Metadata-Version: 2.0
Name: tabulator
Version: 1.12.1
Summary: Consistent interface for stream reading and writing tabular data (csv/xls/json/etc)
Home-page: https://github.com/frictionlessdata/tabulator-py
Author: Open Knowledge Foundation
Author-email: info@okfn.org
License: MIT
Keywords: frictionless data
Platform: UNKNOWN
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Classifier: Environment :: Web Environment
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Internet :: WWW/HTTP :: Dynamic Content
Classifier: Topic :: Software Development :: Libraries :: Python Modules
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Requires-Dist: click (>=6.0,<7.0)
Requires-Dist: ijson (<3.0,>=2.0)
Requires-Dist: jsonlines (>=1.1,<2.0)
Requires-Dist: linear-tsv (>=1.0,<2.0)
Requires-Dist: openpyxl (<3.0,>=2.4)
Requires-Dist: requests (<3.0,>=2.8)
Requires-Dist: six (>=1.9,<2.0)
Requires-Dist: sqlalchemy (>=0.9.6,<2.0)
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tabulator-py
============

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| |Gitter|

A library for reading and writing tabular data (csv/xls/json/etc).

    Version v1.0 includes deprecated API removal and provisional API
    changes. Please read a `migration guide <#v10>`__.

Features
--------

-  supports various formats:
   csv/tsv/xls/xlsx/json/ndjson/ods/gsheet/inline/sql/etc
-  reads data from local, remote, stream or text sources
-  streams data instead of using a lot of memory
-  processes data via simple user processors
-  saves data using the same interface
-  custom loaders, parsers and writers
-  support for compressed files

Getting started
---------------

Installation
~~~~~~~~~~~~

The package use semantic versioning. It means that major versions could
include breaking changes. It's highly recommended to specify
``tabulator`` version range if you use ``setup.py`` or
``requirements.txt`` file e.g. ``tabulator<2.0``.

.. code:: bash

    $ pip install tabulator # OR "sudo pip install tabulator"

Examples
~~~~~~~~

It's pretty simple to start with ``tabulator``:

.. code:: python

    from tabulator import Stream

    with Stream('path.csv', headers=1) as stream:
        stream.headers # [header1, header2, ..]
        for row in stream:
            row  # [value1, value2, ..]

There is an
`examples <https://github.com/frictionlessdata/tabulator-py/tree/master/examples>`__
directory containing other code listings.

Documentation
-------------

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.

