python模块：csv

 """
 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 collections import OrderedDict
 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 = OrderedDict(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))
         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, 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 = 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(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 = 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()):

                 for thisType in [int, float, complex]:
                     try:
                         thisType(row[col])
                         break
                     except (ValueError, OverflowError):
                         pass
                 else:
                     # 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

csv