dictwalk 2.2.0

Dict path traversal and mutation utilities

dictwalk

This library is the result started as an idea to write jq queries for a Python Dict for a reason I can't event remember anymore.

It's current form emerged because I kept asking the AI to add one more feature, then one more, then one more.

Then I asked the AI to come up with features and I said screw it add them all.

This is bored-developer + tokens to burn + scope creep in library form.

This is now just a very advanced hammer in search of a nail, could your usecase be that nail?

I know what you're thinking, this could replace those horrible nested dict functions that I have to mainain in one of my projects. Don't do it's not worth it.

Now in version 1, it's been converted to rust, so now it's much much faster and doing nothing important.

dictwalk is a small utility for traversing and mutating nested Python dict/list data using path expressions. The implementation is now Rust-first (PyO3 extension), called from Python. There is no pure-Python execution backend.

It supports:

• Deep reads (get)
• Existence checks (exists)
• In-place writes (set)
• In-place removals (unset)
• Predicate filtering for lists
• Wildcards (*, **)
• Transform/filter pipelines (|$filter)

Jump To

• Quick Start
• Path Syntax
• API
• Examples
• Filter Functions
• Development

Requirements

• Python >=3.10
• Rust toolchain (for source builds)

Installation

From source:

pip install .

This builds the Rust extension module (dictwalk._dictwalk_rs) during install.

For local development:

uv sync
make rust-build

Quick Start

from dictwalk import dictwalk

data = {
    "a": {
        "users": [
            {"id": 1, "name": "Ada", "active": True},
            {"id": 2, "name": "Lin", "active": False},
        ]
    }
}

# Read
names = dictwalk.get(data, "a.users[].name")
# ["Ada", "Lin"]

# Filter and map
active_names = dictwalk.get(data, "a.users[?.active==True].name[]")
# ["Ada"]

# Write
dictwalk.set(data, "a.users[?.id==2].active", True)

# Unset
dictwalk.unset(data, "a.users[?.id==1].name")

Path Syntax

Dot traversal

a.b.c

Read nested object keys.

List map

a.items[].id

Apply the next token to every item in a list.

List index and slice

a.items[0]
a.items[-1]
a.items[1:3]

Predicates

a.items[?.id==1]
a.items[?.score>=10]

List predicates support:

• ==, !=, >, <, >=, <=

Predicate filters

Use registered filters on predicate values:

a.items[?.id==$even]
a.items[?.id==$gt(5)&&$lt(10)]
a.items[?.id==!$odd]

Boolean operators in predicate filters:

• && (and)
• || (or)
• ! (not)
• parentheses for grouping

Wildcards

a.*.id
a.**.id

• *: one level
• **: deep descendant traversal

Output transforms

Apply filters to the final read value:

a.value|$double|$string
a.list|$max
a.list|$double[]|$max

API

dictwalk exposed from dictwalk.__init__ is the Rust extension object directly. Python methods call into Rust for get, exists, set, unset, and run_filter_function. The package ships PEP 561 type information (py.typed) for Python type checkers.

dictwalk.get(data, path, default=None, strict=False)

• Returns resolved value.
• If strict=False: resolution failures return default.
• If strict=True: raises DictWalkResolutionError.

Special root token support in read paths:

$$root.x

$$root must be the first token in a read path. Mid-path usage is invalid:

a.b.$$root.x  # raises DictWalkParseError

dictwalk.exists(data, path, strict=False) -> bool

• Returns True if path resolves, else False.
• If strict=True, raises DictWalkResolutionError on resolution failures.

dictwalk.set(data, path, value, *, strict=False, create_missing=True, create_filter_match=True, overwrite_incompatible=True) -> dict

Mutates and returns the same data object.

value can be:

• A direct value (42, "x", {"k": 1})
• A filter string ("$double", "$add(2)|$string")
• A root reference expression:
  • $$root
  • $$root.some.path
  • $$root.some.path|$filter

Notes:

• $$root is valid in value, not in write path.
• With strict=True, parent path must already resolve.

dictwalk.unset(data, path, *, strict=False) -> dict

Removes targeted values in-place and returns the same object.

