MacroPipe: Research & Design – Macro Language Evolution (miniature format + enriched grammar)

[coderabbitai]

Overview

This issue tracks research and design conversation for the MacroPipe macro language, originally raised in PR panodata/tikray#51 by @amotl.

The current implementation uses a simple colon-separator-based positional syntax:

<function>:<arg1>:<arg2>:<arg3>

This works well for URL-based ETL pipelines (e.g. Apprise, CrateDB Toolkit I/O, Kotori, LorryStream, openDAQ, Polars, Proton), for example:

https://guided-path.s3.us-east-1.amazonaws.com/demo_climate_data_export.csv?pipe=json_array_to_wkt_point:coords&pipe=python_to_json:data

Goals

The desired future direction is to:

1. Decouple the macro language completely from the MacroPipe engine, allowing multiple language frontends to compile to the same pipeline execution model.
2. Maintain a super miniature macro language (colon-separator style) for URL-safe, text-only contexts.
3. Optionally support a richer grammar variant with context-sensitive parsing, named arguments, and operator expressions.

Discussion Topics

1. Naming the Miniature Language

Candidate names for the colon-separator-based mini language:

• FlatPipe – reflects the doc note that the language is intentionally flat (no nesting, no named args)
• MicroPipe – nice contrast with MacroPipe (micro vs. macro)
• ColonQL – descriptive, references the colon delimiter
• PipeURL / UrlPipe – highlights the URL-parameter use case
• QuickPipe – emphasizes speed and terseness

2. Enriched Grammar with pyparsing

pyparsing (MIT, PEG-style, Python-native) is a strong candidate for implementing a richer grammar variant. Possible syntax ideas:

filter(total_amount > 40) | select(passenger_count, trip_distance, fare_amount)
rename(source=old_name, target=new_name) | scale(column=price, factor=1.1)

Alternatives worth evaluating:

• lark – faster, EBNF-based, good error messages
• textX – grammar + model in one, inspired by XText
• parsimonious – lightweight PEG, minimal dependencies

3. Improvements to the Colon-Based Format

Even keeping the minimal format, these improvements could help:

• Quoted string arguments to avoid escape proliferation: func:"arg with : colon"
• Named/keyword arguments: func:pos_arg:key=value
• Bracketed multi-value args: select:[col1,col2,col3] instead of comma-in-string
• Type-annotated args: e.g. cast:column:int (already done, worth formalizing)

4. Grammar Decoupling Architecture

A possible layered design:

[Miniature Language Parser]   [Enriched Grammar Parser]
         \                            /
          \                          /
           [Common IR / AST Layer]
                    |
           [MacroPipe Engine (Polars)]

Each frontend parses its syntax and emits a shared intermediate representation (e.g., a list of (function_name, args_dict) tuples), which the engine then resolves and applies.

References

• PR: Add Macropipe transformation unit based on Polars expressions tikray#51
• PR comment: Add Macropipe transformation unit based on Polars expressions tikray#51 (comment)
• Requested by: @amotl

[amotl]

Thank you. The data_algebra package is already using Lark, and certainly shares a few ideas what we had in mind. However, while some of its details are pretty excellent, its coverage of topics is significantly larger, and most importantly, development seems to have stalled. However, MacroPipe could possibly reuse the Lark grammar in python3_lark.py and parse_by_lark.py, which is said to be a modified Lark grammar for Python-like expressions for the purpose we are aiming at here.

@coderabbitai: Can you use that to create Python code that implements a transpiler from that near-Python language to Polars expressions? Maybe using polars_model.py is the right choice already, see also test_polars.py.

[coderabbitai]

Tip

For best results, initiate chat on the files or code changes.

🧩 Analysis chain

Great idea, @amotl! Let me first read the relevant data_algebra source files to understand the Lark grammar and Polars model before writing the transpiler.

---

🏁 Script executed:

#!/bin/bash
# Fetch the key data_algebra files from GitHub (raw content)
echo "=== python3_lark.py ==="
curl -fsSL "https://raw.githubusercontent.com/WinVector/data_algebra/main/data_algebra/python3_lark.py"

echo ""
echo "=== parse_by_lark.py ==="
curl -fsSL "https://raw.githubusercontent.com/WinVector/data_algebra/main/data_algebra/parse_by_lark.py"

echo ""
echo "=== polars_model.py (first 300 lines) ==="
curl -fsSL "https://raw.githubusercontent.com/WinVector/data_algebra/main/data_algebra/polars_model.py" | head -300

echo ""
echo "=== test_polars.py (first 200 lines) ==="
curl -fsSL "https://raw.githubusercontent.com/WinVector/data_algebra/main/tests/test_polars.py" | head -200

Length of output: 36804

---

@amotl, I've read the relevant files. The approach is clean: we reuse the grammar string verbatim (MIT-licensed), skip the data_algebra IR entirely, and walk the Lark parse tree ourselves to emit pl.Expr objects directly. No data_algebra runtime dependency needed.

Below is a self-contained polars_expr_transpiler.py module you could drop into src/tikray/macropipe/:

# src/tikray/macropipe/polars_expr_transpiler.py
"""
Transpiler: near-Python expression string  →  polars.Expr

Grammar
-------
Reuses the modified Lark/Python-3 grammar originally from data_algebra
(https://github.com/WinVector/data_algebra, MIT licence).  The grammar
string is self-contained here so there is no runtime dependency on
data_algebra.

Supported surface syntax
------------------------
  Literals          42  3.14  "hello"  True  False  None
  Column refs       total_amount          →  pl.col("total_amount")
  Arithmetic        a + b  a * b  a ** 2  -a
  Comparison        a > 40  a == b  a != b  a <= b
  Logic             a and b   a or b   not a
  Method calls      price.round(2)  col.str.to_uppercase()  col.cast("int")
  Free functions    col("x")  lit(42)  coalesce(a, b)  concat_str(a, b, sep=", ")
  Lists             [1, 2, 3]   (used as argument to .is_in() etc.)

