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BUG: Interchange object data buffer has the wrong dtype / from_dataframe incorrect #55227

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Merged
merged 11 commits into from
Nov 7, 2023
Merged

BUG: Interchange object data buffer has the wrong dtype / from_dataframe incorrect #55227

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9876c64
use buffer dtype in interchange from_dataframe
MarcoGorelli Sep 21, 2023
0b936b3
Merge remote-tracking branch 'upstream/main' into use-buffer-dtype-in…
MarcoGorelli Oct 7, 2023
00ad9c7
wip
MarcoGorelli Oct 7, 2023
d54f950
wip
MarcoGorelli Oct 7, 2023
adeceb9
add failing test
MarcoGorelli Oct 7, 2023
3557b4a
wip
MarcoGorelli Oct 10, 2023
975c87c
Merge remote-tracking branch 'upstream/main' into use-buffer-dtype-in…
MarcoGorelli Oct 10, 2023
0ef179a
simplify
MarcoGorelli Oct 10, 2023
df996ac
remove unnecessary assertion
MarcoGorelli Oct 10, 2023
0bea19b
dont double-extract bit width
MarcoGorelli Oct 10, 2023
d04ac92
Update pandas/core/interchange/from_dataframe.py
MarcoGorelli Oct 10, 2023
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60 changes: 37 additions & 23 deletions pandas/core/interchange/from_dataframe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -266,21 +266,29 @@ def string_column_to_ndarray(col: Column) -> tuple[np.ndarray, Any]:

assert buffers["offsets"], "String buffers must contain offsets"
# Retrieve the data buffer containing the UTF-8 code units
data_buff, protocol_data_dtype = buffers["data"]
# We're going to reinterpret the buffer as uint8, so make sure we can do it safely
assert protocol_data_dtype[1] == 8
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This assertion can indeed be deleted, as we can assume bit width 8 if the column dtype is STRING or LARGE_STRING.

assert protocol_data_dtype[2] in (
ArrowCTypes.STRING,
ArrowCTypes.LARGE_STRING,
) # format_str == utf-8
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This assertion is valid, but it should be on col.dtype[2] rather than protocol_data_dtype[2]

# Convert the buffers to NumPy arrays. In order to go from STRING to
# an equivalent ndarray, we claim that the buffer is uint8 (i.e., a byte array)
data_dtype = (
DtypeKind.UINT,
8,
ArrowCTypes.UINT8,
Endianness.NATIVE,
)
data_buff, data_dtype = buffers["data"]
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We can simply ignore the data dtype here, as we know what it needs to be (we set it later).

Suggested change
data_buff, data_dtype = buffers["data"]
data_buff, _ = buffers["data"]


if (data_dtype[1] == 8) and (
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Out of curiosity what is the 8 here supposed to represent?

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hey - from https://data-apis.org/dataframe-protocol/latest/API.html, it's number of bits:

    @property
    @abstractmethod
    def dtype(self) -> Dtype:
        """
        Dtype description as a tuple ``(kind, bit-width, format string, endianness)``.

        Bit-width : the number of bits as an integer
        Format string : data type description format string in Apache Arrow C
                        Data Interface format.
        Endianness : current only native endianness (``=``) is supported

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@WillAyd WillAyd Sep 22, 2023 •
edited
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Are string dtypes supposed to have an 8 bit association? That is kind of confusing for variable length types, granted I know very little of how this interchange is supposed to work

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I think the idea is that strings are meant to be utf-8, and so each character can be represented with 8bits

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Hmm interesting. Well keep in mind that UTF-8 doesn't mean a character is 8 bits; it is still 1-4 bytes

In arrow-adbc I've seen this assigned the value of 0

https://github.com/apache/arrow-adbc/blob/0d8707a5ee2622ba959b069cd173bfe6ee2aaff3/c/driver/postgresql/statement.cc#L225

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The UFT-8 array, consisting of the actual strings and offsets array, so here the buffer representing all string data (so which is typically much longer as the logical length of the array) can be seen as simply a buffer of bytes ("bytearray"), so so in numpy / buffer interface terms you can view such an array as one bitwidth 8

In arrow-adbc I've seen this assigned the value of 0

That's something postgres specific, I think

data_dtype[2]
in (
ArrowCTypes.STRING,
ArrowCTypes.LARGE_STRING,
)
): # format_str == utf-8
# temporary workaround to keep backwards compatibility due to
# https://github.com/pandas-dev/pandas/issues/54781

# We're going to reinterpret the buffer as uint8, so make sure we can do it
# safely

# Convert the buffers to NumPy arrays. In order to go from STRING to
# an equivalent ndarray, we claim that the buffer is uint8 (i.e., a byte array)
data_dtype = (
DtypeKind.UINT,
8,
ArrowCTypes.UINT8,
Endianness.NATIVE,
)
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This does not need to be in an if-block. We can simply disregard the data buffer dtype - for string columns, this will ALWAYS be as listed here. This was already the case in the previous code! Only the assertions were wrong.

# Specify zero offset as we don't want to chunk the string data
data = buffer_to_ndarray(data_buff, data_dtype, offset=0, length=data_buff.bufsize)

Expand Down Expand Up @@ -378,16 +386,22 @@ def datetime_column_to_ndarray(col: Column) -> tuple[np.ndarray | pd.Series, Any
buffers = col.get_buffers()

_, _, format_str, _ = col.dtype
dbuf, dtype = buffers["data"]
# Consider dtype being `uint` to get number of units passed since the 01.01.1970
data = buffer_to_ndarray(
dbuf,
(
dbuf, data_dtype = buffers["data"]

if data_dtype[0] == DtypeKind.DATETIME:
# temporary workaround to keep backwards compatibility due to
# https://github.com/pandas-dev/pandas/issues/54781
# Consider dtype being `uint` to get number of units passed since the 01.01.1970
data_dtype = (
DtypeKind.UINT,
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Shouldn't this be an INT? Timestamps are backed by 64 bit signed integers in arrow

https://github.com/apache/arrow/blob/772a01c080ad57eb11e9323f5347472b769d45de/format/Schema.fbs#L264

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Yes, AFAIK that should be signed INT

dtype[1],
getattr(ArrowCTypes, f"UINT{dtype[1]}"),
data_dtype[1],
getattr(ArrowCTypes, f"UINT{data_dtype[1]}"),
Endianness.NATIVE,
),
)

data = buffer_to_ndarray(
dbuf,
data_dtype,
offset=col.offset,
length=col.size(),
)
Expand Down