[Python-checkins] python/dist/src/Lib/csv/util sniffer.py,NONE,1.1
montanaro@users.sourceforge.net
montanaro@users.sourceforge.net
Thu, 20 Mar 2003 15:31:26 -0800
Update of /cvsroot/python/python/dist/src/Lib/csv/util
In directory sc8-pr-cvs1:/tmp/cvs-serv25444/Lib/csv/util
Added Files:
sniffer.py
Log Message:
forgot Cliff's sniffer
--- NEW FILE: sniffer.py ---
"""
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<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.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