Quickstart

Jetxl reads Arrow memory. Polars, Pandas and PyArrow can all hand it Arrow, so the only difference between them is how you get there.

Write a sheet

Polars
Pandas
PyArrow
Plain dicts

import polars as pl
import jetxl as jet

df = pl.DataFrame({
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "Salary": [50000.0, 60000.0, 75000.0],
})

jet.write_sheet_arrow(df.to_arrow(), "output.xlsx")

import pandas as pd
import jetxl as jet

df = pd.DataFrame({
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "Salary": [50000.0, 60000.0, 75000.0],
})

jet.write_sheet_arrow(df.to_arrow(), "output.xlsx")

import pyarrow as pa
import jetxl as jet

table = pa.table({
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "Salary": [50000.0, 60000.0, 75000.0],
})

jet.write_sheet_arrow(table, "output.xlsx")

import jetxl as jet

data = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "Salary": [50000.0, 60000.0, 75000.0],
}

jet.write_sheet(data, "output.xlsx")

You don’t need a DataFrame library for this path, but it’s slower and supports fewer options. See the dictionary functions.

.to_arrow() on a Polars or Pandas frame is a conversion, not a copy of the data. It’s what lets Jetxl read your values in place. Polars DataFrames also work when passed directly, without the call.

Make it presentable

The defaults produce a plain grid. Four arguments turn it into something you’d send to someone:

jet.write_sheet_arrow(
    df.to_arrow(),
    "output.xlsx",
    sheet_name="Employees",
    styled_headers=True,   # bolds the header row
    freeze_rows=1,         # header stays visible when scrolling
    auto_filter=True,      # filter dropdowns on each column
    auto_width=True,       # size columns to their contents
)

If you only ever remember four options, remember these. They account for most of the difference between a raw export and a readable one.

Format the numbers

Raw floats read poorly. Name a format per column:

jet.write_sheet_arrow(
    df.to_arrow(),
    "output.xlsx",
    styled_headers=True,
    column_formats={
        "Salary": "currency",   # $50,000.00
        "Age": "integer",       # 25
    },
)

There are sixteen built-in names, and any Excel format code also works. See Number formats.

Where to go next

Conventions

Colors, indexing, and error behavior.

Formatting basics

Headers, widths, freezing, filters.

Charts

Six chart types with full styling.

Recipes

Complete scripts for whole reports.

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Guides

Recipes

Reference

About

Write a sheet

Make it presentable

Format the numbers

Where to go next

Conventions

Formatting basics

Charts

Recipes

​Write a sheet

​Make it presentable

​Format the numbers

​Where to go next

Conventions

Formatting basics

Charts

Recipes

Write a sheet

Make it presentable

Format the numbers

Where to go next