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When cvs.Error is raised when TypeError is caught, the TypeError display and 'During handling' note is just noise with duplicate information. Suppress with 'from None'.
514 lines
19 KiB
Python
514 lines
19 KiB
Python
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r"""
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CSV parsing and writing.
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This module provides classes that assist in the reading and writing
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of Comma Separated Value (CSV) files, and implements the interface
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described by PEP 305. Although many CSV files are simple to parse,
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the format is not formally defined by a stable specification and
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is subtle enough that parsing lines of a CSV file with something
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like line.split(",") is bound to fail. The module supports three
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basic APIs: reading, writing, and registration of dialects.
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DIALECT REGISTRATION:
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Readers and writers support a dialect argument, which is a convenient
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handle on a group of settings. When the dialect argument is a string,
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it identifies one of the dialects previously registered with the module.
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If it is a class or instance, the attributes of the argument are used as
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the settings for the reader or writer:
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class excel:
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delimiter = ','
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quotechar = '"'
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escapechar = None
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doublequote = True
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skipinitialspace = False
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lineterminator = '\r\n'
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quoting = QUOTE_MINIMAL
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SETTINGS:
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* quotechar - specifies a one-character string to use as the
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quoting character. It defaults to '"'.
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* delimiter - specifies a one-character string to use as the
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field separator. It defaults to ','.
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* skipinitialspace - specifies how to interpret spaces which
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immediately follow a delimiter. It defaults to False, which
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means that spaces immediately following a delimiter is part
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of the following field.
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* lineterminator - specifies the character sequence which should
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terminate rows.
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* quoting - controls when quotes should be generated by the writer.
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It can take on any of the following module constants:
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csv.QUOTE_MINIMAL means only when required, for example, when a
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field contains either the quotechar or the delimiter
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csv.QUOTE_ALL means that quotes are always placed around fields.
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csv.QUOTE_NONNUMERIC means that quotes are always placed around
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fields which do not parse as integers or floating point
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numbers.
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csv.QUOTE_STRINGS means that quotes are always placed around
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fields which are strings. Note that the Python value None
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is not a string.
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csv.QUOTE_NOTNULL means that quotes are only placed around fields
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that are not the Python value None.
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csv.QUOTE_NONE means that quotes are never placed around fields.
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* escapechar - specifies a one-character string used to escape
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the delimiter when quoting is set to QUOTE_NONE.
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* doublequote - controls the handling of quotes inside fields. When
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True, two consecutive quotes are interpreted as one during read,
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and when writing, each quote character embedded in the data is
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written as two quotes
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"""
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import re
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import types
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from _csv import Error, writer, reader, register_dialect, \
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unregister_dialect, get_dialect, list_dialects, \
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field_size_limit, \
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QUOTE_MINIMAL, QUOTE_ALL, QUOTE_NONNUMERIC, QUOTE_NONE, \
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QUOTE_STRINGS, QUOTE_NOTNULL
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from _csv import Dialect as _Dialect
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from io import StringIO
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__all__ = ["QUOTE_MINIMAL", "QUOTE_ALL", "QUOTE_NONNUMERIC", "QUOTE_NONE",
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"QUOTE_STRINGS", "QUOTE_NOTNULL",
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"Error", "Dialect", "excel", "excel_tab",
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"field_size_limit", "reader", "writer",
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"register_dialect", "get_dialect", "list_dialects", "Sniffer",
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"unregister_dialect", "DictReader", "DictWriter",
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"unix_dialect"]
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__version__ = "1.0"
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class Dialect:
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"""Describe a CSV dialect.
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This must be subclassed (see csv.excel). Valid attributes are:
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delimiter, quotechar, escapechar, doublequote, skipinitialspace,
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lineterminator, quoting.
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"""
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_name = ""
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_valid = False
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# placeholders
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delimiter = None
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quotechar = None
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escapechar = None
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doublequote = None
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skipinitialspace = None
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lineterminator = None
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quoting = None
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def __init__(self):
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if self.__class__ != Dialect:
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self._valid = True
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self._validate()
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def _validate(self):
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try:
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_Dialect(self)
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except TypeError as e:
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# Re-raise to get a traceback showing more user code.
