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is None, the next row read is used as the fieldnames. In the common case, this means the programmer doesn't need to know the fieldnames ahead of time. The first row of the file will be used. In the uncommon case, this means the programmer can set the reader's fieldnames attribute to None at any time and have the next row read as the next set of fieldnames, so a csv file can contain several "sections", each with different fieldnames.
432 lines
16 KiB
Python
432 lines
16 KiB
Python
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"""
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csv.py - read/write/investigate CSV files
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"""
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import re
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from _csv import Error, __version__, writer, reader, register_dialect, \
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unregister_dialect, get_dialect, list_dialects, \
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QUOTE_MINIMAL, QUOTE_ALL, QUOTE_NONNUMERIC, QUOTE_NONE, \
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__doc__
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try:
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from cStringIO import StringIO
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except ImportError:
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from StringIO import StringIO
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__all__ = [ "QUOTE_MINIMAL", "QUOTE_ALL", "QUOTE_NONNUMERIC", "QUOTE_NONE",
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"Error", "Dialect", "excel", "excel_tab", "reader", "writer",
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"register_dialect", "get_dialect", "list_dialects", "Sniffer",
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"unregister_dialect", "__version__", "DictReader", "DictWriter" ]
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class Dialect:
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_name = ""
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_valid = False
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# placeholders
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delimiter = None
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quotechar = None
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escapechar = None
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doublequote = None
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skipinitialspace = None
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lineterminator = None
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quoting = None
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def __init__(self):
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if self.__class__ != Dialect:
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self._valid = True
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errors = self._validate()
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if errors != []:
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raise Error, "Dialect did not validate: %s" % ", ".join(errors)
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def _validate(self):
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errors = []
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if not self._valid:
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errors.append("can't directly instantiate Dialect class")
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if self.delimiter is None:
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errors.append("delimiter character not set")
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elif (not isinstance(self.delimiter, str) or
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len(self.delimiter) > 1):
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errors.append("delimiter must be one-character string")
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if self.quotechar is None:
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if self.quoting != QUOTE_NONE:
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errors.append("quotechar not set")
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elif (not isinstance(self.quotechar, str) or
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len(self.quotechar) > 1):
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errors.append("quotechar must be one-character string")
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if self.lineterminator is None:
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errors.append("lineterminator not set")
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elif not isinstance(self.lineterminator, str):
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errors.append("lineterminator must be a string")
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if self.doublequote not in (True, False):
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errors.append("doublequote parameter must be True or False")
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if self.skipinitialspace not in (True, False):
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errors.append("skipinitialspace parameter must be True or False")
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if self.quoting is None:
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errors.append("quoting parameter not set")
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if self.quoting is QUOTE_NONE:
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if (not isinstance(self.escapechar, (unicode, str)) or
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len(self.escapechar) > 1):
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errors.append("escapechar must be a one-character string or unicode object")
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return errors
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class excel(Dialect):
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delimiter = ','
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quotechar = '"'
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doublequote = True
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skipinitialspace = False
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lineterminator = '\r\n'
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quoting = QUOTE_MINIMAL
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register_dialect("excel", excel)
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class excel_tab(excel):
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delimiter = '\t'
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register_dialect("excel-tab", excel_tab)
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class DictReader:
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def __init__(self, f, fieldnames=None, restkey=None, restval=None,
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dialect="excel", *args, **kwds):
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self.fieldnames = fieldnames # list of keys for the dict
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self.restkey = restkey # key to catch long rows
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self.restval = restval # default value for short rows
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self.reader = reader(f, dialect, *args, **kwds)
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def __iter__(self):
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return self
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def next(self):
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row = self.reader.next()
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if self.fieldnames is None:
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self.fieldnames = row
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row = self.reader.next()
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# unlike the basic reader, we prefer not to return blanks,
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# because we will typically wind up with a dict full of None
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# values
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while row == []:
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row = self.reader.next()
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d = dict(zip(self.fieldnames, row))
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lf = len(self.fieldnames)
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lr = len(row)
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if lf < lr:
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d[self.restkey] = row[lf:]
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elif lf > lr:
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for key in self.fieldnames[lr:]:
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d[key] = self.restval
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return d
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class DictWriter:
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def __init__(self, f, fieldnames, restval="", extrasaction="raise",
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dialect="excel", *args, **kwds):
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self.fieldnames = fieldnames # list of keys for the dict
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self.restval = restval # for writing short dicts
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if extrasaction.lower() not in ("raise", "ignore"):
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raise ValueError, \
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("extrasaction (%s) must be 'raise' or 'ignore'" %
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extrasaction)
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self.extrasaction = extrasaction
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self.writer = writer(f, dialect, *args, **kwds)
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def _dict_to_list(self, rowdict):
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if self.extrasaction == "raise":
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for k in rowdict.keys():
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if k not in self.fieldnames:
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raise ValueError, "dict contains fields not in fieldnames"
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return [rowdict.get(key, self.restval) for key in self.fieldnames]
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def writerow(self, rowdict):
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return self.writer.writerow(self._dict_to_list(rowdict))
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def writerows(self, rowdicts):
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rows = []
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for rowdict in rowdicts:
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rows.append(self._dict_to_list(rowdict))
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return self.writer.writerows(rows)
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# Guard Sniffer's type checking against builds that exclude complex()
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try:
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complex
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except NameError:
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complex = float
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class Sniffer:
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'''
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"Sniffs" the format of a CSV file (i.e. delimiter, quotechar)
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Returns a Dialect object.
