Consistent interface for stream reading and writing tabular data (csv/xls/json/etc).
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Latest commit 273f4ff Jul 17, 2018

README.md

tabulator-py

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

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.

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

Examples

It's pretty simple to start with tabulator:

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 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:

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.

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:

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:

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:

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.

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:

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 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 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 section.
  • encoding (str) - source encoding with None (detect) as default. See encoding section.
  • compression (str) - source compression like zip with None (detect) as default. See compression section.
  • allow_html (bool) - a flag to allow html. See 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 section.
  • bytes_sample_size (int) - sample size in bytes for operations like encoding detection. See bytes sample size section.
  • ignore_blank_headers (bool) - a flag to ignore any column having a blank header. See ignore blank headers section.
  • force_strings (bool) - if True all output will be converted to strings. See 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 section.
  • skip_rows (int/str[]) - list of rows to skip by row number or row comment. Example: skip_rows=[1, 2, -1, -3, '#', '//'] - rows 1, 2 and rows 1, 3 from the end and all rows started with # and // will be skipped. See 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 section.
  • custom_loaders (dict) - loaders keyed by scheme. See a section below. See custom loaders section.
  • custom_parsers (dict) - custom parsers keyed by format. See a section below. See custom parsers section.
  • custom_writers (dict) - custom writers keyed by format. See a section below. See custom writers section.
  • <name> (<type>) - loader/parser options. See in the scheme/format section
  • (Stream) - returns Stream class instance

stream.closed

  • (bool) - returnsTrue 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 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 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:

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.

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

Options:

  • http_session - a requests.Session object. Read more in the requests docs.
  • 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.

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.

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.

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

Operations:

  • read
  • write

Options:

  • delimiter
  • doublequote
  • escapechar
  • quotechar
  • quoting
  • skipinitialspace
  • lineterminator

See options reference in Python documentation.

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).

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.

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.

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).

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.

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.

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.

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.

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.

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:

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:

# 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:

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

It's possible to use multiline headers:

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:

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:

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

Provide user defined compression e.g. gz:

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:

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:

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:

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:

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:

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:

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:

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 (1 is the first row, -1 is the last)
  • string is a first row chars indicating that row is a comment

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

source = [['John', 1], ['Alex', 2], ['#Sam', 3], ['Mike', 4], ['John', 5]]
with Stream(source, skip_rows=[1, 2, -1, '#']) 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.

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:

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

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

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:

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:

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:

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:

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.

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:

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

$ tabulator

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.

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:

$ 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:

$ 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:

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.

v1.14

Updated behaviour:

  • Now xls booleans will be parsed as booleans not integers

v1.13

New API added:

  • the skip_rows argument now supports negative numbers to skip rows from the end

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