Stream
~~~~~~

The ``Stream`` class represents a tabular stream. It takes the
``source`` argument in a form of source string or object:

::

    <scheme>://path/to/file.<format>

and uses corresponding ``Loader`` and ``Parser`` to open and start to
iterate over the tabular stream. Also user can pass ``scheme`` and
``format`` explicitly as constructor arguments. There are also alot
other options described in sections below.

Let's create a simple stream object to read csv file:

.. code:: python

    from tabulator import Stream

    stream = Stream('data.csv')

This action just instantiate a stream instance. There is no actual IO
interactions or source validity checks. We need to open the stream
object.

.. code:: python

    stream.open()

This call will validate data source, open underlaying stream and read
the data sample (if it's not disabled). All possible exceptions will be
raised on ``stream.open`` call not on constructor call.

After work with the stream is done it could be closed:

.. code:: python

    stream.close()

The ``Stream`` class supports Python context manager interface so calls
above could be written using ``with`` syntax. It's a common and
recommended way to use ``tabulator`` stream:

.. code:: pytnon

    with Stream('data.csv') as stream:
      # use stream

Now we could iterate over rows in our tabular data source. It's
important to understand that ``tabulator`` uses underlaying streams not
loading it to memory (just one row at time). So the ``stream.iter()``
interface is the most effective way to use the stream:

.. code:: python

    for row in stream.iter():
      row # [value1, value2, ..]

But if you need all the data in one call you could use ``stream.read()``
function instead of ``stream.iter()`` function. But if you just run it
after code snippet above the ``stream.read()`` call will return an empty
list. That another important following of stream nature of ``tabulator``
- the ``Stream`` instance just iterates over an underlaying stream. The
underlaying stream has internal pointer (for example as file-like object
has). So after we've iterated over all rows in the first listing the
pointer is set to the end of stream.

.. code:: python

    stream.read() # []

The recommended way is to iterate (or read) over stream just once (and
save data to memory if needed). But there is a possibility to reset the
steram pointer. For some sources it will not be effective (another HTTP
request for remote source). But if you work with local file as a source
for example it's just a cheap ``file.seek()`` call:

::

    stream.reset()
    stream.read() # [[value1, value2, ..], ..]

The ``Stream`` class supports saving tabular data stream to the
filesystem. Let's reset stream again (dont' forget about the pointer)
and save it to the disk:

::

    stream.reset()
    stream.save('data-copy.csv')

The full session will be looking like this:

.. code:: python

    from tabulator import Stream

    with Stream('data.csv') as stream:
      for row in stream.iter():
        row # [value1, value2, ..]
      stream.reset()
      stream.read() # [[value1, value2, ..], ..]
      stream.reset()
      stream.save('data-copy.csv')

It's just a pretty basic ``Stream`` introduction. Please read the full
documentation below and about ``Stream`` arguments in more detail in
following sections. There are many other goodies like headers
extraction, keyed output, post parse processors and many more!

``Stream(source, **options)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Create stream class instance.

-  ``source (any)`` - stream source in a form based on ``scheme``
   argument
-  ``headers (list/int)`` - headers list or row number containing
   headers or row numbers range containing headers. If number is given
   for plain source headers row and all rows before will be removed and
   for keyed source no rows will be removed. See
   `headers <https://github.com/frictionlessdata/tabulator-py#headers>`__
   section.
-  ``scheme (str)`` - source scheme with ``file`` as default. For the
   most cases scheme will be inferred from source. See a list of
   supported schemas below. See
   `schemes <https://github.com/frictionlessdata/tabulator-py#schemes>`__
   section.
-  ``format (str)`` - source format with ``None`` (detect) as default.
   For the most cases format will be inferred from source. See a list of
   supported formats below. See
   `formats <https://github.com/frictionlessdata/tabulator-py#formats>`__
   section.
-  ``encoding (str)`` - source encoding with ``None`` (detect) as
   default. See
   `encoding <https://github.com/frictionlessdata/tabulator-py#encoding>`__
   section.
-  ``compression (str)`` - source compression like ``zip`` with ``None``
   (detect) as default. See
   `compression <https://github.com/frictionlessdata/tabulator-py#compression>`__
   section.
-  ``allow_html (bool)`` - a flag to allow html. See `allow
   html <https://github.com/frictionlessdata/tabulator-py#allow-html>`__
   section.
-  ``sample_size (int)`` - rows count for table.sample. Set to "0" to
   prevent any parsing activities before actual table.iter call. In this
   case headers will not be extracted from the source. See `sample
   size <https://github.com/frictionlessdata/tabulator-py#sample-size>`__
   section.
-  ``bytes_sample_size (int)`` - sample size in bytes for operations
   like encoding detection. See `bytes sample
   size <https://github.com/frictionlessdata/tabulator-py#bytes-sample-size>`__
   section.
-  ``ignore_blank_headers (bool)`` - a flag to ignore any column having
   a blank header. See `ignore blank
   headers <https://github.com/frictionlessdata/tabulator-py#ignore-blank-headers>`__
   section.
-  ``force_strings (bool)`` - if ``True`` all output will be converted
   to strings. See `force
   strings <https://github.com/frictionlessdata/tabulator-py#force-strings>`__
   section.
-  ``force_parse (bool)`` - if ``True`` on row parsing error a stream
   will return an empty row instead of raising an exception. See `force
   parse <https://github.com/frictionlessdata/tabulator-py#force-parse>`__
   section.
-  ``skip_rows (int/str[])`` - list of rows to skip by row number or row
   comment. Example: ``skip_rows=[1, 2, '#', '//']`` - rows 1, 2 and all
   rows started with ``#`` and ``//`` will be skipped. See `skip
   rows <https://github.com/frictionlessdata/tabulator-py#skip-rows>`__
   section.
-  ``post_parse (generator[])`` - post parse processors (hooks).
   Signature to follow is
   ``processor(extended_rows) -> yield (row_number, headers, row)``
   which should yield one extended row per yield instruction. See `post
   parse <https://github.com/frictionlessdata/tabulator-py#post-parse>`__
   section.
-  ``custom_loaders (dict)`` - loaders keyed by scheme. See a section
   below. See `custom
   loaders <https://github.com/frictionlessdata/tabulator-py#custom-loaders>`__
   section.
-  ``custom_parsers (dict)`` - custom parsers keyed by format. See a
   section below. See `custom
   parsers <https://github.com/frictionlessdata/tabulator-py#custom-parsers>`__
   section.
-  ``custom_writers (dict)`` - custom writers keyed by format. See a
   section below. See `custom
   writers <https://github.com/frictionlessdata/tabulator-py#custom-writers>`__
   section.
-  ``<name> (<type>)`` - loader/parser options. See in the scheme/format
   section
-  ``(Stream)`` - returns Stream class instance

``stream.closed``
^^^^^^^^^^^^^^^^^

-  ``(bool)`` - returns\ ``True`` if underlaying stream is closed

``stream.open()``
^^^^^^^^^^^^^^^^^

Open stream by opening underlaying stream.

``stream.close()``
^^^^^^^^^^^^^^^^^^

Close stream by closing underlaying stream.

``stream.reset()``
^^^^^^^^^^^^^^^^^^

Reset stream pointer to the first row.

``stream.headers``
^^^^^^^^^^^^^^^^^^

-  ``(str[])`` - returns data headers

``stream.scheme``
^^^^^^^^^^^^^^^^^

-  ``(str)`` - returns an actual scheme

``stream.format``
^^^^^^^^^^^^^^^^^

-  ``(str)`` - returns an actual format

``stream.encoding``
^^^^^^^^^^^^^^^^^^^

-  ``(str)`` - returns an actual encoding

``stream.sample``
^^^^^^^^^^^^^^^^^

-  ``(list)`` - returns data sample

``stream.iter(keyed=False, extended=False)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Iter stream rows. See `keyed and extended
rows <https://github.com/frictionlessdata/tabulator-py#https://github.com/frictionlessdata/tabulator-py#keyed-and-extended-rows>`__
section.

-  ``keyed (bool)`` - if True yield keyed rows
-  ``extended (bool)`` - if True yield extended rows
-  ``(any[]/any{})`` - yields row/keyed row/extended row

``stream.read(keyed=False, extended=False, limit=None)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Read table rows with count limit. See `keyed and extended
rows <https://github.com/frictionlessdata/tabulator-py#https://github.com/frictionlessdata/tabulator-py#keyed-and-extended-rows>`__
section.

-  ``keyed (bool)`` - return keyed rows
-  ``extended (bool)`` - return extended rows
-  ``limit (int)`` - rows count limit
-  ``(list)`` - returns rows/keyed rows/extended rows

``stream.save(target, format=None,  encoding=None, **options)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Save stream to filesystem.

-  ``target (str)`` - stream target
-  ``format (str)`` - saving format. See supported formats
-  ``encoding (str)`` - saving encoding
-  ``options (dict)`` - writer options

Schemes
~~~~~~~

