Delayed

def delayed(func=None, *, pure=None, nout=None, name=None, traverse=True):
    """
    Decorator to create delayed functions or wrap objects.
    
    Parameters:
    - func: Function to make delayed (None for decorator use)
    - pure: Whether function is pure (no side effects, enables caching)
    - nout: Number of outputs for functions returning multiple values
    - name: Custom name for tasks in graph
    - traverse: Whether to traverse and delay nested collections
    
    Returns:
    Delayed function or object
    """

class Delayed:
    """
    Lazy evaluation wrapper for building task graphs.
    
    Properties:
    - key: str - Unique identifier for this computation
    - dask: dict - Task graph dictionary
    """
    
    def compute(self, scheduler=None, get=None, **kwargs):
        """
        Compute delayed object and return result.
        
        Parameters:
        - scheduler: Scheduler to use ('threads', 'processes', etc.)
        - get: Custom scheduler function
        - **kwargs: Additional scheduler arguments
        
        Returns:
        Computed result of the delayed operation
        """
    
    def persist(self, scheduler=None, get=None, **kwargs):
        """
        Persist delayed object in memory for reuse.
        
        Parameters:
        - scheduler: Scheduler for persistence
        - get: Custom scheduler function
        - **kwargs: Additional scheduler arguments
        
        Returns:
        dask.delayed.Delayed: Persisted delayed object
        """
    
    def visualize(self, filename=None, format=None, optimize_graph=False, 
                 **kwargs):
        """
        Visualize the task graph for this delayed object.
        
        Parameters:
        - filename: Output file path
        - format: Output format ('png', 'svg', 'pdf', etc.)
        - optimize_graph: Whether to optimize before visualization
        - **kwargs: Additional graphviz parameters
        
        Returns:
        Graphviz object or None if filename specified
        """
    
    def __call__(self, *args, **kwargs):
        """Call delayed object as function with arguments."""
    
    def __getattr__(self, attr):
        """Access attributes of delayed object."""
    
    def __getitem__(self, key):
        """Index into delayed object."""
    
    def __setitem__(self, key, value):
        """Set item in delayed object."""
    
    def __iter__(self):
        """Iterate over delayed object."""
    
    def __len__(self):
        """Get length of delayed object."""
    
    def __bool__(self):
        """Boolean evaluation of delayed object."""

# Arithmetic operations create new delayed objects
# delayed_obj + other -> Delayed
# delayed_obj - other -> Delayed  
# delayed_obj * other -> Delayed
# delayed_obj / other -> Delayed
# delayed_obj // other -> Delayed
# delayed_obj % other -> Delayed
# delayed_obj ** other -> Delayed

# Comparison operations
# delayed_obj == other -> Delayed
# delayed_obj != other -> Delayed
# delayed_obj < other -> Delayed
# delayed_obj <= other -> Delayed
# delayed_obj > other -> Delayed
# delayed_obj >= other -> Delayed

# Logical operations
# delayed_obj & other -> Delayed
# delayed_obj | other -> Delayed
# delayed_obj ^ other -> Delayed
# ~delayed_obj -> Delayed

# Unary operations
# -delayed_obj -> Delayed
# +delayed_obj -> Delayed
# abs(delayed_obj) -> Delayed

@delayed
def custom_function(arg1, arg2, **kwargs):
    """
    Custom delayed function example.
    
    Any function can be made delayed using the @delayed decorator.
    The function will not execute until .compute() is called.
    """
    # Function implementation
    return result

# Delayed wrapper for existing functions
delayed_func = delayed(existing_function)

# Delayed values
delayed_value = delayed(some_value)

# Function composition creates task graphs
@delayed
def step1(data):
    return process_data(data)

@delayed  
def step2(processed_data):
    return analyze_data(processed_data)

@delayed
def step3(analysis_result):
    return generate_report(analysis_result)

# Build computation pipeline
data = delayed(load_data())
processed = step1(data)
analyzed = step2(processed)  
report = step3(analyzed)

# Execute entire pipeline
final_result = report.compute()

@delayed(nout=2)
def function_with_multiple_returns(data):
    """
    Function returning multiple values.
    
    The nout parameter specifies number of return values,
    enabling proper unpacking of delayed results.
    """
    result1 = process_part1(data)
    result2 = process_part2(data)
    return result1, result2

# Unpack multiple returns
data = delayed(load_data())
part1, part2 = function_with_multiple_returns(data)

# Use each part independently
analysis1 = delayed(analyze)(part1)
analysis2 = delayed(analyze)(part2)

# Combine results
final = delayed(combine)(analysis1, analysis2)
result = final.compute()

@delayed(pure=True)
def expensive_pure_function(data):
    """
    Pure function with no side effects.
    
