Copyright © 2015 W3C® (MIT, ERCIM, Keio, Beihang). W3C liability, trademark and document use rules apply.
Tabular data is routinely transferred on the web in a variety of formats, including variants on CSV, tab-delimited files, fixed field formats, spreadsheets, HTML tables, and SQL dumps. This document outlines a data model, or infoset, for tabular data and metadata about that tabular data that can be used as a basis for validation, display, or creating other formats. It also contains some non-normative guidance for publishing tabular data as CSV and how that maps into the tabular data model.
An annotated model of tabular data can be supplemented by separate metadata about the table. This specification defines how implementations should locate that metadata, given a file containing tabular data. The standard syntax for that metadata is defined in [tabular-metadata]. Note, however, that applications may have other means to create annotated tables, e.g., through some application specific API-s; this model does not depend on the specificities described in [tabular-metadata].
This section describes the status of this document at the time of its publication. Other documents may supersede this document. A list of current W3C publications and the latest revision of this technical report can be found in the W3C technical reports index at http://www.w3.org/TR/.
The CSV on the Web Working Group was chartered to produce a Recommendation "Access methods for CSV Metadata" as well as Recommendations for "Metadata vocabulary for CSV data" and "Mapping mechanism to transforming CSV into various Formats (e.g., RDF, JSON, or XML)". This document aims to primarily satisfy the first of those Recommendations (see section 5. Locating Metadata), though it also specifies an underlying model for tabular data and is therefore a basis for the other chartered Recommendations.
This definition of CSV used in this document is based on IETF's [RFC4180] which is an Informational RFC. The working group's expectation is that future suggestions to refine RFC 4180 will be relayed to the IETF (e.g. around encoding and line endings) and contribute to its discussions about moving CSV to the Standards track.
Many files containing tabular data embed metadata, for example in lines before the header row of an otherwise standard CSV document. This specification does not define any formats for embedding metadata within CSV files, aside from the titles of columns in the header row which is defined in CSV. We would encourage groups that define tabular data formats to also define a mapping into the annotated tabular data model defined in this document.
This document was published by the CSV on the Web Working Group as a Working Draft. This document is intended to become a W3C Recommendation. If you wish to make comments regarding this document, please send them to public-csv-wg@w3.org (subscribe, archives). All comments are welcome.
Publication as a Working Draft does not imply endorsement by the W3C Membership. This is a draft document and may be updated, replaced or obsoleted by other documents at any time. It is inappropriate to cite this document as other than work in progress.
This document was produced by a group operating under the 5 February 2004 W3C Patent Policy. W3C maintains a public list of any patent disclosures made in connection with the deliverables of the group; that page also includes instructions for disclosing a patent. An individual who has actual knowledge of a patent which the individual believes contains Essential Claim(s) must disclose the information in accordance with section 6 of the W3C Patent Policy.
This document is governed by the 1 August 2014 W3C Process Document.
Tabular data is data that is structured into rows, each of which contains information about some thing. Each row contains the same number of cells (although some of these cells may be empty), which provide values of properties of the thing described by the row. In tabular data, cells within the same column provide values for the same property of the things described by each row. This is what differentiates tabular data from other line-oriented formats.
Tabular data is routinely transferred on the web in a textual format called CSV, but the definition of CSV in practice is very loose. Some people use the term to mean any delimited text file. Others stick more closely to the most standard definition of CSV that there is, [RFC4180]. Appendix A describes the various ways in which CSV is defined. This specification refers to such files, as well as tab-delimited files, fixed field formats, spreadsheets, HTML tables, and SQL dumps as tabular data files.
In section 4. Tabular Data Models, this document defines a model for tabular data that abstracts away from the varying syntaxes that are used for when exchanging tabular data. The model includes annotations, or metadata, about collections of individual tables, rows, columns, and cells. These annotations are typically supplied through separate metadata files; section 5. Locating Metadata defines how these metadata files can be located, while [tabular-metadata] defines what they contain.
Once an annotated table has been created, it can be processed in various ways, such as display, validation, or conversion into other formats. This processing is described in section 6. Processing Tables.
This specification does not normatively define a format for exchanging tabular data. However, it does provide some best practice guidelines for publishing tabular data as CSV, in section section 7. Best Practice CSV, and for parsing both this syntax and those similar to it, in section 8. Parsing Tabular Data.
As well as sections marked as non-normative, all authoring guidelines, diagrams, examples, and notes in this specification are non-normative. Everything else in this specification is normative.
The key words MAY, MUST, MUST NOT, SHOULD, and SHOULD NOT are to be interpreted as described in [RFC2119].
This specification makes use of the compact IRI Syntax; please refer to the Compact IRIs from [JSON-LD].
Within examples, this specification makes use of the following namespaces:
dc
:http://purl.org/dc/terms/
rdfs
:http://www.w3.org/2000/01/rdf-schema#
schema
:http://schema.org/
xsd
:http://www.w3.org/2001/XMLSchema#
The following typographic conventions are used in this specification:
markup
markup definition reference
markup external definition reference
Notes are in light green boxes with a green left border and with a "Note" header in green. Notes are normative or informative depending on the whether they are in a normative or informative section, respectively.
Examples are in light khaki boxes, with khaki left border, and with a numbered "Example" header in khaki. Examples are always informative. The content of the example is in monospace font and may be syntax colored.
This section defines an annotated tabular data model: a model for tables that are annotated with metadata. Annotations provide information about the cells, rows, columns, tables, and groups of tables with which they are associated. The values of these annotations may be lists, structured objects, or atomic values. Core annotations are those that affect the behavior of processors defined in this specification, but other annotations may also be present on any of the components of the model.
Annotations may be described directly in [tabular-metadata], be embedded in a tabular data file, or created during the process of generating an annotated table.
In this document, the term annotation refers to any metadata associated with an object in the annotated tabular data model. These are not necessarily web annotations in the sense of [annotation-model].
A group of tables comprises a set of annotated tables and a set of annotations that relate to that group of tables. The core annotations of a group of tables are:
null
if this is undefined.Groups of tables MAY in addition have any number of annotations which provide information about the group of tables. Annotations on a group of tables may include:
When originating from [tabular-metadata], these annotations arise from common properties defined on table group descriptions within metadata documents.
An annotated table is a table that is annotated with additional metadata. The core annotations of a table are:
null
if this is undefined.null
.null
if this is undefined.The table MAY in addition have any number of other annotations. Annotations on a table may include:
When originating from [tabular-metadata], these annotations arise from common properties defined on table group descriptions within metadata documents.
A column represents a vertical arrangement of cells within a table. The core annotations of a column are:
null
.null
.false
, the column is a real column, which exists in the source data for the table.Columns MAY in addition have any number of other annotations, such as a description. When originating from [tabular-metadata], these annotations arise from common properties defined on table group descriptions within metadata documents.
A row represents a horizontal arrangement of cells within a table. The core annotations of a row are:
null
.Rows MAY have any number of additional annotations. The annotations on a row provide additional metadata about the information held in the row, such as:
Neither this specification nor [tabular-metadata] defines a method to specify such annotations. Implementations MAY define a method for adding annotations to rows by interpreting notes on the table.
A cell represents a cell at the intersection of a row and a column within a table. The core annotations of a cell are:
null
.null
.null
. For example, annotations might enable a processor to understand the string value of the cell as representing a number or a date. By default, if the string value is an empty string, the semantic value of the cell is null
.null
.
There presence or absence of quotes around a value within a CSV file is a syntactic detail that is not reflected in the tabular data model. In other words, there is no distinction in the model between the second value in a,,z
and the second value in a,"",z
.
Cells MAY have any number of additional annotations. The annotations on a cell provide metadata about the value held in the cell, particularly when this overrides the information provided for the column and row that the cell falls within. Annotations on a cell might be:
Neither this specification nor [tabular-metadata] defines a method to specify such annotations. Implementations MAY define a method for adding annotations to cells by interpreting notes on the table.
Columns and Cells within tables may be annotated with a datatype which indicates the type of the values obtained by parsing the string value of the cell.
Datatypes are based on a subset of those defined in [xmlschema11-2] with aliases. The annotated tabular data model limits cell values to have datatypes as shown on the diagram:
anyAtomicType
xml
, a sub-type of string
, which indicates the value is an XML fragmenthtml
, a sub-type of string
, which indicates the value is an HTML fragmentjson
, a sub-type of string
, which indicates the value is serialized JSONThe core annotations of a datatype are:
Datatypes MAY have any number of additional annotations. The annotations on a datatype provide metadata about the datatype such as title or description. These arise from common properties defined on datatype descriptions within metadata documents, as defined in [tabular-metadata].
The length, minimum length and maximum length annotations indicate the exact, minimum and maximum lengths for cell string values.