There is a list of all supported schemes.

file
^^^^

The default scheme. Source should be a file in local filesystem. You
could provide a string or a ``pathlib.Path`` instance:

.. code:: python

    stream = Stream('data.csv')
    stream = Stream(pathlib.Path('data.csv'))

http/https/ftp/ftps
^^^^^^^^^^^^^^^^^^^

    In Python 2 ``tabulator`` can't stream remote data source because of
    underlaying libraries limitation. The whole data source will be
    loaded to the memory. In Python 3 there is no such a problem and
    ``tabulator`` is able to stream remote data source as expected.

Source should be a file available via one of this protocols in the web.

.. code:: python

    stream = Stream('http://example.com/data.csv')

Options:

-  http\_session - a ``requests.Session`` object. Read more in the
   ``requests``
   `docs <http://docs.python-requests.org/en/master/user/advanced/#session-objects>`__.
-  http\_stream - use HTTP streaming when possible. It's enabled by
   default. Disable if you'd like to preload the whole file into memory
   first.

stream
^^^^^^

Source should be a file-like python object which supports corresponding
protocol.

.. code:: python

    stream = Stream(open('data.csv'))

text
^^^^

Source should be a string containing tabular data. In this case
``format`` has to be explicitely passed because it's not possible to
infer it from source string.

.. code:: python

    stream = Stream('text://name,age\nJohn, 21\n', format='csv')

