polars

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Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

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polars-librarydataframe-processinglazy-evaluationpandas-replacementetl-pipelines

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Polars

Overview

Polars is a lightning-fast DataFrame library for Python and Rust built on Apache Arrow. 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.

Quick Start

Installation and Basic Usage

Install Polars:
python
uv pip install polars
Basic DataFrame creation and operations:
python
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:
python
# 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
python
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
python
lf = pl.scan_csv("file.csv")  # Doesn't read yet
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:
python
# 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:
python
# 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:
python
# 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,
    pl.col("value") * 100,
)

Group By and Aggregations

Group data and compute aggregations:
python
# 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:
python
# 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): Preserves original row order
  • explode
    : Faster but groups rows together
  • join
    : Creates list columns

Data I/O

Supported Formats

Polars supports reading and writing:
  • CSV, Parquet, JSON, Excel
  • Databases (via connectors)
  • Cloud storage (S3, Azure, GCS)
  • Google BigQuery
  • Multiple/partitioned files

Common I/O Operations

CSV:
python
# 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):
python
df = pl.read_parquet("file.parquet")
df.write_parquet("output.parquet")
JSON:
python
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:
python
# 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:
python
# Vertical (stack rows)
pl.concat([df1, df2], how="vertical")

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

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

Pivot and Unpivot

Reshape data:
python
# Pivot (wide format)
df.pivot(values="sales", index="date", columns="product")

# 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
  • Strict typing: No silent type conversions
  • Lazy evaluation: Available via LazyFrame
  • Parallel by default: Operations parallelized automatically

Common Operation Mappings

OperationPandasPolars
Select column
df["col"]
df.select("col")
Filter
df[df["col"] > 10]
df.filter(pl.col("col") > 10)
Add column
df.assign(x=...)
df.with_columns(x=...)
Group by
df.groupby("col").agg(...)
df.group_by("col").agg(...)
Window
df.groupby("col").transform(...)
df.with_columns(...).over("col")

Key Syntax Patterns

Pandas sequential (slow):
python
df.assign(
    col_a=lambda df_: df_.value * 10,
    col_b=lambda df_: df_.value * 100
)
Polars parallel (fast):
python
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:
    python
    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 for very large data:
    python
    lf.collect(streaming=True)
  4. Select only needed columns early:
    python
    # Good: Select columns early
lf.select("col1", "col2").filter(...)

# Bad: Filter on all columns first
lf.filter(...).select("col1", "col2")
  5. Use appropriate data types:
    • Categorical for low-cardinality strings
    • Appropriate integer sizes (i32 vs i64)
    • Date types for temporal data

Expression Patterns

Conditional operations:
python
pl.when(condition).then(value).otherwise(other_value)
Column operations across multiple columns:
python
df.select(pl.col("^.*_value$") * 2)  # Regex pattern
Null handling:
python
pl.col("x").fill_null(0)
pl.col("x").is_null()
pl.col("x").drop_nulls()
For additional best practices and patterns, load 
references/best_practices.md
.

Resources

This skill includes comprehensive reference documentation:

references/

  • core_concepts.md
    - Detailed explanations of expressions, lazy evaluation, and type system
  • operations.md
    - Comprehensive guide to all common operations with examples
  • pandas_migration.md
    - Complete migration guide from pandas to Polars
  • io_guide.md
    - Data I/O operations for all supported formats
  • transformations.md
    - Joins, concatenation, pivots, and reshaping operations
  • best_practices.md
    - Performance optimization tips and common patterns
Load these references as needed when users require detailed information about specific topics.

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