The length of a value is determined as follows:
null
, its length is zeroThe minimum, maximum, minimum exclusive, and maximum exclusive annotations indicate limits on cell string values. These apply to numeric, date/time, and duration types.
Validation of cell string values against these datatypes is as defined in [xmlschema11-2].
As described in section 4. Tabular Data Models, tabular data may have a number of annotations associated with it. Here we describe the different methods that can be used to locate metadata that provides those annotations.
In most methods of locating metadata described here, metadata is provided within separate documents. The syntax of these documents is defined in [tabular-metadata].
When creating a set of annotations from metadata, if the same property is specified in two locations then information "closer" to the end user or interpreter of the document overrides other information, and information "closer" to the document itself overrides information "further" from the document. Explicitly, the order of precedence is:
Link
header associated with the tabular data file, see section 5.3 Link Header
Processors MUST always use overriding metadata and embedded metadata. If the overriding metadata does not originate from a complete metadata file itself (for example, if it is generated from command-line options when invoking the implementation), processors MUST attempt to locate the first metadata document from the Link
header, the file-specific metadata, or the directory-specific metadata. The located metadata from these locations MUST be merged as defined in [tabular-metadata].
If no metadata is supplied or found, then the following Metadata is used as the default metadata:
{ "@type": "TableGroup", "tables": [] }
Processors SHOULD provide users with the facility to provide their own metadata for tabular data files that they process. This might be provided:
For example, a processor might be invoked with:
$ csvlint data.csv --datatypes:string,float,string,string
to enable the testing of the types of values in the columns of a CSV file, or with:
$ csvlint data.csv --schema:schema.json
to supply a schema that describes the contents of the file, against which it can be validated.
Metadata supplied in this way is called overriding, or user-supplied, metadata. Implementations SHOULD define how any options they define are mapped into the vocabulary defined in [tabular-metadata]. If the user selects existing metadata files, implementations MUST NOT use metadata located through the link header (as described in section 5.3 Link Header), file-specific metadata (as described in section 5.4 Standard File Metadata) or directory-specific metadata (as described in section 5.5 Standard Directory Metadata).
Most syntaxes for tabular data provide a facility for embedding metadata within the tabular data file itself. The definition of a syntax for tabular data SHOULD include a description of how the syntax maps to an annotated data model, and in particular how any embedded metadata is mapped into the vocabulary defined in [tabular-metadata]. Parsing based on the default dialect for CSV, as described in 8. Parsing Tabular Data, will extract column titles from the first row of a CSV file.
GID,On Street,Species,Trim Cycle,Inventory Date 1,ADDISON AV,Celtis australis,Large Tree Routine Prune,10/18/2010 2,EMERSON ST,Liquidambar styraciflua,Large Tree Routine Prune,6/2/2010
The results of this can be found in section 8.2.1 Simple Example.
For another example, the following tab-delimited file contains embedded metadata where it is assumed that comments may be added using a #
, and that the column types may be indicated using a #datatype
annotation:
# publisher City of Palo Alto # updated 12/31/2010 #name GID on_street species trim_cycle inventory_date #datatype string string string string date:M/D/YYYY GID On Street Species Trim Cycle Inventory Date 1 ADDISON AV Celtis australis Large Tree Routine Prune 10/18/2010 2 EMERSON ST Liquidambar styraciflua Large Tree Routine Prune 6/2/2010
A processor that recognises this format may be able to extract and make sense of this embedded metadata.
If the user has not supplied a metadata file as overriding metadata, described in section 5.1 Overriding Metadata, then when retrieving a tabular data file via HTTP, processors MUST retrieve the metadata file referenced by any Link
header with:
rel="describedby"
andtype="application/csvm+json"
, type="application/ld+json"
or type="application/json"
so long as this referenced metadata file describes the retrieved tabular data file (ie, contains a table description whose url
matches the request URL).
If there is more than one valid metadata file linked to through multiple Link
headers, then implementations MUST use the metadata file referenced by the last Link
header.
For example, when the response to requesting a tab-separated file looks like:
HTTP/1.1 200 OK Content-Type: text/tab-separated-values ... Link: <metadata.ttl>; rel="describedBy"; type="text/turtle" Link: <metadata.json>; rel="describedBy"; type="application/csvm+json"
an implementation must use the referenced metadata.json
to supply metadata for processing the file.
If the metadata file found at this location does not explicitly include a reference to the requested tabular data file then it MUST be ignored.
If the user has not supplied a metadata file as overriding metadata, described in section 5.1 Overriding Metadata, and no applicable metadata file has been discovered through a Link
header, described in section 5.3 Link Header, processors MUST attempt to locate a file-specific metadata document. This method MUST NOT be used if the URL of the tabular data file contains a query component.
In this case, processors MUST construct a URL for the file-specific metadata document by appending -metadata.json
to the location of the tabular data file (with any fragment component of that URL removed) and attempt to retrieve a metadata document from that location.
For example, if the tabular data file is at http://example.org/south-west/devon.csv
then processors must attempt to locate a metadata document at http://example.org/south-west/devon.csv-metadata.json
. On the other hand, if the tabular data file is at http://example.com/index.php?file=43178.csv
then, because the URL of the tabular data file includes a query component, the processor must not attempt to look for a file-specific metadata document.
If the metadata file found at this location does not explicitly include a reference to the relevant tabular data file then it MUST be ignored.
If the user has not supplied a metadata file as overriding metadata, described in section 5.1 Overriding Metadata, and no applicable metadata file has been discovered through a Link
header, described in section 5.3 Link Header, or from the standard file metadata location, described in section 5.4 Standard File Metadata, processors MUST attempt to locate a directory-level metadata document. This method MUST NOT be used if the URL of the tabular data file contains a query component.
Processors MUST locate and retrieve the common metadata document for a directory by resolving the relative URL metadata.json
against the base URL of the tabular data file and fetching the resulting URL.
For example, if the tabular data file is at http://example.org/south-west/devon.csv
then processors must attempt to locate a metadata file at http://example.org/south-west/metadata.json
. On the other hand, if the tabular data file is at http://example.com/index.php?file=43178.csv
then, because the URL of the tabular data file includes a query component, the processor must not attempt to look for a directory-level metadata document.
If the metadata file found at this location does not explicitly include a reference to the relevant tabular data file then it MUST be ignored.
This section describes how particular types of applications should process tabular data and metadata files.
In many cases, an application will start processing from a metadata file. In that case, the initial metadata file is treated as overriding metadata and the application MUST NOT continue to retrieve other available metadata about each of the tabular data files referenced by that initial metadata file other than embedded metadata.
In other cases, applications will start from a tabular data file, such as a CSV file, and locate metadata from that file. This metadata will be used to process the file as if the processor were starting from that metadata file.
For example, if a validator is passed a locally authored metadata file spending.json
, which contains:
{ "tableSchema": "government-spending.csv", "tables": [{ "url": "http://example.org/east-sussex-2015-03.csv", }, { "url": "http://example.org/east-sussex-2015-02.csv" }, ... ] }
the validator would validate all the listed tables, using the locally defined schema at government-spending.csv
. It would also use the metadata embedded in the referenced CSV files; for example, when processing http://example.org/east-sussex-2015-03.csv
, it would use embedded metadata within that file.
If a validator is passed a tabular data file http://example.org/east-sussex-2015-03.csv
,
the validator would use the metadata located from the CSV file: the first metadata file found through the Link
headers found when retrieving that file, or at http://example.org/east-sussex-2015-03.csv-metadata.json
or http://example.org/metadata.json
.
Starting with a metadata file can remove the need to perform additional requests to locate linked, file-specific, or directory-specific metadata
After locating metadata, metadata is merged into a single table group description. When starting with a metadata file, this involves merging the provided metadata file with the embedded metadata for each tabular data file referenced by that file. When starting with a tabular data file, this involves locating the first metadata file as described in section 5. Locating Metadata and merging all relevant metadata into a single descriptor.
If processing starts with a tabular data file, implementations:
UMT
) (see section 5.1 Overriding Metadata).UMT
does not describe a table or group of tables describing the tabular data file, retrieve the first metadata file (FM
) as described in section 5. Locating Metadata:
tables
entry where the url
property is set from that of the tabular data file.UMM = merge(UMT, FM)
or UMT
if no FM
is loaded.If the process starts with a metadata file:
UMM
(which is treated as overriding metadata, see section 5.1 Overriding Metadata).UMM
in order, create one or more annotated tables:
DD
) from UMM
for the table associated with the tabular data file. If there is no such dialect description, extract the first available dialect description from a group of tables in which the tabular data file is described. Otherwise use the default dialect description.DD
based on HTTP headers found when retrieving the tabular data file:
Parse the tabular data file, using DD
as a guide, to create a basic tabular data model (T
) and extract embedded metadata (EM
), for example from the header line.
