pandas vs. Polars for Genomic Data Analysis

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-11-11

pandas is the mature, row-indexed DataFrame library that most existing bioinformatics Python code is built on, while Polars is a newer, Rust-backed DataFrame engine that processes tabular data in parallel and can stream data larger than your available RAM. The short rule of thumb is this: if your genomic tables fit comfortably in memory, your pipeline is small, and you depend on libraries that expect pandas objects, stay with pandas. If you are joining or filtering multi-gigabyte variant files, running interval overlaps on millions of regions, or you want code that scales from a laptop to a cluster-sized file without rewriting, use Polars.

This article walks through the real difference between pandas and Polars for genomics work, gives you the commands and code patterns for each, compares them on the criteria that actually decide pipeline choices, and shows where the performance gap is large enough to change how you design a workflow.

Why this choice matters in bioinformatics

Python is one of the dominant languages for bioinformatics because it sits at the intersection of three needs. It reads and writes the text-based formats genomics runs on (FASTA, FASTQ, BED, GTF, VCF). It glues together command-line tools such as samtools, BWA, and GATK from shell scripts or workflow managers like Snakemake and Nextflow. And it provides statistics and plotting through libraries such as SciPy, statsmodels, and matplotlib.

Nearly every one of those analyses passes through the same intermediate structure: a DataFrame, which is a table of rows and columns where each column has a name and a type. A variant call file turned into a table is a DataFrame. A gene annotation file is a DataFrame. A count matrix of genes by samples is a DataFrame. The library you choose to hold that table determines how fast your pipeline runs, how much memory it needs, and how much code you write.

pandas has been the default choice since the late 2000s and is the most widely used Python DataFrame package. It has also been criticized for inefficiencies and scalability problems on large datasets, which is exactly the gap Polars was designed to close with a native backend written in Rust [1]. In genomics, where a single whole-genome variant file can run to tens of gigabytes and an annotation database can hold tens of millions of intervals, that gap is not an abstract benchmark. It decides whether a script finishes in minutes on your workstation or crashes overnight.

Before comparing the two engines, it helps to name the data types they will hold. Most genomic tabular work falls into a few recurring shapes:

The first four of these are where the pandas vs. Polars question is decided in practice.

What pandas is and where it came from

pandas appeared in 2008 and became the standard table library for Python. It builds on NumPy arrays, stores each column as a block of typed values, and attaches a row index and column labels. Three properties shaped how genomics code was written around it.

First, pandas is eager. Every operation you write executes immediately and produces a new object in memory. df[df["QUAL"] > 30] runs the filter the moment the line executes. There is no query planner, so the library cannot reorder your steps to reduce work.

Second, pandas is single-threaded for most operations. It does not parallelize a groupby, a merge, or a filter across your CPU cores. On a 16-core machine, a pandas merge typically uses one core.

Third, the surrounding ecosystem expects pandas. Seaborn expects a pandas DataFrame. scikit-learn historically expected one. Many bioinformatics libraries, including parts of Bioconda-packaged tooling and many journal-published scripts, accept or return pandas objects. If you have ever followed a biopython tutorial, you saw the same pattern: Biopython parses sequences and annotations into its own objects, and then almost every downstream step in the tutorial converts them into a pandas DataFrame for summarizing.

Those properties are fine for tables up to a few hundred thousand rows. A gene count matrix with 30,000 rows and 500 samples loads instantly. A filtered subset of variants for one chromosome does too. When you use pandas in those settings, you are trading some speed for compatibility with essentially every Python library ever written for data.

The problems start with scale. Loading a 20 GB VCF into pandas can consume several times that size in RAM, because pandas materializes the whole table and its index before you filter anything. Grouping a million-row table by gene and applying a custom function runs on one core and can take minutes. Chained operations each allocate a new copy of the data, so memory use compounds. These are the inefficiencies that modern alternatives such as Polars aim to address [1].

What Polars is and how it differs

Polars is a DataFrame library with its computational core written in Rust, exposed to Python through a thin interface. Three design choices distinguish it from pandas, and each one maps directly onto a genomic workload.

Parallel execution by default. Polars splits column operations across all available CPU cores. A groupby on 64 cores runs roughly like a groupby designed for 64 cores. For genomics, where filtering a variant table or aggregating counts per gene is embarrassingly parallel work, this alone often produces an order-of-magnitude speedup with no change to the logic of your code.

Lazy evaluation and a query optimizer. Polars offers two modes. Eager mode behaves like pandas. Lazy mode, entered by starting a chain with pl.scan_csv() instead of pl.read_csv(), builds a logical plan of your operations and optimizes it before executing anything. If you filter a table and then select three columns, the optimizer pushes the filter down to the file reader so it never materializes the discarded rows or columns. For a compressed VCF, this is the difference between decompressing 20 GB and decompressing the 2 percent of records you actually wanted.

Out-of-core and streaming processing. Polars can process datasets larger than physical memory by streaming batches through the query plan rather than loading everything at once. Out-of-core means the data stays on disk and flows through the engine in chunks. Applied to genomics, a join between a 30 GB variant table and an annotation table can run on a 16 GB laptop, slowly, instead of not at all.

Columnar memory based on Apache Arrow. Polars stores data in the Apache Arrow columnar format, the same representation used by Apache Parquet files, the R arrow package, and many database engines. Arrow layout makes vectorized operations and zero-copy handoffs between tools cheap. This matters for genomics because the field is converging on Arrow-based interchange: query engines like Apache DataFusion read it natively, and libraries that accept Arrow tables can hand data to Polars without a conversion copy.

There is one more behavioral difference that trips people up. pandas uses a row index; Polars does not, treating rows as unordered records identified positionally. Code that relies on .loc[] label lookups, index alignment during merges, or index-based slicing needs rethinking. Genomics code that relies on index alignment is often fragile anyway, since the position of a row in a VCF carries no meaning, so most analysts find the loss minor.

The core comparison table

Read the last two rows first if you are skimming. The ecosystem row and the interval row decide most real-world choices in genomics.

Interval operations: where the gap is measured

Genomic analyses constantly ask questions about relationships between features represented as intervals along a one-dimensional coordinate system, such as positions on a chromosome. Does this variant fall inside this exon? How many reads cover this promoter? Which transposon insertions are nearest to which gene? These interval queries are the backbone of annotation, peak calling validation, and variant interpretation, and they are computationally expensive because a naive implementation compares every interval against every other interval.

For years, the Python answer was Bioframe, which performs interval operations on pandas DataFrames. It works, it is well documented, and it is memory-bound. A benchmark study of polars-bio, a Python library built on Polars-compatible DataFrames with its core operations implemented in Rust using the Apache DataFusion query engine and Apache Arrow, compared it against Bioframe on real-world workloads [1].

The real-world comparison ran 10 million query intervals against 1.2 million reference intervals. In that setting, polars-bio ran overlap queries 6.5 times faster, nearest queries 15.5 times faster, count_overlaps queries 38 times faster, and coverage queries 15 times faster than Bioframe [1]. Those four operations are not exotic. They are the daily bread of annotation pipelines.

On equally sized synthetic sets of 10 million intervals against 10 million intervals, where the workload is harder and the data volume dominates, the speedups narrow but remain large: 1.6 times for overlaps, 5.5 times for nearest, 6 times for count_overlaps, and 6 times for coverage [1]. The pattern in these numbers is instructive. The gap is largest for count_overlaps, an operation that benefits most from parallelism, and smallest for raw overlaps at equal size, where the I/O and the combinatorics of the query itself constrain both engines.

The memory result may matter more than the speed results for people working on laptops or shared HPC nodes with limited allocations. In streaming mode, on real and synthetic interval pairs, polars-bio used 90 times and 15 times less memory respectively [1]. If your current pipeline dies with an out-of-memory error at the overlap step, streaming interval operations are the fix, and streaming is a Polars-native capability that pandas simply does not offer.

polars-bio is compatible with both Polars and pandas DataFrame formats, which is worth emphasizing [1]. You do not have to choose one ecosystem for interval work. If the rest of your script is pandas, you can hand a pandas DataFrame to polars-bio for the overlap step and take the result back, capturing the Rust performance inside an otherwise pandas pipeline. That interop pattern is the least disruptive upgrade path for existing code.

Side-by-side code: the same job in both libraries

The most useful way to understand the difference between pandas and Polars for genomics is to write the same task in both. Here is a recurring pattern: load a VCF, keep high-quality biallelic SNVs, and count variants per chromosome.

pandas version:

import pandas as pd

vcf = pd.read_csv(
    "variants.vcf.gz",
    sep="\t",
    comment="#",
    header=None,
    names=["CHROM", "POS", "ID", "REF", "ALT", "QUAL", "FILTER", "INFO"],
    dtype={"CHROM": "category", "REF": "string", "ALT": "string"},
)

snvs = vcf[
    (vcf["QUAL"] > 30)
    & (vcf["REF"].str.len() == 1)
    & (vcf["ALT"].str.len() == 1)
]

counts = snvs.groupby("CHROM").size()

Polars lazy version:

import polars as pl

counts = (
    pl.scan_csv(
        "variants.vcf.gz",
        separator="\t",
        comment_prefix="#",
        has_header=False,
        new_columns=["CHROM", "POS", "ID", "REF", "ALT", "QUAL", "FILTER", "INFO"],
    )
    .filter(
        (pl.col("QUAL") > 30)
        & (pl.col("REF").str.len_chars() == 1)
        & (pl.col("ALT").str.len_chars() == 1)
    )
    .group_by("CHROM")
    .len()
    .collect()
)

Both scripts produce the same table. Three differences are visible in the code itself. The Polars version starts with scan_csv, which keeps the file on disk and builds a plan. The filter is expressed as a single expression tree the optimizer can analyze, so Polars pushes the QUAL and allele-length predicates into the gzip reader and only decompresses matching rows. The collect() call at the end triggers execution, and the groupby runs across all cores.

The pandas version decompresses the entire file into RAM before filtering. On a 10 GB VCF, that means roughly 10 GB of decompressed text parsed into objects before a single filter runs. On a 16 GB workstation alongside a genome browser and an IDE, that alone can end the session.

Here is the equivalent interval operation with polars-bio, using the operation names the library provides for overlap queries against Bioframe-compatible interval DataFrames [1]:

import polars_bio as pb
import polars as pl

variants = pl.read_csv(
    "snvs.bed", separator="\t",
    has_header=False,
    new_columns=["chrom", "start", "end"],
)
exons = pl.read_csv(
    "exons.bed", separator="\t",
    has_header=False,
    new_columns=["chrom", "start", "end", "gene"],
)

overlaps = pb.overlap(variants, exons)

The same call accepts pandas DataFrames instead of Polars ones if that is what your pipeline already holds [1]. Variants of the call, named for the operations they perform, cover nearest, count_overlaps, and coverage, the four query types benchmarked in the study [1].

Worked scenarios from real pipelines

Abstract comparisons hide the decision boundaries. These four scenarios cover the situations where the choice actually changes outcomes.

Scenario 1: QC summaries on a small cohort

You have 48 WGS samples, and you want a per-sample table of variant counts by consequence class. The joined annotation table is 4 million rows. Any engine handles this in seconds. Use pandas. The reason is not speed, it is friction. Every plotting library, every statsmodels call, and every snippet on Biostars assumes pandas. This is a clear case of when to use pandas in genomics: the dataset fits in a fraction of your RAM and the surrounding tools expect pandas objects.

Scenario 2: Annotating 200 million read alignments against a RepeatMasker track

You need count_overlaps between a large alignment table and a repeat annotation. This is precisely the workload where the benchmark gap is largest. The study measured count_overlaps running 38 times faster than Bioframe on a real-world 10 million versus 1.2 million interval workload, and 6 times faster at equal sizes of 10 million versus 10 million [1]. Streaming mode cut memory use dramatically as well [1]. A pandas-based loop here is not slow, it is infeasible on many machines. Use Polars or polars-bio.

Scenario 3: Reproducing a published pipeline

A 2019 paper published its analysis scripts, written in pandas 0.25, with index-based merges and positional lookups. You need to reproduce the figures exactly. Rewriting in Polars introduces two risks: subtle differences in default join behavior and floating-point handling, and no way to validate intermediate outputs against the paper without rerunning both versions. Reproduce first in pandas, in a pinned environment (conda create -n repro python=3.9 pandas=0.25.3). Migrate to Polars afterward if the reproduction will become a production pipeline. When deciding between pandas or Polars for genomics, legacy reproduction is one of the strongest arguments for pandas.

