Polars vs Pandas: Which DataFrame Library Should You Use in 2026?

Name

Soren Atelier

Updated on 2/12/2026

If you work with data in Python, you have almost certainly hit a wall with Pandas. You load a 2 GB CSV file and watch your machine grind to a halt. A GroupBy aggregation on 50 million rows takes minutes when you expected seconds. You try to parallelize your pipeline and discover that Pandas is fundamentally single-threaded. The library that taught the world tabular data analysis in Python was never designed for the scale of data that modern teams handle daily.

This is not a minor inconvenience. Slow data pipelines block entire teams. Data scientists wait for notebooks to finish instead of iterating on analysis. Engineers build workarounds -- chunking data, spinning up Spark clusters for jobs that should run on a laptop, or rewriting Python in SQL. The cost is measured in hours of lost productivity, delayed insights, and infrastructure bills that grow faster than the data itself.

Polars has emerged as the strongest alternative. Built from the ground up in Rust with Apache Arrow as its memory backbone, Polars routinely processes data 5 to 30 times faster than Pandas while using a fraction of the memory. It supports lazy evaluation, automatic multi-threaded execution, and a query optimizer that rewrites your code to run efficiently. But Pandas is not standing still -- version 2.x brought Arrow-backed dtypes and significant performance improvements. The ecosystem around Pandas remains unmatched.

This article provides a direct, practical comparison of Polars and Pandas in 2026. It covers syntax differences, performance benchmarks, memory usage, ecosystem compatibility, and gives clear guidance on when to use each library.

What Is Pandas?

Pandas is the foundational data manipulation library for Python. Released in 2008 by Wes McKinney, it introduced the DataFrame abstraction to Python and became the standard tool for data cleaning, transformation, and analysis. As of 2026, Pandas has over 45,000 GitHub stars and is installed as a dependency in virtually every data science project.

Key characteristics of Pandas:

• Eager evaluation: Every operation executes immediately when called
• NumPy-backed arrays: Traditionally uses NumPy arrays under the hood (with Arrow backend available since 2.0)
• Single-threaded execution: Operations run on one CPU core by default
• Mature API: Comprehensive documentation, thousands of tutorials, and deep integration with scikit-learn, matplotlib, seaborn, and the entire PyData ecosystem
• Mutable DataFrames: Supports in-place modifications

Pandas 2.x introduced optional PyArrow-backed dtypes, improving memory efficiency for string-heavy data and enabling better interoperability with other Arrow-based tools. However, the core execution model remains single-threaded and eager.

What Is Polars?

Polars is a DataFrame library written in Rust, created by Ritchie Vink in 2020. It uses Apache Arrow as its in-memory columnar format, enabling zero-copy data sharing with other Arrow-compatible tools. Polars was designed from scratch to solve the performance limitations that are inherent to Pandas architecture.

Key characteristics of Polars:

• Lazy and eager evaluation: Supports both modes; lazy mode enables query optimization before execution
• Apache Arrow memory model: Columnar storage with efficient cache utilization
• Automatic multi-threading: Parallelizes operations across all available CPU cores without user intervention
• Query optimizer: Rewrites and optimizes execution plans (predicate pushdown, projection pushdown, join reordering)
• Streaming execution: Can process datasets larger than RAM
• Immutable DataFrames: All operations return new DataFrames; no in-place mutation
• GPU support: Optional NVIDIA GPU acceleration for in-memory workloads

Polars offers both a Python API and a native Rust API. The Python API feels familiar to Pandas users but uses method chaining and expression-based syntax that enables the optimizer to work effectively.

Polars vs Pandas: Complete Comparison Table

Syntax Comparison: Side-by-Side Code Examples

The best way to understand the practical differences is to see the same operations written in both libraries. The following examples demonstrate common data tasks.

Reading a CSV File

Pandas:

import pandas as pd
 
df = pd.read_csv("sales_data.csv")
print(df.head())

Polars:

import polars as pl
 
df = pl.read_csv("sales_data.csv")
print(df.head())

For simple CSV reads, the syntax is nearly identical. Polars will be faster because it reads columns in parallel and uses Arrow's memory format directly. On a 1 GB CSV, Polars typically finishes in under 2 seconds compared to 8-10 seconds for Pandas.