Examples
--------
  parse_expr("total_amount > 40", columns={"total_amount"})
  # → pl.col("total_amount") > pl.lit(40)

  parse_expr("fare_amount.round(2)", columns={"fare_amount"})
  # → pl.col("fare_amount").round(2)

  parse_expr("passenger_count * trip_distance", columns={"passenger_count", "trip_distance"})
  # → pl.col("passenger_count") * pl.col("trip_distance")
"""

from __future__ import annotations

import ast as _ast
import math
from typing import Any, Optional, Set

import lark
import lark.lexer
import lark.tree
import polars as pl

# ---------------------------------------------------------------------------
# Grammar (MIT licence – Erez Shinan / WinVector)
# Modified only: %+% → concat, %?% → coalesce, %/% → float_divide
# ---------------------------------------------------------------------------
_GRAMMAR = r"""
single_input: _NEWLINE | simple_stmt | compound_stmt _NEWLINE
file_input: (_NEWLINE | stmt)*
eval_input: testlist _NEWLINE*

decorator: "@" dotted_name [ "(" [arguments] ")" ] _NEWLINE
decorators: decorator+
decorated: decorators (classdef | funcdef | async_funcdef)
async_funcdef: "async" funcdef
funcdef: "def" NAME "(" parameters? ")" ["->" test] ":" suite
parameters: paramvalue ("," paramvalue)* ["," SLASH] ["," [starparams | kwparams]]
          | starparams | kwparams
SLASH: "/"
starparams: "*" typedparam? ("," paramvalue)* ["," kwparams]
kwparams: "**" typedparam
?paramvalue: typedparam ["=" test]
?typedparam: NAME [":" test]
varargslist: (vfpdef ["=" test] ("," vfpdef ["=" test])* ["," [ "*" [vfpdef] ("," vfpdef ["=" test])* ["," ["**" vfpdef [","]]] | "**" vfpdef [","]]]
  | "*" [vfpdef] ("," vfpdef ["=" test])* ["," ["**" vfpdef [","]]]
  | "**" vfpdef [","])
vfpdef: NAME
?stmt: simple_stmt | compound_stmt
?simple_stmt: small_stmt (";" small_stmt)* [";"] _NEWLINE
?small_stmt: (expr_stmt | del_stmt | pass_stmt | flow_stmt | import_stmt | global_stmt | nonlocal_stmt | assert_stmt)
?expr_stmt: testlist_star_expr (annassign | augassign (yield_expr|testlist)
         | ("=" (yield_expr|testlist_star_expr))*)
annassign: ":" test ["=" test]
?testlist_star_expr: (test|star_expr) ("," (test|star_expr))* [","]
!augassign: ("+=" | "-=" | "*=" | "@=" | "/=" | "%=" | "&=" | "|=" | "^=" | "<<=" | ">>=" | "**=" | "//=")
del_stmt: "del" exprlist
pass_stmt: "pass"
?flow_stmt: break_stmt | continue_stmt | return_stmt | raise_stmt | yield_stmt
break_stmt: "break"
continue_stmt: "continue"
return_stmt: "return" [testlist]
yield_stmt: yield_expr
raise_stmt: "raise" [test ["from" test]]
import_stmt: import_name | import_from
import_name: "import" dotted_as_names
import_from: "from" (dots? dotted_name | dots) "import" ("*" | "(" import_as_names ")" | import_as_names)
!dots: "."+
import_as_name: NAME ["as" NAME]
dotted_as_name: dotted_name ["as" NAME]
import_as_names: import_as_name ("," import_as_name)* [","]
dotted_as_names: dotted_as_name ("," dotted_as_name)*
dotted_name: NAME ("." NAME)*
global_stmt: "global" NAME ("," NAME)*
nonlocal_stmt: "nonlocal" NAME ("," NAME)*
assert_stmt: "assert" test ["," test]
compound_stmt: if_stmt | while_stmt | for_stmt | try_stmt | with_stmt | funcdef | classdef | decorated | async_stmt
async_stmt: "async" (funcdef | with_stmt | for_stmt)
if_stmt: "if" test ":" suite ("elif" test ":" suite)* ["else" ":" suite]
while_stmt: "while" test ":" suite ["else" ":" suite]
for_stmt: "for" exprlist "in" testlist ":" suite ["else" ":" suite]
try_stmt: ("try" ":" suite ((except_clause ":" suite)+ ["else" ":" suite] ["finally" ":" suite] | "finally" ":" suite))
with_stmt: "with" with_item ("," with_item)*  ":" suite
with_item: test ["as" expr]
except_clause: "except" [test ["as" NAME]]
suite: simple_stmt | _NEWLINE _INDENT stmt+ _DEDENT
?test: or_test ("if" or_test "else" test)? | lambdef
?test_nocond: or_test | lambdef_nocond
lambdef: "lambda" [varargslist] ":" test
lambdef_nocond: "lambda" [varargslist] ":" test_nocond
?or_test: and_test ("or" and_test)*
?and_test: not_test ("and" not_test)*
?not_test: "not" not_test -> not
         | comparison
?comparison: expr (_comp_op expr)*
star_expr: "*" expr
?expr: xor_expr ("|" xor_expr)*
?xor_expr: and_expr ("^" and_expr)*
?and_expr: shift_expr ("&" shift_expr)*
?shift_expr: arith_expr (_shift_op arith_expr)*
?arith_expr: term (_add_op term)*
?term: factor (_mul_op factor)*
?factor: _factor_op factor | power
!_factor_op: "+"|"-"|"~"
!_add_op: "+"|"-"
!_shift_op: "<<"|">>"
!_mul_op: "*"|"/"|"%+%"|"%?%"|"%"|"//"|"%/%"
!_comp_op: "<"|">"|"=="|">="|"<="|"<>"|"!="|"in"|"not" "in"|"is"|"is" "not"
?power: await_expr ("**" factor)?
?await_expr: AWAIT? atom_expr
AWAIT: "await"
?atom_expr: atom_expr "(" [arguments] ")"   -> funccall
          | atom_expr "[" subscriptlist "]" -> getitem
          | atom_expr "." NAME              -> getattr
          | atom
?atom: "(" [yield_expr|tuplelist_comp] ")" -> tuple
     | "[" [testlist_comp] "]"  -> list
     | "{" [dict_comp] "}" -> dict
     | "{" set_comp "}" -> set
     | NAME -> var
     | number | string+
     | "(" test ")"
     | "..." -> ellipsis
     | "None"    -> const_none
     | "True"    -> const_true
     | "False"   -> const_false
?testlist_comp: test | tuplelist_comp
tuplelist_comp: (test|star_expr) (comp_for | ("," (test|star_expr))+ [","] | ",")
?subscriptlist: subscript
              | subscript (("," subscript)+ [","] | ",") -> subscript_tuple
subscript: test | ([test] ":" [test] [sliceop]) -> slice
sliceop: ":" [test]
exprlist: (expr|star_expr)
        | (expr|star_expr) (("," (expr|star_expr))+ [","]|",") -> exprlist_tuple
testlist: test | testlist_tuple
testlist_tuple: test (("," test)+ [","] | ",")
dict_comp: key_value comp_for
         | (key_value | "**" expr) ("," (key_value | "**" expr))* [","]
key_value: test ":"  test
set_comp: test comp_for
        | (test|star_expr) ("," (test | star_expr))* [","]
classdef: "class" NAME ["(" [arguments] ")"] ":" suite
arguments: argvalue ("," argvalue)*  ("," [ starargs | kwargs])?
         | starargs | kwargs
         | test comp_for
starargs: "*" test ("," "*" test)* ("," argvalue)* ["," kwargs]
kwargs: "**" test
?argvalue: test ("=" test)?
comp_iter: comp_for | comp_if | async_for
async_for: "async" "for" exprlist "in" or_test [comp_iter]
comp_for: "for" exprlist "in" or_test [comp_iter]
comp_if: "if" test_nocond [comp_iter]
encoding_decl: NAME
yield_expr: "yield" [yield_arg]
yield_arg: "from" test | testlist
number: DEC_NUMBER | HEX_NUMBER | BIN_NUMBER | OCT_NUMBER | FLOAT_NUMBER | IMAG_NUMBER
string: STRING | LONG_STRING
%import python (NAME, COMMENT, STRING, LONG_STRING)
%import python (DEC_NUMBER, HEX_NUMBER, OCT_NUMBER, BIN_NUMBER, FLOAT_NUMBER, IMAG_NUMBER)
_NEWLINE: ( /\r?\n[\t ]*/ | COMMENT )+
%ignore /[\t \f]+/
%ignore /\\[\t \f]*\r?\n/
%ignore COMMENT
%declare _INDENT _DEDENT
"""

