title stringlengths 1 185 | diff stringlengths 0 32.2M | body stringlengths 0 123k ⌀ | url stringlengths 57 58 | created_at stringlengths 20 20 | closed_at stringlengths 20 20 | merged_at stringlengths 20 20 ⌀ | updated_at stringlengths 20 20 |
|---|---|---|---|---|---|---|---|
Bug46381 - Add `storage_option` parameter to to_excel method in Styler | diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 8089fc58db07d..86e30024c51b1 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -2132,7 +2132,11 @@ def _repr_data_resource_(self):
# I/O Methods
@final
- @doc(klass="object", storage_options=_shared_docs["storage_options"])
+ @doc(
+ klass="object",
+ storage_options=_shared_docs["storage_options"],
+ storage_options_versionadded="1.2.0",
+ )
def to_excel(
self,
excel_writer,
@@ -2218,7 +2222,7 @@ def to_excel(
is to be frozen.
{storage_options}
- .. versionadded:: 1.2.0
+ .. versionadded:: {storage_options_versionadded}
See Also
--------
diff --git a/pandas/io/formats/style.py b/pandas/io/formats/style.py
index 10b607da45ca8..4c143733ac77e 100644
--- a/pandas/io/formats/style.py
+++ b/pandas/io/formats/style.py
@@ -28,6 +28,7 @@
Level,
QuantileInterpolation,
Scalar,
+ StorageOptions,
WriteBuffer,
)
from pandas.compat._optional import import_optional_dependency
@@ -549,6 +550,7 @@ def set_tooltips(
NDFrame.to_excel,
klass="Styler",
storage_options=_shared_docs["storage_options"],
+ storage_options_versionadded="1.5.0",
)
def to_excel(
self,
@@ -568,6 +570,7 @@ def to_excel(
inf_rep: str = "inf",
verbose: bool = True,
freeze_panes: tuple[int, int] | None = None,
+ storage_options: StorageOptions = None,
) -> None:
from pandas.io.formats.excel import ExcelFormatter
@@ -590,6 +593,7 @@ def to_excel(
startcol=startcol,
freeze_panes=freeze_panes,
engine=engine,
+ storage_options=storage_options,
)
@overload
diff --git a/pandas/tests/io/excel/test_style.py b/pandas/tests/io/excel/test_style.py
index 9bbe61c90d973..00f6ccb96a905 100644
--- a/pandas/tests/io/excel/test_style.py
+++ b/pandas/tests/io/excel/test_style.py
@@ -1,9 +1,15 @@
import contextlib
+import time
import numpy as np
import pytest
-from pandas import DataFrame
+import pandas.util._test_decorators as td
+
+from pandas import (
+ DataFrame,
+ read_excel,
+)
import pandas._testing as tm
from pandas.io.excel import ExcelWriter
@@ -206,3 +212,27 @@ def custom_converter(css):
with contextlib.closing(openpyxl.load_workbook(path)) as wb:
assert wb["custom"].cell(2, 2).font.color.value == "00111222"
+
+
+@pytest.mark.single_cpu
+@td.skip_if_not_us_locale
+def test_styler_to_s3(s3_resource, s3so):
+ # GH#46381
+
+ mock_bucket_name, target_file = "pandas-test", "test.xlsx"
+ df = DataFrame({"x": [1, 2, 3], "y": [2, 4, 6]})
+ styler = df.style.set_sticky(axis="index")
+ styler.to_excel(f"s3://{mock_bucket_name}/{target_file}", storage_options=s3so)
+ timeout = 5
+ while True:
+ if target_file in (
+ obj.key for obj in s3_resource.Bucket("pandas-test").objects.all()
+ ):
+ break
+ time.sleep(0.1)
+ timeout -= 0.1
+ assert timeout > 0, "Timed out waiting for file to appear on moto"
+ result = read_excel(
+ f"s3://{mock_bucket_name}/{target_file}", index_col=0, storage_options=s3so
+ )
+ tm.assert_frame_equal(result, df)
| - [x] closes #46381
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46491 | 2022-03-24T01:45:44Z | 2022-07-09T16:32:52Z | 2022-07-09T16:32:52Z | 2022-07-10T13:25:50Z |
BLD: Fix black to match pre-commit black version | diff --git a/environment.yml b/environment.yml
index 5402be3d4a00b..f60c68b8d7638 100644
--- a/environment.yml
+++ b/environment.yml
@@ -18,7 +18,7 @@ dependencies:
- cython>=0.29.24
# code checks
- - black=21.5b2
+ - black=22.1.0
- cpplint
- flake8=4.0.1
- flake8-bugbear=21.3.2 # used by flake8, find likely bugs
diff --git a/requirements-dev.txt b/requirements-dev.txt
index c671dc08a263e..f25c51dd58a1c 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -6,7 +6,7 @@ python-dateutil>=2.8.1
pytz
asv < 0.5.0
cython>=0.29.24
-black==21.5b2
+black==22.1.0
cpplint
flake8==4.0.1
flake8-bugbear==21.3.2
| Black version is not synced now across relevant files (pre-commit's version is higher than requirements-dev's and environment's). This PR aims to fix that.
I am not aware of any issue mentioning this problem, hence no reference. | https://api.github.com/repos/pandas-dev/pandas/pulls/46490 | 2022-03-23T21:45:39Z | 2022-03-24T12:06:03Z | 2022-03-24T12:06:03Z | 2022-03-30T21:33:03Z |
Fix CI | diff --git a/pandas/_libs/tslibs/tzconversion.pyx b/pandas/_libs/tslibs/tzconversion.pyx
index ff77566e1d559..9190585b2882d 100644
--- a/pandas/_libs/tslibs/tzconversion.pyx
+++ b/pandas/_libs/tslibs/tzconversion.pyx
@@ -534,7 +534,7 @@ cdef const int64_t[:] _tz_convert_from_utc(const int64_t[:] stamps, tzinfo tz):
int64_t[::1] result
- if is_utc(tz):
+ if is_utc(tz) or tz is None:
# Much faster than going through the "standard" pattern below
return stamps.copy()
| - [ ] closes #xxxx (Replace xxxx with the Github issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46489 | 2022-03-23T21:10:56Z | 2022-03-24T12:08:38Z | 2022-03-24T12:08:38Z | 2022-03-24T15:20:14Z |
PERF: Slow performance of to_dict (#46470) | diff --git a/doc/source/whatsnew/v2.0.0.rst b/doc/source/whatsnew/v2.0.0.rst
index 20e99d007c798..8188916a06008 100644
--- a/doc/source/whatsnew/v2.0.0.rst
+++ b/doc/source/whatsnew/v2.0.0.rst
@@ -606,6 +606,7 @@ Performance improvements
- Performance improvement in :class:`DataFrameGroupBy` and :class:`SeriesGroupBy` when ``by`` is a categorical type and ``observed=False`` (:issue:`49596`)
- Performance improvement in :func:`read_stata` with parameter ``index_col`` set to ``None`` (the default). Now the index will be a :class:`RangeIndex` instead of :class:`Int64Index` (:issue:`49745`)
- Performance improvement in :func:`merge` when not merging on the index - the new index will now be :class:`RangeIndex` instead of :class:`Int64Index` (:issue:`49478`)
+- Performance improvement in :meth:`DataFrame.to_dict` and :meth:`Series.to_dict` when using any non-object dtypes (:issue:`46470`)
.. ---------------------------------------------------------------------------
.. _whatsnew_200.bug_fixes:
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 7b181a3e8e391..acd8af0241e68 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -1813,6 +1813,28 @@ def to_numpy(
return result
+ def _create_data_for_split_and_tight_to_dict(
+ self, are_all_object_dtype_cols: bool, object_dtype_indices: list[int]
+ ) -> list:
+ """
+ Simple helper method to create data for to ``to_dict(orient="split")`` and
+ ``to_dict(orient="tight")`` to create the main output data
+ """
+ if are_all_object_dtype_cols:
+ data = [
+ list(map(maybe_box_native, t))
+ for t in self.itertuples(index=False, name=None)
+ ]
+ else:
+ data = [list(t) for t in self.itertuples(index=False, name=None)]
+ if object_dtype_indices:
+ # If we have object_dtype_cols, apply maybe_box_naive after list
+ # comprehension for perf
+ for row in data:
+ for i in object_dtype_indices:
+ row[i] = maybe_box_native(row[i])
+ return data
+
@overload
def to_dict(
self,
@@ -1952,30 +1974,50 @@ def to_dict(
"'index=False' is only valid when 'orient' is 'split' or 'tight'"
)
+ if orient == "series":
+ # GH46470 Return quickly if orient series to avoid creating dtype objects
+ return into_c((k, v) for k, v in self.items())
+
+ object_dtype_indices = [
+ i
+ for i, col_dtype in enumerate(self.dtypes.values)
+ if is_object_dtype(col_dtype)
+ ]
+ are_all_object_dtype_cols = len(object_dtype_indices) == len(self.dtypes)
+
if orient == "dict":
return into_c((k, v.to_dict(into)) for k, v in self.items())
elif orient == "list":
+ object_dtype_indices_as_set = set(object_dtype_indices)
return into_c(
- (k, list(map(maybe_box_native, v.tolist()))) for k, v in self.items()
+ (
+ k,
+ list(map(maybe_box_native, v.tolist()))
+ if i in object_dtype_indices_as_set
+ else v.tolist(),
+ )
+ for i, (k, v) in enumerate(self.items())
)
elif orient == "split":
+ data = self._create_data_for_split_and_tight_to_dict(
+ are_all_object_dtype_cols, object_dtype_indices
+ )
+
return into_c(
((("index", self.index.tolist()),) if index else ())
+ (
("columns", self.columns.tolist()),
- (
- "data",
- [
- list(map(maybe_box_native, t))
- for t in self.itertuples(index=False, name=None)
- ],
- ),
+ ("data", data),
)
)
elif orient == "tight":
+ data = self._create_data_for_split_and_tight_to_dict(
+ are_all_object_dtype_cols, object_dtype_indices
+ )
+
return into_c(
((("index", self.index.tolist()),) if index else ())
+ (
@@ -1992,26 +2034,65 @@ def to_dict(
+ (("column_names", list(self.columns.names)),)
)
- elif orient == "series":
- return into_c((k, v) for k, v in self.items())
-
elif orient == "records":
columns = self.columns.tolist()
- rows = (
- dict(zip(columns, row))
- for row in self.itertuples(index=False, name=None)
- )
- return [
- into_c((k, maybe_box_native(v)) for k, v in row.items()) for row in rows
- ]
+ if are_all_object_dtype_cols:
+ rows = (
+ dict(zip(columns, row))
+ for row in self.itertuples(index=False, name=None)
+ )
+ return [
+ into_c((k, maybe_box_native(v)) for k, v in row.items())
+ for row in rows
+ ]
+ else:
+ data = [
+ into_c(zip(columns, t))
+ for t in self.itertuples(index=False, name=None)
+ ]
+ if object_dtype_indices:
+ object_dtype_indices_as_set = set(object_dtype_indices)
+ object_dtype_cols = {
+ col
+ for i, col in enumerate(self.columns)
+ if i in object_dtype_indices_as_set
+ }
+ for row in data:
+ for col in object_dtype_cols:
+ row[col] = maybe_box_native(row[col])
+ return data
elif orient == "index":
if not self.index.is_unique:
raise ValueError("DataFrame index must be unique for orient='index'.")
- return into_c(
- (t[0], dict(zip(self.columns, map(maybe_box_native, t[1:]))))
- for t in self.itertuples(name=None)
- )
+ columns = self.columns.tolist()
+ if are_all_object_dtype_cols:
+ return into_c(
+ (t[0], dict(zip(self.columns, map(maybe_box_native, t[1:]))))
+ for t in self.itertuples(name=None)
+ )
+ elif object_dtype_indices:
+ object_dtype_indices_as_set = set(object_dtype_indices)
+ is_object_dtype_by_index = [
+ i in object_dtype_indices_as_set for i in range(len(self.columns))
+ ]
+ return into_c(
+ (
+ t[0],
+ {
+ columns[i]: maybe_box_native(v)
+ if is_object_dtype_by_index[i]
+ else v
+ for i, v in enumerate(t[1:])
+ },
+ )
+ for t in self.itertuples(name=None)
+ )
+ else:
+ return into_c(
+ (t[0], dict(zip(self.columns, t[1:])))
+ for t in self.itertuples(name=None)
+ )
else:
raise ValueError(f"orient '{orient}' not understood")
diff --git a/pandas/core/series.py b/pandas/core/series.py
index dceec40ae666a..e4c4bd305fc57 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -1812,7 +1812,13 @@ def to_dict(self, into: type[dict] = dict) -> dict:
"""
# GH16122
into_c = com.standardize_mapping(into)
- return into_c((k, maybe_box_native(v)) for k, v in self.items())
+
+ if is_object_dtype(self):
+ return into_c((k, maybe_box_native(v)) for k, v in self.items())
+ else:
+ # Not an object dtype => all types will be the same so let the default
+ # indexer return native python type
+ return into_c((k, v) for k, v in self.items())
def to_frame(self, name: Hashable = lib.no_default) -> DataFrame:
"""
diff --git a/pandas/tests/frame/methods/test_to_dict.py b/pandas/tests/frame/methods/test_to_dict.py
index 521f6ead2e69e..c76699cafd481 100644
--- a/pandas/tests/frame/methods/test_to_dict.py
+++ b/pandas/tests/frame/methods/test_to_dict.py
@@ -379,6 +379,16 @@ def test_to_dict_orient_tight(self, index, columns):
"b": [float, float, float],
},
),
+ ( # Make sure we have one df which is all object type cols
+ {
+ "a": [1, "hello", 3],
+ "b": [1.1, "world", 3.3],
+ },
+ {
+ "a": [int, str, int],
+ "b": [float, str, float],
+ },
+ ),
),
)
def test_to_dict_returns_native_types(self, orient, data, expected_types):
| Improves performance of `to_dict` method for dataframes and series
For `orient=index` and `orient=list` performance has decreased but this is because, prior to this PR, we were not coercing types to native python types, we now are. (I assume we want this, but maybe not?)
```
N: 5,000,000
With objects (3 int cols, 3 float fields, 3 string fields)
dict: old: 16.27s, new: 16.50s
list: old: 1.33s, new: 6.28s
split: old: 20.40s, new: 19.77s
records: old: 27.31s, new: 19.75s
index: old: 17.20s, new: 28.44s
tight: old: 20.95s, new: 18.88s
Without objects (3 int cols, 3 float fields)
dict: old: 7.49s, new: 7.48s
list: old: 1.04s, new: 1.07s
split: old: 12.98s, new: 6.32s
records: old: 17.64s, new: 7.20s
index: old: 14.13s, new: 14.44s
tight: old: 12.87s, new: 6.62s
```
- [x] closes #46470 (Replace xxxx with the Github issue number)
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature. | https://api.github.com/repos/pandas-dev/pandas/pulls/46487 | 2022-03-23T20:47:48Z | 2022-11-22T21:59:15Z | 2022-11-22T21:59:15Z | 2022-11-23T00:35:50Z |
WEB: Add benchmarks grant write up as a blog post | diff --git a/web/pandas/community/blog/asv-pandas-grant.md b/web/pandas/community/blog/asv-pandas-grant.md
new file mode 100644
index 0000000000000..205c2222fec94
--- /dev/null
+++ b/web/pandas/community/blog/asv-pandas-grant.md
@@ -0,0 +1,141 @@
+Title: Write up of the NumFOCUS grant to improve pandas benchmarks and diversity
+Date: 2022-04-01
+
+# Write up of the NumFOCUS grant to improve pandas benchmarks and diversity
+
+*By Lucy Jiménez and Dorothy Kabarozi B.*
+
+We want to share our experience working on **Improvements to the**
+**ASV benchmarking framework and diversity efforts** sponsored by
+[NumFOCUS](https://numfocus.org/) to the [pandas](https://pandas.pydata.org/)
+project.
+
+This grant focused on two aspects: the first one is to improve the
+[asv library](https://asv.readthedocs.io/en/stable/), a tool used by
+benchmarking Python packages and used by pandas; this project was
+unmaintained, and the codebase was quite old; additionally, it didn't
+adhere to modern standards, had Python 2 compatibility code that could
+be removed, and also the CI could be improved. The second aspect is
+encouraging more underrepresented groups to contribute to open source
+projects. This grant was held over 10 weeks, working around 20 hours a
+week. It was developed by Dorothy Kabarozi B. from Uganda and Lucy
+Jiménez from Colombia, under the mentoring of Marc Garcia.
+
+## Why were we part of the grant?
+
+Even when we come from different backgrounds, Dorothy from systems
+engineering and Lucy from computational chemistry, we have always been
+interested in participating and contributing to open source software
+projects. For that reason, we have been running the PyLadies meetups in
+our communities ([PyLadies Kampala](https://twitter.com/pyladieskla),
+[PyLadies Colombia](https://twitter.com/pyladies_co)) and have always
+been on the lookout for any opportunities that lead us to contribute.
+
+It all happened through Marc Garcia; he had put out a call through a post
+on social media to mentor ladies from diverse backgrounds. Dorothy got to
+be part of the pandas mentorship group. At the same time, Lucy was
+co-organizer of the SciPy Latam conference, and it is from here she met
+Marc, who was the speaker at that conference, and through this mutual
+connection, we were able to learn about this benchmarks grant.
+
+In brief, by attending conferences, meetups, and social media, you can
+make connections and links that will lead you to these opportunities.
+
+## Learning from the source code
+
+At the beginning of the grant, we started from the basics. We noticed that
+we could improve our skills in managing Git and GitHub. For example, we had
+some troubles with the git workflow, so we had to read and practice more
+about it. One of the valuable resources was the explanation from Marc about
+[how to make an open source contribution](https://tubedu.org/w/kjnHEg72j76StmSFmjzbnE),
+which we invite you to take a look at it.
+
+We learned a lot from the source code and gained immense knowledge about
+best practices and code quality through this grant. We have been working
+on: updating the code to improve the style to follow the PEP-8 guidelines,
+removing Python 2 compatibility code and six dependencies, and finding
+unused code and removing it. We also learned about GitHub actions, and we
+started building the CI on GitHub actions for the asv package; for that we
+have been working on add linting with Flake8, testing with pytest, building
+docs, and running CI on different python versions.
+
+Additionally, we were able to identify bugs in the source code, review
+pull request from other contributors, and create new issues, something we
+thought only maintainers could do but not contributors. Finally, not only
+is reviewing the code itself a learning experience, but also the structure
+and folder hierarchy in the project started to be more transparent.
+
+## Our experience
+
+For this grant, we had a fantastic Mentor, Marc Garcia. He was always
+willing to share his knowledge, explain unclear concepts and share helpful
+feedback. Whenever we would implement that feedback, it felt easier to work
+on more issues faster. We felt the growth from the time we started on this
+project, and we will carry it along as we contribute to more open source
+projects; this all goes back to Marc for his fantastic mentorship. It is
+also important to note that we received feedback from other contributors,
+stakeholders, and core devs during this process, which gave us a broader
+look at the work in open source projects.
+
+We also built a strong teamwork partnership. We helped each other a lot as
+we had numerous one-on-one calls to understand the tasks better. We always
+looked for ways to support each other from the technical side and encouraged
+each other when needed. For us, it was professional and human growth.
+
+## Running an open source software sprint
+
+The knowledge and experience acquired in this process allowed us to
+organize two virtual sprints. The events were carried out in the company
+of local PyLadies communities; the first one was on February 26th with
+[PyLadies Kampala](https://twitter.com/pyladieskla) and on March 21
+with [PyLadies Colombia](https://bit.ly/sprint-asv).
+
+While organizing these events, we learned how to organize and conduct a
+virtual sprint. Some participants in the sprint ultimately had no idea
+about open source, and it was great explaining open source concepts and
+taking them through the Git workflow. Finally, they were able to make their
+first contribution. We learned how to follow up on contributors, helping
+them along the way until their PRs were merged and by reviewing their
+contributions on GitHub.
+
+The most outstanding achievement was mentoring new contributors and
+sharing the knowledge acquired from this grant with others participants
+in our respective communities. Most new contributors after the experience
+have gone ahead to apply for outreach and the upcoming
+[Google Summer of Code](https://summerofcode.withgoogle.com/)
+to apply the skills they learned from these sprints.
+
+## Conclusion
+
+In conclusion, we learned a lot from this experience from the code part,
+the workflow on the open source projects, how to be resilient in difficult
+moments, and encouraging more women and people from our local communities
+to contribute to open source projects.
+
+Finally, if you want to be part of an open source project, we invite you
+to check out GitHub repos for different projects you are interested in and
+search for the easy issues to work on and get started. Also, you can contact
+the maintainers of the projects with specific questions, search for the
+open source communities in your country or contact us for more help.
+
+## Acknowledgments
+
+Many thanks to [NumFOCUS](https://numfocus.org/) for giving us this support
+through [Small Development Grants](https://numfocus.org/programs/small-development-grants)
+and Marc for the excellent mentoring he generously gave us throughout these
+weeks.
+
+We are looking forward to contributing more and impacting our communities
+and the open source community!
+
+___
+If you want to know more, please don't hesitate to connect with us through
+these channels:
+
+*Lucy Jiménez*
+* [Twitter](https://twitter.com/JimenezLucyJ)
+* [LinkedIn](https://www.linkedin.com/in/lucy-j/)
+
+*Dorothy Kabarozi*
+* [Twitter](https://twitter.com/kizdorothy)
+* [LinkedIn](https://www.linkedin.com/in/dorothy-kabarozi/)
| Closes #45049
Create a new entry on the blog for the asv-pandas grant. | https://api.github.com/repos/pandas-dev/pandas/pulls/46483 | 2022-03-23T10:53:55Z | 2022-04-07T19:47:02Z | 2022-04-07T19:47:01Z | 2022-04-07T19:47:35Z |
BUG: Allow passing of args/kwargs to groupby.apply with str func | diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst
index 50a5bf383de77..dbf9547f561d2 100644
--- a/doc/source/whatsnew/v1.5.0.rst
+++ b/doc/source/whatsnew/v1.5.0.rst
@@ -527,6 +527,7 @@ Groupby/resample/rolling
- Bug in :meth:`GroupBy.cummin` and :meth:`GroupBy.cummax` with nullable dtypes incorrectly altering the original data in place (:issue:`46220`)
- Bug in :meth:`GroupBy.cummax` with ``int64`` dtype with leading value being the smallest possible int64 (:issue:`46382`)
- Bug in :meth:`GroupBy.max` with empty groups and ``uint64`` dtype incorrectly raising ``RuntimeError`` (:issue:`46408`)
+- Bug in :meth:`.GroupBy.apply` would fail when ``func`` was a string and args or kwargs were supplied (:issue:`46479`)
-
Reshaping
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 4a617d1b63639..41e5aa628fcc8 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -1363,10 +1363,19 @@ def apply(self, func, *args, **kwargs) -> NDFrameT:
func = com.is_builtin_func(func)
- # this is needed so we don't try and wrap strings. If we could
- # resolve functions to their callable functions prior, this
- # wouldn't be needed
- if args or kwargs:
+ if isinstance(func, str):
+ if hasattr(self, func):
+ res = getattr(self, func)
+ if callable(res):
+ return res(*args, **kwargs)
+ elif args or kwargs:
+ raise ValueError(f"Cannot pass arguments to property {func}")
+ return res
+
+ else:
+ raise TypeError(f"apply func should be callable, not '{func}'")
+
+ elif args or kwargs:
if callable(func):
@wraps(func)
@@ -1382,15 +1391,6 @@ def f(g):
raise ValueError(
"func must be a callable if args or kwargs are supplied"
)
- elif isinstance(func, str):
- if hasattr(self, func):
- res = getattr(self, func)
- if callable(res):
- return res()
- return res
-
- else:
- raise TypeError(f"apply func should be callable, not '{func}'")
else:
f = func
diff --git a/pandas/tests/groupby/test_apply.py b/pandas/tests/groupby/test_apply.py
index 6c6812a25fcd9..ba8b77f8acec3 100644
--- a/pandas/tests/groupby/test_apply.py
+++ b/pandas/tests/groupby/test_apply.py
@@ -1247,3 +1247,12 @@ def test_apply_nonmonotonic_float_index(arg, idx):
expected = DataFrame({"col": arg}, index=idx)
result = expected.groupby("col").apply(lambda x: x)
tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("args, kwargs", [([True], {}), ([], {"numeric_only": True})])
+def test_apply_str_with_args(df, args, kwargs):
+ # GH#46479
+ gb = df.groupby("A")
+ result = gb.apply("sum", *args, **kwargs)
+ expected = gb.sum(numeric_only=True)
+ tm.assert_frame_equal(result, expected)
| - [x] closes #46479 (Replace xxxx with the Github issue number)
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
Precursor to #46072, where users doing `.groupby(...).apply("sum")` may get a warning that would not be able to be silenced without passing numeric_only. | https://api.github.com/repos/pandas-dev/pandas/pulls/46481 | 2022-03-23T02:17:50Z | 2022-03-25T23:00:33Z | 2022-03-25T23:00:33Z | 2022-03-26T01:33:40Z |
ENH: implement astype_overflowsafe for dt64 | diff --git a/pandas/_libs/tslibs/conversion.pyx b/pandas/_libs/tslibs/conversion.pyx
index 00f83be0b51c4..132d742b78e9c 100644
--- a/pandas/_libs/tslibs/conversion.pyx
+++ b/pandas/_libs/tslibs/conversion.pyx
@@ -31,6 +31,7 @@ from pandas._libs.tslibs.np_datetime cimport (
NPY_DATETIMEUNIT,
NPY_FR_ns,
_string_to_dts,
+ astype_overflowsafe,
check_dts_bounds,
dt64_to_dtstruct,
dtstruct_to_dt64,
@@ -215,54 +216,20 @@ def ensure_datetime64ns(arr: ndarray, copy: bool = True):
-------
ndarray with dtype datetime64[ns]
"""
- cdef:
- Py_ssize_t i, n = arr.size
- const int64_t[:] ivalues
- int64_t[:] iresult
- NPY_DATETIMEUNIT unit
- npy_datetimestruct dts
-
- shape = (<object>arr).shape
-
if (<object>arr).dtype.byteorder == ">":
# GH#29684 we incorrectly get OutOfBoundsDatetime if we dont swap
dtype = arr.dtype
arr = arr.astype(dtype.newbyteorder("<"))
if arr.size == 0:
+ # Fastpath; doesn't matter but we have old tests for result.base
+ # being arr.
result = arr.view(DT64NS_DTYPE)
if copy:
result = result.copy()
return result
- if arr.dtype.kind != "M":
- raise TypeError("ensure_datetime64ns arr must have datetime64 dtype")
- unit = get_unit_from_dtype(arr.dtype)
- if unit == NPY_DATETIMEUNIT.NPY_FR_GENERIC:
- # without raising explicitly here, we end up with a SystemError
- # built-in function ensure_datetime64ns returned a result with an error
- raise ValueError("datetime64/timedelta64 must have a unit specified")
-
- if unit == NPY_FR_ns:
- # Check this before allocating result for perf, might save some memory
- if copy:
- return arr.copy()
- return arr
-
- ivalues = arr.view(np.int64).ravel("K")
-
- result = np.empty_like(arr, dtype=DT64NS_DTYPE)
- iresult = result.ravel("K").view(np.int64)
-
- for i in range(n):
- if ivalues[i] != NPY_NAT:
- pandas_datetime_to_datetimestruct(ivalues[i], unit, &dts)
- iresult[i] = dtstruct_to_dt64(&dts)
- check_dts_bounds(&dts)
- else:
- iresult[i] = NPY_NAT
-
- return result
+ return astype_overflowsafe(arr, DT64NS_DTYPE, copy=copy)
def ensure_timedelta64ns(arr: ndarray, copy: bool = True):
diff --git a/pandas/_libs/tslibs/np_datetime.pxd b/pandas/_libs/tslibs/np_datetime.pxd
index dd1494404e933..211b47cc3dc20 100644
--- a/pandas/_libs/tslibs/np_datetime.pxd
+++ b/pandas/_libs/tslibs/np_datetime.pxd
@@ -52,6 +52,8 @@ cdef extern from "numpy/ndarraytypes.h":
NPY_FR_as
NPY_FR_GENERIC
+ int64_t NPY_DATETIME_NAT # elswhere we call this NPY_NAT
+
cdef extern from "src/datetime/np_datetime.h":
ctypedef struct pandas_timedeltastruct:
int64_t days
@@ -67,7 +69,7 @@ cdef extern from "src/datetime/np_datetime.h":
cdef bint cmp_scalar(int64_t lhs, int64_t rhs, int op) except -1
-cdef check_dts_bounds(npy_datetimestruct *dts)
+cdef check_dts_bounds(npy_datetimestruct *dts, NPY_DATETIMEUNIT unit=?)
cdef int64_t dtstruct_to_dt64(npy_datetimestruct* dts) nogil
cdef void dt64_to_dtstruct(int64_t dt64, npy_datetimestruct* out) nogil
@@ -86,3 +88,9 @@ cdef int _string_to_dts(str val, npy_datetimestruct* dts,
bint want_exc) except? -1
cdef NPY_DATETIMEUNIT get_unit_from_dtype(cnp.dtype dtype)
+
+cpdef cnp.ndarray astype_overflowsafe(
+ cnp.ndarray values, # ndarray[datetime64[anyunit]]
+ cnp.dtype dtype, # ndarray[datetime64[anyunit]]
+ bint copy=*,
+)
diff --git a/pandas/_libs/tslibs/np_datetime.pyi b/pandas/_libs/tslibs/np_datetime.pyi
index 5227de4e72f44..222e770ef03d5 100644
--- a/pandas/_libs/tslibs/np_datetime.pyi
+++ b/pandas/_libs/tslibs/np_datetime.pyi
@@ -4,3 +4,6 @@ class OutOfBoundsDatetime(ValueError): ...
# only exposed for testing
def py_get_unit_from_dtype(dtype: np.dtype): ...
+def astype_overflowsafe(
+ arr: np.ndarray, dtype: np.dtype, copy: bool = ...
+) -> np.ndarray: ...
diff --git a/pandas/_libs/tslibs/np_datetime.pyx b/pandas/_libs/tslibs/np_datetime.pyx
index 32d2f1ca4e406..f7f7a29671d48 100644
--- a/pandas/_libs/tslibs/np_datetime.pyx
+++ b/pandas/_libs/tslibs/np_datetime.pyx
@@ -22,7 +22,10 @@ import_datetime()
cimport numpy as cnp
cnp.import_array()
-from numpy cimport int64_t
+from numpy cimport (
+ int64_t,
+ ndarray,
+)
from pandas._libs.tslibs.util cimport get_c_string_buf_and_size
@@ -36,7 +39,12 @@ cdef extern from "src/datetime/np_datetime.h":
pandas_timedeltastruct *result
) nogil
+ # AS, FS, PS versions exist but are not imported because they are not used.
npy_datetimestruct _NS_MIN_DTS, _NS_MAX_DTS
+ npy_datetimestruct _US_MIN_DTS, _US_MAX_DTS
+ npy_datetimestruct _MS_MIN_DTS, _MS_MAX_DTS
+ npy_datetimestruct _S_MIN_DTS, _S_MAX_DTS
+ npy_datetimestruct _M_MIN_DTS, _M_MAX_DTS
PyArray_DatetimeMetaData get_datetime_metadata_from_dtype(cnp.PyArray_Descr *dtype);
@@ -119,22 +127,40 @@ class OutOfBoundsDatetime(ValueError):
pass
-cdef inline check_dts_bounds(npy_datetimestruct *dts):
+cdef check_dts_bounds(npy_datetimestruct *dts, NPY_DATETIMEUNIT unit=NPY_FR_ns):
"""Raises OutOfBoundsDatetime if the given date is outside the range that
can be represented by nanosecond-resolution 64-bit integers."""
cdef:
bint error = False
-
- if (dts.year <= 1677 and
- cmp_npy_datetimestruct(dts, &_NS_MIN_DTS) == -1):
+ npy_datetimestruct cmp_upper, cmp_lower
+
+ if unit == NPY_FR_ns:
+ cmp_upper = _NS_MAX_DTS
+ cmp_lower = _NS_MIN_DTS
+ elif unit == NPY_FR_us:
+ cmp_upper = _US_MAX_DTS
+ cmp_lower = _US_MIN_DTS
+ elif unit == NPY_FR_ms:
+ cmp_upper = _MS_MAX_DTS
+ cmp_lower = _MS_MIN_DTS
+ elif unit == NPY_FR_s:
+ cmp_upper = _S_MAX_DTS
+ cmp_lower = _S_MIN_DTS
+ elif unit == NPY_FR_m:
+ cmp_upper = _M_MAX_DTS
+ cmp_lower = _M_MIN_DTS
+ else:
+ raise NotImplementedError(unit)
+
+ if cmp_npy_datetimestruct(dts, &cmp_lower) == -1:
error = True
- elif (dts.year >= 2262 and
- cmp_npy_datetimestruct(dts, &_NS_MAX_DTS) == 1):
+ elif cmp_npy_datetimestruct(dts, &cmp_upper) == 1:
error = True
if error:
fmt = (f'{dts.year}-{dts.month:02d}-{dts.day:02d} '
f'{dts.hour:02d}:{dts.min:02d}:{dts.sec:02d}')
+ # TODO: "nanosecond" in the message assumes NPY_FR_ns
raise OutOfBoundsDatetime(f'Out of bounds nanosecond timestamp: {fmt}')
@@ -202,3 +228,68 @@ cdef inline int _string_to_dts(str val, npy_datetimestruct* dts,
buf = get_c_string_buf_and_size(val, &length)
return parse_iso_8601_datetime(buf, length, want_exc,
dts, out_local, out_tzoffset)
+
+
+cpdef ndarray astype_overflowsafe(
+ ndarray values,
+ cnp.dtype dtype,
+ bint copy=True,
+):
+ """
+ Convert an ndarray with datetime64[X] to datetime64[Y], raising on overflow.
+ """
+ if values.descr.type_num != cnp.NPY_DATETIME:
+ # aka values.dtype.kind != "M"
+ raise TypeError("astype_overflowsafe values must have datetime64 dtype")
+ if dtype.type_num != cnp.NPY_DATETIME:
+ raise TypeError("astype_overflowsafe dtype must be datetime64")
+
+ cdef:
+ NPY_DATETIMEUNIT from_unit = get_unit_from_dtype(values.dtype)
+ NPY_DATETIMEUNIT to_unit = get_unit_from_dtype(dtype)
+
+ if (
+ from_unit == NPY_DATETIMEUNIT.NPY_FR_GENERIC
+ or to_unit == NPY_DATETIMEUNIT.NPY_FR_GENERIC
+ ):
+ # without raising explicitly here, we end up with a SystemError
+ # built-in function [...] returned a result with an error
+ raise ValueError("datetime64 values and dtype must have a unit specified")
+
+ if from_unit == to_unit:
+ # Check this before allocating result for perf, might save some memory
+ if copy:
+ return values.copy()
+ return values
+
+ cdef:
+ ndarray i8values = values.view("i8")
+
+ # equiv: result = np.empty((<object>values).shape, dtype="i8")
+ ndarray iresult = cnp.PyArray_EMPTY(
+ values.ndim, values.shape, cnp.NPY_INT64, 0
+ )
+
+ cnp.broadcast mi = cnp.PyArray_MultiIterNew2(iresult, i8values)
+ cnp.flatiter it
+ Py_ssize_t i, N = values.size
+ int64_t value, new_value
+ npy_datetimestruct dts
+
+ for i in range(N):
+ # Analogous to: item = values[i]
+ value = (<int64_t*>cnp.PyArray_MultiIter_DATA(mi, 1))[0]
+
+ if value == NPY_DATETIME_NAT:
+ new_value = NPY_DATETIME_NAT
+ else:
+ pandas_datetime_to_datetimestruct(value, from_unit, &dts)
+ check_dts_bounds(&dts, to_unit)
+ new_value = npy_datetimestruct_to_datetime(to_unit, &dts)
+
+ # Analogous to: iresult[i] = new_value
+ (<int64_t*>cnp.PyArray_MultiIter_DATA(mi, 0))[0] = new_value
+
+ cnp.PyArray_MultiIter_NEXT(mi)
+
+ return iresult.view(dtype)
diff --git a/pandas/_libs/tslibs/src/datetime/np_datetime.c b/pandas/_libs/tslibs/src/datetime/np_datetime.c
index 12e20df256293..69c073d223e67 100644
--- a/pandas/_libs/tslibs/src/datetime/np_datetime.c
+++ b/pandas/_libs/tslibs/src/datetime/np_datetime.c
@@ -27,10 +27,40 @@ This file is derived from NumPy 1.7. See NUMPY_LICENSE.txt
#include <numpy/ndarraytypes.h>
#include "np_datetime.h"
+
+const npy_datetimestruct _AS_MIN_DTS = {
+ 1969, 12, 31, 23, 59, 50, 776627, 963145, 224193};
+const npy_datetimestruct _FS_MIN_DTS = {
+ 1969, 12, 31, 21, 26, 16, 627963, 145224, 193000};
+const npy_datetimestruct _PS_MIN_DTS = {
+ 1969, 9, 16, 5, 57, 7, 963145, 224193, 0};
const npy_datetimestruct _NS_MIN_DTS = {
1677, 9, 21, 0, 12, 43, 145224, 193000, 0};
+const npy_datetimestruct _US_MIN_DTS = {
+ -290308, 12, 21, 19, 59, 05, 224193, 0, 0};
+const npy_datetimestruct _MS_MIN_DTS = {
+ -292275055, 5, 16, 16, 47, 4, 193000, 0, 0};
+const npy_datetimestruct _S_MIN_DTS = {
+ -292277022657, 1, 27, 8, 29, 53, 0, 0, 0};
+const npy_datetimestruct _M_MIN_DTS = {
+ -17536621475646, 5, 4, 5, 53, 0, 0, 0, 0};
+
+const npy_datetimestruct _AS_MAX_DTS = {
+ 1970, 1, 1, 0, 0, 9, 223372, 36854, 775807};
+const npy_datetimestruct _FS_MAX_DTS = {
+ 1970, 1, 1, 2, 33, 43, 372036, 854775, 807000};
+const npy_datetimestruct _PS_MAX_DTS = {
+ 1970, 4, 17, 18, 2, 52, 36854, 775807, 0};
const npy_datetimestruct _NS_MAX_DTS = {
2262, 4, 11, 23, 47, 16, 854775, 807000, 0};
+const npy_datetimestruct _US_MAX_DTS = {
+ 294247, 1, 10, 4, 0, 54, 775807, 0, 0};
+const npy_datetimestruct _MS_MAX_DTS = {
+ 292278994, 8, 17, 7, 12, 55, 807000, 0, 0};
+const npy_datetimestruct _S_MAX_DTS = {
+ 292277026596, 12, 4, 15, 30, 7, 0, 0, 0};
+const npy_datetimestruct _M_MAX_DTS = {
+ 17536621479585, 8, 30, 18, 7, 0, 0, 0, 0};
const int days_per_month_table[2][12] = {
diff --git a/pandas/_libs/tslibs/src/datetime/np_datetime.h b/pandas/_libs/tslibs/src/datetime/np_datetime.h
index 8e58be1ca8383..065f09a6d93b5 100644
--- a/pandas/_libs/tslibs/src/datetime/np_datetime.h
+++ b/pandas/_libs/tslibs/src/datetime/np_datetime.h
@@ -28,8 +28,22 @@ typedef struct {
npy_int32 hrs, min, sec, ms, us, ns, seconds, microseconds, nanoseconds;
} pandas_timedeltastruct;
+extern const npy_datetimestruct _AS_MIN_DTS;
+extern const npy_datetimestruct _AS_MAX_DTS;
+extern const npy_datetimestruct _FS_MIN_DTS;
+extern const npy_datetimestruct _FS_MAX_DTS;
+extern const npy_datetimestruct _PS_MIN_DTS;
+extern const npy_datetimestruct _PS_MAX_DTS;
extern const npy_datetimestruct _NS_MIN_DTS;
extern const npy_datetimestruct _NS_MAX_DTS;
+extern const npy_datetimestruct _US_MIN_DTS;
+extern const npy_datetimestruct _US_MAX_DTS;
+extern const npy_datetimestruct _MS_MIN_DTS;
+extern const npy_datetimestruct _MS_MAX_DTS;
+extern const npy_datetimestruct _S_MIN_DTS;
+extern const npy_datetimestruct _S_MAX_DTS;
+extern const npy_datetimestruct _M_MIN_DTS;
+extern const npy_datetimestruct _M_MAX_DTS;
// stuff pandas needs
// ----------------------------------------------------------------------------
diff --git a/pandas/_libs/tslibs/tzconversion.pyx b/pandas/_libs/tslibs/tzconversion.pyx
index ff77566e1d559..9190585b2882d 100644
--- a/pandas/_libs/tslibs/tzconversion.pyx
+++ b/pandas/_libs/tslibs/tzconversion.pyx
@@ -534,7 +534,7 @@ cdef const int64_t[:] _tz_convert_from_utc(const int64_t[:] stamps, tzinfo tz):
int64_t[::1] result
- if is_utc(tz):
+ if is_utc(tz) or tz is None:
# Much faster than going through the "standard" pattern below
return stamps.copy()
diff --git a/pandas/tests/tslibs/test_np_datetime.py b/pandas/tests/tslibs/test_np_datetime.py
index 00a2f90217434..8fff63eaeac05 100644
--- a/pandas/tests/tslibs/test_np_datetime.py
+++ b/pandas/tests/tslibs/test_np_datetime.py
@@ -1,6 +1,13 @@
import numpy as np
+import pytest
-from pandas._libs.tslibs.np_datetime import py_get_unit_from_dtype
+from pandas._libs.tslibs.np_datetime import (
+ OutOfBoundsDatetime,
+ astype_overflowsafe,
+ py_get_unit_from_dtype,
+)
+
+import pandas._testing as tm
def test_get_unit_from_dtype():
@@ -35,3 +42,50 @@ def test_get_unit_from_dtype():
assert py_get_unit_from_dtype(np.dtype("m8[ps]")) == 11
assert py_get_unit_from_dtype(np.dtype("m8[fs]")) == 12
assert py_get_unit_from_dtype(np.dtype("m8[as]")) == 13
+
+
+class TestAstypeOverflowSafe:
+ def test_pass_non_dt64_array(self):
+ # check that we raise, not segfault
+ arr = np.arange(5)
+ dtype = np.dtype("M8[ns]")
+
+ msg = "astype_overflowsafe values must have datetime64 dtype"
+ with pytest.raises(TypeError, match=msg):
+ astype_overflowsafe(arr, dtype, copy=True)
+
+ with pytest.raises(TypeError, match=msg):
+ astype_overflowsafe(arr, dtype, copy=False)
+
+ def test_pass_non_dt64_dtype(self):
+ # check that we raise, not segfault
+ arr = np.arange(5, dtype="i8").view("M8[D]")
+ dtype = np.dtype("m8[ns]")
+
+ msg = "astype_overflowsafe dtype must be datetime64"
+ with pytest.raises(TypeError, match=msg):
+ astype_overflowsafe(arr, dtype, copy=True)
+
+ with pytest.raises(TypeError, match=msg):
+ astype_overflowsafe(arr, dtype, copy=False)
+
+ def test_astype_overflowsafe(self):
+ dtype = np.dtype("M8[ns]")
+
+ dt = np.datetime64("2262-04-05", "D")
+ arr = dt + np.arange(10, dtype="m8[D]")
+
+ # arr.astype silently overflows, so this
+ wrong = arr.astype(dtype)
+ roundtrip = wrong.astype(arr.dtype)
+ assert not (wrong == roundtrip).all()
+
+ msg = "Out of bounds nanosecond timestamp"
+ with pytest.raises(OutOfBoundsDatetime, match=msg):
+ astype_overflowsafe(arr, dtype)
+
+ # But converting to microseconds is fine, and we match numpy's results.
+ dtype2 = np.dtype("M8[us]")
+ result = astype_overflowsafe(arr, dtype2)
+ expected = arr.astype(dtype2)
+ tm.assert_numpy_array_equal(result, expected)
| - [ ] closes #xxxx (Replace xxxx with the Github issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
Plenty of room for improvement. | https://api.github.com/repos/pandas-dev/pandas/pulls/46478 | 2022-03-23T01:11:48Z | 2022-03-24T14:26:48Z | 2022-03-24T14:26:48Z | 2022-03-24T15:27:14Z |
BUG: Use isinstance for is_iterator on Cygwin | diff --git a/pandas/_libs/lib.pyx b/pandas/_libs/lib.pyx
index 18a58902075f2..9a26bdfd36f11 100644
--- a/pandas/_libs/lib.pyx
+++ b/pandas/_libs/lib.pyx
@@ -268,7 +268,10 @@ def is_iterator(obj: object) -> bool:
>>> is_iterator(1)
False
"""
- return PyIter_Check(obj)
+ IF UNAME_SYSNAME.startswith("CYGWIN"):
+ return isinstance(obj, abc.Iterator)
+ ELSE:
+ return PyIter_Check(obj)
def item_from_zerodim(val: object) -> object:
| Fixes #45158
Keep original implementation if not on Cygwin, and choose the implementation at compile time to avoid performance hits from the extra branch.
[Test results on Cygwin are available are available here](https://github.com/DWesl/pandas/runs/5648361139?check_suite_focus=true)
- [X] closes #45158
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [X] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46477 | 2022-03-22T19:11:49Z | 2022-04-24T02:39:13Z | null | 2022-04-24T02:39:13Z |
DEPR: Timedelta.delta | diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst
index 14e9fda1b910c..c200b442d6c91 100644
--- a/doc/source/whatsnew/v1.5.0.rst
+++ b/doc/source/whatsnew/v1.5.0.rst
@@ -350,6 +350,7 @@ Other Deprecations
- Deprecated treating all-bool ``object``-dtype columns as bool-like in :meth:`DataFrame.any` and :meth:`DataFrame.all` with ``bool_only=True``, explicitly cast to bool instead (:issue:`46188`)
- Deprecated behavior of method :meth:`DataFrame.quantile`, attribute ``numeric_only`` will default False. Including datetime/timedelta columns in the result (:issue:`7308`).
- Deprecated :attr:`Timedelta.freq` and :attr:`Timedelta.is_populated` (:issue:`46430`)
+- Deprecated :attr:`Timedelta.delta` (:issue:`46476`)
-
.. ---------------------------------------------------------------------------
diff --git a/pandas/_libs/tslibs/timedeltas.pyx b/pandas/_libs/tslibs/timedeltas.pyx
index fa0db6a46deef..627006a7f32c0 100644
--- a/pandas/_libs/tslibs/timedeltas.pyx
+++ b/pandas/_libs/tslibs/timedeltas.pyx
@@ -1014,6 +1014,12 @@ cdef class _Timedelta(timedelta):
>>> td.delta
42
"""
+ # Deprecated GH#46476
+ warnings.warn(
+ "Timedelta.delta is deprecated and will be removed in a future version.",
+ FutureWarning,
+ stacklevel=1,
+ )
return self.value
@property
diff --git a/pandas/tests/scalar/timedelta/test_timedelta.py b/pandas/tests/scalar/timedelta/test_timedelta.py
index 36520a1983aac..1ec93c69def99 100644
--- a/pandas/tests/scalar/timedelta/test_timedelta.py
+++ b/pandas/tests/scalar/timedelta/test_timedelta.py
@@ -681,3 +681,10 @@ def test_is_populated_deprecated():
with pytest.raises(AttributeError, match="is not writable"):
td.is_populated = 1
+
+
+def test_delta_deprecated():
+ # GH#46476
+ td = Timedelta(123456546, unit="ns")
+ with tm.assert_produces_warning(FutureWarning, match="Timedelta.delta is"):
+ td.delta
| - [ ] closes #xxxx (Replace xxxx with the Github issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
It's redundant and promises to become confusing once we have non-nano td64. | https://api.github.com/repos/pandas-dev/pandas/pulls/46476 | 2022-03-22T18:24:10Z | 2022-03-23T00:39:48Z | 2022-03-23T00:39:48Z | 2022-03-23T01:06:57Z |
TYP: fix mid and length for Interval and Intervalarray | diff --git a/pandas/_libs/interval.pyi b/pandas/_libs/interval.pyi
index 67cb604083c6b..bad6013aa58b6 100644
--- a/pandas/_libs/interval.pyi
+++ b/pandas/_libs/interval.pyi
@@ -32,8 +32,6 @@ class _LengthDescriptor:
def __get__(
self, instance: Interval[_OrderableTimesT], owner: Any
) -> Timedelta: ...
- @overload
- def __get__(self, instance: IntervalTree, owner: Any) -> np.ndarray: ...
class _MidDescriptor:
@overload
@@ -42,8 +40,6 @@ class _MidDescriptor:
def __get__(
self, instance: Interval[_OrderableTimesT], owner: Any
) -> _OrderableTimesT: ...
- @overload
- def __get__(self, instance: IntervalTree, owner: Any) -> np.ndarray: ...
class IntervalMixin:
@property
@@ -54,8 +50,6 @@ class IntervalMixin:
def open_left(self) -> bool: ...
@property
def open_right(self) -> bool: ...
- mid: _MidDescriptor
- length: _LengthDescriptor
@property
def is_empty(self) -> bool: ...
def _check_closed_matches(self, other: IntervalMixin, name: str = ...) -> None: ...
@@ -67,6 +61,8 @@ class Interval(IntervalMixin, Generic[_OrderableT]):
def right(self: Interval[_OrderableT]) -> _OrderableT: ...
@property
def closed(self) -> IntervalClosedType: ...
+ mid: _MidDescriptor
+ length: _LengthDescriptor
def __init__(
self,
left: _OrderableT,
@@ -162,6 +158,10 @@ class IntervalTree(IntervalMixin):
closed: IntervalClosedType = ...,
leaf_size: int = ...,
): ...
+ @property
+ def mid(self) -> np.ndarray: ...
+ @property
+ def length(self) -> np.ndarray: ...
def get_indexer(self, target) -> npt.NDArray[np.intp]: ...
def get_indexer_non_unique(
self, target
diff --git a/pandas/core/arrays/interval.py b/pandas/core/arrays/interval.py
index 63f8fc0262199..e14eec419377c 100644
--- a/pandas/core/arrays/interval.py
+++ b/pandas/core/arrays/interval.py
@@ -7,6 +7,7 @@
)
import textwrap
from typing import (
+ TYPE_CHECKING,
Sequence,
TypeVar,
Union,
@@ -93,6 +94,10 @@
unpack_zerodim_and_defer,
)
+if TYPE_CHECKING:
+ from pandas import Index
+
+
IntervalArrayT = TypeVar("IntervalArrayT", bound="IntervalArray")
IntervalOrNA = Union[Interval, float]
@@ -1230,7 +1235,7 @@ def right(self):
return Index(self._right, copy=False)
@property
- def length(self):
+ def length(self) -> Index:
"""
Return an Index with entries denoting the length of each Interval in
the IntervalArray.
@@ -1238,7 +1243,7 @@ def length(self):
return self.right - self.left
@property
- def mid(self):
+ def mid(self) -> Index:
"""
Return the midpoint of each Interval in the IntervalArray as an Index.
"""
| In the process of doing the types in the MS stubs, discovered that `length` and `mid` have really different return types for `Interval` and `IntervalArray`, so this PR fixes that by moving `length` and `mid` out of `IntervalMixin`
Not typing the rest of `IntervalArray` yet.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46472 | 2022-03-22T13:20:38Z | 2022-03-22T23:29:52Z | 2022-03-22T23:29:52Z | 2023-02-13T21:02:55Z |
Fix problem about loongarch install by pip | diff --git a/setup.cfg b/setup.cfg
index 34c9f56f06371..9eecf4826fcd4 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -31,9 +31,10 @@ project_urls =
[options]
packages = find:
install_requires =
- numpy>=1.18.5; platform_machine!='aarch64' and platform_machine!='arm64' and python_version<'3.10'
+ numpy>=1.18.5; platform_machine!='aarch64' and platform_machine!='arm64' and platform_machine!='loongarch64' and python_version<'3.10'
numpy>=1.19.2; platform_machine=='aarch64' and python_version<'3.10'
numpy>=1.20.0; platform_machine=='arm64' and python_version<'3.10'
+ numpy>=1.22.0; platform_machine=='loongarch64'
numpy>=1.21.0; python_version>='3.10'
python-dateutil>=2.8.1
pytz>=2020.1
| - [x] closes #xxxx (Replace xxxx with the Github issue number)
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
From numpy [v1.22.0](https://github.com/numpy/numpy/releases/tag/v1.22.0) support loongarch | https://api.github.com/repos/pandas-dev/pandas/pulls/46467 | 2022-03-22T07:47:45Z | 2022-05-24T20:54:19Z | null | 2022-05-24T20:54:19Z |
updated head and tail docstrings | diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 8bfd9f3cd962d..98e0ab43f2a09 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -5284,6 +5284,8 @@ def head(self: NDFrameT, n: int = 5) -> NDFrameT:
For negative values of `n`, this function returns all rows except
the last `n` rows, equivalent to ``df[:-n]``.
+ If n is larger than the number of rows, this function returns all rows.
+
Parameters
----------
n : int, default 5
@@ -5357,6 +5359,8 @@ def tail(self: NDFrameT, n: int = 5) -> NDFrameT:
For negative values of `n`, this function returns all rows except
the first `n` rows, equivalent to ``df[n:]``.
+ If n is larger than the number of rows, this function returns all rows.
+
Parameters
----------
n : int, default 5
| - [ ] closes #46079
| https://api.github.com/repos/pandas-dev/pandas/pulls/46466 | 2022-03-22T03:40:58Z | 2022-03-22T20:20:35Z | 2022-03-22T20:20:35Z | 2022-03-22T20:20:54Z |
ENH: implement pandas_timedelta_to_timedeltastruct for other resos | diff --git a/pandas/_libs/tslibs/np_datetime.pyi b/pandas/_libs/tslibs/np_datetime.pyi
index 222e770ef03d5..922fe8c4a4dce 100644
--- a/pandas/_libs/tslibs/np_datetime.pyi
+++ b/pandas/_libs/tslibs/np_datetime.pyi
@@ -4,6 +4,7 @@ class OutOfBoundsDatetime(ValueError): ...
# only exposed for testing
def py_get_unit_from_dtype(dtype: np.dtype): ...
+def py_td64_to_tdstruct(td64: int, unit: int) -> dict: ...
def astype_overflowsafe(
arr: np.ndarray, dtype: np.dtype, copy: bool = ...
) -> np.ndarray: ...
diff --git a/pandas/_libs/tslibs/np_datetime.pyx b/pandas/_libs/tslibs/np_datetime.pyx
index f7f7a29671d48..2a0b4ceeb98f8 100644
--- a/pandas/_libs/tslibs/np_datetime.pyx
+++ b/pandas/_libs/tslibs/np_datetime.pyx
@@ -189,6 +189,14 @@ cdef inline void td64_to_tdstruct(int64_t td64,
return
+# just exposed for testing at the moment
+def py_td64_to_tdstruct(int64_t td64, NPY_DATETIMEUNIT unit):
+ cdef:
+ pandas_timedeltastruct tds
+ pandas_timedelta_to_timedeltastruct(td64, unit, &tds)
+ return tds # <- returned as a dict to python
+
+
cdef inline int64_t pydatetime_to_dt64(datetime val,
npy_datetimestruct *dts):
"""
diff --git a/pandas/_libs/tslibs/src/datetime/np_datetime.c b/pandas/_libs/tslibs/src/datetime/np_datetime.c
index 69c073d223e67..f5400cf8da4df 100644
--- a/pandas/_libs/tslibs/src/datetime/np_datetime.c
+++ b/pandas/_libs/tslibs/src/datetime/np_datetime.c
@@ -712,7 +712,8 @@ void pandas_timedelta_to_timedeltastruct(npy_timedelta td,
npy_int64 sfrac;
npy_int64 ifrac;
int sign;
- npy_int64 DAY_NS = 86400000000000LL;
+ npy_int64 per_day;
+ npy_int64 per_sec;
/* Initialize the output to all zeros */
memset(out, 0, sizeof(pandas_timedeltastruct));
@@ -720,11 +721,14 @@ void pandas_timedelta_to_timedeltastruct(npy_timedelta td,
switch (base) {
case NPY_FR_ns:
+ per_day = 86400000000000LL;
+ per_sec = 1000LL * 1000LL * 1000LL;
+
// put frac in seconds
- if (td < 0 && td % (1000LL * 1000LL * 1000LL) != 0)
- frac = td / (1000LL * 1000LL * 1000LL) - 1;
+ if (td < 0 && td % per_sec != 0)
+ frac = td / per_sec - 1;
else
- frac = td / (1000LL * 1000LL * 1000LL);
+ frac = td / per_sec;
if (frac < 0) {
sign = -1;
@@ -768,12 +772,12 @@ void pandas_timedelta_to_timedeltastruct(npy_timedelta td,
}
sfrac = (out->hrs * 3600LL + out->min * 60LL
- + out->sec) * (1000LL * 1000LL * 1000LL);
+ + out->sec) * per_sec;
if (sign < 0)
out->days = -out->days;
- ifrac = td - (out->days * DAY_NS + sfrac);
+ ifrac = td - (out->days * per_day + sfrac);
if (ifrac != 0) {
out->ms = ifrac / (1000LL * 1000LL);
@@ -786,10 +790,222 @@ void pandas_timedelta_to_timedeltastruct(npy_timedelta td,
out->us = 0;
out->ns = 0;
}
+ break;
+
+ case NPY_FR_us:
+
+ per_day = 86400000000LL;
+ per_sec = 1000LL * 1000LL;
+
+ // put frac in seconds
+ if (td < 0 && td % per_sec != 0)
+ frac = td / per_sec - 1;
+ else
+ frac = td / per_sec;
+
+ if (frac < 0) {
+ sign = -1;
+
+ // even fraction
+ if ((-frac % 86400LL) != 0) {
+ out->days = -frac / 86400LL + 1;
+ frac += 86400LL * out->days;
+ } else {
+ frac = -frac;
+ }
+ } else {
+ sign = 1;
+ out->days = 0;
+ }
+
+ if (frac >= 86400) {
+ out->days += frac / 86400LL;
+ frac -= out->days * 86400LL;
+ }
+
+ if (frac >= 3600) {
+ out->hrs = frac / 3600LL;
+ frac -= out->hrs * 3600LL;
+ } else {
+ out->hrs = 0;
+ }
+
+ if (frac >= 60) {
+ out->min = frac / 60LL;
+ frac -= out->min * 60LL;
+ } else {
+ out->min = 0;
+ }
+
+ if (frac >= 0) {
+ out->sec = frac;
+ frac -= out->sec;
+ } else {
+ out->sec = 0;
+ }
+
+ sfrac = (out->hrs * 3600LL + out->min * 60LL
+ + out->sec) * per_sec;
+
+ if (sign < 0)
+ out->days = -out->days;
+
+ ifrac = td - (out->days * per_day + sfrac);
+
+ if (ifrac != 0) {
+ out->ms = ifrac / 1000LL;
+ ifrac -= out->ms * 1000LL;
+ out->us = ifrac / 1L;
+ ifrac -= out->us * 1L;
+ out->ns = ifrac;
+ } else {
+ out->ms = 0;
+ out->us = 0;
+ out->ns = 0;
+ }
+ break;
+
+ case NPY_FR_ms:
+
+ per_day = 86400000LL;
+ per_sec = 1000LL;
+
+ // put frac in seconds
+ if (td < 0 && td % per_sec != 0)
+ frac = td / per_sec - 1;
+ else
+ frac = td / per_sec;
+
+ if (frac < 0) {
+ sign = -1;
+
+ // even fraction
+ if ((-frac % 86400LL) != 0) {
+ out->days = -frac / 86400LL + 1;
+ frac += 86400LL * out->days;
+ } else {
+ frac = -frac;
+ }
+ } else {
+ sign = 1;
+ out->days = 0;
+ }
+
+ if (frac >= 86400) {
+ out->days += frac / 86400LL;
+ frac -= out->days * 86400LL;
+ }
+
+ if (frac >= 3600) {
+ out->hrs = frac / 3600LL;
+ frac -= out->hrs * 3600LL;
+ } else {
+ out->hrs = 0;
+ }
+
+ if (frac >= 60) {
+ out->min = frac / 60LL;
+ frac -= out->min * 60LL;
+ } else {
+ out->min = 0;
+ }
+
+ if (frac >= 0) {
+ out->sec = frac;
+ frac -= out->sec;
+ } else {
+ out->sec = 0;
+ }
+
+ sfrac = (out->hrs * 3600LL + out->min * 60LL
+ + out->sec) * per_sec;
+
+ if (sign < 0)
+ out->days = -out->days;
+
+ ifrac = td - (out->days * per_day + sfrac);
+
+ if (ifrac != 0) {
+ out->ms = ifrac;
+ out->us = 0;
+ out->ns = 0;
+ } else {
+ out->ms = 0;
+ out->us = 0;
+ out->ns = 0;
+ }
+ break;
+
+ case NPY_FR_s:
+ // special case where we can simplify many expressions bc per_sec=1
+
+ per_day = 86400000LL;
+ per_sec = 1L;
+
+ // put frac in seconds
+ if (td < 0 && td % per_sec != 0)
+ frac = td / per_sec - 1;
+ else
+ frac = td / per_sec;
+
+ if (frac < 0) {
+ sign = -1;
- out->seconds = out->hrs * 3600 + out->min * 60 + out->sec;
- out->microseconds = out->ms * 1000 + out->us;
- out->nanoseconds = out->ns;
+ // even fraction
+ if ((-frac % 86400LL) != 0) {
+ out->days = -frac / 86400LL + 1;
+ frac += 86400LL * out->days;
+ } else {
+ frac = -frac;
+ }
+ } else {
+ sign = 1;
+ out->days = 0;
+ }
+
+ if (frac >= 86400) {
+ out->days += frac / 86400LL;
+ frac -= out->days * 86400LL;
+ }
+
+ if (frac >= 3600) {
+ out->hrs = frac / 3600LL;
+ frac -= out->hrs * 3600LL;
+ } else {
+ out->hrs = 0;
+ }
+
+ if (frac >= 60) {
+ out->min = frac / 60LL;
+ frac -= out->min * 60LL;
+ } else {
+ out->min = 0;
+ }
+
+ if (frac >= 0) {
+ out->sec = frac;
+ frac -= out->sec;
+ } else {
+ out->sec = 0;
+ }
+
+ sfrac = (out->hrs * 3600LL + out->min * 60LL
+ + out->sec) * per_sec;
+
+ if (sign < 0)
+ out->days = -out->days;
+
+ ifrac = td - (out->days * per_day + sfrac);
+
+ if (ifrac != 0) {
+ out->ms = 0;
+ out->us = 0;
+ out->ns = 0;
+ } else {
+ out->ms = 0;
+ out->us = 0;
+ out->ns = 0;
+ }
break;
default:
@@ -797,6 +1013,10 @@ void pandas_timedelta_to_timedeltastruct(npy_timedelta td,
"NumPy timedelta metadata is corrupted with "
"invalid base unit");
}
+
+ out->seconds = out->hrs * 3600 + out->min * 60 + out->sec;
+ out->microseconds = out->ms * 1000 + out->us;
+ out->nanoseconds = out->ns;
}
diff --git a/pandas/tests/tslibs/test_np_datetime.py b/pandas/tests/tslibs/test_np_datetime.py
index 8fff63eaeac05..336c7d30d5f77 100644
--- a/pandas/tests/tslibs/test_np_datetime.py
+++ b/pandas/tests/tslibs/test_np_datetime.py
@@ -5,6 +5,7 @@
OutOfBoundsDatetime,
astype_overflowsafe,
py_get_unit_from_dtype,
+ py_td64_to_tdstruct,
)
import pandas._testing as tm
@@ -44,6 +45,71 @@ def test_get_unit_from_dtype():
assert py_get_unit_from_dtype(np.dtype("m8[as]")) == 13
+def test_td64_to_tdstruct():
+ val = 12454636234 # arbitrary value
+
+ res1 = py_td64_to_tdstruct(val, 10) # ns
+ exp1 = {
+ "days": 0,
+ "hrs": 0,
+ "min": 0,
+ "sec": 12,
+ "ms": 454,
+ "us": 636,
+ "ns": 234,
+ "seconds": 12,
+ "microseconds": 454636,
+ "nanoseconds": 234,
+ }
+ assert res1 == exp1
+
+ res2 = py_td64_to_tdstruct(val, 9) # us
+ exp2 = {
+ "days": 0,
+ "hrs": 3,
+ "min": 27,
+ "sec": 34,
+ "ms": 636,
+ "us": 234,
+ "ns": 0,
+ "seconds": 12454,
+ "microseconds": 636234,
+ "nanoseconds": 0,
+ }
+ assert res2 == exp2
+
+ res3 = py_td64_to_tdstruct(val, 8) # ms
+ exp3 = {
+ "days": 144,
+ "hrs": 3,
+ "min": 37,
+ "sec": 16,
+ "ms": 234,
+ "us": 0,
+ "ns": 0,
+ "seconds": 13036,
+ "microseconds": 234000,
+ "nanoseconds": 0,
+ }
+ assert res3 == exp3
+
+ # Note this out of bounds for nanosecond Timedelta
+ res4 = py_td64_to_tdstruct(val, 7) # s
+ exp4 = {
+ "days": 144150,
+ "hrs": 21,
+ "min": 10,
+ "sec": 34,
+ "ms": 0,
+ "us": 0,
+ "ns": 0,
+ "seconds": 76234,
+ "microseconds": 0,
+ "nanoseconds": 0,
+ }
+ assert res4 == exp4
+
+
class TestAstypeOverflowSafe:
def test_pass_non_dt64_array(self):
# check that we raise, not segfault
| - [ ] closes #xxxx (Replace xxxx with the Github issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
cc @WillAyd IIRC you implemented the original here. The new cases this implements are mostly copy/paste, so it wouldn't surprise me if there is a more elegant alternative. Thoughts? | https://api.github.com/repos/pandas-dev/pandas/pulls/46465 | 2022-03-21T23:54:55Z | 2022-03-31T02:17:01Z | 2022-03-31T02:17:01Z | 2022-03-31T02:25:31Z |
Backport PR #46461 on branch 1.4.x (Update install.rst) | diff --git a/doc/source/getting_started/install.rst b/doc/source/getting_started/install.rst
index 8fe0fd1f78e61..8106efcfcfd98 100644
--- a/doc/source/getting_started/install.rst
+++ b/doc/source/getting_started/install.rst
@@ -276,7 +276,7 @@ Computation
========================= ================== =============================================================
Dependency Minimum Version Notes
========================= ================== =============================================================
-SciPy 1.14.1 Miscellaneous statistical functions
+SciPy 1.4.1 Miscellaneous statistical functions
numba 0.50.1 Alternative execution engine for rolling operations
(see :ref:`Enhancing Performance <enhancingperf.numba>`)
xarray 0.15.1 pandas-like API for N-dimensional data
| Backport PR #46461: Update install.rst | https://api.github.com/repos/pandas-dev/pandas/pulls/46464 | 2022-03-21T23:09:13Z | 2022-03-23T01:30:40Z | 2022-03-23T01:30:40Z | 2022-03-23T01:30:40Z |
REF: implement periods_per_day | diff --git a/pandas/_libs/tslibs/dtypes.pxd b/pandas/_libs/tslibs/dtypes.pxd
index 3686ee216b546..f61409fc16653 100644
--- a/pandas/_libs/tslibs/dtypes.pxd
+++ b/pandas/_libs/tslibs/dtypes.pxd
@@ -1,8 +1,11 @@
+from numpy cimport int64_t
+
from pandas._libs.tslibs.np_datetime cimport NPY_DATETIMEUNIT
cdef str npy_unit_to_abbrev(NPY_DATETIMEUNIT unit)
cdef NPY_DATETIMEUNIT freq_group_code_to_npy_unit(int freq) nogil
+cdef int64_t periods_per_day(NPY_DATETIMEUNIT reso=*)
cdef dict attrname_to_abbrevs
diff --git a/pandas/_libs/tslibs/dtypes.pyx b/pandas/_libs/tslibs/dtypes.pyx
index 02b78cfa530c9..48a06b2c01ae4 100644
--- a/pandas/_libs/tslibs/dtypes.pyx
+++ b/pandas/_libs/tslibs/dtypes.pyx
@@ -307,6 +307,36 @@ cdef NPY_DATETIMEUNIT freq_group_code_to_npy_unit(int freq) nogil:
return NPY_DATETIMEUNIT.NPY_FR_D
+cdef int64_t periods_per_day(NPY_DATETIMEUNIT reso=NPY_DATETIMEUNIT.NPY_FR_ns):
+ """
+ How many of the given time units fit into a single day?
+ """
+ cdef:
+ int64_t day_units
+
+ if reso == NPY_DATETIMEUNIT.NPY_FR_ps:
+ # pico is the smallest unit for which we don't overflow, so
+ # we exclude fempto and atto
+ day_units = 24 * 3600 * 1_000_000_000_000
+ elif reso == NPY_DATETIMEUNIT.NPY_FR_ns:
+ day_units = 24 * 3600 * 1_000_000_000
+ elif reso == NPY_DATETIMEUNIT.NPY_FR_us:
+ day_units = 24 * 3600 * 1_000_000
+ elif reso == NPY_DATETIMEUNIT.NPY_FR_ms:
+ day_units = 24 * 3600 * 1_000
+ elif reso == NPY_DATETIMEUNIT.NPY_FR_s:
+ day_units = 24 * 3600
+ elif reso == NPY_DATETIMEUNIT.NPY_FR_m:
+ day_units = 24 * 60
+ elif reso == NPY_DATETIMEUNIT.NPY_FR_h:
+ day_units = 24
+ elif reso == NPY_DATETIMEUNIT.NPY_FR_D:
+ day_units = 1
+ else:
+ raise NotImplementedError(reso)
+ return day_units
+
+
cdef dict _reso_str_map = {
Resolution.RESO_NS.value: "nanosecond",
Resolution.RESO_US.value: "microsecond",
diff --git a/pandas/_libs/tslibs/vectorized.pyx b/pandas/_libs/tslibs/vectorized.pyx
index 9849bbce8c564..07121396df4a2 100644
--- a/pandas/_libs/tslibs/vectorized.pyx
+++ b/pandas/_libs/tslibs/vectorized.pyx
@@ -21,7 +21,9 @@ cnp.import_array()
from .conversion cimport normalize_i8_stamp
from .dtypes import Resolution
+
from .ccalendar cimport DAY_NANOS
+from .dtypes cimport c_Resolution
from .nattype cimport (
NPY_NAT,
c_NaT as NaT,
@@ -168,27 +170,19 @@ def ints_to_pydatetime(
# -------------------------------------------------------------------------
-cdef:
- int RESO_US = Resolution.RESO_US.value
- int RESO_MS = Resolution.RESO_MS.value
- int RESO_SEC = Resolution.RESO_SEC.value
- int RESO_MIN = Resolution.RESO_MIN.value
- int RESO_HR = Resolution.RESO_HR.value
- int RESO_DAY = Resolution.RESO_DAY.value
-
-cdef inline int _reso_stamp(npy_datetimestruct *dts):
+cdef inline c_Resolution _reso_stamp(npy_datetimestruct *dts):
if dts.us != 0:
if dts.us % 1000 == 0:
- return RESO_MS
- return RESO_US
+ return c_Resolution.RESO_MS
+ return c_Resolution.RESO_US
elif dts.sec != 0:
- return RESO_SEC
+ return c_Resolution.RESO_SEC
elif dts.min != 0:
- return RESO_MIN
+ return c_Resolution.RESO_MIN
elif dts.hour != 0:
- return RESO_HR
- return RESO_DAY
+ return c_Resolution.RESO_HR
+ return c_Resolution.RESO_DAY
@cython.wraparound(False)
@@ -205,7 +199,7 @@ def get_resolution(const int64_t[:] stamps, tzinfo tz=None) -> Resolution:
str typ
npy_datetimestruct dts
- int reso = RESO_DAY, curr_reso
+ c_Resolution reso = c_Resolution.RESO_DAY, curr_reso
if is_utc(tz) or tz is None:
use_utc = True
| I'm finding I'm needing this in a bunch of non-nano branches. Having a really hard time scoping said branches to allow for meaningful tests, but am optimistic that will improve before long.
The Resolution->c_Resolution thing is unrelated, just has been bothering me. | https://api.github.com/repos/pandas-dev/pandas/pulls/46462 | 2022-03-21T21:51:39Z | 2022-03-21T23:22:47Z | 2022-03-21T23:22:47Z | 2022-03-21T23:28:02Z |
Update install.rst | diff --git a/doc/source/getting_started/install.rst b/doc/source/getting_started/install.rst
index dc02460a671d6..e312889f2eb6a 100644
--- a/doc/source/getting_started/install.rst
+++ b/doc/source/getting_started/install.rst
@@ -276,7 +276,7 @@ Computation
========================= ================== =============================================================
Dependency Minimum Version Notes
========================= ================== =============================================================
-SciPy 1.14.1 Miscellaneous statistical functions
+SciPy 1.4.1 Miscellaneous statistical functions
numba 0.50.1 Alternative execution engine for rolling operations
(see :ref:`Enhancing Performance <enhancingperf.numba>`)
xarray 0.15.1 pandas-like API for N-dimensional data
| - [ ] closes #46455
| https://api.github.com/repos/pandas-dev/pandas/pulls/46461 | 2022-03-21T21:09:59Z | 2022-03-21T23:08:32Z | 2022-03-21T23:08:32Z | 2022-03-21T23:09:21Z |
BUG: tz_localize(UTC) not making a copy | diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst
index 251faeea28dc6..4c13dc2756a0e 100644
--- a/doc/source/whatsnew/v1.5.0.rst
+++ b/doc/source/whatsnew/v1.5.0.rst
@@ -384,6 +384,7 @@ Datetimelike
- Bug in :class:`Timestamp` construction when passing datetime components as positional arguments and ``tzinfo`` as a keyword argument incorrectly raising (:issue:`31929`)
- Bug in :meth:`Index.astype` when casting from object dtype to ``timedelta64[ns]`` dtype incorrectly casting ``np.datetime64("NaT")`` values to ``np.timedelta64("NaT")`` instead of raising (:issue:`45722`)
- Bug in :meth:`SeriesGroupBy.value_counts` index when passing categorical column (:issue:`44324`)
+- Bug in :meth:`DatetimeIndex.tz_localize` localizing to UTC failing to make a copy of the underlying data (:issue:`46460`)
-
Timedelta
diff --git a/pandas/_libs/tslibs/tzconversion.pyx b/pandas/_libs/tslibs/tzconversion.pyx
index bc57cb8aaed83..ff77566e1d559 100644
--- a/pandas/_libs/tslibs/tzconversion.pyx
+++ b/pandas/_libs/tslibs/tzconversion.pyx
@@ -64,8 +64,6 @@ cdef int64_t tz_localize_to_utc_single(
return val - _tz_localize_using_tzinfo_api(val, tz, to_utc=True)
elif is_fixed_offset(tz):
- # TODO: in this case we should be able to use get_utcoffset,
- # that returns None for e.g. 'dateutil//usr/share/zoneinfo/Etc/GMT-9'
_, deltas, _ = get_dst_info(tz)
delta = deltas[0]
return val - delta
@@ -121,9 +119,10 @@ timedelta-like}
Py_ssize_t delta_idx_offset, delta_idx, pos_left, pos_right
int64_t *tdata
int64_t v, left, right, val, v_left, v_right, new_local, remaining_mins
- int64_t first_delta
+ int64_t first_delta, delta
int64_t shift_delta = 0
- ndarray[int64_t] trans, result, result_a, result_b, dst_hours
+ ndarray[int64_t] trans, result_a, result_b, dst_hours
+ int64_t[::1] result
npy_datetimestruct dts
bint infer_dst = False, is_dst = False, fill = False
bint shift_forward = False, shift_backward = False
@@ -132,7 +131,7 @@ timedelta-like}
# Vectorized version of DstTzInfo.localize
if is_utc(tz) or tz is None:
- return vals
+ return vals.copy()
result = np.empty(n, dtype=np.int64)
@@ -143,7 +142,18 @@ timedelta-like}
result[i] = NPY_NAT
else:
result[i] = v - _tz_localize_using_tzinfo_api(v, tz, to_utc=True)
- return result
+ return result.base # to return underlying ndarray
+
+ elif is_fixed_offset(tz):
+ _, deltas, _ = get_dst_info(tz)
+ delta = deltas[0]
+ for i in range(n):
+ v = vals[i]
+ if v == NPY_NAT:
+ result[i] = NPY_NAT
+ else:
+ result[i] = v - delta
+ return result.base # to return underlying ndarray
# silence false-positive compiler warning
ambiguous_array = np.empty(0, dtype=bool)
@@ -298,7 +308,7 @@ timedelta-like}
stamp = _render_tstamp(val)
raise pytz.NonExistentTimeError(stamp)
- return result
+ return result.base # .base to get underlying ndarray
cdef inline Py_ssize_t bisect_right_i8(int64_t *data,
diff --git a/pandas/core/arrays/datetimes.py b/pandas/core/arrays/datetimes.py
index 35ae9623d353d..9ffe33e0cf38e 100644
--- a/pandas/core/arrays/datetimes.py
+++ b/pandas/core/arrays/datetimes.py
@@ -456,9 +456,13 @@ def _generate_range(
endpoint_tz = start.tz if start is not None else end.tz
if tz is not None and endpoint_tz is None:
- i8values = tzconversion.tz_localize_to_utc(
- i8values, tz, ambiguous=ambiguous, nonexistent=nonexistent
- )
+
+ if not timezones.is_utc(tz):
+ # short-circuit tz_localize_to_utc which would make
+ # an unnecessary copy with UTC but be a no-op.
+ i8values = tzconversion.tz_localize_to_utc(
+ i8values, tz, ambiguous=ambiguous, nonexistent=nonexistent
+ )
# i8values is localized datetime64 array -> have to convert
# start/end as well to compare
@@ -2126,6 +2130,8 @@ def _sequence_to_dt64ns(
if tz is not None:
# Convert tz-naive to UTC
tz = timezones.maybe_get_tz(tz)
+ # TODO: if tz is UTC, are there situations where we *don't* want a
+ # copy? tz_localize_to_utc always makes one.
data = tzconversion.tz_localize_to_utc(
data.view("i8"), tz, ambiguous=ambiguous
)
diff --git a/pandas/tests/indexes/datetimes/test_timezones.py b/pandas/tests/indexes/datetimes/test_timezones.py
index 51bc054010aca..a07f21f785828 100644
--- a/pandas/tests/indexes/datetimes/test_timezones.py
+++ b/pandas/tests/indexes/datetimes/test_timezones.py
@@ -327,6 +327,17 @@ def test_tz_convert_unsorted(self, tzstr):
# -------------------------------------------------------------
# DatetimeIndex.tz_localize
+ def test_tz_localize_utc_copies(self, utc_fixture):
+ # GH#46460
+ times = ["2015-03-08 01:00", "2015-03-08 02:00", "2015-03-08 03:00"]
+ index = DatetimeIndex(times)
+
+ res = index.tz_localize(utc_fixture)
+ assert not tm.shares_memory(res, index)
+
+ res2 = index._data.tz_localize(utc_fixture)
+ assert not tm.shares_memory(index._data, res2)
+
def test_dti_tz_localize_nonexistent_raise_coerce(self):
# GH#13057
times = ["2015-03-08 01:00", "2015-03-08 02:00", "2015-03-08 03:00"]
diff --git a/pandas/tests/tslibs/test_conversion.py b/pandas/tests/tslibs/test_conversion.py
index 41eb7ae85d032..d0864ae8e1b7b 100644
--- a/pandas/tests/tslibs/test_conversion.py
+++ b/pandas/tests/tslibs/test_conversion.py
@@ -50,6 +50,18 @@ def _compare_local_to_utc(tz_didx, naive_didx):
tm.assert_numpy_array_equal(result, expected)
+def test_tz_localize_to_utc_copies():
+ # GH#46460
+ arr = np.arange(5, dtype="i8")
+ result = tzconversion.tz_convert_from_utc(arr, tz=UTC)
+ tm.assert_numpy_array_equal(result, arr)
+ assert not np.shares_memory(arr, result)
+
+ result = tzconversion.tz_convert_from_utc(arr, tz=None)
+ tm.assert_numpy_array_equal(result, arr)
+ assert not np.shares_memory(arr, result)
+
+
def test_tz_convert_single_matches_tz_convert_hourly(tz_aware_fixture):
tz = tz_aware_fixture
tz_didx = date_range("2014-03-01", "2015-01-10", freq="H", tz=tz)
| - [ ] closes #xxxx (Replace xxxx with the Github issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46460 | 2022-03-21T18:02:01Z | 2022-03-22T23:24:08Z | 2022-03-22T23:24:08Z | 2022-03-24T02:34:14Z |
Add On being part of the pandas Benchmarks Grant blog | diff --git a/web/pandas/community/blog/benchmarks_grant_blog.md b/web/pandas/community/blog/benchmarks_grant_blog.md
new file mode 100644
index 0000000000000..e88a58202d343
--- /dev/null
+++ b/web/pandas/community/blog/benchmarks_grant_blog.md
@@ -0,0 +1,59 @@
+Title: On being part of the pandas Benchmarks Grant while contributing to asv
+Date: 2022-03-21
+
+# On being part of the pandas Benchmarks Grant while contributing to asv
+
+*I didn't know that I would probably upgrade from contributing to documentation to fixing bugs and code in Open source. For some reason, I couldn't find my way around huge codebases, this fear kept lingering on until the [asv](https://github.com/airspeed-velocity/asv) project.*
+
+I've been trying to contribute to open source for about two years now. And all this came to pass a few weeks ago with the pandas benchmarks grant.
+
+I had learned about Open source from one of the Pyladies Kampala meetups and I managed to make my first contribution to a project by editing documentation and even still it took me longer to get my contribution merged. Meanwhile, all this open-source lingo was all new to me at the time, and was so scared of actually trying to contribute to any other project.
+
+My ears had always been out for any mentorship opportunities to contribute to open source. While scrolling on Twitter one day, I landed on Marc Garcia's tweet of how he wanted to mentor ladies into contributing to OS. This is how I commented and shared my email and got under his mentorship for a short while, then the pandemic happened. Long story short, late last year he had a call for contributors for a diversity grant, benchmarks grant, and a permanent role. Gladly I and an amazing teammate Lucy were considered for the benchmarks grant that was later merged with the diversity grant.
+
+This was such a great opportunity for me as I was working on this when my son was just weeks old. Below are some of my takeaways from this experience and I hope it can help some newbies to contribute to open source.
+
+## Challenges:
+
+My greatest challenge in the first weeks of the project was right within me, FEAR.
+Fear to ask and show that I had not understood what to do about the project. This really ate up a lot of my time and I kept going in circles. A couple of hours trying to understand the project and running it locally took me almost a week but if I had asked my mentor, who was always willing to help it would have been easier for me and not have a bunch of wasted hours.
+
+Another challenge for me was my git workflow was not good at all. After forking and cloning the [asv](https://github.com/airspeed-velocity/asv) project, I went straight to working on my first issue without branching out. I noticed that I hadn't branched out after pushing changes to my origin master branch.
+
+I didn't know how to reverse what I had done, hence all the branches I created after that had extra changes I had made at first in the local master It was a total mess that I had to learn the hard way by deleting my fork and redoing it all over again but this time very cautious not to mess up my origin master branch.
+
+Later on, I learned another way around it, so for newbies if you go through what I just explained, follow the steps below and it will reset your local master branch to the upstream version and push it to your origin repository. Assuming that upstream is the original repository and origin is your fork on your GitHub profile:
+
+```
+git checkout master # make sure you are in the master branch
+
+git pull upstream master # pulls all new commits on upstream master
+
+git reset --hard upstream/master # deletes all local master changes
+
+git push origin master --force # deletes all origin master changes
+
+```
+
+## Learning from the source code:
+
+I was able to learn a lot from the source code and gained immense knowledge about best practices and code quality. I was able to learn about Github actions, I was able to identify bugs in the source code and create new issues (something I thought only maintainers can do but not contributors)and improve source code to the latest Flake8 standards.
+
+Not only is reviewing the code itself a learning experience but also the structure and folder hierarchy in the project started to be more clear. Noticed this more when the pytest fixtures were all over the test files and had to move one by one to the conftest.py file so that we could have a clear demarcation between the function under test and setup and teardown step code.
+
+Fixtures are nothing but regular functions that are run by pytest before executing an actual test function.
+
+## Teamwork and collaboration:
+
+For this project, I had an amazing Mentor, Marc Garcia, he was always willing to share his knowledge, explain unclear concepts and share helpful feedback along the way. Whenever I would implement that feedback it felt easier to work on more issues faster . I felt the growth from the time I started on this project and I will carry it along as I contribute to more OOS projects and this really all goes back to Marc for his amazing mentorship.
+
+I also worked with Lucy Jimenez who really helped me a lot as we had numerous one-on-one calls just to understand the tasks better, she always reminded me with her strong quote "You can do it" which pushed me through the uncomfortable learning phases.
+
+In conclusion, I learned a lot from this experience. I was able to carry out a virtual sprint and had a few ladies contribute to the asv project and they have gone ahead to apply for Outreachy and the upcoming Google Summer of Code programs. Many many thanks to Numfocus for giving us this support through small grants.
+
+Looking forward to contributing more and making an impact in my community and also the open-source community.
+If you liked this article, you can connect with me through these channels:
+
+- [GitHub](https://github.com/dorothykiz1)
+- [Twitter](https://twitter.com/kizdorothy)
+- [LinkedIn](https://www.linkedin.com/in/dorothy-kabarozi/)
\ No newline at end of file
| The pandas Benchmarks Grant blog
| https://api.github.com/repos/pandas-dev/pandas/pulls/46459 | 2022-03-21T17:25:33Z | 2022-03-23T07:51:46Z | null | 2022-03-23T07:51:46Z |
BUG: url regex in `style_render` does not pass colon and other valid | diff --git a/doc/source/whatsnew/v1.4.2.rst b/doc/source/whatsnew/v1.4.2.rst
index 4cbb8118055af..13f3e9a0d0a8c 100644
--- a/doc/source/whatsnew/v1.4.2.rst
+++ b/doc/source/whatsnew/v1.4.2.rst
@@ -31,7 +31,7 @@ Bug fixes
~~~~~~~~~
- Fix some cases for subclasses that define their ``_constructor`` properties as general callables (:issue:`46018`)
- Fixed "longtable" formatting in :meth:`.Styler.to_latex` when ``column_format`` is given in extended format (:issue:`46037`)
--
+- Fixed incorrect rendering in :meth:`.Styler.format` with ``hyperlinks="html"`` when the url contains a colon or other special characters (:issue:`46389`)
.. ---------------------------------------------------------------------------
diff --git a/pandas/io/formats/style_render.py b/pandas/io/formats/style_render.py
index 0227bb1ef7cc6..4e3f86d21b228 100644
--- a/pandas/io/formats/style_render.py
+++ b/pandas/io/formats/style_render.py
@@ -1589,7 +1589,7 @@ def _render_href(x, format):
href = r"\href{{{0}}}{{{0}}}"
else:
raise ValueError("``hyperlinks`` format can only be 'html' or 'latex'")
- pat = r"(https?:\/\/|ftp:\/\/|www.)[\w/\-?=%.]+\.[\w/\-&?=%.]+"
+ pat = r"((http|ftp)s?:\/\/|www.)[\w/\-?=%.:@]+\.[\w/\-&?=%.,':;~!@#$*()\[\]]+"
return re.sub(pat, lambda m: href.format(m.group(0)), x)
return x
diff --git a/pandas/tests/io/formats/style/test_html.py b/pandas/tests/io/formats/style/test_html.py
index 2abc963525977..bf4f79d0e89e6 100644
--- a/pandas/tests/io/formats/style/test_html.py
+++ b/pandas/tests/io/formats/style/test_html.py
@@ -778,8 +778,20 @@ def test_hiding_index_columns_multiindex_trimming():
("no scheme, no top-level: www.web", False, "www.web"),
("https scheme: https://www.web.com", True, "https://www.web.com"),
("ftp scheme: ftp://www.web", True, "ftp://www.web"),
+ ("ftps scheme: ftps://www.web", True, "ftps://www.web"),
("subdirectories: www.web.com/directory", True, "www.web.com/directory"),
("Multiple domains: www.1.2.3.4", True, "www.1.2.3.4"),
+ ("with port: http://web.com:80", True, "http://web.com:80"),
+ (
+ "full net_loc scheme: http://user:pass@web.com",
+ True,
+ "http://user:pass@web.com",
+ ),
+ (
+ "with valid special chars: http://web.com/,.':;~!@#$*()[]",
+ True,
+ "http://web.com/,.':;~!@#$*()[]",
+ ),
],
)
def test_rendered_links(type, text, exp, found):
| URLs containing some valid characters such as colon in port numbers get
cut off when html-formatting. As a workaround, expanded the regex to
match a wider variety of URLs.
- [x] closes #46389
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added an entry in the latest `doc/source/whatsnew/v1.4.2.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46457 | 2022-03-21T11:49:41Z | 2022-03-24T18:16:28Z | 2022-03-24T18:16:27Z | 2022-03-24T18:46:43Z |
DOC: conveting Index object into a set(#46440) | diff --git a/doc/source/user_guide/indexing.rst b/doc/source/user_guide/indexing.rst
index a94681924d211..f8c93c04c980a 100644
--- a/doc/source/user_guide/indexing.rst
+++ b/doc/source/user_guide/indexing.rst
@@ -1553,9 +1553,7 @@ Index objects
-------------
The pandas :class:`~pandas.Index` class and its subclasses can be viewed as
-implementing an *ordered multiset*. Duplicates are allowed. However, if you try
-to convert an :class:`~pandas.Index` object with duplicate entries into a
-``set``, an exception will be raised.
+implementing an *ordered multiset*. Duplicates are allowed.
:class:`~pandas.Index` also provides the infrastructure necessary for
lookups, data alignment, and reindexing. The easiest way to create an
| - [ ] closes #46446 (Replace xxxx with the Github issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46454 | 2022-03-21T06:04:57Z | 2022-03-22T03:30:26Z | null | 2022-03-22T03:30:26Z |
REF: Move value_counts, take, factorize to ArrowExtensionArray | diff --git a/pandas/core/arrays/_mixins.py b/pandas/core/arrays/_mixins.py
index b037f278872a9..6a904b8cdb042 100644
--- a/pandas/core/arrays/_mixins.py
+++ b/pandas/core/arrays/_mixins.py
@@ -43,6 +43,7 @@
)
from pandas.core.dtypes.common import (
+ is_array_like,
is_bool_dtype,
is_dtype_equal,
is_integer,
@@ -69,7 +70,10 @@
from pandas.core.array_algos.transforms import shift
from pandas.core.arrays.base import ExtensionArray
from pandas.core.construction import extract_array
-from pandas.core.indexers import check_array_indexer
+from pandas.core.indexers import (
+ check_array_indexer,
+ validate_indices,
+)
from pandas.core.sorting import nargminmax
NDArrayBackedExtensionArrayT = TypeVar(
@@ -86,6 +90,8 @@
NumpyValueArrayLike,
)
+ from pandas import Series
+
def ravel_compat(meth: F) -> F:
"""
@@ -599,6 +605,159 @@ def copy(self: ArrowExtensionArrayT) -> ArrowExtensionArrayT:
"""
return type(self)(self._data)
+ @doc(ExtensionArray.factorize)
+ def factorize(self, na_sentinel: int = -1) -> tuple[np.ndarray, ExtensionArray]:
+ encoded = self._data.dictionary_encode()
+ indices = pa.chunked_array(
+ [c.indices for c in encoded.chunks], type=encoded.type.index_type
+ ).to_pandas()
+ if indices.dtype.kind == "f":
+ indices[np.isnan(indices)] = na_sentinel
+ indices = indices.astype(np.int64, copy=False)
+
+ if encoded.num_chunks:
+ uniques = type(self)(encoded.chunk(0).dictionary)
+ else:
+ uniques = type(self)(pa.array([], type=encoded.type.value_type))
+
+ return indices.values, uniques
+
+ def take(
+ self,
+ indices: TakeIndexer,
+ allow_fill: bool = False,
+ fill_value: Any = None,
+ ):
+ """
+ Take elements from an array.
+
+ Parameters
+ ----------
+ indices : sequence of int or one-dimensional np.ndarray of int
+ Indices to be taken.
+ allow_fill : bool, default False
+ How to handle negative values in `indices`.
+
+ * False: negative values in `indices` indicate positional indices
+ from the right (the default). This is similar to
+ :func:`numpy.take`.
+
+ * True: negative values in `indices` indicate
+ missing values. These values are set to `fill_value`. Any other
+ other negative values raise a ``ValueError``.
+
+ fill_value : any, optional
+ Fill value to use for NA-indices when `allow_fill` is True.
+ This may be ``None``, in which case the default NA value for
+ the type, ``self.dtype.na_value``, is used.
+
+ For many ExtensionArrays, there will be two representations of
+ `fill_value`: a user-facing "boxed" scalar, and a low-level
+ physical NA value. `fill_value` should be the user-facing version,
+ and the implementation should handle translating that to the
+ physical version for processing the take if necessary.
+
+ Returns
+ -------
+ ExtensionArray
+
+ Raises
+ ------
+ IndexError
+ When the indices are out of bounds for the array.
+ ValueError
+ When `indices` contains negative values other than ``-1``
+ and `allow_fill` is True.
+
+ See Also
+ --------
+ numpy.take
+ api.extensions.take
+
+ Notes
+ -----
+ ExtensionArray.take is called by ``Series.__getitem__``, ``.loc``,
+ ``iloc``, when `indices` is a sequence of values. Additionally,
+ it's called by :meth:`Series.reindex`, or any other method
+ that causes realignment, with a `fill_value`.
+ """
+ # TODO: Remove once we got rid of the (indices < 0) check
+ if not is_array_like(indices):
+ indices_array = np.asanyarray(indices)
+ else:
+ # error: Incompatible types in assignment (expression has type
+ # "Sequence[int]", variable has type "ndarray")
+ indices_array = indices # type: ignore[assignment]
+
+ if len(self._data) == 0 and (indices_array >= 0).any():
+ raise IndexError("cannot do a non-empty take")
+ if indices_array.size > 0 and indices_array.max() >= len(self._data):
+ raise IndexError("out of bounds value in 'indices'.")
