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Add an is_dtype function #199

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Merged
merged 9 commits into from
Aug 2, 2023
Merged

Add an is_dtype function #199

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10751b0
add is_type function
MarcoGorelli Jul 12, 2023
27614e7
fixup
MarcoGorelli Jul 12, 2023
5da9d7b
fixup sphinx
MarcoGorelli Jul 12, 2023
bac8540
remove Date
MarcoGorelli Jul 26, 2023
b8b49ce
remove temporal and string for now
MarcoGorelli Aug 1, 2023
e7dea4a
Merge branch 'main' into is-dtype
MarcoGorelli Aug 1, 2023
f4e5ef7
Merge remote-tracking branch 'upstream/main' into is-dtype
MarcoGorelli Aug 2, 2023
13663a2
Merge branch 'is-dtype' of github.com:MarcoGorelli/dataframe-api into…
MarcoGorelli Aug 2, 2023
7e1fc9d
fixup
MarcoGorelli Aug 2, 2023
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33 changes: 33 additions & 0 deletions spec/API_specification/dataframe_api/__init__.py
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Expand Up @@ -32,6 +32,7 @@
"Float64",
"Float32",
"Bool",
"is_dtype",
]


Expand Down Expand Up @@ -239,3 +240,35 @@ class Float32:

class Bool:
"""Boolean type with 8 bits of precision."""


def is_dtype(dtype: Any, kind: str | tuple[str, ...]) -> bool:
"""
Returns a boolean indicating whether a provided dtype is of a specified data type “kind”.

Parameters
----------
dtype: Any
The input dtype.
kind: str
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@MarcoGorelli MarcoGorelli Jul 12, 2023 •
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the array api also allows dtype, but I don't think we need that here. If you want to check whether a dtype is of a specific dtype, you can just use isinstance, e.g.

isinstance(column.dtype, namespace.Float64)

I think this function is more useful for checking conceptual hierarchies, like 'integral'

So, for now, I'm only allowing str | tuple[str, ...]

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Part of the reason for allowing dtype instances is to allow arbitrary composition of dtype sets beyond the supported kinds and allow an escape hatch beyond the kinds officially supported by the array API standard.

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can't you do that with

is_dtype(column.dtype, 'integral') or isinstance(column.dtype, MyFancyDType)

?

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Yes, but that becomes a bit unwieldy quickly if you want to arbitrarily combine multiple "kinds" and/or individual dtypes.

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I'd suggest keeping this simple to begin with, we can always increase complexity later if necessary

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I'd have made the same comment as @kgryte, I think it'll be needed and is better than using isinstance. However, it's a fully backwards-compatible extension, so I'm fine with leaving this comment unresolved here and a follow-up PR to extend the functionality to:

kind: str | tuple[str, ...] | DType | tuple[DType, ..]

data type kind.
The function must return a boolean indicating whether
the input dtype is of a specified data type kind.
The following dtype kinds must be supported:

- 'bool': boolean data type (Bool).
- 'signed integer': signed integer data types (Int8, Int16, Int32, Int64).
- 'unsigned integer': unsigned integer data types (UInt8, UInt16, UInt32, UInt64).
- 'floating': floating-point data types (Float32, Float64).
- 'integral': integer data types. Shorthand for ('signed integer', 'unsigned integer').
- 'numeric': numeric data types. Shorthand for ('integral', 'floating').

If kind is a tuple, the tuple specifies a union of dtypes and/or kinds,
and the function must return a boolean indicating whether the input dtype
is either equal to a specified dtype or belongs to at least one specified
data type kind.

Returns
-------
bool
"""
1 change: 1 addition & 0 deletions spec/API_specification/index.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -27,6 +27,7 @@ of objects and functions in the top-level namespace. The latter are:
Float64
Float32
Bool
is_dtype
column_from_sequence
column_from_1d_array
dataframe_from_dict
Expand Down