pandas.read_xml — pandas 2.2.3 documentation (original) (raw)
pandas.read_xml(path_or_buffer, *, xpath='./*', namespaces=None, elems_only=False, attrs_only=False, names=None, dtype=None, converters=None, parse_dates=None, encoding='utf-8', parser='lxml', stylesheet=None, iterparse=None, compression='infer', storage_options=None, dtype_backend=<no_default>)[source]#
Read XML document into a DataFrame object.
Added in version 1.3.0.
Parameters:
path_or_bufferstr, 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.
Deprecated since version 2.1.0: Passing xml literal strings is deprecated. Wrap literal xml input in io.StringIO
or io.BytesIO
instead.
xpathstr, optional, default ‘./*’
The XPath
to parse required set of nodes for migration toDataFrame.``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.
namespacesdict, 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 asxmlns=’’ 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_onlybool, 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_onlybool, optional, default False
Parse only the attributes at the specified xpath
. By default, all attributes are returned.
nameslist-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.
dtypeType 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.
Added in version 1.5.0.
convertersdict, optional
Dict of functions for converting values in certain columns. Keys can either be integers or column labels.
Added in version 1.5.0.
parse_datesbool 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’
Added in version 1.5.0.
encodingstr, 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.
stylesheetstr, 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.
iterparsedict, 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"]}
Added in version 1.5.0.
compressionstr or dict, default ‘infer’
For on-the-fly decompression of on-disk data. If ‘infer’ and ‘path_or_buffer’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’ (otherwise no compression). If using ‘zip’ or ‘tar’, the ZIP file must contain only one data file to be read in. Set to None
for no decompression. Can also be a dict with key 'method'
set to one of {'zip'
, 'gzip'
, 'bz2'
, 'zstd'
, 'xz'
, 'tar'
} and other key-value pairs are forwarded tozipfile.ZipFile
, gzip.GzipFile
,bz2.BZ2File
, zstandard.ZstdDecompressor
, lzma.LZMAFile
ortarfile.TarFile
, respectively. As an example, the following could be passed for Zstandard decompression using a custom compression dictionary:compression={'method': 'zstd', 'dict_data': my_compression_dict}
.
Added in version 1.5.0: Added support for .tar files.
Changed in version 1.4.0: Zstandard support.
storage_optionsdict, optional
Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request
as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open
. Please see fsspec
and urllib
for more details, and for more examples on storage options refer here.
dtype_backend{‘numpy_nullable’, ‘pyarrow’}, default ‘numpy_nullable’
Back-end data type applied to the resultant DataFrame(still experimental). Behaviour is as follows:
"numpy_nullable"
: returns nullable-dtype-backed DataFrame(default)."pyarrow"
: returns pyarrow-backed nullable ArrowDtypeDataFrame.
Added in version 2.0.
Returns:
df
A DataFrame.
See also
Convert a JSON string to pandas object.
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 aDataFrame
(row by column).
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 DataFrame or raise exceptions due to issues with XML document, xpath
, or other parameters.
See the read_xml documentation in the IO section of the docs for more information in using this method to parse XML files to DataFrames.
Examples
from io import StringIO xml = ''' ... ... ... square ... 360 ... 4.0 ... ... ... circle ... 360 ... ... ... ... triangle ... 180 ... 3.0 ... ... '''
df = pd.read_xml(StringIO(xml)) df shape degrees sides 0 square 360 4.0 1 circle 360 NaN 2 triangle 180 3.0
xml = ''' ... ... ... ... ... '''
df = pd.read_xml(StringIO(xml), xpath=".//row") df shape degrees sides 0 square 360 4.0 1 circle 360 NaN 2 triangle 180 3.0
xml = ''' ... <doc:data xmlns:doc="" title="undefined" rel="noopener noreferrer">https://example.com"> ... doc:row ... doc:shapesquare ... doc:degrees360 ... doc:sides4.0 ... ... doc:row ... doc:shapecircle ... doc:degrees360 ... doc:sides/ ... ... doc:row ... doc:shapetriangle ... doc:degrees180 ... doc:sides3.0 ... ... '''
df = pd.read_xml(StringIO(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
xml_data = ''' ... ... ... 0 ... 1 ... 2.5 ... True ... a ... 2019-12-31 00:00:00 ... ... ... 1 ... 4.5 ... False ... b ... 2019-12-31 00:00:00 ... ... ... '''
df = pd.read_xml(StringIO(xml_data), ... dtype_backend="numpy_nullable", ... parse_dates=["e"]) df index a b c d e 0 0 1 2.5 True a 2019-12-31 1 1 4.5 False b 2019-12-31