Reading a Parquet File

Pandas:

df = pd.read_parquet("sales_data.parquet")

Polars (lazy -- only reads needed columns):

df = pl.scan_parquet("sales_data.parquet")
# No data loaded yet -- just a query plan
result = df.select("product", "revenue", "date").collect()

This is where Polars shines. scan_parquet creates a lazy frame that only reads the columns and rows you actually use. If your Parquet file has 100 columns but you only need 3, Polars skips the other 97 entirely. Pandas reads all 100 columns into memory.

Filtering Rows

Pandas:

# Filter rows where revenue > 1000 and region is "North"
filtered = df[(df["revenue"] > 1000) & (df["region"] == "North")]

Polars:

# Filter rows where revenue > 1000 and region is "North"
filtered = df.filter(
    (pl.col("revenue") > 1000) & (pl.col("region") == "North")
)

Polars uses the pl.col() expression system instead of bracket indexing. This is not just syntactic preference -- expressions allow the query optimizer to push filters down to the data source and parallelize the evaluation.

GroupBy Aggregation

Pandas:

result = df.groupby("category").agg(
    total_revenue=("revenue", "sum"),
    avg_price=("price", "mean"),
    order_count=("order_id", "count")
)

Polars:

result = df.group_by("category").agg(
    total_revenue=pl.col("revenue").sum(),
    avg_price=pl.col("price").mean(),
    order_count=pl.col("order_id").count()
)

Both APIs support named aggregations. The Polars expression syntax is more explicit and composable. For instance, you can easily chain operations within an aggregation: pl.col("revenue").filter(pl.col("status") == "completed").sum() -- something that requires more convoluted code in Pandas.

Joining Two DataFrames

Pandas:

merged = pd.merge(
    orders, customers,
    left_on="customer_id",
    right_on="id",
    how="left"
)

Polars:

merged = orders.join(
    customers,
    left_on="customer_id",
    right_on="id",
    how="left"
)

The join syntax is similar between the two libraries. Polars performs joins faster because it hashes and probes in parallel across multiple threads and can reorder joins in lazy mode for optimal execution.

Adding and Transforming Columns

Pandas:

df["profit_margin"] = (df["revenue"] - df["cost"]) / df["revenue"] * 100
df["year"] = pd.to_datetime(df["date"]).dt.year
df["category_upper"] = df["category"].str.upper()

Polars:

df = df.with_columns(
    profit_margin=((pl.col("revenue") - pl.col("cost")) / pl.col("revenue") * 100),
    year=pl.col("date").cast(pl.Date).dt.year(),
    category_upper=pl.col("category").str.to_uppercase()
)

Polars uses with_columns() to add or transform multiple columns in a single call. All three transformations above execute in parallel. In Pandas, each line runs sequentially and creates intermediate copies of the data.

Chaining Operations (Full Pipeline)

Pandas:

result = (
    df[df["status"] == "completed"]
    .groupby("product_category")
    .agg(total_revenue=("revenue", "sum"))
    .sort_values("total_revenue", ascending=False)
    .head(10)
)

Polars (lazy mode):

result = (
    df.lazy()
    .filter(pl.col("status") == "completed")
    .group_by("product_category")
    .agg(total_revenue=pl.col("revenue").sum())
    .sort("total_revenue", descending=True)
    .head(10)
    .collect()
)

The lazy pipeline in Polars builds an execution plan and optimizes it before running. The optimizer might push the filter before the scan, project only the needed columns, or rearrange operations for efficiency. You get these optimizations automatically just by calling .lazy() at the start and .collect() at the end.

Performance Benchmarks

Real-world benchmarks consistently show Polars outperforming Pandas by significant margins. The following numbers are based on published benchmarks from 2025 and the Polars PDS-H benchmark suite.

CSV Loading (1 GB file, ~10M rows)

Polars reads CSVs 5x faster and uses approximately 87% less memory. This is because Polars reads columns in parallel and stores data in Arrow's columnar format, which is more compact than Pandas' row-oriented NumPy arrays with Python object overhead.

GroupBy Aggregation (10M rows, 5 groups)

Polars completes group-by operations 5-10x faster. The parallelized hash-based aggregation across all CPU cores is the primary reason. Pandas processes each group sequentially on a single thread.

Sort (10M rows)

Sorting shows the largest performance gap -- up to 11x faster in Polars. Sorting is one of Pandas' biggest bottlenecks because it relies on single-threaded NumPy sort implementations.

Join (Two DataFrames, 10M and 1M rows)

Polars joins run 3-8x faster depending on join type and key cardinality. The parallel hash join implementation is particularly effective for large fact-dimension joins common in analytical workloads.

Key Takeaway on Performance

For datasets under 100,000 rows, both libraries feel instant. The performance gap becomes meaningful starting around 1 million rows and grows larger as data size increases. If you regularly work with datasets above 10 million rows on a single machine, Polars provides a substantial productivity boost just from reduced wait times.

Memory Usage: How Polars Stays Lean

Memory efficiency is one of Polars' strongest advantages:

1. Apache Arrow columnar format: Data is stored in contiguous memory blocks per column. This is more cache-friendly than Pandas' block-manager approach and avoids Python object overhead for strings and mixed types.

2. Lazy evaluation avoids intermediate copies: In Pandas, each chained operation creates a new copy of the data. A five-step transformation pipeline might allocate five copies of your DataFrame. Polars' lazy mode builds an optimized plan that minimizes intermediate allocations.

3. Projection pushdown: When reading from Parquet or scanning data lazily, Polars only loads the columns your query actually uses. Pandas loads everything.

4. Predicate pushdown: Filters are pushed to the data source. If you filter a Parquet file to 10% of its rows, Polars reads only the matching row groups from disk. Pandas reads all rows first, then filters in memory.

5. Streaming execution: For datasets larger than available RAM, Polars can process data in streaming batches without requiring the entire dataset in memory.

In practical terms, a pipeline that causes an Out of Memory error in Pandas on a 16 GB machine might run comfortably in Polars using 4-6 GB.

Lazy Evaluation: The Polars Query Optimizer

Lazy evaluation is the feature that most fundamentally separates Polars from Pandas. When you call .lazy() on a Polars DataFrame (or use scan_csv / scan_parquet), operations are not executed immediately. Instead, Polars builds a logical plan -- a directed graph of operations -- and then optimizes it before execution.

The optimizer performs several transformations automatically:

# This lazy pipeline gets automatically optimized
result = (
    pl.scan_parquet("huge_dataset.parquet")  # 100 columns, 500M rows
    .filter(pl.col("country") == "US")       # Optimizer pushes this to file scan
    .select("name", "revenue", "country")    # Optimizer projects only 3 columns
    .group_by("name")
    .agg(pl.col("revenue").sum())
    .sort("revenue", descending=True)
    .head(20)
    .collect()
)

What the optimizer does with this pipeline:

• Projection pushdown: Only reads "name", "revenue", and "country" from the Parquet file (ignoring the other 97 columns)
• Predicate pushdown: Applies the country == "US" filter at the Parquet row-group level, skipping entire chunks of data that contain no US records
• Common subexpression elimination: Reuses computed results when the same expression appears multiple times
• Join reordering: When multiple joins are chained, the optimizer picks the most efficient order

You can inspect the optimized plan before executing:

plan = (
    pl.scan_parquet("data.parquet")
    .filter(pl.col("value") > 100)
    .select("id", "value")
)
print(plan.explain(optimized=True))

Pandas has no equivalent to this. Every Pandas operation runs eagerly, and any optimization must be done manually by the developer.

Ecosystem and Compatibility

Where Pandas Wins on Ecosystem

Pandas has an unmatched ecosystem built over 17 years:

• scikit-learn: Expects Pandas DataFrames as input. While Polars can convert to Pandas for model training, the extra step is friction.
• matplotlib and seaborn: Accept Pandas DataFrames and Series directly for plotting. Polars requires conversion.
• statsmodels: Built on Pandas and NumPy. No native Polars support.
• Jupyter integration: Pandas DataFrames render natively in notebooks. Polars also renders well, but some notebook extensions assume Pandas.
• File format support: Pandas supports Excel, HDF5, SQL databases, clipboard, fixed-width text, and dozens of other formats. Polars supports CSV, Parquet, JSON, IPC/Arrow, Avro, and databases, but not Excel natively (requires conversion).
• Google Colab / cloud notebooks: Pre-installed and assumed in most cloud data science environments.

Where Polars Is Catching Up

Polars' ecosystem is growing rapidly:

• DuckDB integration: DuckDB can query Polars DataFrames directly via SQL without copying data, combining SQL and expression-based workflows.
• Streamlit: Added native Polars support. You can pass pl.DataFrame objects directly to Streamlit display functions.
• Arrow ecosystem: Any tool that works with Apache Arrow (including Spark, DuckDB, DataFusion, and others) can exchange data with Polars at zero copy cost.
• Conversion methods: df.to_pandas() and pl.from_pandas() make switching between the two libraries straightforward.

Visual Exploration with PyGWalker

One tool that bridges the gap between Polars and Pandas is PyGWalker (opens in a new tab), an open-source Python library that turns any DataFrame into an interactive, Tableau-like visualization interface directly inside Jupyter notebooks. PyGWalker works with both Pandas and Polars DataFrames natively, so you can explore your data visually regardless of which library you use for processing.

import pygwalker as pyg
 
# Works with Pandas
import pandas as pd
df_pandas = pd.read_csv("data.csv")
pyg.walk(df_pandas)
 
# Also works with Polars
import polars as pl
df_polars = pl.read_csv("data.csv")
pyg.walk(df_polars)

This is particularly useful in the Polars workflow where you process data at high speed and then need to visually explore patterns, outliers, or distributions without writing plotting code. PyGWalker gives you drag-and-drop chart creation on top of the DataFrame you already have.

Learning Curve

Coming from Pandas to Polars

If you already know Pandas, learning Polars takes roughly one to two weeks of active use to become comfortable. The core concepts -- DataFrames, columns, filtering, grouping, joining -- are identical. What changes is the syntax and mental model:

Key differences to internalize:

1. No index: Polars DataFrames do not have a row index. If you rely heavily on .loc[], .iloc[], or set_index() in Pandas, you will need to adjust. Polars uses filter() and column-based selection for everything.

2. Expression-based API: Instead of df["col"], you use pl.col("col"). Expressions are composable and can be optimized.

3. Method chaining over assignment: Polars encourages building pipelines with method chaining rather than mutating a DataFrame line by line.

4. Lazy by default for file scans: scan_csv() and scan_parquet() return lazy frames. You call .collect() to execute.

5. Strict typing: Polars is stricter about data types. You cannot mix integers and strings in a column the way Pandas sometimes allows with object dtype.

Starting Fresh

For someone new to both libraries, Polars is arguably easier to learn. The expression API is more consistent (no confusion between df.groupby and df.agg patterns that changed between Pandas versions). The lack of an index removes an entire category of common Pandas pitfalls (unexpected index alignment issues, resetting the index, multi-level indexing confusion).

However, Pandas has far more learning resources available: books, university courses, Stack Overflow answers, and tutorials. Polars documentation is well-written but smaller in volume.

When to Use Pandas

Pandas is the right choice when:

• Your data fits in memory and is under 1 million rows: Pandas is fast enough, and the ecosystem support is unmatched.
• You need deep ML ecosystem integration: scikit-learn, statsmodels, and many ML libraries expect Pandas DataFrames.
• Your team already knows Pandas: The cost of retraining a team can outweigh the performance benefits for smaller datasets.
• You work with Excel files frequently: Pandas' read_excel() and to_excel() are battle-tested.
• You need niche I/O formats: HDF5, Stata, SAS, SPSS, fixed-width text -- Pandas supports formats that Polars does not.
• Interactive notebook exploration on small data: For quick, ad-hoc analysis on small CSVs, Pandas' familiarity and ecosystem integration make it the pragmatic choice.

