Polars: Working with Large Datasets

Key Insights

• Polars’ lazy evaluation model with query optimization can process multi-gigabyte datasets using a fraction of the memory pandas requires, often with 10-50x performance improvements
• The expression-based API isn’t just syntactic sugar—it enables automatic parallelization and predicate pushdown that fundamentally changes how you approach data processing
• Streaming mode lets you process datasets larger than your available RAM without resorting to chunked iteration or distributed computing frameworks

Introduction to Polars

Pandas has been the default choice for data manipulation in Python for over a decade. But if you’ve ever tried to process a 10GB CSV file on a laptop with 16GB of RAM, you know the pain. Pandas loads everything into memory, often using 2-5x the file size due to its internal representation.

Polars takes a different approach. Built in Rust with a Python API, it’s designed from the ground up for performance on large datasets. It uses Apache Arrow as its memory model, enabling zero-copy data sharing and efficient columnar operations. More importantly, it introduces lazy evaluation with query optimization—something pandas simply cannot do.

I’ve migrated several production data pipelines from pandas to Polars over the past year. The results have been consistent: 10-50x faster execution, 60-80% memory reduction, and code that’s often more readable. Let’s dig into how to actually use it.

Lazy vs Eager Evaluation

The most important concept in Polars is the distinction between lazy and eager execution. Eager evaluation works like pandas—operations execute immediately and return results. Lazy evaluation builds a query plan that only executes when you explicitly request results.

import polars as pl

# Eager: loads entire file into memory immediately
df_eager = pl.read_csv("sales_data.csv")

# Lazy: creates a query plan, reads nothing yet
lf_lazy = pl.scan_csv("sales_data.csv")

# The lazy frame only executes when you call .collect()
df_result = lf_lazy.collect()

Why does this matter? With lazy evaluation, Polars can optimize your entire query before executing anything. It can push filters down to the scan level (reading fewer rows from disk), eliminate unused columns (reading less data), and reorder operations for efficiency.

Consider this pipeline:

# Lazy pipeline with multiple operations
result = (
    pl.scan_csv("sales_data.csv")
    .filter(pl.col("region") == "North America")
    .select(["date", "product_id", "revenue"])
    .group_by("product_id")
    .agg(pl.col("revenue").sum())
    .collect()
)

Polars doesn’t read the entire file, then filter, then select columns. It optimizes the plan to only read the columns it needs and skip rows that don’t match the filter during the initial scan. This optimization happens automatically.

Memory-Efficient Data Loading

When working with large files, how you load data matters as much as what you do with it. Polars provides several controls for memory-efficient loading.

# Control schema explicitly to avoid inference overhead
# and ensure optimal memory usage
schema = {
    "transaction_id": pl.Int64,
    "customer_id": pl.Int32,  # Use smaller types when possible
    "amount": pl.Float32,     # Float32 vs Float64 halves memory
    "category": pl.Categorical,  # Categorical for repeated strings
    "timestamp": pl.Datetime,
}

lf = pl.scan_csv(
    "transactions_50gb.csv",
    dtypes=schema,
    n_rows=None,  # Read all rows (default)
    rechunk=False,  # Don't consolidate chunks (saves memory during load)
    low_memory=True,  # Reduce memory pressure during parsing
)

The low_memory option tells Polars to use a slower but more memory-efficient parsing strategy. Combined with explicit dtypes (avoiding schema inference on large files) and appropriate data types, you can dramatically reduce memory footprint.

For truly massive files, you can preview the schema without loading data:

# Infer schema from first 1000 rows without loading the file
schema = pl.scan_csv("massive_file.csv").schema
print(schema)

# Then load with optimized types
optimized_schema = {
    col: pl.Categorical if dtype == pl.Utf8 else dtype
    for col, dtype in schema.items()
}

Expression-Based Operations

Polars’ expression API is where the library really shines. Unlike pandas’ mix of bracket notation, .loc, .iloc, and method chaining, Polars uses a consistent expression system.

# Complex data transformation pipeline
result = (
    pl.scan_parquet("events/*.parquet")
    .filter(
        (pl.col("event_type").is_in(["purchase", "subscription"]))
        & (pl.col("timestamp") > pl.lit("2024-01-01").str.to_datetime())
    )
    .with_columns([
        # Create derived columns
        (pl.col("price") * pl.col("quantity")).alias("total_value"),
        pl.col("user_id").cast(pl.Utf8).alias("user_id_str"),
        pl.col("timestamp").dt.month().alias("month"),
    ])
    .group_by(["month", "event_type"])
    .agg([
        pl.count().alias("event_count"),
        pl.col("total_value").sum().alias("revenue"),
        pl.col("total_value").mean().alias("avg_order_value"),
        pl.col("user_id").n_unique().alias("unique_users"),
    ])
    .sort(["month", "revenue"], descending=[False, True])
    .collect()
)

Every operation in this chain is an expression that Polars can analyze, optimize, and parallelize. The with_columns operations run in parallel across CPU cores. The aggregations are computed in a single pass.

