chalkdf 3.38.32

DataFrame utilities for Chalk AI, backed by libchalk

chalkdf

DataFrame utilities for building fast, portable data pipelines on top of Apache Arrow — powered by Chalk’s libchalk execution engine.

The API centers on two concepts:

• chalkdf.DataFrame: a lightweight, eager plan that you can materialize to Arrow.
• chalkdf.LazyFrame: a serializable, chainable description of a plan that can be round-tripped to/from a protobuf and converted to a DataFrame for execution.

You build expressions with Chalk’s Python underscore DSL (from chalk import _) and function registry (import chalk.functions as F). We also ship pragmatic testing helpers (chalkdf.Testing) to make it easy to compare results in your unit tests.

Installation

• Requires Python 3.10–3.13
• Works on Linux and macOS 15+

Install from PyPI:

pip install chalkdf chalkpy

Quickstart

Below are minimal, self‑contained examples that mirror the public API used in the tests.

Create from Arrow and select columns

import pyarrow as pa
from chalkdf import DataFrame

tbl = pa.table({"a": [1, 2, 3], "b": [10, 20, 30], "c": [100, 200, 300]})
df = DataFrame.from_arrow(tbl)

out = df.select("c", "a")
print(out.run())
# ┌─────┬─────┐
# │  c  │  a  │
# ├─────┼─────┤
# │ 100 │  1  │
# │ 200 │  2  │
# │ 300 │  3  │
# └─────┴─────┘

Add or replace columns with expressions

import pyarrow as pa
import chalk.functions as F
from chalk import _
from chalkdf import DataFrame

tbl = pa.table({"a": pa.array([1, 2, 3], pa.int64()), "b": pa.array([10, 20, 30], pa.int64())})
df = DataFrame.from_arrow(tbl)

out = df.with_columns(
    {
        "sum": _.a + _.b,
        "flag": F.if_then_else(_.a < 2, "small", "big"),
    }
)
print(out.run())

Filter, slice, and order

import pyarrow as pa
from chalk import _
from chalkdf import DataFrame

tbl = pa.table({"a": pa.array([1, 2, 3, 4, 5], pa.int64()), "b": pa.array([10, 20, 30, 40, 50], pa.int64())})
df = DataFrame.from_arrow(tbl)

out = df.filter(_.a > 2).slice(1, 2).order_by(("b", "descending"))
print(out.run())

Force caching of a reused subcomputation

DataFrame.replay() is an escape hatch for tests and carefully inspected plans where you need to force a subcomputation to be cached behind an explicit replay boundary. Most plans should let the optimizer place replay nodes automatically; use this only when you know a shared subplan is expensive enough to compute once and reuse.

from chalk import _
from chalkdf import DataFrame

df = DataFrame.from_dict({"id": [1, 2, 3], "amount": [10, 20, 30]})
shared = df.with_columns({"amount_cents": _.amount * 100}).replay("shared_amount_cents")

Rename and explode

import pyarrow as pa
from chalkdf import DataFrame

tbl = pa.table({"id": [1, 2], "vals": pa.array([[1, 2], []], type=pa.list_(pa.int64()))})
df = DataFrame.from_arrow(tbl)

renamed = df.rename({"vals": "values"})
exploded = renamed.explode("values")
print(exploded.run())  # (1,1), (1,2)

Group by and aggregate

import pyarrow as pa
from chalk import _
from chalkdf import DataFrame

tbl = pa.table({"g": [1, 1, 2, 2, 2], "v": pa.array([10, 20, 1, 2, 3], pa.int64())})
df = DataFrame.from_arrow(tbl)

out = df.agg(["g"], _.v.sum().alias("v_sum"))
print(out.run())  # rows may be unordered

Joins

Use a list of join keys when both sides share column names. When names differ, pass a mapping of left columns to their right-hand counterparts.

import pyarrow as pa
from chalkdf import DataFrame

left = DataFrame.from_arrow(pa.table({"key": [1, 2, 3], "x": [10, 20, 30]}))
right = DataFrame.from_arrow(pa.table({"key": [2, 3, 4], "y": [200, 300, 400]}))

joined = left.join(right, on=["key"], how="inner").select("key", "x", "y")
print(joined.run())

# Or with an explicit mapping of left->right keys
joined2 = left.join(right, on={"key": "key"}, how="inner")

# When column names differ, map each left column to its right counterpart
joined3 = left.join(right, on={"key": "lookup_key"}, how="left")

Scan Parquet files

You can construct a DataFrame that scans one or more Parquet files without loading them eagerly. Use local file:// URIs (or remote URIs when running in an environment with appropriate access):

from chalkdf import DataFrame

df = DataFrame.scan(
    ["file:///path/to/data.parquet"],
    name="my_table",
)

print(df.run())  # materializes and prints a preview

Lazy plans (experimental)

LazyFrame records a chain of operations and can round‑trip to a protobuf for transport or persistence. You can reconstruct the same lazy plan later and convert it to a DataFrame to execute.

from chalk import _
from chalkdf import LazyFrame

lf = (
    LazyFrame.from_dict({"c1": [1, 2, 3], "c2": [4, 5, 6]})
    .select("c1", "c2")
    .slice(0, length=10)
    .filter(_.c1 > 0)
)

proto = lf.to_proto()
lf2 = LazyFrame.from_proto(proto)
assert lf == lf2  # structural equality of the recorded plan

df = lf2._convert_to_df()  # convert to a DataFrame for execution
print(df.run())

Notes:

• LazyFrame._convert_to_df() is currently a private/experimental helper.
• For named tables, use LazyFrame.named_table("table_name", schema) as a root.

Testing helpers

Use chalkdf.Testing.assert_frame_equal to compare DataFrame results in unit tests. It materializes both frames to Arrow and supports relaxed comparisons.

import pyarrow as pa
from chalkdf import Testing, DataFrame

left = DataFrame.from_arrow(pa.table({"a": [1.000001], "b": [2.0]}))
right = DataFrame.from_arrow(pa.table({"a": [1.0], "b": [2.0]}))

Testing.assert_frame_equal(left, right, atol=1e-5, rtol=0.0)

# Ignore row or column order when needed
Testing.assert_frame_equal(left, right, check_row_order=False, check_column_order=False)

Why chalkdf?

• Built on Apache Arrow memory formats for zero‑copy interop.
• Runs on Chalk’s native engine for performance and portability.
• Small, predictable API surface suited for services and batch jobs.