This specification provides a non-normative definition for parsing CSV-based files, including the extraction of embedded metadata, in section 8. Parsing Tabular Data. This specification does not define any syntax for embedded metadata beyond this; whatever syntax is used, it's assumed that metadata can be mapped to the vocabulary defined in [tabular-metadata].
Content-Language
HTTP header was found when retrieving the tabular data file, and the value provides a single language, set the lang inherited property to this value in EM
.M = merge(UMM, EM)
using the merge algorithm defined in [tabular-metadata].M
to add annotations to the tabular data model T
as described in Section 2 Annotating Tables in [tabular-metadata].
In the case of starting with a metadata file, UMM
will describe a table or group of tables, and no other metadata files will be retrieved. Thus the metadata file must provide all applicable metadata aside from that embedded within the tabular data files themselves.
Unlike many other data formats, tabular data is designed to be read by humans. For that reason, it's common for data to be represented within tabular data in a human-readable way. The datatype, default, lang, null, required, and separator annotations provide the information needed to parse the string value of a cell into its (semantic) value annotation. This is used:
The process of parsing a cell creates a cell with annotations based on the original string value, parsed value and other column annotations and adds the cell to the list of cells in a row and cells in a column:
After parsing, the cell value can be:
null
,The process of parsing the string value into a single value or a list of values is as follows:
string
, json
, xml
, html
or anyAtomicType
, replace all carriage return (#xD
), line feed (#xA
), and tab (#x9
) characters with space characters.string
, json
, xml
, html
, anyAtomicType
, or normalizedString
, strip leading and trailing whitespace from the string value and replace all instances of two or more whitespace characters with a single space character.null
and the normalized string is an empty string, the cell value is an empty list. If the column required annotation is true
, add an error to the list of errors for the cell.null
, the cell value is a list of values; set the list annotation on the cell to true
, and create the cell value created by:
null
.string
or anyAtomicType
, strip leading and trailing whitespace from these strings.null
. If the column separator annotation is null
and the column required annotation is true
, add an error to the list of errors for the cell.string
, or there is no datatype
, the value has an associated language from the column lang annotation. If there are any errors, add them to the list of errors for the cell; in this case the value has a datatype of string
; if the datatype base is string
, or there is no datatype
, the value has an associated language from the column lang annotation.The final value (or values) become the value annotation on the cell.
If there is a about URL annotation on the column, it becomes the about URL annotation on the cell, after being transformed into an absolute URL as described in URI Template Properties of [tabular-metadata].
If there is a property URL annotation on the column, it becomes the property URL annotation on the cell, after being transformed into an absolute URL as described in URI Template Properties of [tabular-metadata].
If there is a value URL annotation on the column, it becomes the value URL annotation on the cell, after being transformed into an absolute URL as described in URI Template Properties of [tabular-metadata]. The value URL annotation is null
if the cell value is null
and the column virtual annotation is false
.
This section is non-normative.
When datatype annotation is available, the value of a cell is the same as its string value. For example, a cell with a string value of "99"
would similarly have the (semantic) value "99"
.
If a datatype base is provided for the cell, that is used to create a (semantic) value for the cell. For example, if the metadata contains:
"datatype": "integer"
for the cell with the string value "99"
then the value of that cell will be the integer 99
. A cell whose string value was not a valid integer (such as "one"
or "1.0"
) would be assigned that string value as its (semantic) value annotation, but also have a validation error listed in its errors annotation.
Sometimes data uses special codes to indicate unknown or null values. For example, a particular column might contain a number that is expected to be between 1
and 10
, with the string 99
used in the original tabular data file to indicate a null value. The metadata for such a column would include:
"datatype": { "base": "integer", "minimum": 1, "maximum": 10 }, "null": "99"
In this case, a cell with a string value of "5"
would have the (semantic) value of the integer 5
; a cell with a string value of "99"
would have the value null
.
Similarly, a cell may be assigned a default value if the string value for the cell is empty. A configuration such as:
"datatype": { "base": "integer", "minimum": 1, "maximum": 10 }, "default": "5"
In this case, a cell whose string value is ""
would be assigned the value of the integer 5
. A cell whose string value contains whitespace, such as a single tab character, would also be assigned the value of the integer 5
: when the datatype is something other than string
or anyAtomicType
, leading and trailing whitespace is stripped from string values before the remainder of the processing is carried out.
Cells can contain sequences of values. For example, a cell might have the string value "1 5 7.0"
. In this case, the separator is a space character. The appropriate configuration would be:
"datatype": { "base": "integer", "minimum": 1, "maximum": 10 }, "default": "5", "separator": " "
and this would mean that the cell's value would be an array containing two integers and a string: [1, 5, "7.0"]
. The final value of the array is a string because it is not a valid integer; the cell's errors annotation will also contain a validation error.
Also, with this configuration, if the string value of the cell were ""
(ie it was an empty cell) the value of the cell would be an empty list.
A cell value can be inserted into a URL created using a URI template property such as valueUrl
. For example, if a cell with the string value "1 5 7.0"
were in a column named values
, defined with:
"datatype": "decimal", "separator": " ", "valueUrl": "{?values}"
then after expansion of the URI template, the resulting valueUrl
would be ?values=1.0,5.0,7.0
. The canonical representations of the decimal values are used within the URL.
It is not uncommon for numbers within tabular data to be formatted for human consumption, which may involve using commas for decimal points, grouping digits in the number using commas, or adding currency symbols or percent signs to the number.
If the datatype base is a numeric type, the datatype format annotation indicates the expected format for that number. Its value MUST be either a single string or an object with one or more of the properties:
"."
.","
.Authors are encouraged to be conservative in the regular expressions that they use, sticking to the basic features of regular expressions that are likely to be supported across implementations.
If the datatype format annotation is a single string, this is interpreted in the same way as if it were an object with a pattern property whose value is that string.
When parsing the string value of a cell against this format specification, implementations MUST recognise and parse numbers that consist of:
+
or -
sign,0-9
),0-9
) and the character specified as the groupChar,0-9
),E
followed by an optional +
or -
sign followed by one or more decimal digits (0-9
), or%
) or per-mille (‰
) sign.or that are one of the special values:
NaN
,INF
, or-INF
.Implementations MUST add a validation error to the errors annotation for the cell if the string being parsed:
integer
or one of its sub-values,decimal
or one of its sub-values, orNaN
, INF
, or -INF
, if the datatype base is decimal
or one of its sub-values.
Implementations MUST use the sign, exponent, percent, and per-mille signs when parsing the string value of a cell to provide the value of the cell. For example, the string value "-25%"
must be interpreted as -0.25
and the string value "1E6"
as 1000000
.
Boolean values may be represented in many ways aside from the standard 1
and 0
or true
and false
.
If the datatype base for a cell is boolean
, the datatype format annotation provides the true and false values expected, separated by |
. For example if format
is Y|N
then cells must hold either Y
or N
with Y
meaning true
and N
meaning false
.
The resulting cell value will be one or more boolean true
or false
values.
Dates and times are commonly represented in tabular data in formats other than those defined in [xmlschema11-2].
If the datatype base is a date or time type, the datatype format annotation indicates the expected format for that date or time.
The supported date and time formats listed here are expressed in terms of the date field symbols defined in [UAX35] and MUST be interpreted by implementations as defined in that specification.
The following date formats MUST be recognised by implementations:
yyyy-MM-dd
e.g., 2015-03-22
yyyyMMdd
e.g., 20150322
dd-MM-yyyy
e.g., 22-03-2015
d-M-yyyy
e.g., 22-3-2015
MM-dd-yyyy
e.g., 03-22-2015
M-d-yyyy
e.g., 3-22-2015
dd/MM/yyyy
e.g., 22/03/2015
d/M/yyyy
e.g., 22/3/2015
MM/dd/yyyy
e.g., 03/22/2015
M/d/yyyy
e.g., 3/22/2015
dd.MM.yyyy
e.g., 22.03.2015
d.M.yyyy
e.g., 22.3.2015
MM.dd.yyyy
e.g., 03.22.2015
M.d.yyyy
e.g., 3.22.2015
The following time formats MUST be recognised by implementations:
HH:mm:ss
e.g., 15:02:37
HHmmss
e.g., 150237
HH:mm
e.g., 15:02
HHmm
e.g., 1502
The following date/time formats MUST be recognised by implementations:
yyyy-MM-ddTHH:mm:ss
e.g., 2015-03-15T15:02:37
yyyy-MM-ddTHH:mm
e.g., 2015-03-15T15:02
M/d/yyyy HH:mm
for 3/22/2015 15:02
or dd.MM.yyyy HH:mm:ss
for 22.03.2015 15:02:37
Implementations MUST also recognise date, time, and date/time formats that end with timezone markers consisting of between one and three x
s or X
s, possibly after a single space. These MUST be interpreted as follows:
X
e.g., -08
, +0530
, or Z
(minutes are optional)XX
e.g., -0800
, +0530
, or Z
XXX
e.g., -08:00
, +05:30
, or Z
x
e.g., -08
or +0530
(Z
is not permitted)xx
e.g., -0800
or +0530
(Z
is not permitted)xxx
e.g., -08:00
or +05:30
(Z
is not permitted)
For example, formats could include yyyy-MM-ddTHH:mm:ssXXX
for 2015-03-15T15:02:37Z
or 2015-03-15T15:02:37-05:00
, or HH:mm x
for 15:02 -05
.
