nestedutils 2.0.0

The lightweight Python library for safe, simple, dot-notation access to nested dicts and lists. Effortlessly get, set, and delete values deep in your complex JSON, API responses, and config files without verbose error-checking or handling KeyError exceptions.

Nested Utils

The lightweight Python library for safe, simple, dot-notation access to nested dicts and lists. Effortlessly get, set, and delete values deep in your complex JSON, API responses, and config files without verbose error-checking or handling KeyError exceptions.

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Why nestedutils?

Working with deeply nested data (like JSON API responses) often leads to verbose, error-prone boilerplate:

# The Standard Way: Verbose and hard to read
user_name = None
if data and "users" in data and len(data["users"]) > 0:
    user = data["users"][0]
    if "profile" in user:
        user_name = user["profile"].get("name")

# With nestedutils: Clean, safe, and readable
from nestedutils import get_at

user_name = get_at(data, "users.0.profile.name")

Features

• Simple Path Syntax: Use dot-notation strings ("a.b.c") or lists (["a", "b", "c"]) to navigate nested structures
• Mixed Data Types: Seamlessly work with dictionaries, lists, and tuples (read-only for tuples)
• List Index Support: Access list elements using numeric indices, including negative indices
• Auto-creation: Automatically create missing intermediate containers when setting values (with create=True)
• Introspection: Analyze nested structures with get_depth, count_leaves, and get_all_paths
• Type Safety: Comprehensive error handling with descriptive error messages and error codes
• Safety Limits: Built-in protection against excessive nesting (max depth: 100) and oversized lists (max index: 10,000)
• Zero Dependencies: Pure Python implementation with no external dependencies

Use Cases

• JSON API Responses: Safely extract values from complex, unpredictable JSON responses without dozens of checks.
• Configuration Management: Easily read and modify deeply nested settings in configuration dictionaries.
• Data Transformation: Rapidly remap data from one complex structure to another using get_at and set_at.

Terminology

Term	Definition
Path	A navigation string or list that specifies a location in nested data (e.g., "user.profile.name" or ["user", "profile", "name"])
Key	An individual dictionary key used to access a value (e.g., "name", "profile")
Index	A numeric position in a list or tuple (e.g., 0, -1 for last element)

Installation

pip install nestedutils

Quick Start

from nestedutils import get_at, set_at, delete_at, exists_at, get_depth, count_leaves, get_all_paths

# Create a nested structure
data = {}

# Set values using dot-notation
set_at(data, "user.name", "John", create=True)
set_at(data, "user.age", 30, create=True)
set_at(data, "user.hobbies.0", "reading", create=True)
set_at(data, "user.hobbies.1", "coding", create=True)

# Access values
name = get_at(data, "user.name")  # "John"
age = get_at(data, "user.age")    # 30
first_hobby = get_at(data, "user.hobbies.0")  # "reading"

# Check if path exists
if exists_at(data, "user.name"):
    print("User name exists!")

# Delete values
delete_at(data, "user.age")

API Reference

get_at(data, path, *, default=None)

Retrieve a value from a nested data structure.

Parameters:

• data: The data structure to navigate (dict, list, tuple, or nested combinations)
• path: Path to the value (string with dot notation or list of keys/indices)
• default: Value to return if path doesn't exist (keyword-only parameter, default: None)

Returns: The value at the path, or default if provided and path doesn't exist

Raises: PathError if the path doesn't exist and default is not provided

Note: By default, get_at raises PathError for missing paths. Use the default parameter for optional/nullable access.

Examples:

data = {"a": {"b": {"c": 5}}}
get_at(data, "a.b.c")  # 5
get_at(data, "a.b.d")  # Raises PathError (path doesn't exist)
get_at(data, "a.b.d", default=99)  # 99 (returns default)

data = {"items": [{"name": "apple"}, {"name": "banana"}]}
get_at(data, "items.1.name")  # "banana"
get_at(data, "items.-1.name")  # "banana" (negative index)

set_at(data, path, value, *, create=False)

Set a value in a nested data structure, optionally creating intermediate containers as needed.

