| |
| """ |
| 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 |
| |
| try: |
| from cStringIO import StringIO |
| except ImportError: |
| from StringIO import StringIO |
| |
| __all__ = [ "QUOTE_MINIMAL", "QUOTE_ALL", "QUOTE_NONNUMERIC", "QUOTE_NONE", |
| "Error", "Dialect", "excel", "excel_tab", "reader", "writer", |
| "register_dialect", "get_dialect", "list_dialects", "Sniffer", |
| "unregister_dialect", "__version__", "DictReader", "DictWriter" ] |
| |
| class Dialect: |
| """Describe an Excel 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, 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 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) |
| |
| def __iter__(self): |
| return self |
| |
| def next(self): |
| row = self.reader.next() |
| if self.fieldnames is None: |
| self.fieldnames = row |
| row = self.reader.next() |
| |
| # 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 = self.reader.next() |
| 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 _dict_to_list(self, rowdict): |
| if self.extrasaction == "raise": |
| for k in rowdict.keys(): |
| if k not in self.fieldnames: |
| raise ValueError, "dict contains fields not in fieldnames" |
| 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): |
| rows = [] |
| for rowdict in rowdicts: |
| rows.append(self._dict_to_list(rowdict)) |
| return self.writer.writerows(rows) |
| |
| # 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, 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 = '' |
| doublequote = False |
| |
| 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 ('(?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.DOTALL | re.MULTILINE) |
| 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 and (delimiters is None or key in delimiters): |
| 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 _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 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, {}) |
| # 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 = 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) |
| # (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 = 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 = rdr.next() # 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 columnTypes.keys(): |
| |
| for thisType in [int, long, float, complex]: |
| try: |
| thisType(row[col]) |
| break |
| except (ValueError, OverflowError): |
| pass |
| else: |
| # fallback to length of string |
| thisType = len(row[col]) |
| |
| # treat longs as ints |
| if thisType == long: |
| thisType = int |
| |
| 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 |