The cell value will one or more dates/time values extracted using the format
.
For simplicity, this version of this standard does not support abbreviated or full month or day names, or double digit years. Future versions of this standard may support other date and time formats, or general purpose date/time pattern strings. Authors of schemas SHOULD use appropriate regular expressions, along with the string
datatype, for dates and times that use a format other than that specified here.
Durations MUST be formatted and interpreted as defined in [xmlschema11-2], using the [ISO8601] format -?PnYnMnDTnHnMnS
. For example, the duration P1Y1D
is used for a year and a day; the duration PT2H30M
for 2 hours and 30 minutes.
If the datatype base is a duration type, the datatype format annotation provides a regular expression for the string values, in the syntax and processed as defined by [ECMASCRIPT].
Authors are encouraged to be conservative in the regular expressions that they use, sticking to the basic features of regular expressions that are likely to be supported across implementations.
The cell value will be one or more durations extracted using the format
.
If the datatype base is not numeric, boolean
, a date/time type, or a duration type, the datatype format annotation provides a regular expression for the string values, in the syntax and processed as defined by [ECMASCRIPT].
Authors are encouraged to be conservative in the regular expressions that they use, sticking to the basic features of regular expressions that are likely to be supported across implementations.
Values that are labelled as html
, xml
, or json
SHOULD NOT be validated against those formats.
Metadata creators who wish to check the syntax of HTML, XML, or JSON within tabular data should use the datatype format annotation to specify a regular expression against which such values will be tested.
This section is non-normative.
When displaying tables, implementations should:
There are two levels of bidirectionality to consider when displaying tables: the directionality of the table (i.e., whether the columns should be arranged left-to-right or right-to-left) and the directionality of the content of individual cells.
The direction annotation on the table provides information about the desired display of the table. If direction is ltr
then the first column should be displayed on the left and the last column on the right. If direction is rtl
then the first column should be displayed on the right and the last column on the left.
If direction is default
then tables should be displayed with attention to the bidirectionality of the content of the file. Specifically, the values of the cells in the table should be scanned breadth first: from the first cell in the first column through to the last cell in the first row, down to the last cell in the last column. If the first character in the table with a strong type as defined in [BIDI] indicates a RTL directionality, the table should be displayed with the first column on the right and the last column on the left. Otherwise, the table should be displayed with the first column on the left and the last column on the right. Characters such as whitespace, quotes, commas, and numbers do not have a strong type, and therefore are skipped when identifying the character that determines the directionality of the table.
Implementations should enable user preferences to override the indicated metadata about the directionality of the table.
Once the directionality of the table has been determined, each cell within the table should be considered as a separate paragraph, as defined by the Unicode Bidirectional Algorithm (UBA) in [BIDI]. The default directionality for the cell is determined by looking at the text direction annotation for the cell.
Thus, as defined by the UBA, if a cell contains no characters with a strong type (if it's a number or date for example) then the way the cell is displayed should be determined by the text direction annotation of the cell. However, when the cell contains characters with a strong type (such as letters) then they should be displayed according to the Unicode Bidirectional Algorithm as described in [BIDI].
Validators test whether given tabular data files adhere to the structure defined within a schema. Validators MUST raise errors as defined in [tabular-metadata] and in the following situations:
Processors MAY continue validating after encountering the first error.
Conversions of tabular data to other formats operate over a annotated table constructed as defined in Annotating Tables in [tabular-metadata]. The mechanics of these conversions to other formats are defined in other specifications such as [csv2json] and [csv2rdf].
Conversion specifications MUST define a default mapping from an annotated table that lacks any annotations (i.e., that is equivalent to an un-annotated table).
Conversion specifications MUST use the property value of the propertyUrl
of a column as the basis for naming machine-readable fields in the target format, such as the name of the equivalent element or attribute in XML, property in JSON or property URI in RDF.
Conversion specifications MAY use any of the annotations found on an annotated table group, table, column, row or cell, including non-core annotations, to adjust the mapping into another format.
Conversion specifications MAY define additional annotations, not defined in this specification, which are specifically used when converting to the target format of the conversion. For example, a conversion to XML might specify a http://example.org/conversion/xml/element-or-attribute
property on columns that determines whether a particular column is represented through an element or an attribute in the data.
This section is non-normative.
There is no standard for CSV, and there are many variants of CSV used on the web today. This section defines a method for expressing tabular data adhering to the annotated tabular data model in CSV. Authors are encouraged to adhere to the constraints described in this section as implementations should process such CSV files consistently.
This syntax is not compliant with text/csv
as defined in [RFC4180] in that it permits line endings other than CRLF
. Supporting LF
line endings is important for data formats that are used on non-Windows platforms. However, all files that adhere to [RFC4180]'s definition of CSV meet the constraints described in this section.
Developing a standard for CSV is outside the scope of the Working Group. The details here aim to help shape any future standard.
The appropriate content type for a CSV file is text/csv
. For example, when a CSV file is transmitted via HTTP, the HTTP response should include a Content-Type
header with the value text/csv
:
Content-Type: text/csv
CSV files should be encoded using UTF-8. If a CSV file is not encoded using UTF-8, the encoding should be specified through the charset
parameter in the Content-Type
header:
Content-Type: text/csv;charset=ISO-8859-1
The ends of rows in a CSV file should be either CRLF
(U+000D U+000A
) or LF
(U+000A
). Line endings within escaped cells are not normalised.
Each line of a CSV file should contain the same number of comma-separated values.
Values that contain commas, line endings, or double quotes should be escaped by having the entire value wrapped in double quotes. There should not be whitespace before or after the double quotes. Within these escaped cells, any double quotes should be escaped with two double quotes (""
).
The first line of a CSV file should contain a comma-separated list of names of columns. This is known as the header line and provides titles for the columns. There are no constraints on these titles.
If a CSV file does not include a header line, this should be specified using the header
parameter of the media type:
Content-Type: text/csv;header=absent
This grammar is a generalization of that defined in [RFC4180] and is included for reference only.
The EBNF used here is defined in XML 1.0 [EBNF-NOTATION].
This section is non-normative.
As described in section 7. Best Practice CSV, there may be many formats which an application might interpret into the tabular data model described in section 4. Tabular Data Models, including using different separators or fixed format tables, multiple tables within a single file, or ones that have metadata lines before a table header.
Standardising the parsing of CSV is outside the chartered scope of the Working Group. This non-normative section is intended to help the creators of parsers handle the wide variety of CSV-based formats that they may encounter due to the current lack of standardisation of the format.
This section describes an algorithm for parsing formats that do not adhere to the constraints described in section 7. Best Practice CSV, as well as those that do, and extracting embedded metadata. The parsing algorithm uses the following flags. These may be set by metadata properties found while Locating Metadata, including through user input (see Overriding Metadata), or through the inclusion of a dialect description within a metadata file:
rdfs:comment
annotation to the table. This is set by the commentPrefix
property of a dialect description. The default is null
, which means no rows are treated as comments. A value other than null
may mean that the source numbers of rows are different from their numbers.delimiter
property of a dialect description. The default is ,
.encoding
property of a dialect description. The default is utf-8
.null
, set by the doubleQuote
property of a dialect description. The default is "
(such that ""
is used to escape "
within an escaped cell).header
or headerRowCount
property of a dialect description. The default is 1
. A value other than 0
will mean that the source numbers of rows will be different from their numbers.lineTerminators
property of a dialect description. The default is [CRLF, LF]
.null
, set by the quoteChar
property of a dialect description. The default is "
.skipBlankRows
property of a dialect description. The default is false
. A value other than false
may mean that the source numbers of rows are different from their numbers.skipColumns
property of a dialect description. The default is 0
. A value other than 0
will mean that the source numbers of columns will be different from their numbers.skipRows
property of a dialect description. The default is 0
. A value greater than 0
will mean that the source numbers of rows will be different from their numbers.true
, false
, start
, or end
, set by the skipInitialSpace
or trim
property of a dialect description. The default is false
.The algorithm for using these flags to parse a document containing tabular data to create a basic annotated tabular data model and to extract embedded metadata is as follows:
null
null
ltr
false
false
{ "@context": "http://www.w3.org/ns/csvw", "rdfs:comment": [] "tableSchema": { "columns": [] } }
url
property on M to that URL.