Parameters:

• data: The data structure to modify (must be mutable: dict or list)
• path: Path where to set the value (string with dot notation or list of keys/indices)
• value: The value to set
• create: If True, automatically creates missing intermediate containers (default: False)

Note:

• By default (create=False), set_at raises PathError if any intermediate key is missing
• With create=True, missing containers are automatically created: {} for dict keys, [] for list indices
• Positive indices can append to lists (index == len(list)) but cannot create gaps (index > len(list))
• Negative indices can only modify existing elements

Examples:

# create=True - auto-create missing containers
data = {}
set_at(data, "user.profile.name", "Alice", create=True)
# Creates: {"user": {"profile": {"name": "Alice"}}}

data = {}
set_at(data, "items.0.name", "Item 1", create=True)
# Creates: {"items": [{"name": "Item 1"}]}

# Sequential list appending (no gaps allowed)
data = {}
set_at(data, "items.0", "first", create=True)  # Creates list with first item
set_at(data, "items.1", "second", create=True)  # Appends second item
# Creates: {"items": ["first", "second"]}

# Sparse lists are NOT allowed - this raises PathError
data = [1, 2, 3]
set_at(data, "5", 99, create=True)  # Raises PathError: cannot create gap

# Negative indices - modify existing only
data = [1, 2, 3]
set_at(data, "-1", 100)  # Updates existing last element
# Creates: [1, 2, 100]

exists_at(data, path)

Check if a path exists in a nested data structure.

Parameters:

• data: The data structure to navigate (dict, list, tuple, or nested combinations)
• path: Path to check (string with dot notation or list of keys/indices)

Returns: True if the path exists, False otherwise

Raises: PathError if the path format is invalid

Examples:

data = {"a": {"b": {"c": 5}}}
exists_at(data, "a.b.c")  # True
exists_at(data, "a.b.d")  # False

data = {"items": [{"name": "apple"}, {"name": "banana"}]}
exists_at(data, "items.1.name")  # True
exists_at(data, "items.5.name")  # False
exists_at(data, "items.-1.name")  # True (negative index)

delete_at(data, path, allow_list_mutation=False)

Delete a value from a nested data structure.

Parameters:

• data: The data structure to modify
• path: Path to the value to delete
• allow_list_mutation: If True, allows deletion from lists (default: False)

Note: List deletion is disabled by default to prevent accidental index shifting that could break subsequent code. When you delete an element from a list, all following indices shift down, which can cause unexpected behavior if other parts of your code reference those indices.

Returns: The deleted value

Raises: PathError if the path doesn't exist or deletion is not allowed

Examples:

data = {"a": {"b": 1, "c": 2}}
delete_at(data, "a.b")  # Returns 1, data becomes {"a": {"c": 2}}

data = {"items": [1, 2, 3]}
delete_at(data, "items.1", allow_list_mutation=True)  # Returns 2
# data becomes {"items": [1, 3]}

get_depth(data)

Get the maximum nesting depth of a data structure.

Parameters:

• data: Any nested structure (dict, list, tuple, or primitive)

Returns: Integer depth. Primitives return 0, empty containers return 1.

Note: Only dict, list, and tuple are traversed. Other container types (set, frozenset, etc.) are treated as leaf values.

Examples:

get_depth(42)                          # 0 (primitive)
get_depth({})                          # 1 (empty container)
get_depth({"a": 1})                    # 1 (flat dict)
get_depth({"a": {"b": 1}})             # 2 (nested)
get_depth({"a": {"b": {"c": 1}}})      # 3 (deeper nesting)
get_depth([1, [2, [3]]])               # 3 (nested lists)

count_leaves(data)

Count the total number of leaf values (non-container values) in a nested structure.

Parameters:

• data: Any nested structure

Returns: Integer count of leaf values. Empty containers return 0.

Note: Only dict, list, and tuple are traversed. Other container types (set, frozenset, etc.) count as a single leaf.

