polars-dataframes

Polars DataFrames

Overview

Polars is a high-performance DataFrame library for Python built on Apache Arrow with a Rust backend. It provides an expression-based API with lazy evaluation and automatic parallelization for efficient data processing, transformation, and analysis.

When to Use

• Processing tabular datasets from 100 MB to 100 GB that fit in RAM
• ETL pipelines requiring fast read/transform/write cycles
• Replacing pandas when performance matters (10–100x speedup typical)
• Lazy query pipelines with automatic optimization (predicate/projection pushdown)
• Joining, pivoting, and reshaping large tables
• Reading Parquet, CSV, JSON, or cloud-stored data efficiently
• Window functions and complex grouped aggregations
• For larger-than-RAM data, use Dask or Vaex instead
• For GPU-accelerated DataFrames, use cuDF instead

Prerequisites

pip install polars
# Optional extras:
pip install polars[all]          # All I/O backends
pip install polars[pandas]       # Pandas interop
pip install polars[numpy]        # NumPy interop
pip install connectorx sqlalchemy  # Database connectivity

Quick Start

import polars as pl

# Create DataFrame
df = pl.DataFrame({
    "name": ["Alice", "Bob", "Charlie", "Diana"],
    "dept": ["Sales", "Eng", "Sales", "Eng"],
    "salary": [70000, 85000, 72000, 90000],
})

# Expression-based pipeline
result = (
    df.filter(pl.col("salary") > 71000)
    .with_columns(bonus=pl.col("salary") * 0.1)
    .group_by("dept")
    .agg(
        pl.col("salary").mean().alias("avg_salary"),
        pl.len().alias("count"),
    )
)
print(result)
# shape: (2, 3)
# ┌───────┬────────────┬───────┐
# │ dept  ┆ avg_salary ┆ count │
# ├───────┼────────────┼───────┤
# │ Eng   ┆ 87500.0    ┆ 2     │
# │ Sales ┆ 72000.0    ┆ 1     │
# └───────┴────────────┴───────┘

Core API

1. DataFrame Operations

Select, filter, add/modify columns, sort, and sample rows.

import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3, 4, 5],
    "name": ["Alice", "Bob", "Charlie", "Diana", "Eve"],
    "age": [25, 30, 35, 28, 32],
    "score": [88.5, 92.0, 76.3, 95.1, 84.7],
})

# Select columns (with computed expressions)
selected = df.select(
    "name",
    pl.col("age"),
    (pl.col("score") / 100).alias("score_pct"),
)
print(selected.shape)  # (5, 3)

# Filter rows (multiple conditions → implicit AND)
filtered = df.filter(
    pl.col("age") > 27,
    pl.col("score") > 80,
)
print(filtered.shape)  # (3, 4) — Bob, Diana, Eve

# Add columns (preserves existing)
enriched = df.with_columns(
    grade=pl.when(pl.col("score") >= 90).then(pl.lit("A"))
           .when(pl.col("score") >= 80).then(pl.lit("B"))
           .otherwise(pl.lit("C")),
    age_months=pl.col("age") * 12,
)
print(enriched.columns)
# ['id', 'name', 'age', 'score', 'grade', 'age_months']

# Sort
df.sort("score", descending=True).head(3)

2. GroupBy & Aggregations

Group rows and compute summary statistics.

import polars as pl

sales = pl.DataFrame({
    "region": ["East", "West", "East", "West", "East", "West"],
    "product": ["A", "A", "B", "B", "A", "B"],
    "revenue": [100, 150, 200, 180, 120, 210],
    "units": [10, 15, 20, 18, 12, 21],
})

# Basic group_by
summary = sales.group_by("region").agg(
    pl.col("revenue").sum().alias("total_rev"),
    pl.col("revenue").mean().alias("avg_rev"),
    pl.len().alias("n_transactions"),
)
print(summary)

# Multiple keys + conditional aggregation
by_rp = sales.group_by("region", "product").agg(
    pl.col("revenue").sum(),
    (pl.col("units") > 15).sum().alias("large_orders"),
)
print(by_rp)

# Window functions with over() — add group stats without collapsing rows
enriched = sales.with_columns(
    region_avg=pl.col("revenue").mean().over("region"),
    rank_in_region=pl.col("revenue").rank(descending=True).over("region"),
    pct_of_region=pl.col("revenue") / pl.col("revenue").sum().over("region"),
)
print(enriched.select("region", "product", "revenue", "region_avg", "rank_in_region"))

