatypes

.. _codec standard library module: https://docs.python.org/3.4/library/

        codecs.html 

.. _working with missing data: http://pandas.pydata.org/pandas-docs/stable/

        missing_data.html

.. _powerful CSV export capabilities: http://pandas.pydata.org/pandas-docs/

        stable/io.html#writing-to-csv-format

.. _SQLalchemy engine string: http://docs.sqlalchemy.org/en/rel_0_9/core/

        engines.html

.. _Pandas HDF compression docs: http://pandas.pydata.org/pandas-docs/stable/

        io.html#compression

.. _Pandas data columns docs: http://pandas.pydata.org/pandas-docs/stable/

        io.html#query-via-data-columns