# ---------------------------------------------------------------------------
# Type aliases
# ---------------------------------------------------------------------------
_TYPE_MAP: dict[str, Any] = {
    "int":      pl.Int64,
    "int32":    pl.Int32,
    "int64":    pl.Int64,
    "float":    pl.Float64,
    "float32":  pl.Float32,
    "float64":  pl.Float64,
    "str":      pl.Utf8,
    "utf8":     pl.Utf8,
    "bool":     pl.Boolean,
    "date":     pl.Date,
    "datetime": pl.Datetime,
}

# ---------------------------------------------------------------------------
# Binary operator table  (infix token → callable(left, right) → pl.Expr)
# ---------------------------------------------------------------------------
_BINARY_OPS: dict[str, Any] = {
    "+":   lambda l, r: l + r,
    "-":   lambda l, r: l - r,
    "*":   lambda l, r: l * r,
    "/":   lambda l, r: l / r,
    "//":  lambda l, r: l // r,
    "%":   lambda l, r: l % r,
    "**":  lambda l, r: l ** r,
    "==":  lambda l, r: l == r,
    "!=":  lambda l, r: l != r,
    "<>":  lambda l, r: l != r,
    "<":   lambda l, r: l < r,
    "<=":  lambda l, r: l <= r,
    ">":   lambda l, r: l > r,
    ">=":  lambda l, r: l >= r,
    # data_algebra extensions
    "%+%": lambda l, r: pl.concat_str([l, r]),
    "%?%": lambda l, r: pl.coalesce([l, r]),
    "%/%": lambda l, r: l / r,           # float divide (same as /)
}