Formats
~~~~~~~

There is a list of all supported formats. Formats support ``read``
operation could be opened by ``Stream.open()`` and formats support
``write`` operation could be used in ``Stream.save()``.

csv
^^^

Source should be parsable by csv parser.

.. code:: python

    stream = Stream('data.csv', delimiter=',')

Operations:

-  read
-  write

Options:

-  delimiter
-  doublequote
-  escapechar
-  quotechar
-  quoting
-  skipinitialspace
-  lineterminator

See options reference in `Python
documentation <https://docs.python.org/3/library/csv.html#dialects-and-formatting-parameters>`__.

datapackage
^^^^^^^^^^^

    This format is not included to package by default. To use it please
    install ``tabulator`` with an ``datapackage`` extras:
    ``$ pip install tabulator[datapackage]``

Source should be a valid Tabular Data Package see
(https://frictionlessdata.io).

.. code:: python

    stream = Stream('datapackage.json', resource=1)

Operations:

-  read

Options:

-  resource - resource index (starting from 0) or resource name

gsheet
^^^^^^

Source should be a link to publicly available Google Spreadsheet.

.. code:: python

    stream = Stream('https://docs.google.com/spreadsheets/d/<id>?usp=sharing')
    stream = Stream('https://docs.google.com/spreadsheets/d/<id>edit#gid=<gid>')

inline
^^^^^^

Source should be a list of lists or a list of dicts.

.. code:: python

    stream = Stream([['name', 'age'], ['John', 21], ['Alex', 33]])
    stream = Stream([{'name': 'John', 'age': 21}, {'name': 'Alex', 'age': 33}])

Operations:

-  read

json
^^^^

Source should be a valid JSON document containing array of arrays or
array of objects (see ``inline`` format example).

.. code:: python

    stream = Stream('data.json', property='key1.key2')

Operations:

-  read

Options:

-  property - path to tabular data property separated by dots. For
   example having data structure like ``{"response": {"data": [...]}}``
   you should set property to ``response.data``.

ndjson
^^^^^^

Source should be parsable by ndjson parser.

.. code:: python

    stream = Stream('data.ndjson')

Operations:

-  read

ods
^^^

    This format is not included to package by default. To use it please
    install ``tabulator`` with an ``ods`` extras:
    ``$ pip install tabulator[ods]``

Source should be a valid Open Office document.

.. code:: python

    stream = Stream('data.ods', sheet=1)

Operations:

-  read

Options:

-  sheet - sheet number starting from 1 OR sheet name

sql
^^^

Source should be a valid database URL supported by ``sqlalchemy``.

.. code:: python

    stream = Stream('postgresql://name:pass@host:5432/database', table='data')

Operations:

-  read

Options:

-  table - database table name to read data (REQUIRED)
-  order\_by - SQL expression to order rows e.g. ``name desc``

tsv
^^^

Source should be parsable by tsv parser.

.. code:: python

    stream = Stream('data.tsv')

Operations:

-  read

xls/xlsx
^^^^^^^^

    For ``xls`` format ``tabulator`` can't stream data source because of
    underlaying libraries limitation. The whole data source will be
    loaded to the memory. For ``xlsx`` format there is no such a problem
    and ``tabulator`` is able to stream data source as expected.

Source should be a valid Excel document.

.. code:: python

    stream = Stream('data.xls', sheet=1)

Operations:

-  read

Options:

-  sheet - sheet number starting from 1 OR sheet name
-  fill\_merged\_cells - if ``True`` it will unmerge and fill all merged
   cells by a visible value. With this option enabled the parser can't
   stream data and load the whole document into memory.

Headers
~~~~~~~

By default ``Stream`` considers all data source rows as values:

.. code:: python

    with Stream([['name', 'age'], ['Alex', 21]]):
      stream.headers # None
      stream.read() # [['name', 'age'], ['Alex', 21]]

To alter this behaviour ``headers`` argument is supported by ``Stream``
constructor. This argument could be an integer - row number starting
from 1 containing headers:

.. code:: python

    # Integer
    with Stream([['name', 'age'], ['Alex', 21]], headers=1):
      stream.headers # ['name', 'age']
      stream.read() # [['Alex', 21]]

Or it could be a list of strings - user-defined headers:

.. code:: python

    with Stream([['Alex', 21]], headers=['name', 'age']):
      stream.headers # ['name', 'age']
      stream.read() # [['Alex', 21]]

It's possible to use multiline headers:

.. code:: python

    with Stream('data.xlsx', headers=[1,3], fill_merged_cells=True):
      stream.headers # ['header from row 1-3']
      stream.read() # [['value1', 'value2', 'value3']]

If ``headers`` is a row number/range and data source is not keyed all
rows before headers and headers will be removed from data stream (see
first example).

