Dataset Management

def merge(file_list, verify_schema=True, open_with=None, root=False):
    """
    Create logical dataset from multiple parquet files.
    
    The files must either be in the same directory or at the same level
    within a structured directory where directories provide partitioning
    information. Schemas should be consistent across files.
    
    Parameters:
    - file_list: list, paths to parquet files or ParquetFile instances
    - verify_schema: bool, verify schema consistency across all files
    - open_with: function, file opening function with (path, mode) signature
    - root: str, dataset root directory for partitioning disambiguation
    
    Returns:
    ParquetFile: Merged dataset representation with combined metadata
    """

def metadata_from_many(file_list, verify_schema=False, open_with=None,
                       root=False, fs=None):
    """
    Create FileMetaData that points to multiple parquet files.
    
    Parameters:
    - file_list: list, paths to parquet files to consolidate
    - verify_schema: bool, assert that schemas in each file are identical
    - open_with: function, file opening function
    - root: str, top directory of dataset tree
    - fs: fsspec.AbstractFileSystem, filesystem interface
    
    Returns:
    tuple: (basepath, FileMetaData) with consolidated metadata
    """

def analyse_paths(file_list, root=False):
    """
    Consolidate list of file paths into parquet relative paths.
    
    Parameters:
    - file_list: list, file paths to analyze
    - root: str or False, base directory path
    
    Returns:
    tuple: (basepath, relative_paths) with common base and relative paths
    """

def get_file_scheme(paths):
    """
    Determine partitioning scheme from file paths.
    
    Parameters:
    - paths: list, file paths to analyze (from row_group.columns[0].file_path)
    
    Returns:
    str: Partitioning scheme ('empty', 'simple', 'flat', 'hive', 'drill', 'other')
    """

def partitions(row_group, only_values=False):
    """
    Extract partition values from row group file path.
    
    Parameters:
    - row_group: RowGroup object or str, row group or file path
    - only_values: bool, return only values (True) or full path (False)
    
    Returns:
    str: Partition values separated by '/' or full partition path
    """

def part_ids(row_groups):
    """
    Extract part file IDs from row group file paths.
    
    Finds integers matching "**part.*.parquet" pattern in paths.
    
    Parameters:
    - row_groups: list, row group objects to analyze
    
    Returns:
    dict: Mapping from part ID to (row_group_id, part_name) tuple
    """

def groupby_types(cats):
    """
    Group partitioning categories by their data types.
    
    Parameters:
    - cats: dict, partition categories mapping column names to values
    
    Returns:
    dict: Categories grouped by inferred type
    """

def check_column_names(columns, path):
    """
    Validate column names for parquet compatibility.
    
    Parameters:
    - columns: list, column names to validate
    - path: str, file path for error reporting
    
    Returns:
    list: Validated column names
    
    Raises:
    ValueError: If column names are invalid
    """

def ex_from_sep(sep):
    """
    Create regular expression from path separator.
    
    Parameters:
    - sep: str, path separator character
    
    Returns:
    re.Pattern: Regular expression for path matching
    """

# ParquetFile method
def write_row_groups(self, data, row_group_offsets=None, sort_key=None,
                     sort_pnames=False, compression=None, write_fmd=True,
                     open_with=None, mkdirs=None, stats="auto"):
    """
    Write data as new row groups to existing dataset.
    
    Parameters:
    - data: pandas.DataFrame or iterable, data to add to dataset
    - row_group_offsets: int or list, row group size specification
    - sort_key: function, sorting function for row group ordering
    - sort_pnames: bool, align part file names with row group positions
    - compression: str or dict, compression settings
    - write_fmd: bool, write updated common metadata to disk
    - open_with: function, file opening function
    - mkdirs: function, directory creation function
    - stats: bool or list, statistics calculation control
    """

# ParquetFile method
def remove_row_groups(self, rgs, sort_pnames=False, write_fmd=True,
                      open_with=None, remove_with=None):
    """
    Remove row groups from disk and update metadata.
    
    Cannot be applied to simple file scheme datasets. Removes files
    and updates common metadata accordingly.
    
    Parameters:
    - rgs: RowGroup or list, row group(s) to remove
    - sort_pnames: bool, align part file names after removal
    - write_fmd: bool, write updated common metadata
    - open_with: function, file opening function
    - remove_with: function, file removal function
    """

def overwrite(dirpath, data, row_group_offsets=None, sort_pnames=True,
              compression=None, open_with=None, mkdirs=None,
              remove_with=None, stats=True):
    """
    Overwrite partitions in existing hive-formatted parquet dataset.
    
    Row groups with partition values overlapping with new data are
    removed before new data is added. Only supports overwrite_partitioned
    mode currently.
    
    Parameters:
    - dirpath: str, directory path to parquet dataset
    - data: pandas.DataFrame, new data to write
    - row_group_offsets: int or list, row group size specification
    - sort_pnames: bool, align part file names with positions
    - compression: str or dict, compression settings
    - open_with: function, file opening function
    - mkdirs: function, directory creation function
    - remove_with: function, file removal function
    - stats: bool or list, statistics calculation control
    """

def write_common_metadata(fn, fmd, open_with=None, no_row_groups=True):
    """
    Write parquet schema information to shared metadata file.
    
    For hive-style parquet datasets, creates _metadata and _common_metadata
    files containing schema and file organization information.
    
