"Error", "Dialect", "__doc__", "excel", "excel_tab",

"field_size_limit", "reader", "writer",

"register_dialect", "get_dialect", "list_dialects", "Sniffer",

"unregister_dialect", "__version__", "DictReader", "DictWriter",

"""Describe a CSV dialect.

_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

"""Describe the usual properties of Excel-generated CSV files."""

delimiter = ','

quotechar = '"'

doublequote = True

skipinitialspace = False

lineterminator = '\r\n'

"""Describe the usual properties of Unix-generated CSV files."""

delimiter = ','

quotechar = '"'

doublequote = True

skipinitialspace = False

lineterminator = '\n'

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

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):

'''

def __init__(self):

# in case there is more than one possible delimiter

self.preferred = [',', '\t', ';', ' ', ':']

def sniff(self, sample, delimiters=None):

"""

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):

"""

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):

"""

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()):

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