From 86954016384c53745d6144af80da5957ad2e82fd Mon Sep 17 00:00:00 2001
From: Joris Van den Bossche <jorisvandenbossche@gmail.com>
Date: Tue, 3 Dec 2024 19:34:25 +0100
Subject: [PATCH] PERF: improve construct_1d_object_array_from_listlike
(#60461)
* PERF: improve construct_1d_object_array_from_listlike
* use np.fromiter and update annotation
---
pandas/core/dtypes/cast.py | 12 +++++-------
1 file changed, 5 insertions(+), 7 deletions(-)
diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py
index 137a49c448..02b9291da9 100644
--- a/pandas/core/dtypes/cast.py
+++ b/pandas/core/dtypes/cast.py
@@ -87,8 +87,8 @@ from pandas.io._util import _arrow_dtype_mapping
if TYPE_CHECKING:
from collections.abc import (
+ Collection,
Sequence,
- Sized,
)
from pandas._typing import (
@@ -1581,7 +1581,7 @@ def _maybe_box_and_unbox_datetimelike(value: Scalar, dtype: DtypeObj):
return _maybe_unbox_datetimelike(value, dtype)
-def construct_1d_object_array_from_listlike(values: Sized) -> np.ndarray:
+def construct_1d_object_array_from_listlike(values: Collection) -> np.ndarray:
"""
Transform any list-like object in a 1-dimensional numpy array of object
dtype.
@@ -1599,11 +1599,9 @@ def construct_1d_object_array_from_listlike(values: Sized) -> np.ndarray:
-------
1-dimensional numpy array of dtype object
"""
- # numpy will try to interpret nested lists as further dimensions, hence
- # making a 1D array that contains list-likes is a bit tricky:
- result = np.empty(len(values), dtype="object")
- result[:] = values
- return result
+ # numpy will try to interpret nested lists as further dimensions in np.array(),
+ # hence explicitly making a 1D array using np.fromiter
+ return np.fromiter(values, dtype="object", count=len(values))
def maybe_cast_to_integer_array(arr: list | np.ndarray, dtype: np.dtype) -> np.ndarray:
--
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