    Parameters:
    - fn: str, metadata file path to write
    - fmd: FileMetaData, metadata information to write
    - open_with: function, file opening function
    - no_row_groups: bool, exclude row group info (for _common_metadata)
    """

def consolidate_categories(fmd):
    """
    Consolidate categorical metadata across row groups.
    
    Updates pandas metadata to reflect the maximum number of categories
    found across all row groups for each categorical column.
    
    Parameters:
    - fmd: FileMetaData, metadata to consolidate
    """

def update_custom_metadata(obj, custom_metadata):
    """
    Update custom metadata in thrift object or parquet file.
    
    Supports adding, updating, and removing (with None values) metadata
    entries without affecting other metadata or data contents.
    
    Parameters:
    - obj: ThriftObject or ParquetFile, target for metadata update
    - custom_metadata: dict, metadata updates to apply
    """

def get_fs(fn, open_with, mkdirs):
    """
    Get filesystem object and normalize parameters from file path.
    
    Detects and configures appropriate filesystem interface for given
    path, supporting local files, cloud storage, and custom backends.
    
    Parameters:
    - fn: str, file path or URL
    - open_with: function, file opening function
    - mkdirs: function, directory creation function
    
    Returns:
    tuple: (filesystem, normalized_path, open_function, mkdir_function)
    """

def join_path(*path):
    """
    Join path components with forward slashes.
    
    Parameters:
    - *path: str, path components to join
    
    Returns:
    str: Joined path with forward slash separators
    """

from fastparquet import merge, ParquetFile

# Merge multiple parquet files into logical dataset
file_list = [
    'part_000.parquet',
    'part_001.parquet', 
    'part_002.parquet'
]

# Create merged dataset
merged_pf = merge(file_list, verify_schema=True)

# Read from merged dataset
df = merged_pf.to_pandas()
print(f"Total rows: {merged_pf.count()}")
print(f"File scheme: {merged_pf.file_scheme}")

# Work with hive-style partitioned dataset
partitioned_pf = ParquetFile('/path/to/partitioned/dataset/')

# Check partitioning information
print(f"Partitions: {list(partitioned_pf.cats.keys())}")
print(f"Partition values: {partitioned_pf.cats}")

# Add new data to partitioned dataset
new_data = pd.DataFrame({
    'id': range(100, 200),
    'value': range(200, 300),
    'year': [2024] * 100,
    'month': [1] * 50 + [2] * 50
})

partitioned_pf.write_row_groups(
    new_data,
    compression='SNAPPY',
    stats=True
)

# Remove specific row groups
pf = ParquetFile('dataset/')

# Get row groups to remove (e.g., old data)
old_rgs = [rg for rg in pf.row_groups if some_condition(rg)]

# Remove old row groups
pf.remove_row_groups(
    old_rgs,
    sort_pnames=True,  # Realign part file names
    write_fmd=True     # Update metadata
)

# Add new row groups
pf.write_row_groups(new_data, compression='GZIP')

from fastparquet import overwrite

# Overwrite specific partitions with new data
new_partition_data = pd.DataFrame({
    'id': range(1000),
    'value': range(1000),
    'year': [2024] * 1000,
    'month': [3] * 1000  # This will overwrite month=3 partition
})

overwrite(
    'partitioned_dataset/',
    new_partition_data,
    compression='SNAPPY',
    sort_pnames=True
)

from fastparquet.util import update_custom_metadata
from fastparquet.writer import update_file_custom_metadata

# Update metadata in ParquetFile object
pf = ParquetFile('data.parquet')
update_custom_metadata(pf, {
    'processing_version': '2.0',
    'last_updated': '2024-01-15',
    'deprecated_field': None  # Remove this field
})

# Update metadata directly in file
update_file_custom_metadata('data.parquet', {
    'created_by': 'updated_application',
    'schema_version': '1.2'
})

from fastparquet.util import get_file_scheme, analyse_paths

# Analyze file organization
file_paths = [
    'year=2023/month=1/part_000.parquet',
    'year=2023/month=2/part_001.parquet',
    'year=2024/month=1/part_002.parquet'
]

scheme = get_file_scheme(file_paths)
print(f"Detected scheme: {scheme}")  # Output: 'hive'

# Analyze path structure
basepath, relative_paths = analyse_paths(file_paths)
print(f"Base path: {basepath}")
print(f"Relative paths: {relative_paths}")

# File scheme types
FileScheme = Literal['empty', 'simple', 'flat', 'hive', 'drill', 'other']

# Row group specification
RowGroupSpec = Union[int, List[int]]

# File system interface
FileSystemInterface = Any  # fsspec.AbstractFileSystem compatible

# File opening function
OpenFunction = Callable[[str, str], Any]  # (path, mode) -> file-like

# Directory creation function  
MkdirsFunction = Callable[[str], None]  # (path) -> None

# File removal function
RemoveFunction = Callable[[List[str]], None]  # (paths) -> None

# Sort key function for row groups
SortKeyFunction = Callable[[Any], Union[int, str]]  # (row_group) -> sort_key

# Partition information
PartitionInfo = Dict[str, List[Any]]  # column_name -> list of values

# Path analysis result
PathAnalysis = Tuple[str, List[str]]  # (basepath, relative_paths)