# ---------------------------------------------------------------------------
# Method call table  method_name → callable(expr, positional_args) → pl.Expr
# ---------------------------------------------------------------------------
_METHOD_MAP: dict[str, Any] = {
    # ── aggregations ────────────────────────────────────────────────────────
    "sum":       lambda e, a: e.sum(),
    "mean":      lambda e, a: e.mean(),
    "median":    lambda e, a: e.median(),
    "min":       lambda e, a: e.min(),
    "max":       lambda e, a: e.max(),
    "std":       lambda e, a: e.std(),
    "var":       lambda e, a: e.var(),
    "count":     lambda e, a: e.count(),
    "n_unique":  lambda e, a: e.n_unique(),
    "first":     lambda e, a: e.first(),
    "last":      lambda e, a: e.last(),
    "mode":      lambda e, a: e.mode(),
    # ── cumulative ──────────────────────────────────────────────────────────
    "cumsum":    lambda e, a: e.cum_sum(),
    "cumprod":   lambda e, a: e.cum_prod(),
    "cummax":    lambda e, a: e.cum_max(),
    "cummin":    lambda e, a: e.cum_min(),
    # ── numeric ─────────────────────────────────────────────────────────────
    "abs":       lambda e, a: e.abs(),
    "round":     lambda e, a: e.round(int(a[0])) if a else e.round(0),
    "ceil":      lambda e, a: e.ceil(),
    "floor":     lambda e, a: e.floor(),
    "sqrt":      lambda e, a: e.sqrt(),
    "log":       lambda e, a: e.log(a[0] if a else math.e),
    "log1p":     lambda e, a: e.log1p(),
    "exp":       lambda e, a: e.exp(),
    "sign":      lambda e, a: e.sign(),
    "diff":      lambda e, a: e.diff(n=int(a[0])) if a else e.diff(),
    "shift":     lambda e, a: e.shift(int(a[0])) if a else e.shift(1),
    "pct_change":lambda e, a: e.pct_change(),
    "clip":      lambda e, a: e.clip(lower_bound=a[0], upper_bound=a[1]),
    # ── null/nan handling ───────────────────────────────────────────────────
    "is_null":       lambda e, a: e.is_null(),
    "is_not_null":   lambda e, a: e.is_not_null(),
    "is_nan":        lambda e, a: e.is_nan(),
    "is_infinite":   lambda e, a: e.is_infinite(),
    "is_finite":     lambda e, a: e.is_finite(),
    "fill_null":     lambda e, a: e.fill_null(a[0]),
    "fill_nan":      lambda e, a: e.fill_nan(a[0]),
    "drop_nulls":    lambda e, a: e.drop_nulls(),
    "forward_fill":  lambda e, a: e.forward_fill(),
    "backward_fill": lambda e, a: e.backward_fill(),
    # ── string methods  (via .str accessor) ─────────────────────────────────
    "str_contains":      lambda e, a: e.str.contains(a[0]),
    "str_starts_with":   lambda e, a: e.str.starts_with(a[0]),
    "str_ends_with":     lambda e, a: e.str.ends_with(a[0]),
    "str_to_uppercase":  lambda e, a: e.str.to_uppercase(),
    "str_to_lowercase":  lambda e, a: e.str.to_lowercase(),
    "str_replace":       lambda e, a: e.str.replace(a[0], a[1]),
    "str_replace_all":   lambda e, a: e.str.replace_all(a[0], a[1]),
    "str_strip_chars":   lambda e, a: e.str.strip_chars(),
    "str_len_chars":     lambda e, a: e.str.len_chars(),
    "str_slice":         lambda e, a: e.str.slice(int(a[0]), int(a[1]) if len(a) > 1 else None),
    "str_split":         lambda e, a: e.str.split(a[0]),
    # ── casting / renaming ──────────────────────────────────────────────────
    "cast":  lambda e, a: e.cast(_TYPE_MAP[str(a[0])]),
    "alias": lambda e, a: e.alias(str(a[0])),
    # ── membership / sorting ────────────────────────────────────────────────
    "is_in":   lambda e, a: e.is_in(a[0] if len(a) == 1 else pl.Series(a)),
    "sort":    lambda e, a: e.sort(descending=(bool(a[0]) if a else False)),
    "reverse": lambda e, a: e.reverse(),
    "arg_sort":lambda e, a: e.arg_sort(),
    "sample":  lambda e, a: e.sample(n=int(a[0])) if a else e.sample(fraction=0.5),
    # ── boolean ─────────────────────────────────────────────────────────────
    "not_": lambda e, a: e.not_(),
    "all":  lambda e, a: e.all(),
    "any":  lambda e, a: e.any(),
}

# ── chained accessor namespaces  (e.g. col.str.to_uppercase()) ──────────────
# Returns a "proxy" that dispatches further method calls on the right accessor.
_ACCESSOR_MAP: dict[str, Any] = {
    "str":  lambda e: e.str,
    "dt":   lambda e: e.dt,
    "list": lambda e: e.list,
    "arr":  lambda e: e.arr,
    "cat":  lambda e: e.cat,
}

# ---------------------------------------------------------------------------
# Free-function table  fn_name → callable(positional_args) → pl.Expr
# ---------------------------------------------------------------------------
_FUNCTION_MAP: dict[str, Any] = {
    "col":          lambda a: pl.col(str(a[0])),
    "lit":          lambda a: pl.lit(a[0]),
    "coalesce":     lambda a: pl.coalesce(a),
    "concat_str":   lambda a: pl.concat_str(a[:-1], separator=str(a[-1])) if len(a) > 1 else pl.concat_str(a),
    "sum":          lambda a: a[0].sum(),
    "mean":         lambda a: a[0].mean(),
    "min":          lambda a: a[0].min(),
    "max":          lambda a: a[0].max(),
    "count":        lambda a: a[0].count(),
    "when":         lambda a: pl.when(a[0]),
    "all":          lambda a: pl.all() if not a else pl.all(*[str(x) for x in a]),
    "any":          lambda a: pl.any(*[str(x) for x in a]) if a else pl.any(),
    "len":          lambda a: a[0].len() if a else pl.len(),
    "struct":       lambda a: pl.struct(a),
    "date":         lambda a: pl.date(int(a[0]), int(a[1]), int(a[2])),
    "datetime":     lambda a: pl.datetime(int(a[0]), int(a[1]), int(a[2])),
}

# ---------------------------------------------------------------------------
# Parser (module-level singleton)
# ---------------------------------------------------------------------------
_parser = lark.Lark(
    _GRAMMAR,
    parser="lalr",
    start="test",
)


# ---------------------------------------------------------------------------
# Tree walker → pl.Expr
# ---------------------------------------------------------------------------

def _walk(
    node: lark.tree.Tree | lark.lexer.Token,
    columns: Optional[Set[str]],
) -> Any:
    """
    Recursively walk a Lark parse tree and produce a polars.Expr (or a raw
    Python scalar for literal leaf nodes used as arguments).

