Skip to content

BUG: pd.factorize should not upconvert unique values unnecessarily #41132

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 4 commits into from
Apr 26, 2021
Merged

BUG: pd.factorize should not upconvert unique values unnecessarily #41132

Show file tree
Hide file tree
Changes from 3 commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
6cd9380
BUG: pd.factorize upconverted unique values (from e.g. int8 -> int64)
topper-123 Apr 24, 2021
929cc37
Add gh issue number
topper-123 Apr 24, 2021
23adaf2
fix platform dtype
topper-123 Apr 24, 2021
8bfe94c
simplify
topper-123 Apr 24, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 1 addition & 0 deletions doc/source/whatsnew/v1.3.0.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -705,6 +705,7 @@ Conversion
- Bug in :class:`DataFrame` failing to raise ``TypeError`` when constructing from a ``frozenset`` (:issue:`40163`)
- Bug in :class:`Index` construction silently ignoring a passed ``dtype`` when the data cannot be cast to that dtype (:issue:`21311`)
- Bug in :meth:`StringArray.astype` falling back to numpy and raising when converting to ``dtype='categorical'`` (:issue:`40450`)
- Bug in :func:`factorize` where, when given an array with a numeric numpy dtype lower than int64, uint64 and float64, the unique values did not keep their original dtype (:issue:`41132`)
- Bug in :class:`DataFrame` construction with a dictionary containing an arraylike with ``ExtensionDtype`` and ``copy=True`` failing to make a copy (:issue:`38939`)
-

Expand Down
9 changes: 6 additions & 3 deletions pandas/core/algorithms.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -143,11 +143,14 @@ def _ensure_data(values: ArrayLike) -> tuple[np.ndarray, DtypeObj]:
# until our algos support uint8 directly (see TODO)
return np.asarray(values).astype("uint64"), np.dtype("bool")
elif is_signed_integer_dtype(values):
return ensure_int64(values), np.dtype("int64")
dtype = getattr(values, "dtype", np.dtype("int64"))
return ensure_int64(values), dtype
elif is_unsigned_integer_dtype(values):
return ensure_uint64(values), np.dtype("uint64")
dtype = getattr(values, "dtype", np.dtype("uint64"))
return ensure_uint64(values), dtype
elif is_float_dtype(values):
return ensure_float64(values), np.dtype("float64")
dtype = getattr(values, "dtype", np.dtype("float64"))
return ensure_float64(values), dtype
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

i think this code was written before we had non-64 hashtables. i expect a lot of this casting can now be avoided

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Probably yeah, and if we can avoid them, it'll speed some things up nicely.

But that's for a different PR? I got a feeling there's a lot of detail hidden in that change.

Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

fair enough

elif is_complex_dtype(values):

# ignore the fact that we are casting to float
Expand Down
32 changes: 23 additions & 9 deletions pandas/tests/test_algos.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -95,29 +95,32 @@ def test_basic(self):
exp = np.array(["a", "b", "c"], dtype=object)
tm.assert_numpy_array_equal(uniques, exp)

codes, uniques = algos.factorize(list(reversed(range(5))))
arr = np.arange(5, dtype=np.intp)[::-1]

codes, uniques = algos.factorize(arr)
exp = np.array([0, 1, 2, 3, 4], dtype=np.intp)
tm.assert_numpy_array_equal(codes, exp)
exp = np.array([4, 3, 2, 1, 0], dtype=np.int64)
exp = np.array([4, 3, 2, 1, 0], dtype=arr.dtype)
tm.assert_numpy_array_equal(uniques, exp)

codes, uniques = algos.factorize(list(reversed(range(5))), sort=True)

codes, uniques = algos.factorize(arr, sort=True)
exp = np.array([4, 3, 2, 1, 0], dtype=np.intp)
tm.assert_numpy_array_equal(codes, exp)
exp = np.array([0, 1, 2, 3, 4], dtype=np.int64)
exp = np.array([0, 1, 2, 3, 4], dtype=arr.dtype)
tm.assert_numpy_array_equal(uniques, exp)

codes, uniques = algos.factorize(list(reversed(np.arange(5.0))))
arr = np.arange(5.0)[::-1]

codes, uniques = algos.factorize(arr)
exp = np.array([0, 1, 2, 3, 4], dtype=np.intp)
tm.assert_numpy_array_equal(codes, exp)
exp = np.array([4.0, 3.0, 2.0, 1.0, 0.0], dtype=np.float64)
exp = np.array([4.0, 3.0, 2.0, 1.0, 0.0], dtype=arr.dtype)
tm.assert_numpy_array_equal(uniques, exp)

codes, uniques = algos.factorize(list(reversed(np.arange(5.0))), sort=True)
codes, uniques = algos.factorize(arr, sort=True)
exp = np.array([4, 3, 2, 1, 0], dtype=np.intp)
tm.assert_numpy_array_equal(codes, exp)
exp = np.array([0.0, 1.0, 2.0, 3.0, 4.0], dtype=np.float64)
exp = np.array([0.0, 1.0, 2.0, 3.0, 4.0], dtype=arr.dtype)
tm.assert_numpy_array_equal(uniques, exp)

def test_mixed(self):
Expand Down Expand Up @@ -246,6 +249,17 @@ def test_complex_sorting(self):
with pytest.raises(TypeError, match=msg):
algos.factorize(x17[::-1], sort=True)

def test_numeric_dtype_factorize(self, any_real_dtype):
# GH41132
dtype = any_real_dtype
data = np.array([1, 2, 2, 1], dtype=dtype)
expected_codes = np.array([0, 1, 1, 0], dtype=np.intp)
expected_uniques = np.array([1, 2], dtype=dtype)

codes, uniques = algos.factorize(data)
tm.assert_numpy_array_equal(codes, expected_codes)
tm.assert_numpy_array_equal(uniques, expected_uniques)

def test_float64_factorize(self, writable):
data = np.array([1.0, 1e8, 1.0, 1e-8, 1e8, 1.0], dtype=np.float64)
data.setflags(write=writable)
Expand Down