from __future__ import annotations
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|
import ctypes
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import re
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from typing import Any
|
|
import numpy as np
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from pandas._config import using_string_dtype
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from pandas.compat._optional import import_optional_dependency
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from pandas.errors import SettingWithCopyError
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import pandas as pd
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from pandas.core.interchange.dataframe_protocol import (
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Buffer,
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Column,
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ColumnNullType,
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DataFrame as DataFrameXchg,
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DtypeKind,
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)
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from pandas.core.interchange.utils import (
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ArrowCTypes,
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Endianness,
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)
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_NP_DTYPES: dict[DtypeKind, dict[int, Any]] = {
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DtypeKind.INT: {8: np.int8, 16: np.int16, 32: np.int32, 64: np.int64},
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DtypeKind.UINT: {8: np.uint8, 16: np.uint16, 32: np.uint32, 64: np.uint64},
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DtypeKind.FLOAT: {32: np.float32, 64: np.float64},
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DtypeKind.BOOL: {1: bool, 8: bool},
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}
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def from_dataframe(df, allow_copy: bool = True) -> pd.DataFrame:
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"""
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Build a ``pd.DataFrame`` from any DataFrame supporting the interchange protocol.
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.. note::
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For new development, we highly recommend using the Arrow C Data Interface
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alongside the Arrow PyCapsule Interface instead of the interchange protocol.
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From pandas 2.3 onwards, `from_dataframe` uses the PyCapsule Interface,
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only falling back to the interchange protocol if that fails.
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.. warning::
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Due to severe implementation issues, we recommend only considering using the
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interchange protocol in the following cases:
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- converting to pandas: for pandas >= 2.0.3
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- converting from pandas: for pandas >= 3.0.0
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Parameters
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----------
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df : DataFrameXchg
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Object supporting the interchange protocol, i.e. `__dataframe__` method.
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allow_copy : bool, default: True
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Whether to allow copying the memory to perform the conversion
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(if false then zero-copy approach is requested).
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Returns
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-------
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pd.DataFrame
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Examples
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--------
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>>> df_not_necessarily_pandas = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
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>>> interchange_object = df_not_necessarily_pandas.__dataframe__()
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>>> interchange_object.column_names()
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Index(['A', 'B'], dtype='object')
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>>> df_pandas = (pd.api.interchange.from_dataframe
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... (interchange_object.select_columns_by_name(['A'])))
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>>> df_pandas
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A
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0 1
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1 2
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These methods (``column_names``, ``select_columns_by_name``) should work
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for any dataframe library which implements the interchange protocol.
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"""
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if isinstance(df, pd.DataFrame):
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return df
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if hasattr(df, "__arrow_c_stream__"):
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try:
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pa = import_optional_dependency("pyarrow", min_version="14.0.0")
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except ImportError:
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# fallback to _from_dataframe
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pass
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else:
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try:
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return pa.table(df).to_pandas(zero_copy_only=not allow_copy)
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except pa.ArrowInvalid as e:
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raise RuntimeError(e) from e
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if not hasattr(df, "__dataframe__"):
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raise ValueError("`df` does not support __dataframe__")
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return _from_dataframe(
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df.__dataframe__(allow_copy=allow_copy), allow_copy=allow_copy
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)
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def _from_dataframe(df: DataFrameXchg, allow_copy: bool = True):
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"""
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Build a ``pd.DataFrame`` from the DataFrame interchange object.
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Parameters
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----------
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df : DataFrameXchg
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Object supporting the interchange protocol, i.e. `__dataframe__` method.
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allow_copy : bool, default: True
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Whether to allow copying the memory to perform the conversion
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(if false then zero-copy approach is requested).
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Returns
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-------
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pd.DataFrame
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"""
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pandas_dfs = []
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for chunk in df.get_chunks():
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pandas_df = protocol_df_chunk_to_pandas(chunk)
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pandas_dfs.append(pandas_df)
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if not allow_copy and len(pandas_dfs) > 1:
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raise RuntimeError(
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"To join chunks a copy is required which is forbidden by allow_copy=False"
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)
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if not pandas_dfs:
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pandas_df = protocol_df_chunk_to_pandas(df)
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elif len(pandas_dfs) == 1:
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pandas_df = pandas_dfs[0]
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else:
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pandas_df = pd.concat(pandas_dfs, axis=0, ignore_index=True, copy=False)
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index_obj = df.metadata.get("pandas.index", None)
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if index_obj is not None:
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pandas_df.index = index_obj
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return pandas_df
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def protocol_df_chunk_to_pandas(df: DataFrameXchg) -> pd.DataFrame:
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"""
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Convert interchange protocol chunk to ``pd.DataFrame``.