At terminal paths this can:

• Remove dict keys
• Remove list indexes/slices
• Remove list items matching a filter

Examples

This section contains practical examples for:

• dictwalk.get
• dictwalk.set
• dictwalk.unset

from dictwalk import dictwalk

Shared Sample Data

data = {
    "a": {
        "b": {"c": 1},
        "users": [
            {"id": 1, "name": "Ada", "active": True, "score": 10},
            {"id": 2, "name": "Lin", "active": False, "score": 20},
            {"id": 3, "name": "Mia", "active": True, "score": 30},
        ],
        "groups": {
            "g1": {"u1": {"id": 1, "debug": True}},
            "g2": {"nested": {"u2": {"id": 2, "debug": False}}},
        },
    },
    "x": 2,
    "profile": {"name": "Dict Walk", "tags": ["py", "paths", "json"]},
}

get Examples

Basic traversal:

dictwalk.get(data, "a.b.c")
# 1

Root object:

dictwalk.get(data, ".")
# full data object

Root token:

dictwalk.get(data, "$$root.x")
# 2

Invalid mid-path root token:

dictwalk.get(data, "a.b.$$root.x")
# raises DictWalkParseError

Missing path with default:

dictwalk.get(data, "a.b.missing", default="n/a")
# "n/a"

Strict mode:

dictwalk.get(data, "a.b.missing", strict=True)
# raises DictWalkResolutionError

List map:

dictwalk.get(data, "a.users[].name")
# ["Ada", "Lin", "Mia"]

List index and negative index:

dictwalk.get(data, "a.users[0].name")
# "Ada"

dictwalk.get(data, "a.users[-1].name")
# "Mia"

List slice:

dictwalk.get(data, "a.users[1:3].name[]")
# ["Lin", "Mia"]

Predicate filters:

dictwalk.get(data, "a.users[?.id==2].name[]")
# ["Lin"]

dictwalk.get(data, "a.users[?.score>10].name[]")
# ["Lin", "Mia"]

dictwalk.get(data, "a.users[?.score<=20].name[]")
# ["Ada", "Lin"]

Predicate path filters:

dictwalk.get(data, "a.users[?.id==$even].name[]")
# ["Lin"]

dictwalk.get(data, "a.users[?.id==$gt(1)&&$lt(3)].name[]")
# ["Lin"]

dictwalk.get(data, "a.users[?.id==!$odd].name[]")
# ["Lin"]

Predicate root (?.):

dictwalk.get({"items": ["hi", "hello", "yo"]}, "items[?.|$len>2]")
# ["hello"]

Nested predicates:

dictwalk.get(
    {"a": [{"b": [{"c": 1}, {"c": 2}], "d": 10}, {"b": [{"c": 3}], "d": 20}]},
    "a[?.b[?.c==2]].d",
)
# [10]

Wildcards:

dictwalk.get(data, "a.groups.*.id")
# [1]

dictwalk.get(data, "a.groups.**.id")
# [1, 2]

Output transforms:

dictwalk.get(data, "a.b.c|$double")
# 2

dictwalk.get(data, "a.b.c|$double|$string")
# "2"

dictwalk.get(data, "a.users[].score|$sum")
# 60

dictwalk.get(data, "profile.tags|$join(',')")
# "py,paths,json"

set Examples

All set operations mutate and return the same data object.