    :param node:    A Lark Tree or Token.
    :param columns: Set of known column names.  Any NAME token that appears in
                    this set becomes pl.col("name").  Pass None to treat *all*
                    NAME tokens as column references.
    :raises NameError: for unknown names when *columns* is provided.
    :raises ValueError: for unsupported syntax.
    :return: pl.Expr or a raw Python scalar.
    """

    # ── leaf tokens ────────────────────────────────────────────────────────
    if isinstance(node, lark.lexer.Token):
        tt = node.type
        if tt == "DEC_NUMBER":
            return int(node)
        if tt == "FLOAT_NUMBER":
            return float(node)
        if tt == "STRING":
            return _ast.literal_eval(str(node))
        if tt == "NAME":
            name = str(node)
            if columns is None or name in columns:
                return pl.col(name)
            raise NameError(
                f"Unknown name '{name}'. "
                "Pass it as a column or use col() / lit() explicitly."
            )
        raise ValueError(f"Unhandled Token type '{tt}': {node!r}")

    # ── tree nodes ─────────────────────────────────────────────────────────
    assert isinstance(node, lark.tree.Tree)
    kind = node.data

    # ─ pass-through wrappers ───────────────────────────────────────────────
    if kind in ("single_input", "number", "string", "var"):
        return _walk(node.children[0], columns)

    # ─ constants ───────────────────────────────────────────────────────────
    if kind == "const_true":
        return pl.lit(True)
    if kind == "const_false":
        return pl.lit(False)
    if kind == "const_none":
        return pl.lit(None)

    # ─ arithmetic / comparison chains  a op b op c … ───────────────────────
    if kind in ("arith_expr", "term", "comparison"):
        nc = len(node.children)
        if (nc < 3) or (nc % 2 != 1):
            raise ValueError(f"Unexpected child count in '{kind}': {nc}")
        result = _walk(node.children[0], columns)
        for i in range((nc - 1) // 2):
            op_tok = str(node.children[2 * i + 1])
            right  = _walk(node.children[2 * i + 2], columns)
            fn = _BINARY_OPS.get(op_tok)
            if fn is None:
                raise ValueError(f"Unsupported binary operator: '{op_tok}'")
            result = fn(result, right)
        return result

    # ─ power  a ** b ───────────────────────────────────────────────────────
    if kind == "power":
        base = _walk(node.children[0], columns)
        for child in node.children[1:]:
            exp = _walk(child, columns)
            base = base ** exp
        return base

    # ─ unary factor  -x  +x  ~x ────────────────────────────────────────────
    if kind == "factor":
        op_tok = str(node.children[0])
        operand = _walk(node.children[1], columns)
        if op_tok == "-":
            return -operand
        if op_tok == "+":
            return +operand
        if op_tok == "~":
            return ~operand
        raise ValueError(f"Unsupported unary operator: '{op_tok}'")

    # ─ and / or  (short-circuit logic on Polars Exprs) ────────────────────
    if kind == "or_test":
        kids = [_walk(c, columns) for c in node.children]
        result = kids[0]
        for k in kids[1:]:
            result = result | k
        return result

    if kind == "and_test":
        kids = [_walk(c, columns) for c in node.children]
        result = kids[0]
        for k in kids[1:]:
            result = result & k
        return result

    # ─ not ─────────────────────────────────────────────────────────────────
    if kind == "not":
        return ~_wrap_expr(_walk(node.children[0], columns))

    # ─ attribute access  obj.attr  ─────────────────────────────────────────
    # Handled lazily inside funccall; a bare getattr produces an accessor proxy.
    if kind == "getattr":
        obj  = _walk(node.children[0], columns)
        attr = str(node.children[1])
        # If it is an accessor namespace (.str, .dt, …) return the accessor
        if attr in _ACCESSOR_MAP:
            return _ACCESSOR_MAP[attr](obj)
        # Otherwise it is a method that will be called in the funccall parent
        # — we shouldn't reach here as a standalone node, but handle gracefully
        raise ValueError(
            f"Attribute access '.{attr}' without a following call is not supported. "
            "Did you mean '.{attr}()'?"
        )

    # ─ function / method call  f(args)  or  obj.method(args) ──────────────
    if kind == "funccall":
        callee  = node.children[0]
        raw_args_node = node.children[1] if len(node.children) > 1 else None
        args: list[Any] = []
        if raw_args_node is not None:
            args = [_walk(c, columns) for c in raw_args_node.children]

        # Method call:  obj.method(args)
        if isinstance(callee, lark.tree.Tree) and callee.data == "getattr":
            obj    = _walk(callee.children[0], columns)
            method = str(callee.children[1])
            # Accessor-based method (obj.str.contains / obj.dt.year etc.)
            # Here obj is already the accessor proxy.
            if not isinstance(obj, pl.Expr):
                # obj is an accessor proxy (e.g. polars StringNameSpace)
                # — just call the attribute on it
                return getattr(obj, method)(*args)
            fn = _METHOD_MAP.get(method)
            if fn is not None:
                return fn(obj, args)
            # Fallback: attempt direct attribute call (future-proof)
            return getattr(obj, method)(*args)

        # Free function call:  f(args)
        if isinstance(callee, lark.tree.Tree) and callee.data == "var":
            fn_name = str(callee.children[0])
        elif isinstance(callee, lark.lexer.Token):
            fn_name = str(callee)
        else:
            raise ValueError(f"Cannot resolve callee: {callee!r}")

        fn = _FUNCTION_MAP.get(fn_name)
        if fn is not None:
            return fn(args)
        raise ValueError(f"Unknown function: '{fn_name}'")

    # ─ list / tuple / set literals → Python list (for .is_in() args etc.) ─
    if kind in ("list", "tuple", "set"):
        children_node = node.children[0] if node.children else None
        if children_node is None:
            return []
        if isinstance(children_node, lark.tree.Tree) and children_node.data in (
            "tuplelist_comp",
        ):
            return [_walk(c, columns) for c in children_node.children]
        return [_walk(children_node, columns)]

    # ─ assignment (error) ─────────────────────────────────────────────────
    if kind == "expr_stmt":
        raise ValueError("Use '==' for comparison, not '='")

    raise ValueError(f"Unsupported grammar node: '{kind}'")


def _wrap_expr(v: Any) -> pl.Expr:
    """Coerce a raw Python scalar to pl.lit() if it is not already a pl.Expr."""
    if isinstance(v, pl.Expr):
        return v
    return pl.lit(v)


# ---------------------------------------------------------------------------
# Public API
# ---------------------------------------------------------------------------

def parse_expr(
    source: str,
    *,
    columns: Optional[Set[str]] = None,
) -> pl.Expr:
    """
    Parse a near-Python expression string and return the equivalent
    ``polars.Expr``.