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Parameters
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----------
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df : DataFrameXchg
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Returns
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-------
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pd.DataFrame
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"""
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columns: dict[str, Any] = {}
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buffers = [] # hold on to buffers, keeps memory alive
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for name in df.column_names():
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if not isinstance(name, str):
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raise ValueError(f"Column {name} is not a string")
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if name in columns:
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raise ValueError(f"Column {name} is not unique")
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col = df.get_column_by_name(name)
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dtype = col.dtype[0]
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if dtype in (
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DtypeKind.INT,
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DtypeKind.UINT,
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DtypeKind.FLOAT,
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DtypeKind.BOOL,
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):
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columns[name], buf = primitive_column_to_ndarray(col)
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elif dtype == DtypeKind.CATEGORICAL:
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columns[name], buf = categorical_column_to_series(col)
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elif dtype == DtypeKind.STRING:
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columns[name], buf = string_column_to_ndarray(col)
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elif dtype == DtypeKind.DATETIME:
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columns[name], buf = datetime_column_to_ndarray(col)
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else:
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raise NotImplementedError(f"Data type {dtype} not handled yet")
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buffers.append(buf)
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pandas_df = pd.DataFrame(columns)
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pandas_df.attrs["_INTERCHANGE_PROTOCOL_BUFFERS"] = buffers
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return pandas_df
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def primitive_column_to_ndarray(col: Column) -> tuple[np.ndarray, Any]:
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"""
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Convert a column holding one of the primitive dtypes to a NumPy array.
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A primitive type is one of: int, uint, float, bool.
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Parameters
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----------
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col : Column
|
|
Returns
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-------
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tuple
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Tuple of np.ndarray holding the data and the memory owner object
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that keeps the memory alive.
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"""
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buffers = col.get_buffers()
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data_buff, data_dtype = buffers["data"]
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data = buffer_to_ndarray(
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data_buff, data_dtype, offset=col.offset, length=col.size()
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)
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data = set_nulls(data, col, buffers["validity"])
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return data, buffers
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def categorical_column_to_series(col: Column) -> tuple[pd.Series, Any]:
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"""
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Convert a column holding categorical data to a pandas Series.
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Parameters
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----------
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col : Column
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Returns
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-------
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tuple
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Tuple of pd.Series holding the data and the memory owner object
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that keeps the memory alive.
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"""
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categorical = col.describe_categorical
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if not categorical["is_dictionary"]:
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raise NotImplementedError("Non-dictionary categoricals not supported yet")
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cat_column = categorical["categories"]
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if hasattr(cat_column, "_col"):
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# Item "Column" of "Optional[Column]" has no attribute "_col"
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# Item "None" of "Optional[Column]" has no attribute "_col"
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categories = np.array(cat_column._col) # type: ignore[union-attr]
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else:
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raise NotImplementedError(
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"Interchanging categorical columns isn't supported yet, and our "
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"fallback of using the `col._col` attribute (a ndarray) failed."
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)
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buffers = col.get_buffers()
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codes_buff, codes_dtype = buffers["data"]
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codes = buffer_to_ndarray(
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codes_buff, codes_dtype, offset=col.offset, length=col.size()
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)
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# Doing module in order to not get ``IndexError`` for
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# out-of-bounds sentinel values in `codes`
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if len(categories) > 0:
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values = categories[codes % len(categories)]
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else:
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values = codes
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cat = pd.Categorical(
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values, categories=categories, ordered=categorical["is_ordered"]
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)
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data = pd.Series(cat)
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data = set_nulls(data, col, buffers["validity"])
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return data, buffers
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def string_column_to_ndarray(col: Column) -> tuple[np.ndarray, Any]:
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"""
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Convert a column holding string data to a NumPy array.
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Parameters
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----------
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col : Column
|
|
Returns
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-------
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tuple
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Tuple of np.ndarray holding the data and the memory owner object
|
that keeps the memory alive.
|
"""
|
null_kind, sentinel_val = col.describe_null
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if null_kind not in (
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ColumnNullType.NON_NULLABLE,
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ColumnNullType.USE_BITMASK,
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ColumnNullType.USE_BYTEMASK,
|
):
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raise NotImplementedError(
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f"{null_kind} null kind is not yet supported for string columns."