Encoding
~~~~~~~~

``Stream`` constructor accepts ``encoding`` argument to ensure needed
encoding will be used. As a value argument supported by python encoding
name (e.g. 'latin1', 'utf-8', ..) could be used:

.. code:: python

    with Stream(source, encoding='latin1') as stream:
      stream.read()

By default an encoding will be detected automatically. If you experience
a *UnicodeDecodeError* parsing your file, try setting this argument to
'utf-8'.

Compression
~~~~~~~~~~~

``Stream`` constructor accepts ``compression`` argument to ensure that
needed compression will be used. By default compression will be inferred
from file name:

.. code:: python

    with Stream('http://example.com/data.csv.zip') as stream:
      stream.read()

Provide user defined compression e.g. ``gz``:

.. code:: python

    with Stream('data.csv.ext', compression='zip') as stream:
      stream.read()

At the moment ``tabulator`` supports:

-  ``zip`` compression (Python3)
-  ``gz`` compression (Python3)

Allow html
~~~~~~~~~~

By default ``Stream`` will raise ``exceptions.FormatError`` on
``stream.open()`` call if html contents is detected. It's not a tabular
format and for example providing link to csv file inside html (e.g.
GitHub page) is a common mistake.

But sometimes this default behaviour is not what is needed. For example
you write custom parser which should support html contents. In this case
``allow_html`` option for ``Stream`` could be used:

.. code:: python

    with Stream(sorce_with_html, allow_html=True) as stream:
      stream.read() # no exception on open

Sample size
~~~~~~~~~~~

By default ``Stream`` will read some data on ``stream.open()`` call in
advance. This data is provided as ``stream.sample``. The size of this
sample could be set in rows using ``sample_size`` argument of stream
constructor:

.. code:: python

    with Stream(two_rows_source, sample_size=1) as stream:
      stream.sample # only first row
      stream.read() # first and second rows

Data sample could be really useful if you want to implement some initial
data checks without moving stream pointer as ``stream.iter/read`` do.
But if you don't want any interactions with an actual source before
first ``stream.iter/read`` call just disable data smapling with
``sample_size=0``.

Bytes sample size
~~~~~~~~~~~~~~~~~

On initial reading stage ``tabulator`` should detect contents encoding.
The argument ``bytes_sample_size`` customizes how many bytes will be
read to detect encoding:

.. code:: python

    source = 'data/special/latin1.csv'
    with Stream(source) as stream:
        stream.encoding # 'iso8859-2'
    with Stream(source, sample_size=0, bytes_sample_size=10) as stream:
        stream.encoding # 'utf-8'

In this example our data file doesn't include ``iso8859-2`` characters
in first 10 bytes. So we could see the difference in encoding detection.
Note ``sample_size`` usage here - these two parameters are independent.
Here we use ``sample_size=0`` to prevent rows sample creation (will fail
with bad encoding).

Ignore blank headers
~~~~~~~~~~~~~~~~~~~~

Some data tables could have blank headers. For example it could be an
empty strings in ``csv`` or ``None`` values in inline data. By default
``tabulator`` processes it as an ordinary header:

::

    source = 'text://header1,,header3\nvalue1,value2,value3'
    with Stream(source, format='csv', headers=1) as stream:
        stream.headers # ['header1', '', 'header3']
        stream.read(keyed=True) # {'header1': 'value1', '': 'value2', 'header3': 'value3'}

But sometimes it's not a desired behavior. You could ignore columns with
a blank header completely using an ``ignore_blank_headers`` flag:

::

    source = 'text://header1,,header3\nvalue1,value2,value3'
    with Stream(source, format='csv', headers=1, ignore_blank_headers=True) as stream:
        stream.headers # ['header1', 'header3']
        stream.read(keyed=True) # {'header1': 'value1', 'header3': 'value3'}

Force strings
~~~~~~~~~~~~~

Because ``tabulator`` support not only sources with string data
representation as ``csv`` but also sources supporting different data
types as ``json`` or ``inline`` there is a ``Stream`` option
``force_strings`` to stringify all data values on reading.

Here how stream works without forcing strings:

.. code:: python

    with Stream([['string', 1, datetime.time(17, 00)]]) as stream:
      stream.read() # [['string', 1, datetime.time(17, 00)]]

The same data source using ``force_strings`` option:

.. code:: python

    with Stream([['string', 1]], force_strings=True) as stream:
      stream.read() # [['string', '1', '17:00:00']]

For all temporal values stream will use ISO format. But if your data
source doesn't support temporal values (for instance ``json`` format)
``Stream`` just returns it as it is without converting to ISO format.

Force parse
~~~~~~~~~~~

Some data source could be partially mailformed for a parser. For example
``inline`` source could have good rows (lists or dicts) and bad rows
(for example strings). By default ``stream.iter/read`` will raise
``exceptions.SourceError`` on the first bad row:

.. code:: python

    with Stream([[1], 'bad', [3]]) as stream:
      stream.read() # raise exceptions.SourceError

With ``force_parse`` option for ``Stream`` constructor this default
behaviour could be changed. If it's set to ``True`` non-parsable rows
will be returned as empty rows:

.. code:: python

    with Stream([[1], 'bad', [3]]) as stream:
      stream.read() # [[1], [], [3]]

Skip rows
~~~~~~~~~

It's a very common situation when your tabular data contains some rows
you want to skip. It could be blank rows or commented rows. ``Stream``
constructors accepts ``skip_rows`` argument to make it possible. Value
of this argument should be a list of integers and strings where:

-  integer is a row number starting from 1
-  string is a first row chars indicating that row is a comment

Let's skip first, second and commented by '#' symbol rows:

.. code:: python

    source = [['John', 1], ['Alex', 2], ['#Sam', 3], ['Mike', 4]]
    with Stream(source, skip_rows=[1, 2, '#']) as stream
      stream.read() # [['Mike', 4]]

Post parse
~~~~~~~~~~

Skipping rows is a very basic ETL (extrac-transform-load) feature. For
more advanced data transormations there are post parse processors.

.. code:: python

    def skip_odd_rows(extended_rows):
        for row_number, headers, row in extended_rows:
            if not row_number % 2:
                yield (row_number, headers, row)

    def multiply_on_two(extended_rows):
        for row_number, headers, row in extended_rows:
            yield (row_number, headers, list(map(lambda value: value * 2, row)))


    with Stream([[1], [2], [3], [4]], post_parse=[skip_odd_rows, multiply_on_two]) as stream:
      stream.read() # [[4], [8]]

Post parse processor gets extended rows (``[row_number, headers, row]``)
iterator and must yields updated extended rows back. This interface is
very powerful because every processors have full control on iteration
process could skip rows, catch exceptions etc.

Processors will be applied to source from left to right. For example in
listing above ``multiply_on_two`` processor gets rows from
``skip_odd_rows`` processor.

Keyed and extended rows
~~~~~~~~~~~~~~~~~~~~~~~

Stream methods ``stream.iter/read()`` accept ``keyed`` and ``extended``
flags to vary data structure of output data row.

By default a stream returns every row as a list:

.. code:: python

    with Stream([['name', 'age'], ['Alex', 21]]) as stream:
      stream.read() # [['Alex', 21]]

With ``keyed=True`` a stream returns every row as a dict:

.. code:: python

    with Stream([['name', 'age'], ['Alex', 21]]) as stream:
      stream.read(keyed=True) # [{'name': 'Alex', 'age': 21}]

And with ``extended=True`` a stream returns every row as a tuple
contining row number starting from 1, headers as a list and row as a
list:

.. code:: python

    with Stream([['name', 'age'], ['Alex', 21]]) as stream:
      stream.read(extended=True) # (1, ['name', 'age'], ['Alex', 21])

Custom loaders
~~~~~~~~~~~~~~

To create a custom loader ``Loader`` interface should be implemented and
passed to ``Stream`` constructor as
``custom_loaders={'scheme': CustomLoader}`` argument.

For example let's implement a custom loader:

.. code:: python

    from tabulator import Loader

    class CustomLoader(Loader):
      options = []
      def __init__(self, bytes_sample_size, **options):
            pass
      def load(self, source, mode='t', encoding=None):
        # load logic

    with Stream(source, custom_loaders={'custom': CustomLoader}) as stream:
      stream.read()

There are more examples in internal ``tabulator.loaders`` module.

``Loader.options``
^^^^^^^^^^^^^^^^^^

List of supported custom options.

``Loader(bytes_sample_size, **options)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

-  ``bytes_sample_size (int)`` - sample size in bytes
-  ``options (dict)`` - loader options
-  ``(Loader)`` - returns ``Loader`` class instance

``loader.load(source, mode='t', encoding=None)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

-  ``source (str)`` - table source
-  ``mode (str)`` - text stream mode: 't' or 'b'
-  ``encoding (str)`` - encoding of source
-  ``(file-like)`` - returns file-like object of bytes or chars based on
   mode argument