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raise Error(str(e)) from None
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class excel(Dialect):
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"""Describe the usual properties of Excel-generated CSV files."""
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delimiter = ','
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quotechar = '"'
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doublequote = True
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skipinitialspace = False
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lineterminator = '\r\n'
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quoting = QUOTE_MINIMAL
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register_dialect("excel", excel)
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class excel_tab(excel):
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"""Describe the usual properties of Excel-generated TAB-delimited files."""
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delimiter = '\t'
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register_dialect("excel-tab", excel_tab)
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class unix_dialect(Dialect):
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"""Describe the usual properties of Unix-generated CSV files."""
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delimiter = ','
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quotechar = '"'
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doublequote = True
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skipinitialspace = False
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lineterminator = '\n'
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quoting = QUOTE_ALL
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register_dialect("unix", unix_dialect)
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class DictReader:
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def __init__(self, f, fieldnames=None, restkey=None, restval=None,
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dialect="excel", *args, **kwds):
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if fieldnames is not None and iter(fieldnames) is fieldnames:
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fieldnames = list(fieldnames)
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self._fieldnames = fieldnames # list of keys for the dict
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self.restkey = restkey # key to catch long rows
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self.restval = restval # default value for short rows
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self.reader = reader(f, dialect, *args, **kwds)
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self.dialect = dialect
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self.line_num = 0
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def __iter__(self):
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return self
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@property
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def fieldnames(self):
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if self._fieldnames is None:
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try:
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self._fieldnames = next(self.reader)
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except StopIteration:
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pass
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self.line_num = self.reader.line_num
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return self._fieldnames
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@fieldnames.setter
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def fieldnames(self, value):
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self._fieldnames = value
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def __next__(self):
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if self.line_num == 0:
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# Used only for its side effect.
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self.fieldnames
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row = next(self.reader)
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self.line_num = self.reader.line_num
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# unlike the basic reader, we prefer not to return blanks,
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# because we will typically wind up with a dict full of None
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# values
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while row == []:
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row = next(self.reader)
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d = dict(zip(self.fieldnames, row))
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lf = len(self.fieldnames)
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lr = len(row)
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if lf < lr:
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d[self.restkey] = row[lf:]
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elif lf > lr:
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for key in self.fieldnames[lr:]:
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d[key] = self.restval
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return d
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__class_getitem__ = classmethod(types.GenericAlias)
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class DictWriter:
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def __init__(self, f, fieldnames, restval="", extrasaction="raise",
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dialect="excel", *args, **kwds):
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if fieldnames is not None and iter(fieldnames) is fieldnames:
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fieldnames = list(fieldnames)
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self.fieldnames = fieldnames # list of keys for the dict
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self.restval = restval # for writing short dicts
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extrasaction = extrasaction.lower()
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if extrasaction not in ("raise", "ignore"):
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raise ValueError("extrasaction (%s) must be 'raise' or 'ignore'"
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% extrasaction)
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self.extrasaction = extrasaction
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self.writer = writer(f, dialect, *args, **kwds)
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def writeheader(self):
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header = dict(zip(self.fieldnames, self.fieldnames))
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return self.writerow(header)
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def _dict_to_list(self, rowdict):
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if self.extrasaction == "raise":
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wrong_fields = rowdict.keys() - self.fieldnames
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if wrong_fields:
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raise ValueError("dict contains fields not in fieldnames: "
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+ ", ".join([repr(x) for x in wrong_fields]))
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return (rowdict.get(key, self.restval) for key in self.fieldnames)
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def writerow(self, rowdict):
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return self.writer.writerow(self._dict_to_list(rowdict))
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def writerows(self, rowdicts):
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return self.writer.writerows(map(self._dict_to_list, rowdicts))
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__class_getitem__ = classmethod(types.GenericAlias)
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class Sniffer:
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'''
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"Sniffs" the format of a CSV file (i.e. delimiter, quotechar)
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Returns a Dialect object.