+
+ if allow_fill:
+ fill_mask = indices_array < 0
+ if fill_mask.any():
+ validate_indices(indices_array, len(self._data))
+ # TODO(ARROW-9433): Treat negative indices as NULL
+ indices_array = pa.array(indices_array, mask=fill_mask)
+ result = self._data.take(indices_array)
+ if isna(fill_value):
+ return type(self)(result)
+ # TODO: ArrowNotImplementedError: Function fill_null has no
+ # kernel matching input types (array[string], scalar[string])
+ result = type(self)(result)
+ result[fill_mask] = fill_value
+ return result
+ # return type(self)(pc.fill_null(result, pa.scalar(fill_value)))
+ else:
+ # Nothing to fill
+ return type(self)(self._data.take(indices))
+ else: # allow_fill=False
+ # TODO(ARROW-9432): Treat negative indices as indices from the right.
+ if (indices_array < 0).any():
+ # Don't modify in-place
+ indices_array = np.copy(indices_array)
+ indices_array[indices_array < 0] += len(self._data)
+ return type(self)(self._data.take(indices_array))
+
+ def value_counts(self, dropna: bool = True) -> Series:
+ """
+ Return a Series containing counts of each unique value.
+
+ Parameters
+ ----------
+ dropna : bool, default True
+ Don't include counts of missing values.
+
+ Returns
+ -------
+ counts : Series
+
+ See Also
+ --------
+ Series.value_counts
+ """
+ from pandas import (
+ Index,
+ Series,
+ )
+
+ vc = self._data.value_counts()
+
+ values = vc.field(0)
+ counts = vc.field(1)
+ if dropna and self._data.null_count > 0:
+ mask = values.is_valid()
+ values = values.filter(mask)
+ counts = counts.filter(mask)
+
+ # No missing values so we can adhere to the interface and return a numpy array.
+ counts = np.array(counts)
+
+ index = Index(type(self)(values))
+
+ return Series(counts, index=index).astype("Int64")
+
@classmethod
def _concat_same_type(
cls: type[ArrowExtensionArrayT], to_concat
diff --git a/pandas/core/arrays/string_arrow.py b/pandas/core/arrays/string_arrow.py
index ac5bfe32ed5f6..154c143ac89df 100644
--- a/pandas/core/arrays/string_arrow.py
+++ b/pandas/core/arrays/string_arrow.py
@@ -3,8 +3,6 @@
from collections.abc import Callable # noqa: PDF001
import re
from typing import (
- TYPE_CHECKING,
- Any,
Union,
overload,
)
@@ -22,7 +20,6 @@
Scalar,
ScalarIndexer,
SequenceIndexer,
- TakeIndexer,
npt,
)
from pandas.compat import (
@@ -31,10 +28,8 @@
pa_version_under3p0,
pa_version_under4p0,
)
-from pandas.util._decorators import doc
from pandas.core.dtypes.common import (
- is_array_like,
is_bool_dtype,
is_dtype_equal,
is_integer,
@@ -48,7 +43,6 @@
from pandas.core.arraylike import OpsMixin
from pandas.core.arrays._mixins import ArrowExtensionArray
-from pandas.core.arrays.base import ExtensionArray
from pandas.core.arrays.boolean import BooleanDtype
from pandas.core.arrays.integer import Int64Dtype
from pandas.core.arrays.numeric import NumericDtype
@@ -59,7 +53,6 @@
from pandas.core.indexers import (
check_array_indexer,
unpack_tuple_and_ellipses,
- validate_indices,
)
from pandas.core.strings.object_array import ObjectStringArrayMixin
@@ -76,10 +69,6 @@
"ge": pc.greater_equal,
}
-
-if TYPE_CHECKING:
- from pandas import Series
-
ArrowStringScalarOrNAT = Union[str, libmissing.NAType]
@@ -214,23 +203,6 @@ def to_numpy(
result[mask] = na_value
return result
- @doc(ExtensionArray.factorize)
- def factorize(self, na_sentinel: int = -1) -> tuple[np.ndarray, ExtensionArray]:
- encoded = self._data.dictionary_encode()
- indices = pa.chunked_array(
- [c.indices for c in encoded.chunks], type=encoded.type.index_type
- ).to_pandas()
- if indices.dtype.kind == "f":
- indices[np.isnan(indices)] = na_sentinel
- indices = indices.astype(np.int64, copy=False)
-
- if encoded.num_chunks:
- uniques = type(self)(encoded.chunk(0).dictionary)
- else:
- uniques = type(self)(pa.array([], type=encoded.type.value_type))
-
- return indices.values, uniques
-
@overload
def __getitem__(self, item: ScalarIndexer) -> ArrowStringScalarOrNAT:
...
@@ -357,104 +329,6 @@ def _maybe_convert_setitem_value(self, value):
raise ValueError("Scalar must be NA or str")
return value
- def take(
- self,
- indices: TakeIndexer,
- allow_fill: bool = False,
- fill_value: Any = None,
- ):
- """
- Take elements from an array.
-
- Parameters
- ----------
- indices : sequence of int or one-dimensional np.ndarray of int
- Indices to be taken.
- allow_fill : bool, default False
- How to handle negative values in `indices`.
-
- * False: negative values in `indices` indicate positional indices
- from the right (the default). This is similar to
- :func:`numpy.take`.
-
- * True: negative values in `indices` indicate
- missing values. These values are set to `fill_value`. Any other
- other negative values raise a ``ValueError``.
-
- fill_value : any, optional
- Fill value to use for NA-indices when `allow_fill` is True.
- This may be ``None``, in which case the default NA value for
- the type, ``self.dtype.na_value``, is used.
-
- For many ExtensionArrays, there will be two representations of
- `fill_value`: a user-facing "boxed" scalar, and a low-level
- physical NA value. `fill_value` should be the user-facing version,
- and the implementation should handle translating that to the
- physical version for processing the take if necessary.
-
- Returns
- -------
- ExtensionArray
-
- Raises
- ------
- IndexError
- When the indices are out of bounds for the array.
- ValueError
- When `indices` contains negative values other than ``-1``
- and `allow_fill` is True.
-
- See Also
- --------
- numpy.take
- api.extensions.take
-
- Notes
- -----
- ExtensionArray.take is called by ``Series.__getitem__``, ``.loc``,
- ``iloc``, when `indices` is a sequence of values. Additionally,
- it's called by :meth:`Series.reindex`, or any other method
- that causes realignment, with a `fill_value`.
- """
- # TODO: Remove once we got rid of the (indices < 0) check
- if not is_array_like(indices):
- indices_array = np.asanyarray(indices)
- else:
- # error: Incompatible types in assignment (expression has type
- # "Sequence[int]", variable has type "ndarray")
- indices_array = indices # type: ignore[assignment]
-
- if len(self._data) == 0 and (indices_array >= 0).any():
- raise IndexError("cannot do a non-empty take")
- if indices_array.size > 0 and indices_array.max() >= len(self._data):
- raise IndexError("out of bounds value in 'indices'.")
-
- if allow_fill:
- fill_mask = indices_array < 0
- if fill_mask.any():
- validate_indices(indices_array, len(self._data))
- # TODO(ARROW-9433): Treat negative indices as NULL
- indices_array = pa.array(indices_array, mask=fill_mask)
- result = self._data.take(indices_array)
- if isna(fill_value):
- return type(self)(result)
- # TODO: ArrowNotImplementedError: Function fill_null has no
- # kernel matching input types (array[string], scalar[string])
- result = type(self)(result)
- result[fill_mask] = fill_value
- return result
- # return type(self)(pc.fill_null(result, pa.scalar(fill_value)))
- else:
- # Nothing to fill
- return type(self)(self._data.take(indices))
- else: # allow_fill=False
- # TODO(ARROW-9432): Treat negative indices as indices from the right.
- if (indices_array < 0).any():
- # Don't modify in-place
- indices_array = np.copy(indices_array)
- indices_array[indices_array < 0] += len(self._data)
- return type(self)(self._data.take(indices_array))
-
def isin(self, values):
if pa_version_under2p0:
return super().isin(values)
@@ -481,44 +355,6 @@ def isin(self, values):
# to False
return np.array(result, dtype=np.bool_)
- def value_counts(self, dropna: bool = True) -> Series:
- """
- Return a Series containing counts of each unique value.
-
- Parameters
- ----------
- dropna : bool, default True
- Don't include counts of missing values.
-
- Returns
- -------
- counts : Series
-
- See Also
- --------
- Series.value_counts
- """
- from pandas import (
- Index,
- Series,
- )
-
- vc = self._data.value_counts()
-
- values = vc.field(0)
- counts = vc.field(1)
- if dropna and self._data.null_count > 0:
- mask = values.is_valid()
- values = values.filter(mask)
- counts = counts.filter(mask)
-
- # No missing values so we can adhere to the interface and return a numpy array.
- counts = np.array(counts)
-
- index = Index(type(self)(values))
-
- return Series(counts, index=index).astype("Int64")
-
def astype(self, dtype, copy: bool = True):
dtype = pandas_dtype(dtype)
| - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
| https://api.github.com/repos/pandas-dev/pandas/pulls/46453 | 2022-03-21T03:07:09Z | 2022-03-28T19:37:39Z | 2022-03-28T19:37:39Z | 2022-03-28T19:48:32Z |
BUG: groupby.value_counts subset arg not working | diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 0811adbeeeda0..f681ed1092b39 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -1694,17 +1694,6 @@ def value_counts(
in_axis_names = {
grouping.name for grouping in self.grouper.groupings if grouping.in_axis
}
- if isinstance(self._selected_obj, Series):
- name = self._selected_obj.name
- keys = [] if name in in_axis_names else [self._selected_obj]
- else:
- keys = [
- # Can't use .values because the column label needs to be preserved
- self._selected_obj.iloc[:, idx]
- for idx, name in enumerate(self._selected_obj.columns)
- if name not in in_axis_names
- ]
-
if subset is not None:
clashing = set(subset) & set(in_axis_names)
if clashing:
@@ -1713,6 +1702,25 @@ def value_counts(
"the groupby column keys"
)
+ if isinstance(self._selected_obj, Series):
+ name = self._selected_obj.name
+ keys = [] if name in in_axis_names else [self._selected_obj]
+ else:
+ if subset:
+ keys = [
+ self._selected_obj.iloc[:, idx]
+ for idx, name in enumerate(self._selected_obj.columns)
+ if name in subset
+ ]
+ else:
+ keys = [
+ # Can't use .values because the column label needs to
+ # be preserved
+ self._selected_obj.iloc[:, idx]
+ for idx, name in enumerate(self._selected_obj.columns)
+ if name not in in_axis_names
+ ]
+
groupings = list(self.grouper.groupings)
for key in keys:
grouper, _, _ = get_grouper(
diff --git a/pandas/tests/groupby/test_value_counts.py b/pandas/tests/groupby/test_value_counts.py
index d38ba84cd1397..0839533c997a3 100644
--- a/pandas/tests/groupby/test_value_counts.py
+++ b/pandas/tests/groupby/test_value_counts.py
@@ -192,3 +192,20 @@ def test_series_groupby_value_counts_on_categorical():
# Name: 0, dtype: int64
tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("columns", [["c1"], ["c2"], ["c2", "c3"], ["c1", "c2", "c3"]])
+def test_series_groupby_value_counts_subset(columns):
+ # GH46383
+ df = DataFrame(
+ {
+ "c1": ["a", "b", "c", "d", "c"],
+ "c2": ["x", "y", "y", "x", "z"],
+ "c3": [2, 1, 1, 2, 1],
+ },
+ index=[0, 1, 1, 2, 0],
+ )
+ result = df.groupby(level=0).value_counts(subset=columns)
+ expected = df.groupby(level=0)[columns].value_counts()
+
+ tm.assert_series_equal(result, expected)
| - [x] closes #46383
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests)
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
It works but I'm not sure it is the right approach. Alternatively I could add an if-else in l.1712 to check for subset and create `keys` with two separate list comprehensions and leave the rest as it was. | https://api.github.com/repos/pandas-dev/pandas/pulls/46452 | 2022-03-20T23:04:23Z | 2022-03-22T13:42:28Z | null | 2022-03-22T13:42:28Z |
TST: GH38947 creating function to test bar plot index | diff --git a/pandas/tests/plotting/test_misc.py b/pandas/tests/plotting/test_misc.py
index 656590cdb02be..9c7996adfed38 100644
--- a/pandas/tests/plotting/test_misc.py
+++ b/pandas/tests/plotting/test_misc.py
@@ -7,6 +7,7 @@
from pandas import (
DataFrame,
+ Index,
Series,
Timestamp,
)
@@ -441,6 +442,25 @@ def test_dictionary_color(self):
colors = [rect.get_color() for rect in ax.get_lines()[0:2]]
assert all(color == expected[index] for index, color in enumerate(colors))
+ def test_bar_plot(self):
+ # GH38947
+ # Test bar plot with string and int index
+ from matplotlib.text import Text
+
+ expected = [Text(0, 0, "0"), Text(1, 0, "Total")]
+
+ df = DataFrame(
+ {
+ "a": [1, 2],
+ },
+ index=Index([0, "Total"]),
+ )
+ plot_bar = df.plot.bar()
+ assert all(
+ (a.get_text() == b.get_text())
+ for a, b in zip(plot_bar.get_xticklabels(), expected)
+ )
+
def test_has_externally_shared_axis_x_axis(self):
# GH33819
# Test _has_externally_shared_axis() works for x-axis
| - [X] closes #38947
- [X] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [X] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [X] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
I just implemented all [changes](https://github.com/pandas-dev/pandas/pull/44033) done by @SabrinaMD
But I updated the small corrections | https://api.github.com/repos/pandas-dev/pandas/pulls/46451 | 2022-03-20T22:49:10Z | 2022-04-24T18:28:11Z | 2022-04-24T18:28:11Z | 2022-04-24T18:28:44Z |
TST: JSONArray test_combine_first sometimes passes | diff --git a/pandas/tests/extension/json/test_json.py b/pandas/tests/extension/json/test_json.py
index 2b6174be2ca0e..f1e8e82d23691 100644
--- a/pandas/tests/extension/json/test_json.py
+++ b/pandas/tests/extension/json/test_json.py
@@ -267,7 +267,11 @@ def test_combine_le(self, data_repeated):
def test_combine_add(self, data_repeated):
super().test_combine_add(data_repeated)
- @pytest.mark.xfail(reason="combine for JSONArray not supported")
+ @pytest.mark.xfail(
+ reason="combine for JSONArray not supported - "
+ "may pass depending on random data",
+ strict=False,
+ )
def test_combine_first(self, data):
super().test_combine_first(data)
| - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
xref https://github.com/pandas-dev/pandas/pull/46427
Encountered a build where this xpassed
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=75611&view=ms.vss-test-web.build-test-results-tab&runId=3135400&resultId=171966&paneView=debug
| https://api.github.com/repos/pandas-dev/pandas/pulls/46450 | 2022-03-20T22:38:41Z | 2022-03-21T23:07:49Z | 2022-03-21T23:07:49Z | 2022-03-22T17:22:32Z |
Typ: Fix io stata type ignores | diff --git a/pandas/io/stata.py b/pandas/io/stata.py
index 71ab667c1b3aa..d9912f2480e07 100644
--- a/pandas/io/stata.py
+++ b/pandas/io/stata.py
@@ -572,7 +572,13 @@ def _cast_to_stata_types(data: DataFrame) -> DataFrame:
"""
ws = ""
# original, if small, if large
- conversion_data = (
+ conversion_data: tuple[
+ tuple[type, type, type],
+ tuple[type, type, type],
+ tuple[type, type, type],
+ tuple[type, type, type],
+ tuple[type, type, type],
+ ] = (
(np.bool_, np.int8, np.int8),
(np.uint8, np.int8, np.int16),
(np.uint16, np.int16, np.int32),
@@ -600,8 +606,7 @@ def _cast_to_stata_types(data: DataFrame) -> DataFrame:
dtype = data[col].dtype
for c_data in conversion_data:
if dtype == c_data[0]:
- # Value of type variable "_IntType" of "iinfo" cannot be "object"
- if data[col].max() <= np.iinfo(c_data[1]).max: # type: ignore[type-var]
+ if data[col].max() <= np.iinfo(c_data[1]).max:
dtype = c_data[1]
else:
dtype = c_data[2]
@@ -967,7 +972,7 @@ def __init__(self) -> None:
(255, np.dtype(np.float64)),
]
)
- self.DTYPE_MAP_XML = {
+ self.DTYPE_MAP_XML: dict[int, np.dtype] = {
32768: np.dtype(np.uint8), # Keys to GSO
65526: np.dtype(np.float64),
65527: np.dtype(np.float32),
@@ -975,9 +980,7 @@ def __init__(self) -> None:
65529: np.dtype(np.int16),
65530: np.dtype(np.int8),
}
- # error: Argument 1 to "list" has incompatible type "str";
- # expected "Iterable[int]" [arg-type]
- self.TYPE_MAP = list(range(251)) + list("bhlfd") # type: ignore[arg-type]
+ self.TYPE_MAP = list(tuple(range(251)) + tuple("bhlfd"))
self.TYPE_MAP_XML = {
# Not really a Q, unclear how to handle byteswap
32768: "Q",
@@ -1296,9 +1299,7 @@ def g(typ: int) -> str | np.dtype:
if typ <= 2045:
return str(typ)
try:
- # error: Incompatible return value type (got "Type[number]", expected
- # "Union[str, dtype]")
- return self.DTYPE_MAP_XML[typ] # type: ignore[return-value]
+ return self.DTYPE_MAP_XML[typ]
except KeyError as err:
raise ValueError(f"cannot convert stata dtype [{typ}]") from err
| xref #37715
Hi all, first time contributing, please let me know if all is OK process-wise (e.g. commit message, if it is OK that the changes are not squashed)
Regarding the changed code - I added c_data_large for consistency (c_data_small was required to fix type: ignore), can remove if you think that it is unnecessary | https://api.github.com/repos/pandas-dev/pandas/pulls/46449 | 2022-03-20T22:22:34Z | 2022-03-23T01:16:21Z | 2022-03-23T01:16:21Z | 2022-03-23T21:28:41Z |
REF: Eliminate another iterchunks call in ArrowExtensionArray | diff --git a/pandas/core/arrays/_mixins.py b/pandas/core/arrays/_mixins.py
index b037f278872a9..d64d6184af6f6 100644
--- a/pandas/core/arrays/_mixins.py
+++ b/pandas/core/arrays/_mixins.py
@@ -4,7 +4,6 @@
from typing import (
TYPE_CHECKING,
Any,
- Iterator,
Literal,
Sequence,
TypeVar,
@@ -696,65 +695,33 @@ def _set_via_chunk_iteration(
"""
Loop through the array chunks and set the new values while
leaving the chunking layout unchanged.
- """
- chunk_indices = self._indices_to_chunk_indices(indices)
- new_data = list(self._data.iterchunks())
-
- for i, c_ind in enumerate(chunk_indices):
- n = len(c_ind)
- if n == 0:
- continue
- c_value, value = value[:n], value[n:]
- new_data[i] = self._replace_with_indices(new_data[i], c_ind, c_value)
-
- return pa.chunked_array(new_data)
-
- def _indices_to_chunk_indices(
- self, indices: npt.NDArray[np.intp]
- ) -> Iterator[npt.NDArray[np.intp]]:
- """
- Convert *sorted* indices for self into a list of ndarrays
- each containing the indices *within* each chunk of the
- underlying ChunkedArray.
Parameters
----------
indices : npt.NDArray[np.intp]
Position indices for the underlying ChunkedArray.
- Returns
- -------
- Generator yielding positional indices for each chunk
+ value : ExtensionDtype.type, Sequence[ExtensionDtype.type], or object
+ value or values to be set of ``key``.
Notes
-----
Assumes that indices is sorted. Caller is responsible for sorting.
"""
- for start, stop in self._chunk_positional_ranges():
+ new_data = []
+ stop = 0
+ for chunk in self._data.iterchunks():
+ start, stop = stop, stop + len(chunk)
if len(indices) == 0 or stop <= indices[0]:
- yield np.array([], dtype=np.intp)
+ new_data.append(chunk)
else:
n = int(np.searchsorted(indices, stop, side="left"))
c_ind = indices[:n] - start
indices = indices[n:]
- yield c_ind
-
- def _chunk_positional_ranges(self) -> tuple[tuple[int, int], ...]:
- """
- Return a tuple of tuples each containing the left (inclusive)
- and right (exclusive) positional bounds of each chunk's values
- within the underlying ChunkedArray.
-
- Returns
- -------
- tuple[tuple]
- """
- ranges = []
- stop = 0
- for c in self._data.iterchunks():
- start, stop = stop, stop + len(c)
- ranges.append((start, stop))
- return tuple(ranges)
+ n = len(c_ind)
+ c_value, value = value[:n], value[n:]
+ new_data.append(self._replace_with_indices(chunk, c_ind, c_value))
+ return pa.chunked_array(new_data)
@classmethod
def _replace_with_indices(
| - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
Turns O(2n) -> O(n) over `self._data.iterchunks()` by combining creating indices + setting values
| https://api.github.com/repos/pandas-dev/pandas/pulls/46448 | 2022-03-20T19:43:17Z | 2022-03-28T19:39:17Z | 2022-03-28T19:39:17Z | 2022-03-28T19:39:21Z |
Fixing typo in docstrings | diff --git a/pandas/_libs/lib.pyx b/pandas/_libs/lib.pyx
index 23faa3e0304a9..bd9453e1fecb6 100644
--- a/pandas/_libs/lib.pyx
+++ b/pandas/_libs/lib.pyx
@@ -60,7 +60,7 @@ cdef extern from "numpy/arrayobject.h":
ctypedef class numpy.dtype [object PyArray_Descr]:
# Use PyDataType_* macros when possible, however there are no macros
- # for accessing some of the fields, so some are defined. Please
+ # for accessing some fields, so some are defined. Please
# ask on cython-dev if you need more.
cdef:
int type_num
@@ -223,7 +223,7 @@ def is_scalar(val: object) -> bool:
cdef inline int64_t get_itemsize(object val):
"""
- Get the itemsize of a NumPy scalar, -1 if not a NumPy scalar.
+ Get the item size of a NumPy scalar, -1 if not a NumPy scalar.
Parameters
----------
diff --git a/pandas/core/dtypes/api.py b/pandas/core/dtypes/api.py
index bb6bfda183802..107c8a5f51773 100644
--- a/pandas/core/dtypes/api.py
+++ b/pandas/core/dtypes/api.py
@@ -20,6 +20,7 @@
is_float,
is_float_dtype,
is_hashable,
+ is_int,
is_int64_dtype,
is_integer,
is_integer_dtype,
| - [ ] closes #xxxx (Replace xxxx with the Github issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46447 | 2022-03-20T19:37:37Z | 2022-03-20T22:27:17Z | null | 2022-03-20T22:27:17Z |
DOC: conveting Index object into a set(#46440) | diff --git a/doc/source/user_guide/indexing.rst b/doc/source/user_guide/indexing.rst
index a94681924d211..f8c93c04c980a 100644
--- a/doc/source/user_guide/indexing.rst
+++ b/doc/source/user_guide/indexing.rst
@@ -1553,9 +1553,7 @@ Index objects
-------------
The pandas :class:`~pandas.Index` class and its subclasses can be viewed as
-implementing an *ordered multiset*. Duplicates are allowed. However, if you try
-to convert an :class:`~pandas.Index` object with duplicate entries into a
-``set``, an exception will be raised.
+implementing an *ordered multiset*. Duplicates are allowed.
:class:`~pandas.Index` also provides the infrastructure necessary for
lookups, data alignment, and reindexing. The easiest way to create an
| - [x] closes #46440 (Replace xxxx with the Github issue number)
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46446 | 2022-03-20T17:29:51Z | 2022-03-21T06:02:25Z | null | 2022-03-21T12:14:14Z |
Fix showing "None" as ylabel in :meth:`Series.plot` when not setting ylabel | diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst
index 8dac952874f89..1fdfd99d3ff53 100644
--- a/doc/source/whatsnew/v1.5.0.rst
+++ b/doc/source/whatsnew/v1.5.0.rst
@@ -109,6 +109,13 @@ did not have the same index as the input.
df.groupby('a', dropna=True).transform(lambda x: x)
df.groupby('a', dropna=True).transform('sum')
+.. _whatsnew_150.notable_bug_fixes.visualization:
+
+Styler
+^^^^^^
+
+- Fix showing "None" as ylabel in :meth:`Series.plot` when not setting ylabel (:issue:`46129`)
+
.. _whatsnew_150.notable_bug_fixes.notable_bug_fix2:
notable_bug_fix2
diff --git a/pandas/plotting/_matplotlib/core.py b/pandas/plotting/_matplotlib/core.py
index 48875114794d9..7559b042ea7e7 100644
--- a/pandas/plotting/_matplotlib/core.py
+++ b/pandas/plotting/_matplotlib/core.py
@@ -458,7 +458,7 @@ def _compute_plot_data(self):
if isinstance(data, ABCSeries):
label = self.label
if label is None and data.name is None:
- label = "None"
+ label = ""
if label is None:
# We'll end up with columns of [0] instead of [None]
data = data.to_frame()
diff --git a/pandas/tests/plotting/frame/test_frame.py b/pandas/tests/plotting/frame/test_frame.py
index 62540a15f47bd..2fab7d0fdbc74 100644
--- a/pandas/tests/plotting/frame/test_frame.py
+++ b/pandas/tests/plotting/frame/test_frame.py
@@ -1189,7 +1189,7 @@ def test_line_label_none(self):
assert ax.get_legend() is None
ax = s.plot(legend=True)
- assert ax.get_legend().get_texts()[0].get_text() == "None"
+ assert ax.get_legend().get_texts()[0].get_text() == ""
@pytest.mark.parametrize(
"props, expected",
diff --git a/pandas/tests/plotting/test_series.py b/pandas/tests/plotting/test_series.py
index d0ed5cb754c40..c49354816b8b0 100644
--- a/pandas/tests/plotting/test_series.py
+++ b/pandas/tests/plotting/test_series.py
@@ -179,7 +179,7 @@ def test_label(self):
self.plt.close()
_, ax = self.plt.subplots()
ax = s.plot(legend=True, ax=ax)
- self._check_legend_labels(ax, labels=["None"])
+ self._check_legend_labels(ax, labels=[""])
self.plt.close()
# get name from index
s.name = "NAME"
| - [ ] closes #46129
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
[here](https://github.com/pandas-dev/pandas/issues/46129#issuecomment-1055224345), Mctoel mentioned that if series.plot(kind=pie) does not specify `ylabel`, "None" will appear as ylabel in the figure.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46445 | 2022-03-20T15:42:05Z | 2022-03-21T23:09:52Z | 2022-03-21T23:09:51Z | 2022-03-21T23:09:55Z |
BUG: ExcelWriter's engine and supported_extensions are properties | diff --git a/pandas/io/excel/_base.py b/pandas/io/excel/_base.py
index 40007a0abe233..030ae9fefda98 100644
--- a/pandas/io/excel/_base.py
+++ b/pandas/io/excel/_base.py
@@ -945,9 +945,9 @@ class ExcelWriter(metaclass=abc.ABCMeta):
# - Mandatory
# - ``write_cells(self, cells, sheet_name=None, startrow=0, startcol=0)``
# --> called to write additional DataFrames to disk
- # - ``supported_extensions`` (tuple of supported extensions), used to
+ # - ``_supported_extensions`` (tuple of supported extensions), used to
# check that engine supports the given extension.
- # - ``engine`` - string that gives the engine name. Necessary to
+ # - ``_engine`` - string that gives the engine name. Necessary to
# instantiate class directly and bypass ``ExcelWriterMeta`` engine
# lookup.
# - ``save(self)`` --> called to save file to disk
@@ -961,6 +961,10 @@ class ExcelWriter(metaclass=abc.ABCMeta):
# You also need to register the class with ``register_writer()``.
# Technically, ExcelWriter implementations don't need to subclass
# ExcelWriter.
+
+ _engine: str
+ _supported_extensions: tuple[str, ...]
+
def __new__(
cls,
path: FilePath | WriteExcelBuffer | ExcelWriter,
@@ -983,7 +987,6 @@ def __new__(
)
# only switch class if generic(ExcelWriter)
-
if cls is ExcelWriter:
if engine is None or (isinstance(engine, str) and engine == "auto"):
if isinstance(path, str):
@@ -1027,16 +1030,14 @@ def __new__(
_path = None
@property
- @abc.abstractmethod
- def supported_extensions(self) -> tuple[str, ...] | list[str]:
+ def supported_extensions(self) -> tuple[str, ...]:
"""Extensions that writer engine supports."""
- pass
+ return self._supported_extensions
@property
- @abc.abstractmethod
def engine(self) -> str:
"""Name of engine."""
- pass
+ return self._engine
@property
@abc.abstractmethod
@@ -1292,12 +1293,7 @@ def check_extension(cls, ext: str) -> Literal[True]:
"""
if ext.startswith("."):
ext = ext[1:]
- # error: "Callable[[ExcelWriter], Any]" has no attribute "__iter__" (not
- # iterable)
- if not any(
- ext in extension
- for extension in cls.supported_extensions # type: ignore[attr-defined]
- ):
+ if not any(ext in extension for extension in cls._supported_extensions):
raise ValueError(f"Invalid extension for engine '{cls.engine}': '{ext}'")
else:
return True
diff --git a/pandas/io/excel/_odswriter.py b/pandas/io/excel/_odswriter.py
index 4f127f7c2f867..f5367df6f228d 100644
--- a/pandas/io/excel/_odswriter.py
+++ b/pandas/io/excel/_odswriter.py
@@ -25,8 +25,8 @@
class ODSWriter(ExcelWriter):
- engine = "odf"
- supported_extensions = (".ods",)
+ _engine = "odf"
+ _supported_extensions = (".ods",)
def __init__(
self,
diff --git a/pandas/io/excel/_openpyxl.py b/pandas/io/excel/_openpyxl.py
index af0ae29a76988..6d70b3f319f37 100644
--- a/pandas/io/excel/_openpyxl.py
+++ b/pandas/io/excel/_openpyxl.py
@@ -36,8 +36,8 @@
class OpenpyxlWriter(ExcelWriter):
- engine = "openpyxl"
- supported_extensions = (".xlsx", ".xlsm")
+ _engine = "openpyxl"
+ _supported_extensions = (".xlsx", ".xlsm")
def __init__(
self,
diff --git a/pandas/io/excel/_util.py b/pandas/io/excel/_util.py
index 6be5ef0f64e16..c315657170a97 100644
--- a/pandas/io/excel/_util.py
+++ b/pandas/io/excel/_util.py
@@ -41,9 +41,7 @@ def register_writer(klass: ExcelWriter_t) -> None:
"""
if not callable(klass):
raise ValueError("Can only register callables as engines")
- engine_name = klass.engine
- # for mypy
- assert isinstance(engine_name, str)
+ engine_name = klass._engine
_writers[engine_name] = klass
diff --git a/pandas/io/excel/_xlsxwriter.py b/pandas/io/excel/_xlsxwriter.py
index f789378720613..302d0281019f5 100644
--- a/pandas/io/excel/_xlsxwriter.py
+++ b/pandas/io/excel/_xlsxwriter.py
@@ -169,8 +169,8 @@ def convert(cls, style_dict, num_format_str=None):
class XlsxWriter(ExcelWriter):
- engine = "xlsxwriter"
- supported_extensions = (".xlsx",)
+ _engine = "xlsxwriter"
+ _supported_extensions = (".xlsx",)
def __init__(
self,
diff --git a/pandas/io/excel/_xlwt.py b/pandas/io/excel/_xlwt.py
index 5f723da61dde4..234d9e72de10d 100644
--- a/pandas/io/excel/_xlwt.py
+++ b/pandas/io/excel/_xlwt.py
@@ -25,8 +25,8 @@
class XlwtWriter(ExcelWriter):
- engine = "xlwt"
- supported_extensions = (".xls",)
+ _engine = "xlwt"
+ _supported_extensions = (".xls",)
def __init__(
self,
diff --git a/pandas/tests/io/excel/test_writers.py b/pandas/tests/io/excel/test_writers.py
index 5b46c0baac165..de855fa30b88d 100644
--- a/pandas/tests/io/excel/test_writers.py
+++ b/pandas/tests/io/excel/test_writers.py
@@ -1313,8 +1313,8 @@ class DummyClass(ExcelWriter):
called_save = False
called_write_cells = False
called_sheets = False
- supported_extensions = ["xlsx", "xls"]
- engine = "dummy"
+ _supported_extensions = ("xlsx", "xls")
+ _engine = "dummy"
def book(self):
pass
| `engine` and `supported_extensions` are declared to be properties in `ExcelWriter` but sub-classes simply assign variables to them. While the differences are subtle, we should either consistently use properties or variables (pyright doesn't like mixing them; mypy doesn't seem to care).
There are more cases where a sub-class does not use a property (found by pyright when enabling reportGeneralTypeIssues):
<details>
```
pandas/core/arrays/datetimes.py:193:20 - error: Expression of type "_scalar_type" cannot be assigned to declared type "property"
pandas/core/arrays/interval.py:200:12 - error: Expression of type "Literal[1]" cannot be assigned to declared type "property"
pandas/core/arrays/period.py:165:20 - error: Expression of type "_scalar_type" cannot be assigned to declared type "property"
pandas/core/arrays/string_.py:94:12 - error: Expression of type "Literal['string']" cannot be assigned to declared type "property"
pandas/core/arrays/string_.py:97:16 - error: Expression of type "NAType" cannot be assigned to declared type "property"
pandas/core/arrays/timedeltas.py:132:20 - error: Expression of type "_scalar_type" cannot be assigned to declared type "property"
pandas/core/dtypes/dtypes.py:176:12 - error: Expression of type "Literal['category']" cannot be assigned to declared type "property"
pandas/core/dtypes/dtypes.py:670:16 - error: Expression of type "NaTType" cannot be assigned to declared type "property"
pandas/core/dtypes/dtypes.py:1045:12 - error: Expression of type "Literal['interval']" cannot be assigned to declared type "property
pandas/core/dtypes/dtypes.py:1394:16 - error: Expression of type "NAType" cannot be assigned to declared type "property"
pandas/core/indexes/numeric.py:372:20 - error: Expression of type "_engine_type" cannot be assigned to declared type "property"
pandas/core/indexes/numeric.py:387:20 - error: Expression of type "_engine_type" cannot be assigned to declared type "property"
pandas/core/indexes/numeric.py:402:20 - error: Expression of type "_engine_type" cannot be assigned to declared type "property"
pandas/core/indexes/range.py:104:20 - error: Expression of type "_engine_type" cannot be assigned to declared type "property"
pandas/core/internals/array_manager.py:706:12 - error: Expression of type "Literal[2]" cannot be assigned to declared type "property
pandas/core/internals/base.py:158:12 - error: Expression of type "Literal[1]" cannot be assigned to declared type "property"
pandas/core/internals/blocks.py:1981:20 - error: Expression of type "Literal[True]" cannot be assigned to declared type "property"
pandas/core/internals/managers.py:1671:23 - error: Expression of type "Literal[True]" cannot be assigned to declared type "property"
pandas/plotting/_matplotlib/core.py:1038:13 - error: Expression of type "Literal['scatter']" cannot be assigned to declared type "property"
pandas/plotting/_matplotlib/core.py:1126:13 - error: Expression of type "Literal['hexbin']" cannot be assigned to declared type "property"
pandas/plotting/_matplotlib/core.py:1156:13 - error: Expression of type "Literal['line']" cannot be assigned to declared type "property"
pandas/plotting/_matplotlib/core.py:1423:13 - error: Expression of type "Literal['bar']" cannot be assigned to declared type "property"
pandas/plotting/_matplotlib/core.py:1606:13 - error: Expression of type "Literal['pie']" cannot be assigned to declared type "property"
pandas/plotting/_matplotlib/hist.py:171:19 - error: Expression of type "Literal['vertical']" cannot be assigned to declared type "property"
``` | https://api.github.com/repos/pandas-dev/pandas/pulls/46444 | 2022-03-20T13:51:27Z | 2022-03-22T21:15:19Z | 2022-03-22T21:15:19Z | 2022-04-01T01:36:32Z |
REGR: RecursionError when attempting to replace np.nan values | diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py
index cba055d5b4345..a375a26fe64ad 100644
--- a/pandas/core/dtypes/cast.py
+++ b/pandas/core/dtypes/cast.py
@@ -1454,7 +1454,7 @@ def _ensure_nanosecond_dtype(dtype: DtypeObj) -> DtypeObj:
# TODO: other value-dependent functions to standardize here include
# dtypes.concat.cast_to_common_type and Index._find_common_type_compat
-def find_result_type(left: ArrayLike, right: Any) -> DtypeObj:
+def find_result_type(left: ArrayLike, right: Any, strict_na: bool = False) -> DtypeObj:
"""
Find the type/dtype for a the result of an operation between these objects.
@@ -1466,6 +1466,7 @@ def find_result_type(left: ArrayLike, right: Any) -> DtypeObj:
----------
left : np.ndarray or ExtensionArray
right : Any
+ strict_na: bool
Returns
-------
@@ -1491,7 +1492,7 @@ def find_result_type(left: ArrayLike, right: Any) -> DtypeObj:
new_dtype = np.result_type(left, right)
- elif is_valid_na_for_dtype(right, left.dtype):
+ elif not strict_na and is_valid_na_for_dtype(right, left.dtype):
# e.g. IntervalDtype[int] and None/np.nan
new_dtype = ensure_dtype_can_hold_na(left.dtype)
diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index 2ff195fa8e5d2..1f44b92b2ec01 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -436,7 +436,7 @@ def split_and_operate(self, func, *args, **kwargs) -> list[Block]:
# Up/Down-casting
@final
- def coerce_to_target_dtype(self, other) -> Block:
+ def coerce_to_target_dtype(self, other, strict_na: bool = False) -> Block:
"""
coerce the current block to a dtype compat for other
we will return a block, possibly object, and not raise
@@ -444,7 +444,7 @@ def coerce_to_target_dtype(self, other) -> Block:
we can also safely try to coerce to the same dtype
and will receive the same block
"""
- new_dtype = find_result_type(self.values, other)
+ new_dtype = find_result_type(self.values, other, strict_na=strict_na)
return self.astype(new_dtype, copy=False)
@@ -601,7 +601,7 @@ def replace(
return blocks
elif self.ndim == 1 or self.shape[0] == 1:
- blk = self.coerce_to_target_dtype(value)
+ blk = self.coerce_to_target_dtype(value, strict_na=True)
return blk.replace(
to_replace=to_replace,
value=value,
diff --git a/pandas/tests/frame/methods/test_replace.py b/pandas/tests/frame/methods/test_replace.py
index 870a64cfa59c9..8879225436586 100644
--- a/pandas/tests/frame/methods/test_replace.py
+++ b/pandas/tests/frame/methods/test_replace.py
@@ -675,6 +675,13 @@ def test_replace_NAT_with_None(self):
expected = DataFrame([None, None])
tm.assert_frame_equal(result, expected)
+ def test_replace_float_nan_with_pd_NA(self):
+ # gh-45725
+ df = DataFrame([np.nan, np.nan], dtype=float)
+ result = df.replace({np.nan: pd.NA})
+ expected = DataFrame([pd.NA, pd.NA], dtype=object)
+ tm.assert_frame_equal(result, expected)
+
def test_replace_value_is_none(self, datetime_frame):
orig_value = datetime_frame.iloc[0, 0]
orig2 = datetime_frame.iloc[1, 0]
| - [ ] closes #45725 (Replace xxxx with the Github issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
`self._can_hold_element(value)` for pd.NA on a float block is `False` so we need to ensure the block is coerced to a dtype with can hold the value to prevent the RecursionError
The `None` case in the issue OP was already fixed by #46404 which short circuits for the list like case and does not split the block for backwards compatibility
The test added here is NOT parametrized for various null values since this is a targeted regression fix of the recursion error and to maintain the pre-regression behavior and NOT looking to make null value handling consistent for replace.
This targeted regression fix is milestoned 1.5 as the regression that resulted in the RecursionError is only on main. | https://api.github.com/repos/pandas-dev/pandas/pulls/46443 | 2022-03-20T13:46:33Z | 2022-07-26T18:26:55Z | null | 2022-07-26T18:26:55Z |
Backport PR #46404 on branch 1.4.x (REGR: DataFrame.replace when the replacement value was explicitly None) | diff --git a/doc/source/whatsnew/v1.4.2.rst b/doc/source/whatsnew/v1.4.2.rst
index 3523f691cae8f..4cbb8118055af 100644
--- a/doc/source/whatsnew/v1.4.2.rst
+++ b/doc/source/whatsnew/v1.4.2.rst
@@ -19,6 +19,7 @@ Fixed regressions
- Fixed memory performance regression in :meth:`Series.fillna` when called on a :class:`DataFrame` column with ``inplace=True`` (:issue:`46149`)
- Provided an alternative solution for passing custom Excel formats in :meth:`.Styler.to_excel`, which was a regression based on stricter CSS validation. Examples available in the documentation for :meth:`.Styler.format` (:issue:`46152`)
- Fixed regression in :meth:`DataFrame.replace` when a replacement value was also a target for replacement (:issue:`46306`)
+- Fixed regression in :meth:`DataFrame.replace` when the replacement value was explicitly ``None`` when passed in a dictionary to ``to_replace`` (:issue:`45601`, :issue:`45836`)
- Fixed regression when setting values with :meth:`DataFrame.loc` losing :class:`MultiIndex` names if :class:`DataFrame` was empty before (:issue:`46317`)
-
diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index e0df274fbc596..8a09e4ff2d5b7 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -874,6 +874,13 @@ def _replace_coerce(
mask=mask,
)
else:
+ if value is None:
+ # gh-45601, gh-45836
+ nb = self.astype(np.dtype(object), copy=False)
+ if nb is self and not inplace:
+ nb = nb.copy()
+ putmask_inplace(nb.values, mask, value)
+ return [nb]
return self.replace(
to_replace=to_replace, value=value, inplace=inplace, mask=mask
)
diff --git a/pandas/tests/frame/methods/test_replace.py b/pandas/tests/frame/methods/test_replace.py
index 4393dbacc6fcf..b84af7b0e0b52 100644
--- a/pandas/tests/frame/methods/test_replace.py
+++ b/pandas/tests/frame/methods/test_replace.py
@@ -661,6 +661,20 @@ def test_replace_simple_nested_dict_with_nonexistent_value(self):
result = df.replace({"col": {-1: "-", 1: "a", 4: "b"}})
tm.assert_frame_equal(expected, result)
+ def test_replace_NA_with_None(self):
+ # gh-45601
+ df = DataFrame({"value": [42, None]}).astype({"value": "Int64"})
+ result = df.replace({pd.NA: None})
+ expected = DataFrame({"value": [42, None]}, dtype=object)
+ tm.assert_frame_equal(result, expected)
+
+ def test_replace_NAT_with_None(self):
+ # gh-45836
+ df = DataFrame([pd.NaT, pd.NaT])
+ result = df.replace({pd.NaT: None, np.NaN: None})
+ expected = DataFrame([None, None])
+ tm.assert_frame_equal(result, expected)
+
def test_replace_value_is_none(self, datetime_frame):
orig_value = datetime_frame.iloc[0, 0]
orig2 = datetime_frame.iloc[1, 0]
| Backport PR #46404: REGR: DataFrame.replace when the replacement value was explicitly None | https://api.github.com/repos/pandas-dev/pandas/pulls/46441 | 2022-03-20T11:10:08Z | 2022-03-20T16:19:04Z | 2022-03-20T16:19:04Z | 2022-03-20T16:19:04Z |
REF: move ArrowStringArray.__setitem__ and related methods to ArrowExtensionArray | diff --git a/pandas/core/arrays/_mixins.py b/pandas/core/arrays/_mixins.py
index b1537fbf2767d..b037f278872a9 100644
--- a/pandas/core/arrays/_mixins.py
+++ b/pandas/core/arrays/_mixins.py
@@ -4,6 +4,7 @@
from typing import (
TYPE_CHECKING,
Any,
+ Iterator,
Literal,
Sequence,
TypeVar,
@@ -28,7 +29,11 @@
npt,
type_t,
)
-from pandas.compat import pa_version_under2p0
+from pandas.compat import (
+ pa_version_under1p01,
+ pa_version_under2p0,
+ pa_version_under5p0,
+)
from pandas.errors import AbstractMethodError
from pandas.util._decorators import doc
from pandas.util._validators import (
@@ -38,7 +43,10 @@
)
from pandas.core.dtypes.common import (
+ is_bool_dtype,
is_dtype_equal,
+ is_integer,
+ is_scalar,
pandas_dtype,
)
from pandas.core.dtypes.dtypes import (
@@ -46,7 +54,10 @@
ExtensionDtype,
PeriodDtype,
)
-from pandas.core.dtypes.missing import array_equivalent
+from pandas.core.dtypes.missing import (
+ array_equivalent,
+ isna,
+)
from pandas.core import missing
from pandas.core.algorithms import (
@@ -65,10 +76,11 @@
"NDArrayBackedExtensionArrayT", bound="NDArrayBackedExtensionArray"
)
-if TYPE_CHECKING:
-
+if not pa_version_under1p01:
import pyarrow as pa
+ import pyarrow.compute as pc
+if TYPE_CHECKING:
from pandas._typing import (
NumpySorter,
NumpyValueArrayLike,
@@ -607,3 +619,195 @@ def _concat_same_type(
chunks = [array for ea in to_concat for array in ea._data.iterchunks()]
arr = pa.chunked_array(chunks)
return cls(arr)
+
+ def __setitem__(self, key: int | slice | np.ndarray, value: Any) -> None:
+ """Set one or more values inplace.
+
+ Parameters
+ ----------
+ key : int, ndarray, or slice
+ When called from, e.g. ``Series.__setitem__``, ``key`` will be
+ one of
+
+ * scalar int
+ * ndarray of integers.
+ * boolean ndarray
+ * slice object
+
+ value : ExtensionDtype.type, Sequence[ExtensionDtype.type], or object
+ value or values to be set of ``key``.
+
+ Returns
+ -------
+ None
+ """
+ key = check_array_indexer(self, key)
+ indices = self._indexing_key_to_indices(key)
+ value = self._maybe_convert_setitem_value(value)
+
+ argsort = np.argsort(indices)
+ indices = indices[argsort]
+
+ if is_scalar(value):
+ value = np.broadcast_to(value, len(self))
+ elif len(indices) != len(value):
+ raise ValueError("Length of indexer and values mismatch")
+ else:
+ value = np.asarray(value)[argsort]
+
+ self._data = self._set_via_chunk_iteration(indices=indices, value=value)
+
+ def _indexing_key_to_indices(
+ self, key: int | slice | np.ndarray
+ ) -> npt.NDArray[np.intp]:
+ """
+ Convert indexing key for self into positional indices.
+
+ Parameters
+ ----------
+ key : int | slice | np.ndarray
+
+ Returns
+ -------
+ npt.NDArray[np.intp]
+ """
+ n = len(self)
+ if isinstance(key, slice):
+ indices = np.arange(n)[key]
+ elif is_integer(key):
+ indices = np.arange(n)[[key]] # type: ignore[index]
+ elif is_bool_dtype(key):
+ key = np.asarray(key)
+ if len(key) != n:
+ raise ValueError("Length of indexer and values mismatch")
+ indices = key.nonzero()[0]
+ else:
+ key = np.asarray(key)
+ indices = np.arange(n)[key]
+ return indices
+
+ def _maybe_convert_setitem_value(self, value):
+ """Maybe convert value to be pyarrow compatible."""
+ raise NotImplementedError()
+
+ def _set_via_chunk_iteration(
+ self, indices: npt.NDArray[np.intp], value: npt.NDArray[Any]
+ ) -> pa.ChunkedArray:
+ """
+ Loop through the array chunks and set the new values while
+ leaving the chunking layout unchanged.
+ """
+ chunk_indices = self._indices_to_chunk_indices(indices)
+ new_data = list(self._data.iterchunks())
+
+ for i, c_ind in enumerate(chunk_indices):
+ n = len(c_ind)
+ if n == 0:
+ continue
+ c_value, value = value[:n], value[n:]
+ new_data[i] = self._replace_with_indices(new_data[i], c_ind, c_value)
+
+ return pa.chunked_array(new_data)
+
+ def _indices_to_chunk_indices(
+ self, indices: npt.NDArray[np.intp]
+ ) -> Iterator[npt.NDArray[np.intp]]:
+ """
+ Convert *sorted* indices for self into a list of ndarrays
+ each containing the indices *within* each chunk of the
+ underlying ChunkedArray.
+
+ Parameters
+ ----------
+ indices : npt.NDArray[np.intp]
+ Position indices for the underlying ChunkedArray.
+
+ Returns
+ -------
+ Generator yielding positional indices for each chunk
+
+ Notes
+ -----
+ Assumes that indices is sorted. Caller is responsible for sorting.
+ """
+ for start, stop in self._chunk_positional_ranges():
+ if len(indices) == 0 or stop <= indices[0]:
+ yield np.array([], dtype=np.intp)
+ else:
+ n = int(np.searchsorted(indices, stop, side="left"))
+ c_ind = indices[:n] - start
+ indices = indices[n:]
+ yield c_ind
+
+ def _chunk_positional_ranges(self) -> tuple[tuple[int, int], ...]:
+ """
+ Return a tuple of tuples each containing the left (inclusive)
+ and right (exclusive) positional bounds of each chunk's values
+ within the underlying ChunkedArray.
+
+ Returns
+ -------
+ tuple[tuple]
+ """
+ ranges = []
+ stop = 0
+ for c in self._data.iterchunks():
+ start, stop = stop, stop + len(c)
+ ranges.append((start, stop))
+ return tuple(ranges)
+
+ @classmethod
+ def _replace_with_indices(
+ cls,
+ chunk: pa.Array,
+ indices: npt.NDArray[np.intp],
+ value: npt.NDArray[Any],
+ ) -> pa.Array:
+ """
+ Replace items selected with a set of positional indices.
+
+ Analogous to pyarrow.compute.replace_with_mask, except that replacement
+ positions are identified via indices rather than a mask.
+
+ Parameters
+ ----------
+ chunk : pa.Array
+ indices : npt.NDArray[np.intp]
+ value : npt.NDArray[Any]
+ Replacement value(s).
+
+ Returns
+ -------
+ pa.Array
+ """
+ n = len(indices)
+
+ if n == 0:
+ return chunk
+
+ start, stop = indices[[0, -1]]
+
+ if (stop - start) == (n - 1):
+ # fast path for a contiguous set of indices
+ arrays = [
+ chunk[:start],
+ pa.array(value, type=chunk.type),
+ chunk[stop + 1 :],
+ ]
+ arrays = [arr for arr in arrays if len(arr)]
+ if len(arrays) == 1:
+ return arrays[0]
+ return pa.concat_arrays(arrays)
+
+ mask = np.zeros(len(chunk), dtype=np.bool_)
+ mask[indices] = True
+
+ if pa_version_under5p0:
+ arr = chunk.to_numpy(zero_copy_only=False)
+ arr[mask] = value
+ return pa.array(arr, type=chunk.type)
+
+ if isna(value).all():
+ return pc.if_else(mask, None, chunk)
+
+ return pc.replace_with_mask(chunk, mask, value)
diff --git a/pandas/core/arrays/string_arrow.py b/pandas/core/arrays/string_arrow.py
index 4af4b501fd5b0..ac5bfe32ed5f6 100644
--- a/pandas/core/arrays/string_arrow.py
+++ b/pandas/core/arrays/string_arrow.py
@@ -30,7 +30,6 @@
pa_version_under2p0,
pa_version_under3p0,
pa_version_under4p0,
- pa_version_under5p0,
)
from pandas.util._decorators import doc
@@ -343,147 +342,20 @@ def insert(self, loc: int, item):
raise TypeError("Scalar must be NA or str")
return super().insert(loc, item)
- def __setitem__(self, key: int | slice | np.ndarray, value: Any) -> None:
- """Set one or more values inplace.
-
- Parameters
- ----------
- key : int, ndarray, or slice
- When called from, e.g. ``Series.__setitem__``, ``key`` will be
- one of
-
- * scalar int
- * ndarray of integers.
- * boolean ndarray
- * slice object
-
- value : ExtensionDtype.type, Sequence[ExtensionDtype.type], or object
- value or values to be set of ``key``.
-
- Returns
- -------
- None
- """
- key = check_array_indexer(self, key)
- indices = self._key_to_indices(key)
-
+ def _maybe_convert_setitem_value(self, value):
+ """Maybe convert value to be pyarrow compatible."""
if is_scalar(value):
if isna(value):
value = None
elif not isinstance(value, str):
raise ValueError("Scalar must be NA or str")
- value = np.broadcast_to(value, len(indices))
else:
value = np.array(value, dtype=object, copy=True)
- for i, v in enumerate(value):
- if isna(v):
- value[i] = None
- elif not isinstance(v, str):
+ value[isna(value)] = None
+ for v in value:
+ if not (v is None or isinstance(v, str)):
raise ValueError("Scalar must be NA or str")
-
- if len(indices) != len(value):
- raise ValueError("Length of indexer and values mismatch")
-
- argsort = np.argsort(indices)
- indices = indices[argsort]
- value = value[argsort]
-
- self._data = self._set_via_chunk_iteration(indices=indices, value=value)
-
- def _key_to_indices(self, key: int | slice | np.ndarray) -> npt.NDArray[np.intp]:
- """Convert indexing key for self to positional indices."""
- if isinstance(key, slice):
- indices = np.arange(len(self))[key]
- elif is_bool_dtype(key):
- key = np.asarray(key)
- if len(key) != len(self):
- raise ValueError("Length of indexer and values mismatch")
- indices = key.nonzero()[0]
- else:
- key_arr = np.array([key]) if is_integer(key) else np.asarray(key)
- indices = np.arange(len(self))[key_arr]
- return indices
-
- def _set_via_chunk_iteration(
- self, indices: npt.NDArray[np.intp], value: npt.NDArray[Any]
- ) -> pa.ChunkedArray:
- """
- Loop through the array chunks and set the new values while
- leaving the chunking layout unchanged.
- """
-
- chunk_indices = self._within_chunk_indices(indices)
- new_data = []
-
- for i, chunk in enumerate(self._data.iterchunks()):
-
- c_ind = chunk_indices[i]
- n = len(c_ind)
- c_value, value = value[:n], value[n:]
-
- if n == 1:
- # fast path
- chunk = self._set_single_index_in_chunk(chunk, c_ind[0], c_value[0])
- elif n > 0:
- mask = np.zeros(len(chunk), dtype=np.bool_)
- mask[c_ind] = True
- if not pa_version_under5p0:
- if c_value is None or isna(np.array(c_value)).all():
- chunk = pc.if_else(mask, None, chunk)
- else:
- chunk = pc.replace_with_mask(chunk, mask, c_value)
- else:
- # The pyarrow compute functions were added in
- # version 5.0. For prior versions we implement
- # our own by converting to numpy and back.
- chunk = chunk.to_numpy(zero_copy_only=False)
- chunk[mask] = c_value
- chunk = pa.array(chunk, type=pa.string())
-
- new_data.append(chunk)
-
- return pa.chunked_array(new_data)
-
- @staticmethod
- def _set_single_index_in_chunk(chunk: pa.Array, index: int, value: Any) -> pa.Array:
- """Set a single position in a pyarrow array."""
- assert is_scalar(value)
- return pa.concat_arrays(
- [
- chunk[:index],
- pa.array([value], type=pa.string()),
- chunk[index + 1 :],
- ]
- )
-
- def _within_chunk_indices(
- self, indices: npt.NDArray[np.intp]
- ) -> list[npt.NDArray[np.intp]]:
- """
- Convert indices for self into a list of ndarrays each containing
- the indices *within* each chunk of the chunked array.
- """
- # indices must be sorted
- chunk_indices = []
- for start, stop in self._chunk_ranges():
- if len(indices) == 0 or indices[0] >= stop:
- c_ind = np.array([], dtype=np.intp)
- else:
- n = int(np.searchsorted(indices, stop, side="left"))
- c_ind = indices[:n] - start
- indices = indices[n:]
- chunk_indices.append(c_ind)
- return chunk_indices
-
- def _chunk_ranges(self) -> list[tuple]:
- """
- Return a list of tuples each containing the left (inclusive)
- and right (exclusive) bounds of each chunk.
- """
- lengths = [len(c) for c in self._data.iterchunks()]
- stops = np.cumsum(lengths)
- starts = np.concatenate([[0], stops[:-1]])
- return list(zip(starts, stops))
+ return value
def take(
self,
| - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
Moved/refactored `ArrowStringArray.__setitem__` and related methods to `ArrowExtensionArray`. These methods should be generic enough to support any pyarrow type. I left `_maybe_convert_setitem_value` to be implemented by the subclasses as I figured there may need to be type specific logic there. | https://api.github.com/repos/pandas-dev/pandas/pulls/46439 | 2022-03-20T04:02:44Z | 2022-03-20T16:14:30Z | 2022-03-20T16:14:29Z | 2022-03-20T23:18:48Z |
BLD: Remove windows shim | diff --git a/pandas/_libs/src/headers/ms_inttypes.h b/pandas/_libs/src/headers/ms_inttypes.h
deleted file mode 100644
index 1be380337b07f..0000000000000
--- a/pandas/_libs/src/headers/ms_inttypes.h
+++ /dev/null
@@ -1,305 +0,0 @@
-// ISO C9x compliant inttypes.h for Microsoft Visual Studio
-// Based on ISO/IEC 9899:TC2 Committee draft (May 6, 2005) WG14/N1124
-//
-// Copyright (c) 2006 Alexander Chemeris
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// 1. Redistributions of source code must retain the above copyright notice,
-// this list of conditions and the following disclaimer.
-//
-// 2. Redistributions in binary form must reproduce the above copyright
-// notice, this list of conditions and the following disclaimer in the
-// documentation and/or other materials provided with the distribution.
-//
-// 3. The name of the author may be used to endorse or promote products
-// derived from this software without specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR IMPLIED
-// WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
-// MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO
-// EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
-// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
-// OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
-// WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
-// OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
-// ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-//
-///////////////////////////////////////////////////////////////////////////////
-
-#ifndef _MSC_VER // [
-#error "Use this header only with Microsoft Visual C++ compilers!"
-#endif // _MSC_VER ]
-
-#ifndef _MSC_INTTYPES_H_ // [
-#define _MSC_INTTYPES_H_
-
-#if _MSC_VER > 1000
-#pragma once
-#endif
-
-#include "ms_stdint.h"
-
-// 7.8 Format conversion of integer types
-
-typedef struct {
- intmax_t quot;
- intmax_t rem;
-} imaxdiv_t;
-
-// 7.8.1 Macros for format specifiers
-
-#if !defined(__cplusplus) || defined(__STDC_FORMAT_MACROS) // [ See footnote 185 at page 198
-
-// The fprintf macros for signed integers are:
-#define PRId8 "d"
-#define PRIi8 "i"
-#define PRIdLEAST8 "d"
-#define PRIiLEAST8 "i"
-#define PRIdFAST8 "d"
-#define PRIiFAST8 "i"
-
-#define PRId16 "hd"
-#define PRIi16 "hi"
-#define PRIdLEAST16 "hd"
-#define PRIiLEAST16 "hi"
-#define PRIdFAST16 "hd"
-#define PRIiFAST16 "hi"
-
-#define PRId32 "I32d"
-#define PRIi32 "I32i"
-#define PRIdLEAST32 "I32d"
-#define PRIiLEAST32 "I32i"
-#define PRIdFAST32 "I32d"
-#define PRIiFAST32 "I32i"
-
-#define PRId64 "I64d"
-#define PRIi64 "I64i"
-#define PRIdLEAST64 "I64d"
-#define PRIiLEAST64 "I64i"
-#define PRIdFAST64 "I64d"
-#define PRIiFAST64 "I64i"
-
-#define PRIdMAX "I64d"
-#define PRIiMAX "I64i"
-
-#define PRIdPTR "Id"
-#define PRIiPTR "Ii"
-
-// The fprintf macros for unsigned integers are:
-#define PRIo8 "o"
-#define PRIu8 "u"
-#define PRIx8 "x"
-#define PRIX8 "X"
-#define PRIoLEAST8 "o"
-#define PRIuLEAST8 "u"
-#define PRIxLEAST8 "x"
-#define PRIXLEAST8 "X"
-#define PRIoFAST8 "o"
-#define PRIuFAST8 "u"
-#define PRIxFAST8 "x"
-#define PRIXFAST8 "X"
-
-#define PRIo16 "ho"
-#define PRIu16 "hu"
-#define PRIx16 "hx"
-#define PRIX16 "hX"
-#define PRIoLEAST16 "ho"
-#define PRIuLEAST16 "hu"
-#define PRIxLEAST16 "hx"
-#define PRIXLEAST16 "hX"
-#define PRIoFAST16 "ho"
-#define PRIuFAST16 "hu"
-#define PRIxFAST16 "hx"
-#define PRIXFAST16 "hX"
-
-#define PRIo32 "I32o"
-#define PRIu32 "I32u"
-#define PRIx32 "I32x"
-#define PRIX32 "I32X"
-#define PRIoLEAST32 "I32o"
-#define PRIuLEAST32 "I32u"
-#define PRIxLEAST32 "I32x"
-#define PRIXLEAST32 "I32X"
-#define PRIoFAST32 "I32o"
-#define PRIuFAST32 "I32u"
-#define PRIxFAST32 "I32x"
-#define PRIXFAST32 "I32X"
-
-#define PRIo64 "I64o"
-#define PRIu64 "I64u"
-#define PRIx64 "I64x"
-#define PRIX64 "I64X"
-#define PRIoLEAST64 "I64o"
-#define PRIuLEAST64 "I64u"
-#define PRIxLEAST64 "I64x"
-#define PRIXLEAST64 "I64X"
-#define PRIoFAST64 "I64o"
-#define PRIuFAST64 "I64u"
-#define PRIxFAST64 "I64x"
-#define PRIXFAST64 "I64X"
-
-#define PRIoMAX "I64o"
-#define PRIuMAX "I64u"
-#define PRIxMAX "I64x"
-#define PRIXMAX "I64X"
-
-#define PRIoPTR "Io"
-#define PRIuPTR "Iu"
-#define PRIxPTR "Ix"
-#define PRIXPTR "IX"
-
-// The fscanf macros for signed integers are:
-#define SCNd8 "d"
-#define SCNi8 "i"
-#define SCNdLEAST8 "d"
-#define SCNiLEAST8 "i"
-#define SCNdFAST8 "d"
-#define SCNiFAST8 "i"
-
-#define SCNd16 "hd"
-#define SCNi16 "hi"
-#define SCNdLEAST16 "hd"
-#define SCNiLEAST16 "hi"
-#define SCNdFAST16 "hd"
-#define SCNiFAST16 "hi"
-
-#define SCNd32 "ld"
-#define SCNi32 "li"
-#define SCNdLEAST32 "ld"
-#define SCNiLEAST32 "li"
-#define SCNdFAST32 "ld"
-#define SCNiFAST32 "li"
-
-#define SCNd64 "I64d"
-#define SCNi64 "I64i"
-#define SCNdLEAST64 "I64d"
-#define SCNiLEAST64 "I64i"
-#define SCNdFAST64 "I64d"
-#define SCNiFAST64 "I64i"
-
-#define SCNdMAX "I64d"
-#define SCNiMAX "I64i"
-
-#ifdef _WIN64 // [
-# define SCNdPTR "I64d"
-# define SCNiPTR "I64i"
-#else // _WIN64 ][
-# define SCNdPTR "ld"
-# define SCNiPTR "li"
-#endif // _WIN64 ]
-
-// The fscanf macros for unsigned integers are:
-#define SCNo8 "o"
-#define SCNu8 "u"
-#define SCNx8 "x"
-#define SCNX8 "X"
-#define SCNoLEAST8 "o"
-#define SCNuLEAST8 "u"
-#define SCNxLEAST8 "x"
-#define SCNXLEAST8 "X"
-#define SCNoFAST8 "o"
-#define SCNuFAST8 "u"
-#define SCNxFAST8 "x"
-#define SCNXFAST8 "X"
-
-#define SCNo16 "ho"
-#define SCNu16 "hu"
-#define SCNx16 "hx"
-#define SCNX16 "hX"
-#define SCNoLEAST16 "ho"
-#define SCNuLEAST16 "hu"
-#define SCNxLEAST16 "hx"
-#define SCNXLEAST16 "hX"
-#define SCNoFAST16 "ho"
-#define SCNuFAST16 "hu"
-#define SCNxFAST16 "hx"
-#define SCNXFAST16 "hX"
-
-#define SCNo32 "lo"
-#define SCNu32 "lu"
-#define SCNx32 "lx"
-#define SCNX32 "lX"
-#define SCNoLEAST32 "lo"
-#define SCNuLEAST32 "lu"
-#define SCNxLEAST32 "lx"
-#define SCNXLEAST32 "lX"
-#define SCNoFAST32 "lo"
-#define SCNuFAST32 "lu"
-#define SCNxFAST32 "lx"
-#define SCNXFAST32 "lX"
-
-#define SCNo64 "I64o"
-#define SCNu64 "I64u"
-#define SCNx64 "I64x"
-#define SCNX64 "I64X"
-#define SCNoLEAST64 "I64o"
-#define SCNuLEAST64 "I64u"
-#define SCNxLEAST64 "I64x"
-#define SCNXLEAST64 "I64X"
-#define SCNoFAST64 "I64o"
-#define SCNuFAST64 "I64u"
-#define SCNxFAST64 "I64x"
-#define SCNXFAST64 "I64X"
-
-#define SCNoMAX "I64o"
-#define SCNuMAX "I64u"
-#define SCNxMAX "I64x"
-#define SCNXMAX "I64X"
-
-#ifdef _WIN64 // [
-# define SCNoPTR "I64o"
-# define SCNuPTR "I64u"
-# define SCNxPTR "I64x"
-# define SCNXPTR "I64X"
-#else // _WIN64 ][
-# define SCNoPTR "lo"
-# define SCNuPTR "lu"
-# define SCNxPTR "lx"
-# define SCNXPTR "lX"
-#endif // _WIN64 ]
-
-#endif // __STDC_FORMAT_MACROS ]
-
-// 7.8.2 Functions for greatest-width integer types
-
-// 7.8.2.1 The imaxabs function
-#define imaxabs _abs64
-
-// 7.8.2.2 The imaxdiv function
-
-// This is modified version of div() function from Microsoft's div.c found
-// in %MSVC.NET%\crt\src\div.c
-#ifdef STATIC_IMAXDIV // [
-static
-#else // STATIC_IMAXDIV ][
-_inline
-#endif // STATIC_IMAXDIV ]
-imaxdiv_t __cdecl imaxdiv(intmax_t numer, intmax_t denom)
-{
- imaxdiv_t result;
-
- result.quot = numer / denom;
- result.rem = numer % denom;
-
- if (numer < 0 && result.rem > 0) {
- // did division wrong; must fix up
- ++result.quot;
- result.rem -= denom;
- }
-
- return result;
-}
-
-// 7.8.2.3 The strtoimax and strtoumax functions
-#define strtoimax _strtoi64
-#define strtoumax _strtoui64
-
-// 7.8.2.4 The wcstoimax and wcstoumax functions
-#define wcstoimax _wcstoi64
-#define wcstoumax _wcstoui64
-
-
-#endif // _MSC_INTTYPES_H_ ]
diff --git a/pandas/_libs/src/headers/ms_stdint.h b/pandas/_libs/src/headers/ms_stdint.h
deleted file mode 100644
index c66fbb817c0d1..0000000000000
--- a/pandas/_libs/src/headers/ms_stdint.h
+++ /dev/null
@@ -1,247 +0,0 @@
-// ISO C9x compliant stdint.h for Microsoft Visual Studio
-// Based on ISO/IEC 9899:TC2 Committee draft (May 6, 2005) WG14/N1124
-//
-// Copyright (c) 2006-2008 Alexander Chemeris
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// 1. Redistributions of source code must retain the above copyright notice,
-// this list of conditions and the following disclaimer.
-//
-// 2. Redistributions in binary form must reproduce the above copyright
-// notice, this list of conditions and the following disclaimer in the
-// documentation and/or other materials provided with the distribution.
-//
-// 3. The name of the author may be used to endorse or promote products
-// derived from this software without specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR IMPLIED
-// WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
-// MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO
-// EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
-// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
-// OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
-// WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
-// OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
-// ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-//
-///////////////////////////////////////////////////////////////////////////////
-
-#ifndef _MSC_VER // [
-#error "Use this header only with Microsoft Visual C++ compilers!"
-#endif // _MSC_VER ]
-
-#ifndef _MSC_STDINT_H_ // [
-#define _MSC_STDINT_H_
-
-#if _MSC_VER > 1000
-#pragma once
-#endif
-
-#include <limits.h>
-
-// For Visual Studio 6 in C++ mode and for many Visual Studio versions when
-// compiling for ARM we should wrap <wchar.h> include with 'extern "C++" {}'
-// or compiler give many errors like this:
-// error C2733: second C linkage of overloaded function 'wmemchr' not allowed
-#ifdef __cplusplus
-extern "C" {
-#endif
-# include <wchar.h>
-#ifdef __cplusplus
-}
-#endif
-
-// Define _W64 macros to mark types changing their size, like intptr_t.
-#ifndef _W64
-# if !defined(__midl) && (defined(_X86_) || defined(_M_IX86)) && _MSC_VER >= 1300
-# define _W64 __w64
-# else
-# define _W64
-# endif
-#endif
-
-
-// 7.18.1 Integer types
-
-// 7.18.1.1 Exact-width integer types
-
-// Visual Studio 6 and Embedded Visual C++ 4 doesn't
-// realize that, e.g. char has the same size as __int8
-// so we give up on __intX for them.
-#if (_MSC_VER < 1300)
- typedef signed char int8_t;
- typedef signed short int16_t;
- typedef signed int int32_t;
- typedef unsigned char uint8_t;
- typedef unsigned short uint16_t;
- typedef unsigned int uint32_t;
-#else
- typedef signed __int8 int8_t;
- typedef signed __int16 int16_t;
- typedef signed __int32 int32_t;
- typedef unsigned __int8 uint8_t;
- typedef unsigned __int16 uint16_t;
- typedef unsigned __int32 uint32_t;
-#endif
-typedef signed __int64 int64_t;
-typedef unsigned __int64 uint64_t;
-
-
-// 7.18.1.2 Minimum-width integer types
-typedef int8_t int_least8_t;
-typedef int16_t int_least16_t;
-typedef int32_t int_least32_t;
-typedef int64_t int_least64_t;
-typedef uint8_t uint_least8_t;
-typedef uint16_t uint_least16_t;
-typedef uint32_t uint_least32_t;
-typedef uint64_t uint_least64_t;
-
-// 7.18.1.3 Fastest minimum-width integer types
-typedef int8_t int_fast8_t;
-typedef int16_t int_fast16_t;
-typedef int32_t int_fast32_t;
-typedef int64_t int_fast64_t;
-typedef uint8_t uint_fast8_t;
-typedef uint16_t uint_fast16_t;
-typedef uint32_t uint_fast32_t;
-typedef uint64_t uint_fast64_t;
-
-// 7.18.1.4 Integer types capable of holding object pointers
-#ifdef _WIN64 // [
- typedef signed __int64 intptr_t;
- typedef unsigned __int64 uintptr_t;
-#else // _WIN64 ][
- typedef _W64 signed int intptr_t;
- typedef _W64 unsigned int uintptr_t;
-#endif // _WIN64 ]
-
-// 7.18.1.5 Greatest-width integer types
-typedef int64_t intmax_t;
-typedef uint64_t uintmax_t;
-
-
-// 7.18.2 Limits of specified-width integer types
-
-#if !defined(__cplusplus) || defined(__STDC_LIMIT_MACROS) // [ See footnote 220 at page 257 and footnote 221 at page 259
-
-// 7.18.2.1 Limits of exact-width integer types
-#define INT8_MIN ((int8_t)_I8_MIN)
-#define INT8_MAX _I8_MAX
-#define INT16_MIN ((int16_t)_I16_MIN)
-#define INT16_MAX _I16_MAX
-#define INT32_MIN ((int32_t)_I32_MIN)
-#define INT32_MAX _I32_MAX
-#define INT64_MIN ((int64_t)_I64_MIN)
-#define INT64_MAX _I64_MAX
-#define UINT8_MAX _UI8_MAX
-#define UINT16_MAX _UI16_MAX
-#define UINT32_MAX _UI32_MAX
-#define UINT64_MAX _UI64_MAX
-
-// 7.18.2.2 Limits of minimum-width integer types
-#define INT_LEAST8_MIN INT8_MIN
-#define INT_LEAST8_MAX INT8_MAX
-#define INT_LEAST16_MIN INT16_MIN
-#define INT_LEAST16_MAX INT16_MAX
-#define INT_LEAST32_MIN INT32_MIN
-#define INT_LEAST32_MAX INT32_MAX
-#define INT_LEAST64_MIN INT64_MIN
-#define INT_LEAST64_MAX INT64_MAX
-#define UINT_LEAST8_MAX UINT8_MAX
-#define UINT_LEAST16_MAX UINT16_MAX
-#define UINT_LEAST32_MAX UINT32_MAX
-#define UINT_LEAST64_MAX UINT64_MAX
-
-// 7.18.2.3 Limits of fastest minimum-width integer types
-#define INT_FAST8_MIN INT8_MIN
-#define INT_FAST8_MAX INT8_MAX
-#define INT_FAST16_MIN INT16_MIN
-#define INT_FAST16_MAX INT16_MAX
-#define INT_FAST32_MIN INT32_MIN
-#define INT_FAST32_MAX INT32_MAX
-#define INT_FAST64_MIN INT64_MIN
-#define INT_FAST64_MAX INT64_MAX
-#define UINT_FAST8_MAX UINT8_MAX
-#define UINT_FAST16_MAX UINT16_MAX
-#define UINT_FAST32_MAX UINT32_MAX
-#define UINT_FAST64_MAX UINT64_MAX
-
-// 7.18.2.4 Limits of integer types capable of holding object pointers
-#ifdef _WIN64 // [
-# define INTPTR_MIN INT64_MIN
-# define INTPTR_MAX INT64_MAX
-# define UINTPTR_MAX UINT64_MAX
-#else // _WIN64 ][
-# define INTPTR_MIN INT32_MIN
-# define INTPTR_MAX INT32_MAX
-# define UINTPTR_MAX UINT32_MAX
-#endif // _WIN64 ]
-
-// 7.18.2.5 Limits of greatest-width integer types
-#define INTMAX_MIN INT64_MIN
-#define INTMAX_MAX INT64_MAX
-#define UINTMAX_MAX UINT64_MAX
-
-// 7.18.3 Limits of other integer types
-
-#ifdef _WIN64 // [
-# define PTRDIFF_MIN _I64_MIN
-# define PTRDIFF_MAX _I64_MAX
-#else // _WIN64 ][
-# define PTRDIFF_MIN _I32_MIN
-# define PTRDIFF_MAX _I32_MAX
-#endif // _WIN64 ]
-
-#define SIG_ATOMIC_MIN INT_MIN
-#define SIG_ATOMIC_MAX INT_MAX
-
-#ifndef SIZE_MAX // [
-# ifdef _WIN64 // [
-# define SIZE_MAX _UI64_MAX
-# else // _WIN64 ][
-# define SIZE_MAX _UI32_MAX
-# endif // _WIN64 ]
-#endif // SIZE_MAX ]
-
-// WCHAR_MIN and WCHAR_MAX are also defined in <wchar.h>
-#ifndef WCHAR_MIN // [
-# define WCHAR_MIN 0
-#endif // WCHAR_MIN ]
-#ifndef WCHAR_MAX // [
-# define WCHAR_MAX _UI16_MAX
-#endif // WCHAR_MAX ]
-
-#define WINT_MIN 0
-#define WINT_MAX _UI16_MAX
-
-#endif // __STDC_LIMIT_MACROS ]
-
-
-// 7.18.4 Limits of other integer types
-
-#if !defined(__cplusplus) || defined(__STDC_CONSTANT_MACROS) // [ See footnote 224 at page 260
-
-// 7.18.4.1 Macros for minimum-width integer constants
-
-#define INT8_C(val) val##i8
-#define INT16_C(val) val##i16
-#define INT32_C(val) val##i32
-#define INT64_C(val) val##i64
-
-#define UINT8_C(val) val##ui8
-#define UINT16_C(val) val##ui16
-#define UINT32_C(val) val##ui32
-#define UINT64_C(val) val##ui64
-
-// 7.18.4.2 Macros for greatest-width integer constants
-#define INTMAX_C INT64_C
-#define UINTMAX_C UINT64_C
-
-#endif // __STDC_CONSTANT_MACROS ]
-
-
-#endif // _MSC_STDINT_H_ ]
diff --git a/pandas/_libs/src/headers/stdint.h b/pandas/_libs/src/headers/stdint.h
deleted file mode 100644
index 8746bf132d0f7..0000000000000
--- a/pandas/_libs/src/headers/stdint.h
+++ /dev/null
@@ -1,10 +0,0 @@
-#ifndef _PANDAS_STDINT_H_
-#define _PANDAS_STDINT_H_
-
-#if defined(_MSC_VER) && (_MSC_VER < 1900)
-#include "ms_stdint.h"
-#else
-#include <stdint.h>
-#endif
-
-#endif
diff --git a/pandas/_libs/src/parser/tokenizer.h b/pandas/_libs/src/parser/tokenizer.h
index d403435cfca9e..eea9bfd4828d6 100644
--- a/pandas/_libs/src/parser/tokenizer.h
+++ b/pandas/_libs/src/parser/tokenizer.h
@@ -19,7 +19,7 @@ See LICENSE for the license
#define ERROR_OVERFLOW 2
#define ERROR_INVALID_CHARS 3
-#include "../headers/stdint.h"
+#include <stdint.h>
#include "../inline_helper.h"
#include "../headers/portable.h"
diff --git a/pandas/_libs/util.pxd b/pandas/_libs/util.pxd
index df88c896ac593..18009a1a25de4 100644
--- a/pandas/_libs/util.pxd
+++ b/pandas/_libs/util.pxd
@@ -1,18 +1,17 @@
cimport numpy as cnp
+from libc.stdint cimport (
+ INT8_MAX,
+ INT8_MIN,
+ INT16_MAX,
+ INT16_MIN,
+ INT32_MAX,
+ INT32_MIN,
+ INT64_MAX,
+ INT64_MIN,
+ UINT8_MAX,
+ UINT16_MAX,
+ UINT32_MAX,
+ UINT64_MAX,
+)
from pandas._libs.tslibs.util cimport *
-
-
-cdef extern from "src/headers/stdint.h":
- enum: UINT8_MAX
- enum: UINT16_MAX
- enum: UINT32_MAX
- enum: UINT64_MAX
- enum: INT8_MIN
- enum: INT8_MAX
- enum: INT16_MIN
- enum: INT16_MAX
- enum: INT32_MAX
- enum: INT32_MIN
- enum: INT64_MAX
- enum: INT64_MIN
| Just to see if this breaks the world. | https://api.github.com/repos/pandas-dev/pandas/pulls/46437 | 2022-03-20T02:18:40Z | 2022-04-23T22:09:54Z | 2022-04-23T22:09:54Z | 2022-04-24T02:43:58Z |
REF: avoid python-space calls in _get_dst_hours | diff --git a/pandas/_libs/tslibs/tzconversion.pyx b/pandas/_libs/tslibs/tzconversion.pyx
index 705c4cef5c05d..bc57cb8aaed83 100644
--- a/pandas/_libs/tslibs/tzconversion.pyx
+++ b/pandas/_libs/tslibs/tzconversion.pyx
@@ -15,7 +15,6 @@ from cpython.datetime cimport (
import_datetime()
-from dateutil.tz import tzutc
import numpy as np
import pytz
@@ -40,7 +39,6 @@ from pandas._libs.tslibs.np_datetime cimport (
)
from pandas._libs.tslibs.timezones cimport (
get_dst_info,
- get_utcoffset,
is_fixed_offset,
is_tzlocal,
is_utc,
@@ -194,6 +192,8 @@ timedelta-like}
result_b[:] = NPY_NAT
for i in range(n):
+ # This loops resembles the "Find the two best possibilities" block
+ # in pytz's DstTZInfo.localize method.
val = vals[i]
if val == NPY_NAT:
continue
@@ -339,27 +339,37 @@ cdef inline str _render_tstamp(int64_t val):
cdef ndarray[int64_t] _get_dst_hours(
# vals only needed here to potential render an exception message
- ndarray[int64_t] vals,
+ const int64_t[:] vals,
ndarray[int64_t] result_a,
ndarray[int64_t] result_b,
):
cdef:
- Py_ssize_t n = vals.shape[0]
- ndarray[uint8_t, cast=True] both_nat, both_eq
+ Py_ssize_t i, n = vals.shape[0]
+ ndarray[uint8_t, cast=True] mismatch
ndarray[int64_t] delta, dst_hours
- ndarray trans_idx, grp, a_idx, b_idx, one_diff
+ ndarray[intp_t] switch_idxs, trans_idx, grp, a_idx, b_idx, one_diff
list trans_grp
+ intp_t switch_idx
+ int64_t left, right
dst_hours = np.empty(n, dtype=np.int64)
dst_hours[:] = NPY_NAT
- # Get the ambiguous hours (given the above, these are the hours
- # where result_a != result_b and neither of them are NAT)
- both_nat = np.logical_and(result_a != NPY_NAT, result_b != NPY_NAT)
- both_eq = result_a == result_b
- trans_idx = np.squeeze(np.nonzero(np.logical_and(both_nat, ~both_eq)))
+ mismatch = np.zeros(n, dtype=bool)
+
+ for i in range(n):
+ left = result_a[i]
+ right = result_b[i]
+
+ # Get the ambiguous hours (given the above, these are the hours
+ # where result_a != result_b and neither of them are NAT)
+ if left != right and left != NPY_NAT and right != NPY_NAT:
+ mismatch[i] = 1
+
+ trans_idx = mismatch.nonzero()[0]
+
if trans_idx.size == 1:
- stamp = _render_tstamp(vals[trans_idx])
+ stamp = _render_tstamp(vals[trans_idx[0]])
raise pytz.AmbiguousTimeError(
f"Cannot infer dst time from {stamp} as there "
"are no repeated times"
@@ -385,14 +395,14 @@ cdef ndarray[int64_t] _get_dst_hours(
# Find the index for the switch and pull from a for dst and b
# for standard
- switch_idx = (delta <= 0).nonzero()[0]
- if switch_idx.size > 1:
+ switch_idxs = (delta <= 0).nonzero()[0]
+ if switch_idxs.size > 1:
raise pytz.AmbiguousTimeError(
- f"There are {switch_idx.size} dst switches when "
+ f"There are {switch_idxs.size} dst switches when "
"there should only be 1."
)
- switch_idx = switch_idx[0] + 1 # TODO: declare type for switch_idx
+ switch_idx = switch_idxs[0] + 1
# Pull the only index and adjust
a_idx = grp[:switch_idx]
b_idx = grp[switch_idx:]
| There's still a few more to be gotten, but its a start. | https://api.github.com/repos/pandas-dev/pandas/pulls/46436 | 2022-03-20T01:22:33Z | 2022-03-20T16:22:11Z | 2022-03-20T16:22:11Z | 2022-03-20T18:45:11Z |
categorical type column and interval data to a CSV file | diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index 26569b571724d..caa5967c64584 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -2263,7 +2263,8 @@ def to_native_types(
values = algos.take_nd(
values.categories._values,
ensure_platform_int(values._codes),
- fill_value=na_rep,
+ # GH#46297 remove fill_value=na_rep,
+ # categorical interval allow excecute to_cvs
)
values = ensure_wrapped_if_datetimelike(values)
diff --git a/pandas/tests/io/formats/test_to_csv.py b/pandas/tests/io/formats/test_to_csv.py
index d3f8e27c47e98..8d6c5b2bacd3c 100644
--- a/pandas/tests/io/formats/test_to_csv.py
+++ b/pandas/tests/io/formats/test_to_csv.py
@@ -315,6 +315,15 @@ def test_to_csv_date_format_in_categorical(self):
ser = ser.astype("category")
assert ser.to_csv(index=False, date_format="%Y-%m-%d") == expected
+ def test_to_cvs_interval_format_in_categorical(self):
+ # GH#46297
+ df = pd.DataFrame(index=[0], columns=["a"])
+ df.at[0, "a"] = pd.Interval(pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-02"))
+ df["a"] = df["a"].astype("category")
+ result = df.to_csv(index=False, date_format="%Y-%m-%d")
+ expected = tm.convert_rows_list_to_csv_str(['a','"(2020-01-01, 2020-01-02]"'])
+ assert result == expected
+
def test_to_csv_float_ea_float_format(self):
# GH#45991
df = DataFrame({"a": [1.1, 2.02, pd.NA, 6.000006], "b": "c"})
| - [x] closes #46297
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46435 | 2022-03-19T21:31:22Z | 2022-03-23T13:45:51Z | null | 2022-03-23T13:45:51Z |
CLN: rename _tz_localize_using_tzinfo_api, standardize patterns | diff --git a/pandas/_libs/tslibs/conversion.pyx b/pandas/_libs/tslibs/conversion.pyx
index 792b6d1c35b2f..00f83be0b51c4 100644
--- a/pandas/_libs/tslibs/conversion.pyx
+++ b/pandas/_libs/tslibs/conversion.pyx
@@ -71,7 +71,7 @@ from pandas._libs.tslibs.nattype cimport (
)
from pandas._libs.tslibs.tzconversion cimport (
bisect_right_i8,
- tz_convert_utc_to_tzlocal,
+ localize_tzinfo_api,
tz_localize_to_utc_single,
)
@@ -556,7 +556,7 @@ cdef _TSObject _create_tsobject_tz_using_offset(npy_datetimestruct dts,
if is_utc(tz):
pass
elif is_tzlocal(tz):
- tz_convert_utc_to_tzlocal(obj.value, tz, &obj.fold)
+ localize_tzinfo_api(obj.value, tz, &obj.fold)
else:
trans, deltas, typ = get_dst_info(tz)
@@ -725,7 +725,7 @@ cdef inline void _localize_tso(_TSObject obj, tzinfo tz):
elif obj.value == NPY_NAT:
pass
elif is_tzlocal(tz):
- local_val = tz_convert_utc_to_tzlocal(obj.value, tz, &obj.fold)
+ local_val = obj.value + localize_tzinfo_api(obj.value, tz, &obj.fold)
dt64_to_dtstruct(local_val, &obj.dts)
else:
# Adjust datetime64 timestamp, recompute datetimestruct
diff --git a/pandas/_libs/tslibs/tzconversion.pxd b/pandas/_libs/tslibs/tzconversion.pxd
index 0837a5c436197..136e62985995e 100644
--- a/pandas/_libs/tslibs/tzconversion.pxd
+++ b/pandas/_libs/tslibs/tzconversion.pxd
@@ -2,7 +2,7 @@ from cpython.datetime cimport tzinfo
from numpy cimport int64_t
-cdef int64_t tz_convert_utc_to_tzlocal(
+cdef int64_t localize_tzinfo_api(
int64_t utc_val, tzinfo tz, bint* fold=*
) except? -1
cpdef int64_t tz_convert_from_utc_single(int64_t val, tzinfo tz)
diff --git a/pandas/_libs/tslibs/tzconversion.pyx b/pandas/_libs/tslibs/tzconversion.pyx
index 7efe9412e43b9..705c4cef5c05d 100644
--- a/pandas/_libs/tslibs/tzconversion.pyx
+++ b/pandas/_libs/tslibs/tzconversion.pyx
@@ -7,6 +7,7 @@ from cython import Py_ssize_t
from cpython.datetime cimport (
PyDelta_Check,
datetime,
+ datetime_new,
import_datetime,
timedelta,
tzinfo,
@@ -43,6 +44,7 @@ from pandas._libs.tslibs.timezones cimport (
is_fixed_offset,
is_tzlocal,
is_utc,
+ utc_pytz,
)
@@ -61,7 +63,7 @@ cdef int64_t tz_localize_to_utc_single(
return val
elif is_tzlocal(tz):
- return _tz_convert_tzlocal_utc(val, tz, to_utc=True)
+ return val - _tz_localize_using_tzinfo_api(val, tz, to_utc=True)
elif is_fixed_offset(tz):
# TODO: in this case we should be able to use get_utcoffset,
@@ -142,7 +144,7 @@ timedelta-like}
if v == NPY_NAT:
result[i] = NPY_NAT
else:
- result[i] = _tz_convert_tzlocal_utc(v, tz, to_utc=True)
+ result[i] = v - _tz_localize_using_tzinfo_api(v, tz, to_utc=True)
return result
# silence false-positive compiler warning
@@ -402,7 +404,7 @@ cdef ndarray[int64_t] _get_dst_hours(
# ----------------------------------------------------------------------
# Timezone Conversion
-cdef int64_t tz_convert_utc_to_tzlocal(
+cdef int64_t localize_tzinfo_api(
int64_t utc_val, tzinfo tz, bint* fold=NULL
) except? -1:
"""
@@ -416,12 +418,13 @@ cdef int64_t tz_convert_utc_to_tzlocal(
Returns
-------
- local_val : int64_t
+ delta : int64_t
+ Value to add when converting from utc.
"""
- return _tz_convert_tzlocal_utc(utc_val, tz, to_utc=False, fold=fold)
+ return _tz_localize_using_tzinfo_api(utc_val, tz, to_utc=False, fold=fold)
-cpdef int64_t tz_convert_from_utc_single(int64_t val, tzinfo tz):
+cpdef int64_t tz_convert_from_utc_single(int64_t utc_val, tzinfo tz):
"""
Convert the val (in i8) from UTC to tz
@@ -429,7 +432,7 @@ cpdef int64_t tz_convert_from_utc_single(int64_t val, tzinfo tz):
Parameters
----------
- val : int64
+ utc_val : int64
tz : tzinfo
Returns
@@ -443,22 +446,22 @@ cpdef int64_t tz_convert_from_utc_single(int64_t val, tzinfo tz):
int64_t* tdata
intp_t pos
- if val == NPY_NAT:
- return val
+ if utc_val == NPY_NAT:
+ return utc_val
if is_utc(tz):
- return val
+ return utc_val
elif is_tzlocal(tz):
- return _tz_convert_tzlocal_utc(val, tz, to_utc=False)
+ return utc_val + _tz_localize_using_tzinfo_api(utc_val, tz, to_utc=False)
elif is_fixed_offset(tz):
_, deltas, _ = get_dst_info(tz)
delta = deltas[0]
- return val + delta
+ return utc_val + delta
else:
trans, deltas, _ = get_dst_info(tz)
tdata = <int64_t*>cnp.PyArray_DATA(trans)
- pos = bisect_right_i8(tdata, val, trans.shape[0]) - 1
- return val + deltas[pos]
+ pos = bisect_right_i8(tdata, utc_val, trans.shape[0]) - 1
+ return utc_val + deltas[pos]
def tz_convert_from_utc(const int64_t[:] vals, tzinfo tz):
@@ -486,13 +489,13 @@ def tz_convert_from_utc(const int64_t[:] vals, tzinfo tz):
@cython.boundscheck(False)
@cython.wraparound(False)
-cdef const int64_t[:] _tz_convert_from_utc(const int64_t[:] vals, tzinfo tz):
+cdef const int64_t[:] _tz_convert_from_utc(const int64_t[:] stamps, tzinfo tz):
"""
Convert the given values (in i8) either to UTC or from UTC.
Parameters
----------
- vals : int64 ndarray
+ stamps : int64 ndarray
tz : tzinfo
Returns
@@ -500,18 +503,20 @@ cdef const int64_t[:] _tz_convert_from_utc(const int64_t[:] vals, tzinfo tz):
converted : ndarray[int64_t]
"""
cdef:
- int64_t[::1] converted, deltas
- Py_ssize_t i, ntrans = -1, n = vals.shape[0]
- int64_t val, delta = 0 # avoid not-initialized-warning
- intp_t pos
+ Py_ssize_t i, ntrans = -1, n = stamps.shape[0]
ndarray[int64_t] trans
+ int64_t[::1] deltas
int64_t* tdata = NULL
+ intp_t pos
+ int64_t utc_val, local_val, delta = NPY_NAT
+ bint use_utc = False, use_tzlocal = False, use_fixed = False
str typ
- bint use_tzlocal = False, use_fixed = False, use_utc = True
+
+ int64_t[::1] result
if is_utc(tz):
# Much faster than going through the "standard" pattern below
- return vals.copy()
+ return stamps.copy()
if is_utc(tz) or tz is None:
use_utc = True
@@ -520,59 +525,62 @@ cdef const int64_t[:] _tz_convert_from_utc(const int64_t[:] vals, tzinfo tz):
else:
trans, deltas, typ = get_dst_info(tz)
ntrans = trans.shape[0]
-
if typ not in ["pytz", "dateutil"]:
- # FixedOffset, we know len(deltas) == 1
- delta = deltas[0]
+ # static/fixed; in this case we know that len(delta) == 1
use_fixed = True
+ delta = deltas[0]
else:
tdata = <int64_t*>cnp.PyArray_DATA(trans)
- converted = np.empty(n, dtype=np.int64)
+ result = np.empty(n, dtype=np.int64)
for i in range(n):
- val = vals[i]
- if val == NPY_NAT:
- converted[i] = NPY_NAT
+ utc_val = stamps[i]
+ if utc_val == NPY_NAT:
+ result[i] = NPY_NAT
continue
# The pattern used in vectorized.pyx checks for use_utc here,
# but we handle that case above.
if use_tzlocal:
- converted[i] = _tz_convert_tzlocal_utc(val, tz, to_utc=False)
+ local_val = utc_val + _tz_localize_using_tzinfo_api(utc_val, tz, to_utc=False)
elif use_fixed:
- converted[i] = val + delta
+ local_val = utc_val + delta
else:
- pos = bisect_right_i8(tdata, val, ntrans) - 1
- converted[i] = val + deltas[pos]
+ pos = bisect_right_i8(tdata, utc_val, ntrans) - 1
+ local_val = utc_val + deltas[pos]
+
+ result[i] = local_val
- return converted
+ return result
# OSError may be thrown by tzlocal on windows at or close to 1970-01-01
# see https://github.com/pandas-dev/pandas/pull/37591#issuecomment-720628241
-cdef int64_t _tz_convert_tzlocal_utc(int64_t val, tzinfo tz, bint to_utc=True,
- bint* fold=NULL) except? -1:
+cdef int64_t _tz_localize_using_tzinfo_api(
+ int64_t val, tzinfo tz, bint to_utc=True, bint* fold=NULL
+) except? -1:
"""
- Convert the i8 representation of a datetime from a tzlocal timezone to
- UTC, or vice-versa.
+ Convert the i8 representation of a datetime from a general-cast timezone to
+ UTC, or vice-versa using the datetime/tzinfo API.