When to Use Polars

Polars is the right choice when:

• Your data regularly exceeds 1 million rows: The performance difference becomes meaningful and grows with data size.
• You are building data pipelines: Lazy evaluation and query optimization produce faster, more efficient pipelines without manual tuning.
• Memory is a constraint: Polars uses significantly less memory, enabling larger datasets on the same hardware.
• You need parallelism without complexity: Polars parallelizes automatically. No multiprocessing, no dask, no infrastructure changes.
• You work with Parquet files: Polars' predicate and projection pushdown on Parquet files is a major efficiency win.
• You are starting a new project with no legacy Pandas code: There is no migration cost, and Polars' API is clean and consistent.
• You process data for downstream Arrow-compatible tools: DuckDB, Spark, DataFusion, and other tools in the Arrow ecosystem exchange data with Polars at zero copy cost.

Can You Use Both? The Hybrid Approach

Many teams adopt a hybrid approach: use Polars for the heavy data processing steps and convert to Pandas for visualization or ML model training. The conversion between the two is lightweight and fast.

import polars as pl
import pandas as pd
 
# Process data with Polars (fast)
processed = (
    pl.scan_parquet("large_dataset.parquet")
    .filter(pl.col("year") >= 2024)
    .group_by("category")
    .agg(
        pl.col("revenue").sum().alias("total_revenue"),
        pl.col("orders").count().alias("order_count")
    )
    .sort("total_revenue", descending=True)
    .collect()
)
 
# Convert to Pandas for ML or visualization (small result set)
pandas_df = processed.to_pandas()
 
# Use with scikit-learn
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(pandas_df[["order_count"]], pandas_df["total_revenue"])

This pattern gives you the speed of Polars for data wrangling and the ecosystem of Pandas for downstream tasks. The conversion overhead is negligible when the result set is small after aggregation.

AI-Assisted Data Analysis with RunCell

Whether you choose Polars or Pandas, working with data in Jupyter notebooks can be accelerated with AI assistance. RunCell (opens in a new tab) is an AI agent built for Jupyter that helps data scientists write, debug, and optimize their data analysis code. It understands both Pandas and Polars syntax and can suggest the most efficient approach for your specific task -- including recommending when a Polars pipeline would outperform a Pandas equivalent. If you frequently switch between the two libraries, an AI coding assistant that understands both can significantly reduce friction.

Migration Guide: Moving from Pandas to Polars

If you are considering migrating existing Pandas code to Polars, here is a quick reference for the most common operations:

The Future of Both Libraries

Pandas is not going away. Its 2.x releases continue to improve performance, and the optional Arrow backend narrows the gap with Polars for certain operations. The massive ecosystem of tools built on Pandas ensures its relevance for years to come.

Polars is gaining momentum rapidly. With backing from a dedicated company (Polars Inc.), regular releases, growing community contributions, and increasing adoption in production data engineering pipelines, Polars is becoming a standard tool in the modern data stack. GPU acceleration, improved SQL support, and deeper ecosystem integrations are on the roadmap.

The trend is clear: the Python data ecosystem is moving toward Apache Arrow as the common memory format, and both libraries are converging on that standard. This means interoperability between Polars, Pandas, DuckDB, and other tools will only get better.

FAQ

Conclusion

The choice between Polars and Pandas in 2026 is not about one being universally better than the other. It is about matching the tool to the job.

Pandas remains the best choice for small-to-medium datasets, ML workflows that depend on scikit-learn, quick exploratory analysis, and projects where team familiarity matters more than raw performance. Its ecosystem is unrivaled, and Pandas 2.x continues to improve.

Polars is the better choice when performance matters: large datasets, data pipelines, memory-constrained environments, and new projects that benefit from lazy evaluation and automatic parallelism. Its speed advantage is not marginal -- it is often an order of magnitude.

The most effective approach for many teams is to use both. Process your data with Polars where speed counts, convert to Pandas where the ecosystem requires it, and use tools like PyGWalker (opens in a new tab) that work with both DataFrames for visual exploration. The Python data ecosystem is converging on Apache Arrow, making this kind of interoperability easier every year.

Whatever you choose, the fact that Python developers now have a genuine high-performance alternative to Pandas -- without leaving the Python ecosystem -- is a significant step forward for data analysis.