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'''
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def __init__(self):
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# in case there is more than one possible delimiter
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self.preferred = [',', '\t', ';', ' ', ':']
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def sniff(self, sample, delimiters=None):
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"""
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Returns a dialect (or None) corresponding to the sample
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"""
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quotechar, doublequote, delimiter, skipinitialspace = \
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self._guess_quote_and_delimiter(sample, delimiters)
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if not delimiter:
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delimiter, skipinitialspace = self._guess_delimiter(sample,
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delimiters)
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if not delimiter:
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raise Error("Could not determine delimiter")
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class dialect(Dialect):
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_name = "sniffed"
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lineterminator = '\r\n'
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quoting = QUOTE_MINIMAL
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# escapechar = ''
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dialect.doublequote = doublequote
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dialect.delimiter = delimiter
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# _csv.reader won't accept a quotechar of ''
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dialect.quotechar = quotechar or '"'
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dialect.skipinitialspace = skipinitialspace
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return dialect
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def _guess_quote_and_delimiter(self, data, delimiters):
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"""
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Looks for text enclosed between two identical quotes
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(the probable quotechar) which are preceded and followed
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by the same character (the probable delimiter).
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For example:
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,'some text',
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The quote with the most wins, same with the delimiter.
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If there is no quotechar the delimiter can't be determined
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this way.
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"""
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matches = []
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for restr in (r'(?P<delim>[^\w\n"\'])(?P<space> ?)(?P<quote>["\']).*?(?P=quote)(?P=delim)', # ,".*?",
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r'(?:^|\n)(?P<quote>["\']).*?(?P=quote)(?P<delim>[^\w\n"\'])(?P<space> ?)', # ".*?",
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r'(?P<delim>[^\w\n"\'])(?P<space> ?)(?P<quote>["\']).*?(?P=quote)(?:$|\n)', # ,".*?"
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r'(?:^|\n)(?P<quote>["\']).*?(?P=quote)(?:$|\n)'): # ".*?" (no delim, no space)
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regexp = re.compile(restr, re.DOTALL | re.MULTILINE)
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matches = regexp.findall(data)
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if matches:
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break
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if not matches:
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# (quotechar, doublequote, delimiter, skipinitialspace)
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return ('', False, None, 0)
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quotes = {}
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delims = {}
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spaces = 0
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groupindex = regexp.groupindex
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for m in matches:
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n = groupindex['quote'] - 1
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key = m[n]
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if key:
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quotes[key] = quotes.get(key, 0) + 1
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try:
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n = groupindex['delim'] - 1
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key = m[n]
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except KeyError:
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continue
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if key and (delimiters is None or key in delimiters):
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delims[key] = delims.get(key, 0) + 1
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try:
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n = groupindex['space'] - 1
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except KeyError:
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continue
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if m[n]:
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spaces += 1
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quotechar = max(quotes, key=quotes.get)
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if delims:
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delim = max(delims, key=delims.get)
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skipinitialspace = delims[delim] == spaces
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if delim == '\n': # most likely a file with a single column
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delim = ''
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else:
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# there is *no* delimiter, it's a single column of quoted data
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delim = ''
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skipinitialspace = 0
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# if we see an extra quote between delimiters, we've got a
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# double quoted format
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dq_regexp = re.compile(
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r"((%(delim)s)|^)\W*%(quote)s[^%(delim)s\n]*%(quote)s[^%(delim)s\n]*%(quote)s\W*((%(delim)s)|$)" % \
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{'delim':re.escape(delim), 'quote':quotechar}, re.MULTILINE)
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if dq_regexp.search(data):
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doublequote = True
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else:
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doublequote = False
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return (quotechar, doublequote, delim, skipinitialspace)
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def _guess_delimiter(self, data, delimiters):
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"""
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The delimiter /should/ occur the same number of times on
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each row. However, due to malformed data, it may not. We don't want
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an all or nothing approach, so we allow for small variations in this
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number.
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1) build a table of the frequency of each character on every line.
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2) build a table of frequencies of this frequency (meta-frequency?),
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e.g. 'x occurred 5 times in 10 rows, 6 times in 1000 rows,
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7 times in 2 rows'
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3) use the mode of the meta-frequency to determine the /expected/
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frequency for that character
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4) find out how often the character actually meets that goal
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5) the character that best meets its goal is the delimiter
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For performance reasons, the data is evaluated in chunks, so it can
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try and evaluate the smallest portion of the data possible, evaluating
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additional chunks as necessary.