- Private, not intended for use outside of tslibs.conversion
+ Private, not intended for use outside of tslibs.tzconversion.
Parameters
----------
val : int64_t
tz : tzinfo
to_utc : bint
- True if converting tzlocal _to_ UTC, False if going the other direction
+ True if converting _to_ UTC, False if going the other direction.
fold : bint*, default NULL
pointer to fold: whether datetime ends up in a fold or not
- after adjustment
+ after adjustment.
Only passed with to_utc=False.
Returns
-------
- result : int64_t
+ delta : int64_t
+ Value to add when converting from utc, subtract when converting to utc.
Notes
-----
@@ -586,23 +594,21 @@ cdef int64_t _tz_convert_tzlocal_utc(int64_t val, tzinfo tz, bint to_utc=True,
dt64_to_dtstruct(val, &dts)
- dt = datetime(dts.year, dts.month, dts.day, dts.hour,
- dts.min, dts.sec, dts.us)
-
- # tz.utcoffset only makes sense if datetime
- # is _wall time_, so if val is a UTC timestamp convert to wall time
+ # datetime_new is cython-optimized constructor
if not to_utc:
- dt = dt.replace(tzinfo=tzutc())
+ # tz.utcoffset only makes sense if datetime
+ # is _wall time_, so if val is a UTC timestamp convert to wall time
+ dt = datetime_new(dts.year, dts.month, dts.day, dts.hour,
+ dts.min, dts.sec, dts.us, utc_pytz)
dt = dt.astimezone(tz)
if fold is not NULL:
# NB: fold is only passed with to_utc=False
fold[0] = dt.fold
+ else:
+ dt = datetime_new(dts.year, dts.month, dts.day, dts.hour,
+ dts.min, dts.sec, dts.us, None)
td = tz.utcoffset(dt)
delta = int(td.total_seconds() * 1_000_000_000)
-
- if to_utc:
- return val - delta
- else:
- return val + delta
+ return delta
diff --git a/pandas/_libs/tslibs/vectorized.pyx b/pandas/_libs/tslibs/vectorized.pyx
index 3f47a19563b61..9849bbce8c564 100644
--- a/pandas/_libs/tslibs/vectorized.pyx
+++ b/pandas/_libs/tslibs/vectorized.pyx
@@ -21,7 +21,7 @@ cnp.import_array()
from .conversion cimport normalize_i8_stamp
from .dtypes import Resolution
-
+from .ccalendar cimport DAY_NANOS
from .nattype cimport (
NPY_NAT,
c_NaT as NaT,
@@ -40,7 +40,7 @@ from .timezones cimport (
)
from .tzconversion cimport (
bisect_right_i8,
- tz_convert_utc_to_tzlocal,
+ localize_tzinfo_api,
)
# -------------------------------------------------------------------------
@@ -83,17 +83,18 @@ def ints_to_pydatetime(
ndarray[object] of type specified by box
"""
cdef:
- Py_ssize_t i, ntrans =- 1, n = len(stamps)
+ Py_ssize_t i, ntrans = -1, n = stamps.shape[0]
ndarray[int64_t] trans
int64_t[::1] deltas
int64_t* tdata = NULL
intp_t pos
+ int64_t utc_val, local_val, delta = NPY_NAT
+ bint use_utc = False, use_tzlocal = False, use_fixed = False
+ str typ
+
npy_datetimestruct dts
tzinfo new_tz
- str typ
- int64_t value, local_val, delta = NPY_NAT # dummy for delta
ndarray[object] result = np.empty(n, dtype=object)
- bint use_utc = False, use_tzlocal = False, use_fixed = False
bint use_pytz = False
bint use_date = False, use_time = False, use_ts = False, use_pydt = False
@@ -127,22 +128,22 @@ def ints_to_pydatetime(
use_pytz = typ == "pytz"
for i in range(n):
+ utc_val = stamps[i]
new_tz = tz
- value = stamps[i]
- if value == NPY_NAT:
+ if utc_val == NPY_NAT:
result[i] = <object>NaT
continue
if use_utc:
- local_val = value
+ local_val = utc_val
elif use_tzlocal:
- local_val = tz_convert_utc_to_tzlocal(value, tz)
+ local_val = utc_val + localize_tzinfo_api(utc_val, tz)
elif use_fixed:
- local_val = value + delta
+ local_val = utc_val + delta
else:
- pos = bisect_right_i8(tdata, value, ntrans) - 1
- local_val = value + deltas[pos]
+ pos = bisect_right_i8(tdata, utc_val, ntrans) - 1
+ local_val = utc_val + deltas[pos]
if use_pytz:
# find right representation of dst etc in pytz timezone
@@ -151,7 +152,7 @@ def ints_to_pydatetime(
dt64_to_dtstruct(local_val, &dts)
if use_ts:
- result[i] = create_timestamp_from_ts(value, dts, new_tz, freq, fold)
+ result[i] = create_timestamp_from_ts(utc_val, dts, new_tz, freq, fold)
elif use_pydt:
result[i] = datetime(
dts.year, dts.month, dts.day, dts.hour, dts.min, dts.sec, dts.us,
@@ -194,15 +195,17 @@ cdef inline int _reso_stamp(npy_datetimestruct *dts):
@cython.boundscheck(False)
def get_resolution(const int64_t[:] stamps, tzinfo tz=None) -> Resolution:
cdef:
- Py_ssize_t i, ntrans=-1, n = len(stamps)
- npy_datetimestruct dts
- int reso = RESO_DAY, curr_reso
+ Py_ssize_t i, ntrans = -1, n = stamps.shape[0]
ndarray[int64_t] trans
int64_t[::1] deltas
int64_t* tdata = NULL
intp_t pos
- int64_t local_val, delta = NPY_NAT
+ int64_t utc_val, local_val, delta = NPY_NAT
bint use_utc = False, use_tzlocal = False, use_fixed = False
+ str typ
+
+ npy_datetimestruct dts
+ int reso = RESO_DAY, curr_reso
if is_utc(tz) or tz is None:
use_utc = True
@@ -219,18 +222,19 @@ def get_resolution(const int64_t[:] stamps, tzinfo tz=None) -> Resolution:
tdata = <int64_t*>cnp.PyArray_DATA(trans)
for i in range(n):
- if stamps[i] == NPY_NAT:
+ utc_val = stamps[i]
+ if utc_val == NPY_NAT:
continue
if use_utc:
- local_val = stamps[i]
+ local_val = utc_val
elif use_tzlocal:
- local_val = tz_convert_utc_to_tzlocal(stamps[i], tz)
+ local_val = utc_val + localize_tzinfo_api(utc_val, tz)
elif use_fixed:
- local_val = stamps[i] + delta
+ local_val = utc_val + delta
else:
- pos = bisect_right_i8(tdata, stamps[i], ntrans) - 1
- local_val = stamps[i] + deltas[pos]
+ pos = bisect_right_i8(tdata, utc_val, ntrans) - 1
+ local_val = utc_val + deltas[pos]
dt64_to_dtstruct(local_val, &dts)
curr_reso = _reso_stamp(&dts)
@@ -260,15 +264,16 @@ cpdef ndarray[int64_t] normalize_i8_timestamps(const int64_t[:] stamps, tzinfo t
result : int64 ndarray of converted of normalized nanosecond timestamps
"""
cdef:
- Py_ssize_t i, ntrans =- 1, n = len(stamps)
- int64_t[::1] result = np.empty(n, dtype=np.int64)
+ Py_ssize_t i, ntrans = -1, n = stamps.shape[0]
ndarray[int64_t] trans
int64_t[::1] deltas
int64_t* tdata = NULL
- str typ
- Py_ssize_t pos
- int64_t local_val, delta = NPY_NAT
+ intp_t pos
+ int64_t utc_val, local_val, delta = NPY_NAT
bint use_utc = False, use_tzlocal = False, use_fixed = False
+ str typ
+
+ int64_t[::1] result = np.empty(n, dtype=np.int64)
if is_utc(tz) or tz is None:
use_utc = True
@@ -285,19 +290,20 @@ cpdef ndarray[int64_t] normalize_i8_timestamps(const int64_t[:] stamps, tzinfo t
tdata = <int64_t*>cnp.PyArray_DATA(trans)
for i in range(n):
- if stamps[i] == NPY_NAT:
+ utc_val = stamps[i]
+ if utc_val == NPY_NAT:
result[i] = NPY_NAT
continue
if use_utc:
- local_val = stamps[i]
+ local_val = utc_val
elif use_tzlocal:
- local_val = tz_convert_utc_to_tzlocal(stamps[i], tz)
+ local_val = utc_val + localize_tzinfo_api(utc_val, tz)
elif use_fixed:
- local_val = stamps[i] + delta
+ local_val = utc_val + delta
else:
- pos = bisect_right_i8(tdata, stamps[i], ntrans) - 1
- local_val = stamps[i] + deltas[pos]
+ pos = bisect_right_i8(tdata, utc_val, ntrans) - 1
+ local_val = utc_val + deltas[pos]
result[i] = normalize_i8_stamp(local_val)
@@ -322,15 +328,14 @@ def is_date_array_normalized(const int64_t[:] stamps, tzinfo tz=None) -> bool:
is_normalized : bool True if all stamps are normalized
"""
cdef:
- Py_ssize_t i, ntrans =- 1, n = len(stamps)
+ Py_ssize_t i, ntrans = -1, n = stamps.shape[0]
ndarray[int64_t] trans
int64_t[::1] deltas
int64_t* tdata = NULL
intp_t pos
- int64_t local_val, delta = NPY_NAT
- str typ
- int64_t day_nanos = 24 * 3600 * 1_000_000_000
+ int64_t utc_val, local_val, delta = NPY_NAT
bint use_utc = False, use_tzlocal = False, use_fixed = False
+ str typ
if is_utc(tz) or tz is None:
use_utc = True
@@ -347,17 +352,18 @@ def is_date_array_normalized(const int64_t[:] stamps, tzinfo tz=None) -> bool:
tdata = <int64_t*>cnp.PyArray_DATA(trans)
for i in range(n):
+ utc_val = stamps[i]
if use_utc:
- local_val = stamps[i]
+ local_val = utc_val
elif use_tzlocal:
- local_val = tz_convert_utc_to_tzlocal(stamps[i], tz)
+ local_val = utc_val + localize_tzinfo_api(utc_val, tz)
elif use_fixed:
- local_val = stamps[i] + delta
+ local_val = utc_val + delta
else:
- pos = bisect_right_i8(tdata, stamps[i], ntrans) - 1
- local_val = stamps[i] + deltas[pos]
+ pos = bisect_right_i8(tdata, utc_val, ntrans) - 1
+ local_val = utc_val + deltas[pos]
- if local_val % day_nanos != 0:
+ if local_val % DAY_NANOS != 0:
return False
return True
@@ -370,15 +376,17 @@ def is_date_array_normalized(const int64_t[:] stamps, tzinfo tz=None) -> bool:
@cython.boundscheck(False)
def dt64arr_to_periodarr(const int64_t[:] stamps, int freq, tzinfo tz):
cdef:
- Py_ssize_t i, ntrans =- 1, n = len(stamps)
- int64_t[::1] result = np.empty(n, dtype=np.int64)
+ Py_ssize_t i, ntrans = -1, n = stamps.shape[0]
ndarray[int64_t] trans
int64_t[::1] deltas
int64_t* tdata = NULL
intp_t pos
- npy_datetimestruct dts
- int64_t local_val, delta = NPY_NAT
+ int64_t utc_val, local_val, delta = NPY_NAT
bint use_utc = False, use_tzlocal = False, use_fixed = False
+ str typ
+
+ npy_datetimestruct dts
+ int64_t[::1] result = np.empty(n, dtype=np.int64)
if is_utc(tz) or tz is None:
use_utc = True
@@ -395,19 +403,20 @@ def dt64arr_to_periodarr(const int64_t[:] stamps, int freq, tzinfo tz):
tdata = <int64_t*>cnp.PyArray_DATA(trans)
for i in range(n):
- if stamps[i] == NPY_NAT:
+ utc_val = stamps[i]
+ if utc_val == NPY_NAT:
result[i] = NPY_NAT
continue
if use_utc:
- local_val = stamps[i]
+ local_val = utc_val
elif use_tzlocal:
- local_val = tz_convert_utc_to_tzlocal(stamps[i], tz)
+ local_val = utc_val + localize_tzinfo_api(utc_val, tz)
elif use_fixed:
- local_val = stamps[i] + delta
+ local_val = utc_val + delta
else:
- pos = bisect_right_i8(tdata, stamps[i], ntrans) - 1
- local_val = stamps[i] + deltas[pos]
+ pos = bisect_right_i8(tdata, utc_val, ntrans) - 1
+ local_val = utc_val + deltas[pos]
dt64_to_dtstruct(local_val, &dts)
result[i] = get_period_ordinal(&dts, freq)
| Maximized places where code is exactly copy/pasted so its easy to check it stays in sync (since so far the prospect of de-duplicating the affected code is not looking good)
Small optimizations in _tz_convert_tzlocal_utc, which is renamed to reflect the fact that it is actually the General Case, not just tzlocal. | https://api.github.com/repos/pandas-dev/pandas/pulls/46434 | 2022-03-19T20:52:05Z | 2022-03-19T22:32:29Z | 2022-03-19T22:32:29Z | 2022-03-19T23:04:23Z |
BENCH: Fix makeCategoricalIndex | diff --git a/asv_bench/benchmarks/categoricals.py b/asv_bench/benchmarks/categoricals.py
index b30ad9f7b4905..ff0b3b2fb651d 100644
--- a/asv_bench/benchmarks/categoricals.py
+++ b/asv_bench/benchmarks/categoricals.py
@@ -187,7 +187,7 @@ def time_remove_categories(self):
class Rank:
def setup(self):
N = 10**5
- ncats = 20
+ ncats = 15
self.s_str = pd.Series(tm.makeCategoricalIndex(N, ncats)).astype(str)
self.s_str_cat = pd.Series(self.s_str, dtype="category")
| - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
@jreback looks like https://github.com/pandas-dev/pandas/pull/46429 was still making the benchmarks job fail. Locally constructing with `ncats = 15` works.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46433 | 2022-03-19T18:27:08Z | 2022-03-19T21:59:14Z | 2022-03-19T21:59:14Z | 2022-03-19T21:59:18Z |
Mw test | diff --git a/README.md b/README.md
deleted file mode 100644
index 2216f59965354..0000000000000
--- a/README.md
+++ /dev/null
@@ -1,173 +0,0 @@
-<div align="center">
- <img src="https://pandas.pydata.org/static/img/pandas.svg"><br>
-</div>
-
------------------
-
-# pandas: powerful Python data analysis toolkit
-[](https://pypi.org/project/pandas/)
-[](https://anaconda.org/anaconda/pandas/)
-[](https://doi.org/10.5281/zenodo.3509134)
-[](https://pypi.org/project/pandas/)
-[](https://github.com/pandas-dev/pandas/blob/main/LICENSE)
-[](https://dev.azure.com/pandas-dev/pandas/_build/latest?definitionId=1&branch=main)
-[](https://codecov.io/gh/pandas-dev/pandas)
-[](https://pepy.tech/project/pandas)
-[](https://gitter.im/pydata/pandas)
-[](https://numfocus.org)
-[](https://github.com/psf/black)
-[](https://pycqa.github.io/isort/)
-
-## What is it?
-
-**pandas** is a Python package that provides fast, flexible, and expressive data
-structures designed to make working with "relational" or "labeled" data both
-easy and intuitive. It aims to be the fundamental high-level building block for
-doing practical, **real world** data analysis in Python. Additionally, it has
-the broader goal of becoming **the most powerful and flexible open source data
-analysis / manipulation tool available in any language**. It is already well on
-its way towards this goal.
-
-## Main Features
-Here are just a few of the things that pandas does well:
-
- - Easy handling of [**missing data**][missing-data] (represented as
- `NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data
- - Size mutability: columns can be [**inserted and
- deleted**][insertion-deletion] from DataFrame and higher dimensional
- objects
- - Automatic and explicit [**data alignment**][alignment]: objects can
- be explicitly aligned to a set of labels, or the user can simply
- ignore the labels and let `Series`, `DataFrame`, etc. automatically
- align the data for you in computations
- - Powerful, flexible [**group by**][groupby] functionality to perform
- split-apply-combine operations on data sets, for both aggregating
- and transforming data
- - Make it [**easy to convert**][conversion] ragged,
- differently-indexed data in other Python and NumPy data structures
- into DataFrame objects
- - Intelligent label-based [**slicing**][slicing], [**fancy
- indexing**][fancy-indexing], and [**subsetting**][subsetting] of
- large data sets
- - Intuitive [**merging**][merging] and [**joining**][joining] data
- sets
- - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of
- data sets
- - [**Hierarchical**][mi] labeling of axes (possible to have multiple
- labels per tick)
- - Robust IO tools for loading data from [**flat files**][flat-files]
- (CSV and delimited), [**Excel files**][excel], [**databases**][db],
- and saving/loading data from the ultrafast [**HDF5 format**][hdfstore]
- - [**Time series**][timeseries]-specific functionality: date range
- generation and frequency conversion, moving window statistics,
- date shifting and lagging
-
-
- [missing-data]: https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html
- [insertion-deletion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#column-selection-addition-deletion
- [alignment]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html?highlight=alignment#intro-to-data-structures
- [groupby]: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#group-by-split-apply-combine
- [conversion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#dataframe
- [slicing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#slicing-ranges
- [fancy-indexing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced
- [subsetting]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-indexing
- [merging]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#database-style-dataframe-or-named-series-joining-merging
- [joining]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#joining-on-index
- [reshape]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html
- [pivot-table]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html
- [mi]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#hierarchical-indexing-multiindex
- [flat-files]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#csv-text-files
- [excel]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#excel-files
- [db]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#sql-queries
- [hdfstore]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#hdf5-pytables
- [timeseries]: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#time-series-date-functionality
-
-## Where to get it
-The source code is currently hosted on GitHub at:
-https://github.com/pandas-dev/pandas
-
-Binary installers for the latest released version are available at the [Python
-Package Index (PyPI)](https://pypi.org/project/pandas) and on [Conda](https://docs.conda.io/en/latest/).
-
-```sh
-# conda
-conda install pandas
-```
-
-```sh
-# or PyPI
-pip install pandas
-```
-
-## Dependencies
-- [NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays](https://www.numpy.org)
-- [python-dateutil - Provides powerful extensions to the standard datetime module](https://dateutil.readthedocs.io/en/stable/index.html)
-- [pytz - Brings the Olson tz database into Python which allows accurate and cross platform timezone calculations](https://github.com/stub42/pytz)
-
-See the [full installation instructions](https://pandas.pydata.org/pandas-docs/stable/install.html#dependencies) for minimum supported versions of required, recommended and optional dependencies.
-
-## Installation from sources
-To install pandas from source you need [Cython](https://cython.org/) in addition to the normal
-dependencies above. Cython can be installed from PyPI:
-
-```sh
-pip install cython
-```
-
-In the `pandas` directory (same one where you found this file after
-cloning the git repo), execute:
-
-```sh
-python setup.py install
-```
-
-or for installing in [development mode](https://pip.pypa.io/en/latest/cli/pip_install/#install-editable):
-
-
-```sh
-python -m pip install -e . --no-build-isolation --no-use-pep517
-```
-
-If you have `make`, you can also use `make develop` to run the same command.
-
-or alternatively
-
-```sh
-python setup.py develop
-```
-
-See the full instructions for [installing from source](https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html#installing-from-source).
-
-## License
-[BSD 3](LICENSE)
-
-## Documentation
-The official documentation is hosted on PyData.org: https://pandas.pydata.org/pandas-docs/stable
-
-## Background
-Work on ``pandas`` started at [AQR](https://www.aqr.com/) (a quantitative hedge fund) in 2008 and
-has been under active development since then.
-
-## Getting Help
-
-For usage questions, the best place to go to is [StackOverflow](https://stackoverflow.com/questions/tagged/pandas).
-Further, general questions and discussions can also take place on the [pydata mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata).
-
-## Discussion and Development
-Most development discussions take place on GitHub in this repo. Further, the [pandas-dev mailing list](https://mail.python.org/mailman/listinfo/pandas-dev) can also be used for specialized discussions or design issues, and a [Gitter channel](https://gitter.im/pydata/pandas) is available for quick development related questions.
-
-## Contributing to pandas [](https://www.codetriage.com/pandas-dev/pandas)
-
-All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.
-
-A detailed overview on how to contribute can be found in the **[contributing guide](https://pandas.pydata.org/docs/dev/development/contributing.html)**.
-
-If you are simply looking to start working with the pandas codebase, navigate to the [GitHub "issues" tab](https://github.com/pandas-dev/pandas/issues) and start looking through interesting issues. There are a number of issues listed under [Docs](https://github.com/pandas-dev/pandas/issues?labels=Docs&sort=updated&state=open) and [good first issue](https://github.com/pandas-dev/pandas/issues?labels=good+first+issue&sort=updated&state=open) where you could start out.
-
-You can also triage issues which may include reproducing bug reports, or asking for vital information such as version numbers or reproduction instructions. If you would like to start triaging issues, one easy way to get started is to [subscribe to pandas on CodeTriage](https://www.codetriage.com/pandas-dev/pandas).
-
-Or maybe through using pandas you have an idea of your own or are looking for something in the documentation and thinking ‘this can be improved’...you can do something about it!
-
-Feel free to ask questions on the [mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata) or on [Gitter](https://gitter.im/pydata/pandas).
-
-As contributors and maintainers to this project, you are expected to abide by pandas' code of conduct. More information can be found at: [Contributor Code of Conduct](https://github.com/pandas-dev/pandas/blob/main/.github/CODE_OF_CONDUCT.md)
diff --git a/test.md b/test.md
new file mode 100644
index 0000000000000..e69de29bb2d1d
| - [ ] closes #xxxx (Replace xxxx with the Github issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46432 | 2022-03-19T09:55:59Z | 2022-03-19T09:58:37Z | null | 2022-03-19T09:58:37Z |
ENH: use same value counts for roll_var to remove floating point artifacts | diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst
index 08500019143ed..9f2bbaa8d5120 100644
--- a/doc/source/whatsnew/v1.5.0.rst
+++ b/doc/source/whatsnew/v1.5.0.rst
@@ -603,6 +603,7 @@ Groupby/resample/rolling
- Bug in :meth:`GroupBy.max` with empty groups and ``uint64`` dtype incorrectly raising ``RuntimeError`` (:issue:`46408`)
- Bug in :meth:`.GroupBy.apply` would fail when ``func`` was a string and args or kwargs were supplied (:issue:`46479`)
- Bug in :meth:`SeriesGroupBy.apply` would incorrectly name its result when there was a unique group (:issue:`46369`)
+- Bug in :meth:`Rolling.var` and :meth:`Rolling.std` would give non-zero result with window of same values (:issue:`42064`)
- Bug in :meth:`.Rolling.var` would segfault calculating weighted variance when window size was larger than data size (:issue:`46760`)
- Bug in :meth:`Grouper.__repr__` where ``dropna`` was not included. Now it is (:issue:`46754`)
diff --git a/pandas/_libs/window/aggregations.pyx b/pandas/_libs/window/aggregations.pyx
index e58f4c2aa8e42..a1120cd559d57 100644
--- a/pandas/_libs/window/aggregations.pyx
+++ b/pandas/_libs/window/aggregations.pyx
@@ -278,15 +278,15 @@ def roll_mean(const float64_t[:] values, ndarray[int64_t] start,
cdef inline float64_t calc_var(int64_t minp, int ddof, float64_t nobs,
- float64_t ssqdm_x) nogil:
+ float64_t ssqdm_x, int64_t num_consecutive_same_value) nogil:
cdef:
float64_t result
# Variance is unchanged if no observation is added or removed
if (nobs >= minp) and (nobs > ddof):
- # pathological case
- if nobs == 1:
+ # pathological case & repeatedly same values case
+ if nobs == 1 or num_consecutive_same_value >= nobs:
result = 0
else:
result = ssqdm_x / (nobs - <float64_t>ddof)
@@ -297,7 +297,8 @@ cdef inline float64_t calc_var(int64_t minp, int ddof, float64_t nobs,
cdef inline void add_var(float64_t val, float64_t *nobs, float64_t *mean_x,
- float64_t *ssqdm_x, float64_t *compensation) nogil:
+ float64_t *ssqdm_x, float64_t *compensation,
+ int64_t *num_consecutive_same_value, float64_t *prev_value) nogil:
""" add a value from the var calc """
cdef:
float64_t delta, prev_mean, y, t
@@ -307,6 +308,15 @@ cdef inline void add_var(float64_t val, float64_t *nobs, float64_t *mean_x,
return
nobs[0] = nobs[0] + 1
+
+ # GH#42064, record num of same values to remove floating point artifacts
+ if val == prev_value[0]:
+ num_consecutive_same_value[0] += 1
+ else:
+ # reset to 1 (include current value itself)
+ num_consecutive_same_value[0] = 1
+ prev_value[0] = val
+
# Welford's method for the online variance-calculation
# using Kahan summation
# https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
@@ -352,9 +362,8 @@ def roll_var(const float64_t[:] values, ndarray[int64_t] start,
"""
cdef:
float64_t mean_x, ssqdm_x, nobs, compensation_add,
- float64_t compensation_remove,
- float64_t val, prev, delta, mean_x_old
- int64_t s, e
+ float64_t compensation_remove, prev_value
+ int64_t s, e, num_consecutive_same_value
Py_ssize_t i, j, N = len(start)
ndarray[float64_t] output
bint is_monotonic_increasing_bounds
@@ -376,9 +385,13 @@ def roll_var(const float64_t[:] values, ndarray[int64_t] start,
# never removed
if i == 0 or not is_monotonic_increasing_bounds or s >= end[i - 1]:
+ prev_value = values[s]
+ num_consecutive_same_value = 0
+
mean_x = ssqdm_x = nobs = compensation_add = compensation_remove = 0
for j in range(s, e):
- add_var(values[j], &nobs, &mean_x, &ssqdm_x, &compensation_add)
+ add_var(values[j], &nobs, &mean_x, &ssqdm_x, &compensation_add,
+ &num_consecutive_same_value, &prev_value)
else:
@@ -392,9 +405,10 @@ def roll_var(const float64_t[:] values, ndarray[int64_t] start,
# calculate adds
for j in range(end[i - 1], e):
- add_var(values[j], &nobs, &mean_x, &ssqdm_x, &compensation_add)
+ add_var(values[j], &nobs, &mean_x, &ssqdm_x, &compensation_add,
+ &num_consecutive_same_value, &prev_value)
- output[i] = calc_var(minp, ddof, nobs, ssqdm_x)
+ output[i] = calc_var(minp, ddof, nobs, ssqdm_x, num_consecutive_same_value)
if not is_monotonic_increasing_bounds:
nobs = 0.0
diff --git a/pandas/core/_numba/kernels/sum_.py b/pandas/core/_numba/kernels/sum_.py
index c2e81b4990ba9..b67452e4071d9 100644
--- a/pandas/core/_numba/kernels/sum_.py
+++ b/pandas/core/_numba/kernels/sum_.py
@@ -81,7 +81,7 @@ def sliding_sum(
val, nobs, sum_x, compensation_add
)
- if nobs == 0 == nobs:
+ if nobs == 0 == min_periods:
result = 0.0
elif nobs >= min_periods:
result = sum_x
diff --git a/pandas/core/_numba/kernels/var_.py b/pandas/core/_numba/kernels/var_.py
index 2e5660673701b..b1c72832d249b 100644
--- a/pandas/core/_numba/kernels/var_.py
+++ b/pandas/core/_numba/kernels/var_.py
@@ -16,9 +16,22 @@
@numba.jit(nopython=True, nogil=True, parallel=False)
def add_var(
- val: float, nobs: int, mean_x: float, ssqdm_x: float, compensation: float
-) -> tuple[int, float, float, float]:
+ val: float,
+ nobs: int,
+ mean_x: float,
+ ssqdm_x: float,
+ compensation: float,
+ num_consecutive_same_value: int,
+ prev_value: float,
+) -> tuple[int, float, float, float, int, float]:
if not np.isnan(val):
+
+ if val == prev_value:
+ num_consecutive_same_value += 1
+ else:
+ num_consecutive_same_value = 1
+ prev_value = val
+
nobs += 1
prev_mean = mean_x - compensation
y = val - compensation
@@ -30,7 +43,7 @@ def add_var(
else:
mean_x = 0
ssqdm_x += (val - prev_mean) * (val - mean_x)
- return nobs, mean_x, ssqdm_x, compensation
+ return nobs, mean_x, ssqdm_x, compensation, num_consecutive_same_value, prev_value
@numba.jit(nopython=True, nogil=True, parallel=False)
@@ -79,10 +92,27 @@ def sliding_var(
s = start[i]
e = end[i]
if i == 0 or not is_monotonic_increasing_bounds:
+
+ prev_value = values[s]
+ num_consecutive_same_value = 0
+
for j in range(s, e):
val = values[j]
- nobs, mean_x, ssqdm_x, compensation_add = add_var(
- val, nobs, mean_x, ssqdm_x, compensation_add
+ (
+ nobs,
+ mean_x,
+ ssqdm_x,
+ compensation_add,
+ num_consecutive_same_value,
+ prev_value,
+ ) = add_var(
+ val,
+ nobs,
+ mean_x,
+ ssqdm_x,
+ compensation_add,
+ num_consecutive_same_value,
+ prev_value,
)
else:
for j in range(start[i - 1], s):
@@ -93,12 +123,25 @@ def sliding_var(
for j in range(end[i - 1], e):
val = values[j]
- nobs, mean_x, ssqdm_x, compensation_add = add_var(
- val, nobs, mean_x, ssqdm_x, compensation_add
+ (
+ nobs,
+ mean_x,
+ ssqdm_x,
+ compensation_add,
+ num_consecutive_same_value,
+ prev_value,
+ ) = add_var(
+ val,
+ nobs,
+ mean_x,
+ ssqdm_x,
+ compensation_add,
+ num_consecutive_same_value,
+ prev_value,
)
if nobs >= min_periods and nobs > ddof:
- if nobs == 1:
+ if nobs == 1 or num_consecutive_same_value >= nobs:
result = 0.0
else:
result = ssqdm_x / (nobs - ddof)
diff --git a/pandas/core/window/rolling.py b/pandas/core/window/rolling.py
index ac3d8b3dabb2b..5006db44849e0 100644
--- a/pandas/core/window/rolling.py
+++ b/pandas/core/window/rolling.py
@@ -2150,10 +2150,7 @@ def median(
The default ``ddof`` of 1 used in :meth:`Series.std` is different
than the default ``ddof`` of 0 in :func:`numpy.std`.
- A minimum of one period is required for the rolling calculation.
-
- The implementation is susceptible to floating point imprecision as
- shown in the example below.\n
+ A minimum of one period is required for the rolling calculation.\n
"""
).replace("\n", "", 1),
create_section_header("Examples"),
@@ -2161,13 +2158,13 @@ def median(
"""
>>> s = pd.Series([5, 5, 6, 7, 5, 5, 5])
>>> s.rolling(3).std()
- 0 NaN
- 1 NaN
- 2 5.773503e-01
- 3 1.000000e+00
- 4 1.000000e+00
- 5 1.154701e+00
- 6 2.580957e-08
+ 0 NaN
+ 1 NaN
+ 2 0.577350
+ 3 1.000000
+ 4 1.000000
+ 5 1.154701
+ 6 0.000000
dtype: float64
"""
).replace("\n", "", 1),
@@ -2212,10 +2209,7 @@ def std(
The default ``ddof`` of 1 used in :meth:`Series.var` is different
than the default ``ddof`` of 0 in :func:`numpy.var`.
- A minimum of one period is required for the rolling calculation.
-
- The implementation is susceptible to floating point imprecision as
- shown in the example below.\n
+ A minimum of one period is required for the rolling calculation.\n
"""
).replace("\n", "", 1),
create_section_header("Examples"),
@@ -2223,13 +2217,13 @@ def std(
"""
>>> s = pd.Series([5, 5, 6, 7, 5, 5, 5])
>>> s.rolling(3).var()
- 0 NaN
- 1 NaN
- 2 3.333333e-01
- 3 1.000000e+00
- 4 1.000000e+00
- 5 1.333333e+00
- 6 6.661338e-16
+ 0 NaN
+ 1 NaN
+ 2 0.333333
+ 3 1.000000
+ 4 1.000000
+ 5 1.333333
+ 6 0.000000
dtype: float64
"""
).replace("\n", "", 1),
diff --git a/pandas/tests/window/test_numba.py b/pandas/tests/window/test_numba.py
index 0e7fe24420171..4cf6306dc39e5 100644
--- a/pandas/tests/window/test_numba.py
+++ b/pandas/tests/window/test_numba.py
@@ -79,7 +79,20 @@ def f(x, *args):
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
- "data", [DataFrame(np.eye(5)), Series(range(5), name="foo")]
+ "data",
+ [
+ DataFrame(np.eye(5)),
+ DataFrame(
+ [
+ [5, 7, 7, 7, np.nan, np.inf, 4, 3, 3, 3],
+ [5, 7, 7, 7, np.nan, np.inf, 7, 3, 3, 3],
+ [np.nan, np.nan, 5, 6, 7, 5, 5, 5, 5, 5],
+ ]
+ ).T,
+ Series(range(5), name="foo"),
+ Series([20, 10, 10, np.inf, 1, 1, 2, 3]),
+ Series([20, 10, 10, np.nan, 10, 1, 2, 3]),
+ ],
)
def test_numba_vs_cython_rolling_methods(
self,
@@ -95,7 +108,7 @@ def test_numba_vs_cython_rolling_methods(
engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
- roll = data.rolling(2, step=step)
+ roll = data.rolling(3, step=step)
result = getattr(roll, method)(
engine="numba", engine_kwargs=engine_kwargs, **kwargs
)
diff --git a/pandas/tests/window/test_rolling.py b/pandas/tests/window/test_rolling.py
index 89c90836ae957..5f7709c62a1e2 100644
--- a/pandas/tests/window/test_rolling.py
+++ b/pandas/tests/window/test_rolling.py
@@ -1203,6 +1203,9 @@ def test_rolling_var_numerical_issues(func, third_value, values):
result = getattr(ds.rolling(2), func)()
expected = Series([np.nan] + values)
tm.assert_series_equal(result, expected)
+ # GH 42064
+ # new `roll_var` will output 0.0 correctly
+ tm.assert_series_equal(result == 0, expected == 0)
def test_timeoffset_as_window_parameter_for_corr():
@@ -1509,6 +1512,9 @@ def test_rolling_var_floating_artifact_precision():
result = s.rolling(3).var()
expected = Series([np.nan, np.nan, 4 / 3, 0])
tm.assert_series_equal(result, expected, atol=1.0e-15, rtol=1.0e-15)
+ # GH 42064
+ # new `roll_var` will output 0.0 correctly
+ tm.assert_series_equal(result == 0, expected == 0)
def test_rolling_std_small_values():
@@ -1769,3 +1775,77 @@ def test_step_not_integer_raises():
def test_step_not_positive_raises():
with pytest.raises(ValueError, match="step must be >= 0"):
DataFrame(range(2)).rolling(1, step=-1)
+
+
+@pytest.mark.parametrize(
+ ["values", "window", "min_periods", "expected"],
+ [
+ [
+ [20, 10, 10, np.inf, 1, 1, 2, 3],
+ 3,
+ 1,
+ [np.nan, 50, 100 / 3, 0, 40.5, 0, 1 / 3, 1],
+ ],
+ [
+ [20, 10, 10, np.nan, 10, 1, 2, 3],
+ 3,
+ 1,
+ [np.nan, 50, 100 / 3, 0, 0, 40.5, 73 / 3, 1],
+ ],
+ [
+ [np.nan, 5, 6, 7, 5, 5, 5],
+ 3,
+ 3,
+ [np.nan] * 3 + [1, 1, 4 / 3, 0],
+ ],
+ [
+ [5, 7, 7, 7, np.nan, np.inf, 4, 3, 3, 3],
+ 3,
+ 3,
+ [np.nan] * 2 + [4 / 3, 0] + [np.nan] * 4 + [1 / 3, 0],
+ ],
+ [
+ [5, 7, 7, 7, np.nan, np.inf, 7, 3, 3, 3],
+ 3,
+ 3,
+ [np.nan] * 2 + [4 / 3, 0] + [np.nan] * 4 + [16 / 3, 0],
+ ],
+ [
+ [5, 7] * 4,
+ 3,
+ 3,
+ [np.nan] * 2 + [4 / 3] * 6,
+ ],
+ [
+ [5, 7, 5, np.nan, 7, 5, 7],
+ 3,
+ 2,
+ [np.nan, 2, 4 / 3] + [2] * 3 + [4 / 3],
+ ],
+ ],
+)
+def test_rolling_var_same_value_count_logic(values, window, min_periods, expected):
+ # GH 42064.
+
+ expected = Series(expected)
+ sr = Series(values)
+
+ # With new algo implemented, result will be set to .0 in rolling var
+ # if sufficient amount of consecutively same values are found.
+ result_var = sr.rolling(window, min_periods=min_periods).var()
+
+ # use `assert_series_equal` twice to check for equality,
+ # because `check_exact=True` will fail in 32-bit tests due to
+ # precision loss.
+
+ # 1. result should be close to correct value
+ # non-zero values can still differ slightly from "truth"
+ # as the result of online algorithm
+ tm.assert_series_equal(result_var, expected)
+ # 2. zeros should be exactly the same since the new algo takes effect here
+ tm.assert_series_equal(expected == 0, result_var == 0)
+
+ # std should also pass as it's just a sqrt of var
+ result_std = sr.rolling(window, min_periods=min_periods).std()
+ tm.assert_series_equal(result_std, np.sqrt(expected))
+ tm.assert_series_equal(expected == 0, result_std == 0)
| ENH: use same value counts for roll_var to remove floating point artifacts
Please refer to https://github.com/pandas-dev/pandas/issues/42064#issuecomment-1072934337 for the explaination of this new method.
- [ ] closes #42064 (Replace xxxx with the Github issue number)
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46431 | 2022-03-19T06:32:25Z | 2022-04-19T21:25:13Z | 2022-04-19T21:25:13Z | 2022-04-20T01:51:09Z |
DEPR: Timedelta.freq, Timedelta.is_populated | diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst
index 74639cc693cb7..20c8dfae0de5e 100644
--- a/doc/source/whatsnew/v1.5.0.rst
+++ b/doc/source/whatsnew/v1.5.0.rst
@@ -342,6 +342,7 @@ Other Deprecations
- Deprecated allowing non-keyword arguments in :meth:`ExtensionArray.argsort` (:issue:`46134`)
- Deprecated treating all-bool ``object``-dtype columns as bool-like in :meth:`DataFrame.any` and :meth:`DataFrame.all` with ``bool_only=True``, explicitly cast to bool instead (:issue:`46188`)
- Deprecated behavior of method :meth:`DataFrame.quantile`, attribute ``numeric_only`` will default False. Including datetime/timedelta columns in the result (:issue:`7308`).
+- Deprecated :attr:`Timedelta.freq` and :attr:`Timedelta.is_populated` (:issue:`46430`)
-
.. ---------------------------------------------------------------------------
diff --git a/pandas/_libs/tslibs/timedeltas.pxd b/pandas/_libs/tslibs/timedeltas.pxd
index fed1f2d326819..f054a22ed7ad7 100644
--- a/pandas/_libs/tslibs/timedeltas.pxd
+++ b/pandas/_libs/tslibs/timedeltas.pxd
@@ -11,8 +11,7 @@ cdef bint is_any_td_scalar(object obj)
cdef class _Timedelta(timedelta):
cdef readonly:
int64_t value # nanoseconds
- object freq # frequency reference
- bint is_populated # are my components populated
+ bint _is_populated # are my components populated
int64_t _d, _h, _m, _s, _ms, _us, _ns
cpdef timedelta to_pytimedelta(_Timedelta self)
diff --git a/pandas/_libs/tslibs/timedeltas.pyi b/pandas/_libs/tslibs/timedeltas.pyi
index 28c2f7db62158..f4711f728907d 100644
--- a/pandas/_libs/tslibs/timedeltas.pyi
+++ b/pandas/_libs/tslibs/timedeltas.pyi
@@ -146,3 +146,7 @@ class Timedelta(timedelta):
def __hash__(self) -> int: ...
def isoformat(self) -> str: ...
def to_numpy(self) -> np.timedelta64: ...
+ @property
+ def freq(self) -> None: ...
+ @property
+ def is_populated(self) -> bool: ...
diff --git a/pandas/_libs/tslibs/timedeltas.pyx b/pandas/_libs/tslibs/timedeltas.pyx
index 6b7ebf96633b3..5df316f86dd02 100644
--- a/pandas/_libs/tslibs/timedeltas.pyx
+++ b/pandas/_libs/tslibs/timedeltas.pyx
@@ -826,13 +826,32 @@ cdef _to_py_int_float(v):
cdef class _Timedelta(timedelta):
# cdef readonly:
# int64_t value # nanoseconds
- # object freq # frequency reference
- # bint is_populated # are my components populated
+ # bint _is_populated # are my components populated
# int64_t _d, _h, _m, _s, _ms, _us, _ns
# higher than np.ndarray and np.matrix
__array_priority__ = 100
+ @property
+ def freq(self) -> None:
+ # GH#46430
+ warnings.warn(
+ "Timedelta.freq is deprecated and will be removed in a future version",
+ FutureWarning,
+ stacklevel=1,
+ )
+ return None
+
+ @property
+ def is_populated(self) -> bool:
+ # GH#46430
+ warnings.warn(
+ "Timedelta.is_populated is deprecated and will be removed in a future version",
+ FutureWarning,
+ stacklevel=1,
+ )
+ return self._is_populated
+
def __hash__(_Timedelta self):
if self._has_ns():
return hash(self.value)
@@ -881,7 +900,7 @@ cdef class _Timedelta(timedelta):
"""
compute the components
"""
- if self.is_populated:
+ if self._is_populated:
return
cdef:
@@ -898,7 +917,7 @@ cdef class _Timedelta(timedelta):
self._seconds = tds.seconds
self._microseconds = tds.microseconds
- self.is_populated = 1
+ self._is_populated = 1
cpdef timedelta to_pytimedelta(_Timedelta self):
"""
@@ -1389,7 +1408,7 @@ class Timedelta(_Timedelta):
# make timedelta happy
td_base = _Timedelta.__new__(cls, microseconds=int(value) // 1000)
td_base.value = value
- td_base.is_populated = 0
+ td_base._is_populated = 0
return td_base
def __setstate__(self, state):
diff --git a/pandas/tests/scalar/timedelta/test_timedelta.py b/pandas/tests/scalar/timedelta/test_timedelta.py
index 7a32c932aee77..36520a1983aac 100644
--- a/pandas/tests/scalar/timedelta/test_timedelta.py
+++ b/pandas/tests/scalar/timedelta/test_timedelta.py
@@ -659,3 +659,25 @@ def test_timedelta_attribute_precision():
result += td.nanoseconds
expected = td.value
assert result == expected
+
+
+def test_freq_deprecated():
+ # GH#46430
+ td = Timedelta(123456546, unit="ns")
+ with tm.assert_produces_warning(FutureWarning, match="Timedelta.freq"):
+ freq = td.freq
+
+ assert freq is None
+
+ with pytest.raises(AttributeError, match="is not writable"):
+ td.freq = offsets.Day()
+
+
+def test_is_populated_deprecated():
+ # GH#46430
+ td = Timedelta(123456546, unit="ns")
+ with tm.assert_produces_warning(FutureWarning, match="Timedelta.is_populated"):
+ td.is_populated
+
+ with pytest.raises(AttributeError, match="is not writable"):
+ td.is_populated = 1
| - [ ] closes #xxxx (Replace xxxx with the Github issue number)
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46430 | 2022-03-19T04:29:44Z | 2022-03-20T16:34:14Z | 2022-03-20T16:34:14Z | 2022-03-20T18:47:11Z |
TST: Ensure makeCategoricalIndex categories are unique | diff --git a/asv_bench/benchmarks/categoricals.py b/asv_bench/benchmarks/categoricals.py
index 2b60871cbf82b..b30ad9f7b4905 100644
--- a/asv_bench/benchmarks/categoricals.py
+++ b/asv_bench/benchmarks/categoricals.py
@@ -187,7 +187,7 @@ def time_remove_categories(self):
class Rank:
def setup(self):
N = 10**5
- ncats = 100
+ ncats = 20
self.s_str = pd.Series(tm.makeCategoricalIndex(N, ncats)).astype(str)
self.s_str_cat = pd.Series(self.s_str, dtype="category")
diff --git a/pandas/_testing/__init__.py b/pandas/_testing/__init__.py
index 1b236cdc99bed..fc48317114e23 100644
--- a/pandas/_testing/__init__.py
+++ b/pandas/_testing/__init__.py
@@ -305,7 +305,7 @@ def makeUnicodeIndex(k=10, name=None):
def makeCategoricalIndex(k=10, n=3, name=None, **kwargs):
"""make a length k index or n categories"""
- x = rands_array(nchars=4, size=n)
+ x = rands_array(nchars=4, size=n, replace=False)
return CategoricalIndex(
Categorical.from_codes(np.arange(k) % n, categories=x), name=name, **kwargs
)
diff --git a/pandas/_testing/_random.py b/pandas/_testing/_random.py
index a646d7639a4e6..f90e18a2020c5 100644
--- a/pandas/_testing/_random.py
+++ b/pandas/_testing/_random.py
@@ -14,12 +14,12 @@ def randbool(size=(), p: float = 0.5):
)
-def rands_array(nchars, size, dtype="O"):
+def rands_array(nchars, size, dtype="O", replace=True):
"""
Generate an array of byte strings.
"""
retval = (
- np.random.choice(RANDS_CHARS, size=nchars * np.prod(size))
+ np.random.choice(RANDS_CHARS, size=nchars * np.prod(size), replace=replace)
.view((np.str_, nchars))
.reshape(size)
)
| - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
If the flaky lottery is won and the random categories are not unique can raise
```
ImportError while loading conftest 'D:\a\1\s\pandas\conftest.py'.
pandas\conftest.py:564: in <module>
"categorical": tm.makeCategoricalIndex(100),
pandas\_testing\__init__.py:310: in makeCategoricalIndex
Categorical.from_codes(np.arange(k) % n, categories=x), name=name, **kwargs
pandas\core\arrays\categorical.py:678: in from_codes
dtype = CategoricalDtype._from_values_or_dtype(
pandas\core\dtypes\dtypes.py:298: in _from_values_or_dtype
dtype = CategoricalDtype(categories, ordered)
pandas\core\dtypes\dtypes.py:185: in __init__
self._finalize(categories, ordered, fastpath=False)
pandas\core\dtypes\dtypes.py:339: in _finalize
categories = self.validate_categories(categories, fastpath=fastpath)
pandas\core\dtypes\dtypes.py:536: in validate_categories
raise ValueError("Categorical categories must be unique")
E ValueError: Categorical categories must be unique
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/46429 | 2022-03-19T03:12:32Z | 2022-03-19T18:06:52Z | 2022-03-19T18:06:52Z | 2022-05-19T03:02:54Z |
TYP: rename | diff --git a/pandas/_typing.py b/pandas/_typing.py
index 30244e025e430..35c057df43322 100644
--- a/pandas/_typing.py
+++ b/pandas/_typing.py
@@ -141,7 +141,7 @@
]
# For functions like rename that convert one label to another
-Renamer = Union[Mapping[Hashable, Any], Callable[[Hashable], Hashable]]
+Renamer = Union[Mapping[Any, Hashable], Callable[[Any], Hashable]]
# to maintain type information across generic functions and parametrization
T = TypeVar("T")
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index d56e4ef451954..6d4deb79b4898 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -5030,6 +5030,51 @@ def drop( # type: ignore[override]
errors=errors,
)
+ @overload
+ def rename(
+ self,
+ mapper: Renamer | None = ...,
+ *,
+ index: Renamer | None = ...,
+ columns: Renamer | None = ...,
+ axis: Axis | None = ...,
+ copy: bool = ...,
+ inplace: Literal[True],
+ level: Level | None = ...,
+ errors: IgnoreRaise = ...,
+ ) -> None:
+ ...
+
+ @overload
+ def rename(
+ self,
+ mapper: Renamer | None = ...,
+ *,
+ index: Renamer | None = ...,
+ columns: Renamer | None = ...,
+ axis: Axis | None = ...,
+ copy: bool = ...,
+ inplace: Literal[False] = ...,
+ level: Level | None = ...,
+ errors: IgnoreRaise = ...,
+ ) -> DataFrame:
+ ...
+
+ @overload
+ def rename(
+ self,
+ mapper: Renamer | None = ...,
+ *,
+ index: Renamer | None = ...,
+ columns: Renamer | None = ...,
+ axis: Axis | None = ...,
+ copy: bool = ...,
+ inplace: bool = ...,
+ level: Level | None = ...,
+ errors: IgnoreRaise = ...,
+ ) -> DataFrame | None:
+ ...
+
def rename(
self,
mapper: Renamer | None = None,
@@ -5040,7 +5085,7 @@ def rename(
copy: bool = True,
inplace: bool = False,
level: Level | None = None,
- errors: str = "ignore",
+ errors: IgnoreRaise = "ignore",
) -> DataFrame | None:
"""
Alter axes labels.
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 700a8f6a39f8d..2e65e3139ffa1 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -984,117 +984,8 @@ def _rename(
level: Level | None = None,
errors: str = "ignore",
) -> NDFrameT | None:
- """
- Alter axes input function or functions. Function / dict values must be
- unique (1-to-1). Labels not contained in a dict / Series will be left
- as-is. Extra labels listed don't throw an error. Alternatively, change
- ``Series.name`` with a scalar value (Series only).
-
- Parameters
- ----------
- %(axes)s : scalar, list-like, dict-like or function, optional
- Scalar or list-like will alter the ``Series.name`` attribute,
- and raise on DataFrame.
- dict-like or functions are transformations to apply to
- that axis' values
- copy : bool, default True
- Also copy underlying data.
- inplace : bool, default False
- Whether to return a new {klass}. If True then value of copy is
- ignored.
- level : int or level name, default None
- In case of a MultiIndex, only rename labels in the specified
- level.
- errors : {'ignore', 'raise'}, default 'ignore'
- If 'raise', raise a `KeyError` when a dict-like `mapper`, `index`,
- or `columns` contains labels that are not present in the Index
- being transformed.
- If 'ignore', existing keys will be renamed and extra keys will be
- ignored.
-
- Returns
- -------
- renamed : {klass} (new object)
-
- Raises
- ------
- KeyError
- If any of the labels is not found in the selected axis and
- "errors='raise'".
+ # called by Series.rename and DataFrame.rename
- See Also
- --------
- NDFrame.rename_axis
-
- Examples
- --------
- >>> s = pd.Series([1, 2, 3])
- >>> s
- 0 1
- 1 2
- 2 3
- dtype: int64
- >>> s.rename("my_name") # scalar, changes Series.name
- 0 1
- 1 2
- 2 3
- Name: my_name, dtype: int64
- >>> s.rename(lambda x: x ** 2) # function, changes labels
- 0 1
- 1 2
- 4 3
- dtype: int64
- >>> s.rename({1: 3, 2: 5}) # mapping, changes labels
- 0 1
- 3 2
- 5 3
- dtype: int64
-
- Since ``DataFrame`` doesn't have a ``.name`` attribute,
- only mapping-type arguments are allowed.
-
- >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
- >>> df.rename(2)
- Traceback (most recent call last):
- ...
- TypeError: 'int' object is not callable
-
- ``DataFrame.rename`` supports two calling conventions
-
- * ``(index=index_mapper, columns=columns_mapper, ...)``
- * ``(mapper, axis={'index', 'columns'}, ...)``
-
- We *highly* recommend using keyword arguments to clarify your
- intent.
-
- >>> df.rename(index=str, columns={"A": "a", "B": "c"})
- a c
- 0 1 4
- 1 2 5
- 2 3 6
-
- >>> df.rename(index=str, columns={"A": "a", "C": "c"})
- a B
- 0 1 4
- 1 2 5
- 2 3 6
-
- Using axis-style parameters
-
- >>> df.rename(str.lower, axis='columns')
- a b
- 0 1 4
- 1 2 5
- 2 3 6
-
- >>> df.rename({1: 2, 2: 4}, axis='index')
- A B
- 0 1 4
- 2 2 5
- 4 3 6
-
- See the :ref:`user guide <basics.rename>` for more.
- """
if mapper is None and index is None and columns is None:
raise TypeError("must pass an index to rename")
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 91c35d7555705..f649cce985474 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -2204,8 +2204,7 @@ def size(self) -> DataFrame | Series:
result = self._obj_1d_constructor(result)
if not self.as_index:
- # Item "None" of "Optional[Series]" has no attribute "reset_index"
- result = result.rename("size").reset_index() # type: ignore[union-attr]
+ result = result.rename("size").reset_index()
return self._reindex_output(result, fill_value=0)
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 83c5e8206952c..1d3509cac0edd 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -42,6 +42,7 @@
IndexKeyFunc,
Level,
NaPosition,
+ Renamer,
SingleManager,
SortKind,
StorageOptions,
@@ -4617,15 +4618,54 @@ def align(
broadcast_axis=broadcast_axis,
)
+ @overload
def rename(
self,
- index=None,
+ index: Renamer | Hashable | None = ...,
*,
- axis=None,
- copy=True,
- inplace=False,
- level=None,
- errors="ignore",
+ axis: Axis | None = ...,
+ copy: bool = ...,
+ inplace: Literal[True],
+ level: Level | None = ...,
+ errors: IgnoreRaise = ...,
+ ) -> None:
+ ...
+
+ @overload
+ def rename(
+ self,
+ index: Renamer | Hashable | None = ...,
+ *,
+ axis: Axis | None = ...,
+ copy: bool = ...,
+ inplace: Literal[False] = ...,
+ level: Level | None = ...,
+ errors: IgnoreRaise = ...,
+ ) -> Series:
+ ...
+
+ @overload
+ def rename(
+ self,
+ index: Renamer | Hashable | None = ...,
+ *,
+ axis: Axis | None = ...,
+ copy: bool = ...,
+ inplace: bool = ...,
+ level: Level | None = ...,
+ errors: IgnoreRaise = ...,
+ ) -> Series | None:
+ ...
+
+ def rename(
+ self,
+ index: Renamer | Hashable | None = None,
+ *,
+ axis: Axis | None = None,
+ copy: bool = True,
+ inplace: bool = False,
+ level: Level | None = None,
+ errors: IgnoreRaise = "ignore",
) -> Series | None:
"""
Alter Series index labels or name.
@@ -4691,8 +4731,16 @@ def rename(
axis = self._get_axis_number(axis)
if callable(index) or is_dict_like(index):
+ # error: Argument 1 to "_rename" of "NDFrame" has incompatible
+ # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
+ # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
+ # Hashable], Callable[[Any], Hashable], None]"
return super()._rename(
- index, copy=copy, inplace=inplace, level=level, errors=errors
+ index, # type: ignore[arg-type]
+ copy=copy,
+ inplace=inplace,
+ level=level,
+ errors=errors,
)
else:
return self._set_name(index, inplace=inplace)
diff --git a/pandas/io/json/_normalize.py b/pandas/io/json/_normalize.py
index 4a2e49fd85f45..e77d60d2d4950 100644
--- a/pandas/io/json/_normalize.py
+++ b/pandas/io/json/_normalize.py
@@ -520,11 +520,7 @@ def _recursive_extract(data, path, seen_meta, level=0):
result = DataFrame(records)
if record_prefix is not None:
- # Incompatible types in assignment (expression has type "Optional[DataFrame]",
- # variable has type "DataFrame")
- result = result.rename( # type: ignore[assignment]
- columns=lambda x: f"{record_prefix}{x}"
- )
+ result = result.rename(columns=lambda x: f"{record_prefix}{x}")
# Data types, a problem
for k, v in meta_vals.items():
| tested with mypy and pyright:
```py
import pandas as pd
def bool_() -> bool:
...
df = pd.DataFrame({"a": []})
ser = pd.Series({"a": 1})
# DataFrame/Series
reveal_type(df.rename(columns={"a": "b"}))
reveal_type(df.rename(columns={"a": "b"}, inplace=False))
reveal_type(ser.rename("a"))
reveal_type(ser.rename("a", inplace=False))
# None
reveal_type(df.rename(columns={"a": "b"}, inplace=True))
reveal_type(ser.rename("a", inplace=True))
# both
reveal_type(df.rename(columns={"a": "b"}, inplace=bool_()))
reveal_type(ser.rename("a", inplace=bool_()))
``` | https://api.github.com/repos/pandas-dev/pandas/pulls/46428 | 2022-03-19T02:59:17Z | 2022-04-03T03:20:40Z | 2022-04-03T03:20:40Z | 2022-05-26T01:59:14Z |
TST: Convert skip -> xfail | diff --git a/pandas/tests/extension/base/methods.py b/pandas/tests/extension/base/methods.py
index 6379dfe2efefe..b829b017d5fb1 100644
--- a/pandas/tests/extension/base/methods.py
+++ b/pandas/tests/extension/base/methods.py
@@ -18,7 +18,7 @@ class BaseMethodsTests(BaseExtensionTests):
def test_value_counts_default_dropna(self, data):
# make sure we have consistent default dropna kwarg
if not hasattr(data, "value_counts"):
- pytest.skip("value_counts is not implemented")
+ pytest.skip(f"value_counts is not implemented for {type(data)}")
sig = inspect.signature(data.value_counts)
kwarg = sig.parameters["dropna"]
assert kwarg.default is True
diff --git a/pandas/tests/extension/json/test_json.py b/pandas/tests/extension/json/test_json.py
index 84491adb30ef6..2b6174be2ca0e 100644
--- a/pandas/tests/extension/json/test_json.py
+++ b/pandas/tests/extension/json/test_json.py
@@ -190,13 +190,14 @@ def test_series_constructor_scalar_with_index(self, data, dtype):
class TestReshaping(BaseJSON, base.BaseReshapingTests):
- @pytest.mark.skip(reason="Different definitions of NA")
+ @pytest.mark.xfail(reason="Different definitions of NA")
def test_stack(self):
"""
The test does .astype(object).stack(). If we happen to have
any missing values in `data`, then we'll end up with different
rows since we consider `{}` NA, but `.astype(object)` doesn't.
"""
+ super().test_stack()
@pytest.mark.xfail(reason="dict for NA")
def test_unstack(self, data, index):
@@ -214,16 +215,18 @@ class TestIndex(BaseJSON, base.BaseIndexTests):
class TestMissing(BaseJSON, base.BaseMissingTests):
- @pytest.mark.skip(reason="Setting a dict as a scalar")
+ @pytest.mark.xfail(reason="Setting a dict as a scalar")
def test_fillna_series(self):
"""We treat dictionaries as a mapping in fillna, not a scalar."""
+ super().test_fillna_series()
- @pytest.mark.skip(reason="Setting a dict as a scalar")
+ @pytest.mark.xfail(reason="Setting a dict as a scalar")
def test_fillna_frame(self):
"""We treat dictionaries as a mapping in fillna, not a scalar."""
+ super().test_fillna_frame()
-unhashable = pytest.mark.skip(reason="Unhashable")
+unhashable = pytest.mark.xfail(reason="Unhashable")
class TestReduce(base.BaseNoReduceTests):
@@ -233,16 +236,16 @@ class TestReduce(base.BaseNoReduceTests):
class TestMethods(BaseJSON, base.BaseMethodsTests):
@unhashable
def test_value_counts(self, all_data, dropna):
- pass
+ super().test_value_counts(all_data, dropna)
@unhashable
def test_value_counts_with_normalize(self, data):
- pass
+ super().test_value_counts_with_normalize(data)
@unhashable
def test_sort_values_frame(self):
# TODO (EA.factorize): see if _values_for_factorize allows this.
- pass
+ super().test_sort_values_frame()
@pytest.mark.parametrize("ascending", [True, False])
def test_sort_values(self, data_for_sorting, ascending, sort_by_key):
@@ -256,45 +259,46 @@ def test_sort_values_missing(
data_missing_for_sorting, ascending, sort_by_key
)
- @pytest.mark.skip(reason="combine for JSONArray not supported")
+ @pytest.mark.xfail(reason="combine for JSONArray not supported")
def test_combine_le(self, data_repeated):
- pass
+ super().test_combine_le(data_repeated)
- @pytest.mark.skip(reason="combine for JSONArray not supported")
+ @pytest.mark.xfail(reason="combine for JSONArray not supported")
def test_combine_add(self, data_repeated):
- pass
+ super().test_combine_add(data_repeated)
- @pytest.mark.skip(reason="combine for JSONArray not supported")
+ @pytest.mark.xfail(reason="combine for JSONArray not supported")
def test_combine_first(self, data):
- pass
+ super().test_combine_first(data)
@unhashable
def test_hash_pandas_object_works(self, data, kind):
super().test_hash_pandas_object_works(data, kind)
- @pytest.mark.skip(reason="broadcasting error")
+ @pytest.mark.xfail(reason="broadcasting error")
def test_where_series(self, data, na_value):
# Fails with
# *** ValueError: operands could not be broadcast together
# with shapes (4,) (4,) (0,)
super().test_where_series(data, na_value)
- @pytest.mark.skip(reason="Can't compare dicts.")
+ @pytest.mark.xfail(reason="Can't compare dicts.")
def test_searchsorted(self, data_for_sorting):
super().test_searchsorted(data_for_sorting)
- @pytest.mark.skip(reason="Can't compare dicts.")
+ @pytest.mark.xfail(reason="Can't compare dicts.")
def test_equals(self, data, na_value, as_series):
- pass
+ super().test_equals(data, na_value, as_series)
class TestCasting(BaseJSON, base.BaseCastingTests):
- @pytest.mark.skip(reason="failing on np.array(self, dtype=str)")
+ @pytest.mark.xfail(reason="failing on np.array(self, dtype=str)")
def test_astype_str(self):
"""This currently fails in NumPy on np.array(self, dtype=str) with
*** ValueError: setting an array element with a sequence
"""
+ super().test_astype_str()
# We intentionally don't run base.BaseSetitemTests because pandas'
@@ -310,6 +314,7 @@ def test_groupby_extension_transform(self):
I think this is what we want, i.e. `.name` should be the original
values, and not the values for factorization.
"""
+ super().test_groupby_extension_transform()
@unhashable
def test_groupby_extension_apply(self):
@@ -322,6 +327,7 @@ def test_groupby_extension_apply(self):
I suspect that once we support Index[ExtensionArray],
we'll be able to dispatch unique.
"""
+ super().test_groupby_extension_apply()
@unhashable
def test_groupby_extension_agg(self):
@@ -329,6 +335,7 @@ def test_groupby_extension_agg(self):
This fails when we get to tm.assert_series_equal when left.index
contains dictionaries, which are not hashable.
"""
+ super().test_groupby_extension_agg()
@unhashable
def test_groupby_extension_no_sort(self):
@@ -336,6 +343,7 @@ def test_groupby_extension_no_sort(self):
This fails when we get to tm.assert_series_equal when left.index
contains dictionaries, which are not hashable.
"""
+ super().test_groupby_extension_no_sort()
@pytest.mark.xfail(reason="GH#39098: Converts agg result to object")
def test_groupby_agg_extension(self, data_for_grouping):
@@ -354,10 +362,11 @@ def test_add_series_with_extension_array(self, data):
with pytest.raises(TypeError, match="unsupported"):
ser + data
+ @pytest.mark.xfail(reason="not implemented")
def test_divmod_series_array(self):
# GH 23287
# skipping because it is not implemented
- pass
+ super().test_divmod_series_array()
def _check_divmod_op(self, s, op, other, exc=NotImplementedError):
return super()._check_divmod_op(s, op, other, exc=TypeError)
diff --git a/pandas/tests/extension/test_sparse.py b/pandas/tests/extension/test_sparse.py
index dda067ac01e9b..d13f6dab1cc9b 100644
--- a/pandas/tests/extension/test_sparse.py
+++ b/pandas/tests/extension/test_sparse.py
@@ -266,10 +266,10 @@ def test_fillna_series_method(self, data_missing):
with tm.assert_produces_warning(PerformanceWarning):
super().test_fillna_limit_backfill(data_missing)
- @pytest.mark.skip(reason="Unsupported")
+ @pytest.mark.xfail(reason="Unsupported")
def test_fillna_series(self):
# this one looks doable.
- pass
+ super(self).test_fillna_series()
def test_fillna_frame(self, data_missing):
# Have to override to specify that fill_value will change.
@@ -337,9 +337,9 @@ def test_fillna_copy_series(self, data_missing):
assert ser._values is not result._values
assert ser._values.to_dense() is arr.to_dense()
- @pytest.mark.skip(reason="Not Applicable")
+ @pytest.mark.xfail(reason="Not Applicable")
def test_fillna_length_mismatch(self, data_missing):
- pass
+ super().test_fillna_length_mismatch(data_missing)
def test_where_series(self, data, na_value):
assert data[0] != data[1]
diff --git a/pandas/tests/indexes/test_common.py b/pandas/tests/indexes/test_common.py
index 31e82545646b5..05c66191ca3a2 100644
--- a/pandas/tests/indexes/test_common.py
+++ b/pandas/tests/indexes/test_common.py
@@ -454,12 +454,16 @@ def test_sort_values_invalid_na_position(index_with_missing, na_position):
@pytest.mark.parametrize("na_position", ["first", "last"])
-def test_sort_values_with_missing(index_with_missing, na_position):
+def test_sort_values_with_missing(index_with_missing, na_position, request):
# GH 35584. Test that sort_values works with missing values,
# sort non-missing and place missing according to na_position
if isinstance(index_with_missing, CategoricalIndex):
- pytest.skip("missing value sorting order not well-defined")
+ request.node.add_marker(
+ pytest.mark.xfail(
+ reason="missing value sorting order not well-defined", strict=False
+ )
+ )
missing_count = np.sum(index_with_missing.isna())
not_na_vals = index_with_missing[index_with_missing.notna()].values
diff --git a/pandas/tests/io/excel/test_writers.py b/pandas/tests/io/excel/test_writers.py
index 5b46c0baac165..06c106ed22329 100644
--- a/pandas/tests/io/excel/test_writers.py
+++ b/pandas/tests/io/excel/test_writers.py
@@ -21,6 +21,7 @@
option_context,
)
import pandas._testing as tm
+from pandas.util.version import Version
from pandas.io.excel import (
ExcelFile,
@@ -1087,8 +1088,8 @@ def test_comment_empty_line(self, path):
result = pd.read_excel(path, comment="#")
tm.assert_frame_equal(result, expected)
- def test_datetimes(self, path):
-
+ def test_datetimes(self, path, request):
+ openpyxl = pytest.importorskip("openpyxl")
# Test writing and reading datetimes. For issue #9139. (xref #9185)
datetimes = [
datetime(2013, 1, 13, 1, 2, 3),
@@ -1106,10 +1107,15 @@ def test_datetimes(self, path):
write_frame = DataFrame({"A": datetimes})
write_frame.to_excel(path, "Sheet1")
- if path.endswith("xlsx") or path.endswith("xlsm"):
- pytest.skip(
- "Defaults to openpyxl and fails with floating point error on "
- "datetimes; may be fixed on newer versions of openpyxl - GH #38644"
+ if (path.endswith("xlsx") or path.endswith("xlsm")) and Version(
+ openpyxl.__version__
+ ) < Version("3.0.6"):
+ request.node.add_marker(
+ pytest.mark.xfail(
+ reason="Defaults to openpyxl and fails with "
+ "floating point error on datetimes; may be fixed on "
+ "newer versions of openpyxl - GH #38644"
+ )
)
read_frame = pd.read_excel(path, sheet_name="Sheet1", header=0)
diff --git a/pandas/tests/io/formats/test_info.py b/pandas/tests/io/formats/test_info.py
index 6bcf971e5bb05..54b5e699cd034 100644
--- a/pandas/tests/io/formats/test_info.py
+++ b/pandas/tests/io/formats/test_info.py
@@ -388,7 +388,7 @@ def test_info_memory_usage_deep_not_pypy():
assert df_object.memory_usage(deep=True).sum() > df_object.memory_usage().sum()
-@pytest.mark.skipif(not PYPY, reason="on PyPy deep=True does not change result")
+@pytest.mark.xfail(not PYPY, reason="on PyPy deep=True does not change result")
def test_info_memory_usage_deep_pypy():
df_with_object_index = DataFrame({"a": [1]}, index=["foo"])
assert (
| - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
| https://api.github.com/repos/pandas-dev/pandas/pulls/46427 | 2022-03-19T00:15:26Z | 2022-03-20T16:22:44Z | 2022-03-20T16:22:44Z | 2022-03-20T17:44:51Z |
CI: Add pytest-asyncio to PyPy CI | diff --git a/.github/workflows/posix.yml b/.github/workflows/posix.yml
index 4380e8dfa2e57..bc8791afc69f7 100644
--- a/.github/workflows/posix.yml
+++ b/.github/workflows/posix.yml
@@ -162,7 +162,7 @@ jobs:
shell: bash
run: |
# TODO: re-enable cov, its slowing the tests down though
- pip install Cython numpy python-dateutil pytz pytest>=6.0 pytest-xdist>=1.31.0 hypothesis>=5.5.3
+ pip install Cython numpy python-dateutil pytz pytest>=6.0 pytest-xdist>=1.31.0 pytest-asyncio hypothesis>=5.5.3
if: ${{ env.IS_PYPY == 'true' }}
- name: Build Pandas
| - [ ] closes #46421 (Replace xxxx with the Github issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46426 | 2022-03-18T23:26:17Z | 2022-03-19T17:45:20Z | 2022-03-19T17:45:20Z | 2022-03-30T10:49:09Z |
ENH: support zoneinfo tzinfos | diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst
index 3d3d3a487ac37..c7cb87a1b2c55 100644
--- a/doc/source/whatsnew/v1.5.0.rst
+++ b/doc/source/whatsnew/v1.5.0.rst
@@ -481,7 +481,7 @@ Timedelta
Time Zones
^^^^^^^^^^
--
+- Bug in :class:`Timestamp` constructor raising when passed a ``ZoneInfo`` tzinfo object (:issue:`46425`)
-
Numeric
diff --git a/pandas/_libs/tslibs/conversion.pyx b/pandas/_libs/tslibs/conversion.pyx
index ac8dd1d536627..77b9b8ce25e41 100644
--- a/pandas/_libs/tslibs/conversion.pyx
+++ b/pandas/_libs/tslibs/conversion.pyx
@@ -56,6 +56,7 @@ from pandas._libs.tslibs.timezones cimport (
is_fixed_offset,
is_tzlocal,
is_utc,
+ is_zoneinfo,
maybe_get_tz,
tz_compare,
utc_pytz as UTC,
@@ -532,7 +533,7 @@ cdef _TSObject _create_tsobject_tz_using_offset(npy_datetimestruct dts,
# see PEP 495 https://www.python.org/dev/peps/pep-0495/#the-fold-attribute
if is_utc(tz):
pass
- elif is_tzlocal(tz):
+ elif is_tzlocal(tz) or is_zoneinfo(tz):
localize_tzinfo_api(obj.value, tz, &obj.fold)
else:
trans, deltas, typ = get_dst_info(tz)
diff --git a/pandas/_libs/tslibs/timezones.pxd b/pandas/_libs/tslibs/timezones.pxd
index 13f196a567952..d1f46b39b2940 100644
--- a/pandas/_libs/tslibs/timezones.pxd
+++ b/pandas/_libs/tslibs/timezones.pxd
@@ -9,6 +9,7 @@ cdef tzinfo utc_pytz
cpdef bint is_utc(tzinfo tz)
cdef bint is_tzlocal(tzinfo tz)
+cdef bint is_zoneinfo(tzinfo tz)
cdef bint treat_tz_as_pytz(tzinfo tz)
diff --git a/pandas/_libs/tslibs/timezones.pyx b/pandas/_libs/tslibs/timezones.pyx
index 6e3f7a370e5dd..22a154be5fcad 100644
--- a/pandas/_libs/tslibs/timezones.pyx
+++ b/pandas/_libs/tslibs/timezones.pyx
@@ -3,6 +3,14 @@ from datetime import (
timezone,
)
+try:
+ # py39+
+ import zoneinfo
+ from zoneinfo import ZoneInfo
+except ImportError:
+ zoneinfo = None
+ ZoneInfo = None
+
from cpython.datetime cimport (
datetime,
timedelta,
@@ -42,18 +50,43 @@ cdef tzinfo utc_stdlib = timezone.utc
cdef tzinfo utc_pytz = UTC
cdef tzinfo utc_dateutil_str = dateutil_gettz("UTC") # NB: *not* the same as tzutc()
+cdef tzinfo utc_zoneinfo = None
+
# ----------------------------------------------------------------------
+cdef inline bint is_utc_zoneinfo(tzinfo tz):
+ # Workaround for cases with missing tzdata
+ # https://github.com/pandas-dev/pandas/pull/46425#discussion_r830633025
+ if tz is None or zoneinfo is None:
+ return False
+
+ global utc_zoneinfo
+ if utc_zoneinfo is None:
+ try:
+ utc_zoneinfo = ZoneInfo("UTC")
+ except zoneinfo.ZoneInfoNotFoundError:
+ return False
+
+ return tz is utc_zoneinfo
+
+
cpdef inline bint is_utc(tzinfo tz):
return (
tz is utc_pytz
or tz is utc_stdlib
or isinstance(tz, _dateutil_tzutc)
or tz is utc_dateutil_str
+ or is_utc_zoneinfo(tz)
)
+cdef inline bint is_zoneinfo(tzinfo tz):
+ if ZoneInfo is None:
+ return False
+ return isinstance(tz, ZoneInfo)
+
+
cdef inline bint is_tzlocal(tzinfo tz):
return isinstance(tz, _dateutil_tzlocal)
@@ -210,6 +243,8 @@ cdef inline bint is_fixed_offset(tzinfo tz):
return 1
else:
return 0
+ elif is_zoneinfo(tz):
+ return 0
# This also implicitly accepts datetime.timezone objects which are
# considered fixed
return 1
@@ -264,6 +299,8 @@ cdef object get_dst_info(tzinfo tz):
# e.g. pytz.FixedOffset, matplotlib.dates._UTC,
# psycopg2.tz.FixedOffsetTimezone
num = int(get_utcoffset(tz, None).total_seconds()) * 1_000_000_000
+ # If we have e.g. ZoneInfo here, the get_utcoffset call will return None,
+ # so the total_seconds() call will raise AttributeError.
return (np.array([NPY_NAT + 1], dtype=np.int64),
np.array([num], dtype=np.int64),
"unknown")
@@ -291,13 +328,13 @@ cdef object get_dst_info(tzinfo tz):
# deltas
deltas = np.array([v.offset for v in (
tz._ttinfo_before,) + tz._trans_idx], dtype='i8')
- deltas *= 1000000000
+ deltas *= 1_000_000_000
typ = 'dateutil'
elif is_fixed_offset(tz):
trans = np.array([NPY_NAT + 1], dtype=np.int64)
deltas = np.array([tz._ttinfo_std.offset],
- dtype='i8') * 1000000000
+ dtype='i8') * 1_000_000_000
typ = 'fixed'
else:
# 2018-07-12 this is not reached in the tests, and this case
diff --git a/pandas/_libs/tslibs/tzconversion.pyx b/pandas/_libs/tslibs/tzconversion.pyx
index cee2de0cf0f4a..806df7928a5a1 100644
--- a/pandas/_libs/tslibs/tzconversion.pyx
+++ b/pandas/_libs/tslibs/tzconversion.pyx
@@ -42,6 +42,7 @@ from pandas._libs.tslibs.timezones cimport (
is_fixed_offset,
is_tzlocal,
is_utc,
+ is_zoneinfo,
utc_pytz,
)
@@ -60,7 +61,7 @@ cdef int64_t tz_localize_to_utc_single(
elif is_utc(tz) or tz is None:
return val
- elif is_tzlocal(tz):
+ elif is_tzlocal(tz) or is_zoneinfo(tz):
return val - _tz_localize_using_tzinfo_api(val, tz, to_utc=True)
elif is_fixed_offset(tz):
@@ -135,7 +136,7 @@ timedelta-like}
result = np.empty(n, dtype=np.int64)
- if is_tzlocal(tz):
+ if is_tzlocal(tz) or is_zoneinfo(tz):
for i in range(n):
v = vals[i]
if v == NPY_NAT:
@@ -484,8 +485,8 @@ cdef int64_t tz_convert_from_utc_single(
if is_utc(tz):
return utc_val
- elif is_tzlocal(tz):
- return utc_val + _tz_localize_using_tzinfo_api(utc_val, tz, to_utc=False)
+ elif is_tzlocal(tz) or is_zoneinfo(tz):
+ return utc_val + _tz_localize_using_tzinfo_api(utc_val, tz, to_utc=False, fold=fold)
else:
trans, deltas, typ = get_dst_info(tz)
tdata = <int64_t*>cnp.PyArray_DATA(trans)
@@ -569,7 +570,7 @@ cdef const int64_t[:] _tz_convert_from_utc(const int64_t[:] stamps, tzinfo tz):
if is_utc(tz) or tz is None:
use_utc = True
- elif is_tzlocal(tz):
+ elif is_tzlocal(tz) or is_zoneinfo(tz):
use_tzlocal = True
else:
trans, deltas, typ = get_dst_info(tz)
diff --git a/pandas/_libs/tslibs/vectorized.pyx b/pandas/_libs/tslibs/vectorized.pyx
index a37e348154e22..34b9e88e046e9 100644
--- a/pandas/_libs/tslibs/vectorized.pyx
+++ b/pandas/_libs/tslibs/vectorized.pyx
@@ -40,6 +40,7 @@ from .timezones cimport (
get_dst_info,
is_tzlocal,
is_utc,
+ is_zoneinfo,
)
from .tzconversion cimport (
bisect_right_i8,
@@ -117,7 +118,7 @@ def ints_to_pydatetime(
if is_utc(tz) or tz is None:
use_utc = True
- elif is_tzlocal(tz):
+ elif is_tzlocal(tz) or is_zoneinfo(tz):
use_tzlocal = True
else:
trans, deltas, typ = get_dst_info(tz)
@@ -204,7 +205,7 @@ def get_resolution(const int64_t[:] stamps, tzinfo tz=None) -> Resolution:
if is_utc(tz) or tz is None:
use_utc = True
- elif is_tzlocal(tz):
+ elif is_tzlocal(tz) or is_zoneinfo(tz):
use_tzlocal = True
else:
trans, deltas, typ = get_dst_info(tz)
@@ -272,7 +273,7 @@ cpdef ndarray[int64_t] normalize_i8_timestamps(const int64_t[:] stamps, tzinfo t
if is_utc(tz) or tz is None:
use_utc = True
- elif is_tzlocal(tz):
+ elif is_tzlocal(tz) or is_zoneinfo(tz):
use_tzlocal = True
else:
trans, deltas, typ = get_dst_info(tz)
@@ -334,7 +335,7 @@ def is_date_array_normalized(const int64_t[:] stamps, tzinfo tz=None) -> bool:
if is_utc(tz) or tz is None:
use_utc = True
- elif is_tzlocal(tz):
+ elif is_tzlocal(tz) or is_zoneinfo(tz):
use_tzlocal = True
else:
trans, deltas, typ = get_dst_info(tz)
@@ -385,7 +386,7 @@ def dt64arr_to_periodarr(const int64_t[:] stamps, int freq, tzinfo tz):
if is_utc(tz) or tz is None:
use_utc = True
- elif is_tzlocal(tz):
+ elif is_tzlocal(tz) or is_zoneinfo(tz):
use_tzlocal = True
else:
trans, deltas, typ = get_dst_info(tz)
diff --git a/pandas/conftest.py b/pandas/conftest.py
index 8c10a0375d4da..ecdc0f10b1f56 100644
--- a/pandas/conftest.py
+++ b/pandas/conftest.py
@@ -75,6 +75,11 @@
del pa
has_pyarrow = True
+zoneinfo = None
+if pd.compat.PY39:
+ # Import "zoneinfo" could not be resolved (reportMissingImports)
+ import zoneinfo # type: ignore[no-redef]
+
# Until https://github.com/numpy/numpy/issues/19078 is sorted out, just suppress
suppress_npdev_promotion_warning = pytest.mark.filterwarnings(
"ignore:Promotion of numbers and bools:FutureWarning"
@@ -1166,6 +1171,8 @@ def iris(datapath):
timezone(timedelta(hours=1)),
timezone(timedelta(hours=-1), name="foo"),
]
+if zoneinfo is not None:
+ TIMEZONES.extend([zoneinfo.ZoneInfo("US/Pacific"), zoneinfo.ZoneInfo("UTC")])
TIMEZONE_IDS = [repr(i) for i in TIMEZONES]
@@ -1191,7 +1198,12 @@ def tz_aware_fixture(request):
tz_aware_fixture2 = tz_aware_fixture
-@pytest.fixture(params=["utc", "dateutil/UTC", utc, tzutc(), timezone.utc])
+_UTCS = ["utc", "dateutil/UTC", utc, tzutc(), timezone.utc]
+if zoneinfo is not None:
+ _UTCS.append(zoneinfo.ZoneInfo("UTC"))
+
+
+@pytest.fixture(params=_UTCS)
def utc_fixture(request):
"""
Fixture to provide variants of UTC timezone strings and tzinfo objects.
diff --git a/pandas/tests/indexes/datetimes/test_constructors.py b/pandas/tests/indexes/datetimes/test_constructors.py
index b1e764ceb7009..4ac2c15b7d98e 100644
--- a/pandas/tests/indexes/datetimes/test_constructors.py
+++ b/pandas/tests/indexes/datetimes/test_constructors.py
@@ -15,6 +15,7 @@
OutOfBoundsDatetime,
conversion,
)
+from pandas.compat import PY39
import pandas as pd
from pandas import (
@@ -31,6 +32,9 @@
period_array,
)
+if PY39:
+ import zoneinfo
+
class TestDatetimeIndex:
@pytest.mark.parametrize(
@@ -1128,7 +1132,12 @@ def test_timestamp_constructor_retain_fold(tz, fold):
assert result == expected
-@pytest.mark.parametrize("tz", ["dateutil/Europe/London"])
+_tzs = ["dateutil/Europe/London"]
+if PY39:
+ _tzs = ["dateutil/Europe/London", zoneinfo.ZoneInfo("Europe/London")]
+
+
+@pytest.mark.parametrize("tz", _tzs)
@pytest.mark.parametrize(
"ts_input,fold_out",
[
@@ -1148,6 +1157,7 @@ def test_timestamp_constructor_infer_fold_from_value(tz, ts_input, fold_out):
result = ts.fold
expected = fold_out
assert result == expected
+ # TODO: belongs in Timestamp tests?
@pytest.mark.parametrize("tz", ["dateutil/Europe/London"])
| - [x] closes #37654 (Replace xxxx with the Github issue number)
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
The tzlocal-handling code is effectively using the tzinfo/datetime API "correctly", so this adapts zoneinfo cases to go through that path.
We may be able to eek out some extra perf by combining tzlocal/zoneinfo checks or something. Just did the simplest thing here.
cc @pganssle note the comment about the cache in timezones.pyx L55. Is this dangerous and if so can you suggest an alternative?
Potentially closes #43516, haven't checked. | https://api.github.com/repos/pandas-dev/pandas/pulls/46425 | 2022-03-18T22:24:59Z | 2022-04-18T00:55:27Z | 2022-04-18T00:55:26Z | 2022-04-18T14:24:47Z |
CI/TYP: require mypy error-codes in ignore comments | diff --git a/pandas/_libs/tslibs/timestamps.pyi b/pandas/_libs/tslibs/timestamps.pyi
index 7a33be1793797..f641c7fe1a12a 100644
--- a/pandas/_libs/tslibs/timestamps.pyi
+++ b/pandas/_libs/tslibs/timestamps.pyi
@@ -133,10 +133,10 @@ class Timestamp(datetime):
def utcoffset(self) -> timedelta | None: ...
def tzname(self) -> str | None: ...
def dst(self) -> timedelta | None: ...
- def __le__(self, other: datetime) -> bool: ... # type: ignore
- def __lt__(self, other: datetime) -> bool: ... # type: ignore
- def __ge__(self, other: datetime) -> bool: ... # type: ignore
- def __gt__(self, other: datetime) -> bool: ... # type: ignore
+ def __le__(self, other: datetime) -> bool: ... # type: ignore[override]
+ def __lt__(self, other: datetime) -> bool: ... # type: ignore[override]
+ def __ge__(self, other: datetime) -> bool: ... # type: ignore[override]
+ def __gt__(self, other: datetime) -> bool: ... # type: ignore[override]
# error: Signature of "__add__" incompatible with supertype "date"/"datetime"
@overload # type: ignore[override]
def __add__(self, other: np.ndarray) -> np.ndarray: ...
@@ -145,7 +145,7 @@ class Timestamp(datetime):
self: _DatetimeT, other: timedelta | np.timedelta64 | Tick
) -> _DatetimeT: ...
def __radd__(self: _DatetimeT, other: timedelta) -> _DatetimeT: ...
- @overload # type: ignore
+ @overload # type: ignore[override]
def __sub__(self, other: datetime) -> Timedelta: ...
@overload
def __sub__(
diff --git a/pyproject.toml b/pyproject.toml
index 90c1cba90a9aa..c5f89076a29fa 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -97,6 +97,7 @@ warn_unreachable = false # GH#27396
# Suppressing errors
show_none_errors = true
ignore_errors = false
+enable_error_code = "ignore-without-code"
# Miscellaneous strictness flags
allow_untyped_globals = false
allow_redefinition = false
| `enable_error_code = "ignore-without-code"` is a new feature added in mypy 0.940. | https://api.github.com/repos/pandas-dev/pandas/pulls/46424 | 2022-03-18T21:54:36Z | 2022-03-19T17:50:17Z | 2022-03-19T17:50:17Z | 2022-04-01T01:36:34Z |
TYP: drop | diff --git a/pandas/_typing.py b/pandas/_typing.py
index 8cab908d62507..e3b3a4774f558 100644
--- a/pandas/_typing.py
+++ b/pandas/_typing.py
@@ -297,6 +297,9 @@ def closed(self) -> bool:
else:
TakeIndexer = Any
+# Shared by functions such as drop and astype
+IgnoreRaise = Literal["ignore", "raise"]
+
# Windowing rank methods
WindowingRankType = Literal["average", "min", "max"]
@@ -311,7 +314,7 @@ def closed(self) -> bool:
# datetime and NaTType
DatetimeNaTType = Union[datetime, "NaTType"]
-DateTimeErrorChoices = Literal["ignore", "raise", "coerce"]
+DateTimeErrorChoices = Union[IgnoreRaise, Literal["coerce"]]
# sort_index
SortKind = Literal["quicksort", "mergesort", "heapsort", "stable"]
diff --git a/pandas/core/common.py b/pandas/core/common.py
index 62c2034505589..6c3d2c91ab012 100644
--- a/pandas/core/common.py
+++ b/pandas/core/common.py
@@ -250,7 +250,9 @@ def asarray_tuplesafe(values, dtype: NpDtype | None = None) -> np.ndarray:
return result
-def index_labels_to_array(labels, dtype: NpDtype | None = None) -> np.ndarray:
+def index_labels_to_array(
+ labels: np.ndarray | Iterable, dtype: NpDtype | None = None
+) -> np.ndarray:
"""
Transform label or iterable of labels to array, for use in Index.
diff --git a/pandas/core/dtypes/astype.py b/pandas/core/dtypes/astype.py
index daae491b09c8a..8d1427976276c 100644
--- a/pandas/core/dtypes/astype.py
+++ b/pandas/core/dtypes/astype.py
@@ -19,6 +19,7 @@
from pandas._typing import (
ArrayLike,
DtypeObj,
+ IgnoreRaise,
)
from pandas.errors import IntCastingNaNError
from pandas.util._exceptions import find_stack_level
@@ -235,7 +236,7 @@ def astype_array(values: ArrayLike, dtype: DtypeObj, copy: bool = False) -> Arra
def astype_array_safe(
- values: ArrayLike, dtype, copy: bool = False, errors: str = "raise"
+ values: ArrayLike, dtype, copy: bool = False, errors: IgnoreRaise = "raise"
) -> ArrayLike:
"""
Cast array (ndarray or ExtensionArray) to the new dtype.
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 4d279b22a4980..0013ddf73cddc 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -58,6 +58,7 @@
FloatFormatType,
FormattersType,
Frequency,
+ IgnoreRaise,
IndexKeyFunc,
IndexLabel,
Level,
@@ -4831,17 +4832,61 @@ def reindex(self, *args, **kwargs) -> DataFrame:
kwargs.pop("labels", None)
return super().reindex(**kwargs)
- @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "labels"])
+ @overload
+ def drop(
+ self,
+ labels: Hashable | list[Hashable] = ...,
+ *,
+ axis: Axis = ...,
+ index: Hashable | list[Hashable] = ...,
+ columns: Hashable | list[Hashable] = ...,
+ level: Level | None = ...,
+ inplace: Literal[True],
+ errors: IgnoreRaise = ...,
+ ) -> None:
+ ...
+
+ @overload
def drop(
self,
- labels=None,
+ labels: Hashable | list[Hashable] = ...,
+ *,
+ axis: Axis = ...,
+ index: Hashable | list[Hashable] = ...,
+ columns: Hashable | list[Hashable] = ...,
+ level: Level | None = ...,
+ inplace: Literal[False] = ...,
+ errors: IgnoreRaise = ...,
+ ) -> DataFrame:
+ ...
+
+ @overload
+ def drop(
+ self,
+ labels: Hashable | list[Hashable] = ...,
+ *,
+ axis: Axis = ...,
+ index: Hashable | list[Hashable] = ...,
+ columns: Hashable | list[Hashable] = ...,
+ level: Level | None = ...,
+ inplace: bool = ...,
+ errors: IgnoreRaise = ...,
+ ) -> DataFrame | None:
+ ...
+
+ # error: Signature of "drop" incompatible with supertype "NDFrame"
+ # github.com/python/mypy/issues/12387
+ @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "labels"])
+ def drop( # type: ignore[override]
+ self,
+ labels: Hashable | list[Hashable] = None,
axis: Axis = 0,
- index=None,
- columns=None,
+ index: Hashable | list[Hashable] = None,
+ columns: Hashable | list[Hashable] = None,
level: Level | None = None,
inplace: bool = False,
- errors: str = "raise",
- ):
+ errors: IgnoreRaise = "raise",
+ ) -> DataFrame | None:
"""
Drop specified labels from rows or columns.
@@ -11187,7 +11232,7 @@ def where(
inplace=False,
axis=None,
level=None,
- errors="raise",
+ errors: IgnoreRaise = "raise",
try_cast=lib.no_default,
):
return super().where(cond, other, inplace, axis, level, errors, try_cast)
@@ -11202,7 +11247,7 @@ def mask(
inplace=False,
axis=None,
level=None,
- errors="raise",
+ errors: IgnoreRaise = "raise",
try_cast=lib.no_default,
):
return super().mask(cond, other, inplace, axis, level, errors, try_cast)
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 983fb8f0edf12..8bfd9f3cd962d 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -44,6 +44,7 @@
DtypeArg,
DtypeObj,
FilePath,
+ IgnoreRaise,
IndexKeyFunc,
IndexLabel,
IntervalClosedType,
@@ -71,6 +72,7 @@
)
from pandas.util._decorators import (
deprecate_kwarg,
+ deprecate_nonkeyword_arguments,
doc,
rewrite_axis_style_signature,
)
@@ -4270,16 +4272,59 @@ def reindex_like(
return self.reindex(**d)
+ @overload
def drop(
self,
- labels=None,
- axis=0,
- index=None,
- columns=None,
- level=None,
+ labels: Hashable | list[Hashable] = ...,
+ *,
+ axis: Axis = ...,
+ index: Hashable | list[Hashable] = ...,
+ columns: Hashable | list[Hashable] = ...,
+ level: Level | None = ...,
+ inplace: Literal[True],
+ errors: IgnoreRaise = ...,
+ ) -> None:
+ ...
+
+ @overload
+ def drop(
+ self: NDFrameT,
+ labels: Hashable | list[Hashable] = ...,
+ *,
+ axis: Axis = ...,
+ index: Hashable | list[Hashable] = ...,
+ columns: Hashable | list[Hashable] = ...,
+ level: Level | None = ...,
+ inplace: Literal[False] = ...,
+ errors: IgnoreRaise = ...,
+ ) -> NDFrameT:
+ ...
+
+ @overload
+ def drop(
+ self: NDFrameT,
+ labels: Hashable | list[Hashable] = ...,
+ *,
+ axis: Axis = ...,
+ index: Hashable | list[Hashable] = ...,
+ columns: Hashable | list[Hashable] = ...,
+ level: Level | None = ...,
+ inplace: bool_t = ...,
+ errors: IgnoreRaise = ...,
+ ) -> NDFrameT | None:
+ ...
+
+ @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "labels"])
+ def drop(
+ self: NDFrameT,
+ labels: Hashable | list[Hashable] = None,
+ axis: Axis = 0,
+ index: Hashable | list[Hashable] = None,
+ columns: Hashable | list[Hashable] = None,
+ level: Level | None = None,
inplace: bool_t = False,
- errors: str = "raise",
- ):
+ errors: IgnoreRaise = "raise",
+ ) -> NDFrameT | None:
inplace = validate_bool_kwarg(inplace, "inplace")
@@ -4312,7 +4357,7 @@ def _drop_axis(
labels,
axis,
level=None,
- errors: str = "raise",
+ errors: IgnoreRaise = "raise",
only_slice: bool_t = False,
) -> NDFrameT:
"""
@@ -5826,7 +5871,7 @@ def dtypes(self):
return self._constructor_sliced(data, index=self._info_axis, dtype=np.object_)
def astype(
- self: NDFrameT, dtype, copy: bool_t = True, errors: str = "raise"
+ self: NDFrameT, dtype, copy: bool_t = True, errors: IgnoreRaise = "raise"
) -> NDFrameT:
"""
Cast a pandas object to a specified dtype ``dtype``.