3. Joins

Combine DataFrames on shared keys.

import polars as pl

customers = pl.DataFrame({
    "cid": [1, 2, 3, 4],
    "name": ["Alice", "Bob", "Charlie", "Diana"],
})
orders = pl.DataFrame({
    "oid": [101, 102, 103, 104],
    "cid": [1, 2, 1, 5],
    "amount": [100, 200, 150, 300],
})

# Inner join — only matching rows
inner = customers.join(orders, on="cid", how="inner")
print(inner.shape)  # (3, 4) — cid 1 (×2), cid 2

# Left join — all left rows, nulls where no match
left = customers.join(orders, on="cid", how="left")
print(left.shape)  # (4, 4) — Charlie and Diana have null amount

# Anti join — left rows WITHOUT a match in right
no_orders = customers.join(orders, on="cid", how="anti")
print(no_orders["name"].to_list())  # ['Charlie', 'Diana']

# Join on different column names
customers.join(orders, left_on="cid", right_on="cid", suffix="_order")

# Asof join — match to nearest timestamp (time-series alignment)
quotes = pl.DataFrame({
    "time": [1.0, 2.0, 3.0, 4.0],
    "price": [100, 101, 102, 103],
}).cast({"time": pl.Float64})

trades = pl.DataFrame({
    "time": [1.5, 3.2],
    "qty": [50, 75],
}).cast({"time": pl.Float64})

result = trades.join_asof(quotes, on="time", strategy="backward")
print(result)
# time=1.5 matched price=100, time=3.2 matched price=102

4. Reshaping

Pivot, unpivot, explode, and transpose operations.

import polars as pl

# --- Pivot (long → wide) ---
long = pl.DataFrame({
    "date": ["Jan", "Jan", "Feb", "Feb"],
    "product": ["A", "B", "A", "B"],
    "sales": [100, 150, 120, 160],
})
wide = long.pivot(values="sales", index="date", columns="product")
print(wide)
# date | A   | B
# Jan  | 100 | 150
# Feb  | 120 | 160

# --- Unpivot (wide → long) ---
back_to_long = wide.unpivot(
    index="date", on=["A", "B"],
    variable_name="product", value_name="sales",
)
print(back_to_long.shape)  # (4, 3)

# --- Explode list columns ---
nested = pl.DataFrame({
    "id": [1, 2],
    "tags": [["a", "b", "c"], ["d", "e"]],
})
flat = nested.explode("tags")
print(flat.shape)  # (5, 2)

5. Data I/O

Read and write CSV, Parquet, JSON, Excel, databases, and cloud storage.

import polars as pl

# --- CSV ---
df = pl.read_csv("data.csv")
df.write_csv("output.csv")

# --- Parquet (recommended for performance) ---
df = pl.read_parquet("data.parquet")
df.write_parquet("output.parquet", compression="zstd")

# --- JSON / NDJSON ---
df = pl.read_ndjson("data.ndjson")
df.write_ndjson("output.ndjson")

# --- Excel ---
df = pl.read_excel("data.xlsx", sheet_name="Sheet1")
df.write_excel("output.xlsx")

# --- Lazy scan (preferred for large files) ---
lf = pl.scan_csv("large.csv")
result = lf.filter(pl.col("value") > 0).select("id", "value").collect()
print(result.shape)

# --- Database ---
df = pl.read_database_uri(
    "SELECT * FROM users WHERE age > 25",
    uri="postgresql://user:pass@localhost/db",
)

# --- Cloud storage (S3, GCS, Azure) ---
df = pl.read_parquet("s3://bucket/data.parquet")
df = pl.scan_parquet("gs://bucket/data/*.parquet").collect()

# --- Partitioned Parquet (Hive-style) ---
df.write_parquet("output_dir", partition_by=["year", "month"])
lf = pl.scan_parquet("output_dir/**/*.parquet")

6. Expression API

String, datetime, list, and conditional operations.

import polars as pl
from datetime import date

df = pl.DataFrame({
    "text": ["Hello World", "foo bar", "POLARS"],
    "dt": [date(2023, 1, 15), date(2023, 6, 30), date(2024, 12, 1)],
    "values": [[1, 2, 3], [4, 5], [6]],
})

