Polars

Polars

Overview

Polars is a columnar DataFrame library for Python and Rust with Arrow interoperability. Work with Polars' expression-based API, lazy evaluation framework, and high-performance data manipulation capabilities for efficient data processing, pandas migration, and data pipeline optimization.

Reviewed against the official stable documentation and native Polars 1.44.2. Local regression tests cover the corrected APIs and scientific failure cases; cloud, GPU, BigQuery, and remote database recipes are illustrative and require provider setup. Fragments using undefined columns, paths, or ... require adaptation to the dataset.

Quick Start

Installation and Basic Usage

Install the current stable Polars release verified during this refresh:

uv pip install "polars==1.44.2"

Install optional integrations only when needed:

uv pip install "polars[excel,database,fsspec,pandas,numpy]==1.44.2"

Basic DataFrame creation and operations:

import polars as pl

# Create DataFrame
df = pl.DataFrame({
    "name": ["Alice", "Bob", "Charlie"],
    "age": [25, 30, 35],
    "city": ["NY", "LA", "SF"]
})

# Select columns
df.select("name", "age")

# Filter rows
df.filter(pl.col("age") > 25)

# Add computed columns
df.with_columns(
    age_plus_10=pl.col("age") + 10
)

Core Concepts

Expressions

Expressions are the fundamental building blocks of Polars operations. They describe transformations on data and can be composed, reused, and optimized.

Key principles:

• Use pl.col("column_name") to reference columns
• Chain methods to build complex transformations
• Expressions are lazy and only execute within contexts (select, with_columns, filter, group_by)

Example:

# Expression-based computation
df.select(
    pl.col("name"),
    (pl.col("age") * 12).alias("age_in_months")
)

Lazy vs Eager Evaluation

Eager (DataFrame): Operations execute immediately

df = pl.read_csv("file.csv")  # Reads immediately
result = df.filter(pl.col("age") > 25)  # Executes immediately

Lazy (LazyFrame): Operations build a query plan, optimized before execution

lf = pl.scan_csv("file.csv")  # Builds a plan; schema inference can read the source
result = lf.filter(pl.col("age") > 25).select("name", "age")
df = result.collect()  # Now executes optimized query

When to use lazy:

• Working with large datasets
• Complex query pipelines
• When only some columns/rows are needed
• Performance is critical

Benefits of lazy evaluation:

• Automatic query optimization
• Predicate pushdown
• Projection pushdown
• Parallel execution

For detailed concepts, load references/core_concepts.md.

Common Operations

Select

Select and manipulate columns:

# Select specific columns
df.select("name", "age")

# Select with expressions
df.select(
    pl.col("name"),
    (pl.col("age") * 2).alias("double_age")
)

# Select all columns matching a pattern
df.select(pl.col("^.*_id$"))

Filter

Filter rows by conditions:

# Single condition
df.filter(pl.col("age") > 25)

# Multiple conditions (cleaner than using &)
df.filter(
    pl.col("age") > 25,
    pl.col("city") == "NY"
)

# Complex conditions
df.filter(
    (pl.col("age") > 25) | (pl.col("city") == "LA")
)

With Columns

Add or modify columns while preserving existing ones:

# Add new columns
df.with_columns(
    age_plus_10=pl.col("age") + 10,
    name_upper=pl.col("name").str.to_uppercase()
)

# Parallel computation (all columns computed in parallel)
df.with_columns(
    (pl.col("value") * 10).alias("value_times_10"),
    (pl.col("value") * 100).alias("value_times_100"),
)

Group By and Aggregations

Group data and compute aggregations:

# Basic grouping
df.group_by("city").agg(
    pl.col("age").mean().alias("avg_age"),
    pl.len().alias("count")
)

# Multiple group keys
df.group_by("city", "department").agg(
    pl.col("salary").sum()
)

# Conditional aggregations
df.group_by("city").agg(
    (pl.col("age") > 30).sum().alias("over_30")
)

For detailed operation patterns, load references/operations.md.

