""":mod:`pandas.io.html` is a module containing functionality for dealing with
HTML IO.
"""
import os
import re
import numbers
import collections
from distutils.version import LooseVersion
import numpy as np
from pandas.types.common import is_list_like
from pandas.io.common import (EmptyDataError, _is_url, urlopen,
parse_url, _validate_header_arg)
from pandas.io.parsers import TextParser
from pandas.compat import (lrange, lmap, u, string_types, iteritems,
raise_with_traceback, binary_type)
from pandas import Series
from pandas.core.common import AbstractMethodError
from pandas.formats.printing import pprint_thing
_IMPORTS = False
_HAS_BS4 = False
_HAS_LXML = False
_HAS_HTML5LIB = False
def _importers():
# import things we need
# but make this done on a first use basis
global _IMPORTS
if _IMPORTS:
return
_IMPORTS = True
global _HAS_BS4, _HAS_LXML, _HAS_HTML5LIB
try:
import bs4 # noqa
_HAS_BS4 = True
except ImportError:
pass
try:
import lxml # noqa
_HAS_LXML = True
except ImportError:
pass
try:
import html5lib # noqa
_HAS_HTML5LIB = True
except ImportError:
pass
#############
# READ HTML #
#############
_RE_WHITESPACE = re.compile(r'[\r\n]+|\s{2,}')
char_types = string_types + (binary_type,)
def _remove_whitespace(s, regex=_RE_WHITESPACE):
"""Replace extra whitespace inside of a string with a single space.
Parameters
----------
s : str or unicode
The string from which to remove extra whitespace.
regex : regex
The regular expression to use to remove extra whitespace.
Returns
-------
subd : str or unicode
`s` with all extra whitespace replaced with a single space.
"""
return regex.sub(' ', s.strip())
def _get_skiprows(skiprows):
"""Get an iterator given an integer, slice or container.
Parameters
----------
skiprows : int, slice, container
The iterator to use to skip rows; can also be a slice.
Raises
------
TypeError
* If `skiprows` is not a slice, integer, or Container
Returns
-------
it : iterable
A proper iterator to use to skip rows of a DataFrame.
"""
if isinstance(skiprows, slice):
return lrange(skiprows.start or 0, skiprows.stop, skiprows.step or 1)
elif isinstance(skiprows, numbers.Integral) or is_list_like(skiprows):
return skiprows
elif skiprows is None:
return 0
raise TypeError('%r is not a valid type for skipping rows' %
type(skiprows).__name__)
def _read(obj):
"""Try to read from a url, file or string.
Parameters
----------
obj : str, unicode, or file-like
Returns
-------
raw_text : str
"""
if _is_url(obj):
with urlopen(obj) as url:
text = url.read()
elif hasattr(obj, 'read'):
text = obj.read()
elif isinstance(obj, char_types):
text = obj
try:
if os.path.isfile(text):
with open(text, 'rb') as f:
return f.read()
except (TypeError, ValueError):
pass
else:
raise TypeError("Cannot read object of type %r" % type(obj).__name__)
return text
class _HtmlFrameParser(object):
"""Base class for parsers that parse HTML into DataFrames.
Parameters
----------
io : str or file-like
This can be either a string of raw HTML, a valid URL using the HTTP,
FTP, or FILE protocols or a file-like object.
match : str or regex
The text to match in the document.
attrs : dict
List of HTML <table> element attributes to match.
Attributes
----------
io : str or file-like
raw HTML, URL, or file-like object
match : regex
The text to match in the raw HTML
attrs : dict-like
A dictionary of valid table attributes to use to search for table
elements.
Notes
-----
To subclass this class effectively you must override the following methods:
* :func:`_build_doc`
* :func:`_text_getter`
* :func:`_parse_td`
* :func:`_parse_tables`
* :func:`_parse_tr`
* :func:`_parse_thead`
* :func:`_parse_tbody`
* :func:`_parse_tfoot`
See each method's respective documentation for details on their
functionality.
"""
def __init__(self, io, match, attrs, encoding):
self.io = io
self.match = match
self.attrs = attrs
self.encoding = encoding
def parse_tables(self):
tables = self._parse_tables(self._build_doc(), self.match, self.attrs)
return (self._build_table(table) for table in tables)
def _parse_raw_data(self, rows):
"""Parse the raw data into a list of lists.
Parameters
----------
rows : iterable of node-like
A list of row elements.
text_getter : callable
A callable that gets the text from an individual node. This must be
defined by subclasses.
column_finder : callable
A callable that takes a row node as input and returns a list of the
column node in that row. This must be defined by subclasses.
Returns
-------
data : list of list of strings
"""
data = [[_remove_whitespace(self._text_getter(col)) for col in
self._parse_td(row)] for row in rows]
return data
def _text_getter(self, obj):
"""Return the text of an individual DOM node.
Parameters
----------
obj : node-like
A DOM node.
Returns
-------
text : str or unicode
The text from an individual DOM node.
"""
raise AbstractMethodError(self)
def _parse_td(self, obj):
"""Return the td elements from a row element.
Parameters
----------
obj : node-like
Returns
-------
columns : list of node-like
These are the elements of each row, i.e., the columns.
"""
raise AbstractMethodError(self)
def _parse_tables(self, doc, match, attrs):
"""Return all tables from the parsed DOM.
Parameters
----------
doc : tree-like
The DOM from which to parse the table element.
match : str or regular expression
The text to search for in the DOM tree.
attrs : dict
A dictionary of table attributes that can be used to disambiguate
mutliple tables on a page.
Raises
------
ValueError
* If `match` does not match any text in the document.
Returns
-------
tables : list of node-like
A list of <table> elements to be parsed into raw data.
"""
raise AbstractMethodError(self)
def _parse_tr(self, table):
"""Return the list of row elements from the parsed table element.
Parameters
----------
table : node-like
A table element that contains row elements.
Returns
-------
rows : list of node-like
A list row elements of a table, usually <tr> or <th> elements.
"""
raise AbstractMethodError(self)
def _parse_thead(self, table):
"""Return the header of a table.
Parameters
----------
table : node-like
A table element that contains row elements.
Returns
-------
thead : node-like
A <thead>...</thead> element.
"""
raise AbstractMethodError(self)
def _parse_tbody(self, table):
"""Return the body of the table.
Parameters
----------
table : node-like
A table element that contains row elements.
Returns
-------
tbody : node-like
A <tbody>...</tbody> element.
"""
raise AbstractMethodError(self)
def _parse_tfoot(self, table):
"""Return the footer of the table if any.
Parameters
----------
table : node-like
A table element that contains row elements.
Returns
-------
tfoot : node-like
A <tfoot>...</tfoot> element.
"""
raise AbstractMethodError(self)
def _build_doc(self):
"""Return a tree-like object that can be used to iterate over the DOM.
Returns
-------
obj : tree-like
"""
raise AbstractMethodError(self)
def _build_table(self, table):
header = self._parse_raw_thead(table)
body = self._parse_raw_tbody(table)
footer = self._parse_raw_tfoot(table)
return header, body, footer
def _parse_raw_thead(self, table):
thead = self._parse_thead(table)
res = []
if thead:
res = lmap(self._text_getter, self._parse_th(thead[0]))
return np.atleast_1d(
np.array(res).squeeze()) if res and len(res) == 1 else res
def _parse_raw_tfoot(self, table):
tfoot = self._parse_tfoot(table)
res = []
if tfoot:
res = lmap(self._text_getter, self._parse_td(tfoot[0]))
return np.atleast_1d(
np.array(res).squeeze()) if res and len(res) == 1 else res
def _parse_raw_tbody(self, table):
tbody = self._parse_tbody(table)
try:
res = self._parse_tr(tbody[0])
except IndexError:
res = self._parse_tr(table)
return self._parse_raw_data(res)
class _BeautifulSoupHtml5LibFrameParser(_HtmlFrameParser):
"""HTML to DataFrame parser that uses BeautifulSoup under the hood.
See Also
--------
pandas.io.html._HtmlFrameParser
pandas.io.html._LxmlFrameParser
Notes
-----
Documentation strings for this class are in the base class
:class:`pandas.io.html._HtmlFrameParser`.
"""
def __init__(self, *args, **kwargs):
super(_BeautifulSoupHtml5LibFrameParser, self).__init__(*args,
**kwargs)
from bs4 import SoupStrainer
self._strainer = SoupStrainer('table')
def _text_getter(self, obj):
return obj.text
def _parse_td(self, row):
return row.find_all(('td', 'th'))
def _parse_tr(self, element):
return element.find_all('tr')
def _parse_th(self, element):
return element.find_all('th')
def _parse_thead(self, table):
return table.find_all('thead')
def _parse_tbody(self, table):
return table.find_all('tbody')
def _parse_tfoot(self, table):
return table.find_all('tfoot')
def _parse_tables(self, doc, match, attrs):
element_name = self._strainer.name
tables = doc.find_all(element_name, attrs=attrs)
if not tables:
raise ValueError('No tables found')
result = []
unique_tables = set()
for table in tables:
if (table not in unique_tables and
table.find(text=match) is not None):
result.append(table)
unique_tables.add(table)
if not result:
raise ValueError("No tables found matching pattern %r" %
match.pattern)
return result
def _setup_build_doc(self):
raw_text = _read(self.io)
if not raw_text:
raise ValueError('No text parsed from document: %s' % self.io)
return raw_text
def _build_doc(self):
from bs4 import BeautifulSoup
return BeautifulSoup(self._setup_build_doc(), features='html5lib',
from_encoding=self.encoding)
def _build_xpath_expr(attrs):
"""Build an xpath expression to simulate bs4's ability to pass in kwargs to
search for attributes when using the lxml parser.
Parameters
----------
attrs : dict
A dict of HTML attributes. These are NOT checked for validity.
Returns
-------
expr : unicode
An XPath expression that checks for the given HTML attributes.
"""
# give class attribute as class_ because class is a python keyword
if 'class_' in attrs:
attrs['class'] = attrs.pop('class_')
s = [u("@%s=%r") % (k, v) for k, v in iteritems(attrs)]
return u('[%s]') % ' and '.join(s)
_re_namespace = {'re': 'http://exslt.org/regular-expressions'}
_valid_schemes = 'http', 'file', 'ftp'
class _LxmlFrameParser(_HtmlFrameParser):
"""HTML to DataFrame parser that uses lxml under the hood.
Warning
-------
This parser can only handle HTTP, FTP, and FILE urls.
See Also
--------
_HtmlFrameParser
_BeautifulSoupLxmlFrameParser
Notes
-----
Documentation strings for this class are in the base class
:class:`_HtmlFrameParser`.
"""
def __init__(self, *args, **kwargs):
super(_LxmlFrameParser, self).__init__(*args, **kwargs)
def _text_getter(self, obj):
return obj.text_content()
def _parse_td(self, row):
return row.xpath('.//td|.//th')
def _parse_tr(self, table):
expr = './/tr[normalize-space()]'
return table.xpath(expr)
def _parse_tables(self, doc, match, kwargs):
pattern = match.pattern
# 1. check all descendants for the given pattern and only search tables
# 2. go up the tree until we find a table
query = '//table//*[re:test(text(), %r)]/ancestor::table'
xpath_expr = u(query) % pattern
# if any table attributes were given build an xpath expression to
# search for them
if kwargs:
xpath_expr += _build_xpath_expr(kwargs)
tables = doc.xpath(xpath_expr, namespaces=_re_namespace)
if not tables:
raise ValueError("No tables found matching regex %r" % pattern)
return tables
def _build_doc(self):
"""
Raises
------
ValueError
* If a URL that lxml cannot parse is passed.
Exception
* Any other ``Exception`` thrown. For example, trying to parse a
URL that is syntactically correct on a machine with no internet
connection will fail.
See Also
--------
pandas.io.html._HtmlFrameParser._build_doc
"""
from lxml.html import parse, fromstring, HTMLParser
from lxml.etree import XMLSyntaxError
parser = HTMLParser(recover=False, encoding=self.encoding)
try:
# try to parse the input in the simplest way
r = parse(self.io, parser=parser)
try:
r = r.getroot()
except AttributeError:
pass
except (UnicodeDecodeError, IOError):
# if the input is a blob of html goop
if not _is_url(self.io):
r = fromstring(self.io, parser=parser)
try:
r = r.getroot()
except AttributeError:
pass
else:
# not a url
scheme = parse_url(self.io).scheme
if scheme not in _valid_schemes:
# lxml can't parse it
msg = ('%r is not a valid url scheme, valid schemes are '
'%s') % (scheme, _valid_schemes)
raise ValueError(msg)
else:
# something else happened: maybe a faulty connection
raise
else:
if not hasattr(r, 'text_content'):
raise XMLSyntaxError("no text parsed from document", 0, 0, 0)
return r
def _parse_tbody(self, table):
return table.xpath('.//tbody')
def _parse_thead(self, table):
return table.xpath('.//thead')
def _parse_tfoot(self, table):
return table.xpath('.//tfoot')
def _parse_raw_thead(self, table):
expr = './/thead//th'
return [_remove_whitespace(x.text_content()) for x in
table.xpath(expr)]
def _parse_raw_tfoot(self, table):
expr = './/tfoot//th|//tfoot//td'
return [_remove_whitespace(x.text_content()) for x in
table.xpath(expr)]
def _expand_elements(body):
lens = Series(lmap(len, body))
lens_max = lens.max()
not_max = lens[lens != lens_max]
empty = ['']
for ind, length in iteritems(not_max):
body[ind] += empty * (lens_max - length)
def _data_to_frame(**kwargs):
head, body, foot = kwargs.pop('data')
header = kwargs.pop('header')
kwargs['skiprows'] = _get_skiprows(kwargs['skiprows'])
if head:
body = [head] + body
if header is None: # special case when a table has <th> elements
header = 0
if foot:
body += [foot]
# fill out elements of body that are "ragged"
_expand_elements(body)
tp = TextParser(body, header=header, **kwargs)
df = tp.read()
return df
_valid_parsers = {'lxml': _LxmlFrameParser, None: _LxmlFrameParser,
'html5lib': _BeautifulSoupHtml5LibFrameParser,
'bs4': _BeautifulSoupHtml5LibFrameParser}
def _parser_dispatch(flavor):
"""Choose the parser based on the input flavor.
Parameters
----------
flavor : str
The type of parser to use. This must be a valid backend.
Returns
-------
cls : _HtmlFrameParser subclass
The parser class based on the requested input flavor.
Raises
------
ValueError
* If `flavor` is not a valid backend.
ImportError
* If you do not have the requested `flavor`
"""
valid_parsers = list(_valid_parsers.keys())
if flavor not in valid_parsers:
raise ValueError('%r is not a valid flavor, valid flavors are %s' %
(flavor, valid_parsers))
if flavor in ('bs4', 'html5lib'):
if not _HAS_HTML5LIB:
raise ImportError("html5lib not found, please install it")
if not _HAS_BS4:
raise ImportError(
"BeautifulSoup4 (bs4) not found, please install it")
import bs4
if bs4.__version__ == LooseVersion('4.2.0'):
raise ValueError("You're using a version"
" of BeautifulSoup4 (4.2.0) that has been"
" known to cause problems on certain"
" operating systems such as Debian. "
"Please install a version of"
" BeautifulSoup4 != 4.2.0, both earlier"
" and later releases will work.")
else:
if not _HAS_LXML:
raise ImportError("lxml not found, please install it")
return _valid_parsers[flavor]
def _print_as_set(s):
return '{%s}' % ', '.join([pprint_thing(el) for el in s])
def _validate_flavor(flavor):
if flavor is None:
flavor = 'lxml', 'bs4'
elif isinstance(flavor, string_types):
flavor = flavor,
elif isinstance(flavor, collections.Iterable):
if not all(isinstance(flav, string_types) for flav in flavor):
raise TypeError('Object of type %r is not an iterable of strings' %
type(flavor).__name__)
else:
fmt = '{0!r}' if isinstance(flavor, string_types) else '{0}'
fmt += ' is not a valid flavor'
raise ValueError(fmt.format(flavor))
flavor = tuple(flavor)
valid_flavors = set(_valid_parsers)
flavor_set = set(flavor)
if not flavor_set & valid_flavors:
raise ValueError('%s is not a valid set of flavors, valid flavors are '
'%s' % (_print_as_set(flavor_set),
_print_as_set(valid_flavors)))
return flavor
def _parse(flavor, io, match, attrs, encoding, **kwargs):
flavor = _validate_flavor(flavor)
compiled_match = re.compile(match) # you can pass a compiled regex here
# hack around python 3 deleting the exception variable
retained = None
for flav in flavor:
parser = _parser_dispatch(flav)
p = parser(io, compiled_match, attrs, encoding)
try:
tables = p.parse_tables()
except Exception as caught:
retained = caught
else:
break
else:
raise_with_traceback(retained)
ret = []
for table in tables:
try:
ret.append(_data_to_frame(data=table, **kwargs))
except EmptyDataError: # empty table
continue
return ret
[docs]def read_html(io, match='.+', flavor=None, header=None, index_col=None,
skiprows=None, attrs=None, parse_dates=False,
tupleize_cols=False, thousands=',', encoding=None,
decimal='.', converters=None, na_values=None,
keep_default_na=True):
r"""Read HTML tables into a ``list`` of ``DataFrame`` objects.
Parameters
----------
io : str or file-like
A URL, a file-like object, or a raw string containing HTML. Note that
lxml only accepts the http, ftp and file url protocols. If you have a
URL that starts with ``'https'`` you might try removing the ``'s'``.
match : str or compiled regular expression, optional
The set of tables containing text matching this regex or string will be
returned. Unless the HTML is extremely simple you will probably need to
pass a non-empty string here. Defaults to '.+' (match any non-empty
string). The default value will return all tables contained on a page.
This value is converted to a regular expression so that there is
consistent behavior between Beautiful Soup and lxml.
flavor : str or None, container of strings
The parsing engine to use. 'bs4' and 'html5lib' are synonymous with
each other, they are both there for backwards compatibility. The
default of ``None`` tries to use ``lxml`` to parse and if that fails it
falls back on ``bs4`` + ``html5lib``.
header : int or list-like or None, optional
The row (or list of rows for a :class:`~pandas.MultiIndex`) to use to
make the columns headers.
index_col : int or list-like or None, optional
The column (or list of columns) to use to create the index.
skiprows : int or list-like or slice or None, optional
0-based. Number of rows to skip after parsing the column integer. If a
sequence of integers or a slice is given, will skip the rows indexed by
that sequence. Note that a single element sequence means 'skip the nth
row' whereas an integer means 'skip n rows'.
attrs : dict or None, optional
This is a dictionary of attributes that you can pass to use to identify
the table in the HTML. These are not checked for validity before being
passed to lxml or Beautiful Soup. However, these attributes must be
valid HTML table attributes to work correctly. For example, ::
attrs = {'id': 'table'}
is a valid attribute dictionary because the 'id' HTML tag attribute is
a valid HTML attribute for *any* HTML tag as per `this document
<http://www.w3.org/TR/html-markup/global-attributes.html>`__. ::
attrs = {'asdf': 'table'}
is *not* a valid attribute dictionary because 'asdf' is not a valid
HTML attribute even if it is a valid XML attribute. Valid HTML 4.01
table attributes can be found `here
<http://www.w3.org/TR/REC-html40/struct/tables.html#h-11.2>`__. A
working draft of the HTML 5 spec can be found `here
<http://www.w3.org/TR/html-markup/table.html>`__. It contains the
latest information on table attributes for the modern web.
parse_dates : bool, optional
See :func:`~pandas.read_csv` for more details.
tupleize_cols : bool, optional
If ``False`` try to parse multiple header rows into a
:class:`~pandas.MultiIndex`, otherwise return raw tuples. Defaults to
``False``.
thousands : str, optional
Separator to use to parse thousands. Defaults to ``','``.
encoding : str or None, optional
The encoding used to decode the web page. Defaults to ``None``.``None``
preserves the previous encoding behavior, which depends on the
underlying parser library (e.g., the parser library will try to use
the encoding provided by the document).
decimal : str, default '.'
Character to recognize as decimal point (e.g. use ',' for European
data).
.. versionadded:: 0.19.0
converters : dict, default None
Dict of functions for converting values in certain columns. Keys can
either be integers or column labels, values are functions that take one
input argument, the cell (not column) content, and return the
transformed content.
.. versionadded:: 0.19.0
na_values : iterable, default None
Custom NA values
.. versionadded:: 0.19.0
keep_default_na : bool, default True
If na_values are specified and keep_default_na is False the default NaN
values are overridden, otherwise they're appended to
.. versionadded:: 0.19.0
Returns
-------
dfs : list of DataFrames
Notes
-----
Before using this function you should read the :ref:`gotchas about the
HTML parsing libraries <html-gotchas>`.
Expect to do some cleanup after you call this function. For example, you
might need to manually assign column names if the column names are
converted to NaN when you pass the `header=0` argument. We try to assume as
little as possible about the structure of the table and push the
idiosyncrasies of the HTML contained in the table to the user.
This function searches for ``<table>`` elements and only for ``<tr>``
and ``<th>`` rows and ``<td>`` elements within each ``<tr>`` or ``<th>``
element in the table. ``<td>`` stands for "table data".
Similar to :func:`~pandas.read_csv` the `header` argument is applied
**after** `skiprows` is applied.
This function will *always* return a list of :class:`DataFrame` *or*
it will fail, e.g., it will *not* return an empty list.
Examples
--------
See the :ref:`read_html documentation in the IO section of the docs
<io.read_html>` for some examples of reading in HTML tables.
See Also
--------
pandas.read_csv
"""
_importers()
# Type check here. We don't want to parse only to fail because of an
# invalid value of an integer skiprows.
if isinstance(skiprows, numbers.Integral) and skiprows < 0:
raise ValueError('cannot skip rows starting from the end of the '
'data (you passed a negative value)')
_validate_header_arg(header)
return _parse(flavor=flavor, io=io, match=match, header=header,
index_col=index_col, skiprows=skiprows,
parse_dates=parse_dates, tupleize_cols=tupleize_cols,
thousands=thousands, attrs=attrs, encoding=encoding,
decimal=decimal, converters=converters, na_values=na_values,
keep_default_na=keep_default_na)