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)
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buffers = col.get_buffers()
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assert buffers["offsets"], "String buffers must contain offsets"
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# Retrieve the data buffer containing the UTF-8 code units
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data_buff, _ = buffers["data"]
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# We're going to reinterpret the buffer as uint8, so make sure we can do it safely
|
assert col.dtype[2] in (
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ArrowCTypes.STRING,
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ArrowCTypes.LARGE_STRING,
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) # format_str == utf-8
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# Convert the buffers to NumPy arrays. In order to go from STRING to
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# an equivalent ndarray, we claim that the buffer is uint8 (i.e., a byte array)
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data_dtype = (
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DtypeKind.UINT,
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8,
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ArrowCTypes.UINT8,
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Endianness.NATIVE,
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)
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# Specify zero offset as we don't want to chunk the string data
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data = buffer_to_ndarray(data_buff, data_dtype, offset=0, length=data_buff.bufsize)
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# Retrieve the offsets buffer containing the index offsets demarcating
|
# the beginning and the ending of each string
|
offset_buff, offset_dtype = buffers["offsets"]
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# Offsets buffer contains start-stop positions of strings in the data buffer,
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# meaning that it has more elements than in the data buffer, do `col.size() + 1`
|
# here to pass a proper offsets buffer size
|
offsets = buffer_to_ndarray(
|
offset_buff, offset_dtype, offset=col.offset, length=col.size() + 1
|
)
|
|
null_pos = None
|
if null_kind in (ColumnNullType.USE_BITMASK, ColumnNullType.USE_BYTEMASK):
|
validity = buffers["validity"]
|
if validity is not None:
|
valid_buff, valid_dtype = validity
|
null_pos = buffer_to_ndarray(
|
valid_buff, valid_dtype, offset=col.offset, length=col.size()
|
)
|
if sentinel_val == 0:
|
null_pos = ~null_pos
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# Assemble the strings from the code units
|
str_list: list[None | float | str] = [None] * col.size()
|
for i in range(col.size()):
|
# Check for missing values
|
if null_pos is not None and null_pos[i]:
|
str_list[i] = np.nan
|
continue
|
|
# Extract a range of code units
|
units = data[offsets[i] : offsets[i + 1]]
|
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# Convert the list of code units to bytes
|
str_bytes = bytes(units)
|
|
# Create the string
|
string = str_bytes.decode(encoding="utf-8")
|
|
# Add to our list of strings
|
str_list[i] = string
|
|
if using_string_dtype():
|
res = pd.Series(str_list, dtype="str")
|
else:
|
res = np.asarray(str_list, dtype="object") # type: ignore[assignment]
|
|
return res, buffers # type: ignore[return-value]
|
|
|
def parse_datetime_format_str(format_str, data) -> pd.Series | np.ndarray:
|
"""Parse datetime `format_str` to interpret the `data`."""
|
# timestamp 'ts{unit}:tz'
|
timestamp_meta = re.match(r"ts([smun]):(.*)", format_str)
|
if timestamp_meta:
|
unit, tz = timestamp_meta.group(1), timestamp_meta.group(2)
|
if unit != "s":
|
# the format string describes only a first letter of the unit, so
|
# add one extra letter to convert the unit to numpy-style:
|
# 'm' -> 'ms', 'u' -> 'us', 'n' -> 'ns'
|
unit += "s"
|
data = data.astype(f"datetime64[{unit}]")
|
if tz != "":
|
data = pd.Series(data).dt.tz_localize("UTC").dt.tz_convert(tz)
|
return data
|
|
# date 'td{Days/Ms}'
|
date_meta = re.match(r"td([Dm])", format_str)
|
if date_meta:
|
unit = date_meta.group(1)
|
if unit == "D":
|
# NumPy doesn't support DAY unit, so converting days to seconds
|
# (converting to uint64 to avoid overflow)
|
data = (data.astype(np.uint64) * (24 * 60 * 60)).astype("datetime64[s]")
|
elif unit == "m":
|
data = data.astype("datetime64[ms]")
|
else:
|
raise NotImplementedError(f"Date unit is not supported: {unit}")
|
return data
|
|
raise NotImplementedError(f"DateTime kind is not supported: {format_str}")
|
|
|
def datetime_column_to_ndarray(col: Column) -> tuple[np.ndarray | pd.Series, Any]:
|
"""
|
Convert a column holding DateTime data to a NumPy array.
|
|
Parameters
|
----------
|
col : Column
|
|
Returns
|
-------
|
tuple
|
Tuple of np.ndarray holding the data and the memory owner object
|
that keeps the memory alive.
|
"""
|
buffers = col.get_buffers()
|
|
_, col_bit_width, format_str, _ = col.dtype
|
dbuf, _ = buffers["data"]
|
# Consider dtype being `uint` to get number of units passed since the 01.01.1970
|
|
data = buffer_to_ndarray(
|
dbuf,
|
(
|
DtypeKind.INT,
|
col_bit_width,
|
getattr(ArrowCTypes, f"INT{col_bit_width}"),
|
Endianness.NATIVE,
|
),
|
offset=col.offset,
|
length=col.size(),
|
)
|
|
data = parse_datetime_format_str(format_str, data) # type: ignore[assignment]
|
data = set_nulls(data, col, buffers["validity"])
|
return data, buffers
|
|
|
def buffer_to_ndarray(
|
buffer: Buffer,
|
dtype: tuple[DtypeKind, int, str, str],
|
*,
|
length: int,
|
offset: int = 0,
|
) -> np.ndarray:
|
"""
|
Build a NumPy array from the passed buffer.
