cpython/Lib/csv.py at 014dd301b5a075a73430eb4e583e851f49d03e29 · python/cpython · GitHub

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"""

csv.py - read/write/investigate CSV files

"""

import re

import types

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, \

QUOTE_STRINGS, QUOTE_NOTNULL, \

__doc__

from _csv import Dialect as _Dialect

from io import StringIO

__all__ = ["QUOTE_MINIMAL", "QUOTE_ALL", "QUOTE_NONNUMERIC", "QUOTE_NONE",

"QUOTE_STRINGS", "QUOTE_NOTNULL",

"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):

if fieldnames is not None and iter(fieldnames) is fieldnames:

fieldnames = list(fieldnames)

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_getitem__ = classmethod(types.GenericAlias)

class DictWriter:

def __init__(self, f, fieldnames, restval="", extrasaction="raise",

dialect="excel", *args, **kwds):

if fieldnames is not None and iter(fieldnames) is fieldnames:

fieldnames = list(fieldnames)

self.fieldnames = fieldnames # list of keys for the dict

self.restval = restval # for writing short dicts

extrasaction = extrasaction.lower()

if extrasaction 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))

__class_getitem__ = classmethod(types.GenericAlias)

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 isinstance(colType, int): # 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

cpython/Lib/csv.py at 014dd301b5a075a73430eb4e583e851f49d03e29 · python/cpython · GitHub

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