    Setting pure=True enables caching of results,
    improving performance for repeated calls with same arguments.
    """
    # Expensive computation
    return expensive_result

# Results will be cached automatically
data = delayed(load_data())
result1 = expensive_pure_function(data)
result2 = expensive_pure_function(data)  # Uses cached result

combined = delayed(combine)(result1, result2)
final = combined.compute()

# Convert collections to delayed
import dask.array as da
import dask.dataframe as dd

array = da.random.random((1000, 1000), chunks=(100, 100))
delayed_array = array.to_delayed()

dataframe = dd.read_csv('data.csv')  
delayed_dataframe = dataframe.to_delayed()

# Convert delayed to collections
delayed_values = [delayed(load_partition)(i) for i in range(10)]
bag = db.from_delayed(delayed_values)

delayed_dfs = [delayed(load_dataframe)(f) for f in files]
combined_df = dd.from_delayed(delayed_dfs)

@delayed
def risky_function(data):
    """Function that might raise exceptions."""
    if data is None:
        raise ValueError("Data cannot be None")
    return process_data(data)

@delayed
def safe_wrapper(data):
    """Wrapper with error handling."""
    try:
        return risky_function(data)
    except ValueError as e:
        return f"Error: {e}"

# Errors are raised during compute()
data = delayed(None)
result = safe_wrapper(data)
output = result.compute()  # Returns error message

from dask.delayed import delayed
import pandas as pd

@delayed
def load_data(filename):
    return pd.read_csv(filename)

@delayed
def clean_data(df):
    return df.dropna().reset_index(drop=True)

@delayed
def analyze_data(df):
    return df.describe()

@delayed
def save_results(analysis, filename):
    analysis.to_csv(filename)
    return f"Saved to {filename}"

# Build computation graph
filename = 'data.csv'
raw_data = load_data(filename)
clean_data_result = clean_data(raw_data)
analysis = analyze_data(clean_data_result)
save_status = save_results(analysis, 'results.csv')

# Execute computation
result = save_status.compute()
print(result)

from dask.delayed import delayed
import pandas as pd

@delayed
def process_file(filename):
    """Process single file."""
    df = pd.read_csv(filename)
    # Complex processing logic
    return df.groupby('category').value.sum()

@delayed
def combine_results(results):
    """Combine results from multiple files."""
    return pd.concat(results, axis=1).fillna(0)

# Process multiple files in parallel
files = ['file1.csv', 'file2.csv', 'file3.csv', 'file4.csv']
processed = [process_file(f) for f in files]

# Combine all results
combined = combine_results(processed)
final_result = combined.compute()

from dask.delayed import delayed

@delayed
def generate_data(seed, size):
    """Generate synthetic data."""
    import numpy as np
    np.random.seed(seed)
    return np.random.random(size)

@delayed
def process_batch(data_batch, method='mean'):
    """Process a batch of data."""
    if method == 'mean':
        return data_batch.mean()
    elif method == 'sum':
        return data_batch.sum()
    else:
        return data_batch.std()

@delayed
def aggregate_results(results):
    """Aggregate all batch results."""
    return sum(results) / len(results)

# Dynamic graph based on parameters
n_batches = 5
batch_size = 1000
method = 'mean'

# Generate data batches
data_batches = [generate_data(i, batch_size) for i in range(n_batches)]

# Process each batch
batch_results = [process_batch(batch, method) for batch in data_batches]

# Aggregate final result
final_result = aggregate_results(batch_results)
answer = final_result.compute()

from dask.delayed import delayed

class DelayedProcessor:
    """Class with delayed methods for data processing."""
    
    def __init__(self, params):
        self.params = delayed(params)
    
    @delayed
    def load_data(self, source):
        """Load data from source."""
        # Implementation
        return loaded_data
    
    @delayed  
    def preprocess(self, data):
        """Preprocess the data."""
        # Use self.params for configuration
        return preprocessed_data
    
    @delayed
    def train_model(self, data):
        """Train model on data."""
        return trained_model
    
    @delayed
    def evaluate(self, model, test_data):
        """Evaluate model performance."""
        return evaluation_metrics

# Use delayed class methods
processor = DelayedProcessor({'param1': 10, 'param2': 'setting'})

# Build computation pipeline
raw_data = processor.load_data('data_source')
clean_data = processor.preprocess(raw_data)
model = processor.train_model(clean_data)

test_data = processor.load_data('test_source')
clean_test = processor.preprocess(test_data)
metrics = processor.evaluate(model, clean_test)

# Execute entire pipeline
final_metrics = metrics.compute()

import dask.array as da
import dask.dataframe as dd
from dask.delayed import delayed

@delayed
def custom_analysis(array_data, df_data):
    """Custom analysis combining array and dataframe."""
    array_stats = {
        'mean': array_data.mean(),
        'std': array_data.std()
    }
    
    df_stats = {
        'row_count': len(df_data),
        'col_count': len(df_data.columns)
    }
    
    return {'array': array_stats, 'dataframe': df_stats}

# Create collections
array = da.random.random((10000, 100), chunks=(1000, 100))
dataframe = dd.read_csv('large_file.csv')

# Convert to delayed for custom processing
delayed_array = array.compute_chunk_sizes().to_delayed().flatten()[0]
delayed_df = dataframe.to_delayed()[0]

# Custom analysis
analysis = custom_analysis(delayed_array, delayed_df)
result = analysis.compute()