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"""
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data = list(filter(None, data.split('\n')))
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ascii = [chr(c) for c in range(127)] # 7-bit ASCII
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# build frequency tables
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chunkLength = min(10, len(data))
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iteration = 0
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charFrequency = {}
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modes = {}
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delims = {}
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start, end = 0, chunkLength
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while start < len(data):
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iteration += 1
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for line in data[start:end]:
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for char in ascii:
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metaFrequency = charFrequency.get(char, {})
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# must count even if frequency is 0
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freq = line.count(char)
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# value is the mode
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metaFrequency[freq] = metaFrequency.get(freq, 0) + 1
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charFrequency[char] = metaFrequency
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for char in charFrequency.keys():
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items = list(charFrequency[char].items())
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if len(items) == 1 and items[0][0] == 0:
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continue
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# get the mode of the frequencies
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if len(items) > 1:
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modes[char] = max(items, key=lambda x: x[1])
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# adjust the mode - subtract the sum of all
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# other frequencies
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items.remove(modes[char])
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modes[char] = (modes[char][0], modes[char][1]
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- sum(item[1] for item in items))
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else:
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modes[char] = items[0]
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# build a list of possible delimiters
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modeList = modes.items()
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total = float(min(chunkLength * iteration, len(data)))
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# (rows of consistent data) / (number of rows) = 100%
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consistency = 1.0
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# minimum consistency threshold
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threshold = 0.9
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while len(delims) == 0 and consistency >= threshold:
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for k, v in modeList:
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if v[0] > 0 and v[1] > 0:
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if ((v[1]/total) >= consistency and
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(delimiters is None or k in delimiters)):
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delims[k] = v
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consistency -= 0.01
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if len(delims) == 1:
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delim = list(delims.keys())[0]
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skipinitialspace = (data[0].count(delim) ==
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data[0].count("%c " % delim))
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return (delim, skipinitialspace)
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# analyze another chunkLength lines
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start = end
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end += chunkLength
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if not delims:
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return ('', 0)
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# if there's more than one, fall back to a 'preferred' list
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if len(delims) > 1:
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for d in self.preferred:
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if d in delims.keys():
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skipinitialspace = (data[0].count(d) ==
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data[0].count("%c " % d))
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return (d, skipinitialspace)
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# nothing else indicates a preference, pick the character that
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# dominates(?)
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items = [(v,k) for (k,v) in delims.items()]
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items.sort()
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delim = items[-1][1]
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skipinitialspace = (data[0].count(delim) ==
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data[0].count("%c " % delim))
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return (delim, skipinitialspace)
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def has_header(self, sample):
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# Creates a dictionary of types of data in each column. If any
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# column is of a single type (say, integers), *except* for the first
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# row, then the first row is presumed to be labels. If the type
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# can't be determined, it is assumed to be a string in which case
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# the length of the string is the determining factor: if all of the
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# rows except for the first are the same length, it's a header.
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# Finally, a 'vote' is taken at the end for each column, adding or
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# subtracting from the likelihood of the first row being a header.
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rdr = reader(StringIO(sample), self.sniff(sample))
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header = next(rdr) # assume first row is header
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columns = len(header)
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columnTypes = {}
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for i in range(columns): columnTypes[i] = None
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checked = 0
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for row in rdr:
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# arbitrary number of rows to check, to keep it sane
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if checked > 20:
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break
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checked += 1
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if len(row) != columns:
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continue # skip rows that have irregular number of columns
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for col in list(columnTypes.keys()):
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thisType = complex
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try:
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thisType(row[col])
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except (ValueError, OverflowError):
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# fallback to length of string
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thisType = len(row[col])
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if thisType != columnTypes[col]:
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if columnTypes[col] is None: # add new column type
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columnTypes[col] = thisType
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else:
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# type is inconsistent, remove column from
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# consideration
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del columnTypes[col]
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# finally, compare results against first row and "vote"
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# on whether it's a header
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hasHeader = 0
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for col, colType in columnTypes.items():
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if isinstance(colType, int): # it's a length
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if len(header[col]) != colType:
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hasHeader += 1
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else:
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hasHeader -= 1
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else: # attempt typecast
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try:
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colType(header[col])
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except (ValueError, TypeError):
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hasHeader += 1
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else:
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hasHeader -= 1
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return hasHeader > 0
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