Convert XLSX to ARFF

Weka and other machine-learning tools commonly use ARFF, while source data is often delivered as an Excel workbook. Conversion is needed when the target application cannot import XLSX directly or requires explicit attribute types, missing-value markers, and dataset metadata.

What the XLSX format is

XLSX is the XML-based workbook format used by Microsoft Excel and supported by LibreOffice Calc, Apple Numbers, and Google Sheets through Excel export. A workbook can contain multiple worksheets, formulas, formatting, merged cells, charts, hidden rows, and typed values, but data-mining software normally reads one rectangular worksheet range.

ARFF has no direct equivalent for workbook layout, charts, formatting, comments, filters, or merged cells. Conversion extracts cell values and creates a tabular dataset; it does not preserve the workbook as an editable spreadsheet.

What the ARFF format is

ARFF, or Attribute-Relation File Format, is Weka's plain-text dataset format. A typical file contains a @relation declaration, one @attribute declaration per column, and a @data section containing records.

Common attribute types are numeric, string, date, and nominal values declared with a finite list such as {yes,no}. A missing value is written as ?. Explicit declarations reduce ambiguity when Weka performs classification, clustering, feature selection, or other analysis.

Convert with Weka

Weka is the most direct desktop option because its ExcelLoader can read XLSX files and the Explorer can save the loaded dataset as ARFF.

1. Install Weka from the official Weka distribution and start Explorer.
2. Choose Preprocess → Open file and select the .xlsx workbook.
3. If the workbook contains several worksheets, open the loader options and select the required sheet. ExcelLoader commonly uses -S 2 for the second sheet; option names and numbering should be confirmed with ExcelLoader -h for the installed Weka version.
4. Check the displayed attribute names, types, and instance count. Remove title rows or change loader settings if the first row is not a header.
5. Choose the Explorer's Save control and save the dataset with an .arff extension.

Weka's Excel support depends on the Weka release and its bundled or installed spreadsheet-reading libraries. If XLSX is not recognized, update Weka or use the Python method below rather than renaming the file.

Convert with Python

Python is preferable for repeatable conversions, worksheet selection, column cleanup, and batch processing. Install pandas, the XLSX reader, and an ARFF writer:

pip install pandas openpyxl liac-arff

This example treats numeric columns as numeric, converts other columns to text, uses small fixed-value columns as nominal attributes, and writes missing cells as ARFF missing values:

import pandas as pd
import arff

df = pd.read_excel('input.xlsx', sheet_name=0)with open('output.arff', 'w', encoding='utf-8') as file:

Replace sheet_name=0 with a worksheet name or zero-based index. Review the generated declarations, especially for columns containing both numbers and text. Rename duplicate or blank headers before writing the file; ARFF attribute names must be usable and unambiguous.

Use an online converter as an intermediate step

Direct XLSX-to-ARFF web conversion is uncommon and usually provides little control over worksheet selection or ARFF type declarations. A reputable service such as CloudConvert can convert XLSX to CSV; download the CSV, open it in Weka with Preprocess → Open file, inspect the inferred types, and save it as ARFF. CSV conversion loses workbook features and still requires type checking, so do not upload confidential workbooks unless the service's data-handling terms are acceptable.

Quality and compatibility checks

• Headers: Remove title rows, blank header cells, and duplicate names. The first row is commonly interpreted as the attribute-name row.
• Formulas: Readers generally obtain a formula's cached result rather than its formula expression. Recalculate and save the workbook in Excel or LibreOffice before conversion if cached results may be stale.
• Dates: Excel serial dates and locale-specific date strings can be misinterpreted. Normalize dates to one format, or declare an ARFF date attribute with an explicit pattern such as date 'yyyy-MM-dd'.
• Nominal values: A nominal declaration must cover every nonmissing value in that column. Use nominal types for small, fixed categories and string for free text or high-cardinality columns.
• Missing cells: Empty cells should become ?. Do not replace them with numeric zero unless zero represents the actual measurement.
• Multiple worksheets: ARFF describes one relation, so each worksheet normally requires a separate ARFF file. Charts, formatting, merged cells, filters, and workbook metadata are discarded.
• Encoding: Write and inspect the result as UTF-8 when names or values contain accented, non-Latin, or special characters.