    Parameters
    ----------
    source:
        Expression string, e.g. ``"total_amount > 40"`` or
        ``"fare_amount.round(2)"`` or
        ``"passenger_count * trip_distance"``.
    columns:
        Set of column names known to exist in the target frame.
        Any ``NAME`` token found in this set is turned into
        ``pl.col(name)``.  Pass ``None`` (default) to treat *all*
        bare names as column references.

    Returns
    -------
    polars.Expr
    """
    tree = _parser.parse(source.strip())
    expr = _walk(tree, columns)
    return _wrap_expr(expr)

---

Usage sketch inside MacroPipe

from tikray.macropipe.polars_expr_transpiler import parse_expr
import polars as pl

lf = pl.scan_csv("trips.csv")

# Filter step
pred = parse_expr("total_amount > 40 and passenger_count >= 1",
                  columns={"total_amount", "passenger_count"})
lf = lf.filter(pred)

# Derived column
derived = parse_expr("fare_amount.round(2)", columns={"fare_amount"})
lf = lf.with_columns(derived.alias("fare_rounded"))

# Cast
cast_expr = parse_expr("passenger_count.cast('int')", columns={"passenger_count"})
lf = lf.with_columns(cast_expr)

result = lf.collect()

---

Notes and trade-offs

Topic	Decision
data_algebra dependency	None at runtime – grammar string is self-contained (MIT)
Entry point	start="test" (same as parse_by_lark.py)
Column resolution	columns=None → all bare names are columns; pass a set for strict mode
Accessor chains	col.str.contains(…), col.dt.year supported via _ACCESSOR_MAP proxy
and/or	Maps to &/| on pl.Expr (correct for Polars lazy evaluation)
Scalars as arguments	Left as raw Python values so Polars can infer types (round(2), cast("int"))
Missing ops	when/then/otherwise, list comprehensions, slices – can be added incrementally

The grammar is intentionally reused verbatim so we can later layer a FlatPipe (colon-based) frontend on top of the same IR if desired, as sketched in the decoupling architecture in the issue description.

---

🧠 Learnings used

Learnt from: amotl
Repo: panodata/tikray PR: 51
File: src/tikray/macropipe/core.py:42-45
Timestamp: 2026-04-12T14:26:34.439Z
Learning: In `src/tikray/macropipe/core.py` (Python, tikray project), the MacroPipe recipe expression parser should use pure Python `re.findall(r'(?:[^:\\]|\\.)+', expression)` for escape-aware colon-delimited tokenization (splitting on bare `:` while preserving `\:`), followed by `a.replace('\\:', ':')` on each arg token to unescape literal colons. The method that applies recipes to a LazyFrame is named `apply(lf: pl.LazyFrame)` (not `process`). Polars-based parsing via `pl.Series` is discouraged here since the regex runs only once and pure Python is preferred.

[amotl]

@coderabbitai: That looks pretty decent from a distance, and the trade-offs don't feel too signifcant to be able to make an excellent start. Effectively, you are only enumerating a few missing operations as a trade-off, while the code checks all the other boxes well that are super important to get right from the start, like column resolution details, accessor chains, and boolean operators? That's too good to be true.

Of course, all the credits go to @JohnMount and @erezsh for laying out superb ergonomics and modularity in their prior art publications. 🙇

[coderabbitai]

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@amotl, thank you for the kind words — and yes, full credit to @JohnMount and @erezsh for the excellent foundations. 🙇

To answer your question honestly though: it is a bit too good to be true, and I should flag the real bugs I spotted on closer inspection before they bite you in testing:

---

🐛 Bug 1 — Accessor chain double-getattr (.str.method() / .dt.method() broken)

For col.str.contains("x"), the Lark tree is:

funccall
  getattr
    getattr(var(col), "str")   ← inner getattr
  "contains"
  arguments(...)

In the funccall branch, I call _walk(callee.children[0], columns) — which recurses into the inner getattr node. But my getattr handler raises ValueError for bare attribute access (no following call). So all chained accessor calls (.str.*, .dt.*) would throw at runtime.

Fix needed: the getattr handler should return an accessor proxy lazily, and funccall must detect a non-pl.Expr proxy and dispatch via getattr(proxy, method)(*args).

---

🐛 Bug 2 — arguments / argvalue node not handled in _walk

The grammar rule is ?argvalue: test ("=" test)?. The arguments node's children are argvalue Trees, not raw test Trees. My _walk function has no argvalue case, so this line:

args = [_walk(c, columns) for c in raw_args_node.children]

would pass an unhandled tree node to _walk and raise ValueError on virtually every function call with arguments.

Fix needed: add an argvalue case to _walk (returning the value from the first child for positional args, or a (key, value) tuple for keyword args), and let funccall separate positional vs. keyword arguments.

---

⚠️ Concern 3 — LALR + _INDENT/_DEDENT + %import python

The grammar declares %declare _INDENT _DEDENT which normally requires Lark's IndentationAware postlex. Without it, Lark(..., parser="lalr") on the full Python grammar may silently misparse or raise on construction. For expression-only use, switching to parser="earley", ambiguity="resolve" or stripping the grammar down to eval_input and test rules only would be safer.

---

What actually does work

• The operator and comparison chains (arith_expr, term, comparison, power) ✅
• Boolean and/or/not → &/|/~ ✅
• Literal constants and pl.lit() wrapping ✅
• Column resolution (strict-mode columns set vs. permissive None) ✅
• The free-function table (col(), lit(), coalesce(), etc.) ✅ — as long as Bug 2 is fixed

---

So the skeleton is sound and the architecture is right, but Bugs 1 and 2 are genuine showstoppers that need patching before any real test would pass. Would you like me to produce a corrected version addressing these?

[amotl]

So the skeleton is sound and the architecture is right, but Bugs 1 and 2 are genuine showstoppers that need patching before any real test would pass. Would you like me to produce a corrected version addressing these?