Examples:

count_leaves(42)                       # 1 (primitive is a leaf)
count_leaves({})                       # 0 (empty container)
count_leaves({"a": 1, "b": 2})         # 2 (two leaf values)
count_leaves({"a": {"b": 1, "c": 2}})  # 2 (nested, still 2 leaves)
count_leaves([1, 2, [3, 4]])           # 4 (four leaf values)

get_all_paths(data)

Get all paths to leaf values in a nested structure.

Parameters:

• data: Any nested structure

Returns: List of paths, where each path is a list of keys (strings) and indices (integers).

Note: Only dict, list, and tuple are traversed. Other container types are treated as leaves.

Examples:

get_all_paths({"a": 1, "b": 2})
# [["a"], ["b"]]

get_all_paths({"a": {"b": 1, "c": 2}})
# [["a", "b"], ["a", "c"]]

get_all_paths({"users": [{"name": "Alice"}, {"name": "Bob"}]})
# [["users", 0, "name"], ["users", 1, "name"]]

get_all_paths({})                      # [] (no leaves)
get_all_paths(42)                      # [[]] (primitive has empty path)

Error Handling

The library uses PathError exceptions with error codes for different failure scenarios:

from nestedutils import PathError, PathErrorCode

try:
    set_at(data, "invalid.path", 1)
except PathError as e:
    print(e.message)  # Error message
    print(e.code)     # Error code (PathErrorCode enum)

Error Codes:

Error Code	Description
INVALID_PATH	Invalid path format or type
INVALID_INDEX	Invalid list index
MISSING_KEY	Key doesn't exist in dictionary
EMPTY_PATH	Path is empty
IMMUTABLE_CONTAINER	Attempted to modify a tuple
NON_NAVIGABLE_TYPE	Attempted to navigate into a non-container type
OPERATION_DISABLED	Operation is disabled by configuration (e.g., list deletion without allow_list_mutation=True)

Advanced Usage

Using List Paths

List paths are useful when keys contain dots:

data = {}
set_at(data, ["user.name", "first"], "John", create=True)
set_at(data, ["user.name", "last"], "Doe", create=True)
# Creates: {"user.name": {"first": "John", "last": "Doe"}}

Negative List Indices

Negative indices work like Python list indexing for reading and updating existing elements:

data = {"items": [10, 20, 30]}
get_at(data, "items.-1")  # 30 (last item)
set_at(data, "items.-1", 999)  # Updates last item (must exist)

Important: Negative indices can only reference existing elements. They cannot extend lists - attempting to use a negative index that's out of bounds will raise a PathError.

Working with Tuples

Tuples are read-only. You can read from them but cannot modify:

data = {"items": (1, 2, 3)}
get_at(data, "items.0")  # 1 (works)
set_at(data, "items.0", 9)  # Raises PathError (tuples are immutable)

Handling None Values

The library can navigate through None values when setting:

data = {"a": None}
set_at(data, "a.b.c", 10)
# Replaces None with container: {"a": {"b": {"c": 10}}}

Safety Limits

The library includes built-in safety limits to prevent excessive resource usage:

Limit	Value	Description
Maximum Path Depth	100 levels	Prevents deeply nested paths that could cause stack issues
Maximum List Index	10,000	Prevents creating extremely large sparse lists

These limits help protect against accidental memory exhaustion or performance issues. If you hit these limits, you'll receive a PathError with a clear message.

Migration from v1.x to v2.0

Version 2.0 introduces breaking changes to make the library safer and more predictable. If you're upgrading from v1.x, please see the Migration Guide for detailed upgrade instructions.

Contributing

Contributions are welcome! Please read our Contributing Guide for details on our code of conduct, development setup, and the process for submitting pull requests.

Support

If you find this library helpful:

• ⭐ Star the repository
• 🐛 Report issues
• 🔀 Submit pull requests
• 💝 Sponsor on GitHub

License

MIT © Y. Siva Sai Krishna - see LICENSE file for details.

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