cpython/Lib/csv.py at 3.15 · python/cpython · GitHub

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r"""

CSV parsing and writing.

This module provides classes that assist in the reading and writing

of Comma Separated Value (CSV) files, and implements the interface

described by PEP 305. Although many CSV files are simple to parse,

the format is not formally defined by a stable specification and

is subtle enough that parsing lines of a CSV file with something

like line.split(",") is bound to fail. The module supports three

basic APIs: reading, writing, and registration of dialects.

DIALECT REGISTRATION:

Readers and writers support a dialect argument, which is a convenient

handle on a group of settings. When the dialect argument is a string,

it identifies one of the dialects previously registered with the module.

If it is a class or instance, the attributes of the argument are used as

the settings for the reader or writer:

class excel:

delimiter = ','

quotechar = '"'

escapechar = None

doublequote = True

skipinitialspace = False

lineterminator = '\r\n'

quoting = QUOTE_MINIMAL

SETTINGS:

* quotechar - specifies a one-character string to use as the

quoting character. It defaults to '"'.

* delimiter - specifies a one-character string to use as the

field separator. It defaults to ','.

* skipinitialspace - specifies how to interpret spaces which

immediately follow a delimiter. It defaults to False, which

means that spaces immediately following a delimiter is part

of the following field.

* lineterminator - specifies the character sequence which should

terminate rows.

* quoting - controls when quotes should be generated by the writer.

It can take on any of the following module constants:

csv.QUOTE_MINIMAL means only when required, for example, when a

field contains either the quotechar or the delimiter

csv.QUOTE_ALL means that quotes are always placed around fields.

csv.QUOTE_NONNUMERIC means that quotes are always placed around

fields which do not parse as integers or floating-point

numbers.

csv.QUOTE_STRINGS means that quotes are always placed around

fields which are strings. Note that the Python value None

is not a string.

csv.QUOTE_NOTNULL means that quotes are only placed around fields

that are not the Python value None.

csv.QUOTE_NONE means that quotes are never placed around fields.

* escapechar - specifies a one-character string used to escape

the delimiter when quoting is set to QUOTE_NONE.

* doublequote - controls the handling of quotes inside fields. When

True, two consecutive quotes are interpreted as one during read,

and when writing, each quote character embedded in the data is

written as two quotes

"""

import types

from _csv import Error, 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

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", "excel", "excel_tab",

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

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

"unregister_dialect", "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:

# Re-raise to get a traceback showing more user code.

raise Error(str(e)) from None

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

If several delimiters fit the sample equally well, the

delimiters listed in the preferred attribute are preferred, in

that order, no matter how many times each of them occurs.

"""

sample = sample.replace('\r\n', '\n').replace('\r', '\n')

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.

"""

import re

# The body of a quoted field ends at the first quote which is

# not doubled, as it does for a reader. A lazy ".*?" scans to

# the end of the sample instead, from every start: quadratically.

body = r'(?:(?P=quote){2}|(?!(?P=quote)).)*+'

matches = []

for restr in (r'(?P<delim>[^\w\n"\'])(?P<space> ?)(?P<quote>["\'])%s(?P=quote)(?P=delim)', # ,"...",

r'(?:^|\n)(?P<quote>["\'])%s(?P=quote)(?P<delim>[^\w\n"\'])(?P<space> ?)', # "...",

r'(?P<delim>[^\w\n"\'])(?P<space> ?)(?P<quote>["\'])%s(?P=quote)(?:$|\n)', # ,"..."

r'(?:^|\n)(?P<quote>["\'])%s(?P=quote)(?:$|\n)'): # "..." (no delim, no space)

regexp = re.compile(restr % body, 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

# A doubled quote character inside a quoted field means

# a double quoted format. Match whole fields, so that a match

# cannot slide across field boundaries.

doublequote = False

if delim:

dq_regexp = re.compile(

r"(?:(?<=%(delim)s)|^)%(space)s%(quote)s" # ,"

r"((?:%(quote)s%(quote)s|[^%(quote)s]++)*+)" # the body

r"%(quote)s(?:%(delim)s|$)" # ",

% {'delim': re.escape(delim), 'quote': quotechar,

# Skipping spaces after a space rescans them.

'space': ' *+' if delim != ' ' else ''},

re.MULTILINE)

dquotechar = quotechar * 2

doublequote = any(dquotechar in m[1]

for m in dq_regexp.finditer(data))

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.

"""

from collections import Counter, defaultdict

data = list(filter(None, data.split('\n')))

# build frequency tables

chunkLength = min(10, len(data))

iteration = 0

num_lines = 0

# {char -> {count_per_line -> num_lines_with_that_count}}

char_frequency = defaultdict(Counter)

modes = {}

delims = {}

start, end = 0, chunkLength

while start < len(data):

iteration += 1

for line in data[start:end]:

num_lines += 1

for char, count in Counter(line).items():

if char.isascii():

char_frequency[char][count] += 1

for char, counts in char_frequency.items():

items = list(counts.items())

missed_lines = num_lines - sum(counts.values())

if missed_lines:

# Store the number of lines 'char' was missing from.

items.append((0, missed_lines))

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

def __getattr__(name):

if name == "__version__":

from warnings import _deprecated

_deprecated("__version__", remove=(3, 20))

return "1.0" # Do not change

raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

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