Working with ODS in Python

Updated May 16, 2026

OpenDocument Spreadsheet (ODS) file handling with sheet selection, advanced header processing, and high-performance data operations.

Installation

pip install fairspec

Getting Started

ODS format is handled by the XLSX plugin, which provides:

load_xlsx_table - Load ODS files into tables
save_xlsx_table - Save tables to ODS files
XlsxPlugin - Plugin for framework integration

For example:

from fairspec import load_xlsx_table, Resource

table = load_xlsx_table(Resource(data="table.ods"))
# the column types will be automatically inferred

Basic Usage

Loading ODS Files

from fairspec import load_xlsx_table, Resource
from fairspec_metadata import XlsxFileDialect

# Load a simple ODS file
table = load_xlsx_table(Resource(data="data.ods"))

# Load with custom format (specify sheet)
table = load_xlsx_table(Resource(
    data="data.ods",
    fileDialect=XlsxFileDialect(format="ods", sheetName="Sheet2"),
))

# Load multiple ODS files (concatenated)
table = load_xlsx_table(Resource(data=["part1.ods", "part2.ods", "part3.ods"]))

Saving ODS Files

from fairspec import save_xlsx_table
from fairspec_metadata import XlsxFileDialect

# Save with default options
save_xlsx_table(table, path="output.ods", fileDialect=XlsxFileDialect(format="ods"))

# Save with custom sheet name
save_xlsx_table(table, path="output.ods", fileDialect=XlsxFileDialect(
    format="ods",
    sheetName="Data",
))

Advanced Features

Sheet Selection

from fairspec import load_xlsx_table, Resource
from fairspec_metadata import XlsxFileDialect

# Select by sheet number (1-indexed)
table = load_xlsx_table(Resource(
    data="workbook.ods",
    fileDialect=XlsxFileDialect(format="ods", sheetNumber=2),
))

# Select by sheet name
table = load_xlsx_table(Resource(
    data="workbook.ods",
    fileDialect=XlsxFileDialect(format="ods", sheetName="Sales Data"),
))

Multi-Header Row Processing

from fairspec import load_xlsx_table, Resource
from fairspec_metadata import XlsxFileDialect

# ODS with multiple header rows
table = load_xlsx_table(Resource(
    data="multi-header.ods",
    fileDialect=XlsxFileDialect(
        format="ods",
        headerRows=[1, 2],
        headerJoin="_",
    ),
))
# Resulting columns: ["Year_Quarter", "2023_Q1", "2023_Q2", "2024_Q1", "2024_Q2"]

Comment Row Handling

from fairspec import load_xlsx_table, Resource
from fairspec_metadata import XlsxFileDialect

# Skip specific comment rows
table = load_xlsx_table(Resource(
    data="with-comments.ods",
    fileDialect=XlsxFileDialect(
        format="ods",
        commentRows=[1, 2],
        headerRows=[3],
    ),
))

# Skip rows with comment prefix
table = load_xlsx_table(Resource(
    data="data.ods",
    fileDialect=XlsxFileDialect(
        format="ods",
        commentPrefix="#",
        headerRows=[1],
    ),
))

Remote File Loading

from fairspec import load_xlsx_table, Resource

# Load from URL
table = load_xlsx_table(Resource(data="https://example.com/data.ods"))

# Load multiple remote files
table = load_xlsx_table(Resource(data=[
    "https://api.example.com/data-2023.ods",
    "https://api.example.com/data-2024.ods",
]))

Column Selection

from fairspec import load_xlsx_table, Resource
from fairspec_metadata import XlsxFileDialect

# Select specific columns
table = load_xlsx_table(Resource(
    data="data.ods",
    fileDialect=XlsxFileDialect(format="ods", columnNames=["name", "age", "city"]),
))

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