Polars vs pandas: Choosing the Right Python DataFrame Library for Your Data Workflow

Pandas is an open-source library written for the Python programming language which provides fast and adaptable data structures and data analysis tools. It is the most widely used DataFrame library in Python. It’s mature, feature-rich and has an extensive ecosystem with lots of integrations. Pandas enjoys extensive documentation, community support and mature plotting libraries. It’s popular for small-to-medium sized datasets and exploratory analysis.

Polars is a fast, Rust-based, columnar DataFrame library with a Python API. It’s designed for speed, with built-in parallelism and “lazy execution” (not executed immediately) for bigger-than-memory workloads.

Depending on your data processing requirements, Pandas works fine for data science on datasets up to a few million rows. If you’re doing ETL, analytics or working on big tables, Polaris is generally more efficient.

When to Use pandas for Your Workflow

Pandas shines when flexibility, speed of iteration and ecosystem compatibility matter more than extreme scale. It’s the defacto standard DataFrame library. It prioritizes flexibility and offers deep integrations with Scikit-learn. NumPy, Matplotlib, statsmodels and many machine learning tools.

It works with legacy codebases and is familiar to data processing teams who use it for interactive analysis and exploratory data work where flexibility matters most. Its row-based format excels for smaller to medium-sized datasets for ad-hoc analysis, notebook-based workflows and rapid prototyping.

With pandas, you can run any Python function, whereas Polars strongly discourages arbitrary Python execution. With pandas, in-place changes and step-by-step editing are normal, allowing users to mutate state over time. With Polars, DataFrames are effectively immutable.

You can execute pandas API on Apache Spark 3.2. This lets you evenly distribute pandas workloads, ensuring everything gets done the way it should.

For exploratory data analysis, pandas provides fast, interactive operations, easy slicing/filtering/grouping and quick visual inspections. It’s often used for data validation/auditing and cleaning raw data for missing values, inconsistent formats, duplicates or mixed data types.

For business analytics and reporting where data teams need to generate metrics on a set time scale, pandas makes groupby + aggregation simple with easy reshaping, and outputs directly to CSV/Excel.

When data science teams prepare data for ML models, pandas makes experimentation easy with natural column-based feature creation and tight integration with scikit-learn. It’s often used for rapid prototyping and proofs of concept before writing logic in SQL, Spark or production pipelines.

Even finance and non-technical business teams use pandas to automate Excel-based workflows.

Learn more:

Working with pandas DataFrames

Learn pandas data analysis

When to Use Polars for Your Workflow

Polars shines when performance, scalability and reliability matter more than ad-hoc flexibility. Thanks to its Rust engine, multithreading, columnar memory model and lazy execution engine, Polars can handle surprisingly large ETL workloads on a single machine where memory efficiency is critical. Lazy execution means operations are not executed immediately, but are recorded, optimized and executed only when output is explicitly requested. This can result in huge performance gains because it creates one optimized execution plan instead of doing each operation step-by-step. Data transformations are planned first and executed later, allowing the system to optimize the entire pipeline for maximum speed and efficiency.

For production data pipelines requiring consistent high performance and speed-critical workflows, Polars is multi-threading by default to take advantage of all available CPU cores and processing each chunk of the DataFrame on a different thread. This makes it dramatically faster than traditional single-threaded DataFrame libraries like pandas.

When performing joins on tens of millions of rows, such as joining clickstream logs with user metadata, Polars’ joins are multithreaded and the columnar data reduces unnecessary memory copying.

For usage scenarios involving large datasets, complex transformations or multi-step pipelines, Polars benefits from parallel processing, where each row can be processed independently, splitting the join operations across multiple cores and performing hash partitioning in parallel. For multi-step query pipelines with many transformations, Polars can optimize and run the whole pipeline in parallel. Using parallel streaming plus lazy evaluation allows Polars to process datasets larger than RAM. Parallel processing and lazy evaluation also aid large file scanning operations (CSV/Parquet files).

Polars also gains major performance advantages by using columnar storage built on Apache Arrow for query optimization. In columnar storage, data is stored column-by-column, not row-by-row. This allows Polars to read only the columns required, minimizing disk I/O and memory access, making it more efficient for analytical processing. It can operate directly on Apache Arrow’s continuous memory buffers without copying data.

If you are doing ML feature engineering and exploration on extremely large datasets, joining large fact tables, doing heavy aggregations and OLAP analytics, time series workloads, massive file scanning, bigger-than-memory processing and batch processing with tight SLAs, Polars might be the better choice.

• Internal Link: Apache Spark (anchor: distributed data processing)
• Internal Link: data pipelines (anchor: building scalable data pipelines)

Data Representation and Architecture

The data representation models and architectures of pandas and Polars differ on purpose. The row-based storage used by pandas stores complete rows continuously in memory, while the columnar storage found in Polars stores each column contiguously. Each method can impact performance, depending on the types of queries you run.

For analytical queries, columnar storage typically performs better because the query only has to touch the columns needed, whereas row stores must read full rows.

Columns have uniform types leading to better compression ratios, and vectorization enables fast batch processing.

For transactional queries, such as OLTP workloads, row-based storage is preferred since an entire row is stored together so fetching a full record requires a single read, and updating a row only modifies one compact region of memory.

The charts below show the mean performance ratios comparing row-based and columnar DataFrame libraries (in this case, Koalas and Dask).

Polars' columnar format allows for faster aggregations. Since each column is stored contiguously in memory, it can stream through a single column without scanning unrelated data and it parallelizes aggregations across CPU cores. For large datasets, columnar storage reduces RAM pressure because it only reads the columns needed by the query.

The columnar layout in Polars allows vectorized execution using Apache Arrow, enabling zero-copy data sharing. Polars can perform filtering, slicing, without copying the underlying data buffers.

The row-based storage model used by pandas means each row of a DataFrame is stored as a collection of Python objects grouped together. This model is optimized for operations that retrieve or modify complete records. It can fetch all data for a record in one lookup, making it better suited for many small, mixed workload operations rather than large vectors.  It supports heterogeneous data types such as Python objects, strings, numbers, lists and nested data. Such flexibility is useful for messy, real-world data, JSON within CSV records, and mixed type feature sets.

For queries that require accessing many or all columns for a single row, such as retrieving user-level records and serializing row-level data for APIs, pandas doesn’t need to reconstruct the row by accessing multiple column buffers. It’s also faster for workloads with frequent mutations because it allows in-place mutation of DataFrame cells

When the data fits comfortably in memory, pandas is very convenient and provides fast enough performance for small-to-medium datasets.

Performance: Evaluating Speed and Resource Use

Polars is generally faster and more resource-efficient than pandas, especially for data engineering type work and as data and complexity grow. Polars is columnar, multi-threaded by default, and can run lazy/optimized query plans. Pandas is mostly single-threaded for DataFrame ops and uses eager evaluation where every line runs immediately and materializes intermediate DataFrames. Pandas can be faster on small data and some simple vectorized operations, and it’s more flexible—but that flexibility can cost CPU/memory.

The graph below shows how thread counts can impact performance.

With Polars’ LazyFrame query planning and optimizer, your code builds a query play first and Polars optimizes the plan and executes when you tell it to. That alone accounts for most of Polar's speed and memory use advantage.

In pandas, eager evaluation means it computes immediately, creates an intermediate object in memory and then passes that intermediate to the next step, so you suffer speed for multiple passes over the data (often creating multiple full-size intermediates). Since pandas can’t see the whole pipeline, it can’t globally optimize. But pandas is strong when data fits comfortably in memory, when operations are small and interactive, and when you want immediate feedback after each line. As a rule of thumb, chose pandas when:

• you’re doing quick EDA
• datasets are small/medium
• you want step-by-step inspection and debugging
• your logic is highly custom Python (row-wise)

Choose Polars when:

• you’re doing repeatable ETL/analytics pipelines
• datasets are large or wide
• you read Parquet/Arrow a lot
• you care about speed, memory and fewer intermediate copies

Because of their philosophical differences (pandas built for flexibility and Polars built for speed), the two libraries handle missing data and null values differently, which can also impact performance.

Pandas can treat several different values as “missing,” which keeps it flexible but sometimes inconsistent and can slow operations due to Python object handling. Polars uses “null” as the only missing value across all data types to closely match SQL semantics which is faster and more memory efficient at scale.

As seen in the graph below, showing runtime comparisons for representative workflows, when pandas is forced to perform Python-level (per row) execution on large datasets, it creates lots of intermediate copies and operations slow down.

Polars also can see performance bottlenecks when it breaks vectorization and prevents query optimization, or when lazy mode is not used for big pipelines. Polars optimization can also blow up with very large many-to-many joins.

The graph below shows pandas memory consumption increasing linearly with data size.

Performance guidance:

• If your workload is groupby/join/scan large Parquet, Polars usually wins.
• If your workflow is interactive EDA with lots of custom Python logic, pandas is often more convenient.

Benchmarking

 To understand the performance differences, the following are some benchmarking approaches you can implement:

 Quick ad-hoc

• Use time.perf_counter() for wall time
• Repeat multiple times
• Report median/p95

Repeatable microbenchmarks (for a team / PRs)

• Use pytest-benchmark or asv
• Run on a stable machine (or pinned CI runner)
• Save results across commits

Production-like benchmarking (most meaningful)

• Real dataset shape & size
• Cold vs warm cache runs
• End-to-end pipeline timing
• Memory + CPU tracking

To make the comparisons fair, use the same input format, match data types, use the same groupings, keys, outputs, and control threading (out of the box behavior or single core apples-to-apples).

• When performance differences matter most for particular use cases
• Real-world average time improvements with Polars on large datasets

Handling Missing Data and Data Types

The way a DataFrame library handles missing data and data types affects correctness, data quality, performance and ease of use. Pandas offers flexible but sometimes inconsistent handling of missing data and dtypes, while Polars enforces a single null model with strong typing—leading to safer, faster and more predictable behavior, especially at scale.

The missing data model for pandas treats several values – NaN (float), None, NaT (datetime), pd.NA (nullable scalar) – as missing values. This aids in flexibility but can be inconsistent when different data types handle missing data differently. When filling missing values, pandas may change the data type unexpectedly. The ambiguous null semantics makes it harder for pandas to detect data quality issues.

Polars uses a single missing value (null) and uses the same behavior across all data types and all data types are nullable by default. This typically produces predictable behavior and better performance. When filling with missing values, Polars is explicit and preserves the data type. Polars’ consistent null handling usually results in fewer data quality errors.

There are also considerations with how the different memory models affect data type conversions and interoperability. pandas historically leans on NumPy (row-ish, Python-objects that can contain mixed data types) while Polaris is Arrow-native columnar which makes it more straightforward when wiring it into the rest of the Python data stack.

Here are some best practices for maintaining data integrity while using both DataFrame libraries:

• For both libraries…

Enforce uniqueness and key database constraints such as primary key uniqueness, foreign key validity and expected row counts/partitions. Validate joins to prevent silent row explosions. Use consistent, deterministic transformations–they are much easier to test and reproduce. Store “source-of-truth” data in Parquet with stable schema to preserve types. And don’t wait until the end to validate. Validate at key points such as after ingestion, after major transformations, and after publishing.

• With pandas…

Set data types explicitly at read time whenever possible, and prefer nullable data types such as Int64, boolean, string, or datetime64[ns] so that pandas doesn’t fall back to object. Normalize missing values early and watch for silent issues such as NaN == Naan. Avoid chain indexing and row-wise for core logic.

• With Polars…

Define schema and data types explicitly and rely on Polars’ strict typing. Use null consistently and prefer expression-based null handling.

Syntax and API Transitions

• Core API differences: Polars chaining vs. pandas operations
  • With Polars, you typically build a single chained, expression-based pipeline, and in lazy mode, Polars can optimize the whole chain. In pandas, you often write a sequence of statements that mutate in the eager (step-by-step) method.
• Side-by-side code examples: filtering, grouping, aggregations
  • Filtering and selecting (chaining)
    • pandas

result = pdf[pdf["country"] == "US"][["user_id", "revenue"]]

• Polars

result = (

pldf

.filter(pl.col("country") == "US")

.select(["user_id", "revenue"])

)

• Grouping and aggregating:
  • pandas

rev_by_user = (

pdf

.groupby("user_id", as_index=False)["revenue"]

.sum()

)

• Polars

rev_by_user = (

pldf

.group_by("user_id")

.agg(pl.col("revenue").sum())

)

Polars syntax fundamentals:

There are two concepts that matter most when learning Polars: expressions and lazy vs eager execution. Polars is built around expressions, a column-wise computation (similar to SQL) that describes what you want to compute and an engine that decides how to compute it efficiently. Expressions are not executed immediately. They are building blocks in the “lazy” mode of operation where the operations build a query plan and executions happen only when you call.

Conversely, in eager mode (pandas behavior), operations run immediately, which makes it good for exploration and debugging, but slows down for large-scale pipelines. Polars can offer eager execution for interactivity and lazy execution for optimized, large-scale pipelines.

Converting existing Pandas code to Polars

Conversion usually means:

• replace df[...] row/col indexing with .filter() / .select()
• replace in-place assignment with .with_columns()
• replace .apply() with native expressions (whenever possible)
• consider lazy mode for file-backed ETL

Example conversion:

         Original pandas:

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

df = df[df["country"] == "US"][["user_id", "revenue", "ts"]]

df["revenue"] = df["revenue"].fillna(0)

df["day"] = pd.to_datetime(df["ts"]).dt.date

)

out = (

            df.groupby(["user_id", "day"], as_index=False)

        .agg(total_revenue=("revenue", "sum"))

Polars lazy optimized:

import polars as pl

out = (

    pl.scan_parquet("events.parquet")

      .filter(pl.col("country") == "US")

   .select(["user_id", "revenue", "ts"])

   .with_columns([

          pl.col("revenue").fill_null(0),

          pl.col("ts").dt.date().alias("day"),

   ])

      .group_by(["user_id", "day"])

      .agg(pl.col("revenue").sum().alias("total_revenue"))

   .collect()

)

When a team switches data libraries (for example, pandas to Polars, or adding Polars alongside pandas), the learning curve is less about syntax and more about mindset, workflows and risk management. The pandas mindset is imperative, step-by-step, mutate as you go, and inspect after every line. Polars’ mindset is declarative, expression-based where build transformations as pipelines with immutable data and uses SQL-like query planning.

The learning challenge is to start thinking column-wise and declarative rather than row-by-row. Debugging and inspection habits need to change – think in transformations, not states.

With Polars, the data type strictness can feel hostile when it forces schema consistency and fails fast on data type issues, but those failures prevent silent data quality bugs. The challenge is to treat data type errors as data quality signals, not annoyances.

Teams may also feel tooling gaps when switching to Polars as almost every Python data tool accepts pandas and there is a vast pandas ecosystem with documentation. Consider a hybrid approach when legacy tools are needed, focus on Polars for heavy data prep and pandas for modeling and plotting.

API compatibility layers exist for reusing pandas-like DataFrame code on top of Polars. These adapters support the same method names/signations as pandas with similar behaviors and can translate calls into Polars’ native operations. But be careful, an API layer is not conversion, and it can introduce semantic gaps and hide performance pitfalls.

Here are some common refactoring patterns and migration strategies when moving from one DataFrame stack to another.

Common refactoring patterns (pandas to Polaris):

Replace boolean indexing with .filter() and .select()

•     pandas

df2 = df[df["x"] > 0][["id", "x"]]

•         Polars

df2 = df.filter(pl.col("x") > 0).select(["id", "x"])

Replace in-place mutation with .with_columns()

• pandas

df["y"] = df["x"] * 2

• Polars

df = df.with_columns((pl.col("x") * 2).alias("y"))

Replace np.where / conditional assignment with when/then/otherwise

• pandas

df["tier"] = np.where(df["revenue"] >= 100, "high", "low")

• Polars

df = df.with_columns(

  pl.when(pl.col("revenue") >= 100).then("high").otherwise("low").alias("tier")

)

• Rewrite groupby aggregations into expression-based .agg(...)
  • pandas

out = df.groupby("k", as_index=False).agg(total=("v","sum"), users=("id","nunique"))

• Polars

out = df.group_by("k").agg(

  pl.col("v").sum().alias("total"),

  pl.col("id").n_unique().alias("users"),

)

Prefer lazy scans for file-backed ETL

• pandas

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

• Polars

out = (

.scan_parquet("events.parquet")

.filter(pl.col("country") == "US")

.select(["user_id","revenue"])

.group_by("user_id")

.agg(pl.col("revenue").sum().alias("rev"))

.collect()

)

Replace .apply() with native expressions (or isolate UDFs)

• Pandas

Most pandas migrations stall at .apply(axis=1)

• Polars

Try to express it with Polars expressions (str.*, dt.*, list.*, when/then).
If unavoidable, isolate a UDF to a small column/subset and specify return_dtype.

• Internal Link: Python programming (anchor: Python for data analysis)

Integration and Ecosystem Compatibility

Polars and pandas are designed to work together, but they are built on different execution and type models. Interoperability exists via explicit conversion points, not shared internals. Since both libraries can speak Apache Arrow, Arrow can be a key interoperability layer, enabling efficient columnar transfer and cleaner schema preservation.

• Use Parquet or Arrow tables as the interchange format
• Avoid CSV for cross-library workflows

Interoperability is explicit and intentional. There is no shared execution engine or index semantics. There is also no guarantee of zero-copy. Always validate.

Converting data between formats: to_pandas() and import polars:

• pandas to Polars
  • pandas columns are converted to Arrow-compatible Polars types
  • pandas Index is dropped unless you reset it
  • object columns are inspected and coerced (often to Utf8 or error)
  • Best practices
    • Call pd_df.reset_index() if the index matters
    • Normalize dtypes first:
      • use string, Int64, boolean
      • avoid mixed-type object columns
• Polars to pandas
  • Polars columns are converted to pandas (often Arrow-backed if available)
  • A default RangeIndex is created
  • Nulls are mapped to pandas missing representations
  • Best practices
    • Convert once at the boundary, not repeatedly
    • Validate dtypes after conversion (especially ints + nulls)
  • Rule of thumb: Convert at workflow boundaries, not inside loops or hot paths.

When integrating with visualization libraries and plotting tools, most Python plotting libraries expect pandas (or NumPy arrays). Polars integrates well, but you’ll often convert to pandas at the plotting boundary—or pass arrays/columns directly.

For database connectivity and file format support, pandas is best for ad-hoc reads and ecosystem compatibility. Polars is best for large files, Parquet and file-centric analytics. Pandas supports PostgreSQL, MySQL, SQL Server, Oracle, SQLite and any database with a SQLAlchemy driver. Polars is not a full database client. It expects data to arrive as files or Arrow tables. Some databases and tools can output Arrow directly, which Polars can ingest efficiently.

Both support CSV parsing. Polars is very fast with lower memory overhead, while pandas has very flexible parsing and handles messy CSVs well, but parsing tends to be CPU-heavy and memory use can spike.

Polars is superior for Parquet. Pandas can read Parquet but operations are eager only with limited pushdown compared to Polars. With streaming execution and a Arrow-native columnar engine, Polars can produce results with orders of magnitude speedups on large datasets.

Machine learning (ML) library integration and compatibility is one of the biggest practical factors when choosing between pandas and Polars or running both. Most ML libraries expect NumPy arrays (X: np.ndarray, y: np.ndarray), pandas DataFrames/Series (common in sklearn workflows) or Arrow. Many libraries treat pandas as the default tabular container. So, if your ML stack is mostly sklearn and related ecosystem, pandas remains the path of least friction.

Most ML libraries do not yet accept Polars DataFrames directly as first-class inputs. Polars is great for feature engineering but plan to convert at the boundary. It’s recommended to do heavy data prep in Polars and convert to pandas or NumPy for model training and inference.

Here’s a quick checklist for feeding data into ML:

• No mixed-type columns
• All features numeric or encoded
• Nulls handled (imputed/dropped/missing-aware model)
• Feature order stable
• Feature names preserved (if needed)
• Train/inference schema validation in place

Production considerations

When you move pandas or Polars workloads from notebooks into production, the “gotchas” are usually less about syntax and more about runtime, packaging, performance predictability and operability. Validate behavior under the actual memory/CPU limits of your deployment target. Choose strategies like column pruning, early filtering and streaming/lazy scans for file-based workloads.

For runtime and packaging, make sure your production Python version matches what you test locally. Polars ships native code (Rust) and pandas depends on NumPy or optional engines like PyArrow and fastparquet. Parquet/Arrow is usually best for production, offering better schema stability, faster reads and fewer data type surprises than CSV.

