Core DataFrame Operations

def from_cudf(data, npartitions=None, chunksize=None, sort=True, name=None):
    """
    Create a Dask-cuDF collection from a cuDF object.

    Parameters:
    - data: cudf.DataFrame, cudf.Series, or cudf.Index - Source cuDF object
    - npartitions: int, optional - Number of partitions to create
    - chunksize: int, optional - Approximate size of each partition in rows
    - sort: bool, default True - Whether to sort by index
    - name: str, optional - Name for the collection

    Returns:
    DataFrame, Series, or Index - Corresponding Dask-cuDF collection

    Raises:
    - NotImplementedError: If data has MultiIndex (not supported)
    
    Notes:
    - Either npartitions or chunksize should be specified
    - Uses Dask's from_pandas internally with cuDF backend
    """

class DataFrame:
    """
    A distributed Dask DataFrame backed by cuDF DataFrames.
    
    Provides GPU-accelerated operations with pandas-like API.
    Most Dask DataFrame operations are supported with GPU acceleration.
    """
    
    def __init__(self, dsk, name, meta, divisions):
        """
        Initialize DataFrame.
        
        Parameters:
        - dsk: dict - Task graph
        - name: str - Collection name
        - meta: cudf.DataFrame - Metadata object
        - divisions: tuple - Index divisions
        """

class Series:
    """
    A distributed Dask Series backed by cuDF Series.
    
    Provides GPU-accelerated operations for single-column data.
    """
    
    def __init__(self, dsk, name, meta, divisions):
        """
        Initialize Series.
        
        Parameters:
        - dsk: dict - Task graph
        - name: str - Collection name  
        - meta: cudf.Series - Metadata object
        - divisions: tuple - Index divisions
        """

class Index:
    """
    A distributed Dask Index backed by cuDF Index.
    
    Provides GPU-accelerated index operations.
    """
    
    def __init__(self, dsk, name, meta, divisions):
        """
        Initialize Index.
        
        Parameters:
        - dsk: dict - Task graph
        - name: str - Collection name
        - meta: cudf.Index - Metadata object  
        - divisions: tuple - Index divisions
        """

def concat(dfs, axis=0, join='outer', ignore_index=False, **kwargs):
    """
    Concatenate Dask-cuDF objects along axis.
    
    This is an alias to dask.dataframe.concat that works with cuDF backend.
    
    Parameters:
    - dfs: sequence - Objects to concatenate
    - axis: int, default 0 - Axis to concatenate along
    - join: str, default 'outer' - How to handle indexes
    - ignore_index: bool, default False - Reset index in result
    - **kwargs: Additional arguments passed to cudf.concat
    
    Returns:
    DataFrame or Series - Concatenated result
    """

def from_delayed(dasks, meta=None, divisions=None, prefix='from-delayed', verify_meta=True):
    """
    Create DataFrame from list of delayed objects.
    
    This function is a thin wrapper around dask.dataframe.from_delayed,
    creating Dask-cuDF collections from Dask delayed tasks that return cuDF objects.
    
    Parameters:
    - dasks: list of Delayed objects - Tasks that return cuDF DataFrames/Series
    - meta: DataFrame or Series, optional - Metadata defining structure and dtypes
    - divisions: sequence, optional - Index divisions for result
    - prefix: str, default 'from-delayed' - Task name prefix in graph
    - verify_meta: bool, default True - Verify metadata consistency
    
    Returns:
    DataFrame or Series - Dask-cuDF collection from delayed tasks
    
    Notes:
    - Automatically uses cuDF backend for delayed task results
    - Tasks should return cuDF DataFrame or Series objects
    - Meta parameter should match the structure of delayed task outputs
    """

import cudf
import dask_cudf

# Create cuDF DataFrame
cudf_df = cudf.DataFrame({
    'a': [1, 2, 3, 4, 5, 6],
    'b': [10, 20, 30, 40, 50, 60],
    'c': ['x', 'y', 'z', 'x', 'y', 'z']
})

# Convert to Dask-cuDF with 2 partitions
ddf = dask_cudf.from_cudf(cudf_df, npartitions=2)

# Access partition information
print(f"Partitions: {ddf.npartitions}")
print(f"Columns: {list(ddf.columns)}")

# Create cuDF Series
cudf_series = cudf.Series([1, 4, 9, 16, 25, 36], name='squares')

# Convert to Dask-cuDF Series
dseries = dask_cudf.from_cudf(cudf_series, npartitions=3)

# Perform operations
result = dseries.sum().compute()
print(f"Sum of squares: {result}")

# Create multiple DataFrames
df1 = cudf.DataFrame({'x': [1, 2], 'y': [3, 4]})
df2 = cudf.DataFrame({'x': [5, 6], 'y': [7, 8]})

# Convert to Dask-cuDF
ddf1 = dask_cudf.from_cudf(df1, npartitions=1)
ddf2 = dask_cudf.from_cudf(df2, npartitions=1)

# Concatenate
combined = dask_cudf.concat([ddf1, ddf2])
result = combined.compute()

### Creating DataFrame from Delayed Tasks

```python
import dask
import cudf
import dask_cudf

# Create delayed tasks that return cuDF DataFrames
@dask.delayed
def load_partition(i):
    # Simulate loading data partition
    return cudf.DataFrame({
        'id': range(i*100, (i+1)*100),
        'value': range(i*100, (i+1)*100)
    })

# Create list of delayed tasks
delayed_tasks = [load_partition(i) for i in range(5)]

# Convert to Dask-cuDF DataFrame
ddf = dask_cudf.from_delayed(
    delayed_tasks,
    meta=cudf.DataFrame({'id': [], 'value': []}, dtype='int64')
)

print(f"Created DataFrame with {ddf.npartitions} partitions")
print(ddf.compute().head())