Custom parsers
~~~~~~~~~~~~~~

To create a custom parser ``Parser`` interface should be implemented and
passed to ``Stream`` constructor as
``custom_parsers={'format': CustomParser}`` argument.

For example let's implement a custom parser:

.. code:: python

    from tabulator import Parser

    class CustomParser(Parser):
      options = []
      def __init__(self, loader, force_parse, **options):
        self.__loader = loader
      @property
      def closed(self):
        return False
      def open(self, source, encoding=None):
        # open logic
      def close(self):
        # close logic
      def reset(self):
        raise NotImplemenedError()
      @property
      def extended_rows():
        # extended rows logic

    with Stream(source, custom_parsers={'custom': CustomParser}) as stream:
      stream.read()

There are more examples in internal ``tabulator.parsers`` module.

``Parser.options``
^^^^^^^^^^^^^^^^^^

List of supported custom options.

``Parser(loader, force_parse, **options)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Create parser class instance.

-  ``loader (Loader)`` - loader instance
-  ``force_parse (bool)`` - if True parser must yield (row\_number,
   None, []) if there is an row in parsing error instead of stopping the
   iteration by raising an exception
-  ``options (dict)`` - parser options
-  ``(Parser)`` - returns ``Parser`` class instance

``parser.closed``
^^^^^^^^^^^^^^^^^

-  ``(bool)`` - returns ``True`` if parser is closed

``parser.open(source, encoding=None)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

| Open underlaying stream. Parser gets byte or text stream from loader
| to start emit items from this stream.

-  ``source (str)`` - table source
-  ``encoding (str)`` - encoding of source

``parser.close()``
^^^^^^^^^^^^^^^^^^

Close underlaying stream.

``parser.reset()``
^^^^^^^^^^^^^^^^^^

Reset items and underlaying stream. After reset call iterations over
items will start from scratch.

``parser.encoding``
^^^^^^^^^^^^^^^^^^^

-  ``(str)`` - returns an actual encoding

``parser.extended_rows``
^^^^^^^^^^^^^^^^^^^^^^^^

-  ``(iterator)`` - returns extended rows iterator

Custom writers
~~~~~~~~~~~~~~

To create a custom writer ``Writer`` interface should be implemented and
passed to ``Stream`` constructor as
``custom_writers={'format': CustomWriter}`` argument.

For example let's implement a custom writer:

.. code:: python

    from tabulator import Writer

    class CustomWriter(Writer):
      options = []
      def __init__(self, **options):
            pass
      def save(self, source, target, headers=None, encoding=None):
        # save logic

    with Stream(source, custom_writers={'custom': CustomWriter}) as stream:
      stream.save(target)

There are more examples in internal ``tabulator.writers`` module.

``Writer.options``
^^^^^^^^^^^^^^^^^^

List of supported custom options.

``Writer(**options)``
^^^^^^^^^^^^^^^^^^^^^

Create writer class instance.

-  ``options (dict)`` - writer options
-  ``(Writer)`` - returns ``Writer`` class instance

``writer.save(source, target, headers=None, encoding=None)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Save source data to target.

-  ``source (str)`` - data source
-  ``source (str)`` - save target
-  ``headers (str[])`` - optional headers
-  ``encoding (str)`` - encoding of source

Validate
~~~~~~~~

For cases you don't need open the source but want to know is it
supported by ``tabulator`` or not you could use ``validate`` function.
It also let you know what exactly is not supported raising correspondig
exception class.

.. code:: python

    from tabulator import validate, exceptions

    try:
      tabular = validate('data.csv')
    except exceptions.TabulatorException:
      tabular = False

``validate(source, scheme=None, format=None)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Validate if this source has supported scheme and format.

-  ``source (any)`` - data source
-  ``scheme (str)`` - data scheme
-  ``format (str)`` - data format
-  ``(exceptions.SchemeError)`` - raises if scheme is not supported
-  ``(exceptions.FormatError)`` - raises if format is not supported
-  ``(bool)`` - returns ``True`` if scheme/format is supported

Exceptions
~~~~~~~~~~

``exceptions.TabulatorException``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Base class for all ``tabulator`` exceptions.

``exceptions.IOError``
^^^^^^^^^^^^^^^^^^^^^^

All underlaying input-output errors.

``exceptions.HTTPError``
^^^^^^^^^^^^^^^^^^^^^^^^

All underlaying HTTP errors.

``exceptions.SourceError``
^^^^^^^^^^^^^^^^^^^^^^^^^^

This class of exceptions covers all source errors like bad data
structure for JSON.

``exceptions.SchemeError``
^^^^^^^^^^^^^^^^^^^^^^^^^^

For example this exceptions will be used if you provide not supported
source scheme like ``bad://source.csv``.