|
|
Parameters
|
----------
|
buffer : Buffer
|
Buffer to build a NumPy array from.
|
dtype : tuple
|
Data type of the buffer conforming protocol dtypes format.
|
offset : int, default: 0
|
Number of elements to offset from the start of the buffer.
|
length : int, optional
|
If the buffer is a bit-mask, specifies a number of bits to read
|
from the buffer. Has no effect otherwise.
|
|
Returns
|
-------
|
np.ndarray
|
|
Notes
|
-----
|
The returned array doesn't own the memory. The caller of this function is
|
responsible for keeping the memory owner object alive as long as
|
the returned NumPy array is being used.
|
"""
|
kind, bit_width, _, _ = dtype
|
|
column_dtype = _NP_DTYPES.get(kind, {}).get(bit_width, None)
|
if column_dtype is None:
|
raise NotImplementedError(f"Conversion for {dtype} is not yet supported.")
|
|
# TODO: No DLPack yet, so need to construct a new ndarray from the data pointer
|
# and size in the buffer plus the dtype on the column. Use DLPack as NumPy supports
|
# it since https://github.com/numpy/numpy/pull/19083
|
ctypes_type = np.ctypeslib.as_ctypes_type(column_dtype)
|
|
if bit_width == 1:
|
assert length is not None, "`length` must be specified for a bit-mask buffer."
|
pa = import_optional_dependency("pyarrow")
|
arr = pa.BooleanArray.from_buffers(
|
pa.bool_(),
|
length,
|
[None, pa.foreign_buffer(buffer.ptr, length)],
|
offset=offset,
|
)
|
return np.asarray(arr)
|
else:
|
data_pointer = ctypes.cast(
|
buffer.ptr + (offset * bit_width // 8), ctypes.POINTER(ctypes_type)
|
)
|
if length > 0:
|
return np.ctypeslib.as_array(data_pointer, shape=(length,))
|
return np.array([], dtype=ctypes_type)
|
|
|
def set_nulls(
|
data: np.ndarray | pd.Series,
|
col: Column,
|
validity: tuple[Buffer, tuple[DtypeKind, int, str, str]] | None,
|
allow_modify_inplace: bool = True,
|
):
|
"""
|
Set null values for the data according to the column null kind.
|
|
Parameters
|
----------
|
data : np.ndarray or pd.Series
|
Data to set nulls in.
|
col : Column
|
Column object that describes the `data`.
|
validity : tuple(Buffer, dtype) or None
|
The return value of ``col.buffers()``. We do not access the ``col.buffers()``
|
here to not take the ownership of the memory of buffer objects.
|
allow_modify_inplace : bool, default: True
|
Whether to modify the `data` inplace when zero-copy is possible (True) or always
|
modify a copy of the `data` (False).
|
|
Returns
|
-------
|
np.ndarray or pd.Series
|
Data with the nulls being set.
|
"""
|
if validity is None:
|
return data
|
null_kind, sentinel_val = col.describe_null
|
null_pos = None
|
|
if null_kind == ColumnNullType.USE_SENTINEL:
|
null_pos = pd.Series(data) == sentinel_val
|
elif null_kind in (ColumnNullType.USE_BITMASK, ColumnNullType.USE_BYTEMASK):
|
assert validity, "Expected to have a validity buffer for the mask"
|
valid_buff, valid_dtype = validity
|
null_pos = buffer_to_ndarray(
|
valid_buff, valid_dtype, offset=col.offset, length=col.size()
|
)
|
if sentinel_val == 0:
|
null_pos = ~null_pos
|
elif null_kind in (ColumnNullType.NON_NULLABLE, ColumnNullType.USE_NAN):
|
pass
|
else:
|
raise NotImplementedError(f"Null kind {null_kind} is not yet supported.")
|
|
if null_pos is not None and np.any(null_pos):
|
if not allow_modify_inplace:
|
data = data.copy()
|
try:
|
data[null_pos] = None
|
except TypeError:
|
# TypeError happens if the `data` dtype appears to be non-nullable
|
# in numpy notation (bool, int, uint). If this happens,
|
# cast the `data` to nullable float dtype.
|
data = data.astype(float)
|
data[null_pos] = None
|
except SettingWithCopyError:
|
# `SettingWithCopyError` may happen for datetime-like with missing values.
|
data = data.copy()
|
data[null_pos] = None
|
|
return data
|
