Polars and Excel: The Fast Way to Read .xlsx Files (2026 Guide)

While Parquet is the fastest format, the business world runs on Excel. Polars read Excel Via a read_excel function to load these files directly into a high-performance DataFrame.

Step 1: Installation

Polars needs an “engine” to read .xlsx files. You can use openpyxl or xlsx2csv.

pip install polars openpyxl

Step 2: Reading the Excel File

The pl.read_excel() function works just like pd.read_excel.

import polars as pl

# 1. Read the file
# Polars automatically uses the 'openpyxl' engine
df = pl.read_excel("my_report.xlsx")

print(df.head())

Advanced Options

Excel files are tricky. They often have multiple sheets or headers in the wrong place.

1. Specifying a Sheet

• sheet_name="Sheet2" (by name)
• sheet_id=1 (by index, where 1 is the first sheet)

# Read the second sheet in the workbook
df_sales = pl.read_excel("my_report.xlsx", sheet_id=2)

2. Skipping Rows (Bad Headers)

If your real header is on row 3, you can use read_csv‘s “skip” logic inside the read_excel_options.

# This tells the 'xlsx2csv' engine to skip the first 2 rows
df_clean = pl.read_excel(
    "my_report.xlsx",
    read_excel_options={"skip_rows": 2}
)

This gives you the power of Polars while still being able to work with legacy Excel files.