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'''
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def __init__(self):
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# in case there is more than one possible delimiter
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self.preferred = [',', '\t', ';', ' ', ':']
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def sniff(self, sample, delimiters=None):
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"""
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Returns a dialect (or None) corresponding to the sample
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"""
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quotechar, delimiter, skipinitialspace = \
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self._guess_quote_and_delimiter(sample, delimiters)
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if delimiter is None:
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delimiter, skipinitialspace = self._guess_delimiter(sample,
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delimiters)
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class dialect(Dialect):
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_name = "sniffed"
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lineterminator = '\r\n'
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quoting = QUOTE_MINIMAL
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# escapechar = ''
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doublequote = False
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dialect.delimiter = delimiter
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# _csv.reader won't accept a quotechar of ''
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dialect.quotechar = quotechar or '"'
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dialect.skipinitialspace = skipinitialspace
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return dialect
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def _guess_quote_and_delimiter(self, data, delimiters):
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"""
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Looks for text enclosed between two identical quotes
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(the probable quotechar) which are preceded and followed
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by the same character (the probable delimiter).
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For example:
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,'some text',
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The quote with the most wins, same with the delimiter.
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If there is no quotechar the delimiter can't be determined
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this way.
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"""
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matches = []
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for restr in ('(?P<delim>[^\w\n"\'])(?P<space> ?)(?P<quote>["\']).*?(?P=quote)(?P=delim)', # ,".*?",
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'(?:^|\n)(?P<quote>["\']).*?(?P=quote)(?P<delim>[^\w\n"\'])(?P<space> ?)', # ".*?",
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'(?P<delim>>[^\w\n"\'])(?P<space> ?)(?P<quote>["\']).*?(?P=quote)(?:$|\n)', # ,".*?"
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'(?:^|\n)(?P<quote>["\']).*?(?P=quote)(?:$|\n)'): # ".*?" (no delim, no space)
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regexp = re.compile(restr, re.DOTALL | re.MULTILINE)
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matches = regexp.findall(data)
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if matches:
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break
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if not matches:
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return ('', None, 0) # (quotechar, delimiter, skipinitialspace)
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quotes = {}
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delims = {}
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spaces = 0
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for m in matches:
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n = regexp.groupindex['quote'] - 1
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key = m[n]
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if key:
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quotes[key] = quotes.get(key, 0) + 1
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try:
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n = regexp.groupindex['delim'] - 1
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key = m[n]
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except KeyError:
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continue
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if key and (delimiters is None or key in delimiters):
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delims[key] = delims.get(key, 0) + 1
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try:
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n = regexp.groupindex['space'] - 1
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except KeyError:
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continue
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if m[n]:
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spaces += 1
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quotechar = reduce(lambda a, b, quotes = quotes:
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(quotes[a] > quotes[b]) and a or b, quotes.keys())
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if delims:
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delim = reduce(lambda a, b, delims = delims:
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(delims[a] > delims[b]) and a or b, delims.keys())
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skipinitialspace = delims[delim] == spaces
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if delim == '\n': # most likely a file with a single column
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delim = ''
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else:
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# there is *no* delimiter, it's a single column of quoted data
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delim = ''
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skipinitialspace = 0
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return (quotechar, delim, skipinitialspace)
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def _guess_delimiter(self, data, delimiters):
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"""
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The delimiter /should/ occur the same number of times on
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each row. However, due to malformed data, it may not. We don't want
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an all or nothing approach, so we allow for small variations in this
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number.
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1) build a table of the frequency of each character on every line.
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2) build a table of freqencies of this frequency (meta-frequency?),
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e.g. 'x occurred 5 times in 10 rows, 6 times in 1000 rows,
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7 times in 2 rows'
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3) use the mode of the meta-frequency to determine the /expected/
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frequency for that character
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4) find out how often the character actually meets that goal
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5) the character that best meets its goal is the delimiter
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For performance reasons, the data is evaluated in chunks, so it can
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try and evaluate the smallest portion of the data possible, evaluating
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additional chunks as necessary.