Aggregations and Window Functions

Aggregation Functions

Common aggregations within group_by context:

• pl.len() - count rows
• pl.col("x").sum() - sum values
• pl.col("x").mean() - average
• pl.col("x").min() / pl.col("x").max() - extremes
• pl.first() / pl.last() - first/last values

Window Functions with over()

Apply aggregations while preserving row count:

# Add group statistics to each row
df.with_columns(
    avg_age_by_city=pl.col("age").mean().over("city"),
    rank_in_city=pl.col("salary").rank().over("city")
)

# Multiple grouping columns
df.with_columns(
    group_avg=pl.col("value").mean().over("category", "region")
)

Mapping strategies:

• group_to_rows (default): Maps results back to rows; scalar aggregates broadcast.
• explode: Changes row layout/count; use in select, not alongside original rows.
• join: Joins grouped values back as lists; can consume substantial memory.

Data I/O

Supported Formats

Polars supports reading and writing:

• CSV, Parquet, JSON, Excel
• Databases (via connectors)
• Cloud storage (S3, Azure, GCS)
• Google BigQuery through its SDK or a supported database connector
• Multiple/partitioned files

Common I/O Operations

CSV:

# Eager
df = pl.read_csv("file.csv")
df.write_csv("output.csv")

# Lazy (preferred for large files)
lf = pl.scan_csv("file.csv")
result = lf.filter(...).select(...).collect()

Parquet (recommended for performance):

df = pl.read_parquet("file.parquet")
df.write_parquet("output.parquet")

JSON:

df = pl.read_json("file.json")
df.write_json("output.json")

For comprehensive I/O documentation, load references/io_guide.md.

Transformations

Joins

Combine DataFrames:

# Inner join
df1.join(df2, on="id", how="inner")

# Left join
df1.join(df2, on="id", how="left")

# Join on different column names
df1.join(df2, left_on="user_id", right_on="id")

Concatenation

Stack DataFrames:

# Vertical (stack rows)
pl.concat([df1, df2], how="vertical")

# Horizontal (add columns)
pl.concat([df1, df2], how="horizontal_extend")

# Diagonal (union with different schemas)
pl.concat([df1, df2], how="diagonal")

Pivot and Unpivot

Reshape data:

# Pivot (wide format)
df.pivot(on="product", values="sales", index="date")

# Unpivot (long format)
df.unpivot(index="id", on=["col1", "col2"])

For detailed transformation examples, load references/transformations.md.

Pandas Migration

Polars offers significant performance improvements over pandas with a cleaner API. Key differences:

Conceptual Differences

• No index: Polars uses integer positions only
• Typed columns: Schema inference and coercion exist; validate the resulting schema
• Lazy evaluation: Available via LazyFrame
• Parallel by default: Operations parallelized automatically

Common Operation Mappings

Key Syntax Patterns

Pandas assignment:

df.assign(
    col_a=lambda df_: df_.value * 10,
    col_b=lambda df_: df_.value * 100
)

Polars independent expressions:

df.with_columns(
    col_a=pl.col("value") * 10,
    col_b=pl.col("value") * 100,
)

For comprehensive migration guide, load references/pandas_migration.md.

Best Practices

Performance Optimization

1. Use lazy evaluation for large datasets:

   lf = pl.scan_csv("large.csv")  # Don't use read_csv
   result = lf.filter(...).select(...).collect()

2. Avoid Python functions in hot paths:

   • Stay within expression API for parallelization
   • Use .map_elements() only when necessary
   • Prefer native Polars operations

3. Use streaming to reduce intermediate memory:

   lf.collect(engine="streaming")

   The returned DataFrame still must fit memory. Use lf.sink_parquet("output.parquet") for a direct file output; some operations still need substantial memory.

4. Let the optimizer push down filters and projections:

   lf.filter(pl.col("age") > 25).select("name", "age")

   Retain filter dependencies and inspect explain(). Moving a filter across an aggregation or outer join can change the answer.

5. Use appropriate data types:

   • Categorical for low-cardinality strings
   • Appropriate integer sizes (i32 vs i64)
   • Date types for temporal data