PEP: 305 Title: CSV File API Version: $Revision$ Last-Modified: $Date$ Author: Skip Montanaro , Kevin Altis , Cliff Wells Status: Draft Type: Informational Content-Type: text/x-rst Created: 26-Jan-2003 Post-History: Abstract ======== 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 bound to fail. This PEP defines an API for reading and writing CSV files which should make it possible for programmers to select a CSV module which meets their requirements. Existing Modules ================ Three widely available modules enable programmers to read and write CSV files: - Object Craft's CSV module [1]_ - Cliff Wells's Python-DSV module [2]_ - Laurence Tratt's ASV module [3]_ 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 in the module APIs, the programmer has to also deal with semantic differences between the packages. Rationale ========= By defining common APIs for reading and writing CSV files, we make it easier for programmers to choose an appropriate module to suit their needs, and make it easier to switch between modules if their needs change. This PEP also forms a set of requirements for creation of a module which will hopefully be incorporated into the Python distribution. Module Interface ================ The module supports two basic APIs, one for reading and one for writing. The reading interface is:: reader(fileobj [, dialect='excel2000'] [, quotechar='"'] [, delimiter=','] [, skipinitialspace=False]) A reader object is an iterable which takes a file-like object opened for reading as the sole required parameter. It also accepts four optional parameters (discussed below). Readers are typically used as follows:: csvreader = csv.reader(file("some.csv")) for row in csvreader: process(row) The writing interface is similar:: writer(fileobj [, dialect='excel2000'] [, quotechar='"'] [, delimiter=','] [, skipinitialspace=False]) A writer object is a wrapper around a file-like object opened for writing. It accepts the same four optional parameters as the reader constructor. Writers are typically used as follows:: csvwriter = csv.writer(file("some.csv", "w")) for row in someiterable: csvwriter.write(row) Optional Parameters ------------------- Both the reader and writer constructors take four optional keyword parameters: - dialect is an easy way of specifying a complete set of format constraints for a reader or writer. Most people will know what application generated a CSV file or what application will process the CSV file they are generating, but not the precise settings necessary. The only dialect defined initially is "excel2000". The dialect parameter is interpreted in a case-insensitive manner. - quotechar specifies a one-character string to use as the quoting character. It defaults to '"'. - delimiter specifies a one-character string to use as the field separator. It defaults to ','. - skipinitialspace specifies how to interpret whitespace which immediately follows a delimiter. It defaults to False, which means that whitespace immediate following a delimiter is part of the following field. When processing a dialect setting and one or more of the other optional parameters, the dialect parameter is processed first, then the others are processed. This makes it easy to choose a dialect, then override one or more of the settings. For example, if a CSV file was generated by Excel 2000 using single quotes as the quote character and TAB as the delimiter, you could create a reader like:: csvreader = csv.reader(file("some.csv"), dialect="excel2000", quotechar="'", delimiter='\t') Other details of how Excel generates CSV files would be handled automatically. Testing ======= TBD. Issues ====== - 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. - What about alternate escape conventions? When Excel exports a file, it appears only the field delimiter needs to be escaped. It accomplishes this by quoting the entire field, then doubling any quote characters which appear in the field. It also quotes a field if the first character is a quote character. It would seem we need to support two modes: escape-by-quoting and escape-by-prefix. In addition, for the second mode, we'd have to specify the escape character (presumably defaulting to a backslash character). - Should there be a "fully quoted" mode for writing? What about "fully quoted except for numeric values"? - What about end-of-line? If I generate a CSV file on a Unix system, will Excel properly recognize the LF-only line terminators? - What about conversion to other file formats? Is the list-of-lists output from the csvreader sufficient to feed into other writers? - What about an option to generate list-of-dict output from the reader and accept list-of-dicts by the writer? This makes manipulating individual rows easier since each one is independent, but you lose field order when writing and have to tell the writer object the order the fields should appear in the file. - Are quote character and delimiters limited to single characters? I had a client not that long ago who wrote their own flat file format with a delimiter of ":::". - How should rows of different lengths be handled? The options seem to be: * raise an exception when a row is encountered whose length differs from the previous row * silently return short rows * allow the caller to specify the desired row length and what to do when rows of a different length are encountered: ignore, truncate, pad, raise exception, etc. References ========== .. [1] csv module, Object Craft (http://www.object-craft.com.au/projects/csv) .. [2] Python-DSV module, Wells (http://sourceforge.net/projects/python-dsv/) .. [3] 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. Copyright ========= This document has been placed in the public domain. .. Local Variables: mode: indented-text indent-tabs-mode: nil sentence-end-double-space: t fill-column: 70 End: