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Inzynierka_Gwiazdy/machine_learning/Lib/site-packages/pandas/io/xml.py

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Machine learning
2023-09-20 19:46:58 +02:00
"""
:mod:`pandas.io.xml` is a module for reading XML.
"""
from __future__ import annotations
import io
from typing import (
Any,
Callable,
Sequence,
)
from pandas._libs import lib
from pandas._typing import (
TYPE_CHECKING,
CompressionOptions,
ConvertersArg,
DtypeArg,
DtypeBackend,
FilePath,
ParseDatesArg,
ReadBuffer,
StorageOptions,
XMLParsers,
)
from pandas.compat._optional import import_optional_dependency
from pandas.errors import (
AbstractMethodError,
ParserError,
)
from pandas.util._decorators import doc
from pandas.util._validators import check_dtype_backend
from pandas.core.dtypes.common import is_list_like
from pandas.core.shared_docs import _shared_docs
from pandas.io.common import (
file_exists,
get_handle,
infer_compression,
is_fsspec_url,
is_url,
stringify_path,
)
from pandas.io.parsers import TextParser
if TYPE_CHECKING:
from xml.etree.ElementTree import Element
from lxml import etree
from pandas import DataFrame
@doc(
storage_options=_shared_docs["storage_options"],
decompression_options=_shared_docs["decompression_options"] % "path_or_buffer",
)
class _XMLFrameParser:
"""
Internal subclass to parse XML into DataFrames.
Parameters
----------
path_or_buffer : a valid JSON str, path object or file-like object
Any valid string path is acceptable. The string could be a URL. Valid
URL schemes include http, ftp, s3, and file.
xpath : str or regex
The XPath expression to parse required set of nodes for
migration to `Data Frame`. `etree` supports limited XPath.
namespaces : dict
The namespaces defined in XML document (`xmlns:namespace='URI')
as dicts with key being namespace and value the URI.
elems_only : bool
Parse only the child elements at the specified `xpath`.
attrs_only : bool
Parse only the attributes at the specified `xpath`.
names : list
Column names for Data Frame of parsed XML data.
dtype : dict
Data type for data or columns. E.g. {{'a': np.float64,
'b': np.int32, 'c': 'Int64'}}
.. versionadded:: 1.5.0
converters : dict, optional
Dict of functions for converting values in certain columns. Keys can
either be integers or column labels.
.. versionadded:: 1.5.0
parse_dates : bool or list of int or names or list of lists or dict
Converts either index or select columns to datetimes
.. versionadded:: 1.5.0
encoding : str
Encoding of xml object or document.
stylesheet : str or file-like
URL, file, file-like object, or a raw string containing XSLT,
`etree` does not support XSLT but retained for consistency.
iterparse : dict, optional
Dict with row element as key and list of descendant elements
and/or attributes as value to be retrieved in iterparsing of
XML document.
.. versionadded:: 1.5.0
{decompression_options}
.. versionchanged:: 1.4.0 Zstandard support.
{storage_options}
See also
--------
pandas.io.xml._EtreeFrameParser
pandas.io.xml._LxmlFrameParser
Notes
-----
To subclass this class effectively you must override the following methods:`
* :func:`parse_data`
* :func:`_parse_nodes`
* :func:`_iterparse_nodes`
* :func:`_parse_doc`
* :func:`_validate_names`
* :func:`_validate_path`
See each method's respective documentation for details on their
functionality.
"""
def __init__(
self,
path_or_buffer: FilePath | ReadBuffer[bytes] | ReadBuffer[str],
xpath: str,
namespaces: dict[str, str] | None,
elems_only: bool,
attrs_only: bool,
names: Sequence[str] | None,
dtype: DtypeArg | None,
converters: ConvertersArg | None,
parse_dates: ParseDatesArg | None,
encoding: str | None,
stylesheet: FilePath | ReadBuffer[bytes] | ReadBuffer[str] | None,
iterparse: dict[str, list[str]] | None,
compression: CompressionOptions,
storage_options: StorageOptions,
) -> None:
self.path_or_buffer = path_or_buffer
self.xpath = xpath
self.namespaces = namespaces
self.elems_only = elems_only
self.attrs_only = attrs_only
self.names = names
self.dtype = dtype
self.converters = converters
self.parse_dates = parse_dates
self.encoding = encoding
self.stylesheet = stylesheet
self.iterparse = iterparse
self.is_style = None
self.compression = compression
self.storage_options = storage_options
def parse_data(self) -> list[dict[str, str | None]]:
"""
Parse xml data.
This method will call the other internal methods to
validate xpath, names, parse and return specific nodes.
"""
raise AbstractMethodError(self)
def _parse_nodes(self, elems: list[Any]) -> list[dict[str, str | None]]:
"""
Parse xml nodes.
This method will parse the children and attributes of elements
in xpath, conditionally for only elements, only attributes
or both while optionally renaming node names.
Raises
------
ValueError
* If only elements and only attributes are specified.
Notes
-----
Namespace URIs will be removed from return node values. Also,
elements with missing children or attributes compared to siblings
will have optional keys filled with None values.
"""
dicts: list[dict[str, str | None]]
if self.elems_only and self.attrs_only:
raise ValueError("Either element or attributes can be parsed not both.")
if self.elems_only:
if self.names:
dicts = [
{
**(
{el.tag: el.text.strip()}
if el.text and not el.text.isspace()
else {}
),
**{
nm: ch.text.strip() if ch.text else None
for nm, ch in zip(self.names, el.findall("*"))
},
}
for el in elems
]
else:
dicts = [
{
ch.tag: ch.text.strip() if ch.text else None
for ch in el.findall("*")
}
for el in elems
]
elif self.attrs_only:
dicts = [
{k: v.strip() if v else None for k, v in el.attrib.items()}
for el in elems
]
else:
if self.names:
dicts = [
{
**el.attrib,
**(
{el.tag: el.text.strip()}
if el.text and not el.text.isspace()
else {}
),
**{
nm: ch.text.strip() if ch.text else None
for nm, ch in zip(self.names, el.findall("*"))
},
}
for el in elems
]
else:
dicts = [
{
**el.attrib,
**(
{el.tag: el.text.strip()}
if el.text and not el.text.isspace()
else {}
),
**{
ch.tag: ch.text.strip() if ch.text else None
for ch in el.findall("*")
},
}
for el in elems
]
dicts = [
{k.split("}")[1] if "}" in k else k: v for k, v in d.items()} for d in dicts
]
keys = list(dict.fromkeys([k for d in dicts for k in d.keys()]))
dicts = [{k: d[k] if k in d.keys() else None for k in keys} for d in dicts]
if self.names:
dicts = [dict(zip(self.names, d.values())) for d in dicts]
return dicts
def _iterparse_nodes(self, iterparse: Callable) -> list[dict[str, str | None]]:
"""
Iterparse xml nodes.
This method will read in local disk, decompressed XML files for elements
and underlying descendants using iterparse, a method to iterate through
an XML tree without holding entire XML tree in memory.
Raises
------
TypeError
* If `iterparse` is not a dict or its dict value is not list-like.
ParserError
* If `path_or_buffer` is not a physical file on disk or file-like object.
* If no data is returned from selected items in `iterparse`.
Notes
-----
Namespace URIs will be removed from return node values. Also,
elements with missing children or attributes in submitted list
will have optional keys filled with None values.
"""
dicts: list[dict[str, str | None]] = []
row: dict[str, str | None] | None = None
if not isinstance(self.iterparse, dict):
raise TypeError(
f"{type(self.iterparse).__name__} is not a valid type for iterparse"
)
row_node = next(iter(self.iterparse.keys())) if self.iterparse else ""
if not is_list_like(self.iterparse[row_node]):
raise TypeError(
f"{type(self.iterparse[row_node])} is not a valid type "
"for value in iterparse"
)
if (not hasattr(self.path_or_buffer, "read")) and (
not isinstance(self.path_or_buffer, str)
or is_url(self.path_or_buffer)
or is_fsspec_url(self.path_or_buffer)
or self.path_or_buffer.startswith(("<?xml", "<"))
or infer_compression(self.path_or_buffer, "infer") is not None
):
raise ParserError(
"iterparse is designed for large XML files that are fully extracted on "
"local disk and not as compressed files or online sources."
)
iterparse_repeats = len(self.iterparse[row_node]) != len(
set(self.iterparse[row_node])
)
for event, elem in iterparse(self.path_or_buffer, events=("start", "end")):
curr_elem = elem.tag.split("}")[1] if "}" in elem.tag else elem.tag
if event == "start":
if curr_elem == row_node:
row = {}
if row is not None:
if self.names and iterparse_repeats:
for col, nm in zip(self.iterparse[row_node], self.names):
if curr_elem == col:
elem_val = elem.text.strip() if elem.text else None
if elem_val not in row.values() and nm not in row:
row[nm] = elem_val
if col in elem.attrib:
if elem.attrib[col] not in row.values() and nm not in row:
row[nm] = elem.attrib[col]
else:
for col in self.iterparse[row_node]:
if curr_elem == col:
row[col] = elem.text.strip() if elem.text else None
if col in elem.attrib:
row[col] = elem.attrib[col]
if event == "end":
if curr_elem == row_node and row is not None:
dicts.append(row)
row = None
elem.clear()
if hasattr(elem, "getprevious"):
while (
elem.getprevious() is not None and elem.getparent() is not None
):
del elem.getparent()[0]
if dicts == []:
raise ParserError("No result from selected items in iterparse.")
keys = list(dict.fromkeys([k for d in dicts for k in d.keys()]))
dicts = [{k: d[k] if k in d.keys() else None for k in keys} for d in dicts]
if self.names:
dicts = [dict(zip(self.names, d.values())) for d in dicts]
return dicts
def _validate_path(self) -> list[Any]:
"""
Validate xpath.
This method checks for syntax, evaluation, or empty nodes return.
Raises
------
SyntaxError
* If xpah is not supported or issues with namespaces.
ValueError
* If xpah does not return any nodes.
"""
raise AbstractMethodError(self)
def _validate_names(self) -> None:
"""
Validate names.
This method will check if names is a list-like and aligns
with length of parse nodes.
Raises
------
ValueError
* If value is not a list and less then length of nodes.
"""
raise AbstractMethodError(self)
def _parse_doc(
self, raw_doc: FilePath | ReadBuffer[bytes] | ReadBuffer[str]
) -> Element | etree._Element:
"""
Build tree from path_or_buffer.
This method will parse XML object into tree
either from string/bytes or file location.
"""
raise AbstractMethodError(self)
class _EtreeFrameParser(_XMLFrameParser):
"""
Internal class to parse XML into DataFrames with the Python
standard library XML module: `xml.etree.ElementTree`.
"""
def parse_data(self) -> list[dict[str, str | None]]:
from xml.etree.ElementTree import iterparse
if self.stylesheet is not None:
raise ValueError(
"To use stylesheet, you need lxml installed and selected as parser."
)
if self.iterparse is None:
self.xml_doc = self._parse_doc(self.path_or_buffer)
elems = self._validate_path()
self._validate_names()
xml_dicts: list[dict[str, str | None]] = (
self._parse_nodes(elems)
if self.iterparse is None
else self._iterparse_nodes(iterparse)
)
return xml_dicts
def _validate_path(self) -> list[Any]:
"""
Notes
-----
`etree` supports limited XPath. If user attempts a more complex
expression syntax error will raise.
"""
msg = (
"xpath does not return any nodes or attributes. "
"Be sure to specify in `xpath` the parent nodes of "
"children and attributes to parse. "
"If document uses namespaces denoted with "
"xmlns, be sure to define namespaces and "
"use them in xpath."
)
try:
elems = self.xml_doc.findall(self.xpath, namespaces=self.namespaces)
children = [ch for el in elems for ch in el.findall("*")]
attrs = {k: v for el in elems for k, v in el.attrib.items()}
if elems is None:
raise ValueError(msg)
if elems is not None:
if self.elems_only and children == []:
raise ValueError(msg)
if self.attrs_only and attrs == {}:
raise ValueError(msg)
if children == [] and attrs == {}:
raise ValueError(msg)
except (KeyError, SyntaxError):
raise SyntaxError(
"You have used an incorrect or unsupported XPath "
"expression for etree library or you used an "
"undeclared namespace prefix."
)
return elems
def _validate_names(self) -> None:
children: list[Any]
if self.names:
if self.iterparse:
children = self.iterparse[next(iter(self.iterparse))]
else:
parent = self.xml_doc.find(self.xpath, namespaces=self.namespaces)
children = parent.findall("*") if parent else []
if is_list_like(self.names):
if len(self.names) < len(children):
raise ValueError(
"names does not match length of child elements in xpath."
)
else:
raise TypeError(
f"{type(self.names).__name__} is not a valid type for names"
)
def _parse_doc(
self, raw_doc: FilePath | ReadBuffer[bytes] | ReadBuffer[str]
) -> Element:
from xml.etree.ElementTree import (
XMLParser,
parse,
)
handle_data = get_data_from_filepath(
filepath_or_buffer=raw_doc,
encoding=self.encoding,
compression=self.compression,
storage_options=self.storage_options,
)
with preprocess_data(handle_data) as xml_data:
curr_parser = XMLParser(encoding=self.encoding)
document = parse(xml_data, parser=curr_parser)
return document.getroot()
class _LxmlFrameParser(_XMLFrameParser):
"""
Internal class to parse XML into DataFrames with third-party
full-featured XML library, `lxml`, that supports
XPath 1.0 and XSLT 1.0.
"""
def parse_data(self) -> list[dict[str, str | None]]:
"""
Parse xml data.
This method will call the other internal methods to
validate xpath, names, optionally parse and run XSLT,
and parse original or transformed XML and return specific nodes.
"""
from lxml.etree import iterparse
if self.iterparse is None:
self.xml_doc = self._parse_doc(self.path_or_buffer)
if self.stylesheet:
self.xsl_doc = self._parse_doc(self.stylesheet)
self.xml_doc = self._transform_doc()
elems = self._validate_path()
self._validate_names()
xml_dicts: list[dict[str, str | None]] = (
self._parse_nodes(elems)
if self.iterparse is None
else self._iterparse_nodes(iterparse)
)
return xml_dicts
def _validate_path(self) -> list[Any]:
msg = (
"xpath does not return any nodes or attributes. "
"Be sure to specify in `xpath` the parent nodes of "
"children and attributes to parse. "
"If document uses namespaces denoted with "
"xmlns, be sure to define namespaces and "
"use them in xpath."
)
elems = self.xml_doc.xpath(self.xpath, namespaces=self.namespaces)
children = [ch for el in elems for ch in el.xpath("*")]
attrs = {k: v for el in elems for k, v in el.attrib.items()}
if elems == []:
raise ValueError(msg)
if elems != []:
if self.elems_only and children == []:
raise ValueError(msg)
if self.attrs_only and attrs == {}:
raise ValueError(msg)
if children == [] and attrs == {}:
raise ValueError(msg)
return elems
def _validate_names(self) -> None:
children: list[Any]
if self.names:
if self.iterparse:
children = self.iterparse[next(iter(self.iterparse))]
else:
children = self.xml_doc.xpath(
self.xpath + "[1]/*", namespaces=self.namespaces
)
if is_list_like(self.names):
if len(self.names) < len(children):
raise ValueError(
"names does not match length of child elements in xpath."
)
else:
raise TypeError(
f"{type(self.names).__name__} is not a valid type for names"
)
def _parse_doc(
self, raw_doc: FilePath | ReadBuffer[bytes] | ReadBuffer[str]
) -> etree._Element:
from lxml.etree import (
XMLParser,
fromstring,
parse,
)
handle_data = get_data_from_filepath(
filepath_or_buffer=raw_doc,
encoding=self.encoding,
compression=self.compression,
storage_options=self.storage_options,
)
with preprocess_data(handle_data) as xml_data:
curr_parser = XMLParser(encoding=self.encoding)
if isinstance(xml_data, io.StringIO):
if self.encoding is None:
raise TypeError(
"Can not pass encoding None when input is StringIO."
)
document = fromstring(
xml_data.getvalue().encode(self.encoding), parser=curr_parser
)
else:
document = parse(xml_data, parser=curr_parser)
return document
def _transform_doc(self) -> etree._XSLTResultTree:
"""
Transform original tree using stylesheet.
This method will transform original xml using XSLT script into
am ideally flatter xml document for easier parsing and migration
to Data Frame.
"""
from lxml.etree import XSLT
transformer = XSLT(self.xsl_doc)
new_doc = transformer(self.xml_doc)
return new_doc
def get_data_from_filepath(
filepath_or_buffer: FilePath | bytes | ReadBuffer[bytes] | ReadBuffer[str],
encoding: str | None,
compression: CompressionOptions,
storage_options: StorageOptions,
) -> str | bytes | ReadBuffer[bytes] | ReadBuffer[str]:
"""
Extract raw XML data.
The method accepts three input types:
1. filepath (string-like)
2. file-like object (e.g. open file object, StringIO)
3. XML string or bytes
This method turns (1) into (2) to simplify the rest of the processing.
It returns input types (2) and (3) unchanged.
"""
if not isinstance(filepath_or_buffer, bytes):
filepath_or_buffer = stringify_path(filepath_or_buffer)
if (
isinstance(filepath_or_buffer, str)
and not filepath_or_buffer.startswith(("<?xml", "<"))
) and (
not isinstance(filepath_or_buffer, str)
or is_url(filepath_or_buffer)
or is_fsspec_url(filepath_or_buffer)
or file_exists(filepath_or_buffer)
):
with get_handle(
filepath_or_buffer,
"r",
encoding=encoding,
compression=compression,
storage_options=storage_options,
) as handle_obj:
filepath_or_buffer = (
handle_obj.handle.read()
if hasattr(handle_obj.handle, "read")
else handle_obj.handle
)
return filepath_or_buffer
def preprocess_data(data) -> io.StringIO | io.BytesIO:
"""
Convert extracted raw data.
This method will return underlying data of extracted XML content.
The data either has a `read` attribute (e.g. a file object or a
StringIO/BytesIO) or is a string or bytes that is an XML document.
"""
if isinstance(data, str):
data = io.StringIO(data)
elif isinstance(data, bytes):
data = io.BytesIO(data)
return data
def _data_to_frame(data, **kwargs) -> DataFrame:
"""
Convert parsed data to Data Frame.
This method will bind xml dictionary data of keys and values
into named columns of Data Frame using the built-in TextParser
class that build Data Frame and infers specific dtypes.
"""
tags = next(iter(data))
nodes = [list(d.values()) for d in data]
try:
with TextParser(nodes, names=tags, **kwargs) as tp:
return tp.read()
except ParserError:
raise ParserError(
"XML document may be too complex for import. "
"Try to flatten document and use distinct "
"element and attribute names."
)
def _parse(
path_or_buffer: FilePath | ReadBuffer[bytes] | ReadBuffer[str],
xpath: str,
namespaces: dict[str, str] | None,
elems_only: bool,
attrs_only: bool,
names: Sequence[str] | None,
dtype: DtypeArg | None,
converters: ConvertersArg | None,
parse_dates: ParseDatesArg | None,
encoding: str | None,
parser: XMLParsers,
stylesheet: FilePath | ReadBuffer[bytes] | ReadBuffer[str] | None,
iterparse: dict[str, list[str]] | None,
compression: CompressionOptions,
storage_options: StorageOptions,
dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
**kwargs,
) -> DataFrame:
"""
Call internal parsers.
This method will conditionally call internal parsers:
LxmlFrameParser and/or EtreeParser.
Raises
------
ImportError
* If lxml is not installed if selected as parser.
ValueError
* If parser is not lxml or etree.
"""
p: _EtreeFrameParser | _LxmlFrameParser
if parser == "lxml":
lxml = import_optional_dependency("lxml.etree", errors="ignore")
if lxml is not None:
p = _LxmlFrameParser(
path_or_buffer,
xpath,
namespaces,
elems_only,
attrs_only,
names,
dtype,
converters,
parse_dates,
encoding,
stylesheet,
iterparse,
compression,
storage_options,
)
else:
raise ImportError("lxml not found, please install or use the etree parser.")
elif parser == "etree":
p = _EtreeFrameParser(
path_or_buffer,
xpath,
namespaces,
elems_only,
attrs_only,
names,
dtype,
converters,
parse_dates,
encoding,
stylesheet,
iterparse,
compression,
storage_options,
)
else:
raise ValueError("Values for parser can only be lxml or etree.")
data_dicts = p.parse_data()
return _data_to_frame(
data=data_dicts,
dtype=dtype,
converters=converters,
parse_dates=parse_dates,
dtype_backend=dtype_backend,
**kwargs,
)
@doc(
storage_options=_shared_docs["storage_options"],
decompression_options=_shared_docs["decompression_options"] % "path_or_buffer",
)
def read_xml(
path_or_buffer: FilePath | ReadBuffer[bytes] | ReadBuffer[str],
*,
xpath: str = "./*",
namespaces: dict[str, str] | None = None,
elems_only: bool = False,
attrs_only: bool = False,
names: Sequence[str] | None = None,
dtype: DtypeArg | None = None,
converters: ConvertersArg | None = None,
parse_dates: ParseDatesArg | None = None,
# encoding can not be None for lxml and StringIO input
encoding: str | None = "utf-8",
parser: XMLParsers = "lxml",
stylesheet: FilePath | ReadBuffer[bytes] | ReadBuffer[str] | None = None,
iterparse: dict[str, list[str]] | None = None,
compression: CompressionOptions = "infer",
storage_options: StorageOptions = None,
dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
) -> DataFrame:
r"""
Read XML document into a ``DataFrame`` object.
.. versionadded:: 1.3.0
Parameters
----------
path_or_buffer : str, path object, or file-like object
String, path object (implementing ``os.PathLike[str]``), or file-like
object implementing a ``read()`` function. The string can be any valid XML
string or a path. The string can further be a URL. Valid URL schemes
include http, ftp, s3, and file.
xpath : str, optional, default './\*'
The XPath to parse required set of nodes for migration to DataFrame.
XPath should return a collection of elements and not a single
element. Note: The ``etree`` parser supports limited XPath
expressions. For more complex XPath, use ``lxml`` which requires
installation.
namespaces : dict, optional
The namespaces defined in XML document as dicts with key being
namespace prefix and value the URI. There is no need to include all
namespaces in XML, only the ones used in ``xpath`` expression.
Note: if XML document uses default namespace denoted as
`xmlns='<URI>'` without a prefix, you must assign any temporary
namespace prefix such as 'doc' to the URI in order to parse
underlying nodes and/or attributes. For example, ::
namespaces = {{"doc": "https://example.com"}}
elems_only : bool, optional, default False
Parse only the child elements at the specified ``xpath``. By default,
all child elements and non-empty text nodes are returned.
attrs_only : bool, optional, default False
Parse only the attributes at the specified ``xpath``.
By default, all attributes are returned.
names : list-like, optional
Column names for DataFrame of parsed XML data. Use this parameter to
rename original element names and distinguish same named elements and
attributes.
dtype : Type name or dict of column -> type, optional
Data type for data or columns. E.g. {{'a': np.float64, 'b': np.int32,
'c': 'Int64'}}
Use `str` or `object` together with suitable `na_values` settings
to preserve and not interpret dtype.
If converters are specified, they will be applied INSTEAD
of dtype conversion.
.. versionadded:: 1.5.0
converters : dict, optional
Dict of functions for converting values in certain columns. Keys can either
be integers or column labels.
.. versionadded:: 1.5.0
parse_dates : bool or list of int or names or list of lists or dict, default False
Identifiers to parse index or columns to datetime. The behavior is as follows:
* boolean. If True -> try parsing the index.
* list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3
each as a separate date column.
* list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as
a single date column.
* dict, e.g. {{'foo' : [1, 3]}} -> parse columns 1, 3 as date and call
result 'foo'
.. versionadded:: 1.5.0
encoding : str, optional, default 'utf-8'
Encoding of XML document.
parser : {{'lxml','etree'}}, default 'lxml'
Parser module to use for retrieval of data. Only 'lxml' and
'etree' are supported. With 'lxml' more complex XPath searches
and ability to use XSLT stylesheet are supported.
stylesheet : str, path object or file-like object
A URL, file-like object, or a raw string containing an XSLT script.
This stylesheet should flatten complex, deeply nested XML documents
for easier parsing. To use this feature you must have ``lxml`` module
installed and specify 'lxml' as ``parser``. The ``xpath`` must
reference nodes of transformed XML document generated after XSLT
transformation and not the original XML document. Only XSLT 1.0
scripts and not later versions is currently supported.
iterparse : dict, optional
The nodes or attributes to retrieve in iterparsing of XML document
as a dict with key being the name of repeating element and value being
list of elements or attribute names that are descendants of the repeated
element. Note: If this option is used, it will replace ``xpath`` parsing
and unlike xpath, descendants do not need to relate to each other but can
exist any where in document under the repeating element. This memory-
efficient method should be used for very large XML files (500MB, 1GB, or 5GB+).
For example, ::
iterparse = {{"row_element": ["child_elem", "attr", "grandchild_elem"]}}
.. versionadded:: 1.5.0
{decompression_options}
.. versionchanged:: 1.4.0 Zstandard support.
{storage_options}
dtype_backend : {{"numpy_nullable", "pyarrow"}}, defaults to NumPy backed DataFrames
Which dtype_backend to use, e.g. whether a DataFrame should have NumPy
arrays, nullable dtypes are used for all dtypes that have a nullable
implementation when "numpy_nullable" is set, pyarrow is used for all
dtypes if "pyarrow" is set.
The dtype_backends are still experimential.
.. versionadded:: 2.0
Returns
-------
df
A DataFrame.
See Also
--------
read_json : Convert a JSON string to pandas object.
read_html : Read HTML tables into a list of DataFrame objects.
Notes
-----
This method is best designed to import shallow XML documents in
following format which is the ideal fit for the two-dimensions of a
``DataFrame`` (row by column). ::
<root>
<row>
<column1>data</column1>
<column2>data</column2>
<column3>data</column3>
...
</row>
<row>
...
</row>
...
</root>
As a file format, XML documents can be designed any way including
layout of elements and attributes as long as it conforms to W3C
specifications. Therefore, this method is a convenience handler for
a specific flatter design and not all possible XML structures.
However, for more complex XML documents, ``stylesheet`` allows you to
temporarily redesign original document with XSLT (a special purpose
language) for a flatter version for migration to a DataFrame.
This function will *always* return a single :class:`DataFrame` or raise
exceptions due to issues with XML document, ``xpath``, or other
parameters.
See the :ref:`read_xml documentation in the IO section of the docs
<io.read_xml>` for more information in using this method to parse XML
files to DataFrames.
Examples
--------
>>> xml = '''<?xml version='1.0' encoding='utf-8'?>
... <data xmlns="http://example.com">
... <row>
... <shape>square</shape>
... <degrees>360</degrees>
... <sides>4.0</sides>
... </row>
... <row>
... <shape>circle</shape>
... <degrees>360</degrees>
... <sides/>
... </row>
... <row>
... <shape>triangle</shape>
... <degrees>180</degrees>
... <sides>3.0</sides>
... </row>
... </data>'''
>>> df = pd.read_xml(xml)
>>> df
shape degrees sides
0 square 360 4.0
1 circle 360 NaN
2 triangle 180 3.0
>>> xml = '''<?xml version='1.0' encoding='utf-8'?>
... <data>
... <row shape="square" degrees="360" sides="4.0"/>
... <row shape="circle" degrees="360"/>
... <row shape="triangle" degrees="180" sides="3.0"/>
... </data>'''
>>> df = pd.read_xml(xml, xpath=".//row")
>>> df
shape degrees sides
0 square 360 4.0
1 circle 360 NaN
2 triangle 180 3.0
>>> xml = '''<?xml version='1.0' encoding='utf-8'?>
... <doc:data xmlns:doc="https://example.com">
... <doc:row>
... <doc:shape>square</doc:shape>
... <doc:degrees>360</doc:degrees>
... <doc:sides>4.0</doc:sides>
... </doc:row>
... <doc:row>
... <doc:shape>circle</doc:shape>
... <doc:degrees>360</doc:degrees>
... <doc:sides/>
... </doc:row>
... <doc:row>
... <doc:shape>triangle</doc:shape>
... <doc:degrees>180</doc:degrees>
... <doc:sides>3.0</doc:sides>
... </doc:row>
... </doc:data>'''
>>> df = pd.read_xml(xml,
... xpath="//doc:row",
... namespaces={{"doc": "https://example.com"}})
>>> df
shape degrees sides
0 square 360 4.0
1 circle 360 NaN
2 triangle 180 3.0
"""
check_dtype_backend(dtype_backend)
return _parse(
path_or_buffer=path_or_buffer,
xpath=xpath,
namespaces=namespaces,
elems_only=elems_only,
attrs_only=attrs_only,
names=names,
dtype=dtype,
converters=converters,
parse_dates=parse_dates,
encoding=encoding,
parser=parser,
stylesheet=stylesheet,
iterparse=iterparse,
compression=compression,
storage_options=storage_options,
dtype_backend=dtype_backend,
)
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