polars-expr-hopper¶

Declarative, schema-aware expression application for Polars

Define your filters and transformations upfront, and let them apply automatically as soon as the columns they need exist. No more manually checking schemas or ordering your pipeline steps: just declare what you want, and the hopper handles the timing.

Powered by polars-config-meta for persistent DataFrame-level metadata.

Simplify data pipelines by storing your expressions in a single location and letting them apply as soon as the corresponding columns exist in the DataFrame schema.

Why use this?¶

Imagine you're building a data pipeline where:

• You want to filter rows early to minimise downstream processing
• But the columns you need to filter on don't exist yet: they're created mid-pipeline
• You don't want to scatter filter logic throughout your code or manually track "when can I apply this?"

polars-expr-hopper lets you declare all your expressions upfront. They sit in a "hopper" and apply themselves the moment their required columns appear in the DataFrame schema.

Real-world example: CLI tools¶

This pattern shines when building CLI tools where users specify filters via arguments. Instead of complex logic to determine when each filter can run, you just:

1. Parse all user-provided filters into Polars expressions
2. Add them to the hopper
3. Call apply_ready_filters() after each pipeline stage

Filters automatically apply as soon as possible, minimising the rows flowing through expensive operations (like API calls or joins).

See octopolars for a real implementation using this pattern, specifically the apply_ready_exprs() call.

Installation¶

pip install polars-expr-hopper

The polars dependency is required but not included in the package by default. It is shipped as an optional extra which can be activated by passing it in square brackets:

pip install polars-expr-hopper[polars]           # for standard Polars
pip install polars-expr-hopper[polars-lts-cpu]   # for older CPUs

Requirements¶

• Python 3.9+
• Polars (any recent version, installed via [polars] or [polars-lts-cpu] extras)
• (Optional) pyarrow if you want Parquet I/O features that preserve metadata in the hopper

Features¶

• DataFrame-Level Expression Management: Store multiple Polars expressions on a DataFrame via the .hopper namespace.
• Apply When Ready: Each expression is automatically applied once the DataFrame has all columns required by that expression.
• Namespace Plugin: Access everything through df.hopper.*(...)—no subclassing or monkey-patching.
• Metadata Preservation: Transformations called through df.hopper.<method>() keep the same expression hopper on the new DataFrame.
• No Central Orchestration: Avoid fiddly pipeline step names or schemas—just attach your expressions once, and they get applied in the right order automatically.
• Optional Serialisation: If you want to store or share expressions across runs (e.g., Parquet round-trip), you can serialise them to JSON or binary and restore them later—without forcing overhead in normal usage.

Usage¶

Basic Usage Example¶

import polars as pl
import polars_hopper  # Registers the .hopper namespace

# Suppose we're building a pipeline that:
# 1. Starts with user data
# 2. Later enriches it with age information from another source
# 3. We want to filter out user_id=0 immediately, and filter by age>18 as soon as age exists

df = pl.DataFrame({
    "user_id": [1, 2, 3, 0],
    "name": ["Alice", "Bob", "Charlie", "NullUser"]
})

# Declare BOTH filters upfront, even though 'age' doesn't exist yet
df.hopper.add_filters(pl.col("user_id") != 0)
df.hopper.add_filters(pl.col("age") > 18)  # 'age' column doesn't exist yet!

# Apply what we can now (only the user_id filter runs)
df = df.hopper.apply_ready_filters()
# NullUser row is gone, age filter stays pending

# Later in the pipeline: enrich with age data
df = df.hopper.with_columns(pl.Series("age", [25, 15, 30]))

# Now the age filter can apply
df = df.hopper.apply_ready_filters()
# Only rows with age > 18 remain, and that expr is removed from the hopper

How It Works¶

In brief:

1. Add expressions to the hopper via df.hopper.add_filters(), add_selects(), or add_addcols()
2. Expressions wait until their required columns (detected via .meta.root_names()) exist in the DataFrame
3. Call apply_ready_*() after pipeline stages ⇢ ready expressions apply and remove themselves
4. Metadata travels with the DataFrame through transformations (powered by polars-config-meta)

This enables a "declare once, apply when ready" pattern that simplifies complex pipelines.

Implementation details¶

Internally, polars-expr-hopper attaches a small “manager” object (a plugin namespace) to each DataFrame. This manager leverages polars-config-meta to store data in df.config_meta.get_metadata(), keyed by the id(df).

1. List of In-Memory Expressions: - Maintains a hopper_filters list of Polars expressions (pl.Expr) in the DataFrame’s metadata. - Avoids Python callables or lambdas so that .meta.root_names() can be used for schema checks and optional serialisation is possible.

2. Automatic Column Check (apply_ready_filters()) - On apply_ready_filters(), each expression’s required columns (via .meta.root_names()) are compared to the current DataFrame schema. - Expressions referencing missing columns remain pending. - Expressions referencing all present columns are applied via df.filter(expr). - Successfully applied expressions are removed from the hopper.

3. Metadata Preservation - Because we rely on polars-config-meta, transformations called through df.hopper.select(...), df.hopper.with_columns(...), etc. automatically copy the same hopper_filters list to the new DataFrame. - This ensures pending expressions remain valid throughout your pipeline until their columns finally appear.

4. No Monkey-Patching - Polars’ plugin system is used, so there is no monkey-patching of core Polars classes. - The plugin registers a .hopper namespace—just like df.config_meta, but specialised for expression management.

Together, these features allow you to:

• store a set of Polars expressions in one place
• apply them as soon as their required columns exist
• easily carry them forward through the pipeline

All without global orchestration or repeated expression checks.

This was motivated by wanting a way to make a flexible CLI tool and express filters for the results at different steps, without a proliferation of CLI flags. From there, the idea of a 'queue' which was pulled from on demand, in FIFO order but on the condition that the schema must be amenable was born.

This idea could be extended to select statements, but initially filtering was the primary deliverable.

API Methods¶

• add_filters(*exprs: tuple[pl.Expr, ...]) Add a new predicate (lambda, function, Polars expression, etc.) to the hopper.

• apply_ready_filters() -> pl.DataFrame Check each stored expression’s root names. If the columns exist, df.filter(expr) is applied. Successfully applied expressions are removed.

• list_filters() -> List[pl.Expr] Inspect the still-pending expressions in the hopper.
• serialise_filters(format="binary"|"json") -> List[str|bytes] Convert expressions to JSON strings or binary bytes.
• deserialise_filters(serialised_list, format="binary"|"json") Re-create in-memory pl.Expr objects from the serialised data, overwriting any existing expressions.

Contributing¶

Maintained by Louis Maddox. Contributions welcome!

1. Issues & Discussions: Please open a GitHub issue or discussion for bugs, feature requests, or questions.
2. Pull Requests: PRs are welcome! - Install the dev extra (e.g. with uv): uv pip install -e .[dev] - Run tests (when available) and include updates to docs or examples if relevant. - If reporting a bug, please include the version and any error messages/tracebacks.

License¶

This project is licensed under the MIT License.