|Title:||CSV File API|
|Author:||Kevin Altis <altis at semi-retired.com>, Dave Cole <djc at object-craft.com.au>, Andrew McNamara <andrewm at object-craft.com.au>, Skip Montanaro <skip at pobox.com>, Cliff Wells <LogiplexSoftware at earthlink.net>|
|Discussions-To:||<csv at python.org>|
The Comma Separated Values (CSV) file format is the most common import and export format for spreadsheets and databases. Although many CSV files are simple to parse, the format is not formally defined by a stable specification and is subtle enough that parsing lines of a CSV file with something like line.split(",") is eventually bound to fail. This PEP defines an API for reading and writing CSV files. It is accompanied by a corresponding module which implements the API.
- Better motivation for the choice of passing a file object to the constructors. See http://mail.python.org/pipermail/csv/2003-January/000179.html
- Unicode. ugh.
This PEP is about doing one thing well: parsing tabular data which may use a variety of field separators, quoting characters, quote escape mechanisms and line endings. The authors intend the proposed module to solve this one parsing problem efficiently. The authors do not intend to address any of these related topics:
- data interpretation (is a field containing the string "10" supposed to be a string, a float or an int? is it a number in base 10, base 16 or base 2? is a number in quotes a number or a string?)
- locale-specific data representation (should the number 1.23 be written as "1.23" or "1,23" or "1 23"?) -- this may eventually be addressed.
- fixed width tabular data - can already be parsed reliably.
Often, CSV files are formatted simply enough that you can get by reading them line-by-line and splitting on the commas which delimit the fields. This is especially true if all the data being read is numeric. This approach may work for a while, then come back to bite you in the butt when somebody puts something unexpected in the data like a comma. As you dig into the problem you may eventually come to the conclusion that you can solve the problem using regular expressions. This will work for a while, then break mysteriously one day. The problem grows, so you dig deeper and eventually realize that you need a purpose-built parser for the format.
CSV formats are not well-defined and different implementations have a number of subtle corner cases. It has been suggested that the "V" in the acronym stands for "Vague" instead of "Values". Different delimiters and quoting characters are just the start. Some programs generate whitespace after each delimiter which is not part of the following field. Others quote embedded quoting characters by doubling them, others by prefixing them with an escape character. The list of weird ways to do things can seem endless.
All this variability means it is difficult for programmers to reliably parse CSV files from many sources or generate CSV files designed to be fed to specific external programs without a thorough understanding of those sources and programs. This PEP and the software which accompany it attempt to make the process less fragile.
This problem has been tackled before. At least three modules currently available in the Python community enable programmers to read and write CSV files:
Each has a different API, making it somewhat difficult for programmers to switch between them. More of a problem may be that they interpret some of the CSV corner cases differently, so even after surmounting the differences between the different module APIs, the programmer has to also deal with semantic differences between the packages.
This PEP supports three basic APIs, one to read and parse CSV files, one to write them, and one to identify different CSV dialects to the readers and writers.
CSV readers are created with the reader factory function:
obj = reader(iterable [, dialect='excel'] [optional keyword args])
A reader object is an iterator which takes an iterable object returning lines as the sole required parameter. If it supports a binary mode (file objects do), the iterable argument to the reader function must have been opened in binary mode. This gives the reader object full control over the interpretation of the file's contents. The optional dialect parameter is discussed below. The reader function also accepts several optional keyword arguments which define specific format settings for the parser (see the section "Formatting Parameters"). Readers are typically used as follows:
csvreader = csv.reader(file("some.csv")) for row in csvreader: process(row)
Each row returned by a reader object is a list of strings or Unicode objects.
When both a dialect parameter and individual formatting parameters are passed to the constructor, first the dialect is queried for formatting parameters, then individual formatting parameters are examined.
Creating writers is similar:
obj = writer(fileobj [, dialect='excel'], [optional keyword args])
A writer object is a wrapper around a file-like object opened for writing in binary mode (if such a distinction is made). It accepts the same optional keyword parameters as the reader constructor.
Writers are typically used as follows:
csvwriter = csv.writer(file("some.csv", "w")) for row in someiterable: csvwriter.writerow(row)
To generate a set of field names as the first row of the CSV file, the programmer must explicitly write it, e.g.:
csvwriter = csv.writer(file("some.csv", "w"), fieldnames=names) csvwriter.write(names) for row in someiterable: csvwriter.write(row)
or arrange for it to be the first row in the iterable being written.
Because CSV is a somewhat ill-defined format, there are plenty of ways one CSV file can differ from another, yet contain exactly the same data. Many tools which can import or export tabular data allow the user to indicate the field delimiter, quote character, line terminator, and other characteristics of the file. These can be fairly easily determined, but are still mildly annoying to figure out, and make for fairly long function calls when specified individually.
To try and minimize the difficulty of figuring out and specifying a bunch of formatting parameters, reader and writer objects support a dialect argument which is just a convenient handle on a group of these lower level parameters. When a dialect is given as a string it identifies one of the dialects known to the module via its registration functions, otherwise it must be an instance of the Dialect class as described below.
Dialects will generally be named after applications or organizations which define specific sets of format constraints. Two dialects are defined in the module as of this writing, "excel", which describes the default format constraints for CSV file export by Excel 97 and Excel 2000, and "excel-tab", which is the same as "excel" but specifies an ASCII TAB character as the field delimiter.
Dialects are implemented as attribute only classes to enable users to construct variant dialects by subclassing. The "excel" dialect is a subclass of Dialect and is defined as follows:
class Dialect: # placeholders delimiter = None quotechar = None escapechar = None doublequote = None skipinitialspace = None lineterminator = None quoting = None class excel(Dialect): delimiter = ',' quotechar = '"' doublequote = True skipinitialspace = False lineterminator = '\r\n' quoting = QUOTE_MINIMAL
The "excel-tab" dialect is defined as:
class exceltsv(excel): delimiter = '\t'
(For a description of the individual formatting parameters see the section "Formatting Parameters".)
To enable string references to specific dialects, the module defines several functions:
dialect = get_dialect(name) names = list_dialects() register_dialect(name, dialect) unregister_dialect(name)
get_dialect() returns the dialect instance associated with the given name. list_dialects() returns a list of all registered dialect names. register_dialects() associates a string name with a dialect class. unregister_dialect() deletes a name/dialect association.
In addition to the dialect argument, both the reader and writer constructors take several specific formatting parameters, specified as keyword parameters. The formatting parameters understood are:
- quotechar specifies a one-character string to use as the quoting character. It defaults to '"'. Setting this to None has the same effect as setting quoting to csv.QUOTE_NONE.
- delimiter specifies a one-character string to use as the field separator. It defaults to ','.
- escapechar specifies a one-character string used to escape the delimiter when quotechar is set to None.
- skipinitialspace specifies how to interpret whitespace which immediately follows a delimiter. It defaults to False, which means that whitespace immediately following a delimiter is part of the following field.
- lineterminator specifies the character sequence which should terminate rows.
- quoting controls when quotes should be generated by the writer.
It can take on any of the following module constants:
- csv.QUOTE_MINIMAL means only when required, for example, when a field contains either the quotechar or the delimiter
- csv.QUOTE_ALL means that quotes are always placed around fields.
- csv.QUOTE_NONNUMERIC means that quotes are always placed around nonnumeric fields.
- csv.QUOTE_NONE means that quotes are never placed around fields.
- doublequote controls the handling of quotes inside fields. When True two consecutive quotes are interpreted as one during read, and when writing, each quote is written as two quotes.
When processing a dialect setting and one or more of the other optional parameters, the dialect parameter is processed before the individual formatting parameters. This makes it easy to choose a dialect, then override one or more of the settings without defining a new dialect class. For example, if a CSV file was generated by Excel 2000 using single quotes as the quote character and a colon as the delimiter, you could create a reader like:
csvreader = csv.reader(file("some.csv"), dialect="excel", quotechar="'", delimiter=':')
Other details of how Excel generates CSV files would be handled automatically because of the reference to the "excel" dialect.
Reader objects are iterables whose next() method returns a sequence of strings, one string per field in the row.
Writer objects have two methods, writerow() and writerows(). The former accepts an iterable (typically a list) of fields which are to be written to the output. The latter accepts a list of iterables and calls writerow() for each.
Should a parameter control how consecutive delimiters are interpreted? Our thought is "no". Consecutive delimiters should always denote an empty field.
What about Unicode? Is it sufficient to pass a file object gotten from codecs.open()? For example:
csvreader = csv.reader(codecs.open("some.csv", "r", "cp1252")) csvwriter = csv.writer(codecs.open("some.csv", "w", "utf-8"))
In the first example, text would be assumed to be encoded as cp1252. Should the system be aggressive in converting to Unicode or should Unicode strings only be returned if necessary?
In the second example, the file will take care of automatically encoding Unicode strings as utf-8 before writing to disk.
Note: As of this writing, the csv module doesn't handle Unicode data.
What about alternate escape conventions? If the dialect in use includes an escapechar parameter which is not None and the quoting parameter is set to QUOTE_NONE, delimiters appearing within fields will be prefixed by the escape character when writing and are expected to be prefixed by the escape character when reading.
Should there be a "fully quoted" mode for writing? What about "fully quoted except for numeric values"? Both are implemented (QUOTE_ALL and QUOTE_NONNUMERIC, respectively).
What about end-of-line? If I generate a CSV file on a Unix system, will Excel properly recognize the LF-only line terminators? Files must be opened for reading or writing as appropriate using binary mode. Specify the lineterminator sequence as '\r\n'. The resulting file will be written correctly.
What about an option to generate dicts from the reader and accept dicts by the writer? See the DictReader and DictWriter classes in csv.py.
Are quote character and delimiters limited to single characters? For the time being, yes.
How should rows of different lengths be handled? Interpretation of the data is the application's job. There is no such thing as a "short row" or a "long row" at this level.
|||(1, 2) csv module, Python Sandbox (http://cvs.sourceforge.net/cgi-bin/viewcvs.cgi/python/python/nondist/sandbox/csv/)|
|||(1, 2) csv module, Object Craft (http://www.object-craft.com.au/projects/csv)|
|||Python-DSV module, Wells (http://sourceforge.net/projects/python-dsv/)|
|||ASV module, Tratt (http://tratt.net/laurie/python/asv/)|
There are many references to other CSV-related projects on the Web. A few are included here.
This document has been placed in the public domain.