Polars is the fast Rust-powered DataFrame library that has been replacing pandas in 2026 data pipelines. If your script raises ModuleNotFoundError: No module named ‘polars’, the fix is one pip command, but you should know which build to install for your CPU and how to enable optional features.
Two builds available:polars for modern CPUs (AVX2 / x86-64-v3), or polars-lts-cpu for older CPUs (pre-2013) and ARM emulation. Pick the right one or you will get a SIGILL crash before the import even errors.
Step 1: Install polars
# Modern CPUs (most laptops 2014+):
pip install polars
# Older CPUs or VMs with no AVX2:
pip install polars-lts-cpu
# With all optional features (Excel, Parquet, async, plotting):
pip install 'polars[all]'
# Specific extras:
pip install 'polars[numpy,pandas,pyarrow,xlsx2csv,openpyxl,plot]'
Step 2: Verify the install
import polars as pl
df = pl.DataFrame({
'name': ['Alice', 'Bob', 'Carol'],
'age': [25, 30, 35],
})
print(df)
print(pl.__version__)
Step 3: Common quick conversions
import pandas as pd
import polars as pl
# pandas DataFrame -> polars
pl_df = pl.from_pandas(pandas_df)
# polars -> pandas (uses Arrow zero-copy when possible)
pandas_df = pl_df.to_pandas()
# Read CSV (much faster than pandas on large files)
pl.read_csv('big_file.csv')
# Lazy mode (streaming, lazy query optimization)
pl.scan_csv('big_file.csv').filter(pl.col('age') > 30).collect()
Why this error happens
| Cause | Fix |
|---|---|
| Never installed | pip install polars |
| SIGILL on import (no AVX2) | pip uninstall polars then pip install polars-lts-cpu |
| Wrong venv | Activate the right venv first |
| Conda env mismatch | conda install -c conda-forge polars |
| M1/M2/M3 Mac via Rosetta | Install native arm64 Python then reinstall |
Frequently Asked Questions
Is polars faster than pandas in 2026?
Yes, often 5-30x on group-by, joins, and aggregations on dataframes over 1 million rows. Polars is multithreaded by default, uses Apache Arrow memory, and has a query optimizer. For tiny dataframes under 10k rows, pandas can be comparable or slightly faster due to overhead.
Should I use polars or polars-lts-cpu?
Use polars on any laptop or server from 2014 or later (Haswell+) with AVX2 support. Use polars-lts-cpu on older CPUs, in Docker containers without AVX2 passthrough, or if you get “Illegal instruction” on import. Check with: cat /proc/cpuinfo | grep avx2 (Linux) or sysctl -n machdep.cpu.features (Mac).
Can I use polars with Jupyter Notebook?
Yes. Install in your notebook: !pip install polars. Polars DataFrames render as HTML tables in Jupyter automatically. For larger displays, use pl.Config.set_tbl_rows(100) to show more rows.
Does polars support reading Excel files?
Yes, with extras: pip install ‘polars[xlsx2csv,openpyxl,calamine]’. Then pl.read_excel(‘file.xlsx’). For large Excel files, calamine is the fastest engine.
Can I mix polars and pandas in the same script?
Yes. Convert with pl.from_pandas(df) and pl_df.to_pandas(). Conversions use Arrow zero-copy when dtypes allow, so memory cost is minimal. Common pattern: load + transform with polars, hand off to a pandas-based library (scikit-learn, statsmodels) for the final step.