Input / Output & Merge Reference

Polynx re-exports all public Polars top-level functions with automatic argument unwrapping and return-value wrapping.

Standard Polars IO & Construction Functions

Polynx exports all public Polars functions directly from polynx (e.g., read_csv, read_parquet, scan_csv, scan_parquet, concat, col, lit, etc.).

For full documentation on these native IO and construction functions, please consult the Official Polars IO Reference.

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Functions

merge

Pandas-style join interface for DataFrame or LazyFrame with automatic column suffix handling on overlapping names.

plx.merge(
    left: DataFrame | LazyFrame,
    right: DataFrame | LazyFrame,
    on: str | list[str] | None = None,
    how: str = "inner",
    suffixes: tuple[str, str] = ("_x", "_y")
) -> DataFrame | LazyFrame

Parameters:

Parameter	Type	Default	Description
left	DataFrame or LazyFrame	required	Left table.
right	DataFrame or LazyFrame	required	Right table.
on	str, list[str], or None	None	Join column key(s).
how	str	"inner"	Join strategy: "inner", "left", "right", or "outer".
suffixes	tuple[str, str]	("_x", "_y")	Suffixes appended to overlapping columns not in on.

Returns: - DataFrame or LazyFrame: Joined dataset.

Examples:

import polynx as plx

df1 = plx.DataFrame({'id': [1, 2], 'val': [10, 20]})
df2 = plx.DataFrame({'id': [1, 2], 'val': [30, 40]})

merged = plx.merge(df1, df2, on='id', suffixes=('_a', '_b'))
# Columns: ['id', 'val_a', 'val_b']

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Inherited Polars Functions

All public Polars functions are accessible directly from the polynx namespace:

Data Creation & IO

# Concat DataFrames or LazyFrames
df_all = plx.concat([df1, df2])

# Reading and Writing
df = plx.read_csv("data.csv")
df = plx.read_parquet("data.parquet")

Expression Builders

# Column references and literals
plx.col("Sales")
plx.lit(100)

# Native conditional expressions
plx.when(plx.col("Sales") > 50).then(1).otherwise(0)

All functions perform deep unwrapping on inputs and wrap return types back into polynx instances.