Basic nested write:

obj = {}
dictwalk.set(obj, "a.b.c", 5)
# {"a": {"b": {"c": 5}}}

Create list path via map:

obj = {}
dictwalk.set(obj, "a.items[].value", 1)
# {"a": {"items": [{"value": 1}]}}

Update list values with map:

obj = {"a": {"nums": [1, 2, 3]}}
dictwalk.set(obj, "a.nums[]", 9)
# {"a": {"nums": [9, 9, 9]}}

Transform existing values:

obj = {"a": {"nums": [1, 2, 3]}}
dictwalk.set(obj, "a.nums[]", "$double")
# {"a": {"nums": [2, 4, 6]}}

dictwalk.set(obj, "a.nums[]", "$add(1)|$string")
# {"a": {"nums": ["3", "5", "7"]}}

Filtered write:

obj = {"a": {"users": [{"id": 1, "active": False}, {"id": 2, "active": False}]}}
dictwalk.set(obj, "a.users[?.id==2].active", True)
# {"a": {"users": [{"id": 1, "active": False}, {"id": 2, "active": True}]}}

Operator filter write:

obj = {"a": {"users": [{"id": 1, "score": 10}, {"id": 2, "score": 20}, {"id": 3, "score": 30}]}}
dictwalk.set(obj, "a.users[?.id>1].score", 0)
# {"a": {"users": [{"id": 1, "score": 10}, {"id": 2, "score": 0}, {"id": 3, "score": 0}]}}

Index and slice write:

obj = {"a": {"nums": [10, 20, 30, 40]}}
dictwalk.set(obj, "a.nums[1]", 99)
# {"a": {"nums": [10, 99, 30, 40]}}

dictwalk.set(obj, "a.nums[1:3]", 0)
# {"a": {"nums": [10, 0, 0, 40]}}

Wildcard write:

obj = {"a": {"u1": {"enabled": False}, "u2": {"enabled": False}}}
dictwalk.set(obj, "a.*.enabled", True)
# {"a": {"u1": {"enabled": True}, "u2": {"enabled": True}}}

Deep wildcard write:

obj = {"a": {"g1": {"u1": {"enabled": False}}, "g2": {"nested": {"u2": {"enabled": False}}}}}
dictwalk.set(obj, "a.**.enabled", True)
# {"a": {"g1": {"u1": {"enabled": True}}, "g2": {"nested": {"u2": {"enabled": True}}}}}

$$root value expressions:

obj = {"a": {"items": [{"v": 0}, {"v": 0}]}, "source": 9}
dictwalk.set(obj, "a.items[].v", "$$root.source")
# {"a": {"items": [{"v": 9}, {"v": 9}]}, "source": 9}

dictwalk.set(obj, "a.items[].v", "$$root.source|$double")
# {"a": {"items": [{"v": 18}, {"v": 18}]}, "source": 9}

Strict write:

obj = {}
dictwalk.set(obj, "a.b.c", 1, strict=True)
# raises DictWalkResolutionError

Write options:

obj = {}
dictwalk.set(obj, "a.b.c", 1, create_missing=False)
# {}

obj = {"a": {"users": [{"id": "1", "c": 10}]}}
dictwalk.set(obj, "a.users[?.id==3].c", 99, create_filter_match=False)
# unchanged

obj = {"a": 1}
dictwalk.set(obj, "a.b", 2, overwrite_incompatible=False)
# {"a": 1}

unset Examples

All unset operations mutate and return the same data object.

Remove nested key:

obj = {"a": {"b": {"c": 1, "d": 2}}}
dictwalk.unset(obj, "a.b.c")
# {"a": {"b": {"d": 2}}}

Remove mapped key from all list items:

obj = {"a": {"users": [{"id": 1, "name": "Ada"}, {"id": 2, "name": "Lin"}]}}
dictwalk.unset(obj, "a.users[].name")
# {"a": {"users": [{"id": 1}, {"id": 2}]}}

Remove field from filtered matches:

obj = {"a": {"users": [{"id": 1, "score": 10}, {"id": 2, "score": 20}]}}
dictwalk.unset(obj, "a.users[?.id==2].score")
# {"a": {"users": [{"id": 1, "score": 10}, {"id": 2}]}}