Scenario 4: A variant-review web service

A clinical or research review tool queries subsets of a large VCF on request. Loading the full table into pandas at service startup consumes the host's memory. Polars lazy scanning with pushed-down filters, ideally with the file converted to Parquet and sorted by chromosome and position, lets each request read only the relevant slice from disk. The same design works for cohort-level annotation databases. This is a production-scaling question, and the out-of-core capability decides it.

Scenario 5: Sequence parsing and feature extraction

You are extracting coding sequences from a genome assembly and computing codon usage. The parsing belongs to Biopython, whose SeqIO and SeqFeature objects handle FASTA, GenBank, and GFF correctly, including the quirks of each format. The summarization afterwards, codon frequencies per gene, is a table job. The standard biopython tutorial pattern applies: parse with Biopython, convert records to a list of dictionaries, build a DataFrame, aggregate. For a 30,000-gene annotation, pandas is entirely sufficient and the code is shorter. If the same logic must later process every transcript in a pangenome with hundreds of assemblies, the aggregation step is where a Polars version pays off.

When to use pandas

Given the speed numbers, the case for pandas needs to be made honestly, and it is still strong.

Ecosystem gravity. If your step hands a table to Seaborn, statsmodels, scikit-learn, an R script through rpy2, a notebook widget, or a published package that type-checks for pd.DataFrame, converting to Polars and back adds friction and copies. Most bioinformatics packages on Bioconda that touch tables accept pandas. Polars interoperates through Arrow, but each conversion is a place for dtype surprises.

Teaching and reproducibility. Course materials, tutorials, and the majority of published supplementary code use pandas. If your goal is to be understood by the next person who reads your notebook, pandas remains the lingua franca of python for bioinformatics.

Small data, fast iteration. A 50 MB table loads in under a second in either engine. At that size the performance difference is unmeasurable, and pandas' API, error messages, and documentation are more familiar to more collaborators.

Row-wise operations that resist vectorization. pandas' apply and its index model, whatever their performance faults, make some row-oriented transformations straightforward to write. Polars is faster but its expression system rewards columnar thinking, and some genuinely row-dependent logic takes more effort to express.

A concrete rule: use pandas when the largest table in the pipeline is under roughly 1 GB in memory, when every downstream consumer expects pd.DataFrame, or when the code is a reproduction or a one-off analysis where migration risk outweighs runtime.

When to use Polars

Multi-gigabyte tables. Any table that makes you check free -h before loading is a Polars candidate. Lazy scanning with filter pushdown means you pay only for the rows and columns you keep.

Interval queries at scale. The benchmarked speedups for overlap, nearest, count_overlaps, and coverage queries, and the streaming memory reductions, apply directly to annotation and coverage workloads [1].

Production pipelines. Snakemake and Nextflow rules that run on shared clusters benefit from predictable memory use. A Polars streaming step that uses a fixed memory budget is easier to schedule than a pandas step whose footprint scales with input size.

CPU-heavy aggregations. Cohort-level counting, per-sample QC aggregation, matrix slicing, and groupby operations all parallelize across cores automatically.

Future-proofing for Arrow. The Arrow columnar format is becoming interchange infrastructure in data engineering generally, and genomics tooling is following. Data written by Polars in Parquet reads into R, DuckDB, and cloud warehouses without conversion.

Migrating a pipeline from pandas to Polars

For teams that decide to switch, the migration has a reliable shape. Do not rewrite everything at once.

Step 1: Find the boundary. Profile the pipeline. Identify the two or three steps where runtime or memory peaks. Usually that is one big load, one big merge, or one interval operation.

Step 2: Convert at the boundary. Keep the rest in pandas. Read the heavy input with Polars, do the heavy transformation, and call .to_pandas() at the end. Polars supports this conversion directly through Arrow. Most pipelines get 80 percent of the benefit from converting one hot loop.

Step 3: Replace index-dependent logic. Search for .loc, .iloc, set_index, and index-aligned arithmetic. Replace with explicit column-based joins and with_row_index() where position genuinely matters.

Step 4: Move to lazy where it helps. Convert read_* to scan_* at the entry points and add collect() at the end. Check the optimized query plan with .explain() to confirm that filters were pushed down.

Step 5: Handle interval operations with polars-bio rather than reimplementing interval logic, since the tested implementations are available and compatible with both DataFrame formats [1].

Expect the common friction points: string handling differs (str namespace method names changed across Polars versions), null semantics differ (Polars distinguishes NaN from null), and categorical types map differently. Pin your Polars version in requirements.txt because the API has evolved quickly between minor releases.

Common Mistakes and Limitations

Mistake: Benchmarking with the wrong data shape. The performance ordering between engines depends on interval counts, asymmetry between query and reference sets, and the operation type. The published results span 1.6-fold to 38-fold depending on exactly those variables. Do not assume the largest speedup applies to your workload. Benchmark with a realistic sample of your own data.

Mistake: Keeping the pandas mental model of the index. Code that merges on index, or that assumes the DataFrame preserves a meaningful row order after a groupby, produces silently wrong results after translation to Polars. In Polars, order is not guaranteed unless you ask for a sort.

Mistake: Assuming eager Polars is lazy. Calling pl.read_csv() gives eager execution with no pushdown. If you see full-file load times, check that you used scan_csv and collect().

Mistake: Using pandas for interval joins implemented in pure Python. Nested-loop overlap code in pandas is orders of magnitude slower than any specialized library. If you cannot use polars-bio or Bioframe, at least sort intervals and use a sweep algorithm rather than a cross join.

Limitation: The Rust backend does not make algorithms faster, only implementations. Polars and polars-bio speed up the same algorithms you would write in pandas. A bad join strategy in Polars is still a bad join strategy. The 38-fold count_overlaps result reflects a well-implemented parallel engine against a single-threaded one [1], not a change in computational complexity.

Limitation: API churn. Polars has renamed and reorganized methods across versions. Code from a tutorial written a year ago may fail on current Polars. Pin versions and read the changelog when upgrading.

Limitation: Interop copies. Converting between pandas and Polars copies data through Arrow. Crossing the boundary repeatedly in a loop negates the performance benefit. Convert once, at a boundary, and stay on one side.

Limitation: Biology is not benchmarked by any DataFrame engine. Faster interval joins do not validate coordinates, genome builds, or coordinate systems (zero-based half-open in BED, one-based closed in GTF and VCF). No library fixes an off-by-one error, and mixing a zero-based interval table with a one-based one produces plausible-looking wrong answers. Individual analysis decisions still require domain judgment specific to each project.

Frequently Asked Questions

Is Polars always faster than pandas for genomic data?

No. On small tables, under a few hundred megabytes, the difference is negligible and pandas is often more convenient. The measured advantages of the Polars ecosystem appear on large interval and variant workloads, where published benchmarks show speedups ranging from 1.6-fold to 38-fold depending on the operation and dataset sizes [1].

Can I use Biopython with Polars?

Yes, though not directly. Biopython parses sequences, alignments, and annotations into its own object types and does not produce Polars DataFrames. The standard pattern is to parse with Biopython, extract record fields into a list of dictionaries, and construct a DataFrame from that list in either pandas or Polars for the tabular part of the analysis.

Do I have to rewrite my pandas code to use Polars?

No. Polars DataFrames convert to and from pandas through Arrow, and interval libraries built for Polars also accept pandas DataFrames [1]. The practical migration path is to keep your pipeline in pandas and convert only the performance-critical steps, then move fully to Polars only if the whole-pipeline benefits justify it.

What file formats work with Polars in genomic workflows?

Polars reads CSV, TSV, and compressed variants of both, plus Parquet, JSON, and IPC/Arrow files natively. Since VCF, BED, and GTF are tab-delimited text at their core, they load with scan_csv after skipping header lines. For binary formats such as BAM, CRAM, and BCF, decode first with samtools or a format-specific reader, then hand the tabular output to Polars.

When should I use pandas instead of Polars?

Use pandas when your tables fit comfortably in memory, when downstream libraries expect pandas objects, when you are reproducing published code, or when you are teaching and want the syntax most collaborators already know. The question of when to use pandas is mostly a question of scale and ecosystem, not capability.

What is polars-bio and do I need it?

polars-bio is a Python library for fast, parallel, and out-of-core operations on large genomic interval datasets, built on Rust with the Apache DataFusion query engine and Apache Arrow, and compatible with both Polars and pandas DataFrames [1]. You need it if your pipeline performs overlap, nearest, count_overlaps, or coverage queries on large interval sets, particularly on machines with limited memory. For small annotation jobs, simpler tools are fine.

Sources

1. polars-bio-fast, scalable, and out-of-core operations on large genomic interval datasets.