1
.
null
and the row content begins with the comment prefix, strip that prefix from the row content, and add the resulting string to the M.rdfs:comment
array.M.rdfs:comment
array.1
to the source row number.null
and the row content begins with the comment prefix, strip that prefix from the row content, and add the resulting string to the M.rdfs:comment
array.M.tableSchema.columns
, create a new one with a title
property whose value is an array containing a single value that is the value at index i in the list of cell values.M.tableSchema.columns[i].title
.1
to the source row number.1
.1
.null
and the row content begins with the comment prefix, strip that prefix from the row content, and add the resulting string to the M.rdfs:comment
array.true
, add 1
to the source row number and move on to process the next row.1
):
null
string
false
false
ltr
null
null
null
1
to the source column number.1
to the source row number.M.rdfs:comment
is an empty array, remove the rdfs:comment
property from M.To read a row to provide row content, perform the following steps:
To read a quoted value to provide a quoted value, perform the following steps:
To parse a row to provide a list of cell values, perform the following steps:
false
.false
, set the quoted flag to true
, and move on to process the next character. If the current cell value is not an empty string, raise an error.false
, and move on to process the next character. If the next character is not the delimiter, raise an error.true
, append the delimiter character to the current cell value and move on to process the next character.To trim a cell value to provide a trimmed cell value, perform the following steps:
true
or start
then remove any leading whitespace from the start of the trimmed cell value and move on to the next step.true
or end
then remove any trailing whitespace from the end of the trimmed cell value and move on to the next step.This parsing algorithm does not account for the possibility of there being more than one area of tabular data within a single CSV file.
This section is non-normative.
Bidirectional content does not alter the definition of rows or the assignment of cells to columns. Whether or not a CSV file contains right-to-left characters, the first column's content is the first cell of each row, which is the text prior to the first occurrence of a comma within that row.
For example, Egyptian Referendum results are available as a CSV file at https://egelections-2011.appspot.com/Referendum2012/results/csv/EG.csv. Over the wire and in non-Unicode-aware text editors, the CSV looks like:
المحافظة,نسبة موافق,نسبة غير موافق,عدد الناخبين,الأصوات الصحيحة,الأصوات الباطلة,نسبة المشاركة,موافق,غير موافق القليوبية,60.0,40.0,"2,639,808","853,125","15,224",32.9,"512,055","341,070" الجيزة,66.7,33.3,"4,383,701","1,493,092","24,105",34.6,"995,417","497,675" القاهرة,43.2,56.8,"6,580,478","2,254,698","36,342",34.8,"974,371","1,280,327" قنا,84.5,15.5,"1,629,713","364,509","6,743",22.8,"307,839","56,670" ...
Within this CSV file, the first column appears as the content of each line before the first comma and is named المحافظة
(appearing at the start of each row as المحافظة
in the example, which is displaying the relevant characters from left to right in the order they appear "on the wire").
The CSV translates to a table model that looks like:
Column / Row | column 1 | column 2 | column 3 | column 4 | column 5 | column 6 | column 7 | column 8 | column 9 |
---|---|---|---|---|---|---|---|---|---|
column names | المحافظة | نسبة موافق | نسبة غير موافق | عدد الناخبين | الأصوات الصحيحة | الأصوات الباطلة | نسبة المشاركة | موافق | غير موافق |
row 1 | القليوبية | 60.0 | 40.0 | 2,639,808 | 853,125 | 15,224 | 32.9 | 512,055 | 341,070 |
row 2 | الجيزة | 66.7 | 33.3 | 4,383,701 | 1,493,092 | 24,105 | 34.6 | 995,417 | 497,675 |
row 3 | القاهرة | 43.2 | 56.8 | 6,580,478 | 2,254,698 | 36,342 | 34.8 | 974,371 | 1,280,327 |
row 4 | قنا | 84.5 | 15.5 | 1,629,713 | 364,509 | 6,743 | 22.8 | 307,839 | 56,670 |
The fragment identifier #col=3
identifies the third of the columns, named نسبة غير موافق
(appearing as نسبة غير موافق
in the example).
section 6.3.1 Bidirectional Tables defines how this table model should be displayed by compliant applications, and how metadata can affect the display. The default is for the display to be determined by the content of the table. For example, if this CSV were turned into an HTML table for display into a web page, it should be displayed with the first column on the right and the last on the left, as follows:
غير موافق | موافق | نسبة المشاركة | الأصوات الباطلة | الأصوات الصحيحة | عدد الناخبين | نسبة غير موافق | نسبة موافق | المحافظة |
---|---|---|---|---|---|---|---|---|
341,070 | 512,055 | 32.9 | 15,224 | 853,125 | 2,639,808 | 40.0 | 60.0 | القليوبية |
497,675 | 995,417 | 34.6 | 24,105 | 1,493,092 | 4,383,701 | 33.3 | 66.7 | الجيزة |
1,280,327 | 974,371 | 34.8 | 36,342 | 2,254,698 | 6,580,478 | 56.8 | 43.2 | القاهرة |
56,670 | 307,839 | 22.8 | 6,743 | 364,509 | 1,629,713 | 15.5 | 84.5 | قنا |
The fragment identifier #col=3
still identifies the third of the columns, named نسبة غير موافق
, which appears in the HTML display as the third column from the right and is what those who read right-to-left would think of as the third column.
Note that this display matches that shown on the original website.
An alternative approach is for the CSV to be parsed into a table model in which the columns are numbered in the reverse, for tables which are either marked as or detected to be right-to-left tables. For example, we could introduce a bidi=rtl
or similar media type parameter, and use this to determine whether the first column in table generated from the CSV is the text before the first comma in each line or the text after the last comma in the line.
In the example above, if the CSV were served with bidi=rtl
, or the table was detected as being a right-to-left table, then the column numbering in the model would be reversed:
Column / Row | column 9 | column 8 | column 7 | column 6 | column 5 | column 4 | column 3 | column 2 | column 1 |
---|---|---|---|---|---|---|---|---|---|
row 1 (header) | المحافظة | نسبة موافق | نسبة غير موافق | عدد الناخبين | الأصوات الصحيحة | الأصوات الباطلة | نسبة المشاركة | موافق | غير موافق |
row 2 | القليوبية | 60.0 | 40.0 | 2,639,808 | 853,125 | 15,224 | 32.9 | 512,055 | 341,070 |
row 3 | الجيزة | 66.7 | 33.3 | 4,383,701 | 1,493,092 | 24,105 | 34.6 | 995,417 | 497,675 |
row 4 | القاهرة | 43.2 | 56.8 | 6,580,478 | 2,254,698 | 36,342 | 34.8 | 974,371 | 1,280,327 |
row 5 | قنا | 84.5 | 15.5 | 1,629,713 | 364,509 | 6,743 | 22.8 | 307,839 | 56,670 |
This would require a change to [RFC7111] but that might be required by updates to the definition of text/csv
in any case. With the change, the fragment identifier #col=3
would then refer to the third column from the right, named نسبة المشاركة
.
If the model were defined in this way, there would be no need to determine the order of the columns when displayed using a metadata property. Columns would always be displayed with the first column (numbered 1 in the model) on the left. The final display in HTML, for example, would be exactly as above. The only difference would be that #col=3
would refer to the third column from the left.
We note that using media type parameters is problematic because publishers might not have the ability to set them on their servers, and because they can easily get lost as a file is republished or emailed between people.
We invite comment on the best way to approach bidirectionality in CSV files.
A simple CSV file that complies to the constraints described in section 7. Best Practice CSV, at http://example.org/tree-ops.csv
, might look like:
GID,On Street,Species,Trim Cycle,Inventory Date 1,ADDISON AV,Celtis australis,Large Tree Routine Prune,10/18/2010 2,EMERSON ST,Liquidambar styraciflua,Large Tree Routine Prune,6/2/2010
Parsing this file results in an annotated tabular data model of a single table T with five columns and two rows. The columns have the annotations shown in the following table:
id | core annotations | ||||
---|---|---|---|---|---|
table | number | source number | cells | titles | |
C1 | T | 1 | 1 | C1.1, C2.1 | GID |
C2 | T | 2 | 2 | C1.2, C2.2 | On Street |
C3 | T | 3 | 3 | C1.3, C2.3 | Species |
C4 | T | 4 | 4 | C1.4, C2.4 | Trim Cycle |
C5 | T | 5 | 5 | C1.5, C2.5 | Inventory Date |
The extracted embedded metadata, as defined in [tabular-metadata], would look like:
{ "@type": "Table", "url": "http://example.org/tree-ops.csv", "tableSchema": { "columns": [ {"titles": [ "GID" ]}, {"titles": [ "On Street" ]}, {"titles": [ "Species" ]}, {"titles": [ "Trim Cycle" ]}, {"titles": [ "Inventory Date" ]} ] } }
The rows have the annotations shown in the following table:
id | core annotations | |||
---|---|---|---|---|
table | number | source number | cells | |
R1 | T | 1 | 2 | C1.1, C1.2, C1.3, C1.4, C1.5 |
R2 | T | 2 | 3 | C2.1, C2.2, C2.3, C2.4, C2.5 |
The source number of each row is offset by one from the number of each row because in the source CSV file, the header line is the first line. It is possible to reconstruct a [RFC7111] compliant reference to the first record in the original CSV file (http://example.org/tree-ops.csv#row=2
) using the value of the row's source number. This enables implementations to retain provenance between the table model and the original file.
