Skip to main content

Consistent interface for stream reading and writing tabular data (csv/xls/json/etc)

Project description

tabulator-py

Travis Coveralls PyPi Gitter

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 and FTP.
  • 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).

Table of Contents

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+.

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.

Options

On this section, we'll see all different options supported by the Stream class.

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()
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'}
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]]
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

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.

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 only)

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.

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 only)

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')

Adding support for new file sources, formats, and writers

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.

Validate

You can check if a source can be loaded by tabulator using the validate function.

from tabulator import validate, exceptions

try:
    tabular = validate('data.csv')
except exceptions.SchemeError:
    # The file scheme isn't supported
except exceptions.FormatError:
    # The file format isn't supported

Exceptions

All the exceptions thrown by tabulator inherit from tabulator.exceptions.TabulatorException, so you can use it as a way to catch any tabulator exception. You can learn about the other exceptions thrown by looking into the tabulator.exceptions module.

API Reference

The API reference is written as docstrings in the tabulator classes. A good place to start is the Stream class, which manages all loading and parsing of data files.

Contributing

This project follows the Open Knowledge International coding standards.

We recommend you to use virtualenv to isolate this project from the rest of the packages in your machine.

To install the project and its development dependencies, run:

$ make install

To run the tests, use:

$ 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.

v1.19

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

Release history Release notifications

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
tabulator-1.19.3-py2.py3-none-any.whl (51.7 kB) Copy SHA256 hash SHA256 Wheel py2.py3
tabulator-1.19.3.tar.gz (44.2 kB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page