""" 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[^\w\n"\'])(?P ?)(?P["\']).*?(?P=quote)(?P=delim)', # ,".*?", '(?:^|\n)(?P["\']).*?(?P=quote)(?P[^\w\n"\'])(?P ?)', # ".*?", '(?P>[^\w\n"\'])(?P ?)(?P["\']).*?(?P=quote)(?:$|\n)', # ,".*?" '(?:^|\n)(?P["\']).*?(?P=quote)(?:$|\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