Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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317 lines
11 KiB
317 lines
11 KiB
""" parquet compat """
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from typing import Any, AnyStr, Dict, List, Optional
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from warnings import catch_warnings
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from pandas._typing import FilePathOrBuffer
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from pandas.compat._optional import import_optional_dependency
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from pandas.errors import AbstractMethodError
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from pandas import DataFrame, get_option
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from pandas.io.common import _expand_user, get_filepath_or_buffer, is_fsspec_url
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def get_engine(engine: str) -> "BaseImpl":
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""" return our implementation """
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if engine == "auto":
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engine = get_option("io.parquet.engine")
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if engine == "auto":
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# try engines in this order
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engine_classes = [PyArrowImpl, FastParquetImpl]
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error_msgs = ""
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for engine_class in engine_classes:
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try:
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return engine_class()
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except ImportError as err:
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error_msgs += "\n - " + str(err)
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raise ImportError(
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"Unable to find a usable engine; "
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"tried using: 'pyarrow', 'fastparquet'.\n"
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"A suitable version of "
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"pyarrow or fastparquet is required for parquet "
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"support.\n"
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"Trying to import the above resulted in these errors:"
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f"{error_msgs}"
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)
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if engine == "pyarrow":
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return PyArrowImpl()
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elif engine == "fastparquet":
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return FastParquetImpl()
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raise ValueError("engine must be one of 'pyarrow', 'fastparquet'")
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class BaseImpl:
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@staticmethod
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def validate_dataframe(df: DataFrame):
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if not isinstance(df, DataFrame):
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raise ValueError("to_parquet only supports IO with DataFrames")
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# must have value column names (strings only)
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if df.columns.inferred_type not in {"string", "empty"}:
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raise ValueError("parquet must have string column names")
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# index level names must be strings
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valid_names = all(
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isinstance(name, str) for name in df.index.names if name is not None
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)
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if not valid_names:
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raise ValueError("Index level names must be strings")
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def write(self, df: DataFrame, path, compression, **kwargs):
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raise AbstractMethodError(self)
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def read(self, path, columns=None, **kwargs):
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raise AbstractMethodError(self)
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class PyArrowImpl(BaseImpl):
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def __init__(self):
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import_optional_dependency(
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"pyarrow", extra="pyarrow is required for parquet support."
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)
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import pyarrow.parquet
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# import utils to register the pyarrow extension types
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import pandas.core.arrays._arrow_utils # noqa
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self.api = pyarrow
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def write(
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self,
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df: DataFrame,
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path: FilePathOrBuffer[AnyStr],
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compression: Optional[str] = "snappy",
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index: Optional[bool] = None,
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partition_cols: Optional[List[str]] = None,
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**kwargs,
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):
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self.validate_dataframe(df)
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from_pandas_kwargs: Dict[str, Any] = {"schema": kwargs.pop("schema", None)}
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if index is not None:
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from_pandas_kwargs["preserve_index"] = index
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table = self.api.Table.from_pandas(df, **from_pandas_kwargs)
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if is_fsspec_url(path) and "filesystem" not in kwargs:
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# make fsspec instance, which pyarrow will use to open paths
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import_optional_dependency("fsspec")
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import fsspec.core
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fs, path = fsspec.core.url_to_fs(path)
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kwargs["filesystem"] = fs
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else:
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path = _expand_user(path)
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if partition_cols is not None:
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# writes to multiple files under the given path
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self.api.parquet.write_to_dataset(
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table,
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path,
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compression=compression,
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partition_cols=partition_cols,
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**kwargs,
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)
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else:
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# write to single output file
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self.api.parquet.write_table(table, path, compression=compression, **kwargs)
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def read(self, path, columns=None, **kwargs):
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if is_fsspec_url(path) and "filesystem" not in kwargs:
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import_optional_dependency("fsspec")
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import fsspec.core
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fs, path = fsspec.core.url_to_fs(path)
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should_close = False
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else:
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fs = kwargs.pop("filesystem", None)
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should_close = False
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path = _expand_user(path)
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if not fs:
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path, _, _, should_close = get_filepath_or_buffer(path)
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kwargs["use_pandas_metadata"] = True
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result = self.api.parquet.read_table(
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path, columns=columns, filesystem=fs, **kwargs
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).to_pandas()
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if should_close:
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path.close()
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return result
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class FastParquetImpl(BaseImpl):
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def __init__(self):
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# since pandas is a dependency of fastparquet
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# we need to import on first use
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fastparquet = import_optional_dependency(
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"fastparquet", extra="fastparquet is required for parquet support."
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)
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self.api = fastparquet
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def write(
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self,
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df: DataFrame,
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path,
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compression="snappy",
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index=None,
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partition_cols=None,
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**kwargs,
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):
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self.validate_dataframe(df)
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# thriftpy/protocol/compact.py:339:
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# DeprecationWarning: tostring() is deprecated.
