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Generate and load SPSS files based on `Table
Schema <>`__ descriptors.


- implements ``tableschema.Storage`` interface

Getting Started


The package use semantic versioning. It means that major versions could
include breaking changes. It's highly recommended to specify ``package``
version range in your ``setup/requirements`` file e.g.

.. code:: bash

pip install tableschema-spss


Code examples in this readme requires Python 3.3+ interpreter. You could
see even more example in
`examples <>`__

For this example your schema should be compatible with SPSS storage

.. code:: python

from tableschema import Table

# Load and save table to SPSS
table = Table('data.csv', schema='schema.json')'data', storage='spss', base_path='dir/path')


The whole public API of this package is described here and follows
semantic versioning rules. Everyting outside of this readme are private
API and could be changed without any notification on any new version.


Package implements `Tabular
Storage <>`__
interface (see full documentation on the link):


This driver provides an additional API:


- ``base_path (str)`` - a valid directory path where .sav files can be
created and read. If no base\_path is provided, the Storage object
methods will accept file paths rather than bucket names.


List all .sav and .zsav files at base path. Bucket list is only
maintained if Storage has a valid base path, otherwise will return None.

- ``(str[]/None)`` - returns bucket list or None

With a base path

We can get storage with a specified base path this way:

.. code:: python

from tableschema_spss import Storage

storage_base_path = 'path/to/storage/dir'
storage = Storage(storage_base_path)

We can then interact with storage buckets ('buckets' are SPSS .sav/.zsav
files in this context):

.. code:: python

storage.buckets # list buckets in storage
storage.create('bucket', descriptor)
storage.delete('bucket') # deletes named bucket
storage.delete() # deletes all buckets in storage
storage.describe('bucket') # return tableschema descriptor
storage.iter('bucket') # yields rows'bucket') # return rows
storage.write('bucket', rows)

Without a base path

We can also create storage without a base path this way:

.. code:: python

from tableschema_spss import Storage

storage = Storage() # no base path argument

Then we can specify SPSS files directly by passing their file path
(instead of bucket names):

.. code:: python

storage.create('data/my-bucket.sav', descriptor)
storage.delete('data/my-bucket.sav') # deletes named file
storage.describe('data/my-bucket.sav') # return tableschema descriptor
storage.iter('data/my-bucket.sav') # yields rows'data/my-bucket.sav') # return rows
storage.write('data/my-bucket.sav', rows)

Note that storage without base paths does not maintain an internal list
of buckets, so calling ``storage.buckets`` will return ``None``.

Reading .sav files

When reading SPSS data, SPSS date formats, ``DATE``, ``JDATE``,
``EDATE``, ``SDATE``, ``ADATE``, ``DATETIME``, and ``TIME`` are
transformed into Python ``date``, ``datetime``, and ``time`` objects,
where appropriate.

Other SPSS date formats, ``WKDAY``, ``MONTH``, ``MOYR``, ``WKYR``,
``QYR``, and ``DTIME`` are not supported for native transformation and
will be returned as strings.

Creating .sav files

When creating SPSS files from Table Schemas, ``date``, ``datetime``, and
``time`` field types must have a format property defined with the
following patterns:

- ``date``: ``%Y-%m-%d``
- ``datetime``: ``%Y-%m-%d %H:%M:%S``
- ``time``: ``%H:%M:%S.%f``

Table Schema descriptors passed to ``storage.create()`` should include a
custom ``spss:format`` property, defining the SPSS type format the data
is expected to represent. E.g.:

.. code:: json

"fields": [
"name": "person_id",
"type": "integer",
"spss:format": "F8"
"name": "name",
"type": "string",
"spss:format": "A10"
"type": "number",
"name": "salary",
"title": "Current Salary",
"spss:format": "DOLLAR8"
"type": "date",
"name": "bdate",
"title": "Date of Birth",
"format": "%Y-%m-%d",
"spss:format": "ADATE10"


The project follows the `Open Knowledge International coding
standards <>`__.

| Recommended way to get started is to create and activate a project
virtual environment.
| To install package and development dependencies into active


$ make install

To run tests with linting and coverage:

.. code:: bash

$ make test

| For linting ``pylama`` configured in ``pylama.ini`` is used. On this
stage it's already
| installed into your environment and could be used separately with more
fine-grained control
| as described in documentation -

For example to sort results by error type:

.. code:: bash

$ pylama --sort <path>

| For testing ``tox`` configured in ``tox.ini`` is used.
| It's already installed into your environment and could be used
separately with more fine-grained control as described in documentation

| For example to check subset of tests against Python 2 environment with
increased verbosity.
| All positional arguments and options after ``--`` will be passed to

.. code:: bash

tox -e py27 -- -v tests/<path>

| Under the hood ``tox`` uses ``pytest`` configured in ``pytest.ini``,
| and ``mock`` packages. This packages are available only in tox


Here described only breaking and the most important changes. The full
changelog and documentation for all released versions could be found in
nicely formatted `commit
history <>`__.


Initial driver implementation.

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