Dictionary functions

The legacy dictionary interface, kept for backward compatibility

These predate the Arrow interface and support far fewer options. Use write_sheet_arrow unless you specifically need to avoid a DataFrame dependency.

write_sheet

jet.write_sheet(columns, filename, sheet_name=None, charts=None) -> None

columns · dict[str, list] · required

Column name to list of values. Every list must be the same length.

filename · str · required

Output path.

sheet_name · str | None · default: "None"

Tab label.

charts · list[dict] | None

Charts, same shape as the Arrow functions.

jet.write_sheet(
    {
        "Name":   ["Alice", "Bob", "Charlie"],
        "Age":    [25, 30, 35],
        "Salary": [50000.0, 60000.0, 75000.0],
    },
    "output.xlsx",
)

write_sheets

jet.write_sheets(sheets_data, filename, num_threads) -> None

sheets_data · list[dict] · required

Each with name and columns.

filename · str · required

Output path.

num_threads · int · required

Required and positional.

jet.write_sheets(
    [
        {"name": "Sales",    "columns": sales_data},
        {"name": "Expenses", "columns": expense_data},
    ],
    "output.xlsx",
    num_threads=2,
)

What you give up

Formatting

No column_formats, cell_styles, conditional_formats, tables, images or validation. Charts are the one extra supported.

Speed

Values come from Python lists rather than Arrow buffers, so the zero-copy advantage doesn’t apply. The published benchmark puts this path at roughly twice the time of the Arrow one.

Supported types

str, int, float, bool, datetime, and None.

NaN and infinity become empty cells here too, matching the Arrow path. See Data types.