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# Use tobytes() instead.
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if "partition_on" in kwargs and partition_cols is not None:
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raise ValueError(
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"Cannot use both partition_on and "
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"partition_cols. Use partition_cols for partitioning data"
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)
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elif "partition_on" in kwargs:
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partition_cols = kwargs.pop("partition_on")
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if partition_cols is not None:
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kwargs["file_scheme"] = "hive"
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if is_fsspec_url(path):
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fsspec = import_optional_dependency("fsspec")
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# if filesystem is provided by fsspec, file must be opened in 'wb' mode.
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kwargs["open_with"] = lambda path, _: fsspec.open(path, "wb").open()
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else:
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path, _, _, _ = get_filepath_or_buffer(path)
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with catch_warnings(record=True):
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self.api.write(
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path,
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df,
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compression=compression,
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write_index=index,
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partition_on=partition_cols,
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**kwargs,
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)
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def read(self, path, columns=None, **kwargs):
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if is_fsspec_url(path):
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fsspec = import_optional_dependency("fsspec")
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open_with = lambda path, _: fsspec.open(path, "rb").open()
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parquet_file = self.api.ParquetFile(path, open_with=open_with)
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else:
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path, _, _, _ = get_filepath_or_buffer(path)
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parquet_file = self.api.ParquetFile(path)
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return parquet_file.to_pandas(columns=columns, **kwargs)
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def to_parquet(
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df: DataFrame,
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path: FilePathOrBuffer[AnyStr],
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engine: str = "auto",
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compression: Optional[str] = "snappy",
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index: Optional[bool] = None,
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partition_cols: Optional[List[str]] = None,
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**kwargs,
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):
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"""
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Write a DataFrame to the parquet format.
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Parameters
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----------
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df : DataFrame
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path : str or file-like object
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If a string, it will be used as Root Directory path
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when writing a partitioned dataset. By file-like object,
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we refer to objects with a write() method, such as a file handler
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(e.g. via builtin open function) or io.BytesIO. The engine
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fastparquet does not accept file-like objects.
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.. versionchanged:: 0.24.0
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engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto'
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Parquet library to use. If 'auto', then the option
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``io.parquet.engine`` is used. The default ``io.parquet.engine``
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behavior is to try 'pyarrow', falling back to 'fastparquet' if
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'pyarrow' is unavailable.
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compression : {'snappy', 'gzip', 'brotli', None}, default 'snappy'
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Name of the compression to use. Use ``None`` for no compression.
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index : bool, default None
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If ``True``, include the dataframe's index(es) in the file output. If
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``False``, they will not be written to the file.
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If ``None``, similar to ``True`` the dataframe's index(es)
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will be saved. However, instead of being saved as values,
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the RangeIndex will be stored as a range in the metadata so it
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doesn't require much space and is faster. Other indexes will
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be included as columns in the file output.
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.. versionadded:: 0.24.0
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partition_cols : str or list, optional, default None
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Column names by which to partition the dataset.
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Columns are partitioned in the order they are given.
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Must be None if path is not a string.
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.. versionadded:: 0.24.0
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kwargs
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Additional keyword arguments passed to the engine
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"""
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if isinstance(partition_cols, str):
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partition_cols = [partition_cols]
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impl = get_engine(engine)
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return impl.write(
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df,
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path,
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compression=compression,
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index=index,
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partition_cols=partition_cols,
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**kwargs,
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)
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def read_parquet(path, engine: str = "auto", columns=None, **kwargs):
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"""
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Load a parquet object from the file path, returning a DataFrame.
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Parameters
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----------
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path : str, path object or file-like object
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Any valid string path is acceptable. The string could be a URL. Valid
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URL schemes include http, ftp, s3, and file. For file URLs, a host is
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expected. A local file could be:
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``file://localhost/path/to/table.parquet``.
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A file URL can also be a path to a directory that contains multiple
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partitioned parquet files. Both pyarrow and fastparquet support
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paths to directories as well as file URLs. A directory path could be:
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``file://localhost/path/to/tables`` or ``s3://bucket/partition_dir``
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If you want to pass in a path object, pandas accepts any
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``os.PathLike``.
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By file-like object, we refer to objects with a ``read()`` method,
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such as a file handler (e.g. via builtin ``open`` function)
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or ``StringIO``.
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engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto'
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Parquet library to use. If 'auto', then the option
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``io.parquet.engine`` is used. The default ``io.parquet.engine``
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behavior is to try 'pyarrow', falling back to 'fastparquet' if
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'pyarrow' is unavailable.
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columns : list, default=None
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If not None, only these columns will be read from the file.
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**kwargs
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Any additional kwargs are passed to the engine.
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Returns
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-------
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DataFrame
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"""
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impl = get_engine(engine)
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return impl.read(path, columns=columns, **kwargs)
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