forgot Cliff's sniffer
diff --git a/Lib/csv/util/sniffer.py b/Lib/csv/util/sniffer.py
new file mode 100644
index 0000000..7b9b060
--- /dev/null
+++ b/Lib/csv/util/sniffer.py
@@ -0,0 +1,289 @@
+"""
+dialect = Sniffer().sniff(file('csv/easy.csv'))
+print "delimiter", dialect.delimiter
+print "quotechar", dialect.quotechar
+print "skipinitialspace", dialect.skipinitialspace
+"""
+
+from csv import csv
+import re
+
+# ------------------------------------------------------------------------------
+class Sniffer:
+ """
+ "Sniffs" the format of a CSV file (i.e. delimiter, quotechar)
+ Returns a csv.Dialect object.
+ """
+ def __init__(self, sample = 16 * 1024):
+ # in case there is more than one possible delimiter
+ self.preferred = [',', '\t', ';', ' ', ':']
+
+ # amount of data (in bytes) to sample
+ self.sample = sample
+
+
+ def sniff(self, fileobj):
+ """
+ Takes a file-like object and returns a dialect (or None)
+ """
+
+ self.fileobj = fileobj
+
+ data = fileobj.read(self.sample)
+
+ quotechar, delimiter, skipinitialspace = self._guessQuoteAndDelimiter(data)
+ if delimiter is None:
+ delimiter, skipinitialspace = self._guessDelimiter(data)
+
+ class Dialect(csv.Dialect):
+ _name = "sniffed"
+ lineterminator = '\r\n'
+ quoting = csv.QUOTE_MINIMAL
+ # escapechar = ''
+ doublequote = False
+ Dialect.delimiter = delimiter
+ Dialect.quotechar = quotechar
+ Dialect.skipinitialspace = skipinitialspace
+
+ self.dialect = Dialect
+ return self.dialect
+
+
+ def hasHeaders(self):
+ return self._hasHeaders(self.fileobj, self.dialect)
+
+
+ def register_dialect(self, name = 'sniffed'):
+ csv.register_dialect(name, self.dialect)
+
+
+ def _guessQuoteAndDelimiter(self, data):
+ """
+ Looks for text enclosed between two identical quotes
+ (the probable quotechar) which are preceded and followed
+ by the same character (the probable delimiter).
+ For example:
+ ,'some text',
+ The quote with the most wins, same with the delimiter.
+ If there is no quotechar the delimiter can't be determined
+ this way.
+ """
+
+ matches = []
+ for restr in ('(?P<delim>[^\w\n"\'])(?P<space> ?)(?P<quote>["\']).*?(?P=quote)(?P=delim)', # ,".*?",
+ '(?:^|\n)(?P<quote>["\']).*?(?P=quote)(?P<delim>[^\w\n"\'])(?P<space> ?)', # ".*?",
+ '(?P<delim>>[^\w\n"\'])(?P<space> ?)(?P<quote>["\']).*?(?P=quote)(?:$|\n)', # ,".*?"
+ '(?:^|\n)(?P<quote>["\']).*?(?P=quote)(?:$|\n)'): # ".*?" (no delim, no space)
+ regexp = re.compile(restr, re.S | re.M)
+ matches = regexp.findall(data)
+ if matches:
+ break
+
+ if not matches:
+ return ('', None, 0) # (quotechar, delimiter, skipinitialspace)
+
+ quotes = {}
+ delims = {}
+ spaces = 0
+ for m in matches:
+ n = regexp.groupindex['quote'] - 1
+ key = m[n]
+ if key:
+ quotes[key] = quotes.get(key, 0) + 1
+ try:
+ n = regexp.groupindex['delim'] - 1
+ key = m[n]
+ except KeyError:
+ continue
+ if key:
+ delims[key] = delims.get(key, 0) + 1
+ try:
+ n = regexp.groupindex['space'] - 1
+ except KeyError:
+ continue
+ if m[n]:
+ spaces += 1
+
+ quotechar = reduce(lambda a, b, quotes = quotes:
+ (quotes[a] > quotes[b]) and a or b, quotes.keys())
+
+ if delims:
+ delim = reduce(lambda a, b, delims = delims:
+ (delims[a] > delims[b]) and a or b, delims.keys())
+ skipinitialspace = delims[delim] == spaces
+ if delim == '\n': # most likely a file with a single column
+ delim = ''
+ else:
+ # there is *no* delimiter, it's a single column of quoted data
+ delim = ''
+ skipinitialspace = 0
+
+ return (quotechar, delim, skipinitialspace)
+
+
+ def _guessDelimiter(self, data):
+ """
+ The delimiter /should/ occur the same number of times on
+ each row. However, due to malformed data, it may not. We don't want
+ an all or nothing approach, so we allow for small variations in this
+ number.
+ 1) build a table of the frequency of each character on every line.
+ 2) build a table of freqencies of this frequency (meta-frequency?),
+ e.g. "x occurred 5 times in 10 rows, 6 times in 1000 rows,
+ 7 times in 2 rows"
+ 3) use the mode of the meta-frequency to determine the /expected/
+ frequency for that character
+ 4) find out how often the character actually meets that goal
+ 5) the character that best meets its goal is the delimiter
+ For performance reasons, the data is evaluated in chunks, so it can
+ try and evaluate the smallest portion of the data possible, evaluating
+ additional chunks as necessary.
+ """
+
+ data = filter(None, data.split('\n'))
+
+ ascii = [chr(c) for c in range(127)] # 7-bit ASCII
+
+ # build frequency tables
+ chunkLength = min(10, len(data))
+ iteration = 0
+ charFrequency = {}
+ modes = {}
+ delims = {}
+ start, end = 0, min(chunkLength, len(data))
+ while start < len(data):
+ iteration += 1
+ for line in data[start:end]:
+ for char in ascii:
+ metafrequency = charFrequency.get(char, {})
+ freq = line.strip().count(char) # must count even if frequency is 0
+ metafrequency[freq] = metafrequency.get(freq, 0) + 1 # value is the mode
+ charFrequency[char] = metafrequency
+
+ for char in charFrequency.keys():
+ items = charFrequency[char].items()
+ if len(items) == 1 and items[0][0] == 0:
+ continue
+ # get the mode of the frequencies
+ if len(items) > 1:
+ modes[char] = reduce(lambda a, b: a[1] > b[1] and a or b, items)
+ # adjust the mode - subtract the sum of all other frequencies
+ items.remove(modes[char])
+ modes[char] = (modes[char][0], modes[char][1]
+ - reduce(lambda a, b: (0, a[1] + b[1]), items)[1])
+ else:
+ modes[char] = items[0]
+
+ # build a list of possible delimiters
+ modeList = modes.items()
+ total = float(chunkLength * iteration)
+ consistency = 1.0 # (rows of consistent data) / (number of rows) = 100%
+ threshold = 0.9 # minimum consistency threshold
+ while len(delims) == 0 and consistency >= threshold:
+ for k, v in modeList:
+ if v[0] > 0 and v[1] > 0:
+ if (v[1]/total) >= consistency:
+ delims[k] = v
+ consistency -= 0.01
+
+ if len(delims) == 1:
+ delim = delims.keys()[0]
+ skipinitialspace = data[0].count(delim) == data[0].count("%c " % delim)
+ return (delim, skipinitialspace)
+
+ # analyze another chunkLength lines
+ start = end
+ end += chunkLength
+
+ if not delims:
+ return ('', 0)
+
+ # if there's more than one, fall back to a 'preferred' list
+ if len(delims) > 1:
+ for d in self.preferred:
+ if d in delims.keys():
+ skipinitialspace = data[0].count(d) == data[0].count("%c " % d)
+ return (d, skipinitialspace)
+
+ # finally, just return the first damn character in the list
+ delim = delims.keys()[0]
+ skipinitialspace = data[0].count(delim) == data[0].count("%c " % delim)
+ return (delim, skipinitialspace)
+
+
+ def _hasHeaders(self, fileobj, dialect):
+ # Creates a dictionary of types of data in each column. If any column
+ # is of a single type (say, integers), *except* for the first row, then the first
+ # row is presumed to be labels. If the type can't be determined, it is assumed to
+ # be a string in which case the length of the string is the determining factor: if
+ # all of the rows except for the first are the same length, it's a header.
+ # Finally, a 'vote' is taken at the end for each column, adding or subtracting from
+ # the likelihood of the first row being a header.
+
+ def seval(item):
+ """
+ Strips parens from item prior to calling eval in an attempt to make it safer
+ """
+ return eval(item.replace('(', '').replace(')', ''))
+
+ fileobj.seek(0) # rewind the fileobj - this might not work for some file-like objects...
+
+ reader = csv.reader(fileobj,
+ delimiter = dialect.delimiter,
+ quotechar = dialect.quotechar,
+ skipinitialspace = dialect.skipinitialspace)
+
+ header = reader.next() # assume first row is header
+
+ columns = len(header)
+ columnTypes = {}
+ for i in range(columns): columnTypes[i] = None
+
+ checked = 0
+ for row in reader:
+ if checked > 20: # arbitrary number of rows to check, to keep it sane
+ break
+ checked += 1
+
+ if len(row) != columns:
+ continue # skip rows that have irregular number of columns
+
+ for col in columnTypes.keys():
+ try:
+ try:
+ # is it a built-in type (besides string)?
+ thisType = type(seval(row[col]))
+ except OverflowError:
+ # a long int?
+ thisType = type(seval(row[col] + 'L'))
+ thisType = type(0) # treat long ints as int
+ except:
+ # fallback to length of string
+ thisType = len(row[col])
+
+ if thisType != columnTypes[col]:
+ if columnTypes[col] is None: # add new column type
+ columnTypes[col] = thisType
+ else: # type is inconsistent, remove column from consideration
+ del columnTypes[col]
+
+ # finally, compare results against first row and "vote" on whether it's a header
+ hasHeader = 0
+ for col, colType in columnTypes.items():
+ if type(colType) == type(0): # it's a length
+ if len(header[col]) != colType:
+ hasHeader += 1
+ else:
+ hasHeader -= 1
+ else: # attempt typecast
+ try:
+ eval("%s(%s)" % (colType.__name__, header[col]))
+ except:
+ hasHeader += 1
+ else:
+ hasHeader -= 1
+
+ return hasHeader > 0
+
+
+