Constant memory mode

constant_memory=True writes each row to disk as it is produced instead of holding the sheet in RAM, which bounds memory use for very large exports. The trade-off is that options needing a second pass over the finished sheet cannot be applied -- those are skipped with a warning rather than silently dropped, and the capability matrix lists exactly which survive.

Constant Memory Mode (Large Files)

For very large files (millions of rows), use constant_memory=True to minimize RAM usage:

import xlsxturbo
import polars as pl

# Generate a large DataFrame
large_df = pl.DataFrame({
    'id': range(1_000_000),
    'value': [i * 1.5 for i in range(1_000_000)]
})

# Use constant_memory mode for large files
xlsxturbo.df_to_xlsx(large_df, "big_file.xlsx", constant_memory=True)

# Also works with dfs_to_xlsx
xlsxturbo.dfs_to_xlsx([
    (large_df, "Data")
], "multi_sheet.xlsx", constant_memory=True)

Note: Constant memory mode emits a RuntimeWarning and disables some features that require random access: - table_style (Excel tables) - freeze_panes - row_heights - autofit - conditional_formats - formula_columns - merged_ranges - hyperlinks - comments - validations - rich_text - images - checkboxes - textboxes - charts - sparklines - cells

Plain column_widths, header_format, and column_formats remain supported.