# String operations
strings = df.select(
    lower=pl.col("text").str.to_lowercase(),
    length=pl.col("text").str.len_chars(),
    contains_o=pl.col("text").str.contains("o"),
    split=pl.col("text").str.split(" "),
)
print(strings)

# Datetime operations
dates = df.select(
    year=pl.col("dt").dt.year(),
    month=pl.col("dt").dt.month(),
    weekday=pl.col("dt").dt.weekday(),
    quarter=pl.col("dt").dt.quarter(),
)
print(dates)

# List operations
lists = df.select(
    list_len=pl.col("values").list.len(),
    list_sum=pl.col("values").list.sum(),
    first=pl.col("values").list.first(),
)
print(lists)

# Conditional expressions (when/then/otherwise)
df = pl.DataFrame({"score": [45, 72, 88, 95, 60]})
result = df.with_columns(
    grade=pl.when(pl.col("score") >= 90).then(pl.lit("A"))
           .when(pl.col("score") >= 80).then(pl.lit("B"))
           .when(pl.col("score") >= 70).then(pl.lit("C"))
           .otherwise(pl.lit("F")),
)
print(result)

# Null handling
df2 = pl.DataFrame({"x": [1, None, 3, None, 5]})
filled = df2.with_columns(
    filled=pl.col("x").fill_null(0),
    forward=pl.col("x").fill_null(strategy="forward"),
    is_null=pl.col("x").is_null(),
)
print(filled)

# Multi-column operations with regex selector
df3 = pl.DataFrame({"val_a": [1, 2], "val_b": [3, 4], "name": ["x", "y"]})
doubled = df3.select(pl.col("^val_.*$") * 2)
print(doubled)

7. Lazy Evaluation

Build optimized query plans before execution.

import polars as pl

# Lazy mode: build plan, optimize, then execute
lf = pl.scan_csv("large_dataset.csv")

result = (
    lf
    .select("user_id", "category", "amount", "date")  # projection pushdown
    .filter(pl.col("amount") > 100)                     # predicate pushdown
    .with_columns(pl.col("date").str.to_date())
    .group_by("category")
    .agg(
        pl.col("amount").sum().alias("total"),
        pl.col("user_id").n_unique().alias("unique_users"),
    )
    .sort("total", descending=True)
)

# Inspect the optimized plan
print(result.explain())

# Execute
df = result.collect()
print(df)

# Streaming mode for very large data
lf = pl.scan_parquet("data/*.parquet")
result = (
    lf
    .filter(pl.col("year") >= 2023)
    .group_by("region")
    .agg(pl.col("sales").sum())
    .collect(streaming=True)  # processes in batches
)
print(result)

# Sink directly to file (no full materialization)
lf.filter(pl.col("active")).sink_parquet("filtered_output.parquet")

Key Concepts

Lazy vs Eager Comparison

Aspect	Eager (DataFrame)	Lazy (LazyFrame)
Created by	pl.read_*(), pl.DataFrame()	pl.scan_*(), df.lazy()
Execution	Immediate	On .collect()
Optimization	None	Predicate/projection pushdown, join reordering
Streaming	No	collect(streaming=True)
Best for	Small data, interactive	Large data, pipelines

Polars Data Types

Type	Python equivalent	Notes
Int8/16/32/64	int	Choose smallest sufficient size
UInt8/16/32/64	int	Unsigned
Float32/64	float	Float64 default
Boolean	bool
Utf8	str	String type
Categorical	—	Low-cardinality strings (faster groupby)
Date	datetime.date	Date without time
Datetime	datetime.datetime	With microsecond precision
Duration	datetime.timedelta	Time difference
List	list	Variable-length lists
Struct	dict	Named fields
Null	None	All-null column

Key Differences from Pandas

• No index: Row access by position only; no .loc/.iloc with labels
• Strict typing: No silent type coercion; explicit .cast() required
• Expressions, not methods: pl.col("x").mean() instead of df["x"].mean()
• Parallel by default: All column operations run in parallel
• Lazy evaluation: Available via LazyFrame for query optimization

Common Workflows

1. ETL Pipeline (CSV → Clean → Parquet)

import polars as pl

# Extract
lf = pl.scan_csv(
    "raw_data.csv",
    dtypes={"id": pl.Int64, "date": pl.Utf8, "amount": pl.Float64},
)

# Transform
cleaned = (
    lf
    .with_columns(pl.col("date").str.to_date("%Y-%m-%d"))
    .filter(pl.col("amount").is_not_null())
    .with_columns(
        year=pl.col("date").dt.year(),
        month=pl.col("date").dt.month(),
        amount_log=pl.col("amount").log(),
    )
    .drop_nulls()
)

# Load
cleaned.collect().write_parquet("clean_data.parquet", compression="zstd")
print("ETL complete")

2. Multi-Source Join and Aggregation

import polars as pl

# Simulate three data sources
users = pl.DataFrame({
    "uid": [1, 2, 3, 4],
    "name": ["Alice", "Bob", "Charlie", "Diana"],
    "region": ["East", "West", "East", "West"],
})
orders = pl.DataFrame({
    "oid": range(1, 7),
    "uid": [1, 1, 2, 3, 3, 3],
    "amount": [100, 200, 150, 50, 75, 125],
})
products = pl.DataFrame({
    "oid": range(1, 7),
    "category": ["Elec", "Books", "Elec", "Books", "Elec", "Elec"],
})

# Join → aggregate
result = (
    orders
    .join(users, on="uid", how="left")
    .join(products, on="oid", how="left")
    .group_by("region", "category")
    .agg(
        pl.col("amount").sum().alias("total"),
        pl.col("amount").mean().alias("avg_order"),
        pl.len().alias("n_orders"),
    )
    .sort("total", descending=True)
)
print(result)

3. Time-Series Feature Engineering

Uses: GroupBy, Window functions, Joins, Expression API.

1. Load time-series data with pl.scan_csv() or pl.scan_parquet()
2. Parse dates: .with_columns(pl.col("date").str.to_date())
3. Sort by entity and date: .sort("entity_id", "date")
4. Add lag features: pl.col("value").shift(n).over("entity_id")
5. Add rolling statistics: pl.col("value").rolling_mean(window_size=7).over("entity_id")
6. Compute percent change: (pl.col("value") - pl.col("value").shift(1)) / pl.col("value").shift(1)
7. Collect and write: .collect().write_parquet("features.parquet")

Key Parameters

Parameter	Function	Default	Range/Options	Effect
how	.join()	"inner"	inner, left, outer, cross, semi, anti	Join type
strategy	.join_asof()	"backward"	backward, forward, nearest	Asof match direction
streaming	.collect()	False	True/False	Process in batches for large data
compression	.write_parquet()	"zstd"	snappy, gzip, brotli, lz4, zstd, uncompressed	Parquet compression
partition_by	.write_parquet()	None	List of columns	Hive-style partitioning
rechunk	pl.concat()	False	True/False	Rechunk memory after concat
aggregate_function	.pivot()	"first"	first, sum, mean, max, min, count	Duplicate handling in pivot
n_rows	pl.read_csv()	None	Positive int	Limit rows read (for sampling)
parallel	pl.read_csv()	"auto"	auto, columns, row_groups, none	Parallel reading strategy
dtypes	pl.read_csv()	None	Dict of column→type	Override type inference

Best Practices

1. Use lazy mode for large datasets: pl.scan_csv() not pl.read_csv(). Enables query optimization and streaming.

2. Stay in the expression API: Avoid .map_elements() (runs Python, no parallelism). Prefer native Polars operations — string, datetime, list namespaces cover most needs.

3. Select early, filter early: Place .select() and .filter() as early as possible in lazy pipelines. The optimizer can push these down but explicit placement helps.

4. Use Categorical for low-cardinality strings: df.with_columns(pl.col("region").cast(pl.Categorical)) — dramatically speeds up groupby and joins on repeated string values.

5. Prefer Parquet over CSV: Parquet preserves types, supports predicate pushdown, and is 5–10x smaller. Use compression="zstd" for best compression/speed balance.

6. Anti-pattern — Python loops over rows: Never iterate rows with for row in df.iter_rows() for computation. Use expressions instead.

7. Anti-pattern — chaining .with_columns() calls: Combine multiple column additions into a single .with_columns() call for parallel execution.

Common Recipes

Recipe: Pandas Migration Pattern

import polars as pl
import pandas as pd

# Convert pandas → polars
pd_df = pd.DataFrame({"col": [1, 2, 3], "group": ["a", "b", "a"]})
pl_df = pl.from_pandas(pd_df)

# Key operation mapping:
# pandas: df["col"]              → polars: df.select("col")
# pandas: df[df["col"] > 1]     → polars: df.filter(pl.col("col") > 1)
# pandas: df.assign(x=...)      → polars: df.with_columns(x=...)
# pandas: df.groupby().agg()    → polars: df.group_by().agg()
# pandas: df.groupby().transform → polars: pl.col(...).over(...)
# pandas: df.merge()            → polars: df.join()
# pandas: df.melt()             → polars: df.unpivot()

# Convert back
pd_result = pl_df.to_pandas()

Recipe: Complex Aggregation Report

import polars as pl

df = pl.DataFrame({
    "dept": ["Sales", "Eng", "Sales", "Eng", "Sales", "Eng"],
    "level": ["Jr", "Sr", "Sr", "Jr", "Jr", "Sr"],
    "salary": [50000, 95000, 75000, 70000, 55000, 100000],
})

report = (
    df.group_by("dept", "level")
    .agg(
        pl.col("salary").mean().alias("avg_sal"),
        pl.col("salary").median().alias("med_sal"),
        pl.col("salary").std().alias("std_sal"),
        pl.len().alias("count"),
    )
    .pivot(values="avg_sal", index="dept", columns="level")
    .with_columns(
        diff=pl.col("Sr") - pl.col("Jr"),
    )
)
print(report)

Recipe: Reading Multiple Files with Schema Alignment

import polars as pl
from pathlib import Path

# Read multiple CSVs with potentially different columns
files = sorted(Path("data/").glob("*.csv"))
dfs = [pl.read_csv(f) for f in files]

# Diagonal concat handles mismatched schemas (fills nulls)
combined = pl.concat(dfs, how="diagonal")
print(f"Combined: {combined.shape}")
print(f"Columns: {combined.columns}")

# Or use lazy scan for Parquet (automatic parallel)
lf = pl.scan_parquet("data/**/*.parquet")
result = lf.filter(pl.col("date") > "2023-01-01").collect()

Troubleshooting

Problem	Cause	Solution
SchemaError: column not found	Column name typo or case mismatch	Check df.columns; Polars is case-sensitive
ComputeError: cannot cast	Type mismatch in operation	Use .cast(pl.Type) explicitly
OutOfMemoryError on collect	Data too large for eager mode	Use lf.collect(streaming=True) or filter first
Slow .map_elements()	Python UDF prevents parallelism	Rewrite using native expressions (str/dt/list namespaces)
Join produces more rows than expected	Duplicate keys in right DataFrame	Deduplicate first: df.unique(subset=["key"])
InvalidOperationError: join on different types	Key columns have different dtypes	Cast both to same type: .cast(pl.Int64)
.over() returns wrong values	Forgetting to include all group columns	Include all grouping columns in .over("col1", "col2")
Parquet file unreadable	Written with incompatible compression	Specify compression="snappy" for maximum compatibility
CSV dates read as strings	No automatic date parsing in CSV reader	Parse after reading: pl.col("date").str.to_date("%Y-%m-%d")
concat fails with different schemas	Columns don't match across DataFrames	Use how="diagonal" to fill missing columns with null

Bundled Resources

• references/pandas_migration.md — Pandas-to-Polars migration guide with operation mapping tables (selection, filtering, column ops, aggregation, window functions, joins, reshaping, string ops, datetime ops, missing data, I/O), interoperability code, common migration patterns with side-by-side code, migration pitfalls, and migration checklist.

  • Covers: all operation mapping content from original pandas_migration.md
  • Relocated inline: key pandas differences summary → SKILL.md Key Concepts "Key Differences from Pandas" section; basic conversion recipe → SKILL.md Common Recipes "Pandas Migration Pattern"
  • Omitted: anti-pattern code examples for row iteration and sequential pipe — covered in io_best_practices.md

• references/advanced_operations.md — Rolling windows (time-based and row-based), cumulative operations (cum_sum/max/min/prod), shift/lag/lead with grouped contexts, struct operations (create/access/unnest), list column manipulation (stats, eval, filter, explode), unique/duplicate detection, advanced sorting (nulls_last, expression-based, top-N per group), column renaming (dict, suffix/prefix/programmatic), sampling (fixed n, fraction, bootstrap), transpose, and advanced reshaping patterns (wide-long-wide, nested JSON to flat, multi-level unpivot, horizontal concat).

  • Covers: advanced operations from original operations.md + transformations from original transformations.md not in main SKILL.md
  • Relocated inline: basic selection/filtering → SKILL.md Core API section 1; groupby/aggregation → section 2; basic joins/asof → section 3; basic pivot/unpivot/explode/concat → section 4; string/date/list/conditional basics → section 6; basic window functions → section 2
  • Omitted: join performance tips (simple; covered in SKILL.md Best Practices); concatenation options (rechunk covered in Key Parameters table)

• references/io_best_practices.md — Full I/O format guide (CSV options, Parquet options with partitioning, JSON/NDJSON, Excel multi-sheet, Arrow IPC), database connectivity (PostgreSQL, MySQL, SQLite, BigQuery), cloud storage (S3, Azure, GCS), in-memory format conversions (dict, NumPy, pandas, Arrow), format selection decision guide, schema management and error handling, expression composition and reuse patterns, column selection patterns (by type, regex, exclude), memory management (estimated_size, type optimization, streaming), pipeline functions for composable transforms, testing/debugging (query plans, schema validation, profiling), performance anti-patterns (sequential pipe, many DataFrames, in-place mutation, unspecified types), and version compatibility notes.

  • Covers: all I/O content from original io_guide.md + expression/memory/testing/performance content from original best_practices.md + format selection and version notes from original core_concepts.md
  • Relocated inline: basic CSV/Parquet/JSON/Excel/database read/write → SKILL.md Core API section 5; lazy vs eager comparison → SKILL.md Key Concepts table; basic expression context/syntax → SKILL.md section 6; parallelization/type system concepts → SKILL.md Key Concepts + Best Practices; null handling → SKILL.md section 6; categorical recommendation → SKILL.md Best Practices item 4
  • Omitted: detailed expression fundamentals (what are expressions, expression contexts) — fully covered in SKILL.md Core API; basic conditional logic examples — covered in SKILL.md section 6; basic aggregation patterns — covered in SKILL.md section 2

Per-Reference-File Disposition (Original 6 files)

Original File	Lines	Disposition	Target
operations.md	603	Consolidated	Advanced ops → references/advanced_operations.md; basic selection/filter/groupby/window/string/date → SKILL.md Core API sections 1-2, 6
transformations.md	550	Consolidated	Reshaping/transpose → references/advanced_operations.md; basic joins/pivot/unpivot/explode/concat → SKILL.md Core API sections 3-4
io_guide.md	558	Consolidated	Full I/O detail → references/io_best_practices.md; basic read/write → SKILL.md Core API section 5
best_practices.md	650	Consolidated	Expression reuse, memory, testing, anti-patterns → references/io_best_practices.md; core best practices → SKILL.md Best Practices
core_concepts.md	379	Consolidated	Format selection, version notes → references/io_best_practices.md; data types, lazy/eager, parallelism → SKILL.md Key Concepts
pandas_migration.md	418	Migrated	→ references/pandas_migration.md (expanded with window/string/datetime/missing data tables)

Intentional Omissions

• Row iteration examples (operations.md): Not documented as a positive capability; only referenced as anti-pattern in Best Practices
• Expression fundamentals tutorial (core_concepts.md): Expression syntax, contexts, and expansion are fully covered by SKILL.md Core API sections; a separate tutorial would duplicate
• Detailed parallelization internals (core_concepts.md): "What gets parallelized" list omitted — users only need the Best Practices guidance to stay in the expression API
• Copy-on-write comparison (core_concepts.md): Pandas 2.0+ copy-on-write details omitted — migration-focused, not Polars-centric

Related Skills

• zarr-python — Chunked array storage; Polars can read/write Parquet that Zarr processes
• matplotlib-scientific-plotting — Visualization; convert to pandas with .to_pandas() for plotting
• scikit-learn-machine-learning — ML pipelines; use .to_numpy() or .to_pandas() for sklearn input

References

• Polars User Guide: https://docs.pola.rs/
• Polars API Reference: https://docs.pola.rs/api/python/stable/reference/
• GitHub: https://github.com/pola-rs/polars
• Polars Cookbook: https://docs.pola.rs/user-guide/