@@ -9139,7 +9184,7 @@ def _where(
inplace=False,
axis=None,
level=None,
- errors="raise",
+ errors: IgnoreRaise = "raise",
):
"""
Equivalent to public method `where`, except that `other` is not
@@ -9278,7 +9323,7 @@ def where(
inplace=False,
axis=None,
level=None,
- errors="raise",
+ errors: IgnoreRaise = "raise",
try_cast=lib.no_default,
):
"""
@@ -9431,7 +9476,7 @@ def mask(
inplace=False,
axis=None,
level=None,
- errors="raise",
+ errors: IgnoreRaise = "raise",
try_cast=lib.no_default,
):
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index ab6e1c47057eb..8b9c537631d94 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -9,6 +9,7 @@
Any,
Callable,
Hashable,
+ Iterable,
Literal,
Sequence,
TypeVar,
@@ -46,6 +47,7 @@
Dtype,
DtypeObj,
F,
+ IgnoreRaise,
Shape,
npt,
)
@@ -6810,7 +6812,11 @@ def insert(self, loc: int, item) -> Index:
# TODO(2.0) can use Index instead of self._constructor
return self._constructor._with_infer(new_values, name=self.name)
- def drop(self, labels, errors: str_t = "raise") -> Index:
+ def drop(
+ self,
+ labels: Index | np.ndarray | Iterable[Hashable],
+ errors: IgnoreRaise = "raise",
+ ) -> Index:
"""
Make new Index with passed list of labels deleted.
diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index 2ff195fa8e5d2..26569b571724d 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -27,6 +27,7 @@
ArrayLike,
DtypeObj,
F,
+ IgnoreRaise,
Shape,
npt,
)
@@ -501,7 +502,9 @@ def dtype(self) -> DtypeObj:
return self.values.dtype
@final
- def astype(self, dtype: DtypeObj, copy: bool = False, errors: str = "raise"):
+ def astype(
+ self, dtype: DtypeObj, copy: bool = False, errors: IgnoreRaise = "raise"
+ ):
"""
Coerce to the new dtype.
diff --git a/pandas/core/series.py b/pandas/core/series.py
index f53c82972a602..00384ec26f71d 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -38,6 +38,7 @@
Dtype,
DtypeObj,
FillnaOptions,
+ IgnoreRaise,
IndexKeyFunc,
Level,
NaPosition,
@@ -4763,17 +4764,61 @@ def reindex(self, *args, **kwargs) -> Series:
kwargs.update({"index": index})
return super().reindex(**kwargs)
- @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "labels"])
+ @overload
def drop(
self,
- labels=None,
- axis=0,
- index=None,
- columns=None,
- level=None,
- inplace=False,
- errors="raise",
+ labels: Hashable | list[Hashable] = ...,
+ *,
+ axis: Axis = ...,
+ index: Hashable | list[Hashable] = ...,
+ columns: Hashable | list[Hashable] = ...,
+ level: Level | None = ...,
+ inplace: Literal[True],
+ errors: IgnoreRaise = ...,
+ ) -> None:
+ ...
+
+ @overload
+ def drop(
+ self,
+ labels: Hashable | list[Hashable] = ...,
+ *,
+ axis: Axis = ...,
+ index: Hashable | list[Hashable] = ...,
+ columns: Hashable | list[Hashable] = ...,
+ level: Level | None = ...,
+ inplace: Literal[False] = ...,
+ errors: IgnoreRaise = ...,
) -> Series:
+ ...
+
+ @overload
+ def drop(
+ self,
+ labels: Hashable | list[Hashable] = ...,
+ *,
+ axis: Axis = ...,
+ index: Hashable | list[Hashable] = ...,
+ columns: Hashable | list[Hashable] = ...,
+ level: Level | None = ...,
+ inplace: bool = ...,
+ errors: IgnoreRaise = ...,
+ ) -> Series | None:
+ ...
+
+ # error: Signature of "drop" incompatible with supertype "NDFrame"
+ # github.com/python/mypy/issues/12387
+ @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "labels"])
+ def drop( # type: ignore[override]
+ self,
+ labels: Hashable | list[Hashable] = None,
+ axis: Axis = 0,
+ index: Hashable | list[Hashable] = None,
+ columns: Hashable | list[Hashable] = None,
+ level: Level | None = None,
+ inplace: bool = False,
+ errors: IgnoreRaise = "raise",
+ ) -> Series | None:
"""
Return Series with specified index labels removed.
diff --git a/pandas/io/json/_normalize.py b/pandas/io/json/_normalize.py
index 36a7949a9f1e3..4a2e49fd85f45 100644
--- a/pandas/io/json/_normalize.py
+++ b/pandas/io/json/_normalize.py
@@ -16,7 +16,10 @@
import numpy as np
from pandas._libs.writers import convert_json_to_lines
-from pandas._typing import Scalar
+from pandas._typing import (
+ IgnoreRaise,
+ Scalar,
+)
from pandas.util._decorators import deprecate
import pandas as pd
@@ -244,7 +247,7 @@ def _json_normalize(
meta: str | list[str | list[str]] | None = None,
meta_prefix: str | None = None,
record_prefix: str | None = None,
- errors: str = "raise",
+ errors: IgnoreRaise = "raise",
sep: str = ".",
max_level: int | None = None,
) -> DataFrame:
diff --git a/pandas/plotting/_matplotlib/misc.py b/pandas/plotting/_matplotlib/misc.py
index 6583328f916f1..083d85ef0876d 100644
--- a/pandas/plotting/_matplotlib/misc.py
+++ b/pandas/plotting/_matplotlib/misc.py
@@ -27,6 +27,7 @@
from pandas import (
DataFrame,
+ Index,
Series,
)
@@ -378,6 +379,7 @@ def parallel_coordinates(
ncols = len(df.columns)
# determine values to use for xticks
+ x: list[int] | Index
if use_columns is True:
if not np.all(np.isreal(list(df.columns))):
raise ValueError("Columns must be numeric to be used as xticks")
| Tested with mypy and pyright:
```py
import pandas as pd
def bool_() -> bool:
...
df = pd.DataFrame({"a": []})
ser = pd.Series({"a": 1, "b": 2})
# DataFrame/Series
reveal_type(df.drop(columns="a"))
reveal_type(df.drop(columns="a", inplace=False))
reveal_type(ser.drop(index="a"))
reveal_type(ser.drop(index="a", inplace=False))
# None
reveal_type(df.drop(columns="a", inplace=True))
reveal_type(ser.drop(index="a", inplace=True))
# both
reveal_type(df.drop(columns="a", inplace=bool_()))
reveal_type(ser.drop(index="a", inplace=bool_()))
``` | https://api.github.com/repos/pandas-dev/pandas/pulls/46423 | 2022-03-18T21:20:50Z | 2022-03-21T23:09:13Z | 2022-03-21T23:09:13Z | 2022-04-01T01:36:33Z |
BUG: Categorical of booleans has object .categories | diff --git a/pandas/tests/series/accessors/test_cat_accessor.py b/pandas/tests/series/accessors/test_cat_accessor.py
index 1a038839a67c9..53158482fde46 100644
--- a/pandas/tests/series/accessors/test_cat_accessor.py
+++ b/pandas/tests/series/accessors/test_cat_accessor.py
@@ -282,3 +282,12 @@ def test_set_categories_setitem(self):
# values should not be coerced to NaN
assert list(df["Sex"]) == ["female", "male", "male"]
assert list(df["Survived"]) == ["Yes", "No", "Yes"]
+
+ def test_categorical_of_booleans_is_boolean(self):
+ # https://github.com/pandas-dev/pandas/issues/46313
+ df = DataFrame(
+ {"int_cat": [1, 2, 3], "bool_cat": [True, False, False]}, dtype="category"
+ )
+ value = df["bool_cat"].cat.categories.dtype
+ expected = np.dtype(np.bool_)
+ assert value is expected
| - [x] closes #46313
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
This patch adds a test for checking regression behavior as described in #46313. The bug was reported to be fixed and I could not reproduce the bug in the latest version as Mar.18, 2022. So, only a test was added.
This is my first contribution to the open-source project. Please let me know if something needs to be fixed. Thanks! | https://api.github.com/repos/pandas-dev/pandas/pulls/46422 | 2022-03-18T19:55:35Z | 2022-03-20T18:01:48Z | 2022-03-20T18:01:48Z | 2022-03-20T18:01:57Z |
CLN: assorted | diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst
index c4a760efd9a40..a373870f268ae 100644
--- a/doc/source/whatsnew/v1.5.0.rst
+++ b/doc/source/whatsnew/v1.5.0.rst
@@ -466,6 +466,7 @@ Groupby/resample/rolling
- Bug in :meth:`.GroupBy.cumsum` with ``timedelta64[ns]`` dtype failing to recognize ``NaT`` as a null value (:issue:`46216`)
- Bug in :meth:`GroupBy.cummin` and :meth:`GroupBy.cummax` with nullable dtypes incorrectly altering the original data in place (:issue:`46220`)
- Bug in :meth:`GroupBy.cummax` with ``int64`` dtype with leading value being the smallest possible int64 (:issue:`46382`)
+- Bug in :meth:`GroupBy.max` with empty groups and ``uint64`` dtype incorrectly raising ``RuntimeError`` (:issue:`46408`)
-
Reshaping
diff --git a/pandas/_libs/hashing.pyx b/pandas/_libs/hashing.pyx
index 39caf04ddf2f8..9c44e3f4ccf4a 100644
--- a/pandas/_libs/hashing.pyx
+++ b/pandas/_libs/hashing.pyx
@@ -53,7 +53,7 @@ def hash_object_array(
"""
cdef:
Py_ssize_t i, n
- uint64_t[:] result
+ uint64_t[::1] result
bytes data, k
uint8_t *kb
uint64_t *lens
diff --git a/pandas/_libs/hashtable_class_helper.pxi.in b/pandas/_libs/hashtable_class_helper.pxi.in
index 6ddf8d42b9baa..8a2b9c2f77627 100644
--- a/pandas/_libs/hashtable_class_helper.pxi.in
+++ b/pandas/_libs/hashtable_class_helper.pxi.in
@@ -503,7 +503,7 @@ cdef class {{name}}HashTable(HashTable):
int ret = 0
{{c_type}} val
khiter_t k
- intp_t[:] locs = np.empty(n, dtype=np.intp)
+ intp_t[::1] locs = np.empty(n, dtype=np.intp)
with nogil:
for i in range(n):
@@ -561,7 +561,7 @@ cdef class {{name}}HashTable(HashTable):
"""
cdef:
Py_ssize_t i, idx, count = count_prior, n = len(values)
- intp_t[:] labels
+ intp_t[::1] labels
int ret = 0
{{c_type}} val, na_value2
khiter_t k
@@ -710,7 +710,7 @@ cdef class {{name}}HashTable(HashTable):
# tuple[np.ndarray[np.intp], np.ndarray[{{dtype}}]]
cdef:
Py_ssize_t i, n = len(values)
- intp_t[:] labels
+ intp_t[::1] labels
Py_ssize_t idx, count = 0
int ret = 0
{{c_type}} val
@@ -848,7 +848,7 @@ cdef class StringHashTable(HashTable):
object val
const char *v
khiter_t k
- intp_t[:] locs = np.empty(n, dtype=np.intp)
+ intp_t[::1] locs = np.empty(n, dtype=np.intp)
# these by-definition *must* be strings
vecs = <const char **>malloc(n * sizeof(char *))
@@ -946,8 +946,8 @@ cdef class StringHashTable(HashTable):
"""
cdef:
Py_ssize_t i, idx, count = count_prior, n = len(values)
- intp_t[:] labels
- int64_t[:] uindexer
+ intp_t[::1] labels
+ int64_t[::1] uindexer
int ret = 0
object val
const char *v
@@ -1168,7 +1168,7 @@ cdef class PyObjectHashTable(HashTable):
int ret = 0
object val
khiter_t k
- intp_t[:] locs = np.empty(n, dtype=np.intp)
+ intp_t[::1] locs = np.empty(n, dtype=np.intp)
for i in range(n):
val = values[i]
@@ -1223,7 +1223,7 @@ cdef class PyObjectHashTable(HashTable):
"""
cdef:
Py_ssize_t i, idx, count = count_prior, n = len(values)
- intp_t[:] labels
+ intp_t[::1] labels
int ret = 0
object val
khiter_t k
diff --git a/pandas/_libs/hashtable_func_helper.pxi.in b/pandas/_libs/hashtable_func_helper.pxi.in
index 6b1fe25154e30..11a45bb194c03 100644
--- a/pandas/_libs/hashtable_func_helper.pxi.in
+++ b/pandas/_libs/hashtable_func_helper.pxi.in
@@ -85,7 +85,9 @@ cdef value_count_{{dtype}}(const {{dtype}}_t[:] values, bint dropna):
{{endif}}
# collect counts in the order corresponding to result_keys:
- cdef int64_t[:] result_counts = np.empty(table.size, dtype=np.int64)
+ cdef:
+ int64_t[::1] result_counts = np.empty(table.size, dtype=np.int64)
+
for i in range(table.size):
{{if dtype == 'object'}}
k = kh_get_{{ttype}}(table, result_keys.data[i])
@@ -366,7 +368,7 @@ def mode(ndarray[htfunc_t] values, bint dropna):
ndarray[htfunc_t] keys
ndarray[htfunc_t] modes
- int64_t[:] counts
+ int64_t[::1] counts
int64_t count, max_count = -1
Py_ssize_t nkeys, k, j = 0
diff --git a/pandas/_libs/interval.pyx b/pandas/_libs/interval.pyx
index aba635e19995a..4c8419b78e2b8 100644
--- a/pandas/_libs/interval.pyx
+++ b/pandas/_libs/interval.pyx
@@ -5,11 +5,11 @@ from operator import (
)
from cpython.datetime cimport (
- PyDateTime_IMPORT,
PyDelta_Check,
+ import_datetime,
)
-PyDateTime_IMPORT
+import_datetime()
from cpython.object cimport (
Py_EQ,
diff --git a/pandas/_libs/lib.pyx b/pandas/_libs/lib.pyx
index c86eb80da93f7..18a58902075f2 100644
--- a/pandas/_libs/lib.pyx
+++ b/pandas/_libs/lib.pyx
@@ -9,9 +9,9 @@ from cython import Py_ssize_t
from cpython.datetime cimport (
PyDate_Check,
PyDateTime_Check,
- PyDateTime_IMPORT,
PyDelta_Check,
PyTime_Check,
+ import_datetime,
)
from cpython.iterator cimport PyIter_Check
from cpython.number cimport PyNumber_Check
@@ -27,7 +27,7 @@ from cpython.tuple cimport (
)
from cython cimport floating
-PyDateTime_IMPORT
+import_datetime()
import numpy as np
@@ -2470,8 +2470,8 @@ def maybe_convert_objects(ndarray[object] objects,
ndarray[int64_t] ints
ndarray[uint64_t] uints
ndarray[uint8_t] bools
- int64_t[:] idatetimes
- int64_t[:] itimedeltas
+ int64_t[::1] idatetimes
+ int64_t[::1] itimedeltas
Seen seen = Seen()
object val
float64_t fval, fnan = np.nan
diff --git a/pandas/_libs/ops.pyx b/pandas/_libs/ops.pyx
index ac8a7f2cc57f7..df6a17cc4cc5e 100644
--- a/pandas/_libs/ops.pyx
+++ b/pandas/_libs/ops.pyx
@@ -194,7 +194,7 @@ def scalar_binop(object[:] values, object val, object op) -> ndarray:
"""
cdef:
Py_ssize_t i, n = len(values)
- object[:] result
+ object[::1] result
object x
result = np.empty(n, dtype=object)
@@ -231,7 +231,7 @@ def vec_binop(object[:] left, object[:] right, object op) -> ndarray:
"""
cdef:
Py_ssize_t i, n = len(left)
- object[:] result
+ object[::1] result
if n != <Py_ssize_t>len(right):
raise ValueError(f'Arrays were different lengths: {n} vs {len(right)}')
diff --git a/pandas/_libs/parsers.pyx b/pandas/_libs/parsers.pyx
index 59a86751964e6..6d96b5c0c9f03 100644
--- a/pandas/_libs/parsers.pyx
+++ b/pandas/_libs/parsers.pyx
@@ -1457,7 +1457,7 @@ cdef _categorical_convert(parser_t *parser, int64_t col,
const char *word = NULL
int64_t NA = -1
- int64_t[:] codes
+ int64_t[::1] codes
int64_t current_category = 0
char *errors = "strict"
diff --git a/pandas/_libs/tslib.pyx b/pandas/_libs/tslib.pyx
index 063b95953bd91..9dfc438319148 100644
--- a/pandas/_libs/tslib.pyx
+++ b/pandas/_libs/tslib.pyx
@@ -5,13 +5,13 @@ import cython
from cpython.datetime cimport (
PyDate_Check,
PyDateTime_Check,
- PyDateTime_IMPORT,
datetime,
+ import_datetime,
tzinfo,
)
# import datetime C API
-PyDateTime_IMPORT
+import_datetime()
cimport numpy as cnp
@@ -63,7 +63,6 @@ from pandas._libs.tslibs.timestamps cimport _Timestamp
from pandas._libs.tslibs.timestamps import Timestamp
# Note: this is the only non-tslibs intra-pandas dependency here
-
from pandas._libs.missing cimport checknull_with_nat_and_na
from pandas._libs.tslibs.tzconversion cimport tz_localize_to_utc_single
diff --git a/pandas/_libs/tslibs/conversion.pyx b/pandas/_libs/tslibs/conversion.pyx
index 7dce3cad9d339..9c5f8b18b38bc 100644
--- a/pandas/_libs/tslibs/conversion.pyx
+++ b/pandas/_libs/tslibs/conversion.pyx
@@ -18,13 +18,13 @@ import pytz
from cpython.datetime cimport (
PyDate_Check,
PyDateTime_Check,
- PyDateTime_IMPORT,
datetime,
+ import_datetime,
time,
tzinfo,
)
-PyDateTime_IMPORT
+import_datetime()
from pandas._libs.tslibs.base cimport ABCTimestamp
from pandas._libs.tslibs.np_datetime cimport (
diff --git a/pandas/_libs/tslibs/dtypes.pyx b/pandas/_libs/tslibs/dtypes.pyx
index 43f33b98b8eaa..02b78cfa530c9 100644
--- a/pandas/_libs/tslibs/dtypes.pyx
+++ b/pandas/_libs/tslibs/dtypes.pyx
@@ -165,7 +165,7 @@ class FreqGroup(Enum):
FR_MS = c_FreqGroup.FR_MS
FR_US = c_FreqGroup.FR_US
FR_NS = c_FreqGroup.FR_NS
- FR_UND = -c_FreqGroup.FR_UND # undefined
+ FR_UND = c_FreqGroup.FR_UND # undefined
@staticmethod
def from_period_dtype_code(code: int) -> "FreqGroup":
diff --git a/pandas/_libs/tslibs/fields.pyi b/pandas/_libs/tslibs/fields.pyi
index 415b4329310c0..571a327b46df8 100644
--- a/pandas/_libs/tslibs/fields.pyi
+++ b/pandas/_libs/tslibs/fields.pyi
@@ -22,7 +22,7 @@ def get_date_field(
field: str,
) -> npt.NDArray[np.int32]: ...
def get_timedelta_field(
- tdindex: np.ndarray, # const int64_t[:]
+ tdindex: npt.NDArray[np.int64], # const int64_t[:]
field: str,
) -> npt.NDArray[np.int32]: ...
def isleapyear_arr(
@@ -31,7 +31,7 @@ def isleapyear_arr(
def build_isocalendar_sarray(
dtindex: npt.NDArray[np.int64], # const int64_t[:]
) -> np.ndarray: ...
-def get_locale_names(name_type: str, locale: str | None = ...): ...
+def _get_locale_names(name_type: str, locale: str | None = ...): ...
class RoundTo:
@property
diff --git a/pandas/_libs/tslibs/fields.pyx b/pandas/_libs/tslibs/fields.pyx
index c1915e719f515..6d8b27e0ad75a 100644
--- a/pandas/_libs/tslibs/fields.pyx
+++ b/pandas/_libs/tslibs/fields.pyx
@@ -152,7 +152,7 @@ def get_date_name_field(const int64_t[:] dtindex, str field, object locale=None)
if locale is None:
names = np.array(DAYS_FULL, dtype=np.object_)
else:
- names = np.array(get_locale_names('f_weekday', locale),
+ names = np.array(_get_locale_names('f_weekday', locale),
dtype=np.object_)
for i in range(count):
if dtindex[i] == NPY_NAT:
@@ -167,7 +167,7 @@ def get_date_name_field(const int64_t[:] dtindex, str field, object locale=None)
if locale is None:
names = np.array(MONTHS_FULL, dtype=np.object_)
else:
- names = np.array(get_locale_names('f_month', locale),
+ names = np.array(_get_locale_names('f_month', locale),
dtype=np.object_)
for i in range(count):
if dtindex[i] == NPY_NAT:
@@ -574,7 +574,7 @@ def build_isocalendar_sarray(const int64_t[:] dtindex):
return out
-def get_locale_names(name_type: str, locale: object = None):
+def _get_locale_names(name_type: str, locale: object = None):
"""
Returns an array of localized day or month names.
@@ -650,7 +650,7 @@ class RoundTo:
return 4
-cdef inline ndarray[int64_t] _floor_int64(int64_t[:] values, int64_t unit):
+cdef inline ndarray[int64_t] _floor_int64(const int64_t[:] values, int64_t unit):
cdef:
Py_ssize_t i, n = len(values)
ndarray[int64_t] result = np.empty(n, dtype="i8")
@@ -668,7 +668,7 @@ cdef inline ndarray[int64_t] _floor_int64(int64_t[:] values, int64_t unit):
return result
-cdef inline ndarray[int64_t] _ceil_int64(int64_t[:] values, int64_t unit):
+cdef inline ndarray[int64_t] _ceil_int64(const int64_t[:] values, int64_t unit):
cdef:
Py_ssize_t i, n = len(values)
ndarray[int64_t] result = np.empty(n, dtype="i8")
diff --git a/pandas/_libs/tslibs/nattype.pxd b/pandas/_libs/tslibs/nattype.pxd
index 5e5f4224f902f..e878fa7629f25 100644
--- a/pandas/_libs/tslibs/nattype.pxd
+++ b/pandas/_libs/tslibs/nattype.pxd
@@ -4,7 +4,6 @@ from numpy cimport int64_t
cdef int64_t NPY_NAT
-cdef bint _nat_scalar_rules[6]
cdef set c_nat_strings
cdef class _NaT(datetime):
diff --git a/pandas/_libs/tslibs/nattype.pyx b/pandas/_libs/tslibs/nattype.pyx
index e6a70177463b8..8ddbbfba49614 100644
--- a/pandas/_libs/tslibs/nattype.pyx
+++ b/pandas/_libs/tslibs/nattype.pyx
@@ -3,11 +3,13 @@ import warnings
from cpython.datetime cimport (
PyDate_Check,
PyDateTime_Check,
- PyDateTime_IMPORT,
PyDelta_Check,
datetime,
+ import_datetime,
timedelta,
)
+
+import_datetime()
from cpython.object cimport (
Py_EQ,
Py_GE,
@@ -18,10 +20,6 @@ from cpython.object cimport (
PyObject_RichCompare,
)
-PyDateTime_IMPORT
-
-from cpython.version cimport PY_MINOR_VERSION
-
import numpy as np
cimport numpy as cnp
@@ -43,14 +41,6 @@ cdef set c_nat_strings = nat_strings
cdef int64_t NPY_NAT = util.get_nat()
iNaT = NPY_NAT # python-visible constant
-cdef bint _nat_scalar_rules[6]
-_nat_scalar_rules[Py_EQ] = False
-_nat_scalar_rules[Py_NE] = True
-_nat_scalar_rules[Py_LT] = False
-_nat_scalar_rules[Py_LE] = False
-_nat_scalar_rules[Py_GT] = False
-_nat_scalar_rules[Py_GE] = False
-
# ----------------------------------------------------------------------
@@ -107,7 +97,6 @@ def __nat_unpickle(*args):
cdef class _NaT(datetime):
# cdef readonly:
# int64_t value
- # object freq
# higher than np.ndarray and np.matrix
__array_priority__ = 100
@@ -115,16 +104,16 @@ cdef class _NaT(datetime):
def __richcmp__(_NaT self, object other, int op):
if util.is_datetime64_object(other) or PyDateTime_Check(other):
# We treat NaT as datetime-like for this comparison
- return _nat_scalar_rules[op]
+ return op == Py_NE
elif util.is_timedelta64_object(other) or PyDelta_Check(other):
# We treat NaT as timedelta-like for this comparison
- return _nat_scalar_rules[op]
+ return op == Py_NE
elif util.is_array(other):
if other.dtype.kind in "mM":
result = np.empty(other.shape, dtype=np.bool_)
- result.fill(_nat_scalar_rules[op])
+ result.fill(op == Py_NE)
elif other.dtype.kind == "O":
result = np.array([PyObject_RichCompare(self, x, op) for x in other])
elif op == Py_EQ:
@@ -510,8 +499,7 @@ class NaTType(_NaT):
utcoffset = _make_error_func("utcoffset", datetime)
# "fromisocalendar" was introduced in 3.8
- if PY_MINOR_VERSION >= 8:
- fromisocalendar = _make_error_func("fromisocalendar", datetime)
+ fromisocalendar = _make_error_func("fromisocalendar", datetime)
# ----------------------------------------------------------------------
# The remaining methods have docstrings copy/pasted from the analogous
diff --git a/pandas/_libs/tslibs/np_datetime.pxd b/pandas/_libs/tslibs/np_datetime.pxd
index c2bbc4fe764fe..d2b85369ecc57 100644
--- a/pandas/_libs/tslibs/np_datetime.pxd
+++ b/pandas/_libs/tslibs/np_datetime.pxd
@@ -8,6 +8,7 @@ from numpy cimport (
)
+# TODO(cython3): most of these can be cimported directly from numpy
cdef extern from "numpy/ndarrayobject.h":
ctypedef int64_t npy_timedelta
ctypedef int64_t npy_datetime
@@ -59,6 +60,9 @@ cdef extern from "src/datetime/np_datetime.h":
NPY_DATETIMEUNIT fr,
npy_datetimestruct *result) nogil
+ npy_datetime npy_datetimestruct_to_datetime(NPY_DATETIMEUNIT fr,
+ npy_datetimestruct *d) nogil
+
cdef bint cmp_scalar(int64_t lhs, int64_t rhs, int op) except -1
diff --git a/pandas/_libs/tslibs/np_datetime.pyx b/pandas/_libs/tslibs/np_datetime.pyx
index 79a58478d630a..b0b431e5bb3cd 100644
--- a/pandas/_libs/tslibs/np_datetime.pyx
+++ b/pandas/_libs/tslibs/np_datetime.pyx
@@ -6,7 +6,7 @@ from cpython.datetime cimport (
PyDateTime_GET_DAY,
PyDateTime_GET_MONTH,
PyDateTime_GET_YEAR,
- PyDateTime_IMPORT,
+ import_datetime,
)
from cpython.object cimport (
Py_EQ,
@@ -17,7 +17,7 @@ from cpython.object cimport (
Py_NE,
)
-PyDateTime_IMPORT
+import_datetime()
from numpy cimport int64_t
@@ -28,13 +28,6 @@ cdef extern from "src/datetime/np_datetime.h":
int cmp_npy_datetimestruct(npy_datetimestruct *a,
npy_datetimestruct *b)
- npy_datetime npy_datetimestruct_to_datetime(NPY_DATETIMEUNIT fr,
- npy_datetimestruct *d) nogil
-
- void pandas_datetime_to_datetimestruct(npy_datetime val,
- NPY_DATETIMEUNIT fr,
- npy_datetimestruct *result) nogil
-
void pandas_timedelta_to_timedeltastruct(npy_timedelta val,
NPY_DATETIMEUNIT fr,
pandas_timedeltastruct *result
diff --git a/pandas/_libs/tslibs/offsets.pyx b/pandas/_libs/tslibs/offsets.pyx
index f19d34d99c814..fef98199d3dbc 100644
--- a/pandas/_libs/tslibs/offsets.pyx
+++ b/pandas/_libs/tslibs/offsets.pyx
@@ -8,15 +8,15 @@ import cython
from cpython.datetime cimport (
PyDate_Check,
PyDateTime_Check,
- PyDateTime_IMPORT,
PyDelta_Check,
date,
datetime,
+ import_datetime,
time as dt_time,
timedelta,
)
-PyDateTime_IMPORT
+import_datetime()
from dateutil.easter import easter
from dateutil.relativedelta import relativedelta
@@ -2392,7 +2392,7 @@ cdef class SemiMonthOffset(SingleConstructorOffset):
int64_t[:] i8other = dtarr.view("i8")
Py_ssize_t i, count = len(i8other)
int64_t val
- int64_t[:] out = np.empty(count, dtype="i8")
+ int64_t[::1] out = np.empty(count, dtype="i8")
npy_datetimestruct dts
int months, to_day, nadj, n = self.n
int days_in_month, day, anchor_dom = self.day_of_month
@@ -2577,7 +2577,7 @@ cdef class Week(SingleConstructorOffset):
cdef:
Py_ssize_t i, count = len(i8other)
int64_t val
- int64_t[:] out = np.empty(count, dtype="i8")
+ int64_t[::1] out = np.empty(count, dtype="i8")
npy_datetimestruct dts
int wday, days, weeks, n = self.n
int anchor_weekday = self.weekday
@@ -3774,7 +3774,7 @@ cdef shift_quarters(
"""
cdef:
Py_ssize_t count = len(dtindex)
- int64_t[:] out = np.empty(count, dtype="int64")
+ int64_t[::1] out = np.empty(count, dtype="int64")
if day_opt not in ["start", "end", "business_start", "business_end"]:
raise ValueError("day must be None, 'start', 'end', "
@@ -3800,7 +3800,7 @@ def shift_months(const int64_t[:] dtindex, int months, object day_opt=None):
Py_ssize_t i
npy_datetimestruct dts
int count = len(dtindex)
- int64_t[:] out = np.empty(count, dtype="int64")
+ int64_t[::1] out = np.empty(count, dtype="int64")
if day_opt is None:
with nogil:
@@ -3827,7 +3827,7 @@ def shift_months(const int64_t[:] dtindex, int months, object day_opt=None):
@cython.wraparound(False)
@cython.boundscheck(False)
cdef inline void _shift_months(const int64_t[:] dtindex,
- int64_t[:] out,
+ int64_t[::1] out,
Py_ssize_t count,
int months,
str day_opt) nogil:
@@ -3859,7 +3859,7 @@ cdef inline void _shift_months(const int64_t[:] dtindex,
@cython.wraparound(False)
@cython.boundscheck(False)
cdef inline void _shift_quarters(const int64_t[:] dtindex,
- int64_t[:] out,
+ int64_t[::1] out,
Py_ssize_t count,
int quarters,
int q1start_month,
@@ -3906,7 +3906,7 @@ cdef ndarray[int64_t] _shift_bdays(const int64_t[:] i8other, int periods):
"""
cdef:
Py_ssize_t i, n = len(i8other)
- int64_t[:] result = np.empty(n, dtype="i8")
+ int64_t[::1] result = np.empty(n, dtype="i8")
int64_t val, res
int wday, nadj, days
npy_datetimestruct dts
diff --git a/pandas/_libs/tslibs/parsing.pyx b/pandas/_libs/tslibs/parsing.pyx
index 942a07760b86e..53f2dd87c20f7 100644
--- a/pandas/_libs/tslibs/parsing.pyx
+++ b/pandas/_libs/tslibs/parsing.pyx
@@ -17,7 +17,6 @@ from cpython.datetime cimport (
tzinfo,
)
from cpython.object cimport PyObject_Str
-from cpython.version cimport PY_VERSION_HEX
import_datetime()
@@ -196,11 +195,9 @@ cdef inline object _parse_delimited_date(str date_string, bint dayfirst):
),
stacklevel=4,
)
- if PY_VERSION_HEX >= 0x03060100:
- # In Python <= 3.6.0 there is no range checking for invalid dates
- # in C api, thus we call faster C version for 3.6.1 or newer
- return datetime_new(year, month, day, 0, 0, 0, 0, None), reso
- return datetime(year, month, day, 0, 0, 0, 0, None), reso
+ # In Python <= 3.6.0 there is no range checking for invalid dates
+ # in C api, thus we call faster C version for 3.6.1 or newer
+ return datetime_new(year, month, day, 0, 0, 0, 0, None), reso
raise DateParseError(f"Invalid date specified ({month}/{day})")
@@ -573,7 +570,9 @@ cdef dateutil_parse(
""" lifted from dateutil to get resolution"""
cdef:
- object res, attr, ret, tzdata
+ str attr
+ datetime ret
+ object res, tzdata
object reso = None
dict repl = {}
@@ -637,7 +636,7 @@ def try_parse_dates(
) -> np.ndarray:
cdef:
Py_ssize_t i, n
- object[:] result
+ object[::1] result
n = len(values)
result = np.empty(n, dtype='O')
@@ -681,7 +680,7 @@ def try_parse_date_and_time(
) -> np.ndarray:
cdef:
Py_ssize_t i, n
- object[:] result
+ object[::1] result
n = len(dates)
# TODO(cython3): Use len instead of `shape[0]`
@@ -719,7 +718,7 @@ def try_parse_year_month_day(
) -> np.ndarray:
cdef:
Py_ssize_t i, n
- object[:] result
+ object[::1] result
n = len(years)
# TODO(cython3): Use len instead of `shape[0]`
@@ -742,7 +741,7 @@ def try_parse_datetime_components(object[:] years,
cdef:
Py_ssize_t i, n
- object[:] result
+ object[::1] result
int secs
double float_secs
double micros
@@ -1095,7 +1094,7 @@ def concat_date_cols(tuple date_cols, bint keep_trivial_numbers=True) -> np.ndar
object[::1] iters_view
flatiter it
cnp.ndarray[object] result
- object[:] result_view
+ object[::1] result_view
if col_count == 0:
return np.zeros(0, dtype=object)
diff --git a/pandas/_libs/tslibs/period.pyx b/pandas/_libs/tslibs/period.pyx
index 182502ba9ad7f..986bbd8c8f856 100644
--- a/pandas/_libs/tslibs/period.pyx
+++ b/pandas/_libs/tslibs/period.pyx
@@ -36,13 +36,13 @@ import cython
from cpython.datetime cimport (
PyDate_Check,
PyDateTime_Check,
- PyDateTime_IMPORT,
PyDelta_Check,
datetime,
+ import_datetime,
)
# import datetime C API
-PyDateTime_IMPORT
+import_datetime()
from pandas._libs.tslibs.np_datetime cimport (
NPY_DATETIMEUNIT,
@@ -62,7 +62,6 @@ cdef extern from "src/datetime/np_datetime.h":
cimport pandas._libs.tslibs.util as util
-from pandas._libs.tslibs.timedeltas import Timedelta
from pandas._libs.tslibs.timestamps import Timestamp
from pandas._libs.tslibs.ccalendar cimport (
@@ -104,7 +103,6 @@ from pandas._libs.tslibs.parsing import parse_time_string
from pandas._libs.tslibs.nattype cimport (
NPY_NAT,
- _nat_scalar_rules,
c_NaT as NaT,
c_nat_strings as nat_strings,
checknull_with_nat,
@@ -1673,7 +1671,7 @@ cdef class _Period(PeriodMixin):
self._require_matching_freq(other)
return PyObject_RichCompareBool(self.ordinal, other.ordinal, op)
elif other is NaT:
- return _nat_scalar_rules[op]
+ return op == Py_NE
elif util.is_array(other):
# GH#44285
if cnp.PyArray_IsZeroDim(other):
@@ -1828,10 +1826,10 @@ cdef class _Period(PeriodMixin):
if end:
if freq == "B" or self.freq == "B":
# roll forward to ensure we land on B date
- adjust = Timedelta(1, "D") - Timedelta(1, "ns")
+ adjust = np.timedelta64(1, "D") - np.timedelta64(1, "ns")
return self.to_timestamp(how="start") + adjust
endpoint = (self + self.freq).to_timestamp(how='start')
- return endpoint - Timedelta(1, 'ns')
+ return endpoint - np.timedelta64(1, "ns")
if freq is None:
freq = self._dtype._get_to_timestamp_base()
diff --git a/pandas/_libs/tslibs/strptime.pyx b/pandas/_libs/tslibs/strptime.pyx
index d214694fb659d..bc3e68671b7ec 100644
--- a/pandas/_libs/tslibs/strptime.pyx
+++ b/pandas/_libs/tslibs/strptime.pyx
@@ -72,8 +72,8 @@ def array_strptime(ndarray[object] values, object fmt, bint exact=True, errors='
cdef:
Py_ssize_t i, n = len(values)
npy_datetimestruct dts
- int64_t[:] iresult
- object[:] result_timezone
+ int64_t[::1] iresult
+ object[::1] result_timezone
int year, month, day, minute, hour, second, weekday, julian
int week_of_year, week_of_year_start, parse_code, ordinal
int iso_week, iso_year
diff --git a/pandas/_libs/tslibs/timedeltas.pyx b/pandas/_libs/tslibs/timedeltas.pyx
index 6b7ebf96633b3..76a14a56e1cc2 100644
--- a/pandas/_libs/tslibs/timedeltas.pyx
+++ b/pandas/_libs/tslibs/timedeltas.pyx
@@ -22,12 +22,12 @@ cnp.import_array()
from cpython.datetime cimport (
PyDateTime_Check,
- PyDateTime_IMPORT,
PyDelta_Check,
+ import_datetime,
timedelta,
)
-PyDateTime_IMPORT
+import_datetime()
cimport pandas._libs.tslibs.util as util
diff --git a/pandas/_libs/tslibs/timestamps.pyx b/pandas/_libs/tslibs/timestamps.pyx
index aad49bd70b120..2afceb827e49a 100644
--- a/pandas/_libs/tslibs/timestamps.pyx
+++ b/pandas/_libs/tslibs/timestamps.pyx
@@ -25,10 +25,10 @@ cnp.import_array()
from cpython.datetime cimport ( # alias bc `tzinfo` is a kwarg below
PyDate_Check,
PyDateTime_Check,
- PyDateTime_IMPORT,
PyDelta_Check,
PyTZInfo_Check,
datetime,
+ import_datetime,
time,
tzinfo as tzinfo_type,
)
@@ -43,7 +43,7 @@ from cpython.object cimport (
PyObject_RichCompareBool,
)
-PyDateTime_IMPORT
+import_datetime()
from pandas._libs.tslibs cimport ccalendar
from pandas._libs.tslibs.base cimport ABCTimestamp
diff --git a/pandas/_libs/tslibs/tzconversion.pyx b/pandas/_libs/tslibs/tzconversion.pyx
index 1a1aa6dfec5a0..7efe9412e43b9 100644
--- a/pandas/_libs/tslibs/tzconversion.pyx
+++ b/pandas/_libs/tslibs/tzconversion.pyx
@@ -5,14 +5,14 @@ import cython
from cython import Py_ssize_t
from cpython.datetime cimport (
- PyDateTime_IMPORT,
PyDelta_Check,
datetime,
+ import_datetime,
timedelta,
tzinfo,
)
-PyDateTime_IMPORT
+import_datetime()
from dateutil.tz import tzutc
import numpy as np
diff --git a/pandas/_libs/tslibs/util.pxd b/pandas/_libs/tslibs/util.pxd
index 150516aadffc6..492b7d519551f 100644
--- a/pandas/_libs/tslibs/util.pxd
+++ b/pandas/_libs/tslibs/util.pxd
@@ -14,7 +14,6 @@ cdef extern from *:
cdef extern from "Python.h":
# Note: importing extern-style allows us to declare these as nogil
# functions, whereas `from cpython cimport` does not.
- bint PyUnicode_Check(object obj) nogil
bint PyBool_Check(object obj) nogil
bint PyFloat_Check(object obj) nogil
bint PyComplex_Check(object obj) nogil
diff --git a/pandas/_libs/tslibs/vectorized.pyx b/pandas/_libs/tslibs/vectorized.pyx
index ada6d7f6495bf..3f47a19563b61 100644
--- a/pandas/_libs/tslibs/vectorized.pyx
+++ b/pandas/_libs/tslibs/vectorized.pyx
@@ -89,7 +89,7 @@ def ints_to_pydatetime(
int64_t* tdata = NULL
intp_t pos
npy_datetimestruct dts
- object dt, new_tz
+ tzinfo new_tz
str typ
int64_t value, local_val, delta = NPY_NAT # dummy for delta
ndarray[object] result = np.empty(n, dtype=object)
@@ -190,6 +190,8 @@ cdef inline int _reso_stamp(npy_datetimestruct *dts):
return RESO_DAY
+@cython.wraparound(False)
+@cython.boundscheck(False)
def get_resolution(const int64_t[:] stamps, tzinfo tz=None) -> Resolution:
cdef:
Py_ssize_t i, ntrans=-1, n = len(stamps)
@@ -259,7 +261,7 @@ cpdef ndarray[int64_t] normalize_i8_timestamps(const int64_t[:] stamps, tzinfo t
"""
cdef:
Py_ssize_t i, ntrans =- 1, n = len(stamps)
- int64_t[:] result = np.empty(n, dtype=np.int64)
+ int64_t[::1] result = np.empty(n, dtype=np.int64)
ndarray[int64_t] trans
int64_t[::1] deltas
int64_t* tdata = NULL
@@ -369,7 +371,7 @@ def is_date_array_normalized(const int64_t[:] stamps, tzinfo tz=None) -> bool:
def dt64arr_to_periodarr(const int64_t[:] stamps, int freq, tzinfo tz):
cdef:
Py_ssize_t i, ntrans =- 1, n = len(stamps)
- int64_t[:] result = np.empty(n, dtype=np.int64)
+ int64_t[::1] result = np.empty(n, dtype=np.int64)
ndarray[int64_t] trans
int64_t[::1] deltas
int64_t* tdata = NULL
diff --git a/pandas/core/indexes/datetimelike.py b/pandas/core/indexes/datetimelike.py
index 5c6fa8d771210..25b7a5c3d3689 100644
--- a/pandas/core/indexes/datetimelike.py
+++ b/pandas/core/indexes/datetimelike.py
@@ -92,16 +92,12 @@ class DatetimeIndexOpsMixin(NDArrayBackedExtensionIndex):
freqstr: str | None
_resolution_obj: Resolution
- # error: "Callable[[Any], Any]" has no attribute "fget"
- hasnans = cast(
- bool,
- cache_readonly(
- DatetimeLikeArrayMixin._hasna.fget # type: ignore[attr-defined]
- ),
- )
-
# ------------------------------------------------------------------------
+ @cache_readonly
+ def hasnans(self) -> bool:
+ return self._data._hasna
+
def equals(self, other: Any) -> bool:
"""
Determines if two Index objects contain the same elements.
diff --git a/pandas/tests/groupby/transform/test_transform.py b/pandas/tests/groupby/transform/test_transform.py
index 2a660583f1396..0f450adf67b23 100644
--- a/pandas/tests/groupby/transform/test_transform.py
+++ b/pandas/tests/groupby/transform/test_transform.py
@@ -1357,6 +1357,7 @@ def test_null_group_str_reducer(request, dropna, reduction_func):
tm.assert_equal(result, expected)
+@pytest.mark.filterwarnings("ignore:tshift is deprecated:FutureWarning")
def test_null_group_str_transformer(
request, using_array_manager, dropna, transformation_func
):
| - [ ] closes #xxxx (Replace xxxx with the Github issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46420 | 2022-03-18T16:24:04Z | 2022-03-19T17:50:01Z | 2022-03-19T17:50:01Z | 2022-03-19T18:14:03Z |
TST: Additional tests to iloc setitem | diff --git a/pandas/tests/indexing/test_iloc.py b/pandas/tests/indexing/test_iloc.py
index 426192ab46914..a0509b889e036 100644
--- a/pandas/tests/indexing/test_iloc.py
+++ b/pandas/tests/indexing/test_iloc.py
@@ -21,11 +21,13 @@
Interval,
NaT,
Series,
+ Timestamp,
array,
concat,
date_range,
interval_range,
isna,
+ to_datetime,
)
import pandas._testing as tm
from pandas.api.types import is_scalar
@@ -1188,6 +1190,24 @@ def test_iloc_getitem_int_single_ea_block_view(self):
arr[2] = arr[-1]
assert ser[0] == arr[-1]
+ def test_iloc_setitem_multicolumn_to_datetime(self):
+
+ # GH#20511
+ df = DataFrame({"A": ["2022-01-01", "2022-01-02"], "B": ["2021", "2022"]})
+
+ df.iloc[:, [0]] = DataFrame({"A": to_datetime(["2021", "2022"])})
+ expected = DataFrame(
+ {
+ "A": [
+ Timestamp("2021-01-01 00:00:00"),
+ Timestamp("2022-01-01 00:00:00"),
+ ],
+ "B": ["2021", "2022"],
+ },
+ dtype=object,
+ )
+ tm.assert_frame_equal(df, expected)
+
class TestILocErrors:
# NB: this test should work for _any_ Series we can pass as
@@ -1363,6 +1383,24 @@ def test_frame_iloc_setitem_callable(self):
exp.iloc[[1, 3], [0]] = [-5, -5]
tm.assert_frame_equal(res, exp)
+ def test_frame_iloc_setitem_callable_multicolumn_to_datetime(self):
+
+ # GH#20511
+ df = DataFrame({"A": ["2022-01-01", "2022-01-02"], "B": ["2021", "2022"]})
+
+ df.iloc[:, [0]] = df.iloc[:, [0]].apply(to_datetime)
+ expected = DataFrame(
+ {
+ "A": [
+ Timestamp("2022-01-01 00:00:00"),
+ Timestamp("2022-01-02 00:00:00"),
+ ],
+ "B": ["2021", "2022"],
+ },
+ dtype=object,
+ )
+ tm.assert_frame_equal(df, expected)
+
class TestILocSeries:
def test_iloc(self):
| This tests make sure when converting multiple columns to datetimes
and when assiging back it remains as datetime not as unix date
as mentioned in GH #20511.
- [x] closes #20511
- [x] Tests added and passed
- [x] All code checks passed
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46419 | 2022-03-18T16:03:16Z | 2022-05-10T04:31:29Z | null | 2022-05-10T04:31:50Z |
CLN: ArrowStringArray._cmp_method - use ChunkedArray.to_numpy() | diff --git a/pandas/core/arrays/string_arrow.py b/pandas/core/arrays/string_arrow.py
index e39ebd3afd2ff..eb3b3b8d29647 100644
--- a/pandas/core/arrays/string_arrow.py
+++ b/pandas/core/arrays/string_arrow.py
@@ -332,8 +332,11 @@ def _cmp_method(self, other, op):
else:
return NotImplemented
- # TODO(ARROW-9429): Add a .to_numpy() to ChunkedArray
- return BooleanArray._from_sequence(result.to_pandas().values)
+ if pa_version_under2p0:
+ result = result.to_pandas().values
+ else:
+ result = result.to_numpy()
+ return BooleanArray._from_sequence(result)
def insert(self, loc: int, item):
if not isinstance(item, str) and item is not libmissing.NA:
| - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
Addresses minor TODO comment in ArrowStringArray._cmp_method.
Also, better perf:
```
import numpy as np
import pyarrow as pa
ca = pa.chunked_array([
np.random.choice([0, 1], 1000),
np.random.choice([0, 1], 1000),
], type=pa.bool_())
%timeit ca.to_pandas().values
45.9 µs ± 2.75 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
%timeit ca.to_numpy()
3.8 µs ± 273 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
``` | https://api.github.com/repos/pandas-dev/pandas/pulls/46417 | 2022-03-18T14:10:30Z | 2022-03-18T16:15:16Z | 2022-03-18T16:15:16Z | 2022-03-20T23:18:48Z |
TST: fix pytest collection errors with pytest-xdist | diff --git a/pandas/tests/tseries/offsets/test_offsets.py b/pandas/tests/tseries/offsets/test_offsets.py
index bf2afe50bdb01..fd764a60d0644 100644
--- a/pandas/tests/tseries/offsets/test_offsets.py
+++ b/pandas/tests/tseries/offsets/test_offsets.py
@@ -563,9 +563,9 @@ def test_mul(self):
assert DateOffset(2) == 2 * DateOffset(1)
assert DateOffset(2) == DateOffset(1) * 2
- @pytest.mark.parametrize("relativedelta_kwd", list(liboffsets._relativedelta_kwds))
- def test_constructor(self, relativedelta_kwd, request):
- if relativedelta_kwd == "millisecond":
+ @pytest.mark.parametrize("kwd", sorted(liboffsets._relativedelta_kwds))
+ def test_constructor(self, kwd, request):
+ if kwd == "millisecond":
request.node.add_marker(
pytest.mark.xfail(
raises=NotImplementedError,
@@ -573,9 +573,9 @@ def test_constructor(self, relativedelta_kwd, request):
"yet supported.",
)
)
- offset = DateOffset(**{relativedelta_kwd: 2})
- assert offset.kwds == {relativedelta_kwd: 2}
- assert getattr(offset, relativedelta_kwd) == 2
+ offset = DateOffset(**{kwd: 2})
+ assert offset.kwds == {kwd: 2}
+ assert getattr(offset, kwd) == 2
def test_default_constructor(self):
assert (self.d + DateOffset(2)) == datetime(2008, 1, 4)
| xref https://github.com/pandas-dev/pandas/pull/43598#discussion_r829985695 | https://api.github.com/repos/pandas-dev/pandas/pulls/46416 | 2022-03-18T13:50:23Z | 2022-03-18T16:15:35Z | 2022-03-18T16:15:35Z | 2022-03-20T16:28:31Z |
REGR: Bar plot axis timestamp to string conversion error | diff --git a/pandas/tests/plotting/test_misc.py b/pandas/tests/plotting/test_misc.py
index 64c736d6314e4..656590cdb02be 100644
--- a/pandas/tests/plotting/test_misc.py
+++ b/pandas/tests/plotting/test_misc.py
@@ -8,6 +8,7 @@
from pandas import (
DataFrame,
Series,
+ Timestamp,
)
import pandas._testing as tm
from pandas.tests.plotting.common import (
@@ -549,3 +550,15 @@ def test_externally_shared_axes(self):
assert not plots[0][0].xaxis.get_label().get_visible()
assert plots[0][1].xaxis.get_label().get_visible()
assert not plots[0][2].xaxis.get_label().get_visible()
+
+ def test_plot_bar_axis_units_timestamp_conversion(self):
+ # GH 38736
+ # Ensure string x-axis from the second plot will not be converted to datetime
+ # due to axis data from first plot
+ df = DataFrame(
+ [1.0],
+ index=[Timestamp("2022-02-22 22:22:22")],
+ )
+ _check_plot_works(df.plot)
+ s = Series({"A": 1.0})
+ _check_plot_works(s.plot.bar)
| Added regression test to prevent bar plot from attempting to convert x-axis from string to timestamp due to previous plot's axis units.
Problematic example:
https://github.com/pandas-dev/pandas/blob/fb353446651067fafec03301256e93d59065c520/pandas/plotting/_matplotlib/core.py#L1433
- [x] closes #38736
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46413 | 2022-03-18T09:16:07Z | 2022-03-20T17:43:37Z | 2022-03-20T17:43:36Z | 2022-03-20T17:44:01Z |
CI: bump mypy&pyright | diff --git a/.github/workflows/code-checks.yml b/.github/workflows/code-checks.yml
index 58bef05940af1..f32fed3b3ee68 100644
--- a/.github/workflows/code-checks.yml
+++ b/.github/workflows/code-checks.yml
@@ -74,7 +74,7 @@ jobs:
- name: Install pyright
# note: keep version in sync with .pre-commit-config.yaml
- run: npm install -g pyright@1.1.212
+ run: npm install -g pyright@1.1.230
- name: Build Pandas
id: build
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 2c58ccb52f9e5..aeb49c2b1a545 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -85,7 +85,7 @@ repos:
types: [python]
stages: [manual]
# note: keep version in sync with .github/workflows/code-checks.yml
- additional_dependencies: ['pyright@1.1.212']
+ additional_dependencies: ['pyright@1.1.230']
- repo: local
hooks:
- id: flake8-rst
diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst
index b1caeaf5e4726..b90a877012e6f 100644
--- a/doc/source/whatsnew/v1.5.0.rst
+++ b/doc/source/whatsnew/v1.5.0.rst
@@ -177,7 +177,7 @@ If installed, we now require:
+-----------------+-----------------+----------+---------+
| Package | Minimum Version | Required | Changed |
+=================+=================+==========+=========+
-| mypy (dev) | 0.931 | | X |
+| mypy (dev) | 0.941 | | X |
+-----------------+-----------------+----------+---------+
diff --git a/environment.yml b/environment.yml
index 2c6a6c95569d6..5402be3d4a00b 100644
--- a/environment.yml
+++ b/environment.yml
@@ -24,7 +24,7 @@ dependencies:
- flake8-bugbear=21.3.2 # used by flake8, find likely bugs
- flake8-comprehensions=3.7.0 # used by flake8, linting of unnecessary comprehensions
- isort>=5.2.1 # check that imports are in the right order
- - mypy=0.931
+ - mypy=0.941
- pre-commit>=2.9.2
- pycodestyle # used by flake8
- pyupgrade
diff --git a/pandas/core/arrays/sparse/array.py b/pandas/core/arrays/sparse/array.py
index 15ec1006b4f80..c2a13961687fd 100644
--- a/pandas/core/arrays/sparse/array.py
+++ b/pandas/core/arrays/sparse/array.py
@@ -774,8 +774,7 @@ def fillna(
elif method is not None:
msg = "fillna with 'method' requires high memory usage."
warnings.warn(msg, PerformanceWarning)
- # Need type annotation for "new_values" [var-annotated]
- new_values = np.asarray(self) # type: ignore[var-annotated]
+ new_values = np.asarray(self)
# interpolate_2d modifies new_values inplace
interpolate_2d(new_values, method=method, limit=limit)
return type(self)(new_values, fill_value=self.fill_value)
diff --git a/pandas/core/computation/ops.py b/pandas/core/computation/ops.py
index 0109e0d2add11..9c54065de0353 100644
--- a/pandas/core/computation/ops.py
+++ b/pandas/core/computation/ops.py
@@ -580,7 +580,8 @@ def __init__(self, op: str, operand) -> None:
def __call__(self, env):
operand = self.operand(env)
- return self.func(operand)
+ # error: Cannot call function of unknown type
+ return self.func(operand) # type: ignore[operator]
def __repr__(self) -> str:
return pprint_thing(f"{self.op}({self.operand})")
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index d00a2778848ae..983fb8f0edf12 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -1481,9 +1481,15 @@ def equals(self, other: object) -> bool_t:
def __neg__(self):
def blk_func(values: ArrayLike):
if is_bool_dtype(values.dtype):
- return operator.inv(values)
+ # error: Argument 1 to "inv" has incompatible type "Union
+ # [ExtensionArray, ndarray[Any, Any]]"; expected
+ # "_SupportsInversion[ndarray[Any, dtype[bool_]]]"
+ return operator.inv(values) # type: ignore[arg-type]
else:
- return operator.neg(values)
+ # error: Argument 1 to "neg" has incompatible type "Union
+ # [ExtensionArray, ndarray[Any, Any]]"; expected
+ # "_SupportsNeg[ndarray[Any, dtype[Any]]]"
+ return operator.neg(values) # type: ignore[arg-type]
new_data = self._mgr.apply(blk_func)
res = self._constructor(new_data)
@@ -1495,7 +1501,10 @@ def blk_func(values: ArrayLike):
if is_bool_dtype(values.dtype):
return values.copy()
else:
- return operator.pos(values)
+ # error: Argument 1 to "pos" has incompatible type "Union
+ # [ExtensionArray, ndarray[Any, Any]]"; expected
+ # "_SupportsPos[ndarray[Any, dtype[Any]]]"
+ return operator.pos(values) # type: ignore[arg-type]
new_data = self._mgr.apply(blk_func)
res = self._constructor(new_data)
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 37fee7058f902..ab6e1c47057eb 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -4088,7 +4088,11 @@ def _get_nearest_indexer(
op = operator.lt if self.is_monotonic_increasing else operator.le
indexer = np.where(
- op(left_distances, right_distances) | (right_indexer == -1),
+ # error: Argument 1&2 has incompatible type "Union[ExtensionArray,
+ # ndarray[Any, Any]]"; expected "Union[SupportsDunderLE,
+ # SupportsDunderGE, SupportsDunderGT, SupportsDunderLT]"
+ op(left_distances, right_distances) # type: ignore[arg-type]
+ | (right_indexer == -1),
left_indexer,
right_indexer,
)
diff --git a/pandas/io/formats/style.py b/pandas/io/formats/style.py
index 764787dd962e9..a3899fb880e6c 100644
--- a/pandas/io/formats/style.py
+++ b/pandas/io/formats/style.py
@@ -3875,14 +3875,22 @@ def _highlight_between(
)
g_left = (
- ops[0](data, left)
+ # error: Argument 2 to "ge" has incompatible type "Union[str, float,
+ # Period, Timedelta, Interval[Any], datetime64, timedelta64, datetime,
+ # Sequence[Any], ndarray[Any, Any], NDFrame]"; expected "Union
+ # [SupportsDunderLE, SupportsDunderGE, SupportsDunderGT, SupportsDunderLT]"
+ ops[0](data, left) # type: ignore[arg-type]
if left is not None
else np.full(data.shape, True, dtype=bool)
)
if isinstance(g_left, (DataFrame, Series)):
g_left = g_left.where(pd.notna(g_left), False)
l_right = (
- ops[1](data, right)
+ # error: Argument 2 to "le" has incompatible type "Union[str, float,
+ # Period, Timedelta, Interval[Any], datetime64, timedelta64, datetime,
+ # Sequence[Any], ndarray[Any, Any], NDFrame]"; expected "Union
+ # [SupportsDunderLE, SupportsDunderGE, SupportsDunderGT, SupportsDunderLT]"
+ ops[1](data, right) # type: ignore[arg-type]
if right is not None
else np.full(data.shape, True, dtype=bool)
)
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 94a1d344b4849..c671dc08a263e 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -12,7 +12,7 @@ flake8==4.0.1
flake8-bugbear==21.3.2
flake8-comprehensions==3.7.0
isort>=5.2.1
-mypy==0.931
+mypy==0.941
pre-commit>=2.9.2
pycodestyle
pyupgrade
| `ExtensionArray` does not implement any of the magic `<>!` methods. Might be worth adding abstract methods and force sub-classes to implement them? @jbrockmendel | https://api.github.com/repos/pandas-dev/pandas/pulls/46411 | 2022-03-18T02:29:17Z | 2022-03-18T14:33:49Z | 2022-03-18T14:33:49Z | 2022-03-18T14:33:49Z |
REF: implement get_unit_from_dtype | diff --git a/pandas/_libs/tslibs/conversion.pyx b/pandas/_libs/tslibs/conversion.pyx
index 7dce3cad9d339..0adf6f722c9ce 100644
--- a/pandas/_libs/tslibs/conversion.pyx
+++ b/pandas/_libs/tslibs/conversion.pyx
@@ -36,6 +36,7 @@ from pandas._libs.tslibs.np_datetime cimport (
dtstruct_to_dt64,
get_datetime64_unit,
get_datetime64_value,
+ get_unit_from_dtype,
npy_datetime,
npy_datetimestruct,
pandas_datetime_to_datetimestruct,
@@ -234,7 +235,9 @@ def ensure_datetime64ns(arr: ndarray, copy: bool = True):
result = result.copy()
return result
- unit = get_datetime64_unit(arr.flat[0])
+ if arr.dtype.kind != "M":
+ raise TypeError("ensure_datetime64ns arr must have datetime64 dtype")
+ unit = get_unit_from_dtype(arr.dtype)
if unit == NPY_DATETIMEUNIT.NPY_FR_GENERIC:
# without raising explicitly here, we end up with a SystemError
# built-in function ensure_datetime64ns returned a result with an error
diff --git a/pandas/_libs/tslibs/np_datetime.pxd b/pandas/_libs/tslibs/np_datetime.pxd
index c2bbc4fe764fe..4ab10bc431d09 100644
--- a/pandas/_libs/tslibs/np_datetime.pxd
+++ b/pandas/_libs/tslibs/np_datetime.pxd
@@ -1,3 +1,4 @@
+cimport numpy as cnp
from cpython.datetime cimport (
date,
datetime,
@@ -79,3 +80,5 @@ cdef NPY_DATETIMEUNIT get_datetime64_unit(object obj) nogil
cdef int _string_to_dts(str val, npy_datetimestruct* dts,
int* out_local, int* out_tzoffset,
bint want_exc) except? -1
+
+cdef NPY_DATETIMEUNIT get_unit_from_dtype(cnp.dtype dtype)
diff --git a/pandas/_libs/tslibs/np_datetime.pyi b/pandas/_libs/tslibs/np_datetime.pyi
index db0c277b73bd5..5227de4e72f44 100644
--- a/pandas/_libs/tslibs/np_datetime.pyi
+++ b/pandas/_libs/tslibs/np_datetime.pyi
@@ -1 +1,6 @@
+import numpy as np
+
class OutOfBoundsDatetime(ValueError): ...
+
+# only exposed for testing
+def py_get_unit_from_dtype(dtype: np.dtype): ...
diff --git a/pandas/_libs/tslibs/np_datetime.pyx b/pandas/_libs/tslibs/np_datetime.pyx
index 79a58478d630a..5f4ef84a79586 100644
--- a/pandas/_libs/tslibs/np_datetime.pyx
+++ b/pandas/_libs/tslibs/np_datetime.pyx
@@ -19,6 +19,9 @@ from cpython.object cimport (
PyDateTime_IMPORT
+cimport numpy as cnp
+
+cnp.import_array()
from numpy cimport int64_t
from pandas._libs.tslibs.util cimport get_c_string_buf_and_size
@@ -42,6 +45,8 @@ cdef extern from "src/datetime/np_datetime.h":
npy_datetimestruct _NS_MIN_DTS, _NS_MAX_DTS
+ PyArray_DatetimeMetaData get_datetime_metadata_from_dtype(cnp.PyArray_Descr *dtype);
+
cdef extern from "src/datetime/np_datetime_strings.h":
int parse_iso_8601_datetime(const char *str, int len, int want_exc,
npy_datetimestruct *out,
@@ -74,6 +79,22 @@ cdef inline NPY_DATETIMEUNIT get_datetime64_unit(object obj) nogil:
"""
return <NPY_DATETIMEUNIT>(<PyDatetimeScalarObject*>obj).obmeta.base
+
+cdef NPY_DATETIMEUNIT get_unit_from_dtype(cnp.dtype dtype):
+ # NB: caller is responsible for ensuring this is *some* datetime64 or
+ # timedelta64 dtype, otherwise we can segfault
+ cdef:
+ cnp.PyArray_Descr* descr = <cnp.PyArray_Descr*>dtype
+ PyArray_DatetimeMetaData meta
+ meta = get_datetime_metadata_from_dtype(descr)
+ return meta.base
+
+
+def py_get_unit_from_dtype(dtype):
+ # for testing get_unit_from_dtype; adds 896 bytes to the .so file.
+ return get_unit_from_dtype(dtype)
+
+
# ----------------------------------------------------------------------
# Comparison
diff --git a/pandas/_libs/tslibs/src/datetime/np_datetime.c b/pandas/_libs/tslibs/src/datetime/np_datetime.c
index 8f59f53a555d8..12e20df256293 100644
--- a/pandas/_libs/tslibs/src/datetime/np_datetime.c
+++ b/pandas/_libs/tslibs/src/datetime/np_datetime.c
@@ -768,3 +768,15 @@ void pandas_timedelta_to_timedeltastruct(npy_timedelta td,
"invalid base unit");
}
}
+
+
+/*
+ * This function returns a pointer to the DateTimeMetaData
+ * contained within the provided datetime dtype.
+ *
+ * Copied near-verbatim from numpy/core/src/multiarray/datetime.c
+ */
+PyArray_DatetimeMetaData
+get_datetime_metadata_from_dtype(PyArray_Descr *dtype) {
+ return (((PyArray_DatetimeDTypeMetaData *)dtype->c_metadata)->meta);
+}
diff --git a/pandas/_libs/tslibs/src/datetime/np_datetime.h b/pandas/_libs/tslibs/src/datetime/np_datetime.h
index 0bbc24ed822c5..8e58be1ca8383 100644
--- a/pandas/_libs/tslibs/src/datetime/np_datetime.h
+++ b/pandas/_libs/tslibs/src/datetime/np_datetime.h
@@ -75,5 +75,12 @@ int cmp_npy_datetimestruct(const npy_datetimestruct *a,
void
add_minutes_to_datetimestruct(npy_datetimestruct *dts, int minutes);
+/*
+ * This function returns the DateTimeMetaData
+ * contained within the provided datetime dtype.
+ */
+PyArray_DatetimeMetaData get_datetime_metadata_from_dtype(
+ PyArray_Descr *dtype);
+
#endif // PANDAS__LIBS_TSLIBS_SRC_DATETIME_NP_DATETIME_H_
diff --git a/pandas/tests/tslibs/test_np_datetime.py b/pandas/tests/tslibs/test_np_datetime.py
new file mode 100644
index 0000000000000..00a2f90217434
--- /dev/null
+++ b/pandas/tests/tslibs/test_np_datetime.py
@@ -0,0 +1,37 @@
+import numpy as np
+
+from pandas._libs.tslibs.np_datetime import py_get_unit_from_dtype
+
+
+def test_get_unit_from_dtype():
+ # datetime64
+ assert py_get_unit_from_dtype(np.dtype("M8[Y]")) == 0
+ assert py_get_unit_from_dtype(np.dtype("M8[M]")) == 1
+ assert py_get_unit_from_dtype(np.dtype("M8[W]")) == 2
+ # B has been deprecated and removed -> no 3
+ assert py_get_unit_from_dtype(np.dtype("M8[D]")) == 4
+ assert py_get_unit_from_dtype(np.dtype("M8[h]")) == 5
+ assert py_get_unit_from_dtype(np.dtype("M8[m]")) == 6
+ assert py_get_unit_from_dtype(np.dtype("M8[s]")) == 7
+ assert py_get_unit_from_dtype(np.dtype("M8[ms]")) == 8
+ assert py_get_unit_from_dtype(np.dtype("M8[us]")) == 9
+ assert py_get_unit_from_dtype(np.dtype("M8[ns]")) == 10
+ assert py_get_unit_from_dtype(np.dtype("M8[ps]")) == 11
+ assert py_get_unit_from_dtype(np.dtype("M8[fs]")) == 12
+ assert py_get_unit_from_dtype(np.dtype("M8[as]")) == 13
+
+ # timedelta64
+ assert py_get_unit_from_dtype(np.dtype("m8[Y]")) == 0
+ assert py_get_unit_from_dtype(np.dtype("m8[M]")) == 1
+ assert py_get_unit_from_dtype(np.dtype("m8[W]")) == 2
+ # B has been deprecated and removed -> no 3
+ assert py_get_unit_from_dtype(np.dtype("m8[D]")) == 4
+ assert py_get_unit_from_dtype(np.dtype("m8[h]")) == 5
+ assert py_get_unit_from_dtype(np.dtype("m8[m]")) == 6
+ assert py_get_unit_from_dtype(np.dtype("m8[s]")) == 7
+ assert py_get_unit_from_dtype(np.dtype("m8[ms]")) == 8
+ assert py_get_unit_from_dtype(np.dtype("m8[us]")) == 9
+ assert py_get_unit_from_dtype(np.dtype("m8[ns]")) == 10
+ assert py_get_unit_from_dtype(np.dtype("m8[ps]")) == 11
+ assert py_get_unit_from_dtype(np.dtype("m8[fs]")) == 12
+ assert py_get_unit_from_dtype(np.dtype("m8[as]")) == 13
| Finding I need this in a bunch of non-nano branches.
cpplint gave a warning I didn't know what to do with so ignored; pls advise.
```
pandas/_libs/tslibs/src/datetime/np_datetime.c:787: Are you taking an address of a cast? This is dangerous: could be a temp var. Take the address before doing the cast, rather than after [runtime/casting] [4]
```
@mattip would this be a good candidate for adding to numpy's `__init__.pxd`? | https://api.github.com/repos/pandas-dev/pandas/pulls/46410 | 2022-03-18T01:07:50Z | 2022-03-18T20:32:45Z | 2022-03-18T20:32:45Z | 2022-03-18T20:35:24Z |
BUG: avoid RuntimeError in groupby.max | diff --git a/pandas/_libs/groupby.pyx b/pandas/_libs/groupby.pyx
index d84cc4873a250..899c56a0a075e 100644
--- a/pandas/_libs/groupby.pyx
+++ b/pandas/_libs/groupby.pyx
@@ -1047,6 +1047,7 @@ def group_last(
const uint8_t[:, :] mask,
uint8_t[:, ::1] result_mask=None,
Py_ssize_t min_count=-1,
+ bint is_datetimelike=False,
) -> None:
"""
Only aggregates on axis=0
@@ -1056,7 +1057,6 @@ def group_last(
iu_64_floating_obj_t val
ndarray[iu_64_floating_obj_t, ndim=2] resx
ndarray[int64_t, ndim=2] nobs
- bint runtime_error = False
bint uses_mask = mask is not None
bint isna_entry
@@ -1116,8 +1116,7 @@ def group_last(
if uses_mask:
isna_entry = mask[i, j]
else:
- isna_entry = _treat_as_na(val, True)
- # TODO: Sure we always want is_datetimelike=True?
+ isna_entry = _treat_as_na(val, is_datetimelike)
if not isna_entry:
nobs[lab, j] += 1
@@ -1125,26 +1124,30 @@ def group_last(
for i in range(ncounts):
for j in range(K):
+ # TODO(cython3): the entire next block can be shared
+ # across 3 places once conditional-nogil is available
if nobs[i, j] < min_count:
+ # if we are integer dtype, not is_datetimelike, and
+ # not uses_mask, then getting here implies that
+ # counts[i] < min_count, which means we will
+ # be cast to float64 and masked at the end
+ # of WrappedCythonOp._call_cython_op. So we can safely
+ # set a placeholder value in out[i, j].
if uses_mask:
result_mask[i, j] = True
elif iu_64_floating_obj_t is int64_t:
- # TODO: only if datetimelike?
+ # Per above, this is a placeholder in
+ # non-is_datetimelike cases.
out[i, j] = NPY_NAT
elif iu_64_floating_obj_t is uint64_t:
- runtime_error = True
- break
+ # placeholder, see above
+ out[i, j] = 0
else:
out[i, j] = NAN
else:
out[i, j] = resx[i, j]
- if runtime_error:
- # We cannot raise directly above because that is within a nogil
- # block.
- raise RuntimeError("empty group with uint64_t")
-
# TODO(cython3): GH#31710 use memorviews once cython 0.30 is released so we can
# use `const iu_64_floating_obj_t[:, :] values`
@@ -1159,6 +1162,7 @@ def group_nth(
uint8_t[:, ::1] result_mask=None,
int64_t min_count=-1,
int64_t rank=1,
+ bint is_datetimelike=False,
) -> None:
"""
Only aggregates on axis=0
@@ -1168,7 +1172,6 @@ def group_nth(
iu_64_floating_obj_t val
ndarray[iu_64_floating_obj_t, ndim=2] resx
ndarray[int64_t, ndim=2] nobs
- bint runtime_error = False
bint uses_mask = mask is not None
bint isna_entry
@@ -1230,8 +1233,7 @@ def group_nth(
if uses_mask:
isna_entry = mask[i, j]
else:
- isna_entry = _treat_as_na(val, True)
- # TODO: Sure we always want is_datetimelike=True?
+ isna_entry = _treat_as_na(val, is_datetimelike)
if not isna_entry:
nobs[lab, j] += 1
@@ -1241,25 +1243,27 @@ def group_nth(
for i in range(ncounts):
for j in range(K):
if nobs[i, j] < min_count:
+ # if we are integer dtype, not is_datetimelike, and
+ # not uses_mask, then getting here implies that
+ # counts[i] < min_count, which means we will
+ # be cast to float64 and masked at the end
+ # of WrappedCythonOp._call_cython_op. So we can safely
+ # set a placeholder value in out[i, j].
if uses_mask:
result_mask[i, j] = True
out[i, j] = 0
elif iu_64_floating_obj_t is int64_t:
- # TODO: only if datetimelike?
+ # Per above, this is a placeholder in
+ # non-is_datetimelike cases.
out[i, j] = NPY_NAT
elif iu_64_floating_obj_t is uint64_t:
- runtime_error = True
- break
+ # placeholder, see above
+ out[i, j] = 0
else:
out[i, j] = NAN
else:
out[i, j] = resx[i, j]
- if runtime_error:
- # We cannot raise directly above because that is within a nogil
- # block.
- raise RuntimeError("empty group with uint64_t")
-
@cython.boundscheck(False)
@cython.wraparound(False)
@@ -1386,7 +1390,6 @@ cdef group_min_max(
Py_ssize_t i, j, N, K, lab, ngroups = len(counts)
iu_64_floating_t val, nan_val
ndarray[iu_64_floating_t, ndim=2] group_min_or_max
- bint runtime_error = False
int64_t[:, ::1] nobs
bint uses_mask = mask is not None
bint isna_entry
@@ -1403,7 +1406,6 @@ cdef group_min_max(
group_min_or_max[:] = _get_min_or_max(<iu_64_floating_t>0, compute_max, is_datetimelike)
if iu_64_floating_t is int64_t:
- # TODO: only if is_datetimelike?
nan_val = NPY_NAT
elif iu_64_floating_t is uint64_t:
# NB: We do not define nan_val because there is no such thing
@@ -1442,25 +1444,30 @@ cdef group_min_max(
for i in range(ngroups):
for j in range(K):
if nobs[i, j] < min_count:
+ # if we are integer dtype, not is_datetimelike, and
+ # not uses_mask, then getting here implies that
+ # counts[i] < min_count, which means we will
+ # be cast to float64 and masked at the end
+ # of WrappedCythonOp._call_cython_op. So we can safely
+ # set a placeholder value in out[i, j].
if uses_mask:
result_mask[i, j] = True
# set out[i, j] to 0 to be deterministic, as
# it was initialized with np.empty. Also ensures
# we can downcast out if appropriate.
out[i, j] = 0
+ elif iu_64_floating_t is int64_t:
+ # Per above, this is a placeholder in
+ # non-is_datetimelike cases.
+ out[i, j] = nan_val
elif iu_64_floating_t is uint64_t:
- runtime_error = True
- break
+ # placeholder, see above
+ out[i, j] = 0
else:
out[i, j] = nan_val
else:
out[i, j] = group_min_or_max[i, j]
- if runtime_error:
- # We cannot raise directly above because that is within a nogil
- # block.
- raise RuntimeError("empty group with uint64_t")
-
@cython.wraparound(False)
@cython.boundscheck(False)
diff --git a/pandas/core/groupby/ops.py b/pandas/core/groupby/ops.py
index 130c69400ea5f..15bc262997075 100644
--- a/pandas/core/groupby/ops.py
+++ b/pandas/core/groupby/ops.py
@@ -519,7 +519,7 @@ def _call_cython_op(
result = maybe_fill(np.empty(out_shape, dtype=out_dtype))
if self.kind == "aggregate":
counts = np.zeros(ngroups, dtype=np.int64)
- if self.how in ["min", "max", "mean"]:
+ if self.how in ["min", "max", "mean", "last", "first"]:
func(
out=result,
counts=counts,
@@ -530,16 +530,6 @@ def _call_cython_op(
result_mask=result_mask,
is_datetimelike=is_datetimelike,
)
- elif self.how in ["first", "last"]:
- func(
- out=result,
- counts=counts,
- values=values,
- labels=comp_ids,
- min_count=min_count,
- mask=mask,
- result_mask=result_mask,
- )
elif self.how in ["add"]:
# We support datetimelike
func(
diff --git a/pandas/tests/resample/test_datetime_index.py b/pandas/tests/resample/test_datetime_index.py
index 8a96643b9834f..fbc3b385e5098 100644
--- a/pandas/tests/resample/test_datetime_index.py
+++ b/pandas/tests/resample/test_datetime_index.py
@@ -1856,15 +1856,11 @@ def test_resample_unsigned_int(any_unsigned_int_numpy_dtype):
)
df = df.loc[(df.index < "2000-01-02") | (df.index > "2000-01-03"), :]
- if any_unsigned_int_numpy_dtype == "uint64":
- with pytest.raises(RuntimeError, match="empty group with uint64_t"):
- df.resample("D").max()
- else:
- result = df.resample("D").max()
-
- expected = DataFrame(
- [1, np.nan, 0],
- columns=["x"],
- index=date_range(start="2000-01-01", end="2000-01-03 23", freq="D"),
- )
- tm.assert_frame_equal(result, expected)
+ result = df.resample("D").max()
+
+ expected = DataFrame(
+ [1, np.nan, 0],
+ columns=["x"],
+ index=date_range(start="2000-01-01", end="2000-01-03 23", freq="D"),
+ )
+ tm.assert_frame_equal(result, expected)
| - [ ] closes #xxxx (Replace xxxx with the Github issue number)
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
Addresses most of the remaining TODOs (except those waiting on cython3) i wanted to get done before pushing forward on more nullable cases. | https://api.github.com/repos/pandas-dev/pandas/pulls/46408 | 2022-03-17T20:53:56Z | 2022-03-18T00:33:12Z | 2022-03-18T00:33:12Z | 2022-03-18T01:08:17Z |
BUG: Fixed Unicode decoding error in `Period.strftime` when a locale-specific directive is used | diff --git a/.github/workflows/ubuntu.yml b/.github/workflows/ubuntu.yml
index 301e7804ddbd8..9b3b3df9cbd0c 100644
--- a/.github/workflows/ubuntu.yml
+++ b/.github/workflows/ubuntu.yml
@@ -42,18 +42,26 @@ jobs:
- name: "Minimum Versions"
env_file: actions-38-minimum_versions.yaml
pattern: "not slow and not network and not single_cpu"
- - name: "Locale: it_IT.utf8"
+ - name: "Locale: it_IT"
env_file: actions-38.yaml
pattern: "not slow and not network and not single_cpu"
extra_apt: "language-pack-it"
+ # Use the utf8 version as the default, it has no bad side-effect.
lang: "it_IT.utf8"
lc_all: "it_IT.utf8"
- - name: "Locale: zh_CN.utf8"
+ # Also install it_IT (its encoding is ISO8859-1) but do not activate it.
+ # It will be temporarily activated during tests with locale.setlocale
+ extra_loc: "it_IT"
+ - name: "Locale: zh_CN"
env_file: actions-38.yaml
pattern: "not slow and not network and not single_cpu"
extra_apt: "language-pack-zh-hans"
+ # Use the utf8 version as the default, it has no bad side-effect.
lang: "zh_CN.utf8"
lc_all: "zh_CN.utf8"
+ # Also install zh_CN (its encoding is gb2312) but do not activate it.
+ # It will be temporarily activated during tests with locale.setlocale
+ extra_loc: "zh_CN"
- name: "Copy-on-Write"
env_file: actions-310.yaml
pattern: "not slow and not network and not single_cpu"
@@ -148,6 +156,12 @@ jobs:
# xsel for clipboard tests
run: sudo apt-get update && sudo apt-get install -y xsel ${{ env.EXTRA_APT }}
+ - name: Generate extra locales
+ # These extra locales will be available for locale.setlocale() calls in tests
+ run: |
+ sudo locale-gen ${{ matrix.extra_loc }}
+ if: ${{ matrix.extra_loc }}
+
- name: Set up Conda
uses: ./.github/actions/setup-conda
with:
diff --git a/doc/source/whatsnew/v1.6.0.rst b/doc/source/whatsnew/v1.6.0.rst
index c393b8a57f805..9d7e4c4dd5200 100644
--- a/doc/source/whatsnew/v1.6.0.rst
+++ b/doc/source/whatsnew/v1.6.0.rst
@@ -175,7 +175,7 @@ I/O
Period
^^^^^^
--
+- Bug in :meth:`Period.strftime` and :meth:`PeriodIndex.strftime`, raising ``UnicodeDecodeError`` when a locale-specific directive was passed (:issue:`46319`)
-
Plotting
diff --git a/pandas/_libs/tslibs/period.pyx b/pandas/_libs/tslibs/period.pyx
index d2d4838bfafc0..25f6386001ef6 100644
--- a/pandas/_libs/tslibs/period.pyx
+++ b/pandas/_libs/tslibs/period.pyx
@@ -1162,7 +1162,8 @@ cdef str period_format(int64_t value, int freq, object fmt=None):
return "NaT"
if isinstance(fmt, str):
- fmt = fmt.encode("utf-8")
+ # Encode using current locale, in case fmt contains non-utf8 chars
+ fmt = <bytes>util.string_encode_locale(fmt)
if fmt is None:
freq_group = get_freq_group(freq)
@@ -1231,7 +1232,8 @@ cdef str _period_strftime(int64_t value, int freq, bytes fmt):
# Execute c_strftime to process the usual datetime directives
formatted = c_strftime(&dts, <char*>fmt)
- result = util.char_to_string(formatted)
+ # Decode result according to current locale
+ result = util.char_to_string_locale(formatted)
free(formatted)
# Now we will fill the placeholders corresponding to our additional directives
diff --git a/pandas/_libs/tslibs/util.pxd b/pandas/_libs/tslibs/util.pxd
index 492b7d519551f..a28aace5d2f15 100644
--- a/pandas/_libs/tslibs/util.pxd
+++ b/pandas/_libs/tslibs/util.pxd
@@ -26,6 +26,10 @@ cdef extern from "Python.h":
const char* PyUnicode_AsUTF8AndSize(object obj,
Py_ssize_t* length) except NULL
+ object PyUnicode_EncodeLocale(object obj, const char *errors) nogil
+ object PyUnicode_DecodeLocale(const char *str, const char *errors) nogil
+
+
from numpy cimport (
float64_t,
int64_t,
@@ -219,3 +223,13 @@ cdef inline const char* get_c_string_buf_and_size(str py_string,
cdef inline const char* get_c_string(str py_string) except NULL:
return get_c_string_buf_and_size(py_string, NULL)
+
+
+cdef inline bytes string_encode_locale(str py_string):
+ """As opposed to PyUnicode_Encode, use current system locale to encode."""
+ return PyUnicode_EncodeLocale(py_string, NULL)
+
+
+cdef inline object char_to_string_locale(const char* data):
+ """As opposed to PyUnicode_FromString, use current system locale to decode."""
+ return PyUnicode_DecodeLocale(data, NULL)
diff --git a/pandas/tests/io/formats/test_format.py b/pandas/tests/io/formats/test_format.py
index 443aa743a6444..4f8ab78c13766 100644
--- a/pandas/tests/io/formats/test_format.py
+++ b/pandas/tests/io/formats/test_format.py
@@ -1,9 +1,14 @@
"""
Test output formatting for Series/DataFrame, including to_string & reprs
"""
-from datetime import datetime
+from contextlib import nullcontext
+from datetime import (
+ datetime,
+ time,
+)
from io import StringIO
import itertools
+import locale
from operator import methodcaller
import os
from pathlib import Path
@@ -46,6 +51,13 @@
use_32bit_repr = is_platform_windows() or not IS64
+def get_local_am_pm():
+ """Return the AM and PM strings returned by strftime in current locale."""
+ am_local = time(1).strftime("%p")
+ pm_local = time(13).strftime("%p")
+ return am_local, pm_local
+
+
@pytest.fixture(params=["string", "pathlike", "buffer"])
def filepath_or_buffer_id(request):
"""
@@ -3219,6 +3231,67 @@ def test_period_tz(self):
per = dt.to_period(freq="H")
assert per.format()[0] == "2013-01-01 00:00"
+ @pytest.mark.parametrize(
+ "locale_str",
+ [
+ pytest.param(None, id=str(locale.getlocale())),
+ "it_IT.utf8",
+ "it_IT", # Note: encoding will be 'ISO8859-1'
+ "zh_CN.utf8",
+ "zh_CN", # Note: encoding will be 'gb2312'
+ ],
+ )
+ def test_period_non_ascii_fmt(self, locale_str):
+ # GH#46468 non-ascii char in input format string leads to wrong output
+
+ # Skip if locale cannot be set
+ if locale_str is not None and not tm.can_set_locale(locale_str, locale.LC_ALL):
+ pytest.skip(f"Skipping as locale '{locale_str}' cannot be set on host.")
+
+ # Change locale temporarily for this test.
+ with tm.set_locale(locale_str, locale.LC_ALL) if locale_str else nullcontext():
+ # Scalar
+ per = pd.Period("2018-03-11 13:00", freq="H")
+ assert per.strftime("%y é") == "18 é"
+
+ # Index
+ per = pd.period_range("2003-01-01 01:00:00", periods=2, freq="12h")
+ formatted = per.format(date_format="%y é")
+ assert formatted[0] == "03 é"
+ assert formatted[1] == "03 é"
+
+ @pytest.mark.parametrize(
+ "locale_str",
+ [
+ pytest.param(None, id=str(locale.getlocale())),
+ "it_IT.utf8",
+ "it_IT", # Note: encoding will be 'ISO8859-1'
+ "zh_CN.utf8",
+ "zh_CN", # Note: encoding will be 'gb2312'
+ ],
+ )
+ def test_period_custom_locale_directive(self, locale_str):
+ # GH#46319 locale-specific directive leads to non-utf8 c strftime char* result
+
+ # Skip if locale cannot be set
+ if locale_str is not None and not tm.can_set_locale(locale_str, locale.LC_ALL):
+ pytest.skip(f"Skipping as locale '{locale_str}' cannot be set on host.")
+
+ # Change locale temporarily for this test.
+ with tm.set_locale(locale_str, locale.LC_ALL) if locale_str else nullcontext():
+ # Get locale-specific reference
+ am_local, pm_local = get_local_am_pm()
+
+ # Scalar
+ per = pd.Period("2018-03-11 13:00", freq="H")
+ assert per.strftime("%p") == pm_local
+
+ # Index
+ per = pd.period_range("2003-01-01 01:00:00", periods=2, freq="12h")
+ formatted = per.format(date_format="%y %I:%M:%S%p")
+ assert formatted[0] == f"03 01:00:00{am_local}"
+ assert formatted[1] == f"03 01:00:00{pm_local}"
+
class TestDatetimeIndexFormat:
def test_datetime(self):
| - [x] closes #46319 , closes #46468
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
Note: this was extracted from #46116 to decompose the big PR into reviewable bits. More to come soon | https://api.github.com/repos/pandas-dev/pandas/pulls/46405 | 2022-03-17T13:57:38Z | 2022-09-08T15:19:54Z | 2022-09-08T15:19:54Z | 2022-10-13T16:59:51Z |
REGR: DataFrame.replace when the replacement value was explicitly None | diff --git a/doc/source/whatsnew/v1.4.2.rst b/doc/source/whatsnew/v1.4.2.rst
index 3523f691cae8f..4cbb8118055af 100644
--- a/doc/source/whatsnew/v1.4.2.rst
+++ b/doc/source/whatsnew/v1.4.2.rst
@@ -19,6 +19,7 @@ Fixed regressions
- Fixed memory performance regression in :meth:`Series.fillna` when called on a :class:`DataFrame` column with ``inplace=True`` (:issue:`46149`)
- Provided an alternative solution for passing custom Excel formats in :meth:`.Styler.to_excel`, which was a regression based on stricter CSS validation. Examples available in the documentation for :meth:`.Styler.format` (:issue:`46152`)
- Fixed regression in :meth:`DataFrame.replace` when a replacement value was also a target for replacement (:issue:`46306`)
+- Fixed regression in :meth:`DataFrame.replace` when the replacement value was explicitly ``None`` when passed in a dictionary to ``to_replace`` (:issue:`45601`, :issue:`45836`)
- Fixed regression when setting values with :meth:`DataFrame.loc` losing :class:`MultiIndex` names if :class:`DataFrame` was empty before (:issue:`46317`)
-
diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index cdf2c47ad3f60..2ff195fa8e5d2 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -777,6 +777,13 @@ def _replace_coerce(
mask=mask,
)
else:
+ if value is None:
+ # gh-45601, gh-45836
+ nb = self.astype(np.dtype(object), copy=False)
+ if nb is self and not inplace:
+ nb = nb.copy()
+ putmask_inplace(nb.values, mask, value)
+ return [nb]
return self.replace(
to_replace=to_replace, value=value, inplace=inplace, mask=mask
)
diff --git a/pandas/tests/frame/methods/test_replace.py b/pandas/tests/frame/methods/test_replace.py
index d8cd477cdcff8..870a64cfa59c9 100644
--- a/pandas/tests/frame/methods/test_replace.py
+++ b/pandas/tests/frame/methods/test_replace.py
@@ -661,6 +661,20 @@ def test_replace_simple_nested_dict_with_nonexistent_value(self):
result = df.replace({"col": {-1: "-", 1: "a", 4: "b"}})
tm.assert_frame_equal(expected, result)
+ def test_replace_NA_with_None(self):
+ # gh-45601
+ df = DataFrame({"value": [42, None]}).astype({"value": "Int64"})
+ result = df.replace({pd.NA: None})
+ expected = DataFrame({"value": [42, None]}, dtype=object)
+ tm.assert_frame_equal(result, expected)
+
+ def test_replace_NAT_with_None(self):
+ # gh-45836
+ df = DataFrame([pd.NaT, pd.NaT])
+ result = df.replace({pd.NaT: None, np.NaN: None})
+ expected = DataFrame([None, None])
+ tm.assert_frame_equal(result, expected)
+
def test_replace_value_is_none(self, datetime_frame):
orig_value = datetime_frame.iloc[0, 0]
orig2 = datetime_frame.iloc[1, 0]
| - [ ] closes #45601
- [ ] closes #45836
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46404 | 2022-03-17T13:33:13Z | 2022-03-19T18:26:22Z | 2022-03-19T18:26:21Z | 2022-03-21T00:58:48Z |
Revert " BUG: RecursionError when attempting to replace np.nan values (#45725)" | diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index 69f66973d0954..cdf2c47ad3f60 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -568,7 +568,6 @@ def replace(
# go through replace_list
values = self.values
- value = self._standardize_fill_value(value) # GH#45725
if isinstance(values, Categorical):
# TODO: avoid special-casing
diff --git a/pandas/tests/frame/methods/test_replace.py b/pandas/tests/frame/methods/test_replace.py
index 2eb300a8905b8..d8cd477cdcff8 100644
--- a/pandas/tests/frame/methods/test_replace.py
+++ b/pandas/tests/frame/methods/test_replace.py
@@ -661,21 +661,6 @@ def test_replace_simple_nested_dict_with_nonexistent_value(self):
result = df.replace({"col": {-1: "-", 1: "a", 4: "b"}})
tm.assert_frame_equal(expected, result)
- def test_replace_numpy_nan(self, nulls_fixture):
- # GH#45725 ensure numpy.nan can be replaced with all other null types
- to_replace = np.nan
- value = nulls_fixture
- dtype = object
- df = DataFrame({"A": [to_replace]}, dtype=dtype)
- expected = DataFrame({"A": [value]}, dtype=dtype)
-
- result = df.replace({to_replace: value}).astype(dtype=dtype)
- tm.assert_frame_equal(result, expected)
-
- # same thing but different calling convention
- result = df.replace(to_replace, value).astype(dtype=dtype)
- tm.assert_frame_equal(result, expected)
-
def test_replace_value_is_none(self, datetime_frame):
orig_value = datetime_frame.iloc[0, 0]
orig2 = datetime_frame.iloc[1, 0]
diff --git a/pandas/tests/series/methods/test_replace.py b/pandas/tests/series/methods/test_replace.py
index 7177648df27ce..77c9cf4013bd7 100644
--- a/pandas/tests/series/methods/test_replace.py
+++ b/pandas/tests/series/methods/test_replace.py
@@ -36,23 +36,6 @@ def test_replace_explicit_none(self):
assert expected.iloc[-1] is None
tm.assert_series_equal(result, expected)
- def test_replace_numpy_nan(self, nulls_fixture):
- # GH#45725 ensure numpy.nan can be replaced with all other null types
- to_replace = np.nan
- value = nulls_fixture
- dtype = object
- ser = pd.Series([to_replace], dtype=dtype)
- expected = pd.Series([value], dtype=dtype)
-
- result = ser.replace({to_replace: value}).astype(dtype=dtype)
- tm.assert_series_equal(result, expected)
- assert result.dtype == dtype
-
- # same thing but different calling convention
- result = ser.replace(to_replace, value).astype(dtype=dtype)
- tm.assert_series_equal(result, expected)
- assert result.dtype == dtype
-
def test_replace_noop_doesnt_downcast(self):
# GH#44498
ser = pd.Series([None, None, pd.Timestamp("2021-12-16 17:31")], dtype=object)
| Reverts pandas-dev/pandas#45749
see https://github.com/pandas-dev/pandas/pull/45749#issuecomment-1070837621 | https://api.github.com/repos/pandas-dev/pandas/pulls/46403 | 2022-03-17T12:01:12Z | 2022-03-17T23:02:43Z | 2022-03-17T23:02:43Z | 2022-03-18T09:44:31Z |
CI: Use shared dependency file for ARM | diff --git a/.circleci/config.yml b/.circleci/config.yml
index a16492b022264..612552f4eac59 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -8,7 +8,7 @@ jobs:
environment:
ENV_FILE: ci/deps/circle-38-arm64.yaml
PYTEST_WORKERS: auto
- PATTERN: "not slow and not network and not clipboard and not arm_slow"
+ PATTERN: "not single_cpu and not slow and not network and not clipboard and not arm_slow and not db"
PYTEST_TARGET: "pandas"
PANDAS_CI: "1"
steps:
diff --git a/ci/deps/circle-38-arm64.yaml b/ci/deps/circle-38-arm64.yaml
index 7e5b56ebe081f..f38c401ca49f8 100644
--- a/ci/deps/circle-38-arm64.yaml
+++ b/ci/deps/circle-38-arm64.yaml
@@ -4,19 +4,52 @@ channels:
dependencies:
- python=3.8
- # tools
- - cython>=0.29.24
+ # test dependencies
+ - cython=0.29.24
- pytest>=6.0
+ - pytest-cov
- pytest-xdist>=1.31
- hypothesis>=5.5.3
+ - psutil
- pytest-asyncio
+ - boto3
- # pandas dependencies
- - botocore>=1.11
- - flask
- - moto
- - numpy
+ # required dependencies
- python-dateutil
+ - numpy
- pytz
+
+ # optional dependencies
+ - beautifulsoup4
+ - blosc
+ - bottleneck
+ - brotlipy
+ - fastparquet
+ - fsspec
+ - html5lib
+ - gcsfs
+ - jinja2
+ - lxml
+ - matplotlib
+ - numba
+ - numexpr
+ - openpyxl
+ - odfpy
+ - pandas-gbq
+ - psycopg2
+ - pyarrow
+ - pymysql
+ # Not provided on ARM
+ #- pyreadstat
+ - pytables
+ - python-snappy
+ - pyxlsb
+ - s3fs
+ - scipy
+ - sqlalchemy
+ - tabulate
+ - xarray
+ - xlrd
+ - xlsxwriter
+ - xlwt
- zstandard
- - pip
| - [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
| https://api.github.com/repos/pandas-dev/pandas/pulls/46401 | 2022-03-17T04:14:11Z | 2022-03-18T00:30:25Z | 2022-03-18T00:30:25Z | 2022-03-30T10:46:05Z |
REF/PERF: ArrowStringArray.__setitem__ | diff --git a/asv_bench/benchmarks/array.py b/asv_bench/benchmarks/array.py
index 103df0fd94847..b58200911749e 100644
--- a/asv_bench/benchmarks/array.py
+++ b/asv_bench/benchmarks/array.py
@@ -2,6 +2,8 @@
import pandas as pd
+from .pandas_vb_common import tm
+
class BooleanArray:
def setup(self):
@@ -39,3 +41,32 @@ def time_constructor(self):
def time_from_integer_array(self):
pd.array(self.values_integer, dtype="Int64")
+
+
+class ArrowStringArray:
+
+ params = [False, True]
+ param_names = ["multiple_chunks"]
+
+ def setup(self, multiple_chunks):
+ try:
+ import pyarrow as pa
+ except ImportError:
+ raise NotImplementedError
+ strings = tm.rands_array(3, 10_000)
+ if multiple_chunks:
+ chunks = [strings[i : i + 100] for i in range(0, len(strings), 100)]
+ self.array = pd.arrays.ArrowStringArray(pa.chunked_array(chunks))
+ else:
+ self.array = pd.arrays.ArrowStringArray(pa.array(strings))
+
+ def time_setitem(self, multiple_chunks):
+ for i in range(200):
+ self.array[i] = "foo"
+
+ def time_setitem_list(self, multiple_chunks):
+ indexer = list(range(0, 50)) + list(range(-50, 0))
+ self.array[indexer] = ["foo"] * len(indexer)
+
+ def time_setitem_slice(self, multiple_chunks):
+ self.array[::10] = "foo"
diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst
index 089ba62e461d1..a16a615e70496 100644
--- a/doc/source/whatsnew/v1.5.0.rst
+++ b/doc/source/whatsnew/v1.5.0.rst
@@ -315,6 +315,7 @@ Performance improvements
- Performance improvement in :func:`merge` when left and/or right are empty (:issue:`45838`)
- Performance improvement in :meth:`DataFrame.join` when left and/or right are empty (:issue:`46015`)
- Performance improvement in :meth:`DataFrame.reindex` and :meth:`Series.reindex` when target is a :class:`MultiIndex` (:issue:`46235`)
+- Performance improvement when setting values in a pyarrow backed string array (:issue:`46400`)
- Performance improvement in :func:`factorize` (:issue:`46109`)
- Performance improvement in :class:`DataFrame` and :class:`Series` constructors for extension dtype scalars (:issue:`45854`)
diff --git a/pandas/compat/__init__.py b/pandas/compat/__init__.py
index d0d7d6a1b8da6..5078d87bc91c7 100644
--- a/pandas/compat/__init__.py
+++ b/pandas/compat/__init__.py
@@ -22,6 +22,7 @@
pa_version_under2p0,
pa_version_under3p0,
pa_version_under4p0,
+ pa_version_under5p0,
)
PY39 = sys.version_info >= (3, 9)
@@ -148,4 +149,5 @@ def get_lzma_file():
"pa_version_under2p0",
"pa_version_under3p0",
"pa_version_under4p0",
+ "pa_version_under5p0",
]
diff --git a/pandas/core/arrays/string_arrow.py b/pandas/core/arrays/string_arrow.py
index e39ebd3afd2ff..9b582a4e911a8 100644
--- a/pandas/core/arrays/string_arrow.py
+++ b/pandas/core/arrays/string_arrow.py
@@ -6,7 +6,6 @@
TYPE_CHECKING,
Any,
Union,
- cast,
overload,
)
@@ -31,6 +30,7 @@
pa_version_under2p0,
pa_version_under3p0,
pa_version_under4p0,
+ pa_version_under5p0,
)
from pandas.util._decorators import doc
@@ -362,49 +362,125 @@ def __setitem__(self, key: int | slice | np.ndarray, value: Any) -> None:
None
"""
key = check_array_indexer(self, key)
+ indices = self._key_to_indices(key)
- if is_integer(key):
- key = cast(int, key)
-
- if not is_scalar(value):
- raise ValueError("Must pass scalars with scalar indexer")
- elif isna(value):
+ if is_scalar(value):
+ if isna(value):
value = None
elif not isinstance(value, str):
raise ValueError("Scalar must be NA or str")
+ value = np.broadcast_to(value, len(indices))
+ else:
+ value = np.array(value, dtype=object, copy=True)
+ for i, v in enumerate(value):
+ if isna(v):
+ value[i] = None
+ elif not isinstance(v, str):
+ raise ValueError("Scalar must be NA or str")
+
+ if len(indices) != len(value):
+ raise ValueError("Length of indexer and values mismatch")
+
+ argsort = np.argsort(indices)
+ indices = indices[argsort]
+ value = value[argsort]
+
+ self._data = self._set_via_chunk_iteration(indices=indices, value=value)
+
+ def _key_to_indices(self, key: int | slice | np.ndarray) -> npt.NDArray[np.intp]:
+ """Convert indexing key for self to positional indices."""
+ if isinstance(key, slice):
+ indices = np.arange(len(self))[key]
+ elif is_bool_dtype(key):
+ key = np.asarray(key)
+ if len(key) != len(self):
+ raise ValueError("Length of indexer and values mismatch")
+ indices = key.nonzero()[0]
+ else:
+ key_arr = np.array([key]) if is_integer(key) else np.asarray(key)
+ indices = np.arange(len(self))[key_arr]
+ return indices
- # Slice data and insert in-between
- new_data = [
- *self._data[0:key].chunks,
+ def _set_via_chunk_iteration(
+ self, indices: npt.NDArray[np.intp], value: npt.NDArray[Any]
+ ) -> pa.ChunkedArray:
+ """
+ Loop through the array chunks and set the new values while
+ leaving the chunking layout unchanged.
+ """
+
+ chunk_indices = self._within_chunk_indices(indices)
+ new_data = []
+
+ for i, chunk in enumerate(self._data.iterchunks()):
+
+ c_ind = chunk_indices[i]
+ n = len(c_ind)
+ c_value, value = value[:n], value[n:]
+
+ if n == 1:
+ # fast path
+ chunk = self._set_single_index_in_chunk(chunk, c_ind[0], c_value[0])
+ elif n > 0:
+ mask = np.zeros(len(chunk), dtype=np.bool_)
+ mask[c_ind] = True
+ if not pa_version_under5p0:
+ if c_value is None or isna(np.array(c_value)).all():
+ chunk = pc.if_else(mask, None, chunk)
+ else:
+ chunk = pc.replace_with_mask(chunk, mask, c_value)
+ else:
+ # The pyarrow compute functions were added in
+ # version 5.0. For prior versions we implement
+ # our own by converting to numpy and back.
+ chunk = chunk.to_numpy(zero_copy_only=False)
+ chunk[mask] = c_value
+ chunk = pa.array(chunk, type=pa.string())
+
+ new_data.append(chunk)
+
+ return pa.chunked_array(new_data)
+
+ @staticmethod
+ def _set_single_index_in_chunk(chunk: pa.Array, index: int, value: Any) -> pa.Array:
+ """Set a single position in a pyarrow array."""
+ assert is_scalar(value)
+ return pa.concat_arrays(
+ [
+ chunk[:index],
pa.array([value], type=pa.string()),
- *self._data[(key + 1) :].chunks,
+ chunk[index + 1 :],
]
- self._data = pa.chunked_array(new_data)
- else:
- # Convert to integer indices and iteratively assign.
- # TODO: Make a faster variant of this in Arrow upstream.
- # This is probably extremely slow.
-
- # Convert all possible input key types to an array of integers
- if isinstance(key, slice):
- key_array = np.array(range(len(self))[key])
- elif is_bool_dtype(key):
- # TODO(ARROW-9430): Directly support setitem(booleans)
- key_array = np.argwhere(key).flatten()
- else:
- # TODO(ARROW-9431): Directly support setitem(integers)
- key_array = np.asanyarray(key)
+ )
- if is_scalar(value):
- value = np.broadcast_to(value, len(key_array))
+ def _within_chunk_indices(
+ self, indices: npt.NDArray[np.intp]
+ ) -> list[npt.NDArray[np.intp]]:
+ """
+ Convert indices for self into a list of ndarrays each containing
+ the indices *within* each chunk of the chunked array.
+ """
+ # indices must be sorted
+ chunk_indices = []
+ for start, stop in self._chunk_ranges():
+ if len(indices) == 0 or indices[0] >= stop:
+ c_ind = np.array([], dtype=np.intp)
else:
- value = np.asarray(value)
+ n = int(np.searchsorted(indices, stop, side="left"))
+ c_ind = indices[:n] - start
+ indices = indices[n:]
+ chunk_indices.append(c_ind)
+ return chunk_indices
- if len(key_array) != len(value):
- raise ValueError("Length of indexer and values mismatch")
-
- for k, v in zip(key_array, value):
- self[k] = v
+ def _chunk_ranges(self) -> list[tuple]:
+ """
+ Return a list of tuples each containing the left (inclusive)
+ and right (exclusive) bounds of each chunk.
+ """
+ lengths = [len(c) for c in self._data.iterchunks()]
+ stops = np.cumsum(lengths)
+ starts = np.concatenate([[0], stops[:-1]])
+ return list(zip(starts, stops))
def take(
self,
diff --git a/pandas/tests/arrays/string_/test_string_arrow.py b/pandas/tests/arrays/string_/test_string_arrow.py
index 265afa89d6530..de1b7a9c603af 100644
--- a/pandas/tests/arrays/string_/test_string_arrow.py
+++ b/pandas/tests/arrays/string_/test_string_arrow.py
@@ -132,3 +132,68 @@ def test_pyarrow_not_installed_raises():
with pytest.raises(ImportError, match=msg):
ArrowStringArray._from_sequence(["a", None, "b"])
+
+
+@skip_if_no_pyarrow
+@pytest.mark.parametrize("multiple_chunks", [False, True])
+@pytest.mark.parametrize(
+ "key, value, expected",
+ [
+ (-1, "XX", ["a", "b", "c", "d", "XX"]),
+ (1, "XX", ["a", "XX", "c", "d", "e"]),
+ (1, None, ["a", None, "c", "d", "e"]),
+ (1, pd.NA, ["a", None, "c", "d", "e"]),
+ ([1, 3], "XX", ["a", "XX", "c", "XX", "e"]),
+ ([1, 3], ["XX", "YY"], ["a", "XX", "c", "YY", "e"]),
+ ([1, 3], ["XX", None], ["a", "XX", "c", None, "e"]),
+ ([1, 3], ["XX", pd.NA], ["a", "XX", "c", None, "e"]),
+ ([0, -1], ["XX", "YY"], ["XX", "b", "c", "d", "YY"]),
+ ([-1, 0], ["XX", "YY"], ["YY", "b", "c", "d", "XX"]),
+ (slice(3, None), "XX", ["a", "b", "c", "XX", "XX"]),
+ (slice(2, 4), ["XX", "YY"], ["a", "b", "XX", "YY", "e"]),
+ (slice(3, 1, -1), ["XX", "YY"], ["a", "b", "YY", "XX", "e"]),
+ (slice(None), "XX", ["XX", "XX", "XX", "XX", "XX"]),
+ ([False, True, False, True, False], ["XX", "YY"], ["a", "XX", "c", "YY", "e"]),
+ ],
+)
+def test_setitem(multiple_chunks, key, value, expected):
+ import pyarrow as pa
+
+ result = pa.array(list("abcde"))
+ expected = pa.array(expected)
+
+ if multiple_chunks:
+ result = pa.chunked_array([result[:3], result[3:]])
+ expected = pa.chunked_array([expected[:3], expected[3:]])
+
+ result = ArrowStringArray(result)
+ expected = ArrowStringArray(expected)
+
+ result[key] = value
+ tm.assert_equal(result, expected)
+ assert result._data.num_chunks == expected._data.num_chunks
+
+
+@skip_if_no_pyarrow
+def test_setitem_invalid_indexer_raises():
+ import pyarrow as pa
+
+ arr = ArrowStringArray(pa.array(list("abcde")))
+
+ with pytest.raises(IndexError, match=None):
+ arr[5] = "foo"
+
+ with pytest.raises(IndexError, match=None):
+ arr[-6] = "foo"
+
+ with pytest.raises(IndexError, match=None):
+ arr[[0, 5]] = "foo"
+
+ with pytest.raises(IndexError, match=None):
+ arr[[0, -6]] = "foo"
+
+ with pytest.raises(IndexError, match=None):
+ arr[[True, True, False]] = "foo"
+
+ with pytest.raises(ValueError, match=None):
+ arr[[0, 1]] = ["foo", "bar", "baz"]
| - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added an entry in the latest `doc/source/whatsnew/v1.5.0.rst` file if fixing a bug or adding a new feature.
This PR improves the performance of `ArrowStringArray.__setitem__` and avoids some existing behavior that can lead to "over-chunking" of the underlying pyarrow `ChunkedArray` as well as exponential performance.
```
import pandas as pd
import pandas._testing as tm
import pyarrow as pa
ca = pa.chunked_array([
tm.rands_array(3, 1_000),
tm.rands_array(3, 1_000),
tm.rands_array(3, 1_000),
])
num_chunks = []
for n in [1000, 2000, 3000]:
arr = pd.arrays.ArrowStringArray(ca)
%timeit arr[:n] = "foo"
num_chunks.append(arr._data.num_chunks)
print("num_chunks:", num_chunks)
```
```
# main
1.72 s ± 142 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
7.23 s ± 761 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
17.5 s ± 1.26 s per loop (mean ± std. dev. of 7 runs, 1 loop each)
num_chunks: [1002, 2001, 3001]
```
```
# PR
88.5 µs ± 1.12 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
145 µs ± 2.55 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
199 µs ± 3.54 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
num_chunks: [3, 3, 3]
```
ASVs added:
```
before after ratio
<main> <arrow-setitem>
- 42.6±0.7ms 28.7±0.6ms 0.67 array.ArrowStringArray.time_setitem(True)
- 22.5±0.3ms 9.86±0.2ms 0.44 array.ArrowStringArray.time_setitem(False)
- 6.84±0.06ms 430±6μs 0.06 array.ArrowStringArray.time_setitem_list(False)
- 17.4±0.1ms 403±5μs 0.02 array.ArrowStringArray.time_setitem_list(True)
- 1.10±0.01s 3.51±0.04ms 0.00 array.ArrowStringArray.time_setitem_slice(True)
- 1.05±0.02s 259±3μs 0.00 array.ArrowStringArray.time_setitem_slice(False)
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/46400 | 2022-03-17T03:49:06Z | 2022-03-18T16:17:07Z | 2022-03-18T16:17:07Z | 2022-03-20T23:18:48Z |
REF: separate out _get_dst_hours | diff --git a/pandas/_libs/tslibs/tzconversion.pyx b/pandas/_libs/tslibs/tzconversion.pyx
index 111d16c27fb3f..1a1aa6dfec5a0 100644
--- a/pandas/_libs/tslibs/tzconversion.pyx
+++ b/pandas/_libs/tslibs/tzconversion.pyx
@@ -115,21 +115,19 @@ timedelta-like}
localized : ndarray[int64_t]
"""
cdef:
- int64_t[::1] deltas
- ndarray[uint8_t, cast=True] ambiguous_array, both_nat, both_eq
+ const int64_t[::1] deltas
+ ndarray[uint8_t, cast=True] ambiguous_array
Py_ssize_t i, isl, isr, idx, pos, ntrans, n = vals.shape[0]
Py_ssize_t delta_idx_offset, delta_idx, pos_left, pos_right
int64_t *tdata
int64_t v, left, right, val, v_left, v_right, new_local, remaining_mins
int64_t first_delta
int64_t shift_delta = 0
- ndarray[int64_t] trans, result, result_a, result_b, dst_hours, delta
- ndarray trans_idx, grp, a_idx, b_idx, one_diff
+ ndarray[int64_t] trans, result, result_a, result_b, dst_hours
npy_datetimestruct dts
bint infer_dst = False, is_dst = False, fill = False
bint shift_forward = False, shift_backward = False
bint fill_nonexist = False
- list trans_grp
str stamp
# Vectorized version of DstTzInfo.localize
@@ -223,49 +221,20 @@ timedelta-like}
# silence false-positive compiler warning
dst_hours = np.empty(0, dtype=np.int64)
if infer_dst:
- dst_hours = np.empty(n, dtype=np.int64)
- dst_hours[:] = NPY_NAT
-
- # Get the ambiguous hours (given the above, these are the hours
- # where result_a != result_b and neither of them are NAT)
- both_nat = np.logical_and(result_a != NPY_NAT, result_b != NPY_NAT)
- both_eq = result_a == result_b
- trans_idx = np.squeeze(np.nonzero(np.logical_and(both_nat, ~both_eq)))
- if trans_idx.size == 1:
- stamp = _render_tstamp(vals[trans_idx])
- raise pytz.AmbiguousTimeError(
- f"Cannot infer dst time from {stamp} as there "
- f"are no repeated times")
- # Split the array into contiguous chunks (where the difference between
- # indices is 1). These are effectively dst transitions in different
- # years which is useful for checking that there is not an ambiguous
- # transition in an individual year.
- if trans_idx.size > 0:
- one_diff = np.where(np.diff(trans_idx) != 1)[0] + 1
- trans_grp = np.array_split(trans_idx, one_diff)
-
- # Iterate through each day, if there are no hours where the
- # delta is negative (indicates a repeat of hour) the switch
- # cannot be inferred
- for grp in trans_grp:
-
- delta = np.diff(result_a[grp])
- if grp.size == 1 or np.all(delta > 0):
- stamp = _render_tstamp(vals[grp[0]])
- raise pytz.AmbiguousTimeError(stamp)
-
- # Find the index for the switch and pull from a for dst and b
- # for standard
- switch_idx = (delta <= 0).nonzero()[0]
- if switch_idx.size > 1:
- raise pytz.AmbiguousTimeError(
- f"There are {switch_idx.size} dst switches when "
- f"there should only be 1.")
- switch_idx = switch_idx[0] + 1
- # Pull the only index and adjust
- a_idx = grp[:switch_idx]
- b_idx = grp[switch_idx:]
- dst_hours[grp] = np.hstack((result_a[a_idx], result_b[b_idx]))
+ dst_hours = _get_dst_hours(vals, result_a, result_b)
+
+ # Pre-compute delta_idx_offset that will be used if we go down non-existent
+ # paths.
+ # Shift the delta_idx by if the UTC offset of
+ # the target tz is greater than 0 and we're moving forward
+ # or vice versa
+ first_delta = deltas[0]
+ if (shift_forward or shift_delta > 0) and first_delta > 0:
+ delta_idx_offset = 1
+ elif (shift_backward or shift_delta < 0) and first_delta < 0:
+ delta_idx_offset = 1
+ else:
+ delta_idx_offset = 0
for i in range(n):
val = vals[i]
@@ -290,7 +259,8 @@ timedelta-like}
stamp = _render_tstamp(val)
raise pytz.AmbiguousTimeError(
f"Cannot infer dst time from {stamp}, try using the "
- f"'ambiguous' argument")
+ "'ambiguous' argument"
+ )
elif left != NPY_NAT:
result[i] = left
elif right != NPY_NAT:
@@ -305,7 +275,7 @@ timedelta-like}
# time
if -1 < shift_delta + remaining_mins < HOUR_NANOS:
raise ValueError(
- f"The provided timedelta will relocalize on a "
+ "The provided timedelta will relocalize on a "
f"nonexistent time: {nonexistent}"
)
new_local = val + shift_delta
@@ -318,16 +288,6 @@ timedelta-like}
delta_idx = bisect_right_i8(tdata, new_local, ntrans)
- # Shift the delta_idx by if the UTC offset of
- # the target tz is greater than 0 and we're moving forward
- # or vice versa
- first_delta = deltas[0]
- if (shift_forward or shift_delta > 0) and first_delta > 0:
- delta_idx_offset = 1
- elif (shift_backward or shift_delta < 0) and first_delta < 0:
- delta_idx_offset = 1
- else:
- delta_idx_offset = 0
delta_idx = delta_idx - delta_idx_offset
result[i] = new_local - deltas[delta_idx]
elif fill_nonexist:
@@ -375,6 +335,70 @@ cdef inline str _render_tstamp(int64_t val):
return str(Timestamp(val))
+cdef ndarray[int64_t] _get_dst_hours(
+ # vals only needed here to potential render an exception message
+ ndarray[int64_t] vals,
+ ndarray[int64_t] result_a,
+ ndarray[int64_t] result_b,
+):
+ cdef:
+ Py_ssize_t n = vals.shape[0]
+ ndarray[uint8_t, cast=True] both_nat, both_eq
+ ndarray[int64_t] delta, dst_hours
+ ndarray trans_idx, grp, a_idx, b_idx, one_diff
+ list trans_grp
+
+ dst_hours = np.empty(n, dtype=np.int64)
+ dst_hours[:] = NPY_NAT
+
+ # Get the ambiguous hours (given the above, these are the hours
+ # where result_a != result_b and neither of them are NAT)
+ both_nat = np.logical_and(result_a != NPY_NAT, result_b != NPY_NAT)
+ both_eq = result_a == result_b
+ trans_idx = np.squeeze(np.nonzero(np.logical_and(both_nat, ~both_eq)))
+ if trans_idx.size == 1:
+ stamp = _render_tstamp(vals[trans_idx])
+ raise pytz.AmbiguousTimeError(
+ f"Cannot infer dst time from {stamp} as there "
+ "are no repeated times"
+ )
+
+ # Split the array into contiguous chunks (where the difference between
+ # indices is 1). These are effectively dst transitions in different
+ # years which is useful for checking that there is not an ambiguous
+ # transition in an individual year.
+ if trans_idx.size > 0:
+ one_diff = np.where(np.diff(trans_idx) != 1)[0] + 1
+ trans_grp = np.array_split(trans_idx, one_diff)
+
+ # Iterate through each day, if there are no hours where the
+ # delta is negative (indicates a repeat of hour) the switch
+ # cannot be inferred
+ for grp in trans_grp:
+
+ delta = np.diff(result_a[grp])
+ if grp.size == 1 or np.all(delta > 0):
+ stamp = _render_tstamp(vals[grp[0]])
+ raise pytz.AmbiguousTimeError(stamp)
+
+ # Find the index for the switch and pull from a for dst and b
+ # for standard
+ switch_idx = (delta <= 0).nonzero()[0]
+ if switch_idx.size > 1:
+ raise pytz.AmbiguousTimeError(
+ f"There are {switch_idx.size} dst switches when "
+ "there should only be 1."
+ )
+
+ switch_idx = switch_idx[0] + 1 # TODO: declare type for switch_idx
+ # Pull the only index and adjust
+ a_idx = grp[:switch_idx]
+ b_idx = grp[switch_idx:]
+ dst_hours[grp] = np.hstack((result_a[a_idx], result_b[b_idx]))
+
+ return dst_hours
+
+
# ----------------------------------------------------------------------
# Timezone Conversion
| There's a lot going on in tz_localize_to_utc, trying to split it into more tightly-scoped pieces. Future goals are a) identifying nano-specific pieces and b) getting rid of python-space numpy calls. | https://api.github.com/repos/pandas-dev/pandas/pulls/46399 | 2022-03-17T01:37:37Z | 2022-03-17T23:02:14Z | 2022-03-17T23:02:14Z | 2022-03-17T23:06:53Z |
REF: Localizer class to de-duplicate tzconversion code | diff --git a/pandas/_libs/tslibs/vectorized.pyx b/pandas/_libs/tslibs/vectorized.pyx
index 34b9e88e046e9..5829bf5492ef8 100644
--- a/pandas/_libs/tslibs/vectorized.pyx
+++ b/pandas/_libs/tslibs/vectorized.pyx
@@ -32,10 +32,7 @@ from .np_datetime cimport (
)
from .offsets cimport BaseOffset
from .period cimport get_period_ordinal
-from .timestamps cimport (
- create_timestamp_from_ts,
- normalize_i8_stamp,
-)
+from .timestamps cimport create_timestamp_from_ts
from .timezones cimport (
get_dst_info,
is_tzlocal,
@@ -47,6 +44,54 @@ from .tzconversion cimport (
localize_tzinfo_api,
)
+
+cdef const int64_t[::1] _deltas_placeholder = np.array([], dtype=np.int64)
+
+
+@cython.freelist(16)
+@cython.internal
+@cython.final
+cdef class Localizer:
+ cdef:
+ tzinfo tz
+ bint use_utc, use_fixed, use_tzlocal, use_dst, use_pytz
+ ndarray trans
+ Py_ssize_t ntrans
+ const int64_t[::1] deltas
+ int64_t delta
+
+ @cython.initializedcheck(False)
+ @cython.boundscheck(False)
+ def __cinit__(self, tzinfo tz):
+ self.tz = tz
+ self.use_utc = self.use_tzlocal = self.use_fixed = False
+ self.use_dst = self.use_pytz = False
+ self.ntrans = -1 # placeholder
+ self.delta = -1 # placeholder
+ self.deltas = _deltas_placeholder
+
+ if is_utc(tz) or tz is None:
+ self.use_utc = True
+
+ elif is_tzlocal(tz) or is_zoneinfo(tz):
+ self.use_tzlocal = True
+
+ else:
+ trans, deltas, typ = get_dst_info(tz)
+ self.trans = trans
+ self.ntrans = trans.shape[0]
+ self.deltas = deltas
+
+ if typ != "pytz" and typ != "dateutil":
+ # static/fixed; in this case we know that len(delta) == 1
+ self.use_fixed = True
+ self.delta = deltas[0]
+ else:
+ self.use_dst = True
+ if typ == "pytz":
+ self.use_pytz = True
+
+
# -------------------------------------------------------------------------
@@ -87,19 +132,14 @@ def ints_to_pydatetime(
ndarray[object] of type specified by box
"""
cdef:
- Py_ssize_t i, ntrans = -1, n = stamps.shape[0]
- ndarray[int64_t] trans
- int64_t[::1] deltas
+ Localizer info = Localizer(tz)
+ int64_t utc_val, local_val
+ Py_ssize_t pos, i, n = stamps.shape[0]
int64_t* tdata = NULL
- intp_t pos
- int64_t utc_val, local_val, delta = NPY_NAT
- bint use_utc = False, use_tzlocal = False, use_fixed = False
- str typ
npy_datetimestruct dts
tzinfo new_tz
ndarray[object] result = np.empty(n, dtype=object)
- bint use_pytz = False
bint use_date = False, use_time = False, use_ts = False, use_pydt = False
if box == "date":
@@ -116,20 +156,8 @@ def ints_to_pydatetime(
"box must be one of 'datetime', 'date', 'time' or 'timestamp'"
)
- if is_utc(tz) or tz is None:
- use_utc = True
- elif is_tzlocal(tz) or is_zoneinfo(tz):
- use_tzlocal = True
- else:
- trans, deltas, typ = get_dst_info(tz)
- ntrans = trans.shape[0]
- if typ not in ["pytz", "dateutil"]:
- # static/fixed; in this case we know that len(delta) == 1
- use_fixed = True
- delta = deltas[0]
- else:
- tdata = <int64_t*>cnp.PyArray_DATA(trans)
- use_pytz = typ == "pytz"
+ if info.use_dst:
+ tdata = <int64_t*>cnp.PyArray_DATA(info.trans)
for i in range(n):
utc_val = stamps[i]
@@ -139,17 +167,17 @@ def ints_to_pydatetime(
result[i] = <object>NaT
continue
- if use_utc:
+ if info.use_utc:
local_val = utc_val
- elif use_tzlocal:
+ elif info.use_tzlocal:
local_val = utc_val + localize_tzinfo_api(utc_val, tz)
- elif use_fixed:
- local_val = utc_val + delta
+ elif info.use_fixed:
+ local_val = utc_val + info.delta
else:
- pos = bisect_right_i8(tdata, utc_val, ntrans) - 1
- local_val = utc_val + deltas[pos]
+ pos = bisect_right_i8(tdata, utc_val, info.ntrans) - 1
+ local_val = utc_val + info.deltas[pos]
- if use_pytz:
+ if info.use_pytz:
# find right representation of dst etc in pytz timezone
new_tz = tz._tzinfos[tz._transition_info[pos]]
@@ -191,46 +219,31 @@ cdef inline c_Resolution _reso_stamp(npy_datetimestruct *dts):
@cython.boundscheck(False)
def get_resolution(const int64_t[:] stamps, tzinfo tz=None) -> Resolution:
cdef:
- Py_ssize_t i, ntrans = -1, n = stamps.shape[0]
- ndarray[int64_t] trans
- int64_t[::1] deltas
+ Localizer info = Localizer(tz)
+ int64_t utc_val, local_val
+ Py_ssize_t pos, i, n = stamps.shape[0]
int64_t* tdata = NULL
- intp_t pos
- int64_t utc_val, local_val, delta = NPY_NAT
- bint use_utc = False, use_tzlocal = False, use_fixed = False
- str typ
npy_datetimestruct dts
c_Resolution reso = c_Resolution.RESO_DAY, curr_reso
- if is_utc(tz) or tz is None:
- use_utc = True
- elif is_tzlocal(tz) or is_zoneinfo(tz):
- use_tzlocal = True
- else:
- trans, deltas, typ = get_dst_info(tz)
- ntrans = trans.shape[0]
- if typ not in ["pytz", "dateutil"]:
- # static/fixed; in this case we know that len(delta) == 1
- use_fixed = True
- delta = deltas[0]
- else:
- tdata = <int64_t*>cnp.PyArray_DATA(trans)
+ if info.use_dst:
+ tdata = <int64_t*>cnp.PyArray_DATA(info.trans)
for i in range(n):
utc_val = stamps[i]
if utc_val == NPY_NAT:
continue
- if use_utc:
+ if info.use_utc:
local_val = utc_val
- elif use_tzlocal:
+ elif info.use_tzlocal:
local_val = utc_val + localize_tzinfo_api(utc_val, tz)
- elif use_fixed:
- local_val = utc_val + delta
+ elif info.use_fixed:
+ local_val = utc_val + info.delta
else:
- pos = bisect_right_i8(tdata, utc_val, ntrans) - 1
- local_val = utc_val + deltas[pos]
+ pos = bisect_right_i8(tdata, utc_val, info.ntrans) - 1
+ local_val = utc_val + info.deltas[pos]
dt64_to_dtstruct(local_val, &dts)
curr_reso = _reso_stamp(&dts)
@@ -242,6 +255,8 @@ def get_resolution(const int64_t[:] stamps, tzinfo tz=None) -> Resolution:
# -------------------------------------------------------------------------
+
+@cython.cdivision(False)
@cython.wraparound(False)
@cython.boundscheck(False)
cpdef ndarray[int64_t] normalize_i8_timestamps(const int64_t[:] stamps, tzinfo tz):
@@ -260,30 +275,15 @@ cpdef ndarray[int64_t] normalize_i8_timestamps(const int64_t[:] stamps, tzinfo t
result : int64 ndarray of converted of normalized nanosecond timestamps
"""
cdef:
- Py_ssize_t i, ntrans = -1, n = stamps.shape[0]
- ndarray[int64_t] trans
- int64_t[::1] deltas
+ Localizer info = Localizer(tz)
+ int64_t utc_val, local_val
+ Py_ssize_t pos, i, n = stamps.shape[0]
int64_t* tdata = NULL
- intp_t pos
- int64_t utc_val, local_val, delta = NPY_NAT
- bint use_utc = False, use_tzlocal = False, use_fixed = False
- str typ
int64_t[::1] result = np.empty(n, dtype=np.int64)
- if is_utc(tz) or tz is None:
- use_utc = True
- elif is_tzlocal(tz) or is_zoneinfo(tz):
- use_tzlocal = True
- else:
- trans, deltas, typ = get_dst_info(tz)
- ntrans = trans.shape[0]
- if typ not in ["pytz", "dateutil"]:
- # static/fixed; in this case we know that len(delta) == 1
- use_fixed = True
- delta = deltas[0]
- else:
- tdata = <int64_t*>cnp.PyArray_DATA(trans)
+ if info.use_dst:
+ tdata = <int64_t*>cnp.PyArray_DATA(info.trans)
for i in range(n):
utc_val = stamps[i]
@@ -291,17 +291,17 @@ cpdef ndarray[int64_t] normalize_i8_timestamps(const int64_t[:] stamps, tzinfo t
result[i] = NPY_NAT
continue
- if use_utc:
+ if info.use_utc:
local_val = utc_val
- elif use_tzlocal:
+ elif info.use_tzlocal:
local_val = utc_val + localize_tzinfo_api(utc_val, tz)
- elif use_fixed:
- local_val = utc_val + delta
+ elif info.use_fixed:
+ local_val = utc_val + info.delta
else:
- pos = bisect_right_i8(tdata, utc_val, ntrans) - 1
- local_val = utc_val + deltas[pos]
+ pos = bisect_right_i8(tdata, utc_val, info.ntrans) - 1
+ local_val = utc_val + info.deltas[pos]
- result[i] = normalize_i8_stamp(local_val)
+ result[i] = local_val - (local_val % DAY_NANOS)
return result.base # `.base` to access underlying ndarray
@@ -324,40 +324,25 @@ def is_date_array_normalized(const int64_t[:] stamps, tzinfo tz=None) -> bool:
is_normalized : bool True if all stamps are normalized
"""
cdef:
- Py_ssize_t i, ntrans = -1, n = stamps.shape[0]
- ndarray[int64_t] trans
- int64_t[::1] deltas
+ Localizer info = Localizer(tz)
+ int64_t utc_val, local_val
+ Py_ssize_t pos, i, n = stamps.shape[0]
int64_t* tdata = NULL
- intp_t pos
- int64_t utc_val, local_val, delta = NPY_NAT
- bint use_utc = False, use_tzlocal = False, use_fixed = False
- str typ
-
- if is_utc(tz) or tz is None:
- use_utc = True
- elif is_tzlocal(tz) or is_zoneinfo(tz):
- use_tzlocal = True
- else:
- trans, deltas, typ = get_dst_info(tz)
- ntrans = trans.shape[0]
- if typ not in ["pytz", "dateutil"]:
- # static/fixed; in this case we know that len(delta) == 1
- use_fixed = True
- delta = deltas[0]
- else:
- tdata = <int64_t*>cnp.PyArray_DATA(trans)
+
+ if info.use_dst:
+ tdata = <int64_t*>cnp.PyArray_DATA(info.trans)
for i in range(n):
utc_val = stamps[i]
- if use_utc:
+ if info.use_utc:
local_val = utc_val
- elif use_tzlocal:
+ elif info.use_tzlocal:
local_val = utc_val + localize_tzinfo_api(utc_val, tz)
- elif use_fixed:
- local_val = utc_val + delta
+ elif info.use_fixed:
+ local_val = utc_val + info.delta
else:
- pos = bisect_right_i8(tdata, utc_val, ntrans) - 1
- local_val = utc_val + deltas[pos]
+ pos = bisect_right_i8(tdata, utc_val, info.ntrans) - 1
+ local_val = utc_val + info.deltas[pos]
if local_val % DAY_NANOS != 0:
return False
@@ -372,31 +357,16 @@ def is_date_array_normalized(const int64_t[:] stamps, tzinfo tz=None) -> bool:
@cython.boundscheck(False)
def dt64arr_to_periodarr(const int64_t[:] stamps, int freq, tzinfo tz):
cdef:
- Py_ssize_t i, ntrans = -1, n = stamps.shape[0]
- ndarray[int64_t] trans
- int64_t[::1] deltas
+ Localizer info = Localizer(tz)
+ int64_t utc_val, local_val
+ Py_ssize_t pos, i, n = stamps.shape[0]
int64_t* tdata = NULL
- intp_t pos
- int64_t utc_val, local_val, delta = NPY_NAT
- bint use_utc = False, use_tzlocal = False, use_fixed = False
- str typ
npy_datetimestruct dts
int64_t[::1] result = np.empty(n, dtype=np.int64)
- if is_utc(tz) or tz is None:
- use_utc = True
- elif is_tzlocal(tz) or is_zoneinfo(tz):
- use_tzlocal = True
- else:
- trans, deltas, typ = get_dst_info(tz)
- ntrans = trans.shape[0]
- if typ not in ["pytz", "dateutil"]:
- # static/fixed; in this case we know that len(delta) == 1
- use_fixed = True
- delta = deltas[0]
- else:
- tdata = <int64_t*>cnp.PyArray_DATA(trans)
+ if info.use_dst:
+ tdata = <int64_t*>cnp.PyArray_DATA(info.trans)
for i in range(n):
utc_val = stamps[i]
@@ -404,15 +374,15 @@ def dt64arr_to_periodarr(const int64_t[:] stamps, int freq, tzinfo tz):
result[i] = NPY_NAT
continue
- if use_utc:
+ if info.use_utc:
local_val = utc_val
- elif use_tzlocal:
+ elif info.use_tzlocal:
local_val = utc_val + localize_tzinfo_api(utc_val, tz)
- elif use_fixed:
- local_val = utc_val + delta
+ elif info.use_fixed:
+ local_val = utc_val + info.delta
else:
- pos = bisect_right_i8(tdata, utc_val, ntrans) - 1
- local_val = utc_val + deltas[pos]
+ pos = bisect_right_i8(tdata, utc_val, info.ntrans) - 1
+ local_val = utc_val + info.deltas[pos]
dt64_to_dtstruct(local_val, &dts)
result[i] = get_period_ordinal(&dts, freq)
| Second attempt at #46246. Perf impact isn't _as_ bad, but is still ugly, see asvs posted below.
The lines:
```
if info.use_utc:
local_val = stamps[i]
elif info.use_tzlocal:
local_val = tz_convert_utc_to_tzlocal(stamps[i], tz)
elif info.use_fixed:
local_val = stamps[i] + info.delta
else:
pos = bisect_right_i8(tdata, stamps[i], info.ntrans) - 1
local_val = stamps[i] + info.deltas[pos]
```
are repeated 5 times in this file, once more with small edits in tzconversion.pyx, and potentially a couple others. So I'd _really_ like to turn them into either a Localizer method or just a module-level function. Doing so causes even bigger performance hits.
Also these lines are liable to change as we add zoneinfo and non-nano support, so making them a function would be helpful for keeping them in sync.
The only idea I have for troubleshooting perf is to move the `info.use_foo` checks (or the info.delta and info.deltas lookups) outside of the loop, but that doesn't seem to make any difference.
Asking for help from the big guns cc @jreback @WillAyd @realead @da-woods
```
time asv continuous -E virtualenv -f 1.01 main HEAD -b tslibs.normalize -b TimeDT64ArrToPeriodArr -b TimeIntsToPydatetime -b TimeResolution --record-samples --append-samples
[...]
before after ratio
[49937b93] [c1085df7]
<perf-gb-mask> <ref-localizer-2>
+ 1.62±0.05μs 2.45±0.03μs 1.51 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 5000, datetime.timezone.utc)
+ 1.89±0.02ms 2.68±0.4ms 1.41 tslibs.normalize.Normalize.time_is_date_array_normalized(1000000, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 4.19±0.1μs 5.70±0.6μs 1.36 tslibs.resolution.TimeResolution.time_get_resolution('us', 0, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 4.57±0.1μs 6.12±0.2μs 1.34 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 3000, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 1.90±0.1μs 2.51±0.3μs 1.32 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 7000, None)
+ 4.11±0.06μs 5.42±0.07μs 1.32 tslibs.resolution.TimeResolution.time_get_resolution('m', 1, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 4.00±0.09μs 5.23±0.2μs 1.31 tslibs.resolution.TimeResolution.time_get_resolution('us', 1, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 4.04±0.09μs 5.27±0.5μs 1.30 tslibs.resolution.TimeResolution.time_get_resolution('m', 0, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 974±10ns 1.26±0.1μs 1.29 tslibs.resolution.TimeResolution.time_get_resolution('ns', 0, tzlocal())
+ 4.76±0.1μs 6.13±0.4μs 1.29 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 2011, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 1.76±0.08μs 2.26±0.1μs 1.28 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4000, tzlocal())
+ 1.77±0.02μs 2.26±0.3μs 1.28 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 6000, tzlocal())
+ 1.67±0.03μs 2.13±0.01μs 1.28 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4006, None)
+ 2.12±0.07μs 2.70±0.05μs 1.27 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 1.70±0.05μs 2.16±0.02μs 1.27 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 3000, tzlocal())
+ 1.64±0.01μs 2.07±0.04μs 1.27 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 7000, datetime.timezone.utc)
+ 2.27±0.04μs 2.87±0.03μs 1.27 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 951±10ns 1.20±0.01μs 1.26 tslibs.resolution.TimeResolution.time_get_resolution('us', 1, datetime.timezone.utc)
+ 244±20μs 308±30μs 1.26 tslibs.resolution.TimeResolution.time_get_resolution('us', 10000, datetime.timezone.utc)
+ 8.57±0.2μs 10.8±0.6μs 1.26 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 4000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 923±7ns 1.16±0.04μs 1.26 tslibs.resolution.TimeResolution.time_get_resolution('s', 0, datetime.timezone.utc)
+ 1.70±0.03μs 2.14±0.03μs 1.26 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 8000, None)
+ 4.40±0.05μs 5.53±0.2μs 1.26 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 4000, None)
+ 1.67±0.05μs 2.10±0.01μs 1.26 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4000, None)
+ 1.53±0.04μs 1.92±0.1μs 1.26 tslibs.resolution.TimeResolution.time_get_resolution('ns', 1, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 2.31±0.03μs 2.90±0.03μs 1.25 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 5000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 1.42±0.03μs 1.78±0.04μs 1.25 tslibs.resolution.TimeResolution.time_get_resolution('us', 1, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 1.64±0.02μs 2.05±0.03μs 1.25 tslibs.resolution.TimeResolution.time_get_resolution('m', 1, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 1.97±0.07μs 2.47±0.3μs 1.25 tslibs.normalize.Normalize.time_normalize_i8_timestamps(100, datetime.timezone.utc)
+ 7.16±0.05μs 8.95±0.6μs 1.25 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 3000, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 2.28±0.04μs 2.85±0.2μs 1.25 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 7000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 4.94±0.08μs 6.15±0.2μs 1.25 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 7000, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 1.03±0.1μs 1.28±0.01μs 1.25 tslibs.resolution.TimeResolution.time_get_resolution('us', 1, None)
+ 2.08±0.04μs 2.58±0.02μs 1.24 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 5000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 286±9μs 355±4μs 1.24 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(10000, 9000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 1.67±0.04μs 2.07±0.03μs 1.24 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4006, datetime.timezone.utc)
+ 1.03±0.04μs 1.28±0μs 1.24 tslibs.resolution.TimeResolution.time_get_resolution('s', 0, None)
+ 4.96±0.1μs 6.14±0.1μs 1.24 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 10000, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 2.10±0.03μs 2.60±0.05μs 1.24 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4006, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 4.15±0.07μs 5.13±0.03μs 1.24 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 6000, None)
+ 4.68±0.1μs 5.78±0.1μs 1.24 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 2011, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 4.95±0.1μs 6.12±0.1μs 1.23 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4006, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 674±20μs 832±70μs 1.23 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(10000, 9000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 4.84±0.1μs 5.96±0.2μs 1.23 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4000, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 6.37±0.09μs 7.83±0.2μs 1.23 tslibs.resolution.TimeResolution.time_get_resolution('m', 100, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 5.13±0.04μs 6.29±0.2μs 1.23 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 10000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 5.00±0.4μs 6.11±0.1μs 1.22 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 8000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 3.67±0.07ms 4.49±0.07ms 1.22 tslibs.resolution.TimeResolution.time_get_resolution('s', 100, tzlocal())
+ 1.92±0.09μs 2.35±0.02μs 1.22 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 9000, None)
+ 1.55±0.03μs 1.89±0.03μs 1.22 tslibs.resolution.TimeResolution.time_get_resolution('m', 0, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 4.92±0.3μs 6.02±0.2μs 1.22 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 5000, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 1.06±0.03μs 1.30±0.1μs 1.22 tslibs.resolution.TimeResolution.time_get_resolution('m', 0, None)
+ 208±4μs 254±0.9μs 1.22 tslibs.resolution.TimeResolution.time_get_resolution('m', 10000, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 9.37±0.08μs 11.4±0.06μs 1.22 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 5000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 27.7±2ms 33.8±0.2ms 1.22 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1000000, 7000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 2.32±0.03μs 2.83±0.1μs 1.22 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 8000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 2.30±0.03μs 2.81±0.03μs 1.22 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 6000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 5.41±0.2μs 6.60±0.6μs 1.22 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 7000, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 1.77±0.06μs 2.16±0.02μs 1.22 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 7000, tzlocal())
+ 1.45±0.01μs 1.76±0.05μs 1.22 tslibs.resolution.TimeResolution.time_get_resolution('m', 1, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 4.02±0.02μs 4.89±0.5μs 1.22 tslibs.resolution.TimeResolution.time_get_resolution('D', 1, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 2.46±0.08μs 2.98±0.09μs 1.22 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 10000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 2.11±0.07μs 2.56±0.2μs 1.22 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 7000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 2.97±0.02μs 3.61±0.06μs 1.21 tslibs.resolution.TimeResolution.time_get_resolution('m', 100, None)
+ 2.05±0.03μs 2.48±0.08μs 1.21 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 3000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 4.87±0.2μs 5.91±0.2μs 1.21 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 6000, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 7.60±0.4μs 9.21±0.2μs 1.21 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 11000, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 1.01±0.01μs 1.22±0.03μs 1.21 tslibs.resolution.TimeResolution.time_get_resolution('us', 0, None)
+ 7.10±0.2μs 8.61±0.2μs 1.21 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 1011, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 1.73±0.04μs 2.09±0.02μs 1.21 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4000, datetime.timezone.utc)
+ 4.11±0.2μs 4.98±0.2μs 1.21 tslibs.resolution.TimeResolution.time_get_resolution('D', 0, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 69.1±3ms 83.7±5ms 1.21 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1000000, 4006, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 64.8±0.6ms 78.5±4ms 1.21 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1000000, 8000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 4.02±0.05μs 4.86±0.09μs 1.21 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 1011, datetime.timezone.utc)
+ 1.85±0.2μs 2.24±0.04μs 1.21 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 1000, None)
+ 28.1±0.6ms 34.0±2ms 1.21 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1000000, 10000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 205±4μs 248±1μs 1.21 tslibs.resolution.TimeResolution.time_get_resolution('m', 10000, None)
+ 5.13±0.2μs 6.18±0.3μs 1.21 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 2000, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 4.17±0.06μs 5.03±0.5μs 1.20 tslibs.resolution.TimeResolution.time_get_resolution('ns', 1, datetime.timezone(datetime.timedelta(seconds=3600)))
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+ 551±5μs 663±40μs 1.20 tslibs.resolution.TimeResolution.time_get_resolution('s', 10000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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+ 5.08±0.06μs 6.11±0.04μs 1.20 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 9000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 1.76±0.04μs 2.11±0.02μs 1.20 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 6000, None)
+ 2.55±0.07μs 3.06±0.3μs 1.20 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 4000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 24.9±1μs 29.9±0.7μs 1.20 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 100, datetime.timezone.utc)
+ 2.29±0.1μs 2.75±0.07μs 1.20 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 3000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 4.52±0.3μs 5.43±0.06μs 1.20 tslibs.resolution.TimeResolution.time_get_resolution('h', 1, datetime.timezone(datetime.timedelta(seconds=3600)))
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+ 1.35±0.02μs 1.62±0.01μs 1.20 tslibs.resolution.TimeResolution.time_get_resolution('s', 0, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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+ 5.27±0.09μs 6.29±0.4μs 1.19 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 12000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 21.9±0.5μs 26.2±0.6μs 1.19 tslibs.normalize.Normalize.time_is_date_array_normalized(10000, datetime.timezone(datetime.timedelta(seconds=3600)))
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+ 2.31±0.08μs 2.75±0.1μs 1.19 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 2011, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
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+ 27.4±0.6ms 32.5±2ms 1.19 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1000000, 6000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 5.16±0.2μs 6.13±0.1μs 1.19 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 12000, datetime.timezone(datetime.timedelta(seconds=3600)))
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+ 2.07±0.05μs 2.45±0.2μs 1.19 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 0, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 1.60±0.01μs 1.90±0.03μs 1.19 tslibs.resolution.TimeResolution.time_get_resolution('us', 0, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
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+ 2.24±0.2μs 2.65±0.02μs 1.18 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 10000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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+ 2.24±0.07μs 2.65±0.2μs 1.18 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 1, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 1.78±0.03μs 2.10±0.2μs 1.18 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 0, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 2.02±0.02μs 2.39±0.2μs 1.18 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 2011, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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+ 26.2±0.7μs 30.9±0.3μs 1.18 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 100, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 1.89±0.1μs 2.23±0.03μs 1.18 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 5000, None)
+ 340±6ms 401±20ms 1.18 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('datetime', 1000000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 25.3±0.4ms 29.9±0.5ms 1.18 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1000000, 6000, datetime.timezone.utc)
+ 65.1±1ms 76.8±0.5ms 1.18 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1000000, 6000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 2.07±0.03μs 2.44±0.1μs 1.18 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 11000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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+ 67.4±1ms 79.3±3ms 1.18 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1000000, 7000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 1.46±0.1μs 1.72±0.07μs 1.18 tslibs.resolution.TimeResolution.time_get_resolution('h', 0, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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+ 1.46±0.03μs 1.72±0.04μs 1.18 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 0, None)
+ 1.88±0.09μs 2.21±0.1μs 1.18 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 9000, tzlocal())
+ 5.28±0.02μs 6.20±0.08μs 1.17 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 4000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 2.40±0.1μs 2.82±0.07μs 1.17 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4006, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 5.14±0.2μs 6.04±0.05μs 1.17 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 11000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
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+ 1.70±0.08μs 1.99±0.04μs 1.17 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 9000, tzlocal())
+ 1.81±0.2μs 2.13±0.05μs 1.17 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 7000, datetime.timezone.utc)
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+ 207±10μs 242±3μs 1.17 tslibs.resolution.TimeResolution.time_get_resolution('m', 10000, datetime.timezone.utc)
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+ 1.74±0.1μs 2.03±0.08μs 1.17 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 12000, datetime.timezone.utc)
+ 1.05±0.04μs 1.23±0.02μs 1.17 tslibs.resolution.TimeResolution.time_get_resolution('m', 1, None)
+ 8.17±0.04μs 9.52±0.4μs 1.17 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 5000, datetime.timezone(datetime.timedelta(seconds=3600)))
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+ 26.2±0.3ms 29.9±0.8ms 1.14 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1000000, 11000, datetime.timezone.utc)
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+ 354±9ms 403±20ms 1.14 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(10000, 9000, tzlocal())
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+ 266±5μs 302±10μs 1.14 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(10000, 1011, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 239±8μs 271±10μs 1.14 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(10000, 1011, datetime.timezone.utc)
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+ 202±2μs 229±10μs 1.13 tslibs.resolution.TimeResolution.time_get_resolution('s', 10000, datetime.timezone.utc)
+ 3.83±0.1ms 4.33±0.2ms 1.13 tslibs.resolution.TimeResolution.time_get_resolution('ns', 100, tzlocal())
+ 1.74±0.04μs 1.97±0.04μs 1.13 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 6000, datetime.timezone.utc)
+ 1.82±0.02μs 2.06±0.05μs 1.13 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 1, datetime.timezone.utc)
+ 6.80±0.5μs 7.69±0.2μs 1.13 tslibs.resolution.TimeResolution.time_get_resolution('h', 100, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 2.61±0.09μs 2.95±0.05μs 1.13 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 1011, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 195±3μs 220±9μs 1.13 tslibs.resolution.TimeResolution.time_get_resolution('D', 10000, datetime.timezone.utc)
+ 2.12±0.07μs 2.39±0.2μs 1.13 tslibs.normalize.Normalize.time_normalize_i8_timestamps(100, None)
+ 1.84±0.1μs 2.07±0.2μs 1.13 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 6000, datetime.timezone.utc)
+ 2.39±0.1μs 2.69±0.2μs 1.13 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 2011, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 5.14±0.3μs 5.77±0.07μs 1.12 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 1000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 61.1±3ms 68.6±0.3ms 1.12 tslibs.resolution.TimeResolution.time_get_resolution('m', 1000000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 1.88±0.06μs 2.11±0.03μs 1.12 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 11000, None)
+ 1.99±0.2μs 2.23±0.03μs 1.12 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 2011, tzlocal())
+ 39.6±1μs 44.4±0.6μs 1.12 tslibs.resolution.TimeResolution.time_get_resolution('m', 1, tzlocal())
+ 450±3ms 504±20ms 1.12 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 1000000, datetime.timezone.utc)
+ 1.90±0.1μs 2.13±0.02μs 1.12 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 4000, None)
+ 442±3ms 495±30ms 1.12 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('datetime', 1000000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 3.57±0.02ms 3.99±0.2ms 1.12 tslibs.resolution.TimeResolution.time_get_resolution('h', 100, tzlocal())
+ 1.90±0.2μs 2.12±0.03μs 1.12 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 1000, tzlocal())
+ 2.48±0.02μs 2.77±0.02μs 1.12 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 1011, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 2.40±0.09μs 2.68±0.06μs 1.12 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 4006, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 5.21±0.1μs 5.80±0.4μs 1.11 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 9000, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 42.4±2μs 47.2±0.3μs 1.11 tslibs.resolution.TimeResolution.time_get_resolution('s', 1, tzlocal())
+ 3.79±0.1ms 4.21±0.01ms 1.11 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 100, tzlocal())
+ 4.46±0.04μs 4.96±0.2μs 1.11 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 0, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 5.48±0.4μs 6.09±0.07μs 1.11 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 3000, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 1.88±0.1μs 2.09±0.03μs 1.11 tslibs.normalize.Normalize.time_normalize_i8_timestamps(1, None)
+ 5.27±0.2μs 5.86±0.2μs 1.11 tslibs.normalize.Normalize.time_normalize_i8_timestamps(1, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 223±2μs 248±20μs 1.11 tslibs.resolution.TimeResolution.time_get_resolution('s', 10000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 3.94±0.06μs 4.38±0.06μs 1.11 tslibs.resolution.TimeResolution.time_get_resolution('h', 100, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 1.83±0.1μs 2.03±0.02μs 1.11 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 4000, datetime.timezone.utc)
+ 5.97±0.4μs 6.63±0.6μs 1.11 tslibs.normalize.Normalize.time_normalize_i8_timestamps(100, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 1.34±0.03μs 1.49±0.03μs 1.11 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 0, datetime.timezone.utc)
+ 1.94±0.04μs 2.15±0.02μs 1.11 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 4006, None)
+ 2.33±0.09μs 2.58±0.04μs 1.11 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 1000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 1.84±0.08μs 2.04±0.09μs 1.11 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 1000, datetime.timezone.utc)
+ 2.59±0.05μs 2.86±0.02μs 1.11 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 12000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 6.05±0.1μs 6.69±0.2μs 1.11 tslibs.resolution.TimeResolution.time_get_resolution('ns', 100, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 7.79±0.7μs 8.60±0.03μs 1.10 tslibs.resolution.TimeResolution.time_get_resolution('h', 100, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 28.3±0.7ms 31.3±0.5ms 1.10 tslibs.normalize.Normalize.time_normalize_i8_timestamps(1000000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 1.77±0.2μs 1.95±0.04μs 1.10 tslibs.resolution.TimeResolution.time_get_resolution('h', 1, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 5.52±0.4μs 6.06±0.1μs 1.10 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 11000, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 1.88±0.03μs 2.07±0.04μs 1.10 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 11000, tzlocal())
+ 605±8μs 664±10μs 1.10 tslibs.resolution.TimeResolution.time_get_resolution('m', 10000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 949±20ns 1.04±0.05μs 1.10 tslibs.resolution.TimeResolution.time_get_resolution('us', 0, datetime.timezone.utc)
+ 1.95±0.1μs 2.14±0.04μs 1.10 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 4000, tzlocal())
+ 5.58±0.2μs 6.12±0.05μs 1.10 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 1000, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 919±30ns 1.01±0.03μs 1.10 tslibs.resolution.TimeResolution.time_get_resolution('ns', 1, datetime.timezone.utc)
+ 4.77±0.1μs 5.21±0.3μs 1.09 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 9000, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 328±3ms 358±5ms 1.09 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('datetime', 1000000, None)
+ 3.69±0.1ms 4.03±0.2ms 1.09 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 2011, tzlocal())
+ 2.07±0.01μs 2.26±0.1μs 1.09 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 9000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 335±5ms 365±30ms 1.09 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('datetime', 1000000, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 63.2±2ms 68.9±0.9ms 1.09 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1000000, 1000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 2.56±0.1μs 2.79±0.04μs 1.09 tslibs.normalize.Normalize.time_normalize_i8_timestamps(1, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 1.90±0.1μs 2.07±0.03μs 1.09 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 2000, datetime.timezone.utc)
+ 1.97±0.03μs 2.14±0.07μs 1.08 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 1, None)
+ 2.03±0.09μs 2.20±0.08μs 1.08 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 0, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 7.09±0.06μs 7.66±0.4μs 1.08 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 2000, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 1.58±0.06μs 1.70±0.05μs 1.08 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 0, tzlocal())
+ 35.2±0.8μs 38.0±0.7μs 1.08 tslibs.normalize.Normalize.time_is_date_array_normalized(10000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 21.8±1ms 23.5±0.2ms 1.08 tslibs.resolution.TimeResolution.time_get_resolution('D', 1000000, None)
+ 2.01±0.1μs 2.17±0.09μs 1.08 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 2000, tzlocal())
+ 1.39±0.02μs 1.50±0.06μs 1.08 tslibs.resolution.TimeResolution.time_get_resolution('ns', 1, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 2.61±0.08μs 2.81±0.04μs 1.07 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 3000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 338±4ms 363±20ms 1.07 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('datetime', 1000000, datetime.timezone.utc)
+ 549±30ns 590±20ns 1.07 tslibs.normalize.Normalize.time_is_date_array_normalized(1, None)
+ 2.44±0.1μs 2.62±0.02μs 1.07 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 11000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 1.94±0.08μs 2.08±0.04μs 1.07 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 4006, datetime.timezone.utc)
+ 5.17±0.3μs 5.52±0.04μs 1.07 tslibs.normalize.Normalize.time_normalize_i8_timestamps(100, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 3.40±0.1μs 3.63±0.02μs 1.07 tslibs.resolution.TimeResolution.time_get_resolution('h', 100, None)
+ 2.67±0.1ms 2.83±0.02ms 1.06 tslibs.normalize.Normalize.time_normalize_i8_timestamps(1000000, datetime.timezone.utc)
+ 2.52±0.1μs 2.67±0.02μs 1.06 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 11000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 1.51±0.05μs 1.57±0.02μs 1.04 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 0, tzlocal())
+ 43.7±0.2μs 45.5±2μs 1.04 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 100, datetime.timezone(datetime.timedelta(seconds=3600)))
- 3.53±0.03ms 3.36±0.01ms 0.95 tslibs.normalize.Normalize.time_normalize_i8_timestamps(1000000, datetime.timezone(datetime.timedelta(seconds=3600)))
- 1.34±0.04ms 1.27±0.02ms 0.95 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('date', 10000, None)
- 279±20ms 265±4ms 0.95 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 1000000, datetime.timezone(datetime.timedelta(seconds=3600)))
- 276±9ms 261±2ms 0.95 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 1000000, datetime.timezone.utc)
- 151±3ms 142±2ms 0.94 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('date', 1000000, None)
- 2.29±0.1μs 2.09±0.04μs 0.91 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('datetime', 0, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 1.57±0.03μs 1.41±0.09μs 0.90 tslibs.resolution.TimeResolution.time_get_resolution('ns', 0, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 3.54±0.2ms 3.07±0.05ms 0.87 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('datetime', 10000, datetime.timezone.utc)
``` | https://api.github.com/repos/pandas-dev/pandas/pulls/46397 | 2022-03-16T21:34:43Z | 2022-04-18T22:31:06Z | 2022-04-18T22:31:06Z | 2022-04-18T23:33:59Z |
TYP/CI: Fix failing clipboard typing | diff --git a/pandas/io/clipboard/__init__.py b/pandas/io/clipboard/__init__.py
index 2ebe03f5f298c..6a39b20869497 100644
--- a/pandas/io/clipboard/__init__.py
+++ b/pandas/io/clipboard/__init__.py
@@ -94,7 +94,8 @@ class PyperclipException(RuntimeError):
class PyperclipWindowsException(PyperclipException):
def __init__(self, message) -> None:
- message += f" ({ctypes.WinError()})"
+ # attr only exists on Windows, so typing fails on other platforms
+ message += f" ({ctypes.WinError()})" # type: ignore[attr-defined]
super().__init__(message)
| - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
| https://api.github.com/repos/pandas-dev/pandas/pulls/46395 | 2022-03-16T15:22:45Z | 2022-03-16T18:12:45Z | 2022-03-16T18:12:45Z | 2022-03-16T20:12:31Z |
CI: Use conda-forge PyPy | diff --git a/.github/workflows/posix.yml b/.github/workflows/posix.yml
index b86dcea59edb8..35c40f2a4aa54 100644
--- a/.github/workflows/posix.yml
+++ b/.github/workflows/posix.yml
@@ -155,24 +155,11 @@ jobs:
channel-priority: flexible
environment-file: ${{ env.ENV_FILE }}
use-only-tar-bz2: true
- if: ${{ env.IS_PYPY == 'false' }} # No pypy3.8 support
- name: Upgrade Arrow version
run: conda install -n pandas-dev -c conda-forge --no-update-deps pyarrow=${{ matrix.pyarrow_version }}
if: ${{ matrix.pyarrow_version }}
- - name: Setup PyPy
- uses: actions/setup-python@v3
- with:
- python-version: "pypy-3.8"
- if: ${{ env.IS_PYPY == 'true' }}
-
- - name: Setup PyPy dependencies
- run: |
- # TODO: re-enable cov, its slowing the tests down though
- pip install Cython numpy python-dateutil pytz pytest>=6.0 pytest-xdist>=1.31.0 pytest-asyncio>=0.17 hypothesis>=5.5.3
- if: ${{ env.IS_PYPY == 'true' }}
-
- name: Build Pandas
uses: ./.github/actions/build_pandas
diff --git a/ci/deps/actions-pypy-38.yaml b/ci/deps/actions-pypy-38.yaml
index ad05d2ab2dacc..eda35ee14ec65 100644
--- a/ci/deps/actions-pypy-38.yaml
+++ b/ci/deps/actions-pypy-38.yaml
@@ -11,6 +11,7 @@ dependencies:
- cython>=0.29.24
- pytest>=6.0
- pytest-cov
+ - pytest-asyncio
- pytest-xdist>=1.31
- hypothesis>=5.5.3
| - [ ] closes #xxxx (Replace xxxx with the Github issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46394 | 2022-03-16T14:28:37Z | 2022-05-16T22:06:00Z | 2022-05-16T22:06:00Z | 2022-05-17T09:17:11Z |
REGR: only convert at end for Block.replace_list | diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index 2ff195fa8e5d2..0dda83f4e9c3f 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -555,6 +555,8 @@ def replace(
to_replace,
value,
inplace: bool = False,
+ # conversion is done at the end if we're called from replace_list
+ convert: bool = True,
# mask may be pre-computed if we're called from replace_list
mask: npt.NDArray[np.bool_] | None = None,
) -> list[Block]:
@@ -572,6 +574,7 @@ def replace(
if isinstance(values, Categorical):
# TODO: avoid special-casing
blk = self if inplace else self.copy()
+ # TODO: should we pass convert here? moot point if special casing avoided.
blk.values._replace(to_replace=to_replace, value=value, inplace=True)
return [blk]
@@ -592,7 +595,7 @@ def replace(
elif self._can_hold_element(value):
blk = self if inplace else self.copy()
putmask_inplace(blk.values, mask, value)
- if not (self.is_object and value is None):
+ if convert and not (self.is_object and value is None):
# if the user *explicitly* gave None, we keep None, otherwise
# may downcast to NaN
blocks = blk.convert(numeric=False, copy=False)
@@ -606,6 +609,7 @@ def replace(
to_replace=to_replace,
value=value,
inplace=True,
+ convert=convert,
mask=mask,
)
@@ -619,6 +623,7 @@ def replace(
to_replace=to_replace,
value=value,
inplace=True,
+ convert=convert,
mask=mask[i : i + 1],
)
)
@@ -630,8 +635,10 @@ def _replace_regex(
to_replace,
value,
inplace: bool = False,
+ # conversion is done at the end if we're called from replace_list
convert: bool = True,
- mask=None,
+ # mask may be pre-computed if we're called from replace_list
+ mask: npt.NDArray[np.bool_] | None = None,
) -> list[Block]:
"""
Replace elements by the given value.
@@ -664,7 +671,10 @@ def _replace_regex(
replace_regex(new_values, rx, value, mask)
block = self.make_block(new_values)
- return block.convert(numeric=False, copy=False)
+ if convert:
+ return block.convert(numeric=False, copy=False)
+ else:
+ return [block]
@final
def replace_list(
@@ -707,7 +717,7 @@ def replace_list(
masks = [extract_bool_array(x) for x in masks] # type: ignore[arg-type]
rb = [self if inplace else self.copy()]
- for i, (src, dest) in enumerate(pairs):
+ for i, (to_replace, value) in enumerate(pairs):
convert = i == src_len # only convert once at the end
new_rb: list[Block] = []
@@ -723,8 +733,8 @@ def replace_list(
m = mib[blk_num : blk_num + 1]
result = blk._replace_coerce(
- to_replace=src,
- value=dest,
+ to_replace=to_replace,
+ value=value,
mask=m,
inplace=inplace,
regex=regex,
@@ -785,7 +795,11 @@ def _replace_coerce(
putmask_inplace(nb.values, mask, value)
return [nb]
return self.replace(
- to_replace=to_replace, value=value, inplace=inplace, mask=mask
+ to_replace=to_replace,
+ value=value,
+ inplace=inplace,
+ convert=False,
+ mask=mask,
)
# ---------------------------------------------------------------------
| In Block.replace_list there is some logic to only convert at the end.
before #40082 the convert parameter of Block._replace_regex was honored, but #40082 changed it not convert. This caused a regression, #44864 and was then changed to always convert in #44897. In this PR, we restore the original behavior and honor the convert parameter, which is unused on master.
before #44940, the conversion was not done in Block._replace_coerce for the non-regex case. #44940 changed Block._replace_coerce to dispatch to Block._replace for the non-regex case. Block._replace converts by default.
i've removed _replace_coerce since it appears that the only logic there now is to decide whether to use Block.replace or Block._replace_regex and is only called from Block.replace_list. (The signatures for Block.replace or Block._replace_regex are now consistent in this PR)
I've labelled as a regression but not sure whether we would want to backport this. I have therefore marked as draft and will wait till the reported regressions #45836 and #45601 that are caused by #44940 to be fixed first to avoid merge conflicts with the backport if we don't backport this.
Alternatively we remove the convert logic from Block.replace_list as dead code. | https://api.github.com/repos/pandas-dev/pandas/pulls/46393 | 2022-03-16T11:22:14Z | 2022-07-26T18:26:26Z | null | 2022-07-26T18:26:26Z |
Added the round( ) function to avoid error while multiplication of float numbers issue: #46362 | diff --git a/doc/source/whatsnew/v1.4.2.rst b/doc/source/whatsnew/v1.4.2.rst
index 06f1f406c3816..b1d44b1388fdc 100644
--- a/doc/source/whatsnew/v1.4.2.rst
+++ b/doc/source/whatsnew/v1.4.2.rst
@@ -30,7 +30,7 @@ Bug fixes
~~~~~~~~~
- Fix some cases for subclasses that define their ``_constructor`` properties as general callables (:issue:`46018`)
- Fixed "longtable" formatting in :meth:`.Styler.to_latex` when ``column_format`` is given in extended format (:issue:`46037`)
--
+
.. ---------------------------------------------------------------------------
diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst
index 8dac952874f89..6aa64380b0a90 100644
--- a/doc/source/whatsnew/v1.5.0.rst
+++ b/doc/source/whatsnew/v1.5.0.rst
@@ -362,6 +362,7 @@ Conversion
- Bug in :meth:`Series.astype` and :meth:`DataFrame.astype` from floating dtype to unsigned integer dtype failing to raise in the presence of negative values (:issue:`45151`)
- Bug in :func:`array` with ``FloatingDtype`` and values containing float-castable strings incorrectly raising (:issue:`45424`)
- Bug when comparing string and datetime64ns objects causing ``OverflowError`` exception. (:issue:`45506`)
+- Bug in :meth:`format.py` the percentiles values are converted to integer dtype, which ignores the floating point error while multiplying. (:issue:`46362`)
Strings
^^^^^^^
diff --git a/pandas/io/formats/format.py b/pandas/io/formats/format.py
index 609fc2a45aa21..c337b2922f36e 100644
--- a/pandas/io/formats/format.py
+++ b/pandas/io/formats/format.py
@@ -1713,10 +1713,10 @@ def format_percentiles(
percentiles = 100 * percentiles
- int_idx = np.isclose(percentiles.astype(int), percentiles)
+ int_idx = np.isclose(percentiles.round(), percentiles)
if np.all(int_idx):
- out = percentiles.astype(int).astype(str)
+ out = percentiles.round().astype(int).astype(str)
return [i + "%" for i in out]
unique_pcts = np.unique(percentiles)
@@ -1729,7 +1729,7 @@ def format_percentiles(
).astype(int)
prec = max(1, prec)
out = np.empty_like(percentiles, dtype=object)
- out[int_idx] = percentiles[int_idx].astype(int).astype(str)
+ out[int_idx] = percentiles[int_idx].round().astype(int).astype(str)
out[~int_idx] = percentiles[~int_idx].round(prec).astype(str)
return [i + "%" for i in out]
diff --git a/pandas/tests/io/formats/test_format.py b/pandas/tests/io/formats/test_format.py
index adcaeba5cfd8d..a8e260eaf6e51 100644
--- a/pandas/tests/io/formats/test_format.py
+++ b/pandas/tests/io/formats/test_format.py
@@ -3262,6 +3262,11 @@ def test_format_percentiles():
expected = ["0%", "50%", "2.0%", "50%", "66.67%", "99.99%"]
assert result == expected
+ # Issue #46362
+ result = fmt.format_percentiles([0.281,0.57,0.58,0.29])
+ expected = ['28.1%', '57%', '58%', '29%']
+ assert result == expected
+
msg = r"percentiles should all be in the interval \[0,1\]"
with pytest.raises(ValueError, match=msg):
fmt.format_percentiles([0.1, np.nan, 0.5])
| - Added round( ) function to round the values and then convert it to int type while they are passed into the list-out, so that we can avoid conversations of int(28.999999999999996) to 28.
- if we round the value first it will be converted into 29 and then formating them to int type.
- ✅ closes #46362
- ✅ [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- ✅ All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- ✅ Added an entry in the latest `doc/source/whatsnew/v1.4.2.rst`
| https://api.github.com/repos/pandas-dev/pandas/pulls/46388 | 2022-03-16T07:36:25Z | 2022-05-24T21:01:59Z | null | 2022-05-24T21:01:59Z |
TST: skip -> xfail | diff --git a/pandas/tests/frame/methods/test_between_time.py b/pandas/tests/frame/methods/test_between_time.py
index d8a742c644e9e..eb5cfbc2bfbe8 100644
--- a/pandas/tests/frame/methods/test_between_time.py
+++ b/pandas/tests/frame/methods/test_between_time.py
@@ -18,7 +18,7 @@
class TestBetweenTime:
- @td.skip_if_has_locale
+ @td.skip_if_not_us_locale
def test_between_time_formats(self, frame_or_series):
# GH#11818
rng = date_range("1/1/2000", "1/5/2000", freq="5min")
diff --git a/pandas/tests/groupby/test_quantile.py b/pandas/tests/groupby/test_quantile.py
index 1badc4aa7995a..bf06495f935cd 100644
--- a/pandas/tests/groupby/test_quantile.py
+++ b/pandas/tests/groupby/test_quantile.py
@@ -34,10 +34,13 @@
],
)
@pytest.mark.parametrize("q", [0, 0.25, 0.5, 0.75, 1])
-def test_quantile(interpolation, a_vals, b_vals, q):
+def test_quantile(interpolation, a_vals, b_vals, q, request):
if interpolation == "nearest" and q == 0.5 and b_vals == [4, 3, 2, 1]:
- pytest.skip(
- "Unclear numpy expectation for nearest result with equidistant data"
+ request.node.add_marker(
+ pytest.mark.xfail(
+ reason="Unclear numpy expectation for nearest "
+ "result with equidistant data"
+ )
)
a_expected = pd.Series(a_vals).quantile(q, interpolation=interpolation)
diff --git a/pandas/tests/io/formats/test_series_info.py b/pandas/tests/io/formats/test_series_info.py
index fc83b6c3d23ea..761dd07dbef51 100644
--- a/pandas/tests/io/formats/test_series_info.py
+++ b/pandas/tests/io/formats/test_series_info.py
@@ -116,7 +116,7 @@ def test_info_shows_dtypes():
assert name in res
-@pytest.mark.skipif(PYPY, reason="on PyPy deep=True doesn't change result")
+@pytest.mark.xfail(PYPY, reason="on PyPy deep=True doesn't change result")
def test_info_memory_usage_deep_not_pypy():
s_with_object_index = Series({"a": [1]}, index=["foo"])
assert s_with_object_index.memory_usage(
@@ -127,7 +127,7 @@ def test_info_memory_usage_deep_not_pypy():
assert s_object.memory_usage(deep=True) > s_object.memory_usage()
-@pytest.mark.skipif(not PYPY, reason="on PyPy deep=True does not change result")
+@pytest.mark.xfail(not PYPY, reason="on PyPy deep=True does not change result")
def test_info_memory_usage_deep_pypy():
s_with_object_index = Series({"a": [1]}, index=["foo"])
assert s_with_object_index.memory_usage(
diff --git a/pandas/tests/io/json/test_ujson.py b/pandas/tests/io/json/test_ujson.py
index 93318bed2a6af..e82a888f47388 100644
--- a/pandas/tests/io/json/test_ujson.py
+++ b/pandas/tests/io/json/test_ujson.py
@@ -945,9 +945,11 @@ def test_array_numpy_labelled(self):
class TestPandasJSONTests:
- def test_dataframe(self, orient, numpy):
+ def test_dataframe(self, request, orient, numpy):
if orient == "records" and numpy:
- pytest.skip("Not idiomatic pandas")
+ request.node.add_marker(
+ pytest.mark.xfail(reason=f"Not idiomatic pandas if orient={orient}")
+ )
dtype = get_int32_compat_dtype(numpy, orient)
diff --git a/pandas/tests/io/parser/common/test_read_errors.py b/pandas/tests/io/parser/common/test_read_errors.py
index 2274646ae7c69..47f1052808e0c 100644
--- a/pandas/tests/io/parser/common/test_read_errors.py
+++ b/pandas/tests/io/parser/common/test_read_errors.py
@@ -241,11 +241,16 @@ def test_null_byte_char(all_parsers):
@td.check_file_leaks
-def test_open_file(all_parsers):
+def test_open_file(request, all_parsers):
# GH 39024
parser = all_parsers
if parser.engine == "c":
- pytest.skip("'c' engine does not support sep=None with delim_whitespace=False")
+ request.node.add_marker(
+ pytest.mark.xfail(
+ reason=f"{parser.engine} engine does not support sep=None "
+ f"with delim_whitespace=False"
+ )
+ )
with tm.ensure_clean() as path:
file = Path(path)
diff --git a/pandas/tests/io/parser/dtypes/test_dtypes_basic.py b/pandas/tests/io/parser/dtypes/test_dtypes_basic.py
index 4d99b3c3c8c85..eded3c1126bad 100644
--- a/pandas/tests/io/parser/dtypes/test_dtypes_basic.py
+++ b/pandas/tests/io/parser/dtypes/test_dtypes_basic.py
@@ -191,31 +191,39 @@ def test_delimiter_with_usecols_and_parse_dates(all_parsers):
@pytest.mark.parametrize("thousands", ["_", None])
-def test_decimal_and_exponential(python_parser_only, numeric_decimal, thousands):
+def test_decimal_and_exponential(
+ request, python_parser_only, numeric_decimal, thousands
+):
# GH#31920
- decimal_number_check(python_parser_only, numeric_decimal, thousands, None)
+ decimal_number_check(request, python_parser_only, numeric_decimal, thousands, None)
@pytest.mark.parametrize("thousands", ["_", None])
@pytest.mark.parametrize("float_precision", [None, "legacy", "high", "round_trip"])
def test_1000_sep_decimal_float_precision(
- c_parser_only, numeric_decimal, float_precision, thousands
+ request, c_parser_only, numeric_decimal, float_precision, thousands
):
# test decimal and thousand sep handling in across 'float_precision'
# parsers
- decimal_number_check(c_parser_only, numeric_decimal, thousands, float_precision)
+ decimal_number_check(
+ request, c_parser_only, numeric_decimal, thousands, float_precision
+ )
text, value = numeric_decimal
text = " " + text + " "
if isinstance(value, str): # the negative cases (parse as text)
value = " " + value + " "
- decimal_number_check(c_parser_only, (text, value), thousands, float_precision)
+ decimal_number_check(
+ request, c_parser_only, (text, value), thousands, float_precision
+ )
-def decimal_number_check(parser, numeric_decimal, thousands, float_precision):
+def decimal_number_check(request, parser, numeric_decimal, thousands, float_precision):
# GH#31920
value = numeric_decimal[0]
- if thousands is None and "_" in value:
- pytest.skip("Skip test if no thousands sep is defined and sep is in value")
+ if thousands is None and value in ("1_,", "1_234,56", "1_234,56e0"):
+ request.node.add_marker(
+ pytest.mark.xfail(reason=f"thousands={thousands} and sep is in {value}")
+ )
df = parser.read_csv(
StringIO(value),
float_precision=float_precision,
diff --git a/pandas/tests/io/parser/test_compression.py b/pandas/tests/io/parser/test_compression.py
index d97b594623023..576542306c164 100644
--- a/pandas/tests/io/parser/test_compression.py
+++ b/pandas/tests/io/parser/test_compression.py
@@ -91,7 +91,7 @@ def test_zip_error_invalid_zip(parser_and_data):
@skip_pyarrow
@pytest.mark.parametrize("filename", [None, "test.{ext}"])
-def test_compression(parser_and_data, compression_only, buffer, filename):
+def test_compression(request, parser_and_data, compression_only, buffer, filename):
parser, data, expected = parser_and_data
compress_type = compression_only
@@ -99,7 +99,11 @@ def test_compression(parser_and_data, compression_only, buffer, filename):
filename = filename if filename is None else filename.format(ext=ext)
if filename and buffer:
- pytest.skip("Cannot deduce compression from buffer of compressed data.")
+ request.node.add_marker(
+ pytest.mark.xfail(
+ reason="Cannot deduce compression from buffer of compressed data."
+ )
+ )
with tm.ensure_clean(filename=filename) as path:
tm.write_to_compressed(compress_type, path, data)
diff --git a/pandas/tests/io/parser/test_unsupported.py b/pandas/tests/io/parser/test_unsupported.py
index f346fad7acecf..2f28697daf9e2 100644
--- a/pandas/tests/io/parser/test_unsupported.py
+++ b/pandas/tests/io/parser/test_unsupported.py
@@ -191,11 +191,13 @@ def test_close_file_handle_on_invalid_usecols(all_parsers):
os.unlink(fname)
-def test_invalid_file_inputs(all_parsers):
+def test_invalid_file_inputs(request, all_parsers):
# GH#45957
parser = all_parsers
if parser.engine == "python":
- pytest.skip("Python engine supports lists.")
+ request.node.add_marker(
+ pytest.mark.xfail(reason=f"{parser.engine} engine supports lists.")
+ )
with pytest.raises(ValueError, match="Invalid"):
parser.read_csv([])
diff --git a/pandas/tests/io/test_sql.py b/pandas/tests/io/test_sql.py
index 31add4743d1e9..e4318f1d79102 100644
--- a/pandas/tests/io/test_sql.py
+++ b/pandas/tests/io/test_sql.py
@@ -1719,7 +1719,7 @@ def test_default_date_load(self):
# MySQL SHOULD be converted.
assert issubclass(df.DateCol.dtype.type, np.datetime64)
- def test_datetime_with_timezone(self):
+ def test_datetime_with_timezone(self, request):
# edge case that converts postgresql datetime with time zone types
# to datetime64[ns,psycopg2.tz.FixedOffsetTimezone..], which is ok
# but should be more natural, so coerce to datetime64[ns] for now
@@ -1760,7 +1760,9 @@ def check(col):
# GH11216
df = read_sql_query("select * from types", self.conn)
if not hasattr(df, "DateColWithTz"):
- pytest.skip("no column with datetime with time zone")
+ request.node.add_marker(
+ pytest.mark.xfail(reason="no column with datetime with time zone")
+ )
# this is parsed on Travis (linux), but not on macosx for some reason
# even with the same versions of psycopg2 & sqlalchemy, possibly a
@@ -1772,7 +1774,9 @@ def check(col):
"select * from types", self.conn, parse_dates=["DateColWithTz"]
)
if not hasattr(df, "DateColWithTz"):
- pytest.skip("no column with datetime with time zone")
+ request.node.add_marker(
+ pytest.mark.xfail(reason="no column with datetime with time zone")
+ )
col = df.DateColWithTz
assert is_datetime64tz_dtype(col.dtype)
assert str(col.dt.tz) == "UTC"
@@ -2275,8 +2279,9 @@ def test_get_engine_auto_error_message(self):
class _TestSQLAlchemyConn(_EngineToConnMixin, _TestSQLAlchemy):
+ @pytest.mark.xfail(reason="Nested transactions rollbacks don't work with Pandas")
def test_transactions(self):
- pytest.skip("Nested transactions rollbacks don't work with Pandas")
+ super().test_transactions()
class _TestSQLiteAlchemy:
diff --git a/pandas/tests/series/indexing/test_setitem.py b/pandas/tests/series/indexing/test_setitem.py
index dd83ac49eda86..992f67c2affc6 100644
--- a/pandas/tests/series/indexing/test_setitem.py
+++ b/pandas/tests/series/indexing/test_setitem.py
@@ -759,10 +759,6 @@ def test_series_where(self, obj, key, expected, val, is_inplace):
self._check_inplace(is_inplace, orig, arr, obj)
def test_index_where(self, obj, key, expected, val):
- if obj.dtype.kind == "c" or expected.dtype.kind == "c":
- # TODO(Index[complex]): Should become unreachable
- pytest.skip("test not applicable for this dtype")
-
mask = np.zeros(obj.shape, dtype=bool)
mask[key] = True
@@ -770,10 +766,6 @@ def test_index_where(self, obj, key, expected, val):
tm.assert_index_equal(res, Index(expected, dtype=expected.dtype))
def test_index_putmask(self, obj, key, expected, val):
- if obj.dtype.kind == "c" or expected.dtype.kind == "c":
- # TODO(Index[complex]): Should become unreachable
- pytest.skip("test not applicable for this dtype")
-
mask = np.zeros(obj.shape, dtype=bool)
mask[key] = True
diff --git a/pandas/tests/series/test_arithmetic.py b/pandas/tests/series/test_arithmetic.py
index 1ffdb10369134..c8044a44b48ee 100644
--- a/pandas/tests/series/test_arithmetic.py
+++ b/pandas/tests/series/test_arithmetic.py
@@ -823,11 +823,13 @@ def test_series_inplace_ops(self, dtype1, dtype2, dtype_expected, dtype_mul):
tm.assert_series_equal(ser1, expected)
-def test_none_comparison(series_with_simple_index):
+def test_none_comparison(request, series_with_simple_index):
series = series_with_simple_index
if len(series) < 1:
- pytest.skip("Test doesn't make sense on empty data")
+ request.node.add_marker(
+ pytest.mark.xfail(reason="Test doesn't make sense on empty data")
+ )
# bug brought up by #1079
# changed from TypeError in 0.17.0
diff --git a/pandas/tests/series/test_constructors.py b/pandas/tests/series/test_constructors.py
index 3b7ae28be68fa..e416b1f625993 100644
--- a/pandas/tests/series/test_constructors.py
+++ b/pandas/tests/series/test_constructors.py
@@ -801,31 +801,6 @@ def test_constructor_floating_data_int_dtype(self, frame_or_series):
obj = frame_or_series(list(arr), dtype="i8")
tm.assert_equal(obj, expected)
- @td.skip_if_no("dask")
- def test_construct_dask_float_array_int_dtype_match_ndarray(self):
- # GH#40110 make sure we treat a float-dtype dask array with the same
- # rules we would for an ndarray
- import dask.dataframe as dd
-
- arr = np.array([1, 2.5, 3])
- darr = dd.from_array(arr)
-
- res = Series(darr)
- expected = Series(arr)
- tm.assert_series_equal(res, expected)
-
- res = Series(darr, dtype="i8")
- expected = Series(arr, dtype="i8")
- tm.assert_series_equal(res, expected)
-
- msg = "In a future version, passing float-dtype values containing NaN"
- arr[2] = np.nan
- with tm.assert_produces_warning(FutureWarning, match=msg):
- res = Series(darr, dtype="i8")
- with tm.assert_produces_warning(FutureWarning, match=msg):
- expected = Series(arr, dtype="i8")
- tm.assert_series_equal(res, expected)
-
def test_constructor_coerce_float_fail(self, any_int_numpy_dtype):
# see gh-15832
# Updated: make sure we treat this list the same as we would treat
@@ -1989,9 +1964,7 @@ def test_numpy_array(input_dict, expected):
tm.assert_numpy_array_equal(result, expected)
-@pytest.mark.skipif(
- not np_version_under1p19, reason="check failure on numpy below 1.19"
-)
+@pytest.mark.xfail(not np_version_under1p19, reason="check failure on numpy below 1.19")
def test_numpy_array_np_v1p19():
with pytest.raises(KeyError, match="0"):
np.array([Series({1: 1})])
diff --git a/pandas/tests/strings/test_extract.py b/pandas/tests/strings/test_extract.py
index 0f4ffccd8ad7f..04c355c6c78c5 100644
--- a/pandas/tests/strings/test_extract.py
+++ b/pandas/tests/strings/test_extract.py
@@ -174,13 +174,13 @@ def test_extract_expand_capture_groups(any_string_dtype):
tm.assert_frame_equal(result, expected)
-def test_extract_expand_capture_groups_index(index, any_string_dtype):
+def test_extract_expand_capture_groups_index(request, index, any_string_dtype):
# https://github.com/pandas-dev/pandas/issues/6348
# not passing index to the extractor
data = ["A1", "B2", "C"]
if len(index) < len(data):
- pytest.skip("Index too short")
+ request.node.add_marker(pytest.mark.xfail(reason="Index too short."))
index = index[: len(data)]
s = Series(data, index=index, dtype=any_string_dtype)
diff --git a/pandas/tests/test_downstream.py b/pandas/tests/test_downstream.py
index ce7c7a634f5a9..c1e7a8ae883ae 100644
--- a/pandas/tests/test_downstream.py
+++ b/pandas/tests/test_downstream.py
@@ -11,7 +11,10 @@
import pandas.util._test_decorators as td
import pandas as pd
-from pandas import DataFrame
+from pandas import (
+ DataFrame,
+ Series,
+)
import pandas._testing as tm
# geopandas, xarray, fsspec, fastparquet all produce these
@@ -70,7 +73,7 @@ def test_dask_ufunc():
import dask.array as da
import dask.dataframe as dd
- s = pd.Series([1.5, 2.3, 3.7, 4.0])
+ s = Series([1.5, 2.3, 3.7, 4.0])
ds = dd.from_pandas(s, npartitions=2)
result = da.fix(ds).compute()
@@ -80,6 +83,32 @@ def test_dask_ufunc():
pd.set_option("compute.use_numexpr", olduse)
+@td.skip_if_no("dask")
+def test_construct_dask_float_array_int_dtype_match_ndarray():
+ # GH#40110 make sure we treat a float-dtype dask array with the same
+ # rules we would for an ndarray
+ import dask.dataframe as dd
+
+ arr = np.array([1, 2.5, 3])
+ darr = dd.from_array(arr)
+
+ res = Series(darr)
+ expected = Series(arr)
+ tm.assert_series_equal(res, expected)
+
+ res = Series(darr, dtype="i8")
+ expected = Series(arr, dtype="i8")
+ tm.assert_series_equal(res, expected)
+
+ msg = "In a future version, passing float-dtype values containing NaN"
+ arr[2] = np.nan
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ res = Series(darr, dtype="i8")
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ expected = Series(arr, dtype="i8")
+ tm.assert_series_equal(res, expected)
+
+
def test_xarray(df):
xarray = import_module("xarray") # noqa:F841
@@ -224,7 +253,7 @@ def test_torch_frame_construction(using_array_manager):
if not using_array_manager:
assert np.shares_memory(df, val_tensor)
- ser = pd.Series(val_tensor[0])
+ ser = Series(val_tensor[0])
assert np.shares_memory(ser, val_tensor)
diff --git a/pandas/tests/tools/test_to_datetime.py b/pandas/tests/tools/test_to_datetime.py
index 6907b1f334f53..7597d4345cfce 100644
--- a/pandas/tests/tools/test_to_datetime.py
+++ b/pandas/tests/tools/test_to_datetime.py
@@ -315,7 +315,7 @@ def test_to_datetime_format_microsecond(self, cache):
def test_to_datetime_format_time(self, cache, value, format, dt):
assert to_datetime(value, format=format, cache=cache) == dt
- @td.skip_if_has_locale
+ @td.skip_if_not_us_locale
def test_to_datetime_with_non_exact(self, cache):
# GH 10834
# 8904
@@ -1738,7 +1738,7 @@ def test_to_datetime_with_space_in_series(self, cache):
result_ignore = to_datetime(ser, errors="ignore", cache=cache)
tm.assert_series_equal(result_ignore, ser)
- @td.skip_if_has_locale
+ @td.skip_if_not_us_locale
def test_to_datetime_with_apply(self, cache):
# this is only locale tested with US/None locales
# GH 5195
@@ -1748,7 +1748,7 @@ def test_to_datetime_with_apply(self, cache):
result = td.apply(to_datetime, format="%b %y", cache=cache)
tm.assert_series_equal(result, expected)
- @td.skip_if_has_locale
+ @td.skip_if_not_us_locale
def test_to_datetime_with_apply_with_empty_str(self, cache):
# this is only locale tested with US/None locales
# GH 5195
diff --git a/pandas/tests/tslibs/test_parsing.py b/pandas/tests/tslibs/test_parsing.py
index 3d2daec442c38..279a84b174e36 100644
--- a/pandas/tests/tslibs/test_parsing.py
+++ b/pandas/tests/tslibs/test_parsing.py
@@ -183,7 +183,7 @@ def test_guess_datetime_format_with_dayfirst(dayfirst, expected):
assert result == expected
-@td.skip_if_has_locale
+@td.skip_if_not_us_locale
@pytest.mark.parametrize(
"string,fmt",
[
diff --git a/pandas/tests/tslibs/test_timezones.py b/pandas/tests/tslibs/test_timezones.py
index 7ab0ad0856af0..aa10ab15f4744 100644
--- a/pandas/tests/tslibs/test_timezones.py
+++ b/pandas/tests/tslibs/test_timezones.py
@@ -23,9 +23,6 @@ def test_is_utc(utc_fixture):
@pytest.mark.parametrize("tz_name", list(pytz.common_timezones))
def test_cache_keys_are_distinct_for_pytz_vs_dateutil(tz_name):
- if tz_name == "UTC":
- pytest.skip("UTC: special case in dateutil")
-
tz_p = timezones.maybe_get_tz(tz_name)
tz_d = timezones.maybe_get_tz("dateutil/" + tz_name)
diff --git a/pandas/util/_test_decorators.py b/pandas/util/_test_decorators.py
index 10322a25ffd18..d7eba6b8319fb 100644
--- a/pandas/util/_test_decorators.py
+++ b/pandas/util/_test_decorators.py
@@ -115,12 +115,6 @@ def _skip_if_no_mpl():
return True
-def _skip_if_has_locale():
- lang, _ = locale.getlocale()
- if lang is not None:
- return True
-
-
def _skip_if_not_us_locale():
lang, _ = locale.getlocale()
if lang != "en_US":
@@ -198,9 +192,6 @@ def skip_if_no(package: str, min_version: str | None = None):
skip_if_mpl = pytest.mark.skipif(not _skip_if_no_mpl(), reason="matplotlib is present")
skip_if_32bit = pytest.mark.skipif(not IS64, reason="skipping for 32 bit")
skip_if_windows = pytest.mark.skipif(is_platform_windows(), reason="Running on Windows")
-skip_if_has_locale = pytest.mark.skipif(
- _skip_if_has_locale(), reason=f"Specific locale is set {locale.getlocale()[0]}"
-)
skip_if_not_us_locale = pytest.mark.skipif(
_skip_if_not_us_locale(), reason=f"Specific locale is set {locale.getlocale()[0]}"
)
| - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
| https://api.github.com/repos/pandas-dev/pandas/pulls/46387 | 2022-03-16T03:04:26Z | 2022-03-17T23:01:26Z | 2022-03-17T23:01:26Z | 2022-03-18T00:31:45Z |
CI: Test Pyarrow Less | diff --git a/.github/workflows/posix.yml b/.github/workflows/posix.yml
index bc8791afc69f7..9ad80ea96de31 100644
--- a/.github/workflows/posix.yml
+++ b/.github/workflows/posix.yml
@@ -26,9 +26,6 @@ jobs:
matrix:
env_file: [actions-38.yaml, actions-39.yaml, actions-310.yaml]
pattern: ["not single_cpu", "single_cpu"]
- # Don't test pyarrow v2/3: Causes timeouts in read_csv engine
- # even if tests are skipped/xfailed
- pyarrow_version: ["5", "7"]
include:
- env_file: actions-38-downstream_compat.yaml
pattern: "not slow and not network and not single_cpu"
@@ -62,8 +59,27 @@ jobs:
pandas_testing_mode: "deprecate"
test_args: "-W error"
name: "Numpy Dev"
+ # Test lowest & highest supported pyarrow
+ # Don't test pyarrow v2/3: Causes timeouts in read_csv engine
+ # even if tests are skipped/xfailed
+ - env_file: actions-38.yaml
+ pattern: "not single_cpu"
+ pyarrow_version: 5
+ name: "Pyarrow 5"
+ - env_file: actions-38.yaml
+ pattern: "single_cpu"
+ pyarrow_version: 5
+ name: "Pyarrow 5 (single)"
+ - env_file: actions-38.yaml
+ pattern: "not single_cpu"
+ pyarrow_version: latest
+ name: "Latest Pyarrow"
+ - env_file: actions-38.yaml
+ pattern: "single_cpu"
+ pyarrow_version: latest
+ name: "Latest Pyarrow (single)"
fail-fast: false
- name: ${{ matrix.name || format('{0} pyarrow={1} {2}', matrix.env_file, matrix.pyarrow_version, matrix.pattern) }}
+ name: ${{ matrix.name || format('{0} {1}', matrix.env_file, matrix.pattern) }}
env:
ENV_FILE: ci/deps/${{ matrix.env_file }}
PATTERN: ${{ matrix.pattern }}
@@ -149,7 +165,13 @@ jobs:
if: ${{ env.IS_PYPY == 'false' }} # No pypy3.8 support
- name: Upgrade Arrow version
- run: conda install -n pandas-dev -c conda-forge --no-update-deps pyarrow=${{ matrix.pyarrow_version }}
+ run: |
+ conda remove --force pyarrow
+ if [[ ${{matrix.pyarrow_version}} == 'latest' ]]; then
+ conda install -n pandas-dev -c conda-forge --no-update-deps pyarrow
+ else
+ conda install -n pandas-dev -c conda-forge --no-update-deps pyarrow=${{ matrix.pyarrow_version }}
+ fi
if: ${{ matrix.pyarrow_version }}
- name: Setup PyPy
diff --git a/ci/deps/actions-310.yaml b/ci/deps/actions-310.yaml
index 37e7ea04a348a..82c741384ac62 100644
--- a/ci/deps/actions-310.yaml
+++ b/ci/deps/actions-310.yaml
@@ -31,8 +31,7 @@ dependencies:
- jinja2
- lxml
- matplotlib
- # TODO: uncomment after numba supports py310
- #- numba
+ - numba
- numexpr
- openpyxl
- odfpy
@@ -40,7 +39,8 @@ dependencies:
- psycopg2
- pymysql
- pytables
- - pyarrow
+ # Installed separately since mamba doesn't get latest version properly
+ #- pyarrow
- pyreadstat
- python-snappy
- pyxlsb
diff --git a/ci/deps/actions-38.yaml b/ci/deps/actions-38.yaml
index 9c46eca4ab989..ee75b443882e6 100644
--- a/ci/deps/actions-38.yaml
+++ b/ci/deps/actions-38.yaml
@@ -37,7 +37,8 @@ dependencies:
- odfpy
- pandas-gbq
- psycopg2
- - pyarrow
+ # Installed separately since mamba doesn't get latest version properly
+ #- pyarrow
- pymysql
- pyreadstat
- pytables
diff --git a/ci/deps/actions-39.yaml b/ci/deps/actions-39.yaml
index 89b647372d7bc..3086d55e89e38 100644
--- a/ci/deps/actions-39.yaml
+++ b/ci/deps/actions-39.yaml
@@ -38,7 +38,8 @@ dependencies:
- pandas-gbq
- psycopg2
- pymysql
- - pyarrow
+ # Installed separately since mamba doesn't get latest version properly
+ #- pyarrow
- pyreadstat
- pytables
- python-snappy
| - [ ] closes #xxxx (Replace xxxx with the Github issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
CI has been really clogged w/ 2-3hr queue times.
Cut down on number of builds by only testing Pyarrow on one python version(if behavior is different based on Python version, that is pyarrow's problem).
If compat continues to be an issue in the future, I would suggest that pyarrow test a "pandas-dev" build, similar to our numpydev builds. I have added one of these builds to fastparquet in the past, and it seems to be effective as there haven't been any recent issues with new fastparquet versions breaking CI IIRC. | https://api.github.com/repos/pandas-dev/pandas/pulls/46386 | 2022-03-16T02:12:42Z | 2022-05-02T17:47:09Z | null | 2022-05-02T17:47:13Z |
Parse start/end only if Index of type Datetime | diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 37fee7058f902..6016c13cce1e3 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -6663,7 +6663,8 @@ def slice_locs(
# GH 16785: If start and end happen to be date strings with UTC offsets
# attempt to parse and check that the offsets are the same
- if isinstance(start, (str, datetime)) and isinstance(end, (str, datetime)):
+ if (self.is_type_compatible("datetime") and
+ isinstance(start, (str, datetime)) and isinstance(end, (str, datetime))):
try:
ts_start = Timestamp(start)
ts_end = Timestamp(end)
| Many of the existing tests are already failing if compiling with [sanitizers](https://github.com/google/sanitizers):
> runtime error: signed integer overflow: 253370764800000000 * 1000 cannot be represented in type 'long'
One pretty common use case when this happens, is when using a `TimedeltaIndex`, and passing a string representing a time delta to index it (e.g. '12 hours'). The existing code still tries to perform a Datetime specific check which leads to a cast attempt `Timestamp('12 hours')`, which in normal conditions is caught as a python exception by the `try: except:` and ignored (hence, the casting is ignored). However, it still leads to an unnecessary overflow happening in between which is flagged as an error if compiling with sanitizers.
As far as I can tell, there is not reason to run this Datetime specific check in the first place, unless we are dealing with an indexer for Datetime objects, hence the proposed change.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46385 | 2022-03-16T01:45:06Z | 2022-04-25T09:29:01Z | null | 2022-04-25T09:29:01Z |
Bug gb cummax | diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst
index 8dac952874f89..b18d5aaac6539 100644
--- a/doc/source/whatsnew/v1.5.0.rst
+++ b/doc/source/whatsnew/v1.5.0.rst
@@ -454,6 +454,7 @@ Groupby/resample/rolling
- Bug in :meth:`DataFrameGroupby.cumsum` with ``skipna=False`` giving incorrect results (:issue:`46216`)
- Bug in :meth:`.GroupBy.cumsum` with ``timedelta64[ns]`` dtype failing to recognize ``NaT`` as a null value (:issue:`46216`)
- Bug in :meth:`GroupBy.cummin` and :meth:`GroupBy.cummax` with nullable dtypes incorrectly altering the original data in place (:issue:`46220`)
+- Bug in :meth:`GroupBy.cummax` with ``int64`` dtype with leading value being the smallest possible int64 (:issue:`46382`)
-
Reshaping
diff --git a/pandas/_libs/groupby.pyx b/pandas/_libs/groupby.pyx
index ff5258e37e352..d84cc4873a250 100644
--- a/pandas/_libs/groupby.pyx
+++ b/pandas/_libs/groupby.pyx
@@ -989,13 +989,18 @@ cdef inline bint _treat_as_na(numeric_object_t val, bint is_datetimelike) nogil:
return False
-cdef numeric_t _get_min_or_max(numeric_t val, bint compute_max):
+cdef numeric_t _get_min_or_max(numeric_t val, bint compute_max, bint is_datetimelike):
"""
Find either the min or the max supported by numeric_t; 'val' is a placeholder
to effectively make numeric_t an argument.
"""
if numeric_t is int64_t:
- return -_int64_max if compute_max else util.INT64_MAX
+ if compute_max and is_datetimelike:
+ return -_int64_max
+ # Note(jbrockmendel) 2022-03-15 for reasons unknown, using util.INT64_MIN
+ # instead of NPY_NAT here causes build warnings and failure in
+ # test_cummax_i8_at_implementation_bound
+ return NPY_NAT if compute_max else util.INT64_MAX
elif numeric_t is int32_t:
return util.INT32_MIN if compute_max else util.INT32_MAX
elif numeric_t is int16_t:
@@ -1395,7 +1400,7 @@ cdef group_min_max(
nobs = np.zeros((<object>out).shape, dtype=np.int64)
group_min_or_max = np.empty_like(out)
- group_min_or_max[:] = _get_min_or_max(<iu_64_floating_t>0, compute_max)
+ group_min_or_max[:] = _get_min_or_max(<iu_64_floating_t>0, compute_max, is_datetimelike)
if iu_64_floating_t is int64_t:
# TODO: only if is_datetimelike?
@@ -1564,7 +1569,7 @@ cdef group_cummin_max(
bint isna_entry
accum = np.empty((ngroups, (<object>values).shape[1]), dtype=values.dtype)
- accum[:] = _get_min_or_max(<iu_64_floating_t>0, compute_max)
+ accum[:] = _get_min_or_max(<iu_64_floating_t>0, compute_max, is_datetimelike)
na_val = _get_na_val(<iu_64_floating_t>0, is_datetimelike)
diff --git a/pandas/tests/groupby/test_function.py b/pandas/tests/groupby/test_function.py
index ec7f3200b682b..7440b63e78b65 100644
--- a/pandas/tests/groupby/test_function.py
+++ b/pandas/tests/groupby/test_function.py
@@ -830,6 +830,18 @@ def test_cummax(dtypes_for_minmax):
tm.assert_series_equal(result, expected)
+def test_cummax_i8_at_implementation_bound():
+ # the minimum value used to be treated as NPY_NAT+1 instead of NPY_NAT
+ # for int64 dtype GH#46382
+ ser = Series([pd.NaT.value + n for n in range(5)])
+ df = DataFrame({"A": 1, "B": ser, "C": ser.view("M8[ns]")})
+ gb = df.groupby("A")
+
+ res = gb.cummax()
+ exp = df[["B", "C"]]
+ tm.assert_frame_equal(res, exp)
+
+
@pytest.mark.parametrize("method", ["cummin", "cummax"])
@pytest.mark.parametrize("dtype", ["float", "Int64", "Float64"])
@pytest.mark.parametrize(
| - [ ] closes #xxxx (Replace xxxx with the Github issue number)
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46382 | 2022-03-15T23:36:00Z | 2022-03-16T02:01:52Z | 2022-03-16T02:01:52Z | 2022-03-16T20:51:15Z |
Backport PR #46354 on branch 1.4.x (DOC: minor fixes to 1.4.2 release notes) | diff --git a/doc/source/whatsnew/v1.4.2.rst b/doc/source/whatsnew/v1.4.2.rst
index 06f1f406c3816..3523f691cae8f 100644
--- a/doc/source/whatsnew/v1.4.2.rst
+++ b/doc/source/whatsnew/v1.4.2.rst
@@ -18,8 +18,8 @@ Fixed regressions
- Fixed regression in :func:`read_csv` killing python process when invalid file input was given for ``engine="c"`` (:issue:`45957`)
- Fixed memory performance regression in :meth:`Series.fillna` when called on a :class:`DataFrame` column with ``inplace=True`` (:issue:`46149`)
- Provided an alternative solution for passing custom Excel formats in :meth:`.Styler.to_excel`, which was a regression based on stricter CSS validation. Examples available in the documentation for :meth:`.Styler.format` (:issue:`46152`)
-- Fixed regression in :meth:`DataFrame.replace` when a replacement value was also a target for replacement (:issue:`46335`)
-- Fixed regression in :meth:`DataFrame.loc.__setitem__` losing :class:`MultiIndex` names if :class:`DataFrame` was empty before (:issue:`46317`)
+- Fixed regression in :meth:`DataFrame.replace` when a replacement value was also a target for replacement (:issue:`46306`)
+- Fixed regression when setting values with :meth:`DataFrame.loc` losing :class:`MultiIndex` names if :class:`DataFrame` was empty before (:issue:`46317`)
-
.. ---------------------------------------------------------------------------
| Backport PR #46354: DOC: minor fixes to 1.4.2 release notes | https://api.github.com/repos/pandas-dev/pandas/pulls/46380 | 2022-03-15T23:12:06Z | 2022-03-16T01:41:45Z | 2022-03-16T01:41:45Z | 2022-03-16T01:41:45Z |
BUG: Nullable _get_common_dtype match non-nullable behavior | diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst
index 610592bb3ce1f..ed0cfe0408ba9 100644
--- a/doc/source/whatsnew/v1.5.0.rst
+++ b/doc/source/whatsnew/v1.5.0.rst
@@ -462,6 +462,7 @@ Reshaping
- Bug in :func:`concat` between a :class:`Series` with integer dtype and another with :class:`CategoricalDtype` with integer categories and containing ``NaN`` values casting to object dtype instead of ``float64`` (:issue:`45359`)
- Bug in :func:`get_dummies` that selected object and categorical dtypes but not string (:issue:`44965`)
- Bug in :meth:`DataFrame.align` when aligning a :class:`MultiIndex` to a :class:`Series` with another :class:`MultiIndex` (:issue:`46001`)
+- Bug in concanenation with ``IntegerDtype``, or ``FloatingDtype`` arrays where the resulting dtype did not mirror the behavior of the non-nullable dtypes (:issue:`46379`)
-
Sparse
diff --git a/pandas/core/arrays/boolean.py b/pandas/core/arrays/boolean.py
index 5294b4061ad44..b40d7904e33e4 100644
--- a/pandas/core/arrays/boolean.py
+++ b/pandas/core/arrays/boolean.py
@@ -137,18 +137,6 @@ def __from_arrow__(
else:
return BooleanArray._concat_same_type(results)
- def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
- # Handle only boolean + np.bool_ -> boolean, since other cases like
- # Int64 + boolean -> Int64 will be handled by the other type
- if all(
- isinstance(t, BooleanDtype)
- or (isinstance(t, np.dtype) and (np.issubdtype(t, np.bool_)))
- for t in dtypes
- ):
- return BooleanDtype()
- else:
- return None
-
def coerce_to_array(
values, mask=None, copy: bool = False
diff --git a/pandas/core/arrays/floating.py b/pandas/core/arrays/floating.py
index 49a71922f331b..0c14fac57d9db 100644
--- a/pandas/core/arrays/floating.py
+++ b/pandas/core/arrays/floating.py
@@ -2,8 +2,6 @@
import numpy as np
-from pandas._typing import DtypeObj
-
from pandas.core.dtypes.common import is_float_dtype
from pandas.core.dtypes.dtypes import register_extension_dtype
@@ -37,20 +35,6 @@ def construct_array_type(cls) -> type[FloatingArray]:
"""
return FloatingArray
- def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
- # for now only handle other floating types
- if not all(isinstance(t, FloatingDtype) for t in dtypes):
- return None
- np_dtype = np.find_common_type(
- # error: Item "ExtensionDtype" of "Union[Any, ExtensionDtype]" has no
- # attribute "numpy_dtype"
- [t.numpy_dtype for t in dtypes], # type: ignore[union-attr]
- [],
- )
- if np.issubdtype(np_dtype, np.floating):
- return FLOAT_STR_TO_DTYPE[str(np_dtype)]
- return None
-
@classmethod
def _str_to_dtype_mapping(cls):
return FLOAT_STR_TO_DTYPE
diff --git a/pandas/core/arrays/integer.py b/pandas/core/arrays/integer.py
index 9ef3939656ecd..24e5fa1bef552 100644
--- a/pandas/core/arrays/integer.py
+++ b/pandas/core/arrays/integer.py
@@ -2,12 +2,9 @@
import numpy as np
-from pandas._typing import DtypeObj
-
from pandas.core.dtypes.base import register_extension_dtype
from pandas.core.dtypes.common import is_integer_dtype
-from pandas.core.arrays.masked import BaseMaskedDtype
from pandas.core.arrays.numeric import (
NumericArray,
NumericDtype,
@@ -38,38 +35,6 @@ def construct_array_type(cls) -> type[IntegerArray]:
"""
return IntegerArray
- def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
- # we only handle nullable EA dtypes and numeric numpy dtypes
- if not all(
- isinstance(t, BaseMaskedDtype)
- or (
- isinstance(t, np.dtype)
- and (np.issubdtype(t, np.number) or np.issubdtype(t, np.bool_))
- )
- for t in dtypes
- ):
- return None
- np_dtype = np.find_common_type(
- # error: List comprehension has incompatible type List[Union[Any,
- # dtype, ExtensionDtype]]; expected List[Union[dtype, None, type,
- # _SupportsDtype, str, Tuple[Any, Union[int, Sequence[int]]],
- # List[Any], _DtypeDict, Tuple[Any, Any]]]
- [
- t.numpy_dtype # type: ignore[misc]
- if isinstance(t, BaseMaskedDtype)
- else t
- for t in dtypes
- ],
- [],
- )
- if np.issubdtype(np_dtype, np.integer):
- return INT_STR_TO_DTYPE[str(np_dtype)]
- elif np.issubdtype(np_dtype, np.floating):
- from pandas.core.arrays.floating import FLOAT_STR_TO_DTYPE
-
- return FLOAT_STR_TO_DTYPE[str(np_dtype)]
- return None
-
@classmethod
def _str_to_dtype_mapping(cls):
return INT_STR_TO_DTYPE
diff --git a/pandas/core/dtypes/dtypes.py b/pandas/core/dtypes/dtypes.py
index 946892822720c..66eed0a75fa19 100644
--- a/pandas/core/dtypes/dtypes.py
+++ b/pandas/core/dtypes/dtypes.py
@@ -1437,3 +1437,22 @@ def from_numpy_dtype(cls, dtype: np.dtype) -> BaseMaskedDtype:
return FLOAT_STR_TO_DTYPE[dtype.name]
else:
raise NotImplementedError(dtype)
+
+ def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
+ # We unwrap any masked dtypes, find the common dtype we would use
+ # for that, then re-mask the result.
+ from pandas.core.dtypes.cast import find_common_type
+
+ new_dtype = find_common_type(
+ [
+ dtype.numpy_dtype if isinstance(dtype, BaseMaskedDtype) else dtype
+ for dtype in dtypes
+ ]
+ )
+ if not isinstance(new_dtype, np.dtype):
+ # If we ever support e.g. Masked[DatetimeArray] then this will change
+ return None
+ try:
+ return type(self).from_numpy_dtype(new_dtype)
+ except (KeyError, NotImplementedError):
+ return None
diff --git a/pandas/tests/arrays/integer/test_concat.py b/pandas/tests/arrays/integer/test_concat.py
index 2e8ef506140af..feba574da548f 100644
--- a/pandas/tests/arrays/integer/test_concat.py
+++ b/pandas/tests/arrays/integer/test_concat.py
@@ -15,11 +15,13 @@
(["UInt8", "Int8"], "Int16"),
(["Int32", "UInt32"], "Int64"),
(["Int64", "UInt64"], "Float64"),
- (["Int64", "boolean"], "Int64"),
- (["UInt8", "boolean"], "UInt8"),
+ (["Int64", "boolean"], "object"),
+ (["UInt8", "boolean"], "object"),
],
)
def test_concat_series(to_concat_dtypes, result_dtype):
+ # we expect the same dtypes as we would get with non-masked inputs,
+ # just masked where available.
result = pd.concat([pd.Series([0, 1, pd.NA], dtype=t) for t in to_concat_dtypes])
expected = pd.concat([pd.Series([0, 1, pd.NA], dtype=object)] * 2).astype(
@@ -47,11 +49,13 @@ def test_concat_series(to_concat_dtypes, result_dtype):
(["UInt8", "int8"], "Int16"),
(["Int32", "uint32"], "Int64"),
(["Int64", "uint64"], "Float64"),
- (["Int64", "bool"], "Int64"),
- (["UInt8", "bool"], "UInt8"),
+ (["Int64", "bool"], "object"),
+ (["UInt8", "bool"], "object"),
],
)
def test_concat_series_with_numpy(to_concat_dtypes, result_dtype):
+ # we expect the same dtypes as we would get with non-masked inputs,
+ # just masked where available.
s1 = pd.Series([0, 1, pd.NA], dtype=to_concat_dtypes[0])
s2 = pd.Series(np.array([0, 1], dtype=to_concat_dtypes[1]))
| - [ ] closes #xxxx (Replace xxxx with the Github issue number)
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46379 | 2022-03-15T22:50:16Z | 2022-03-17T02:03:31Z | 2022-03-17T02:03:31Z | 2022-03-17T03:21:33Z |
REF: disallow ints from delta_to_nanoseconds | diff --git a/pandas/_libs/tslibs/timedeltas.pyx b/pandas/_libs/tslibs/timedeltas.pyx
index 8eaf86b3d193f..f8f8a6f81d801 100644
--- a/pandas/_libs/tslibs/timedeltas.pyx
+++ b/pandas/_libs/tslibs/timedeltas.pyx
@@ -173,11 +173,11 @@ cpdef int64_t delta_to_nanoseconds(delta) except? -1:
if is_tick_object(delta):
return delta.nanos
if isinstance(delta, _Timedelta):
- delta = delta.value
+ return delta.value
+
if is_timedelta64_object(delta):
return get_timedelta64_value(ensure_td64ns(delta))
- if is_integer_object(delta):
- return delta
+
if PyDelta_Check(delta):
try:
return (
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index 7a7bad02fde72..e070f23b35315 100644
--- a/pandas/core/arrays/datetimelike.py
+++ b/pandas/core/arrays/datetimelike.py
@@ -1316,7 +1316,9 @@ def __add__(self, other):
elif is_integer_dtype(other_dtype):
if not is_period_dtype(self.dtype):
raise integer_op_not_supported(self)
- result = cast("PeriodArray", self)._addsub_int_array(other, operator.add)
+ result = cast("PeriodArray", self)._addsub_int_array_or_scalar(
+ other, operator.add
+ )
else:
# Includes Categorical, other ExtensionArrays
# For PeriodDtype, if self is a TimedeltaArray and other is a
@@ -1376,7 +1378,9 @@ def __sub__(self, other):
elif is_integer_dtype(other_dtype):
if not is_period_dtype(self.dtype):
raise integer_op_not_supported(self)
- result = cast("PeriodArray", self)._addsub_int_array(other, operator.sub)
+ result = cast("PeriodArray", self)._addsub_int_array_or_scalar(
+ other, operator.sub
+ )
else:
# Includes ExtensionArrays, float_dtype
return NotImplemented
diff --git a/pandas/core/arrays/period.py b/pandas/core/arrays/period.py
index 2368af0de1bf3..b2a8dfddc6b68 100644
--- a/pandas/core/arrays/period.py
+++ b/pandas/core/arrays/period.py
@@ -747,8 +747,8 @@ def _sub_period_array(self, other):
new_values[mask] = NaT
return new_values
- def _addsub_int_array(
- self, other: np.ndarray, op: Callable[[Any, Any], Any]
+ def _addsub_int_array_or_scalar(
+ self, other: np.ndarray | int, op: Callable[[Any, Any], Any]
) -> PeriodArray:
"""
Add or subtract array of integers; equivalent to applying
@@ -756,7 +756,7 @@ def _addsub_int_array(
Parameters
----------
- other : np.ndarray[integer-dtype]
+ other : np.ndarray[int64] or int
op : {operator.add, operator.sub}
Returns
@@ -775,12 +775,7 @@ def _add_offset(self, other: BaseOffset):
assert not isinstance(other, Tick)
self._require_matching_freq(other, base=True)
-
- # Note: when calling parent class's _add_timedeltalike_scalar,
- # it will call delta_to_nanoseconds(delta). Because delta here
- # is an integer, delta_to_nanoseconds will return it unchanged.
- result = super()._add_timedeltalike_scalar(other.n)
- return type(self)(result, freq=self.freq)
+ return self._addsub_int_array_or_scalar(other.n, operator.add)
def _add_timedeltalike_scalar(self, other):
"""
@@ -800,10 +795,8 @@ def _add_timedeltalike_scalar(self, other):
# special handling for np.timedelta64("NaT"), avoid calling
# _check_timedeltalike_freq_compat as that would raise TypeError
other = self._check_timedeltalike_freq_compat(other)
+ other = np.timedelta64(other, "ns")
- # Note: when calling parent class's _add_timedeltalike_scalar,
- # it will call delta_to_nanoseconds(delta). Because delta here
- # is an integer, delta_to_nanoseconds will return it unchanged.
return super()._add_timedeltalike_scalar(other)
def _add_timedelta_arraylike(self, other):
@@ -828,7 +821,7 @@ def _add_timedelta_arraylike(self, other):
# all-NaT TimedeltaIndex is equivalent to a single scalar td64 NaT
return self + np.timedelta64("NaT")
- ordinals = self._addsub_int_array(delta, operator.add).asi8
+ ordinals = self._addsub_int_array_or_scalar(delta, operator.add).asi8
return type(self)(ordinals, dtype=self.dtype)
def _check_timedeltalike_freq_compat(self, other):
diff --git a/pandas/core/indexes/period.py b/pandas/core/indexes/period.py
index a296ce130bbf5..fc7b122689ebb 100644
--- a/pandas/core/indexes/period.py
+++ b/pandas/core/indexes/period.py
@@ -272,7 +272,7 @@ def __new__(
def values(self) -> np.ndarray:
return np.asarray(self, dtype=object)
- def _maybe_convert_timedelta(self, other):
+ def _maybe_convert_timedelta(self, other) -> int | npt.NDArray[np.int64]:
"""
Convert timedelta-like input to an integer multiple of self.freq
diff --git a/pandas/tests/tslibs/test_timedeltas.py b/pandas/tests/tslibs/test_timedeltas.py
index a0ec563d1f48f..7d7de7492a07b 100644
--- a/pandas/tests/tslibs/test_timedeltas.py
+++ b/pandas/tests/tslibs/test_timedeltas.py
@@ -30,9 +30,6 @@
24 * 3600e9 + 111,
), # GH43764
(offsets.Nano(125), 125),
- (1, 1),
- (np.int64(2), 2),
- (np.int32(3), 3),
],
)
def test_delta_to_nanoseconds(obj, expected):
@@ -46,6 +43,15 @@ def test_delta_to_nanoseconds_error():
with pytest.raises(TypeError, match="<class 'numpy.ndarray'>"):
delta_to_nanoseconds(obj)
+ with pytest.raises(TypeError, match="float"):
+ delta_to_nanoseconds(1.5)
+ with pytest.raises(TypeError, match="int"):
+ delta_to_nanoseconds(1)
+ with pytest.raises(TypeError, match="int"):
+ delta_to_nanoseconds(np.int64(2))
+ with pytest.raises(TypeError, match="int"):
+ delta_to_nanoseconds(np.int32(3))
+
def test_huge_nanoseconds_overflow():
# GH 32402
| chipping away at non-nano roadblocks | https://api.github.com/repos/pandas-dev/pandas/pulls/46378 | 2022-03-15T22:42:16Z | 2022-03-16T00:45:40Z | 2022-03-16T00:45:40Z | 2022-03-16T01:01:44Z |
BUG:error in reading a boolean column with blanks from excel file #45903 | diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst
index 358d9447b131d..6bb86a90e487a 100644
--- a/doc/source/whatsnew/v1.5.0.rst
+++ b/doc/source/whatsnew/v1.5.0.rst
@@ -569,6 +569,7 @@ I/O
- Bug in Parquet roundtrip for Interval dtype with ``datetime64[ns]`` subtype (:issue:`45881`)
- Bug in :func:`read_excel` when reading a ``.ods`` file with newlines between xml elements (:issue:`45598`)
- Bug in :func:`read_parquet` when ``engine="fastparquet"`` where the file was not closed on error (:issue:`46555`)
+- Bug in :func:`read_excel` when reading a ``.xlsx`` file with a boolean column that contains blank lines (:issue:`45903`)
Period
^^^^^^
diff --git a/pandas/core/arrays/boolean.py b/pandas/core/arrays/boolean.py
index 35b9de3f7af93..f7f276f5ed330 100644
--- a/pandas/core/arrays/boolean.py
+++ b/pandas/core/arrays/boolean.py
@@ -289,6 +289,7 @@ class BooleanArray(BaseMaskedArray):
# Fill values used for any/all
_truthy_value = True
_falsey_value = False
+ _NONE_VALUES = {"nan", "NaN", "None", "NA", "null", "NULL"}
_TRUE_VALUES = {"True", "TRUE", "true", "1", "1.0"}
_FALSE_VALUES = {"False", "FALSE", "false", "0", "0.0"}
@@ -321,8 +322,8 @@ def _from_sequence_of_strings(
false_values_union = cls._FALSE_VALUES.union(false_values or [])
def map_string(s):
- if isna(s):
- return s
+ if isna(s) or s in cls._NONE_VALUES:
+ return None
elif s in true_values_union:
return True
elif s in false_values_union:
diff --git a/pandas/io/parsers/base_parser.py b/pandas/io/parsers/base_parser.py
index 2851ea36c8a33..b0562b06e9fea 100644
--- a/pandas/io/parsers/base_parser.py
+++ b/pandas/io/parsers/base_parser.py
@@ -785,7 +785,7 @@ def _cast_types(self, values, cast_type, column):
try:
if is_bool_dtype(cast_type):
return array_type._from_sequence_of_strings(
- values,
+ values.astype(str),
dtype=cast_type,
true_values=self.true_values,
false_values=self.false_values,
diff --git a/pandas/tests/io/data/excel/test_types.ods b/pandas/tests/io/data/excel/test_types.ods
index c9a82bfff810b..881c89c1f42db 100644
Binary files a/pandas/tests/io/data/excel/test_types.ods and b/pandas/tests/io/data/excel/test_types.ods differ
diff --git a/pandas/tests/io/data/excel/test_types.xls b/pandas/tests/io/data/excel/test_types.xls
index 2d387603a8307..bae1dc4815dad 100644
Binary files a/pandas/tests/io/data/excel/test_types.xls and b/pandas/tests/io/data/excel/test_types.xls differ
diff --git a/pandas/tests/io/data/excel/test_types.xlsb b/pandas/tests/io/data/excel/test_types.xlsb
index e7403aa288263..9c5ecddb7dff7 100644
Binary files a/pandas/tests/io/data/excel/test_types.xlsb and b/pandas/tests/io/data/excel/test_types.xlsb differ
diff --git a/pandas/tests/io/data/excel/test_types.xlsm b/pandas/tests/io/data/excel/test_types.xlsm
index 4c8c10e40effe..0da5807c66b7e 100644
Binary files a/pandas/tests/io/data/excel/test_types.xlsm and b/pandas/tests/io/data/excel/test_types.xlsm differ
diff --git a/pandas/tests/io/data/excel/test_types.xlsx b/pandas/tests/io/data/excel/test_types.xlsx
index 561bb2c5d6714..b6f3ae43b8060 100644
Binary files a/pandas/tests/io/data/excel/test_types.xlsx and b/pandas/tests/io/data/excel/test_types.xlsx differ
diff --git a/pandas/tests/io/excel/test_readers.py b/pandas/tests/io/excel/test_readers.py
index 1e0f74ea41453..2600f674fc6e7 100644
--- a/pandas/tests/io/excel/test_readers.py
+++ b/pandas/tests/io/excel/test_readers.py
@@ -420,8 +420,10 @@ def test_reader_special_dtypes(self, request, read_ext):
datetime(2013, 12, 14),
datetime(2015, 3, 14),
],
+ "BoolColWithBlank": [True, False, None, True, False],
},
)
+
basename = "test_types"
# should read in correctly and infer types
| - [x] closes #45903
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added an entry in the latest `doc/source/whatsnew/v1.5.0.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46374 | 2022-03-15T13:37:10Z | 2022-06-10T21:37:22Z | null | 2022-06-10T21:37:22Z |
TST: Fix warnings & skip -> xfail | diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index 1b0ae40949d5b..86cf0c79759f5 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -45,7 +45,7 @@ jobs:
/opt/python/cp38-cp38/bin/python -m venv ~/virtualenvs/pandas-dev && \
. ~/virtualenvs/pandas-dev/bin/activate && \
python -m pip install --no-deps -U pip wheel 'setuptools<60.0.0' && \
- pip install cython numpy python-dateutil pytz pytest pytest-xdist hypothesis && \
+ pip install cython numpy python-dateutil pytz pytest pytest-xdist pytest-asyncio hypothesis && \
python setup.py build_ext -q -j2 && \
python -m pip install --no-build-isolation -e . && \
pytest -m 'not slow and not network and not clipboard and not single_cpu' pandas --junitxml=test-data.xml"
diff --git a/ci/deps/actions-310-numpydev.yaml b/ci/deps/actions-310-numpydev.yaml
index 3e32665d5433f..a5eb8a69e19da 100644
--- a/ci/deps/actions-310-numpydev.yaml
+++ b/ci/deps/actions-310-numpydev.yaml
@@ -9,6 +9,7 @@ dependencies:
- pytest-cov
- pytest-xdist>=1.31
- hypothesis>=5.5.3
+ - pytest-asyncio
# pandas dependencies
- python-dateutil
diff --git a/ci/deps/circle-38-arm64.yaml b/ci/deps/circle-38-arm64.yaml
index 60608c3ee1a86..7e5b56ebe081f 100644
--- a/ci/deps/circle-38-arm64.yaml
+++ b/ci/deps/circle-38-arm64.yaml
@@ -9,6 +9,7 @@ dependencies:
- pytest>=6.0
- pytest-xdist>=1.31
- hypothesis>=5.5.3
+ - pytest-asyncio
# pandas dependencies
- botocore>=1.11
diff --git a/pandas/tests/arrays/string_/test_string.py b/pandas/tests/arrays/string_/test_string.py
index 8b2aea5c2e2e1..59b9d2f2f8908 100644
--- a/pandas/tests/arrays/string_/test_string.py
+++ b/pandas/tests/arrays/string_/test_string.py
@@ -523,7 +523,7 @@ def test_memory_usage(dtype):
# GH 33963
if dtype.storage == "pyarrow":
- pytest.skip("not applicable")
+ pytest.skip(f"not applicable for {dtype.storage}")
series = pd.Series(["a", "b", "c"], dtype=dtype)
diff --git a/pandas/tests/extension/base/ops.py b/pandas/tests/extension/base/ops.py
index 1d3d736ca7ee2..a1d232b737da7 100644
--- a/pandas/tests/extension/base/ops.py
+++ b/pandas/tests/extension/base/ops.py
@@ -114,17 +114,22 @@ def test_add_series_with_extension_array(self, data):
self.assert_series_equal(result, expected)
@pytest.mark.parametrize("box", [pd.Series, pd.DataFrame])
- def test_direct_arith_with_ndframe_returns_not_implemented(self, data, box):
+ def test_direct_arith_with_ndframe_returns_not_implemented(
+ self, request, data, box
+ ):
# EAs should return NotImplemented for ops with Series/DataFrame
# Pandas takes care of unboxing the series and calling the EA's op.
other = pd.Series(data)
if box is pd.DataFrame:
other = other.to_frame()
- if hasattr(data, "__add__"):
- result = data.__add__(other)
- assert result is NotImplemented
- else:
- raise pytest.skip(f"{type(data).__name__} does not implement add")
+ if not hasattr(data, "__add__"):
+ request.node.add_marker(
+ pytest.mark.xfail(
+ reason=f"{type(data).__name__} does not implement add"
+ )
+ )
+ result = data.__add__(other)
+ assert result is NotImplemented
class BaseComparisonOpsTests(BaseOpsUtil):
diff --git a/pandas/tests/extension/json/test_json.py b/pandas/tests/extension/json/test_json.py
index f13ef65267304..84491adb30ef6 100644
--- a/pandas/tests/extension/json/test_json.py
+++ b/pandas/tests/extension/json/test_json.py
@@ -156,10 +156,10 @@ def test_contains(self, data):
class TestConstructors(BaseJSON, base.BaseConstructorsTests):
- @pytest.mark.skip(reason="not implemented constructor from dtype")
+ @pytest.mark.xfail(reason="not implemented constructor from dtype")
def test_from_dtype(self, data):
# construct from our dtype & string dtype
- pass
+ super(self).test_from_dtype(data)
@pytest.mark.xfail(reason="RecursionError, GH-33900")
def test_series_constructor_no_data_with_index(self, dtype, na_value):
diff --git a/pandas/tests/extension/test_categorical.py b/pandas/tests/extension/test_categorical.py
index d21110e078709..1e17bf33c806c 100644
--- a/pandas/tests/extension/test_categorical.py
+++ b/pandas/tests/extension/test_categorical.py
@@ -50,7 +50,7 @@ def data():
"""Length-100 array for this type.
* data[0] and data[1] should both be non missing
- * data[0] and data[1] should not gbe equal
+ * data[0] and data[1] should not be equal
"""
return Categorical(make_data())
@@ -86,7 +86,7 @@ class TestDtype(base.BaseDtypeTests):
class TestInterface(base.BaseInterfaceTests):
- @pytest.mark.skip(reason="Memory usage doesn't match")
+ @pytest.mark.xfail(reason="Memory usage doesn't match")
def test_memory_usage(self, data):
# Is this deliberate?
super().test_memory_usage(data)
@@ -149,13 +149,7 @@ class TestIndex(base.BaseIndexTests):
class TestMissing(base.BaseMissingTests):
- @pytest.mark.skip(reason="Not implemented")
- def test_fillna_limit_pad(self, data_missing):
- super().test_fillna_limit_pad(data_missing)
-
- @pytest.mark.skip(reason="Not implemented")
- def test_fillna_limit_backfill(self, data_missing):
- super().test_fillna_limit_backfill(data_missing)
+ pass
class TestReduce(base.BaseNoReduceTests):
@@ -163,7 +157,7 @@ class TestReduce(base.BaseNoReduceTests):
class TestMethods(base.BaseMethodsTests):
- @pytest.mark.skip(reason="Unobserved categories included")
+ @pytest.mark.xfail(reason="Unobserved categories included")
def test_value_counts(self, all_data, dropna):
return super().test_value_counts(all_data, dropna)
@@ -184,10 +178,6 @@ def test_combine_add(self, data_repeated):
expected = pd.Series([a + val for a in list(orig_data1)])
self.assert_series_equal(result, expected)
- @pytest.mark.skip(reason="Not Applicable")
- def test_fillna_length_mismatch(self, data_missing):
- super().test_fillna_length_mismatch(data_missing)
-
class TestCasting(base.BaseCastingTests):
@pytest.mark.parametrize("cls", [Categorical, CategoricalIndex])
diff --git a/pandas/tests/extension/test_datetime.py b/pandas/tests/extension/test_datetime.py
index a64b42fad9415..92796c604333d 100644
--- a/pandas/tests/extension/test_datetime.py
+++ b/pandas/tests/extension/test_datetime.py
@@ -175,9 +175,7 @@ class TestMissing(BaseDatetimeTests, base.BaseMissingTests):
class TestReshaping(BaseDatetimeTests, base.BaseReshapingTests):
- @pytest.mark.skip(reason="We have DatetimeTZBlock")
- def test_concat(self, data, in_frame):
- pass
+ pass
class TestSetitem(BaseDatetimeTests, base.BaseSetitemTests):
diff --git a/pandas/tests/extension/test_interval.py b/pandas/tests/extension/test_interval.py
index e2f4d69c489ba..0f916cea9d518 100644
--- a/pandas/tests/extension/test_interval.py
+++ b/pandas/tests/extension/test_interval.py
@@ -121,9 +121,9 @@ def test_reduce_series_numeric(self, data, all_numeric_reductions, skipna):
class TestMethods(BaseInterval, base.BaseMethodsTests):
- @pytest.mark.skip(reason="addition is not defined for intervals")
+ @pytest.mark.xfail(reason="addition is not defined for intervals")
def test_combine_add(self, data_repeated):
- pass
+ super().test_combine_add(data_repeated)
@pytest.mark.xfail(
reason="Raises with incorrect message bc it disallows *all* listlikes "
@@ -134,29 +134,31 @@ def test_fillna_length_mismatch(self, data_missing):
class TestMissing(BaseInterval, base.BaseMissingTests):
- # Index.fillna only accepts scalar `value`, so we have to skip all
+ # Index.fillna only accepts scalar `value`, so we have to xfail all
# non-scalar fill tests.
- unsupported_fill = pytest.mark.skip("Unsupported fillna option.")
+ unsupported_fill = pytest.mark.xfail(
+ reason="Unsupported fillna option for Interval."
+ )
@unsupported_fill
def test_fillna_limit_pad(self):
- pass
+ super().test_fillna_limit_pad()
@unsupported_fill
def test_fillna_series_method(self):
- pass
+ super().test_fillna_series_method()
@unsupported_fill
def test_fillna_limit_backfill(self):
- pass
+ super().test_fillna_limit_backfill()
@unsupported_fill
def test_fillna_no_op_returns_copy(self):
- pass
+ super().test_fillna_no_op_returns_copy()
@unsupported_fill
def test_fillna_series(self):
- pass
+ super().test_fillna_series()
def test_fillna_non_scalar_raises(self, data_missing):
msg = "can only insert Interval objects and NA into an IntervalArray"
@@ -173,9 +175,9 @@ class TestSetitem(BaseInterval, base.BaseSetitemTests):
class TestPrinting(BaseInterval, base.BasePrintingTests):
- @pytest.mark.skip(reason="custom repr")
+ @pytest.mark.xfail(reason="Interval has custom repr")
def test_array_repr(self, data, size):
- pass
+ super().test_array_repr()
class TestParsing(BaseInterval, base.BaseParsingTests):
diff --git a/pandas/tests/extension/test_numpy.py b/pandas/tests/extension/test_numpy.py
index 2e1112ccf2205..ee181101a181a 100644
--- a/pandas/tests/extension/test_numpy.py
+++ b/pandas/tests/extension/test_numpy.py
@@ -208,10 +208,15 @@ def test_series_constructor_scalar_with_index(self, data, dtype):
class TestDtype(BaseNumPyTests, base.BaseDtypeTests):
- @pytest.mark.skip(reason="Incorrect expected.")
- # we unsurprisingly clash with a NumPy name.
- def test_check_dtype(self, data):
- pass
+ def test_check_dtype(self, data, request):
+ if data.dtype.numpy_dtype == "object":
+ request.node.add_marker(
+ pytest.mark.xfail(
+ reason=f"PandasArray expectedly clashes with a "
+ f"NumPy name: {data.dtype.numpy_dtype}"
+ )
+ )
+ super().test_check_dtype(data)
class TestGetitem(BaseNumPyTests, base.BaseGetitemTests):
@@ -345,11 +350,6 @@ def test_fillna_frame(self, data_missing):
class TestReshaping(BaseNumPyTests, base.BaseReshapingTests):
- @pytest.mark.skip(reason="Incorrect expected.")
- def test_merge(self, data, na_value):
- # Fails creating expected (key column becomes a PandasDtype because)
- super().test_merge(data, na_value)
-
@pytest.mark.parametrize(
"in_frame",
[
diff --git a/pandas/tests/extension/test_string.py b/pandas/tests/extension/test_string.py
index 73682620b8353..a4c22e016581d 100644
--- a/pandas/tests/extension/test_string.py
+++ b/pandas/tests/extension/test_string.py
@@ -26,7 +26,7 @@
def split_array(arr):
if arr.dtype.storage != "pyarrow":
- pytest.skip("chunked array n/a")
+ pytest.skip("only applicable for pyarrow chunked array n/a")
def _split_array(arr):
import pyarrow as pa
@@ -156,13 +156,9 @@ def test_reduce_series_numeric(self, data, all_numeric_reductions, skipna):
class TestMethods(base.BaseMethodsTests):
- @pytest.mark.skip(reason="returns nullable")
- def test_value_counts(self, all_data, dropna):
- return super().test_value_counts(all_data, dropna)
-
- @pytest.mark.skip(reason="returns nullable")
+ @pytest.mark.xfail(reason="returns nullable: GH 44692")
def test_value_counts_with_normalize(self, data):
- pass
+ super().test_value_counts_with_normalize(data)
class TestCasting(base.BaseCastingTests):
diff --git a/pandas/tests/plotting/frame/test_frame.py b/pandas/tests/plotting/frame/test_frame.py
index 62540a15f47bd..684d8e661d185 100644
--- a/pandas/tests/plotting/frame/test_frame.py
+++ b/pandas/tests/plotting/frame/test_frame.py
@@ -146,12 +146,11 @@ def test_nullable_int_plot(self):
df = DataFrame(
{
"A": [1, 2, 3, 4, 5],
- "B": [1.0, 2.0, 3.0, 4.0, 5.0],
- "C": [7, 5, np.nan, 3, 2],
+ "B": [1, 2, 3, 4, 5],
+ "C": np.array([7, 5, np.nan, 3, 2], dtype=object),
"D": pd.to_datetime(dates, format="%Y").view("i8"),
"E": pd.to_datetime(dates, format="%Y", utc=True).view("i8"),
- },
- dtype=np.int64,
+ }
)
_check_plot_works(df.plot, x="A", y="B")
diff --git a/pandas/tests/plotting/test_converter.py b/pandas/tests/plotting/test_converter.py
index 04d3397faddb7..0258d2fae116d 100644
--- a/pandas/tests/plotting/test_converter.py
+++ b/pandas/tests/plotting/test_converter.py
@@ -327,7 +327,7 @@ def test_conversion(self):
rs = self.pc.convert(
np.array(
- ["2012-01-01 00:00:00+0000", "2012-01-02 00:00:00+0000"],
+ ["2012-01-01 00:00:00", "2012-01-02 00:00:00"],
dtype="datetime64[ns]",
),
None,
diff --git a/pyproject.toml b/pyproject.toml
index ae4072d37a22d..90c1cba90a9aa 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -56,6 +56,7 @@ markers = [
"arm_slow: mark a test as slow for arm64 architecture",
"arraymanager: mark a test to run with ArrayManager enabled",
]
+asyncio_mode = "strict"
[tool.mypy]
# Import discovery
| - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
| https://api.github.com/repos/pandas-dev/pandas/pulls/46370 | 2022-03-15T04:59:23Z | 2022-03-16T00:38:30Z | 2022-03-16T00:38:30Z | 2022-03-30T10:50:57Z |
DOC: Add docs about using numba with parallel | diff --git a/doc/source/user_guide/enhancingperf.rst b/doc/source/user_guide/enhancingperf.rst
index 0e948504051c6..1a1229f95523b 100644
--- a/doc/source/user_guide/enhancingperf.rst
+++ b/doc/source/user_guide/enhancingperf.rst
@@ -350,6 +350,28 @@ a larger amount of data points (e.g. 1+ million).
In [6]: %timeit roll.apply(f, engine='cython', raw=True)
3.92 s ± 59 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
+If your compute hardware contains multiple CPUs, the largest performance gain can be realized by setting ``parallel`` to ``True``
+to leverage more than 1 CPU. Internally, pandas leverages numba to parallelize computations over the columns of a :class:`DataFrame`;
+therefore, this performance benefit is only beneficial for a :class:`DataFrame` with a large number of columns.
+
+.. code-block:: ipython
+
+ In [1]: import numba
+
+ In [2]: numba.set_num_threads(1)
+
+ In [3]: df = pd.DataFrame(np.random.randn(10_000, 100))
+
+ In [4]: roll = df.rolling(100)
+
+ In [5]: %timeit roll.mean(engine="numba", engine_kwargs={"parallel": True})
+ 347 ms ± 26 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
+
+ In [6]: numba.set_num_threads(2)
+
+ In [7]: %timeit roll.mean(engine="numba", engine_kwargs={"parallel": True})
+ 201 ms ± 2.97 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
+
Custom Function Examples
~~~~~~~~~~~~~~~~~~~~~~~~
| - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
| https://api.github.com/repos/pandas-dev/pandas/pulls/46368 | 2022-03-15T03:28:34Z | 2022-03-15T22:17:39Z | 2022-03-15T22:17:39Z | 2022-03-16T00:31:40Z |
BUG: Fix remaining cases of groupby(...).transform with dropna=True | diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst
index 14e9fda1b910c..42cd34c94b069 100644
--- a/doc/source/whatsnew/v1.5.0.rst
+++ b/doc/source/whatsnew/v1.5.0.rst
@@ -83,32 +83,44 @@ did not have the same index as the input.
.. code-block:: ipython
- In [3]: df.groupby('a', dropna=True).transform(lambda x: x.sum())
+ In [3]: # Value in the last row should be np.nan
+ df.groupby('a', dropna=True).transform('sum')
Out[3]:
b
0 5
1 5
+ 2 5
- In [3]: df.groupby('a', dropna=True).transform(lambda x: x)
+ In [3]: # Should have one additional row with the value np.nan
+ df.groupby('a', dropna=True).transform(lambda x: x.sum())
Out[3]:
b
- 0 2
- 1 3
+ 0 5
+ 1 5
+
+ In [3]: # The value in the last row is np.nan interpreted as an integer
+ df.groupby('a', dropna=True).transform('ffill')
+ Out[3]:
+ b
+ 0 2
+ 1 3
+ 2 -9223372036854775808
- In [3]: df.groupby('a', dropna=True).transform('sum')
+ In [3]: # Should have one additional row with the value np.nan
+ df.groupby('a', dropna=True).transform(lambda x: x)
Out[3]:
b
- 0 5
- 1 5
- 2 5
+ 0 2
+ 1 3
*New behavior*:
.. ipython:: python
+ df.groupby('a', dropna=True).transform('sum')
df.groupby('a', dropna=True).transform(lambda x: x.sum())
+ df.groupby('a', dropna=True).transform('ffill')
df.groupby('a', dropna=True).transform(lambda x: x)
- df.groupby('a', dropna=True).transform('sum')
.. _whatsnew_150.notable_bug_fixes.visualization:
diff --git a/pandas/core/groupby/base.py b/pandas/core/groupby/base.py
index 0c8474c9badc7..ec9a2e4a4b5c0 100644
--- a/pandas/core/groupby/base.py
+++ b/pandas/core/groupby/base.py
@@ -70,7 +70,6 @@ class OutputKey:
"mean",
"median",
"min",
- "ngroup",
"nth",
"nunique",
"prod",
@@ -113,6 +112,7 @@ def maybe_normalize_deprecated_kernels(kernel):
"diff",
"ffill",
"fillna",
+ "ngroup",
"pad",
"pct_change",
"rank",
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 9f80c8f91ac6f..4a617d1b63639 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -62,6 +62,7 @@ class providing the base-class of operations.
)
from pandas.util._exceptions import find_stack_level
+from pandas.core.dtypes.cast import ensure_dtype_can_hold_na
from pandas.core.dtypes.common import (
is_bool_dtype,
is_datetime64_dtype,
@@ -950,7 +951,13 @@ def curried(x):
if name in base.plotting_methods:
return self.apply(curried)
- return self._python_apply_general(curried, self._obj_with_exclusions)
+ result = self._python_apply_general(curried, self._obj_with_exclusions)
+
+ if self.grouper.has_dropped_na and name in base.transformation_kernels:
+ # result will have dropped rows due to nans, fill with null
+ # and ensure index is ordered same as the input
+ result = self._set_result_index_ordered(result)
+ return result
wrapper.__name__ = name
return wrapper
@@ -2608,7 +2615,11 @@ def blk_func(values: ArrayLike) -> ArrayLike:
# then there will be no -1s in indexer, so we can use
# the original dtype (no need to ensure_dtype_can_hold_na)
if isinstance(values, np.ndarray):
- out = np.empty(values.shape, dtype=values.dtype)
+ dtype = values.dtype
+ if self.grouper.has_dropped_na:
+ # dropped null groups give rise to nan in the result
+ dtype = ensure_dtype_can_hold_na(values.dtype)
+ out = np.empty(values.shape, dtype=dtype)
else:
out = type(values)._empty(values.shape, dtype=values.dtype)
@@ -3114,9 +3125,16 @@ def ngroup(self, ascending: bool = True):
"""
with self._group_selection_context():
index = self._selected_obj.index
- result = self._obj_1d_constructor(
- self.grouper.group_info[0], index, dtype=np.int64
- )
+ comp_ids = self.grouper.group_info[0]
+
+ dtype: type
+ if self.grouper.has_dropped_na:
+ comp_ids = np.where(comp_ids == -1, np.nan, comp_ids)
+ dtype = np.float64
+ else:
+ dtype = np.int64
+
+ result = self._obj_1d_constructor(comp_ids, index, dtype=dtype)
if not ascending:
result = self.ngroups - 1 - result
return result
diff --git a/pandas/core/groupby/ops.py b/pandas/core/groupby/ops.py
index 15bc262997075..5f15e11c4740c 100644
--- a/pandas/core/groupby/ops.py
+++ b/pandas/core/groupby/ops.py
@@ -98,15 +98,25 @@
class WrappedCythonOp:
"""
Dispatch logic for functions defined in _libs.groupby
+
+ Parameters
+ ----------
+ kind: str
+ Whether the operation is an aggregate or transform.
+ how: str
+ Operation name, e.g. "mean".
+ has_dropped_na: bool
+ True precisely when dropna=True and the grouper contains a null value.
"""
# Functions for which we do _not_ attempt to cast the cython result
# back to the original dtype.
cast_blocklist = frozenset(["rank", "count", "size", "idxmin", "idxmax"])
- def __init__(self, kind: str, how: str) -> None:
+ def __init__(self, kind: str, how: str, has_dropped_na: bool) -> None:
self.kind = kind
self.how = how
+ self.has_dropped_na = has_dropped_na
_CYTHON_FUNCTIONS = {
"aggregate": {
@@ -194,7 +204,9 @@ def _get_cython_vals(self, values: np.ndarray) -> np.ndarray:
values = ensure_float64(values)
elif values.dtype.kind in ["i", "u"]:
- if how in ["add", "var", "prod", "mean", "ohlc"]:
+ if how in ["add", "var", "prod", "mean", "ohlc"] or (
+ self.kind == "transform" and self.has_dropped_na
+ ):
# result may still include NaN, so we have to cast
values = ensure_float64(values)
@@ -582,6 +594,10 @@ def _call_cython_op(
result = result.T
+ if self.how == "rank" and self.has_dropped_na:
+ # TODO: Wouldn't need this if group_rank supported mask
+ result = np.where(comp_ids < 0, np.nan, result)
+
if self.how not in self.cast_blocklist:
# e.g. if we are int64 and need to restore to datetime64/timedelta64
# "rank" is the only member of cast_blocklist we get here
@@ -959,7 +975,7 @@ def _cython_operation(
"""
assert kind in ["transform", "aggregate"]
- cy_op = WrappedCythonOp(kind=kind, how=how)
+ cy_op = WrappedCythonOp(kind=kind, how=how, has_dropped_na=self.has_dropped_na)
ids, _, _ = self.group_info
ngroups = self.ngroups
diff --git a/pandas/tests/apply/test_frame_transform.py b/pandas/tests/apply/test_frame_transform.py
index 3384f9d46029a..2d4deff8ebc4a 100644
--- a/pandas/tests/apply/test_frame_transform.py
+++ b/pandas/tests/apply/test_frame_transform.py
@@ -139,6 +139,10 @@ def test_transform_bad_dtype(op, frame_or_series, request):
raises=ValueError, reason="GH 40418: rank does not raise a TypeError"
)
)
+ elif op == "ngroup":
+ request.node.add_marker(
+ pytest.mark.xfail(raises=ValueError, reason="ngroup not valid for NDFrame")
+ )
obj = DataFrame({"A": 3 * [object]}) # DataFrame that will fail on most transforms
obj = tm.get_obj(obj, frame_or_series)
@@ -157,9 +161,14 @@ def test_transform_bad_dtype(op, frame_or_series, request):
@pytest.mark.parametrize("op", frame_kernels_raise)
-def test_transform_partial_failure_typeerror(op):
+def test_transform_partial_failure_typeerror(request, op):
# GH 35964
+ if op == "ngroup":
+ request.node.add_marker(
+ pytest.mark.xfail(raises=ValueError, reason="ngroup not valid for NDFrame")
+ )
+
# Using object makes most transform kernels fail
df = DataFrame({"A": 3 * [object], "B": [1, 2, 3]})
diff --git a/pandas/tests/apply/test_str.py b/pandas/tests/apply/test_str.py
index 554fd4174c7a4..2af063dee8b55 100644
--- a/pandas/tests/apply/test_str.py
+++ b/pandas/tests/apply/test_str.py
@@ -243,8 +243,12 @@ def test_agg_cython_table_transform_frame(df, func, expected, axis):
@pytest.mark.parametrize("op", series_transform_kernels)
-def test_transform_groupby_kernel_series(string_series, op):
+def test_transform_groupby_kernel_series(request, string_series, op):
# GH 35964
+ if op == "ngroup":
+ request.node.add_marker(
+ pytest.mark.xfail(raises=ValueError, reason="ngroup not valid for NDFrame")
+ )
# TODO(2.0) Remove after pad/backfill deprecation enforced
op = maybe_normalize_deprecated_kernels(op)
args = [0.0] if op == "fillna" else []
@@ -255,9 +259,15 @@ def test_transform_groupby_kernel_series(string_series, op):
@pytest.mark.parametrize("op", frame_transform_kernels)
-def test_transform_groupby_kernel_frame(axis, float_frame, op):
+def test_transform_groupby_kernel_frame(request, axis, float_frame, op):
# TODO(2.0) Remove after pad/backfill deprecation enforced
op = maybe_normalize_deprecated_kernels(op)
+
+ if op == "ngroup":
+ request.node.add_marker(
+ pytest.mark.xfail(raises=ValueError, reason="ngroup not valid for NDFrame")
+ )
+
# GH 35964
args = [0.0] if op == "fillna" else []
diff --git a/pandas/tests/groupby/test_rank.py b/pandas/tests/groupby/test_rank.py
index 83b8d5c29bbf0..7830c229ece2f 100644
--- a/pandas/tests/groupby/test_rank.py
+++ b/pandas/tests/groupby/test_rank.py
@@ -658,6 +658,8 @@ def test_non_unique_index():
)
result = df.groupby([df.index, "A"]).value.rank(ascending=True, pct=True)
expected = Series(
- [1.0] * 4, index=[pd.Timestamp("20170101", tz="US/Eastern")] * 4, name="value"
+ [1.0, 1.0, 1.0, np.nan],
+ index=[pd.Timestamp("20170101", tz="US/Eastern")] * 4,
+ name="value",
)
tm.assert_series_equal(result, expected)
diff --git a/pandas/tests/groupby/transform/test_transform.py b/pandas/tests/groupby/transform/test_transform.py
index 0f450adf67b23..3042e38d9014c 100644
--- a/pandas/tests/groupby/transform/test_transform.py
+++ b/pandas/tests/groupby/transform/test_transform.py
@@ -168,6 +168,10 @@ def test_transform_axis_1(request, transformation_func):
# TODO(2.0) Remove after pad/backfill deprecation enforced
transformation_func = maybe_normalize_deprecated_kernels(transformation_func)
+ if transformation_func == "ngroup":
+ msg = "ngroup fails with axis=1: #45986"
+ request.node.add_marker(pytest.mark.xfail(reason=msg))
+
warn = None
if transformation_func == "tshift":
warn = FutureWarning
@@ -383,6 +387,15 @@ def test_transform_transformation_func(request, transformation_func):
elif transformation_func == "fillna":
test_op = lambda x: x.transform("fillna", value=0)
mock_op = lambda x: x.fillna(value=0)
+ elif transformation_func == "ngroup":
+ test_op = lambda x: x.transform("ngroup")
+ counter = -1
+
+ def mock_op(x):
+ nonlocal counter
+ counter += 1
+ return Series(counter, index=x.index)
+
elif transformation_func == "tshift":
msg = (
"Current behavior of groupby.tshift is inconsistent with other "
@@ -394,10 +407,14 @@ def test_transform_transformation_func(request, transformation_func):
mock_op = lambda x: getattr(x, transformation_func)()
result = test_op(df.groupby("A"))
- groups = [df[["B"]].iloc[:4], df[["B"]].iloc[4:6], df[["B"]].iloc[6:]]
- expected = concat([mock_op(g) for g in groups])
+ # pass the group in same order as iterating `for ... in df.groupby(...)`
+ # but reorder to match df's index since this is a transform
+ groups = [df[["B"]].iloc[4:6], df[["B"]].iloc[6:], df[["B"]].iloc[:4]]
+ expected = concat([mock_op(g) for g in groups]).sort_index()
+ # sort_index does not preserve the freq
+ expected = expected.set_axis(df.index)
- if transformation_func == "cumcount":
+ if transformation_func in ("cumcount", "ngroup"):
tm.assert_series_equal(result, expected)
else:
tm.assert_frame_equal(result, expected)
@@ -1122,10 +1139,6 @@ def test_transform_agg_by_name(request, reduction_func, obj):
func = reduction_func
g = obj.groupby(np.repeat([0, 1], 3))
- if func == "ngroup": # GH#27468
- request.node.add_marker(
- pytest.mark.xfail(reason="TODO: g.transform('ngroup') doesn't work")
- )
if func == "corrwith" and isinstance(obj, Series): # GH#32293
request.node.add_marker(
pytest.mark.xfail(reason="TODO: implement SeriesGroupBy.corrwith")
@@ -1137,8 +1150,8 @@ def test_transform_agg_by_name(request, reduction_func, obj):
# this is the *definition* of a transformation
tm.assert_index_equal(result.index, obj.index)
- if func != "size" and obj.ndim == 2:
- # size returns a Series, unlike other transforms
+ if func not in ("ngroup", "size") and obj.ndim == 2:
+ # size/ngroup return a Series, unlike other transforms
tm.assert_index_equal(result.columns, obj.columns)
# verify that values were broadcasted across each group
@@ -1312,7 +1325,7 @@ def test_null_group_lambda_self(sort, dropna):
def test_null_group_str_reducer(request, dropna, reduction_func):
# GH 17093
- if reduction_func in ("corrwith", "ngroup"):
+ if reduction_func == "corrwith":
msg = "incorrectly raises"
request.node.add_marker(pytest.mark.xfail(reason=msg))
index = [1, 2, 3, 4] # test transform preserves non-standard index
@@ -1358,31 +1371,11 @@ def test_null_group_str_reducer(request, dropna, reduction_func):
@pytest.mark.filterwarnings("ignore:tshift is deprecated:FutureWarning")
-def test_null_group_str_transformer(
- request, using_array_manager, dropna, transformation_func
-):
+def test_null_group_str_transformer(request, dropna, transformation_func):
# GH 17093
- xfails_block = (
- "cummax",
- "cummin",
- "cumsum",
- "fillna",
- "rank",
- "backfill",
- "ffill",
- "bfill",
- "pad",
- )
- xfails_array = ("cummax", "cummin", "cumsum", "fillna", "rank")
if transformation_func == "tshift":
msg = "tshift requires timeseries"
request.node.add_marker(pytest.mark.xfail(reason=msg))
- elif dropna and (
- (not using_array_manager and transformation_func in xfails_block)
- or (using_array_manager and transformation_func in xfails_array)
- ):
- msg = "produces incorrect results when nans are present"
- request.node.add_marker(pytest.mark.xfail(reason=msg))
args = (0,) if transformation_func == "fillna" else ()
df = DataFrame({"A": [1, 1, np.nan], "B": [1, 2, 2]}, index=[1, 2, 3])
gb = df.groupby("A", dropna=dropna)
@@ -1420,10 +1413,6 @@ def test_null_group_str_reducer_series(request, dropna, reduction_func):
msg = "corrwith not implemented for SeriesGroupBy"
request.node.add_marker(pytest.mark.xfail(reason=msg))
- if reduction_func == "ngroup":
- msg = "ngroup fails"
- request.node.add_marker(pytest.mark.xfail(reason=msg))
-
# GH 17093
index = [1, 2, 3, 4] # test transform preserves non-standard index
ser = Series([1, 2, 2, 3], index=index)
@@ -1470,15 +1459,6 @@ def test_null_group_str_transformer_series(request, dropna, transformation_func)
if transformation_func == "tshift":
msg = "tshift requires timeseries"
request.node.add_marker(pytest.mark.xfail(reason=msg))
- elif dropna and transformation_func in (
- "cummax",
- "cummin",
- "cumsum",
- "fillna",
- "rank",
- ):
- msg = "produces incorrect results when nans are present"
- request.node.add_marker(pytest.mark.xfail(reason=msg))
args = (0,) if transformation_func == "fillna" else ()
ser = Series([1, 2, 2], index=[1, 2, 3])
gb = ser.groupby([1, 1, np.nan], dropna=dropna)
@@ -1502,4 +1482,10 @@ def test_null_group_str_transformer_series(request, dropna, transformation_func)
msg = f"{transformation_func} is deprecated"
with tm.assert_produces_warning(warn, match=msg):
result = gb.transform(transformation_func, *args)
- tm.assert_equal(result, expected)
+ if dropna and transformation_func == "fillna":
+ # GH#46369 - result name is the group; remove this block when fixed.
+ tm.assert_equal(result, expected, check_names=False)
+ # This should be None
+ assert result.name == 1.0
+ else:
+ tm.assert_equal(result, expected)
| - [x] closes #17093 (Replace xxxx with the Github issue number)
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46367 | 2022-03-15T03:27:34Z | 2022-03-22T23:25:02Z | 2022-03-22T23:25:01Z | 2023-07-15T11:02:53Z |
Fixed `Period` and `PeriodIndex` formatting | diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst
index 089ba62e461d1..2536a9994399c 100644
--- a/doc/source/whatsnew/v1.5.0.rst
+++ b/doc/source/whatsnew/v1.5.0.rst
@@ -434,6 +434,7 @@ I/O
Period
^^^^^^
- Bug in subtraction of :class:`Period` from :class:`PeriodArray` returning wrong results (:issue:`45999`)
+- Bug in :meth:`Period.strftime` and :meth:`PeriodIndex.strftime`, directives ``%l`` and ``%u`` were giving wrong results (:issue:`46252`)
-
Plotting
diff --git a/pandas/_libs/tslibs/period.pyx b/pandas/_libs/tslibs/period.pyx
index 9c05c6867f71f..182502ba9ad7f 100644
--- a/pandas/_libs/tslibs/period.pyx
+++ b/pandas/_libs/tslibs/period.pyx
@@ -9,6 +9,7 @@ from cpython.object cimport (
PyObject_RichCompareBool,
)
from numpy cimport (
+ int32_t,
int64_t,
ndarray,
)
@@ -1217,10 +1218,14 @@ cdef str _period_strftime(int64_t value, int freq, bytes fmt):
char *formatted
bytes pat, brepl
list found_pat = [False] * len(extra_fmts)
- int year, quarter
+ int quarter
+ int32_t us, ps
str result, repl
get_date_info(value, freq, &dts)
+
+ # Find our additional directives in the pattern and replace them with
+ # placeholders that are not processed by c_strftime
for i in range(len(extra_fmts)):
pat = extra_fmts[i][0]
brepl = extra_fmts[i][1]
@@ -1228,28 +1233,41 @@ cdef str _period_strftime(int64_t value, int freq, bytes fmt):
fmt = fmt.replace(pat, brepl)
found_pat[i] = True
+ # Execute c_strftime to process the usual datetime directives
formatted = c_strftime(&dts, <char*>fmt)
result = util.char_to_string(formatted)
free(formatted)
+ # Now we will fill the placeholders corresponding to our additional directives
+
+ # First prepare the contents
+ # Save these to local vars as dts can be modified by get_yq below
+ us = dts.us
+ ps = dts.ps
+ if any(found_pat[0:3]):
+ # Note: this modifies `dts` in-place so that year becomes fiscal year
+ # However it looses the us and ps
+ quarter = get_yq(value, freq, &dts)
+ else:
+ quarter = 0
+
+ # Now do the filling per se
for i in range(len(extra_fmts)):
if found_pat[i]:
- quarter = get_yq(value, freq, &dts)
-
- if i == 0:
- repl = str(quarter)
- elif i == 1: # %f, 2-digit year
+ if i == 0: # %q, 1-digit quarter.
+ repl = f"{quarter}"
+ elif i == 1: # %f, 2-digit 'Fiscal' year
repl = f"{(dts.year % 100):02d}"
- elif i == 2:
+ elif i == 2: # %F, 'Fiscal' year with a century
repl = str(dts.year)
- elif i == 3:
- repl = f"{(value % 1_000):03d}"
- elif i == 4:
- repl = f"{(value % 1_000_000):06d}"
- elif i == 5:
- repl = f"{(value % 1_000_000_000):09d}"
+ elif i == 3: # %l, milliseconds
+ repl = f"{(us // 1_000):03d}"
+ elif i == 4: # %u, microseconds
+ repl = f"{(us):06d}"
+ elif i == 5: # %n, nanoseconds
+ repl = f"{((us * 1000) + (ps // 1000)):09d}"
result = result.replace(str_extra_fmts[i], repl)
@@ -2302,7 +2320,8 @@ cdef class _Period(PeriodMixin):
containing one or several directives. The method recognizes the same
directives as the :func:`time.strftime` function of the standard Python
distribution, as well as the specific additional directives ``%f``,
- ``%F``, ``%q``. (formatting & docs originally from scikits.timeries).
+ ``%F``, ``%q``, ``%l``, ``%u``, ``%n``.
+ (formatting & docs originally from scikits.timeries).
+-----------+--------------------------------+-------+
| Directive | Meaning | Notes |
@@ -2349,11 +2368,20 @@ cdef class _Period(PeriodMixin):
| | AM or PM. | |
+-----------+--------------------------------+-------+
| ``%q`` | Quarter as a decimal number | |
- | | [01,04] | |
+ | | [1,4] | |
+-----------+--------------------------------+-------+
| ``%S`` | Second as a decimal number | \(4) |
| | [00,61]. | |
+-----------+--------------------------------+-------+
+ | ``%l`` | Millisecond as a decimal number| |
+ | | [000,999]. | |
+ +-----------+--------------------------------+-------+
+ | ``%u`` | Microsecond as a decimal number| |
+ | | [000000,999999]. | |
+ +-----------+--------------------------------+-------+
+ | ``%n`` | Nanosecond as a decimal number | |
+ | | [000000000,999999999]. | |
+ +-----------+--------------------------------+-------+
| ``%U`` | Week number of the year | \(5) |
| | (Sunday as the first day of | |
| | the week) as a decimal number | |
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index 487a39976a0e8..8f0516abe8bb3 100644
--- a/pandas/core/arrays/datetimelike.py
+++ b/pandas/core/arrays/datetimelike.py
@@ -1593,6 +1593,14 @@ def strftime(self, date_format: str) -> npt.NDArray[np.object_]:
of the string format can be found in `python string format
doc <%(URL)s>`__.
+ Formats supported by the C `strftime` API but not by the python string format
+ doc (such as `"%%R"`, `"%%r"`) are not officially supported and should be
+ preferably replaced with their supported equivalents (such as `"%%H:%%M"`,
+ `"%%I:%%M:%%S %%p"`).
+
+ Note that `PeriodIndex` support additional directives, detailed in
+ `Period.strftime`.
+
Parameters
----------
date_format : str
@@ -1609,6 +1617,8 @@ def strftime(self, date_format: str) -> npt.NDArray[np.object_]:
DatetimeIndex.normalize : Return DatetimeIndex with times to midnight.
DatetimeIndex.round : Round the DatetimeIndex to the specified freq.
DatetimeIndex.floor : Floor the DatetimeIndex to the specified freq.
+ Timestamp.strftime : Format a single Timestamp.
+ Period.strftime : Format a single Period.
Examples
--------
diff --git a/pandas/core/arrays/period.py b/pandas/core/arrays/period.py
index b2a8dfddc6b68..fa543f6773634 100644
--- a/pandas/core/arrays/period.py
+++ b/pandas/core/arrays/period.py
@@ -642,17 +642,17 @@ def _format_native_types(
values = self.astype(object)
if date_format:
- formatter = lambda dt: dt.strftime(date_format)
+ formatter = lambda per: per.strftime(date_format)
else:
- formatter = lambda dt: str(dt)
+ formatter = lambda per: str(per)
if self._hasna:
mask = self._isnan
values[mask] = na_rep
imask = ~mask
- values[imask] = np.array([formatter(dt) for dt in values[imask]])
+ values[imask] = np.array([formatter(per) for per in values[imask]])
else:
- values = np.array([formatter(dt) for dt in values])
+ values = np.array([formatter(per) for per in values])
return values
# ------------------------------------------------------------------
diff --git a/pandas/tests/io/formats/test_format.py b/pandas/tests/io/formats/test_format.py
index adcaeba5cfd8d..9ab3e4cf6afac 100644
--- a/pandas/tests/io/formats/test_format.py
+++ b/pandas/tests/io/formats/test_format.py
@@ -1,7 +1,6 @@
"""
Test output formatting for Series/DataFrame, including to_string & reprs
"""
-
from datetime import datetime
from io import StringIO
import itertools
@@ -3167,6 +3166,65 @@ def test_str(self):
assert str(NaT) == "NaT"
+class TestPeriodIndexFormat:
+ def test_period_format_and_strftime_default(self):
+ per = pd.PeriodIndex([datetime(2003, 1, 1, 12), None], freq="H")
+
+ # Default formatting
+ formatted = per.format()
+ assert formatted[0] == "2003-01-01 12:00" # default: minutes not shown
+ assert formatted[1] == "NaT"
+ # format is equivalent to strftime(None)...
+ assert formatted[0] == per.strftime(None)[0]
+ assert per.strftime(None)[1] is np.nan # ...except for NaTs
+
+ # Same test with nanoseconds freq
+ per = pd.period_range("2003-01-01 12:01:01.123456789", periods=2, freq="n")
+ formatted = per.format()
+ assert (formatted == per.strftime(None)).all()
+ assert formatted[0] == "2003-01-01 12:01:01.123456789"
+ assert formatted[1] == "2003-01-01 12:01:01.123456790"
+
+ def test_period_custom(self):
+ # GH#46252 custom formatting directives %l (ms) and %u (us)
+
+ # 3 digits
+ per = pd.period_range("2003-01-01 12:01:01.123", periods=2, freq="l")
+ formatted = per.format(date_format="%y %I:%M:%S (ms=%l us=%u ns=%n)")
+ assert formatted[0] == "03 12:01:01 (ms=123 us=123000 ns=123000000)"
+ assert formatted[1] == "03 12:01:01 (ms=124 us=124000 ns=124000000)"
+
+ # 6 digits
+ per = pd.period_range("2003-01-01 12:01:01.123456", periods=2, freq="u")
+ formatted = per.format(date_format="%y %I:%M:%S (ms=%l us=%u ns=%n)")
+ assert formatted[0] == "03 12:01:01 (ms=123 us=123456 ns=123456000)"
+ assert formatted[1] == "03 12:01:01 (ms=123 us=123457 ns=123457000)"
+
+ # 9 digits
+ per = pd.period_range("2003-01-01 12:01:01.123456789", periods=2, freq="n")
+ formatted = per.format(date_format="%y %I:%M:%S (ms=%l us=%u ns=%n)")
+ assert formatted[0] == "03 12:01:01 (ms=123 us=123456 ns=123456789)"
+ assert formatted[1] == "03 12:01:01 (ms=123 us=123456 ns=123456790)"
+
+ def test_period_tz(self):
+ # Formatting periods created from a datetime with timezone.
+
+ # This timestamp is in 2013 in Europe/Paris but is 2012 in UTC
+ dt = pd.to_datetime(["2013-01-01 00:00:00+01:00"], utc=True)
+
+ # Converting to a period looses the timezone information
+ # Since tz is currently set as utc, we'll see 2012
+ with tm.assert_produces_warning(UserWarning, match="will drop timezone"):
+ per = dt.to_period(freq="H")
+ assert per.format()[0] == "2012-12-31 23:00"
+
+ # If tz is currently set as paris before conversion, we'll see 2013
+ dt = dt.tz_convert("Europe/Paris")
+ with tm.assert_produces_warning(UserWarning, match="will drop timezone"):
+ per = dt.to_period(freq="H")
+ assert per.format()[0] == "2013-01-01 00:00"
+
+
class TestDatetimeIndexFormat:
def test_datetime(self):
formatted = pd.to_datetime([datetime(2003, 1, 1, 12), NaT]).format()
| - [x] closes #46252
- [x] Improved docstring for `PeriodIndex.strftime` and `Period.strftime`
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
Note: this is the first PR extracted from #46116. More to come soon :) | https://api.github.com/repos/pandas-dev/pandas/pulls/46361 | 2022-03-14T15:04:34Z | 2022-03-18T00:34:24Z | 2022-03-18T00:34:23Z | 2022-03-18T10:56:31Z |
CI: Debug Windows stack overflow | diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index e6dc98179b7d6..1b0ae40949d5b 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -45,7 +45,7 @@ jobs:
/opt/python/cp38-cp38/bin/python -m venv ~/virtualenvs/pandas-dev && \
. ~/virtualenvs/pandas-dev/bin/activate && \
python -m pip install --no-deps -U pip wheel 'setuptools<60.0.0' && \
- pip install cython numpy python-dateutil pytz pytest pytest-xdist hypothesis pytest-azurepipelines && \
+ pip install cython numpy python-dateutil pytz pytest pytest-xdist hypothesis && \
python setup.py build_ext -q -j2 && \
python -m pip install --no-build-isolation -e . && \
pytest -m 'not slow and not network and not clipboard and not single_cpu' pandas --junitxml=test-data.xml"
diff --git a/pandas/tests/extension/arrow/test_bool.py b/pandas/tests/extension/arrow/test_bool.py
index bdfbbef937019..0205b8aad9431 100644
--- a/pandas/tests/extension/arrow/test_bool.py
+++ b/pandas/tests/extension/arrow/test_bool.py
@@ -1,6 +1,11 @@
import numpy as np
import pytest
+from pandas.compat import (
+ is_ci_environment,
+ is_platform_windows,
+)
+
import pandas as pd
import pandas._testing as tm
from pandas.api.types import is_bool_dtype
@@ -82,6 +87,10 @@ def test_reduce_series_boolean(self):
pass
+@pytest.mark.skipif(
+ is_ci_environment() and is_platform_windows(),
+ reason="Causes stack overflow on Windows CI",
+)
class TestReduceBoolean(base.BaseBooleanReduceTests):
pass
diff --git a/pandas/tests/extension/json/test_json.py b/pandas/tests/extension/json/test_json.py
index d530a75b74c8f..f13ef65267304 100644
--- a/pandas/tests/extension/json/test_json.py
+++ b/pandas/tests/extension/json/test_json.py
@@ -1,5 +1,6 @@
import collections
import operator
+import sys
import pytest
@@ -163,12 +164,24 @@ def test_from_dtype(self, data):
@pytest.mark.xfail(reason="RecursionError, GH-33900")
def test_series_constructor_no_data_with_index(self, dtype, na_value):
# RecursionError: maximum recursion depth exceeded in comparison
- super().test_series_constructor_no_data_with_index(dtype, na_value)
+ rec_limit = sys.getrecursionlimit()
+ try:
+ # Limit to avoid stack overflow on Windows CI
+ sys.setrecursionlimit(100)
+ super().test_series_constructor_no_data_with_index(dtype, na_value)
+ finally:
+ sys.setrecursionlimit(rec_limit)
@pytest.mark.xfail(reason="RecursionError, GH-33900")
def test_series_constructor_scalar_na_with_index(self, dtype, na_value):
# RecursionError: maximum recursion depth exceeded in comparison
- super().test_series_constructor_scalar_na_with_index(dtype, na_value)
+ rec_limit = sys.getrecursionlimit()
+ try:
+ # Limit to avoid stack overflow on Windows CI
+ sys.setrecursionlimit(100)
+ super().test_series_constructor_scalar_na_with_index(dtype, na_value)
+ finally:
+ sys.setrecursionlimit(rec_limit)
@pytest.mark.xfail(reason="collection as scalar, GH-33901")
def test_series_constructor_scalar_with_index(self, data, dtype):
| ```
Thread 0x0000142c (most recent call first):
File "C:\Miniconda\en.vs\pandas-dev\lib\site-packages\execnet\gateway_base.py", line 400 in read
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\gateway_base.py", line 432 in from_io
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\gateway_base.py", line 967 in _thread_receiver
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\gateway_base.py", line 220 in run
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\gateway_base.py", line 285 in _perform_spawn
Current thread 0x00000520 (most recent call first):
File "D:\a\1\s\pandas\core\construction.py", line 850 in is_empty_data
File "D:\a\1\s\pandas\core\construction.py", line 887 in create_series_with_explicit_dtype
File "D:\a\1\s\pandas\core\series.py", line 515 in _init_dict
File "D:\a\1\s\pandas\core\series.py", line 422 in __init__
File "D:\a\1\s\pandas\core\construction.py", line 889 in create_series_with_explicit_dtype
File "D:\a\1\s\pandas\core\series.py", line 515 in _init_dict
File "D:\a\1\s\pandas\core\series.py", line 422 in __init__
File "D:\a\1\s\pandas\core\construction.py", line 889 in create_series_with_explicit_dtype
File "D:\a\1\s\pandas\core\series.py", line 515 in _init_dict
File "D:\a\1\s\pandas\core\series.py", line 422 in __init__
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/46358 | 2022-03-13T18:16:00Z | 2022-03-15T19:43:34Z | 2022-03-15T19:43:34Z | 2022-03-29T12:23:41Z |
ENH: consistency of input args for boundaries - pd.interval_range | diff --git a/doc/source/user_guide/advanced.rst b/doc/source/user_guide/advanced.rst
index b8df21ab5a5b4..3081c6f7c6a08 100644
--- a/doc/source/user_guide/advanced.rst
+++ b/doc/source/user_guide/advanced.rst
@@ -1082,14 +1082,14 @@ of :ref:`frequency aliases <timeseries.offset_aliases>` with datetime-like inter
pd.interval_range(start=pd.Timedelta("0 days"), periods=3, freq="9H")
-Additionally, the ``closed`` parameter can be used to specify which side(s) the intervals
-are closed on. Intervals are closed on the right side by default.
+Additionally, the ``inclusive`` parameter can be used to specify which side(s) the intervals
+are closed on. Intervals are closed on the both side by default.
.. ipython:: python
- pd.interval_range(start=0, end=4, closed="both")
+ pd.interval_range(start=0, end=4, inclusive="both")
- pd.interval_range(start=0, end=4, closed="neither")
+ pd.interval_range(start=0, end=4, inclusive="neither")
Specifying ``start``, ``end``, and ``periods`` will generate a range of evenly spaced
intervals from ``start`` to ``end`` inclusively, with ``periods`` number of elements
diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst
index 56b1a6317472b..8607294244052 100644
--- a/doc/source/whatsnew/v1.5.0.rst
+++ b/doc/source/whatsnew/v1.5.0.rst
@@ -426,6 +426,7 @@ Other Deprecations
- Deprecated behavior of method :meth:`DataFrame.quantile`, attribute ``numeric_only`` will default False. Including datetime/timedelta columns in the result (:issue:`7308`).
- Deprecated :attr:`Timedelta.freq` and :attr:`Timedelta.is_populated` (:issue:`46430`)
- Deprecated :attr:`Timedelta.delta` (:issue:`46476`)
+- Deprecated the ``closed`` argument in :meth:`interval_range` in favor of ``inclusive`` argument; In a future version passing ``closed`` will raise (:issue:`40245`)
-
.. ---------------------------------------------------------------------------
diff --git a/pandas/core/indexes/interval.py b/pandas/core/indexes/interval.py
index 425e7d3f4432e..f6a20a418c32b 100644
--- a/pandas/core/indexes/interval.py
+++ b/pandas/core/indexes/interval.py
@@ -11,6 +11,7 @@
Hashable,
Literal,
)
+import warnings
import numpy as np
@@ -160,7 +161,7 @@ def _new_IntervalIndex(cls, d):
A new ``IntervalIndex`` is typically constructed using
:func:`interval_range`:
- >>> pd.interval_range(start=0, end=5)
+ >>> pd.interval_range(start=0, end=5, inclusive="right")
IntervalIndex([(0, 1], (1, 2], (2, 3], (3, 4], (4, 5]],
dtype='interval[int64, right]')
@@ -443,7 +444,7 @@ def is_overlapping(self) -> bool:
Intervals that share closed endpoints overlap:
- >>> index = pd.interval_range(0, 3, closed='both')
+ >>> index = pd.interval_range(0, 3, inclusive='both')
>>> index
IntervalIndex([[0, 1], [1, 2], [2, 3]],
dtype='interval[int64, both]')
@@ -452,7 +453,7 @@ def is_overlapping(self) -> bool:
Intervals that only have an open endpoint in common do not overlap:
- >>> index = pd.interval_range(0, 3, closed='left')
+ >>> index = pd.interval_range(0, 3, inclusive='left')
>>> index
IntervalIndex([[0, 1), [1, 2), [2, 3)],
dtype='interval[int64, left]')
@@ -956,7 +957,8 @@ def interval_range(
periods=None,
freq=None,
name: Hashable = None,
- closed: IntervalClosedType = "right",
+ closed: lib.NoDefault = lib.no_default,
+ inclusive: IntervalClosedType | None = None,
) -> IntervalIndex:
"""
Return a fixed frequency IntervalIndex.
@@ -979,6 +981,14 @@ def interval_range(
Whether the intervals are closed on the left-side, right-side, both
or neither.
+ .. deprecated:: 1.5.0
+ Argument `closed` has been deprecated to standardize boundary inputs.
+ Use `inclusive` instead, to set each bound as closed or open.
+ inclusive : {"both", "neither", "left", "right"}, default "both"
+ Include boundaries; Whether to set each bound as closed or open.
+
+ .. versionadded:: 1.5.0
+
Returns
-------
IntervalIndex
@@ -1001,14 +1011,14 @@ def interval_range(
--------
Numeric ``start`` and ``end`` is supported.
- >>> pd.interval_range(start=0, end=5)
+ >>> pd.interval_range(start=0, end=5, inclusive="right")
IntervalIndex([(0, 1], (1, 2], (2, 3], (3, 4], (4, 5]],
dtype='interval[int64, right]')
Additionally, datetime-like input is also supported.
>>> pd.interval_range(start=pd.Timestamp('2017-01-01'),
- ... end=pd.Timestamp('2017-01-04'))
+ ... end=pd.Timestamp('2017-01-04'), inclusive="right")
IntervalIndex([(2017-01-01, 2017-01-02], (2017-01-02, 2017-01-03],
(2017-01-03, 2017-01-04]],
dtype='interval[datetime64[ns], right]')
@@ -1017,7 +1027,7 @@ def interval_range(
endpoints of the individual intervals within the ``IntervalIndex``. For
numeric ``start`` and ``end``, the frequency must also be numeric.
- >>> pd.interval_range(start=0, periods=4, freq=1.5)
+ >>> pd.interval_range(start=0, periods=4, freq=1.5, inclusive="right")
IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0]],
dtype='interval[float64, right]')
@@ -1025,7 +1035,7 @@ def interval_range(
convertible to a DateOffset.
>>> pd.interval_range(start=pd.Timestamp('2017-01-01'),
- ... periods=3, freq='MS')
+ ... periods=3, freq='MS', inclusive="right")
IntervalIndex([(2017-01-01, 2017-02-01], (2017-02-01, 2017-03-01],
(2017-03-01, 2017-04-01]],
dtype='interval[datetime64[ns], right]')
@@ -1033,17 +1043,40 @@ def interval_range(
Specify ``start``, ``end``, and ``periods``; the frequency is generated
automatically (linearly spaced).
- >>> pd.interval_range(start=0, end=6, periods=4)
+ >>> pd.interval_range(start=0, end=6, periods=4, inclusive="right")
IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0]],
dtype='interval[float64, right]')
- The ``closed`` parameter specifies which endpoints of the individual
+ The ``inclusive`` parameter specifies which endpoints of the individual
intervals within the ``IntervalIndex`` are closed.
- >>> pd.interval_range(end=5, periods=4, closed='both')
+ >>> pd.interval_range(end=5, periods=4, inclusive='both')
IntervalIndex([[1, 2], [2, 3], [3, 4], [4, 5]],
dtype='interval[int64, both]')
"""
+ if inclusive is not None and not isinstance(closed, lib.NoDefault):
+ raise ValueError(
+ "Deprecated argument `closed` cannot be passed "
+ "if argument `inclusive` is not None"
+ )
+ elif not isinstance(closed, lib.NoDefault):
+ warnings.warn(
+ "Argument `closed` is deprecated in favor of `inclusive`.",
+ FutureWarning,
+ stacklevel=2,
+ )
+ if closed is None:
+ inclusive = "both"
+ elif closed in ("both", "neither", "left", "right"):
+ inclusive = closed
+ else:
+ raise ValueError(
+ "Argument `closed` has to be either"
+ "'both', 'neither', 'left' or 'right'"
+ )
+ elif inclusive is None:
+ inclusive = "both"
+
start = maybe_box_datetimelike(start)
end = maybe_box_datetimelike(end)
endpoint = start if start is not None else end
@@ -1120,4 +1153,4 @@ def interval_range(
else:
breaks = timedelta_range(start=start, end=end, periods=periods, freq=freq)
- return IntervalIndex.from_breaks(breaks, name=name, closed=closed)
+ return IntervalIndex.from_breaks(breaks, name=name, closed=inclusive)
diff --git a/pandas/tests/frame/methods/test_round.py b/pandas/tests/frame/methods/test_round.py
index dd9206940bcd6..77cadfff55e2f 100644
--- a/pandas/tests/frame/methods/test_round.py
+++ b/pandas/tests/frame/methods/test_round.py
@@ -210,7 +210,7 @@ def test_round_nonunique_categorical(self):
def test_round_interval_category_columns(self):
# GH#30063
- columns = pd.CategoricalIndex(pd.interval_range(0, 2))
+ columns = pd.CategoricalIndex(pd.interval_range(0, 2, inclusive="right"))
df = DataFrame([[0.66, 1.1], [0.3, 0.25]], columns=columns)
result = df.round()
diff --git a/pandas/tests/groupby/aggregate/test_cython.py b/pandas/tests/groupby/aggregate/test_cython.py
index 96009be5d12e3..9631de7833cf4 100644
--- a/pandas/tests/groupby/aggregate/test_cython.py
+++ b/pandas/tests/groupby/aggregate/test_cython.py
@@ -214,7 +214,7 @@ def test_cython_agg_empty_buckets_nanops(observed):
result = df.groupby(pd.cut(df["a"], grps), observed=observed)._cython_agg_general(
"add", alt=None, numeric_only=True
)
- intervals = pd.interval_range(0, 20, freq=5)
+ intervals = pd.interval_range(0, 20, freq=5, inclusive="right")
expected = DataFrame(
{"a": [0, 0, 36, 0]},
index=pd.CategoricalIndex(intervals, name="a", ordered=True),
diff --git a/pandas/tests/indexes/interval/test_astype.py b/pandas/tests/indexes/interval/test_astype.py
index c253a745ef5a2..4cdbe2bbcf12b 100644
--- a/pandas/tests/indexes/interval/test_astype.py
+++ b/pandas/tests/indexes/interval/test_astype.py
@@ -117,7 +117,7 @@ def test_subtype_integer(self, subtype_start, subtype_end):
@pytest.mark.xfail(reason="GH#15832")
def test_subtype_integer_errors(self):
# int64 -> uint64 fails with negative values
- index = interval_range(-10, 10)
+ index = interval_range(-10, 10, inclusive="right")
dtype = IntervalDtype("uint64", "right")
# Until we decide what the exception message _should_ be, we
@@ -133,7 +133,7 @@ class TestFloatSubtype(AstypeTests):
"""Tests specific to IntervalIndex with float subtype"""
indexes = [
- interval_range(-10.0, 10.0, closed="neither"),
+ interval_range(-10.0, 10.0, inclusive="neither"),
IntervalIndex.from_arrays(
[-1.5, np.nan, 0.0, 0.0, 1.5], [-0.5, np.nan, 1.0, 1.0, 3.0], closed="both"
),
@@ -170,7 +170,7 @@ def test_subtype_integer_with_non_integer_borders(self, subtype):
def test_subtype_integer_errors(self):
# float64 -> uint64 fails with negative values
- index = interval_range(-10.0, 10.0)
+ index = interval_range(-10.0, 10.0, inclusive="right")
dtype = IntervalDtype("uint64", "right")
msg = re.escape(
"Cannot convert interval[float64, right] to interval[uint64, right]; "
@@ -191,10 +191,10 @@ class TestDatetimelikeSubtype(AstypeTests):
"""Tests specific to IntervalIndex with datetime-like subtype"""
indexes = [
- interval_range(Timestamp("2018-01-01"), periods=10, closed="neither"),
+ interval_range(Timestamp("2018-01-01"), periods=10, inclusive="neither"),
interval_range(Timestamp("2018-01-01"), periods=10).insert(2, NaT),
interval_range(Timestamp("2018-01-01", tz="US/Eastern"), periods=10),
- interval_range(Timedelta("0 days"), periods=10, closed="both"),
+ interval_range(Timedelta("0 days"), periods=10, inclusive="both"),
interval_range(Timedelta("0 days"), periods=10).insert(2, NaT),
]
diff --git a/pandas/tests/indexes/interval/test_interval.py b/pandas/tests/indexes/interval/test_interval.py
index 37c13c37d070b..8880cab2ce29b 100644
--- a/pandas/tests/indexes/interval/test_interval.py
+++ b/pandas/tests/indexes/interval/test_interval.py
@@ -167,10 +167,10 @@ def test_delete(self, closed):
@pytest.mark.parametrize(
"data",
[
- interval_range(0, periods=10, closed="neither"),
- interval_range(1.7, periods=8, freq=2.5, closed="both"),
- interval_range(Timestamp("20170101"), periods=12, closed="left"),
- interval_range(Timedelta("1 day"), periods=6, closed="right"),
+ interval_range(0, periods=10, inclusive="neither"),
+ interval_range(1.7, periods=8, freq=2.5, inclusive="both"),
+ interval_range(Timestamp("20170101"), periods=12, inclusive="left"),
+ interval_range(Timedelta("1 day"), periods=6, inclusive="right"),
],
)
def test_insert(self, data):
@@ -868,9 +868,9 @@ def test_nbytes(self):
@pytest.mark.parametrize("new_closed", ["left", "right", "both", "neither"])
def test_set_closed(self, name, closed, new_closed):
# GH 21670
- index = interval_range(0, 5, closed=closed, name=name)
+ index = interval_range(0, 5, inclusive=closed, name=name)
result = index.set_closed(new_closed)
- expected = interval_range(0, 5, closed=new_closed, name=name)
+ expected = interval_range(0, 5, inclusive=new_closed, name=name)
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize("bad_closed", ["foo", 10, "LEFT", True, False])
diff --git a/pandas/tests/indexes/interval/test_interval_range.py b/pandas/tests/indexes/interval/test_interval_range.py
index 2f28c33a3bbc6..63e7f3aa2b120 100644
--- a/pandas/tests/indexes/interval/test_interval_range.py
+++ b/pandas/tests/indexes/interval/test_interval_range.py
@@ -34,25 +34,25 @@ def test_constructor_numeric(self, closed, name, freq, periods):
# defined from start/end/freq
result = interval_range(
- start=start, end=end, freq=freq, name=name, closed=closed
+ start=start, end=end, freq=freq, name=name, inclusive=closed
)
tm.assert_index_equal(result, expected)
# defined from start/periods/freq
result = interval_range(
- start=start, periods=periods, freq=freq, name=name, closed=closed
+ start=start, periods=periods, freq=freq, name=name, inclusive=closed
)
tm.assert_index_equal(result, expected)
# defined from end/periods/freq
result = interval_range(
- end=end, periods=periods, freq=freq, name=name, closed=closed
+ end=end, periods=periods, freq=freq, name=name, inclusive=closed
)
tm.assert_index_equal(result, expected)
# GH 20976: linspace behavior defined from start/end/periods
result = interval_range(
- start=start, end=end, periods=periods, name=name, closed=closed
+ start=start, end=end, periods=periods, name=name, inclusive=closed
)
tm.assert_index_equal(result, expected)
@@ -67,19 +67,19 @@ def test_constructor_timestamp(self, closed, name, freq, periods, tz):
# defined from start/end/freq
result = interval_range(
- start=start, end=end, freq=freq, name=name, closed=closed
+ start=start, end=end, freq=freq, name=name, inclusive=closed
)
tm.assert_index_equal(result, expected)
# defined from start/periods/freq
result = interval_range(
- start=start, periods=periods, freq=freq, name=name, closed=closed
+ start=start, periods=periods, freq=freq, name=name, inclusive=closed
)
tm.assert_index_equal(result, expected)
# defined from end/periods/freq
result = interval_range(
- end=end, periods=periods, freq=freq, name=name, closed=closed
+ end=end, periods=periods, freq=freq, name=name, inclusive=closed
)
tm.assert_index_equal(result, expected)
@@ -88,7 +88,7 @@ def test_constructor_timestamp(self, closed, name, freq, periods, tz):
# matches expected only for non-anchored offsets and tz naive
# (anchored/DST transitions cause unequal spacing in expected)
result = interval_range(
- start=start, end=end, periods=periods, name=name, closed=closed
+ start=start, end=end, periods=periods, name=name, inclusive=closed
)
tm.assert_index_equal(result, expected)
@@ -102,25 +102,25 @@ def test_constructor_timedelta(self, closed, name, freq, periods):
# defined from start/end/freq
result = interval_range(
- start=start, end=end, freq=freq, name=name, closed=closed
+ start=start, end=end, freq=freq, name=name, inclusive=closed
)
tm.assert_index_equal(result, expected)
# defined from start/periods/freq
result = interval_range(
- start=start, periods=periods, freq=freq, name=name, closed=closed
+ start=start, periods=periods, freq=freq, name=name, inclusive=closed
)
tm.assert_index_equal(result, expected)
# defined from end/periods/freq
result = interval_range(
- end=end, periods=periods, freq=freq, name=name, closed=closed
+ end=end, periods=periods, freq=freq, name=name, inclusive=closed
)
tm.assert_index_equal(result, expected)
# GH 20976: linspace behavior defined from start/end/periods
result = interval_range(
- start=start, end=end, periods=periods, name=name, closed=closed
+ start=start, end=end, periods=periods, name=name, inclusive=closed
)
tm.assert_index_equal(result, expected)
@@ -163,7 +163,9 @@ def test_no_invalid_float_truncation(self, start, end, freq):
breaks = [0.5, 2.0, 3.5, 5.0, 6.5]
expected = IntervalIndex.from_breaks(breaks)
- result = interval_range(start=start, end=end, periods=4, freq=freq)
+ result = interval_range(
+ start=start, end=end, periods=4, freq=freq, inclusive="right"
+ )
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize(
@@ -184,7 +186,7 @@ def test_no_invalid_float_truncation(self, start, end, freq):
def test_linspace_dst_transition(self, start, mid, end):
# GH 20976: linspace behavior defined from start/end/periods
# accounts for the hour gained/lost during DST transition
- result = interval_range(start=start, end=end, periods=2)
+ result = interval_range(start=start, end=end, periods=2, inclusive="right")
expected = IntervalIndex.from_breaks([start, mid, end])
tm.assert_index_equal(result, expected)
@@ -353,3 +355,17 @@ def test_errors(self):
msg = "Start and end cannot both be tz-aware with different timezones"
with pytest.raises(TypeError, match=msg):
interval_range(start=start, end=end)
+
+ def test_interval_range_error_and_warning(self):
+ # GH 40245
+
+ msg = (
+ "Deprecated argument `closed` cannot "
+ "be passed if argument `inclusive` is not None"
+ )
+ with pytest.raises(ValueError, match=msg):
+ interval_range(end=5, periods=4, closed="both", inclusive="both")
+
+ msg = "Argument `closed` is deprecated in favor of `inclusive`"
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ interval_range(end=5, periods=4, closed="right")
diff --git a/pandas/tests/indexes/interval/test_setops.py b/pandas/tests/indexes/interval/test_setops.py
index 059b0b75f4190..51a1d36398aa4 100644
--- a/pandas/tests/indexes/interval/test_setops.py
+++ b/pandas/tests/indexes/interval/test_setops.py
@@ -194,7 +194,7 @@ def test_set_incompatible_types(self, closed, op_name, sort):
tm.assert_index_equal(result, expected)
# GH 19016: incompatible dtypes -> cast to object
- other = interval_range(Timestamp("20180101"), periods=9, closed=closed)
+ other = interval_range(Timestamp("20180101"), periods=9, inclusive=closed)
expected = getattr(index.astype(object), op_name)(other, sort=sort)
if op_name == "difference":
expected = index
diff --git a/pandas/tests/indexing/test_coercion.py b/pandas/tests/indexing/test_coercion.py
index 2d54a9ba370ca..4504c55698a9a 100644
--- a/pandas/tests/indexing/test_coercion.py
+++ b/pandas/tests/indexing/test_coercion.py
@@ -709,7 +709,7 @@ def test_fillna_datetime64tz(self, index_or_series, fill_val, fill_dtype):
],
)
def test_fillna_interval(self, index_or_series, fill_val):
- ii = pd.interval_range(1.0, 5.0, closed="right").insert(1, np.nan)
+ ii = pd.interval_range(1.0, 5.0, inclusive="right").insert(1, np.nan)
assert isinstance(ii.dtype, pd.IntervalDtype)
obj = index_or_series(ii)
diff --git a/pandas/tests/indexing/test_loc.py b/pandas/tests/indexing/test_loc.py
index 4702b5e5c4504..3f8e4401808b7 100644
--- a/pandas/tests/indexing/test_loc.py
+++ b/pandas/tests/indexing/test_loc.py
@@ -1508,12 +1508,12 @@ def test_loc_getitem_interval_index(self):
def test_loc_getitem_interval_index2(self):
# GH#19977
- index = pd.interval_range(start=0, periods=3, closed="both")
+ index = pd.interval_range(start=0, periods=3, inclusive="both")
df = DataFrame(
[[1, 2, 3], [4, 5, 6], [7, 8, 9]], index=index, columns=["A", "B", "C"]
)
- index_exp = pd.interval_range(start=0, periods=2, freq=1, closed="both")
+ index_exp = pd.interval_range(start=0, periods=2, freq=1, inclusive="both")
expected = Series([1, 4], index=index_exp, name="A")
result = df.loc[1, "A"]
tm.assert_series_equal(result, expected)
diff --git a/pandas/tests/io/excel/test_writers.py b/pandas/tests/io/excel/test_writers.py
index e475c9a4c6145..26c283a7dcc45 100644
--- a/pandas/tests/io/excel/test_writers.py
+++ b/pandas/tests/io/excel/test_writers.py
@@ -282,7 +282,10 @@ def test_multiindex_interval_datetimes(self, ext):
[
range(4),
pd.interval_range(
- start=pd.Timestamp("2020-01-01"), periods=4, freq="6M"
+ start=pd.Timestamp("2020-01-01"),
+ periods=4,
+ freq="6M",
+ inclusive="right",
),
]
)
diff --git a/pandas/tests/series/indexing/test_setitem.py b/pandas/tests/series/indexing/test_setitem.py
index 992f67c2affc6..1e84a05e2ae97 100644
--- a/pandas/tests/series/indexing/test_setitem.py
+++ b/pandas/tests/series/indexing/test_setitem.py
@@ -779,7 +779,7 @@ def test_index_putmask(self, obj, key, expected, val):
pytest.param(
# GH#45568 setting a valid NA value into IntervalDtype[int] should
# cast to IntervalDtype[float]
- Series(interval_range(1, 5)),
+ Series(interval_range(1, 5, inclusive="right")),
Series(
[Interval(1, 2), np.nan, Interval(3, 4), Interval(4, 5)],
dtype="interval[float64]",
@@ -1356,7 +1356,7 @@ class TestPeriodIntervalCoercion(CoercionTest):
@pytest.fixture(
params=[
period_range("2016-01-01", periods=3, freq="D"),
- interval_range(1, 5),
+ interval_range(1, 5, inclusive="right"),
]
)
def obj(self, request):
diff --git a/pandas/tests/util/test_assert_interval_array_equal.py b/pandas/tests/util/test_assert_interval_array_equal.py
index 8cc4ade3d7e95..243f357d7298c 100644
--- a/pandas/tests/util/test_assert_interval_array_equal.py
+++ b/pandas/tests/util/test_assert_interval_array_equal.py
@@ -9,7 +9,7 @@
[
{"start": 0, "periods": 4},
{"start": 1, "periods": 5},
- {"start": 5, "end": 10, "closed": "left"},
+ {"start": 5, "end": 10, "inclusive": "left"},
],
)
def test_interval_array_equal(kwargs):
@@ -19,8 +19,8 @@ def test_interval_array_equal(kwargs):
def test_interval_array_equal_closed_mismatch():
kwargs = {"start": 0, "periods": 5}
- arr1 = interval_range(closed="left", **kwargs).values
- arr2 = interval_range(closed="right", **kwargs).values
+ arr1 = interval_range(inclusive="left", **kwargs).values
+ arr2 = interval_range(inclusive="right", **kwargs).values
msg = """\
IntervalArray are different
| - [x] xref #40245
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46355 | 2022-03-13T16:26:36Z | 2022-04-04T20:57:36Z | 2022-04-04T20:57:36Z | 2022-05-31T16:12:54Z |
DOC: minor fixes to 1.4.2 release notes | diff --git a/doc/source/whatsnew/v1.4.2.rst b/doc/source/whatsnew/v1.4.2.rst
index 06f1f406c3816..3523f691cae8f 100644
--- a/doc/source/whatsnew/v1.4.2.rst
+++ b/doc/source/whatsnew/v1.4.2.rst
@@ -18,8 +18,8 @@ Fixed regressions
- Fixed regression in :func:`read_csv` killing python process when invalid file input was given for ``engine="c"`` (:issue:`45957`)
- Fixed memory performance regression in :meth:`Series.fillna` when called on a :class:`DataFrame` column with ``inplace=True`` (:issue:`46149`)
- Provided an alternative solution for passing custom Excel formats in :meth:`.Styler.to_excel`, which was a regression based on stricter CSS validation. Examples available in the documentation for :meth:`.Styler.format` (:issue:`46152`)
-- Fixed regression in :meth:`DataFrame.replace` when a replacement value was also a target for replacement (:issue:`46335`)
-- Fixed regression in :meth:`DataFrame.loc.__setitem__` losing :class:`MultiIndex` names if :class:`DataFrame` was empty before (:issue:`46317`)
+- Fixed regression in :meth:`DataFrame.replace` when a replacement value was also a target for replacement (:issue:`46306`)
+- Fixed regression when setting values with :meth:`DataFrame.loc` losing :class:`MultiIndex` names if :class:`DataFrame` was empty before (:issue:`46317`)
-
.. ---------------------------------------------------------------------------
| null | https://api.github.com/repos/pandas-dev/pandas/pulls/46354 | 2022-03-13T14:30:57Z | 2022-03-15T23:11:38Z | 2022-03-15T23:11:38Z | 2022-03-16T09:06:12Z |
TST: xfail(strict=False) flaky hashtable test | diff --git a/pandas/tests/libs/test_hashtable.py b/pandas/tests/libs/test_hashtable.py
index 6af81468ef1ef..bed427da5dd12 100644
--- a/pandas/tests/libs/test_hashtable.py
+++ b/pandas/tests/libs/test_hashtable.py
@@ -246,7 +246,16 @@ def test_lookup_overflow(self, writable):
(ht.Float64HashTable, ht.Float64Vector, "float64", False),
(ht.Int64HashTable, ht.Int64Vector, "int64", False),
(ht.Int32HashTable, ht.Int32Vector, "int32", False),
- (ht.UInt64HashTable, ht.UInt64Vector, "uint64", False),
+ pytest.param(
+ ht.UInt64HashTable,
+ ht.UInt64Vector,
+ "uint64",
+ False,
+ marks=pytest.mark.xfail(
+ reason="Sometimes doesn't raise.",
+ strict=False,
+ ),
+ ),
],
)
def test_vector_resize(
| - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
Example: https://app.circleci.com/pipelines/github/pandas-dev/pandas/8766/workflows/4a9ce4f4-0e1c-49aa-b035-ffcb6334be45/jobs/39610
| https://api.github.com/repos/pandas-dev/pandas/pulls/46350 | 2022-03-12T22:56:27Z | 2022-03-15T22:39:08Z | 2022-03-15T22:39:08Z | 2022-03-16T00:31:54Z |
PERF: Index[StringArray] | diff --git a/pandas/core/arrays/string_.py b/pandas/core/arrays/string_.py
index b5432f0d1346c..f3db5598e306c 100644
--- a/pandas/core/arrays/string_.py
+++ b/pandas/core/arrays/string_.py
@@ -42,10 +42,10 @@
ExtensionArray,
FloatingArray,
IntegerArray,
- PandasArray,
)
from pandas.core.arrays.floating import FloatingDtype
from pandas.core.arrays.integer import IntegerDtype
+from pandas.core.arrays.numpy_ import PandasArray
from pandas.core.construction import extract_array
from pandas.core.indexers import check_array_indexer
from pandas.core.missing import isna
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index c673848bc022a..6005ce1fe836c 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -147,6 +147,7 @@
validate_tz_from_dtype,
)
from pandas.core.arrays.sparse import SparseDtype
+from pandas.core.arrays.string_ import StringArray
from pandas.core.base import (
IndexOpsMixin,
PandasObject,
@@ -5032,7 +5033,11 @@ def _get_engine_target(self) -> ArrayLike:
Get the ndarray or ExtensionArray that we can pass to the IndexEngine
constructor.
"""
- return self._values
+ vals = self._values
+ if isinstance(vals, StringArray):
+ # GH#45652 much more performant than ExtensionEngine
+ return vals._ndarray
+ return vals
def _from_join_target(self, result: np.ndarray) -> ArrayLike:
"""
| - [x] closes #45652 (Replace xxxx with the Github issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
bandaid until we can improve perf in ExtensionEngine | https://api.github.com/repos/pandas-dev/pandas/pulls/46349 | 2022-03-12T22:32:11Z | 2022-03-15T23:40:38Z | 2022-03-15T23:40:38Z | 2022-03-15T23:41:17Z |
Series.map Performance Improvement When Using Dictionaries | diff --git a/pandas/core/base.py b/pandas/core/base.py
index 74e119a42c974..7a1836a89fb10 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -69,7 +69,6 @@
from pandas.core.arraylike import OpsMixin
from pandas.core.arrays import ExtensionArray
from pandas.core.construction import (
- create_series_with_explicit_dtype,
ensure_wrapped_if_datetimelike,
extract_array,
)
@@ -796,7 +795,7 @@ def _reduce(
return func(skipna=skipna, **kwds)
@final
- def _map_values(self, mapper, na_action=None):
+ def _map_values(self, mapper, na_action: Literal["ignore"] | None = None):
"""
An internal function that maps values using the input
correspondence (which can be a dict, Series, or function).
@@ -820,24 +819,20 @@ def _map_values(self, mapper, na_action=None):
# as we know that we are not going to have to yield
# python types
if is_dict_like(mapper):
+ dict_with_default = mapper
if isinstance(mapper, dict) and hasattr(mapper, "__missing__"):
# If a dictionary subclass defines a default value method,
# convert mapper to a lookup function (GH #15999).
- dict_with_default = mapper
mapper = lambda x: dict_with_default[x]
else:
- # Dictionary does not have a default. Thus it's safe to
- # convert to an Series for efficiency.
- # we specify the keys here to handle the
- # possibility that they are tuples
-
- # The return value of mapping with an empty mapper is
- # expected to be pd.Series(np.nan, ...). As np.nan is
- # of dtype float64 the return value of this method should
- # be float64 as well
- mapper = create_series_with_explicit_dtype(
- mapper, dtype_if_empty=np.float64
- )
+ # Dictionary does not have a default. Since the default
+ # behavior for missing values in a dictionary mapping is
+ # to map them to np.nan, it is safe to convert the
+ # dictionary to a defaultdict which returns np.nan for
+ # missing keys for efficiency (GH #46248). For even better
+ # performance, ReadOnlyNanDefaultDict provides the
+ # necessary functionality, without copying the dictionary.
+ mapper = lambda x: dict_with_default.get(x, np.nan)
if isinstance(mapper, ABCSeries):
# Since values were input this means we came from either
@@ -853,7 +848,6 @@ def _map_values(self, mapper, na_action=None):
indexer = mapper.index.get_indexer(values)
new_values = algorithms.take_nd(mapper._values, indexer)
-
return new_values
# we must convert to python types
| - [x] closes #46248
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
Hello, first time contributing to pandas so if I did something wrong, please let me know!
In the discussion for #46248 I proposed a very simple solution using `collections.defaultdict`. However, after implementing the change, I found a performance improvement of 10x, but that was still a far stretch from the 1000-10000x improvement I was expecting. After more testing I found that the majority of the time was now being spent by copying the dictionary to the `defaultdict`. To avoid unnecessarily copying the dictionary's data, I use `dict.get` with a default of `np.nan` for dictionaries that do not implement the `__missing__` method ~~added `ReadOnlyNanDefaultDict` which gives us the behavior of `defaultdict(lambda: np.nan, dictionary_object)` without copying the data. As given by the name, it is also read only since that is all that is needed by `IndexOpsMixin._map_values`.~~ With this change, I see the huge performance boost of 1000-10000x.
This is a slight deviation from the `defaultdict` solution proposed in the issue, but it performs much better and behaves in the same way.
Lastly, I tried to add type hints for `IndexOpsMixin._map_values` but could not find the proper way of typing `Series` without causing a circular import. Is `"Series"` the way to go, or is there something better? | https://api.github.com/repos/pandas-dev/pandas/pulls/46348 | 2022-03-12T22:05:25Z | 2022-05-07T02:59:46Z | null | 2022-05-07T02:59:46Z |
Fix snappy version | diff --git a/ci/deps/actions-310.yaml b/ci/deps/actions-310.yaml
index 8f333c72f002c..37e7ea04a348a 100644
--- a/ci/deps/actions-310.yaml
+++ b/ci/deps/actions-310.yaml
@@ -42,10 +42,10 @@ dependencies:
- pytables
- pyarrow
- pyreadstat
+ - python-snappy
- pyxlsb
- s3fs
- scipy
- - snappy
- sqlalchemy
- tabulate
- xarray
diff --git a/ci/deps/actions-38-downstream_compat.yaml b/ci/deps/actions-38-downstream_compat.yaml
index 7386649f1c627..40f48884f1822 100644
--- a/ci/deps/actions-38-downstream_compat.yaml
+++ b/ci/deps/actions-38-downstream_compat.yaml
@@ -38,14 +38,14 @@ dependencies:
- odfpy
- pandas-gbq
- psycopg2
- - pymysql
- - pytables
- pyarrow
+ - pymysql
- pyreadstat
+ - pytables
+ - python-snappy
- pyxlsb
- s3fs
- scipy
- - snappy
- sqlalchemy
- tabulate
- xarray
diff --git a/ci/deps/actions-38-minimum_versions.yaml b/ci/deps/actions-38-minimum_versions.yaml
index 5726001ea1290..abba5ddd60325 100644
--- a/ci/deps/actions-38-minimum_versions.yaml
+++ b/ci/deps/actions-38-minimum_versions.yaml
@@ -40,14 +40,14 @@ dependencies:
- openpyxl=3.0.3
- pandas-gbq=0.14.0
- psycopg2=2.8.4
- - pymysql=0.10.1
- - pytables=3.6.1
- pyarrow=1.0.1
+ - pymysql=0.10.1
- pyreadstat=1.1.0
+ - pytables=3.6.1
+ - python-snappy=0.6.0
- pyxlsb=1.0.6
- s3fs=0.4.0
- scipy=1.4.1
- - snappy=1.1.8
- sqlalchemy=1.4.0
- tabulate=0.8.7
- xarray=0.15.1
diff --git a/ci/deps/actions-38.yaml b/ci/deps/actions-38.yaml
index 85a25571bb9fd..9c46eca4ab989 100644
--- a/ci/deps/actions-38.yaml
+++ b/ci/deps/actions-38.yaml
@@ -37,14 +37,14 @@ dependencies:
- odfpy
- pandas-gbq
- psycopg2
- - pymysql
- - pytables
- pyarrow
+ - pymysql
- pyreadstat
+ - pytables
+ - python-snappy
- pyxlsb
- s3fs
- scipy
- - snappy
- sqlalchemy
- tabulate
- xarray
diff --git a/ci/deps/actions-39.yaml b/ci/deps/actions-39.yaml
index 8cff9539300f0..89b647372d7bc 100644
--- a/ci/deps/actions-39.yaml
+++ b/ci/deps/actions-39.yaml
@@ -38,13 +38,13 @@ dependencies:
- pandas-gbq
- psycopg2
- pymysql
- - pytables
- pyarrow
- pyreadstat
+ - pytables
+ - python-snappy
- pyxlsb
- s3fs
- scipy
- - snappy
- sqlalchemy
- tabulate
- xarray
diff --git a/doc/source/getting_started/install.rst b/doc/source/getting_started/install.rst
index 8706292d60def..dc02460a671d6 100644
--- a/doc/source/getting_started/install.rst
+++ b/doc/source/getting_started/install.rst
@@ -411,6 +411,6 @@ Compression
Dependency Minimum Version Notes
========================= ================== =============================================================
brotli 0.7.0 Brotli compression
-snappy 1.1.8 Snappy compression
+python-snappy 0.6.0 Snappy compression
Zstandard 0.15.2 Zstandard compression
========================= ================== =============================================================
diff --git a/pandas/compat/_optional.py b/pandas/compat/_optional.py
index baae5c913c05d..e69bee5c647d8 100644
--- a/pandas/compat/_optional.py
+++ b/pandas/compat/_optional.py
@@ -35,7 +35,7 @@
"pyxlsb": "1.0.6",
"s3fs": "0.4.0",
"scipy": "1.4.1",
- "snappy": "1.1.8",
+ "snappy": "0.6.0",
"sqlalchemy": "1.4.0",
"tables": "3.6.1",
"tabulate": "0.8.7",
@@ -53,12 +53,13 @@
"bs4": "beautifulsoup4",
"bottleneck": "Bottleneck",
"brotli": "brotlipy",
+ "jinja2": "Jinja2",
"lxml.etree": "lxml",
"odf": "odfpy",
"pandas_gbq": "pandas-gbq",
- "tables": "pytables",
+ "snappy": "python-snappy",
"sqlalchemy": "SQLAlchemy",
- "jinja2": "Jinja2",
+ "tables": "pytables",
}
@@ -72,6 +73,10 @@ def get_version(module: types.ModuleType) -> str:
if module.__name__ == "brotli":
# brotli doesn't contain attributes to confirm it's version
return ""
+ if module.__name__ == "snappy":
+ # snappy doesn't contain attributes to confirm it's version
+ # See https://github.com/andrix/python-snappy/pull/119
+ return ""
raise ImportError(f"Can't determine version for {module.__name__}")
if module.__name__ == "psycopg2":
# psycopg2 appends " (dt dec pq3 ext lo64)" to it's version
| Fix for https://github.com/pandas-dev/pandas/pull/46260
- [ ] closes #xxxx (Replace xxxx with the Github issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46347 | 2022-03-12T21:28:08Z | 2022-03-15T22:39:43Z | 2022-03-15T22:39:43Z | 2022-03-30T10:47:12Z |
Refactor CI code | diff --git a/.circleci/config.yml b/.circleci/config.yml
index 612552f4eac59..0d9e3ade08846 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -13,7 +13,7 @@ jobs:
PANDAS_CI: "1"
steps:
- checkout
- - run: ci/setup_env.sh
+ - run: .circleci/setup_env.sh
- run: PATH=$HOME/miniconda3/envs/pandas-dev/bin:$HOME/miniconda3/condabin:$PATH ci/run_tests.sh
workflows:
diff --git a/ci/setup_env.sh b/.circleci/setup_env.sh
similarity index 100%
rename from ci/setup_env.sh
rename to .circleci/setup_env.sh
diff --git a/.github/actions/build-pandas/action.yml b/.github/actions/build-pandas/action.yml
new file mode 100644
index 0000000000000..b91bf456e0e60
--- /dev/null
+++ b/.github/actions/build-pandas/action.yml
@@ -0,0 +1,39 @@
+name: Build pandas
+description: Rebuilds the C extensions and installs pandas
+runs:
+ using: composite
+ steps:
+ - name: Environment Detail
+ run: |
+ if which micromamba; then
+ micromamba info
+ micromamba list
+ micromamba info | grep -Ei 'environment.+:' | grep -qEiv 'environment.+:.+none'
+ fi
+ if which pip; then
+ pip list
+ fi
+ python --version
+ shell: bash -el {0}
+
+ - name: Get Python version
+ id: get-python-version
+ run: python3 -c "import platform as p; print(f'::set-output name=version::{p.python_implementation()}-{p.python_version()}')"
+ shell: bash -el {0}
+
+ - name: Set up sccache
+ uses: ./.github/actions/setup-sccache
+ with:
+ extra-cache-key: ${{ steps.get-python-version.outputs.version }}
+
+ - name: Build Pandas
+ run: |
+ which python
+ which pip
+ time DISTUTILS_C_COMPILER_LAUNCHER=sccache python setup.py build_ext -v -j 2
+ pip install -v -e . --no-build-isolation --no-use-pep517 --no-index
+ shell: bash -el {0}
+
+ - name: Build Version
+ run: pushd /tmp && python -c "import pandas; pandas.show_versions();" && popd
+ shell: bash -el {0}
diff --git a/.github/actions/build_pandas/action.yml b/.github/actions/build_pandas/action.yml
deleted file mode 100644
index 2e4bfea165316..0000000000000
--- a/.github/actions/build_pandas/action.yml
+++ /dev/null
@@ -1,17 +0,0 @@
-name: Build pandas
-description: Rebuilds the C extensions and installs pandas
-runs:
- using: composite
- steps:
-
- - name: Environment Detail
- run: |
- conda info
- conda list
- shell: bash -l {0}
-
- - name: Build Pandas
- run: |
- python setup.py build_ext -j 2
- python -m pip install -e . --no-build-isolation --no-use-pep517 --no-index
- shell: bash -l {0}
diff --git a/.github/actions/run-tests/action.yml b/.github/actions/run-tests/action.yml
new file mode 100644
index 0000000000000..45c9f24118b79
--- /dev/null
+++ b/.github/actions/run-tests/action.yml
@@ -0,0 +1,34 @@
+name: Run tests and report results
+inputs:
+ check-pyarrow-version:
+ required: false
+runs:
+ using: composite
+ steps:
+ - name: Check PyArrow version
+ run: |
+ # Double check that we have the expected PyArrow
+ pushd /tmp
+ python -c "import pandas; pandas.show_versions()" | egrep -i "pyarrow.+: ${{ matrix.check-pyarrow-version }}"
+ popd
+ shell: bash -el {0}
+ if: ${{ inputs.check-pyarrow-version }}
+
+ - name: Test
+ run: ci/run_tests.sh
+ shell: bash -el {0}
+
+ - name: Publish test results
+ uses: actions/upload-artifact@v2
+ with:
+ name: Test results
+ path: test-data.xml
+ if: failure()
+
+ - name: Upload coverage to Codecov
+ uses: codecov/codecov-action@v2
+ with:
+ flags: unittests
+ name: codecov-pandas
+ fail_ci_if_error: false
+ if: failure()
diff --git a/.github/actions/setup-python/action.yml b/.github/actions/setup-python/action.yml
new file mode 100644
index 0000000000000..3345ff72688dd
--- /dev/null
+++ b/.github/actions/setup-python/action.yml
@@ -0,0 +1,65 @@
+name: Setup Python and install requirements
+inputs:
+ python-version:
+ required: true
+ architecture:
+ default: x64
+runs:
+ using: composite
+ steps:
+ # TODO: GH#44980 https://github.com/pypa/setuptools/issues/2941
+ - name: Create temporary requirements.txt
+ run: |
+ # Drop cache at least once per month
+ month_today="$(date '+%Y-%m')"
+ cat > requirements.txt <<EOF
+ # $month_today
+
+ # Python deps
+ pip
+ setuptools<60.0.0
+ wheel
+
+ # Pandas deps
+ # GH 39416
+ numpy
+ cython
+ python-dateutil
+ pytz
+
+ # Test deps
+ git+https://github.com/nedbat/coveragepy.git
+ hypothesis
+ pytest>=6.2.5
+ pytest-xdist
+ pytest-cov
+ pytest-asyncio>=0.17
+ EOF
+ shell: bash -el {0}
+
+ - name: Set up Python
+ uses: actions/setup-python@v3
+ with:
+ python-version: ${{ inputs.python-version }}
+ architecture: ${{ inputs.architecture }}
+ cache: pip
+
+ - name: Fix $PATH on macOS
+ run: |
+ # On macOS, the Python version we installed above is too late in $PATH
+ # to be effective if using "bash -l" (which we need for code that works
+ # with Conda envs).
+ cat >> ~/.bash_profile <<EOF
+ export PATH="\$(echo "\$PATH" | grep -Eio '[^:]+hostedtoolcache/python[^:]+bin'):\$PATH" \
+ EOF
+ shell: bash -el {0}
+ if: ${{ runner.os == 'macOS' }}
+
+ - name: Install dependencies
+ run: |
+ cat requirements.txt
+ # TODO https://github.com/numpy/numpy/issues/21196
+ bits32=$(python -c 'import sys; print(int(sys.maxsize > 2**32))')
+ NPY_DISABLE_SVML=$bits32 pip install -r requirements.txt
+ pip list
+ shell: bash -el {0}
diff --git a/.github/actions/setup-sccache/action.yml b/.github/actions/setup-sccache/action.yml
new file mode 100644
index 0000000000000..9e59cc034fe18
--- /dev/null
+++ b/.github/actions/setup-sccache/action.yml
@@ -0,0 +1,28 @@
+name: Setup sccache
+inputs:
+ extra-cache-key:
+ required: false
+ default: ''
+runs:
+ using: composite
+ steps:
+ - name: Get Date
+ id: get-date
+ run: echo "::set-output name=today::$(/bin/date -u '+%Y%m%d')"
+ shell: bash
+
+ - name: Fix Windows temporary directory
+ # On Windows, for some reason the default temporary directory provided to sccache
+ # may become read-only at some point. Work around by having a private tempdir.
+ id: mktemp
+ run: echo "::set-output name=tmpdir::$(cygpath -w $(mktemp -d))"
+ shell: bash
+ if: ${{ runner.os == 'Windows' }}
+
+ - name: Setup sccache
+ uses: hendrikmuhs/ccache-action@v1.2
+ with:
+ variant: sccache
+ key: ${{ runner.os }}--${{ runner.arch }}--${{ github.workflow }}--${{ steps.get-date.outputs.today }}--${{ inputs.extra-cache-key }}
+ env:
+ TMP: "${{ steps.mktemp.outputs.tmpdir }}"
diff --git a/.github/actions/setup/action.yml b/.github/actions/setup/action.yml
index 9ef00e7a85a6f..668ca94ca6a7d 100644
--- a/.github/actions/setup/action.yml
+++ b/.github/actions/setup/action.yml
@@ -1,12 +1,50 @@
name: Set up pandas
description: Runs all the setup steps required to have a built pandas ready to use
+inputs:
+ environment-file:
+ default: environment.yml
+ pyarrow-version:
+ required: false
+ is-pypy:
+ default: false
+ environment-name:
+ default: pandas-dev
+ python-version:
+ required: false
runs:
using: composite
steps:
- - name: Setting conda path
- run: echo "${HOME}/miniconda3/bin" >> $GITHUB_PATH
- shell: bash -l {0}
+ - name: Set Arrow version in ${{ inputs.environment-file }} to ${{ inputs.pyarrow-version }}
+ run: |
+ grep -q ' - pyarrow' ${{ inputs.environment-file }}
+ sed -i"" -e "s/ - pyarrow/ - pyarrow=${{ inputs.pyarrow-version }}/" ${{ inputs.environment-file }}
+ cat ${{ inputs.environment-file }}
+ shell: bash
+ if: ${{ inputs.pyarrow-version }}
- - name: Setup environment and build pandas
- run: ci/setup_env.sh
- shell: bash -l {0}
+ - name: Pin setuptools (GH#44980)
+ run: |
+ echo ' - setuptools <60' >> ${{ inputs.environment-file }}
+ shell: bash
+
+ - name: Install ${{ inputs.environment-file }} (Python ${{ inputs.python-version }})
+ uses: mamba-org/provision-with-micromamba@main
+ with:
+ environment-name: ${{ inputs.environment-name }}
+ environment-file: ${{ inputs.environment-file }}
+ extra-specs: |
+ ${{ inputs.python-version && format('python={0}', inputs.python-version) }}
+ cache-env: true
+ if: ${{ inputs.is-pypy == 'false' }} # No pypy3.8 support
+
+ - name: Setup PyPy
+ uses: actions/setup-python@v3
+ with:
+ python-version: "pypy-3.8"
+ if: ${{ inputs.is-pypy == 'true' }}
+
+ - name: Setup PyPy dependencies
+ # TODO: re-enable cov, its slowing the tests down though
+ run: pip install Cython numpy python-dateutil pytz pytest>=6.0 pytest-xdist>=1.31.0 hypothesis>=5.5.3 pytest-asyncio>=0.17
+ shell: bash
+ if: ${{ inputs.is-pypy == 'true' }}
diff --git a/.github/workflows/asv-bot.yml b/.github/workflows/asv-bot.yml
index f3946aeb84a63..63cad09e2e223 100644
--- a/.github/workflows/asv-bot.yml
+++ b/.github/workflows/asv-bot.yml
@@ -6,8 +6,7 @@ on:
- created
env:
- ENV_FILE: environment.yml
- COMMENT: ${{github.event.comment.body}}
+ COMMENT: ${{ github.event.comment.body }}
jobs:
autotune:
@@ -17,7 +16,7 @@ jobs:
runs-on: ubuntu-latest
defaults:
run:
- shell: bash -l {0}
+ shell: bash -el {0}
concurrency:
# Set concurrency to prevent abuse(full runs are ~5.5 hours !!!)
@@ -29,24 +28,14 @@ jobs:
steps:
- name: Checkout
- uses: actions/checkout@v2
+ uses: actions/checkout@v3
with:
fetch-depth: 0
- - name: Cache conda
- uses: actions/cache@v2
- with:
- path: ~/conda_pkgs_dir
- key: ${{ runner.os }}-conda-${{ hashFiles('${{ env.ENV_FILE }}') }}
-
- # Although asv sets up its own env, deps are still needed
- # during discovery process
- - uses: conda-incubator/setup-miniconda@v2
- with:
- activate-environment: pandas-dev
- channel-priority: strict
- environment-file: ${{ env.ENV_FILE }}
- use-only-tar-bz2: true
+ # Although asv sets up its own env, deps are still needed
+ # during discovery process
+ - name: Set up Conda
+ uses: ./.github/actions/setup
- name: Run benchmarks
id: bench
diff --git a/.github/workflows/autoupdate-pre-commit-config.yml b/.github/workflows/autoupdate-pre-commit-config.yml
index 3696cba8cf2e6..11484f98995bc 100644
--- a/.github/workflows/autoupdate-pre-commit-config.yml
+++ b/.github/workflows/autoupdate-pre-commit-config.yml
@@ -12,7 +12,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Set up Python
- uses: actions/setup-python@v2
+ uses: actions/setup-python@v3
- name: Cache multiple paths
uses: actions/cache@v2
with:
diff --git a/.github/workflows/code-checks.yml b/.github/workflows/code-checks.yml
index f32fed3b3ee68..667f3da6ce972 100644
--- a/.github/workflows/code-checks.yml
+++ b/.github/workflows/code-checks.yml
@@ -11,7 +11,6 @@ on:
- 1.4.x
env:
- ENV_FILE: environment.yml
PANDAS_CI: 1
jobs:
@@ -24,10 +23,10 @@ jobs:
cancel-in-progress: true
steps:
- name: Checkout
- uses: actions/checkout@v2
+ uses: actions/checkout@v3
- name: Install Python
- uses: actions/setup-python@v2
+ uses: actions/setup-python@v3
with:
python-version: '3.9.7'
@@ -39,7 +38,7 @@ jobs:
runs-on: ubuntu-latest
defaults:
run:
- shell: bash -l {0}
+ shell: bash -el {0}
concurrency:
# https://github.community/t/concurrecy-not-work-for-push/183068/7
@@ -48,24 +47,17 @@ jobs:
steps:
- name: Checkout
- uses: actions/checkout@v2
+ uses: actions/checkout@v3
with:
fetch-depth: 0
- - name: Cache conda
- uses: actions/cache@v2
- with:
- path: ~/conda_pkgs_dir
- key: ${{ runner.os }}-conda-${{ hashFiles('${{ env.ENV_FILE }}') }}
+ - name: Set up Conda
+ uses: ./.github/actions/setup
- - uses: conda-incubator/setup-miniconda@v2
- with:
- mamba-version: "*"
- channels: conda-forge
- activate-environment: pandas-dev
- channel-priority: strict
- environment-file: ${{ env.ENV_FILE }}
- use-only-tar-bz2: true
+ - name: Build Pandas
+ uses: ./.github/actions/build-pandas
+ id: build
+ continue-on-error: true
- name: Install node.js (for pyright)
uses: actions/setup-node@v2
@@ -76,10 +68,6 @@ jobs:
# note: keep version in sync with .pre-commit-config.yaml
run: npm install -g pyright@1.1.230
- - name: Build Pandas
- id: build
- uses: ./.github/actions/build_pandas
-
- name: Run checks on imported code
run: ci/code_checks.sh code
if: ${{ steps.build.outcome == 'success' }}
@@ -105,7 +93,7 @@ jobs:
runs-on: ubuntu-latest
defaults:
run:
- shell: bash -l {0}
+ shell: bash -el {0}
concurrency:
# https://github.community/t/concurrecy-not-work-for-push/183068/7
@@ -114,28 +102,15 @@ jobs:
steps:
- name: Checkout
- uses: actions/checkout@v2
+ uses: actions/checkout@v3
with:
fetch-depth: 0
- - name: Cache conda
- uses: actions/cache@v2
- with:
- path: ~/conda_pkgs_dir
- key: ${{ runner.os }}-conda-${{ hashFiles('${{ env.ENV_FILE }}') }}
-
- - uses: conda-incubator/setup-miniconda@v2
- with:
- mamba-version: "*"
- channels: conda-forge
- activate-environment: pandas-dev
- channel-priority: strict
- environment-file: ${{ env.ENV_FILE }}
- use-only-tar-bz2: true
+ - name: Set up Conda
+ uses: ./.github/actions/setup
- name: Build Pandas
- id: build
- uses: ./.github/actions/build_pandas
+ uses: ./.github/actions/build-pandas
- name: Run ASV benchmarks
run: |
@@ -148,7 +123,6 @@ jobs:
if grep "failed" benchmarks.log > /dev/null ; then
exit 1
fi
- if: ${{ steps.build.outcome == 'success' }}
- name: Publish benchmarks artifact
uses: actions/upload-artifact@v2
@@ -162,7 +136,7 @@ jobs:
runs-on: ubuntu-latest
defaults:
run:
- shell: bash -l {0}
+ shell: bash -el {0}
concurrency:
# https://github.community/t/concurrecy-not-work-for-push/183068/7
@@ -174,7 +148,7 @@ jobs:
run: docker image prune -f
- name: Checkout
- uses: actions/checkout@v2
+ uses: actions/checkout@v3
with:
fetch-depth: 0
diff --git a/.github/workflows/comment_bot.yml b/.github/workflows/comment_bot.yml
index 8f610fd5781ef..5c47a500a0f9e 100644
--- a/.github/workflows/comment_bot.yml
+++ b/.github/workflows/comment_bot.yml
@@ -12,7 +12,7 @@ jobs:
if: startsWith(github.event.comment.body, '@github-actions pre-commit')
runs-on: ubuntu-latest
steps:
- - uses: actions/checkout@v2
+ - uses: actions/checkout@v3
- uses: r-lib/actions/pr-fetch@v2
with:
repo-token: ${{ secrets.GITHUB_TOKEN }}
@@ -23,7 +23,7 @@ jobs:
~/.cache/pre-commit
~/.cache/pip
key: pre-commit-dispatched-${{ runner.os }}-build
- - uses: actions/setup-python@v2
+ - uses: actions/setup-python@v3
with:
python-version: 3.8
- name: Install-pre-commit
diff --git a/.github/workflows/docbuild-and-upload.yml b/.github/workflows/docbuild-and-upload.yml
index 4cce75779d750..8b8154cc53d53 100644
--- a/.github/workflows/docbuild-and-upload.yml
+++ b/.github/workflows/docbuild-and-upload.yml
@@ -11,13 +11,15 @@ on:
- 1.4.x
env:
- ENV_FILE: environment.yml
PANDAS_CI: 1
jobs:
web_and_docs:
name: Doc Build and Upload
runs-on: ubuntu-latest
+ defaults:
+ run:
+ shell: bash -el {0}
concurrency:
# https://github.community/t/concurrecy-not-work-for-push/183068/7
@@ -26,21 +28,21 @@ jobs:
steps:
- name: Checkout
- uses: actions/checkout@v2
+ uses: actions/checkout@v3
with:
fetch-depth: 0
- - name: Set up pandas
+ - name: Set up Conda
uses: ./.github/actions/setup
+ - name: Build pandas
+ uses: ./.github/actions/build-pandas
+
- name: Build website
- run: |
- source activate pandas-dev
- python web/pandas_web.py web/pandas --target-path=web/build
+ run: python web/pandas_web.py web/pandas --target-path=web/build
+
- name: Build documentation
- run: |
- source activate pandas-dev
- doc/make.py --warnings-are-errors
+ run: doc/make.py --warnings-are-errors
- name: Install ssh key
run: |
diff --git a/.github/workflows/macos-windows.yml b/.github/workflows/macos-windows.yml
new file mode 100644
index 0000000000000..8d2df387d5f75
--- /dev/null
+++ b/.github/workflows/macos-windows.yml
@@ -0,0 +1,62 @@
+name: macOS and Windows
+
+on:
+ push:
+ branches:
+ - main
+ - 1.4.x
+ pull_request:
+ branches:
+ - main
+ - 1.4.x
+ paths-ignore:
+ - "doc/**"
+
+env:
+ PANDAS_CI: 1
+ PYTEST_TARGET: pandas
+ PYTEST_WORKERS: auto
+ PATTERN: "not slow and not db and not network and not single_cpu"
+
+jobs:
+ pytest:
+ runs-on: ${{ matrix.os }}
+ defaults:
+ run:
+ shell: bash -el {0}
+ timeout-minutes: 120
+ strategy:
+ matrix:
+ os: [macos-10.15, windows-2019]
+ env_file: [actions-38.yaml, actions-39.yaml, actions-310.yaml]
+ pyarrow_version: [6]
+ fail-fast: false
+ name: ${{ matrix.name || format('{0} {1} pyarrow={2}', matrix.os, matrix.env_file, matrix.pyarrow_version) }}
+ concurrency:
+ # https://github.community/t/concurrecy-not-work-for-push/183068/7
+ group: ${{ github.event_name == 'push' && github.run_number || github.ref }}-${{ matrix.os }}-${{ matrix.env_file }}-${{ matrix.pyarrow_version }}-mac-win
+ cancel-in-progress: true
+
+ steps:
+ - name: Checkout
+ uses: actions/checkout@v3
+ with:
+ fetch-depth: 0
+
+ - name: wmic.exe
+ run: wmic.exe cpu get caption, deviceid, name, numberofcores, maxclockspeed
+ if: ${{ runner.os == 'Windows' }}
+
+ - name: Set up Conda (${{ matrix.env_file }}, Arrow ${{ matrix.pyarrow_version}})
+ uses: ./.github/actions/setup
+ with:
+ environment-file: ci/deps/${{ matrix.env_file }}
+ pyarrow-version: ${{ matrix.pyarrow_version }}
+
+ - name: Build pandas
+ uses: ./.github/actions/build-pandas
+
+ - name: Run tests
+ uses: ./.github/actions/run-tests
+ with:
+ check-pyarrow-version: ${{ matrix.pyarrow_version }}
diff --git a/.github/workflows/python-dev.yml b/.github/workflows/python-dev.yml
index c287827206336..c9446e6cbb6c2 100644
--- a/.github/workflows/python-dev.yml
+++ b/.github/workflows/python-dev.yml
@@ -21,11 +21,11 @@ on:
- "doc/**"
env:
- PYTEST_WORKERS: "auto"
PANDAS_CI: 1
+ PYTEST_TARGET: pandas
+ PYTEST_WORKERS: "auto"
PATTERN: "not slow and not network and not clipboard and not single_cpu"
COVERAGE: true
- PYTEST_TARGET: pandas
jobs:
build:
@@ -35,63 +35,43 @@ jobs:
fail-fast: false
matrix:
os: [ubuntu-latest, macOS-latest, windows-latest]
+ python-version: ["3.11-dev"]
- name: actions-311-dev
+ name: ${{ matrix.python-version }}
timeout-minutes: 80
+ defaults:
+ run:
+ shell: bash -el {0}
concurrency:
#https://github.community/t/concurrecy-not-work-for-push/183068/7
- group: ${{ github.event_name == 'push' && github.run_number || github.ref }}-${{ matrix.os }}-${{ matrix.pytest_target }}-dev
+ group: ${{ github.event_name == 'push' && github.run_number || github.ref }}-${{ matrix.os }}-dev
cancel-in-progress: true
steps:
- - uses: actions/checkout@v2
+ - uses: actions/checkout@v3
with:
fetch-depth: 0
- - name: Set up Python Dev Version
- uses: actions/setup-python@v2
+ - name: Set up Python ${{ matrix.python-version }} and install dependencies
+ uses: ./.github/actions/setup-python
with:
- python-version: '3.11-dev'
+ python-version: ${{ matrix.python-version }}
- # TODO: GH#44980 https://github.com/pypa/setuptools/issues/2941
- - name: Install dependencies
- shell: bash
- run: |
- python -m pip install --upgrade pip "setuptools<60.0.0" wheel
- pip install -i https://pypi.anaconda.org/scipy-wheels-nightly/simple numpy
- pip install git+https://github.com/nedbat/coveragepy.git
- pip install cython python-dateutil pytz hypothesis pytest>=6.2.5 pytest-xdist pytest-cov
- pip list
+ - name: Install NumPy nightly
+ run:
+ pip install -Ui https://pypi.anaconda.org/scipy-wheels-nightly/simple numpy
- - name: Build Pandas
- run: |
- python setup.py build_ext -q -j2
- python -m pip install -e . --no-build-isolation --no-use-pep517
+ - name: Build pandas
+ uses: ./.github/actions/build-pandas
- - name: Build Version
+ - name: Install pandas from sdist
run: |
- python -c "import pandas; pandas.show_versions();"
-
- - name: Test with pytest
- shell: bash
- run: |
- ci/run_tests.sh
-
- - name: Publish test results
- uses: actions/upload-artifact@v2
- with:
- name: Test results
- path: test-data.xml
- if: failure()
-
- - name: Report Coverage
- run: |
- coverage report -m
+ # Double check that we have the expected Python and NumPy
+ python --version
+ pip list
+ python --version | grep -i "$(echo "${{ matrix.python-version }}" | sed 's/-.*//')"
+ pip list | egrep -i "numpy.+dev"
- - name: Upload coverage to Codecov
- uses: codecov/codecov-action@v2
- with:
- flags: unittests
- name: codecov-pandas
- fail_ci_if_error: true
+ - name: Run tests
+ uses: ./.github/actions/run-tests
diff --git a/.github/workflows/sdist.yml b/.github/workflows/sdist.yml
index 431710a49a7dd..c86901d1b8520 100644
--- a/.github/workflows/sdist.yml
+++ b/.github/workflows/sdist.yml
@@ -14,78 +14,83 @@ on:
- "doc/**"
jobs:
- build:
- if: ${{ github.event.label.name == 'Build' || contains(github.event.pull_request.labels.*.name, 'Build') || github.event_name == 'push'}}
+ sdist:
+ #if: ${{ github.event.label.name == 'Build' || contains(github.event.pull_request.labels.*.name, 'Build') || github.event_name == 'push'}}
runs-on: ubuntu-latest
timeout-minutes: 60
defaults:
run:
- shell: bash -l {0}
+ shell: bash -el {0}
strategy:
fail-fast: false
matrix:
- python-version: ["3.8", "3.9", "3.10"]
+ # 'numpy-version' is oldest supported NumPy
+ include:
+ - python-version: "3.8"
+ numpy-version: "1.18.5"
+ - python-version: "3.9"
+ numpy-version: "1.19.4"
+ - python-version: "3.10"
+ numpy-version: "1.21.3"
+
+ name: sdist Python ${{ matrix.python-version }} NumPy ${{ matrix.numpy-version }}
+
concurrency:
# https://github.community/t/concurrecy-not-work-for-push/183068/7
- group: ${{ github.event_name == 'push' && github.run_number || github.ref }}-${{matrix.python-version}}-sdist
+ group: ${{ github.event_name == 'push' && github.run_number || github.ref }}-${{ matrix.python-version }}-sdist
cancel-in-progress: true
steps:
- - uses: actions/checkout@v2
+ - uses: actions/checkout@v3
with:
fetch-depth: 0
- - name: Set up Python
- uses: actions/setup-python@v2
+ - name: Set up Python ${{ matrix.python-version }} and install dependencies
+ uses: ./.github/actions/setup-python
with:
python-version: ${{ matrix.python-version }}
- # TODO: GH#44980 https://github.com/pypa/setuptools/issues/2941
- - name: Install dependencies
- run: |
- python -m pip install --upgrade pip "setuptools<60.0.0" wheel
-
- # GH 39416
- pip install numpy
-
- name: Build pandas sdist
- run: |
- pip list
- python setup.py sdist --formats=gztar
+ run: python setup.py sdist --formats=gztar
- name: Upload sdist artifact
uses: actions/upload-artifact@v3
with:
- name: ${{matrix.python-version}}-sdist.gz
+ name: ${{ matrix.python-version }}-sdist.gz
path: dist/*.gz
- - uses: conda-incubator/setup-miniconda@v2
+ - name: Set up Conda env with Python ${{ matrix.python-version }}, NumPy ${{ matrix.numpy-version }}
+ # TODO: Change this when https://github.com/mamba-org/provision-with-micromamba/pull/45 is merged
+ uses: jonashaag/provision-with-micromamba@cache-failure
with:
- activate-environment: pandas-sdist
+ environment-file: false
+ environment-name: pandas-sdist
channels: conda-forge
- python-version: '${{ matrix.python-version }}'
+ extra-specs: |
+ python=${{ matrix.python-version }}
+ setuptools<60
+ numpy=${{ matrix.numpy-version }}
+ cache-env: true
+
+ - name: Set up sccache
+ uses: ./.github/actions/setup-sccache
+ with:
+ extra-cache-key: ${{ matrix.python-version }}
- # TODO: GH#44980 https://github.com/pypa/setuptools/issues/2941
- name: Install pandas from sdist
run: |
- python -m pip install --upgrade "setuptools<60.0.0"
+ micromamba info
+ # Double check that we have the expected NumPy
pip list
- python -m pip install dist/*.gz
-
- - name: Force oldest supported NumPy
- run: |
- case "${{matrix.python-version}}" in
- 3.8)
- pip install numpy==1.18.5 ;;
- 3.9)
- pip install numpy==1.19.3 ;;
- 3.10)
- pip install numpy==1.21.2 ;;
- esac
+ pip list | egrep -i "numpy.+${{ matrix.numpy-version }}"
+ time DISTUTILS_C_COMPILER_LAUNCHER=sccache python -m pip install -v dist/*.gz
- - name: Import pandas
+ - name: Build Version
run: |
- cd ..
- conda list
- python -c "import pandas; pandas.show_versions();"
+ pushd /tmp
+ python -c "import pandas; pandas.show_versions()"
+ # Double check that we have the expected Python and NumPy
+ python -c "import pandas; pandas.show_versions()" | egrep -i "python.+: ${{ matrix.python-version }}"
+ python -c "import pandas; pandas.show_versions()" | egrep -i "numpy.+: ${{ matrix.numpy-version }}"
+ popd
diff --git a/.github/workflows/posix.yml b/.github/workflows/ubuntu.yml
similarity index 61%
rename from .github/workflows/posix.yml
rename to .github/workflows/ubuntu.yml
index bc8791afc69f7..ecb26f5a5a200 100644
--- a/.github/workflows/posix.yml
+++ b/.github/workflows/ubuntu.yml
@@ -1,4 +1,4 @@
-name: Posix
+name: Ubuntu
on:
push:
@@ -12,15 +12,12 @@ on:
paths-ignore:
- "doc/**"
-env:
- PANDAS_CI: 1
-
jobs:
pytest:
runs-on: ubuntu-latest
defaults:
run:
- shell: bash -l {0}
+ shell: bash -el {0}
timeout-minutes: 120
strategy:
matrix:
@@ -30,41 +27,42 @@ jobs:
# even if tests are skipped/xfailed
pyarrow_version: ["5", "7"]
include:
- - env_file: actions-38-downstream_compat.yaml
+ - name: "Downstream Compat"
+ env_file: actions-38-downstream_compat.yaml
pattern: "not slow and not network and not single_cpu"
pytest_target: "pandas/tests/test_downstream.py"
- name: "Downstream Compat"
- - env_file: actions-38-minimum_versions.yaml
+ - name: "Minimum Versions"
+ env_file: actions-38-minimum_versions.yaml
pattern: "not slow and not network and not single_cpu"
- name: "Minimum Versions"
- - env_file: actions-38.yaml
+ - name: "Locale: it_IT.utf8"
+ env_file: actions-38.yaml
pattern: "not slow and not network and not single_cpu"
extra_apt: "language-pack-it"
lang: "it_IT.utf8"
lc_all: "it_IT.utf8"
- name: "Locale: it_IT.utf8"
- - env_file: actions-38.yaml
+ - name: "Locale: zh_CN.utf8"
+ env_file: actions-38.yaml
pattern: "not slow and not network and not single_cpu"
extra_apt: "language-pack-zh-hans"
lang: "zh_CN.utf8"
lc_all: "zh_CN.utf8"
- name: "Locale: zh_CN.utf8"
- - env_file: actions-38.yaml
+ - name: "Data Manager"
+ env_file: actions-38.yaml
pattern: "not slow and not network and not single_cpu"
pandas_data_manager: "array"
- name: "Data Manager"
- - env_file: actions-pypy-38.yaml
+ - name: "Pypy"
+ env_file: actions-pypy-38.yaml
pattern: "not slow and not network and not single_cpu"
test_args: "--max-worker-restart 0"
- name: "Pypy"
- - env_file: actions-310-numpydev.yaml
+ - name: "Numpy Dev"
+ env_file: actions-310-numpydev.yaml
pattern: "not slow and not network and not single_cpu"
pandas_testing_mode: "deprecate"
test_args: "-W error"
- name: "Numpy Dev"
fail-fast: false
name: ${{ matrix.name || format('{0} pyarrow={1} {2}', matrix.env_file, matrix.pyarrow_version, matrix.pattern) }}
env:
+ PANDAS_CI: 1
ENV_FILE: ci/deps/${{ matrix.env_file }}
PATTERN: ${{ matrix.pattern }}
EXTRA_APT: ${{ matrix.extra_apt || '' }}
@@ -80,7 +78,7 @@ jobs:
COVERAGE: ${{ !contains(matrix.env_file, 'pypy') }}
concurrency:
# https://github.community/t/concurrecy-not-work-for-push/183068/7
- group: ${{ github.event_name == 'push' && github.run_number || github.ref }}-${{ matrix.env_file }}-${{ matrix.pattern }}-${{ matrix.pyarrow_version || '' }}-${{ matrix.extra_apt || '' }}-${{ matrix.pandas_data_manager || '' }}
+ group: ${{ github.event_name == 'push' && github.run_number || github.ref }}-${{ matrix.env_file }}-${{ matrix.pattern }}-${{ matrix.pyarrow_version || '' }}-${{ matrix.extra_apt || '' }}-${{ matrix.pandas_data_manager || '' }}-ubuntu
cancel-in-progress: true
services:
@@ -121,72 +119,26 @@ jobs:
steps:
- name: Checkout
- uses: actions/checkout@v2
+ uses: actions/checkout@v3
with:
fetch-depth: 0
- - name: Cache conda
- uses: actions/cache@v2
- env:
- CACHE_NUMBER: 0
- with:
- path: ~/conda_pkgs_dir
- key: ${{ runner.os }}-conda-${{ env.CACHE_NUMBER }}-${{
- hashFiles('${{ env.ENV_FILE }}') }}
-
- name: Extra installs
# xsel for clipboard tests
run: sudo apt-get update && sudo apt-get install -y libc6-dev-i386 xsel ${{ env.EXTRA_APT }}
- - uses: conda-incubator/setup-miniconda@v2
+ - name: Set up Conda (${{ matrix.env_file }}, Arrow ${{ matrix.pyarrow_version}})
+ uses: ./.github/actions/setup
with:
- mamba-version: "*"
- channels: conda-forge
- activate-environment: pandas-dev
- channel-priority: flexible
environment-file: ${{ env.ENV_FILE }}
- use-only-tar-bz2: true
- if: ${{ env.IS_PYPY == 'false' }} # No pypy3.8 support
+ pyarrow-version: ${{ matrix.pyarrow_version }}
+ is-pypy: ${{ env.IS_PYPY }}
- - name: Upgrade Arrow version
- run: conda install -n pandas-dev -c conda-forge --no-update-deps pyarrow=${{ matrix.pyarrow_version }}
- if: ${{ matrix.pyarrow_version }}
+ - name: Build pandas
+ uses: ./.github/actions/build-pandas
- - name: Setup PyPy
- uses: actions/setup-python@v2
+ - name: Run tests
+ uses: ./.github/actions/run-tests
with:
- python-version: "pypy-3.8"
- if: ${{ env.IS_PYPY == 'true' }}
-
- - name: Setup PyPy dependencies
- shell: bash
- run: |
- # TODO: re-enable cov, its slowing the tests down though
- pip install Cython numpy python-dateutil pytz pytest>=6.0 pytest-xdist>=1.31.0 pytest-asyncio hypothesis>=5.5.3
- if: ${{ env.IS_PYPY == 'true' }}
-
- - name: Build Pandas
- uses: ./.github/actions/build_pandas
-
- - name: Test
- run: ci/run_tests.sh
- # TODO: Don't continue on error for PyPy
+ check-pyarrow-version: ${{ matrix.pyarrow_version }}
continue-on-error: ${{ env.IS_PYPY == 'true' }}
- if: always()
-
- - name: Build Version
- run: pushd /tmp && python -c "import pandas; pandas.show_versions();" && popd
-
- - name: Publish test results
- uses: actions/upload-artifact@v2
- with:
- name: Test results
- path: test-data.xml
- if: failure()
-
- - name: Upload coverage to Codecov
- uses: codecov/codecov-action@v2
- with:
- flags: unittests
- name: codecov-pandas
- fail_ci_if_error: false
diff --git a/.github/workflows/windows-32bit.yml b/.github/workflows/windows-32bit.yml
new file mode 100644
index 0000000000000..ec6519f9d43d9
--- /dev/null
+++ b/.github/workflows/windows-32bit.yml
@@ -0,0 +1,56 @@
+name: Windows 32 bit builds
+
+on:
+ push:
+ branches:
+ - main
+ - 1.4.x
+ pull_request:
+ branches:
+ - main
+ - 1.4.x
+ paths-ignore:
+ - "doc/**"
+
+env:
+ PANDAS_CI: 1
+ PYTEST_TARGET: pandas
+ PYTEST_WORKERS: auto
+ PATTERN: "not slow and not network and not clipboard and not single_cpu"
+ COVERAGE: true
+
+jobs:
+ windows-32bit:
+ runs-on: windows-latest
+ timeout-minutes: 120
+ concurrency:
+ # https://github.community/t/concurrecy-not-work-for-push/183068/7
+ group: ${{ github.event_name == 'push' && github.run_number || github.ref }}-${{ matrix.python-version }}-windows-32bit
+ cancel-in-progress: true
+ strategy:
+ matrix:
+ python-version: [3.9]
+ defaults:
+ run:
+ shell: bash -el {0}
+
+ steps:
+ - name: Checkout
+ uses: actions/checkout@v3
+ with:
+ fetch-depth: 0
+
+ - name: wmic.exe
+ run: wmic.exe cpu get caption, deviceid, name, numberofcores, maxclockspeed
+
+ - name: Set up Python ${{ matrix.python-version }} x86 and install dependencies
+ uses: ./.github/actions/setup-python
+ with:
+ python-version: ${{ matrix.python-version }}
+ architecture: x86
+
+ - name: Build pandas
+ uses: ./.github/actions/build-pandas
+
+ - name: Run tests
+ uses: ./.github/actions/run-tests
diff --git a/azure-pipelines.yml b/azure-pipelines.yml
deleted file mode 100644
index 86cf0c79759f5..0000000000000
--- a/azure-pipelines.yml
+++ /dev/null
@@ -1,59 +0,0 @@
-# Adapted from https://github.com/numba/numba/blob/master/azure-pipelines.yml
-trigger:
- branches:
- include:
- - main
- - 1.4.x
- paths:
- exclude:
- - 'doc/**'
-
-pr:
- autoCancel: true
- branches:
- include:
- - main
- - 1.4.x
-
-variables:
- PYTEST_WORKERS: auto
- PYTEST_TARGET: pandas
- PATTERN: "not slow and not db and not network and not single_cpu"
- PANDAS_CI: 1
-
-jobs:
-- template: ci/azure/posix.yml
- parameters:
- name: macOS
- vmImage: macOS-10.15
-
-- template: ci/azure/windows.yml
- parameters:
- name: Windows
- vmImage: windows-2019
-
-- job: py38_32bit
- pool:
- vmImage: ubuntu-18.04
-
- steps:
- # TODO: GH#44980 https://github.com/pypa/setuptools/issues/2941
- - script: |
- docker pull quay.io/pypa/manylinux2014_i686
- docker run -v $(pwd):/pandas quay.io/pypa/manylinux2014_i686 \
- /bin/bash -xc "cd pandas && \
- /opt/python/cp38-cp38/bin/python -m venv ~/virtualenvs/pandas-dev && \
- . ~/virtualenvs/pandas-dev/bin/activate && \
- python -m pip install --no-deps -U pip wheel 'setuptools<60.0.0' && \
- pip install cython numpy python-dateutil pytz pytest pytest-xdist pytest-asyncio hypothesis && \
- python setup.py build_ext -q -j2 && \
- python -m pip install --no-build-isolation -e . && \
- pytest -m 'not slow and not network and not clipboard and not single_cpu' pandas --junitxml=test-data.xml"
- displayName: 'Run 32-bit manylinux2014 Docker Build / Tests'
-
- - task: PublishTestResults@2
- condition: succeededOrFailed()
- inputs:
- testResultsFiles: '**/test-*.xml'
- failTaskOnFailedTests: true
- testRunTitle: 'Publish test results for Python 3.8-32 bit full Linux'
diff --git a/ci/azure/posix.yml b/ci/azure/posix.yml
deleted file mode 100644
index df1d5049be33d..0000000000000
--- a/ci/azure/posix.yml
+++ /dev/null
@@ -1,50 +0,0 @@
-parameters:
- name: ''
- vmImage: ''
-
-jobs:
-- job: ${{ parameters.name }}
- timeoutInMinutes: 90
- pool:
- vmImage: ${{ parameters.vmImage }}
- strategy:
- matrix:
- py38:
- ENV_FILE: ci/deps/actions-38.yaml
- CONDA_PY: "38"
-
- py39:
- ENV_FILE: ci/deps/actions-39.yaml
- CONDA_PY: "39"
-
- py310:
- ENV_FILE: ci/deps/actions-310.yaml
- CONDA_PY: "310"
-
- steps:
- - script: echo '##vso[task.prependpath]$(HOME)/miniconda3/bin'
- displayName: 'Set conda path'
-
- - script: rm /usr/local/miniconda/pkgs/cache/*.json
- displayName: 'Workaround for mamba-org/mamba#488'
-
- - script: ci/setup_env.sh
- displayName: 'Setup environment and build pandas'
-
- - script: |
- conda run -n pandas-dev --no-capture-output ci/run_tests.sh
- displayName: 'Test'
-
- - script: |
- pushd /tmp
- conda run -n pandas-dev python -c "import pandas; pandas.show_versions()"
- popd
- displayName: 'Build versions'
-
- - task: PublishTestResults@2
- condition: succeededOrFailed()
- inputs:
- failTaskOnFailedTests: true
- testResultsFiles: 'test-data.xml'
- testRunTitle: ${{ format('{0}-$(CONDA_PY)', parameters.name) }}
- displayName: 'Publish test results'
diff --git a/ci/azure/windows.yml b/ci/azure/windows.yml
deleted file mode 100644
index 02c6564579aa2..0000000000000
--- a/ci/azure/windows.yml
+++ /dev/null
@@ -1,58 +0,0 @@
-parameters:
- name: ''
- vmImage: ''
-
-jobs:
-- job: ${{ parameters.name }}
- timeoutInMinutes: 90
- pool:
- vmImage: ${{ parameters.vmImage }}
- strategy:
- matrix:
- py38:
- ENV_FILE: ci/deps/actions-38.yaml
- CONDA_PY: "38"
-
- py39:
- ENV_FILE: ci/deps/actions-39.yaml
- CONDA_PY: "39"
-
- py310:
- ENV_FILE: ci/deps/actions-310.yaml
- CONDA_PY: "310"
-
- steps:
- - powershell: |
- Write-Host "##vso[task.prependpath]$env:CONDA\Scripts"
- Write-Host "##vso[task.prependpath]$HOME/miniconda3/bin"
- displayName: 'Add conda to PATH'
- - bash: conda install -yv -c conda-forge -n base 'mamba>=0.21.2'
- displayName: 'Install mamba'
-
- - bash: |
- # See https://github.com/mamba-org/mamba/issues/1370
- # See https://github.com/mamba-org/mamba/issues/633
- C:\\Miniconda\\condabin\\mamba.bat create -n pandas-dev
- C:\\Miniconda\\condabin\\mamba.bat env update -n pandas-dev --file ci\\deps\\actions-$(CONDA_PY).yaml
- # TODO: GH#44980 https://github.com/pypa/setuptools/issues/2941
- C:\\Miniconda\\condabin\\mamba.bat install -n pandas-dev 'setuptools<60'
- C:\\Miniconda\\condabin\\mamba.bat list -n pandas-dev
- displayName: 'Create anaconda environment'
- - bash: |
- source activate pandas-dev
- conda list
- python setup.py build_ext -q -j 2
- python -m pip install --no-build-isolation -e .
- displayName: 'Build'
- - bash: |
- source activate pandas-dev
- wmic.exe cpu get caption, deviceid, name, numberofcores, maxclockspeed
- ci/run_tests.sh
- displayName: 'Test'
- - task: PublishTestResults@2
- condition: succeededOrFailed()
- inputs:
- failTaskOnFailedTests: true
- testResultsFiles: 'test-data.xml'
- testRunTitle: ${{ format('{0}-$(CONDA_PY)', parameters.name) }}
- displayName: 'Publish test results'
diff --git a/ci/deps/actions-310-numpydev.yaml b/ci/deps/actions-310-numpydev.yaml
index a5eb8a69e19da..401be14aaca02 100644
--- a/ci/deps/actions-310-numpydev.yaml
+++ b/ci/deps/actions-310-numpydev.yaml
@@ -9,7 +9,7 @@ dependencies:
- pytest-cov
- pytest-xdist>=1.31
- hypothesis>=5.5.3
- - pytest-asyncio
+ - pytest-asyncio>=0.17
# pandas dependencies
- python-dateutil
diff --git a/ci/deps/actions-310.yaml b/ci/deps/actions-310.yaml
index 37e7ea04a348a..dac1219245e84 100644
--- a/ci/deps/actions-310.yaml
+++ b/ci/deps/actions-310.yaml
@@ -11,7 +11,7 @@ dependencies:
- pytest-xdist>=1.31
- hypothesis>=5.5.3
- psutil
- - pytest-asyncio
+ - pytest-asyncio>=0.17
- boto3
# required dependencies
diff --git a/ci/deps/actions-38-downstream_compat.yaml b/ci/deps/actions-38-downstream_compat.yaml
index 40f48884f1822..01415122e6076 100644
--- a/ci/deps/actions-38-downstream_compat.yaml
+++ b/ci/deps/actions-38-downstream_compat.yaml
@@ -12,7 +12,7 @@ dependencies:
- pytest-xdist>=1.31
- hypothesis>=5.5.3
- psutil
- - pytest-asyncio
+ - pytest-asyncio>=0.17
- boto3
# required dependencies
diff --git a/ci/deps/actions-38-minimum_versions.yaml b/ci/deps/actions-38-minimum_versions.yaml
index abba5ddd60325..f3a967f67cbc3 100644
--- a/ci/deps/actions-38-minimum_versions.yaml
+++ b/ci/deps/actions-38-minimum_versions.yaml
@@ -13,7 +13,7 @@ dependencies:
- pytest-xdist>=1.31
- hypothesis>=5.5.3
- psutil
- - pytest-asyncio
+ - pytest-asyncio>=0.17
- boto3
# required dependencies
diff --git a/ci/deps/actions-38.yaml b/ci/deps/actions-38.yaml
index 9c46eca4ab989..79cd831051c2f 100644
--- a/ci/deps/actions-38.yaml
+++ b/ci/deps/actions-38.yaml
@@ -11,7 +11,7 @@ dependencies:
- pytest-xdist>=1.31
- hypothesis>=5.5.3
- psutil
- - pytest-asyncio
+ - pytest-asyncio>=0.17
- boto3
# required dependencies
diff --git a/ci/deps/actions-39.yaml b/ci/deps/actions-39.yaml
index 89b647372d7bc..1c681104f3196 100644
--- a/ci/deps/actions-39.yaml
+++ b/ci/deps/actions-39.yaml
@@ -11,7 +11,7 @@ dependencies:
- pytest-xdist>=1.31
- hypothesis>=5.5.3
- psutil
- - pytest-asyncio
+ - pytest-asyncio>=0.17
- boto3
# required dependencies
diff --git a/ci/run_tests.sh b/ci/run_tests.sh
index e6de5caf955fc..026f65d248e6d 100755
--- a/ci/run_tests.sh
+++ b/ci/run_tests.sh
@@ -30,6 +30,10 @@ if [[ "$PATTERN" ]]; then
PYTEST_CMD="$PYTEST_CMD -m \"$PATTERN\""
fi
+which pytest
+which python
+pytest -VV || true
+python -VV || true
echo $PYTEST_CMD
sh -c "$PYTEST_CMD"
diff --git a/environment.yml b/environment.yml
index 5402be3d4a00b..fdcaf1f51550e 100644
--- a/environment.yml
+++ b/environment.yml
@@ -69,7 +69,7 @@ dependencies:
- pytest>=6.0
- pytest-cov
- pytest-xdist>=1.31
- - pytest-asyncio
+ - pytest-asyncio>=0.17
- pytest-instafail
# downstream tests
diff --git a/requirements-dev.txt b/requirements-dev.txt
index c671dc08a263e..cfec4bfee0a67 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -47,7 +47,7 @@ flask
pytest>=6.0
pytest-cov
pytest-xdist>=1.31
-pytest-asyncio
+pytest-asyncio>=0.17
pytest-instafail
seaborn
statsmodels
diff --git a/setup.py b/setup.py
index 62704dc4423c8..78df5e1b9571b 100755
--- a/setup.py
+++ b/setup.py
@@ -74,7 +74,48 @@ def is_platform_mac():
_pxi_dep[module] = pxi_files
-class build_ext(_build_ext):
+class CompilerLauncherMixin:
+ """Add "compiler launchers" to distutils.
+
+ We use this to be able to run the Pandas build using "ccache".
+
+ A compiler launcher is a program that is invoked instead of invoking the
+ compiler directly. It is passed the full compiler invocation command line.
+
+ A similar feature exists in CMake, see
+ https://cmake.org/cmake/help/latest/prop_tgt/LANG_COMPILER_LAUNCHER.html.
+ """
+
+ __is_set_up = False
+
+ def build_extensions(self):
+ # Integrate into "build_ext"
+ self.__setup()
+ super().build_extensions()
+
+ def build_libraries(self):
+ # Integrate into "build_clib"
+ self.__setup()
+ super().build_extensions()
+
+ def __setup(self):
+ if self.__is_set_up:
+ return
+ self.__is_set_up = True
+ compiler_launcher = os.getenv("DISTUTILS_C_COMPILER_LAUNCHER")
+ if compiler_launcher:
+
+ def spawn_with_compiler_launcher(cmd):
+ exclude_programs = ("link.exe",)
+ if not cmd[0].endswith(exclude_programs):
+ cmd = [compiler_launcher] + cmd
+ return original_spawn(cmd)
+
+ original_spawn = self.compiler.spawn
+ self.compiler.spawn = spawn_with_compiler_launcher
+
+
+class build_ext(CompilerLauncherMixin, _build_ext):
@classmethod
def render_templates(cls, pxifiles):
for pxifile in pxifiles:
@@ -334,7 +375,7 @@ def run(self):
extra_compile_args.append("/Z7")
extra_link_args.append("/DEBUG")
else:
- # PANDAS_CI=1 is set by ci/setup_env.sh
+ # PANDAS_CI=1 is set in CI
if os.environ.get("PANDAS_CI", "0") == "1":
extra_compile_args.append("-Werror")
if debugging_symbols_requested:
| I know this is a lot of changes. Happy to split into multiple PRs if it is too much to review.
Side effect of the changes is that most jobs are ~ 10 minutes faster.
- Drop Azure (move to GHA).
- Use mamba-org/provision-with-micromamba to set up Conda envs.
- Cache Conda envs.
- Use hendrikmuhs/ccache-action to cache C compiliation.
- Add Windows 32 bit build.
- Drop Ubuntu 32 bit build. (Can also bring back, have code for that.)
- Factor out common GHA code into reusable actions. | https://api.github.com/repos/pandas-dev/pandas/pulls/46346 | 2022-03-12T20:11:26Z | 2022-04-05T07:09:58Z | null | 2022-04-05T07:09:58Z |
TST: Avoid stack overflow on Windows CI with recursion test | diff --git a/pandas/tests/dtypes/test_inference.py b/pandas/tests/dtypes/test_inference.py
index bf13e6b7b4629..15f6e82419049 100644
--- a/pandas/tests/dtypes/test_inference.py
+++ b/pandas/tests/dtypes/test_inference.py
@@ -17,6 +17,7 @@
import itertools
from numbers import Number
import re
+import sys
import numpy as np
import pytest
@@ -205,8 +206,14 @@ def foo():
inference.is_list_like([])
foo()
- with tm.external_error_raised(RecursionError):
- foo()
+ rec_limit = sys.getrecursionlimit()
+ try:
+ # Limit to avoid stack overflow on Windows CI
+ sys.setrecursionlimit(100)
+ with tm.external_error_raised(RecursionError):
+ foo()
+ finally:
+ sys.setrecursionlimit(rec_limit)
def test_is_list_like_iter_is_none():
| - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
Trying to avoid this outcome: https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=75264&view=logs&jobId=f64eb602-b0ed-5dcc-0a60-a7288ad39597&j=f64eb602-b0ed-5dcc-0a60-a7288ad39597&t=129bd72d-2137-55bd-1811-c4ce50c6dce8
```
.......................Windows fatal exception: stack overflow
Thread 0x0000148c (most recent call first):
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\gateway_base.py", line 400 in read
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\gateway_base.py", line 432 in from_io
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\gateway_base.py", line 967 in _thread_receiver
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\gateway_base.py", line 220 in run
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\gateway_base.py", line 285 in _perform_spawn
Current thread 0x00000750 (most recent call first):
File "D:\a\1\s\pandas\tests\dtypes\test_inference.py", line 206 in foo
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/46345 | 2022-03-12T19:01:38Z | 2022-03-13T00:20:47Z | 2022-03-13T00:20:47Z | 2022-03-13T00:20:50Z |
WIP provision-with-micromamba | diff --git a/.circleci/config.yml b/.circleci/config.yml
deleted file mode 100644
index a16492b022264..0000000000000
--- a/.circleci/config.yml
+++ /dev/null
@@ -1,22 +0,0 @@
-version: 2.1
-
-jobs:
- test-arm:
- machine:
- image: ubuntu-2004:202101-01
- resource_class: arm.medium
- environment:
- ENV_FILE: ci/deps/circle-38-arm64.yaml
- PYTEST_WORKERS: auto
- PATTERN: "not slow and not network and not clipboard and not arm_slow"
- PYTEST_TARGET: "pandas"
- PANDAS_CI: "1"
- steps:
- - checkout
- - run: ci/setup_env.sh
- - run: PATH=$HOME/miniconda3/envs/pandas-dev/bin:$HOME/miniconda3/condabin:$PATH ci/run_tests.sh
-
-workflows:
- test:
- jobs:
- - test-arm
diff --git a/.github/workflows/code-checks.yml b/.github/workflows/code-checks.yml
index 58bef05940af1..ba4ca3c766546 100644
--- a/.github/workflows/code-checks.yml
+++ b/.github/workflows/code-checks.yml
@@ -16,6 +16,7 @@ env:
jobs:
pre_commit:
+ if: false
name: pre-commit
runs-on: ubuntu-latest
concurrency:
@@ -35,6 +36,7 @@ jobs:
uses: pre-commit/action@v2.0.3
typing_and_docstring_validation:
+ if: false
name: Docstring and typing validation
runs-on: ubuntu-latest
defaults:
@@ -52,20 +54,10 @@ jobs:
with:
fetch-depth: 0
- - name: Cache conda
- uses: actions/cache@v2
+ - uses: mamba-org/provision-with-micromamba@main
with:
- path: ~/conda_pkgs_dir
- key: ${{ runner.os }}-conda-${{ hashFiles('${{ env.ENV_FILE }}') }}
-
- - uses: conda-incubator/setup-miniconda@v2
- with:
- mamba-version: "*"
- channels: conda-forge
- activate-environment: pandas-dev
- channel-priority: strict
environment-file: ${{ env.ENV_FILE }}
- use-only-tar-bz2: true
+ cache-env: true
- name: Install node.js (for pyright)
uses: actions/setup-node@v2
@@ -101,6 +93,7 @@ jobs:
if: ${{ steps.build.outcome == 'success' }}
asv-benchmarks:
+ if: false
name: ASV Benchmarks
runs-on: ubuntu-latest
defaults:
@@ -118,20 +111,10 @@ jobs:
with:
fetch-depth: 0
- - name: Cache conda
- uses: actions/cache@v2
- with:
- path: ~/conda_pkgs_dir
- key: ${{ runner.os }}-conda-${{ hashFiles('${{ env.ENV_FILE }}') }}
-
- - uses: conda-incubator/setup-miniconda@v2
+ - uses: mamba-org/provision-with-micromamba@main
with:
- mamba-version: "*"
- channels: conda-forge
- activate-environment: pandas-dev
- channel-priority: strict
environment-file: ${{ env.ENV_FILE }}
- use-only-tar-bz2: true
+ cache-env: true
- name: Build Pandas
id: build
@@ -158,6 +141,7 @@ jobs:
if: failure()
build_docker_dev_environment:
+ if: false
name: Build Docker Dev Environment
runs-on: ubuntu-latest
defaults:
diff --git a/.github/workflows/posix.yml b/.github/workflows/posix.yml
index 4380e8dfa2e57..7758a98074b28 100644
--- a/.github/workflows/posix.yml
+++ b/.github/workflows/posix.yml
@@ -24,44 +24,44 @@ jobs:
timeout-minutes: 120
strategy:
matrix:
- env_file: [actions-38.yaml, actions-39.yaml, actions-310.yaml]
- pattern: ["not single_cpu", "single_cpu"]
+ env_file: [actions-38.yaml] #, actions-39.yaml, actions-310.yaml]
+ pattern: ["not single_cpu"] #, "single_cpu"]
# Don't test pyarrow v2/3: Causes timeouts in read_csv engine
# even if tests are skipped/xfailed
- pyarrow_version: ["5", "7"]
- include:
- - env_file: actions-38-downstream_compat.yaml
- pattern: "not slow and not network and not single_cpu"
- pytest_target: "pandas/tests/test_downstream.py"
- name: "Downstream Compat"
- - env_file: actions-38-minimum_versions.yaml
- pattern: "not slow and not network and not single_cpu"
- name: "Minimum Versions"
- - env_file: actions-38.yaml
- pattern: "not slow and not network and not single_cpu"
- extra_apt: "language-pack-it"
- lang: "it_IT.utf8"
- lc_all: "it_IT.utf8"
- name: "Locale: it_IT.utf8"
- - env_file: actions-38.yaml
- pattern: "not slow and not network and not single_cpu"
- extra_apt: "language-pack-zh-hans"
- lang: "zh_CN.utf8"
- lc_all: "zh_CN.utf8"
- name: "Locale: zh_CN.utf8"
- - env_file: actions-38.yaml
- pattern: "not slow and not network and not single_cpu"
- pandas_data_manager: "array"
- name: "Data Manager"
- - env_file: actions-pypy-38.yaml
- pattern: "not slow and not network and not single_cpu"
- test_args: "--max-worker-restart 0"
- name: "Pypy"
- - env_file: actions-310-numpydev.yaml
- pattern: "not slow and not network and not single_cpu"
- pandas_testing_mode: "deprecate"
- test_args: "-W error"
- name: "Numpy Dev"
+ pyarrow_version: ["5"] #, "7"]
+ # include:
+ # - env_file: actions-38-downstream_compat.yaml
+ # pattern: "not slow and not network and not single_cpu"
+ # pytest_target: "pandas/tests/test_downstream.py"
+ # name: "Downstream Compat"
+ # - env_file: actions-38-minimum_versions.yaml
+ # pattern: "not slow and not network and not single_cpu"
+ # name: "Minimum Versions"
+ # - env_file: actions-38.yaml
+ # pattern: "not slow and not network and not single_cpu"
+ # extra_apt: "language-pack-it"
+ # lang: "it_IT.utf8"
+ # lc_all: "it_IT.utf8"
+ # name: "Locale: it_IT.utf8"
+ # - env_file: actions-38.yaml
+ # pattern: "not slow and not network and not single_cpu"
+ # extra_apt: "language-pack-zh-hans"
+ # lang: "zh_CN.utf8"
+ # lc_all: "zh_CN.utf8"
+ # name: "Locale: zh_CN.utf8"
+ # - env_file: actions-38.yaml
+ # pattern: "not slow and not network and not single_cpu"
+ # pandas_data_manager: "array"
+ # name: "Data Manager"
+ # - env_file: actions-pypy-38.yaml
+ # pattern: "not slow and not network and not single_cpu"
+ # test_args: "--max-worker-restart 0"
+ # name: "Pypy"
+ # - env_file: actions-310-numpydev.yaml
+ # pattern: "not slow and not network and not single_cpu"
+ # pandas_testing_mode: "deprecate"
+ # test_args: "-W error"
+ # name: "Numpy Dev"
fail-fast: false
name: ${{ matrix.name || format('{0} pyarrow={1} {2}', matrix.env_file, matrix.pyarrow_version, matrix.pattern) }}
env:
@@ -125,33 +125,18 @@ jobs:
with:
fetch-depth: 0
- - name: Cache conda
- uses: actions/cache@v2
- env:
- CACHE_NUMBER: 0
- with:
- path: ~/conda_pkgs_dir
- key: ${{ runner.os }}-conda-${{ env.CACHE_NUMBER }}-${{
- hashFiles('${{ env.ENV_FILE }}') }}
-
- name: Extra installs
# xsel for clipboard tests
run: sudo apt-get update && sudo apt-get install -y libc6-dev-i386 xsel ${{ env.EXTRA_APT }}
- - uses: conda-incubator/setup-miniconda@v2
+ - uses: mamba-org/provision-with-micromamba@main
with:
- mamba-version: "*"
- channels: conda-forge
- activate-environment: pandas-dev
- channel-priority: flexible
environment-file: ${{ env.ENV_FILE }}
- use-only-tar-bz2: true
+ extra-specs: |
+ ${{ matrix.pyarrow_version && format('pyarrow={0}', matrix.pyarrow_version) }}
+ cache-env: true
if: ${{ env.IS_PYPY == 'false' }} # No pypy3.8 support
- - name: Upgrade Arrow version
- run: conda install -n pandas-dev -c conda-forge --no-update-deps pyarrow=${{ matrix.pyarrow_version }}
- if: ${{ matrix.pyarrow_version }}
-
- name: Setup PyPy
uses: actions/setup-python@v2
with:
@@ -165,28 +150,28 @@ jobs:
pip install Cython numpy python-dateutil pytz pytest>=6.0 pytest-xdist>=1.31.0 hypothesis>=5.5.3
if: ${{ env.IS_PYPY == 'true' }}
- - name: Build Pandas
- uses: ./.github/actions/build_pandas
-
- - name: Test
- run: ci/run_tests.sh
- # TODO: Don't continue on error for PyPy
- continue-on-error: ${{ env.IS_PYPY == 'true' }}
- if: always()
-
- - name: Build Version
- run: pushd /tmp && python -c "import pandas; pandas.show_versions();" && popd
-
- - name: Publish test results
- uses: actions/upload-artifact@v2
- with:
- name: Test results
- path: test-data.xml
- if: failure()
-
- - name: Upload coverage to Codecov
- uses: codecov/codecov-action@v2
- with:
- flags: unittests
- name: codecov-pandas
- fail_ci_if_error: false
+ # - name: Build Pandas
+ # uses: ./.github/actions/build_pandas
+
+ # - name: Test
+ # run: ci/run_tests.sh
+ # # TODO: Don't continue on error for PyPy
+ # continue-on-error: ${{ env.IS_PYPY == 'true' }}
+ # if: always()
+
+ # - name: Build Version
+ # run: pushd /tmp && python -c "import pandas; pandas.show_versions();" && popd
+
+ # - name: Publish test results
+ # uses: actions/upload-artifact@v2
+ # with:
+ # name: Test results
+ # path: test-data.xml
+ # if: failure()
+
+ # - name: Upload coverage to Codecov
+ # uses: codecov/codecov-action@v2
+ # with:
+ # flags: unittests
+ # name: codecov-pandas
+ # fail_ci_if_error: false
diff --git a/.github/workflows/sdist.yml b/.github/workflows/sdist.yml
index 431710a49a7dd..1d6c4698d23f1 100644
--- a/.github/workflows/sdist.yml
+++ b/.github/workflows/sdist.yml
@@ -15,7 +15,7 @@ on:
jobs:
build:
- if: ${{ github.event.label.name == 'Build' || contains(github.event.pull_request.labels.*.name, 'Build') || github.event_name == 'push'}}
+ #if: ${{ github.event.label.name == 'Build' || contains(github.event.pull_request.labels.*.name, 'Build') || github.event_name == 'push'}}
runs-on: ubuntu-latest
timeout-minutes: 60
defaults:
@@ -25,7 +25,7 @@ jobs:
strategy:
fail-fast: false
matrix:
- python-version: ["3.8", "3.9", "3.10"]
+ python-version: ["3.8"] #, "3.9", "3.10"]
concurrency:
# https://github.community/t/concurrecy-not-work-for-push/183068/7
group: ${{ github.event_name == 'push' && github.run_number || github.ref }}-${{matrix.python-version}}-sdist
@@ -36,56 +36,57 @@ jobs:
with:
fetch-depth: 0
- - name: Set up Python
- uses: actions/setup-python@v2
+ - uses: mamba-org/provision-with-micromamba@main
with:
- python-version: ${{ matrix.python-version }}
+ environment-name: pandas-sdist
+ extra-specs: |
+ python=${{ matrix.python-version }}
+ cache-env: true
- # TODO: GH#44980 https://github.com/pypa/setuptools/issues/2941
- - name: Install dependencies
- run: |
- python -m pip install --upgrade pip "setuptools<60.0.0" wheel
+# - name: Set up Python
+# uses: actions/setup-python@v2
+# with:
+# python-version: ${{ matrix.python-version }}
- # GH 39416
- pip install numpy
+# # TODO: GH#44980 https://github.com/pypa/setuptools/issues/2941
+# - name: Install dependencies
+# run: |
+# python -m pip install --upgrade pip "setuptools<60.0.0" wheel
- - name: Build pandas sdist
- run: |
- pip list
- python setup.py sdist --formats=gztar
+# # GH 39416
+# pip install numpy
- - name: Upload sdist artifact
- uses: actions/upload-artifact@v3
- with:
- name: ${{matrix.python-version}}-sdist.gz
- path: dist/*.gz
+# - name: Build pandas sdist
+# run: |
+# pip list
+# python setup.py sdist --formats=gztar
- - uses: conda-incubator/setup-miniconda@v2
- with:
- activate-environment: pandas-sdist
- channels: conda-forge
- python-version: '${{ matrix.python-version }}'
+# - name: Upload sdist artifact
+# uses: actions/upload-artifact@v3
+# with:
+# name: ${{matrix.python-version}}-sdist.gz
+# path: dist/*.gz
- # TODO: GH#44980 https://github.com/pypa/setuptools/issues/2941
- - name: Install pandas from sdist
- run: |
- python -m pip install --upgrade "setuptools<60.0.0"
- pip list
- python -m pip install dist/*.gz
+# # TODO: GH#44980 https://github.com/pypa/setuptools/issues/2941
+# - name: Install pandas from sdist
+# run: |
+# python -m pip install --upgrade "setuptools<60.0.0"
+# pip list
+# python -m pip install dist/*.gz
- - name: Force oldest supported NumPy
- run: |
- case "${{matrix.python-version}}" in
- 3.8)
- pip install numpy==1.18.5 ;;
- 3.9)
- pip install numpy==1.19.3 ;;
- 3.10)
- pip install numpy==1.21.2 ;;
- esac
+# - name: Force oldest supported NumPy
+# run: |
+# case "${{matrix.python-version}}" in
+# 3.8)
+# pip install numpy==1.18.5 ;;
+# 3.9)
+# pip install numpy==1.19.3 ;;
+# 3.10)
+# pip install numpy==1.21.2 ;;
+# esac
- - name: Import pandas
- run: |
- cd ..
- conda list
- python -c "import pandas; pandas.show_versions();"
+# - name: Import pandas
+# run: |
+# cd ..
+# conda list
+# python -c "import pandas; pandas.show_versions();"
diff --git a/azure-pipelines.yml b/azure-pipelines.yml
deleted file mode 100644
index e6dc98179b7d6..0000000000000
--- a/azure-pipelines.yml
+++ /dev/null
@@ -1,59 +0,0 @@
-# Adapted from https://github.com/numba/numba/blob/master/azure-pipelines.yml
-trigger:
- branches:
- include:
- - main
- - 1.4.x
- paths:
- exclude:
- - 'doc/**'
-
-pr:
- autoCancel: true
- branches:
- include:
- - main
- - 1.4.x
-
-variables:
- PYTEST_WORKERS: auto
- PYTEST_TARGET: pandas
- PATTERN: "not slow and not db and not network and not single_cpu"
- PANDAS_CI: 1
-
-jobs:
-- template: ci/azure/posix.yml
- parameters:
- name: macOS
- vmImage: macOS-10.15
-
-- template: ci/azure/windows.yml
- parameters:
- name: Windows
- vmImage: windows-2019
-
-- job: py38_32bit
- pool:
- vmImage: ubuntu-18.04
-
- steps:
- # TODO: GH#44980 https://github.com/pypa/setuptools/issues/2941
- - script: |
- docker pull quay.io/pypa/manylinux2014_i686
- docker run -v $(pwd):/pandas quay.io/pypa/manylinux2014_i686 \
- /bin/bash -xc "cd pandas && \
- /opt/python/cp38-cp38/bin/python -m venv ~/virtualenvs/pandas-dev && \
- . ~/virtualenvs/pandas-dev/bin/activate && \
- python -m pip install --no-deps -U pip wheel 'setuptools<60.0.0' && \
- pip install cython numpy python-dateutil pytz pytest pytest-xdist hypothesis pytest-azurepipelines && \
- python setup.py build_ext -q -j2 && \
- python -m pip install --no-build-isolation -e . && \
- pytest -m 'not slow and not network and not clipboard and not single_cpu' pandas --junitxml=test-data.xml"
- displayName: 'Run 32-bit manylinux2014 Docker Build / Tests'
-
- - task: PublishTestResults@2
- condition: succeededOrFailed()
- inputs:
- testResultsFiles: '**/test-*.xml'
- failTaskOnFailedTests: true
- testRunTitle: 'Publish test results for Python 3.8-32 bit full Linux'
| - Docstring validation: 5 min (hot cache)
- ASV benchmark: 5 min (hot cache)
-
- [ ] closes #xxxx (Replace xxxx with the Github issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46344 | 2022-03-12T18:50:31Z | 2022-03-12T20:03:17Z | null | 2022-03-12T20:03:17Z |
TYP: Add typing for pd.to_timedelta, fix Timedelta operator return types | diff --git a/pandas/_libs/tslibs/timedeltas.pyi b/pandas/_libs/tslibs/timedeltas.pyi
index 9377261979be4..28c2f7db62158 100644
--- a/pandas/_libs/tslibs/timedeltas.pyi
+++ b/pandas/_libs/tslibs/timedeltas.pyi
@@ -1,19 +1,65 @@
from datetime import timedelta
from typing import (
ClassVar,
+ Literal,
Type,
TypeVar,
overload,
)
import numpy as np
-import numpy.typing as npt
from pandas._libs.tslibs import (
NaTType,
Tick,
)
+from pandas._typing import npt
+# This should be kept consistent with the keys in the dict timedelta_abbrevs
+# in pandas/_libs/tslibs/timedeltas.pyx
+UnitChoices = Literal[
+ "Y",
+ "y",
+ "M",
+ "W",
+ "w",
+ "D",
+ "d",
+ "days",
+ "day",
+ "hours",
+ "hour",
+ "hr",
+ "h",
+ "m",
+ "minute",
+ "min",
+ "minutes",
+ "t",
+ "s",
+ "seconds",
+ "sec",
+ "second",
+ "ms",
+ "milliseconds",
+ "millisecond",
+ "milli",
+ "millis",
+ "l",
+ "us",
+ "microseconds",
+ "microsecond",
+ "µs",
+ "micro",
+ "micros",
+ "u",
+ "ns",
+ "nanoseconds",
+ "nano",
+ "nanos",
+ "nanosecond",
+ "n",
+]
_S = TypeVar("_S", bound=timedelta)
def ints_to_pytimedelta(
@@ -25,7 +71,7 @@ def array_to_timedelta64(
unit: str | None = ...,
errors: str = ...,
) -> np.ndarray: ... # np.ndarray[m8ns]
-def parse_timedelta_unit(unit: str | None) -> str: ...
+def parse_timedelta_unit(unit: str | None) -> UnitChoices: ...
def delta_to_nanoseconds(delta: np.timedelta64 | timedelta | Tick) -> int: ...
class Timedelta(timedelta):
@@ -59,20 +105,20 @@ class Timedelta(timedelta):
def ceil(self: _S, freq: str) -> _S: ...
@property
def resolution_string(self) -> str: ...
- def __add__(self, other: timedelta) -> timedelta: ...
- def __radd__(self, other: timedelta) -> timedelta: ...
- def __sub__(self, other: timedelta) -> timedelta: ...
- def __rsub__(self, other: timedelta) -> timedelta: ...
- def __neg__(self) -> timedelta: ...
- def __pos__(self) -> timedelta: ...
- def __abs__(self) -> timedelta: ...
- def __mul__(self, other: float) -> timedelta: ...
- def __rmul__(self, other: float) -> timedelta: ...
+ def __add__(self, other: timedelta) -> Timedelta: ...
+ def __radd__(self, other: timedelta) -> Timedelta: ...
+ def __sub__(self, other: timedelta) -> Timedelta: ...
+ def __rsub__(self, other: timedelta) -> Timedelta: ...
+ def __neg__(self) -> Timedelta: ...
+ def __pos__(self) -> Timedelta: ...
+ def __abs__(self) -> Timedelta: ...
+ def __mul__(self, other: float) -> Timedelta: ...
+ def __rmul__(self, other: float) -> Timedelta: ...
# error: Signature of "__floordiv__" incompatible with supertype "timedelta"
@overload # type: ignore[override]
def __floordiv__(self, other: timedelta) -> int: ...
@overload
- def __floordiv__(self, other: int | float) -> timedelta: ...
+ def __floordiv__(self, other: int | float) -> Timedelta: ...
@overload
def __floordiv__(
self, other: npt.NDArray[np.timedelta64]
@@ -90,11 +136,13 @@ class Timedelta(timedelta):
@overload
def __truediv__(self, other: timedelta) -> float: ...
@overload
- def __truediv__(self, other: float) -> timedelta: ...
- def __mod__(self, other: timedelta) -> timedelta: ...
- def __divmod__(self, other: timedelta) -> tuple[int, timedelta]: ...
+ def __truediv__(self, other: float) -> Timedelta: ...
+ def __mod__(self, other: timedelta) -> Timedelta: ...
+ def __divmod__(self, other: timedelta) -> tuple[int, Timedelta]: ...
def __le__(self, other: timedelta) -> bool: ...
def __lt__(self, other: timedelta) -> bool: ...
def __ge__(self, other: timedelta) -> bool: ...
def __gt__(self, other: timedelta) -> bool: ...
def __hash__(self) -> int: ...
+ def isoformat(self) -> str: ...
+ def to_numpy(self) -> np.timedelta64: ...
diff --git a/pandas/_libs/tslibs/timedeltas.pyx b/pandas/_libs/tslibs/timedeltas.pyx
index 8eaf86b3d193f..e6b27b4459aae 100644
--- a/pandas/_libs/tslibs/timedeltas.pyx
+++ b/pandas/_libs/tslibs/timedeltas.pyx
@@ -81,6 +81,7 @@ Components = collections.namedtuple(
],
)
+# This should be kept consistent with UnitChoices in pandas/_libs/tslibs/timedeltas.pyi
cdef dict timedelta_abbrevs = {
"Y": "Y",
"y": "Y",
diff --git a/pandas/_typing.py b/pandas/_typing.py
index cabf0e8275d08..2b42a0a3efb44 100644
--- a/pandas/_typing.py
+++ b/pandas/_typing.py
@@ -313,3 +313,4 @@ def closed(self) -> bool:
# datetime and NaTType
DatetimeNaTType = Union[datetime, "NaTType"]
+DateTimeErrorChoices = Literal["ignore", "raise", "coerce"]
diff --git a/pandas/core/tools/datetimes.py b/pandas/core/tools/datetimes.py
index 3ff6e7f09b72a..46fd1cad97440 100644
--- a/pandas/core/tools/datetimes.py
+++ b/pandas/core/tools/datetimes.py
@@ -39,6 +39,7 @@
from pandas._typing import (
AnyArrayLike,
ArrayLike,
+ DateTimeErrorChoices,
Timezone,
)
from pandas.util._exceptions import find_stack_level
@@ -79,6 +80,7 @@
if TYPE_CHECKING:
from pandas._libs.tslibs.nattype import NaTType
+ from pandas._libs.tslibs.timedeltas import UnitChoices
from pandas import (
DataFrame,
@@ -657,7 +659,7 @@ def _adjust_to_origin(arg, origin, unit):
@overload
def to_datetime(
arg: DatetimeScalar,
- errors: str = ...,
+ errors: DateTimeErrorChoices = ...,
dayfirst: bool = ...,
yearfirst: bool = ...,
utc: bool | None = ...,
@@ -674,7 +676,7 @@ def to_datetime(
@overload
def to_datetime(
arg: Series | DictConvertible,
- errors: str = ...,
+ errors: DateTimeErrorChoices = ...,
dayfirst: bool = ...,
yearfirst: bool = ...,
utc: bool | None = ...,
@@ -691,7 +693,7 @@ def to_datetime(
@overload
def to_datetime(
arg: list | tuple | Index | ArrayLike,
- errors: str = ...,
+ errors: DateTimeErrorChoices = ...,
dayfirst: bool = ...,
yearfirst: bool = ...,
utc: bool | None = ...,
@@ -707,7 +709,7 @@ def to_datetime(
def to_datetime(
arg: DatetimeScalarOrArrayConvertible | DictConvertible,
- errors: str = "raise",
+ errors: DateTimeErrorChoices = "raise",
dayfirst: bool = False,
yearfirst: bool = False,
utc: bool | None = None,
@@ -1148,7 +1150,7 @@ def to_datetime(
}
-def _assemble_from_unit_mappings(arg, errors, tz):
+def _assemble_from_unit_mappings(arg, errors: DateTimeErrorChoices, tz):
"""
assemble the unit specified fields from the arg (DataFrame)
Return a Series for actual parsing
@@ -1228,7 +1230,8 @@ def coerce(values):
except (TypeError, ValueError) as err:
raise ValueError(f"cannot assemble the datetimes: {err}") from err
- for u in ["h", "m", "s", "ms", "us", "ns"]:
+ units: list[UnitChoices] = ["h", "m", "s", "ms", "us", "ns"]
+ for u in units:
value = unit_rev.get(u)
if value is not None and value in arg:
try:
diff --git a/pandas/core/tools/timedeltas.py b/pandas/core/tools/timedeltas.py
index 81b2be4e10e62..720d02f0cf59e 100644
--- a/pandas/core/tools/timedeltas.py
+++ b/pandas/core/tools/timedeltas.py
@@ -3,6 +3,12 @@
"""
from __future__ import annotations
+from datetime import timedelta
+from typing import (
+ TYPE_CHECKING,
+ overload,
+)
+
import numpy as np
from pandas._libs import lib
@@ -23,8 +29,61 @@
from pandas.core.arrays.timedeltas import sequence_to_td64ns
-
-def to_timedelta(arg, unit=None, errors="raise"):
+if TYPE_CHECKING:
+ from pandas._libs.tslibs.timedeltas import UnitChoices
+ from pandas._typing import (
+ ArrayLike,
+ DateTimeErrorChoices,
+ )
+
+ from pandas import (
+ Index,
+ Series,
+ TimedeltaIndex,
+ )
+
+
+@overload
+def to_timedelta(
+ arg: str | int | float | timedelta,
+ unit: UnitChoices | None = ...,
+ errors: DateTimeErrorChoices = ...,
+) -> Timedelta:
+ ...
+
+
+@overload
+def to_timedelta(
+ arg: Series,
+ unit: UnitChoices | None = ...,
+ errors: DateTimeErrorChoices = ...,
+) -> Series:
+ ...
+
+
+@overload
+def to_timedelta(
+ arg: list | tuple | range | ArrayLike | Index,
+ unit: UnitChoices | None = ...,
+ errors: DateTimeErrorChoices = ...,
+) -> TimedeltaIndex:
+ ...
+
+
+def to_timedelta(
+ arg: str
+ | int
+ | float
+ | timedelta
+ | list
+ | tuple
+ | range
+ | ArrayLike
+ | Index
+ | Series,
+ unit: UnitChoices | None = None,
+ errors: DateTimeErrorChoices = "raise",
+) -> Timedelta | TimedeltaIndex | Series:
"""
Convert argument to timedelta.
@@ -133,7 +192,11 @@ def to_timedelta(arg, unit=None, errors="raise"):
return _convert_listlike(arg, unit=unit, errors=errors, name=arg.name)
elif isinstance(arg, np.ndarray) and arg.ndim == 0:
# extract array scalar and process below
- arg = lib.item_from_zerodim(arg)
+ # error: Incompatible types in assignment (expression has type "object",
+ # variable has type "Union[str, int, float, timedelta, List[Any],
+ # Tuple[Any, ...], Union[Union[ExtensionArray, ndarray[Any, Any]], Index,
+ # Series]]") [assignment]
+ arg = lib.item_from_zerodim(arg) # type: ignore[assignment]
elif is_list_like(arg) and getattr(arg, "ndim", 1) == 1:
return _convert_listlike(arg, unit=unit, errors=errors)
elif getattr(arg, "ndim", 1) > 1:
diff --git a/pandas/io/sql.py b/pandas/io/sql.py
index e004e9c1ecbcc..0bb27334e1651 100644
--- a/pandas/io/sql.py
+++ b/pandas/io/sql.py
@@ -25,7 +25,10 @@
import numpy as np
import pandas._libs.lib as lib
-from pandas._typing import DtypeArg
+from pandas._typing import (
+ DateTimeErrorChoices,
+ DtypeArg,
+)
from pandas.compat._optional import import_optional_dependency
from pandas.errors import AbstractMethodError
from pandas.util._exceptions import find_stack_level
@@ -86,7 +89,7 @@ def _handle_date_column(
# read_sql like functions.
# Format can take on custom to_datetime argument values such as
# {"errors": "coerce"} or {"dayfirst": True}
- error = format.pop("errors", None) or "ignore"
+ error: DateTimeErrorChoices = format.pop("errors", None) or "ignore"
return to_datetime(col, errors=error, **format)
else:
# Allow passing of formatting string for integers
| Add typing for pd.to_timedelta
Change operators on `Timedelta` to return `Timedelta` rather than `timedelta`
| https://api.github.com/repos/pandas-dev/pandas/pulls/46343 | 2022-03-12T18:43:38Z | 2022-03-16T01:07:03Z | 2022-03-16T01:07:03Z | 2023-02-13T21:02:37Z |
Bug fix - groupby sum removed columns | diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 3d857e4f3e4e7..7af394e744f1a 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -2179,6 +2179,14 @@ def sum(
npfunc=np.sum,
)
+ if isinstance(result, DataFrame):
+ try:
+ dtypes = self.dtypes
+ for column in result.columns:
+ result[column] = result[column].astype(dtypes[column].dtype)
+ except Exception:
+ pass
+
return self._reindex_output(result, fill_value=0)
@final
diff --git a/pandas/tests/groupby/test_groupby.py b/pandas/tests/groupby/test_groupby.py
index 7bf63bb3c2cac..5e830fa105185 100644
--- a/pandas/tests/groupby/test_groupby.py
+++ b/pandas/tests/groupby/test_groupby.py
@@ -20,6 +20,7 @@
Timestamp,
date_range,
to_datetime,
+ util,
)
import pandas._testing as tm
from pandas.core.arrays import BooleanArray
@@ -2654,3 +2655,24 @@ def test_pad_backfill_deprecation():
s.groupby(level=0).backfill()
with tm.assert_produces_warning(FutureWarning, match="pad"):
s.groupby(level=0).pad()
+
+
+def test_groupby_agg_general_dtypes():
+ # GH 44132
+ df = util.testing.makeMixedDataFrame()
+ df = df.to_numpy()
+ df = DataFrame(df)
+ df.columns = ["A", "B", "C", "D"]
+ df = df.set_index("B")
+
+ df1, df2 = df.iloc[:2], df.iloc[2:]
+
+ groupby1 = df1.groupby("B").sum()
+ groupby2 = df2.groupby("B").sum()
+
+ df_conc = pd.concat([groupby1, groupby2], axis=0)
+
+ result = df_conc.groupby(level=0).sum()
+ expected = df.groupby(level=0).sum()
+
+ tm.assert_frame_equal(result, expected)
| - [x] closes #44132
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46342 | 2022-03-12T16:20:35Z | 2022-03-19T10:13:57Z | null | 2022-03-19T10:13:57Z |
PERF: use bisect_right_i8 in vectorized | diff --git a/pandas/_libs/tslibs/vectorized.pyx b/pandas/_libs/tslibs/vectorized.pyx
index bc254b6c5a5cf..0704edb3c5724 100644
--- a/pandas/_libs/tslibs/vectorized.pyx
+++ b/pandas/_libs/tslibs/vectorized.pyx
@@ -9,12 +9,15 @@ from cpython.datetime cimport (
import numpy as np
+cimport numpy as cnp
from numpy cimport (
int64_t,
intp_t,
ndarray,
)
+cnp.import_array()
+
from .conversion cimport normalize_i8_stamp
from .dtypes import Resolution
@@ -35,52 +38,13 @@ from .timezones cimport (
is_tzlocal,
is_utc,
)
-from .tzconversion cimport tz_convert_utc_to_tzlocal
+from .tzconversion cimport (
+ bisect_right_i8,
+ tz_convert_utc_to_tzlocal,
+)
# -------------------------------------------------------------------------
-cdef inline object create_datetime_from_ts(
- int64_t value,
- npy_datetimestruct dts,
- tzinfo tz,
- object freq,
- bint fold,
-):
- """
- Convenience routine to construct a datetime.datetime from its parts.
- """
- return datetime(
- dts.year, dts.month, dts.day, dts.hour, dts.min, dts.sec, dts.us,
- tz, fold=fold,
- )
-
-
-cdef inline object create_date_from_ts(
- int64_t value,
- npy_datetimestruct dts,
- tzinfo tz,
- object freq,
- bint fold
-):
- """
- Convenience routine to construct a datetime.date from its parts.
- """
- # GH#25057 add fold argument to match other func_create signatures
- return date(dts.year, dts.month, dts.day)
-
-
-cdef inline object create_time_from_ts(
- int64_t value,
- npy_datetimestruct dts,
- tzinfo tz,
- object freq,
- bint fold
-):
- """
- Convenience routine to construct a datetime.time from its parts.
- """
- return time(dts.hour, dts.min, dts.sec, dts.us, tz, fold=fold)
-
@cython.wraparound(False)
@cython.boundscheck(False)
@@ -119,29 +83,29 @@ def ints_to_pydatetime(
ndarray[object] of type specified by box
"""
cdef:
- Py_ssize_t i, n = len(stamps)
+ Py_ssize_t i, ntrans =- 1, n = len(stamps)
ndarray[int64_t] trans
int64_t[::1] deltas
- intp_t[:] pos
+ int64_t* tdata = NULL
+ intp_t pos
npy_datetimestruct dts
object dt, new_tz
str typ
int64_t value, local_val, delta = NPY_NAT # dummy for delta
ndarray[object] result = np.empty(n, dtype=object)
- object (*func_create)(int64_t, npy_datetimestruct, tzinfo, object, bint)
bint use_utc = False, use_tzlocal = False, use_fixed = False
bint use_pytz = False
+ bint use_date = False, use_time = False, use_ts = False, use_pydt = False
if box == "date":
assert (tz is None), "tz should be None when converting to date"
-
- func_create = create_date_from_ts
+ use_date = True
elif box == "timestamp":
- func_create = create_timestamp_from_ts
+ use_ts = True
elif box == "time":
- func_create = create_time_from_ts
+ use_time = True
elif box == "datetime":
- func_create = create_datetime_from_ts
+ use_pydt = True
else:
raise ValueError(
"box must be one of 'datetime', 'date', 'time' or 'timestamp'"
@@ -153,12 +117,13 @@ def ints_to_pydatetime(
use_tzlocal = True
else:
trans, deltas, typ = get_dst_info(tz)
+ ntrans = trans.shape[0]
if typ not in ["pytz", "dateutil"]:
# static/fixed; in this case we know that len(delta) == 1
use_fixed = True
delta = deltas[0]
else:
- pos = trans.searchsorted(stamps, side="right") - 1
+ tdata = <int64_t*>cnp.PyArray_DATA(trans)
use_pytz = typ == "pytz"
for i in range(n):
@@ -176,14 +141,26 @@ def ints_to_pydatetime(
elif use_fixed:
local_val = value + delta
else:
- local_val = value + deltas[pos[i]]
+ pos = bisect_right_i8(tdata, value, ntrans) - 1
+ local_val = value + deltas[pos]
- if use_pytz:
- # find right representation of dst etc in pytz timezone
- new_tz = tz._tzinfos[tz._transition_info[pos[i]]]
+ if use_pytz:
+ # find right representation of dst etc in pytz timezone
+ new_tz = tz._tzinfos[tz._transition_info[pos]]
dt64_to_dtstruct(local_val, &dts)
- result[i] = func_create(value, dts, new_tz, freq, fold)
+
+ if use_ts:
+ result[i] = create_timestamp_from_ts(value, dts, new_tz, freq, fold)
+ elif use_pydt:
+ result[i] = datetime(
+ dts.year, dts.month, dts.day, dts.hour, dts.min, dts.sec, dts.us,
+ new_tz, fold=fold,
+ )
+ elif use_date:
+ result[i] = date(dts.year, dts.month, dts.day)
+ else:
+ result[i] = time(dts.hour, dts.min, dts.sec, dts.us, new_tz, fold=fold)
return result
@@ -219,12 +196,13 @@ cdef inline int _reso_stamp(npy_datetimestruct *dts):
def get_resolution(const int64_t[:] stamps, tzinfo tz=None) -> Resolution:
cdef:
- Py_ssize_t i, n = len(stamps)
+ Py_ssize_t i, ntrans=-1, n = len(stamps)
npy_datetimestruct dts
int reso = RESO_DAY, curr_reso
ndarray[int64_t] trans
int64_t[::1] deltas
- intp_t[:] pos
+ int64_t* tdata = NULL
+ intp_t pos
int64_t local_val, delta = NPY_NAT
bint use_utc = False, use_tzlocal = False, use_fixed = False
@@ -234,12 +212,13 @@ def get_resolution(const int64_t[:] stamps, tzinfo tz=None) -> Resolution:
use_tzlocal = True
else:
trans, deltas, typ = get_dst_info(tz)
+ ntrans = trans.shape[0]
if typ not in ["pytz", "dateutil"]:
# static/fixed; in this case we know that len(delta) == 1
use_fixed = True
delta = deltas[0]
else:
- pos = trans.searchsorted(stamps, side="right") - 1
+ tdata = <int64_t*>cnp.PyArray_DATA(trans)
for i in range(n):
if stamps[i] == NPY_NAT:
@@ -252,7 +231,8 @@ def get_resolution(const int64_t[:] stamps, tzinfo tz=None) -> Resolution:
elif use_fixed:
local_val = stamps[i] + delta
else:
- local_val = stamps[i] + deltas[pos[i]]
+ pos = bisect_right_i8(tdata, stamps[i], ntrans) - 1
+ local_val = stamps[i] + deltas[pos]
dt64_to_dtstruct(local_val, &dts)
curr_reso = _reso_stamp(&dts)
@@ -282,12 +262,13 @@ cpdef ndarray[int64_t] normalize_i8_timestamps(const int64_t[:] stamps, tzinfo t
result : int64 ndarray of converted of normalized nanosecond timestamps
"""
cdef:
- Py_ssize_t i, n = len(stamps)
+ Py_ssize_t i, ntrans =- 1, n = len(stamps)
int64_t[:] result = np.empty(n, dtype=np.int64)
ndarray[int64_t] trans
int64_t[::1] deltas
+ int64_t* tdata = NULL
str typ
- Py_ssize_t[:] pos
+ Py_ssize_t pos
int64_t local_val, delta = NPY_NAT
bint use_utc = False, use_tzlocal = False, use_fixed = False
@@ -297,12 +278,13 @@ cpdef ndarray[int64_t] normalize_i8_timestamps(const int64_t[:] stamps, tzinfo t
use_tzlocal = True
else:
trans, deltas, typ = get_dst_info(tz)
+ ntrans = trans.shape[0]
if typ not in ["pytz", "dateutil"]:
# static/fixed; in this case we know that len(delta) == 1
use_fixed = True
delta = deltas[0]
else:
- pos = trans.searchsorted(stamps, side="right") - 1
+ tdata = <int64_t*>cnp.PyArray_DATA(trans)
for i in range(n):
if stamps[i] == NPY_NAT:
@@ -316,7 +298,8 @@ cpdef ndarray[int64_t] normalize_i8_timestamps(const int64_t[:] stamps, tzinfo t
elif use_fixed:
local_val = stamps[i] + delta
else:
- local_val = stamps[i] + deltas[pos[i]]
+ pos = bisect_right_i8(tdata, stamps[i], ntrans) - 1
+ local_val = stamps[i] + deltas[pos]
result[i] = normalize_i8_stamp(local_val)
@@ -341,10 +324,11 @@ def is_date_array_normalized(const int64_t[:] stamps, tzinfo tz=None) -> bool:
is_normalized : bool True if all stamps are normalized
"""
cdef:
- Py_ssize_t i, n = len(stamps)
+ Py_ssize_t i, ntrans =- 1, n = len(stamps)
ndarray[int64_t] trans
int64_t[::1] deltas
- intp_t[:] pos
+ int64_t* tdata = NULL
+ intp_t pos
int64_t local_val, delta = NPY_NAT
str typ
int64_t day_nanos = 24 * 3600 * 1_000_000_000
@@ -356,12 +340,13 @@ def is_date_array_normalized(const int64_t[:] stamps, tzinfo tz=None) -> bool:
use_tzlocal = True
else:
trans, deltas, typ = get_dst_info(tz)
+ ntrans = trans.shape[0]
if typ not in ["pytz", "dateutil"]:
# static/fixed; in this case we know that len(delta) == 1
use_fixed = True
delta = deltas[0]
else:
- pos = trans.searchsorted(stamps, side="right") - 1
+ tdata = <int64_t*>cnp.PyArray_DATA(trans)
for i in range(n):
if use_utc:
@@ -371,7 +356,8 @@ def is_date_array_normalized(const int64_t[:] stamps, tzinfo tz=None) -> bool:
elif use_fixed:
local_val = stamps[i] + delta
else:
- local_val = stamps[i] + deltas[pos[i]]
+ pos = bisect_right_i8(tdata, stamps[i], ntrans) - 1
+ local_val = stamps[i] + deltas[pos]
if local_val % day_nanos != 0:
return False
@@ -386,11 +372,12 @@ def is_date_array_normalized(const int64_t[:] stamps, tzinfo tz=None) -> bool:
@cython.boundscheck(False)
def dt64arr_to_periodarr(const int64_t[:] stamps, int freq, tzinfo tz):
cdef:
- Py_ssize_t i, n = len(stamps)
+ Py_ssize_t i, ntrans =- 1, n = len(stamps)
int64_t[:] result = np.empty(n, dtype=np.int64)
ndarray[int64_t] trans
int64_t[::1] deltas
- Py_ssize_t[:] pos
+ int64_t* tdata = NULL
+ intp_t pos
npy_datetimestruct dts
int64_t local_val, delta = NPY_NAT
bint use_utc = False, use_tzlocal = False, use_fixed = False
@@ -401,12 +388,13 @@ def dt64arr_to_periodarr(const int64_t[:] stamps, int freq, tzinfo tz):
use_tzlocal = True
else:
trans, deltas, typ = get_dst_info(tz)
+ ntrans = trans.shape[0]
if typ not in ["pytz", "dateutil"]:
# static/fixed; in this case we know that len(delta) == 1
use_fixed = True
delta = deltas[0]
else:
- pos = trans.searchsorted(stamps, side="right") - 1
+ tdata = <int64_t*>cnp.PyArray_DATA(trans)
for i in range(n):
if stamps[i] == NPY_NAT:
@@ -420,7 +408,8 @@ def dt64arr_to_periodarr(const int64_t[:] stamps, int freq, tzinfo tz):
elif use_fixed:
local_val = stamps[i] + delta
else:
- local_val = stamps[i] + deltas[pos[i]]
+ pos = bisect_right_i8(tdata, stamps[i], ntrans) - 1
+ local_val = stamps[i] + deltas[pos]
dt64_to_dtstruct(local_val, &dts)
result[i] = get_period_ordinal(&dts, freq)
| - [ ] closes #xxxx (Replace xxxx with the Github issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/46341 | 2022-03-12T16:15:36Z | 2022-03-15T22:40:02Z | 2022-03-15T22:40:02Z | 2022-03-15T23:40:34Z |
Backport PR #46335 on branch 1.4.x (BUG: replace with value also being replaced) | diff --git a/doc/source/whatsnew/v1.4.2.rst b/doc/source/whatsnew/v1.4.2.rst
index 2bdbeb0ab6991..06f1f406c3816 100644
--- a/doc/source/whatsnew/v1.4.2.rst
+++ b/doc/source/whatsnew/v1.4.2.rst
@@ -18,6 +18,7 @@ Fixed regressions
- Fixed regression in :func:`read_csv` killing python process when invalid file input was given for ``engine="c"`` (:issue:`45957`)
- Fixed memory performance regression in :meth:`Series.fillna` when called on a :class:`DataFrame` column with ``inplace=True`` (:issue:`46149`)
- Provided an alternative solution for passing custom Excel formats in :meth:`.Styler.to_excel`, which was a regression based on stricter CSS validation. Examples available in the documentation for :meth:`.Styler.format` (:issue:`46152`)
+- Fixed regression in :meth:`DataFrame.replace` when a replacement value was also a target for replacement (:issue:`46335`)
- Fixed regression in :meth:`DataFrame.loc.__setitem__` losing :class:`MultiIndex` names if :class:`DataFrame` was empty before (:issue:`46317`)
-
diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index 5810f3515c4ae..e0df274fbc596 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -708,9 +708,18 @@ def replace(
else:
# split so that we only upcast where necessary
- return self.split_and_operate(
- type(self).replace, to_replace, value, inplace=True
- )
+ blocks = []
+ for i, nb in enumerate(self._split()):
+ blocks.extend(
+ type(self).replace(
+ nb,
+ to_replace=to_replace,
+ value=value,
+ inplace=True,
+ mask=mask[i : i + 1],
+ )
+ )
+ return blocks
@final
def _replace_regex(
diff --git a/pandas/tests/frame/methods/test_replace.py b/pandas/tests/frame/methods/test_replace.py
index b9024b4233d7f..4393dbacc6fcf 100644
--- a/pandas/tests/frame/methods/test_replace.py
+++ b/pandas/tests/frame/methods/test_replace.py
@@ -1519,3 +1519,10 @@ def test_replace_regex_dtype_frame(self, regex):
expected_df2 = DataFrame({"A": [1], "B": ["1"]})
result_df2 = df2.replace(to_replace="0", value=1, regex=regex)
tm.assert_frame_equal(result_df2, expected_df2)
+
+ def test_replace_with_value_also_being_replaced(self):
+ # GH46306
+ df = DataFrame({"A": [0, 1, 2], "B": [1, 0, 2]})
+ result = df.replace({0: 1, 1: np.nan})
+ expected = DataFrame({"A": [1, np.nan, 2], "B": [np.nan, 1, 2]})
+ tm.assert_frame_equal(result, expected)
| Backport PR #46335: BUG: replace with value also being replaced | https://api.github.com/repos/pandas-dev/pandas/pulls/46340 | 2022-03-12T15:11:50Z | 2022-03-12T16:38:55Z | 2022-03-12T16:38:55Z | 2022-03-12T16:38:55Z |
REF: de-duplicate hard-coded unit code | diff --git a/pandas/_libs/tslib.pyx b/pandas/_libs/tslib.pyx
index 5c852e85efdc0..063b95953bd91 100644
--- a/pandas/_libs/tslib.pyx
+++ b/pandas/_libs/tslib.pyx
@@ -243,7 +243,7 @@ def array_with_unit_to_datetime(
)
return result, tz
- m, p = precision_from_unit(unit)
+ m, _ = precision_from_unit(unit)
if is_raise:
# try a quick conversion to i8/f8
diff --git a/pandas/_libs/tslibs/dtypes.pxd b/pandas/_libs/tslibs/dtypes.pxd
index ae25de38c5bfd..3686ee216b546 100644
--- a/pandas/_libs/tslibs/dtypes.pxd
+++ b/pandas/_libs/tslibs/dtypes.pxd
@@ -1,7 +1,13 @@
+from pandas._libs.tslibs.np_datetime cimport NPY_DATETIMEUNIT
+
+
+cdef str npy_unit_to_abbrev(NPY_DATETIMEUNIT unit)
+cdef NPY_DATETIMEUNIT freq_group_code_to_npy_unit(int freq) nogil
+
cdef dict attrname_to_abbrevs
cdef enum c_FreqGroup:
- # Mirrors FreqGroup in the .pxy file
+ # Mirrors FreqGroup in the .pyx file
FR_ANN = 1000
FR_QTR = 2000
FR_MTH = 3000
@@ -17,6 +23,20 @@ cdef enum c_FreqGroup:
FR_UND = -10000 # undefined
+cdef enum c_Resolution:
+ # Mirrors Resolution in the .pyx file
+ RESO_NS = 0
+ RESO_US = 1
+ RESO_MS = 2
+ RESO_SEC = 3
+ RESO_MIN = 4
+ RESO_HR = 5
+ RESO_DAY = 6
+ RESO_MTH = 7
+ RESO_QTR = 8
+ RESO_YR = 9
+
+
cdef enum PeriodDtypeCode:
# Annual freqs with various fiscal year ends.
# eg, 2005 for A_FEB runs Mar 1, 2004 to Feb 28, 2005
diff --git a/pandas/_libs/tslibs/dtypes.pyi b/pandas/_libs/tslibs/dtypes.pyi
index 8c642bc847a29..ab66677a8be3a 100644
--- a/pandas/_libs/tslibs/dtypes.pyi
+++ b/pandas/_libs/tslibs/dtypes.pyi
@@ -2,6 +2,8 @@ from enum import Enum
from pandas._libs.tslibs.offsets import BaseOffset
+# These are not public API, but are exposed in the .pyi file because they
+# are imported in tests.
_attrname_to_abbrevs: dict[str, str]
_period_code_map: dict[str, int]
@@ -10,13 +12,11 @@ class PeriodDtypeBase:
# actually __cinit__
def __new__(cls, code: int): ...
- def freq_group_code(self) -> int: ...
- def date_offset(self) -> BaseOffset: ...
- @classmethod
- def from_date_offset(cls, offset: BaseOffset) -> PeriodDtypeBase: ...
+ def _freq_group_code(self) -> int: ...
@property
- def resolution(self) -> Resolution: ...
+ def _resolution_obj(self) -> Resolution: ...
def _get_to_timestamp_base(self) -> int: ...
+ def _freqstr(self) -> str: ...
class FreqGroup(Enum):
FR_ANN: int
@@ -33,7 +33,7 @@ class FreqGroup(Enum):
FR_NS: int
FR_UND: int
@staticmethod
- def get_freq_group(code: int) -> FreqGroup: ...
+ def from_period_dtype_code(code: int) -> FreqGroup: ...
class Resolution(Enum):
RESO_NS: int
@@ -49,10 +49,10 @@ class Resolution(Enum):
def __lt__(self, other: Resolution) -> bool: ...
def __ge__(self, other: Resolution) -> bool: ...
@property
- def freq_group(self) -> FreqGroup: ...
- @property
def attrname(self) -> str: ...
@classmethod
def from_attrname(cls, attrname: str) -> Resolution: ...
@classmethod
- def get_reso_from_freq(cls, freq: str) -> Resolution: ...
+ def get_reso_from_freqstr(cls, freq: str) -> Resolution: ...
+ @property
+ def attr_abbrev(self) -> str: ...
diff --git a/pandas/_libs/tslibs/dtypes.pyx b/pandas/_libs/tslibs/dtypes.pyx
index b64a360510ab9..43f33b98b8eaa 100644
--- a/pandas/_libs/tslibs/dtypes.pyx
+++ b/pandas/_libs/tslibs/dtypes.pyx
@@ -2,6 +2,8 @@
# originals
from enum import Enum
+from pandas._libs.tslibs.np_datetime cimport NPY_DATETIMEUNIT
+
cdef class PeriodDtypeBase:
"""
@@ -23,32 +25,24 @@ cdef class PeriodDtypeBase:
return self._dtype_code == other._dtype_code
@property
- def freq_group_code(self) -> int:
+ def _freq_group_code(self) -> int:
# See also: libperiod.get_freq_group
return (self._dtype_code // 1000) * 1000
@property
- def resolution(self) -> "Resolution":
- fgc = self.freq_group_code
- return Resolution.from_freq_group(FreqGroup(fgc))
+ def _resolution_obj(self) -> "Resolution":
+ fgc = self._freq_group_code
+ freq_group = FreqGroup(fgc)
+ abbrev = _reverse_period_code_map[freq_group.value].split("-")[0]
+ if abbrev == "B":
+ return Resolution.RESO_DAY
+ attrname = _abbrev_to_attrnames[abbrev]
+ return Resolution.from_attrname(attrname)
@property
- def date_offset(self):
- """
- Corresponding DateOffset object.
-
- This mapping is mainly for backward-compatibility.
- """
- from .offsets import to_offset
-
- freqstr = _reverse_period_code_map.get(self._dtype_code)
-
- return to_offset(freqstr)
-
- @classmethod
- def from_date_offset(cls, offset):
- code = offset._period_dtype_code
- return cls(code)
+ def _freqstr(self) -> str:
+ # Will be passed to to_offset in Period._maybe_convert_freq
+ return _reverse_period_code_map.get(self._dtype_code)
cpdef int _get_to_timestamp_base(self):
"""
@@ -73,52 +67,52 @@ cdef class PeriodDtypeBase:
_period_code_map = {
# Annual freqs with various fiscal year ends.
# eg, 2005 for A-FEB runs Mar 1, 2004 to Feb 28, 2005
- "A-DEC": 1000, # Annual - December year end
- "A-JAN": 1001, # Annual - January year end
- "A-FEB": 1002, # Annual - February year end
- "A-MAR": 1003, # Annual - March year end
- "A-APR": 1004, # Annual - April year end
- "A-MAY": 1005, # Annual - May year end
- "A-JUN": 1006, # Annual - June year end
- "A-JUL": 1007, # Annual - July year end
- "A-AUG": 1008, # Annual - August year end
- "A-SEP": 1009, # Annual - September year end
- "A-OCT": 1010, # Annual - October year end
- "A-NOV": 1011, # Annual - November year end
+ "A-DEC": PeriodDtypeCode.A_DEC, # Annual - December year end
+ "A-JAN": PeriodDtypeCode.A_JAN, # Annual - January year end
+ "A-FEB": PeriodDtypeCode.A_FEB, # Annual - February year end
+ "A-MAR": PeriodDtypeCode.A_MAR, # Annual - March year end
+ "A-APR": PeriodDtypeCode.A_APR, # Annual - April year end
+ "A-MAY": PeriodDtypeCode.A_MAY, # Annual - May year end
+ "A-JUN": PeriodDtypeCode.A_JUN, # Annual - June year end
+ "A-JUL": PeriodDtypeCode.A_JUL, # Annual - July year end
+ "A-AUG": PeriodDtypeCode.A_AUG, # Annual - August year end
+ "A-SEP": PeriodDtypeCode.A_SEP, # Annual - September year end
+ "A-OCT": PeriodDtypeCode.A_OCT, # Annual - October year end
+ "A-NOV": PeriodDtypeCode.A_NOV, # Annual - November year end
# Quarterly frequencies with various fiscal year ends.
# eg, Q42005 for Q-OCT runs Aug 1, 2005 to Oct 31, 2005
- "Q-DEC": 2000, # Quarterly - December year end
- "Q-JAN": 2001, # Quarterly - January year end
- "Q-FEB": 2002, # Quarterly - February year end
- "Q-MAR": 2003, # Quarterly - March year end
- "Q-APR": 2004, # Quarterly - April year end
- "Q-MAY": 2005, # Quarterly - May year end
- "Q-JUN": 2006, # Quarterly - June year end
- "Q-JUL": 2007, # Quarterly - July year end
- "Q-AUG": 2008, # Quarterly - August year end
- "Q-SEP": 2009, # Quarterly - September year end
- "Q-OCT": 2010, # Quarterly - October year end
- "Q-NOV": 2011, # Quarterly - November year end
-
- "M": 3000, # Monthly
-
- "W-SUN": 4000, # Weekly - Sunday end of week
- "W-MON": 4001, # Weekly - Monday end of week
- "W-TUE": 4002, # Weekly - Tuesday end of week
- "W-WED": 4003, # Weekly - Wednesday end of week
- "W-THU": 4004, # Weekly - Thursday end of week
- "W-FRI": 4005, # Weekly - Friday end of week
- "W-SAT": 4006, # Weekly - Saturday end of week
-
- "B": 5000, # Business days
- "D": 6000, # Daily
- "H": 7000, # Hourly
- "T": 8000, # Minutely
- "S": 9000, # Secondly
- "L": 10000, # Millisecondly
- "U": 11000, # Microsecondly
- "N": 12000, # Nanosecondly
+ "Q-DEC": PeriodDtypeCode.Q_DEC, # Quarterly - December year end
+ "Q-JAN": PeriodDtypeCode.Q_JAN, # Quarterly - January year end
+ "Q-FEB": PeriodDtypeCode.Q_FEB, # Quarterly - February year end
+ "Q-MAR": PeriodDtypeCode.Q_MAR, # Quarterly - March year end
+ "Q-APR": PeriodDtypeCode.Q_APR, # Quarterly - April year end
+ "Q-MAY": PeriodDtypeCode.Q_MAY, # Quarterly - May year end
+ "Q-JUN": PeriodDtypeCode.Q_JUN, # Quarterly - June year end
+ "Q-JUL": PeriodDtypeCode.Q_JUL, # Quarterly - July year end
+ "Q-AUG": PeriodDtypeCode.Q_AUG, # Quarterly - August year end
+ "Q-SEP": PeriodDtypeCode.Q_SEP, # Quarterly - September year end
+ "Q-OCT": PeriodDtypeCode.Q_OCT, # Quarterly - October year end
+ "Q-NOV": PeriodDtypeCode.Q_NOV, # Quarterly - November year end
+
+ "M": PeriodDtypeCode.M, # Monthly
+
+ "W-SUN": PeriodDtypeCode.W_SUN, # Weekly - Sunday end of week
+ "W-MON": PeriodDtypeCode.W_MON, # Weekly - Monday end of week
+ "W-TUE": PeriodDtypeCode.W_TUE, # Weekly - Tuesday end of week
+ "W-WED": PeriodDtypeCode.W_WED, # Weekly - Wednesday end of week
+ "W-THU": PeriodDtypeCode.W_THU, # Weekly - Thursday end of week
+ "W-FRI": PeriodDtypeCode.W_FRI, # Weekly - Friday end of week
+ "W-SAT": PeriodDtypeCode.W_SAT, # Weekly - Saturday end of week
+
+ "B": PeriodDtypeCode.B, # Business days
+ "D": PeriodDtypeCode.D, # Daily
+ "H": PeriodDtypeCode.H, # Hourly
+ "T": PeriodDtypeCode.T, # Minutely
+ "S": PeriodDtypeCode.S, # Secondly
+ "L": PeriodDtypeCode.L, # Millisecondly
+ "U": PeriodDtypeCode.U, # Microsecondly
+ "N": PeriodDtypeCode.N, # Nanosecondly
}
_reverse_period_code_map = {
@@ -131,7 +125,7 @@ _period_code_map.update({"Y" + key[1:]: _period_code_map[key]
_period_code_map.update({
"Q": 2000, # Quarterly - December year end (default quarterly)
- "A": 1000, # Annual
+ "A": PeriodDtypeCode.A, # Annual
"W": 4000, # Weekly
"C": 5000, # Custom Business Day
})
@@ -159,41 +153,38 @@ cdef dict _abbrev_to_attrnames = {v: k for k, v in attrname_to_abbrevs.items()}
class FreqGroup(Enum):
# Mirrors c_FreqGroup in the .pxd file
- FR_ANN = 1000
- FR_QTR = 2000
- FR_MTH = 3000
- FR_WK = 4000
- FR_BUS = 5000
- FR_DAY = 6000
- FR_HR = 7000
- FR_MIN = 8000
- FR_SEC = 9000
- FR_MS = 10000
- FR_US = 11000
- FR_NS = 12000
- FR_UND = -10000 # undefined
+ FR_ANN = c_FreqGroup.FR_ANN
+ FR_QTR = c_FreqGroup.FR_QTR
+ FR_MTH = c_FreqGroup.FR_MTH
+ FR_WK = c_FreqGroup.FR_WK
+ FR_BUS = c_FreqGroup.FR_BUS
+ FR_DAY = c_FreqGroup.FR_DAY
+ FR_HR = c_FreqGroup.FR_HR
+ FR_MIN = c_FreqGroup.FR_MIN
+ FR_SEC = c_FreqGroup.FR_SEC
+ FR_MS = c_FreqGroup.FR_MS
+ FR_US = c_FreqGroup.FR_US
+ FR_NS = c_FreqGroup.FR_NS
+ FR_UND = -c_FreqGroup.FR_UND # undefined
@staticmethod
- def get_freq_group(code: int) -> "FreqGroup":
- # See also: PeriodDtypeBase.freq_group_code
+ def from_period_dtype_code(code: int) -> "FreqGroup":
+ # See also: PeriodDtypeBase._freq_group_code
code = (code // 1000) * 1000
return FreqGroup(code)
class Resolution(Enum):
-
- # Note: cython won't allow us to reference the cdef versions at the
- # module level
- RESO_NS = 0
- RESO_US = 1
- RESO_MS = 2
- RESO_SEC = 3
- RESO_MIN = 4
- RESO_HR = 5
- RESO_DAY = 6
- RESO_MTH = 7
- RESO_QTR = 8
- RESO_YR = 9
+ RESO_NS = c_Resolution.RESO_NS
+ RESO_US = c_Resolution.RESO_US
+ RESO_MS = c_Resolution.RESO_MS
+ RESO_SEC = c_Resolution.RESO_SEC
+ RESO_MIN = c_Resolution.RESO_MIN
+ RESO_HR = c_Resolution.RESO_HR
+ RESO_DAY = c_Resolution.RESO_DAY
+ RESO_MTH = c_Resolution.RESO_MTH
+ RESO_QTR = c_Resolution.RESO_QTR
+ RESO_YR = c_Resolution.RESO_YR
def __lt__(self, other):
return self.value < other.value
@@ -202,29 +193,9 @@ class Resolution(Enum):
return self.value >= other.value
@property
- def freq_group(self) -> FreqGroup:
- if self == Resolution.RESO_NS:
- return FreqGroup.FR_NS
- elif self == Resolution.RESO_US:
- return FreqGroup.FR_US
- elif self == Resolution.RESO_MS:
- return FreqGroup.FR_MS
- elif self == Resolution.RESO_SEC:
- return FreqGroup.FR_SEC
- elif self == Resolution.RESO_MIN:
- return FreqGroup.FR_MIN
- elif self == Resolution.RESO_HR:
- return FreqGroup.FR_HR
- elif self == Resolution.RESO_DAY:
- return FreqGroup.FR_DAY
- elif self == Resolution.RESO_MTH:
- return FreqGroup.FR_MTH
- elif self == Resolution.RESO_QTR:
- return FreqGroup.FR_QTR
- elif self == Resolution.RESO_YR:
- return FreqGroup.FR_ANN
- else:
- raise ValueError(self) # pragma: no cover
+ def attr_abbrev(self) -> str:
+ # string that we can pass to to_offset
+ return _attrname_to_abbrevs[self.attrname]
@property
def attrname(self) -> str:
@@ -254,7 +225,7 @@ class Resolution(Enum):
return cls(_str_reso_map[attrname])
@classmethod
- def get_reso_from_freq(cls, freq: str) -> "Resolution":
+ def get_reso_from_freqstr(cls, freq: str) -> "Resolution":
"""
Return resolution code against frequency str.
@@ -262,10 +233,10 @@ class Resolution(Enum):
Examples
--------
- >>> Resolution.get_reso_from_freq('H')
+ >>> Resolution.get_reso_from_freqstr('H')
<Resolution.RESO_HR: 5>
- >>> Resolution.get_reso_from_freq('H') == Resolution.RESO_HR
+ >>> Resolution.get_reso_from_freqstr('H') == Resolution.RESO_HR
True
"""
try:
@@ -283,13 +254,57 @@ class Resolution(Enum):
return cls.from_attrname(attr_name)
- @classmethod
- def from_freq_group(cls, freq_group: FreqGroup) -> "Resolution":
- abbrev = _reverse_period_code_map[freq_group.value].split("-")[0]
- if abbrev == "B":
- return cls.RESO_DAY
- attrname = _abbrev_to_attrnames[abbrev]
- return cls.from_attrname(attrname)
+
+cdef str npy_unit_to_abbrev(NPY_DATETIMEUNIT unit):
+ if unit == NPY_DATETIMEUNIT.NPY_FR_ns or unit == NPY_DATETIMEUNIT.NPY_FR_GENERIC:
+ # generic -> default to nanoseconds
+ return "ns"
+ elif unit == NPY_DATETIMEUNIT.NPY_FR_us:
+ return "us"
+ elif unit == NPY_DATETIMEUNIT.NPY_FR_ms:
+ return "ms"
+ elif unit == NPY_DATETIMEUNIT.NPY_FR_s:
+ return "s"
+ elif unit == NPY_DATETIMEUNIT.NPY_FR_m:
+ return "m"
+ elif unit == NPY_DATETIMEUNIT.NPY_FR_h:
+ return "h"
+ elif unit == NPY_DATETIMEUNIT.NPY_FR_D:
+ return "D"
+ elif unit == NPY_DATETIMEUNIT.NPY_FR_W:
+ return "W"
+ elif unit == NPY_DATETIMEUNIT.NPY_FR_M:
+ return "M"
+ elif unit == NPY_DATETIMEUNIT.NPY_FR_Y:
+ return "Y"
+ else:
+ raise NotImplementedError(unit)
+
+
+cdef NPY_DATETIMEUNIT freq_group_code_to_npy_unit(int freq) nogil:
+ """
+ Convert the freq to the corresponding NPY_DATETIMEUNIT to pass
+ to npy_datetimestruct_to_datetime.
+ """
+ if freq == FR_MTH:
+ return NPY_DATETIMEUNIT.NPY_FR_M
+ elif freq == FR_DAY:
+ return NPY_DATETIMEUNIT.NPY_FR_D
+ elif freq == FR_HR:
+ return NPY_DATETIMEUNIT.NPY_FR_h
+ elif freq == FR_MIN:
+ return NPY_DATETIMEUNIT.NPY_FR_m
+ elif freq == FR_SEC:
+ return NPY_DATETIMEUNIT.NPY_FR_s
+ elif freq == FR_MS:
+ return NPY_DATETIMEUNIT.NPY_FR_ms
+ elif freq == FR_US:
+ return NPY_DATETIMEUNIT.NPY_FR_us
+ elif freq == FR_NS:
+ return NPY_DATETIMEUNIT.NPY_FR_ns
+ elif freq == FR_UND:
+ # Default to Day
+ return NPY_DATETIMEUNIT.NPY_FR_D
cdef dict _reso_str_map = {
diff --git a/pandas/_libs/tslibs/period.pyx b/pandas/_libs/tslibs/period.pyx
index 54cae834d7024..9c05c6867f71f 100644
--- a/pandas/_libs/tslibs/period.pyx
+++ b/pandas/_libs/tslibs/period.pyx
@@ -95,6 +95,7 @@ from pandas._libs.tslibs.dtypes cimport (
FR_WK,
PeriodDtypeBase,
attrname_to_abbrevs,
+ freq_group_code_to_npy_unit,
)
from pandas._libs.tslibs.parsing cimport quarter_to_myear
@@ -689,8 +690,7 @@ cdef char* c_strftime(npy_datetimestruct *dts, char *fmt):
# Conversion between date_info and npy_datetimestruct
cdef inline int get_freq_group(int freq) nogil:
- # Note: this is equivalent to libfrequencies.get_freq_group, specialized
- # to integer argument.
+ # See also FreqGroup.get_freq_group
return (freq // 1000) * 1000
@@ -806,36 +806,10 @@ cdef int64_t get_period_ordinal(npy_datetimestruct *dts, int freq) nogil:
unix_date = npy_datetimestruct_to_datetime(NPY_FR_D, dts)
return DtoB(dts, 0, unix_date)
- unit = get_unit(freq)
+ unit = freq_group_code_to_npy_unit(freq)
return npy_datetimestruct_to_datetime(unit, dts)
-cdef NPY_DATETIMEUNIT get_unit(int freq) nogil:
- """
- Convert the freq to the corresponding NPY_DATETIMEUNIT to pass
- to npy_datetimestruct_to_datetime.
- """
- if freq == FR_MTH:
- return NPY_DATETIMEUNIT.NPY_FR_M
- elif freq == FR_DAY:
- return NPY_DATETIMEUNIT.NPY_FR_D
- elif freq == FR_HR:
- return NPY_DATETIMEUNIT.NPY_FR_h
- elif freq == FR_MIN:
- return NPY_DATETIMEUNIT.NPY_FR_m
- elif freq == FR_SEC:
- return NPY_DATETIMEUNIT.NPY_FR_s
- elif freq == FR_MS:
- return NPY_DATETIMEUNIT.NPY_FR_ms
- elif freq == FR_US:
- return NPY_DATETIMEUNIT.NPY_FR_us
- elif freq == FR_NS:
- return NPY_DATETIMEUNIT.NPY_FR_ns
- elif freq == FR_UND:
- # Default to Day
- return NPY_DATETIMEUNIT.NPY_FR_D
-
-
cdef void get_date_info(int64_t ordinal, int freq, npy_datetimestruct *dts) nogil:
cdef:
int64_t unix_date, nanos
@@ -1649,7 +1623,7 @@ cdef class _Period(PeriodMixin):
if isinstance(freq, int):
# We already have a dtype code
dtype = PeriodDtypeBase(freq)
- freq = dtype.date_offset
+ freq = dtype._freqstr
freq = to_offset(freq)
diff --git a/pandas/_libs/tslibs/timedeltas.pyx b/pandas/_libs/tslibs/timedeltas.pyx
index 8eaf86b3d193f..e88fb42adfc78 100644
--- a/pandas/_libs/tslibs/timedeltas.pyx
+++ b/pandas/_libs/tslibs/timedeltas.pyx
@@ -36,6 +36,7 @@ from pandas._libs.tslibs.conversion cimport (
cast_from_unit,
precision_from_unit,
)
+from pandas._libs.tslibs.dtypes cimport npy_unit_to_abbrev
from pandas._libs.tslibs.nattype cimport (
NPY_NAT,
c_NaT as NaT,
@@ -192,32 +193,6 @@ cpdef int64_t delta_to_nanoseconds(delta) except? -1:
raise TypeError(type(delta))
-cdef str npy_unit_to_abbrev(NPY_DATETIMEUNIT unit):
- if unit == NPY_DATETIMEUNIT.NPY_FR_ns or unit == NPY_DATETIMEUNIT.NPY_FR_GENERIC:
- # generic -> default to nanoseconds
- return "ns"
- elif unit == NPY_DATETIMEUNIT.NPY_FR_us:
- return "us"
- elif unit == NPY_DATETIMEUNIT.NPY_FR_ms:
- return "ms"
- elif unit == NPY_DATETIMEUNIT.NPY_FR_s:
- return "s"
- elif unit == NPY_DATETIMEUNIT.NPY_FR_m:
- return "m"
- elif unit == NPY_DATETIMEUNIT.NPY_FR_h:
- return "h"
- elif unit == NPY_DATETIMEUNIT.NPY_FR_D:
- return "D"
- elif unit == NPY_DATETIMEUNIT.NPY_FR_W:
- return "W"
- elif unit == NPY_DATETIMEUNIT.NPY_FR_M:
- return "M"
- elif unit == NPY_DATETIMEUNIT.NPY_FR_Y:
- return "Y"
- else:
- raise NotImplementedError(unit)
-
-
@cython.overflowcheck(True)
cdef object ensure_td64ns(object ts):
"""
diff --git a/pandas/_libs/tslibs/vectorized.pyx b/pandas/_libs/tslibs/vectorized.pyx
index bc254b6c5a5cf..f63cb4f58609a 100644
--- a/pandas/_libs/tslibs/vectorized.pyx
+++ b/pandas/_libs/tslibs/vectorized.pyx
@@ -191,16 +191,12 @@ def ints_to_pydatetime(
# -------------------------------------------------------------------------
cdef:
- int RESO_NS = 0
- int RESO_US = 1
- int RESO_MS = 2
- int RESO_SEC = 3
- int RESO_MIN = 4
- int RESO_HR = 5
- int RESO_DAY = 6
- int RESO_MTH = 7
- int RESO_QTR = 8
- int RESO_YR = 9
+ int RESO_US = Resolution.RESO_US.value
+ int RESO_MS = Resolution.RESO_MS.value
+ int RESO_SEC = Resolution.RESO_SEC.value
+ int RESO_MIN = Resolution.RESO_MIN.value
+ int RESO_HR = Resolution.RESO_HR.value
+ int RESO_DAY = Resolution.RESO_DAY.value
cdef inline int _reso_stamp(npy_datetimestruct *dts):
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index 7a7bad02fde72..d58555a49e2e6 100644
--- a/pandas/core/arrays/datetimelike.py
+++ b/pandas/core/arrays/datetimelike.py
@@ -956,7 +956,7 @@ def _resolution_obj(self) -> Resolution | None:
if freqstr is None:
return None
try:
- return Resolution.get_reso_from_freq(freqstr)
+ return Resolution.get_reso_from_freqstr(freqstr)
except KeyError:
return None
diff --git a/pandas/core/arrays/timedeltas.py b/pandas/core/arrays/timedeltas.py
index afc20fcc54376..dc63cd92bbb2b 100644
--- a/pandas/core/arrays/timedeltas.py
+++ b/pandas/core/arrays/timedeltas.py
@@ -1023,6 +1023,7 @@ def sequence_to_td64ns(
# cast the unit, multiply base/frac separately
# to avoid precision issues from float -> int
mask = np.isnan(data)
+ # The next few lines are effectively a vectorized 'cast_from_unit'
m, p = precision_from_unit(unit or "ns")
base = data.astype(np.int64)
frac = data - base
diff --git a/pandas/core/indexes/datetimes.py b/pandas/core/indexes/datetimes.py
index 4acdc7e6c7556..3954cb28c2aca 100644
--- a/pandas/core/indexes/datetimes.py
+++ b/pandas/core/indexes/datetimes.py
@@ -573,8 +573,7 @@ def _parsed_string_to_bounds(self, reso: Resolution, parsed: datetime):
-------
lower, upper: pd.Timestamp
"""
- grp = reso.freq_group
- per = Period(parsed, freq=grp.value)
+ per = Period(parsed, freq=reso.attr_abbrev)
start, end = per.start_time, per.end_time
# GH 24076
diff --git a/pandas/core/indexes/period.py b/pandas/core/indexes/period.py
index a296ce130bbf5..3105bef6d506f 100644
--- a/pandas/core/indexes/period.py
+++ b/pandas/core/indexes/period.py
@@ -446,8 +446,9 @@ def get_loc(self, key, method=None, tolerance=None):
# TODO: pass if method is not None, like DTI does?
raise KeyError(key) from err
- if reso == self.dtype.resolution:
- # the reso < self.dtype.resolution case goes through _get_string_slice
+ if reso == self.dtype._resolution_obj:
+ # the reso < self.dtype._resolution_obj case goes
+ # through _get_string_slice
key = Period(parsed, freq=self.freq)
loc = self.get_loc(key, method=method, tolerance=tolerance)
# Recursing instead of falling through matters for the exception
@@ -495,14 +496,13 @@ def _maybe_cast_slice_bound(self, label, side: str, kind=lib.no_default):
return super()._maybe_cast_slice_bound(label, side, kind=kind)
def _parsed_string_to_bounds(self, reso: Resolution, parsed: datetime):
- grp = reso.freq_group
- iv = Period(parsed, freq=grp.value)
+ iv = Period(parsed, freq=reso.attr_abbrev)
return (iv.asfreq(self.freq, how="start"), iv.asfreq(self.freq, how="end"))
def _can_partial_date_slice(self, reso: Resolution) -> bool:
assert isinstance(reso, Resolution), (type(reso), reso)
# e.g. test_getitem_setitem_periodindex
- return reso > self.dtype.resolution
+ return reso > self.dtype._resolution_obj
def period_range(
diff --git a/pandas/plotting/_matplotlib/converter.py b/pandas/plotting/_matplotlib/converter.py
index 6c40770b4fac5..e5fb6b1dafa98 100644
--- a/pandas/plotting/_matplotlib/converter.py
+++ b/pandas/plotting/_matplotlib/converter.py
@@ -533,34 +533,35 @@ def has_level_label(label_flags: np.ndarray, vmin: float) -> bool:
def _daily_finder(vmin, vmax, freq: BaseOffset):
# error: "BaseOffset" has no attribute "_period_dtype_code"
dtype_code = freq._period_dtype_code # type: ignore[attr-defined]
+ freq_group = FreqGroup.from_period_dtype_code(dtype_code)
periodsperday = -1
if dtype_code >= FreqGroup.FR_HR.value:
- if dtype_code == FreqGroup.FR_NS.value:
+ if freq_group == FreqGroup.FR_NS:
periodsperday = 24 * 60 * 60 * 1000000000
- elif dtype_code == FreqGroup.FR_US.value:
+ elif freq_group == FreqGroup.FR_US:
periodsperday = 24 * 60 * 60 * 1000000
- elif dtype_code == FreqGroup.FR_MS.value:
+ elif freq_group == FreqGroup.FR_MS:
periodsperday = 24 * 60 * 60 * 1000
- elif dtype_code == FreqGroup.FR_SEC.value:
+ elif freq_group == FreqGroup.FR_SEC:
periodsperday = 24 * 60 * 60
- elif dtype_code == FreqGroup.FR_MIN.value:
+ elif freq_group == FreqGroup.FR_MIN:
periodsperday = 24 * 60
- elif dtype_code == FreqGroup.FR_HR.value:
+ elif freq_group == FreqGroup.FR_HR:
periodsperday = 24
else: # pragma: no cover
raise ValueError(f"unexpected frequency: {dtype_code}")
periodsperyear = 365 * periodsperday
periodspermonth = 28 * periodsperday
- elif dtype_code == FreqGroup.FR_BUS.value:
+ elif freq_group == FreqGroup.FR_BUS:
periodsperyear = 261
periodspermonth = 19
- elif dtype_code == FreqGroup.FR_DAY.value:
+ elif freq_group == FreqGroup.FR_DAY:
periodsperyear = 365
periodspermonth = 28
- elif FreqGroup.get_freq_group(dtype_code) == FreqGroup.FR_WK:
+ elif freq_group == FreqGroup.FR_WK:
periodsperyear = 52
periodspermonth = 3
else: # pragma: no cover
@@ -898,14 +899,13 @@ def _annual_finder(vmin, vmax, freq):
def get_finder(freq: BaseOffset):
# error: "BaseOffset" has no attribute "_period_dtype_code"
dtype_code = freq._period_dtype_code # type: ignore[attr-defined]
- fgroup = (dtype_code // 1000) * 1000
- fgroup = FreqGroup(fgroup)
+ fgroup = FreqGroup.from_period_dtype_code(dtype_code)
if fgroup == FreqGroup.FR_ANN:
return _annual_finder
elif fgroup == FreqGroup.FR_QTR:
return _quarterly_finder
- elif dtype_code == FreqGroup.FR_MTH.value:
+ elif fgroup == FreqGroup.FR_MTH:
return _monthly_finder
elif (dtype_code >= FreqGroup.FR_BUS.value) or fgroup == FreqGroup.FR_WK:
return _daily_finder
@@ -919,7 +919,7 @@ class TimeSeries_DateLocator(Locator):
Parameters
----------
- freq : {var}
+ freq : BaseOffset
Valid frequency specifier.
minor_locator : {False, True}, optional
Whether the locator is for minor ticks (True) or not.
@@ -933,7 +933,7 @@ class TimeSeries_DateLocator(Locator):
def __init__(
self,
- freq,
+ freq: BaseOffset,
minor_locator=False,
dynamic_mode=True,
base=1,
@@ -1010,7 +1010,7 @@ class TimeSeries_DateFormatter(Formatter):
Parameters
----------
- freq : {int, string}
+ freq : BaseOffset
Valid frequency specifier.
minor_locator : bool, default False
Whether the current formatter should apply to minor ticks (True) or
@@ -1021,7 +1021,7 @@ class TimeSeries_DateFormatter(Formatter):
def __init__(
self,
- freq,
+ freq: BaseOffset,
minor_locator: bool = False,
dynamic_mode: bool = True,
plot_obj=None,
diff --git a/pandas/tests/tseries/frequencies/test_freq_code.py b/pandas/tests/tseries/frequencies/test_freq_code.py
index 41257bb3f67fd..e961fdc295c96 100644
--- a/pandas/tests/tseries/frequencies/test_freq_code.py
+++ b/pandas/tests/tseries/frequencies/test_freq_code.py
@@ -38,12 +38,12 @@ def test_get_to_timestamp_base(freqstr, exp_freqstr):
],
)
def test_get_attrname_from_abbrev(freqstr, expected):
- assert Resolution.get_reso_from_freq(freqstr).attrname == expected
+ assert Resolution.get_reso_from_freqstr(freqstr).attrname == expected
@pytest.mark.parametrize("freq", ["D", "H", "T", "S", "L", "U", "N"])
def test_get_freq_roundtrip2(freq):
- obj = Resolution.get_reso_from_freq(freq)
+ obj = Resolution.get_reso_from_freqstr(freq)
result = _attrname_to_abbrevs[obj.attrname]
assert freq == result
| I had hoped to go further and get rid of one of the dtypes.pyx/pxd classes entirely, but ATM i dont think thats feasible. | https://api.github.com/repos/pandas-dev/pandas/pulls/46338 | 2022-03-12T03:38:14Z | 2022-03-16T00:36:18Z | 2022-03-16T00:36:18Z | 2022-03-16T01:22:12Z |
BUG: replace with value also being replaced | diff --git a/doc/source/whatsnew/v1.4.2.rst b/doc/source/whatsnew/v1.4.2.rst
index 2bdbeb0ab6991..06f1f406c3816 100644
--- a/doc/source/whatsnew/v1.4.2.rst
+++ b/doc/source/whatsnew/v1.4.2.rst
@@ -18,6 +18,7 @@ Fixed regressions
- Fixed regression in :func:`read_csv` killing python process when invalid file input was given for ``engine="c"`` (:issue:`45957`)
- Fixed memory performance regression in :meth:`Series.fillna` when called on a :class:`DataFrame` column with ``inplace=True`` (:issue:`46149`)
- Provided an alternative solution for passing custom Excel formats in :meth:`.Styler.to_excel`, which was a regression based on stricter CSS validation. Examples available in the documentation for :meth:`.Styler.format` (:issue:`46152`)
+- Fixed regression in :meth:`DataFrame.replace` when a replacement value was also a target for replacement (:issue:`46335`)
- Fixed regression in :meth:`DataFrame.loc.__setitem__` losing :class:`MultiIndex` names if :class:`DataFrame` was empty before (:issue:`46317`)
-
diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index 3693edbae7d95..69f66973d0954 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -612,9 +612,18 @@ def replace(
else:
# split so that we only upcast where necessary
- return self.split_and_operate(
- type(self).replace, to_replace, value, inplace=True
- )
+ blocks = []
+ for i, nb in enumerate(self._split()):
+ blocks.extend(
+ type(self).replace(
+ nb,
+ to_replace=to_replace,
+ value=value,
+ inplace=True,
+ mask=mask[i : i + 1],
+ )
+ )
+ return blocks
@final
def _replace_regex(
diff --git a/pandas/tests/frame/methods/test_replace.py b/pandas/tests/frame/methods/test_replace.py
index 6b53ef400e53d..2eb300a8905b8 100644
--- a/pandas/tests/frame/methods/test_replace.py
+++ b/pandas/tests/frame/methods/test_replace.py
@@ -1542,3 +1542,10 @@ def test_replace_regex_dtype_frame(self, regex):
expected_df2 = DataFrame({"A": [1], "B": ["1"]})
result_df2 = df2.replace(to_replace="0", value=1, regex=regex)
tm.assert_frame_equal(result_df2, expected_df2)
+
+ def test_replace_with_value_also_being_replaced(self):
+ # GH46306
+ df = DataFrame({"A": [0, 1, 2], "B": [1, 0, 2]})
+ result = df.replace({0: 1, 1: np.nan})
+ expected = DataFrame({"A": [1, np.nan, 2], "B": [np.nan, 1, 2]})
+ tm.assert_frame_equal(result, expected)
| - [x] closes #46306
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added an entry in `doc/source/whatsnew/v1.4.2.rst`
| https://api.github.com/repos/pandas-dev/pandas/pulls/46335 | 2022-03-12T00:15:53Z | 2022-03-12T15:11:05Z | 2022-03-12T15:11:05Z | 2022-03-20T23:18:58Z |
Backport PR #46322 on branch 1.4.x (Regression in loc.setitem losing mi names when df is empty) | diff --git a/doc/source/whatsnew/v1.4.2.rst b/doc/source/whatsnew/v1.4.2.rst
index badda6a73d1c8..0b1c7af594110 100644
--- a/doc/source/whatsnew/v1.4.2.rst
+++ b/doc/source/whatsnew/v1.4.2.rst
@@ -17,6 +17,7 @@ Fixed regressions
- Fixed regression in :meth:`DataFrame.drop` and :meth:`Series.drop` when :class:`Index` had extension dtype and duplicates (:issue:`45860`)
- Fixed memory performance regression in :meth:`Series.fillna` when called on a :class:`DataFrame` column with ``inplace=True`` (:issue:`46149`)
- Provided an alternative solution for passing custom Excel formats in :meth:`.Styler.to_excel`, which was a regression based on stricter CSS validation. Examples available in the documentation for :meth:`.Styler.format` (:issue:`46152`)
+- Fixed regression in :meth:`DataFrame.loc.__setitem__` losing :class:`MultiIndex` names if :class:`DataFrame` was empty before (:issue:`46317`)
-
.. ---------------------------------------------------------------------------
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index 0c3640e12e3b5..60de68052639c 100644
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -2003,7 +2003,14 @@ def _setitem_with_indexer_missing(self, indexer, value):
# We will ignore the existing dtypes instead of using
# internals.concat logic
df = value.to_frame().T
- df.index = Index([indexer], name=self.obj.index.name)
+
+ idx = self.obj.index
+ if isinstance(idx, MultiIndex):
+ name = idx.names
+ else:
+ name = idx.name
+
+ df.index = Index([indexer], name=name)
if not has_dtype:
# i.e. if we already had a Series or ndarray, keep that
# dtype. But if we had a list or dict, then do inference
diff --git a/pandas/tests/frame/indexing/test_indexing.py b/pandas/tests/frame/indexing/test_indexing.py
index b186a55b221c8..76da8e3c38b0f 100644
--- a/pandas/tests/frame/indexing/test_indexing.py
+++ b/pandas/tests/frame/indexing/test_indexing.py
@@ -1288,6 +1288,18 @@ def test_loc_expand_empty_frame_keep_index_name(self):
expected = DataFrame({"b": [1]}, index=Index([0], name="a"))
tm.assert_frame_equal(df, expected)
+ def test_loc_expand_empty_frame_keep_midx_names(self):
+ # GH#46317
+ df = DataFrame(
+ columns=["d"], index=MultiIndex.from_tuples([], names=["a", "b", "c"])
+ )
+ df.loc[(1, 2, 3)] = "foo"
+ expected = DataFrame(
+ {"d": ["foo"]},
+ index=MultiIndex.from_tuples([(1, 2, 3)], names=["a", "b", "c"]),
+ )
+ tm.assert_frame_equal(df, expected)
+
class TestDataFrameIndexingUInt64:
def test_setitem(self, uint64_frame):
| Backport PR #46322: Regression in loc.setitem losing mi names when df is empty | https://api.github.com/repos/pandas-dev/pandas/pulls/46334 | 2022-03-11T23:45:18Z | 2022-03-12T03:15:35Z | 2022-03-12T03:15:34Z | 2022-03-12T03:15:35Z |
Backport PR #46325 on branch 1.4.x (Regression in read csv causing segfault for invalid file input) | diff --git a/doc/source/whatsnew/v1.4.2.rst b/doc/source/whatsnew/v1.4.2.rst
index badda6a73d1c8..239f02b0b6bcf 100644
--- a/doc/source/whatsnew/v1.4.2.rst
+++ b/doc/source/whatsnew/v1.4.2.rst
@@ -15,6 +15,7 @@ including other versions of pandas.
Fixed regressions
~~~~~~~~~~~~~~~~~
- Fixed regression in :meth:`DataFrame.drop` and :meth:`Series.drop` when :class:`Index` had extension dtype and duplicates (:issue:`45860`)
+- Fixed regression in :func:`read_csv` killing python process when invalid file input was given for ``engine="c"`` (:issue:`45957`)
- Fixed memory performance regression in :meth:`Series.fillna` when called on a :class:`DataFrame` column with ``inplace=True`` (:issue:`46149`)
- Provided an alternative solution for passing custom Excel formats in :meth:`.Styler.to_excel`, which was a regression based on stricter CSS validation. Examples available in the documentation for :meth:`.Styler.format` (:issue:`46152`)
-
diff --git a/pandas/io/parsers/readers.py b/pandas/io/parsers/readers.py
index 577c38deb4ed4..7d0e78ce43b71 100644
--- a/pandas/io/parsers/readers.py
+++ b/pandas/io/parsers/readers.py
@@ -1227,6 +1227,10 @@ def _make_engine(
assert self.handles is not None
f = self.handles.handle
+ elif engine != "python":
+ msg = f"Invalid file path or buffer object type: {type(f)}"
+ raise ValueError(msg)
+
try:
return mapping[engine](f, **self.options)
except Exception:
diff --git a/pandas/tests/io/parser/test_unsupported.py b/pandas/tests/io/parser/test_unsupported.py
index fad5c3d785e97..e360b9a49474d 100644
--- a/pandas/tests/io/parser/test_unsupported.py
+++ b/pandas/tests/io/parser/test_unsupported.py
@@ -189,3 +189,13 @@ def test_close_file_handle_on_invalide_usecols(all_parsers):
parser.read_csv(fname, usecols=["col1", "col2", "col3"])
# unlink fails on windows if file handles still point to it
os.unlink(fname)
+
+
+def test_invalid_file_inputs(all_parsers):
+ # GH#45957
+ parser = all_parsers
+ if parser.engine == "python":
+ pytest.skip("Python engine supports lists.")
+
+ with pytest.raises(ValueError, match="Invalid"):
+ parser.read_csv([])
| Backport PR #46325: Regression in read csv causing segfault for invalid file input | https://api.github.com/repos/pandas-dev/pandas/pulls/46333 | 2022-03-11T23:44:53Z | 2022-03-12T03:15:42Z | 2022-03-12T03:15:42Z | 2022-03-12T03:15:43Z |
REF/PERF: MultiIndex.get_locs to use boolean arrays internally | diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst
index 089ba62e461d1..b2bf596dd8dd4 100644
--- a/doc/source/whatsnew/v1.5.0.rst
+++ b/doc/source/whatsnew/v1.5.0.rst
@@ -310,7 +310,7 @@ Performance improvements
- Performance improvement in :meth:`.GroupBy.diff` (:issue:`16706`)
- Performance improvement in :meth:`.GroupBy.transform` when broadcasting values for user-defined functions (:issue:`45708`)
- Performance improvement in :meth:`.GroupBy.transform` for user-defined functions when only a single group exists (:issue:`44977`)
-- Performance improvement in :meth:`MultiIndex.get_locs` (:issue:`45681`, :issue:`46040`)
+- Performance improvement in :meth:`DataFrame.loc` and :meth:`Series.loc` for tuple-based indexing of a :class:`MultiIndex` (:issue:`45681`, :issue:`46040`, :issue:`46330`)
- Performance improvement in :attr:`MultiIndex.values` when the MultiIndex contains levels of type DatetimeIndex, TimedeltaIndex or ExtensionDtypes (:issue:`46288`)
- Performance improvement in :func:`merge` when left and/or right are empty (:issue:`45838`)
- Performance improvement in :meth:`DataFrame.join` when left and/or right are empty (:issue:`46015`)
diff --git a/pandas/core/indexes/multi.py b/pandas/core/indexes/multi.py
index 4daf59b911ce1..1105e7b8a274f 100644
--- a/pandas/core/indexes/multi.py
+++ b/pandas/core/indexes/multi.py
@@ -84,7 +84,6 @@
get_unanimous_names,
)
from pandas.core.indexes.frozen import FrozenList
-from pandas.core.indexes.numeric import Int64Index
from pandas.core.ops.invalid import make_invalid_op
from pandas.core.sorting import (
get_group_index,
@@ -3160,10 +3159,10 @@ def maybe_mi_droplevels(indexer, levels):
return indexer, result_index
def _get_level_indexer(
- self, key, level: int = 0, indexer: Int64Index | None = None
+ self, key, level: int = 0, indexer: npt.NDArray[np.bool_] | None = None
):
# `level` kwarg is _always_ positional, never name
- # return an indexer, boolean array or a slice showing where the key is
+ # return a boolean array or slice showing where the key is
# in the totality of values
# if the indexer is provided, then use this
@@ -3171,36 +3170,26 @@ def _get_level_indexer(
level_codes = self.codes[level]
def convert_indexer(start, stop, step, indexer=indexer, codes=level_codes):
- # given the inputs and the codes/indexer, compute an indexer set
- # if we have a provided indexer, then this need not consider
- # the entire labels set
- r = np.arange(start, stop, step)
-
- if indexer is not None and len(indexer) != len(codes):
-
- # we have an indexer which maps the locations in the labels
- # that we have already selected (and is not an indexer for the
- # entire set) otherwise this is wasteful so we only need to
- # examine locations that are in this set the only magic here is
- # that the result are the mappings to the set that we have
- # selected
- from pandas import Series
-
- mapper = Series(indexer)
- indexer = codes.take(ensure_platform_int(indexer))
- result = Series(Index(indexer).isin(r).nonzero()[0])
- m = result.map(mapper)
- # error: Incompatible types in assignment (expression has type
- # "ndarray", variable has type "Series")
- m = np.asarray(m) # type: ignore[assignment]
-
+ # Compute a bool indexer to identify the positions to take.
+ # If we have an existing indexer, we only need to examine the
+ # subset of positions where the existing indexer is True.
+ if indexer is not None:
+ # we only need to look at the subset of codes where the
+ # existing indexer equals True
+ codes = codes[indexer]
+
+ if step is None or step == 1:
+ new_indexer = (codes >= start) & (codes < stop)
else:
- # error: Incompatible types in assignment (expression has type
- # "ndarray", variable has type "Series")
- m = np.zeros(len(codes), dtype=bool) # type: ignore[assignment]
- m[np.in1d(codes, r, assume_unique=Index(codes).is_unique)] = True
+ r = np.arange(start, stop, step, dtype=codes.dtype)
+ new_indexer = algos.isin(codes, r)
+
+ if indexer is None:
+ return new_indexer
- return m
+ indexer = indexer.copy()
+ indexer[indexer] = new_indexer
+ return indexer
if isinstance(key, slice):
# handle a slice, returning a slice if we can
@@ -3327,62 +3316,41 @@ def get_locs(self, seq):
f"on levels {true_slices}, lexsort depth {self._lexsort_depth}"
)
- n = len(self)
- # indexer is the list of all positions that we want to take; it
- # is created on the first entry in seq and narrowed down as we
- # look at remaining entries
- indexer = None
-
if any(x is Ellipsis for x in seq):
raise NotImplementedError(
"MultiIndex does not support indexing with Ellipsis"
)
- def _convert_to_indexer(r) -> Int64Index:
- # return an indexer
- if isinstance(r, slice):
- m = np.zeros(n, dtype=bool)
- m[r] = True
- r = m.nonzero()[0]
- elif com.is_bool_indexer(r):
- if len(r) != n:
- raise ValueError(
- "cannot index with a boolean indexer "
- "that is not the same length as the "
- "index"
- )
- r = r.nonzero()[0]
- return Int64Index(r)
+ n = len(self)
- def _update_indexer(idxr: Index, indexer: Index | None) -> Index:
- if indexer is None:
- return idxr
- indexer_intersection = indexer.intersection(idxr)
- if indexer_intersection.empty and not idxr.empty and not indexer.empty:
- raise KeyError(seq)
- return indexer_intersection
+ def _to_bool_indexer(indexer) -> npt.NDArray[np.bool_]:
+ if isinstance(indexer, slice):
+ new_indexer = np.zeros(n, dtype=np.bool_)
+ new_indexer[indexer] = True
+ return new_indexer
+ return indexer
+
+ # a bool indexer for the positions we want to take
+ indexer: npt.NDArray[np.bool_] | None = None
for i, k in enumerate(seq):
+ lvl_indexer: npt.NDArray[np.bool_] | slice | None = None
+
if com.is_bool_indexer(k):
- # a boolean indexer, must be the same length!
- k = np.asarray(k)
- lvl_indexer = _convert_to_indexer(k)
- indexer = _update_indexer(lvl_indexer, indexer=indexer)
+ if len(k) != n:
+ raise ValueError(
+ "cannot index with a boolean indexer that "
+ "is not the same length as the index"
+ )
+ lvl_indexer = np.asarray(k)
elif is_list_like(k):
- # a collection of labels to include from this level (these
- # are or'd)
-
- indexers: Int64Index | None = None
+ # a collection of labels to include from this level (these are or'd)
# GH#27591 check if this is a single tuple key in the level
try:
- # Argument "indexer" to "_get_level_indexer" of "MultiIndex"
- # has incompatible type "Index"; expected "Optional[Int64Index]"
- lev_loc = self._get_level_indexer(
- k, level=i, indexer=indexer # type: ignore[arg-type]
- )
+ lvl_indexer = self._get_level_indexer(k, level=i, indexer=indexer)
except (InvalidIndexError, TypeError, KeyError) as err:
# InvalidIndexError e.g. non-hashable, fall back to treating
# this as a sequence of labels
@@ -3394,11 +3362,8 @@ def _update_indexer(idxr: Index, indexer: Index | None) -> Index:
# e.g. slice
raise err
try:
- # Argument "indexer" to "_get_level_indexer" of "MultiIndex"
- # has incompatible type "Index"; expected
- # "Optional[Int64Index]"
- item_lvl_indexer = self._get_level_indexer(
- x, level=i, indexer=indexer # type: ignore[arg-type]
+ item_indexer = self._get_level_indexer(
+ x, level=i, indexer=indexer
)
except KeyError:
# ignore not founds; see discussion in GH#39424
@@ -3418,82 +3383,63 @@ def _update_indexer(idxr: Index, indexer: Index | None) -> Index:
)
continue
else:
- idxrs = _convert_to_indexer(item_lvl_indexer)
-
- if indexers is None:
- indexers = idxrs
+ if lvl_indexer is None:
+ lvl_indexer = _to_bool_indexer(item_indexer)
+ elif isinstance(item_indexer, slice):
+ lvl_indexer[item_indexer] = True # type: ignore[index]
else:
- indexers = indexers.union(idxrs, sort=False)
-
- else:
- idxrs = _convert_to_indexer(lev_loc)
- if indexers is None:
- indexers = idxrs
- else:
- indexers = indexers.union(idxrs, sort=False)
+ lvl_indexer |= item_indexer
- if indexers is not None:
- indexer = _update_indexer(indexers, indexer=indexer)
- else:
+ if lvl_indexer is None:
# no matches we are done
# test_loc_getitem_duplicates_multiindex_empty_indexer
return np.array([], dtype=np.intp)
elif com.is_null_slice(k):
# empty slice
- if indexer is None:
- indexer = Index(np.arange(n))
+ if indexer is None and i == len(seq) - 1:
+ return np.arange(n, dtype=np.intp)
+ continue
- elif isinstance(k, slice):
+ else:
+ # a slice or a single label
+ lvl_indexer = self._get_level_indexer(k, level=i, indexer=indexer)
- # a slice, include BOTH of the labels
- # Argument "indexer" to "_get_level_indexer" of "MultiIndex" has
- # incompatible type "Index"; expected "Optional[Int64Index]"
- lvl_indexer = self._get_level_indexer(
- k,
- level=i,
- indexer=indexer, # type: ignore[arg-type]
- )
- indexer = _update_indexer(
- _convert_to_indexer(lvl_indexer),
- indexer=indexer,
- )
+ # update indexer
+ lvl_indexer = _to_bool_indexer(lvl_indexer)
+ if indexer is None:
+ indexer = lvl_indexer
else:
- # a single label
- lvl_indexer = self._get_loc_level(k, level=i)[0]
- indexer = _update_indexer(
- _convert_to_indexer(lvl_indexer),
- indexer=indexer,
- )
+ indexer &= lvl_indexer
+ if not np.any(indexer) and np.any(lvl_indexer):
+ raise KeyError(seq)
# empty indexer
if indexer is None:
return np.array([], dtype=np.intp)
- assert isinstance(indexer, Int64Index), type(indexer)
- indexer = self._reorder_indexer(seq, indexer)
-
- return indexer._values.astype(np.intp, copy=False)
+ pos_indexer = indexer.nonzero()[0]
+ return self._reorder_indexer(seq, pos_indexer)
# --------------------------------------------------------------------
def _reorder_indexer(
self,
seq: tuple[Scalar | Iterable | AnyArrayLike, ...],
- indexer: Int64Index,
- ) -> Int64Index:
+ indexer: npt.NDArray[np.intp],
+ ) -> npt.NDArray[np.intp]:
"""
- Reorder an indexer of a MultiIndex (self) so that the label are in the
+ Reorder an indexer of a MultiIndex (self) so that the labels are in the
same order as given in seq
Parameters
----------
seq : label/slice/list/mask or a sequence of such
- indexer: an Int64Index indexer of self
+ indexer: a position indexer of self
Returns
-------
- indexer : a sorted Int64Index indexer of self ordered as seq
+ indexer : a sorted position indexer of self ordered as seq
"""
# If the index is lexsorted and the list_like label in seq are sorted
# then we do not need to sort
| - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
Use boolean arrays internally within `MultiIndex.get_locs` rather than int64 indexes. Logical operations show performance improvements over intersecting int64 indexes. The output remains an integer positional indexer.
```
before after ratio
[17dda440] [94121581]
<main> <multiindex-get-locs-bool-arrays>
- 563±10μs 519±8μs 0.92 indexing.MultiIndexing.time_loc_all_scalars(True)
- 33.9±0.6ms 30.4±0.4ms 0.89 indexing.MultiIndexing.time_loc_all_null_slices(True)
- 38.7±1ms 34.5±1ms 0.89 indexing.MultiIndexing.time_loc_all_null_slices(False)
- 1.62±0.02ms 1.43±0.01ms 0.88 indexing.MultiIndexing.time_loc_all_slices(True)
- 6.40±0.06ms 5.53±0.1ms 0.86 indexing.MultiIndexing.time_loc_all_bool_indexers(True)
- 107±1ms 41.6±0.6ms 0.39 indexing.MultiIndexing.time_loc_all_lists(True)
- 34.6±0.8ms 8.24±0.4ms 0.24 indexing.MultiIndexing.time_loc_all_slices(False)
- 236±4ms 23.6±0.2ms 0.10 indexing.MultiIndexing.time_loc_all_lists(False)
- 97.3±0.7ms 9.16±0.4ms 0.09 indexing.MultiIndexing.time_loc_null_slice_plus_slice(False)
- 36.5±0.5ms 1.36±0.03ms 0.04 indexing.MultiIndexing.time_loc_null_slice_plus_slice(True)
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/46330 | 2022-03-11T20:59:28Z | 2022-03-18T01:52:13Z | 2022-03-18T01:52:13Z | 2022-03-20T23:18:48Z |
PERF: faster searchsorted in tzconversion | diff --git a/pandas/_libs/tslibs/conversion.pyx b/pandas/_libs/tslibs/conversion.pyx
index 7ea3c85fe9ddb..7dce3cad9d339 100644
--- a/pandas/_libs/tslibs/conversion.pyx
+++ b/pandas/_libs/tslibs/conversion.pyx
@@ -69,6 +69,7 @@ from pandas._libs.tslibs.nattype cimport (
checknull_with_nat,
)
from pandas._libs.tslibs.tzconversion cimport (
+ bisect_right_i8,
tz_convert_utc_to_tzlocal,
tz_localize_to_utc_single,
)
@@ -536,6 +537,7 @@ cdef _TSObject _create_tsobject_tz_using_offset(npy_datetimestruct dts,
int64_t value # numpy dt64
datetime dt
ndarray[int64_t] trans
+ int64_t* tdata
int64_t[::1] deltas
value = dtstruct_to_dt64(&dts)
@@ -556,7 +558,8 @@ cdef _TSObject _create_tsobject_tz_using_offset(npy_datetimestruct dts,
trans, deltas, typ = get_dst_info(tz)
if typ == 'dateutil':
- pos = trans.searchsorted(obj.value, side='right') - 1
+ tdata = <int64_t*>cnp.PyArray_DATA(trans)
+ pos = bisect_right_i8(tdata, obj.value, trans.shape[0]) - 1
obj.fold = _infer_tsobject_fold(obj, trans, deltas, pos)
# Keep the converter same as PyDateTime's
@@ -708,7 +711,8 @@ cdef inline void _localize_tso(_TSObject obj, tzinfo tz):
ndarray[int64_t] trans
int64_t[::1] deltas
int64_t local_val
- Py_ssize_t pos
+ int64_t* tdata
+ Py_ssize_t pos, ntrans
str typ
assert obj.tzinfo is None
@@ -723,17 +727,20 @@ cdef inline void _localize_tso(_TSObject obj, tzinfo tz):
else:
# Adjust datetime64 timestamp, recompute datetimestruct
trans, deltas, typ = get_dst_info(tz)
+ ntrans = trans.shape[0]
if typ == "pytz":
# i.e. treat_tz_as_pytz(tz)
- pos = trans.searchsorted(obj.value, side="right") - 1
+ tdata = <int64_t*>cnp.PyArray_DATA(trans)
+ pos = bisect_right_i8(tdata, obj.value, ntrans) - 1
local_val = obj.value + deltas[pos]
# find right representation of dst etc in pytz timezone
tz = tz._tzinfos[tz._transition_info[pos]]
elif typ == "dateutil":
# i.e. treat_tz_as_dateutil(tz)
- pos = trans.searchsorted(obj.value, side="right") - 1
+ tdata = <int64_t*>cnp.PyArray_DATA(trans)
+ pos = bisect_right_i8(tdata, obj.value, ntrans) - 1
local_val = obj.value + deltas[pos]
# dateutil supports fold, so we infer fold from value
diff --git a/pandas/_libs/tslibs/tzconversion.pxd b/pandas/_libs/tslibs/tzconversion.pxd
index 3666d00707ac8..0837a5c436197 100644
--- a/pandas/_libs/tslibs/tzconversion.pxd
+++ b/pandas/_libs/tslibs/tzconversion.pxd
@@ -9,3 +9,5 @@ cpdef int64_t tz_convert_from_utc_single(int64_t val, tzinfo tz)
cdef int64_t tz_localize_to_utc_single(
int64_t val, tzinfo tz, object ambiguous=*, object nonexistent=*
) except? -1
+
+cdef Py_ssize_t bisect_right_i8(int64_t *data, int64_t val, Py_ssize_t n)
diff --git a/pandas/_libs/tslibs/tzconversion.pyx b/pandas/_libs/tslibs/tzconversion.pyx
index 2173ca37da7fa..111d16c27fb3f 100644
--- a/pandas/_libs/tslibs/tzconversion.pyx
+++ b/pandas/_libs/tslibs/tzconversion.pyx
@@ -116,9 +116,8 @@ timedelta-like}
"""
cdef:
int64_t[::1] deltas
- int64_t[:] idx_shifted, idx_shifted_left, idx_shifted_right
ndarray[uint8_t, cast=True] ambiguous_array, both_nat, both_eq
- Py_ssize_t i, idx, pos, ntrans, n = vals.shape[0]
+ Py_ssize_t i, isl, isr, idx, pos, ntrans, n = vals.shape[0]
Py_ssize_t delta_idx_offset, delta_idx, pos_left, pos_right
int64_t *tdata
int64_t v, left, right, val, v_left, v_right, new_local, remaining_mins
@@ -194,21 +193,28 @@ timedelta-like}
result_a[:] = NPY_NAT
result_b[:] = NPY_NAT
- idx_shifted_left = (np.maximum(0, trans.searchsorted(
- vals - DAY_NANOS, side='right') - 1)).astype(np.int64)
-
- idx_shifted_right = (np.maximum(0, trans.searchsorted(
- vals + DAY_NANOS, side='right') - 1)).astype(np.int64)
-
for i in range(n):
val = vals[i]
- v_left = val - deltas[idx_shifted_left[i]]
+ if val == NPY_NAT:
+ continue
+
+ # TODO: be careful of overflow in val-DAY_NANOS
+ isl = bisect_right_i8(tdata, val - DAY_NANOS, ntrans) - 1
+ if isl < 0:
+ isl = 0
+
+ v_left = val - deltas[isl]
pos_left = bisect_right_i8(tdata, v_left, ntrans) - 1
# timestamp falls to the left side of the DST transition
if v_left + deltas[pos_left] == val:
result_a[i] = v_left
- v_right = val - deltas[idx_shifted_right[i]]
+ # TODO: be careful of overflow in val+DAY_NANOS
+ isr = bisect_right_i8(tdata, val + DAY_NANOS, ntrans) - 1
+ if isr < 0:
+ isr = 0
+
+ v_right = val - deltas[isr]
pos_right = bisect_right_i8(tdata, v_right, ntrans) - 1
# timestamp falls to the right side of the DST transition
if v_right + deltas[pos_right] == val:
@@ -309,7 +315,9 @@ timedelta-like}
# Subtract 1 since the beginning hour is _inclusive_ of
# nonexistent times
new_local = val - remaining_mins - 1
- delta_idx = trans.searchsorted(new_local, side='right')
+
+ delta_idx = bisect_right_i8(tdata, new_local, ntrans)
+
# Shift the delta_idx by if the UTC offset of
# the target tz is greater than 0 and we're moving forward
# or vice versa
@@ -333,17 +341,22 @@ timedelta-like}
cdef inline Py_ssize_t bisect_right_i8(int64_t *data,
int64_t val, Py_ssize_t n):
+ # Caller is responsible for checking n > 0
+ # This looks very similar to local_search_right in the ndarray.searchsorted
+ # implementation.
cdef:
Py_ssize_t pivot, left = 0, right = n
- assert n >= 1
-
# edge cases
if val > data[n - 1]:
return n
- if val < data[0]:
- return 0
+ # Caller is responsible for ensuring 'val >= data[0]'. This is
+ # ensured by the fact that 'data' comes from get_dst_info where data[0]
+ # is *always* NPY_NAT+1. If that ever changes, we will need to restore
+ # the following disabled check.
+ # if val < data[0]:
+ # return 0
while left < right:
pivot = left + (right - left) // 2
@@ -403,6 +416,7 @@ cpdef int64_t tz_convert_from_utc_single(int64_t val, tzinfo tz):
int64_t delta
int64_t[::1] deltas
ndarray[int64_t, ndim=1] trans
+ int64_t* tdata
intp_t pos
if val == NPY_NAT:
@@ -418,7 +432,8 @@ cpdef int64_t tz_convert_from_utc_single(int64_t val, tzinfo tz):
return val + delta
else:
trans, deltas, _ = get_dst_info(tz)
- pos = trans.searchsorted(val, side="right") - 1
+ tdata = <int64_t*>cnp.PyArray_DATA(trans)
+ pos = bisect_right_i8(tdata, val, trans.shape[0]) - 1
return val + deltas[pos]
@@ -462,10 +477,11 @@ cdef const int64_t[:] _tz_convert_from_utc(const int64_t[:] vals, tzinfo tz):
"""
cdef:
int64_t[::1] converted, deltas
- Py_ssize_t i, n = vals.shape[0]
+ Py_ssize_t i, ntrans = -1, n = vals.shape[0]
int64_t val, delta = 0 # avoid not-initialized-warning
- intp_t[:] pos
+ intp_t pos
ndarray[int64_t] trans
+ int64_t* tdata = NULL
str typ
bint use_tzlocal = False, use_fixed = False, use_utc = True
@@ -479,13 +495,14 @@ cdef const int64_t[:] _tz_convert_from_utc(const int64_t[:] vals, tzinfo tz):
use_tzlocal = True
else:
trans, deltas, typ = get_dst_info(tz)
+ ntrans = trans.shape[0]
if typ not in ["pytz", "dateutil"]:
# FixedOffset, we know len(deltas) == 1
delta = deltas[0]
use_fixed = True
else:
- pos = trans.searchsorted(vals, side="right") - 1
+ tdata = <int64_t*>cnp.PyArray_DATA(trans)
converted = np.empty(n, dtype=np.int64)
@@ -502,7 +519,8 @@ cdef const int64_t[:] _tz_convert_from_utc(const int64_t[:] vals, tzinfo tz):
elif use_fixed:
converted[i] = val + delta
else:
- converted[i] = val + deltas[pos[i]]
+ pos = bisect_right_i8(tdata, val, ntrans) - 1
+ converted[i] = val + deltas[pos]
return converted
| ```
ts = pd.Timestamp(0)
ts2 = ts.tz_localize("US/Pacific")
%timeit ts.tz_localize("US/Pacific")
35.1 µs ± 385 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <- main
16.8 µs ± 312 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <- PR
%timeit ts2.tz_localize(None)
5.1 µs ± 54.5 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <- main
2.7 µs ± 68.4 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <- PR
``` | https://api.github.com/repos/pandas-dev/pandas/pulls/46329 | 2022-03-11T19:34:19Z | 2022-03-11T23:41:01Z | 2022-03-11T23:41:01Z | 2022-03-12T00:35:52Z |
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