tabulator-py

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

Features

• Supports most common tabular formats: CSV, XLS, ODS, JSON, Google Sheets, SQL, and others. See complete list below.
• Loads local and remote data: Supports HTTP, FTP and S3.
• Low memory usage: Only the current row is kept in memory, so you can large datasets.
• Supports compressed files: Using ZIP or GZIP algorithms.
• Extensible: You can add support for custom file formats and loaders (e.g. FTP).

Contents

• Getting started
  • Installation
  • Running on CLI
  • Running on Python
• Documentation
  • Working with Stream
  • Supported schemes
  • Supported file formats
  • Custom file sources and formats
• API Reference
  • cli
  • Stream
  • Loader
  • Parser
  • Writer
  • validate
  • TabulatorException
  • IOError
  • HTTPError
  • SourceError
  • FormatError
  • EncodingError
• Contributing
• Changelog

Getting started

Installation

$ pip install tabulator

Running on CLI

Tabulator ships with a simple CLI called tabulator to read tabular data. For example:

$ tabulator https://github.com/frictionlessdata/tabulator-py/raw/4c1b3943ac98be87b551d87a777d0f7ca4904701/data/table.csv.gz
id,name
1,english
2,中国人

You can see all supported options by running tabulator --help.

Running on Python

from tabulator import Stream

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

You can find other examples in the examples directory.

Documentation

In the following sections, we'll walk through some usage examples of this library. All examples were tested with Python 3.6, but should run fine with Python 3.3+.

Working with Stream

The Stream class represents a tabular stream. It takes the file path as the source argument. For example:

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

It uses this path to determine the file format (e.g. CSV or XLS) and scheme (e.g. HTTP or postgresql). It also supports format extraction from URLs like http://example.com?format=csv. If necessary, you also can define these explicitly.

Let's try it out. First, we create a Stream object passing the path to a CSV file.

import tabulator

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

At this point, the file haven't been read yet. Let's open the stream so we can read the contents.

try:
    stream.open()
except tabulator.TabulatorException as e:
    pass  # Handle exception

This will open the underlying data stream, read a small sample to detect the file encoding, and prepare the data to be read. We catch tabulator.TabulatorException here, in case something goes wrong.

We can now read the file contents. To iterate over each row, we do:

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

The stream.iter() method will return each row data as a list of values. If you prefer, you could call stream.iter(keyed=True) instead, which returns a dictionary with the column names as keys. Either way, this method keeps only a single row in memory at a time. This means it can handle handle large files without consuming too much memory.

If you want to read the entire file, use stream.read(). It accepts the same arguments as stream.iter(), but returns all rows at once.

stream.reset()
rows = stream.read()

Notice that we called stream.reset() before reading the rows. This is because internally, tabulator only keeps a pointer to its current location in the file. If we didn't reset this pointer, we would read starting from where we stopped. For example, if we ran stream.read() again, we would get an empty list, as the internal file pointer is at the end of the file (because we've already read it all). Depending on the file location, it might be necessary to download the file again to rewind (e.g. when the file was loaded from the web).

After we're done, close the stream with:

stream.close()

The entire example looks like:

import tabulator

stream = tabulator.Stream('data.csv')
try:
    stream.open()
except tabulator.TabulatorException as e:
    pass  # Handle exception

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

stream.reset()  # Rewind internal file pointer
rows = stream.read()

stream.close()

It could be rewritten to use Python's context manager interface as:

import tabulator

try:
    with tabulator.Stream('data.csv') as stream:
        for row in stream.iter():
            print(row)

        stream.reset()
        rows = stream.read()
except tabulator.TabulatorException as e:
    pass

This is the preferred way, as Python closes the stream automatically, even if some exception was thrown along the way.

The full API documentation is available as docstrings in the Stream source code.

Headers

By default, tabulator considers that all file rows are values (i.e. there is no header).

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

If you have a header row, you can use the headers argument with the its row number (starting from 1).

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

You can also pass a lists of strings to define the headers explicitly:

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

Tabulator also supports multiline headers for the xls and xlsx formats.

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

Encoding

You can specify the file encoding (e.g. utf-8 and latin1) via the encoding argument.

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

If this argument isn't set, Tabulator will try to infer it from the data. If you get a UnicodeDecodeError while loading a file, try setting the encoding to utf-8.

Compression (Python3-only)

Tabulator supports both ZIP and GZIP compression methods. By default it'll infer from the file name:

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

You can also set it explicitly:

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

Options

• filename: filename in zip file to process (default is first file)

Allow html

The Stream class raises tabulator.exceptions.FormatError if it detects HTML contents. This helps avoiding the relatively common mistake of trying to load a CSV file inside an HTML page, for example on GitHub.

You can disable this behaviour using the allow_html option:

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

Sample size

To detect the file's headers, and run other checks like validating that the file doesn't contain HTML, Tabulator reads a sample of rows on the stream.open() method. This data is available via the stream.sample property. The number of rows used can be defined via the sample_size parameters (defaults to 100).

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

You can disable this by setting sample_size to zero. This way, no data will be read on stream.open().

Bytes sample size

Tabulator needs to read a part of the file to infer its encoding. The bytes_sample_size arguments controls how many bytes will be read for this detection (defaults to 10000).

source = 'data/special/latin1.csv'
with Stream(source) as stream:
    stream.encoding # 'iso8859-2'

You can disable this by setting bytes_sample_size to zero, in which case it'll use the machine locale's default encoding.

Ignore blank headers

When True, tabulator will ignore columns that have blank headers (defaults to False).

# Default behaviour
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'}

# Ignoring columns with blank headers
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'}

Ignore listed/not-listed headers

The option is similar to the ignore_blank_headers. It removes arbitrary columns from the data based on the corresponding column names:

# Ignore listed headers (omit columns)
source = 'text://header1,header2,header3\nvalue1,value2,value3'
with Stream(source, format='csv', headers=1, ignore_listed_headers=['header2']) as stream:
    assert stream.headers == ['header1', 'header3']
    assert stream.read(keyed=True) == [
        {'header1': 'value1', 'header3': 'value3'},
    ]

# Ignore NOT listed headers (pick colums)
source = 'text://header1,header2,header3\nvalue1,value2,value3'
with Stream(source, format='csv', headers=1, ignore_not_listed_headers=['header2']) as stream:
    assert stream.headers == ['header2']
    assert stream.read(keyed=True) == [
        {'header2': 'value2'},
    ]

Force strings

When True, all rows' values will be converted to strings (defaults to False). None values will be converted to empty strings.

# Default behaviour
with Stream([['string', 1, datetime.datetime(2017, 12, 1, 17, 00)]]) as stream:
  stream.read() # [['string', 1, datetime.dateime(2017, 12, 1, 17, 00)]]

# Forcing rows' values as strings
with Stream([['string', 1]], force_strings=True) as stream:
  stream.read() # [['string', '1', '2017-12-01 17:00:00']]

Force parse

When True, don't raise an exception when parsing a malformed row, but simply return an empty row. Otherwise, tabulator raises tabulator.exceptions.SourceError when a row can't be parsed. Defaults to False.

# Default behaviour
with Stream([[1], 'bad', [3]]) as stream:
  stream.read() # raises tabulator.exceptions.SourceError

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

Skip rows

List of row numbers and/or strings to skip. If it's a string, all rows that begin with it will be skipped (e.g. '#' and '//'). If it's the empty string, all rows that begin with an empty column will be skipped.

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]]

If the headers parameter is also set to be an integer, it will use the first not skipped row as a headers.

source = [['#comment'], ['name', 'order'], ['John', 1], ['Alex', 2]]
with Stream(source, headers=1, skip_rows=['#']) as stream:
  stream.headers # [['name', 'order']]
  stream.read() # [['Jogn', 1], ['Alex', 2]]

Post parse

List of functions that can filter or transform rows after they are parsed. These functions receive the extended_rows containing the row's number, headers list, and the row values list. They then process the rows, and yield or discard them, modified or not.

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_by_two(extended_rows):
    for row_number, headers, row in extended_rows:
        doubled_row = list(map(lambda value: value * 2, row))
        yield (row_number, headers, doubled_row)

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

These functions are applied in order, as a simple data pipeline. In the example above, multiply_by_two just sees the rows yielded by skip_odd_rows.

Keyed and extended rows

The methods stream.iter() and stream.read() accept the keyed and extended flag arguments to modify how the rows are returned.

By default, every row is returned as a list of its cells values:

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

With keyed=True, the rows are returned as dictionaries, mapping the column names to their values in the row:

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

And with extended=True, the rows are returned as a tuple of (row_number, headers, row), there row_number is the current row number (starting from 1), headers is a list with the headers names, and row is a list with the rows values:

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

Supported schemes

s3

It loads data from AWS S3. For private files you should provide credentials supported by the boto3 library, for example, corresponding environment variables. Read more about configuring boto3.

stream = Stream('s3://bucket/data.csv')

Options

• s3_endpoint_url - the endpoint URL to use. By default it's https://s3.amazonaws.com. For complex use cases, for example, goodtables's runs on a data package this option can be provided as an environment variable S3_ENDPOINT_URL.

file

The default scheme, a file in the local filesystem.

stream = Stream('data.csv')

http/https/ftp/ftps

In Python 2, tabulator can't stream remote data sources because of a limitation in the underlying libraries. The whole data source will be loaded to the memory. In Python 3 there is no such problem and remote files are streamed.

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

Options

• http_session - a requests.Session object. Read more in the requests docs.
• http_stream - Enables or disables HTTP streaming, when possible (enabled by default). Disable it if you'd like to preload the whole file into memory.
• http_timeout - This timeout will be used for a requests session construction.

stream

The source is a file-like Python object.

with open('data.csv') as fp:
    stream = Stream(fp)

text

The source is a string containing the tabular data. Both scheme and format must be set explicitly, as it's not possible to infer them.

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

Supported file formats

In this section, we'll describe the supported file formats, and their respective configuration options and operations. Some formats only support read operations, while others support both reading and writing.

csv (read & write)

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

Options

It supports all options from the Python CSV library. Check their documentation for more information.

xls/xlsx (read & write)

Tabulator is unable to stream xls files, so the entire file is loaded in memory. Streaming is supported for xlsx files.

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

Options

• sheet: Sheet name or number (starting from 1).
• 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.
• preserve_formatting: if True it will try to preserve text formatting of numeric and temporal cells returning it as strings according to how it looks in a spreadsheet (EXPERIMETAL)
• adjust_floating_point_error: if True it will correct the Excel behaviour regarding floating point numbers

ods (read only)

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)

Options

• sheet: Sheet name or number (starting from 1)

gsheet (read only)

A publicly-accessible 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>')

sql (read & write)

Any database URL supported by sqlalchemy.

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

Options

• table (required): Database table name
• order_by: SQL expression for row ordering (e.g. name DESC)

Data Package (read only)

This format is not included to package by default. You can enable it by installing tabulator using pip install tabulator[datapackage].

A Tabular Data Package.

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

Options

• resource: Resource name or index (starting from 0)

inline (read only)

Either a list of lists, or a list of dicts mapping the column names to their respective values.

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

json (read only)

JSON document containing a list of lists, or a list of dicts mapping the column names to their respective values (see the inline format for an example).

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

Options

• property: JSON Path to the property containing the tabular data. For example, considering the JSON {"response": {"data": [...]}}, the property should be set to response.data.

ndjson (read only)

stream = Stream('data.ndjson')

tsv (read only)

stream = Stream('data.tsv')

html (read only)

This format is not included to package by default. To use it please install tabulator with the html extra: $ pip install tabulator[html]

An HTML table element residing inside an HTML document.

Supports simple tables (no merged cells) with any legal combination of the td, th, tbody & thead elements.

Usually foramt='html' would need to be specified explicitly as web URLs don't always use the .html extension.

stream = Stream('http://example.com/some/page.aspx', format='html' selector='.content .data table#id1')

Options

• selector: CSS selector for specifying which table element to extract. By default it's table, which takes the first table element in the document.

Custom file sources and formats

Tabulator is written with extensibility in mind, allowing you to add support for new tabular file formats, schemes (e.g. ssh), and writers (e.g. MongoDB). There are three components that allow this:

• Loaders
  • Loads a stream from some location (e.g. ssh)
• Parsers
  • Parses a stream of tabular data in some format (e.g. xls)
• Writers
  • Writes tabular data to some destination (e.g. MongoDB)

In this section, we'll see how to write custom classes to extend any of these components.

Custom loaders

You can add support for a new scheme (e.g. ssh) by creating a custom loader. Custom loaders are implemented by inheriting from the Loader class, and implementing its methods. This loader can then be used by Stream to load data by passing it via the custom_loaders={'scheme': CustomLoader} argument.

The skeleton of a custom loader looks like:

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()

You can see examples of how the loaders are implemented by looking in the tabulator.loaders module.

Custom parsers

You can add support for a new file format by creating a custom parser. Similarly to custom loaders, custom parsers are implemented by inheriting from the Parser class, and implementing its methods. This parser can then be used by Stream to parse data by passing it via the custom_parsers={'format': CustomParser} argument.

The skeleton of a custom parser looks like:

from tabulator import Parser

class CustomParser(Parser):
    options = []

    def __init__(self, loader, force_parse, **options):
        self.__loader = loader

    def open(self, source, encoding=None):
        # open logic

    def close(self):
        # close logic

    def reset(self):
        # reset logic

    @property
    def closed(self):
        return False

    @property
    def extended_rows(self):
        # extended rows logic

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

You can see examples of how parsers are implemented by looking in the tabulator.parsers module.

Custom writers

You can add support to write files in a specific format by creating a custom writer. The custom writers are implemented by inheriting from the base Writer class, and implementing its methods. This writer can then be used by Stream to write data via the custom_writers={'format': CustomWriter} argument.

The skeleton of a custom writer looks like:

from tabulator import Writer

class CustomWriter(Writer):
  options = []

  def __init__(self, **options):
      pass

  def write(self, source, target, headers=None, encoding=None):
      # write logic

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

You can see examples of how parsers are implemented by looking in the tabulator.writers module.

API Reference

cli

cli(source, limit, **options)

Command-line interface

Usage: tabulator [OPTIONS] SOURCE

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

Stream

Stream(self, source, headers=None, scheme=None, format=None, encoding=None, compression=None, allow_html=False, sample_size=100, bytes_sample_size=10000, ignore_blank_headers=False, ignore_listed_headers=None, ignore_not_listed_headers=None, multiline_headers_joiner=' ', force_strings=False, force_parse=False, skip_rows=[], post_parse=[], custom_loaders={}, custom_parsers={}, custom_writers={}, **options)

Stream of tabular data.

This is the main tabulator class. It loads a data source, and allows you to stream its parsed contents.

Arguments

• source (str): Path to file as <scheme>://path/to/file.<format>. If not explicitly set, the scheme (file, http, ...) and format (csv, xls, ...) are inferred from the source string.
• headers (Union[int, List[int], List[str]], optional): Either a row number or list of row numbers (in case of multi-line headers) to be considered as headers (rows start counting at 1), or the actual headers defined a list of strings. If not set, all rows will be treated as containing values.
• scheme (str, optional): Scheme for loading the file (file, http, ...). If not set, it'll be inferred from source.
• format (str, optional): File source's format (csv, xls, ...). If not set, it'll be inferred from source. inferred
• encoding (str, optional): Source encoding. If not set, it'll be inferred.
• compression (str, optional): Source file compression (zip, ...). If not set, it'll be inferred.
• allow_html (bool, optional): Allow the file source to be an HTML page. If False, raises exceptions.FormatError if the loaded file is an HTML page. Defaults to False.
• sample_size (int, optional): Controls the number of sample rows used to infer properties from the data (headers, encoding, etc.). Set to 0 to disable sampling, in which case nothing will be inferred from the data. Defaults to config.DEFAULT_SAMPLE_SIZE.
• bytes_sample_size (int, optional): Same as sample_size, but instead of number of rows, controls number of bytes. Defaults to config.DEFAULT_BYTES_SAMPLE_SIZE.
• ignore_blank_headers (bool, optional): When True, ignores all columns that have blank headers. Defaults to False.
• ignore_listed_headers (List[str], optional): When passed, ignores all columns with headers that the given list includes
• ignore_not_listed_headers (List[str], optional): When passed, ignores all columns with headers that the given list DOES NOT include
• multiline_headers_joiner (str, optional): When passed, it's used to join multiline headers as <passed-value>.join(header1_1, header1_2) Defaults to ' ' (space).
• force_strings (bool, optional): When True, casts all data to strings. Defaults to False.
• force_parse (bool, optional): When True, don't raise exceptions when parsing malformed rows, simply returning an empty value. Defaults to False.
• skip_rows (List[Union[int, str, dict]], optional): List of row numbers, strings and regex patterns as dicts to skip. If a string, it'll skip rows that their first cells begin with it e.g. '#' and '//'. To provide a regex pattern use an object like {'type': 'regex', 'value': '^#'} For example: skip_rows=[1, '# comment', {'type': 'regex', 'value': '^# (regex|comment)'}]
• post_parse (List[function], optional): List of generator functions that receives a list of rows and headers, processes them, and yields them (or not). Useful to pre-process the data. Defaults to None.
• custom_loaders (dict, optional): Dictionary with keys as scheme names, and values as their respective Loader class implementations. Defaults to None.
• custom_parsers (dict, optional): Dictionary with keys as format names, and values as their respective Parser class implementations. Defaults to None.
• custom_loaders (dict, optional): Dictionary with keys as writer format names, and values as their respective Writer class implementations. Defaults to None.
• **options (Any, optional): Extra options passed to the loaders and parsers.

stream.closed

Returns True if the underlying stream is closed, False otherwise.

Returns

bool: whether closed

stream.encoding

Stream's encoding

Returns

str: encoding

stream.format

Path's format

Returns

str: format

stream.fragment

Path's fragment

Returns

str: fragment

stream.hash

Returns the SHA256 hash of the read chunks if available

Returns

str/None: SHA256 hash

stream.headers

Headers

Returns

str[]/None: headers if available

stream.sample

Returns the stream's rows used as sample.

These sample rows are used internally to infer characteristics of the source file (e.g. encoding, headers, ...).

Returns

list[]: sample

stream.scheme

Path's scheme

Returns

str: scheme

stream.size

Returns the BYTE count of the read chunks if available

Returns

int/None: BYTE count

stream.open

stream.open(self)

Opens the stream for reading.

Raises:

TabulatorException: if an error

stream.close

stream.close(self)

Closes the stream.

stream.reset

stream.reset(self)

Resets the stream pointer to the beginning of the file.

stream.iter

stream.iter(self, keyed=False, extended=False)

Iterate over the rows.

Each row is returned in a format that depends on the arguments keyed and extended. By default, each row is returned as list of their values.

Arguments

• keyed (bool, optional): When True, each returned row will be a dict mapping the header name to its value in the current row. For example, [{'name': 'J Smith', 'value': '10'}]. Ignored if extended is True. Defaults to False.
• extended (bool, optional): When True, returns each row as a tuple with row number (starts at 1), list of headers, and list of row values. For example, (1, ['name', 'value'], ['J Smith', '10']). Defaults to False.

Raises

• exceptions.TabulatorException: If the stream is closed.

Returns

Iterator[Union[List[Any], Dict[str, Any], Tuple[int, List[str], List[Any]]]]: The row itself. The format depends on the values of keyed and extended arguments.

stream.read

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

Returns a list of rows.

Arguments

• keyed (bool, optional): See :func:Stream.iter.
• extended (bool, optional): See :func:Stream.iter.
• limit (int, optional): Number of rows to return. If None, returns all rows. Defaults to None.

Returns

List[Union[List[Any], Dict[str, Any], Tuple[int, List[str], List[Any]]]]: The list of rows. The format depends on the values of keyed and extended arguments.

stream.save

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

Save stream to the local filesystem.

Arguments:

target (str): Path where to save the stream.
format (str, optional):
    The format the stream will be saved as. If
    None, detects from the ``target`` path. Defaults to None.
encoding (str, optional):
    Saved file encoding. Defaults to ``config.DEFAULT_ENCODING``.
**options: Extra options passed to the writer.

Loader

Loader(self, bytes_sample_size, **options)

Abstract class implemented by the data loaders

The loaders inherit and implement this class' methods to add support for a new scheme (e.g. ssh).

Arguments

• bytes_sample_size (int): Sample size in bytes
• **options (dict): Loader options

loader.options

Built-in mutable sequence.

If no argument is given, the constructor creates a new empty list. The argument must be an iterable if specified.

loader.load

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

Load source file.

Arguments

• source (str): Path to tabular source file.
• mode (str, optional): Text stream mode, t (text) or b (binary). Defaults to t.
• encoding (str, optional): Source encoding. Auto-detect by default.

Returns

Union[TextIO, BinaryIO]: I/O stream opened either as text or binary.

Parser

Parser(self, loader, force_parse, **options)

Abstract class implemented by the data parsers.

The parsers inherit and implement this class' methods to add support for a new file type.

Arguments

• loader (tabulator.Loader): Loader instance to read the file.
• force_parse (bool): When True, the parser yields an empty extended row tuple (row_number, None, []) when there is an error parsing a row. Otherwise, it stops the iteration by raising the exception tabulator.exceptions.SourceError.
• **options (dict): Loader options

parser.closed

Flag telling if the parser is closed.

Returns

bool: whether closed

parser.encoding

Encoding

Returns

str: encoding

parser.extended_rows

Returns extended rows iterator.