Polars uses multi-threading by default. Consider setting/controlling thread usage via environment configuration in production. Polars’ lazy optimization can improve throughput, but very small jobs might see planning overhead.

Production pipelines should enforce data types and nullability expectations explicitly (both libraries require you to assert constraints). Add checks around joins to prevent silent row explosions.

For observability, track runtime, row counts, null counts for key columns and output sizes per run. Add “stop-the-line” checks at boundaries (before publishing outputs). And ensure errors surface with actionable context (which partition/file/table, which check failed).

Validate outputs (row counts, aggregates, null rates) and performance budgets (time/memory thresholds). Run tests in containers that match production OS/glibc to avoid native wheel surprises.

Practical Migration Strategies

Migration strategies for moving a team from pandas to Polars or adopting Polars alongside pandas:

• Strangler pattern – When you need low risk and continuous delivery, replace one segment at a time with Polars while keeping the old pandas running. Convert at boundaries.
• Use both – When your bottleneck is ETL/aggregation but you rely on pandas-native tooling downstream, Use Polars for I/O joins, groupbys and feature computation; convert the final result to pandas for scikit-learn, plotting, stats libraries.
• Full rewrite of a single pipeline – When you want a clear success story and reusable patterns, pick one pipeline end-to-end and rewrite it fully in Polars to use as the internal reference implementation.
• Dual-run parity – When correctness is critical, run pandas and Polars versions side-by-side for a period, compare outputs, metrics and costs; switch over once parity is proven. 

Performance profiling – To identify optimization opportunities, start tracking, wall time (how long a user waits), peak memory, row counts, column counts and output correctness. Most pipelines bottleneck in one of: I/O, join, groupby, sort, string parsing, or Python UDF. Add simple timers around those stages. Use pandas (Python) profilers when you suspect Python-level work and Polars profilers to inspect a lazy query plan. Make one targeted change and rerun the same benchmark to compare.

Team training and knowledge transfer considerations

Your goal is to succeed without stalling delivery or losing confidence in the data. Make sure the team understands the motivations and can tie them to real wins, i.e. which problem(s) are we solving, which workloads benefit the most, and what is not going to change. Appoint ownership (migration lead, reviewers, decision-makers) for accountability.

Use real company pipelines for examples for relevance and buy-in. Since Polars is closer to SQL but with Python syntax, the biggest changes are conceptual mindset shifts:

• Imperative to declarative
• Row-wise to column-wise
• Mutable state to immutable pipelines
• Immediate execution to lazy planning and execution

Sequence the training in layers so that teams feel productive early. Perhaps start with filtering, selecting and grouping before moving on to expressions and null handling and data type differences. Then tackle lazy execution and optimization before migration and production patterns. Establish a hybrid phase with clear guidance on what pandas is allowed for to reduce anxiety. For faster knowledge transfer, pair experienced Polars users with pandas-heavy users.

Validate correctness publicly to build trust and measure and share wins.

FAQs

• Is pandas better than Polars? Neither is universally better; choice depends on specific workflow requirements, dataset size and performance needs.
• What is better, Polars or pandas? Pandas excels for interactive analysis and ecosystem integration; Polars performs better for large-scale production pipelines.
• Is Polars a replacement for pandas? Polars complements rather than replaces pandas; both serve different use cases effectively.
• Is it worth switching to Polars? It depends on whether you're processing large datasets where Polars' lazy mode and query optimization deliver measurable benefits.

Conclusion

When deciding which DataFrame library makes sense for your teams, there is no blanket answer. Typically, pandas is better suited for small-to-medium sized datasets and exploratory analysis, while Polars, with its lazy execution, is better suited for high performance on large (even bigger-than-memory) workloads. Depending on your use cases, you may end up using both, so test small portions for specific workflows with both libraries and evaluate based on your actual data processing tasks.

Your teams need to understand the strengths and weaknesses of columnar vs row-based storage and their implications for different query patterns. The core API, syntax, data format and database connection differences will require a learning curve when switching between DataFrame libraries.

Resources for further learning and experimentation:

Building scalable data pipelines

Introduction to Python

Distributed data processing

Working with Pandas DataFrames

Learn Pandas data analysis