Expressions also compose naturally:

# Define reusable expressions
revenue_expr = (pl.col("price") * pl.col("quantity")).alias("revenue")
is_high_value = pl.col("revenue") > 1000

# Use them in queries
high_value_sales = (
    pl.scan_csv("sales.csv")
    .with_columns(revenue_expr)
    .filter(is_high_value)
    .collect()
)

Optimizing Query Performance

Polars provides tools to understand and optimize query execution. The .explain() method shows you the optimized query plan.

query = (
    pl.scan_csv("large_dataset.csv")
    .filter(pl.col("status") == "active")
    .select(["id", "name", "value"])
    .group_by("name")
    .agg(pl.col("value").sum())
)

# View the optimized plan
print(query.explain())

The output shows predicate pushdown (filters moved to scan) and projection pushdown (only required columns read):

AGGREGATE
    [col("value").sum()] BY [col("name")]
    CSV SCAN large_dataset.csv
    PROJECT 3/10 COLUMNS  # Only reading 3 of 10 columns
    SELECTION: col("status") == "active"  # Filter at scan level

Common optimization patterns:

# BAD: Filter after collecting intermediate results
df = pl.scan_csv("data.csv").collect()
filtered = df.filter(pl.col("x") > 100)

# GOOD: Keep operations lazy until the end
filtered = (
    pl.scan_csv("data.csv")
    .filter(pl.col("x") > 100)
    .collect()
)

# BAD: Multiple separate aggregations
mean_val = df.select(pl.col("x").mean()).item()
sum_val = df.select(pl.col("x").sum()).item()

# GOOD: Single pass aggregation
stats = df.select([
    pl.col("x").mean().alias("mean"),
    pl.col("x").sum().alias("sum"),
])

Handling Out-of-Core Data

When your dataset exceeds available RAM, Polars’ streaming mode processes data in batches without loading everything at once.

# Process a 100GB file on a machine with 16GB RAM
(
    pl.scan_csv("massive_logs.csv")
    .filter(pl.col("level") == "ERROR")
    .group_by("service")
    .agg([
        pl.count().alias("error_count"),
        pl.col("message").first().alias("sample_message"),
    ])
    .collect(streaming=True)  # Enable streaming execution
)

For ETL pipelines that transform large datasets, use sink_parquet to write results directly to disk without materializing in memory:

# Transform and write without loading full dataset into memory
(
    pl.scan_csv("raw_data/*.csv")
    .with_columns([
        pl.col("timestamp").str.to_datetime(),
        pl.col("amount").cast(pl.Float64),
    ])
    .filter(pl.col("amount") > 0)
    .sink_parquet(
        "processed_data/output.parquet",
        compression="zstd",
        row_group_size=100_000,
    )
)

This processes the entire pipeline in streaming fashion, writing output incrementally. I’ve used this pattern to process 500GB datasets on a laptop.

Polars vs Pandas: When to Switch

Not every project needs Polars. Here’s my practical guidance:

Use Polars when:

• Dataset size exceeds 1GB
• You’re doing aggregations or joins on large tables
• Performance matters for production pipelines
• You want lazy evaluation and query optimization

Stick with pandas when:

• Dataset fits comfortably in memory (under 1GB)
• You need specific pandas-only functionality
• Your team isn’t ready to learn a new API
• You’re doing quick exploratory analysis on small data

Interoperability is straightforward:

import pandas as pd
import polars as pl

# Pandas to Polars (zero-copy when possible)
pandas_df = pd.read_csv("data.csv")
polars_df = pl.from_pandas(pandas_df)

# Polars to Pandas
polars_df = pl.read_csv("data.csv")
pandas_df = polars_df.to_pandas()

# Use Polars for heavy lifting, pandas for compatibility
result = (
    pl.scan_parquet("big_data.parquet")
    .group_by("category")
    .agg(pl.col("value").sum())
    .collect()
    .to_pandas()  # Convert for libraries that need pandas
)

The migration path is incremental. Start by replacing your slowest pandas operations with Polars equivalents. Most teams I’ve worked with see immediate wins on aggregation and join operations, which often provides enough justification to expand usage.

Polars isn’t a drop-in replacement for pandas—the API is different enough that you’ll need to learn new patterns. But those patterns are more consistent and often more readable. Once you internalize the expression-based approach, you’ll find yourself writing cleaner data transformation code regardless of which library you’re using.