The extended rows are tuples containing (row_number, headers, row),

Raises

• SourceError: If force_parse is False and a row can't be parsed, this exception will be raised. Otherwise, an empty extended row is returned (i.e. (row_number, None, [])).
• Returns:
• Iterator[Tuple[int, List[str], List[Any]]]: Extended rows containing (row_number, headers, row), where headers is a list of the header names (can be None), and row is a list of row values.

parser.options

Built-in mutable sequence.

If no argument is given, the constructor creates a new empty list. The argument must be an iterable if specified.

parser.open

parser.open(self, source, encoding=None)

Open underlying file stream in the beginning of the file.

The parser gets a byte or text stream from the tabulator.Loader instance and start emitting items.

Arguments

• source (str): Path to source table.
• encoding (str, optional): Source encoding. Auto-detect by default.

Returns

None

parser.close

parser.close(self)

Closes underlying file stream.

parser.reset

parser.reset(self)

Resets underlying stream and current items list.

After reset() is called, iterating over the items will start from the beginning.

Writer

Writer(self, **options)

Abstract class implemented by the data writers.

The writers inherit and implement this class' methods to add support for a new file destination.

Arguments

• **options (dict): Writer options.

writer.options

Built-in mutable sequence.

If no argument is given, the constructor creates a new empty list. The argument must be an iterable if specified.

writer.write

writer.write(self, source, target, headers, encoding=None)

Writes source data to target.

Arguments

• source (str): Source data.
• target (str): Write target.
• headers (List[str]): List of header names.
• encoding (str, optional): Source file encoding.

validate

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

Check if tabulator is able to load the source.

Arguments

• source (Union[str, IO]): The source path or IO object.
• scheme (str, optional): The source scheme. Auto-detect by default.
• format (str, optional): The source file format. Auto-detect by default.

Raises

• SchemeError: The file scheme is not supported.
• FormatError: The file format is not supported.

Returns

bool: Whether tabulator is able to load the source file.

TabulatorException

TabulatorException(self, /, *args, **kwargs)

Base class for all tabulator exceptions.

IOError

IOError(self, /, *args, **kwargs)

Local loading error

HTTPError

HTTPError(self, /, *args, **kwargs)

Remote loading error

SourceError

SourceError(self, /, *args, **kwargs)

The source file could not be parsed correctly.

FormatError

FormatError(self, /, *args, **kwargs)

The file format is unsupported or invalid.

EncodingError

EncodingError(self, /, *args, **kwargs)

Encoding error

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

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.

v1.34

• Added multiline_headers_joiner argument (#291)

v1.33

• Added support for regex patterns in skip_rows (#290)

v1.32

• Added ability to skip columns (#293)

v1.31

• Added xlsx writer
• Added html reader

v1.30

• Added adjust_floating_point_error parameter to the xlsx parser

v1.29

• Implemented the stream.size and stream.hash properties

v1.28

• Added SQL writer

v1.27

• Added http_timeout argument for the http/https format

v1.26

• Added stream.fragment field showing e.g. Excel sheet's or DP resource's name

v1.25

• Added support for the s3 file scheme (data loading from AWS S3)

v1.24

• Added support for compressed file-like objects

v1.23

• Added a setter for the stream.headers property

v1.22

• The headers parameter will now use the first not skipped row if the skip_rows parameter is provided and there are comments on the top of a data source (see #264)

v1.21

• Implemented experimental preserve_formatting for xlsx

v1.20

• Added support for specifying filename in zip source

v1.19

Updated behaviour:

• For ods format the boolean, integer and datatime native types are detected now

v1.18

Updated behaviour:

• For xls format the boolean, integer and datatime native types are detected now

v1.17

Updated behaviour:

• Added support for Python 3.7

v1.16

New API added:

• skip_rows support for an empty string to skip rows with an empty first column

v1.15

New API added:

• Format will be extracted from URLs like http://example.com?format=csv

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 starting from the end

v1.12

Updated behaviour:

• Instead of raising an exception, a UserWarning warning will be emitted if an option isn't recognized.

v1.11

New API added:

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

v1.10

New API added:

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

v1.9

Improved behaviour:

• The headers argument allows to set the order for keyed sources and cherry-pick values

v1.8

New API added:

• Formats XLS/XLSX/ODS supports sheet names passed via the sheet argument
• The Stream constructor accepts an ignore_blank_headers option

v1.7

Improved behaviour:

• Rebased datapackage format on datapackage@1 library

v1.6

New API added:

• Argument source for the Stream constructor can 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 option 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 the Loader/Parser/Writer API - please use an updated version

v0.15

Provisional API added:

• Unofficial support for Stream arguments custom_loaders/parsers