@coderabbitai: I'd like to get an opinion about possible improvements, sure. However, please create a separate ticket for corresponding evaluations, so we don't overload this one. Thanks.

[coderabbitai]

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🧩 Analysis chain

Sure, let me create a dedicated issue now.

---

🏁 Script executed:

#!/bin/bash
gh issue create \
  --repo panodata/macropipe \
  --assignee amotl \
  --title "polars_expr_transpiler: Fix accessor chains, argument parsing, and grammar parser concerns" \
  --body "## Overview

This issue tracks concrete bugs and improvements identified in the \`polars_expr_transpiler.py\` module proposed in `#7` (the Lark-grammar-based near-Python → Polars expression transpiler).

Backlink: https://github.com/panodata/macropipe/issues/7#issuecomment-TODO
Requested by: `@amotl`

---

## Bug 1 — Accessor chain double-\`getattr\` (\`.str.method()\` / \`.dt.method()\` broken)

For an expression like \`col.str.contains(\"x\")\`, the Lark parse tree is:

\`\`\`
funccall
  getattr
    getattr(var(col), \"str\")   ← inner getattr
  \"contains\"
  arguments(...)
\`\`\`

In the \`funccall\` branch, \`_walk(callee.children[0], columns)\` recurses into the **inner \`getattr\`** node. But the current \`getattr\` handler raises \`ValueError\` for bare attribute access (no following call). So all chained accessor calls (\`.str.*\`, \`.dt.*\`, \`.arr.*\`, \`.cat.*\`) would throw at runtime.

**Fix needed:** The \`getattr\` handler should return an accessor proxy lazily, and \`funccall\` must detect a non-\`pl.Expr\` proxy and dispatch via \`getattr(proxy, method)(*args)\`.

---

## Bug 2 — \`arguments\` / \`argvalue\` node not handled in \`_walk\`

The grammar rule is \`?argvalue: test (\"=\" test)?\`. The \`arguments\` node's children are \`argvalue\` Trees, not raw \`test\` Trees. The current \`_walk\` function has no \`argvalue\` case, so:

\`\`\`python
args = [_walk(c, columns) for c in raw_args_node.children]
\`\`\`

passes an unhandled tree node to \`_walk\` and raises \`ValueError\` on virtually **every function call with arguments**.

**Fix needed:** Add an \`argvalue\` case to \`_walk\` (returning the value from the first child for positional args, or a \`(key, value)\` tuple for keyword args). The \`funccall\` handler should then separate positional vs. keyword arguments before dispatching.

---

## Concern 3 — LALR parser + \`_INDENT\`/\`_DEDENT\` + \`%import python\`

The grammar declares \`%declare _INDENT _DEDENT\` which normally requires Lark's \`IndentationAware\` postlex. Without it, \`Lark(..., parser=\"lalr\")\` on the full Python grammar may silently misparse or raise on construction.

**Options to evaluate:**
- Switch to \`parser=\"earley\", ambiguity=\"resolve\"\` for correctness.
- Strip the grammar down to only \`eval_input\` and \`test\` rules (expression-only subset), eliminating the need for indentation handling entirely.
- Use Lark's built-in \`python.lark\` grammar with a custom postlex.

---

## What already works correctly (no changes needed)

- Operator and comparison chains (\`arith_expr\`, \`term\`, \`comparison\`, \`power\`) ✅
- Boolean \`and\`/\`or\`/\`not\` → \`&\`/\`|\`/\`~\` ✅
- Literal constants and \`pl.lit()\` wrapping ✅
- Column resolution (strict-mode \`columns\` set vs. permissive \`None\`) ✅
- Free-function table (\`col()\`, \`lit()\`, \`coalesce()\`, etc.) ✅ — pending Bug 2 fix

---

## Possible future enhancements

- \`when/then/otherwise\` chaining
- List comprehensions
- Slice expressions
- Named/keyword argument pass-through for free functions
"

Length of output: 47

---

@amotl, done! The follow-up tracking issue has been opened at:

👉 #10 — polars_expr_transpiler: Fix accessor chains, argument parsing, and grammar parser concerns

It covers Bug 1 (accessor chain double-getattr), Bug 2 (argvalue node unhandled in _walk), Concern 3 (LALR + _INDENT/_DEDENT grammar mismatch), a summary of what already works correctly, and a short list of possible future enhancements. Assigned to you.

(ᵔᴥᵔ)づ♡

[amotl]

@coderabbitai: Thank you. Using Lark's built-in python.lark grammar with a custom postlex, or other adjustments to ignore indentations like elaborated here and in GH-10, also sounds very intriguing.

[coderabbitai]

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@amotl, great — and it turns out this is cleanly solved by Lark's own tooling. Here is a concrete rundown of the three practical paths, from simplest to most powerful:

---

Option A — start='eval_input' + PythonIndenter (least code, most idiomatic)

The official Lark example loads python.lark directly from the package via Lark.open_from_package('lark', 'python.lark', ['grammars'], parser='lalr', postlex=PythonIndenter(), start='file_input').

For expression-only use, swap start='file_input' for start='eval_input'. The eval_input rule is defined as testlist _NEWLINE* — a single expression followed by optional newlines — so _INDENT/_DEDENT tokens are never generated and PythonIndenter becomes a no-op postlex that costs nothing:

from lark import Lark
from lark.indenter import PythonIndenter

_parser = Lark.open_from_package(
    'lark', 'python.lark', ['grammars'],
    parser='lalr',
    postlex=PythonIndenter(),
    start='eval_input',
)

This replaces the entire embedded _GRAMMAR string in the transpiler and immediately fixes Concern 3 from GH-10.

---

Option B — selective %import python (minimal grammar surface, no postlex needed)

You can selectively import only what you need from the built-in grammar — e.g. %import python (eval_input, atom, var, expr, testlist_star_expr, _NEWLINE, NAME) — and extend it with custom rules, while still supplying PythonIndenter() as postlex.

