dotc 0.4.1

DOTC (like Yahtzee) - Access Nested Dicts and Lists via Dots

DOTC 🎯

Access nested Python data structures using simple dot notation

Transform complex nested dictionaries and lists into easily navigable objects with dot notation access.

Quick Start

pip install dotc

from dotc import Dotc

# Your nested data
data = {
    'user': {
        'name': 'Alice',
        'scores': [85, 92, 78]
    }
}

# Convert to dot-accessible object
d = Dotc(data)

# Access with simple dots
print(d.user.name)      # Alice
print(d.user.scores._0) # 85

✨ Core Features

🔍 Simple Dot Access

Access nested data structures naturally:

data = {'a': 1, 'b': {'c': 3, 'd': [4, 5, 6]}}
d = Dotc(data)

d.a           # Returns: 1
d.b.c         # Returns: 3
d.b.d._0      # Returns: 4 (list access with _index)

🚀 Instant Results with Spawn

Get both the object and a specific value in one call during instantiation:

# Get object and value simultaneously
staff_data = {
    'staff': {
        'coders': ['mike', 'jeremie', 'trey', 'donnie']
    }
}

# During instantiation with _pathget
dc, result = Dotc(staff_data, _pathget='staff.coders.0')
print(result)  # mike

# You can also use different paths
dc2, result2 = Dotc(staff_data, _pathget='staff.coders.1')
print(result2)  # jeremie

dc3, result3 = Dotc(staff_data, _pathget='staff.coders.2')
print(result3)  # trey

🎯 Programmatic Path Access with __call__

Use the call method to get values by path after instantiation:

staff_data = {
    'staff': {
        'coders': ['mike', 'jeremie', 'trey', 'donnie']
    }
}

# Create with initial path extraction
dc, result = Dotc(staff_data, _pathget='staff.coders.0')
print(result)  # mike

# Then use __call__ for additional path queries
result2 = dc('staff.coders.1')  # jeremie
result3 = dc('staff.coders.3')  # donnie

🛡️ Safe Access with Defaults

Never worry about missing keys:

d = Dotc({'a': {'b': 1}})

d.a.b         # Returns: 1
d.a.missing   # Returns: None (default)
d.x.y.z       # Returns: None (safe traversal)

🔄 Full Data Resolution

Get the complete resolved data structure:

d = Dotc({'a': 1, 'b': {'c': [2, 3]}})

d._           # Returns: {'a': 1, 'b': {'c': [2, 3]}}
d.b._         # Returns: {'c': [2, 3]}
d.b.c._       # Returns: [2, 3]

📖 Tutorial

Basic Usage

from dotc import Dotc

# Simple dictionary
data = {'name': 'John', 'age': 30}
d = Dotc(data)
print(d.name)  # John
print(d.age)   # 30

Nested Dictionaries

data = {
    'person': {
        'details': {
            'name': 'Jane',
            'location': 'NYC'
        }
    }
}

d = Dotc(data)
print(d.person.details.name)      # Jane
print(d.person.details.location)  # NYC

Working with Lists

data = {
    'fruits': ['apple', 'banana', 'cherry'],
    'numbers': [1, 2, 3, 4, 5]
}

d = Dotc(data)
print(d.fruits._0)    # apple
print(d.fruits._1)    # banana
print(d.numbers._4)   # 5

Mixed Nested Structures

data = {
    'users': [
        {'name': 'Alice', 'scores': [95, 87]},
        {'name': 'Bob', 'scores': [78, 92]}
    ]
}

d = Dotc(data)
print(d.users._0.name)        # Alice
print(d.users._0.scores._0)   # 95
print(d.users._1.scores._1)   # 92

🎯 Advanced Features

Inspection and Debugging

d = Dotc({'a': 1, 'b': {'c': [1, 2, 3]}})

# Basic inspection
d._show()

# Verbose inspection
d._show(v=1)

# Inspect specific parts
d._show(d.b, v=1)

Using DataPath for Programmatic Access

For cases where you need programmatic path traversal:

from dotc import DataPath

data = {'a': {'b': [1, 2, 3]}}
d = Dotc(data)

dp = DataPath()
result = dp.get('a.b.0', d)  # Returns: 1

# Works with regular Python objects too
result = dp.get('a.b.0', data)  # Returns: 1

🔧 Configuration Options

d = Dotc(
    data={'a': 1},
    node='custom_name',      # Custom node name
    default='N/A',           # Custom default value
    _strict=1,              # Raise errors for missing keys
    _debug=1                # Enable debug output
)

🎪 Use Cases

Configuration Management

config = {
    'database': {'host': 'localhost', 'port': 5432},
    'api': {'timeout': 30, 'retries': 3}
}

cfg = Dotc(config)
db_host = cfg.database.host      # localhost
api_timeout = cfg.api.timeout    # 30

JSON API Response Handling

api_response = {
    'data': {
        'user': {'id': 123, 'profile': {'email': '[e-mail]'}}
    }
}

resp = Dotc(api_response)
email = resp.data.user.profile.email  # [e-mail]

Data Processing Pipelines

# Get object and extract value in one step
staff_data = {
    'staff': {
        'coders': ['mike', 'jeremie', 'trey', 'donnie'],
        'metrics': {'team_size': 4, 'experience': 'senior'}
    }
}

# Extract initial value during instantiation
processor, first_coder = Dotc(staff_data, _pathget='staff.coders.0')
print(f"Lead developer: {first_coder}")  # Lead developer: mike

# Use the same object for additional queries
team_size = processor('staff.metrics.team_size')
print(f"Team size: {team_size}")  # Team size: 4

📚 API Reference

Class Methods

• Dotc(data, node=None, default=None, _pathget=None, **kwargs) - Create a new Dotc object
• When _pathget is provided, returns (instance, result) tuple

Instance Methods

• obj(path) - Get value at path programmatically
• obj._ - Get fully resolved data structure
• obj._show(verbosity=0) - Inspect object structure

Utility Classes

• DataPath.get(path, obj, default) - Static path traversal

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DOTC - Making nested data navigation as easy as ABC! 🎯