Remove items at terminal filtered path:

obj = {"a": {"users": [{"id": 1}, {"id": 2}, {"id": 3}]}}
dictwalk.unset(obj, "a.users[?.id>1]")
# {"a": {"users": [{"id": 1}]}}

In-place list filtering with unset:

obj = {"a": {"b": [1, 2, 3, 4, 5]}}

# Keep even values (remove non-matches)
dictwalk.unset(obj, "a.b[?.|$even==False]")
# {"a": {"b": [2, 4]}}

# Remove even values
dictwalk.unset(obj, "a.b[?.|$even==True]")
# {"a": {"b": [1, 3, 5]}}

General predicate style for in-place filtering:

• Keep condition P: unset P == False
• Remove condition P: unset P == True
• For scalar-list current-item predicates, use ?.|$filter... on the left side.

Remove list index and slice:

obj = {"a": {"nums": [10, 20, 30, 40]}}
dictwalk.unset(obj, "a.nums[1]")
# {"a": {"nums": [10, 30, 40]}}

dictwalk.unset(obj, "a.nums[1:3]")
# {"a": {"nums": [10]}}

Unset with slice + nested field:

obj = {"a": {"users": [{"id": 1, "debug": True}, {"id": 2, "debug": False}, {"id": 3, "debug": True}]}}
dictwalk.unset(obj, "a.users[1:3].debug")
# {"a": {"users": [{"id": 1, "debug": True}, {"id": 2}, {"id": 3}]}}

Wildcard unset:

obj = {"a": {"u1": {"debug": True, "id": 1}, "u2": {"debug": False, "id": 2}}}
dictwalk.unset(obj, "a.*.debug")
# {"a": {"u1": {"id": 1}, "u2": {"id": 2}}}

Deep wildcard unset:

obj = {"a": {"g1": {"u1": {"debug": True, "id": 1}}, "g2": {"nested": {"u2": {"debug": False, "id": 2}}}}}
dictwalk.unset(obj, "a.**.debug")
# {"a": {"g1": {"u1": {"id": 1}}, "g2": {"nested": {"u2": {"id": 2}}}}}

Strict unset:

obj = {"a": {"b": {}}}
dictwalk.unset(obj, "a.b.c", strict=True)
# raises DictWalkResolutionError

Filter Functions

This section documents the built-in path filters available in dictwalk.

Use filters in:

• Output transforms: a.b.c|$double|$string
• Predicate expressions: a.items[?.id==$even]
• Write transforms: dictwalk.set(data, "a.items[]", "$inc")

Usage notes:

• Syntax: $name or $name(arg1, arg2, ...)
• Pipe multiple filters with |
• Add [] to map over list values in transform context (example: $double[])
• Predicate boolean composition supports &&, ||, !, and parentheses

Numeric:

• $inc: add 1
• $dec: subtract 1
• $double: multiply by 2
• $square: multiply by itself
• $add(amount): add amount
• $sub(amount): subtract amount
• $mul(factor): multiply by factor
• $div(divisor): divide (returns None when divisor is 0)
• $idiv(divisor): integer divide/floor divide (returns None when divisor is 0)
• $mod(divisor): modulo (returns None when divisor is 0)
• $neg: negate value
• $pow(exponent): raise value to exponent
• $rpow(base): raise base to value
• $sqrt: square root (returns None for negative input)
• $root(degree): nth root (returns None for invalid input)
• $round(ndigits=0): round value
• $floor: floor
• $ceil: ceil
• $abs: absolute value
• $clamp(min_value, max_value): clamp to bounds
• $sign: -1, 0, or 1
• $log(base=e): logarithm (returns None for invalid input)
• $exp: exponential
• $pct(percent): percent of value (x * percent/100)

Comparison/predicates:

• $even: true if even int
• $odd: true if odd int
• $gt(threshold): greater than threshold
• $lt(threshold): less than threshold
• $gte(threshold): greater than or equal
• $lte(threshold): less than or equal
• $between(min_value, max_value): inclusive range check
• $contains(value): membership for str/list/tuple/set/dict
• $in(values): check if current value is in provided container
• $type_is(name): type-name comparison (case-insensitive)
• $is_empty: None or zero-length container
• $non_empty: inverse of $is_empty

Conversion:

• $string: str(x)
• $int: int(x)
• $float: float(x)
• $decimal: Decimal(x)
• $bool: truthy conversion with string handling ("true", "1", "yes", etc.)
• $quote: wrap in double quotes

String:

• $lower: lowercase string
• $upper: uppercase string
• $title: title case
• $strip(chars=None): strip chars
• $replace(old, new): replace substring
• $split(sep=None): split into list
• $join(sep): join list-like values
• $startswith(prefix): startswith check
• $endswith(suffix): endswith check
• $matches(pattern): regex search check

Collections:

• $len: length
• $max: max for list/tuple, otherwise passthrough
• $min: min for list/tuple, otherwise passthrough
• $unique: deduplicate list while preserving order
• $reverse: reverse list/tuple order
• $chunk(size): split list/tuple into chunks of size (returns None for size <= 0)
• $flatten: flatten one level of nested list/tuple items into a new list
• $flatten_deep: recursively flatten nested list/tuple items into a new list
• $sorted(reverse=False): sort list/tuple
• $first: first item for list/tuple
• $last: last item for list/tuple
• $pick(*keys): keep only selected dict keys
• $unpick(*keys): remove selected dict keys

Statistics:

• $sum: sum for list/tuple, otherwise passthrough
• $avg: average for list/tuple, otherwise passthrough
• $pctile(p): percentile of list/tuple (p in 0..100, linear interpolation)
• $median: median of list/tuple
• $q1: 25th percentile of list/tuple
• $q3: 75th percentile of list/tuple
• $iqr: interquartile range (q3 - q1)
• $mode: most frequent value in list/tuple (ties pick first encountered)
• $stdev: population standard deviation of list/tuple

Null/fallback:

• $default(value): fallback when current value is None
• $coalesce(*values): first non-None among current value and provided values

Date/time:

• $to_datetime(fmt=None): parse datetime
• $timestamp: convert datetime-like to unix timestamp
• $age_seconds: seconds from datetime to now
• $before(dt): datetime comparison
• $after(dt): datetime comparison

Filter usage examples:

from dictwalk import dictwalk

data = {"a": {"scores": [10, 20, 30], "name": "  ada  ", "created": "2024-01-01T00:00:00Z"}}

dictwalk.get(data, "a.scores|$sum")
# 60

dictwalk.get({"a": {"nested": [[1, 2], [3], 4]}}, "a.nested|$flatten")
# [1, 2, 3, 4]

dictwalk.get({"a": {"nested": [[1, [2, [3]]], 4]}}, "a.nested|$flatten_deep")
# [1, 2, 3, 4]

dictwalk.get({"a": {"items": [1, 2, 3, 4, 5]}}, "a.items|$chunk(2)")
# [[1, 2], [3, 4], [5]]

dictwalk.get(data, "a.name|$strip|$title")
# "Ada"

dictwalk.get(data, "a.created|$to_datetime|$timestamp")
# 1704067200.0

dictwalk.get({"a": {"users": [{"id": 1}, {"id": 2}]}}, "a.users[?.id==$even].id[]")
# [2]

Errors

From dictwalk.errors:

• DictWalkError (base)
• DictWalkParseError
• DictWalkOperatorError
• DictWalkResolutionError

Use strict=True when you want explicit failures instead of fallback defaults.

Development

Run tests:

make test

Build Rust extension:

make rust-build

Run lint/type/dependency checks:

make lint
make type
make deptry

Run everything:

make ci

Direct tox usage:

uv run tox -e py310,py311,py312,py313,py314,lint,type,deptry