jorisvandenbossche commented on code in PR #14804: URL: https://github.com/apache/arrow/pull/14804#discussion_r1049330105
########## python/pyarrow/interchange/from_dataframe.py: ########## @@ -0,0 +1,529 @@ +# 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) + batches.append(batch) + + table = pa.Table.from_batches(batches) + return table + + +def protocol_df_chunk_to_pyarrow(df: DataFrameObject) -> 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) + elif dtype == DtypeKind.BOOL: + columns[name], buf = bool_8_column_to_array(col) + elif dtype == DtypeKind.CATEGORICAL: + columns[name], buf = categorical_column_to_dictionary(col) + elif dtype == DtypeKind.DATETIME: + columns[name], buf = datetime_column_to_array(col) + 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) -> 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() + data = buffers_to_array(buffers, col.size(), col.describe_null, col.offset) + return data, buffers + + +def bool_8_column_to_array(col: ColumnObject) -> 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. + """ + 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 +) -> 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.offset) + + if null_kind == ColumnNullType.USE_SENTINEL: + 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) -> 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: + 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 tz != "": + raise NotImplementedError("Timezones are not supported yet") + 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 + + # TODO + # date 'td{Days/Ms}' Review Comment: I think it is fine to only support timestamp. I assume the protocol doesn't actually support `date` and `time`, although `date` could be seen as datetime with day resolution (which you can represent with numpy's datetime64 dtype), but I don't know if that is officially allowed by the protocol. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