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"""
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data = filter(None, data.split('\n'))
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ascii = [chr(c) for c in range(127)] # 7-bit ASCII
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# build frequency tables
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chunkLength = min(10, len(data))
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iteration = 0
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charFrequency = {}
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modes = {}
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delims = {}
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start, end = 0, min(chunkLength, len(data))
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while start < len(data):
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iteration += 1
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for line in data[start:end]:
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for char in ascii:
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metaFrequency = charFrequency.get(char, {})
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# must count even if frequency is 0
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freq = line.strip().count(char)
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# value is the mode
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metaFrequency[freq] = metaFrequency.get(freq, 0) + 1
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charFrequency[char] = metaFrequency
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for char in charFrequency.keys():
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items = charFrequency[char].items()
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if len(items) == 1 and items[0][0] == 0:
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continue
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# get the mode of the frequencies
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if len(items) > 1:
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modes[char] = reduce(lambda a, b: a[1] > b[1] and a or b,
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items)
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# adjust the mode - subtract the sum of all
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# other frequencies
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items.remove(modes[char])
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modes[char] = (modes[char][0], modes[char][1]
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- reduce(lambda a, b: (0, a[1] + b[1]),
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items)[1])
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else:
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modes[char] = items[0]
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# build a list of possible delimiters
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modeList = modes.items()
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total = float(chunkLength * iteration)
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# (rows of consistent data) / (number of rows) = 100%
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consistency = 1.0
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# minimum consistency threshold
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threshold = 0.9
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while len(delims) == 0 and consistency >= threshold:
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for k, v in modeList:
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if v[0] > 0 and v[1] > 0:
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if ((v[1]/total) >= consistency and
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(delimiters is None or k in delimiters)):
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delims[k] = v
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consistency -= 0.01
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if len(delims) == 1:
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delim = delims.keys()[0]
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skipinitialspace = (data[0].count(delim) ==
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data[0].count("%c " % delim))
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return (delim, skipinitialspace)
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# analyze another chunkLength lines
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start = end
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end += chunkLength
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if not delims:
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return ('', 0)
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# if there's more than one, fall back to a 'preferred' list
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if len(delims) > 1:
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for d in self.preferred:
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if d in delims.keys():
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skipinitialspace = (data[0].count(d) ==
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data[0].count("%c " % d))
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return (d, skipinitialspace)
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# finally, just return the first damn character in the list
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delim = delims.keys()[0]
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skipinitialspace = (data[0].count(delim) ==
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data[0].count("%c " % delim))
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return (delim, skipinitialspace)
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def has_header(self, sample):
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# Creates a dictionary of types of data in each column. If any
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# column is of a single type (say, integers), *except* for the first
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# row, then the first row is presumed to be labels. If the type
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# can't be determined, it is assumed to be a string in which case
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# the length of the string is the determining factor: if all of the
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# rows except for the first are the same length, it's a header.
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# Finally, a 'vote' is taken at the end for each column, adding or
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# subtracting from the likelihood of the first row being a header.
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rdr = reader(StringIO(sample), self.sniff(sample))
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header = rdr.next() # assume first row is header
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columns = len(header)
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columnTypes = {}
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for i in range(columns): columnTypes[i] = None
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checked = 0
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for row in rdr:
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# arbitrary number of rows to check, to keep it sane
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if checked > 20:
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break
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checked += 1
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if len(row) != columns:
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continue # skip rows that have irregular number of columns
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for col in columnTypes.keys():
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for thisType in [int, long, float, complex]:
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try:
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thisType(row[col])
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break
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except (ValueError, OverflowError):
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pass
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else:
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# fallback to length of string
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thisType = len(row[col])
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# treat longs as ints
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if thisType == long:
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thisType = int
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if thisType != columnTypes[col]:
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if columnTypes[col] is None: # add new column type
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columnTypes[col] = thisType
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else:
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# type is inconsistent, remove column from
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# consideration
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del columnTypes[col]
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# finally, compare results against first row and "vote"
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# on whether it's a header
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hasHeader = 0
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for col, colType in columnTypes.items():
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if type(colType) == type(0): # it's a length
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if len(header[col]) != colType:
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hasHeader += 1
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else:
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hasHeader -= 1
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else: # attempt typecast
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try:
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colType(header[col])
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except (ValueError, TypeError):
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hasHeader += 1
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else:
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hasHeader -= 1
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return hasHeader > 0
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