For an expression-only transpiler that never needs compound statements or suite/_INDENT/_DEDENT, importing only test and its dependencies is sufficient, and no postlex is needed at all:

_EXPR_GRAMMAR = r"""
%import python (test, NAME, NUMBER, STRING, LONG_STRING,
                DEC_NUMBER, HEX_NUMBER, OCT_NUMBER, BIN_NUMBER,
                FLOAT_NUMBER, IMAG_NUMBER)

start: test _NEWLINE*
_NEWLINE: /\r?\n/

%ignore /[\t \f]+/
%ignore /\\[\t \f]*\r?\n/
"""

_parser = Lark(_EXPR_GRAMMAR, parser='lalr', start='start')

No %declare _INDENT _DEDENT, no postlex, no ambiguity risk.

---

Option C — %extend on top of the built-in grammar (for future enriched syntax)

Lark's %extend directive lets you add new syntax to the built-in Python grammar — for example, adding a match_stmt compound statement — while reusing PythonIndenter and start=['single_input', 'file_input', 'eval_input'].

This is the path to take if MacroPipe's enriched grammar later needs its own pipe-operator (|) or custom infix operators beyond what Python provides.

---

Recommendation for GH-10

For the immediate bug-fix milestone, Option A is the lowest-friction patch (one Lark.open_from_package call replaces ~250 lines of embedded grammar). Option B is slightly leaner and avoids even pulling in compound-statement rules. Both resolve Concern 3 cleanly, leaving Bugs 1 and 2 as the remaining work items.

( ˘ ³˘)♥

[amotl]

@coderabbitai: Thank you, that sounds too good to be true. We hope everything is reasonably correct here, otherwise it is possible Erez already started facepalming and hair-pulling. ;]

1. So, being able to use chained accessor namespaces (Bug 1) is important to us, and John seems to have solved this detail by using a proxy already. Can you recommend how to solve this detail with your recent proposals to use the vanilla Lark grammar for Python?

   # ── chained accessor namespaces  (e.g. col.str.to_uppercase()) ──────────────
   # Returns a "proxy" that dispatches further method calls on the right accessor.
   _ACCESSOR_MAP: dict[str, Any] = {
       "str":  lambda e: e.str,
       "dt":   lambda e: e.dt,
       "list": lambda e: e.list,
       "arr":  lambda e: e.arr,
       "cat":  lambda e: e.cat,
   }

2. I can't believe that using function arguments would not be possible at all (Bug 2). However, of course this is also of topmost importance. Please only convey information you are absolutely sure about, and let me finally start getting my hands dirty on this. Thanks for all the input so far.

[coderabbitai]

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@amotl, good questions — here are honest, precise answers to both.

---

1. Bug 1 — Accessor chains with the vanilla Lark grammar

The good news is that the _ACCESSOR_MAP proxy pattern already handles this correctly for the known Polars namespaces. My earlier Bug 1 description was overstated. Here is what actually happens for col.str.contains("x"):

The parse tree is:

funccall
  getattr(getattr(var("col"), "str"), "contains")   ← callee
  arguments("x")

In funccall, _walk is called on callee.children[0] = getattr(var("col"), "str"). The getattr handler finds "str" in _ACCESSOR_MAP and returns pl.col("col").str (the StringNameSpace proxy). Back in funccall, obj is not a pl.Expr, so it falls through to getattr(obj, method)(*args) → pl.col("col").str.contains("x"). ✅

This tree structure is identical regardless of whether the grammar comes from the embedded _GRAMMAR string or from Lark.open_from_package('lark', 'python.lark', ...) — both use the same atom_expr rule. So the accessor proxy carries over to Option A with zero changes.

The only real exposure is an attribute name not listed in _ACCESSOR_MAP (e.g., a future Polars namespace not yet enumerated). The fix there is to loosen the getattr handler: instead of raising, attempt getattr(obj, attr) and only raise if the result is None or the attribute doesn't exist:

if kind == "getattr":
    obj  = _walk(node.children[0], columns)
    attr = str(node.children[1])
    accessor_fn = _ACCESSOR_MAP.get(attr)
    if accessor_fn is not None:
        return accessor_fn(obj)          # known namespace → proxy
    if isinstance(obj, pl.Expr):
        # unknown attr on an Expr: return a lazy proxy for the funccall parent
        return getattr(obj, attr)        # e.g. e.some_future_accessor
    raise ValueError(
        f"Attribute '.{attr}' is not a known Polars accessor namespace."
    )

---

2. Bug 2 — Function argument handling (narrower than stated)

You are right to be sceptical. The bug is narrower than I described. In Lark, the ? prefix on ?argvalue makes it a transparent (inline) rule: when argvalue has a single child (a positional argument), Lark hoists the child directly and no argvalue Tree node is produced. So round(2) → arguments.children = [<DEC_NUMBER 2>] — no wrapping, no problem.

The argvalue Tree node is produced only when there are two children: a keyword argument (key=value). So the actual impact is:

Call form	Works?
abs() — no args	✅
round(2) — positional only	✅
col.str.contains("x") — positional only	✅
concat_str(a, b, sep=", ") — keyword arg	❌ argvalue Tree unhandled

The targeted fix for keyword args is:

# inside funccall, replace the flat list comprehension:
pos_args: list[Any] = []
kw_args: dict[str, Any] = {}
if raw_args_node is not None:
    for child in raw_args_node.children:
        if isinstance(child, lark.tree.Tree) and child.data == "argvalue":
            # keyword arg: argvalue(test(key_name), test(value))
            key   = str(child.children[0].children[0])   # NAME token
            value = _walk(child.children[1], columns)
            kw_args[key] = value
        else:
            pos_args.append(_walk(child, columns))

Then pass kw_args to the function/method dispatchers that need it (currently only concat_str and similar variadic free functions need it; method calls generally don't use keyword args in this context).

---

With those two clarifications in hand, the _ACCESSOR_MAP proxy pattern survives unchanged into Option A, and the argument-handling fix is a small, surgical addition. Good luck getting your hands dirty — the foundation is solid. 🙇