jorisvandenbossche commented on code in PR #14804: URL: https://github.com/apache/arrow/pull/14804#discussion_r1054173163
########## python/pyarrow/interchange/from_dataframe.py: ########## @@ -0,0 +1,590 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +from __future__ import annotations + +from typing import ( + Any, +) + +from pyarrow.interchange.column import ( + DtypeKind, + ColumnBuffers, + ColumnNullType, +) + +import ctypes +import numpy as np +import pyarrow as pa +import re + +from pyarrow.interchange.column import Dtype + + +# A typing protocol could be added later to let Mypy validate code using +# `from_dataframe` better. +DataFrameObject = Any +ColumnObject = Any +BufferObject = Any + + +_PYARROW_DTYPES: dict[DtypeKind, dict[int, Any]] = { + DtypeKind.INT: {8: pa.int8(), + 16: pa.int16(), + 32: pa.int32(), + 64: pa.int64()}, + DtypeKind.UINT: {8: pa.uint8(), + 16: pa.uint16(), + 32: pa.uint32(), + 64: pa.uint64()}, + DtypeKind.FLOAT: {16: pa.float16(), + 32: pa.float32(), + 64: pa.float64()}, + DtypeKind.BOOL: {1: pa.bool_()}, + DtypeKind.STRING: {8: pa.string()}, +} + + +def from_dataframe(df: DataFrameObject, allow_copy=True) -> pa.Table: + """ + Build a ``pa.Table`` from any DataFrame supporting the interchange + protocol. + + Parameters + ---------- + df : DataFrameObject + Object supporting the interchange protocol, i.e. `__dataframe__` + method. + allow_copy : bool, default: True + Whether to allow copying the memory to perform the conversion + (if false then zero-copy approach is requested). + + Returns + ------- + pa.Table + """ + if isinstance(df, pa.Table): + return df + + if not hasattr(df, "__dataframe__"): + raise ValueError("`df` does not support __dataframe__") + + return _from_dataframe(df.__dataframe__(allow_copy=allow_copy)) + + +def _from_dataframe(df: DataFrameObject, allow_copy=True): + """ + Build a ``pa.Table`` from the DataFrame interchange object. + + Parameters + ---------- + df : DataFrameObject + Object supporting the interchange protocol, i.e. `__dataframe__` + method. + allow_copy : bool, default: True + Whether to allow copying the memory to perform the conversion + (if false then zero-copy approach is requested). + + Returns + ------- + pa.Table + """ + batches = [] + for chunk in df.get_chunks(): + batch = protocol_df_chunk_to_pyarrow(chunk, allow_copy) + batches.append(batch) + + table = pa.Table.from_batches(batches) + return table + + +def protocol_df_chunk_to_pyarrow( + df: DataFrameObject, + allow_copy: bool = True +) -> pa.Table: + """ + Convert interchange protocol chunk to ``pa.RecordBatch``. + + Parameters + ---------- + df : DataFrameObject + + Returns + ------- + pa.RecordBatch + """ + # We need a dict of columns here, with each column being a PyArrow + # or NumPy array. + columns: dict[str, Any] = {} + buffers = [] # hold on to buffers, keeps memory alive + for name in df.column_names(): + if not isinstance(name, str): + raise ValueError(f"Column {name} is not a string") + if name in columns: + raise ValueError(f"Column {name} is not unique") + col = df.get_column_by_name(name) + dtype = col.dtype[0] + if dtype in ( + DtypeKind.INT, + DtypeKind.UINT, + DtypeKind.FLOAT, + DtypeKind.STRING, + ): + columns[name], buf = column_to_array(col, allow_copy) + elif dtype == DtypeKind.BOOL: + columns[name], buf = bool_8_column_to_array(col, allow_copy) + elif dtype == DtypeKind.CATEGORICAL: + columns[name], buf = categorical_column_to_dictionary( + col, allow_copy) + elif dtype == DtypeKind.DATETIME: + columns[name], buf = datetime_column_to_array(col, allow_copy) + else: + raise NotImplementedError(f"Data type {dtype} not handled yet") + + buffers.append(buf) + + return pa.RecordBatch.from_pydict(columns) + + +def column_to_array( + col: ColumnObject, + allow_copy: bool = True +) -> tuple[pa.Array, Any]: + """ + Convert a column holding one of the primitive dtypes to a PyArrow array. + A primitive type is one of: int, uint, float, bool (1 bit). + + Parameters + ---------- + col : ColumnObject + + Returns + ------- + tuple + Tuple of pa.Array holding the data and the memory owner object + that keeps the memory alive. + """ + buffers = col.get_buffers() + null_count = col.null_count + data = buffers_to_array(buffers, col.size(), + col.describe_null, + col.dtype, col.offset, + allow_copy, null_count) + return data, buffers + + +def bool_8_column_to_array( + col: ColumnObject, + allow_copy: bool = True +) -> tuple[pa.Array, Any]: + """ + Convert a column holding boolean dtype with bit width = 8 to a + PyArrow array. + + Parameters + ---------- + col : ColumnObject + + Returns + ------- + tuple + Tuple of pa.Array holding the data and the memory owner object + that keeps the memory alive. + """ + if not allow_copy: + raise RuntimeError( + "PyArrow Array will always be created out of a NumPy ndarray " + "and a copy is required which is forbidden by allow_copy=False" + ) + buffers = col.get_buffers() + + # Data buffer + data_buff, data_dtype = buffers["data"] + offset = col.offset + data_bit_width = data_dtype[1] + + data = buffer_to_ndarray(data_buff, + data_bit_width, + offset) + + # Validity buffer + try: + validity_buff, validity_dtype = buffers["validity"] + except TypeError: + validity_buff = None + + null_kind, sentinel_val = col.describe_null + bytemask = None + + if validity_buff: + if null_kind in (ColumnNullType.USE_BYTEMASK, + ColumnNullType.USE_BITMASK): + + validity_bit_width = validity_dtype[1] + bytemask = buffer_to_ndarray(validity_buff, + validity_bit_width, + offset) + if sentinel_val == 0: + bytemask = np.invert(bytemask) + else: + raise NotImplementedError(f"{null_kind} null representation " + "is not yet supported for boolean " + "dtype.") + # Output + return pa.array(data, mask=bytemask), buffers + + +def categorical_column_to_dictionary( + col: ColumnObject, + allow_copy: bool = True +) -> tuple[pa.DictionaryArray, Any]: + """ + Convert a column holding categorical data to a pa.DictionaryArray. + + Parameters + ---------- + col : ColumnObject + + Returns + ------- + tuple + Tuple of pa.DictionaryArray holding the data and the memory owner + object that keeps the memory alive. + """ + categorical = col.describe_categorical + null_kind, sentinel_val = col.describe_null + + if not categorical["is_dictionary"]: + raise NotImplementedError( + "Non-dictionary categoricals not supported yet") + + cat_column = categorical["categories"] + dictionary = column_to_array(cat_column)[0] + + buffers = col.get_buffers() + indices = buffers_to_array(buffers, col.size(), + col.describe_null, + col.dtype, col.offset, + allow_copy) + + if null_kind == ColumnNullType.USE_SENTINEL: + if not allow_copy: + raise RuntimeError( + "PyArrow Array will always be created out of a NumPy ndarray " + "and a copy is required which is forbidden by allow_copy=False" + ) + bytemask = [value == sentinel_val for value in indices.to_pylist()] + indices = pa.array(indices.to_pylist(), mask=bytemask) + + dict_array = pa.DictionaryArray.from_arrays(indices, dictionary) + return dict_array, buffers + + +def datetime_column_to_array( + col: ColumnObject, + allow_copy: bool = True +) -> tuple[pa.Array, Any]: + """ + Convert a column holding DateTime data to a NumPy array. + + Parameters + ---------- + col : ColumnObject + + Returns + ------- + tuple + Tuple of pa.Array holding the data and the memory owner object + that keeps the memory alive. + """ + buffers = col.get_buffers() + data_buff, data_type = buffers["data"] + try: + validity_buff, validity_dtype = buffers["validity"] + except TypeError: + validity_buff = None + + format_str = data_type[2] + unit, tz = parse_datetime_format_str(format_str) + data_dtype = pa.timestamp(unit, tz=tz) + + # Data buffer + data_pa_buffer = pa.foreign_buffer(data_buff.ptr, data_buff.bufsize) + + # Validity buffer + validity_pa_buff = validity_buff + bytemask = None + if validity_pa_buff: + validity_pa_buff, bytemask = validity_buffer(validity_buff, + validity_dtype, + col.describe_null, + col.offset) + + # Constructing a pa.Array from data and validity buffer + array = pa.Array.from_buffers( + data_dtype, + col.size(), + [validity_pa_buff, data_pa_buffer], + offset=col.offset, + ) + + null_kind, sentinel_val = col.describe_null + if null_kind == ColumnNullType.USE_SENTINEL: + # pandas iNaT + if sentinel_val == -9223372036854775808: + bytemask = np.isnan(np.array([str(e) for e in array.to_pylist()], + dtype="datetime64[ns]")) + else: + raise NotImplementedError( + f"Sentinel value {sentinel_val} for datetime is not yet " + "supported.") + + # In case a bytemask was constructed with validity_buffer() call + # or with sentinel_value then we have to add the mask to the array + if bytemask is not None: + if not allow_copy: + raise RuntimeError( + "PyArrow Array will always be created out of a NumPy ndarray " + "and a copy is required which is forbidden by allow_copy=False" + ) + return pa.array(array.to_pylist(), mask=bytemask), buffers + else: + return array, buffers + + +def parse_datetime_format_str(format_str): + """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" + + return unit, tz + + raise NotImplementedError(f"DateTime kind is not supported: {format_str}") + + +def buffers_to_array( + buffers: ColumnBuffers, + length: int, + describe_null: ColumnNullType, + validity_dtype: Dtype, + offset: int = 0, + allow_copy: bool = True, + null_count: int = -1 +) -> pa.Array: + """ + Build a PyArrow array from the passed buffer. + + Parameters + ---------- + buffer : ColumnBuffers + Dictionary containing tuples of underlying buffers and + their associated dtype. + length : int + The number of values in the array. + describe_null: ColumnNullType + Null representation the column dtype uses, + as a tuple ``(kind, value)`` + validity_dtype : Dtype, + Dtype description as a tuple ``(kind, bit-width, format string, + endianness)``. + offset : int, default: 0 + Number of elements to offset from the start of the buffer. + allow_copy : bool, default: True + Whether to allow copying the memory to perform the conversion + (if false then zero-copy approach is requested). + null_count : int, default: -1 + + Returns + ------- + pa.Array + + 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 PyArrow array is being used. + """ + data_buff, data_type = buffers["data"] + try: + validity_buff, validity_dtype = buffers["validity"] + except TypeError: + validity_buff = None + try: + offset_buff, _ = buffers["offsets"] + except TypeError: + offset_buff = None + + kind, bit_width, _, _ = data_type + + data_dtype = _PYARROW_DTYPES.get(kind, {}).get(bit_width, None) + if data_dtype is None: + raise NotImplementedError( + f"Conversion for {data_type} is not yet supported.") + + # Construct a pyarrow Buffer + data_pa_buffer = pa.foreign_buffer(data_buff.ptr, data_buff.bufsize) + + # Construct a validity pyarrow Buffer or a bytemask, if exists + validity_pa_buff = validity_buff + _, _, validity_f_string, _ = validity_dtype + bytemask = None + if validity_pa_buff: + validity_pa_buff, bytemask = validity_buffer(validity_buff, + validity_dtype, + describe_null, + offset) + + # Construct a pyarrow Array from buffers + if offset_buff: + # If an offset buffer exists, construct an offset pyarrow Buffer + # and add it to the construction of an array + offset_pa_buffer = pa.py_buffer(offset_buff._x) Review Comment: Avoid accessing the private `_x` ? -- This is an automated message from the Apache Git Service. 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