xml2arrow 0.15.0

Efficiently convert XML data to Apache Arrow format.

xml2arrow-python

A Python package for efficiently converting XML files to Apache Arrow tables using a YAML configuration. Powered by the xml2arrow Rust crate for high performance.

Features

• 🚀 High-performance XML parsing via the xml2arrow Rust crate
• 📊 Declarative mapping from XML structures to Arrow tables using a YAML config file
• 🔄 Nested structure support with parent–child index columns linking related tables
• 🎯 Type conversion including automatic scale and offset transforms for float fields
• 💡 Attribute and element extraction using @-prefixed path segments for attributes
• ⏹️ Early termination via stop_at_paths for efficiently reading only part of a file
• 🐍 Flexible input — accepts file paths, path-like objects, or any file-like object

Installation

pip install xml2arrow

Usage

1. Write a configuration file

The YAML configuration defines which parts of the XML document become tables and how their fields are typed. The full schema is:

parser_options:
  trim_text: <true|false>      # Trim whitespace from text nodes (default: false)
  stop_at_paths: [<xml_path>]  # Stop parsing after these closing tags (optional,
                               # useful for reading only a file header)
tables:
  - name: <table_name>         # Name of the resulting PyArrow RecordBatch
    xml_path: <xml_path>       # Path to the element whose children are rows.
                               # Use "/" to treat the whole document as one row.
    levels: [<level>, ...]     # Parent-link index columns — see "Nested tables"
    fields:
      - name: <field_name>     # Column name
        xml_path: <field_path> # Path to the element or attribute holding the value.
                               # Prefix the last segment with @ for attributes
                               # (e.g. /library/book/@id)
        data_type: <type>      # Arrow data type — see supported types below
        nullable: <true|false> # Whether the field can be null (default: false)
                               # If false, missing/empty tags cause a ParseError.
        scale: <number>        # Multiply float values by this factor (optional)
        offset: <number>       # Add this value to float values after scaling (optional)
                               # value = (value * scale) + offset

Supported data types: Boolean, Int8, UInt8, Int16, UInt16, Int32, UInt32, Int64, UInt64, Float32, Float64, Utf8

Boolean fields accept (case-insensitively): true, false, 1, 0, yes, no, on, off, t, f, y, n.

2. Nested tables and levels

When your XML has a parent–child relationship between tables, levels creates the index columns that link child rows back to their parent rows. Each string in the list names an element at a nesting boundary above the row element, and generates a zero-based uint32 column named <level> in the output.

Note: If a table is defined purely to establish a structural hierarchy (i.e., it has levels defined but an empty fields list), it acts only as a boundary and will be excluded from the final output map.

For example, given stations that each have multiple measurements:

<report>
  <monitoring_stations>
    <monitoring_station>   <!-- boundary → produces <station> index -->
      <measurements>
        <measurement>      <!-- row element for the measurements table -->
          ...
        </measurement>
      </measurements>
    </monitoring_station>
  </monitoring_stations>
</report>

- name: measurements
  xml_path: /report/monitoring_stations/monitoring_station/measurements
  levels: [station, measurement]
  fields: [...]

This produces a <station> column (which parent station each measurement belongs to) and a <measurement> column (the per-station row counter), letting you join the measurements table back to the stations table on <station>.

3. Parse the XML

import polars as pl
from xml2arrow import XmlToArrowParser

parser = XmlToArrowParser("config.yaml")
record_batches = parser.parse("data.xml")  # also accepts pathlib.Path or any file-like object

# Access a table by name
batch = record_batches["measurements"]     # pyarrow.RecordBatch

# Convert to a pandas DataFrame
df = batch.to_pandas()

# Convert to a Polars DataFrame
df = pl.from_arrow(batch)

# Convert to a PyArrow Table
import pyarrow as pa
table = pa.Table.from_batches([batch])

parse() returns a dict[str, pyarrow.RecordBatch] whose keys are the table names defined in your config. Because the values are standard PyArrow RecordBatch objects they integrate directly with pandas, Polars, DuckDB, and any other tool in the Arrow ecosystem.

Example

This example extracts meteorological station data from a nested XML document into three linked Arrow tables.