``exceptions.FormatError``
^^^^^^^^^^^^^^^^^^^^^^^^^^

For example this exceptions will be used if you provide not supported
source format like ``http://source.bad``.

``exceptions.EncodingError``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^

All errors related to encoding problems.

CLI
~~~

    It's a provisional API. If you use it as a part of other program
    please pin concrete ``goodtables`` version to your requirements
    file.

The library ships with a simple CLI to read tabular data:

.. code:: bash

    $ tabulator data/table.csv
    id, name
    1, english
    2, 中国人

``$ tabulator``
^^^^^^^^^^^^^^^

.. code:: bash

    Usage: cli.py [OPTIONS] SOURCE

    Options:
      --headers INTEGER
      --scheme TEXT
      --format TEXT
      --encoding TEXT
      --limit INTEGER
      --help             Show this message and exit.

Contributing
------------

The project follows the `Open Knowledge International coding
standards <https://github.com/okfn/coding-standards>`__.

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

::

    $ 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
- https://pylama.readthedocs.io/en/latest/.

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 -
https://testrun.org/tox/latest/.

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

.. code:: bash

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

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

Changelog
---------

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 <https://github.com/frictionlessdata/tabulator-py/commits/master>`__.

v1.12
~~~~~

Updated behaviour:

-  Now ``UserWarning`` will be emitted on bad options instead of raising
   an exception

v1.11
~~~~~

New API added:

-  Added ``http_session`` argument for ``http/https`` format (it now
   uses ``requests``)
-  Added support for multiline headers: ``headers`` argument now accepts
   ranges like ``[1,3]``

v1.10
~~~~~

New API added:

-  Added support for compressed files i.e. ``zip`` and ``gz`` for
   Python3
-  The ``Stream`` constructor now accepts a ``compression`` argument
-  The ``http/https`` scheme now accepts a ``http_stream`` flag

v1.9
~~~~

Improved behaviour:

-  Now the ``headers`` argument allows to set order for keyed sources
   and cherry-pick values

v1.8
~~~~

New API added:

-  Formats ``XLS/XLSX/ODS`` now supports a sheet name passed as a
   ``sheet`` argument
-  The ``Stream`` constructor now accepts an ``ignore_blank_headers``
   option

v1.7
~~~~

Improved behaviour:

-  Rebased ``datapackage`` format on ``datapackage@1`` libarry

v1.6
~~~~

New API added:

-  Argument ``source`` for the ``Stream`` constructor now could be a
   ``pathlib.Path``

v1.5
~~~~

New API added:

-  Argument ``bytes_sample_size`` for the ``Stream`` constructor

v1.4
~~~~

Improved behaviour:

-  updated encoding name to a canonical form

v1.3
~~~~

New API added:

-  ``stream.scheme``
-  ``stream.format``
-  ``stream.encoding``

Promoted provisional API to stable API:

-  ``Loader`` (custom loaders)
-  ``Parser`` (custom parsers)
-  ``Writer`` (custom writers)
-  ``validate``

v1.2
~~~~

Improved behaviour:

-  autodetect common csv delimiters

v1.1
~~~~

New API added:

-  added ``fill_merged_cells`` argument to ``xls/xlsx`` formats

v1.0
~~~~

New API added:

-  published ``Loader/Parser/Writer`` API
-  added ``Stream`` argument ``force_strings``
-  added ``Stream`` argument ``force_parse``
-  added ``Stream`` argument ``custom_writers``

Deprecated API removal:

-  removed ``topen`` and ``Table`` - use ``Stream`` instead
-  removed ``Stream`` arguments ``loader/parser_options`` - use
   ``**options`` instead

Provisional API changed:

-  updated ``Loader/Parser/Writer`` API - please use an updated version

v0.15
~~~~~

Provisional API added:

-  unofficial support for ``Stream`` arguments
   ``custom_loaders/parsers``

.. |Travis| image:: https://img.shields.io/travis/frictionlessdata/tabulator-py/master.svg
   :target: https://travis-ci.org/frictionlessdata/tabulator-py
.. |Coveralls| image:: http://img.shields.io/coveralls/frictionlessdata/tabulator-py.svg?branch=master
   :target: https://coveralls.io/r/frictionlessdata/tabulator-py?branch=master
.. |PyPi| image:: https://img.shields.io/pypi/v/tabulator.svg
   :target: https://pypi.python.org/pypi/tabulator
.. |Gitter| image:: https://img.shields.io/gitter/room/frictionlessdata/chat.svg
   :target: https://gitter.im/frictionlessdata/chat