The cells have the annotations shown in the following table (note that the values of all the cells in the table are strings, denoted by the double quotes in the table below):
id | core annotations | ||||
---|---|---|---|---|---|
table | column | row | string value | value | |
C1.1 | T | C1 | R1 | "1" | "1" |
C1.2 | T | C2 | R1 | "ADDISON AV" | "ADDISON AV" |
C1.3 | T | C3 | R1 | "Celtis australis" | "Celtis australis" |
C1.4 | T | C4 | R1 | "Large Tree Routine Prune" | "Large Tree Routine Prune" |
C1.5 | T | C5 | R1 | "10/18/2010" | "10/18/2010" |
C2.1 | T | C1 | R2 | "2" | "2" |
C2.2 | T | C2 | R2 | "EMERSON ST" | "EMERSON ST" |
C2.3 | T | C3 | R2 | "Liquidambar styraciflua" | "Liquidambar styraciflua" |
C2.4 | T | C4 | R2 | "Large Tree Routine Prune" | "Large Tree Routine Prune" |
C2.5 | T | C5 | R2 | "6/2/2010" | "6/2/2010" |
The tools that the consumer of this data uses may provide a mechanism for overriding the metadata that has been provided within the file itself. For example, they might enable the consumer to add machine-readable names to the columns, or to mark the fifth column as holding a date in the format M/D/YYYY
. These facilities are implementation defined; the code for invoking a Javascript-based parser might look like:
data.parse({ "column-names": ["GID", "on_street", "species", "trim_cycle", "inventory_date"], "datatypes": ["string", "string", "string", "string", "date"], "formats": [null,null,null,null,"M/D/YYYY"] });
This is equivalent to a metadata file expressed in the syntax defined in [tabular-metadata], looking like:
{ "@type": "Table", "url": "http://example.org/tree-ops.csv", "tableSchema": { "columns": [{ "name": "GID", "datatype": "string" }, { "name": "on_street", "datatype": "string" }, { "name": "species", "datatype": "string" }, { "name": "trim_cycle", "datatype": "string" }, { "name": "inventory_date", "datatype": { "base": "date", "format": "M/d/yyyy" } }] } }
This would be merged with the embedded metadata found in the CSV file, providing the titles for the columns to create:
{ "@type": "Table", "url": "http://example.org/tree-ops.csv", "tableSchema": { "columns": [{ "name": "GID", "titles": "GID", "datatype": "string" }, { "name": "on_street", "titles": "On Street", "datatype": "string" }, { "name": "species", "titles": "Species", "datatype": "string" }, { "name": "trim_cycle", "titles": "Trim Cycle", "datatype": "string" }, { "name": "inventory_date", "titles": "Inventory Date", "datatype": { "base": "date", "format": "M/d/yyyy" } }] } }
The processor can then create an annotated tabular data model that included name
annotations on the columns, and datatype
annotations on the cells, and created cells whose values were of appropriate types (in the case of this Javascript implementation, the cells in the last column would be Date
objects, for example).
Assuming this kind of implementation-defined parsing, the columns would then have the annotations shown in the following table:
id | core annotations | ||||||
---|---|---|---|---|---|---|---|
table | number | source number | cells | name | titles | datatype | |
C1 | T | 1 | 1 | C1.1, C2.1 | GID | GID | string |
C2 | T | 2 | 2 | C1.2, C2.2 | on_street | On Street | string |
C3 | T | 3 | 3 | C1.3, C2.3 | species | Species | string |
C4 | T | 4 | 4 | C1.4, C2.4 | trim_cycle | Trim Cycle | string |
C5 | T | 5 | 5 | C1.5, C2.5 | inventory_date | Inventory Date | { "base": "date", "format": "M/d/yyyy" } |
The cells have the annotations shown in the following table. Because of the overrides provided by the consumer to guide the parsing, and the way the parser works, the cells in the Inventory Date
column (cells C1.5 and C2.5) have values that are parsed dates rather than unparsed strings.
id | core annotations | ||||
---|---|---|---|---|---|
table | column | row | string value | value | |
C1.1 | T | C1 | R1 | "1" | "1" |
C1.2 | T | C2 | R1 | "ADDISON AV" | "ADDISON AV" |
C1.3 | T | C3 | R1 | "Celtis australis" | "Celtis australis" |
C1.4 | T | C4 | R1 | "Large Tree Routine Prune" | "Large Tree Routine Prune" |
C1.5 | T | C5 | R1 | "10/18/2010" | 2010-10-18 |
C2.1 | T | C1 | R2 | "2" | "2" |
C2.2 | T | C2 | R2 | "EMERSON ST" | "EMERSON ST" |
C2.3 | T | C3 | R2 | "Liquidambar styraciflua" | "Liquidambar styraciflua" |
C2.4 | T | C4 | R2 | "Large Tree Routine Prune" | "Large Tree Routine Prune" |
C2.5 | T | C5 | R2 | "6/2/2010" | 2010-06-02 |
A similar set of annotations could be provided through a metadata file, located as discussed in section 5. Locating Metadata and defined in [tabular-metadata]. For example, this might look like:
{ "@context": ["http://www.w3.org/ns/csvw", {"@language": "en"}], "url": "tree-ops.csv", "dc:title": "Tree Operations", "dcat:keyword": ["tree", "street", "maintenance"], "dc:publisher": { "schema:name": "Example Municipality", "schema:url": {"@id": "http://example.org"} }, "dc:license": {"@id": "http://opendefinition.org/licenses/cc-by/"}, "dc:modified": {"@value": "2010-12-31", "@type": "xsd:date"}, "tableSchema": { "columns": [{ "name": "GID", "titles": ["GID", "Generic Identifier"], "dc:description": "An identifier for the operation on a tree.", "datatype": "string", "required": true }, { "name": "on_street", "titles": "On Street", "dc:description": "The street that the tree is on.", "datatype": "string" }, { "name": "species", "titles": "Species", "dc:description": "The species of the tree.", "datatype": "string" }, { "name": "trim_cycle", "titles": "Trim Cycle", "dc:description": "The operation performed on the tree.", "datatype": "string" }, { "name": "inventory_date", "titles": "Inventory Date", "dc:description": "The date of the operation that was performed.", "datatype": {"base": "date", "format": "M/d/yyyy"} }], "primaryKey": "GID", "aboutUrl": "#gid-{GID}" } }
The annotated tabular data model generated from this would be more sophisticated again. The table itself would have the following annotations:
dc:title
{"@value": "Tree Operations", "@language": "en"}
dcat:keyword
[{"@value": "tree", "@language", "en"}, {"@value": "street", "@language": "en"}, {"@value": "maintenance", "@language": "en"}]
dc:publisher
[{ "schema:name": "Example Municipality", "schema:url": {"@id": "http://example.org"} }]
dc:license
{"@id": "http://opendefinition.org/licenses/cc-by/"}
dc:modified
{"@value": "2010-12-31", "@type": "date"}
The columns would have the annotations shown in the following table:
id | core annotations | other annotations | ||||||
---|---|---|---|---|---|---|---|---|
table | number | source number | cells | name | titles | datatype | dc:description | |
C1 | T | 1 | 1 | C1.1, C2.1 | GID | GID , Generic Identifier | string | An identifier for the operation on a tree. |
C2 | T | 2 | 2 | C1.2, C2.2 | on_street | On Street | string | The street that the tree is on. |
C3 | T | 3 | 3 | C1.3, C2.3 | species | Species | string | The species of the tree. |
C4 | T | 4 | 4 | C1.4, C2.4 | trim_cycle | Trim Cycle | string | The operation performed on the tree. |
C5 | T | 5 | 5 | C1.5, C2.5 | inventory_date | Inventory Date | { "base": "date", "format": "M/d/yyyy" } | The date of the operation that was performed. |
The rows have an additional primary key annotation, as shown in the following table:
id | core annotations | ||||
---|---|---|---|---|---|
table | number | source number | cells | primary key | |
R1 | T | 1 | 2 | C1.1, C1.2, C1.3, C1.4, C1.5 | C1.1 |
R2 | T | 2 | 3 | C2.1, C2.2, C2.3, C2.4, C2.5 | C2.1 |
Thanks to the provided metadata, the cells again have the annotations shown in the following table. The metadata file has provided the information to supplement the model with additional annotations but also, for the Inventory Date
column (cells C1.5 and C2.5), have a value that is a parsed date rather than an unparsed string.
