""" csv.py - read/write/investigate CSV files """ import re from _csv import Error, __version__, writer, reader, register_dialect, \ unregister_dialect, get_dialect, list_dialects, \ field_size_limit, \ QUOTE_MINIMAL, QUOTE_ALL, QUOTE_NONNUMERIC, QUOTE_NONE, \ __doc__ from _csv import Dialect as _Dialect from io import StringIO __all__ = ["QUOTE_MINIMAL", "QUOTE_ALL", "QUOTE_NONNUMERIC", "QUOTE_NONE", "Error", "Dialect", "__doc__", "excel", "excel_tab", "field_size_limit", "reader", "writer", "register_dialect", "get_dialect", "list_dialects", "Sniffer", "unregister_dialect", "__version__", "DictReader", "DictWriter", "unix_dialect"] class Dialect: """Describe a CSV dialect. This must be subclassed (see csv.excel). Valid attributes are: delimiter, quotechar, escapechar, doublequote, skipinitialspace, lineterminator, quoting. """ _name = "" _valid = False # placeholders delimiter = None quotechar = None escapechar = None doublequote = None skipinitialspace = None lineterminator = None quoting = None def __init__(self): if self.__class__ != Dialect: self._valid = True self._validate() def _validate(self): try: _Dialect(self) except TypeError as e: # We do this for compatibility with py2.3 raise Error(str(e)) class excel(Dialect): """Describe the usual properties of Excel-generated CSV files.""" delimiter = ',' quotechar = '"' doublequote = True skipinitialspace = False lineterminator = '\r\n' quoting = QUOTE_MINIMAL register_dialect("excel", excel) class excel_tab(excel): """Describe the usual properties of Excel-generated TAB-delimited files.""" delimiter = '\t' register_dialect("excel-tab", excel_tab) class unix_dialect(Dialect): """Describe the usual properties of Unix-generated CSV files.""" delimiter = ',' quotechar = '"' doublequote = True skipinitialspace = False lineterminator = '\n' quoting = QUOTE_ALL register_dialect("unix", unix_dialect) class DictReader: def __init__(self, f, fieldnames=None, restkey=None, restval=None, dialect="excel", *args, **kwds): self._fieldnames = fieldnames # list of keys for the dict self.restkey = restkey # key to catch long rows self.restval = restval # default value for short rows self.reader = reader(f, dialect, *args, **kwds) self.dialect = dialect self.line_num = 0 def __iter__(self): return self @property def fieldnames(self): if self._fieldnames is None: try: self._fieldnames = next(self.reader) except StopIteration: pass self.line_num = self.reader.line_num return self._fieldnames @fieldnames.setter def fieldnames(self, value): self._fieldnames = value def __next__(self): if self.line_num == 0: # Used only for its side effect. self.fieldnames row = next(self.reader) self.line_num = self.reader.line_num # unlike the basic reader, we prefer not to return blanks, # because we will typically wind up with a dict full of None # values while row == []: row = next(self.reader) d = dict(zip(self.fieldnames, row)) lf = len(self.fieldnames) lr = len(row) if lf < lr: d[self.restkey] = row[lf:] elif lf > lr: for key in self.fieldnames[lr:]: d[key] = self.restval return d class DictWriter: def __init__(self, f, fieldnames, restval="", extrasaction="raise", dialect="excel", *args, **kwds): self.fieldnames = fieldnames # list of keys for the dict self.restval = restval # for writing short dicts if extrasaction.lower() not in ("raise", "ignore"): raise ValueError("extrasaction (%s) must be 'raise' or 'ignore'" % extrasaction) self.extrasaction = extrasaction self.writer = writer(f, dialect, *args, **kwds) def writeheader(self): header = dict(zip(self.fieldnames, self.fieldnames)) return self.writerow(header) def _dict_to_list(self, rowdict): if self.extrasaction == "raise": wrong_fields = rowdict.keys() - self.fieldnames if wrong_fields: raise ValueError("dict contains fields not in fieldnames: " + ", ".join([repr(x) for x in wrong_fields])) return (rowdict.get(key, self.restval) for key in self.fieldnames) def writerow(self, rowdict): return self.writer.writerow(self._dict_to_list(rowdict)) def writerows(self, rowdicts): return self.writer.writerows(map(self._dict_to_list, rowdicts)) # Guard Sniffer's type checking against builds that exclude complex() try: complex except NameError: complex = float class Sniffer: ''' "Sniffs" the format of a CSV file (i.e. delimiter, quotechar) Returns a Dialect object. ''' def __init__(self): # in case there is more than one possible delimiter self.preferred = [',', '\t', ';', ' ', ':'] def sniff(self, sample, delimiters=None): """ Returns a dialect (or None) corresponding to the sample """ quotechar, doublequote, delimiter, skipinitialspace = \ self._guess_quote_and_delimiter(sample, delimiters) if not delimiter: delimiter, skipinitialspace = self._guess_delimiter(sample, delimiters) if not delimiter: raise Error("Could not determine delimiter") class dialect(Dialect): _name = "sniffed" lineterminator = '\r\n' quoting = QUOTE_MINIMAL # escapechar = '' dialect.doublequote = doublequote dialect.delimiter = delimiter # _csv.reader won't accept a quotechar of '' dialect.quotechar = quotechar or '"' dialect.skipinitialspace = skipinitialspace return dialect def _guess_quote_and_delimiter(self, data, delimiters): """ 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 (r'(?P<delim>[^\w\n"\'])(?P<space> ?)(?P<quote>["\']).*?(?P=quote)(?P=delim)', # ,".*?", r'(?:^|\n)(?P<quote>["\']).*?(?P=quote)(?P<delim>[^\w\n"\'])(?P<space> ?)', # ".*?", r'(?P<delim>[^\w\n"\'])(?P<space> ?)(?P<quote>["\']).*?(?P=quote)(?:$|\n)', # ,".*?" r'(?:^|\n)(?P<quote>["\']).*?(?P=quote)(?:$|\n)'): # ".*?" (no delim, no space) regexp = re.compile(restr, re.DOTALL | re.MULTILINE) matches = regexp.findall(data) if matches: break if not matches: # (quotechar, doublequote, delimiter, skipinitialspace) return ('', False, None, 0) quotes = {} delims = {} spaces = 0 groupindex = regexp.groupindex for m in matches: n = groupindex['quote'] - 1 key = m[n] if key: quotes[key] = quotes.get(key, 0) + 1 try: n = groupindex['delim'] - 1 key = m[n] except KeyError: continue if key and (delimiters is None or key in delimiters): delims[key] = delims.get(key, 0) + 1 try: n = groupindex['space'] - 1 except KeyError: continue if m[n]: spaces += 1 quotechar = max(quotes, key=quotes.get) if delims: delim = max(delims, key=delims.get) 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 # if we see an extra quote between delimiters, we've got a # double quoted format dq_regexp = re.compile( r"((%(delim)s)|^)\W*%(quote)s[^%(delim)s\n]*%(quote)s[^%(delim)s\n]*%(quote)s\W*((%(delim)s)|$)" % \ {'delim':re.escape(delim), 'quote':quotechar}, re.MULTILINE) if dq_regexp.search(data): doublequote = True else: doublequote = False return (quotechar, doublequote, delim, skipinitialspace) def _guess_delimiter(self, data, delimiters): """ 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 frequencies 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 = list(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, chunkLength while start < len(data): iteration += 1 for line in data[start:end]: for char in ascii: metaFrequency = charFrequency.get(char, {}) # must count even if frequency is 0 freq = line.count(char) # value is the mode metaFrequency[freq] = metaFrequency.get(freq, 0) + 1 charFrequency[char] = metaFrequency for char in charFrequency.keys(): items = list(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] = max(items, key=lambda x: x[1]) # adjust the mode - subtract the sum of all # other frequencies items.remove(modes[char]) modes[char] = (modes[char][0], modes[char][1] - sum(item[1] for item in items)) else: modes[char] = items[0] # build a list of possible delimiters modeList = modes.items() total = float(min(chunkLength * iteration, len(data))) # (rows of consistent data) / (number of rows) = 100% consistency = 1.0 # minimum consistency threshold threshold = 0.9 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 and (delimiters is None or k in delimiters)): delims[k] = v consistency -= 0.01 if len(delims) == 1: delim = list(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) # nothing else indicates a preference, pick the character that # dominates(?) items = [(v,k) for (k,v) in delims.items()] items.sort() delim = items[-1][1] skipinitialspace = (data[0].count(delim) == data[0].count("%c " % delim)) return (delim, skipinitialspace) def has_header(self, sample): # 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. rdr = reader(StringIO(sample), self.sniff(sample)) header = next(rdr) # assume first row is header columns = len(header) columnTypes = {} for i in range(columns): columnTypes[i] = None checked = 0 for row in rdr: # arbitrary number of rows to check, to keep it sane if checked > 20: break checked += 1 if len(row) != columns: continue # skip rows that have irregular number of columns for col in list(columnTypes.keys()): thisType = complex try: thisType(row[col]) except (ValueError, OverflowError): # 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: colType(header[col]) except (ValueError, TypeError): hasHeader += 1 else: hasHeader -= 1 return hasHeader > 0