XML data (stations.xml)

<report>
  <header>
    <title>Meteorological Station Data</title>
    <created_by>National Weather Service</created_by>
    <creation_time>2024-12-30T13:59:15Z</creation_time>
  </header>
  <monitoring_stations>
    <monitoring_station id="MS001">
      <location>
        <latitude>-61.39110459389277</latitude>
        <longitude>48.08662749089257</longitude>
        <elevation>547.1050788360882</elevation>
      </location>
      <measurements>
        <measurement>
          <timestamp>2024-12-30T12:39:15Z</timestamp>
          <temperature unit="C">35.486545480326114</temperature>
          <pressure unit="hPa">950.439973486407</pressure>
          <humidity unit="%">49.77716576844861</humidity>
        </measurement>
        <measurement>
          <timestamp>2024-12-30T12:44:15Z</timestamp>
          <temperature unit="C">29.095166644493865</temperature>
          <pressure unit="hPa">1049.3215015450517</pressure>
          <humidity unit="%">32.5687148391251</humidity>
        </measurement>
      </measurements>
      <metadata>
        <description>Located in the Arctic Tundra area, used for Scientific Research.</description>
        <install_date>2024-03-31</install_date>
      </metadata>
    </monitoring_station>
    <monitoring_station id="MS002">
      <location>
        <latitude>11.891496388319311</latitude>
        <longitude>135.09336983543022</longitude>
        <elevation>174.53349357280004</elevation>
      </location>
      <measurements>
        <measurement>
          <timestamp>2024-12-30T12:39:15Z</timestamp>
          <temperature unit="C">24.791842953632283</temperature>
          <pressure unit="hPa">989.4054287187706</pressure>
          <humidity unit="%">57.70794884397625</humidity>
        </measurement>
        <measurement>
          <timestamp>2024-12-30T12:44:15Z</timestamp>
          <temperature unit="C">15.153690541845911</temperature>
          <pressure unit="hPa">1001.413052919951</pressure>
          <humidity unit="%">45.45094598045342</humidity>
        </measurement>
        <measurement>
          <timestamp>2024-12-30T12:49:15Z</timestamp>
          <temperature unit="C">-4.022555715139081</temperature>
          <pressure unit="hPa">1000.5225751769922</pressure>
          <humidity unit="%">70.40117458947834</humidity>
        </measurement>
        <measurement>
          <timestamp>2024-12-30T12:54:15Z</timestamp>
          <temperature unit="C">25.852920542644185</temperature>
          <pressure unit="hPa">953.762785698162</pressure>
          <humidity unit="%">42.62088244545566</humidity>
        </measurement>
      </measurements>
      <metadata>
        <description>Located in the Desert area, used for Weather Forecasting.</description>
        <install_date>2024-01-17</install_date>
      </metadata>
    </monitoring_station>
  </monitoring_stations>
</report>

Configuration (stations.yaml)

tables:
  - name: report
    xml_path: /
    levels: []
    fields:
      - name: title
        xml_path: /report/header/title
        data_type: Utf8
      - name: created_by
        xml_path: /report/header/created_by
        data_type: Utf8
      - name: creation_time
        xml_path: /report/header/creation_time
        data_type: Utf8

  - name: stations
    xml_path: /report/monitoring_stations
    levels:
      - station
    fields:
      - name: id
        xml_path: /report/monitoring_stations/monitoring_station/@id
        data_type: Utf8
      - name: latitude
        xml_path: /report/monitoring_stations/monitoring_station/location/latitude
        data_type: Float32
      - name: longitude
        xml_path: /report/monitoring_stations/monitoring_station/location/longitude
        data_type: Float32
      - name: elevation
        xml_path: /report/monitoring_stations/monitoring_station/location/elevation
        data_type: Float32
      - name: description
        xml_path: /report/monitoring_stations/monitoring_station/metadata/description
        data_type: Utf8
      - name: install_date
        xml_path: /report/monitoring_stations/monitoring_station/metadata/install_date
        data_type: Utf8

  - name: measurements
    xml_path: /report/monitoring_stations/monitoring_station/measurements
    levels:
      - station      # Links each measurement back to its parent station
      - measurement
    fields:
      - name: timestamp
        xml_path: /report/monitoring_stations/monitoring_station/measurements/measurement/timestamp
        data_type: Utf8
      - name: temperature
        xml_path: /report/monitoring_stations/monitoring_station/measurements/measurement/temperature
        data_type: Float64
        offset: 273.15   # Convert °C → K
      - name: pressure
        xml_path: /report/monitoring_stations/monitoring_station/measurements/measurement/pressure
        data_type: Float64
        scale: 100.0     # Convert hPa → Pa
      - name: humidity
        xml_path: /report/monitoring_stations/monitoring_station/measurements/measurement/humidity
        data_type: Float64

Parsing and using the output

import polars as pl
from xml2arrow import XmlToArrowParser

parser = XmlToArrowParser("stations.yaml")
record_batches = parser.parse("stations.xml")

stations_df = pl.from_arrow(record_batches["stations"])
measurements_df = pl.from_arrow(record_batches["measurements"])

# Join measurements back to their parent station using the <station> index
merged = measurements_df.join(
    stations_df.select(["<station>", "id"]),
    on="<station>",
)
print(merged.select(["id", "timestamp", "temperature", "pressure"]))

Output

- report:
 ┌─────────────────────────────┬──────────────────────────┬──────────────────────┐
 │ title                       ┆ created_by               ┆ creation_time        │
 │ ---                         ┆ ---                      ┆ ---                  │
 │ str                         ┆ str                      ┆ str                  │
 ╞═════════════════════════════╪══════════════════════════╪══════════════════════╡
 │ Meteorological Station Data ┆ National Weather Service ┆ 2024-12-30T13:59:15Z │
 └─────────────────────────────┴──────────────────────────┴──────────────────────┘

- stations:
 ┌───────────┬───────┬────────────┬────────────┬────────────┬────────────────────────┬──────────────┐
 │ <station> ┆ id    ┆ latitude   ┆ longitude  ┆ elevation  ┆ description            ┆ install_date │
 │ ---       ┆ ---   ┆ ---        ┆ ---        ┆ ---        ┆ ---                    ┆ ---          │
 │ u32       ┆ str   ┆ f32        ┆ f32        ┆ f32        ┆ str                    ┆ str          │
 ╞═══════════╪═══════╪════════════╪════════════╪════════════╪════════════════════════╪══════════════╡
 │ 0         ┆ MS001 ┆ -61.391106 ┆ 48.086628  ┆ 547.105103 ┆ Located in the Arctic  ┆ 2024-03-31   │
 │           ┆       ┆            ┆            ┆            ┆ Tundra a…              ┆              │
 │ 1         ┆ MS002 ┆ 11.891497  ┆ 135.093369 ┆ 174.533493 ┆ Located in the Desert  ┆ 2024-01-17   │
 │           ┆       ┆            ┆            ┆            ┆ area, us…              ┆              │
 └───────────┴───────┴────────────┴────────────┴────────────┴────────────────────────┴──────────────┘

- measurements:
 ┌───────────┬───────────────┬──────────────────────┬─────────────┬───────────────┬───────────┐
 │ <station> ┆ <measurement> ┆ timestamp            ┆ temperature ┆ pressure      ┆ humidity  │
 │ ---       ┆ ---           ┆ ---                  ┆ ---         ┆ ---           ┆ ---       │
 │ u32       ┆ u32           ┆ str                  ┆ f64         ┆ f64           ┆ f64       │
 ╞═══════════╪═══════════════╪══════════════════════╪═════════════╪═══════════════╪═══════════╡
 │ 0         ┆ 0             ┆ 2024-12-30T12:39:15Z ┆ 308.636545  ┆ 95043.997349  ┆ 49.777166 │
 │ 0         ┆ 1             ┆ 2024-12-30T12:44:15Z ┆ 302.245167  ┆ 104932.150155 ┆ 32.568715 │
 │ 1         ┆ 0             ┆ 2024-12-30T12:39:15Z ┆ 297.941843  ┆ 98940.542872  ┆ 57.707949 │
 │ 1         ┆ 1             ┆ 2024-12-30T12:44:15Z ┆ 288.303691  ┆ 100141.305292 ┆ 45.450946 │
 │ 1         ┆ 2             ┆ 2024-12-30T12:49:15Z ┆ 269.127444  ┆ 100052.257518 ┆ 70.401175 │
 │ 1         ┆ 3             ┆ 2024-12-30T12:54:15Z ┆ 299.002921  ┆ 95376.27857   ┆ 42.620882 │
 └───────────┴───────────────┴──────────────────────┴─────────────┴───────────────┴───────────┘

The <station> index in the measurements table links each measurement to its parent station by row position, enabling a join on stations.<station> = measurements.<station>.