id | core annotations | |||||
---|---|---|---|---|---|---|
table | column | row | string value | value | about URL | |
C1.1 | T | C1 | R1 | "1" | "1" | http://example.org/tree-ops.csv#gid-1 |
C1.2 | T | C2 | R1 | "ADDISON AV" | "ADDISON AV" | http://example.org/tree-ops.csv#gid-1 |
C1.3 | T | C3 | R1 | "Celtis australis" | "Celtis australis" | http://example.org/tree-ops.csv#gid-1 |
C1.4 | T | C4 | R1 | "Large Tree Routine Prune" | "Large Tree Routine Prune" | http://example.org/tree-ops.csv#gid-1 |
C1.5 | T | C5 | R1 | "10/18/2010" | 2010-10-18 | http://example.org/tree-ops.csv#gid-1 |
C2.1 | T | C1 | R2 | "2" | "2" | http://example.org/tree-ops.csv#gid-2 |
C2.2 | T | C2 | R2 | "EMERSON ST" | "EMERSON ST" | http://example.org/tree-ops.csv#gid-2 |
C2.3 | T | C3 | R2 | "Liquidambar styraciflua" | "Liquidambar styraciflua" | http://example.org/tree-ops.csv#gid-2 |
C2.4 | T | C4 | R2 | "Large Tree Routine Prune" | "Large Tree Routine Prune" | http://example.org/tree-ops.csv#gid-2 |
C2.5 | T | C5 | R2 | "6/2/2010" | 2010-06-02 | http://example.org/tree-ops.csv#gid-2 |
The following slightly amended CSV file contains quoted and missing cell values:
GID,On Street,Species,Trim Cycle,Inventory Date 1,ADDISON AV,"Celtis australis","Large Tree Routine Prune",10/18/2010 2,,"Liquidambar styraciflua","Large Tree Routine Prune",
Parsing this file similarly results in an annotated tabular data model of a single table T with five columns and two rows. The columns and rows have exactly the same annotations as previously, but there are two null
cell values for C2.2 and C2.5. Note that the quoting of values within the CSV makes no difference to either the string value or value of the cell.
id | core annotations | ||||
---|---|---|---|---|---|
table | column | row | string value | value | |
C1.1 | T | C1 | R1 | "1" | "1" |
C1.2 | T | C2 | R1 | "ADDISON AV" | "ADDISON AV" |
C1.3 | T | C3 | R1 | "Celtis australis" | "Celtis australis" |
C1.4 | T | C4 | R1 | "Large Tree Routine Prune" | "Large Tree Routine Prune" |
C1.5 | T | C5 | R1 | "10/18/2010" | "10/18/2010" |
C2.1 | T | C1 | R2 | "2" | "2" |
C2.2 | T | C2 | R2 | "" | null |
C2.3 | T | C3 | R2 | "Liquidambar styraciflua" | "Liquidambar styraciflua" |
C2.4 | T | C4 | R2 | "Large Tree Routine Prune" | "Large Tree Routine Prune" |
C2.5 | T | C5 | R2 | "" | null |
The following example illustrates some of the complexities that can be involved in parsing tabular data, how the flags described above can be used, and how new tabular data formats could be defined that embed additional annotations into the tabular data model.
In this example, the publishers of the data are using an internal convention to supply additional metadata about the tabular data embedded within the file itself. They are also using a tab as a separator rather than a comma.
# publisher City of Palo Alto # updated 12/31/2010 #name GID on_street species trim_cycle inventory_date #datatype string string string string date:M/D/YYYY GID On Street Species Trim Cycle Inventory Date 1 ADDISON AV Celtis australis Large Tree Routine Prune 10/18/2010 2 EMERSON ST Liquidambar styraciflua Large Tree Routine Prune 6/2/2010
Naive parsing of the above data will assume a comma separator and thus results in a single table T with a single column and six rows. The column has the annotations shown in the following table:
id | core annotations | ||||
---|---|---|---|---|---|
table | number | source number | cells | titles | |
C1 | T | 1 | 1 | C1.1, C2.1, C3.1, C4.1, C5.1 | # publisher City of Palo Alto |
The rows have the annotations shown in the following table:
id | core annotations | |||
---|---|---|---|---|
table | number | source number | cells | |
R1 | T | 1 | 2 | C1.1 |
R2 | T | 2 | 3 | C2.1 |
R3 | T | 3 | 4 | C3.1 |
R4 | T | 4 | 5 | C4.1 |
R5 | T | 5 | 6 | C5.1 |
R6 | T | 6 | 7 | C6.1 |
The cells have the annotations shown in the following table (note that the values of all the cells in the table are strings, denoted by the double quotes in the table below):
id | core annotations | ||||
---|---|---|---|---|---|
table | column | row | string value | value | |
C1.1 | T | C1 | R1 | "# updated 12/31/2010" | "# updated 12/31/2010" |
C1.1 | T | C1 | R1 | "#name GID on_street species trim_cycle inventory_date" | "#name GID on_street species trim_cycle inventory_date" |
C2.1 | T | C1 | R2 | "#datatype string string string string date:M/D/YYYY" | "#datatype string string string string date:M/D/YYYY" |
C3.1 | T | C1 | R3 | " GID On Street Species Trim Cycle Inventory Date" | " GID On Street Species Trim Cycle Inventory Date" |
C4.1 | T | C1 | R4 | " 1 ADDISON AV Celtis australis Large Tree Routine Prune 10/18/2010" | " 1 ADDISON AV Celtis australis Large Tree Routine Prune 10/18/2010" |
C5.1 | T | C1 | R5 | " 2 EMERSON ST Liquidambar styraciflua Large Tree Routine Prune 6/2/2010" | " 2 EMERSON ST Liquidambar styraciflua Large Tree Routine Prune 6/2/2010" |
The consumer of the data may use the flags described above to create a more useful set of data from this file. Specifically, they could set:
#
Setting these is done in an implementation-defined way. It could be done, for example, by sniffing the contents of the file itself, through command-line options, or by embedding a dialect description into a metadata file associated with the tabular data, which would look like:
{ "delimiter": "\t", "skipRows": 4, "skipColumns": 1, "commentPrefix": "#" }
With these flags in operation, parsing this file results in an annotated tabular data model of a single table T with five columns and two rows which is largely the same as that created from the original simple example described in section 8.2.1 Simple Example. There are three differences.
First, because the four skipped rows began with the comment prefix, the table itself now has four rdfs:comment
annotations, with the values:
publisher City of Palo Alto
updated 12/31/2010
name GID on_street species trim_cycle inventory_date
datatype string string string string date:M/D/YYYY
Second, because the first column has been skipped, the source number of each of the columns is offset by one from the number of each column:
id | core annotations | ||||
---|---|---|---|---|---|
table | number | source number | cells | titles | |
C1 | T | 1 | 2 | C1.1, C2.1 | GID |
C2 | T | 2 | 3 | C1.2, C2.2 | On Street |
C3 | T | 3 | 4 | C1.3, C2.3 | Species |
C4 | T | 4 | 5 | C1.4, C2.4 | Trim Cycle |
C5 | T | 5 | 6 | C1.5, C2.5 | Inventory Date |
Finally, because four additional rows have been skipped, the source number of each of the rows is offset by five from the row number (the four skipped rows plus the single header row):
id | core annotations | |||
---|---|---|---|---|
table | number | source number | cells | |
R1 | T | 1 | 6 | C1.1, C1.2, C1.3, C1.4, C1.5 |
R2 | T | 2 | 7 | C2.1, C2.2, C2.3, C2.4, C2.5 |
The conventions used in this data (invented for the purpose of this example) are in fact intended to create an annotated tabular data model which includes named annotations on the table itself, on the columns, and on the cells. The creator of these conventions could create a specification for this particular tabular data syntax and register a media type for it. The specification would include statements like:
"#"
, the second column is the name of an annotation on the table and the values of the remaining columns are concatenated to create the value of that annotation.#name
, the remaining cells in the row provide a name
annotation for each column in the table.#datatype
, the remaining cells in the row provide datatype
/format
annotations for the cells within the relevant column, and these are interpreted to create the value for each cell in that column.title
annotations on the columns in the table.Parsers that recognised the format could then build a more sophisticated annotated tabular data model using only the embedded information in the tabular data file. They would extract embedded metadata looking like:
{ "@context": "http://www.w3.org/ns/csvw", "url": "tree-ops.csv", "dc:publisher": "City of Palo Alto", "dc:updated": "12/31/2010", "tableSchema": { "columns": [{ "name": "GID", "titles": "GID", "datatype": "string", }, { "name": "on_street", "titles": "On Street", "datatype": "string" }, { "name": "species", "titles": "Species", "datatype": "string" }, { "name": "trim_cycle", "titles": "Trim Cycle", "datatype": "string" }, { "name": "inventory_date", "titles": "Inventory Date", "datatype": { "base": "date", "format": "M/d/yyyy" } }] } }
As before, the result would be a single table T with five columns and two rows. The table itself would have two annotations:
dc:publisher
{"@value": "City of Palo Alto"}
dc:updated
{"@value": "12/31/2010"}
The columns have the annotations shown in the following table:
id | core annotations | |||||
---|---|---|---|---|---|---|
table | number | source number | cells | name | titles | |
C1 | T | 1 | 2 | C1.1, C2.1 | GID | GID |
C2 | T | 2 | 3 | C1.2, C2.2 | on_street | On Street |
C3 | T | 3 | 4 | C1.3, C2.3 | species | Species |
C4 | T | 4 | 5 | C1.4, C2.4 | trim_cycle | Trim Cycle |
C5 | T | 5 | 6 | C1.5, C2.5 | inventory_date | Inventory Date |
The rows have the annotations shown in the following table, exactly as in previous examples:
id | core annotations | |||
---|---|---|---|---|
table | number | source number | cells | |
R1 | T | 1 | 6 | C1.1, C1.2, C1.3, C1.4, C1.5 |
R2 | T | 2 | 7 | C2.1, C2.2, C2.3, C2.4, C2.5 |
The cells have the annotations shown in the following table. Because of the way the particular tabular data format has been specified, these include additional annotations but also, for the Inventory Date
column (cells C1.5 and C2.5), have a value that is a parsed date rather than an unparsed string.
id | core annotations | ||||
---|---|---|---|---|---|
table | column | row | string value | value | |
C1.1 | T | C1 | R1 | "1" | "1" |
C1.2 | T | C2 | R1 | "ADDISON AV" | "ADDISON AV" |
C1.3 | T | C3 | R1 | "Celtis australis" | "Celtis australis" |
C1.4 | T | C4 | R1 | "Large Tree Routine Prune" | "Large Tree Routine Prune" |
C1.5 | T | C5 | R1 | "10/18/2010" | 2010-10-18 |
C2.1 | T | C1 | R2 | "2" | "2" |
C2.2 | T | C2 | R2 | "EMERSON ST" | "EMERSON ST" |
C2.3 | T | C3 | R2 | "Liquidambar styraciflua" | "Liquidambar styraciflua" |
C2.4 | T | C4 | R2 | "Large Tree Routine Prune" | "Large Tree Routine Prune" |
C2.5 | T | C5 | R2 | "6/2/2010" | 2010-06-02 |
The following example shows a CSV file with multiple header lines:
Who,What,,Where, Organisation,Sector,Subsector,Department,Municipality #org,#sector,#subsector,#adm1,#adm2 UNICEF,Education,Teacher training,Chocó,Quidbó UNICEF,Education,Teacher training,Chocó,Bojayá
Here, the first line contains some grouping titles in the first line, which are not particularly helpful. The lines following those contain useful titles for the columns. Thus the appropriate configuration for a dialect description is:
{ "skipRows": 1, "headerRowCount": 2 }
With this configuration, the table model contains five columns, each of which have two titles, summarised in the following table:
id | core annotations | ||||
---|---|---|---|---|---|
table | number | source number | cells | titles | |
C1 | T | 1 | 1 | C1.1, C2.1 | Organisation , #org |
C2 | T | 2 | 2 | C1.2, C2.2 | Sector , #sector |
C3 | T | 3 | 3 | C1.3, C2.3 | Subsector , #subsector |
C4 | T | 4 | 4 | C1.4, C2.4 | Department , #adm1 |
C5 | T | 5 | 5 | C1.5, C2.5 | Municipality , #adm2 |
As metadata, this would look like:
{ "tableSchema": { "columns": [ { "titles": ["Organisation", "#org"] }, { "titles": ["Sector", "#sector"] }, { "titles": ["Subsector", "#subsector"] }, { "titles": ["Department", "#adm1"] }, { "titles": ["Municipality", "#adm2"] }, ] } }
A separate metadata file could contain just the second of each of these titles, for example:
{ "tableSchema": { "columns": [ { "name": "org", "titles": #org" }, { "name": "sector", "titles": #sector" }, { "name": "subsector", "titles": #subsector" }, { "name": "adm1", "titles": #adm1" }, { "name": "adm2", "titles": #adm2" }, ] } }
This enables people from multiple jurisdictions to use the same tabular data structures without having to use exactly the same titles within their documents.
This section is non-normative.
This appendix outlines various ways in which CSV is defined.
[RFC4180] defines CSV with the following ABNF grammar:
file = [header CRLF] record *(CRLF record) [CRLF] header = name *(COMMA name) record = field *(COMMA field) name = field field = (escaped / non-escaped) escaped = DQUOTE *(TEXTDATA / COMMA / CR / LF / 2DQUOTE) DQUOTE non-escaped = *TEXTDATA COMMA = %x2C CR = %x0D DQUOTE = %x22 LF = %x0A CRLF = CR LF TEXTDATA = %x20-21 / %x23-2B / %x2D-7E
Of particular note here are:
TEXTDATA
indicates that only non-control ASCII characters are permitted within a CSV file. This restriction is routinely ignored in practice, and is impractical on the international web.CRLF
. This makes it harder to produce CSV files on Unix-based systems where the usual line ending is LF
.header
parameter on the media type indicates whether the header is present or not.""
).Excel is a common tool for both creating and reading CSV documents, and therefore the CSV that it produces is a de facto standard.
The following describes the behaviour of Microsoft Excel for Mac 2011 with an English locale. Further testing is needed to see the behaviour of Excel in other situations.
Excel generates CSV files encoded using Windows-1252 with LF
line endings. Characters that cannot be represented within Windows-1252 are replaced by underscores. Only those cells that need escaping (eg because they contain commas or double quotes) are escaped, and double quotes are escaped with two double quotes.
Dates and numbers are formatted as displayed, which means that formatting can lead to information being lost or becoming inconsistent.
When opening CSV files, Excel interprets CSV files saved in UTF-8 as being encoded as Windows-1252 (whether or not a BOM is present). It correctly deals with double quoted cells, except that it converts line breaks within cells into spaces. It understands CRLF
as a line break. It detects dates (formatted as YYYY-MM-DD
) and formats them in the default date formatting for files.
Excel provides more control when importing CSV files into Excel. However, it does not properly understand UTF-8 (with or without BOM). It does however properly understand UTF-16 and can read non-ASCII characters from a UTF-16-encoded file.
A particular quirk in the importing of CSV is that if a cell contains a line break, the final double quote that escapes the cell will be included within it.
When tabular data is copied from Excel, it is copied in a tab-delimited format, with LF
line breaks.
Downloaded CSV files are encoded in UTF-8, without a BOM, and with LF
line endings. Dates and numbers are formatted as they appear within the spreadsheet.
CSV files can be imported as UTF-8 (with or without BOM). CRLF
line endings are correctly recognised. Dates are reformatted to the default date format on load.
Tabular Data Packages place the following restrictions on CSV files:
As a starting point, CSV files included in a Tabular Data Package package must conform to the RFC for CSV (4180 - Common Format and MIME Type for Comma-Separated Values (CSV) Files). In addition:
File names MUST end with
.csv
.Files MUST be encoded as UTF-8.
Files MUST have a single header row. This row MUST be the first row in the file.
Terminology: each column in the CSV file is termed a field and its
name
is the string in that column in the header row.The
name
MUST be unique amongst fields, MUST contain at least one character, and MUST conform to the character restrictions defined for thename
property.Rows in the file MUST NOT contain more fields than are in the header row (though they may contain less).
Each file MUST have an entry in the
tables
array in thedatapackage.json
file.The resource metadata MUST include a
tableSchema
attribute whose value MUST be a valid schema description.All fields in the CSV files MUST be described in the schema description.
CSV files generated by different applications often vary in their syntax, e.g. use of quoting characters, delimiters, etc. To encourage conformance, CSV files in a CSV files in a Tabular Data Package SHOULD:
- Use "," as field delimiters.
- Use
CRLF
(U+000D U+000A
) orLF
(U+000A
) as line terminators.If a CSV file does not follow these rules then its specific CSV dialect MUST be documented. The resource hash for the resource in the
datapackage.json
descriptor MUST:
- Include a
dialect
key that conforms to that described in the CSV Dialect Description Format.Applications processing the CSV file SHOULD read use the
dialect
of the CSV file to guide parsing.
To replicate the findings above, test files which include non-ASCII characters, double quotes, and line breaks within cells are:
This section is non-normative.
The document has undergone substantial changes since the last working draft. Below are some of the changes made: