title stringlengths 1 185 | diff stringlengths 0 32.2M | body stringlengths 0 123k β | url stringlengths 57 58 | created_at stringlengths 20 20 | closed_at stringlengths 20 20 | merged_at stringlengths 20 20 β | updated_at stringlengths 20 20 |
|---|---|---|---|---|---|---|---|
TST: xfail test due to new numexpr version | diff --git a/pandas/tests/frame/test_arithmetic.py b/pandas/tests/frame/test_arithmetic.py
index 24a70e55e2f0e..bb9a76829c77d 100644
--- a/pandas/tests/frame/test_arithmetic.py
+++ b/pandas/tests/frame/test_arithmetic.py
@@ -22,14 +22,12 @@
)
import pandas._testing as tm
from pandas.core.computation import expressions as expr
-from pandas.core.computation.expressions import (
- _MIN_ELEMENTS,
- NUMEXPR_INSTALLED,
-)
+from pandas.core.computation.expressions import _MIN_ELEMENTS
from pandas.tests.frame.common import (
_check_mixed_float,
_check_mixed_int,
)
+from pandas.util.version import Version
@pytest.fixture(autouse=True, params=[0, 1000000], ids=["numexpr", "python"])
@@ -501,10 +499,19 @@ def test_floordiv_axis0(self):
result2 = df.floordiv(ser.values, axis=0)
tm.assert_frame_equal(result2, expected)
- @pytest.mark.skipif(not NUMEXPR_INSTALLED, reason="numexpr not installed")
@pytest.mark.parametrize("opname", ["floordiv", "pow"])
- def test_floordiv_axis0_numexpr_path(self, opname):
+ def test_floordiv_axis0_numexpr_path(self, opname, request):
# case that goes through numexpr and has to fall back to masked_arith_op
+ ne = pytest.importorskip("numexpr")
+ if (
+ Version(ne.__version__) >= Version("2.8.7")
+ and opname == "pow"
+ and "python" in request.node.callspec.id
+ ):
+ request.node.add_marker(
+ pytest.mark.xfail(reason="https://github.com/pydata/numexpr/issues/454")
+ )
+
op = getattr(operator, opname)
arr = np.arange(_MIN_ELEMENTS + 100).reshape(_MIN_ELEMENTS // 100 + 1, -1) * 100
| null | https://api.github.com/repos/pandas-dev/pandas/pulls/55300 | 2023-09-26T18:07:06Z | 2023-09-26T21:02:55Z | 2023-09-26T21:02:55Z | 2023-09-26T21:02:58Z |
Backport PR #55275 on branch 2.1.x (Update pyproject.toml - replace `output_formatting` with `output-formatting`) | diff --git a/.github/workflows/package-checks.yml b/.github/workflows/package-checks.yml
index 04abcf4ce8816..7d20ef92587d9 100644
--- a/.github/workflows/package-checks.yml
+++ b/.github/workflows/package-checks.yml
@@ -24,7 +24,7 @@ jobs:
runs-on: ubuntu-22.04
strategy:
matrix:
- extra: ["test", "performance", "computation", "fss", "aws", "gcp", "excel", "parquet", "feather", "hdf5", "spss", "postgresql", "mysql", "sql-other", "html", "xml", "plot", "output_formatting", "clipboard", "compression", "consortium-standard", "all"]
+ extra: ["test", "performance", "computation", "fss", "aws", "gcp", "excel", "parquet", "feather", "hdf5", "spss", "postgresql", "mysql", "sql-other", "html", "xml", "plot", "output-formatting", "clipboard", "compression", "consortium-standard", "all"]
fail-fast: false
name: Install Extras - ${{ matrix.extra }}
concurrency:
diff --git a/doc/source/getting_started/install.rst b/doc/source/getting_started/install.rst
index ae7c9d4ea9c62..7570f9f33d265 100644
--- a/doc/source/getting_started/install.rst
+++ b/doc/source/getting_started/install.rst
@@ -247,14 +247,14 @@ Dependency Minimum Version pip ext
Visualization
^^^^^^^^^^^^^
-Installable with ``pip install "pandas[plot, output_formatting]"``.
+Installable with ``pip install "pandas[plot, output-formatting]"``.
========================= ================== ================== =============================================================
Dependency Minimum Version pip extra Notes
========================= ================== ================== =============================================================
matplotlib 3.6.1 plot Plotting library
-Jinja2 3.1.2 output_formatting Conditional formatting with DataFrame.style
-tabulate 0.8.10 output_formatting Printing in Markdown-friendly format (see `tabulate`_)
+Jinja2 3.1.2 output-formatting Conditional formatting with DataFrame.style
+tabulate 0.8.10 output-formatting Printing in Markdown-friendly format (see `tabulate`_)
========================= ================== ================== =============================================================
Computation
diff --git a/doc/source/whatsnew/v2.1.2.rst b/doc/source/whatsnew/v2.1.2.rst
index 97aeb56924e65..6fec66ec8d556 100644
--- a/doc/source/whatsnew/v2.1.2.rst
+++ b/doc/source/whatsnew/v2.1.2.rst
@@ -29,7 +29,7 @@ Bug fixes
Other
~~~~~
--
+- Fixed non-working installation of optional dependency group ``output_formatting``. Replacing underscore ``_`` with a dash ``-`` fixes broken dependency resolution. A correct way to use now is ``pip install pandas[output-formatting]``.
-
.. ---------------------------------------------------------------------------
diff --git a/pyproject.toml b/pyproject.toml
index 477c9be6274b3..3cfdadc268160 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -80,7 +80,7 @@ sql-other = ['SQLAlchemy>=1.4.36']
html = ['beautifulsoup4>=4.11.1', 'html5lib>=1.1', 'lxml>=4.8.0']
xml = ['lxml>=4.8.0']
plot = ['matplotlib>=3.6.1']
-output_formatting = ['jinja2>=3.1.2', 'tabulate>=0.8.10']
+output-formatting = ['jinja2>=3.1.2', 'tabulate>=0.8.10']
clipboard = ['PyQt5>=5.15.6', 'qtpy>=2.2.0']
compression = ['zstandard>=0.17.0']
consortium-standard = ['dataframe-api-compat>=0.1.7']
| Backport PR #55275: Update pyproject.toml - replace `output_formatting` with `output-formatting` | https://api.github.com/repos/pandas-dev/pandas/pulls/55291 | 2023-09-26T00:12:26Z | 2023-09-26T05:11:35Z | 2023-09-26T05:11:35Z | 2023-09-26T05:11:35Z |
Backport PR #55276 on branch 2.1.x (Bump pypa/cibuildwheel from 2.15.0 to 2.16.0) | diff --git a/.github/workflows/wheels.yml b/.github/workflows/wheels.yml
index 97d78a1a9afe3..ecb30fefb9ff2 100644
--- a/.github/workflows/wheels.yml
+++ b/.github/workflows/wheels.yml
@@ -138,7 +138,7 @@ jobs:
run: echo "sdist_name=$(cd ./dist && ls -d */)" >> "$GITHUB_ENV"
- name: Build wheels
- uses: pypa/cibuildwheel@v2.15.0
+ uses: pypa/cibuildwheel@v2.16.0
with:
package-dir: ./dist/${{ matrix.buildplat[1] == 'macosx_*' && env.sdist_name || needs.build_sdist.outputs.sdist_file }}
env:
| Backport PR #55276: Bump pypa/cibuildwheel from 2.15.0 to 2.16.0 | https://api.github.com/repos/pandas-dev/pandas/pulls/55288 | 2023-09-25T18:09:59Z | 2023-09-25T20:23:54Z | 2023-09-25T20:23:54Z | 2023-09-25T20:23:54Z |
BUG: df.resample('MS', closed='right') incorrectly places bins | diff --git a/doc/source/whatsnew/v2.1.2.rst b/doc/source/whatsnew/v2.1.2.rst
index 6fec66ec8d556..1a25b848e0f84 100644
--- a/doc/source/whatsnew/v2.1.2.rst
+++ b/doc/source/whatsnew/v2.1.2.rst
@@ -13,6 +13,7 @@ including other versions of pandas.
Fixed regressions
~~~~~~~~~~~~~~~~~
+- Fixed bug in :meth:`DataFrame.resample` where bin edges were not correct for :class:`~pandas.tseries.offsets.MonthBegin` (:issue:`55271`)
- Fixed bug where PDEP-6 warning about setting an item of an incompatible dtype was being shown when creating a new conditional column (:issue:`55025`)
-
@@ -21,7 +22,8 @@ Fixed regressions
Bug fixes
~~~~~~~~~
--
+- Fixed bug in :meth:`DataFrame.resample` not respecting ``closed`` and ``label`` arguments for :class:`~pandas.tseries.offsets.BusinessDay` (:issue:`55282`)
+- Fixed bug in :meth:`DataFrame.resample` where bin edges were not correct for :class:`~pandas.tseries.offsets.BusinessDay` (:issue:`55281`)
-
.. ---------------------------------------------------------------------------
diff --git a/pandas/core/resample.py b/pandas/core/resample.py
index 30d654078bd05..fa30e35e36925 100644
--- a/pandas/core/resample.py
+++ b/pandas/core/resample.py
@@ -2297,7 +2297,17 @@ def _adjust_bin_edges(
) -> tuple[DatetimeIndex, npt.NDArray[np.int64]]:
# Some hacks for > daily data, see #1471, #1458, #1483
- if self.freq != "D" and is_superperiod(self.freq, "D"):
+ if self.freq.name in ("BM", "ME", "W") or self.freq.name.split("-")[0] in (
+ "BQ",
+ "BA",
+ "Q",
+ "A",
+ "W",
+ ):
+ # If the right end-point is on the last day of the month, roll forwards
+ # until the last moment of that day. Note that we only do this for offsets
+ # which correspond to the end of a super-daily period - "month start", for
+ # example, is excluded.
if self.closed == "right":
# GH 21459, GH 9119: Adjust the bins relative to the wall time
edges_dti = binner.tz_localize(None)
diff --git a/pandas/tests/resample/test_datetime_index.py b/pandas/tests/resample/test_datetime_index.py
index d929dfa6e1e59..113e2d8986ad2 100644
--- a/pandas/tests/resample/test_datetime_index.py
+++ b/pandas/tests/resample/test_datetime_index.py
@@ -660,7 +660,7 @@ def test_resample_reresample(unit):
s = Series(np.random.default_rng(2).random(len(dti)), dti)
bs = s.resample("B", closed="right", label="right").mean()
result = bs.resample("8H").mean()
- assert len(result) == 22
+ assert len(result) == 25
assert isinstance(result.index.freq, offsets.DateOffset)
assert result.index.freq == offsets.Hour(8)
@@ -2051,3 +2051,66 @@ def test_resample_M_deprecated():
with tm.assert_produces_warning(UserWarning, match=depr_msg):
result = s.resample("2M").mean()
tm.assert_series_equal(result, expected)
+
+
+def test_resample_ms_closed_right():
+ # https://github.com/pandas-dev/pandas/issues/55271
+ dti = date_range(start="2020-01-31", freq="1min", periods=6000)
+ df = DataFrame({"ts": dti}, index=dti)
+ grouped = df.resample("MS", closed="right")
+ result = grouped.last()
+ expected = DataFrame(
+ {"ts": [datetime(2020, 2, 1), datetime(2020, 2, 4, 3, 59)]},
+ index=DatetimeIndex([datetime(2020, 1, 1), datetime(2020, 2, 1)], freq="MS"),
+ )
+ tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("freq", ["B", "C"])
+def test_resample_c_b_closed_right(freq: str):
+ # https://github.com/pandas-dev/pandas/issues/55281
+ dti = date_range(start="2020-01-31", freq="1min", periods=6000)
+ df = DataFrame({"ts": dti}, index=dti)
+ grouped = df.resample(freq, closed="right")
+ result = grouped.last()
+ expected = DataFrame(
+ {
+ "ts": [
+ datetime(2020, 1, 31),
+ datetime(2020, 2, 3),
+ datetime(2020, 2, 4),
+ datetime(2020, 2, 4, 3, 59),
+ ]
+ },
+ index=DatetimeIndex(
+ [
+ datetime(2020, 1, 30),
+ datetime(2020, 1, 31),
+ datetime(2020, 2, 3),
+ datetime(2020, 2, 4),
+ ],
+ freq=freq,
+ ),
+ )
+ tm.assert_frame_equal(result, expected)
+
+
+def test_resample_b_55282():
+ # https://github.com/pandas-dev/pandas/issues/55282
+ s = Series(
+ [1, 2, 3, 4, 5, 6], index=date_range("2023-09-26", periods=6, freq="12H")
+ )
+ result = s.resample("B", closed="right", label="right").mean()
+ expected = Series(
+ [1.0, 2.5, 4.5, 6.0],
+ index=DatetimeIndex(
+ [
+ datetime(2023, 9, 26),
+ datetime(2023, 9, 27),
+ datetime(2023, 9, 28),
+ datetime(2023, 9, 29),
+ ],
+ freq="B",
+ ),
+ )
+ tm.assert_series_equal(result, expected)
| - [x] closes #55271
- [x] closes #55281
- [x] closes #55282
- [ ] [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 [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
~todo: extra tests for 'B' with closed ='right' (linked issue below), whatsnew~
This fixes a regression from 2.1.0 - but, it also happens to fix two older bugs as a side-effect. So, I've put this in the 2.1.2 milestone | https://api.github.com/repos/pandas-dev/pandas/pulls/55283 | 2023-09-25T15:01:06Z | 2023-09-26T15:57:41Z | 2023-09-26T15:57:41Z | 2023-10-05T12:04:25Z |
Updated `pre-commit` hooks versions. | diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index c01bf65818167..b0b511e1048c6 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -20,11 +20,11 @@ ci:
repos:
- repo: https://github.com/hauntsaninja/black-pre-commit-mirror
# black compiled with mypyc
- rev: 23.7.0
+ rev: 23.9.1
hooks:
- id: black
- repo: https://github.com/astral-sh/ruff-pre-commit
- rev: v0.0.287
+ rev: v0.0.291
hooks:
- id: ruff
args: [--exit-non-zero-on-fix]
@@ -107,7 +107,7 @@ repos:
hooks:
- id: isort
- repo: https://github.com/asottile/pyupgrade
- rev: v3.10.1
+ rev: v3.13.0
hooks:
- id: pyupgrade
args: [--py39-plus]
diff --git a/pyproject.toml b/pyproject.toml
index 4e1c77413efda..df89bd129901f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -187,7 +187,7 @@ environment = {CFLAGS="-g0"}
[tool.black]
target-version = ['py39', 'py310']
-required-version = '23.7.0'
+required-version = '23.9.1'
exclude = '''
(
asv_bench/env
| - [ ] 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
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] 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/55277 | 2023-09-25T09:29:05Z | 2023-09-25T18:08:06Z | 2023-09-25T18:08:06Z | 2023-09-25T18:53:39Z |
Bump pypa/cibuildwheel from 2.15.0 to 2.16.0 | diff --git a/.github/workflows/wheels.yml b/.github/workflows/wheels.yml
index 83d14b51092e6..4c7a7b329777b 100644
--- a/.github/workflows/wheels.yml
+++ b/.github/workflows/wheels.yml
@@ -138,7 +138,7 @@ jobs:
run: echo "sdist_name=$(cd ./dist && ls -d */)" >> "$GITHUB_ENV"
- name: Build wheels
- uses: pypa/cibuildwheel@v2.15.0
+ uses: pypa/cibuildwheel@v2.16.0
with:
package-dir: ./dist/${{ matrix.buildplat[1] == 'macosx_*' && env.sdist_name || needs.build_sdist.outputs.sdist_file }}
env:
| Bumps [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel) from 2.15.0 to 2.16.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/pypa/cibuildwheel/releases">pypa/cibuildwheel's releases</a>.</em></p>
<blockquote>
<h2>v2.16.0</h2>
<ul>
<li>β¨ Add the ability to pass additional flags to a build frontend through the <a href="https://cibuildwheel.readthedocs.io/en/stable/options/#build-frontend">CIBW_BUILD_FRONTEND</a> option (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1588">#1588</a>).</li>
<li>β¨ The environment variable SOURCE_DATE_EPOCH is now automatically passed through to container Linux builds (useful for <a href="https://reproducible-builds.org/docs/source-date-epoch/">reproducible builds</a>!) (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1589">#1589</a>)</li>
<li>π Updates the prerelease CPython 3.12 version to 3.12.0rc2 (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1604">#1604</a>)</li>
<li>π Fix <code>requires_python</code> auto-detection from setup.py when the call to <code>setup()</code> is within an <code>if __name__ == "__main__"</code> block (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1613">#1613</a>)</li>
<li>π Fix a bug that prevented building Linux wheels in Docker on a Windows host (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1573">#1573</a>)</li>
<li>π <code>--only</code> can now select prerelease-pythons (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1564">#1564</a>)</li>
<li>π Docs & examples updates (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1582">#1582</a>, <a href="https://redirect.github.com/pypa/cibuildwheel/issues/1593">#1593</a>, <a href="https://redirect.github.com/pypa/cibuildwheel/issues/1598">#1598</a>, <a href="https://redirect.github.com/pypa/cibuildwheel/issues/1615">#1615</a>)</li>
</ul>
</blockquote>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a href="https://github.com/pypa/cibuildwheel/blob/main/docs/changelog.md">pypa/cibuildwheel's changelog</a>.</em></p>
<blockquote>
<h3>v2.16.0</h3>
<p><em>18 September 2023</em></p>
<ul>
<li>β¨ Add the ability to pass additional flags to a build frontend through the <a href="https://cibuildwheel.readthedocs.io/en/stable/options/#build-frontend">CIBW_BUILD_FRONTEND</a> option (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1588">#1588</a>).</li>
<li>β¨ The environment variable SOURCE_DATE_EPOCH is now automatically passed through to container Linux builds (useful for <a href="https://reproducible-builds.org/docs/source-date-epoch/">reproducible builds</a>!) (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1589">#1589</a>)</li>
<li>π Updates the prerelease CPython 3.12 version to 3.12.0rc2 (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1604">#1604</a>)</li>
<li>π Fix <code>requires_python</code> auto-detection from setup.py when the call to <code>setup()</code> is within an `if <strong>name</strong> == "<strong>main</strong>" block (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1613">#1613</a>)</li>
<li>π Fix a bug that prevented building Linux wheels in Docker on a Windows host (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1573">#1573</a>)</li>
<li>π <code>--only</code> can now select prerelease-pythons (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1564">#1564</a>)</li>
<li>π Docs & examples updates (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1582">#1582</a>, <a href="https://redirect.github.com/pypa/cibuildwheel/issues/1593">#1593</a>, <a href="https://redirect.github.com/pypa/cibuildwheel/issues/1598">#1598</a>, <a href="https://redirect.github.com/pypa/cibuildwheel/issues/1615">#1615</a>)</li>
</ul>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/pypa/cibuildwheel/commit/a873dd9cbf9e3c4c73a1fd11ac31cf835f6eb502"><code>a873dd9</code></a> Bump version: v2.16.0</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/e8ba0d49edd2845a1a46395921609f1b7a194bbf"><code>e8ba0d4</code></a> Merge pull request <a href="https://redirect.github.com/pypa/cibuildwheel/issues/1615">#1615</a> from pypa/dependabot/github_actions/docker/setup-qem...</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/f0feaffbaabd508d48bb83b5d46b83cac7107181"><code>f0feaff</code></a> Merge pull request <a href="https://redirect.github.com/pypa/cibuildwheel/issues/1613">#1613</a> from henryiii/henryiii/fix/mainif</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/80a54b0226a8a4cc643bc24a968570871dd84364"><code>80a54b0</code></a> Merge pull request <a href="https://redirect.github.com/pypa/cibuildwheel/issues/1589">#1589</a> from dalcinl/source_date_epoch</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/76dba0b9ba3b5143ff833d8414b023ecf2ce8a90"><code>76dba0b</code></a> Merge pull request <a href="https://redirect.github.com/pypa/cibuildwheel/issues/1588">#1588</a> from pypa/frontend-flags</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/0954ffaa6586fffcdc720ab2b788ec1abcdf3481"><code>0954ffa</code></a> Merge pull request <a href="https://redirect.github.com/pypa/cibuildwheel/issues/1618">#1618</a> from pypa/rtd-update</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/753cbd1ca526543d317f0678fb5bc16018ed5ee9"><code>753cbd1</code></a> Update RtD config to include mandatory build.os option</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/825d89818ab1c7ca0bb9b9781b0b9beb74925b6d"><code>825d898</code></a> Merge pull request <a href="https://redirect.github.com/pypa/cibuildwheel/issues/1614">#1614</a> from henryiii/henryiii/chore/minor</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/f5e60d647fe700ba6e357c30376e8a48f91e5974"><code>f5e60d6</code></a> fix: include examples too</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/adc991c47b1cb56b6de3bb052686f8081791b21b"><code>adc991c</code></a> [Bot] Update dependencies (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1604">#1604</a>)</li>
<li>Additional commits viewable in <a href="https://github.com/pypa/cibuildwheel/compare/v2.15.0...v2.16.0">compare view</a></li>
</ul>
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</details> | https://api.github.com/repos/pandas-dev/pandas/pulls/55276 | 2023-09-25T08:51:03Z | 2023-09-25T18:08:55Z | 2023-09-25T18:08:55Z | 2023-09-25T18:08:59Z |
Update pyproject.toml - replace `output_formatting` with `output-formatting` | diff --git a/.github/workflows/package-checks.yml b/.github/workflows/package-checks.yml
index 64a94d7fde5a9..a2c42af53c3a8 100644
--- a/.github/workflows/package-checks.yml
+++ b/.github/workflows/package-checks.yml
@@ -24,7 +24,7 @@ jobs:
runs-on: ubuntu-22.04
strategy:
matrix:
- extra: ["test", "performance", "computation", "fss", "aws", "gcp", "excel", "parquet", "feather", "hdf5", "spss", "postgresql", "mysql", "sql-other", "html", "xml", "plot", "output_formatting", "clipboard", "compression", "consortium-standard", "all"]
+ extra: ["test", "performance", "computation", "fss", "aws", "gcp", "excel", "parquet", "feather", "hdf5", "spss", "postgresql", "mysql", "sql-other", "html", "xml", "plot", "output-formatting", "clipboard", "compression", "consortium-standard", "all"]
fail-fast: false
name: Install Extras - ${{ matrix.extra }}
concurrency:
diff --git a/doc/source/getting_started/install.rst b/doc/source/getting_started/install.rst
index 2c0787397e047..4a0ad4ae658c3 100644
--- a/doc/source/getting_started/install.rst
+++ b/doc/source/getting_started/install.rst
@@ -247,14 +247,14 @@ Dependency Minimum Version pip ext
Visualization
^^^^^^^^^^^^^
-Installable with ``pip install "pandas[plot, output_formatting]"``.
+Installable with ``pip install "pandas[plot, output-formatting]"``.
========================= ================== ================== =============================================================
Dependency Minimum Version pip extra Notes
========================= ================== ================== =============================================================
matplotlib 3.6.1 plot Plotting library
-Jinja2 3.1.2 output_formatting Conditional formatting with DataFrame.style
-tabulate 0.8.10 output_formatting Printing in Markdown-friendly format (see `tabulate`_)
+Jinja2 3.1.2 output-formatting Conditional formatting with DataFrame.style
+tabulate 0.8.10 output-formatting Printing in Markdown-friendly format (see `tabulate`_)
========================= ================== ================== =============================================================
Computation
diff --git a/doc/source/whatsnew/v2.1.2.rst b/doc/source/whatsnew/v2.1.2.rst
index 97aeb56924e65..6fec66ec8d556 100644
--- a/doc/source/whatsnew/v2.1.2.rst
+++ b/doc/source/whatsnew/v2.1.2.rst
@@ -29,7 +29,7 @@ Bug fixes
Other
~~~~~
--
+- Fixed non-working installation of optional dependency group ``output_formatting``. Replacing underscore ``_`` with a dash ``-`` fixes broken dependency resolution. A correct way to use now is ``pip install pandas[output-formatting]``.
-
.. ---------------------------------------------------------------------------
diff --git a/pyproject.toml b/pyproject.toml
index 4e1c77413efda..f0d9dbcdf8c61 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -80,7 +80,7 @@ sql-other = ['SQLAlchemy>=1.4.36']
html = ['beautifulsoup4>=4.11.1', 'html5lib>=1.1', 'lxml>=4.8.0']
xml = ['lxml>=4.8.0']
plot = ['matplotlib>=3.6.1']
-output_formatting = ['jinja2>=3.1.2', 'tabulate>=0.8.10']
+output-formatting = ['jinja2>=3.1.2', 'tabulate>=0.8.10']
clipboard = ['PyQt5>=5.15.6', 'qtpy>=2.2.0']
compression = ['zstandard>=0.17.0']
consortium-standard = ['dataframe-api-compat>=0.1.7']
| closes #55290
Simply said, none of this would work (dependencies from `output-formatting` would not be installed):
```
pip install pandas[output-formatting]
pip install pandas[output_formatting]
pip install pandas[output-formatting,xml]
```
When being distributed, underscores are replaces with dashes. So, in the packaged PKG-INFO file you won't find spots of `output_formatting`, but only `output-formatting`:
```
Requires-Dist: lxml>=4.8.0; extra == "xml"
Requires-Dist: jinja2>=3.1.2; extra == "output-formatting"
Requires-Dist: tabulate>=0.8.10; extra == "output-formatting"
```
But on the same time, this:
```
Provides-Extra: output_formatting
```
This fix addresses that. | https://api.github.com/repos/pandas-dev/pandas/pulls/55275 | 2023-09-25T08:28:45Z | 2023-09-26T00:11:06Z | 2023-09-26T00:11:06Z | 2023-09-26T00:11:16Z |
ASV: add GroupBy.sum benchmark with timedelta and integer types | diff --git a/asv_bench/benchmarks/groupby.py b/asv_bench/benchmarks/groupby.py
index b206523dfe851..4567b5b414301 100644
--- a/asv_bench/benchmarks/groupby.py
+++ b/asv_bench/benchmarks/groupby.py
@@ -841,6 +841,23 @@ def time_groupby_sum_multiindex(self):
self.df.groupby(level=[0, 1]).sum()
+class SumTimeDelta:
+ # GH 20660
+ def setup(self):
+ N = 10**4
+ self.df = DataFrame(
+ np.random.randint(1000, 100000, (N, 100)),
+ index=np.random.randint(200, size=(N,)),
+ ).astype("timedelta64[ns]")
+ self.df_int = self.df.copy().astype("int64")
+
+ def time_groupby_sum_timedelta(self):
+ self.df.groupby(lambda x: x).sum()
+
+ def time_groupby_sum_int(self):
+ self.df_int.groupby(lambda x: x).sum()
+
+
class Transform:
def setup(self):
n1 = 400
| - [x] closes #20660
- [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).
- [x] Added new benchmark to `GroupBy.sum` method with `timedelta64` and `int64` types.
| https://api.github.com/repos/pandas-dev/pandas/pulls/55273 | 2023-09-25T03:40:18Z | 2023-09-25T18:20:52Z | 2023-09-25T18:20:52Z | 2023-09-28T20:03:00Z |
This fix enables you to preserve the datetime precision when using the melt method. | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index fa3cef6d9457d..d498c84358448 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -254,6 +254,7 @@ Bug fixes
- Bug in :class:`AbstractHolidayCalendar` where timezone data was not propagated when computing holiday observances (:issue:`54580`)
- Bug in :class:`pandas.core.window.Rolling` where duplicate datetimelike indexes are treated as consecutive rather than equal with ``closed='left'`` and ``closed='neither'`` (:issue:`20712`)
- Bug in :meth:`DataFrame.apply` where passing ``raw=True`` ignored ``args`` passed to the applied function (:issue:`55009`)
+- Bug in :meth:`pandas.DataFrame.melt` where it would not preserve the datetime (:issue:`55254`)
- Bug in :meth:`pandas.read_excel` with a ODS file without cached formatted cell for float values (:issue:`55219`)
Categorical
diff --git a/pandas/core/reshape/melt.py b/pandas/core/reshape/melt.py
index 74e6a6a28ccb0..387d43f47fe9b 100644
--- a/pandas/core/reshape/melt.py
+++ b/pandas/core/reshape/melt.py
@@ -134,7 +134,9 @@ def melt(
mcolumns = id_vars + var_name + [value_name]
- if frame.shape[1] > 0:
+ if frame.shape[1] > 0 and not any(
+ not isinstance(dt, np.dtype) and dt._supports_2d for dt in frame.dtypes
+ ):
mdata[value_name] = concat(
[frame.iloc[:, i] for i in range(frame.shape[1])]
).values
diff --git a/pandas/tests/reshape/test_melt.py b/pandas/tests/reshape/test_melt.py
index 941478066a7d8..ef748e264188c 100644
--- a/pandas/tests/reshape/test_melt.py
+++ b/pandas/tests/reshape/test_melt.py
@@ -459,6 +459,47 @@ def test_melt_ea_columns(self):
)
tm.assert_frame_equal(result, expected)
+ def test_melt_preserves_datetime(self):
+ df = DataFrame(
+ data=[
+ {
+ "type": "A0",
+ "start_date": pd.Timestamp("2023/03/01", tz="Asia/Tokyo"),
+ "end_date": pd.Timestamp("2023/03/10", tz="Asia/Tokyo"),
+ },
+ {
+ "type": "A1",
+ "start_date": pd.Timestamp("2023/03/01", tz="Asia/Tokyo"),
+ "end_date": pd.Timestamp("2023/03/11", tz="Asia/Tokyo"),
+ },
+ ],
+ index=["aaaa", "bbbb"],
+ )
+ result = df.melt(
+ id_vars=["type"],
+ value_vars=["start_date", "end_date"],
+ var_name="start/end",
+ value_name="date",
+ )
+ expected = DataFrame(
+ {
+ "type": {0: "A0", 1: "A1", 2: "A0", 3: "A1"},
+ "start/end": {
+ 0: "start_date",
+ 1: "start_date",
+ 2: "end_date",
+ 3: "end_date",
+ },
+ "date": {
+ 0: pd.Timestamp("2023-03-01 00:00:00+0900", tz="Asia/Tokyo"),
+ 1: pd.Timestamp("2023-03-01 00:00:00+0900", tz="Asia/Tokyo"),
+ 2: pd.Timestamp("2023-03-10 00:00:00+0900", tz="Asia/Tokyo"),
+ 3: pd.Timestamp("2023-03-11 00:00:00+0900", tz="Asia/Tokyo"),
+ },
+ }
+ )
+ tm.assert_frame_equal(result, expected)
+
class TestLreshape:
def test_pairs(self):
|
- [X ] closes #55254 (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 [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ 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/55270 | 2023-09-25T00:44:14Z | 2023-10-03T21:56:40Z | 2023-10-03T21:56:40Z | 2023-10-03T21:56:47Z |
TST: Clean up autouse fixtures | diff --git a/pandas/tests/arithmetic/conftest.py b/pandas/tests/arithmetic/conftest.py
index 7dd5169202ba4..7ec77e5b65b7e 100644
--- a/pandas/tests/arithmetic/conftest.py
+++ b/pandas/tests/arithmetic/conftest.py
@@ -7,23 +7,8 @@
RangeIndex,
)
import pandas._testing as tm
-from pandas.core.computation import expressions as expr
-@pytest.fixture(autouse=True, params=[0, 1000000], ids=["numexpr", "python"])
-def switch_numexpr_min_elements(request):
- _MIN_ELEMENTS = expr._MIN_ELEMENTS
- expr._MIN_ELEMENTS = request.param
- yield request.param
- expr._MIN_ELEMENTS = _MIN_ELEMENTS
-
-
-# ------------------------------------------------------------------
-
-
-# doctest with +SKIP for one fixture fails during setup with
-# 'DoctestItem' object has no attribute 'callspec'
-# due to switch_numexpr_min_elements fixture
@pytest.fixture(params=[1, np.array(1, dtype=np.int64)])
def one(request):
"""
@@ -58,9 +43,6 @@ def one(request):
zeros.extend([0, 0.0, -0.0])
-# doctest with +SKIP for zero fixture fails during setup with
-# 'DoctestItem' object has no attribute 'callspec'
-# due to switch_numexpr_min_elements fixture
@pytest.fixture(params=zeros)
def zero(request):
"""
diff --git a/pandas/tests/arithmetic/test_numeric.py b/pandas/tests/arithmetic/test_numeric.py
index fa17c24fffb26..c93a03ad0f479 100644
--- a/pandas/tests/arithmetic/test_numeric.py
+++ b/pandas/tests/arithmetic/test_numeric.py
@@ -29,6 +29,13 @@
)
+@pytest.fixture(autouse=True, params=[0, 1000000], ids=["numexpr", "python"])
+def switch_numexpr_min_elements(request, monkeypatch):
+ with monkeypatch.context() as m:
+ m.setattr(expr, "_MIN_ELEMENTS", request.param)
+ yield request.param
+
+
@pytest.fixture(params=[Index, Series, tm.to_array])
def box_pandas_1d_array(request):
"""
diff --git a/pandas/tests/frame/test_arithmetic.py b/pandas/tests/frame/test_arithmetic.py
index 1488fa65fabc0..24a70e55e2f0e 100644
--- a/pandas/tests/frame/test_arithmetic.py
+++ b/pandas/tests/frame/test_arithmetic.py
@@ -33,11 +33,10 @@
@pytest.fixture(autouse=True, params=[0, 1000000], ids=["numexpr", "python"])
-def switch_numexpr_min_elements(request):
- _MIN_ELEMENTS = expr._MIN_ELEMENTS
- expr._MIN_ELEMENTS = request.param
- yield request.param
- expr._MIN_ELEMENTS = _MIN_ELEMENTS
+def switch_numexpr_min_elements(request, monkeypatch):
+ with monkeypatch.context() as m:
+ m.setattr(expr, "_MIN_ELEMENTS", request.param)
+ yield request.param
class DummyElement:
@@ -1074,7 +1073,7 @@ def test_frame_with_frame_reindex(self):
],
ids=lambda x: x.__name__,
)
- def test_binop_other(self, op, value, dtype, switch_numexpr_min_elements, request):
+ def test_binop_other(self, op, value, dtype, switch_numexpr_min_elements):
skip = {
(operator.truediv, "bool"),
(operator.pow, "bool"),
diff --git a/pandas/tests/io/formats/test_format.py b/pandas/tests/io/formats/test_format.py
index f3a1ebe23b568..53ee449c2dc0c 100644
--- a/pandas/tests/io/formats/test_format.py
+++ b/pandas/tests/io/formats/test_format.py
@@ -22,8 +22,6 @@
import pytest
import pytz
-from pandas._config import config
-
import pandas as pd
from pandas import (
DataFrame,
@@ -51,17 +49,6 @@ def get_local_am_pm():
return am_local, pm_local
-@pytest.fixture(autouse=True)
-def clean_config():
- curr_deprecated_options = config._deprecated_options.copy()
- curr_registered_options = config._registered_options.copy()
- curr_global_config = config._global_config.copy()
- yield
- config._deprecated_options = curr_deprecated_options
- config._registered_options = curr_registered_options
- config._global_config = curr_global_config
-
-
@pytest.fixture(params=["string", "pathlike", "buffer"])
def filepath_or_buffer_id(request):
"""
@@ -3604,7 +3591,7 @@ def test_repr_html_ipython_config(ip):
df._repr_html_()
"""
)
- result = ip.run_cell(code)
+ result = ip.run_cell(code, silent=True)
assert not result.error_in_exec
diff --git a/pandas/tests/io/test_html.py b/pandas/tests/io/test_html.py
index 6cf90749e5b30..dcee52011a691 100644
--- a/pandas/tests/io/test_html.py
+++ b/pandas/tests/io/test_html.py
@@ -99,15 +99,18 @@ def test_same_ordering(datapath):
assert_framelist_equal(dfs_lxml, dfs_bs4)
-@pytest.mark.parametrize(
- "flavor",
- [
+@pytest.fixture(
+ params=[
pytest.param("bs4", marks=[td.skip_if_no("bs4"), td.skip_if_no("html5lib")]),
pytest.param("lxml", marks=td.skip_if_no("lxml")),
],
)
+def flavor_read_html(request):
+ return partial(read_html, flavor=request.param)
+
+
class TestReadHtml:
- def test_literal_html_deprecation(self):
+ def test_literal_html_deprecation(self, flavor_read_html):
# GH 53785
msg = (
"Passing literal html to 'read_html' is deprecated and "
@@ -116,7 +119,7 @@ def test_literal_html_deprecation(self):
)
with tm.assert_produces_warning(FutureWarning, match=msg):
- self.read_html(
+ flavor_read_html(
"""<table>
<thead>
<tr>
@@ -147,12 +150,7 @@ def spam_data(self, datapath):
def banklist_data(self, datapath):
return datapath("io", "data", "html", "banklist.html")
- @pytest.fixture(autouse=True)
- def set_defaults(self, flavor):
- self.read_html = partial(read_html, flavor=flavor)
- yield
-
- def test_to_html_compat(self):
+ def test_to_html_compat(self, flavor_read_html):
df = (
tm.makeCustomDataframe(
4,
@@ -165,12 +163,12 @@ def test_to_html_compat(self):
.map("{:.3f}".format).astype(float)
)
out = df.to_html()
- res = self.read_html(StringIO(out), attrs={"class": "dataframe"}, index_col=0)[
- 0
- ]
+ res = flavor_read_html(
+ StringIO(out), attrs={"class": "dataframe"}, index_col=0
+ )[0]
tm.assert_frame_equal(res, df)
- def test_dtype_backend(self, string_storage, dtype_backend):
+ def test_dtype_backend(self, string_storage, dtype_backend, flavor_read_html):
# GH#50286
df = DataFrame(
{
@@ -196,7 +194,7 @@ def test_dtype_backend(self, string_storage, dtype_backend):
out = df.to_html(index=False)
with pd.option_context("mode.string_storage", string_storage):
- result = self.read_html(StringIO(out), dtype_backend=dtype_backend)[0]
+ result = flavor_read_html(StringIO(out), dtype_backend=dtype_backend)[0]
expected = DataFrame(
{
@@ -227,16 +225,16 @@ def test_dtype_backend(self, string_storage, dtype_backend):
@pytest.mark.network
@pytest.mark.single_cpu
- def test_banklist_url(self, httpserver, banklist_data):
+ def test_banklist_url(self, httpserver, banklist_data, flavor_read_html):
with open(banklist_data, encoding="utf-8") as f:
httpserver.serve_content(content=f.read())
- df1 = self.read_html(
+ df1 = flavor_read_html(
# lxml cannot find attrs leave out for now
httpserver.url,
match="First Federal Bank of Florida", # attrs={"class": "dataTable"}
)
# lxml cannot find attrs leave out for now
- df2 = self.read_html(
+ df2 = flavor_read_html(
httpserver.url,
match="Metcalf Bank",
) # attrs={"class": "dataTable"})
@@ -245,165 +243,169 @@ def test_banklist_url(self, httpserver, banklist_data):
@pytest.mark.network
@pytest.mark.single_cpu
- def test_spam_url(self, httpserver, spam_data):
+ def test_spam_url(self, httpserver, spam_data, flavor_read_html):
with open(spam_data, encoding="utf-8") as f:
httpserver.serve_content(content=f.read())
- df1 = self.read_html(httpserver.url, match=".*Water.*")
- df2 = self.read_html(httpserver.url, match="Unit")
+ df1 = flavor_read_html(httpserver.url, match=".*Water.*")
+ df2 = flavor_read_html(httpserver.url, match="Unit")
assert_framelist_equal(df1, df2)
@pytest.mark.slow
- def test_banklist(self, banklist_data):
- df1 = self.read_html(banklist_data, match=".*Florida.*", attrs={"id": "table"})
- df2 = self.read_html(banklist_data, match="Metcalf Bank", attrs={"id": "table"})
+ def test_banklist(self, banklist_data, flavor_read_html):
+ df1 = flavor_read_html(
+ banklist_data, match=".*Florida.*", attrs={"id": "table"}
+ )
+ df2 = flavor_read_html(
+ banklist_data, match="Metcalf Bank", attrs={"id": "table"}
+ )
assert_framelist_equal(df1, df2)
- def test_spam(self, spam_data):
- df1 = self.read_html(spam_data, match=".*Water.*")
- df2 = self.read_html(spam_data, match="Unit")
+ def test_spam(self, spam_data, flavor_read_html):
+ df1 = flavor_read_html(spam_data, match=".*Water.*")
+ df2 = flavor_read_html(spam_data, match="Unit")
assert_framelist_equal(df1, df2)
assert df1[0].iloc[0, 0] == "Proximates"
assert df1[0].columns[0] == "Nutrient"
- def test_spam_no_match(self, spam_data):
- dfs = self.read_html(spam_data)
+ def test_spam_no_match(self, spam_data, flavor_read_html):
+ dfs = flavor_read_html(spam_data)
for df in dfs:
assert isinstance(df, DataFrame)
- def test_banklist_no_match(self, banklist_data):
- dfs = self.read_html(banklist_data, attrs={"id": "table"})
+ def test_banklist_no_match(self, banklist_data, flavor_read_html):
+ dfs = flavor_read_html(banklist_data, attrs={"id": "table"})
for df in dfs:
assert isinstance(df, DataFrame)
- def test_spam_header(self, spam_data):
- df = self.read_html(spam_data, match=".*Water.*", header=2)[0]
+ def test_spam_header(self, spam_data, flavor_read_html):
+ df = flavor_read_html(spam_data, match=".*Water.*", header=2)[0]
assert df.columns[0] == "Proximates"
assert not df.empty
- def test_skiprows_int(self, spam_data):
- df1 = self.read_html(spam_data, match=".*Water.*", skiprows=1)
- df2 = self.read_html(spam_data, match="Unit", skiprows=1)
+ def test_skiprows_int(self, spam_data, flavor_read_html):
+ df1 = flavor_read_html(spam_data, match=".*Water.*", skiprows=1)
+ df2 = flavor_read_html(spam_data, match="Unit", skiprows=1)
assert_framelist_equal(df1, df2)
- def test_skiprows_range(self, spam_data):
- df1 = self.read_html(spam_data, match=".*Water.*", skiprows=range(2))
- df2 = self.read_html(spam_data, match="Unit", skiprows=range(2))
+ def test_skiprows_range(self, spam_data, flavor_read_html):
+ df1 = flavor_read_html(spam_data, match=".*Water.*", skiprows=range(2))
+ df2 = flavor_read_html(spam_data, match="Unit", skiprows=range(2))
assert_framelist_equal(df1, df2)
- def test_skiprows_list(self, spam_data):
- df1 = self.read_html(spam_data, match=".*Water.*", skiprows=[1, 2])
- df2 = self.read_html(spam_data, match="Unit", skiprows=[2, 1])
+ def test_skiprows_list(self, spam_data, flavor_read_html):
+ df1 = flavor_read_html(spam_data, match=".*Water.*", skiprows=[1, 2])
+ df2 = flavor_read_html(spam_data, match="Unit", skiprows=[2, 1])
assert_framelist_equal(df1, df2)
- def test_skiprows_set(self, spam_data):
- df1 = self.read_html(spam_data, match=".*Water.*", skiprows={1, 2})
- df2 = self.read_html(spam_data, match="Unit", skiprows={2, 1})
+ def test_skiprows_set(self, spam_data, flavor_read_html):
+ df1 = flavor_read_html(spam_data, match=".*Water.*", skiprows={1, 2})
+ df2 = flavor_read_html(spam_data, match="Unit", skiprows={2, 1})
assert_framelist_equal(df1, df2)
- def test_skiprows_slice(self, spam_data):
- df1 = self.read_html(spam_data, match=".*Water.*", skiprows=1)
- df2 = self.read_html(spam_data, match="Unit", skiprows=1)
+ def test_skiprows_slice(self, spam_data, flavor_read_html):
+ df1 = flavor_read_html(spam_data, match=".*Water.*", skiprows=1)
+ df2 = flavor_read_html(spam_data, match="Unit", skiprows=1)
assert_framelist_equal(df1, df2)
- def test_skiprows_slice_short(self, spam_data):
- df1 = self.read_html(spam_data, match=".*Water.*", skiprows=slice(2))
- df2 = self.read_html(spam_data, match="Unit", skiprows=slice(2))
+ def test_skiprows_slice_short(self, spam_data, flavor_read_html):
+ df1 = flavor_read_html(spam_data, match=".*Water.*", skiprows=slice(2))
+ df2 = flavor_read_html(spam_data, match="Unit", skiprows=slice(2))
assert_framelist_equal(df1, df2)
- def test_skiprows_slice_long(self, spam_data):
- df1 = self.read_html(spam_data, match=".*Water.*", skiprows=slice(2, 5))
- df2 = self.read_html(spam_data, match="Unit", skiprows=slice(4, 1, -1))
+ def test_skiprows_slice_long(self, spam_data, flavor_read_html):
+ df1 = flavor_read_html(spam_data, match=".*Water.*", skiprows=slice(2, 5))
+ df2 = flavor_read_html(spam_data, match="Unit", skiprows=slice(4, 1, -1))
assert_framelist_equal(df1, df2)
- def test_skiprows_ndarray(self, spam_data):
- df1 = self.read_html(spam_data, match=".*Water.*", skiprows=np.arange(2))
- df2 = self.read_html(spam_data, match="Unit", skiprows=np.arange(2))
+ def test_skiprows_ndarray(self, spam_data, flavor_read_html):
+ df1 = flavor_read_html(spam_data, match=".*Water.*", skiprows=np.arange(2))
+ df2 = flavor_read_html(spam_data, match="Unit", skiprows=np.arange(2))
assert_framelist_equal(df1, df2)
- def test_skiprows_invalid(self, spam_data):
+ def test_skiprows_invalid(self, spam_data, flavor_read_html):
with pytest.raises(TypeError, match=("is not a valid type for skipping rows")):
- self.read_html(spam_data, match=".*Water.*", skiprows="asdf")
+ flavor_read_html(spam_data, match=".*Water.*", skiprows="asdf")
- def test_index(self, spam_data):
- df1 = self.read_html(spam_data, match=".*Water.*", index_col=0)
- df2 = self.read_html(spam_data, match="Unit", index_col=0)
+ def test_index(self, spam_data, flavor_read_html):
+ df1 = flavor_read_html(spam_data, match=".*Water.*", index_col=0)
+ df2 = flavor_read_html(spam_data, match="Unit", index_col=0)
assert_framelist_equal(df1, df2)
- def test_header_and_index_no_types(self, spam_data):
- df1 = self.read_html(spam_data, match=".*Water.*", header=1, index_col=0)
- df2 = self.read_html(spam_data, match="Unit", header=1, index_col=0)
+ def test_header_and_index_no_types(self, spam_data, flavor_read_html):
+ df1 = flavor_read_html(spam_data, match=".*Water.*", header=1, index_col=0)
+ df2 = flavor_read_html(spam_data, match="Unit", header=1, index_col=0)
assert_framelist_equal(df1, df2)
- def test_header_and_index_with_types(self, spam_data):
- df1 = self.read_html(spam_data, match=".*Water.*", header=1, index_col=0)
- df2 = self.read_html(spam_data, match="Unit", header=1, index_col=0)
+ def test_header_and_index_with_types(self, spam_data, flavor_read_html):
+ df1 = flavor_read_html(spam_data, match=".*Water.*", header=1, index_col=0)
+ df2 = flavor_read_html(spam_data, match="Unit", header=1, index_col=0)
assert_framelist_equal(df1, df2)
- def test_infer_types(self, spam_data):
+ def test_infer_types(self, spam_data, flavor_read_html):
# 10892 infer_types removed
- df1 = self.read_html(spam_data, match=".*Water.*", index_col=0)
- df2 = self.read_html(spam_data, match="Unit", index_col=0)
+ df1 = flavor_read_html(spam_data, match=".*Water.*", index_col=0)
+ df2 = flavor_read_html(spam_data, match="Unit", index_col=0)
assert_framelist_equal(df1, df2)
- def test_string_io(self, spam_data):
+ def test_string_io(self, spam_data, flavor_read_html):
with open(spam_data, encoding="UTF-8") as f:
data1 = StringIO(f.read())
with open(spam_data, encoding="UTF-8") as f:
data2 = StringIO(f.read())
- df1 = self.read_html(data1, match=".*Water.*")
- df2 = self.read_html(data2, match="Unit")
+ df1 = flavor_read_html(data1, match=".*Water.*")
+ df2 = flavor_read_html(data2, match="Unit")
assert_framelist_equal(df1, df2)
- def test_string(self, spam_data):
+ def test_string(self, spam_data, flavor_read_html):
with open(spam_data, encoding="UTF-8") as f:
data = f.read()
- df1 = self.read_html(StringIO(data), match=".*Water.*")
- df2 = self.read_html(StringIO(data), match="Unit")
+ df1 = flavor_read_html(StringIO(data), match=".*Water.*")
+ df2 = flavor_read_html(StringIO(data), match="Unit")
assert_framelist_equal(df1, df2)
- def test_file_like(self, spam_data):
+ def test_file_like(self, spam_data, flavor_read_html):
with open(spam_data, encoding="UTF-8") as f:
- df1 = self.read_html(f, match=".*Water.*")
+ df1 = flavor_read_html(f, match=".*Water.*")
with open(spam_data, encoding="UTF-8") as f:
- df2 = self.read_html(f, match="Unit")
+ df2 = flavor_read_html(f, match="Unit")
assert_framelist_equal(df1, df2)
@pytest.mark.network
@pytest.mark.single_cpu
- def test_bad_url_protocol(self, httpserver):
+ def test_bad_url_protocol(self, httpserver, flavor_read_html):
httpserver.serve_content("urlopen error unknown url type: git", code=404)
with pytest.raises(URLError, match="urlopen error unknown url type: git"):
- self.read_html("git://github.com", match=".*Water.*")
+ flavor_read_html("git://github.com", match=".*Water.*")
@pytest.mark.slow
@pytest.mark.network
@pytest.mark.single_cpu
- def test_invalid_url(self, httpserver):
+ def test_invalid_url(self, httpserver, flavor_read_html):
httpserver.serve_content("Name or service not known", code=404)
with pytest.raises((URLError, ValueError), match="HTTP Error 404: NOT FOUND"):
- self.read_html(httpserver.url, match=".*Water.*")
+ flavor_read_html(httpserver.url, match=".*Water.*")
@pytest.mark.slow
- def test_file_url(self, banklist_data):
+ def test_file_url(self, banklist_data, flavor_read_html):
url = banklist_data
- dfs = self.read_html(
+ dfs = flavor_read_html(
file_path_to_url(os.path.abspath(url)), match="First", attrs={"id": "table"}
)
assert isinstance(dfs, list)
@@ -411,54 +413,78 @@ def test_file_url(self, banklist_data):
assert isinstance(df, DataFrame)
@pytest.mark.slow
- def test_invalid_table_attrs(self, banklist_data):
+ def test_invalid_table_attrs(self, banklist_data, flavor_read_html):
url = banklist_data
with pytest.raises(ValueError, match="No tables found"):
- self.read_html(
+ flavor_read_html(
url, match="First Federal Bank of Florida", attrs={"id": "tasdfable"}
)
- def _bank_data(self, path, **kwargs):
- return self.read_html(path, match="Metcalf", attrs={"id": "table"}, **kwargs)
-
@pytest.mark.slow
- def test_multiindex_header(self, banklist_data):
- df = self._bank_data(banklist_data, header=[0, 1])[0]
+ def test_multiindex_header(self, banklist_data, flavor_read_html):
+ df = flavor_read_html(
+ banklist_data, match="Metcalf", attrs={"id": "table"}, header=[0, 1]
+ )[0]
assert isinstance(df.columns, MultiIndex)
@pytest.mark.slow
- def test_multiindex_index(self, banklist_data):
- df = self._bank_data(banklist_data, index_col=[0, 1])[0]
+ def test_multiindex_index(self, banklist_data, flavor_read_html):
+ df = flavor_read_html(
+ banklist_data, match="Metcalf", attrs={"id": "table"}, index_col=[0, 1]
+ )[0]
assert isinstance(df.index, MultiIndex)
@pytest.mark.slow
- def test_multiindex_header_index(self, banklist_data):
- df = self._bank_data(banklist_data, header=[0, 1], index_col=[0, 1])[0]
+ def test_multiindex_header_index(self, banklist_data, flavor_read_html):
+ df = flavor_read_html(
+ banklist_data,
+ match="Metcalf",
+ attrs={"id": "table"},
+ header=[0, 1],
+ index_col=[0, 1],
+ )[0]
assert isinstance(df.columns, MultiIndex)
assert isinstance(df.index, MultiIndex)
@pytest.mark.slow
- def test_multiindex_header_skiprows_tuples(self, banklist_data):
- df = self._bank_data(banklist_data, header=[0, 1], skiprows=1)[0]
+ def test_multiindex_header_skiprows_tuples(self, banklist_data, flavor_read_html):
+ df = flavor_read_html(
+ banklist_data,
+ match="Metcalf",
+ attrs={"id": "table"},
+ header=[0, 1],
+ skiprows=1,
+ )[0]
assert isinstance(df.columns, MultiIndex)
@pytest.mark.slow
- def test_multiindex_header_skiprows(self, banklist_data):
- df = self._bank_data(banklist_data, header=[0, 1], skiprows=1)[0]
+ def test_multiindex_header_skiprows(self, banklist_data, flavor_read_html):
+ df = flavor_read_html(
+ banklist_data,
+ match="Metcalf",
+ attrs={"id": "table"},
+ header=[0, 1],
+ skiprows=1,
+ )[0]
assert isinstance(df.columns, MultiIndex)
@pytest.mark.slow
- def test_multiindex_header_index_skiprows(self, banklist_data):
- df = self._bank_data(
- banklist_data, header=[0, 1], index_col=[0, 1], skiprows=1
+ def test_multiindex_header_index_skiprows(self, banklist_data, flavor_read_html):
+ df = flavor_read_html(
+ banklist_data,
+ match="Metcalf",
+ attrs={"id": "table"},
+ header=[0, 1],
+ index_col=[0, 1],
+ skiprows=1,
)[0]
assert isinstance(df.index, MultiIndex)
assert isinstance(df.columns, MultiIndex)
@pytest.mark.slow
- def test_regex_idempotency(self, banklist_data):
+ def test_regex_idempotency(self, banklist_data, flavor_read_html):
url = banklist_data
- dfs = self.read_html(
+ dfs = flavor_read_html(
file_path_to_url(os.path.abspath(url)),
match=re.compile(re.compile("Florida")),
attrs={"id": "table"},
@@ -467,10 +493,10 @@ def test_regex_idempotency(self, banklist_data):
for df in dfs:
assert isinstance(df, DataFrame)
- def test_negative_skiprows(self, spam_data):
+ def test_negative_skiprows(self, spam_data, flavor_read_html):
msg = r"\(you passed a negative value\)"
with pytest.raises(ValueError, match=msg):
- self.read_html(spam_data, match="Water", skiprows=-1)
+ flavor_read_html(spam_data, match="Water", skiprows=-1)
@pytest.fixture
def python_docs(self):
@@ -523,20 +549,20 @@ def python_docs(self):
@pytest.mark.network
@pytest.mark.single_cpu
- def test_multiple_matches(self, python_docs, httpserver):
+ def test_multiple_matches(self, python_docs, httpserver, flavor_read_html):
httpserver.serve_content(content=python_docs)
- dfs = self.read_html(httpserver.url, match="Python")
+ dfs = flavor_read_html(httpserver.url, match="Python")
assert len(dfs) > 1
@pytest.mark.network
@pytest.mark.single_cpu
- def test_python_docs_table(self, python_docs, httpserver):
+ def test_python_docs_table(self, python_docs, httpserver, flavor_read_html):
httpserver.serve_content(content=python_docs)
- dfs = self.read_html(httpserver.url, match="Python")
+ dfs = flavor_read_html(httpserver.url, match="Python")
zz = [df.iloc[0, 0][0:4] for df in dfs]
assert sorted(zz) == ["Pyth", "What"]
- def test_empty_tables(self):
+ def test_empty_tables(self, flavor_read_html):
"""
Make sure that read_html ignores empty tables.
"""
@@ -560,13 +586,13 @@ def test_empty_tables(self):
</tbody>
</table>
"""
- result = self.read_html(StringIO(html))
+ result = flavor_read_html(StringIO(html))
assert len(result) == 1
- def test_multiple_tbody(self):
+ def test_multiple_tbody(self, flavor_read_html):
# GH-20690
# Read all tbody tags within a single table.
- result = self.read_html(
+ result = flavor_read_html(
StringIO(
"""<table>
<thead>
@@ -595,12 +621,12 @@ def test_multiple_tbody(self):
tm.assert_frame_equal(result, expected)
- def test_header_and_one_column(self):
+ def test_header_and_one_column(self, flavor_read_html):
"""
Don't fail with bs4 when there is a header and only one column
as described in issue #9178
"""
- result = self.read_html(
+ result = flavor_read_html(
StringIO(
"""<table>
<thead>
@@ -621,11 +647,11 @@ def test_header_and_one_column(self):
tm.assert_frame_equal(result, expected)
- def test_thead_without_tr(self):
+ def test_thead_without_tr(self, flavor_read_html):
"""
Ensure parser adds <tr> within <thead> on malformed HTML.
"""
- result = self.read_html(
+ result = flavor_read_html(
StringIO(
"""<table>
<thead>
@@ -653,7 +679,7 @@ def test_thead_without_tr(self):
tm.assert_frame_equal(result, expected)
- def test_tfoot_read(self):
+ def test_tfoot_read(self, flavor_read_html):
"""
Make sure that read_html reads tfoot, containing td or th.
Ignores empty tfoot
@@ -685,16 +711,16 @@ def test_tfoot_read(self):
data1 = data_template.format(footer="")
data2 = data_template.format(footer="<tr><td>footA</td><th>footB</th></tr>")
- result1 = self.read_html(StringIO(data1))[0]
- result2 = self.read_html(StringIO(data2))[0]
+ result1 = flavor_read_html(StringIO(data1))[0]
+ result2 = flavor_read_html(StringIO(data2))[0]
tm.assert_frame_equal(result1, expected1)
tm.assert_frame_equal(result2, expected2)
- def test_parse_header_of_non_string_column(self):
+ def test_parse_header_of_non_string_column(self, flavor_read_html):
# GH5048: if header is specified explicitly, an int column should be
# parsed as int while its header is parsed as str
- result = self.read_html(
+ result = flavor_read_html(
StringIO(
"""
<table>
@@ -717,7 +743,7 @@ def test_parse_header_of_non_string_column(self):
tm.assert_frame_equal(result, expected)
@pytest.mark.slow
- def test_banklist_header(self, banklist_data, datapath):
+ def test_banklist_header(self, banklist_data, datapath, flavor_read_html):
from pandas.io.html import _remove_whitespace
def try_remove_ws(x):
@@ -726,7 +752,7 @@ def try_remove_ws(x):
except AttributeError:
return x
- df = self.read_html(banklist_data, match="Metcalf", attrs={"id": "table"})[0]
+ df = flavor_read_html(banklist_data, match="Metcalf", attrs={"id": "table"})[0]
ground_truth = read_csv(
datapath("io", "data", "csv", "banklist.csv"),
converters={"Updated Date": Timestamp, "Closing Date": Timestamp},
@@ -765,19 +791,19 @@ def try_remove_ws(x):
tm.assert_frame_equal(converted, gtnew)
@pytest.mark.slow
- def test_gold_canyon(self, banklist_data):
+ def test_gold_canyon(self, banklist_data, flavor_read_html):
gc = "Gold Canyon"
with open(banklist_data, encoding="utf-8") as f:
raw_text = f.read()
assert gc in raw_text
- df = self.read_html(banklist_data, match="Gold Canyon", attrs={"id": "table"})[
- 0
- ]
+ df = flavor_read_html(
+ banklist_data, match="Gold Canyon", attrs={"id": "table"}
+ )[0]
assert gc in df.to_string()
- def test_different_number_of_cols(self):
- expected = self.read_html(
+ def test_different_number_of_cols(self, flavor_read_html):
+ expected = flavor_read_html(
StringIO(
"""<table>
<thead>
@@ -813,7 +839,7 @@ def test_different_number_of_cols(self):
index_col=0,
)[0]
- result = self.read_html(
+ result = flavor_read_html(
StringIO(
"""<table>
<thead>
@@ -848,9 +874,9 @@ def test_different_number_of_cols(self):
tm.assert_frame_equal(result, expected)
- def test_colspan_rowspan_1(self):
+ def test_colspan_rowspan_1(self, flavor_read_html):
# GH17054
- result = self.read_html(
+ result = flavor_read_html(
StringIO(
"""
<table>
@@ -873,7 +899,7 @@ def test_colspan_rowspan_1(self):
tm.assert_frame_equal(result, expected)
- def test_colspan_rowspan_copy_values(self):
+ def test_colspan_rowspan_copy_values(self, flavor_read_html):
# GH17054
# In ASCII, with lowercase letters being copies:
@@ -881,7 +907,7 @@ def test_colspan_rowspan_copy_values(self):
# X x Y Z W
# A B b z C
- result = self.read_html(
+ result = flavor_read_html(
StringIO(
"""
<table>
@@ -908,7 +934,7 @@ def test_colspan_rowspan_copy_values(self):
tm.assert_frame_equal(result, expected)
- def test_colspan_rowspan_both_not_1(self):
+ def test_colspan_rowspan_both_not_1(self, flavor_read_html):
# GH17054
# In ASCII, with lowercase letters being copies:
@@ -916,7 +942,7 @@ def test_colspan_rowspan_both_not_1(self):
# A B b b C
# a b b b D
- result = self.read_html(
+ result = flavor_read_html(
StringIO(
"""
<table>
@@ -940,7 +966,7 @@ def test_colspan_rowspan_both_not_1(self):
tm.assert_frame_equal(result, expected)
- def test_rowspan_at_end_of_row(self):
+ def test_rowspan_at_end_of_row(self, flavor_read_html):
# GH17054
# In ASCII, with lowercase letters being copies:
@@ -948,7 +974,7 @@ def test_rowspan_at_end_of_row(self):
# A B
# C b
- result = self.read_html(
+ result = flavor_read_html(
StringIO(
"""
<table>
@@ -969,10 +995,10 @@ def test_rowspan_at_end_of_row(self):
tm.assert_frame_equal(result, expected)
- def test_rowspan_only_rows(self):
+ def test_rowspan_only_rows(self, flavor_read_html):
# GH17054
- result = self.read_html(
+ result = flavor_read_html(
StringIO(
"""
<table>
@@ -990,9 +1016,9 @@ def test_rowspan_only_rows(self):
tm.assert_frame_equal(result, expected)
- def test_header_inferred_from_rows_with_only_th(self):
+ def test_header_inferred_from_rows_with_only_th(self, flavor_read_html):
# GH17054
- result = self.read_html(
+ result = flavor_read_html(
StringIO(
"""
<table>
@@ -1018,15 +1044,15 @@ def test_header_inferred_from_rows_with_only_th(self):
tm.assert_frame_equal(result, expected)
- def test_parse_dates_list(self):
+ def test_parse_dates_list(self, flavor_read_html):
df = DataFrame({"date": date_range("1/1/2001", periods=10)})
expected = df.to_html()
- res = self.read_html(StringIO(expected), parse_dates=[1], index_col=0)
+ res = flavor_read_html(StringIO(expected), parse_dates=[1], index_col=0)
tm.assert_frame_equal(df, res[0])
- res = self.read_html(StringIO(expected), parse_dates=["date"], index_col=0)
+ res = flavor_read_html(StringIO(expected), parse_dates=["date"], index_col=0)
tm.assert_frame_equal(df, res[0])
- def test_parse_dates_combine(self):
+ def test_parse_dates_combine(self, flavor_read_html):
raw_dates = Series(date_range("1/1/2001", periods=10))
df = DataFrame(
{
@@ -1034,32 +1060,32 @@ def test_parse_dates_combine(self):
"time": raw_dates.map(lambda x: str(x.time())),
}
)
- res = self.read_html(
+ res = flavor_read_html(
StringIO(df.to_html()), parse_dates={"datetime": [1, 2]}, index_col=1
)
newdf = DataFrame({"datetime": raw_dates})
tm.assert_frame_equal(newdf, res[0])
- def test_wikipedia_states_table(self, datapath):
+ def test_wikipedia_states_table(self, datapath, flavor_read_html):
data = datapath("io", "data", "html", "wikipedia_states.html")
assert os.path.isfile(data), f"{repr(data)} is not a file"
assert os.path.getsize(data), f"{repr(data)} is an empty file"
- result = self.read_html(data, match="Arizona", header=1)[0]
+ result = flavor_read_html(data, match="Arizona", header=1)[0]
assert result.shape == (60, 12)
assert "Unnamed" in result.columns[-1]
assert result["sq mi"].dtype == np.dtype("float64")
assert np.allclose(result.loc[0, "sq mi"], 665384.04)
- def test_wikipedia_states_multiindex(self, datapath):
+ def test_wikipedia_states_multiindex(self, datapath, flavor_read_html):
data = datapath("io", "data", "html", "wikipedia_states.html")
- result = self.read_html(data, match="Arizona", index_col=0)[0]
+ result = flavor_read_html(data, match="Arizona", index_col=0)[0]
assert result.shape == (60, 11)
assert "Unnamed" in result.columns[-1][1]
assert result.columns.nlevels == 2
assert np.allclose(result.loc["Alaska", ("Total area[2]", "sq mi")], 665384.04)
- def test_parser_error_on_empty_header_row(self):
- result = self.read_html(
+ def test_parser_error_on_empty_header_row(self, flavor_read_html):
+ result = flavor_read_html(
StringIO(
"""
<table>
@@ -1083,9 +1109,9 @@ def test_parser_error_on_empty_header_row(self):
)
tm.assert_frame_equal(result[0], expected)
- def test_decimal_rows(self):
+ def test_decimal_rows(self, flavor_read_html):
# GH 12907
- result = self.read_html(
+ result = flavor_read_html(
StringIO(
"""<html>
<body>
@@ -1113,7 +1139,7 @@ def test_decimal_rows(self):
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("arg", [True, False])
- def test_bool_header_arg(self, spam_data, arg):
+ def test_bool_header_arg(self, spam_data, arg, flavor_read_html):
# GH 6114
msg = re.escape(
"Passing a bool to header is invalid. Use header=None for no header or "
@@ -1121,11 +1147,11 @@ def test_bool_header_arg(self, spam_data, arg):
"column names"
)
with pytest.raises(TypeError, match=msg):
- self.read_html(spam_data, header=arg)
+ flavor_read_html(spam_data, header=arg)
- def test_converters(self):
+ def test_converters(self, flavor_read_html):
# GH 13461
- result = self.read_html(
+ result = flavor_read_html(
StringIO(
"""<table>
<thead>
@@ -1150,9 +1176,9 @@ def test_converters(self):
tm.assert_frame_equal(result, expected)
- def test_na_values(self):
+ def test_na_values(self, flavor_read_html):
# GH 13461
- result = self.read_html(
+ result = flavor_read_html(
StringIO(
"""<table>
<thead>
@@ -1177,7 +1203,7 @@ def test_na_values(self):
tm.assert_frame_equal(result, expected)
- def test_keep_default_na(self):
+ def test_keep_default_na(self, flavor_read_html):
html_data = """<table>
<thead>
<tr>
@@ -1195,15 +1221,15 @@ def test_keep_default_na(self):
</table>"""
expected_df = DataFrame({"a": ["N/A", "NA"]})
- html_df = self.read_html(StringIO(html_data), keep_default_na=False)[0]
+ html_df = flavor_read_html(StringIO(html_data), keep_default_na=False)[0]
tm.assert_frame_equal(expected_df, html_df)
expected_df = DataFrame({"a": [np.nan, np.nan]})
- html_df = self.read_html(StringIO(html_data), keep_default_na=True)[0]
+ html_df = flavor_read_html(StringIO(html_data), keep_default_na=True)[0]
tm.assert_frame_equal(expected_df, html_df)
- def test_preserve_empty_rows(self):
- result = self.read_html(
+ def test_preserve_empty_rows(self, flavor_read_html):
+ result = flavor_read_html(
StringIO(
"""
<table>
@@ -1228,8 +1254,8 @@ def test_preserve_empty_rows(self):
tm.assert_frame_equal(result, expected)
- def test_ignore_empty_rows_when_inferring_header(self):
- result = self.read_html(
+ def test_ignore_empty_rows_when_inferring_header(self, flavor_read_html):
+ result = flavor_read_html(
StringIO(
"""
<table>
@@ -1251,7 +1277,7 @@ def test_ignore_empty_rows_when_inferring_header(self):
tm.assert_frame_equal(result, expected)
- def test_multiple_header_rows(self):
+ def test_multiple_header_rows(self, flavor_read_html):
# Issue #13434
expected_df = DataFrame(
data=[("Hillary", 68, "D"), ("Bernie", 74, "D"), ("Donald", 69, "R")]
@@ -1261,20 +1287,20 @@ def test_multiple_header_rows(self):
["Name", "Unnamed: 1_level_1", "Unnamed: 2_level_1"],
]
html = expected_df.to_html(index=False)
- html_df = self.read_html(StringIO(html))[0]
+ html_df = flavor_read_html(StringIO(html))[0]
tm.assert_frame_equal(expected_df, html_df)
- def test_works_on_valid_markup(self, datapath):
+ def test_works_on_valid_markup(self, datapath, flavor_read_html):
filename = datapath("io", "data", "html", "valid_markup.html")
- dfs = self.read_html(filename, index_col=0)
+ dfs = flavor_read_html(filename, index_col=0)
assert isinstance(dfs, list)
assert isinstance(dfs[0], DataFrame)
@pytest.mark.slow
- def test_fallback_success(self, datapath):
+ def test_fallback_success(self, datapath, flavor_read_html):
banklist_data = datapath("io", "data", "html", "banklist.html")
- self.read_html(banklist_data, match=".*Water.*", flavor=["lxml", "html5lib"])
+ flavor_read_html(banklist_data, match=".*Water.*", flavor=["lxml", "html5lib"])
def test_to_html_timestamp(self):
rng = date_range("2000-01-01", periods=10)
@@ -1309,7 +1335,7 @@ def test_to_html_borderless(self):
(False, DataFrame(["foo bar baz qux"]), DataFrame(["foo"])),
],
)
- def test_displayed_only(self, displayed_only, exp0, exp1):
+ def test_displayed_only(self, displayed_only, exp0, exp1, flavor_read_html):
# GH 20027
data = """<html>
<body>
@@ -1331,7 +1357,7 @@ def test_displayed_only(self, displayed_only, exp0, exp1):
</body>
</html>"""
- dfs = self.read_html(StringIO(data), displayed_only=displayed_only)
+ dfs = flavor_read_html(StringIO(data), displayed_only=displayed_only)
tm.assert_frame_equal(dfs[0], exp0)
if exp1 is not None:
@@ -1340,7 +1366,7 @@ def test_displayed_only(self, displayed_only, exp0, exp1):
assert len(dfs) == 1 # Should not parse hidden table
@pytest.mark.parametrize("displayed_only", [True, False])
- def test_displayed_only_with_many_elements(self, displayed_only):
+ def test_displayed_only_with_many_elements(self, displayed_only, flavor_read_html):
html_table = """
<table>
<tr>
@@ -1357,7 +1383,9 @@ def test_displayed_only_with_many_elements(self, displayed_only):
</tr>
</table>
"""
- result = read_html(StringIO(html_table), displayed_only=displayed_only)[0]
+ result = flavor_read_html(StringIO(html_table), displayed_only=displayed_only)[
+ 0
+ ]
expected = DataFrame({"A": [1, 4], "B": [2, 5]})
tm.assert_frame_equal(result, expected)
@@ -1365,23 +1393,23 @@ def test_displayed_only_with_many_elements(self, displayed_only):
"ignore:You provided Unicode markup but also provided a value for "
"from_encoding.*:UserWarning"
)
- def test_encode(self, html_encoding_file):
+ def test_encode(self, html_encoding_file, flavor_read_html):
base_path = os.path.basename(html_encoding_file)
root = os.path.splitext(base_path)[0]
_, encoding = root.split("_")
try:
with open(html_encoding_file, "rb") as fobj:
- from_string = self.read_html(
+ from_string = flavor_read_html(
fobj.read(), encoding=encoding, index_col=0
).pop()
with open(html_encoding_file, "rb") as fobj:
- from_file_like = self.read_html(
+ from_file_like = flavor_read_html(
BytesIO(fobj.read()), encoding=encoding, index_col=0
).pop()
- from_filename = self.read_html(
+ from_filename = flavor_read_html(
html_encoding_file, encoding=encoding, index_col=0
).pop()
tm.assert_frame_equal(from_string, from_file_like)
@@ -1393,10 +1421,10 @@ def test_encode(self, html_encoding_file):
pytest.skip()
raise
- def test_parse_failure_unseekable(self):
+ def test_parse_failure_unseekable(self, flavor_read_html):
# Issue #17975
- if self.read_html.keywords.get("flavor") == "lxml":
+ if flavor_read_html.keywords.get("flavor") == "lxml":
pytest.skip("Not applicable for lxml")
class UnseekableStringIO(StringIO):
@@ -1408,12 +1436,12 @@ def seekable(self):
<table><tr><td>spam<foobr />eggs</td></tr></table>"""
)
- assert self.read_html(bad)
+ assert flavor_read_html(bad)
with pytest.raises(ValueError, match="passed a non-rewindable file object"):
- self.read_html(bad)
+ flavor_read_html(bad)
- def test_parse_failure_rewinds(self):
+ def test_parse_failure_rewinds(self, flavor_read_html):
# Issue #17975
class MockFile:
@@ -1444,12 +1472,12 @@ def __iter__(self) -> Iterator:
good = MockFile("<table><tr><td>spam<br />eggs</td></tr></table>")
bad = MockFile("<table><tr><td>spam<foobr />eggs</td></tr></table>")
- assert self.read_html(good)
- assert self.read_html(bad)
+ assert flavor_read_html(good)
+ assert flavor_read_html(bad)
@pytest.mark.slow
@pytest.mark.single_cpu
- def test_importcheck_thread_safety(self, datapath):
+ def test_importcheck_thread_safety(self, datapath, flavor_read_html):
# see gh-16928
class ErrorThread(threading.Thread):
@@ -1462,8 +1490,8 @@ def run(self):
self.err = None
filename = datapath("io", "data", "html", "valid_markup.html")
- helper_thread1 = ErrorThread(target=self.read_html, args=(filename,))
- helper_thread2 = ErrorThread(target=self.read_html, args=(filename,))
+ helper_thread1 = ErrorThread(target=flavor_read_html, args=(filename,))
+ helper_thread2 = ErrorThread(target=flavor_read_html, args=(filename,))
helper_thread1.start()
helper_thread2.start()
@@ -1472,17 +1500,17 @@ def run(self):
pass
assert None is helper_thread1.err is helper_thread2.err
- def test_parse_path_object(self, datapath):
+ def test_parse_path_object(self, datapath, flavor_read_html):
# GH 37705
file_path_string = datapath("io", "data", "html", "spam.html")
file_path = Path(file_path_string)
- df1 = self.read_html(file_path_string)[0]
- df2 = self.read_html(file_path)[0]
+ df1 = flavor_read_html(file_path_string)[0]
+ df2 = flavor_read_html(file_path)[0]
tm.assert_frame_equal(df1, df2)
- def test_parse_br_as_space(self):
+ def test_parse_br_as_space(self, flavor_read_html):
# GH 29528: pd.read_html() convert <br> to space
- result = self.read_html(
+ result = flavor_read_html(
StringIO(
"""
<table>
@@ -1502,7 +1530,7 @@ def test_parse_br_as_space(self):
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("arg", ["all", "body", "header", "footer"])
- def test_extract_links(self, arg):
+ def test_extract_links(self, arg, flavor_read_html):
gh_13141_data = """
<table>
<tr>
@@ -1565,7 +1593,7 @@ def test_extract_links(self, arg):
elif arg == "header":
head_exp = gh_13141_expected["head_extract"]
- result = self.read_html(StringIO(gh_13141_data), extract_links=arg)[0]
+ result = flavor_read_html(StringIO(gh_13141_data), extract_links=arg)[0]
expected = DataFrame([data_exp, foot_exp], columns=head_exp)
expected = expected.fillna(np.nan)
tm.assert_frame_equal(result, expected)
@@ -1578,7 +1606,7 @@ def test_extract_links_bad(self, spam_data):
with pytest.raises(ValueError, match=msg):
read_html(spam_data, extract_links="incorrect")
- def test_extract_links_all_no_header(self):
+ def test_extract_links_all_no_header(self, flavor_read_html):
# GH 48316
data = """
<table>
@@ -1589,7 +1617,7 @@ def test_extract_links_all_no_header(self):
</tr>
</table>
"""
- result = self.read_html(StringIO(data), extract_links="all")[0]
+ result = flavor_read_html(StringIO(data), extract_links="all")[0]
expected = DataFrame([[("Google.com", "https://google.com")]])
tm.assert_frame_equal(result, expected)
@@ -1601,7 +1629,7 @@ def test_invalid_dtype_backend(self):
with pytest.raises(ValueError, match=msg):
read_html("test", dtype_backend="numpy")
- def test_style_tag(self):
+ def test_style_tag(self, flavor_read_html):
# GH 48316
data = """
<table>
@@ -1622,6 +1650,6 @@ def test_style_tag(self):
</tr>
</table>
"""
- result = self.read_html(StringIO(data))[0]
+ result = flavor_read_html(StringIO(data))[0]
expected = DataFrame(data=[["A1", "B1"], ["A2", "B2"]], columns=["A", "B"])
tm.assert_frame_equal(result, expected)
diff --git a/pandas/tests/series/test_arithmetic.py b/pandas/tests/series/test_arithmetic.py
index 55fc77fb5705f..8547fd6988791 100644
--- a/pandas/tests/series/test_arithmetic.py
+++ b/pandas/tests/series/test_arithmetic.py
@@ -30,11 +30,10 @@
@pytest.fixture(autouse=True, params=[0, 1000000], ids=["numexpr", "python"])
-def switch_numexpr_min_elements(request):
- _MIN_ELEMENTS = expr._MIN_ELEMENTS
- expr._MIN_ELEMENTS = request.param
- yield request.param
- expr._MIN_ELEMENTS = _MIN_ELEMENTS
+def switch_numexpr_min_elements(request, monkeypatch):
+ with monkeypatch.context() as m:
+ m.setattr(expr, "_MIN_ELEMENTS", request.param)
+ yield
def _permute(obj):
diff --git a/pandas/tests/test_expressions.py b/pandas/tests/test_expressions.py
index 1e66cefbcfdd0..dfec99f0786eb 100644
--- a/pandas/tests/test_expressions.py
+++ b/pandas/tests/test_expressions.py
@@ -102,12 +102,6 @@ def _array_mixed2(_mixed2):
@pytest.mark.skipif(not expr.USE_NUMEXPR, reason="not using numexpr")
class TestExpressions:
- @pytest.fixture(autouse=True)
- def save_min_elements(self):
- min_elements = expr._MIN_ELEMENTS
- yield
- expr._MIN_ELEMENTS = min_elements
-
@staticmethod
def call_op(df, other, flex: bool, opname: str):
if flex:
@@ -140,21 +134,24 @@ def call_op(df, other, flex: bool, opname: str):
@pytest.mark.parametrize(
"arith", ["add", "sub", "mul", "mod", "truediv", "floordiv"]
)
- def test_run_arithmetic(self, request, fixture, flex, arith):
+ def test_run_arithmetic(self, request, fixture, flex, arith, monkeypatch):
df = request.getfixturevalue(fixture)
- expr._MIN_ELEMENTS = 0
- result, expected = self.call_op(df, df, flex, arith)
-
- if arith == "truediv":
- assert all(x.kind == "f" for x in expected.dtypes.values)
- tm.assert_equal(expected, result)
+ with monkeypatch.context() as m:
+ m.setattr(expr, "_MIN_ELEMENTS", 0)
+ result, expected = self.call_op(df, df, flex, arith)
- for i in range(len(df.columns)):
- result, expected = self.call_op(df.iloc[:, i], df.iloc[:, i], flex, arith)
if arith == "truediv":
- assert expected.dtype.kind == "f"
+ assert all(x.kind == "f" for x in expected.dtypes.values)
tm.assert_equal(expected, result)
+ for i in range(len(df.columns)):
+ result, expected = self.call_op(
+ df.iloc[:, i], df.iloc[:, i], flex, arith
+ )
+ if arith == "truediv":
+ assert expected.dtype.kind == "f"
+ tm.assert_equal(expected, result)
+
@pytest.mark.parametrize(
"fixture",
[
@@ -168,7 +165,7 @@ def test_run_arithmetic(self, request, fixture, flex, arith):
],
)
@pytest.mark.parametrize("flex", [True, False])
- def test_run_binary(self, request, fixture, flex, comparison_op):
+ def test_run_binary(self, request, fixture, flex, comparison_op, monkeypatch):
"""
tests solely that the result is the same whether or not numexpr is
enabled. Need to test whether the function does the correct thing
@@ -179,18 +176,19 @@ def test_run_binary(self, request, fixture, flex, comparison_op):
with option_context("compute.use_numexpr", False):
other = df.copy() + 1
- expr._MIN_ELEMENTS = 0
- expr.set_test_mode(True)
+ with monkeypatch.context() as m:
+ m.setattr(expr, "_MIN_ELEMENTS", 0)
+ expr.set_test_mode(True)
- result, expected = self.call_op(df, other, flex, arith)
+ result, expected = self.call_op(df, other, flex, arith)
- used_numexpr = expr.get_test_result()
- assert used_numexpr, "Did not use numexpr as expected."
- tm.assert_equal(expected, result)
+ used_numexpr = expr.get_test_result()
+ assert used_numexpr, "Did not use numexpr as expected."
+ tm.assert_equal(expected, result)
- for i in range(len(df.columns)):
- binary_comp = other.iloc[:, i] + 1
- self.call_op(df.iloc[:, i], binary_comp, flex, "add")
+ for i in range(len(df.columns)):
+ binary_comp = other.iloc[:, i] + 1
+ self.call_op(df.iloc[:, i], binary_comp, flex, "add")
def test_invalid(self):
array = np.random.default_rng(2).standard_normal(1_000_001)
@@ -406,7 +404,7 @@ def test_bool_ops_column_name_dtype(self, test_input, expected):
"arith", ("add", "sub", "mul", "mod", "truediv", "floordiv")
)
@pytest.mark.parametrize("axis", (0, 1))
- def test_frame_series_axis(self, axis, arith, _frame):
+ def test_frame_series_axis(self, axis, arith, _frame, monkeypatch):
# GH#26736 Dataframe.floordiv(Series, axis=1) fails
df = _frame
@@ -415,15 +413,16 @@ def test_frame_series_axis(self, axis, arith, _frame):
else:
other = df.iloc[:, 0]
- expr._MIN_ELEMENTS = 0
+ with monkeypatch.context() as m:
+ m.setattr(expr, "_MIN_ELEMENTS", 0)
- op_func = getattr(df, arith)
+ op_func = getattr(df, arith)
- with option_context("compute.use_numexpr", False):
- expected = op_func(other, axis=axis)
+ with option_context("compute.use_numexpr", False):
+ expected = op_func(other, axis=axis)
- result = op_func(other, axis=axis)
- tm.assert_frame_equal(expected, result)
+ result = op_func(other, axis=axis)
+ tm.assert_frame_equal(expected, result)
@pytest.mark.parametrize(
"op",
@@ -436,29 +435,32 @@ def test_frame_series_axis(self, axis, arith, _frame):
)
@pytest.mark.parametrize("box", [DataFrame, Series, Index])
@pytest.mark.parametrize("scalar", [-5, 5])
- def test_python_semantics_with_numexpr_installed(self, op, box, scalar):
+ def test_python_semantics_with_numexpr_installed(
+ self, op, box, scalar, monkeypatch
+ ):
# https://github.com/pandas-dev/pandas/issues/36047
- expr._MIN_ELEMENTS = 0
- data = np.arange(-50, 50)
- obj = box(data)
- method = getattr(obj, op)
- result = method(scalar)
-
- # compare result with numpy
- with option_context("compute.use_numexpr", False):
- expected = method(scalar)
-
- tm.assert_equal(result, expected)
-
- # compare result element-wise with Python
- for i, elem in enumerate(data):
- if box == DataFrame:
- scalar_result = result.iloc[i, 0]
- else:
- scalar_result = result[i]
- try:
- expected = getattr(int(elem), op)(scalar)
- except ZeroDivisionError:
- pass
- else:
- assert scalar_result == expected
+ with monkeypatch.context() as m:
+ m.setattr(expr, "_MIN_ELEMENTS", 0)
+ data = np.arange(-50, 50)
+ obj = box(data)
+ method = getattr(obj, op)
+ result = method(scalar)
+
+ # compare result with numpy
+ with option_context("compute.use_numexpr", False):
+ expected = method(scalar)
+
+ tm.assert_equal(result, expected)
+
+ # compare result element-wise with Python
+ for i, elem in enumerate(data):
+ if box == DataFrame:
+ scalar_result = result.iloc[i, 0]
+ else:
+ scalar_result = result[i]
+ try:
+ expected = getattr(int(elem), op)(scalar)
+ except ZeroDivisionError:
+ pass
+ else:
+ assert scalar_result == expected
| Scoping the autouse functionality closer where it matters for the tests | https://api.github.com/repos/pandas-dev/pandas/pulls/55269 | 2023-09-24T20:43:33Z | 2023-09-25T18:22:34Z | 2023-09-25T18:22:34Z | 2023-09-25T18:22:37Z |
BUG: groupby.idxmax/idxmin consistently raise on unobserved categorical | diff --git a/.github/workflows/code-checks.yml b/.github/workflows/code-checks.yml
index 3bd68c07dcbc3..4260c0836bbea 100644
--- a/.github/workflows/code-checks.yml
+++ b/.github/workflows/code-checks.yml
@@ -124,7 +124,7 @@ jobs:
run: |
cd asv_bench
asv machine --yes
- asv run --quick --dry-run --durations=30 --python=same
+ asv run --quick --dry-run --durations=30 --python=same --show-stderr
build_docker_dev_environment:
name: Build Docker Dev Environment
diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 017a28ffb573a..50d89abbb286a 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -364,6 +364,7 @@ Plotting
Groupby/resample/rolling
^^^^^^^^^^^^^^^^^^^^^^^^
+- Bug in :meth:`DataFrameGroupBy.idxmax`, :meth:`DataFrameGroupBy.idxmin`, :meth:`SeriesGroupBy.idxmax`, and :meth:`SeriesGroupBy.idxmin` would not consistently raise when grouping with ``observed=False`` and unobserved categoricals (:issue:`10694`)
- Fixed bug in :meth:`DataFrame.resample` not respecting ``closed`` and ``label`` arguments for :class:`~pandas.tseries.offsets.BusinessDay` (:issue:`55282`)
- Fixed bug in :meth:`DataFrame.resample` where bin edges were not correct for :class:`~pandas.tseries.offsets.BusinessDay` (:issue:`55281`)
- Fixed bug in :meth:`DataFrame.resample` where bin edges were not correct for :class:`~pandas.tseries.offsets.MonthBegin` (:issue:`55271`)
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index e131d689b6a40..210ce8ce9fcbf 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -11910,7 +11910,7 @@ def _logical_func(
def any(
self,
- axis: Axis = 0,
+ axis: Axis | None = 0,
bool_only: bool_t = False,
skipna: bool_t = True,
**kwargs,
diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 904ab9bdfc6dd..a2f556eba08a4 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -1185,15 +1185,13 @@ def nsmallest(
def idxmin(
self, axis: Axis | lib.NoDefault = lib.no_default, skipna: bool = True
) -> Series:
- result = self._op_via_apply("idxmin", axis=axis, skipna=skipna)
- return result.astype(self.obj.index.dtype) if result.empty else result
+ return self._idxmax_idxmin("idxmin", axis=axis, skipna=skipna)
@doc(Series.idxmax.__doc__)
def idxmax(
self, axis: Axis | lib.NoDefault = lib.no_default, skipna: bool = True
) -> Series:
- result = self._op_via_apply("idxmax", axis=axis, skipna=skipna)
- return result.astype(self.obj.index.dtype) if result.empty else result
+ return self._idxmax_idxmin("idxmax", axis=axis, skipna=skipna)
@doc(Series.corr.__doc__)
def corr(
@@ -2187,22 +2185,9 @@ def idxmax(
Beef co2_emissions
dtype: object
"""
- if axis is not lib.no_default:
- if axis is None:
- axis = self.axis
- axis = self.obj._get_axis_number(axis)
- self._deprecate_axis(axis, "idxmax")
- else:
- axis = self.axis
-
- def func(df):
- return df.idxmax(axis=axis, skipna=skipna, numeric_only=numeric_only)
-
- func.__name__ = "idxmax"
- result = self._python_apply_general(
- func, self._obj_with_exclusions, not_indexed_same=True
+ return self._idxmax_idxmin(
+ "idxmax", axis=axis, numeric_only=numeric_only, skipna=skipna
)
- return result.astype(self.obj.index.dtype) if result.empty else result
def idxmin(
self,
@@ -2282,22 +2267,9 @@ def idxmin(
Beef consumption
dtype: object
"""
- if axis is not lib.no_default:
- if axis is None:
- axis = self.axis
- axis = self.obj._get_axis_number(axis)
- self._deprecate_axis(axis, "idxmin")
- else:
- axis = self.axis
-
- def func(df):
- return df.idxmin(axis=axis, skipna=skipna, numeric_only=numeric_only)
-
- func.__name__ = "idxmin"
- result = self._python_apply_general(
- func, self._obj_with_exclusions, not_indexed_same=True
+ return self._idxmax_idxmin(
+ "idxmin", axis=axis, numeric_only=numeric_only, skipna=skipna
)
- return result.astype(self.obj.index.dtype) if result.empty else result
boxplot = boxplot_frame_groupby
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index a022bfd1bd9bc..e33c4b3579c69 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -2015,10 +2015,14 @@ def _transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
with com.temp_setattr(self, "as_index", True):
# GH#49834 - result needs groups in the index for
# _wrap_transform_fast_result
- if engine is not None:
- kwargs["engine"] = engine
- kwargs["engine_kwargs"] = engine_kwargs
- result = getattr(self, func)(*args, **kwargs)
+ if func in ["idxmin", "idxmax"]:
+ func = cast(Literal["idxmin", "idxmax"], func)
+ result = self._idxmax_idxmin(func, True, *args, **kwargs)
+ else:
+ if engine is not None:
+ kwargs["engine"] = engine
+ kwargs["engine_kwargs"] = engine_kwargs
+ result = getattr(self, func)(*args, **kwargs)
return self._wrap_transform_fast_result(result)
@@ -5720,6 +5724,113 @@ def sample(
sampled_indices = np.concatenate(sampled_indices)
return self._selected_obj.take(sampled_indices, axis=self.axis)
+ def _idxmax_idxmin(
+ self,
+ how: Literal["idxmax", "idxmin"],
+ ignore_unobserved: bool = False,
+ axis: Axis | None | lib.NoDefault = lib.no_default,
+ skipna: bool = True,
+ numeric_only: bool = False,
+ ):
+ """Compute idxmax/idxmin.
+
+ Parameters
+ ----------
+ how: {"idxmin", "idxmax"}
+ Whether to compute idxmin or idxmax.
+ axis : {{0 or 'index', 1 or 'columns'}}, default None
+ The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
+ If axis is not provided, grouper's axis is used.
+ numeric_only : bool, default False
+ Include only float, int, boolean columns.
+ skipna : bool, default True
+ Exclude NA/null values. If an entire row/column is NA, the result
+ will be NA.
+ ignore_unobserved : bool, default False
+ When True and an unobserved group is encountered, do not raise. This used
+ for transform where unobserved groups do not play an impact on the result.
+
+ Returns
+ -------
+ Series or DataFrame
+ idxmax or idxmin for the groupby operation.
+ """
+ if axis is not lib.no_default:
+ if axis is None:
+ axis = self.axis
+ axis = self.obj._get_axis_number(axis)
+ self._deprecate_axis(axis, how)
+ else:
+ axis = self.axis
+
+ if not self.observed and any(
+ ping._passed_categorical for ping in self.grouper.groupings
+ ):
+ expected_len = np.prod(
+ [len(ping.group_index) for ping in self.grouper.groupings]
+ )
+ if len(self.grouper.groupings) == 1:
+ result_len = len(self.grouper.groupings[0].grouping_vector.unique())
+ else:
+ # result_index only contains observed groups in this case
+ result_len = len(self.grouper.result_index)
+ assert result_len <= expected_len
+ has_unobserved = result_len < expected_len
+
+ raise_err: bool | np.bool_ = not ignore_unobserved and has_unobserved
+ # Only raise an error if there are columns to compute; otherwise we return
+ # an empty DataFrame with an index (possibly including unobserved) but no
+ # columns
+ data = self._obj_with_exclusions
+ if raise_err and isinstance(data, DataFrame):
+ if numeric_only:
+ data = data._get_numeric_data()
+ raise_err = len(data.columns) > 0
+ else:
+ raise_err = False
+ if raise_err:
+ raise ValueError(
+ f"Can't get {how} of an empty group due to unobserved categories. "
+ "Specify observed=True in groupby instead."
+ )
+
+ try:
+ if self.obj.ndim == 1:
+ result = self._op_via_apply(how, skipna=skipna)
+ else:
+
+ def func(df):
+ method = getattr(df, how)
+ return method(axis=axis, skipna=skipna, numeric_only=numeric_only)
+
+ func.__name__ = how
+ result = self._python_apply_general(
+ func, self._obj_with_exclusions, not_indexed_same=True
+ )
+ except ValueError as err:
+ name = "argmax" if how == "idxmax" else "argmin"
+ if f"attempt to get {name} of an empty sequence" in str(err):
+ raise ValueError(
+ f"Can't get {how} of an empty group due to unobserved categories. "
+ "Specify observed=True in groupby instead."
+ ) from None
+ raise
+
+ result = result.astype(self.obj.index.dtype) if result.empty else result
+
+ if not skipna:
+ has_na_value = result.isnull().any(axis=None)
+ if has_na_value:
+ warnings.warn(
+ f"The behavior of {type(self).__name__}.{how} with all-NA "
+ "values, or any-NA and skipna=False, is deprecated. In a future "
+ "version this will raise ValueError",
+ FutureWarning,
+ stacklevel=find_stack_level(),
+ )
+
+ return result
+
@doc(GroupBy)
def get_groupby(
diff --git a/pandas/tests/groupby/test_categorical.py b/pandas/tests/groupby/test_categorical.py
index b11240c841420..11291bb89b604 100644
--- a/pandas/tests/groupby/test_categorical.py
+++ b/pandas/tests/groupby/test_categorical.py
@@ -1416,6 +1416,15 @@ def test_series_groupby_on_2_categoricals_unobserved(reduction_func, observed):
return
agg = getattr(series_groupby, reduction_func)
+
+ if not observed and reduction_func in ["idxmin", "idxmax"]:
+ # idxmin and idxmax are designed to fail on empty inputs
+ with pytest.raises(
+ ValueError, match="empty group due to unobserved categories"
+ ):
+ agg(*args)
+ return
+
result = agg(*args)
assert len(result) == expected_length
@@ -1448,6 +1457,15 @@ def test_series_groupby_on_2_categoricals_unobserved_zeroes_or_nans(
series_groupby = df.groupby(["cat_1", "cat_2"], observed=False)["value"]
agg = getattr(series_groupby, reduction_func)
+
+ if reduction_func in ["idxmin", "idxmax"]:
+ # idxmin and idxmax are designed to fail on empty inputs
+ with pytest.raises(
+ ValueError, match="empty group due to unobserved categories"
+ ):
+ agg(*args)
+ return
+
result = agg(*args)
zero_or_nan = _results_for_groupbys_with_missing_categories[reduction_func]
@@ -1514,6 +1532,15 @@ def test_dataframe_groupby_on_2_categoricals_when_observed_is_false(
df_grp = df.groupby(["cat_1", "cat_2"], observed=observed)
args = get_groupby_method_args(reduction_func, df)
+
+ if not observed and reduction_func in ["idxmin", "idxmax"]:
+ # idxmin and idxmax are designed to fail on empty inputs
+ with pytest.raises(
+ ValueError, match="empty group due to unobserved categories"
+ ):
+ getattr(df_grp, reduction_func)(*args)
+ return
+
res = getattr(df_grp, reduction_func)(*args)
expected = _results_for_groupbys_with_missing_categories[reduction_func]
@@ -1883,14 +1910,7 @@ def test_category_order_reducer(
request, as_index, sort, observed, reduction_func, index_kind, ordered
):
# GH#48749
- if (
- reduction_func in ("idxmax", "idxmin")
- and not observed
- and index_kind != "multi"
- ):
- msg = "GH#10694 - idxmax/min fail with unused categories"
- request.node.add_marker(pytest.mark.xfail(reason=msg))
- elif reduction_func == "corrwith" and not as_index:
+ if reduction_func == "corrwith" and not as_index:
msg = "GH#49950 - corrwith with as_index=False may not have grouping column"
request.node.add_marker(pytest.mark.xfail(reason=msg))
elif index_kind != "range" and not as_index:
@@ -1912,6 +1932,15 @@ def test_category_order_reducer(
df = df.set_index(keys)
args = get_groupby_method_args(reduction_func, df)
gb = df.groupby(keys, as_index=as_index, sort=sort, observed=observed)
+
+ if not observed and reduction_func in ["idxmin", "idxmax"]:
+ # idxmin and idxmax are designed to fail on empty inputs
+ with pytest.raises(
+ ValueError, match="empty group due to unobserved categories"
+ ):
+ getattr(gb, reduction_func)(*args)
+ return
+
op_result = getattr(gb, reduction_func)(*args)
if as_index:
result = op_result.index.get_level_values("a").categories
@@ -2114,6 +2143,13 @@ def test_agg_list(request, as_index, observed, reduction_func, test_series, keys
gb = gb["b"]
args = get_groupby_method_args(reduction_func, df)
+ if not observed and reduction_func in ["idxmin", "idxmax"] and keys == ["a1", "a2"]:
+ with pytest.raises(
+ ValueError, match="empty group due to unobserved categories"
+ ):
+ gb.agg([reduction_func], *args)
+ return
+
result = gb.agg([reduction_func], *args)
expected = getattr(gb, reduction_func)(*args)
diff --git a/pandas/tests/groupby/test_function.py b/pandas/tests/groupby/test_function.py
index a92880c87b847..08372541988d0 100644
--- a/pandas/tests/groupby/test_function.py
+++ b/pandas/tests/groupby/test_function.py
@@ -544,6 +544,39 @@ def test_idxmin_idxmax_axis1():
gb2.idxmax(axis=1)
+@pytest.mark.parametrize(
+ "func, values, expected_values, warn",
+ [
+ ("idxmin", [0, 1, 2], [0, 2], None),
+ ("idxmax", [0, 1, 2], [1, 2], None),
+ ("idxmin", [0, np.nan, 2], [np.nan, 2], FutureWarning),
+ ("idxmax", [0, np.nan, 2], [np.nan, 2], FutureWarning),
+ ("idxmin", [1, 0, np.nan], [1, np.nan], FutureWarning),
+ ("idxmax", [1, 0, np.nan], [0, np.nan], FutureWarning),
+ ],
+)
+@pytest.mark.parametrize("test_series", [True, False])
+def test_idxmin_idxmax_skipna_false(func, values, expected_values, warn, test_series):
+ # GH#54234
+ df = DataFrame(
+ {
+ "a": [1, 1, 2],
+ "b": values,
+ }
+ )
+ gb = df.groupby("a")
+ index = Index([1, 2], name="a")
+ expected = DataFrame({"b": expected_values}, index=index)
+ if test_series:
+ gb = gb["b"]
+ expected = expected["b"]
+ klass = "Series" if test_series else "DataFrame"
+ msg = f"The behavior of {klass}GroupBy.{func} with all-NA values"
+ with tm.assert_produces_warning(warn, match=msg):
+ result = getattr(gb, func)(skipna=False)
+ tm.assert_equal(result, expected)
+
+
@pytest.mark.parametrize("numeric_only", [True, False, None])
def test_axis1_numeric_only(request, groupby_func, numeric_only):
if groupby_func in ("idxmax", "idxmin"):
diff --git a/pandas/tests/groupby/test_groupby.py b/pandas/tests/groupby/test_groupby.py
index 4ca8b0e317bd2..e3e4a7efaa307 100644
--- a/pandas/tests/groupby/test_groupby.py
+++ b/pandas/tests/groupby/test_groupby.py
@@ -2001,22 +2001,10 @@ def test_pivot_table_values_key_error():
@pytest.mark.parametrize(
"op", ["idxmax", "idxmin", "min", "max", "sum", "prod", "skew"]
)
-def test_empty_groupby(
- columns, keys, values, method, op, request, using_array_manager, dropna
-):
+def test_empty_groupby(columns, keys, values, method, op, using_array_manager, dropna):
# GH8093 & GH26411
override_dtype = None
- if (
- isinstance(values, Categorical)
- and len(keys) == 1
- and op in ["idxmax", "idxmin"]
- ):
- mark = pytest.mark.xfail(
- raises=ValueError, match="attempt to get arg(min|max) of an empty sequence"
- )
- request.node.add_marker(mark)
-
if isinstance(values, BooleanArray) and op in ["sum", "prod"]:
# We expect to get Int64 back for these
override_dtype = "Int64"
@@ -2061,12 +2049,21 @@ def get_categorical_invalid_expected():
is_dt64 = df.dtypes.iloc[0].kind == "M"
is_cat = isinstance(values, Categorical)
- if isinstance(values, Categorical) and not values.ordered and op in ["min", "max"]:
- msg = f"Cannot perform {op} with non-ordered Categorical"
- with pytest.raises(TypeError, match=msg):
+ if (
+ isinstance(values, Categorical)
+ and not values.ordered
+ and op in ["min", "max", "idxmin", "idxmax"]
+ ):
+ if op in ["min", "max"]:
+ msg = f"Cannot perform {op} with non-ordered Categorical"
+ klass = TypeError
+ else:
+ msg = f"Can't get {op} of an empty group due to unobserved categories"
+ klass = ValueError
+ with pytest.raises(klass, match=msg):
get_result()
- if isinstance(columns, list):
+ if op in ["min", "max"] and isinstance(columns, list):
# i.e. DataframeGroupBy, not SeriesGroupBy
result = get_result(numeric_only=True)
expected = get_categorical_invalid_expected()
diff --git a/pandas/tests/groupby/test_groupby_dropna.py b/pandas/tests/groupby/test_groupby_dropna.py
index d82278c277d48..8065aa63dff81 100644
--- a/pandas/tests/groupby/test_groupby_dropna.py
+++ b/pandas/tests/groupby/test_groupby_dropna.py
@@ -503,18 +503,7 @@ def test_null_is_null_for_dtype(
@pytest.mark.parametrize("index_kind", ["range", "single", "multi"])
-def test_categorical_reducers(
- request, reduction_func, observed, sort, as_index, index_kind
-):
- # GH#36327
- if (
- reduction_func in ("idxmin", "idxmax")
- and not observed
- and index_kind != "multi"
- ):
- msg = "GH#10694 - idxmin/max broken for categorical with observed=False"
- request.node.add_marker(pytest.mark.xfail(reason=msg))
-
+def test_categorical_reducers(reduction_func, observed, sort, as_index, index_kind):
# Ensure there is at least one null value by appending to the end
values = np.append(np.random.default_rng(2).choice([1, 2, None], size=19), None)
df = pd.DataFrame(
@@ -544,6 +533,17 @@ def test_categorical_reducers(
args = (args[0].drop(columns=keys),)
args_filled = (args_filled[0].drop(columns=keys),)
+ gb_keepna = df.groupby(
+ keys, dropna=False, observed=observed, sort=sort, as_index=as_index
+ )
+
+ if not observed and reduction_func in ["idxmin", "idxmax"]:
+ with pytest.raises(
+ ValueError, match="empty group due to unobserved categories"
+ ):
+ getattr(gb_keepna, reduction_func)(*args)
+ return
+
gb_filled = df_filled.groupby(keys, observed=observed, sort=sort, as_index=True)
expected = getattr(gb_filled, reduction_func)(*args_filled).reset_index()
expected["x"] = expected["x"].replace(4, None)
@@ -573,9 +573,6 @@ def test_categorical_reducers(
if as_index:
expected = expected["size"].rename(None)
- gb_keepna = df.groupby(
- keys, dropna=False, observed=observed, sort=sort, as_index=as_index
- )
if as_index or index_kind == "range" or reduction_func == "size":
warn = None
else:
diff --git a/pandas/tests/groupby/test_raises.py b/pandas/tests/groupby/test_raises.py
index f9a2b3d44b117..46bf324fad1d7 100644
--- a/pandas/tests/groupby/test_raises.py
+++ b/pandas/tests/groupby/test_raises.py
@@ -97,22 +97,24 @@ def df_with_cat_col():
return df
-def _call_and_check(klass, msg, how, gb, groupby_func, args):
- if klass is None:
- if how == "method":
- getattr(gb, groupby_func)(*args)
- elif how == "agg":
- gb.agg(groupby_func, *args)
- else:
- gb.transform(groupby_func, *args)
- else:
- with pytest.raises(klass, match=msg):
+def _call_and_check(klass, msg, how, gb, groupby_func, args, warn_msg=""):
+ warn_klass = None if warn_msg == "" else FutureWarning
+ with tm.assert_produces_warning(warn_klass, match=warn_msg):
+ if klass is None:
if how == "method":
getattr(gb, groupby_func)(*args)
elif how == "agg":
gb.agg(groupby_func, *args)
else:
gb.transform(groupby_func, *args)
+ else:
+ with pytest.raises(klass, match=msg):
+ if how == "method":
+ getattr(gb, groupby_func)(*args)
+ elif how == "agg":
+ gb.agg(groupby_func, *args)
+ else:
+ gb.transform(groupby_func, *args)
@pytest.mark.parametrize("how", ["method", "agg", "transform"])
@@ -233,8 +235,7 @@ def test_groupby_raises_string_np(
warn_msg = "using SeriesGroupBy.[sum|mean]"
else:
warn_msg = "using DataFrameGroupBy.[sum|mean]"
- with tm.assert_produces_warning(FutureWarning, match=warn_msg):
- _call_and_check(klass, msg, how, gb, groupby_func_np, ())
+ _call_and_check(klass, msg, how, gb, groupby_func_np, (), warn_msg=warn_msg)
@pytest.mark.parametrize("how", ["method", "agg", "transform"])
@@ -297,13 +298,11 @@ def test_groupby_raises_datetime(
"var": (TypeError, "datetime64 type does not support var operations"),
}[groupby_func]
- warn = None
- warn_msg = f"'{groupby_func}' with datetime64 dtypes is deprecated"
if groupby_func in ["any", "all"]:
- warn = FutureWarning
-
- with tm.assert_produces_warning(warn, match=warn_msg):
- _call_and_check(klass, msg, how, gb, groupby_func, args)
+ warn_msg = f"'{groupby_func}' with datetime64 dtypes is deprecated"
+ else:
+ warn_msg = ""
+ _call_and_check(klass, msg, how, gb, groupby_func, args, warn_msg=warn_msg)
@pytest.mark.parametrize("how", ["agg", "transform"])
@@ -342,8 +341,7 @@ def test_groupby_raises_datetime_np(
warn_msg = "using SeriesGroupBy.[sum|mean]"
else:
warn_msg = "using DataFrameGroupBy.[sum|mean]"
- with tm.assert_produces_warning(FutureWarning, match=warn_msg):
- _call_and_check(klass, msg, how, gb, groupby_func_np, ())
+ _call_and_check(klass, msg, how, gb, groupby_func_np, (), warn_msg=warn_msg)
@pytest.mark.parametrize("func", ["prod", "cumprod", "skew", "var"])
@@ -540,8 +538,7 @@ def test_groupby_raises_category_np(
warn_msg = "using SeriesGroupBy.[sum|mean]"
else:
warn_msg = "using DataFrameGroupBy.[sum|mean]"
- with tm.assert_produces_warning(FutureWarning, match=warn_msg):
- _call_and_check(klass, msg, how, gb, groupby_func_np, ())
+ _call_and_check(klass, msg, how, gb, groupby_func_np, (), warn_msg=warn_msg)
@pytest.mark.parametrize("how", ["method", "agg", "transform"])
@@ -572,6 +569,16 @@ def test_groupby_raises_category_on_category(
return
empty_groups = any(group.empty for group in gb.groups.values())
+ if (
+ not observed
+ and how != "transform"
+ and isinstance(by, list)
+ and isinstance(by[0], str)
+ and by == ["a", "b"]
+ ):
+ assert not empty_groups
+ # TODO: empty_groups should be true due to unobserved categorical combinations
+ empty_groups = True
klass, msg = {
"all": (None, ""),
@@ -617,10 +624,10 @@ def test_groupby_raises_category_on_category(
if not using_copy_on_write
else (None, ""), # no-op with CoW
"first": (None, ""),
- "idxmax": (ValueError, "attempt to get argmax of an empty sequence")
+ "idxmax": (ValueError, "empty group due to unobserved categories")
if empty_groups
else (None, ""),
- "idxmin": (ValueError, "attempt to get argmin of an empty sequence")
+ "idxmin": (ValueError, "empty group due to unobserved categories")
if empty_groups
else (None, ""),
"last": (None, ""),
diff --git a/pandas/tests/groupby/transform/test_transform.py b/pandas/tests/groupby/transform/test_transform.py
index cf3f41e04902c..4a493ef3fd52c 100644
--- a/pandas/tests/groupby/transform/test_transform.py
+++ b/pandas/tests/groupby/transform/test_transform.py
@@ -1637,3 +1637,19 @@ def test_as_index_no_change(keys, df, groupby_func):
result = gb_as_index_true.transform(groupby_func, *args)
expected = gb_as_index_false.transform(groupby_func, *args)
tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize("how", ["idxmax", "idxmin"])
+@pytest.mark.parametrize("numeric_only", [True, False])
+def test_idxmin_idxmax_transform_args(how, skipna, numeric_only):
+ # GH#55268 - ensure *args are passed through when calling transform
+ df = DataFrame({"a": [1, 1, 1, 2], "b": [3.0, 4.0, np.nan, 6.0], "c": list("abcd")})
+ gb = df.groupby("a")
+ msg = f"'axis' keyword in DataFrameGroupBy.{how} is deprecated"
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ result = gb.transform(how, 0, skipna, numeric_only)
+ warn = None if skipna else FutureWarning
+ msg = f"The behavior of DataFrame.{how} with .* any-NA and skipna=False"
+ with tm.assert_produces_warning(warn, match=msg):
+ expected = gb.transform(how, skipna=skipna, numeric_only=numeric_only)
+ tm.assert_frame_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 [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
Part of #10694, but doesn't close it fully.
- When given a non-ordered category with unobserved categories, we currently raise about the unobserved categories. We should instead raise about the non-orderedness for consistency with min/max.
- _python_apply_general does not return the correct dtype when there is a CategoricalIndex will NaN values.
- _python_apply_general fails on a single grouping with unobserved categories even when we're call it from transform where the unobserved categories should have no impact.
- _python_apply_general with an empty DataFrame with no numeric columns returns all the columns even when `numeric_only=True`.
All of these are fixed in #54234 | https://api.github.com/repos/pandas-dev/pandas/pulls/55268 | 2023-09-24T20:10:46Z | 2023-10-08T18:49:58Z | 2023-10-08T18:49:58Z | 2023-10-09T20:25:11Z |
TST: Load iris and types data in sql tests as needed | diff --git a/pandas/tests/io/test_sql.py b/pandas/tests/io/test_sql.py
index 63546b44e92be..c30965feeb586 100644
--- a/pandas/tests/io/test_sql.py
+++ b/pandas/tests/io/test_sql.py
@@ -24,7 +24,6 @@
import pandas as pd
from pandas import (
DataFrame,
- DatetimeTZDtype,
Index,
MultiIndex,
Series,
@@ -86,17 +85,18 @@ def sql_strings():
}
-def iris_table_metadata(dialect: str):
+def iris_table_metadata():
+ import sqlalchemy
from sqlalchemy import (
- REAL,
Column,
+ Double,
Float,
MetaData,
String,
Table,
)
- dtype = Float if dialect == "postgresql" else REAL
+ dtype = Double if Version(sqlalchemy.__version__) >= Version("2.0.0") else Float
metadata = MetaData()
iris = Table(
"iris",
@@ -127,11 +127,11 @@ def create_and_load_iris_sqlite3(conn: sqlite3.Connection, iris_file: Path):
cur.executemany(stmt, reader)
-def create_and_load_iris(conn, iris_file: Path, dialect: str):
+def create_and_load_iris(conn, iris_file: Path):
from sqlalchemy import insert
from sqlalchemy.engine import Engine
- iris = iris_table_metadata(dialect)
+ iris = iris_table_metadata()
with iris_file.open(newline=None, encoding="utf-8") as csvfile:
reader = csv.reader(csvfile)
@@ -198,8 +198,6 @@ def types_table_metadata(dialect: str):
Column("IntColWithNull", Integer),
Column("BoolColWithNull", bool_type),
)
- if dialect == "postgresql":
- types.append_column(Column("DateColWithTz", DateTime(timezone=True)))
return types
@@ -245,6 +243,51 @@ def create_and_load_types(conn, types_data: list[dict], dialect: str):
conn.execute(stmt)
+def create_and_load_postgres_datetz(conn):
+ from sqlalchemy import (
+ Column,
+ DateTime,
+ MetaData,
+ Table,
+ insert,
+ )
+ from sqlalchemy.engine import Engine
+
+ metadata = MetaData()
+ datetz = Table("datetz", metadata, Column("DateColWithTz", DateTime(timezone=True)))
+ datetz_data = [
+ {
+ "DateColWithTz": "2000-01-01 00:00:00-08:00",
+ },
+ {
+ "DateColWithTz": "2000-06-01 00:00:00-07:00",
+ },
+ ]
+ stmt = insert(datetz).values(datetz_data)
+ if isinstance(conn, Engine):
+ with conn.connect() as conn:
+ with conn.begin():
+ datetz.drop(conn, checkfirst=True)
+ datetz.create(bind=conn)
+ conn.execute(stmt)
+ else:
+ with conn.begin():
+ datetz.drop(conn, checkfirst=True)
+ datetz.create(bind=conn)
+ conn.execute(stmt)
+
+ # "2000-01-01 00:00:00-08:00" should convert to
+ # "2000-01-01 08:00:00"
+ # "2000-06-01 00:00:00-07:00" should convert to
+ # "2000-06-01 07:00:00"
+ # GH 6415
+ expected_data = [
+ Timestamp("2000-01-01 08:00:00", tz="UTC"),
+ Timestamp("2000-06-01 07:00:00", tz="UTC"),
+ ]
+ return Series(expected_data, name="DateColWithTz")
+
+
def check_iris_frame(frame: DataFrame):
pytype = frame.dtypes.iloc[0].type
row = frame.iloc[0]
@@ -295,7 +338,6 @@ def types_data():
"BoolCol": False,
"IntColWithNull": 1,
"BoolColWithNull": False,
- "DateColWithTz": "2000-01-01 00:00:00-08:00",
},
{
"TextCol": "first",
@@ -307,7 +349,6 @@ def types_data():
"BoolCol": False,
"IntColWithNull": None,
"BoolColWithNull": None,
- "DateColWithTz": "2000-06-01 00:00:00-07:00",
},
]
@@ -429,7 +470,7 @@ def drop_view(
@pytest.fixture
-def mysql_pymysql_engine(iris_path, types_data):
+def mysql_pymysql_engine():
sqlalchemy = pytest.importorskip("sqlalchemy")
pymysql = pytest.importorskip("pymysql")
engine = sqlalchemy.create_engine(
@@ -437,15 +478,6 @@ def mysql_pymysql_engine(iris_path, types_data):
connect_args={"client_flag": pymysql.constants.CLIENT.MULTI_STATEMENTS},
poolclass=sqlalchemy.pool.NullPool,
)
- insp = sqlalchemy.inspect(engine)
- if not insp.has_table("iris"):
- create_and_load_iris(engine, iris_path, "mysql")
- if not insp.has_table("types"):
- for entry in types_data:
- entry.pop("DateColWithTz")
- create_and_load_types(engine, types_data, "mysql")
- if not insp.has_table("iris_view"):
- create_and_load_iris_view(engine)
yield engine
for view in get_all_views(engine):
drop_view(view, engine)
@@ -455,26 +487,44 @@ def mysql_pymysql_engine(iris_path, types_data):
@pytest.fixture
-def mysql_pymysql_conn(iris_path, mysql_pymysql_engine):
+def mysql_pymysql_engine_iris(mysql_pymysql_engine, iris_path):
+ create_and_load_iris(mysql_pymysql_engine, iris_path)
+ create_and_load_iris_view(mysql_pymysql_engine)
+ yield mysql_pymysql_engine
+
+
+@pytest.fixture
+def mysql_pymysql_engine_types(mysql_pymysql_engine, types_data):
+ create_and_load_types(mysql_pymysql_engine, types_data, "mysql")
+ yield mysql_pymysql_engine
+
+
+@pytest.fixture
+def mysql_pymysql_conn(mysql_pymysql_engine):
with mysql_pymysql_engine.connect() as conn:
yield conn
@pytest.fixture
-def postgresql_psycopg2_engine(iris_path, types_data):
+def mysql_pymysql_conn_iris(mysql_pymysql_engine_iris):
+ with mysql_pymysql_engine_iris.connect() as conn:
+ yield conn
+
+
+@pytest.fixture
+def mysql_pymysql_conn_types(mysql_pymysql_engine_types):
+ with mysql_pymysql_engine_types.connect() as conn:
+ yield conn
+
+
+@pytest.fixture
+def postgresql_psycopg2_engine():
sqlalchemy = pytest.importorskip("sqlalchemy")
pytest.importorskip("psycopg2")
engine = sqlalchemy.create_engine(
"postgresql+psycopg2://postgres:postgres@localhost:5432/pandas",
poolclass=sqlalchemy.pool.NullPool,
)
- insp = sqlalchemy.inspect(engine)
- if not insp.has_table("iris"):
- create_and_load_iris(engine, iris_path, "postgresql")
- if not insp.has_table("types"):
- create_and_load_types(engine, types_data, "postgresql")
- if not insp.has_table("iris_view"):
- create_and_load_iris_view(engine)
yield engine
for view in get_all_views(engine):
drop_view(view, engine)
@@ -483,34 +533,48 @@ def postgresql_psycopg2_engine(iris_path, types_data):
engine.dispose()
+@pytest.fixture
+def postgresql_psycopg2_engine_iris(postgresql_psycopg2_engine, iris_path):
+ create_and_load_iris(postgresql_psycopg2_engine, iris_path)
+ create_and_load_iris_view(postgresql_psycopg2_engine)
+ yield postgresql_psycopg2_engine
+
+
+@pytest.fixture
+def postgresql_psycopg2_engine_types(postgresql_psycopg2_engine, types_data):
+ create_and_load_types(postgresql_psycopg2_engine, types_data, "postgres")
+ yield postgresql_psycopg2_engine
+
+
@pytest.fixture
def postgresql_psycopg2_conn(postgresql_psycopg2_engine):
with postgresql_psycopg2_engine.connect() as conn:
yield conn
+@pytest.fixture
+def postgresql_psycopg2_conn_iris(postgresql_psycopg2_engine_iris):
+ with postgresql_psycopg2_engine_iris.connect() as conn:
+ yield conn
+
+
+@pytest.fixture
+def postgresql_psycopg2_conn_types(postgresql_psycopg2_engine_types):
+ with postgresql_psycopg2_engine_types.connect() as conn:
+ yield conn
+
+
@pytest.fixture
def sqlite_str():
pytest.importorskip("sqlalchemy")
with tm.ensure_clean() as name:
- yield "sqlite:///" + name
+ yield f"sqlite:///{name}"
@pytest.fixture
-def sqlite_engine(sqlite_str, iris_path, types_data):
+def sqlite_engine(sqlite_str):
sqlalchemy = pytest.importorskip("sqlalchemy")
engine = sqlalchemy.create_engine(sqlite_str, poolclass=sqlalchemy.pool.NullPool)
-
- insp = sqlalchemy.inspect(engine)
- if not insp.has_table("iris"):
- create_and_load_iris(engine, iris_path, "sqlite")
- if not insp.has_table("iris_view"):
- create_and_load_iris_view(engine)
- if not insp.has_table("types"):
- for entry in types_data:
- entry.pop("DateColWithTz")
- create_and_load_types(engine, types_data, "sqlite")
-
yield engine
for view in get_all_views(engine):
drop_view(view, engine)
@@ -526,74 +590,68 @@ def sqlite_conn(sqlite_engine):
@pytest.fixture
-def sqlite_iris_str(sqlite_str, iris_path, types_data):
+def sqlite_str_iris(sqlite_str, iris_path):
sqlalchemy = pytest.importorskip("sqlalchemy")
engine = sqlalchemy.create_engine(sqlite_str)
+ create_and_load_iris(engine, iris_path)
+ create_and_load_iris_view(engine)
+ engine.dispose()
+ return sqlite_str
+
- insp = sqlalchemy.inspect(engine)
- if not insp.has_table("iris"):
- create_and_load_iris(engine, iris_path, "sqlite")
- if not insp.has_table("iris_view"):
- create_and_load_iris_view(engine)
- if not insp.has_table("types"):
- for entry in types_data:
- entry.pop("DateColWithTz")
- create_and_load_types(engine, types_data, "sqlite")
+@pytest.fixture
+def sqlite_engine_iris(sqlite_engine, iris_path):
+ create_and_load_iris(sqlite_engine, iris_path)
+ create_and_load_iris_view(sqlite_engine)
+ yield sqlite_engine
+
+
+@pytest.fixture
+def sqlite_conn_iris(sqlite_engine_iris):
+ with sqlite_engine_iris.connect() as conn:
+ yield conn
+
+
+@pytest.fixture
+def sqlite_str_types(sqlite_str, types_data):
+ sqlalchemy = pytest.importorskip("sqlalchemy")
+ engine = sqlalchemy.create_engine(sqlite_str)
+ create_and_load_types(engine, types_data, "sqlite")
engine.dispose()
return sqlite_str
@pytest.fixture
-def sqlite_iris_engine(sqlite_engine, iris_path):
- return sqlite_engine
+def sqlite_engine_types(sqlite_engine, types_data):
+ create_and_load_types(sqlite_engine, types_data, "sqlite")
+ yield sqlite_engine
@pytest.fixture
-def sqlite_iris_conn(sqlite_iris_engine):
- with sqlite_iris_engine.connect() as conn:
+def sqlite_conn_types(sqlite_engine_types):
+ with sqlite_engine_types.connect() as conn:
yield conn
@pytest.fixture
def sqlite_buildin():
with contextlib.closing(sqlite3.connect(":memory:")) as closing_conn:
- create_and_load_iris_view(closing_conn)
with closing_conn as conn:
yield conn
@pytest.fixture
-def sqlite_sqlalchemy_memory_engine(iris_path, types_data):
- sqlalchemy = pytest.importorskip("sqlalchemy")
- engine = sqlalchemy.create_engine("sqlite:///:memory:")
-
- insp = sqlalchemy.inspect(engine)
- if not insp.has_table("iris"):
- create_and_load_iris(engine, iris_path, "sqlite")
- if not insp.has_table("iris_view"):
- create_and_load_iris_view(engine)
- if not insp.has_table("types"):
- for entry in types_data:
- entry.pop("DateColWithTz")
- create_and_load_types(engine, types_data, "sqlite")
-
- yield engine
- for view in get_all_views(engine):
- drop_view(view, engine)
- for tbl in get_all_tables(engine):
- drop_table(tbl, engine)
+def sqlite_buildin_iris(sqlite_buildin, iris_path):
+ create_and_load_iris_sqlite3(sqlite_buildin, iris_path)
+ create_and_load_iris_view(sqlite_buildin)
+ yield sqlite_buildin
@pytest.fixture
-def sqlite_buildin_iris(sqlite_buildin, iris_path, types_data):
- create_and_load_iris_sqlite3(sqlite_buildin, iris_path)
-
- for entry in types_data:
- entry.pop("DateColWithTz")
+def sqlite_buildin_types(sqlite_buildin, types_data):
types_data = [tuple(entry.values()) for entry in types_data]
-
create_and_load_types_sqlite3(sqlite_buildin, types_data)
- return sqlite_buildin
+ yield sqlite_buildin
mysql_connectable = [
@@ -601,39 +659,64 @@ def sqlite_buildin_iris(sqlite_buildin, iris_path, types_data):
pytest.param("mysql_pymysql_conn", marks=pytest.mark.db),
]
+mysql_connectable_iris = [
+ pytest.param("mysql_pymysql_engine_iris", marks=pytest.mark.db),
+ pytest.param("mysql_pymysql_conn_iris", marks=pytest.mark.db),
+]
+
+mysql_connectable_types = [
+ pytest.param("mysql_pymysql_engine_types", marks=pytest.mark.db),
+ pytest.param("mysql_pymysql_conn_types", marks=pytest.mark.db),
+]
postgresql_connectable = [
pytest.param("postgresql_psycopg2_engine", marks=pytest.mark.db),
pytest.param("postgresql_psycopg2_conn", marks=pytest.mark.db),
]
+postgresql_connectable_iris = [
+ pytest.param("postgresql_psycopg2_engine_iris", marks=pytest.mark.db),
+ pytest.param("postgresql_psycopg2_conn_iris", marks=pytest.mark.db),
+]
+
+postgresql_connectable_types = [
+ pytest.param("postgresql_psycopg2_engine_types", marks=pytest.mark.db),
+ pytest.param("postgresql_psycopg2_conn_types", marks=pytest.mark.db),
+]
+
sqlite_connectable = [
- pytest.param("sqlite_engine", marks=pytest.mark.db),
- pytest.param("sqlite_conn", marks=pytest.mark.db),
- pytest.param("sqlite_str", marks=pytest.mark.db),
+ "sqlite_engine",
+ "sqlite_conn",
+ "sqlite_str",
+]
+
+sqlite_connectable_iris = [
+ "sqlite_engine_iris",
+ "sqlite_conn_iris",
+ "sqlite_str_iris",
]
-sqlite_iris_connectable = [
- pytest.param("sqlite_iris_engine", marks=pytest.mark.db),
- pytest.param("sqlite_iris_conn", marks=pytest.mark.db),
- pytest.param("sqlite_iris_str", marks=pytest.mark.db),
+sqlite_connectable_types = [
+ "sqlite_engine_types",
+ "sqlite_conn_types",
+ "sqlite_str_types",
]
sqlalchemy_connectable = mysql_connectable + postgresql_connectable + sqlite_connectable
sqlalchemy_connectable_iris = (
- mysql_connectable + postgresql_connectable + sqlite_iris_connectable
+ mysql_connectable_iris + postgresql_connectable_iris + sqlite_connectable_iris
)
-all_connectable = sqlalchemy_connectable + [
- "sqlite_buildin",
- "sqlite_sqlalchemy_memory_engine",
-]
+sqlalchemy_connectable_types = (
+ mysql_connectable_types + postgresql_connectable_types + sqlite_connectable_types
+)
-all_connectable_iris = sqlalchemy_connectable_iris + [
- "sqlite_buildin_iris",
- "sqlite_sqlalchemy_memory_engine",
-]
+all_connectable = sqlalchemy_connectable + ["sqlite_buildin"]
+
+all_connectable_iris = sqlalchemy_connectable_iris + ["sqlite_buildin_iris"]
+
+all_connectable_types = sqlalchemy_connectable_types + ["sqlite_buildin_types"]
@pytest.mark.parametrize("conn", all_connectable)
@@ -813,10 +896,10 @@ def sample(pd_table, conn, keys, data_iter):
assert count_rows(conn, "test_frame") == len(test_frame1)
-@pytest.mark.parametrize("conn", all_connectable_iris)
+@pytest.mark.parametrize("conn", all_connectable_types)
def test_default_type_conversion(conn, request):
conn_name = conn
- if conn_name == "sqlite_buildin_iris":
+ if conn_name == "sqlite_buildin_types":
request.applymarker(
pytest.mark.xfail(
reason="sqlite_buildin connection does not implement read_sql_table"
@@ -1093,43 +1176,39 @@ def test_read_view_sqlite(sqlite_buildin):
tm.assert_frame_equal(result, expected)
-def test_execute_typeerror(sqlite_iris_engine):
+def test_execute_typeerror(sqlite_engine_iris):
with pytest.raises(TypeError, match="pandas.io.sql.execute requires a connection"):
with tm.assert_produces_warning(
FutureWarning,
match="`pandas.io.sql.execute` is deprecated and "
"will be removed in the future version.",
):
- sql.execute("select * from iris", sqlite_iris_engine)
+ sql.execute("select * from iris", sqlite_engine_iris)
-def test_execute_deprecated(sqlite_buildin_iris):
+def test_execute_deprecated(sqlite_conn_iris):
# GH50185
with tm.assert_produces_warning(
FutureWarning,
match="`pandas.io.sql.execute` is deprecated and "
"will be removed in the future version.",
):
- sql.execute("select * from iris", sqlite_buildin_iris)
+ sql.execute("select * from iris", sqlite_conn_iris)
-@pytest.fixture
-def flavor():
- def func(conn_name):
- if "postgresql" in conn_name:
- return "postgresql"
- elif "sqlite" in conn_name:
- return "sqlite"
- elif "mysql" in conn_name:
- return "mysql"
-
- raise ValueError(f"unsupported connection: {conn_name}")
+def flavor(conn_name):
+ if "postgresql" in conn_name:
+ return "postgresql"
+ elif "sqlite" in conn_name:
+ return "sqlite"
+ elif "mysql" in conn_name:
+ return "mysql"
- return func
+ raise ValueError(f"unsupported connection: {conn_name}")
@pytest.mark.parametrize("conn", all_connectable_iris)
-def test_read_sql_iris_parameter(conn, request, sql_strings, flavor):
+def test_read_sql_iris_parameter(conn, request, sql_strings):
conn_name = conn
conn = request.getfixturevalue(conn)
query = sql_strings["read_parameters"][flavor(conn_name)]
@@ -1141,19 +1220,19 @@ def test_read_sql_iris_parameter(conn, request, sql_strings, flavor):
@pytest.mark.parametrize("conn", all_connectable_iris)
-def test_read_sql_iris_named_parameter(conn, request, sql_strings, flavor):
+def test_read_sql_iris_named_parameter(conn, request, sql_strings):
conn_name = conn
conn = request.getfixturevalue(conn)
query = sql_strings["read_named_parameters"][flavor(conn_name)]
params = {"name": "Iris-setosa", "length": 5.1}
- pandasSQL = pandasSQL_builder(conn)
- with pandasSQL.run_transaction():
- iris_frame = pandasSQL.read_query(query, params=params)
+ with pandasSQL_builder(conn) as pandasSQL:
+ with pandasSQL.run_transaction():
+ iris_frame = pandasSQL.read_query(query, params=params)
check_iris_frame(iris_frame)
@pytest.mark.parametrize("conn", all_connectable_iris)
-def test_read_sql_iris_no_parameter_with_percent(conn, request, sql_strings, flavor):
+def test_read_sql_iris_no_parameter_with_percent(conn, request, sql_strings):
if "mysql" in conn or "postgresql" in conn:
request.applymarker(pytest.mark.xfail(reason="broken test"))
@@ -1322,7 +1401,7 @@ def test_api_execute_sql(conn, request):
tm.equalContents(row, [5.1, 3.5, 1.4, 0.2, "Iris-setosa"])
-@pytest.mark.parametrize("conn", all_connectable_iris)
+@pytest.mark.parametrize("conn", all_connectable_types)
def test_api_date_parsing(conn, request):
conn_name = conn
conn = request.getfixturevalue(conn)
@@ -1378,7 +1457,7 @@ def test_api_date_parsing(conn, request):
]
-@pytest.mark.parametrize("conn", all_connectable_iris)
+@pytest.mark.parametrize("conn", all_connectable_types)
@pytest.mark.parametrize("error", ["ignore", "raise", "coerce"])
@pytest.mark.parametrize(
"read_sql, text, mode",
@@ -1398,7 +1477,7 @@ def test_api_custom_dateparsing_error(
):
conn_name = conn
conn = request.getfixturevalue(conn)
- if text == "types" and conn_name == "sqlite_buildin_iris":
+ if text == "types" and conn_name == "sqlite_buildin_types":
request.applymarker(
pytest.mark.xfail(reason="failing combination of arguments")
)
@@ -1414,14 +1493,13 @@ def test_api_custom_dateparsing_error(
)
if "postgres" in conn_name:
# TODO: clean up types_data_frame fixture
- result = result.drop(columns=["DateColWithTz"])
result["BoolCol"] = result["BoolCol"].astype(int)
result["BoolColWithNull"] = result["BoolColWithNull"].astype(float)
tm.assert_frame_equal(result, expected)
-@pytest.mark.parametrize("conn", all_connectable_iris)
+@pytest.mark.parametrize("conn", all_connectable_types)
def test_api_date_and_index(conn, request):
# Test case where same column appears in parse_date and index_col
conn = request.getfixturevalue(conn)
@@ -2022,7 +2100,7 @@ def test_query_by_select_obj(conn, request):
select,
)
- iris = iris_table_metadata("postgres")
+ iris = iris_table_metadata()
name_select = select(iris).where(iris.c.Name == bindparam("name"))
iris_df = sql.read_sql(name_select, conn, params={"name": "Iris-setosa"})
all_names = set(iris_df["Name"])
@@ -2188,7 +2266,6 @@ def test_roundtrip(conn, request, test_frame1):
@pytest.mark.parametrize("conn", all_connectable_iris)
def test_execute_sql(conn, request):
conn = request.getfixturevalue(conn)
- pandasSQL = pandasSQL_builder(conn)
with pandasSQL_builder(conn) as pandasSQL:
with pandasSQL.run_transaction():
iris_results = pandasSQL.execute("SELECT * FROM iris")
@@ -2220,7 +2297,7 @@ def test_read_table_absent_raises(conn, request):
sql.read_sql_table("this_doesnt_exist", con=conn)
-@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+@pytest.mark.parametrize("conn", sqlalchemy_connectable_types)
def test_sqlalchemy_default_type_conversion(conn, request):
conn_name = conn
if conn_name == "sqlite_str":
@@ -2254,7 +2331,7 @@ def test_bigint(conn, request):
tm.assert_frame_equal(df, result)
-@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+@pytest.mark.parametrize("conn", sqlalchemy_connectable_types)
def test_default_date_load(conn, request):
conn_name = conn
if conn_name == "sqlite_str":
@@ -2270,82 +2347,40 @@ def test_default_date_load(conn, request):
assert issubclass(df.DateCol.dtype.type, np.datetime64)
-@pytest.mark.parametrize("conn", sqlalchemy_connectable_iris)
-def test_datetime_with_timezone(conn, request):
+@pytest.mark.parametrize("conn", postgresql_connectable)
+@pytest.mark.parametrize("parse_dates", [None, ["DateColWithTz"]])
+def test_datetime_with_timezone_query(conn, request, parse_dates):
# 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
-
- def check(col):
- # check that a column is either datetime64[ns]
- # or datetime64[ns, UTC]
- if lib.is_np_dtype(col.dtype, "M"):
- # "2000-01-01 00:00:00-08:00" should convert to
- # "2000-01-01 08:00:00"
- assert col[0] == Timestamp("2000-01-01 08:00:00")
-
- # "2000-06-01 00:00:00-07:00" should convert to
- # "2000-06-01 07:00:00"
- assert col[1] == Timestamp("2000-06-01 07:00:00")
-
- elif isinstance(col.dtype, DatetimeTZDtype):
- assert str(col.dt.tz) == "UTC"
-
- # "2000-01-01 00:00:00-08:00" should convert to
- # "2000-01-01 08:00:00"
- # "2000-06-01 00:00:00-07:00" should convert to
- # "2000-06-01 07:00:00"
- # GH 6415
- expected_data = [
- Timestamp("2000-01-01 08:00:00", tz="UTC"),
- Timestamp("2000-06-01 07:00:00", tz="UTC"),
- ]
- expected = Series(expected_data, name=col.name)
- tm.assert_series_equal(col, expected)
-
- else:
- raise AssertionError(f"DateCol loaded with incorrect type -> {col.dtype}")
-
- # GH11216
conn = request.getfixturevalue(conn)
- df = read_sql_query("select * from types", conn)
- if not hasattr(df, "DateColWithTz"):
- request.applymarker(
- pytest.mark.xfail(reason="no column with datetime with time zone")
- )
+ expected = create_and_load_postgres_datetz(conn)
- # this is parsed on Travis (linux), but not on macosx for some reason
- # even with the same versions of psycopg2 & sqlalchemy, possibly a
- # Postgresql server version difference
+ # GH11216
+ df = read_sql_query("select * from datetz", conn, parse_dates=parse_dates)
col = df.DateColWithTz
- assert isinstance(col.dtype, DatetimeTZDtype)
+ tm.assert_series_equal(col, expected)
- df = read_sql_query("select * from types", conn, parse_dates=["DateColWithTz"])
- if not hasattr(df, "DateColWithTz"):
- request.applymarker(
- pytest.mark.xfail(reason="no column with datetime with time zone")
- )
- col = df.DateColWithTz
- assert isinstance(col.dtype, DatetimeTZDtype)
- assert str(col.dt.tz) == "UTC"
- check(df.DateColWithTz)
+
+@pytest.mark.parametrize("conn", postgresql_connectable)
+def test_datetime_with_timezone_query_chunksize(conn, request):
+ conn = request.getfixturevalue(conn)
+ expected = create_and_load_postgres_datetz(conn)
df = concat(
- list(read_sql_query("select * from types", conn, chunksize=1)),
+ list(read_sql_query("select * from datetz", conn, chunksize=1)),
ignore_index=True,
)
col = df.DateColWithTz
- assert isinstance(col.dtype, DatetimeTZDtype)
- assert str(col.dt.tz) == "UTC"
- expected = sql.read_sql_table("types", conn)
- col = expected.DateColWithTz
- assert isinstance(col.dtype, DatetimeTZDtype)
- tm.assert_series_equal(df.DateColWithTz, expected.DateColWithTz)
-
- # xref #7139
- # this might or might not be converted depending on the postgres driver
- df = sql.read_sql_table("types", conn)
- check(df.DateColWithTz)
+ tm.assert_series_equal(col, expected)
+
+
+@pytest.mark.parametrize("conn", postgresql_connectable)
+def test_datetime_with_timezone_table(conn, request):
+ conn = request.getfixturevalue(conn)
+ expected = create_and_load_postgres_datetz(conn)
+ result = sql.read_sql_table("datetz", conn)
+ tm.assert_frame_equal(result, expected.to_frame())
@pytest.mark.parametrize("conn", sqlalchemy_connectable)
@@ -2403,7 +2438,7 @@ def test_naive_datetimeindex_roundtrip(conn, request):
tm.assert_frame_equal(result, expected, check_names=False)
-@pytest.mark.parametrize("conn", sqlalchemy_connectable_iris)
+@pytest.mark.parametrize("conn", sqlalchemy_connectable_types)
def test_date_parsing(conn, request):
# No Parsing
conn_name = conn
@@ -3235,8 +3270,8 @@ def test_read_sql_dtype(conn, request, func, dtype_backend):
tm.assert_frame_equal(result, expected)
-def test_keyword_deprecation(sqlite_sqlalchemy_memory_engine):
- conn = sqlite_sqlalchemy_memory_engine
+def test_keyword_deprecation(sqlite_engine):
+ conn = sqlite_engine
# GH 54397
msg = (
"Starting with pandas version 3.0 all arguments of to_sql except for the "
@@ -3249,8 +3284,8 @@ def test_keyword_deprecation(sqlite_sqlalchemy_memory_engine):
df.to_sql("example", conn, None, if_exists="replace")
-def test_bigint_warning(sqlite_sqlalchemy_memory_engine):
- conn = sqlite_sqlalchemy_memory_engine
+def test_bigint_warning(sqlite_engine):
+ conn = sqlite_engine
# test no warning for BIGINT (to support int64) is raised (GH7433)
df = DataFrame({"a": [1, 2]}, dtype="int64")
assert df.to_sql(name="test_bigintwarning", con=conn, index=False) == 2
@@ -3259,15 +3294,15 @@ def test_bigint_warning(sqlite_sqlalchemy_memory_engine):
sql.read_sql_table("test_bigintwarning", conn)
-def test_valueerror_exception(sqlite_sqlalchemy_memory_engine):
- conn = sqlite_sqlalchemy_memory_engine
+def test_valueerror_exception(sqlite_engine):
+ conn = sqlite_engine
df = DataFrame({"col1": [1, 2], "col2": [3, 4]})
with pytest.raises(ValueError, match="Empty table name specified"):
df.to_sql(name="", con=conn, if_exists="replace", index=False)
-def test_row_object_is_named_tuple(sqlite_sqlalchemy_memory_engine):
- conn = sqlite_sqlalchemy_memory_engine
+def test_row_object_is_named_tuple(sqlite_engine):
+ conn = sqlite_engine
# GH 40682
# Test for the is_named_tuple() function
# Placed here due to its usage of sqlalchemy
@@ -3305,8 +3340,8 @@ class Test(BaseModel):
assert list(df.columns) == ["id", "string_column"]
-def test_read_sql_string_inference(sqlite_sqlalchemy_memory_engine):
- conn = sqlite_sqlalchemy_memory_engine
+def test_read_sql_string_inference(sqlite_engine):
+ conn = sqlite_engine
# GH#54430
pytest.importorskip("pyarrow")
table = "test"
@@ -3324,8 +3359,8 @@ def test_read_sql_string_inference(sqlite_sqlalchemy_memory_engine):
tm.assert_frame_equal(result, expected)
-def test_roundtripping_datetimes(sqlite_sqlalchemy_memory_engine):
- conn = sqlite_sqlalchemy_memory_engine
+def test_roundtripping_datetimes(sqlite_engine):
+ conn = sqlite_engine
# GH#54877
df = DataFrame({"t": [datetime(2020, 12, 31, 12)]}, dtype="datetime64[ns]")
df.to_sql("test", conn, if_exists="replace", index=False)
@@ -3444,8 +3479,8 @@ def test_self_join_date_columns(postgresql_psycopg2_engine):
pandasSQL.drop_table("person")
-def test_create_and_drop_table(sqlite_sqlalchemy_memory_engine):
- conn = sqlite_sqlalchemy_memory_engine
+def test_create_and_drop_table(sqlite_engine):
+ conn = sqlite_engine
temp_frame = DataFrame({"one": [1.0, 2.0, 3.0, 4.0], "two": [4.0, 3.0, 2.0, 1.0]})
with sql.SQLDatabase(conn) as pandasSQL:
with pandasSQL.run_transaction():
@@ -3568,24 +3603,18 @@ def test_sqlite_illegal_names(sqlite_buildin):
sql.table_exists(c_tbl, conn)
-# -----------------------------------------------------------------------------
-# -- Old tests from 0.13.1 (before refactor using sqlalchemy)
-
-
-_formatters = {
- datetime: "'{}'".format,
- str: "'{}'".format,
- np.str_: "'{}'".format,
- bytes: "'{}'".format,
- float: "{:.8f}".format,
- int: "{:d}".format,
- type(None): lambda x: "NULL",
- np.float64: "{:.10f}".format,
- bool: "'{!s}'".format,
-}
-
-
def format_query(sql, *args):
+ _formatters = {
+ datetime: "'{}'".format,
+ str: "'{}'".format,
+ np.str_: "'{}'".format,
+ bytes: "'{}'".format,
+ float: "{:.8f}".format,
+ int: "{:d}".format,
+ type(None): lambda x: "NULL",
+ np.float64: "{:.10f}".format,
+ bool: "'{!s}'".format,
+ }
processed_args = []
for arg in args:
if isinstance(arg, float) and isna(arg):
| Also factors out timezone data in a fixture to only apply to the single test where it's used | https://api.github.com/repos/pandas-dev/pandas/pulls/55265 | 2023-09-24T16:52:18Z | 2023-10-25T17:18:06Z | 2023-10-25T17:18:06Z | 2023-10-25T17:18:10Z |
TYP: Misc changes for pandas-stubs; use Protocol to avoid str in Sequence | diff --git a/pandas/_typing.py b/pandas/_typing.py
index c2bbebfbe2857..0e2a0881f0122 100644
--- a/pandas/_typing.py
+++ b/pandas/_typing.py
@@ -24,6 +24,7 @@
Type as type_t,
TypeVar,
Union,
+ overload,
)
import numpy as np
@@ -85,6 +86,8 @@
# Name "npt._ArrayLikeInt_co" is not defined [name-defined]
NumpySorter = Optional[npt._ArrayLikeInt_co] # type: ignore[name-defined]
+ from typing import SupportsIndex
+
if sys.version_info >= (3, 10):
from typing import TypeGuard # pyright: ignore[reportUnusedImport]
else:
@@ -109,10 +112,40 @@
# list-like
-# Cannot use `Sequence` because a string is a sequence, and we don't want to
-# accept that. Could refine if https://github.com/python/typing/issues/256 is
-# resolved to differentiate between Sequence[str] and str
-ListLike = Union[AnyArrayLike, list, tuple, range]
+# from https://github.com/hauntsaninja/useful_types
+# includes Sequence-like objects but excludes str and bytes
+_T_co = TypeVar("_T_co", covariant=True)
+
+
+class SequenceNotStr(Protocol[_T_co]):
+ @overload
+ def __getitem__(self, index: SupportsIndex, /) -> _T_co:
+ ...
+
+ @overload
+ def __getitem__(self, index: slice, /) -> Sequence[_T_co]:
+ ...
+
+ def __contains__(self, value: object, /) -> bool:
+ ...
+
+ def __len__(self) -> int:
+ ...
+
+ def __iter__(self) -> Iterator[_T_co]:
+ ...
+
+ def index(self, value: Any, /, start: int = 0, stop: int = ...) -> int:
+ ...
+
+ def count(self, value: Any, /) -> int:
+ ...
+
+ def __reversed__(self) -> Iterator[_T_co]:
+ ...
+
+
+ListLike = Union[AnyArrayLike, SequenceNotStr, range]
# scalars
@@ -120,7 +153,7 @@
DatetimeLikeScalar = Union["Period", "Timestamp", "Timedelta"]
PandasScalar = Union["Period", "Timestamp", "Timedelta", "Interval"]
Scalar = Union[PythonScalar, PandasScalar, np.datetime64, np.timedelta64, date]
-IntStrT = TypeVar("IntStrT", int, str)
+IntStrT = TypeVar("IntStrT", bound=Union[int, str])
# timestamp and timedelta convertible types
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 3e32a6d93b023..432c0a745c7a0 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -240,6 +240,7 @@
Renamer,
Scalar,
Self,
+ SequenceNotStr,
SortKind,
StorageOptions,
Suffixes,
@@ -1187,7 +1188,7 @@ def to_string(
buf: None = ...,
columns: Axes | None = ...,
col_space: int | list[int] | dict[Hashable, int] | None = ...,
- header: bool | list[str] = ...,
+ header: bool | SequenceNotStr[str] = ...,
index: bool = ...,
na_rep: str = ...,
formatters: fmt.FormattersType | None = ...,
@@ -1212,7 +1213,7 @@ def to_string(
buf: FilePath | WriteBuffer[str],
columns: Axes | None = ...,
col_space: int | list[int] | dict[Hashable, int] | None = ...,
- header: bool | list[str] = ...,
+ header: bool | SequenceNotStr[str] = ...,
index: bool = ...,
na_rep: str = ...,
formatters: fmt.FormattersType | None = ...,
@@ -1250,7 +1251,7 @@ def to_string(
buf: FilePath | WriteBuffer[str] | None = None,
columns: Axes | None = None,
col_space: int | list[int] | dict[Hashable, int] | None = None,
- header: bool | list[str] = True,
+ header: bool | SequenceNotStr[str] = True,
index: bool = True,
na_rep: str = "NaN",
formatters: fmt.FormattersType | None = None,
@@ -10563,9 +10564,9 @@ def merge(
self,
right: DataFrame | Series,
how: MergeHow = "inner",
- on: IndexLabel | None = None,
- left_on: IndexLabel | None = None,
- right_on: IndexLabel | None = None,
+ on: IndexLabel | AnyArrayLike | None = None,
+ left_on: IndexLabel | AnyArrayLike | None = None,
+ right_on: IndexLabel | AnyArrayLike | None = None,
left_index: bool = False,
right_index: bool = False,
sort: bool = False,
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 427687d9614f9..738f4cbe6bc43 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -72,6 +72,7 @@
Renamer,
Scalar,
Self,
+ SequenceNotStr,
SortKind,
StorageOptions,
Suffixes,
@@ -3273,7 +3274,7 @@ def to_latex(
self,
buf: None = ...,
columns: Sequence[Hashable] | None = ...,
- header: bool_t | list[str] = ...,
+ header: bool_t | SequenceNotStr[str] = ...,
index: bool_t = ...,
na_rep: str = ...,
formatters: FormattersType | None = ...,
@@ -3300,7 +3301,7 @@ def to_latex(
self,
buf: FilePath | WriteBuffer[str],
columns: Sequence[Hashable] | None = ...,
- header: bool_t | list[str] = ...,
+ header: bool_t | SequenceNotStr[str] = ...,
index: bool_t = ...,
na_rep: str = ...,
formatters: FormattersType | None = ...,
@@ -3330,7 +3331,7 @@ def to_latex(
self,
buf: FilePath | WriteBuffer[str] | None = None,
columns: Sequence[Hashable] | None = None,
- header: bool_t | list[str] = True,
+ header: bool_t | SequenceNotStr[str] = True,
index: bool_t = True,
na_rep: str = "NaN",
formatters: FormattersType | None = None,
diff --git a/pandas/core/methods/describe.py b/pandas/core/methods/describe.py
index 5bb6bebd8a87b..dcdf0067d45b0 100644
--- a/pandas/core/methods/describe.py
+++ b/pandas/core/methods/describe.py
@@ -301,7 +301,7 @@ def describe_timestamp_as_categorical_1d(
names = ["count", "unique"]
objcounts = data.value_counts()
count_unique = len(objcounts[objcounts != 0])
- result = [data.count(), count_unique]
+ result: list[float | Timestamp] = [data.count(), count_unique]
dtype = None
if count_unique > 0:
top, freq = objcounts.index[0], objcounts.iloc[0]
diff --git a/pandas/core/resample.py b/pandas/core/resample.py
index 30d654078bd05..b6323e8c8b5f9 100644
--- a/pandas/core/resample.py
+++ b/pandas/core/resample.py
@@ -1541,7 +1541,7 @@ def count(self):
return result
- def quantile(self, q: float | AnyArrayLike = 0.5, **kwargs):
+ def quantile(self, q: float | list[float] | AnyArrayLike = 0.5, **kwargs):
"""
Return value at the given quantile.
diff --git a/pandas/core/reshape/merge.py b/pandas/core/reshape/merge.py
index 6d1ff07e07c76..4b9fcc80af4bb 100644
--- a/pandas/core/reshape/merge.py
+++ b/pandas/core/reshape/merge.py
@@ -138,9 +138,9 @@ def merge(
left: DataFrame | Series,
right: DataFrame | Series,
how: MergeHow = "inner",
- on: IndexLabel | None = None,
- left_on: IndexLabel | None = None,
- right_on: IndexLabel | None = None,
+ on: IndexLabel | AnyArrayLike | None = None,
+ left_on: IndexLabel | AnyArrayLike | None = None,
+ right_on: IndexLabel | AnyArrayLike | None = None,
left_index: bool = False,
right_index: bool = False,
sort: bool = False,
@@ -187,9 +187,9 @@ def merge(
def _cross_merge(
left: DataFrame,
right: DataFrame,
- on: IndexLabel | None = None,
- left_on: IndexLabel | None = None,
- right_on: IndexLabel | None = None,
+ on: IndexLabel | AnyArrayLike | None = None,
+ left_on: IndexLabel | AnyArrayLike | None = None,
+ right_on: IndexLabel | AnyArrayLike | None = None,
left_index: bool = False,
right_index: bool = False,
sort: bool = False,
@@ -239,7 +239,9 @@ def _cross_merge(
return res
-def _groupby_and_merge(by, left: DataFrame, right: DataFrame, merge_pieces):
+def _groupby_and_merge(
+ by, left: DataFrame | Series, right: DataFrame | Series, merge_pieces
+):
"""
groupby & merge; we are always performing a left-by type operation
@@ -255,7 +257,7 @@ def _groupby_and_merge(by, left: DataFrame, right: DataFrame, merge_pieces):
by = [by]
lby = left.groupby(by, sort=False)
- rby: groupby.DataFrameGroupBy | None = None
+ rby: groupby.DataFrameGroupBy | groupby.SeriesGroupBy | None = None
# if we can groupby the rhs
# then we can get vastly better perf
@@ -295,8 +297,8 @@ def _groupby_and_merge(by, left: DataFrame, right: DataFrame, merge_pieces):
def merge_ordered(
- left: DataFrame,
- right: DataFrame,
+ left: DataFrame | Series,
+ right: DataFrame | Series,
on: IndexLabel | None = None,
left_on: IndexLabel | None = None,
right_on: IndexLabel | None = None,
@@ -737,9 +739,9 @@ def __init__(
left: DataFrame | Series,
right: DataFrame | Series,
how: MergeHow | Literal["asof"] = "inner",
- on: IndexLabel | None = None,
- left_on: IndexLabel | None = None,
- right_on: IndexLabel | None = None,
+ on: IndexLabel | AnyArrayLike | None = None,
+ left_on: IndexLabel | AnyArrayLike | None = None,
+ right_on: IndexLabel | AnyArrayLike | None = None,
left_index: bool = False,
right_index: bool = False,
sort: bool = True,
diff --git a/pandas/core/series.py b/pandas/core/series.py
index d3a2bb1745cd1..fd50a85f3c2e3 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -2141,7 +2141,7 @@ def groupby(
# Statistics, overridden ndarray methods
# TODO: integrate bottleneck
- def count(self):
+ def count(self) -> int:
"""
Return number of non-NA/null observations in the Series.
diff --git a/pandas/io/formats/csvs.py b/pandas/io/formats/csvs.py
index 8d0edd88ffb6c..569c8aaf6cef1 100644
--- a/pandas/io/formats/csvs.py
+++ b/pandas/io/formats/csvs.py
@@ -21,6 +21,7 @@
import numpy as np
from pandas._libs import writers as libwriters
+from pandas._typing import SequenceNotStr
from pandas.util._decorators import cache_readonly
from pandas.core.dtypes.generic import (
@@ -109,7 +110,7 @@ def decimal(self) -> str:
return self.fmt.decimal
@property
- def header(self) -> bool | list[str]:
+ def header(self) -> bool | SequenceNotStr[str]:
return self.fmt.header
@property
@@ -213,7 +214,7 @@ def _need_to_save_header(self) -> bool:
return bool(self._has_aliases or self.header)
@property
- def write_cols(self) -> Sequence[Hashable]:
+ def write_cols(self) -> SequenceNotStr[Hashable]:
if self._has_aliases:
assert not isinstance(self.header, bool)
if len(self.header) != len(self.cols):
@@ -224,7 +225,7 @@ def write_cols(self) -> Sequence[Hashable]:
else:
# self.cols is an ndarray derived from Index._format_native_types,
# so its entries are strings, i.e. hashable
- return cast(Sequence[Hashable], self.cols)
+ return cast(SequenceNotStr[Hashable], self.cols)
@property
def encoded_labels(self) -> list[Hashable]:
diff --git a/pandas/io/formats/format.py b/pandas/io/formats/format.py
index 2297f7945a264..922d0f37bee3a 100644
--- a/pandas/io/formats/format.py
+++ b/pandas/io/formats/format.py
@@ -105,6 +105,7 @@
FloatFormatType,
FormattersType,
IndexLabel,
+ SequenceNotStr,
StorageOptions,
WriteBuffer,
)
@@ -566,7 +567,7 @@ def __init__(
frame: DataFrame,
columns: Axes | None = None,
col_space: ColspaceArgType | None = None,
- header: bool | list[str] = True,
+ header: bool | SequenceNotStr[str] = True,
index: bool = True,
na_rep: str = "NaN",
formatters: FormattersType | None = None,
diff --git a/pandas/tests/io/test_sql.py b/pandas/tests/io/test_sql.py
index f015c9efe7122..e1839fc1b0a67 100644
--- a/pandas/tests/io/test_sql.py
+++ b/pandas/tests/io/test_sql.py
@@ -3161,8 +3161,6 @@ def dtype_backend_data() -> DataFrame:
@pytest.fixture
def dtype_backend_expected():
def func(storage, dtype_backend, conn_name):
- string_array: StringArray | ArrowStringArray
- string_array_na: StringArray | ArrowStringArray
if storage == "python":
string_array = StringArray(np.array(["a", "b", "c"], dtype=np.object_))
string_array_na = StringArray(np.array(["a", "b", pd.NA], dtype=np.object_))
| The first commit addresses some type issues found by running mypy on the tests from pandas-stubs (one big issue is that Index/Series are not included in IndexLabel; there might be cases where that is correct but in many cases they should be included).
The second commit re-writes #47233 with the protocol version of Sequence.
Can split it in two PRs if one of them is controversial. | https://api.github.com/repos/pandas-dev/pandas/pulls/55263 | 2023-09-24T14:25:51Z | 2023-09-26T15:48:56Z | 2023-09-26T15:48:56Z | 2023-12-10T04:33:43Z |
CLN: Remove temp_setattr from groupby.transform | diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index d607baf18d6cb..a022bfd1bd9bc 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -2012,15 +2012,13 @@ def _transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
# If func is a reduction, we need to broadcast the
# result to the whole group. Compute func result
# and deal with possible broadcasting below.
- # Temporarily set observed for dealing with categoricals.
- with com.temp_setattr(self, "observed", True):
- with com.temp_setattr(self, "as_index", True):
- # GH#49834 - result needs groups in the index for
- # _wrap_transform_fast_result
- if engine is not None:
- kwargs["engine"] = engine
- kwargs["engine_kwargs"] = engine_kwargs
- result = getattr(self, func)(*args, **kwargs)
+ with com.temp_setattr(self, "as_index", True):
+ # GH#49834 - result needs groups in the index for
+ # _wrap_transform_fast_result
+ if engine is not None:
+ kwargs["engine"] = engine
+ kwargs["engine_kwargs"] = engine_kwargs
+ result = getattr(self, func)(*args, **kwargs)
return self._wrap_transform_fast_result(result)
| - [ ] 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 [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
Using temp_setattr doesn't actually change anything. All of the objects that `observed` impacts are cached and have already been computed. | https://api.github.com/repos/pandas-dev/pandas/pulls/55262 | 2023-09-24T13:09:08Z | 2023-09-24T17:21:30Z | 2023-09-24T17:21:30Z | 2023-09-24T17:21:36Z |
BUG: DatetimeIndex.union returning object dtype for indexes with the same tz but different units | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 930e03ae7d75a..ca09a69f603ab 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -259,7 +259,7 @@ Categorical
Datetimelike
^^^^^^^^^^^^
--
+- Bug in :meth:`DatetimeIndex.union` returning object dtype for tz-aware indexes with the same timezone but different units (:issue:`55238`)
-
Timedelta
diff --git a/pandas/core/dtypes/dtypes.py b/pandas/core/dtypes/dtypes.py
index beff71d5e9dd9..196c95c3673e9 100644
--- a/pandas/core/dtypes/dtypes.py
+++ b/pandas/core/dtypes/dtypes.py
@@ -928,6 +928,13 @@ def __setstate__(self, state) -> None:
self._tz = state["tz"]
self._unit = state["unit"]
+ def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
+ if all(isinstance(t, DatetimeTZDtype) and t.tz == self.tz for t in dtypes):
+ np_dtype = np.max([cast(DatetimeTZDtype, t).base for t in [self, *dtypes]])
+ unit = np.datetime_data(np_dtype)[0]
+ return type(self)(unit=unit, tz=self.tz)
+ return super()._get_common_dtype(dtypes)
+
@cache_readonly
def index_class(self) -> type_t[DatetimeIndex]:
from pandas import DatetimeIndex
diff --git a/pandas/tests/indexes/datetimes/test_setops.py b/pandas/tests/indexes/datetimes/test_setops.py
index b56bad7f2e833..ca784948a5d29 100644
--- a/pandas/tests/indexes/datetimes/test_setops.py
+++ b/pandas/tests/indexes/datetimes/test_setops.py
@@ -189,6 +189,14 @@ def test_union_with_DatetimeIndex(self, sort):
# Fails with "AttributeError: can't set attribute"
i2.union(i1, sort=sort)
+ def test_union_same_timezone_different_units(self):
+ # GH 55238
+ idx1 = date_range("2000-01-01", periods=3, tz="UTC").as_unit("ms")
+ idx2 = date_range("2000-01-01", periods=3, tz="UTC").as_unit("us")
+ result = idx1.union(idx2)
+ expected = date_range("2000-01-01", periods=3, tz="UTC").as_unit("us")
+ tm.assert_index_equal(result, expected)
+
# TODO: moved from test_datetimelike; de-duplicate with version below
def test_intersection2(self):
first = tm.makeDateIndex(10)
| - [x] closes #55238
- [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 [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [x] Added an entry in the latest `doc/source/whatsnew/v2.2.0.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/55259 | 2023-09-24T01:13:41Z | 2023-09-25T18:42:45Z | 2023-09-25T18:42:45Z | 2023-11-16T12:56:53Z |
BUG: Series[slc]=foo raising with IntervalIndex | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 930e03ae7d75a..38a1c1511af73 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -292,6 +292,7 @@ Interval
- Bug in :class:`Interval` ``__repr__`` not displaying UTC offsets for :class:`Timestamp` bounds. Additionally the hour, minute and second components will now be shown. (:issue:`55015`)
- Bug in :meth:`IntervalIndex.get_indexer` with datetime or timedelta intervals incorrectly matching on integer targets (:issue:`47772`)
- Bug in :meth:`IntervalIndex.get_indexer` with timezone-aware datetime intervals incorrectly matching on a sequence of timezone-naive targets (:issue:`47772`)
+- Bug in setting values on a :class:`Series` with an :class:`IntervalIndex` using a slice incorrectly raising (:issue:`54722`)
-
Indexing
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 8703fef1e5940..e23887159c9c6 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -4204,15 +4204,9 @@ def _convert_slice_indexer(self, key: slice, kind: Literal["loc", "getitem"]):
self._validate_indexer("slice", key.step, "getitem")
return key
- # convert the slice to an indexer here
-
- # special case for interval_dtype bc we do not do partial-indexing
- # on integer Intervals when slicing
- # TODO: write this in terms of e.g. should_partial_index?
- ints_are_positional = self._should_fallback_to_positional or isinstance(
- self.dtype, IntervalDtype
- )
- is_positional = is_index_slice and ints_are_positional
+ # convert the slice to an indexer here; checking that the user didn't
+ # pass a positional slice to loc
+ is_positional = is_index_slice and self._should_fallback_to_positional
# if we are mixed and have integers
if is_positional:
diff --git a/pandas/tests/indexing/interval/test_interval.py b/pandas/tests/indexing/interval/test_interval.py
index 52a1d433712ff..ae25724972fde 100644
--- a/pandas/tests/indexing/interval/test_interval.py
+++ b/pandas/tests/indexing/interval/test_interval.py
@@ -134,6 +134,33 @@ def test_getitem_interval_with_nans(self, frame_or_series, indexer_sl):
tm.assert_equal(result, expected)
+ def test_setitem_interval_with_slice(self):
+ # GH#54722
+ ii = IntervalIndex.from_breaks(range(4, 15))
+ ser = Series(range(10), index=ii)
+
+ orig = ser.copy()
+
+ # This should be a no-op (used to raise)
+ ser.loc[1:3] = 20
+ tm.assert_series_equal(ser, orig)
+
+ ser.loc[6:8] = 19
+ orig.iloc[1:4] = 19
+ tm.assert_series_equal(ser, orig)
+
+ ser2 = Series(range(5), index=ii[::2])
+ orig2 = ser2.copy()
+
+ # this used to raise
+ ser2.loc[6:8] = 22 # <- raises on main, sets on branch
+ orig2.iloc[1] = 22
+ tm.assert_series_equal(ser2, orig2)
+
+ ser2.loc[5:7] = 21
+ orig2.iloc[:2] = 21
+ tm.assert_series_equal(ser2, orig2)
+
class TestIntervalIndexInsideMultiIndex:
def test_mi_intervalindex_slicing_with_scalar(self):
| - [x] closes #54722 (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).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [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/55258 | 2023-09-23T22:06:37Z | 2023-09-25T18:43:41Z | 2023-09-25T18:43:40Z | 2023-09-25T19:49:25Z |
DEPR: Index.insert dtype-inference | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 919ac8b03f936..c2032b0d34536 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -454,6 +454,7 @@ Other Deprecations
- Deprecated allowing non-keyword arguments in :meth:`DataFrame.to_string` except ``buf``. (:issue:`54229`)
- Deprecated allowing non-keyword arguments in :meth:`DataFrame.to_xml` except ``path_or_buffer``. (:issue:`54229`)
- Deprecated allowing passing :class:`BlockManager` objects to :class:`DataFrame` or :class:`SingleBlockManager` objects to :class:`Series` (:issue:`52419`)
+- Deprecated behavior of :meth:`Index.insert` with an object-dtype index silently performing type inference on the result, explicitly call ``result.infer_objects(copy=False)`` for the old behavior instead (:issue:`51363`)
- Deprecated downcasting behavior in :meth:`Series.where`, :meth:`DataFrame.where`, :meth:`Series.mask`, :meth:`DataFrame.mask`, :meth:`Series.clip`, :meth:`DataFrame.clip`; in a future version these will not infer object-dtype columns to non-object dtype, or all-round floats to integer dtype. Call ``result.infer_objects(copy=False)`` on the result for object inference, or explicitly cast floats to ints. To opt in to the future version, use ``pd.set_option("future.no_silent_downcasting", True)`` (:issue:`53656`)
- Deprecated including the groups in computations when using :meth:`.DataFrameGroupBy.apply` and :meth:`.DataFrameGroupBy.resample`; pass ``include_groups=False`` to exclude the groups (:issue:`7155`)
- Deprecated indexing an :class:`Index` with a boolean indexer of length zero (:issue:`55820`)
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 6c9f93d3482a7..3abe77b97fe58 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -6939,14 +6939,24 @@ def insert(self, loc: int, item) -> Index:
loc = loc if loc >= 0 else loc - 1
new_values[loc] = item
- idx = Index._with_infer(new_values, name=self.name)
+ out = Index._with_infer(new_values, name=self.name)
if (
using_pyarrow_string_dtype()
- and is_string_dtype(idx.dtype)
+ and is_string_dtype(out.dtype)
and new_values.dtype == object
):
- idx = idx.astype(new_values.dtype)
- return idx
+ out = out.astype(new_values.dtype)
+ if self.dtype == object and out.dtype != object:
+ # GH#51363
+ warnings.warn(
+ "The behavior of Index.insert with object-dtype is deprecated, "
+ "in a future version this will return an object-dtype Index "
+ "instead of inferring a non-object dtype. To retain the old "
+ "behavior, do `idx.insert(loc, item).infer_objects(copy=False)`",
+ FutureWarning,
+ stacklevel=find_stack_level(),
+ )
+ return out
def drop(
self,
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index e3928621a4e48..c233295b25700 100644
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -1893,7 +1893,15 @@ def _setitem_with_indexer(self, indexer, value, name: str = "iloc"):
# just replacing the block manager here
# so the object is the same
index = self.obj._get_axis(i)
- labels = index.insert(len(index), key)
+ with warnings.catch_warnings():
+ # TODO: re-issue this with setitem-specific message?
+ warnings.filterwarnings(
+ "ignore",
+ "The behavior of Index.insert with object-dtype "
+ "is deprecated",
+ category=FutureWarning,
+ )
+ labels = index.insert(len(index), key)
# We are expanding the Series/DataFrame values to match
# the length of thenew index `labels`. GH#40096 ensure
@@ -2186,7 +2194,14 @@ def _setitem_with_indexer_missing(self, indexer, value):
# and set inplace
if self.ndim == 1:
index = self.obj.index
- new_index = index.insert(len(index), indexer)
+ with warnings.catch_warnings():
+ # TODO: re-issue this with setitem-specific message?
+ warnings.filterwarnings(
+ "ignore",
+ "The behavior of Index.insert with object-dtype is deprecated",
+ category=FutureWarning,
+ )
+ new_index = index.insert(len(index), indexer)
# we have a coerced indexer, e.g. a float
# that matches in an int64 Index, so
diff --git a/pandas/core/internals/managers.py b/pandas/core/internals/managers.py
index cc88312d5b58f..6eb4099b4d830 100644
--- a/pandas/core/internals/managers.py
+++ b/pandas/core/internals/managers.py
@@ -1376,8 +1376,14 @@ def insert(self, loc: int, item: Hashable, value: ArrayLike, refs=None) -> None:
value : np.ndarray or ExtensionArray
refs : The reference tracking object of the value to set.
"""
- # insert to the axis; this could possibly raise a TypeError
- new_axis = self.items.insert(loc, item)
+ with warnings.catch_warnings():
+ # TODO: re-issue this with setitem-specific message?
+ warnings.filterwarnings(
+ "ignore",
+ "The behavior of Index.insert with object-dtype is deprecated",
+ category=FutureWarning,
+ )
+ new_axis = self.items.insert(loc, item)
if value.ndim == 2:
value = value.T
diff --git a/pandas/tests/indexes/test_old_base.py b/pandas/tests/indexes/test_old_base.py
index f08de8e65451c..0fff6abcfc6a5 100644
--- a/pandas/tests/indexes/test_old_base.py
+++ b/pandas/tests/indexes/test_old_base.py
@@ -407,13 +407,20 @@ def test_where(self, listlike_box, simple_index):
tm.assert_index_equal(result, expected)
def test_insert_base(self, index):
- result = index[1:4]
+ trimmed = index[1:4]
if not len(index):
pytest.skip("Not applicable for empty index")
# test 0th element
- assert index[0:4].equals(result.insert(0, index[0]))
+ warn = None
+ if index.dtype == object and index.inferred_type == "boolean":
+ # GH#51363
+ warn = FutureWarning
+ msg = "The behavior of Index.insert with object-dtype is deprecated"
+ with tm.assert_produces_warning(warn, match=msg):
+ result = trimmed.insert(0, index[0])
+ assert index[0:4].equals(result)
def test_insert_out_of_bounds(self, index):
# TypeError/IndexError matches what np.insert raises in these cases
| - [x] closes #51363 (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).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
Doesn't have a "do X to avoid this warning, not sure if this merits future option or temporary keyword. | https://api.github.com/repos/pandas-dev/pandas/pulls/55257 | 2023-09-23T20:45:53Z | 2023-12-08T23:39:11Z | 2023-12-08T23:39:11Z | 2023-12-09T16:42:08Z |
ENH/PERF: add ExtensionArray.duplicated | diff --git a/asv_bench/benchmarks/algorithms.py b/asv_bench/benchmarks/algorithms.py
index 2584e1f13853a..192f19c36b47d 100644
--- a/asv_bench/benchmarks/algorithms.py
+++ b/asv_bench/benchmarks/algorithms.py
@@ -1,6 +1,7 @@
from importlib import import_module
import numpy as np
+import pyarrow as pa
import pandas as pd
@@ -72,7 +73,16 @@ class Duplicated:
params = [
[True, False],
["first", "last", False],
- ["int", "uint", "float", "string", "datetime64[ns]", "datetime64[ns, tz]"],
+ [
+ "int",
+ "uint",
+ "float",
+ "string",
+ "datetime64[ns]",
+ "datetime64[ns, tz]",
+ "timestamp[ms][pyarrow]",
+ "duration[s][pyarrow]",
+ ],
]
param_names = ["unique", "keep", "dtype"]
@@ -87,6 +97,12 @@ def setup(self, unique, keep, dtype):
"datetime64[ns, tz]": pd.date_range(
"2011-01-01", freq="H", periods=N, tz="Asia/Tokyo"
),
+ "timestamp[ms][pyarrow]": pd.Index(
+ np.arange(N), dtype=pd.ArrowDtype(pa.timestamp("ms"))
+ ),
+ "duration[s][pyarrow]": pd.Index(
+ np.arange(N), dtype=pd.ArrowDtype(pa.duration("s"))
+ ),
}[dtype]
if not unique:
data = data.repeat(5)
diff --git a/doc/source/reference/extensions.rst b/doc/source/reference/extensions.rst
index 83f830bb11198..e412793a328a3 100644
--- a/doc/source/reference/extensions.rst
+++ b/doc/source/reference/extensions.rst
@@ -49,6 +49,7 @@ objects.
api.extensions.ExtensionArray.copy
api.extensions.ExtensionArray.view
api.extensions.ExtensionArray.dropna
+ api.extensions.ExtensionArray.duplicated
api.extensions.ExtensionArray.equals
api.extensions.ExtensionArray.factorize
api.extensions.ExtensionArray.fillna
diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 9dc095e6de6ff..7667caa830bb7 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -76,6 +76,7 @@ Other enhancements
- :func:`read_csv` now supports ``on_bad_lines`` parameter with ``engine="pyarrow"``. (:issue:`54480`)
- :meth:`ExtensionArray._explode` interface method added to allow extension type implementations of the ``explode`` method (:issue:`54833`)
+- :meth:`ExtensionArray.duplicated` added to allow extension type implementations of the ``duplicated`` method (:issue:`55255`)
- DataFrame.apply now allows the usage of numba (via ``engine="numba"``) to JIT compile the passed function, allowing for potential speedups (:issue:`54666`)
- Implement masked algorithms for :meth:`Series.value_counts` (:issue:`54984`)
-
@@ -241,6 +242,7 @@ Performance improvements
- Performance improvement in :meth:`DataFrame.groupby` when aggregating pyarrow timestamp and duration dtypes (:issue:`55031`)
- Performance improvement in :meth:`DataFrame.sort_index` and :meth:`Series.sort_index` when indexed by a :class:`MultiIndex` (:issue:`54835`)
- Performance improvement in :meth:`Index.difference` (:issue:`55108`)
+- Performance improvement in :meth:`Series.duplicated` for pyarrow dtypes (:issue:`55255`)
- Performance improvement when indexing with more than 4 keys (:issue:`54550`)
- Performance improvement when localizing time to UTC (:issue:`55241`)
diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py
index c952178f4c998..4ff3de2fc7b2b 100644
--- a/pandas/core/algorithms.py
+++ b/pandas/core/algorithms.py
@@ -55,7 +55,6 @@
)
from pandas.core.dtypes.concat import concat_compat
from pandas.core.dtypes.dtypes import (
- ArrowDtype,
BaseMaskedDtype,
CategoricalDtype,
ExtensionDtype,
@@ -979,14 +978,16 @@ def value_counts_arraylike(
def duplicated(
- values: ArrayLike, keep: Literal["first", "last", False] = "first"
+ values: ArrayLike,
+ keep: Literal["first", "last", False] = "first",
+ mask: npt.NDArray[np.bool_] | None = None,
) -> npt.NDArray[np.bool_]:
"""
Return boolean ndarray denoting duplicate values.
Parameters
----------
- values : nd.array, ExtensionArray or Series
+ values : np.ndarray or ExtensionArray
Array over which to check for duplicate values.
keep : {'first', 'last', False}, default 'first'
- ``first`` : Mark duplicates as ``True`` except for the first
@@ -994,21 +995,15 @@ def duplicated(
- ``last`` : Mark duplicates as ``True`` except for the last
occurrence.
- False : Mark all duplicates as ``True``.
+ mask : ndarray[bool], optional
+ array indicating which elements to exclude from checking
Returns
-------
duplicated : ndarray[bool]
"""
- if hasattr(values, "dtype"):
- if isinstance(values.dtype, ArrowDtype) and values.dtype.kind in "ifub":
- values = values._to_masked() # type: ignore[union-attr]
-
- if isinstance(values.dtype, BaseMaskedDtype):
- values = cast("BaseMaskedArray", values)
- return htable.duplicated(values._data, keep=keep, mask=values._mask)
-
values = _ensure_data(values)
- return htable.duplicated(values, keep=keep)
+ return htable.duplicated(values, keep=keep, mask=mask)
def mode(
diff --git a/pandas/core/arrays/arrow/array.py b/pandas/core/arrays/arrow/array.py
index 4b79d0dbb683e..0579aa3760531 100644
--- a/pandas/core/arrays/arrow/array.py
+++ b/pandas/core/arrays/arrow/array.py
@@ -42,6 +42,7 @@
from pandas.core.dtypes.missing import isna
from pandas.core import (
+ algorithms as algos,
missing,
roperator,
)
@@ -1289,6 +1290,30 @@ def to_numpy(
result[~mask] = data[~mask]._pa_array.to_numpy()
return result
+ @doc(ExtensionArray.duplicated)
+ def duplicated(
+ self, keep: Literal["first", "last", False] = "first"
+ ) -> npt.NDArray[np.bool_]:
+ pa_type = self._pa_array.type
+ if pa.types.is_floating(pa_type) or pa.types.is_integer(pa_type):
+ values = self.to_numpy(na_value=0)
+ elif pa.types.is_boolean(pa_type):
+ values = self.to_numpy(na_value=False)
+ elif pa.types.is_temporal(pa_type):
+ if pa_type.bit_width == 32:
+ pa_type = pa.int32()
+ else:
+ pa_type = pa.int64()
+ arr = self.astype(ArrowDtype(pa_type))
+ values = arr.to_numpy(na_value=0)
+ else:
+ # factorize the values to avoid the performance penalty of
+ # converting to object dtype
+ values = self.factorize()[0]
+
+ mask = self.isna() if self._hasna else None
+ return algos.duplicated(values, keep=keep, mask=mask)
+
def unique(self) -> Self:
"""
Compute the ArrowExtensionArray of unique values.
diff --git a/pandas/core/arrays/base.py b/pandas/core/arrays/base.py
index 933944dbd4632..c06bf7366447b 100644
--- a/pandas/core/arrays/base.py
+++ b/pandas/core/arrays/base.py
@@ -61,6 +61,7 @@
roperator,
)
from pandas.core.algorithms import (
+ duplicated,
factorize_array,
isin,
map_array,
@@ -125,6 +126,7 @@ class ExtensionArray:
astype
copy
dropna
+ duplicated
factorize
fillna
equals
@@ -1116,6 +1118,31 @@ def dropna(self) -> Self:
# error: Unsupported operand type for ~ ("ExtensionArray")
return self[~self.isna()] # type: ignore[operator]
+ def duplicated(
+ self, keep: Literal["first", "last", False] = "first"
+ ) -> npt.NDArray[np.bool_]:
+ """
+ Return boolean ndarray denoting duplicate values.
+
+ Parameters
+ ----------
+ keep : {'first', 'last', False}, default 'first'
+ - ``first`` : Mark duplicates as ``True`` except for the first occurrence.
+ - ``last`` : Mark duplicates as ``True`` except for the last occurrence.
+ - False : Mark all duplicates as ``True``.
+
+ Returns
+ -------
+ ndarray[bool]
+
+ Examples
+ --------
+ >>> pd.array([1, 1, 2, 3, 3], dtype="Int64").duplicated()
+ array([False, True, False, False, True])
+ """
+ mask = self.isna().astype(np.bool_, copy=False)
+ return duplicated(values=self, keep=keep, mask=mask)
+
def shift(self, periods: int = 1, fill_value: object = None) -> ExtensionArray:
"""
Shift values by desired number.
diff --git a/pandas/core/arrays/masked.py b/pandas/core/arrays/masked.py
index 9b85fb0477e6f..56d3711c7d13b 100644
--- a/pandas/core/arrays/masked.py
+++ b/pandas/core/arrays/masked.py
@@ -952,6 +952,14 @@ def copy(self) -> Self:
mask = self._mask.copy()
return self._simple_new(data, mask)
+ @doc(ExtensionArray.duplicated)
+ def duplicated(
+ self, keep: Literal["first", "last", False] = "first"
+ ) -> npt.NDArray[np.bool_]:
+ values = self._data
+ mask = self._mask
+ return algos.duplicated(values, keep=keep, mask=mask)
+
def unique(self) -> Self:
"""
Compute the BaseMaskedArray of unique values.
diff --git a/pandas/core/arrays/sparse/array.py b/pandas/core/arrays/sparse/array.py
index 4d5eef960293f..cf349220e4ba7 100644
--- a/pandas/core/arrays/sparse/array.py
+++ b/pandas/core/arrays/sparse/array.py
@@ -28,6 +28,7 @@
from pandas._libs.tslibs import NaT
from pandas.compat.numpy import function as nv
from pandas.errors import PerformanceWarning
+from pandas.util._decorators import doc
from pandas.util._exceptions import find_stack_level
from pandas.util._validators import (
validate_bool_kwarg,
@@ -830,6 +831,14 @@ def _first_fill_value_loc(self):
diff = np.r_[np.diff(indices), 2]
return indices[(diff > 1).argmax()] + 1
+ @doc(ExtensionArray.duplicated)
+ def duplicated(
+ self, keep: Literal["first", "last", False] = "first"
+ ) -> npt.NDArray[np.bool_]:
+ values = np.asarray(self)
+ mask = np.asarray(self.isna())
+ return algos.duplicated(values, keep=keep, mask=mask)
+
def unique(self) -> Self:
uniques = algos.unique(self.sp_values)
if len(self.sp_values) != len(self):
diff --git a/pandas/core/base.py b/pandas/core/base.py
index 3026189e747bb..d4421560bcea7 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -1365,7 +1365,10 @@ def drop_duplicates(self, *, keep: DropKeep = "first"):
@final
def _duplicated(self, keep: DropKeep = "first") -> npt.NDArray[np.bool_]:
- return algorithms.duplicated(self._values, keep=keep)
+ arr = self._values
+ if isinstance(arr, ExtensionArray):
+ return arr.duplicated(keep=keep)
+ return algorithms.duplicated(arr, keep=keep)
def _arith_method(self, other, op):
res_name = ops.get_op_result_name(self, other)
diff --git a/pandas/tests/extension/base/methods.py b/pandas/tests/extension/base/methods.py
index 4e0bc8d804bab..e10c6ef9a7018 100644
--- a/pandas/tests/extension/base/methods.py
+++ b/pandas/tests/extension/base/methods.py
@@ -248,6 +248,18 @@ def test_sort_values_frame(self, data_for_sorting, ascending):
)
tm.assert_frame_equal(result, expected)
+ @pytest.mark.parametrize("keep", ["first", "last", False])
+ def test_duplicated(self, data, keep):
+ arr = data.take([0, 1, 0, 1])
+ result = arr.duplicated(keep=keep)
+ if keep == "first":
+ expected = np.array([False, False, True, True])
+ elif keep == "last":
+ expected = np.array([True, True, False, False])
+ else:
+ expected = np.array([True, True, True, True])
+ tm.assert_numpy_array_equal(result, expected)
+
@pytest.mark.parametrize("box", [pd.Series, lambda x: x])
@pytest.mark.parametrize("method", [lambda x: x.unique(), pd.unique])
def test_unique(self, data, box, method):
| - [x] closes #27264
- [x] closes #48424
- [x] closes #48788
- [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 [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [x] Added an entry in the latest `doc/source/whatsnew/v2.2.0.rst` file if fixing a bug or adding a new feature.
[updated]
`asv continuous -f 1.1 upstream/main arrow-temporal-duplicated -b algorithms.Duplicated`
```
before after ratio
- 101Β±2ms 51.6Β±3ms 0.51 algorithms.Duplicated.time_duplicated(False, False, 'string[pyarrow]')
- 98.5Β±3ms 47.3Β±4ms 0.48 algorithms.Duplicated.time_duplicated(False, 'first', 'string[pyarrow]')
- 96.0Β±4ms 45.5Β±4ms 0.47 algorithms.Duplicated.time_duplicated(False, 'last', 'string[pyarrow]')
- 1.57Β±0.03s 13.6Β±0.3ms 0.01 algorithms.Duplicated.time_duplicated(False, False, 'timestamp[ms][pyarrow]')
- 1.54Β±0.02s 13.1Β±0.7ms 0.01 algorithms.Duplicated.time_duplicated(False, 'first', 'timestamp[ms][pyarrow]')
- 1.55Β±0s 12.8Β±0.7ms 0.01 algorithms.Duplicated.time_duplicated(False, 'last', 'timestamp[ms][pyarrow]')
``` | https://api.github.com/repos/pandas-dev/pandas/pulls/55255 | 2023-09-23T02:31:29Z | 2023-10-03T15:48:01Z | 2023-10-03T15:48:01Z | 2023-11-16T12:56:51Z |
TYP: towards matplotlib 3.8 | diff --git a/ci/deps/actions-310.yaml b/ci/deps/actions-310.yaml
index b22f8cb34c814..94652e8586d77 100644
--- a/ci/deps/actions-310.yaml
+++ b/ci/deps/actions-310.yaml
@@ -33,7 +33,7 @@ dependencies:
- gcsfs>=2022.11.0
- jinja2>=3.1.2
- lxml>=4.9.2
- - matplotlib>=3.6.3, <3.8
+ - matplotlib>=3.6.3
- numba>=0.56.4
- numexpr>=2.8.4
- odfpy>=1.4.1
diff --git a/ci/deps/actions-311-downstream_compat.yaml b/ci/deps/actions-311-downstream_compat.yaml
index ceea734352fca..bf47bfe3a83ec 100644
--- a/ci/deps/actions-311-downstream_compat.yaml
+++ b/ci/deps/actions-311-downstream_compat.yaml
@@ -34,7 +34,7 @@ dependencies:
- gcsfs>=2022.11.0
- jinja2>=3.1.2
- lxml>=4.9.2
- - matplotlib>=3.6.3, <3.8
+ - matplotlib>=3.6.3
- numba>=0.56.4
- numexpr>=2.8.4
- odfpy>=1.4.1
diff --git a/ci/deps/actions-311.yaml b/ci/deps/actions-311.yaml
index 1c9f349c2eb28..bd9e2059ef477 100644
--- a/ci/deps/actions-311.yaml
+++ b/ci/deps/actions-311.yaml
@@ -33,7 +33,7 @@ dependencies:
- gcsfs>=2022.11.0
- jinja2>=3.1.2
- lxml>=4.9.2
- - matplotlib>=3.6.3, <3.8
+ - matplotlib>=3.6.3
- numba>=0.56.4
- numexpr>=2.8.4
- odfpy>=1.4.1
diff --git a/ci/deps/actions-39.yaml b/ci/deps/actions-39.yaml
index 92b88a34094d2..cf4087a3e4670 100644
--- a/ci/deps/actions-39.yaml
+++ b/ci/deps/actions-39.yaml
@@ -33,7 +33,7 @@ dependencies:
- gcsfs>=2022.11.0
- jinja2>=3.1.2
- lxml>=4.9.2
- - matplotlib>=3.6.3, <3.8
+ - matplotlib>=3.6.3
- numba>=0.56.4
- numexpr>=2.8.4
- odfpy>=1.4.1
diff --git a/ci/deps/circle-310-arm64.yaml b/ci/deps/circle-310-arm64.yaml
index f81b91fcbae3b..abe6145d077ed 100644
--- a/ci/deps/circle-310-arm64.yaml
+++ b/ci/deps/circle-310-arm64.yaml
@@ -33,7 +33,7 @@ dependencies:
- gcsfs>=2022.11.0
- jinja2>=3.1.2
- lxml>=4.9.2
- - matplotlib>=3.6.3, <3.8
+ - matplotlib>=3.6.3
- numba>=0.56.4
- numexpr>=2.8.4
- odfpy>=1.4.1
diff --git a/environment.yml b/environment.yml
index e41389c7f262a..aea71efd72f2c 100644
--- a/environment.yml
+++ b/environment.yml
@@ -35,7 +35,7 @@ dependencies:
- ipython
- jinja2>=3.1.2
- lxml>=4.9.2
- - matplotlib>=3.6.3, <3.8
+ - matplotlib>=3.6.3
- numba>=0.56.4
- numexpr>=2.8.4
- openpyxl>=3.1.0
diff --git a/pandas/plotting/_matplotlib/converter.py b/pandas/plotting/_matplotlib/converter.py
index 5a7ceabbf554e..848fb77c942fb 100644
--- a/pandas/plotting/_matplotlib/converter.py
+++ b/pandas/plotting/_matplotlib/converter.py
@@ -64,6 +64,8 @@
if TYPE_CHECKING:
from collections.abc import Generator
+ from matplotlib.axis import Axis
+
from pandas._libs.tslibs.offsets import BaseOffset
@@ -187,7 +189,7 @@ class TimeFormatter(Formatter):
def __init__(self, locs) -> None:
self.locs = locs
- def __call__(self, x, pos: int = 0) -> str:
+ def __call__(self, x, pos: int | None = 0) -> str:
"""
Return the time of day as a formatted string.
@@ -364,8 +366,14 @@ def get_locator(self, dmin, dmax):
locator = MilliSecondLocator(self.tz)
locator.set_axis(self.axis)
- locator.axis.set_view_interval(*self.axis.get_view_interval())
- locator.axis.set_data_interval(*self.axis.get_data_interval())
+ # error: Item "None" of "Axis | _DummyAxis | _AxisWrapper | None"
+ # has no attribute "get_data_interval"
+ locator.axis.set_view_interval( # type: ignore[union-attr]
+ *self.axis.get_view_interval() # type: ignore[union-attr]
+ )
+ locator.axis.set_data_interval( # type: ignore[union-attr]
+ *self.axis.get_data_interval() # type: ignore[union-attr]
+ )
return locator
return mdates.AutoDateLocator.get_locator(self, dmin, dmax)
@@ -950,6 +958,8 @@ class TimeSeries_DateLocator(Locator):
day : {int}, optional
"""
+ axis: Axis
+
def __init__(
self,
freq: BaseOffset,
@@ -999,7 +1009,9 @@ def __call__(self):
base = self.base
(d, m) = divmod(vmin, base)
vmin = (d + 1) * base
- locs = list(range(vmin, vmax + 1, base))
+ # error: No overload variant of "range" matches argument types "float",
+ # "float", "int"
+ locs = list(range(vmin, vmax + 1, base)) # type: ignore[call-overload]
return locs
def autoscale(self):
@@ -1038,6 +1050,8 @@ class TimeSeries_DateFormatter(Formatter):
Whether the formatter works in dynamic mode or not.
"""
+ axis: Axis
+
def __init__(
self,
freq: BaseOffset,
@@ -1084,7 +1098,7 @@ def set_locs(self, locs) -> None:
(vmin, vmax) = (vmax, vmin)
self._set_default_format(vmin, vmax)
- def __call__(self, x, pos: int = 0) -> str:
+ def __call__(self, x, pos: int | None = 0) -> str:
if self.formatdict is None:
return ""
else:
@@ -1107,6 +1121,8 @@ class TimeSeries_TimedeltaFormatter(Formatter):
Formats the ticks along an axis controlled by a :class:`TimedeltaIndex`.
"""
+ axis: Axis
+
@staticmethod
def format_timedelta_ticks(x, pos, n_decimals: int) -> str:
"""
@@ -1124,7 +1140,7 @@ def format_timedelta_ticks(x, pos, n_decimals: int) -> str:
s = f"{int(d):d} days {s}"
return s
- def __call__(self, x, pos: int = 0) -> str:
+ def __call__(self, x, pos: int | None = 0) -> str:
(vmin, vmax) = tuple(self.axis.get_view_interval())
n_decimals = min(int(np.ceil(np.log10(100 * 10**9 / abs(vmax - vmin)))), 9)
return self.format_timedelta_ticks(x, pos, n_decimals)
diff --git a/pandas/plotting/_matplotlib/core.py b/pandas/plotting/_matplotlib/core.py
index 76b68c6b03dd2..6be8284d2a0be 100644
--- a/pandas/plotting/_matplotlib/core.py
+++ b/pandas/plotting/_matplotlib/core.py
@@ -529,9 +529,16 @@ def _maybe_right_yaxis(self, ax: Axes, axes_num: int) -> Axes:
# otherwise, create twin axes
orig_ax, new_ax = ax, ax.twinx()
# TODO: use Matplotlib public API when available
- new_ax._get_lines = orig_ax._get_lines
- new_ax._get_patches_for_fill = orig_ax._get_patches_for_fill
- orig_ax.right_ax, new_ax.left_ax = new_ax, orig_ax
+ new_ax._get_lines = orig_ax._get_lines # type: ignore[attr-defined]
+ # TODO #54485
+ new_ax._get_patches_for_fill = ( # type: ignore[attr-defined]
+ orig_ax._get_patches_for_fill # type: ignore[attr-defined]
+ )
+ # TODO #54485
+ orig_ax.right_ax, new_ax.left_ax = ( # type: ignore[attr-defined]
+ new_ax,
+ orig_ax,
+ )
if not self._has_plotted_object(orig_ax): # no data on left y
orig_ax.get_yaxis().set_visible(False)
@@ -540,7 +547,7 @@ def _maybe_right_yaxis(self, ax: Axes, axes_num: int) -> Axes:
new_ax.set_yscale("log")
elif self.logy == "sym" or self.loglog == "sym":
new_ax.set_yscale("symlog")
- return new_ax
+ return new_ax # type: ignore[return-value]
@final
@cache_readonly
@@ -1206,12 +1213,15 @@ def _get_errorbars(
@final
def _get_subplots(self, fig: Figure):
- from matplotlib.axes import Subplot
+ if Version(mpl.__version__) < Version("3.8"):
+ from matplotlib.axes import Subplot as Klass
+ else:
+ from matplotlib.axes import Axes as Klass
return [
ax
for ax in fig.get_axes()
- if (isinstance(ax, Subplot) and ax.get_subplotspec() is not None)
+ if (isinstance(ax, Klass) and ax.get_subplotspec() is not None)
]
@final
@@ -1255,8 +1265,10 @@ def _post_plot_logic(self, ax: Axes, data) -> None:
x, y = self.x, self.y
xlabel = self.xlabel if self.xlabel is not None else pprint_thing(x)
ylabel = self.ylabel if self.ylabel is not None else pprint_thing(y)
- ax.set_xlabel(xlabel)
- ax.set_ylabel(ylabel)
+ # error: Argument 1 to "set_xlabel" of "_AxesBase" has incompatible
+ # type "Hashable"; expected "str"
+ ax.set_xlabel(xlabel) # type: ignore[arg-type]
+ ax.set_ylabel(ylabel) # type: ignore[arg-type]
@final
def _plot_colorbar(self, ax: Axes, *, fig: Figure, **kwds):
@@ -1393,7 +1405,7 @@ def _get_norm_and_cmap(self, c_values, color_by_categorical: bool):
else:
cmap = None
- if color_by_categorical:
+ if color_by_categorical and cmap is not None:
from matplotlib import colors
n_cats = len(self.data[c].cat.categories)
@@ -1584,13 +1596,13 @@ def _ts_plot(self, ax: Axes, x, data: Series, style=None, **kwds):
decorate_axes(ax.left_ax, freq, kwds)
if hasattr(ax, "right_ax"):
decorate_axes(ax.right_ax, freq, kwds)
- ax._plot_data.append((data, self._kind, kwds))
+ # TODO #54485
+ ax._plot_data.append((data, self._kind, kwds)) # type: ignore[attr-defined]
lines = self._plot(ax, data.index, np.asarray(data.values), style=style, **kwds)
# set date formatter, locators and rescale limits
- # error: Argument 3 to "format_dateaxis" has incompatible type "Index";
- # expected "DatetimeIndex | PeriodIndex"
- format_dateaxis(ax, ax.freq, data.index) # type: ignore[arg-type]
+ # TODO #54485
+ format_dateaxis(ax, ax.freq, data.index) # type: ignore[arg-type, attr-defined]
return lines
@final
@@ -1606,11 +1618,15 @@ def _initialize_stacker(cls, ax: Axes, stacking_id, n: int) -> None:
if stacking_id is None:
return
if not hasattr(ax, "_stacker_pos_prior"):
- ax._stacker_pos_prior = {}
+ # TODO #54485
+ ax._stacker_pos_prior = {} # type: ignore[attr-defined]
if not hasattr(ax, "_stacker_neg_prior"):
- ax._stacker_neg_prior = {}
- ax._stacker_pos_prior[stacking_id] = np.zeros(n)
- ax._stacker_neg_prior[stacking_id] = np.zeros(n)
+ # TODO #54485
+ ax._stacker_neg_prior = {} # type: ignore[attr-defined]
+ # TODO #54485
+ ax._stacker_pos_prior[stacking_id] = np.zeros(n) # type: ignore[attr-defined]
+ # TODO #54485
+ ax._stacker_neg_prior[stacking_id] = np.zeros(n) # type: ignore[attr-defined]
@final
@classmethod
@@ -1624,9 +1640,17 @@ def _get_stacked_values(
cls._initialize_stacker(ax, stacking_id, len(values))
if (values >= 0).all():
- return ax._stacker_pos_prior[stacking_id] + values
+ # TODO #54485
+ return (
+ ax._stacker_pos_prior[stacking_id] # type: ignore[attr-defined]
+ + values
+ )
elif (values <= 0).all():
- return ax._stacker_neg_prior[stacking_id] + values
+ # TODO #54485
+ return (
+ ax._stacker_neg_prior[stacking_id] # type: ignore[attr-defined]
+ + values
+ )
raise ValueError(
"When stacked is True, each column must be either "
@@ -1640,9 +1664,11 @@ def _update_stacker(cls, ax: Axes, stacking_id: int | None, values) -> None:
if stacking_id is None:
return
if (values >= 0).all():
- ax._stacker_pos_prior[stacking_id] += values
+ # TODO #54485
+ ax._stacker_pos_prior[stacking_id] += values # type: ignore[attr-defined]
elif (values <= 0).all():
- ax._stacker_neg_prior[stacking_id] += values
+ # TODO #54485
+ ax._stacker_neg_prior[stacking_id] += values # type: ignore[attr-defined]
def _post_plot_logic(self, ax: Axes, data) -> None:
from matplotlib.ticker import FixedLocator
@@ -1658,7 +1684,9 @@ def get_label(i):
if self._need_to_set_index:
xticks = ax.get_xticks()
xticklabels = [get_label(x) for x in xticks]
- ax.xaxis.set_major_locator(FixedLocator(xticks))
+ # error: Argument 1 to "FixedLocator" has incompatible type "ndarray[Any,
+ # Any]"; expected "Sequence[float]"
+ ax.xaxis.set_major_locator(FixedLocator(xticks)) # type: ignore[arg-type]
ax.set_xticklabels(xticklabels)
# If the index is an irregular time series, then by default
@@ -1737,9 +1765,11 @@ def _plot( # type: ignore[override]
if stacking_id is None:
start = np.zeros(len(y))
elif (y >= 0).all():
- start = ax._stacker_pos_prior[stacking_id]
+ # TODO #54485
+ start = ax._stacker_pos_prior[stacking_id] # type: ignore[attr-defined]
elif (y <= 0).all():
- start = ax._stacker_neg_prior[stacking_id]
+ # TODO #54485
+ start = ax._stacker_neg_prior[stacking_id] # type: ignore[attr-defined]
else:
start = np.zeros(len(y))
@@ -2005,7 +2035,9 @@ def _decorate_ticks(
ax.set_yticklabels(ticklabels)
if name is not None and self.use_index:
ax.set_ylabel(name)
- ax.set_xlabel(self.xlabel)
+ # error: Argument 1 to "set_xlabel" of "_AxesBase" has incompatible type
+ # "Hashable | None"; expected "str"
+ ax.set_xlabel(self.xlabel) # type: ignore[arg-type]
class PiePlot(MPLPlot):
diff --git a/pandas/plotting/_matplotlib/hist.py b/pandas/plotting/_matplotlib/hist.py
index f5b415f12f37d..de4fd91541a9d 100644
--- a/pandas/plotting/_matplotlib/hist.py
+++ b/pandas/plotting/_matplotlib/hist.py
@@ -199,11 +199,21 @@ def _get_column_weights(weights, i: int, y):
def _post_plot_logic(self, ax: Axes, data) -> None:
if self.orientation == "horizontal":
- ax.set_xlabel("Frequency" if self.xlabel is None else self.xlabel)
- ax.set_ylabel(self.ylabel)
+ # error: Argument 1 to "set_xlabel" of "_AxesBase" has incompatible
+ # type "Hashable"; expected "str"
+ ax.set_xlabel(
+ "Frequency"
+ if self.xlabel is None
+ else self.xlabel # type: ignore[arg-type]
+ )
+ ax.set_ylabel(self.ylabel) # type: ignore[arg-type]
else:
- ax.set_xlabel(self.xlabel)
- ax.set_ylabel("Frequency" if self.ylabel is None else self.ylabel)
+ ax.set_xlabel(self.xlabel) # type: ignore[arg-type]
+ ax.set_ylabel(
+ "Frequency"
+ if self.ylabel is None
+ else self.ylabel # type: ignore[arg-type]
+ )
@property
def orientation(self) -> PlottingOrientation:
@@ -447,8 +457,14 @@ def hist_series(
ax.grid(grid)
axes = np.array([ax])
+ # error: Argument 1 to "set_ticks_props" has incompatible type "ndarray[Any,
+ # dtype[Any]]"; expected "Axes | Sequence[Axes]"
set_ticks_props(
- axes, xlabelsize=xlabelsize, xrot=xrot, ylabelsize=ylabelsize, yrot=yrot
+ axes, # type: ignore[arg-type]
+ xlabelsize=xlabelsize,
+ xrot=xrot,
+ ylabelsize=ylabelsize,
+ yrot=yrot,
)
else:
diff --git a/pandas/plotting/_matplotlib/style.py b/pandas/plotting/_matplotlib/style.py
index a5f34e9434cb7..bf4e4be3bfd82 100644
--- a/pandas/plotting/_matplotlib/style.py
+++ b/pandas/plotting/_matplotlib/style.py
@@ -269,7 +269,9 @@ def _is_single_string_color(color: Color) -> bool:
"""
conv = matplotlib.colors.ColorConverter()
try:
- conv.to_rgba(color)
+ # error: Argument 1 to "to_rgba" of "ColorConverter" has incompatible type
+ # "str | Sequence[float]"; expected "tuple[float, float, float] | ..."
+ conv.to_rgba(color) # type: ignore[arg-type]
except ValueError:
return False
else:
diff --git a/pandas/plotting/_matplotlib/timeseries.py b/pandas/plotting/_matplotlib/timeseries.py
index 5471305b03baf..f0b68e5dde450 100644
--- a/pandas/plotting/_matplotlib/timeseries.py
+++ b/pandas/plotting/_matplotlib/timeseries.py
@@ -126,7 +126,7 @@ def _upsample_others(ax: Axes, freq: BaseOffset, kwargs: dict[str, Any]) -> None
labels.extend(rlabels)
if legend is not None and kwargs.get("legend", True) and len(lines) > 0:
- title = legend.get_title().get_text()
+ title: str | None = legend.get_title().get_text()
if title == "None":
title = None
ax.legend(lines, labels, loc="best", title=title)
@@ -136,7 +136,8 @@ def _replot_ax(ax: Axes, freq: BaseOffset, kwargs: dict[str, Any]):
data = getattr(ax, "_plot_data", None)
# clear current axes and data
- ax._plot_data = []
+ # TODO #54485
+ ax._plot_data = [] # type: ignore[attr-defined]
ax.clear()
decorate_axes(ax, freq, kwargs)
@@ -148,7 +149,8 @@ def _replot_ax(ax: Axes, freq: BaseOffset, kwargs: dict[str, Any]):
series = series.copy()
idx = series.index.asfreq(freq, how="S")
series.index = idx
- ax._plot_data.append((series, plotf, kwds))
+ # TODO #54485
+ ax._plot_data.append((series, plotf, kwds)) # type: ignore[attr-defined]
# for tsplot
if isinstance(plotf, str):
@@ -165,17 +167,23 @@ def _replot_ax(ax: Axes, freq: BaseOffset, kwargs: dict[str, Any]):
def decorate_axes(ax: Axes, freq: BaseOffset, kwargs: dict[str, Any]) -> None:
"""Initialize axes for time-series plotting"""
if not hasattr(ax, "_plot_data"):
- ax._plot_data = []
+ # TODO #54485
+ ax._plot_data = [] # type: ignore[attr-defined]
- ax.freq = freq
+ # TODO #54485
+ ax.freq = freq # type: ignore[attr-defined]
xaxis = ax.get_xaxis()
- xaxis.freq = freq
+ # TODO #54485
+ xaxis.freq = freq # type: ignore[attr-defined]
if not hasattr(ax, "legendlabels"):
- ax.legendlabels = [kwargs.get("label", None)]
+ # TODO #54485
+ ax.legendlabels = [kwargs.get("label", None)] # type: ignore[attr-defined]
else:
ax.legendlabels.append(kwargs.get("label", None))
- ax.view_interval = None
- ax.date_axis_info = None
+ # TODO #54485
+ ax.view_interval = None # type: ignore[attr-defined]
+ # TODO #54485
+ ax.date_axis_info = None # type: ignore[attr-defined]
def _get_ax_freq(ax: Axes):
diff --git a/pandas/plotting/_matplotlib/tools.py b/pandas/plotting/_matplotlib/tools.py
index 8c0e401f991a6..898b5b25e7b01 100644
--- a/pandas/plotting/_matplotlib/tools.py
+++ b/pandas/plotting/_matplotlib/tools.py
@@ -52,10 +52,12 @@ def maybe_adjust_figure(fig: Figure, *args, **kwargs) -> None:
def format_date_labels(ax: Axes, rot) -> None:
# mini version of autofmt_xdate
for label in ax.get_xticklabels():
- label.set_ha("right")
+ label.set_horizontalalignment("right")
label.set_rotation(rot)
fig = ax.get_figure()
- maybe_adjust_figure(fig, bottom=0.2)
+ if fig is not None:
+ # should always be a Figure but can technically be None
+ maybe_adjust_figure(fig, bottom=0.2)
def table(
@@ -76,8 +78,14 @@ def table(
cellText = data.values
+ # error: Argument "cellText" to "table" has incompatible type "ndarray[Any,
+ # Any]"; expected "Sequence[Sequence[str]] | None"
return matplotlib.table.table(
- ax, cellText=cellText, rowLabels=rowLabels, colLabels=colLabels, **kwargs
+ ax,
+ cellText=cellText, # type: ignore[arg-type]
+ rowLabels=rowLabels,
+ colLabels=colLabels,
+ **kwargs,
)
@@ -369,12 +377,12 @@ def _has_externally_shared_axis(ax1: Axes, compare_axis: str) -> bool:
"_has_externally_shared_axis() needs 'x' or 'y' as a second parameter"
)
- axes = axes.get_siblings(ax1)
+ axes_siblings = axes.get_siblings(ax1)
# Retain ax1 and any of its siblings which aren't in the same position as it
ax1_points = ax1.get_position().get_points()
- for ax2 in axes:
+ for ax2 in axes_siblings:
if not np.array_equal(ax1_points, ax2.get_position().get_points()):
return True
diff --git a/pandas/tests/io/test_sql.py b/pandas/tests/io/test_sql.py
index b274866b7c9a8..9ac20774b8c93 100644
--- a/pandas/tests/io/test_sql.py
+++ b/pandas/tests/io/test_sql.py
@@ -3179,7 +3179,9 @@ def dtype_backend_data() -> DataFrame:
@pytest.fixture
def dtype_backend_expected():
- def func(storage, dtype_backend, conn_name):
+ def func(storage, dtype_backend, conn_name) -> DataFrame:
+ string_array: StringArray | ArrowStringArray
+ string_array_na: StringArray | ArrowStringArray
if storage == "python":
string_array = StringArray(np.array(["a", "b", "c"], dtype=np.object_))
string_array_na = StringArray(np.array(["a", "b", pd.NA], dtype=np.object_))
diff --git a/pandas/tests/plotting/common.py b/pandas/tests/plotting/common.py
index fd5a66049bd24..69120160699c2 100644
--- a/pandas/tests/plotting/common.py
+++ b/pandas/tests/plotting/common.py
@@ -328,7 +328,7 @@ def _check_axes_shape(axes, axes_num=None, layout=None, figsize=None):
)
-def _flatten_visible(axes):
+def _flatten_visible(axes: Axes | Sequence[Axes]) -> Sequence[Axes]:
"""
Flatten axes, and filter only visible
@@ -339,8 +339,8 @@ def _flatten_visible(axes):
"""
from pandas.plotting._matplotlib.tools import flatten_axes
- axes = flatten_axes(axes)
- axes = [ax for ax in axes if ax.get_visible()]
+ axes_ndarray = flatten_axes(axes)
+ axes = [ax for ax in axes_ndarray if ax.get_visible()]
return axes
diff --git a/pyright_reportGeneralTypeIssues.json b/pyright_reportGeneralTypeIssues.json
index c059b9c589ecd..e155b34053069 100644
--- a/pyright_reportGeneralTypeIssues.json
+++ b/pyright_reportGeneralTypeIssues.json
@@ -99,6 +99,9 @@
"pandas/io/sql.py",
"pandas/io/stata.py",
"pandas/plotting/_matplotlib/boxplot.py",
+ "pandas/plotting/_matplotlib/core.py",
+ "pandas/plotting/_matplotlib/timeseries.py",
+ "pandas/plotting/_matplotlib/tools.py",
"pandas/tseries/frequencies.py",
"pandas/tseries/holiday.py",
],
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 490e170299030..faf915f4b9716 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -24,7 +24,7 @@ gcsfs>=2022.11.0
ipython
jinja2>=3.1.2
lxml>=4.9.2
-matplotlib>=3.6.3, <3.8
+matplotlib>=3.6.3
numba>=0.56.4
numexpr>=2.8.4
openpyxl>=3.1.0
| Type changes for matplotlib 3.8. There is more work needed:
- failing CI with 3.8 https://github.com/pandas-dev/pandas/pull/55210#issuecomment-1732096783
- refactor the use of matplotlib to not set non-existing attributes matploblib object and to avoid using private methods (see mypy errors below). This can be done in a separate PR (or we add type ignores for now).
Typing errors left with 3.8:
```
pandas/plotting/_matplotlib/timeseries.py:127: error: "Axes" has no attribute "_plot_data"; maybe "plot_date"? [attr-defined]
pandas/plotting/_matplotlib/timeseries.py:139: error: "Axes" has no attribute "_plot_data"; maybe "plot_date"? [attr-defined]
pandas/plotting/_matplotlib/timeseries.py:156: error: "Axes" has no attribute "_plot_data"; maybe "plot_date"? [attr-defined]
pandas/plotting/_matplotlib/timeseries.py:158: error: "Axes" has no attribute "freq" [attr-defined]
pandas/plotting/_matplotlib/timeseries.py:160: error: "XAxis" has no attribute "freq" [attr-defined]
pandas/plotting/_matplotlib/timeseries.py:162: error: "Axes" has no attribute "legendlabels" [attr-defined]
pandas/plotting/_matplotlib/timeseries.py:165: error: "Axes" has no attribute "view_interval" [attr-defined]
pandas/plotting/_matplotlib/timeseries.py:166: error: "Axes" has no attribute "date_axis_info" [attr-defined]
pandas/plotting/_matplotlib/core.py:468: error: "Axes" has no attribute "containers" [attr-defined]
pandas/plotting/_matplotlib/core.py:485: error: "_AxesBase" has no attribute "_get_lines"; maybe "get_lines"? [attr-defined]
pandas/plotting/_matplotlib/core.py:485: error: "Axes" has no attribute "_get_lines" [attr-defined]
pandas/plotting/_matplotlib/core.py:486: error: "_AxesBase" has no attribute "_get_patches_for_fill" [attr-defined]
pandas/plotting/_matplotlib/core.py:486: error: "Axes" has no attribute "_get_patches_for_fill" [attr-defined]
pandas/plotting/_matplotlib/core.py:487: error: "Axes" has no attribute "right_ax" [attr-defined]
pandas/plotting/_matplotlib/core.py:487: error: "_AxesBase" has no attribute "left_ax" [attr-defined]
pandas/plotting/_matplotlib/core.py:1440: error: "Axes" has no attribute "_plot_data"; maybe "plot_date"? [attr-defined]
pandas/plotting/_matplotlib/core.py:1444: error: "Axes" has no attribute "freq" [attr-defined]
pandas/plotting/_matplotlib/core.py:1458: error: "Axes" has no attribute "_stacker_pos_prior" [attr-defined]
pandas/plotting/_matplotlib/core.py:1460: error: "Axes" has no attribute "_stacker_neg_prior" [attr-defined]
pandas/plotting/_matplotlib/core.py:1461: error: "Axes" has no attribute "_stacker_pos_prior" [attr-defined]
pandas/plotting/_matplotlib/core.py:1462: error: "Axes" has no attribute "_stacker_neg_prior" [attr-defined]
pandas/plotting/_matplotlib/core.py:1473: error: "Axes" has no attribute "_stacker_pos_prior" [attr-defined]
pandas/plotting/_matplotlib/core.py:1475: error: "Axes" has no attribute "_stacker_neg_prior" [attr-defined]
pandas/plotting/_matplotlib/core.py:1488: error: "Axes" has no attribute "_stacker_pos_prior" [attr-defined]
pandas/plotting/_matplotlib/core.py:1490: error: "Axes" has no attribute "_stacker_neg_prior" [attr-defined]
pandas/plotting/_matplotlib/core.py:1590: error: "Axes" has no attribute "_stacker_pos_prior" [attr-defined]
pandas/plotting/_matplotlib/core.py:1592: error: "Axes" has no attribute "_stacker_neg_prior" [attr-defined]
```
This PR will obviously fail as it doesn't update matplotlib itself. | https://api.github.com/repos/pandas-dev/pandas/pulls/55253 | 2023-09-22T23:53:31Z | 2023-11-15T21:38:02Z | 2023-11-15T21:38:02Z | 2023-12-10T04:33:46Z |
DEPR: 'A' for yearly frequency and YearEnd in favour of 'Y' | diff --git a/asv_bench/benchmarks/tslibs/period.py b/asv_bench/benchmarks/tslibs/period.py
index a92fbbe8d4dbe..67f3b7736018d 100644
--- a/asv_bench/benchmarks/tslibs/period.py
+++ b/asv_bench/benchmarks/tslibs/period.py
@@ -72,7 +72,7 @@ def time_now(self, freq):
self.per.now(freq)
def time_asfreq(self, freq):
- self.per.asfreq("A")
+ self.per.asfreq("Y")
def time_str(self, freq):
str(self.per)
diff --git a/doc/source/user_guide/io.rst b/doc/source/user_guide/io.rst
index 6bd181740c78d..c2fe277c4f4e5 100644
--- a/doc/source/user_guide/io.rst
+++ b/doc/source/user_guide/io.rst
@@ -2332,7 +2332,7 @@ A few notes on the generated table schema:
.. ipython:: python
- s_per = pd.Series(1, index=pd.period_range("2016", freq="A-DEC", periods=4))
+ s_per = pd.Series(1, index=pd.period_range("2016", freq="Y-DEC", periods=4))
build_table_schema(s_per)
* Categoricals use the ``any`` type and an ``enum`` constraint listing
diff --git a/doc/source/user_guide/timeseries.rst b/doc/source/user_guide/timeseries.rst
index 113efeefb48ce..3ec303323b887 100644
--- a/doc/source/user_guide/timeseries.rst
+++ b/doc/source/user_guide/timeseries.rst
@@ -895,7 +895,7 @@ into ``freq`` keyword arguments. The available date offsets and associated frequ
:class:`~pandas.tseries.offsets.BQuarterEnd`, ``'BQ``, "business quarter end"
:class:`~pandas.tseries.offsets.BQuarterBegin`, ``'BQS'``, "business quarter begin"
:class:`~pandas.tseries.offsets.FY5253Quarter`, ``'REQ'``, "retail (aka 52-53 week) quarter"
- :class:`~pandas.tseries.offsets.YearEnd`, ``'A'``, "calendar year end"
+ :class:`~pandas.tseries.offsets.YearEnd`, ``'Y'``, "calendar year end"
:class:`~pandas.tseries.offsets.YearBegin`, ``'AS'`` or ``'BYS'``,"calendar year begin"
:class:`~pandas.tseries.offsets.BYearEnd`, ``'BA'``, "business year end"
:class:`~pandas.tseries.offsets.BYearBegin`, ``'BAS'``, "business year begin"
@@ -1258,7 +1258,7 @@ frequencies. We will refer to these aliases as *offset aliases*.
"BQ", "business quarter end frequency"
"QS", "quarter start frequency"
"BQS", "business quarter start frequency"
- "A, Y", "year end frequency"
+ "Y", "year end frequency"
"BA, BY", "business year end frequency"
"AS, YS", "year start frequency"
"BAS, BYS", "business year start frequency"
@@ -1321,7 +1321,7 @@ frequencies. We will refer to these aliases as *period aliases*.
"W", "weekly frequency"
"M", "monthly frequency"
"Q", "quarterly frequency"
- "A, Y", "yearly frequency"
+ "Y", "yearly frequency"
"H", "hourly frequency"
"min", "minutely frequency"
"s", "secondly frequency"
@@ -1331,8 +1331,8 @@ frequencies. We will refer to these aliases as *period aliases*.
.. deprecated:: 2.2.0
- Aliases ``T``, ``S``, ``L``, ``U``, and ``N`` are deprecated in favour of the aliases
- ``min``, ``s``, ``ms``, ``us``, and ``ns``.
+ Aliases ``A``, ``T``, ``S``, ``L``, ``U``, and ``N`` are deprecated in favour of the aliases
+ ``Y``, ``min``, ``s``, ``ms``, ``us``, and ``ns``.
Combining aliases
@@ -1383,18 +1383,18 @@ For some frequencies you can specify an anchoring suffix:
"(B)Q(S)\-SEP", "quarterly frequency, year ends in September"
"(B)Q(S)\-OCT", "quarterly frequency, year ends in October"
"(B)Q(S)\-NOV", "quarterly frequency, year ends in November"
- "(B)A(S)\-DEC", "annual frequency, anchored end of December. Same as 'A'"
- "(B)A(S)\-JAN", "annual frequency, anchored end of January"
- "(B)A(S)\-FEB", "annual frequency, anchored end of February"
- "(B)A(S)\-MAR", "annual frequency, anchored end of March"
- "(B)A(S)\-APR", "annual frequency, anchored end of April"
- "(B)A(S)\-MAY", "annual frequency, anchored end of May"
- "(B)A(S)\-JUN", "annual frequency, anchored end of June"
- "(B)A(S)\-JUL", "annual frequency, anchored end of July"
- "(B)A(S)\-AUG", "annual frequency, anchored end of August"
- "(B)A(S)\-SEP", "annual frequency, anchored end of September"
- "(B)A(S)\-OCT", "annual frequency, anchored end of October"
- "(B)A(S)\-NOV", "annual frequency, anchored end of November"
+ "(B)Y(S)\-DEC", "annual frequency, anchored end of December. Same as 'Y'"
+ "(B)Y(S)\-JAN", "annual frequency, anchored end of January"
+ "(B)Y(S)\-FEB", "annual frequency, anchored end of February"
+ "(B)Y(S)\-MAR", "annual frequency, anchored end of March"
+ "(B)Y(S)\-APR", "annual frequency, anchored end of April"
+ "(B)Y(S)\-MAY", "annual frequency, anchored end of May"
+ "(B)Y(S)\-JUN", "annual frequency, anchored end of June"
+ "(B)Y(S)\-JUL", "annual frequency, anchored end of July"
+ "(B)Y(S)\-AUG", "annual frequency, anchored end of August"
+ "(B)Y(S)\-SEP", "annual frequency, anchored end of September"
+ "(B)Y(S)\-OCT", "annual frequency, anchored end of October"
+ "(B)Y(S)\-NOV", "annual frequency, anchored end of November"
These can be used as arguments to ``date_range``, ``bdate_range``, constructors
for ``DatetimeIndex``, as well as various other timeseries-related functions
@@ -1690,7 +1690,7 @@ the end of the interval.
.. warning::
The default values for ``label`` and ``closed`` is '**left**' for all
- frequency offsets except for 'ME', 'A', 'Q', 'BM', 'BA', 'BQ', and 'W'
+ frequency offsets except for 'ME', 'Y', 'Q', 'BM', 'BA', 'BQ', and 'W'
which all have a default of 'right'.
This might unintendedly lead to looking ahead, where the value for a later
@@ -1995,7 +1995,7 @@ Because ``freq`` represents a span of ``Period``, it cannot be negative like "-3
.. ipython:: python
- pd.Period("2012", freq="A-DEC")
+ pd.Period("2012", freq="Y-DEC")
pd.Period("2012-1-1", freq="D")
@@ -2008,7 +2008,7 @@ frequency. Arithmetic is not allowed between ``Period`` with different ``freq``
.. ipython:: python
- p = pd.Period("2012", freq="A-DEC")
+ p = pd.Period("2012", freq="Y-DEC")
p + 1
p - 3
p = pd.Period("2012-01", freq="2M")
@@ -2050,7 +2050,7 @@ return the number of frequency units between them:
.. ipython:: python
- pd.Period("2012", freq="A-DEC") - pd.Period("2002", freq="A-DEC")
+ pd.Period("2012", freq="Y-DEC") - pd.Period("2002", freq="Y-DEC")
PeriodIndex and period_range
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -2184,7 +2184,7 @@ method. Let's start with the fiscal year 2011, ending in December:
.. ipython:: python
- p = pd.Period("2011", freq="A-DEC")
+ p = pd.Period("2011", freq="Y-DEC")
p
We can convert it to a monthly frequency. Using the ``how`` parameter, we can
@@ -2211,10 +2211,10 @@ input period:
p = pd.Period("2011-12", freq="M")
- p.asfreq("A-NOV")
+ p.asfreq("Y-NOV")
Note that since we converted to an annual frequency that ends the year in
-November, the monthly period of December 2011 is actually in the 2012 A-NOV
+November, the monthly period of December 2011 is actually in the 2012 Y-NOV
period.
.. _timeseries.quarterly:
diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 445b93705cde5..b0d4ed730ce57 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -222,6 +222,7 @@ Other Deprecations
- Deprecated downcasting behavior in :meth:`Series.where`, :meth:`DataFrame.where`, :meth:`Series.mask`, :meth:`DataFrame.mask`, :meth:`Series.clip`, :meth:`DataFrame.clip`; in a future version these will not infer object-dtype columns to non-object dtype, or all-round floats to integer dtype. Call ``result.infer_objects(copy=False)`` on the result for object inference, or explicitly cast floats to ints. To opt in to the future version, use ``pd.set_option("future.no_silent_downcasting", True)`` (:issue:`53656`)
- Deprecated including the groups in computations when using :meth:`DataFrameGroupBy.apply` and :meth:`DataFrameGroupBy.resample`; pass ``include_groups=False`` to exclude the groups (:issue:`7155`)
- Deprecated not passing a tuple to :class:`DataFrameGroupBy.get_group` or :class:`SeriesGroupBy.get_group` when grouping by a length-1 list-like (:issue:`25971`)
+- Deprecated string ``A`` denoting frequency in :class:`YearEnd` and strings ``A-DEC``, ``A-JAN``, etc. denoting annual frequencies with various fiscal year ends (:issue:`52536`)
- Deprecated strings ``S``, ``U``, and ``N`` denoting units in :func:`to_timedelta` (:issue:`52536`)
- Deprecated strings ``T``, ``S``, ``L``, ``U``, and ``N`` denoting frequencies in :class:`Minute`, :class:`Second`, :class:`Milli`, :class:`Micro`, :class:`Nano` (:issue:`52536`)
- Deprecated strings ``T``, ``S``, ``L``, ``U``, and ``N`` denoting units in :class:`Timedelta` (:issue:`52536`)
diff --git a/pandas/_libs/tslibs/dtypes.pxd b/pandas/_libs/tslibs/dtypes.pxd
index e050ac5a6c7b7..bda4fcf04234b 100644
--- a/pandas/_libs/tslibs/dtypes.pxd
+++ b/pandas/_libs/tslibs/dtypes.pxd
@@ -13,6 +13,8 @@ cpdef bint is_supported_unit(NPY_DATETIMEUNIT reso)
cpdef freq_to_period_freqstr(freq_n, freq_name)
cdef dict c_OFFSET_TO_PERIOD_FREQSTR
+cdef dict c_OFFSET_DEPR_FREQSTR
+cdef dict c_REVERSE_OFFSET_DEPR_FREQSTR
cdef dict c_DEPR_ABBREVS
cdef dict attrname_to_abbrevs
cdef dict npy_unit_to_attrname
diff --git a/pandas/_libs/tslibs/dtypes.pyi b/pandas/_libs/tslibs/dtypes.pyi
index 72a8fa8ff0b38..d8680ed2d27b4 100644
--- a/pandas/_libs/tslibs/dtypes.pyi
+++ b/pandas/_libs/tslibs/dtypes.pyi
@@ -7,6 +7,7 @@ from pandas._libs.tslibs.timedeltas import UnitChoices
_attrname_to_abbrevs: dict[str, str]
_period_code_map: dict[str, int]
OFFSET_TO_PERIOD_FREQSTR: dict[str, str]
+OFFSET_DEPR_FREQSTR: dict[str, str]
DEPR_ABBREVS: dict[str, UnitChoices]
def periods_per_day(reso: int) -> int: ...
diff --git a/pandas/_libs/tslibs/dtypes.pyx b/pandas/_libs/tslibs/dtypes.pyx
index cca379c620aeb..ebb6e3a240cbe 100644
--- a/pandas/_libs/tslibs/dtypes.pyx
+++ b/pandas/_libs/tslibs/dtypes.pyx
@@ -101,19 +101,19 @@ 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": 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
+ # eg, 2005 for Y-FEB runs Mar 1, 2004 to Feb 28, 2005
+ "Y-DEC": PeriodDtypeCode.A_DEC, # Annual - December year end
+ "Y-JAN": PeriodDtypeCode.A_JAN, # Annual - January year end
+ "Y-FEB": PeriodDtypeCode.A_FEB, # Annual - February year end
+ "Y-MAR": PeriodDtypeCode.A_MAR, # Annual - March year end
+ "Y-APR": PeriodDtypeCode.A_APR, # Annual - April year end
+ "Y-MAY": PeriodDtypeCode.A_MAY, # Annual - May year end
+ "Y-JUN": PeriodDtypeCode.A_JUN, # Annual - June year end
+ "Y-JUL": PeriodDtypeCode.A_JUL, # Annual - July year end
+ "Y-AUG": PeriodDtypeCode.A_AUG, # Annual - August year end
+ "Y-SEP": PeriodDtypeCode.A_SEP, # Annual - September year end
+ "Y-OCT": PeriodDtypeCode.A_OCT, # Annual - October year end
+ "Y-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
@@ -156,22 +156,22 @@ _reverse_period_code_map = {
# Yearly aliases; careful not to put these in _reverse_period_code_map
_period_code_map.update({"Y" + key[1:]: _period_code_map[key]
for key in _period_code_map
- if key.startswith("A-")})
+ if key.startswith("Y-")})
_period_code_map.update({
"Q": 2000, # Quarterly - December year end (default quarterly)
- "A": PeriodDtypeCode.A, # Annual
+ "Y": PeriodDtypeCode.A, # Annual
"W": 4000, # Weekly
"C": 5000, # Custom Business Day
})
cdef set _month_names = {
- x.split("-")[-1] for x in _period_code_map.keys() if x.startswith("A-")
+ x.split("-")[-1] for x in _period_code_map.keys() if x.startswith("Y-")
}
# Map attribute-name resolutions to resolution abbreviations
_attrname_to_abbrevs = {
- "year": "A",
+ "year": "Y",
"quarter": "Q",
"month": "M",
"day": "D",
@@ -192,9 +192,9 @@ OFFSET_TO_PERIOD_FREQSTR: dict = {
"BQS": "Q",
"QS": "Q",
"BQ": "Q",
- "BA": "A",
- "AS": "A",
- "BAS": "A",
+ "BA": "Y",
+ "AS": "Y",
+ "BAS": "Y",
"MS": "M",
"D": "D",
"B": "B",
@@ -205,15 +205,19 @@ OFFSET_TO_PERIOD_FREQSTR: dict = {
"ns": "ns",
"H": "H",
"Q": "Q",
- "A": "A",
+ "Y": "Y",
"W": "W",
"ME": "M",
- "Y": "A",
- "BY": "A",
- "YS": "A",
- "BYS": "A",
+ "BY": "Y",
+ "YS": "Y",
+ "BYS": "Y",
+}
+OFFSET_DEPR_FREQSTR: dict[str, str]= {
+ "M": "ME",
}
cdef dict c_OFFSET_TO_PERIOD_FREQSTR = OFFSET_TO_PERIOD_FREQSTR
+cdef dict c_OFFSET_DEPR_FREQSTR = OFFSET_DEPR_FREQSTR
+cdef dict c_REVERSE_OFFSET_DEPR_FREQSTR = {v: k for k, v in OFFSET_DEPR_FREQSTR.items()}
cpdef freq_to_period_freqstr(freq_n, freq_name):
if freq_n == 1:
@@ -226,6 +230,20 @@ cpdef freq_to_period_freqstr(freq_n, freq_name):
# Map deprecated resolution abbreviations to correct resolution abbreviations
DEPR_ABBREVS: dict[str, str]= {
+ "A": "Y",
+ "a": "Y",
+ "A-DEC": "Y-DEC",
+ "A-JAN": "Y-JAN",
+ "A-FEB": "Y-FEB",
+ "A-MAR": "Y-MAR",
+ "A-APR": "Y-APR",
+ "A-MAY": "Y-MAY",
+ "A-JUN": "Y-JUN",
+ "A-JUL": "Y-JUL",
+ "A-AUG": "Y-AUG",
+ "A-SEP": "Y-SEP",
+ "A-OCT": "Y-OCT",
+ "A-NOV": "Y-NOV",
"T": "min",
"t": "min",
"S": "s",
diff --git a/pandas/_libs/tslibs/offsets.pyx b/pandas/_libs/tslibs/offsets.pyx
index 74398eb0e2405..a24c2ce7f4b8a 100644
--- a/pandas/_libs/tslibs/offsets.pyx
+++ b/pandas/_libs/tslibs/offsets.pyx
@@ -15,6 +15,7 @@ from cpython.datetime cimport (
time as dt_time,
timedelta,
)
+
import warnings
import_datetime()
@@ -48,7 +49,6 @@ from pandas._libs.tslibs.ccalendar import (
)
from pandas.util._exceptions import find_stack_level
-
from pandas._libs.tslibs.ccalendar cimport (
dayofweek,
get_days_in_month,
@@ -58,6 +58,8 @@ from pandas._libs.tslibs.ccalendar cimport (
from pandas._libs.tslibs.conversion cimport localize_pydatetime
from pandas._libs.tslibs.dtypes cimport (
c_DEPR_ABBREVS,
+ c_OFFSET_DEPR_FREQSTR,
+ c_REVERSE_OFFSET_DEPR_FREQSTR,
periods_per_day,
)
from pandas._libs.tslibs.nattype cimport (
@@ -2496,7 +2498,7 @@ cdef class YearEnd(YearOffset):
"""
_default_month = 12
- _prefix = "A"
+ _prefix = "Y"
_day_opt = "end"
cdef readonly:
@@ -4447,7 +4449,7 @@ prefix_mapping = {
offset._prefix: offset
for offset in [
YearBegin, # 'AS'
- YearEnd, # 'A'
+ YearEnd, # 'Y'
BYearBegin, # 'BAS'
BYearEnd, # 'BA'
BusinessDay, # 'B'
@@ -4489,8 +4491,7 @@ _lite_rule_alias = {
"W": "W-SUN",
"Q": "Q-DEC",
- "A": "A-DEC", # YearEnd(month=12),
- "Y": "A-DEC",
+ "Y": "Y-DEC", # YearEnd(month=12),
"AS": "AS-JAN", # YearBegin(month=1),
"YS": "AS-JAN",
"BA": "BA-DEC", # BYearEnd(month=12),
@@ -4615,21 +4616,22 @@ cpdef to_offset(freq, bint is_period=False):
tups = zip(split[0::4], split[1::4], split[2::4])
for n, (sep, stride, name) in enumerate(tups):
- if is_period is False and name == "M":
+ if is_period is False and name in c_OFFSET_DEPR_FREQSTR:
warnings.warn(
- "\'M\' will be deprecated, please use \'ME\' "
- "for \'month end\'",
+ f"\'{name}\' will be deprecated, please use "
+ f"\'{c_OFFSET_DEPR_FREQSTR.get(name)}\' instead.",
UserWarning,
stacklevel=find_stack_level(),
)
- name = "ME"
- if is_period is True and name == "ME":
+ name = c_OFFSET_DEPR_FREQSTR[name]
+ if is_period is True and name in c_REVERSE_OFFSET_DEPR_FREQSTR:
raise ValueError(
- r"for Period, please use \'M\' "
- "instead of \'ME\'"
+ f"for Period, please use "
+ f"\'{c_REVERSE_OFFSET_DEPR_FREQSTR.get(name)}\' "
+ f"instead of \'{name}\'"
)
- elif is_period is True and name == "M":
- name = "ME"
+ elif is_period is True and name in c_OFFSET_DEPR_FREQSTR:
+ name = c_OFFSET_DEPR_FREQSTR.get(name)
if sep != "" and not sep.isspace():
raise ValueError("separator must be spaces")
@@ -4648,6 +4650,7 @@ cpdef to_offset(freq, bint is_period=False):
stacklevel=find_stack_level(),
)
prefix = c_DEPR_ABBREVS[prefix]
+
if prefix in {"D", "H", "min", "s", "ms", "us", "ns"}:
# For these prefixes, we have something like "3H" or
# "2.5T", so we can construct a Timedelta with the
@@ -4661,7 +4664,7 @@ cpdef to_offset(freq, bint is_period=False):
offset *= stride_sign
else:
stride = int(stride)
- offset = _get_offset(name)
+ offset = _get_offset(prefix)
offset = offset * int(np.fabs(stride) * stride_sign)
if delta is None:
diff --git a/pandas/_libs/tslibs/timestamps.pyx b/pandas/_libs/tslibs/timestamps.pyx
index 1b4332c2d26cf..8bf1ebb9bf608 100644
--- a/pandas/_libs/tslibs/timestamps.pyx
+++ b/pandas/_libs/tslibs/timestamps.pyx
@@ -1249,7 +1249,7 @@ cdef class _Timestamp(ABCTimestamp):
>>> ts = pd.Timestamp('2020-03-14T15:32:52.192548651')
>>> # Year end frequency
>>> ts.to_period(freq='Y')
- Period('2020', 'A-DEC')
+ Period('2020', 'Y-DEC')
>>> # Month end frequency
>>> ts.to_period(freq='M')
diff --git a/pandas/core/arrays/arrow/array.py b/pandas/core/arrays/arrow/array.py
index 4b79d0dbb683e..5b5ce4c4d057b 100644
--- a/pandas/core/arrays/arrow/array.py
+++ b/pandas/core/arrays/arrow/array.py
@@ -2467,7 +2467,7 @@ def _round_temporally(
if offset is None:
raise ValueError(f"Must specify a valid frequency: {freq}")
pa_supported_unit = {
- "A": "year",
+ "Y": "year",
"AS": "year",
"Q": "quarter",
"QS": "quarter",
diff --git a/pandas/core/arrays/datetimes.py b/pandas/core/arrays/datetimes.py
index b520f9f4a6deb..67494546010e2 100644
--- a/pandas/core/arrays/datetimes.py
+++ b/pandas/core/arrays/datetimes.py
@@ -2042,7 +2042,7 @@ def isocalendar(self) -> DataFrame:
>>> idx = pd.date_range("2012-01-01", "2015-01-01", freq="Y")
>>> idx
DatetimeIndex(['2012-12-31', '2013-12-31', '2014-12-31'],
- dtype='datetime64[ns]', freq='A-DEC')
+ dtype='datetime64[ns]', freq='Y-DEC')
>>> idx.is_leap_year
array([ True, False, False])
diff --git a/pandas/core/arrays/period.py b/pandas/core/arrays/period.py
index 4532e5bffe7a9..2468535397568 100644
--- a/pandas/core/arrays/period.py
+++ b/pandas/core/arrays/period.py
@@ -548,7 +548,7 @@ def __arrow_array__(self, type=None):
>>> idx = pd.PeriodIndex(["2023", "2024", "2025"], freq="Y")
>>> idx
- PeriodIndex(['2023', '2024', '2025'], dtype='period[A-DEC]')
+ PeriodIndex(['2023', '2024', '2025'], dtype='period[Y-DEC]')
>>> idx.dayofyear
Index([365, 366, 365], dtype='int64')
""",
@@ -712,10 +712,10 @@ def asfreq(self, freq=None, how: str = "E") -> Self:
Examples
--------
- >>> pidx = pd.period_range('2010-01-01', '2015-01-01', freq='A')
+ >>> pidx = pd.period_range('2010-01-01', '2015-01-01', freq='Y')
>>> pidx
PeriodIndex(['2010', '2011', '2012', '2013', '2014', '2015'],
- dtype='period[A-DEC]')
+ dtype='period[Y-DEC]')
>>> pidx.asfreq('M')
PeriodIndex(['2010-12', '2011-12', '2012-12', '2013-12', '2014-12',
@@ -1025,18 +1025,18 @@ def period_array(
Examples
--------
- >>> period_array([pd.Period('2017', freq='A'),
- ... pd.Period('2018', freq='A')])
+ >>> period_array([pd.Period('2017', freq='Y'),
+ ... pd.Period('2018', freq='Y')])
<PeriodArray>
['2017', '2018']
- Length: 2, dtype: period[A-DEC]
+ Length: 2, dtype: period[Y-DEC]
- >>> period_array([pd.Period('2017', freq='A'),
- ... pd.Period('2018', freq='A'),
+ >>> period_array([pd.Period('2017', freq='Y'),
+ ... pd.Period('2018', freq='Y'),
... pd.NaT])
<PeriodArray>
['2017', '2018', 'NaT']
- Length: 3, dtype: period[A-DEC]
+ Length: 3, dtype: period[Y-DEC]
Integers that look like years are handled
diff --git a/pandas/core/dtypes/common.py b/pandas/core/dtypes/common.py
index 9da4eac6a42c8..efee47d1c2686 100644
--- a/pandas/core/dtypes/common.py
+++ b/pandas/core/dtypes/common.py
@@ -402,7 +402,7 @@ def is_period_dtype(arr_or_dtype) -> bool:
False
>>> is_period_dtype(pd.Period("2017-01-01"))
False
- >>> is_period_dtype(pd.PeriodIndex([], freq="A"))
+ >>> is_period_dtype(pd.PeriodIndex([], freq="Y"))
True
"""
warnings.warn(
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 432c0a745c7a0..8b6226a8a3473 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -12068,7 +12068,7 @@ def to_period(
For the yearly frequency
>>> idx.to_period("Y")
- PeriodIndex(['2001', '2002', '2003'], dtype='period[A-DEC]')
+ PeriodIndex(['2001', '2002', '2003'], dtype='period[Y-DEC]')
"""
new_obj = self.copy(deep=copy and not using_copy_on_write())
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 738f4cbe6bc43..d81e21cdbdb1e 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -9173,11 +9173,11 @@ def resample(
Use frame.T.resample(...) instead.
closed : {{'right', 'left'}}, default None
Which side of bin interval is closed. The default is 'left'
- for all frequency offsets except for 'ME', 'A', 'Q', 'BM',
+ for all frequency offsets except for 'ME', 'Y', 'Q', 'BM',
'BA', 'BQ', and 'W' which all have a default of 'right'.
label : {{'right', 'left'}}, default None
Which bin edge label to label bucket with. The default is 'left'
- for all frequency offsets except for 'ME', 'A', 'Q', 'BM',
+ for all frequency offsets except for 'ME', 'Y', 'Q', 'BM',
'BA', 'BQ', and 'W' which all have a default of 'right'.
convention : {{'start', 'end', 's', 'e'}}, default 'start'
For `PeriodIndex` only, controls whether to use the start or
@@ -9348,12 +9348,12 @@ def resample(
assigned to the first quarter of the period.
>>> s = pd.Series([1, 2], index=pd.period_range('2012-01-01',
- ... freq='A',
+ ... freq='Y',
... periods=2))
>>> s
2012 1
2013 2
- Freq: A-DEC, dtype: int64
+ Freq: Y-DEC, dtype: int64
>>> s.resample('Q', convention='start').asfreq()
2012Q1 1.0
2012Q2 NaN
diff --git a/pandas/core/resample.py b/pandas/core/resample.py
index e9b2bacd9e1df..9d6256ca75dd8 100644
--- a/pandas/core/resample.py
+++ b/pandas/core/resample.py
@@ -2101,7 +2101,7 @@ def __init__(
else:
freq = to_offset(freq)
- end_types = {"ME", "A", "Q", "BM", "BA", "BQ", "W"}
+ end_types = {"ME", "Y", "Q", "BM", "BA", "BQ", "W"}
rule = freq.rule_code
if rule in end_types or ("-" in rule and rule[: rule.find("-")] in end_types):
if closed is None:
@@ -2301,7 +2301,7 @@ def _adjust_bin_edges(
"BQ",
"BA",
"Q",
- "A",
+ "Y",
"W",
):
# If the right end-point is on the last day of the month, roll forwards
diff --git a/pandas/core/series.py b/pandas/core/series.py
index fd50a85f3c2e3..8ffc97e7143ef 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -5651,7 +5651,7 @@ def to_timestamp(
2023 1
2024 2
2025 3
- Freq: A-DEC, dtype: int64
+ Freq: Y-DEC, dtype: int64
The resulting frequency of the Timestamps is `YearBegin`
@@ -5670,7 +5670,7 @@ def to_timestamp(
2023-01-31 1
2024-01-31 2
2025-01-31 3
- Freq: A-JAN, dtype: int64
+ Freq: Y-JAN, dtype: int64
"""
if not isinstance(self.index, PeriodIndex):
raise TypeError(f"unsupported Type {type(self.index).__name__}")
@@ -5705,12 +5705,12 @@ def to_period(self, freq: str | None = None, copy: bool | None = None) -> Series
2023 1
2024 2
2025 3
- Freq: A-DEC, dtype: int64
+ Freq: Y-DEC, dtype: int64
Viewing the index
>>> s.index
- PeriodIndex(['2023', '2024', '2025'], dtype='period[A-DEC]')
+ PeriodIndex(['2023', '2024', '2025'], dtype='period[Y-DEC]')
"""
if not isinstance(self.index, DatetimeIndex):
raise TypeError(f"unsupported Type {type(self.index).__name__}")
diff --git a/pandas/tests/arithmetic/test_period.py b/pandas/tests/arithmetic/test_period.py
index ee8391830db4c..bee1e1a385672 100644
--- a/pandas/tests/arithmetic/test_period.py
+++ b/pandas/tests/arithmetic/test_period.py
@@ -286,14 +286,14 @@ def test_parr_cmp_pi_mismatched_freq(self, freq, box_with_array):
msg = rf"Invalid comparison between dtype=period\[{freq}\] and Period"
with pytest.raises(TypeError, match=msg):
- base <= Period("2011", freq="A")
+ base <= Period("2011", freq="Y")
with pytest.raises(TypeError, match=msg):
- Period("2011", freq="A") >= base
+ Period("2011", freq="Y") >= base
# TODO: Could parametrize over boxes for idx?
- idx = PeriodIndex(["2011", "2012", "2013", "2014"], freq="A")
- rev_msg = r"Invalid comparison between dtype=period\[A-DEC\] and PeriodArray"
+ idx = PeriodIndex(["2011", "2012", "2013", "2014"], freq="Y")
+ rev_msg = r"Invalid comparison between dtype=period\[Y-DEC\] and PeriodArray"
idx_msg = rev_msg if box_with_array in [tm.to_array, pd.array] else msg
with pytest.raises(TypeError, match=idx_msg):
base <= idx
@@ -405,18 +405,18 @@ def test_cmp_series_period_series_mixed_freq(self):
# GH#13200
base = Series(
[
- Period("2011", freq="A"),
+ Period("2011", freq="Y"),
Period("2011-02", freq="M"),
- Period("2013", freq="A"),
+ Period("2013", freq="Y"),
Period("2011-04", freq="M"),
]
)
ser = Series(
[
- Period("2012", freq="A"),
+ Period("2012", freq="Y"),
Period("2011-01", freq="M"),
- Period("2013", freq="A"),
+ Period("2013", freq="Y"),
Period("2011-05", freq="M"),
]
)
@@ -934,9 +934,9 @@ def test_pi_add_sub_int_array_freqn_gt1(self):
def test_pi_sub_isub_offset(self):
# offset
# DateOffset
- rng = period_range("2014", "2024", freq="A")
+ rng = period_range("2014", "2024", freq="Y")
result = rng - pd.offsets.YearEnd(5)
- expected = period_range("2009", "2019", freq="A")
+ expected = period_range("2009", "2019", freq="Y")
tm.assert_index_equal(result, expected)
rng -= pd.offsets.YearEnd(5)
tm.assert_index_equal(rng, expected)
@@ -1176,17 +1176,17 @@ def test_pi_sub_isub_timedeltalike_hourly(self, two_hours):
def test_add_iadd_timedeltalike_annual(self):
# offset
# DateOffset
- rng = period_range("2014", "2024", freq="A")
+ rng = period_range("2014", "2024", freq="Y")
result = rng + pd.offsets.YearEnd(5)
- expected = period_range("2019", "2029", freq="A")
+ expected = period_range("2019", "2029", freq="Y")
tm.assert_index_equal(result, expected)
rng += pd.offsets.YearEnd(5)
tm.assert_index_equal(rng, expected)
def test_pi_add_sub_timedeltalike_freq_mismatch_annual(self, mismatched_freq):
other = mismatched_freq
- rng = period_range("2014", "2024", freq="A")
- msg = "Input has different freq(=.+)? from Period.*?\\(freq=A-DEC\\)"
+ rng = period_range("2014", "2024", freq="Y")
+ msg = "Input has different freq(=.+)? from Period.*?\\(freq=Y-DEC\\)"
with pytest.raises(IncompatibleFrequency, match=msg):
rng + other
with pytest.raises(IncompatibleFrequency, match=msg):
diff --git a/pandas/tests/arrays/categorical/test_astype.py b/pandas/tests/arrays/categorical/test_astype.py
index d2f9f6dffab49..7fba150c9113f 100644
--- a/pandas/tests/arrays/categorical/test_astype.py
+++ b/pandas/tests/arrays/categorical/test_astype.py
@@ -32,7 +32,7 @@ def test_astype_nan_to_int(self, cls, values):
[
array(["2019", "2020"], dtype="datetime64[ns, UTC]"),
array([0, 0], dtype="timedelta64[ns]"),
- array([Period("2019"), Period("2020")], dtype="period[A-DEC]"),
+ array([Period("2019"), Period("2020")], dtype="period[Y-DEC]"),
array([Interval(0, 1), Interval(1, 2)], dtype="interval"),
array([1, np.nan], dtype="Int64"),
],
diff --git a/pandas/tests/arrays/period/test_arrow_compat.py b/pandas/tests/arrays/period/test_arrow_compat.py
index 903fc3177aa84..6c04d7c603d4c 100644
--- a/pandas/tests/arrays/period/test_arrow_compat.py
+++ b/pandas/tests/arrays/period/test_arrow_compat.py
@@ -33,7 +33,7 @@ def test_arrow_extension_type():
"data, freq",
[
(pd.date_range("2017", periods=3), "D"),
- (pd.date_range("2017", periods=3, freq="A"), "A-DEC"),
+ (pd.date_range("2017", periods=3, freq="Y"), "Y-DEC"),
],
)
def test_arrow_array(data, freq):
diff --git a/pandas/tests/arrays/period/test_constructors.py b/pandas/tests/arrays/period/test_constructors.py
index 0ea26a6ece7eb..d034162f1b46e 100644
--- a/pandas/tests/arrays/period/test_constructors.py
+++ b/pandas/tests/arrays/period/test_constructors.py
@@ -71,11 +71,11 @@ def test_from_datetime64_freq_2M(freq):
"data, freq, msg",
[
(
- [pd.Period("2017", "D"), pd.Period("2017", "A")],
+ [pd.Period("2017", "D"), pd.Period("2017", "Y")],
None,
"Input has different freq",
),
- ([pd.Period("2017", "D")], "A", "Input has different freq"),
+ ([pd.Period("2017", "D")], "Y", "Input has different freq"),
],
)
def test_period_array_raises(data, freq, msg):
diff --git a/pandas/tests/arrays/test_array.py b/pandas/tests/arrays/test_array.py
index 2746cd91963a0..0aeedf4d03919 100644
--- a/pandas/tests/arrays/test_array.py
+++ b/pandas/tests/arrays/test_array.py
@@ -350,7 +350,7 @@ def test_array_inference(data, expected):
"data",
[
# mix of frequencies
- [pd.Period("2000", "D"), pd.Period("2001", "A")],
+ [pd.Period("2000", "D"), pd.Period("2001", "Y")],
# mix of closed
[pd.Interval(0, 1, closed="left"), pd.Interval(1, 2, closed="right")],
# Mix of timezones
diff --git a/pandas/tests/arrays/test_datetimes.py b/pandas/tests/arrays/test_datetimes.py
index a105852395b3a..fc46e5a372806 100644
--- a/pandas/tests/arrays/test_datetimes.py
+++ b/pandas/tests/arrays/test_datetimes.py
@@ -747,7 +747,7 @@ def test_iter_zoneinfo_fold(self, tz):
assert left.utcoffset() == right2.utcoffset()
def test_date_range_frequency_M_deprecated(self):
- depr_msg = r"\'M\' will be deprecated, please use \'ME\' for \'month end\'"
+ depr_msg = "'M' will be deprecated, please use 'ME' instead."
expected = pd.date_range("1/1/2000", periods=4, freq="2ME")
with tm.assert_produces_warning(UserWarning, match=depr_msg):
diff --git a/pandas/tests/arrays/test_period.py b/pandas/tests/arrays/test_period.py
index d1e954bc2ebe2..43a80a92573c5 100644
--- a/pandas/tests/arrays/test_period.py
+++ b/pandas/tests/arrays/test_period.py
@@ -82,9 +82,9 @@ def test_setitem(key, value, expected):
def test_setitem_raises_incompatible_freq():
arr = PeriodArray(np.arange(3), dtype="period[D]")
with pytest.raises(IncompatibleFrequency, match="freq"):
- arr[0] = pd.Period("2000", freq="A")
+ arr[0] = pd.Period("2000", freq="Y")
- other = PeriodArray._from_sequence(["2000", "2001"], dtype="period[A]")
+ other = PeriodArray._from_sequence(["2000", "2001"], dtype="period[Y]")
with pytest.raises(IncompatibleFrequency, match="freq"):
arr[[0, 1]] = other
diff --git a/pandas/tests/base/test_conversion.py b/pandas/tests/base/test_conversion.py
index db13e979a3c2d..20ee2d443f340 100644
--- a/pandas/tests/base/test_conversion.py
+++ b/pandas/tests/base/test_conversion.py
@@ -192,9 +192,9 @@ def test_iter_box(self):
"datetime64[ns, US/Central]",
),
(
- pd.PeriodIndex([2018, 2019], freq="A"),
+ pd.PeriodIndex([2018, 2019], freq="Y"),
PeriodArray,
- pd.core.dtypes.dtypes.PeriodDtype("A-DEC"),
+ pd.core.dtypes.dtypes.PeriodDtype("Y-DEC"),
),
(pd.IntervalIndex.from_breaks([0, 1, 2]), IntervalArray, "interval"),
(
diff --git a/pandas/tests/dtypes/test_common.py b/pandas/tests/dtypes/test_common.py
index 4507857418e9e..b0ca0f4705194 100644
--- a/pandas/tests/dtypes/test_common.py
+++ b/pandas/tests/dtypes/test_common.py
@@ -274,7 +274,7 @@ def test_is_period_dtype():
assert not com.is_period_dtype(pd.Period("2017-01-01"))
assert com.is_period_dtype(PeriodDtype(freq="D"))
- assert com.is_period_dtype(pd.PeriodIndex([], freq="A"))
+ assert com.is_period_dtype(pd.PeriodIndex([], freq="Y"))
def test_is_interval_dtype():
diff --git a/pandas/tests/frame/methods/test_asfreq.py b/pandas/tests/frame/methods/test_asfreq.py
index a2f8f3e278395..0527bfb16492c 100644
--- a/pandas/tests/frame/methods/test_asfreq.py
+++ b/pandas/tests/frame/methods/test_asfreq.py
@@ -104,7 +104,7 @@ def test_asfreq_keep_index_name(self, frame_or_series):
assert index_name == obj.asfreq("10D").index.name
def test_asfreq_ts(self, frame_or_series):
- index = period_range(freq="A", start="1/1/2001", end="12/31/2010")
+ index = period_range(freq="Y", start="1/1/2001", end="12/31/2010")
obj = DataFrame(
np.random.default_rng(2).standard_normal((len(index), 3)), index=index
)
@@ -235,7 +235,7 @@ def test_asfreq_2ME(self, freq, freq_half):
tm.assert_frame_equal(result, expected)
def test_asfreq_frequency_M_deprecated(self):
- depr_msg = r"\'M\' will be deprecated, please use \'ME\' for \'month end\'"
+ depr_msg = "'M' will be deprecated, please use 'ME' instead."
index = date_range("1/1/2000", periods=4, freq="ME")
df = DataFrame({"s": Series([0.0, 1.0, 2.0, 3.0], index=index)})
diff --git a/pandas/tests/frame/methods/test_join.py b/pandas/tests/frame/methods/test_join.py
index 2d4ac1d4a4444..d9796a5b25c63 100644
--- a/pandas/tests/frame/methods/test_join.py
+++ b/pandas/tests/frame/methods/test_join.py
@@ -22,7 +22,7 @@ def frame_with_period_index():
return DataFrame(
data=np.arange(20).reshape(4, 5),
columns=list("abcde"),
- index=period_range(start="2000", freq="A", periods=4),
+ index=period_range(start="2000", freq="Y", periods=4),
)
diff --git a/pandas/tests/frame/methods/test_reindex.py b/pandas/tests/frame/methods/test_reindex.py
index 0105c41bd0eca..bba86b481eadc 100644
--- a/pandas/tests/frame/methods/test_reindex.py
+++ b/pandas/tests/frame/methods/test_reindex.py
@@ -36,7 +36,7 @@ def test_dti_set_index_reindex_datetimeindex(self):
# GH#6631
df = DataFrame(np.random.default_rng(2).random(6))
idx1 = date_range("2011/01/01", periods=6, freq="ME", tz="US/Eastern")
- idx2 = date_range("2013", periods=6, freq="A", tz="Asia/Tokyo")
+ idx2 = date_range("2013", periods=6, freq="Y", tz="Asia/Tokyo")
df = df.set_index(idx1)
tm.assert_index_equal(df.index, idx1)
diff --git a/pandas/tests/frame/methods/test_set_index.py b/pandas/tests/frame/methods/test_set_index.py
index 5984e591dd6c1..f755ef0c2763d 100644
--- a/pandas/tests/frame/methods/test_set_index.py
+++ b/pandas/tests/frame/methods/test_set_index.py
@@ -493,7 +493,7 @@ def test_set_index_period(self):
idx1 = idx1.append(idx1)
idx2 = period_range("2013-01-01 09:00", periods=2, freq="H")
idx2 = idx2.append(idx2).append(idx2)
- idx3 = period_range("2005", periods=6, freq="A")
+ idx3 = period_range("2005", periods=6, freq="Y")
df = df.set_index(idx1)
df = df.set_index(idx2, append=True)
@@ -694,7 +694,7 @@ def test_set_index_periodindex(self):
# GH#6631
df = DataFrame(np.random.default_rng(2).random(6))
idx1 = period_range("2011/01/01", periods=6, freq="M")
- idx2 = period_range("2013", periods=6, freq="A")
+ idx2 = period_range("2013", periods=6, freq="Y")
df = df.set_index(idx1)
tm.assert_index_equal(df.index, idx1)
diff --git a/pandas/tests/frame/methods/test_to_timestamp.py b/pandas/tests/frame/methods/test_to_timestamp.py
index e72b576fca833..478708ce90488 100644
--- a/pandas/tests/frame/methods/test_to_timestamp.py
+++ b/pandas/tests/frame/methods/test_to_timestamp.py
@@ -16,7 +16,7 @@
import pandas._testing as tm
-def _get_with_delta(delta, freq="A-DEC"):
+def _get_with_delta(delta, freq="Y-DEC"):
return date_range(
to_datetime("1/1/2001") + delta,
to_datetime("12/31/2009") + delta,
@@ -27,7 +27,7 @@ def _get_with_delta(delta, freq="A-DEC"):
class TestToTimestamp:
def test_to_timestamp(self, frame_or_series):
K = 5
- index = period_range(freq="A", start="1/1/2001", end="12/1/2009")
+ index = period_range(freq="Y", start="1/1/2001", end="12/1/2009")
obj = DataFrame(
np.random.default_rng(2).standard_normal((len(index), K)),
index=index,
@@ -36,7 +36,7 @@ def test_to_timestamp(self, frame_or_series):
obj["mix"] = "a"
obj = tm.get_obj(obj, frame_or_series)
- exp_index = date_range("1/1/2001", end="12/31/2009", freq="A-DEC")
+ exp_index = date_range("1/1/2001", end="12/31/2009", freq="Y-DEC")
exp_index = exp_index + Timedelta(1, "D") - Timedelta(1, "ns")
result = obj.to_timestamp("D", "end")
tm.assert_index_equal(result.index, exp_index)
@@ -71,7 +71,7 @@ def test_to_timestamp(self, frame_or_series):
def test_to_timestamp_columns(self):
K = 5
- index = period_range(freq="A", start="1/1/2001", end="12/1/2009")
+ index = period_range(freq="Y", start="1/1/2001", end="12/1/2009")
df = DataFrame(
np.random.default_rng(2).standard_normal((len(index), K)),
index=index,
@@ -82,7 +82,7 @@ def test_to_timestamp_columns(self):
# columns
df = df.T
- exp_index = date_range("1/1/2001", end="12/31/2009", freq="A-DEC")
+ exp_index = date_range("1/1/2001", end="12/31/2009", freq="Y-DEC")
exp_index = exp_index + Timedelta(1, "D") - Timedelta(1, "ns")
result = df.to_timestamp("D", "end", axis=1)
tm.assert_index_equal(result.columns, exp_index)
@@ -122,7 +122,7 @@ def test_to_timestamp_columns(self):
assert result2.columns.freqstr == "AS-JAN"
def test_to_timestamp_invalid_axis(self):
- index = period_range(freq="A", start="1/1/2001", end="12/1/2009")
+ index = period_range(freq="Y", start="1/1/2001", end="12/1/2009")
obj = DataFrame(
np.random.default_rng(2).standard_normal((len(index), 5)), index=index
)
diff --git a/pandas/tests/frame/test_arithmetic.py b/pandas/tests/frame/test_arithmetic.py
index bb9a76829c77d..2c3b732fe7196 100644
--- a/pandas/tests/frame/test_arithmetic.py
+++ b/pandas/tests/frame/test_arithmetic.py
@@ -1236,7 +1236,7 @@ def test_frame_add_tz_mismatch_converts_to_utc(self):
assert result.index.tz is timezone.utc
def test_align_frame(self):
- rng = pd.period_range("1/1/2000", "1/1/2010", freq="A")
+ rng = pd.period_range("1/1/2000", "1/1/2010", freq="Y")
ts = DataFrame(
np.random.default_rng(2).standard_normal((len(rng), 3)), index=rng
)
diff --git a/pandas/tests/frame/test_reductions.py b/pandas/tests/frame/test_reductions.py
index e66557f132c1d..c6eb2c6b047f4 100644
--- a/pandas/tests/frame/test_reductions.py
+++ b/pandas/tests/frame/test_reductions.py
@@ -908,7 +908,7 @@ def test_mean_datetimelike(self):
"A": np.arange(3),
"B": date_range("2016-01-01", periods=3),
"C": pd.timedelta_range("1D", periods=3),
- "D": pd.period_range("2016", periods=3, freq="A"),
+ "D": pd.period_range("2016", periods=3, freq="Y"),
}
)
result = df.mean(numeric_only=True)
@@ -933,7 +933,7 @@ def test_mean_datetimelike_numeric_only_false(self):
tm.assert_series_equal(result, expected)
# mean of period is not allowed
- df["D"] = pd.period_range("2016", periods=3, freq="A")
+ df["D"] = pd.period_range("2016", periods=3, freq="Y")
with pytest.raises(TypeError, match="mean is not implemented for Period"):
df.mean(numeric_only=False)
diff --git a/pandas/tests/frame/test_repr_info.py b/pandas/tests/frame/test_repr_info.py
index 64d516e484991..55c239f7284c1 100644
--- a/pandas/tests/frame/test_repr_info.py
+++ b/pandas/tests/frame/test_repr_info.py
@@ -339,7 +339,7 @@ def test_repr_np_nat_with_object(self, arg, box, expected):
assert result == expected
def test_frame_datetime64_pre1900_repr(self):
- df = DataFrame({"year": date_range("1/1/1700", periods=50, freq="A-DEC")})
+ df = DataFrame({"year": date_range("1/1/1700", periods=50, freq="Y-DEC")})
# it works!
repr(df)
diff --git a/pandas/tests/groupby/test_timegrouper.py b/pandas/tests/groupby/test_timegrouper.py
index a9e67df1fb793..a3dc9e3087c7b 100644
--- a/pandas/tests/groupby/test_timegrouper.py
+++ b/pandas/tests/groupby/test_timegrouper.py
@@ -193,7 +193,7 @@ def test_timegrouper_with_reg_groups(self):
).set_index(["Date", "Buyer"])
msg = "The default value of numeric_only"
- result = df.groupby([Grouper(freq="A"), "Buyer"]).sum(numeric_only=True)
+ result = df.groupby([Grouper(freq="Y"), "Buyer"]).sum(numeric_only=True)
tm.assert_frame_equal(result, expected)
expected = DataFrame(
@@ -336,7 +336,7 @@ def test_timegrouper_with_reg_groups(self):
)
tm.assert_frame_equal(result, expected)
- @pytest.mark.parametrize("freq", ["D", "ME", "A", "Q-APR"])
+ @pytest.mark.parametrize("freq", ["D", "ME", "Y", "Q-APR"])
def test_timegrouper_with_reg_groups_freq(self, freq):
# GH 6764 multiple grouping with/without sort
df = DataFrame(
diff --git a/pandas/tests/indexes/datetimelike_/test_sort_values.py b/pandas/tests/indexes/datetimelike_/test_sort_values.py
index ab1c15f003d4d..cf919bfa29d10 100644
--- a/pandas/tests/indexes/datetimelike_/test_sort_values.py
+++ b/pandas/tests/indexes/datetimelike_/test_sort_values.py
@@ -127,7 +127,7 @@ def test_sort_values_with_freq_periodindex(self, freq):
@pytest.mark.parametrize(
"idx",
[
- PeriodIndex(["2011", "2012", "2013"], name="pidx", freq="A"),
+ PeriodIndex(["2011", "2012", "2013"], name="pidx", freq="Y"),
Index([2011, 2012, 2013], name="idx"), # for compatibility check
],
)
@@ -275,10 +275,10 @@ def test_sort_values_without_freq_datetimeindex(
),
(
PeriodIndex(
- ["2011", "2013", "2015", "2012", "2011"], name="pidx", freq="A"
+ ["2011", "2013", "2015", "2012", "2011"], name="pidx", freq="Y"
),
PeriodIndex(
- ["2011", "2011", "2012", "2013", "2015"], name="pidx", freq="A"
+ ["2011", "2011", "2012", "2013", "2015"], name="pidx", freq="Y"
),
),
(
@@ -308,7 +308,7 @@ def test_sort_values_without_freq_periodindex_nat(self):
def test_order_stability_compat():
# GH#35922. sort_values is stable both for normal and datetime-like Index
- pidx = PeriodIndex(["2011", "2013", "2015", "2012", "2011"], name="pidx", freq="A")
+ pidx = PeriodIndex(["2011", "2013", "2015", "2012", "2011"], name="pidx", freq="Y")
iidx = Index([2011, 2013, 2015, 2012, 2011], name="idx")
ordered1, indexer1 = pidx.sort_values(return_indexer=True, ascending=False)
ordered2, indexer2 = iidx.sort_values(return_indexer=True, ascending=False)
diff --git a/pandas/tests/indexes/datetimes/methods/test_to_period.py b/pandas/tests/indexes/datetimes/methods/test_to_period.py
index 5f266ea0b42a6..7712a4166329c 100644
--- a/pandas/tests/indexes/datetimes/methods/test_to_period.py
+++ b/pandas/tests/indexes/datetimes/methods/test_to_period.py
@@ -60,7 +60,7 @@ def test_to_period_quarterlyish(self, off):
def test_to_period_annualish(self, off):
rng = date_range("01-Jan-2012", periods=8, freq=off)
prng = rng.to_period()
- assert prng.freq == "A-DEC"
+ assert prng.freq == "Y-DEC"
def test_to_period_monthish(self):
offsets = ["MS", "BM"]
diff --git a/pandas/tests/indexes/datetimes/test_constructors.py b/pandas/tests/indexes/datetimes/test_constructors.py
index 61c8cc4a50fe2..7dee58e63fa88 100644
--- a/pandas/tests/indexes/datetimes/test_constructors.py
+++ b/pandas/tests/indexes/datetimes/test_constructors.py
@@ -753,7 +753,7 @@ def test_constructor_invalid_dtype_raises(self, dtype):
DatetimeIndex([1, 2], dtype=dtype)
def test_constructor_name(self):
- idx = date_range(start="2000-01-01", periods=1, freq="A", name="TEST")
+ idx = date_range(start="2000-01-01", periods=1, freq="Y", name="TEST")
assert idx.name == "TEST"
def test_000constructor_resolution(self):
@@ -978,8 +978,8 @@ def test_dti_constructor_years_only(self, tz_naive_fixture):
rng2 = date_range("2014", "2015", freq="MS", tz=tz)
expected2 = date_range("2014-01-01", "2015-01-01", freq="MS", tz=tz)
- rng3 = date_range("2014", "2020", freq="A", tz=tz)
- expected3 = date_range("2014-12-31", "2019-12-31", freq="A", tz=tz)
+ rng3 = date_range("2014", "2020", freq="Y", tz=tz)
+ expected3 = date_range("2014-12-31", "2019-12-31", freq="Y", tz=tz)
rng4 = date_range("2014", "2020", freq="AS", tz=tz)
expected4 = date_range("2014-01-01", "2020-01-01", freq="AS", tz=tz)
@@ -1036,7 +1036,7 @@ def test_constructor_int64_nocopy(self):
assert (index.asi8[50:100] != -1).all()
@pytest.mark.parametrize(
- "freq", ["ME", "Q", "A", "D", "B", "BH", "min", "s", "ms", "us", "H", "ns", "C"]
+ "freq", ["ME", "Q", "Y", "D", "B", "BH", "min", "s", "ms", "us", "H", "ns", "C"]
)
def test_from_freq_recreate_from_data(self, freq):
org = date_range(start="2001/02/01 09:00", freq=freq, periods=1)
diff --git a/pandas/tests/indexes/datetimes/test_date_range.py b/pandas/tests/indexes/datetimes/test_date_range.py
index b93aee1d988de..f0996d7af917d 100644
--- a/pandas/tests/indexes/datetimes/test_date_range.py
+++ b/pandas/tests/indexes/datetimes/test_date_range.py
@@ -7,6 +7,7 @@
time,
timedelta,
)
+import re
import numpy as np
import pytest
@@ -252,12 +253,11 @@ def test_begin_year_alias(self, freq):
)
tm.assert_index_equal(rng, exp)
- @pytest.mark.parametrize("freq", ["A", "Y"])
- def test_end_year_alias(self, freq):
+ def test_end_year_alias(self):
# see gh-9313
- rng = date_range("1/1/2013", "7/1/2017", freq=freq)
+ rng = date_range("1/1/2013", "7/1/2017", freq="Y")
exp = DatetimeIndex(
- ["2013-12-31", "2014-12-31", "2015-12-31", "2016-12-31"], freq=freq
+ ["2013-12-31", "2014-12-31", "2015-12-31", "2016-12-31"], freq="Y"
)
tm.assert_index_equal(rng, exp)
@@ -272,10 +272,10 @@ def test_business_end_year_alias(self, freq):
def test_date_range_negative_freq(self):
# GH 11018
- rng = date_range("2011-12-31", freq="-2A", periods=3)
- exp = DatetimeIndex(["2011-12-31", "2009-12-31", "2007-12-31"], freq="-2A")
+ rng = date_range("2011-12-31", freq="-2Y", periods=3)
+ exp = DatetimeIndex(["2011-12-31", "2009-12-31", "2007-12-31"], freq="-2Y")
tm.assert_index_equal(rng, exp)
- assert rng.freq == "-2A"
+ assert rng.freq == "-2Y"
rng = date_range("2011-01-31", freq="-2ME", periods=3)
exp = DatetimeIndex(["2011-01-31", "2010-11-30", "2010-09-30"], freq="-2ME")
@@ -638,7 +638,7 @@ def test_range_tz_dateutil(self):
assert dr[0] == start
assert dr[2] == end
- @pytest.mark.parametrize("freq", ["1D", "3D", "2ME", "7W", "3H", "A"])
+ @pytest.mark.parametrize("freq", ["1D", "3D", "2ME", "7W", "3H", "Y"])
def test_range_closed(self, freq, inclusive_endpoints_fixture):
begin = datetime(2011, 1, 1)
end = datetime(2014, 1, 1)
@@ -653,7 +653,7 @@ def test_range_closed(self, freq, inclusive_endpoints_fixture):
tm.assert_index_equal(expected_range, result_range)
- @pytest.mark.parametrize("freq", ["1D", "3D", "2ME", "7W", "3H", "A"])
+ @pytest.mark.parametrize("freq", ["1D", "3D", "2ME", "7W", "3H", "Y"])
def test_range_closed_with_tz_aware_start_end(
self, freq, inclusive_endpoints_fixture
):
@@ -674,7 +674,7 @@ def test_range_closed_with_tz_aware_start_end(
tm.assert_index_equal(expected_range, result_range)
- @pytest.mark.parametrize("freq", ["1D", "3D", "2ME", "7W", "3H", "A"])
+ @pytest.mark.parametrize("freq", ["1D", "3D", "2ME", "7W", "3H", "Y"])
def test_range_with_tz_closed_with_tz_aware_start_end(
self, freq, inclusive_endpoints_fixture
):
@@ -839,20 +839,23 @@ def test_freq_dateoffset_with_relateivedelta_nanos(self):
@pytest.mark.parametrize(
"freq,freq_depr",
[
- ("min", "T"),
- ("s", "S"),
- ("ms", "L"),
- ("us", "U"),
- ("ns", "N"),
+ ("2Y", "2A"),
+ ("200Y-MAY", "200A-MAY"),
+ ("2min", "2T"),
+ ("1s", "1S"),
+ ("2ms", "2L"),
+ ("1us", "1U"),
+ ("2ns", "2N"),
],
)
- def test_frequencies_T_S_L_U_N_deprecated(self, freq, freq_depr):
+ def test_frequencies_A_T_S_L_U_N_deprecated(self, freq, freq_depr):
# GH#52536
- msg = f"'{freq_depr}' is deprecated and will be removed in a future version."
+ freq_msg = re.split("[0-9]*", freq_depr, maxsplit=1)[1]
+ msg = f"'{freq_msg}' is deprecated and will be removed in a future version."
- expected = date_range("1/1/2000", periods=4, freq=freq)
+ expected = date_range("1/1/2000", periods=2, freq=freq)
with tm.assert_produces_warning(FutureWarning, match=msg):
- result = date_range("1/1/2000", periods=4, freq=freq_depr)
+ result = date_range("1/1/2000", periods=2, freq=freq_depr)
tm.assert_index_equal(result, expected)
diff --git a/pandas/tests/indexes/datetimes/test_ops.py b/pandas/tests/indexes/datetimes/test_ops.py
index 21dc22bea87dc..ac6d0a97956e4 100644
--- a/pandas/tests/indexes/datetimes/test_ops.py
+++ b/pandas/tests/indexes/datetimes/test_ops.py
@@ -20,7 +20,7 @@ class TestDatetimeIndexOps:
@pytest.mark.parametrize(
"freq,expected",
[
- ("A", "day"),
+ ("Y", "day"),
("Q", "day"),
("ME", "day"),
("D", "day"),
@@ -33,7 +33,7 @@ class TestDatetimeIndexOps:
)
def test_resolution(self, request, tz_naive_fixture, freq, expected):
tz = tz_naive_fixture
- if freq == "A" and not IS64 and isinstance(tz, tzlocal):
+ if freq == "Y" and not IS64 and isinstance(tz, tzlocal):
request.node.add_marker(
pytest.mark.xfail(reason="OverflowError inside tzlocal past 2038")
)
diff --git a/pandas/tests/indexes/period/methods/test_asfreq.py b/pandas/tests/indexes/period/methods/test_asfreq.py
index 89ea4fb6472d0..f9838ce272296 100644
--- a/pandas/tests/indexes/period/methods/test_asfreq.py
+++ b/pandas/tests/indexes/period/methods/test_asfreq.py
@@ -10,7 +10,7 @@
class TestPeriodIndex:
def test_asfreq(self):
- pi1 = period_range(freq="A", start="1/1/2001", end="1/1/2001")
+ pi1 = period_range(freq="Y", start="1/1/2001", end="1/1/2001")
pi2 = period_range(freq="Q", start="1/1/2001", end="1/1/2001")
pi3 = period_range(freq="M", start="1/1/2001", end="1/1/2001")
pi4 = period_range(freq="D", start="1/1/2001", end="1/1/2001")
@@ -26,42 +26,42 @@ def test_asfreq(self):
assert pi1.asfreq("Min", "s") == pi6
assert pi1.asfreq("s", "s") == pi7
- assert pi2.asfreq("A", "s") == pi1
+ assert pi2.asfreq("Y", "s") == pi1
assert pi2.asfreq("M", "s") == pi3
assert pi2.asfreq("D", "s") == pi4
assert pi2.asfreq("H", "s") == pi5
assert pi2.asfreq("Min", "s") == pi6
assert pi2.asfreq("s", "s") == pi7
- assert pi3.asfreq("A", "s") == pi1
+ assert pi3.asfreq("Y", "s") == pi1
assert pi3.asfreq("Q", "s") == pi2
assert pi3.asfreq("D", "s") == pi4
assert pi3.asfreq("H", "s") == pi5
assert pi3.asfreq("Min", "s") == pi6
assert pi3.asfreq("s", "s") == pi7
- assert pi4.asfreq("A", "s") == pi1
+ assert pi4.asfreq("Y", "s") == pi1
assert pi4.asfreq("Q", "s") == pi2
assert pi4.asfreq("M", "s") == pi3
assert pi4.asfreq("H", "s") == pi5
assert pi4.asfreq("Min", "s") == pi6
assert pi4.asfreq("s", "s") == pi7
- assert pi5.asfreq("A", "s") == pi1
+ assert pi5.asfreq("Y", "s") == pi1
assert pi5.asfreq("Q", "s") == pi2
assert pi5.asfreq("M", "s") == pi3
assert pi5.asfreq("D", "s") == pi4
assert pi5.asfreq("Min", "s") == pi6
assert pi5.asfreq("s", "s") == pi7
- assert pi6.asfreq("A", "s") == pi1
+ assert pi6.asfreq("Y", "s") == pi1
assert pi6.asfreq("Q", "s") == pi2
assert pi6.asfreq("M", "s") == pi3
assert pi6.asfreq("D", "s") == pi4
assert pi6.asfreq("H", "s") == pi5
assert pi6.asfreq("s", "s") == pi7
- assert pi7.asfreq("A", "s") == pi1
+ assert pi7.asfreq("Y", "s") == pi1
assert pi7.asfreq("Q", "s") == pi2
assert pi7.asfreq("M", "s") == pi3
assert pi7.asfreq("D", "s") == pi4
diff --git a/pandas/tests/indexes/period/methods/test_astype.py b/pandas/tests/indexes/period/methods/test_astype.py
index e54cd73a35f59..07595b6b8c1dd 100644
--- a/pandas/tests/indexes/period/methods/test_astype.py
+++ b/pandas/tests/indexes/period/methods/test_astype.py
@@ -44,7 +44,7 @@ def test_astype_conversion(self):
expected = Index([str(x) for x in idx], name="idx")
tm.assert_index_equal(result, expected)
- idx = period_range("1990", "2009", freq="A", name="idx")
+ idx = period_range("1990", "2009", freq="Y", name="idx")
result = idx.astype("i8")
tm.assert_index_equal(result, Index(idx.asi8, name="idx"))
tm.assert_numpy_array_equal(result.values, idx.asi8)
diff --git a/pandas/tests/indexes/period/methods/test_is_full.py b/pandas/tests/indexes/period/methods/test_is_full.py
index 490f199a59ed7..b4105bedbe21d 100644
--- a/pandas/tests/indexes/period/methods/test_is_full.py
+++ b/pandas/tests/indexes/period/methods/test_is_full.py
@@ -4,19 +4,19 @@
def test_is_full():
- index = PeriodIndex([2005, 2007, 2009], freq="A")
+ index = PeriodIndex([2005, 2007, 2009], freq="Y")
assert not index.is_full
- index = PeriodIndex([2005, 2006, 2007], freq="A")
+ index = PeriodIndex([2005, 2006, 2007], freq="Y")
assert index.is_full
- index = PeriodIndex([2005, 2005, 2007], freq="A")
+ index = PeriodIndex([2005, 2005, 2007], freq="Y")
assert not index.is_full
- index = PeriodIndex([2005, 2005, 2006], freq="A")
+ index = PeriodIndex([2005, 2005, 2006], freq="Y")
assert index.is_full
- index = PeriodIndex([2006, 2005, 2005], freq="A")
+ index = PeriodIndex([2006, 2005, 2005], freq="Y")
with pytest.raises(ValueError, match="Index is not monotonic"):
index.is_full
diff --git a/pandas/tests/indexes/period/methods/test_shift.py b/pandas/tests/indexes/period/methods/test_shift.py
index 48dc5f0e64d08..d649dd3da0864 100644
--- a/pandas/tests/indexes/period/methods/test_shift.py
+++ b/pandas/tests/indexes/period/methods/test_shift.py
@@ -29,16 +29,16 @@ def test_pi_shift_ndarray(self):
tm.assert_index_equal(result, expected)
def test_shift(self):
- pi1 = period_range(freq="A", start="1/1/2001", end="12/1/2009")
- pi2 = period_range(freq="A", start="1/1/2002", end="12/1/2010")
+ pi1 = period_range(freq="Y", start="1/1/2001", end="12/1/2009")
+ pi2 = period_range(freq="Y", start="1/1/2002", end="12/1/2010")
tm.assert_index_equal(pi1.shift(0), pi1)
assert len(pi1) == len(pi2)
tm.assert_index_equal(pi1.shift(1), pi2)
- pi1 = period_range(freq="A", start="1/1/2001", end="12/1/2009")
- pi2 = period_range(freq="A", start="1/1/2000", end="12/1/2008")
+ pi1 = period_range(freq="Y", start="1/1/2001", end="12/1/2009")
+ pi2 = period_range(freq="Y", start="1/1/2000", end="12/1/2008")
assert len(pi1) == len(pi2)
tm.assert_index_equal(pi1.shift(-1), pi2)
@@ -117,6 +117,6 @@ def test_shift_gh8083(self):
def test_shift_periods(self):
# GH #22458 : argument 'n' was deprecated in favor of 'periods'
- idx = period_range(freq="A", start="1/1/2001", end="12/1/2009")
+ idx = period_range(freq="Y", start="1/1/2001", end="12/1/2009")
tm.assert_index_equal(idx.shift(periods=0), idx)
tm.assert_index_equal(idx.shift(0), idx)
diff --git a/pandas/tests/indexes/period/methods/test_to_timestamp.py b/pandas/tests/indexes/period/methods/test_to_timestamp.py
index 8bb0c3518c835..462f66eef7269 100644
--- a/pandas/tests/indexes/period/methods/test_to_timestamp.py
+++ b/pandas/tests/indexes/period/methods/test_to_timestamp.py
@@ -47,7 +47,7 @@ def test_to_timestamp_non_contiguous(self):
tm.assert_datetime_array_equal(result, expected, check_freq=False)
def test_to_timestamp_freq(self):
- idx = period_range("2017", periods=12, freq="A-DEC")
+ idx = period_range("2017", periods=12, freq="Y-DEC")
result = idx.to_timestamp()
expected = date_range("2017", periods=12, freq="AS-JAN")
tm.assert_index_equal(result, expected)
@@ -72,12 +72,12 @@ def test_to_timestamp_pi_nat(self):
tm.assert_index_equal(result3, exp)
assert result3.freqstr == "3M"
- msg = "Frequency must be positive, because it represents span: -2A"
+ msg = "Frequency must be positive, because it represents span: -2Y"
with pytest.raises(ValueError, match=msg):
- result.to_period(freq="-2A")
+ result.to_period(freq="-2Y")
def test_to_timestamp_preserve_name(self):
- index = period_range(freq="A", start="1/1/2001", end="12/1/2009", name="foo")
+ index = period_range(freq="Y", start="1/1/2001", end="12/1/2009", name="foo")
assert index.name == "foo"
conv = index.to_timestamp("D")
diff --git a/pandas/tests/indexes/period/test_constructors.py b/pandas/tests/indexes/period/test_constructors.py
index a5bdfa11140d1..ac4edb10d9352 100644
--- a/pandas/tests/indexes/period/test_constructors.py
+++ b/pandas/tests/indexes/period/test_constructors.py
@@ -166,7 +166,7 @@ def test_constructor_fromarraylike(self):
msg = "'Period' object is not iterable"
with pytest.raises(TypeError, match=msg):
- PeriodIndex(data=Period("2007", freq="A"))
+ PeriodIndex(data=Period("2007", freq="Y"))
result = PeriodIndex(iter(idx))
tm.assert_index_equal(result, idx)
@@ -418,7 +418,7 @@ def test_constructor_freq_mult(self):
@pytest.mark.parametrize(
"freq_offset, freq_period",
[
- ("A", "A"),
+ ("Y", "Y"),
("ME", "M"),
("D", "D"),
("min", "min"),
@@ -453,7 +453,7 @@ def test_constructor_freq_combined(self):
tm.assert_index_equal(pidx, expected)
def test_constructor(self):
- pi = period_range(freq="A", start="1/1/2001", end="12/1/2009")
+ pi = period_range(freq="Y", start="1/1/2001", end="12/1/2009")
assert len(pi) == 9
pi = period_range(freq="Q", start="1/1/2001", end="12/1/2009")
@@ -526,7 +526,7 @@ def test_constructor(self):
Period("2006-12-31", ("w", 1))
@pytest.mark.parametrize(
- "freq", ["M", "Q", "A", "D", "B", "min", "s", "ms", "us", "ns", "H"]
+ "freq", ["M", "Q", "Y", "D", "B", "min", "s", "ms", "us", "ns", "H"]
)
@pytest.mark.filterwarnings(
r"ignore:Period with BDay freq is deprecated:FutureWarning"
@@ -539,7 +539,7 @@ def test_recreate_from_data(self, freq):
def test_map_with_string_constructor(self):
raw = [2005, 2007, 2009]
- index = PeriodIndex(raw, freq="A")
+ index = PeriodIndex(raw, freq="Y")
expected = Index([str(num) for num in raw])
res = index.map(str)
diff --git a/pandas/tests/indexes/period/test_formats.py b/pandas/tests/indexes/period/test_formats.py
index 87bbb96377a79..67deeccff4e2a 100644
--- a/pandas/tests/indexes/period/test_formats.py
+++ b/pandas/tests/indexes/period/test_formats.py
@@ -55,7 +55,7 @@ def test_representation(self, method):
idx2 = PeriodIndex(["2011-01-01"], freq="D")
idx3 = PeriodIndex(["2011-01-01", "2011-01-02"], freq="D")
idx4 = PeriodIndex(["2011-01-01", "2011-01-02", "2011-01-03"], freq="D")
- idx5 = PeriodIndex(["2011", "2012", "2013"], freq="A")
+ idx5 = PeriodIndex(["2011", "2012", "2013"], freq="Y")
idx6 = PeriodIndex(["2011-01-01 09:00", "2012-02-01 10:00", "NaT"], freq="H")
idx7 = pd.period_range("2013Q1", periods=1, freq="Q")
idx8 = pd.period_range("2013Q1", periods=2, freq="Q")
@@ -73,7 +73,7 @@ def test_representation(self, method):
"dtype='period[D]')"
)
- exp5 = "PeriodIndex(['2011', '2012', '2013'], dtype='period[A-DEC]')"
+ exp5 = "PeriodIndex(['2011', '2012', '2013'], dtype='period[Y-DEC]')"
exp6 = (
"PeriodIndex(['2011-01-01 09:00', '2012-02-01 10:00', 'NaT'], "
@@ -101,7 +101,7 @@ def test_representation_to_series(self):
idx2 = PeriodIndex(["2011-01-01"], freq="D")
idx3 = PeriodIndex(["2011-01-01", "2011-01-02"], freq="D")
idx4 = PeriodIndex(["2011-01-01", "2011-01-02", "2011-01-03"], freq="D")
- idx5 = PeriodIndex(["2011", "2012", "2013"], freq="A")
+ idx5 = PeriodIndex(["2011", "2012", "2013"], freq="Y")
idx6 = PeriodIndex(["2011-01-01 09:00", "2012-02-01 10:00", "NaT"], freq="H")
idx7 = pd.period_range("2013Q1", periods=1, freq="Q")
@@ -125,7 +125,7 @@ def test_representation_to_series(self):
exp5 = """0 2011
1 2012
2 2013
-dtype: period[A-DEC]"""
+dtype: period[Y-DEC]"""
exp6 = """0 2011-01-01 09:00
1 2012-02-01 10:00
@@ -157,7 +157,7 @@ def test_summary(self):
idx2 = PeriodIndex(["2011-01-01"], freq="D")
idx3 = PeriodIndex(["2011-01-01", "2011-01-02"], freq="D")
idx4 = PeriodIndex(["2011-01-01", "2011-01-02", "2011-01-03"], freq="D")
- idx5 = PeriodIndex(["2011", "2012", "2013"], freq="A")
+ idx5 = PeriodIndex(["2011", "2012", "2013"], freq="Y")
idx6 = PeriodIndex(["2011-01-01 09:00", "2012-02-01 10:00", "NaT"], freq="H")
idx7 = pd.period_range("2013Q1", periods=1, freq="Q")
@@ -177,7 +177,7 @@ def test_summary(self):
Freq: D"""
exp5 = """PeriodIndex: 3 entries, 2011 to 2013
-Freq: A-DEC"""
+Freq: Y-DEC"""
exp6 = """PeriodIndex: 3 entries, 2011-01-01 09:00 to NaT
Freq: H"""
diff --git a/pandas/tests/indexes/period/test_indexing.py b/pandas/tests/indexes/period/test_indexing.py
index 109a4a41e2841..f5af550d94ab1 100644
--- a/pandas/tests/indexes/period/test_indexing.py
+++ b/pandas/tests/indexes/period/test_indexing.py
@@ -238,9 +238,9 @@ def test_getitem_day(self, idx_range):
class TestGetLoc:
def test_get_loc_msg(self):
- idx = period_range("2000-1-1", freq="A", periods=10)
- bad_period = Period("2012", "A")
- with pytest.raises(KeyError, match=r"^Period\('2012', 'A-DEC'\)$"):
+ idx = period_range("2000-1-1", freq="Y", periods=10)
+ bad_period = Period("2012", "Y")
+ with pytest.raises(KeyError, match=r"^Period\('2012', 'Y-DEC'\)$"):
idx.get_loc(bad_period)
try:
diff --git a/pandas/tests/indexes/period/test_partial_slicing.py b/pandas/tests/indexes/period/test_partial_slicing.py
index 3a272f53091b5..5bc76340badaf 100644
--- a/pandas/tests/indexes/period/test_partial_slicing.py
+++ b/pandas/tests/indexes/period/test_partial_slicing.py
@@ -14,7 +14,7 @@
class TestPeriodIndex:
def test_getitem_periodindex_duplicates_string_slice(self, using_copy_on_write):
# monotonic
- idx = PeriodIndex([2000, 2007, 2007, 2009, 2009], freq="A-JUN")
+ idx = PeriodIndex([2000, 2007, 2007, 2009, 2009], freq="Y-JUN")
ts = Series(np.random.default_rng(2).standard_normal(len(idx)), index=idx)
original = ts.copy()
@@ -28,7 +28,7 @@ def test_getitem_periodindex_duplicates_string_slice(self, using_copy_on_write):
assert (ts[1:3] == 1).all()
# not monotonic
- idx = PeriodIndex([2000, 2007, 2007, 2009, 2007], freq="A-JUN")
+ idx = PeriodIndex([2000, 2007, 2007, 2009, 2007], freq="Y-JUN")
ts = Series(np.random.default_rng(2).standard_normal(len(idx)), index=idx)
result = ts["2007"]
diff --git a/pandas/tests/indexes/period/test_period.py b/pandas/tests/indexes/period/test_period.py
index bd40fa37897d8..0c445b4cdf770 100644
--- a/pandas/tests/indexes/period/test_period.py
+++ b/pandas/tests/indexes/period/test_period.py
@@ -18,7 +18,7 @@
class TestPeriodIndex:
def test_make_time_series(self):
- index = period_range(freq="A", start="1/1/2001", end="12/1/2009")
+ index = period_range(freq="Y", start="1/1/2001", end="12/1/2009")
series = Series(1, index=index)
assert isinstance(series, Series)
@@ -67,7 +67,7 @@ def test_values(self):
tm.assert_numpy_array_equal(idx.asi8, exp)
def test_period_index_length(self):
- pi = period_range(freq="A", start="1/1/2001", end="12/1/2009")
+ pi = period_range(freq="Y", start="1/1/2001", end="12/1/2009")
assert len(pi) == 9
pi = period_range(freq="Q", start="1/1/2001", end="12/1/2009")
@@ -157,7 +157,7 @@ def test_period_index_length(self):
@pytest.mark.parametrize(
"periodindex",
[
- period_range(freq="A", start="1/1/2001", end="12/1/2005"),
+ period_range(freq="Y", start="1/1/2001", end="12/1/2005"),
period_range(freq="Q", start="1/1/2001", end="12/1/2002"),
period_range(freq="M", start="1/1/2001", end="1/1/2002"),
period_range(freq="D", start="12/1/2001", end="6/1/2001"),
@@ -187,7 +187,7 @@ def test_fields(self, periodindex, field):
assert getattr(x, field) == val
def test_is_(self):
- create_index = lambda: period_range(freq="A", start="1/1/2001", end="12/1/2009")
+ create_index = lambda: period_range(freq="Y", start="1/1/2001", end="12/1/2009")
index = create_index()
assert index.is_(index)
assert not index.is_(create_index())
@@ -199,23 +199,23 @@ def test_is_(self):
assert ind2.is_(index)
assert not index.is_(index[:])
assert not index.is_(index.asfreq("M"))
- assert not index.is_(index.asfreq("A"))
+ assert not index.is_(index.asfreq("Y"))
assert not index.is_(index - 2)
assert not index.is_(index - 0)
def test_index_unique(self):
- idx = PeriodIndex([2000, 2007, 2007, 2009, 2009], freq="A-JUN")
- expected = PeriodIndex([2000, 2007, 2009], freq="A-JUN")
+ idx = PeriodIndex([2000, 2007, 2007, 2009, 2009], freq="Y-JUN")
+ expected = PeriodIndex([2000, 2007, 2009], freq="Y-JUN")
tm.assert_index_equal(idx.unique(), expected)
assert idx.nunique() == 3
def test_negative_ordinals(self):
- Period(ordinal=-1000, freq="A")
- Period(ordinal=0, freq="A")
+ Period(ordinal=-1000, freq="Y")
+ Period(ordinal=0, freq="Y")
- idx1 = PeriodIndex(ordinal=[-1, 0, 1], freq="A")
- idx2 = PeriodIndex(ordinal=np.array([-1, 0, 1]), freq="A")
+ idx1 = PeriodIndex(ordinal=[-1, 0, 1], freq="Y")
+ idx2 = PeriodIndex(ordinal=np.array([-1, 0, 1]), freq="Y")
tm.assert_index_equal(idx1, idx2)
def test_pindex_fieldaccessor_nat(self):
@@ -267,14 +267,14 @@ def test_with_multi_index(self):
def test_map(self):
# test_map_dictlike generally tests
- index = PeriodIndex([2005, 2007, 2009], freq="A")
+ index = PeriodIndex([2005, 2007, 2009], freq="Y")
result = index.map(lambda x: x.ordinal)
exp = Index([x.ordinal for x in index])
tm.assert_index_equal(result, exp)
def test_format_empty(self):
# GH35712
- empty_idx = PeriodIndex([], freq="A")
+ empty_idx = PeriodIndex([], freq="Y")
assert empty_idx.format() == []
assert empty_idx.format(name=True) == [""]
@@ -284,6 +284,17 @@ def test_period_index_frequency_ME_error_message(self):
with pytest.raises(ValueError, match=msg):
PeriodIndex(["2020-01-01", "2020-01-02"], freq="2ME")
+ @pytest.mark.parametrize("freq", ["2A", "A-DEC", "200A-AUG"])
+ def test_a_deprecated_from_time_series(self, freq):
+ # GH#52536
+ freq_msg = freq[freq.index("A") :]
+ msg = f"'{freq_msg}' is deprecated and will be removed in a future version."
+
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ index = period_range(freq=freq, start="1/1/2001", end="12/1/2009")
+ series = Series(1, index=index)
+ assert isinstance(series, Series)
+
def test_maybe_convert_timedelta():
pi = PeriodIndex(["2000", "2001"], freq="D")
diff --git a/pandas/tests/indexes/period/test_period_range.py b/pandas/tests/indexes/period/test_period_range.py
index 63acaba2d4f3e..bee8a1282d08b 100644
--- a/pandas/tests/indexes/period/test_period_range.py
+++ b/pandas/tests/indexes/period/test_period_range.py
@@ -20,7 +20,7 @@ def test_required_arguments(self):
with pytest.raises(ValueError, match=msg):
period_range("2011-1-1", "2012-1-1", "B")
- @pytest.mark.parametrize("freq", ["D", "W", "Q", "A"])
+ @pytest.mark.parametrize("freq", ["D", "W", "Q", "Y"])
def test_construction_from_string(self, freq):
# non-empty
expected = date_range(
diff --git a/pandas/tests/indexes/period/test_pickle.py b/pandas/tests/indexes/period/test_pickle.py
index cb981ab10064f..7d359fdabb6f1 100644
--- a/pandas/tests/indexes/period/test_pickle.py
+++ b/pandas/tests/indexes/period/test_pickle.py
@@ -12,7 +12,7 @@
class TestPickle:
- @pytest.mark.parametrize("freq", ["D", "M", "A"])
+ @pytest.mark.parametrize("freq", ["D", "M", "Y"])
def test_pickle_round_trip(self, freq):
idx = PeriodIndex(["2016-05-16", "NaT", NaT, np.nan], freq=freq)
result = tm.round_trip_pickle(idx)
diff --git a/pandas/tests/indexes/period/test_resolution.py b/pandas/tests/indexes/period/test_resolution.py
index 6c876b4f9366f..98ccfe6569798 100644
--- a/pandas/tests/indexes/period/test_resolution.py
+++ b/pandas/tests/indexes/period/test_resolution.py
@@ -7,7 +7,7 @@ class TestResolution:
@pytest.mark.parametrize(
"freq,expected",
[
- ("A", "year"),
+ ("Y", "year"),
("Q", "quarter"),
("M", "month"),
("D", "day"),
diff --git a/pandas/tests/indexes/period/test_setops.py b/pandas/tests/indexes/period/test_setops.py
index dd05210e417b0..9610db5f0336b 100644
--- a/pandas/tests/indexes/period/test_setops.py
+++ b/pandas/tests/indexes/period/test_setops.py
@@ -81,8 +81,8 @@ def test_union(self, sort):
other6 = period_range("2000-04-01", freq="M", periods=7)
expected6 = period_range("2000-01-01", freq="M", periods=10)
- rng7 = period_range("2003-01-01", freq="A", periods=5)
- other7 = period_range("1998-01-01", freq="A", periods=8)
+ rng7 = period_range("2003-01-01", freq="Y", periods=5)
+ other7 = period_range("1998-01-01", freq="Y", periods=8)
expected7 = PeriodIndex(
[
"2003",
@@ -96,7 +96,7 @@ def test_union(self, sort):
"2001",
"2002",
],
- freq="A",
+ freq="Y",
)
rng8 = PeriodIndex(
@@ -293,9 +293,9 @@ def test_difference(self, sort):
expected6 = PeriodIndex(["2000-02-01", "2000-01-01", "2000-03-01"], freq="M")
period_rng = ["2003", "2007", "2006", "2005", "2004"]
- rng7 = PeriodIndex(period_rng, freq="A")
- other7 = period_range("1998-01-01", freq="A", periods=8)
- expected7 = PeriodIndex(["2007", "2006"], freq="A")
+ rng7 = PeriodIndex(period_rng, freq="Y")
+ other7 = period_range("1998-01-01", freq="Y", periods=8)
+ expected7 = PeriodIndex(["2007", "2006"], freq="Y")
for rng, other, expected in [
(rng1, other1, expected1),
diff --git a/pandas/tests/indexes/period/test_tools.py b/pandas/tests/indexes/period/test_tools.py
index 18668fd357fd8..2a9149844a353 100644
--- a/pandas/tests/indexes/period/test_tools.py
+++ b/pandas/tests/indexes/period/test_tools.py
@@ -27,7 +27,7 @@ class TestPeriodRepresentation:
("us", "1970-01-01"),
("ns", "1970-01-01"),
("M", "1970-01"),
- ("A", 1970),
+ ("Y", 1970),
],
)
@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
@@ -43,7 +43,7 @@ def test_freq(self, freq, base_date):
class TestPeriodIndexConversion:
def test_tolist(self):
- index = period_range(freq="A", start="1/1/2001", end="12/1/2009")
+ index = period_range(freq="Y", start="1/1/2001", end="12/1/2009")
rs = index.tolist()
for x in rs:
assert isinstance(x, Period)
diff --git a/pandas/tests/indexes/test_base.py b/pandas/tests/indexes/test_base.py
index bc04c1c6612f4..632d52c3f68cd 100644
--- a/pandas/tests/indexes/test_base.py
+++ b/pandas/tests/indexes/test_base.py
@@ -980,7 +980,7 @@ def test_str_attribute(self, method):
Index(range(5)),
tm.makeDateIndex(10),
MultiIndex.from_tuples([("foo", "1"), ("bar", "3")]),
- period_range(start="2000", end="2010", freq="A"),
+ period_range(start="2000", end="2010", freq="Y"),
],
)
def test_str_attribute_raises(self, index):
diff --git a/pandas/tests/indexes/timedeltas/test_timedelta_range.py b/pandas/tests/indexes/timedeltas/test_timedelta_range.py
index d0593b3230959..03531547ef042 100644
--- a/pandas/tests/indexes/timedeltas/test_timedelta_range.py
+++ b/pandas/tests/indexes/timedeltas/test_timedelta_range.py
@@ -3,6 +3,7 @@
from pandas import (
Timedelta,
+ TimedeltaIndex,
timedelta_range,
to_timedelta,
)
@@ -119,3 +120,42 @@ def test_timedelta_range_infer_freq(self):
# https://github.com/pandas-dev/pandas/issues/35897
result = timedelta_range("0s", "1s", periods=31)
assert result.freq is None
+
+ @pytest.mark.parametrize(
+ "freq_depr, start, end, expected_values, expected_freq",
+ [
+ (
+ "3.5S",
+ "05:03:01",
+ "05:03:10",
+ ["0 days 05:03:01", "0 days 05:03:04.500000", "0 days 05:03:08"],
+ "3500ms",
+ ),
+ (
+ "2.5T",
+ "5 hours",
+ "5 hours 8 minutes",
+ [
+ "0 days 05:00:00",
+ "0 days 05:02:30",
+ "0 days 05:05:00",
+ "0 days 05:07:30",
+ ],
+ "150s",
+ ),
+ ],
+ )
+ def test_timedelta_range_deprecated_freq(
+ self, freq_depr, start, end, expected_values, expected_freq
+ ):
+ # GH#52536
+ msg = (
+ f"'{freq_depr[-1]}' is deprecated and will be removed in a future version."
+ )
+
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ result = timedelta_range(start=start, end=end, freq=freq_depr)
+ expected = TimedeltaIndex(
+ expected_values, dtype="timedelta64[ns]", freq=expected_freq
+ )
+ tm.assert_index_equal(result, expected)
diff --git a/pandas/tests/indexing/test_loc.py b/pandas/tests/indexing/test_loc.py
index a2693c85e507f..1177d8df6030d 100644
--- a/pandas/tests/indexing/test_loc.py
+++ b/pandas/tests/indexing/test_loc.py
@@ -260,19 +260,19 @@ def test_loc_npstr(self):
@pytest.mark.parametrize(
"msg, key",
[
- (r"Period\('2019', 'A-DEC'\), 'foo', 'bar'", (Period(2019), "foo", "bar")),
- (r"Period\('2019', 'A-DEC'\), 'y1', 'bar'", (Period(2019), "y1", "bar")),
- (r"Period\('2019', 'A-DEC'\), 'foo', 'z1'", (Period(2019), "foo", "z1")),
+ (r"Period\('2019', 'Y-DEC'\), 'foo', 'bar'", (Period(2019), "foo", "bar")),
+ (r"Period\('2019', 'Y-DEC'\), 'y1', 'bar'", (Period(2019), "y1", "bar")),
+ (r"Period\('2019', 'Y-DEC'\), 'foo', 'z1'", (Period(2019), "foo", "z1")),
(
- r"Period\('2018', 'A-DEC'\), Period\('2016', 'A-DEC'\), 'bar'",
+ r"Period\('2018', 'Y-DEC'\), Period\('2016', 'Y-DEC'\), 'bar'",
(Period(2018), Period(2016), "bar"),
),
- (r"Period\('2018', 'A-DEC'\), 'foo', 'y1'", (Period(2018), "foo", "y1")),
+ (r"Period\('2018', 'Y-DEC'\), 'foo', 'y1'", (Period(2018), "foo", "y1")),
(
- r"Period\('2017', 'A-DEC'\), 'foo', Period\('2015', 'A-DEC'\)",
+ r"Period\('2017', 'Y-DEC'\), 'foo', Period\('2015', 'Y-DEC'\)",
(Period(2017), "foo", Period(2015)),
),
- (r"Period\('2017', 'A-DEC'\), 'z1', 'bar'", (Period(2017), "z1", "bar")),
+ (r"Period\('2017', 'Y-DEC'\), 'z1', 'bar'", (Period(2017), "z1", "bar")),
],
)
def test_contains_raise_error_if_period_index_is_in_multi_index(self, msg, key):
diff --git a/pandas/tests/internals/test_internals.py b/pandas/tests/internals/test_internals.py
index 597bc2975268e..b97da11bc4252 100644
--- a/pandas/tests/internals/test_internals.py
+++ b/pandas/tests/internals/test_internals.py
@@ -1335,13 +1335,13 @@ def test_interval_can_hold_element(self, dtype, element):
assert not blk._can_hold_element(elem)
def test_period_can_hold_element_emptylist(self):
- pi = period_range("2016", periods=3, freq="A")
+ pi = period_range("2016", periods=3, freq="Y")
blk = new_block(pi._data.reshape(1, 3), BlockPlacement([1]), ndim=2)
assert blk._can_hold_element([])
def test_period_can_hold_element(self, element):
- pi = period_range("2016", periods=3, freq="A")
+ pi = period_range("2016", periods=3, freq="Y")
elem = element(pi)
self.check_series_setitem(elem, pi, True)
diff --git a/pandas/tests/io/json/test_json_table_schema.py b/pandas/tests/io/json/test_json_table_schema.py
index fc2edc7559a48..d7a4dfca90b5e 100644
--- a/pandas/tests/io/json/test_json_table_schema.py
+++ b/pandas/tests/io/json/test_json_table_schema.py
@@ -150,7 +150,7 @@ def test_as_json_table_type_bool_data(self, bool_type):
pd.to_datetime(["2016"], utc=True),
pd.Series(pd.to_datetime(["2016"])),
pd.Series(pd.to_datetime(["2016"], utc=True)),
- pd.period_range("2016", freq="A", periods=3),
+ pd.period_range("2016", freq="Y", periods=3),
],
)
def test_as_json_table_type_date_data(self, date_data):
@@ -480,9 +480,9 @@ def test_convert_pandas_type_to_json_field_datetime(
assert result == expected
def test_convert_pandas_type_to_json_period_range(self):
- arr = pd.period_range("2016", freq="A-DEC", periods=4)
+ arr = pd.period_range("2016", freq="Y-DEC", periods=4)
result = convert_pandas_type_to_json_field(arr)
- expected = {"name": "values", "type": "datetime", "freq": "A-DEC"}
+ expected = {"name": "values", "type": "datetime", "freq": "Y-DEC"}
assert result == expected
@pytest.mark.parametrize("kind", [pd.Categorical, pd.CategoricalIndex])
diff --git a/pandas/tests/plotting/test_datetimelike.py b/pandas/tests/plotting/test_datetimelike.py
index 220741cf1ec3d..f488ee7da87ac 100644
--- a/pandas/tests/plotting/test_datetimelike.py
+++ b/pandas/tests/plotting/test_datetimelike.py
@@ -102,7 +102,7 @@ def test_is_error_nozeroindex(self):
_check_plot_works(a.plot, yerr=a)
def test_nonnumeric_exclude(self):
- idx = date_range("1/1/1987", freq="A", periods=3)
+ idx = date_range("1/1/1987", freq="Y", periods=3)
df = DataFrame({"A": ["x", "y", "z"], "B": [1, 2, 3]}, idx)
fig, ax = mpl.pyplot.subplots()
@@ -111,13 +111,13 @@ def test_nonnumeric_exclude(self):
mpl.pyplot.close(fig)
def test_nonnumeric_exclude_error(self):
- idx = date_range("1/1/1987", freq="A", periods=3)
+ idx = date_range("1/1/1987", freq="Y", periods=3)
df = DataFrame({"A": ["x", "y", "z"], "B": [1, 2, 3]}, idx)
msg = "no numeric data to plot"
with pytest.raises(TypeError, match=msg):
df["A"].plot()
- @pytest.mark.parametrize("freq", ["s", "min", "H", "D", "W", "M", "Q", "A"])
+ @pytest.mark.parametrize("freq", ["s", "min", "H", "D", "W", "M", "Q", "Y"])
def test_tsplot_period(self, freq):
idx = period_range("12/31/1999", freq=freq, periods=100)
ser = Series(np.random.default_rng(2).standard_normal(len(idx)), idx)
@@ -125,7 +125,7 @@ def test_tsplot_period(self, freq):
_check_plot_works(ser.plot, ax=ax)
@pytest.mark.parametrize(
- "freq", ["s", "min", "H", "D", "W", "ME", "Q-DEC", "A", "1B30Min"]
+ "freq", ["s", "min", "H", "D", "W", "ME", "Q-DEC", "Y", "1B30Min"]
)
def test_tsplot_datetime(self, freq):
idx = date_range("12/31/1999", freq=freq, periods=100)
@@ -165,8 +165,8 @@ def test_get_datevalue(self):
from pandas.plotting._matplotlib.converter import get_datevalue
assert get_datevalue(None, "D") is None
- assert get_datevalue(1987, "A") == 1987
- assert get_datevalue(Period(1987, "A"), "M") == Period("1987-12", "M").ordinal
+ assert get_datevalue(1987, "Y") == 1987
+ assert get_datevalue(Period(1987, "Y"), "M") == Period("1987-12", "M").ordinal
assert get_datevalue("1/1/1987", "D") == Period("1987-1-1", "D").ordinal
def test_ts_plot_format_coord(self):
@@ -176,7 +176,7 @@ def check_format_of_first_point(ax, expected_string):
first_y = first_line.get_ydata()[0]
assert expected_string == ax.format_coord(first_x, first_y)
- annual = Series(1, index=date_range("2014-01-01", periods=3, freq="A-DEC"))
+ annual = Series(1, index=date_range("2014-01-01", periods=3, freq="Y-DEC"))
_, ax = mpl.pyplot.subplots()
annual.plot(ax=ax)
check_format_of_first_point(ax, "t = 2014 y = 1.000000")
@@ -187,14 +187,14 @@ def check_format_of_first_point(ax, expected_string):
daily.plot(ax=ax)
check_format_of_first_point(ax, "t = 2014-01-01 y = 1.000000")
- @pytest.mark.parametrize("freq", ["s", "min", "H", "D", "W", "M", "Q", "A"])
+ @pytest.mark.parametrize("freq", ["s", "min", "H", "D", "W", "M", "Q", "Y"])
def test_line_plot_period_series(self, freq):
idx = period_range("12/31/1999", freq=freq, periods=100)
ser = Series(np.random.default_rng(2).standard_normal(len(idx)), idx)
_check_plot_works(ser.plot, ser.index.freq)
@pytest.mark.parametrize(
- "frqncy", ["1s", "3s", "5min", "7H", "4D", "8W", "11M", "3A"]
+ "frqncy", ["1s", "3s", "5min", "7H", "4D", "8W", "11M", "3Y"]
)
def test_line_plot_period_mlt_series(self, frqncy):
# test period index line plot for series with multiples (`mlt`) of the
@@ -204,14 +204,14 @@ def test_line_plot_period_mlt_series(self, frqncy):
_check_plot_works(s.plot, s.index.freq.rule_code)
@pytest.mark.parametrize(
- "freq", ["s", "min", "H", "D", "W", "ME", "Q-DEC", "A", "1B30Min"]
+ "freq", ["s", "min", "H", "D", "W", "ME", "Q-DEC", "Y", "1B30Min"]
)
def test_line_plot_datetime_series(self, freq):
idx = date_range("12/31/1999", freq=freq, periods=100)
ser = Series(np.random.default_rng(2).standard_normal(len(idx)), idx)
_check_plot_works(ser.plot, ser.index.freq.rule_code)
- @pytest.mark.parametrize("freq", ["s", "min", "H", "D", "W", "ME", "Q", "A"])
+ @pytest.mark.parametrize("freq", ["s", "min", "H", "D", "W", "ME", "Q", "Y"])
def test_line_plot_period_frame(self, freq):
idx = date_range("12/31/1999", freq=freq, periods=100)
df = DataFrame(
@@ -222,7 +222,7 @@ def test_line_plot_period_frame(self, freq):
_check_plot_works(df.plot, df.index.freq)
@pytest.mark.parametrize(
- "frqncy", ["1s", "3s", "5min", "7H", "4D", "8W", "11M", "3A"]
+ "frqncy", ["1s", "3s", "5min", "7H", "4D", "8W", "11M", "3Y"]
)
def test_line_plot_period_mlt_frame(self, frqncy):
# test period index line plot for DataFrames with multiples (`mlt`)
@@ -240,7 +240,7 @@ def test_line_plot_period_mlt_frame(self, frqncy):
@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
@pytest.mark.parametrize(
- "freq", ["s", "min", "H", "D", "W", "ME", "Q-DEC", "A", "1B30Min"]
+ "freq", ["s", "min", "H", "D", "W", "ME", "Q-DEC", "Y", "1B30Min"]
)
def test_line_plot_datetime_frame(self, freq):
idx = date_range("12/31/1999", freq=freq, periods=100)
@@ -254,7 +254,7 @@ def test_line_plot_datetime_frame(self, freq):
_check_plot_works(df.plot, freq)
@pytest.mark.parametrize(
- "freq", ["s", "min", "H", "D", "W", "ME", "Q-DEC", "A", "1B30Min"]
+ "freq", ["s", "min", "H", "D", "W", "ME", "Q-DEC", "Y", "1B30Min"]
)
def test_line_plot_inferred_freq(self, freq):
idx = date_range("12/31/1999", freq=freq, periods=100)
@@ -440,7 +440,7 @@ def test_get_finder(self):
assert conv.get_finder(to_offset("D")) == conv._daily_finder
assert conv.get_finder(to_offset("ME")) == conv._monthly_finder
assert conv.get_finder(to_offset("Q")) == conv._quarterly_finder
- assert conv.get_finder(to_offset("A")) == conv._annual_finder
+ assert conv.get_finder(to_offset("Y")) == conv._annual_finder
assert conv.get_finder(to_offset("W")) == conv._daily_finder
def test_finder_daily(self):
@@ -523,10 +523,10 @@ def test_finder_monthly_long(self):
def test_finder_annual(self):
xp = [1987, 1988, 1990, 1990, 1995, 2020, 2070, 2170]
- xp = [Period(x, freq="A").ordinal for x in xp]
+ xp = [Period(x, freq="Y").ordinal for x in xp]
rs = []
for nyears in [5, 10, 19, 49, 99, 199, 599, 1001]:
- rng = period_range("1987", periods=nyears, freq="A")
+ rng = period_range("1987", periods=nyears, freq="Y")
ser = Series(np.random.default_rng(2).standard_normal(len(rng)), rng)
_, ax = mpl.pyplot.subplots()
ser.plot(ax=ax)
diff --git a/pandas/tests/resample/test_base.py b/pandas/tests/resample/test_base.py
index ae28a010d8435..ad6ff70b14d25 100644
--- a/pandas/tests/resample/test_base.py
+++ b/pandas/tests/resample/test_base.py
@@ -96,7 +96,7 @@ def test_raises_on_non_datetimelike_index():
"but got an instance of 'RangeIndex'"
)
with pytest.raises(TypeError, match=msg):
- xp.resample("A")
+ xp.resample("Y")
@all_ts
diff --git a/pandas/tests/resample/test_datetime_index.py b/pandas/tests/resample/test_datetime_index.py
index 113e2d8986ad2..b40ae7d2bd8c0 100644
--- a/pandas/tests/resample/test_datetime_index.py
+++ b/pandas/tests/resample/test_datetime_index.py
@@ -435,7 +435,7 @@ def test_resample_frame_basic_cy_funcs(f, unit):
g._cython_agg_general(f, alt=None, numeric_only=True)
-@pytest.mark.parametrize("freq", ["A", "ME"])
+@pytest.mark.parametrize("freq", ["Y", "ME"])
def test_resample_frame_basic_M_A(freq, unit):
df = tm.makeTimeDataFrame()
df.index = df.index.as_unit(unit)
@@ -668,8 +668,8 @@ def test_resample_reresample(unit):
@pytest.mark.parametrize(
"freq, expected_kwargs",
[
- ["A-DEC", {"start": "1990", "end": "2000", "freq": "a-dec"}],
- ["A-JUN", {"start": "1990", "end": "2000", "freq": "a-jun"}],
+ ["Y-DEC", {"start": "1990", "end": "2000", "freq": "y-dec"}],
+ ["Y-JUN", {"start": "1990", "end": "2000", "freq": "y-jun"}],
["ME", {"start": "1990-01", "end": "2000-01", "freq": "M"}],
],
)
@@ -1246,7 +1246,7 @@ def test_corner_cases_period(simple_period_range_series):
# miscellaneous test coverage
len0pts = simple_period_range_series("2007-01", "2010-05", freq="M")[:0]
# it works
- result = len0pts.resample("A-DEC").mean()
+ result = len0pts.resample("Y-DEC").mean()
assert len(result) == 0
@@ -2021,7 +2021,7 @@ def test_long_rule_non_nano():
"1900-12-31",
]
).astype("datetime64[s]"),
- freq="200A-DEC",
+ freq="200Y-DEC",
)
expected = Series([1.0, 3.0, 6.5, 4.0, 3.0, 6.5, 4.0, 3.0, 6.5], index=expected_idx)
tm.assert_series_equal(result, expected)
@@ -2044,7 +2044,7 @@ def test_resample_empty_series_with_tz():
def test_resample_M_deprecated():
- depr_msg = r"\'M\' will be deprecated, please use \'ME\' for \'month end\'"
+ depr_msg = "'M' will be deprecated, please use 'ME' instead."
s = Series(range(10), index=date_range("20130101", freq="d", periods=10))
expected = s.resample("2ME").mean()
diff --git a/pandas/tests/resample/test_period_index.py b/pandas/tests/resample/test_period_index.py
index db3804a6600b9..f3e2eecf63d6b 100644
--- a/pandas/tests/resample/test_period_index.py
+++ b/pandas/tests/resample/test_period_index.py
@@ -109,7 +109,7 @@ def test_selection(self, index, freq, kind, kwargs):
def test_annual_upsample_cases(
self, offset, period, conv, meth, month, simple_period_range_series
):
- ts = simple_period_range_series("1/1/1990", "12/31/1991", freq=f"A-{month}")
+ ts = simple_period_range_series("1/1/1990", "12/31/1991", freq=f"Y-{month}")
warn = FutureWarning if period == "B" else None
msg = r"PeriodDtype\[B\] is deprecated"
with tm.assert_produces_warning(warn, match=msg):
@@ -120,20 +120,20 @@ def test_annual_upsample_cases(
def test_basic_downsample(self, simple_period_range_series):
ts = simple_period_range_series("1/1/1990", "6/30/1995", freq="M")
- result = ts.resample("a-dec").mean()
+ result = ts.resample("y-dec").mean()
expected = ts.groupby(ts.index.year).mean()
- expected.index = period_range("1/1/1990", "6/30/1995", freq="a-dec")
+ expected.index = period_range("1/1/1990", "6/30/1995", freq="y-dec")
tm.assert_series_equal(result, expected)
# this is ok
- tm.assert_series_equal(ts.resample("a-dec").mean(), result)
- tm.assert_series_equal(ts.resample("a").mean(), result)
+ tm.assert_series_equal(ts.resample("y-dec").mean(), result)
+ tm.assert_series_equal(ts.resample("y").mean(), result)
@pytest.mark.parametrize(
"rule,expected_error_msg",
[
- ("a-dec", "<YearEnd: month=12>"),
+ ("y-dec", "<YearEnd: month=12>"),
("q-mar", "<QuarterEnd: startingMonth=3>"),
("M", "<MonthEnd>"),
("w-thu", "<Week: weekday=3>"),
@@ -152,7 +152,7 @@ def test_not_subperiod(self, simple_period_range_series, rule, expected_error_ms
@pytest.mark.parametrize("freq", ["D", "2D"])
def test_basic_upsample(self, freq, simple_period_range_series):
ts = simple_period_range_series("1/1/1990", "6/30/1995", freq="M")
- result = ts.resample("a-dec").mean()
+ result = ts.resample("y-dec").mean()
resampled = result.resample(freq, convention="end").ffill()
expected = result.to_timestamp(freq, how="end")
@@ -160,7 +160,7 @@ def test_basic_upsample(self, freq, simple_period_range_series):
tm.assert_series_equal(resampled, expected)
def test_upsample_with_limit(self):
- rng = period_range("1/1/2000", periods=5, freq="A")
+ rng = period_range("1/1/2000", periods=5, freq="Y")
ts = Series(np.random.default_rng(2).standard_normal(len(rng)), rng)
result = ts.resample("M", convention="end").ffill(limit=2)
@@ -168,13 +168,13 @@ def test_upsample_with_limit(self):
tm.assert_series_equal(result, expected)
def test_annual_upsample(self, simple_period_range_series):
- ts = simple_period_range_series("1/1/1990", "12/31/1995", freq="A-DEC")
+ ts = simple_period_range_series("1/1/1990", "12/31/1995", freq="Y-DEC")
df = DataFrame({"a": ts})
rdf = df.resample("D").ffill()
exp = df["a"].resample("D").ffill()
tm.assert_series_equal(rdf["a"], exp)
- rng = period_range("2000", "2003", freq="A-DEC")
+ rng = period_range("2000", "2003", freq="Y-DEC")
ts = Series([1, 2, 3, 4], index=rng)
result = ts.resample("M").ffill()
@@ -391,13 +391,13 @@ def test_weekly_upsample(self, day, target, convention, simple_period_range_seri
def test_resample_to_timestamps(self, simple_period_range_series):
ts = simple_period_range_series("1/1/1990", "12/31/1995", freq="M")
- result = ts.resample("A-DEC", kind="timestamp").mean()
- expected = ts.to_timestamp(how="start").resample("A-DEC").mean()
+ result = ts.resample("Y-DEC", kind="timestamp").mean()
+ expected = ts.to_timestamp(how="start").resample("Y-DEC").mean()
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("month", MONTHS)
def test_resample_to_quarterly(self, simple_period_range_series, month):
- ts = simple_period_range_series("1990", "1992", freq=f"A-{month}")
+ ts = simple_period_range_series("1990", "1992", freq=f"Y-{month}")
quar_ts = ts.resample(f"Q-{month}").ffill()
stamps = ts.to_timestamp("D", how="start")
@@ -415,7 +415,7 @@ def test_resample_to_quarterly(self, simple_period_range_series, month):
@pytest.mark.parametrize("how", ["start", "end"])
def test_resample_to_quarterly_start_end(self, simple_period_range_series, how):
# conforms, but different month
- ts = simple_period_range_series("1990", "1992", freq="A-JUN")
+ ts = simple_period_range_series("1990", "1992", freq="Y-JUN")
result = ts.resample("Q-MAR", convention=how).ffill()
expected = ts.asfreq("Q-MAR", how=how)
expected = expected.reindex(result.index, method="ffill")
@@ -426,21 +426,21 @@ def test_resample_to_quarterly_start_end(self, simple_period_range_series, how):
tm.assert_series_equal(result, expected)
def test_resample_fill_missing(self):
- rng = PeriodIndex([2000, 2005, 2007, 2009], freq="A")
+ rng = PeriodIndex([2000, 2005, 2007, 2009], freq="Y")
s = Series(np.random.default_rng(2).standard_normal(4), index=rng)
stamps = s.to_timestamp()
- filled = s.resample("A").ffill()
- expected = stamps.resample("A").ffill().to_period("A")
+ filled = s.resample("Y").ffill()
+ expected = stamps.resample("Y").ffill().to_period("Y")
tm.assert_series_equal(filled, expected)
def test_cant_fill_missing_dups(self):
- rng = PeriodIndex([2000, 2005, 2005, 2007, 2007], freq="A")
+ rng = PeriodIndex([2000, 2005, 2005, 2007, 2007], freq="Y")
s = Series(np.random.default_rng(2).standard_normal(5), index=rng)
msg = "Reindexing only valid with uniquely valued Index objects"
with pytest.raises(InvalidIndexError, match=msg):
- s.resample("A").ffill()
+ s.resample("Y").ffill()
@pytest.mark.parametrize("freq", ["5min"])
@pytest.mark.parametrize("kind", ["period", None, "timestamp"])
@@ -537,13 +537,13 @@ def test_resample_tz_localized(self):
ts["second"] = np.cumsum(np.random.default_rng(2).standard_normal(len(rng)))
expected = DataFrame(
{
- "first": ts.resample("A").sum()["first"],
- "second": ts.resample("A").mean()["second"],
+ "first": ts.resample("Y").sum()["first"],
+ "second": ts.resample("Y").mean()["second"],
},
columns=["first", "second"],
)
result = (
- ts.resample("A")
+ ts.resample("Y")
.agg({"first": "sum", "second": "mean"})
.reindex(columns=["first", "second"])
)
@@ -573,8 +573,8 @@ def test_quarterly_resampling(self):
rng = period_range("2000Q1", periods=10, freq="Q-DEC")
ts = Series(np.arange(10), index=rng)
- result = ts.resample("A").mean()
- exp = ts.to_timestamp().resample("A").mean().to_period()
+ result = ts.resample("Y").mean()
+ exp = ts.to_timestamp().resample("Y").mean().to_period()
tm.assert_series_equal(result, exp)
def test_resample_weekly_bug_1726(self):
@@ -647,7 +647,7 @@ def test_monthly_convention_span(self):
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
- "from_freq, to_freq", [("D", "ME"), ("Q", "A"), ("ME", "Q"), ("D", "W")]
+ "from_freq, to_freq", [("D", "ME"), ("Q", "Y"), ("ME", "Q"), ("D", "W")]
)
def test_default_right_closed_label(self, from_freq, to_freq):
idx = date_range(start="8/15/2012", periods=100, freq=from_freq)
@@ -676,7 +676,7 @@ def test_all_values_single_bin(self):
index = period_range(start="2012-01-01", end="2012-12-31", freq="M")
s = Series(np.random.default_rng(2).standard_normal(len(index)), index=index)
- result = s.resample("A").mean()
+ result = s.resample("Y").mean()
tm.assert_almost_equal(result.iloc[0], s.mean())
def test_evenly_divisible_with_no_extra_bins(self):
diff --git a/pandas/tests/resample/test_time_grouper.py b/pandas/tests/resample/test_time_grouper.py
index 3e0922228cb74..c00366b2e28ce 100644
--- a/pandas/tests/resample/test_time_grouper.py
+++ b/pandas/tests/resample/test_time_grouper.py
@@ -24,7 +24,7 @@ def test_series():
def test_apply(test_series):
- grouper = Grouper(freq="A", label="right", closed="right")
+ grouper = Grouper(freq="Y", label="right", closed="right")
grouped = test_series.groupby(grouper)
@@ -44,18 +44,18 @@ def test_count(test_series):
expected = test_series.groupby(lambda x: x.year).count()
- grouper = Grouper(freq="A", label="right", closed="right")
+ grouper = Grouper(freq="Y", label="right", closed="right")
result = test_series.groupby(grouper).count()
expected.index = result.index
tm.assert_series_equal(result, expected)
- result = test_series.resample("A").count()
+ result = test_series.resample("Y").count()
expected.index = result.index
tm.assert_series_equal(result, expected)
def test_numpy_reduction(test_series):
- result = test_series.resample("A", closed="right").prod()
+ result = test_series.resample("Y", closed="right").prod()
msg = "using SeriesGroupBy.prod"
with tm.assert_produces_warning(FutureWarning, match=msg):
diff --git a/pandas/tests/reshape/concat/test_concat.py b/pandas/tests/reshape/concat/test_concat.py
index 5dde863f246d1..aa1a74aadae12 100644
--- a/pandas/tests/reshape/concat/test_concat.py
+++ b/pandas/tests/reshape/concat/test_concat.py
@@ -30,8 +30,8 @@
class TestConcatenate:
def test_append_concat(self):
# GH#1815
- d1 = date_range("12/31/1990", "12/31/1999", freq="A-DEC")
- d2 = date_range("12/31/2000", "12/31/2009", freq="A-DEC")
+ d1 = date_range("12/31/1990", "12/31/1999", freq="Y-DEC")
+ d2 = date_range("12/31/2000", "12/31/2009", freq="Y-DEC")
s1 = Series(np.random.default_rng(2).standard_normal(10), d1)
s2 = Series(np.random.default_rng(2).standard_normal(10), d2)
diff --git a/pandas/tests/reshape/test_pivot.py b/pandas/tests/reshape/test_pivot.py
index 8435f4a189c56..2d41b6d355ead 100644
--- a/pandas/tests/reshape/test_pivot.py
+++ b/pandas/tests/reshape/test_pivot.py
@@ -445,10 +445,10 @@ def test_pivot_no_values(self):
tm.assert_frame_equal(res, exp)
res = df.pivot_table(
- index=Grouper(freq="A"), columns=Grouper(key="dt", freq="ME")
+ index=Grouper(freq="Y"), columns=Grouper(key="dt", freq="ME")
)
exp = DataFrame(
- [3.0], index=pd.DatetimeIndex(["2011-12-31"], freq="A"), columns=exp_columns
+ [3.0], index=pd.DatetimeIndex(["2011-12-31"], freq="Y"), columns=exp_columns
)
tm.assert_frame_equal(res, exp)
@@ -1273,7 +1273,7 @@ def test_pivot_timegrouper(self, using_array_manager):
expected = DataFrame(
np.array([10, 18, 3], dtype="int64").reshape(1, 3),
- index=pd.DatetimeIndex([datetime(2013, 12, 31)], freq="A"),
+ index=pd.DatetimeIndex([datetime(2013, 12, 31)], freq="Y"),
columns="Carl Joe Mark".split(),
)
expected.index.name = "Date"
@@ -1281,7 +1281,7 @@ def test_pivot_timegrouper(self, using_array_manager):
result = pivot_table(
df,
- index=Grouper(freq="A"),
+ index=Grouper(freq="Y"),
columns="Buyer",
values="Quantity",
aggfunc="sum",
@@ -1291,7 +1291,7 @@ def test_pivot_timegrouper(self, using_array_manager):
result = pivot_table(
df,
index="Buyer",
- columns=Grouper(freq="A"),
+ columns=Grouper(freq="Y"),
values="Quantity",
aggfunc="sum",
)
diff --git a/pandas/tests/scalar/period/test_asfreq.py b/pandas/tests/scalar/period/test_asfreq.py
index 00285148a3c90..4287a69823aef 100644
--- a/pandas/tests/scalar/period/test_asfreq.py
+++ b/pandas/tests/scalar/period/test_asfreq.py
@@ -18,7 +18,7 @@ class TestFreqConversion:
"""Test frequency conversion of date objects"""
@pytest.mark.filterwarnings("ignore:Period with BDay:FutureWarning")
- @pytest.mark.parametrize("freq", ["A", "Q", "M", "W", "B", "D"])
+ @pytest.mark.parametrize("freq", ["Y", "Q", "M", "W", "B", "D"])
def test_asfreq_near_zero(self, freq):
# GH#19643, GH#19650
per = Period("0001-01-01", freq=freq)
@@ -49,7 +49,7 @@ def test_to_timestamp_out_of_bounds(self):
per.to_timestamp()
def test_asfreq_corner(self):
- val = Period(freq="A", year=2007)
+ val = Period(freq="Y", year=2007)
result1 = val.asfreq("5min")
result2 = val.asfreq("min")
expected = Period("2007-12-31 23:59", freq="min")
@@ -61,11 +61,11 @@ def test_asfreq_corner(self):
def test_conv_annual(self):
# frequency conversion tests: from Annual Frequency
- ival_A = Period(freq="A", year=2007)
+ ival_A = Period(freq="Y", year=2007)
- ival_AJAN = Period(freq="A-JAN", year=2007)
- ival_AJUN = Period(freq="A-JUN", year=2007)
- ival_ANOV = Period(freq="A-NOV", year=2007)
+ ival_AJAN = Period(freq="Y-JAN", year=2007)
+ ival_AJUN = Period(freq="Y-JUN", year=2007)
+ ival_ANOV = Period(freq="Y-NOV", year=2007)
ival_A_to_Q_start = Period(freq="Q", year=2007, quarter=1)
ival_A_to_Q_end = Period(freq="Q", year=2007, quarter=4)
@@ -133,7 +133,7 @@ def test_conv_annual(self):
assert ival_ANOV.asfreq("D", "s") == ival_ANOV_to_D_start
assert ival_ANOV.asfreq("D", "E") == ival_ANOV_to_D_end
- assert ival_A.asfreq("A") == ival_A
+ assert ival_A.asfreq("Y") == ival_A
def test_conv_quarterly(self):
# frequency conversion tests: from Quarterly Frequency
@@ -144,7 +144,7 @@ def test_conv_quarterly(self):
ival_QEJAN = Period(freq="Q-JAN", year=2007, quarter=1)
ival_QEJUN = Period(freq="Q-JUN", year=2007, quarter=1)
- ival_Q_to_A = Period(freq="A", year=2007)
+ ival_Q_to_A = Period(freq="Y", year=2007)
ival_Q_to_M_start = Period(freq="M", year=2007, month=1)
ival_Q_to_M_end = Period(freq="M", year=2007, month=3)
ival_Q_to_W_start = Period(freq="W", year=2007, month=1, day=1)
@@ -175,8 +175,8 @@ def test_conv_quarterly(self):
ival_QEJUN_to_D_start = Period(freq="D", year=2006, month=7, day=1)
ival_QEJUN_to_D_end = Period(freq="D", year=2006, month=9, day=30)
- assert ival_Q.asfreq("A") == ival_Q_to_A
- assert ival_Q_end_of_year.asfreq("A") == ival_Q_to_A
+ assert ival_Q.asfreq("Y") == ival_Q_to_A
+ assert ival_Q_end_of_year.asfreq("Y") == ival_Q_to_A
assert ival_Q.asfreq("M", "s") == ival_Q_to_M_start
assert ival_Q.asfreq("M", "E") == ival_Q_to_M_end
@@ -207,7 +207,7 @@ def test_conv_monthly(self):
ival_M = Period(freq="M", year=2007, month=1)
ival_M_end_of_year = Period(freq="M", year=2007, month=12)
ival_M_end_of_quarter = Period(freq="M", year=2007, month=3)
- ival_M_to_A = Period(freq="A", year=2007)
+ ival_M_to_A = Period(freq="Y", year=2007)
ival_M_to_Q = Period(freq="Q", year=2007, quarter=1)
ival_M_to_W_start = Period(freq="W", year=2007, month=1, day=1)
ival_M_to_W_end = Period(freq="W", year=2007, month=1, day=31)
@@ -231,8 +231,8 @@ def test_conv_monthly(self):
freq="s", year=2007, month=1, day=31, hour=23, minute=59, second=59
)
- assert ival_M.asfreq("A") == ival_M_to_A
- assert ival_M_end_of_year.asfreq("A") == ival_M_to_A
+ assert ival_M.asfreq("Y") == ival_M_to_A
+ assert ival_M_end_of_year.asfreq("Y") == ival_M_to_A
assert ival_M.asfreq("Q") == ival_M_to_Q
assert ival_M_end_of_quarter.asfreq("Q") == ival_M_to_Q
@@ -282,14 +282,14 @@ def test_conv_weekly(self):
ival_W_end_of_year = Period(freq="W", year=2007, month=12, day=31)
ival_W_end_of_quarter = Period(freq="W", year=2007, month=3, day=31)
ival_W_end_of_month = Period(freq="W", year=2007, month=1, day=31)
- ival_W_to_A = Period(freq="A", year=2007)
+ ival_W_to_A = Period(freq="Y", year=2007)
ival_W_to_Q = Period(freq="Q", year=2007, quarter=1)
ival_W_to_M = Period(freq="M", year=2007, month=1)
if Period(freq="D", year=2007, month=12, day=31).weekday == 6:
- ival_W_to_A_end_of_year = Period(freq="A", year=2007)
+ ival_W_to_A_end_of_year = Period(freq="Y", year=2007)
else:
- ival_W_to_A_end_of_year = Period(freq="A", year=2008)
+ ival_W_to_A_end_of_year = Period(freq="Y", year=2008)
if Period(freq="D", year=2007, month=3, day=31).weekday == 6:
ival_W_to_Q_end_of_quarter = Period(freq="Q", year=2007, quarter=1)
@@ -321,8 +321,8 @@ def test_conv_weekly(self):
freq="s", year=2007, month=1, day=7, hour=23, minute=59, second=59
)
- assert ival_W.asfreq("A") == ival_W_to_A
- assert ival_W_end_of_year.asfreq("A") == ival_W_to_A_end_of_year
+ assert ival_W.asfreq("Y") == ival_W_to_A
+ assert ival_W_end_of_year.asfreq("Y") == ival_W_to_A_end_of_year
assert ival_W.asfreq("Q") == ival_W_to_Q
assert ival_W_end_of_quarter.asfreq("Q") == ival_W_to_Q_end_of_quarter
@@ -394,7 +394,7 @@ def test_conv_business(self):
ival_B_end_of_month = Period(freq="B", year=2007, month=1, day=31)
ival_B_end_of_week = Period(freq="B", year=2007, month=1, day=5)
- ival_B_to_A = Period(freq="A", year=2007)
+ ival_B_to_A = Period(freq="Y", year=2007)
ival_B_to_Q = Period(freq="Q", year=2007, quarter=1)
ival_B_to_M = Period(freq="M", year=2007, month=1)
ival_B_to_W = Period(freq="W", year=2007, month=1, day=7)
@@ -414,8 +414,8 @@ def test_conv_business(self):
freq="s", year=2007, month=1, day=1, hour=23, minute=59, second=59
)
- assert ival_B.asfreq("A") == ival_B_to_A
- assert ival_B_end_of_year.asfreq("A") == ival_B_to_A
+ assert ival_B.asfreq("Y") == ival_B_to_A
+ assert ival_B_end_of_year.asfreq("Y") == ival_B_to_A
assert ival_B.asfreq("Q") == ival_B_to_Q
assert ival_B_end_of_quarter.asfreq("Q") == ival_B_to_Q
assert ival_B.asfreq("M") == ival_B_to_M
@@ -452,11 +452,11 @@ def test_conv_daily(self):
ival_B_friday = Period(freq="B", year=2007, month=1, day=5)
ival_B_monday = Period(freq="B", year=2007, month=1, day=8)
- ival_D_to_A = Period(freq="A", year=2007)
+ ival_D_to_A = Period(freq="Y", year=2007)
- ival_Deoq_to_AJAN = Period(freq="A-JAN", year=2008)
- ival_Deoq_to_AJUN = Period(freq="A-JUN", year=2007)
- ival_Deoq_to_ADEC = Period(freq="A-DEC", year=2007)
+ ival_Deoq_to_AJAN = Period(freq="Y-JAN", year=2008)
+ ival_Deoq_to_AJUN = Period(freq="Y-JUN", year=2007)
+ ival_Deoq_to_ADEC = Period(freq="Y-DEC", year=2007)
ival_D_to_QEJAN = Period(freq="Q-JAN", year=2007, quarter=4)
ival_D_to_QEJUN = Period(freq="Q-JUN", year=2007, quarter=3)
@@ -480,13 +480,13 @@ def test_conv_daily(self):
freq="s", year=2007, month=1, day=1, hour=23, minute=59, second=59
)
- assert ival_D.asfreq("A") == ival_D_to_A
+ assert ival_D.asfreq("Y") == ival_D_to_A
- assert ival_D_end_of_quarter.asfreq("A-JAN") == ival_Deoq_to_AJAN
- assert ival_D_end_of_quarter.asfreq("A-JUN") == ival_Deoq_to_AJUN
- assert ival_D_end_of_quarter.asfreq("A-DEC") == ival_Deoq_to_ADEC
+ assert ival_D_end_of_quarter.asfreq("Y-JAN") == ival_Deoq_to_AJAN
+ assert ival_D_end_of_quarter.asfreq("Y-JUN") == ival_Deoq_to_AJUN
+ assert ival_D_end_of_quarter.asfreq("Y-DEC") == ival_Deoq_to_ADEC
- assert ival_D_end_of_year.asfreq("A") == ival_D_to_A
+ assert ival_D_end_of_year.asfreq("Y") == ival_D_to_A
assert ival_D_end_of_quarter.asfreq("Q") == ival_D_to_QEDEC
assert ival_D.asfreq("Q-JAN") == ival_D_to_QEJAN
assert ival_D.asfreq("Q-JUN") == ival_D_to_QEJUN
@@ -523,7 +523,7 @@ def test_conv_hourly(self):
ival_H_end_of_day = Period(freq="H", year=2007, month=1, day=1, hour=23)
ival_H_end_of_bus = Period(freq="H", year=2007, month=1, day=1, hour=23)
- ival_H_to_A = Period(freq="A", year=2007)
+ ival_H_to_A = Period(freq="Y", year=2007)
ival_H_to_Q = Period(freq="Q", year=2007, quarter=1)
ival_H_to_M = Period(freq="M", year=2007, month=1)
ival_H_to_W = Period(freq="W", year=2007, month=1, day=7)
@@ -544,8 +544,8 @@ def test_conv_hourly(self):
freq="s", year=2007, month=1, day=1, hour=0, minute=59, second=59
)
- assert ival_H.asfreq("A") == ival_H_to_A
- assert ival_H_end_of_year.asfreq("A") == ival_H_to_A
+ assert ival_H.asfreq("Y") == ival_H_to_A
+ assert ival_H_end_of_year.asfreq("Y") == ival_H_to_A
assert ival_H.asfreq("Q") == ival_H_to_Q
assert ival_H_end_of_quarter.asfreq("Q") == ival_H_to_Q
assert ival_H.asfreq("M") == ival_H_to_M
@@ -591,7 +591,7 @@ def test_conv_minutely(self):
freq="Min", year=2007, month=1, day=1, hour=0, minute=59
)
- ival_T_to_A = Period(freq="A", year=2007)
+ ival_T_to_A = Period(freq="Y", year=2007)
ival_T_to_Q = Period(freq="Q", year=2007, quarter=1)
ival_T_to_M = Period(freq="M", year=2007, month=1)
ival_T_to_W = Period(freq="W", year=2007, month=1, day=7)
@@ -607,8 +607,8 @@ def test_conv_minutely(self):
freq="s", year=2007, month=1, day=1, hour=0, minute=0, second=59
)
- assert ival_T.asfreq("A") == ival_T_to_A
- assert ival_T_end_of_year.asfreq("A") == ival_T_to_A
+ assert ival_T.asfreq("Y") == ival_T_to_A
+ assert ival_T_end_of_year.asfreq("Y") == ival_T_to_A
assert ival_T.asfreq("Q") == ival_T_to_Q
assert ival_T_end_of_quarter.asfreq("Q") == ival_T_to_Q
assert ival_T.asfreq("M") == ival_T_to_M
@@ -657,7 +657,7 @@ def test_conv_secondly(self):
freq="s", year=2007, month=1, day=1, hour=0, minute=0, second=59
)
- ival_S_to_A = Period(freq="A", year=2007)
+ ival_S_to_A = Period(freq="Y", year=2007)
ival_S_to_Q = Period(freq="Q", year=2007, quarter=1)
ival_S_to_M = Period(freq="M", year=2007, month=1)
ival_S_to_W = Period(freq="W", year=2007, month=1, day=7)
@@ -667,8 +667,8 @@ def test_conv_secondly(self):
ival_S_to_H = Period(freq="H", year=2007, month=1, day=1, hour=0)
ival_S_to_T = Period(freq="Min", year=2007, month=1, day=1, hour=0, minute=0)
- assert ival_S.asfreq("A") == ival_S_to_A
- assert ival_S_end_of_year.asfreq("A") == ival_S_to_A
+ assert ival_S.asfreq("Y") == ival_S_to_A
+ assert ival_S_end_of_year.asfreq("Y") == ival_S_to_A
assert ival_S.asfreq("Q") == ival_S_to_Q
assert ival_S_end_of_quarter.asfreq("Q") == ival_S_to_Q
assert ival_S.asfreq("M") == ival_S_to_M
@@ -707,44 +707,44 @@ def test_conv_microsecond(self):
def test_asfreq_mult(self):
# normal freq to mult freq
- p = Period(freq="A", year=2007)
+ p = Period(freq="Y", year=2007)
# ordinal will not change
- for freq in ["3A", offsets.YearEnd(3)]:
+ for freq in ["3Y", offsets.YearEnd(3)]:
result = p.asfreq(freq)
- expected = Period("2007", freq="3A")
+ expected = Period("2007", freq="3Y")
assert result == expected
assert result.ordinal == expected.ordinal
assert result.freq == expected.freq
# ordinal will not change
- for freq in ["3A", offsets.YearEnd(3)]:
+ for freq in ["3Y", offsets.YearEnd(3)]:
result = p.asfreq(freq, how="S")
- expected = Period("2007", freq="3A")
+ expected = Period("2007", freq="3Y")
assert result == expected
assert result.ordinal == expected.ordinal
assert result.freq == expected.freq
# mult freq to normal freq
- p = Period(freq="3A", year=2007)
+ p = Period(freq="3Y", year=2007)
# ordinal will change because how=E is the default
- for freq in ["A", offsets.YearEnd()]:
+ for freq in ["Y", offsets.YearEnd()]:
result = p.asfreq(freq)
- expected = Period("2009", freq="A")
+ expected = Period("2009", freq="Y")
assert result == expected
assert result.ordinal == expected.ordinal
assert result.freq == expected.freq
# ordinal will not change
- for freq in ["A", offsets.YearEnd()]:
+ for freq in ["Y", offsets.YearEnd()]:
result = p.asfreq(freq, how="s")
- expected = Period("2007", freq="A")
+ expected = Period("2007", freq="Y")
assert result == expected
assert result.ordinal == expected.ordinal
assert result.freq == expected.freq
- p = Period(freq="A", year=2007)
+ p = Period(freq="Y", year=2007)
for freq in ["2M", offsets.MonthEnd(2)]:
result = p.asfreq(freq)
expected = Period("2007-12", freq="2M")
@@ -760,7 +760,7 @@ def test_asfreq_mult(self):
assert result.ordinal == expected.ordinal
assert result.freq == expected.freq
- p = Period(freq="3A", year=2007)
+ p = Period(freq="3Y", year=2007)
for freq in ["2M", offsets.MonthEnd(2)]:
result = p.asfreq(freq)
expected = Period("2009-12", freq="2M")
diff --git a/pandas/tests/scalar/period/test_period.py b/pandas/tests/scalar/period/test_period.py
index 7d07f327e3978..c7d42a83e663c 100644
--- a/pandas/tests/scalar/period/test_period.py
+++ b/pandas/tests/scalar/period/test_period.py
@@ -61,9 +61,9 @@ def test_construction(self):
assert i1 == i2
- i1 = Period("2005", freq="A")
+ i1 = Period("2005", freq="Y")
i2 = Period("2005")
- i3 = Period("2005", freq="a")
+ i3 = Period("2005", freq="y")
assert i1 == i2
assert i1 == i3
@@ -224,7 +224,7 @@ def test_period_constructor_offsets(self):
assert Period("1/1/2005", freq=offsets.MonthEnd()) == Period(
"1/1/2005", freq="M"
)
- assert Period("2005", freq=offsets.YearEnd()) == Period("2005", freq="A")
+ assert Period("2005", freq=offsets.YearEnd()) == Period("2005", freq="Y")
assert Period("2005", freq=offsets.MonthEnd()) == Period("2005", freq="M")
with tm.assert_produces_warning(FutureWarning, match=bday_msg):
assert Period("3/10/12", freq=offsets.BusinessDay()) == Period(
@@ -315,13 +315,13 @@ def test_invalid_arguments(self):
msg = '^Given date string "-2000" not likely a datetime$'
with pytest.raises(ValueError, match=msg):
- Period("-2000", "A")
+ Period("-2000", "Y")
msg = "day is out of range for month"
with pytest.raises(DateParseError, match=msg):
- Period("0", "A")
+ Period("0", "Y")
msg = "Unknown datetime string format, unable to parse"
with pytest.raises(DateParseError, match=msg):
- Period("1/1/-2000", "A")
+ Period("1/1/-2000", "Y")
def test_constructor_corner(self):
expected = Period("2007-01", freq="2M")
@@ -331,8 +331,8 @@ def test_constructor_corner(self):
p = Period("2007-01-01", freq="D")
- result = Period(p, freq="A")
- exp = Period("2007", freq="A")
+ result = Period(p, freq="Y")
+ exp = Period("2007", freq="Y")
assert result == exp
def test_constructor_infer_freq(self):
@@ -360,11 +360,11 @@ def test_constructor_infer_freq(self):
assert p.freq == "us"
def test_multiples(self):
- result1 = Period("1989", freq="2A")
- result2 = Period("1989", freq="A")
+ result1 = Period("1989", freq="2Y")
+ result2 = Period("1989", freq="Y")
assert result1.ordinal == result2.ordinal
- assert result1.freqstr == "2A-DEC"
- assert result2.freqstr == "A-DEC"
+ assert result1.freqstr == "2Y-DEC"
+ assert result2.freqstr == "Y-DEC"
assert result1.freq == offsets.YearEnd(2)
assert result2.freq == offsets.YearEnd()
@@ -390,7 +390,7 @@ def test_period_cons_quarterly(self, month):
@pytest.mark.parametrize("month", MONTHS)
def test_period_cons_annual(self, month):
# bugs in scikits.timeseries
- freq = f"A-{month}"
+ freq = f"Y-{month}"
exp = Period("1989", freq=freq)
stamp = exp.to_timestamp("D", how="end") + timedelta(days=30)
p = Period(stamp, freq=freq)
@@ -428,7 +428,7 @@ def test_period_from_ordinal(self):
assert p == res
assert isinstance(res, Period)
- @pytest.mark.parametrize("freq", ["A", "M", "D", "H"])
+ @pytest.mark.parametrize("freq", ["Y", "M", "D", "H"])
def test_construct_from_nat_string_and_freq(self, freq):
per = Period("NaT", freq=freq)
assert per is NaT
@@ -621,7 +621,7 @@ def test_to_timestamp_mult(self):
"ignore:Period with BDay freq is deprecated:FutureWarning"
)
def test_to_timestamp(self):
- p = Period("1982", freq="A")
+ p = Period("1982", freq="Y")
start_ts = p.to_timestamp(how="S")
aliases = ["s", "StarT", "BEGIn"]
for a in aliases:
@@ -635,7 +635,7 @@ def test_to_timestamp(self):
assert end_ts == p.to_timestamp("D", how=a)
assert end_ts == p.to_timestamp("3D", how=a)
- from_lst = ["A", "Q", "M", "W", "B", "D", "H", "Min", "s"]
+ from_lst = ["Y", "Q", "M", "W", "B", "D", "H", "Min", "s"]
def _ex(p):
if p.freq == "B":
@@ -653,7 +653,7 @@ def _ex(p):
# Frequency other than daily
- p = Period("1985", freq="A")
+ p = Period("1985", freq="Y")
result = p.to_timestamp("H", how="end")
expected = Timestamp(1986, 1, 1) - Timedelta(1, "ns")
@@ -732,7 +732,7 @@ def test_to_timestamp_microsecond(self, ts, expected, freq):
("2000-12-15 13:45:26", "s", "2000-12-15 13:45:26", "s"),
("2000-12-15 13:45:26", "min", "2000-12-15 13:45", "min"),
("2000-12-15 13:45:26", "H", "2000-12-15 13:00", "H"),
- ("2000-12-15", "Y", "2000", "A-DEC"),
+ ("2000-12-15", "Y", "2000", "Y-DEC"),
("2000-12-15", "Q", "2000Q4", "Q-DEC"),
("2000-12-15", "M", "2000-12", "M"),
("2000-12-15", "W", "2000-12-11/2000-12-17", "W-SUN"),
@@ -763,7 +763,7 @@ def test_strftime(self):
class TestPeriodProperties:
"""Test properties such as year, month, weekday, etc...."""
- @pytest.mark.parametrize("freq", ["A", "M", "D", "H"])
+ @pytest.mark.parametrize("freq", ["Y", "M", "D", "H"])
def test_is_leap_year(self, freq):
# GH 13727
p = Period("2000-01-01 00:00:00", freq=freq)
@@ -861,7 +861,7 @@ def test_inner_bounds_start_and_end_time(self, bound, offset, period_property):
assert getattr(period, period_property).floor("s") == expected
def test_start_time(self):
- freq_lst = ["A", "Q", "M", "D", "H", "min", "s"]
+ freq_lst = ["Y", "Q", "M", "D", "H", "min", "s"]
xp = datetime(2012, 1, 1)
for f in freq_lst:
p = Period("2012", freq=f)
@@ -871,7 +871,7 @@ def test_start_time(self):
assert Period("2012", freq="W").start_time == datetime(2011, 12, 26)
def test_end_time(self):
- p = Period("2012", freq="A")
+ p = Period("2012", freq="Y")
def _ex(*args):
return Timestamp(Timestamp(datetime(*args)).as_unit("ns")._value - 1)
@@ -936,7 +936,7 @@ def _ex(*args):
def test_properties_annually(self):
# Test properties on Periods with annually frequency.
- a_date = Period(freq="A", year=2007)
+ a_date = Period(freq="Y", year=2007)
assert a_date.year == 2007
def test_properties_quarterly(self):
@@ -1196,11 +1196,11 @@ def test_add_sub_td64_nat(self, unit):
nat - per
def test_sub_delta(self):
- left, right = Period("2011", freq="A"), Period("2007", freq="A")
+ left, right = Period("2011", freq="Y"), Period("2007", freq="Y")
result = left - right
assert result == 4 * right.freq
- msg = r"Input has different freq=M from Period\(freq=A-DEC\)"
+ msg = r"Input has different freq=M from Period\(freq=Y-DEC\)"
with pytest.raises(IncompatibleFrequency, match=msg):
left - Period("2007-01", freq="M")
@@ -1316,7 +1316,7 @@ def test_sub_n_gt_1_offsets(self, offset, kwd_name, n, normalize):
def test_add_offset(self):
# freq is DateOffset
- for freq in ["A", "2A", "3A"]:
+ for freq in ["Y", "2Y", "3Y"]:
p = Period("2011", freq=freq)
exp = Period("2013", freq=freq)
assert p + offsets.YearEnd(2) == exp
@@ -1467,7 +1467,7 @@ def test_sub_offset(self):
]
)
- for freq in ["A", "2A", "3A"]:
+ for freq in ["Y", "2Y", "3Y"]:
p = Period("2011", freq=freq)
assert p - offsets.YearEnd(2) == Period("2009", freq=freq)
@@ -1589,7 +1589,7 @@ def test_small_year_parsing():
def test_negone_ordinals():
- freqs = ["A", "M", "Q", "D", "H", "min", "s"]
+ freqs = ["Y", "M", "Q", "D", "H", "min", "s"]
period = Period(ordinal=-1, freq="D")
for freq in freqs:
diff --git a/pandas/tests/series/methods/test_align.py b/pandas/tests/series/methods/test_align.py
index e1b3dd4888ef5..2091549b4c3c1 100644
--- a/pandas/tests/series/methods/test_align.py
+++ b/pandas/tests/series/methods/test_align.py
@@ -204,7 +204,7 @@ def test_align_dt64tzindex_mismatched_tzs():
def test_align_periodindex(join_type):
- rng = period_range("1/1/2000", "1/1/2010", freq="A")
+ rng = period_range("1/1/2000", "1/1/2010", freq="Y")
ts = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng)
# TODO: assert something?
diff --git a/pandas/tests/series/test_arithmetic.py b/pandas/tests/series/test_arithmetic.py
index 8547fd6988791..c8f94632bc25f 100644
--- a/pandas/tests/series/test_arithmetic.py
+++ b/pandas/tests/series/test_arithmetic.py
@@ -153,7 +153,7 @@ class TestSeriesArithmetic:
# Some of these may end up in tests/arithmetic, but are not yet sorted
def test_add_series_with_period_index(self):
- rng = pd.period_range("1/1/2000", "1/1/2010", freq="A")
+ rng = pd.period_range("1/1/2000", "1/1/2010", freq="Y")
ts = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng)
result = ts + ts[::2]
@@ -164,7 +164,7 @@ def test_add_series_with_period_index(self):
result = ts + _permute(ts[::2])
tm.assert_series_equal(result, expected)
- msg = "Input has different freq=D from Period\\(freq=A-DEC\\)"
+ msg = "Input has different freq=D from Period\\(freq=Y-DEC\\)"
with pytest.raises(IncompatibleFrequency, match=msg):
ts + ts.asfreq("D", how="end")
diff --git a/pandas/tests/series/test_constructors.py b/pandas/tests/series/test_constructors.py
index d45c655a4c0a2..5a05a1840b644 100644
--- a/pandas/tests/series/test_constructors.py
+++ b/pandas/tests/series/test_constructors.py
@@ -1306,7 +1306,7 @@ def test_construct_from_ints_including_iNaT_scalar_period_dtype(self):
assert isna(series[2])
def test_constructor_period_incompatible_frequency(self):
- data = [Period("2000", "D"), Period("2001", "A")]
+ data = [Period("2000", "D"), Period("2001", "Y")]
result = Series(data)
assert result.dtype == object
assert result.tolist() == data
diff --git a/pandas/tests/series/test_repr.py b/pandas/tests/series/test_repr.py
index f294885fb8f4d..d088482eb05e3 100644
--- a/pandas/tests/series/test_repr.py
+++ b/pandas/tests/series/test_repr.py
@@ -247,7 +247,7 @@ def test_index_repr_in_frame_with_nan(self):
assert repr(s) == exp
def test_format_pre_1900_dates(self):
- rng = date_range("1/1/1850", "1/1/1950", freq="A-DEC")
+ rng = date_range("1/1/1850", "1/1/1950", freq="Y-DEC")
rng.format()
ts = Series(1, index=rng)
repr(ts)
diff --git a/pandas/tests/tseries/frequencies/test_freq_code.py b/pandas/tests/tseries/frequencies/test_freq_code.py
index 417bf0e90201b..ca8db972e2185 100644
--- a/pandas/tests/tseries/frequencies/test_freq_code.py
+++ b/pandas/tests/tseries/frequencies/test_freq_code.py
@@ -27,7 +27,7 @@ def test_get_to_timestamp_base(freqstr, exp_freqstr):
@pytest.mark.parametrize(
"freqstr,expected",
[
- ("A", "year"),
+ ("Y", "year"),
("Q", "quarter"),
("M", "month"),
("D", "day"),
@@ -99,9 +99,9 @@ def test_compatibility(freqstr, expected):
assert ts_np + do == np.datetime64(expected)
-@pytest.mark.parametrize("freq", ["T", "S", "L", "N", "U"])
-def test_units_t_l_deprecated_from__attrname_to_abbrevs(freq):
- # GH 52536
+@pytest.mark.parametrize("freq", ["A", "T", "S", "L", "U", "N"])
+def test_units_A_T_S_L_U_N_deprecated_from_attrname_to_abbrevs(freq):
+ # GH#52536
msg = f"'{freq}' is deprecated and will be removed in a future version."
with tm.assert_produces_warning(FutureWarning, match=msg):
diff --git a/pandas/tests/tseries/frequencies/test_inference.py b/pandas/tests/tseries/frequencies/test_inference.py
index 82cceeac2cd25..0b2978389ea88 100644
--- a/pandas/tests/tseries/frequencies/test_inference.py
+++ b/pandas/tests/tseries/frequencies/test_inference.py
@@ -52,7 +52,7 @@ def base_delta_code_pair(request):
freqs = (
[f"Q-{month}" for month in MONTHS]
- + [f"{annual}-{month}" for annual in ["A", "BA"] for month in MONTHS]
+ + [f"{annual}-{month}" for annual in ["Y", "BA"] for month in MONTHS]
+ ["ME", "BM", "BMS"]
+ [f"WOM-{count}{day}" for count in range(1, 5) for day in DAYS]
+ [f"W-{day}" for day in DAYS]
@@ -167,7 +167,7 @@ def test_monthly_ambiguous():
def test_annual_ambiguous():
rng = DatetimeIndex(["1/31/2000", "1/31/2001", "1/31/2002"])
- assert rng.inferred_freq == "A-JAN"
+ assert rng.inferred_freq == "Y-JAN"
@pytest.mark.parametrize("count", range(1, 5))
@@ -359,7 +359,7 @@ def test_not_monotonic():
rng = DatetimeIndex(["1/31/2000", "1/31/2001", "1/31/2002"])
rng = rng[::-1]
- assert rng.inferred_freq == "-1A-JAN"
+ assert rng.inferred_freq == "-1Y-JAN"
def test_non_datetime_index2():
@@ -479,18 +479,18 @@ def test_series_datetime_index(freq):
"Q@JAN",
"Q@FEB",
"Q@MAR",
- "A@JAN",
- "A@FEB",
- "A@MAR",
- "A@APR",
- "A@MAY",
- "A@JUN",
- "A@JUL",
- "A@AUG",
- "A@SEP",
- "A@OCT",
- "A@NOV",
- "A@DEC",
+ "Y@JAN",
+ "Y@FEB",
+ "Y@MAR",
+ "Y@APR",
+ "Y@MAY",
+ "Y@JUN",
+ "Y@JUL",
+ "Y@AUG",
+ "Y@SEP",
+ "Y@OCT",
+ "Y@NOV",
+ "Y@DEC",
"Y@JAN",
"WOM@1MON",
"WOM@2MON",
diff --git a/pandas/tests/tseries/offsets/test_offsets.py b/pandas/tests/tseries/offsets/test_offsets.py
index a7e9854c38f18..de44cf3f94d26 100644
--- a/pandas/tests/tseries/offsets/test_offsets.py
+++ b/pandas/tests/tseries/offsets/test_offsets.py
@@ -839,7 +839,7 @@ def test_rule_code(self):
"NOV",
"DEC",
]
- base_lst = ["A", "AS", "BA", "BAS", "Q", "QS", "BQ", "BQS"]
+ base_lst = ["Y", "AS", "BA", "BAS", "Q", "QS", "BQ", "BQS"]
for base in base_lst:
for v in suffix_lst:
alias = "-".join([base, v])
@@ -858,7 +858,7 @@ def test_freq_offsets():
class TestReprNames:
def test_str_for_named_is_name(self):
# look at all the amazing combinations!
- month_prefixes = ["A", "AS", "BA", "BAS", "Q", "BQ", "BQS", "QS"]
+ month_prefixes = ["Y", "AS", "BA", "BAS", "Q", "BQ", "BQS", "QS"]
names = [
prefix + "-" + month
for prefix in month_prefixes
diff --git a/pandas/tests/tslibs/test_conversion.py b/pandas/tests/tslibs/test_conversion.py
index c1ab0ba0b5e6f..d0f8923f3ad89 100644
--- a/pandas/tests/tslibs/test_conversion.py
+++ b/pandas/tests/tslibs/test_conversion.py
@@ -73,7 +73,7 @@ def test_tz_convert_single_matches_tz_convert_hourly(tz_aware_fixture):
_compare_local_to_utc(tz_didx, naive_didx)
-@pytest.mark.parametrize("freq", ["D", "A"])
+@pytest.mark.parametrize("freq", ["D", "Y"])
def test_tz_convert_single_matches_tz_convert(tz_aware_fixture, freq):
tz = tz_aware_fixture
tz_didx = date_range("2018-01-01", "2020-01-01", freq=freq, tz=tz)
diff --git a/pandas/tests/tslibs/test_libfrequencies.py b/pandas/tests/tslibs/test_libfrequencies.py
index 83f28f6b5dc01..effd3b4b8b4e5 100644
--- a/pandas/tests/tslibs/test_libfrequencies.py
+++ b/pandas/tests/tslibs/test_libfrequencies.py
@@ -16,10 +16,8 @@
(offsets.QuarterEnd(startingMonth=12).freqstr, "DEC"),
("Q-JAN", "JAN"),
(offsets.QuarterEnd(startingMonth=1).freqstr, "JAN"),
- ("A-DEC", "DEC"),
("Y-DEC", "DEC"),
(offsets.YearEnd().freqstr, "DEC"),
- ("A-MAY", "MAY"),
("Y-MAY", "MAY"),
(offsets.YearEnd(month=5).freqstr, "MAY"),
],
diff --git a/pandas/tests/tslibs/test_parsing.py b/pandas/tests/tslibs/test_parsing.py
index ec3579109e7a4..425decc14251a 100644
--- a/pandas/tests/tslibs/test_parsing.py
+++ b/pandas/tests/tslibs/test_parsing.py
@@ -138,8 +138,8 @@ def test_parsers_quarterly_with_freq_error(date_str, kwargs, msg):
"date_str,freq,expected",
[
("2013Q2", None, datetime(2013, 4, 1)),
- ("2013Q2", "A-APR", datetime(2012, 8, 1)),
- ("2013-Q2", "A-DEC", datetime(2013, 4, 1)),
+ ("2013Q2", "Y-APR", datetime(2012, 8, 1)),
+ ("2013-Q2", "Y-DEC", datetime(2013, 4, 1)),
],
)
def test_parsers_quarterly_with_freq(date_str, freq, expected):
@@ -148,7 +148,7 @@ def test_parsers_quarterly_with_freq(date_str, freq, expected):
@pytest.mark.parametrize(
- "date_str", ["2Q 2005", "2Q-200A", "2Q-200", "22Q2005", "2Q200.", "6Q-20"]
+ "date_str", ["2Q 2005", "2Q-200Y", "2Q-200", "22Q2005", "2Q200.", "6Q-20"]
)
def test_parsers_quarter_invalid(date_str):
if date_str == "6Q-20":
diff --git a/pandas/tests/tslibs/test_period_asfreq.py b/pandas/tests/tslibs/test_period_asfreq.py
index 99f0a82d6711e..ca207e1031653 100644
--- a/pandas/tests/tslibs/test_period_asfreq.py
+++ b/pandas/tests/tslibs/test_period_asfreq.py
@@ -54,7 +54,7 @@ def test_intra_day_conversion_factors(freq1, freq2, expected):
@pytest.mark.parametrize(
- "freq,expected", [("A", 0), ("M", 0), ("W", 1), ("D", 0), ("B", 0)]
+ "freq,expected", [("Y", 0), ("M", 0), ("W", 1), ("D", 0), ("B", 0)]
)
def test_period_ordinal_start_values(freq, expected):
# information for Jan. 1, 1970.
diff --git a/pandas/tseries/frequencies.py b/pandas/tseries/frequencies.py
index 7aa245341cbdd..e77f56a9928ae 100644
--- a/pandas/tseries/frequencies.py
+++ b/pandas/tseries/frequencies.py
@@ -66,7 +66,7 @@
key = f"{_prefix}-{_m}"
OFFSET_TO_PERIOD_FREQSTR[key] = OFFSET_TO_PERIOD_FREQSTR[_prefix]
-for _prefix in ["A", "Q"]:
+for _prefix in ["Y", "Q"]:
for _m in MONTHS:
_alias = f"{_prefix}-{_m}"
OFFSET_TO_PERIOD_FREQSTR[_alias] = _alias
@@ -345,7 +345,7 @@ def _get_annual_rule(self) -> str | None:
if pos_check is None:
return None
else:
- return {"cs": "AS", "bs": "BAS", "ce": "A", "be": "BA"}.get(pos_check)
+ return {"cs": "AS", "bs": "BAS", "ce": "Y", "be": "BA"}.get(pos_check)
def _get_quarterly_rule(self) -> str | None:
if len(self.mdiffs) > 1:
@@ -574,7 +574,7 @@ def _quarter_months_conform(source: str, target: str) -> bool:
def _is_annual(rule: str) -> bool:
rule = rule.upper()
- return rule == "A" or rule.startswith("A-")
+ return rule == "Y" or rule.startswith("Y-")
def _is_quarterly(rule: str) -> bool:
| xref #54275, #54061
deprecated string `'A'` for yearly frequency and YearEnd in favour of `'Y'`.
EDIT:
deprecated annual frequencies with various fiscal year ends:` "A-DEC", "A-JAN"`, etc. in favour of `"Y-DEC", "Y-JAN"`, etc. | https://api.github.com/repos/pandas-dev/pandas/pulls/55252 | 2023-09-22T22:31:44Z | 2023-10-06T09:40:26Z | 2023-10-06T09:40:26Z | 2023-10-06T15:09:10Z |
Backport PR #55249 on branch 2.1.x (BUG: incompatible dtype when creating string column with loc) | diff --git a/doc/source/whatsnew/v2.1.2.rst b/doc/source/whatsnew/v2.1.2.rst
index 2c9b10160d144..97aeb56924e65 100644
--- a/doc/source/whatsnew/v2.1.2.rst
+++ b/doc/source/whatsnew/v2.1.2.rst
@@ -13,7 +13,7 @@ including other versions of pandas.
Fixed regressions
~~~~~~~~~~~~~~~~~
--
+- Fixed bug where PDEP-6 warning about setting an item of an incompatible dtype was being shown when creating a new conditional column (:issue:`55025`)
-
.. ---------------------------------------------------------------------------
diff --git a/pandas/core/dtypes/missing.py b/pandas/core/dtypes/missing.py
index 7117e34b23ca4..8760c8eeca454 100644
--- a/pandas/core/dtypes/missing.py
+++ b/pandas/core/dtypes/missing.py
@@ -624,6 +624,9 @@ def infer_fill_value(val):
return np.array("NaT", dtype=DT64NS_DTYPE)
elif dtype in ["timedelta", "timedelta64"]:
return np.array("NaT", dtype=TD64NS_DTYPE)
+ return np.array(np.nan, dtype=object)
+ elif val.dtype.kind == "U":
+ return np.array(np.nan, dtype=val.dtype)
return np.nan
diff --git a/pandas/tests/frame/indexing/test_indexing.py b/pandas/tests/frame/indexing/test_indexing.py
index b324291bab31e..370cbf0f33174 100644
--- a/pandas/tests/frame/indexing/test_indexing.py
+++ b/pandas/tests/frame/indexing/test_indexing.py
@@ -1890,6 +1890,21 @@ def test_setitem_dict_and_set_disallowed_multiindex(self, key):
df.loc[key] = 1
+def test_adding_new_conditional_column() -> None:
+ # https://github.com/pandas-dev/pandas/issues/55025
+ df = DataFrame({"x": [1]})
+ df.loc[df["x"] == 1, "y"] = "1"
+ expected = DataFrame({"x": [1], "y": ["1"]})
+ tm.assert_frame_equal(df, expected)
+
+ df = DataFrame({"x": [1]})
+ # try inserting something which numpy would store as 'object'
+ value = lambda x: x
+ df.loc[df["x"] == 1, "y"] = value
+ expected = DataFrame({"x": [1], "y": [value]})
+ tm.assert_frame_equal(df, expected)
+
+
class TestSetitemValidation:
# This is adapted from pandas/tests/arrays/masked/test_indexing.py
# but checks for warnings instead of errors.
diff --git a/pandas/tests/frame/indexing/test_set_value.py b/pandas/tests/frame/indexing/test_set_value.py
index cd9ffa0f129a2..32312868adacb 100644
--- a/pandas/tests/frame/indexing/test_set_value.py
+++ b/pandas/tests/frame/indexing/test_set_value.py
@@ -26,10 +26,7 @@ def test_set_value_resize(self, float_frame):
assert float_frame._get_value("foobar", "qux") == 0
res = float_frame.copy()
- with tm.assert_produces_warning(
- FutureWarning, match="Setting an item of incompatible dtype"
- ):
- res._set_value("foobar", "baz", "sam")
+ res._set_value("foobar", "baz", "sam")
assert res["baz"].dtype == np.object_
res = float_frame.copy()
| Backport PR #55249: BUG: incompatible dtype when creating string column with loc | https://api.github.com/repos/pandas-dev/pandas/pulls/55251 | 2023-09-22T19:56:16Z | 2023-09-22T23:47:53Z | 2023-09-22T23:47:53Z | 2023-09-22T23:47:53Z |
BUG: incompatible dtype when creating string column with loc | diff --git a/doc/source/whatsnew/v2.1.2.rst b/doc/source/whatsnew/v2.1.2.rst
index 2c9b10160d144..97aeb56924e65 100644
--- a/doc/source/whatsnew/v2.1.2.rst
+++ b/doc/source/whatsnew/v2.1.2.rst
@@ -13,7 +13,7 @@ including other versions of pandas.
Fixed regressions
~~~~~~~~~~~~~~~~~
--
+- Fixed bug where PDEP-6 warning about setting an item of an incompatible dtype was being shown when creating a new conditional column (:issue:`55025`)
-
.. ---------------------------------------------------------------------------
diff --git a/pandas/core/dtypes/missing.py b/pandas/core/dtypes/missing.py
index 7117e34b23ca4..8760c8eeca454 100644
--- a/pandas/core/dtypes/missing.py
+++ b/pandas/core/dtypes/missing.py
@@ -624,6 +624,9 @@ def infer_fill_value(val):
return np.array("NaT", dtype=DT64NS_DTYPE)
elif dtype in ["timedelta", "timedelta64"]:
return np.array("NaT", dtype=TD64NS_DTYPE)
+ return np.array(np.nan, dtype=object)
+ elif val.dtype.kind == "U":
+ return np.array(np.nan, dtype=val.dtype)
return np.nan
diff --git a/pandas/tests/frame/indexing/test_indexing.py b/pandas/tests/frame/indexing/test_indexing.py
index b324291bab31e..370cbf0f33174 100644
--- a/pandas/tests/frame/indexing/test_indexing.py
+++ b/pandas/tests/frame/indexing/test_indexing.py
@@ -1890,6 +1890,21 @@ def test_setitem_dict_and_set_disallowed_multiindex(self, key):
df.loc[key] = 1
+def test_adding_new_conditional_column() -> None:
+ # https://github.com/pandas-dev/pandas/issues/55025
+ df = DataFrame({"x": [1]})
+ df.loc[df["x"] == 1, "y"] = "1"
+ expected = DataFrame({"x": [1], "y": ["1"]})
+ tm.assert_frame_equal(df, expected)
+
+ df = DataFrame({"x": [1]})
+ # try inserting something which numpy would store as 'object'
+ value = lambda x: x
+ df.loc[df["x"] == 1, "y"] = value
+ expected = DataFrame({"x": [1], "y": [value]})
+ tm.assert_frame_equal(df, expected)
+
+
class TestSetitemValidation:
# This is adapted from pandas/tests/arrays/masked/test_indexing.py
# but checks for warnings instead of errors.
diff --git a/pandas/tests/frame/indexing/test_set_value.py b/pandas/tests/frame/indexing/test_set_value.py
index cd9ffa0f129a2..32312868adacb 100644
--- a/pandas/tests/frame/indexing/test_set_value.py
+++ b/pandas/tests/frame/indexing/test_set_value.py
@@ -26,10 +26,7 @@ def test_set_value_resize(self, float_frame):
assert float_frame._get_value("foobar", "qux") == 0
res = float_frame.copy()
- with tm.assert_produces_warning(
- FutureWarning, match="Setting an item of incompatible dtype"
- ):
- res._set_value("foobar", "baz", "sam")
+ res._set_value("foobar", "baz", "sam")
assert res["baz"].dtype == np.object_
res = float_frame.copy()
| - [ ] closes #55025 (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 [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] 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/55249 | 2023-09-22T16:36:52Z | 2023-09-22T19:55:11Z | 2023-09-22T19:55:11Z | 2023-09-22T19:55:18Z |
Create broken-linkcheck.yml | diff --git a/.github/workflows/broken-linkcheck.yml b/.github/workflows/broken-linkcheck.yml
new file mode 100644
index 0000000000000..10ab5b08a4437
--- /dev/null
+++ b/.github/workflows/broken-linkcheck.yml
@@ -0,0 +1,38 @@
+name: Linkcheck
+on:
+ schedule:
+ # Run monthly on the 1st day of the month
+ - cron: '0 0 1 * *'
+ pull_request:
+ paths:
+ - ".github/workflows/broken-linkcheck.yml"
+ - "doc/make.py"
+jobs:
+ linkcheck:
+ runs-on: ubuntu-latest
+ defaults:
+ run:
+ shell: bash -el {0}
+
+ steps:
+ - name: Checkout
+ uses: actions/checkout@v4
+ with:
+ fetch-depth: 0
+
+ - name: Set up Conda
+ uses: ./.github/actions/setup-conda
+
+ - name: Build Pandas
+ uses: ./.github/actions/build_pandas
+
+ - name: Run linkcheck script
+ working-directory: ./doc
+ run: |
+ set -o pipefail
+ python make.py linkcheck | tee linkcheck.txt
+
+ - name: Display broken links
+ if: failure()
+ working-directory: ./doc
+ run: grep broken linkcheck.txt
diff --git a/doc/source/conf.py b/doc/source/conf.py
index 86d2494707ce2..6b52b52ce5e13 100644
--- a/doc/source/conf.py
+++ b/doc/source/conf.py
@@ -14,6 +14,7 @@
import inspect
import logging
import os
+import re
import sys
import warnings
@@ -798,3 +799,49 @@ def setup(app):
app.add_autodocumenter(AccessorMethodDocumenter)
app.add_autodocumenter(AccessorCallableDocumenter)
app.add_directive("autosummary", PandasAutosummary)
+
+
+# Ignore list for broken links,found in CI run checks for broken-linkcheck.yml
+
+linkcheck_ignore = [
+ "^http://$",
+ "^https://$",
+ *[
+ re.escape(link)
+ for link in [
+ "http://scatterci.github.io/pydata/pandas",
+ "http://specs.frictionlessdata.io/json-table-schema/",
+ "https://cloud.google.com/bigquery/docs/access-control#roles",
+ "https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs#configuration.query",
+ "https://crates.io/crates/calamine",
+ "https://devguide.python.org/setup/#macos",
+ "https://en.wikipedia.org/wiki/Imputation_statistics",
+ "https://en.wikipedia.org/wiki/Imputation_(statistics",
+ "https://github.com/noatamir/pandas-dev",
+ "https://github.com/pandas-dev/pandas/blob/main/pandas/plotting/__init__.py#L1",
+ "https://github.com/pandas-dev/pandas/blob/v0.20.2/pandas/core/generic.py#L568",
+ "https://github.com/pandas-dev/pandas/blob/v0.20.2/pandas/core/frame.py#L1495",
+ "https://github.com/pandas-dev/pandas/issues/174151",
+ "https://gitpod.io/#https://github.com/USERNAME/pandas",
+ "https://manishamde.github.io/blog/2013/03/07/pandas-and-python-top-10/",
+ "https://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.table",
+ "https://nipunbatra.github.io/blog/visualisation/2013/05/01/aggregation-timeseries.html",
+ "https://nbviewer.ipython.org/gist/metakermit/5720498",
+ "https://numpy.org/doc/stable/user/basics.byteswapping.html",
+ "https://pandas-gbq.readthedocs.io/en/latest/changelog.html#changelog-0-8-0",
+ "https://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking",
+ "https://pandas.pydata.org/pandas-docs/stable/ecosystem.html",
+ "https://sqlalchemy.readthedocs.io/en/latest/dialects/index.html",
+ "https://support.sas.com/documentation/cdl/en/lrdict/64316/HTML/default/viewer.htm#a000245912.htm",
+ "https://support.sas.com/documentation/cdl/en/lrdict/64316/HTML/default/viewer.htm#a000214639.htm",
+ "https://support.sas.com/documentation/cdl/en/lrdict/64316/HTML/default/viewer.htm#a002283942.htm",
+ "https://support.sas.com/documentation/cdl/en/lrdict/64316/HTML/default/viewer.htm#a000245965.htm",
+ "https://support.sas.com/documentation/cdl/en/imlug/66845/HTML/default/viewer.htm#imlug_langref_sect455.htm",
+ "https://support.sas.com/documentation/cdl/en/lrdict/64316/HTML/default/viewer.htm#a002284668.htm",
+ "https://support.sas.com/documentation/cdl/en/lrdict/64316/HTML/default/viewer.htm#a002978282.htm",
+ "https://wesmckinney.com/blog/update-on-upcoming-pandas-v0-10-new-file-parser-other-performance-wins/",
+ "https://visualstudio.microsoft.com/downloads/#build-tools-for-visual-studio-2022",
+ "pandas.zip",
+ ]
+ ],
+]
| Created a Github Action to run the Sphinx linkcheck monthly.
- [x] closes #xxxx (Replace xxxx with the GitHub issue number) #45409
- [ ] [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 [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] 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/55246 | 2023-09-22T14:50:51Z | 2023-11-07T21:45:29Z | 2023-11-07T21:45:29Z | 2023-12-05T18:29:39Z |
PERF: Add type-hints in tzconversion.pyx | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 8eab623a2b5f7..ebca1605be8d5 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -238,7 +238,7 @@ Performance improvements
- Performance improvement in :meth:`DataFrame.sort_index` and :meth:`Series.sort_index` when indexed by a :class:`MultiIndex` (:issue:`54835`)
- Performance improvement in :meth:`Index.difference` (:issue:`55108`)
- Performance improvement when indexing with more than 4 keys (:issue:`54550`)
--
+- Performance improvement when localizing time to UTC (:issue:`55241`)
.. ---------------------------------------------------------------------------
.. _whatsnew_220.bug_fixes:
diff --git a/pandas/_libs/tslibs/tzconversion.pyx b/pandas/_libs/tslibs/tzconversion.pyx
index e17d264333264..a96779aa33255 100644
--- a/pandas/_libs/tslibs/tzconversion.pyx
+++ b/pandas/_libs/tslibs/tzconversion.pyx
@@ -462,7 +462,7 @@ cdef str _render_tstamp(int64_t val, NPY_DATETIMEUNIT creso):
cdef _get_utc_bounds(
- ndarray vals,
+ ndarray[int64_t] vals,
int64_t* tdata,
Py_ssize_t ntrans,
const int64_t[::1] deltas,
@@ -472,7 +472,7 @@ cdef _get_utc_bounds(
# result_a) or right of the DST transition (store in result_b)
cdef:
- ndarray result_a, result_b
+ ndarray[int64_t] result_a, result_b
Py_ssize_t i, n = vals.size
int64_t val, v_left, v_right
Py_ssize_t isl, isr, pos_left, pos_right
| - [ ] 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 [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
Part of #55179. With this, I'm seeing time plummet in the benchmark
tslibs.tz_convert.TimeTZConvert.time_tz_localize_to_utc(1000000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
from 159ms (Cython 0.29) and 271ms (Cython 3.0.2) to ~5-6ms on both. | https://api.github.com/repos/pandas-dev/pandas/pulls/55241 | 2023-09-22T11:50:25Z | 2023-09-22T14:59:39Z | 2023-09-22T14:59:39Z | 2023-09-24T15:29:44Z |
DOC: point out that explicitly passing axis=None is deprecated for sum, prod, var, std, sem | diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index c75c6c546aad0..d0fab0037d99d 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -12532,6 +12532,39 @@ def last_valid_index(self) -> Hashable | None:
{examples}
"""
+_sum_prod_doc = """
+{desc}
+
+Parameters
+----------
+axis : {axis_descr}
+ Axis for the function to be applied on.
+ For `Series` this parameter is unused and defaults to 0.
+
+ .. warning::
+
+ The behavior of DataFrame.{name} with ``axis=None`` is deprecated,
+ in a future version this will reduce over both axes and return a scalar
+ To retain the old behavior, pass axis=0 (or do not pass axis).
+
+ .. versionadded:: 2.0.0
+
+skipna : bool, default True
+ Exclude NA/null values when computing the result.
+numeric_only : bool, default False
+ Include only float, int, boolean columns. Not implemented for Series.
+
+{min_count}\
+**kwargs
+ Additional keyword arguments to be passed to the function.
+
+Returns
+-------
+{name1} or scalar\
+{see_also}\
+{examples}
+"""
+
_num_ddof_doc = """
{desc}
@@ -12539,6 +12572,13 @@ def last_valid_index(self) -> Hashable | None:
----------
axis : {axis_descr}
For `Series` this parameter is unused and defaults to 0.
+
+ .. warning::
+
+ The behavior of DataFrame.{name} with ``axis=None`` is deprecated,
+ in a future version this will reduce over both axes and return a scalar
+ To retain the old behavior, pass axis=0 (or do not pass axis).
+
skipna : bool, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA.
@@ -13246,7 +13286,7 @@ def make_doc(name: str, ndim: int) -> str:
kwargs = {"min_count": ""}
elif name == "sum":
- base_doc = _num_doc
+ base_doc = _sum_prod_doc
desc = (
"Return the sum of the values over the requested axis.\n\n"
"This is equivalent to the method ``numpy.sum``."
@@ -13256,7 +13296,7 @@ def make_doc(name: str, ndim: int) -> str:
kwargs = {"min_count": _min_count_stub}
elif name == "prod":
- base_doc = _num_doc
+ base_doc = _sum_prod_doc
desc = "Return the product of the values over the requested axis."
see_also = _stat_func_see_also
examples = _prod_examples
@@ -13540,6 +13580,7 @@ def make_doc(name: str, ndim: int) -> str:
docstr = base_doc.format(
desc=desc,
+ name=name,
name1=name1,
name2=name2,
axis_descr=axis_descr,
| - [x] closes #54547
pointed out in the documentation of the methods: `sum, prod, var, std, sem` that explicitly passing `axis=None` is deprecated.
| https://api.github.com/repos/pandas-dev/pandas/pulls/55240 | 2023-09-22T11:11:57Z | 2023-09-22T16:50:52Z | 2023-09-22T16:50:52Z | 2023-09-22T16:50:58Z |
DOC: Fix doc for first_valid_index | diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index d0fab0037d99d..427687d9614f9 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -12466,11 +12466,6 @@ def first_valid_index(self) -> Hashable | None:
-------
type of index
- Notes
- -----
- If all elements are non-NA/null, returns None.
- Also returns None for empty {klass}.
-
Examples
--------
For Series:
@@ -12481,6 +12476,22 @@ def first_valid_index(self) -> Hashable | None:
>>> s.last_valid_index()
2
+ >>> s = pd.Series([None, None])
+ >>> print(s.first_valid_index())
+ None
+ >>> print(s.last_valid_index())
+ None
+
+ If all elements in Series are NA/null, returns None.
+
+ >>> s = pd.Series()
+ >>> print(s.first_valid_index())
+ None
+ >>> print(s.last_valid_index())
+ None
+
+ If Series is empty, returns None.
+
For DataFrame:
>>> df = pd.DataFrame({{'A': [None, None, 2], 'B': [None, 3, 4]}})
@@ -12493,6 +12504,31 @@ def first_valid_index(self) -> Hashable | None:
1
>>> df.last_valid_index()
2
+
+ >>> df = pd.DataFrame({{'A': [None, None, None], 'B': [None, None, None]}})
+ >>> df
+ A B
+ 0 None None
+ 1 None None
+ 2 None None
+ >>> print(df.first_valid_index())
+ None
+ >>> print(df.last_valid_index())
+ None
+
+ If all elements in DataFrame are NA/null, returns None.
+
+ >>> df = pd.DataFrame()
+ >>> df
+ Empty DataFrame
+ Columns: []
+ Index: []
+ >>> print(df.first_valid_index())
+ None
+ >>> print(df.last_valid_index())
+ None
+
+ If DataFrame is empty, returns None.
"""
return self._find_valid_index(how="first")
| - [x] closes #55187
- [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 [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [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/55236 | 2023-09-22T02:00:41Z | 2023-09-22T23:48:53Z | 2023-09-22T23:48:53Z | 2023-09-22T23:53:17Z |
Typo: 'whenlines' -> 'when lines' | diff --git a/pandas/io/json/_json.py b/pandas/io/json/_json.py
index ecab14a54beff..d596891197aaa 100644
--- a/pandas/io/json/_json.py
+++ b/pandas/io/json/_json.py
@@ -175,7 +175,7 @@ def to_json(
if mode == "a" and (not lines or orient != "records"):
msg = (
- "mode='a' (append) is only supported when"
+ "mode='a' (append) is only supported when "
"lines is True and orient is 'records'"
)
raise ValueError(msg)
diff --git a/pandas/tests/io/json/test_readlines.py b/pandas/tests/io/json/test_readlines.py
index c2f915e33df8a..d7baba87bba31 100644
--- a/pandas/tests/io/json/test_readlines.py
+++ b/pandas/tests/io/json/test_readlines.py
@@ -399,7 +399,7 @@ def test_to_json_append_orient(orient_):
# Test ValueError when orient is not 'records'
df = DataFrame({"col1": [1, 2], "col2": ["a", "b"]})
msg = (
- r"mode='a' \(append\) is only supported when"
+ r"mode='a' \(append\) is only supported when "
"lines is True and orient is 'records'"
)
with pytest.raises(ValueError, match=msg):
@@ -411,7 +411,7 @@ def test_to_json_append_lines():
# Test ValueError when lines is not True
df = DataFrame({"col1": [1, 2], "col2": ["a", "b"]})
msg = (
- r"mode='a' \(append\) is only supported when"
+ r"mode='a' \(append\) is only supported when "
"lines is True and orient is 'records'"
)
with pytest.raises(ValueError, match=msg):
| I noticed that the text gets joined together when this particular error message gets printed. It looks like `whenlines` instead of `when lines`. | https://api.github.com/repos/pandas-dev/pandas/pulls/55233 | 2023-09-21T20:09:46Z | 2023-09-22T00:10:28Z | 2023-09-22T00:10:28Z | 2023-09-22T00:10:35Z |
BUG: Interchange object data buffer has the wrong dtype / from_dataframe incorrect | diff --git a/pandas/core/interchange/from_dataframe.py b/pandas/core/interchange/from_dataframe.py
index 214fbf9f36435..d45ae37890ba7 100644
--- a/pandas/core/interchange/from_dataframe.py
+++ b/pandas/core/interchange/from_dataframe.py
@@ -266,10 +266,9 @@ def string_column_to_ndarray(col: Column) -> tuple[np.ndarray, Any]:
assert buffers["offsets"], "String buffers must contain offsets"
# Retrieve the data buffer containing the UTF-8 code units
- data_buff, protocol_data_dtype = buffers["data"]
+ data_buff, _ = buffers["data"]
# We're going to reinterpret the buffer as uint8, so make sure we can do it safely
- assert protocol_data_dtype[1] == 8
- assert protocol_data_dtype[2] in (
+ assert col.dtype[2] in (
ArrowCTypes.STRING,
ArrowCTypes.LARGE_STRING,
) # format_str == utf-8
@@ -377,15 +376,16 @@ def datetime_column_to_ndarray(col: Column) -> tuple[np.ndarray | pd.Series, Any
"""
buffers = col.get_buffers()
- _, _, format_str, _ = col.dtype
- dbuf, dtype = buffers["data"]
+ _, col_bit_width, format_str, _ = col.dtype
+ dbuf, _ = buffers["data"]
# Consider dtype being `uint` to get number of units passed since the 01.01.1970
+
data = buffer_to_ndarray(
dbuf,
(
- DtypeKind.UINT,
- dtype[1],
- getattr(ArrowCTypes, f"UINT{dtype[1]}"),
+ DtypeKind.INT,
+ col_bit_width,
+ getattr(ArrowCTypes, f"INT{col_bit_width}"),
Endianness.NATIVE,
),
offset=col.offset,
diff --git a/pandas/tests/interchange/test_impl.py b/pandas/tests/interchange/test_impl.py
index 8a25a2c1889f3..db163397ade67 100644
--- a/pandas/tests/interchange/test_impl.py
+++ b/pandas/tests/interchange/test_impl.py
@@ -14,6 +14,7 @@
DtypeKind,
)
from pandas.core.interchange.from_dataframe import from_dataframe
+from pandas.core.interchange.utils import ArrowCTypes
@pytest.fixture
@@ -326,3 +327,24 @@ def test_interchange_from_non_pandas_tz_aware():
dtype="datetime64[us, Asia/Kathmandu]",
)
tm.assert_frame_equal(expected, result)
+
+
+def test_interchange_from_corrected_buffer_dtypes(monkeypatch) -> None:
+ # https://github.com/pandas-dev/pandas/issues/54781
+ df = pd.DataFrame({"a": ["foo", "bar"]}).__dataframe__()
+ interchange = df.__dataframe__()
+ column = interchange.get_column_by_name("a")
+ buffers = column.get_buffers()
+ buffers_data = buffers["data"]
+ buffer_dtype = buffers_data[1]
+ buffer_dtype = (
+ DtypeKind.UINT,
+ 8,
+ ArrowCTypes.UINT8,
+ buffer_dtype[3],
+ )
+ buffers["data"] = (buffers_data[0], buffer_dtype)
+ column.get_buffers = lambda: buffers
+ interchange.get_column_by_name = lambda _: column
+ monkeypatch.setattr(df, "__dataframe__", lambda allow_copy: interchange)
+ pd.api.interchange.from_dataframe(df)
| - [ ] closes #54781 (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 [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
I haven't included a whatsnew note as this isn't user-facing | https://api.github.com/repos/pandas-dev/pandas/pulls/55227 | 2023-09-21T11:25:18Z | 2023-11-07T12:06:56Z | 2023-11-07T12:06:56Z | 2023-11-07T12:06:56Z |
Backport PR #55206 on branch 2.1.x (BUILD: Fix duplicate files warning) | diff --git a/pandas/_libs/meson.build b/pandas/_libs/meson.build
index 1cf2c4343d844..fd632790546f6 100644
--- a/pandas/_libs/meson.build
+++ b/pandas/_libs/meson.build
@@ -114,9 +114,40 @@ foreach ext_name, ext_dict : libs_sources
)
endforeach
-py.install_sources(
+# Basically just __init__.py and the .pyi files
+sources_to_install = [
'__init__.py',
- subdir: 'pandas/_libs'
-)
+ 'algos.pyi',
+ 'arrays.pyi',
+ 'byteswap.pyi',
+ 'groupby.pyi',
+ 'hashing.pyi',
+ 'hashtable.pyi',
+ 'index.pyi',
+ 'indexing.pyi',
+ 'internals.pyi',
+ 'interval.pyi',
+ 'join.pyi',
+ 'json.pyi',
+ 'lib.pyi',
+ 'missing.pyi',
+ 'ops.pyi',
+ 'ops_dispatch.pyi',
+ 'parsers.pyi',
+ 'properties.pyi',
+ 'reshape.pyi',
+ 'sas.pyi',
+ 'sparse.pyi',
+ 'testing.pyi',
+ 'tslib.pyi',
+ 'writers.pyi'
+]
+
+foreach source: sources_to_install
+ py.install_sources(
+ source,
+ subdir: 'pandas/_libs'
+ )
+endforeach
subdir('window')
diff --git a/pandas/_libs/tslibs/meson.build b/pandas/_libs/tslibs/meson.build
index 167695b84514c..a1b0c54d1f48c 100644
--- a/pandas/_libs/tslibs/meson.build
+++ b/pandas/_libs/tslibs/meson.build
@@ -31,7 +31,28 @@ foreach ext_name, ext_dict : tslibs_sources
)
endforeach
-py.install_sources(
+sources_to_install = [
'__init__.py',
- subdir: 'pandas/_libs/tslibs'
-)
+ 'ccalendar.pyi',
+ 'conversion.pyi',
+ 'dtypes.pyi',
+ 'fields.pyi',
+ 'nattype.pyi',
+ 'np_datetime.pyi',
+ 'offsets.pyi',
+ 'parsing.pyi',
+ 'period.pyi',
+ 'strptime.pyi',
+ 'timedeltas.pyi',
+ 'timestamps.pyi',
+ 'timezones.pyi',
+ 'tzconversion.pyi',
+ 'vectorized.pyi'
+]
+
+foreach source: sources_to_install
+ py.install_sources(
+ source,
+ subdir: 'pandas/_libs/tslibs'
+ )
+endforeach
diff --git a/pandas/_libs/window/meson.build b/pandas/_libs/window/meson.build
index 85aa060a26406..ad15644f73a0c 100644
--- a/pandas/_libs/window/meson.build
+++ b/pandas/_libs/window/meson.build
@@ -16,3 +16,16 @@ py.extension_module(
subdir: 'pandas/_libs/window',
install: true
)
+
+sources_to_install = [
+ '__init__.py',
+ 'aggregations.pyi',
+ 'indexers.pyi'
+]
+
+foreach source: sources_to_install
+ py.install_sources(
+ source,
+ subdir: 'pandas/_libs/window'
+ )
+endforeach
diff --git a/pandas/meson.build b/pandas/meson.build
index f02258c98d46a..435103a954d86 100644
--- a/pandas/meson.build
+++ b/pandas/meson.build
@@ -26,7 +26,6 @@ subdir('_libs')
subdirs_list = [
'_config',
- '_libs',
'_testing',
'api',
'arrays',
| Backport PR #55206: BUILD: Fix duplicate files warning | https://api.github.com/repos/pandas-dev/pandas/pulls/55222 | 2023-09-20T17:00:07Z | 2023-09-20T19:16:58Z | 2023-09-20T19:16:58Z | 2023-09-20T19:16:58Z |
Backport PR #55207 on branch 2.1.x (DOC: Add whatsnew for 2.1.2) | diff --git a/doc/source/whatsnew/index.rst b/doc/source/whatsnew/index.rst
index c21fbc28ae68f..baab20845a2a2 100644
--- a/doc/source/whatsnew/index.rst
+++ b/doc/source/whatsnew/index.rst
@@ -16,6 +16,7 @@ Version 2.1
.. toctree::
:maxdepth: 2
+ v2.1.2
v2.1.1
v2.1.0
diff --git a/doc/source/whatsnew/v2.1.0.rst b/doc/source/whatsnew/v2.1.0.rst
index 040ca048d1224..21eef4921ebc0 100644
--- a/doc/source/whatsnew/v2.1.0.rst
+++ b/doc/source/whatsnew/v2.1.0.rst
@@ -892,4 +892,4 @@ Other
Contributors
~~~~~~~~~~~~
-.. contributors:: v2.0.3..v2.1.0|HEAD
+.. contributors:: v2.0.3..v2.1.0
diff --git a/doc/source/whatsnew/v2.1.1.rst b/doc/source/whatsnew/v2.1.1.rst
index bb3fbd4ffc90f..5a1a1516dc30d 100644
--- a/doc/source/whatsnew/v2.1.1.rst
+++ b/doc/source/whatsnew/v2.1.1.rst
@@ -51,3 +51,5 @@ Other
Contributors
~~~~~~~~~~~~
+
+.. contributors:: v2.1.0..v2.1.1|HEAD
diff --git a/doc/source/whatsnew/v2.1.2.rst b/doc/source/whatsnew/v2.1.2.rst
new file mode 100644
index 0000000000000..2c9b10160d144
--- /dev/null
+++ b/doc/source/whatsnew/v2.1.2.rst
@@ -0,0 +1,39 @@
+.. _whatsnew_212:
+
+What's new in 2.1.2 (October ??, 2023)
+---------------------------------------
+
+These are the changes in pandas 2.1.2. See :ref:`release` for a full changelog
+including other versions of pandas.
+
+{{ header }}
+
+.. ---------------------------------------------------------------------------
+.. _whatsnew_212.regressions:
+
+Fixed regressions
+~~~~~~~~~~~~~~~~~
+-
+-
+
+.. ---------------------------------------------------------------------------
+.. _whatsnew_212.bug_fixes:
+
+Bug fixes
+~~~~~~~~~
+-
+-
+
+.. ---------------------------------------------------------------------------
+.. _whatsnew_212.other:
+
+Other
+~~~~~
+-
+-
+
+.. ---------------------------------------------------------------------------
+.. _whatsnew_212.contributors:
+
+Contributors
+~~~~~~~~~~~~
| Backport PR #55207: DOC: Add whatsnew for 2.1.2 | https://api.github.com/repos/pandas-dev/pandas/pulls/55221 | 2023-09-20T13:52:44Z | 2023-09-20T16:27:10Z | 2023-09-20T16:27:10Z | 2023-09-20T16:27:10Z |
Backport PR #55008 on branch 2.1.x (CoW: Clear dead references every time we add a new one) | diff --git a/pandas/_libs/internals.pyx b/pandas/_libs/internals.pyx
index adf4e8c926fa3..859ebce36eaa8 100644
--- a/pandas/_libs/internals.pyx
+++ b/pandas/_libs/internals.pyx
@@ -951,6 +951,11 @@ cdef class BlockValuesRefs:
else:
self.referenced_blocks = []
+ def _clear_dead_references(self) -> None:
+ self.referenced_blocks = [
+ ref for ref in self.referenced_blocks if ref() is not None
+ ]
+
def add_reference(self, blk: SharedBlock) -> None:
"""Adds a new reference to our reference collection.
@@ -959,6 +964,7 @@ cdef class BlockValuesRefs:
blk: SharedBlock
The block that the new references should point to.
"""
+ self._clear_dead_references()
self.referenced_blocks.append(weakref.ref(blk))
def add_index_reference(self, index: object) -> None:
@@ -969,6 +975,7 @@ cdef class BlockValuesRefs:
index : Index
The index that the new reference should point to.
"""
+ self._clear_dead_references()
self.referenced_blocks.append(weakref.ref(index))
def has_reference(self) -> bool:
@@ -981,8 +988,6 @@ cdef class BlockValuesRefs:
-------
bool
"""
- self.referenced_blocks = [
- ref for ref in self.referenced_blocks if ref() is not None
- ]
+ self._clear_dead_references()
# Checking for more references than block pointing to itself
return len(self.referenced_blocks) > 1
| #55008 | https://api.github.com/repos/pandas-dev/pandas/pulls/55220 | 2023-09-20T13:44:15Z | 2023-09-20T15:54:24Z | 2023-09-20T15:54:24Z | 2023-09-30T21:24:28Z |
BUG: manage raw ods file without cell cache | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index da2e30edc80ea..b97198c36891c 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -223,6 +223,7 @@ Bug fixes
- Bug in :class:`AbstractHolidayCalendar` where timezone data was not propagated when computing holiday observances (:issue:`54580`)
- Bug in :class:`pandas.core.window.Rolling` where duplicate datetimelike indexes are treated as consecutive rather than equal with ``closed='left'`` and ``closed='neither'`` (:issue:`20712`)
- Bug in :meth:`DataFrame.apply` where passing ``raw=True`` ignored ``args`` passed to the applied function (:issue:`55009`)
+- Bug in :meth:`pandas.read_excel` with a ODS file without cached formatted cell for float values (:issue:`55219`)
Categorical
^^^^^^^^^^^
diff --git a/pandas/io/excel/_odfreader.py b/pandas/io/excel/_odfreader.py
index 8016dbbaf7f42..277f64f636731 100644
--- a/pandas/io/excel/_odfreader.py
+++ b/pandas/io/excel/_odfreader.py
@@ -150,7 +150,7 @@ def get_sheet_data(
max_row_len = len(table_row)
row_repeat = self._get_row_repeat(sheet_row)
- if self._is_empty_row(sheet_row):
+ if len(table_row) == 0:
empty_rows += row_repeat
else:
# add blank rows to our table
@@ -182,16 +182,6 @@ def _get_column_repeat(self, cell) -> int:
return int(cell.attributes.get((TABLENS, "number-columns-repeated"), 1))
- def _is_empty_row(self, row) -> bool:
- """
- Helper function to find empty rows
- """
- for column in row.childNodes:
- if len(column.childNodes) > 0:
- return False
-
- return True
-
def _get_cell_value(self, cell) -> Scalar | NaTType:
from odf.namespaces import OFFICENS
diff --git a/pandas/tests/io/data/excel/test_unempty_cells.ods b/pandas/tests/io/data/excel/test_unempty_cells.ods
new file mode 100644
index 0000000000000..52458753bae06
Binary files /dev/null and b/pandas/tests/io/data/excel/test_unempty_cells.ods differ
diff --git a/pandas/tests/io/excel/test_odf.py b/pandas/tests/io/excel/test_odf.py
index 25079b235d332..9d49718bec357 100644
--- a/pandas/tests/io/excel/test_odf.py
+++ b/pandas/tests/io/excel/test_odf.py
@@ -48,3 +48,14 @@ def test_read_newlines_between_xml_elements_table():
result = pd.read_excel("test_newlines.ods")
tm.assert_frame_equal(result, expected)
+
+
+def test_read_unempty_cells():
+ expected = pd.DataFrame(
+ [1, np.nan, 3, np.nan, 5],
+ columns=["Column 1"],
+ )
+
+ result = pd.read_excel("test_unempty_cells.ods")
+
+ tm.assert_frame_equal(result, expected)
| - [ ] [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 [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.~~
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
```python
import ezodf
ods = ezodf.newdoc("ods", "test_unempty_cells.ods")
sheet = ezodf.Sheet("base", size=(6, 1))
ods.sheets += sheet
sheet[0, 0].set_value("Column 1")
for i in range(1, 6, 2):
sheet[i, 0].set_value(i)
#sheet[i, 0].append(ezodf.Paragraph(str(i)))
ods.save()
```
should generate
| | Column 1 |
|---:|-----------:|
| 0 | 1 |
| 1 | nan |
| 2 | 3 |
| 3 | nan |
| 4 | 5 |
but it generates (an empty DataFrame)
| Column 1 |
|------------|
A hacky fix in building the ODS file is commented out in the snippet `sheet[i, 0].append(ezodf.Paragraph(str(i)))`.
There is a cache in ODS file and data is correctly read from the origin but the logic to determine if a full row is empty mistakenly relies on the cache.
This PR removes that logic et fixes the bug. | https://api.github.com/repos/pandas-dev/pandas/pulls/55219 | 2023-09-20T13:40:21Z | 2023-09-20T16:57:32Z | 2023-09-20T16:57:32Z | 2023-09-20T16:57:40Z |
Backport PR #55211 on branch 2.1.x (DOC: Add release date for 2.1.1) | diff --git a/doc/source/whatsnew/v2.1.1.rst b/doc/source/whatsnew/v2.1.1.rst
index 6d5da7cdff3b3..060235f0d2bff 100644
--- a/doc/source/whatsnew/v2.1.1.rst
+++ b/doc/source/whatsnew/v2.1.1.rst
@@ -1,6 +1,6 @@
.. _whatsnew_211:
-What's new in 2.1.1 (September XX, 2023)
+What's new in 2.1.1 (September 20, 2023)
----------------------------------------
These are the changes in pandas 2.1.1. See :ref:`release` for a full changelog
| Backport PR #55211: DOC: Add release date for 2.1.1 | https://api.github.com/repos/pandas-dev/pandas/pulls/55218 | 2023-09-20T11:57:26Z | 2023-09-20T13:38:34Z | 2023-09-20T13:38:34Z | 2023-09-20T13:38:34Z |
Backport PR #55050 on branch 2.1.x (Fix docstring of Index.join in base.py) | diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index aa306da017d34..b4ef5380d2b30 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -4586,7 +4586,7 @@ def join(
-------
join_index, (left_indexer, right_indexer)
- Examples
+ Examples
--------
>>> idx1 = pd.Index([1, 2, 3])
>>> idx2 = pd.Index([4, 5, 6])
| Backport PR #55050: Fix docstring of Index.join in base.py | https://api.github.com/repos/pandas-dev/pandas/pulls/55217 | 2023-09-20T11:00:22Z | 2023-09-20T15:52:47Z | 2023-09-20T15:52:47Z | 2023-09-20T15:52:47Z |
Backport PR #55210 on branch 2.1.x (CI: Pin matplotlib < 3.8) | diff --git a/ci/deps/actions-310.yaml b/ci/deps/actions-310.yaml
index edd48604dbfda..5db145e31ebc5 100644
--- a/ci/deps/actions-310.yaml
+++ b/ci/deps/actions-310.yaml
@@ -34,7 +34,7 @@ dependencies:
- gcsfs>=2022.05.0
- jinja2>=3.1.2
- lxml>=4.8.0
- - matplotlib>=3.6.1
+ - matplotlib>=3.6.1, <3.8
- numba>=0.55.2
- numexpr>=2.8.0
- odfpy>=1.4.1
diff --git a/ci/deps/actions-311-downstream_compat.yaml b/ci/deps/actions-311-downstream_compat.yaml
index 2fb6072314b1e..2d50056098a0f 100644
--- a/ci/deps/actions-311-downstream_compat.yaml
+++ b/ci/deps/actions-311-downstream_compat.yaml
@@ -35,7 +35,7 @@ dependencies:
- gcsfs>=2022.05.0
- jinja2>=3.1.2
- lxml>=4.8.0
- - matplotlib>=3.6.1
+ - matplotlib>=3.6.1, <3.8
- numba>=0.55.2
- numexpr>=2.8.0
- odfpy>=1.4.1
diff --git a/ci/deps/actions-311.yaml b/ci/deps/actions-311.yaml
index 029312202bcaa..d56fb9ac9fe90 100644
--- a/ci/deps/actions-311.yaml
+++ b/ci/deps/actions-311.yaml
@@ -34,7 +34,7 @@ dependencies:
- gcsfs>=2022.05.0
- jinja2>=3.1.2
- lxml>=4.8.0
- - matplotlib>=3.6.1
+ - matplotlib>=3.6.1, <3.8
- numba>=0.55.2
- numexpr>=2.8.0
- odfpy>=1.4.1
diff --git a/ci/deps/actions-39.yaml b/ci/deps/actions-39.yaml
index 12893698b5534..1843e6bd8005f 100644
--- a/ci/deps/actions-39.yaml
+++ b/ci/deps/actions-39.yaml
@@ -34,7 +34,7 @@ dependencies:
- gcsfs>=2022.05.0
- jinja2>=3.1.2
- lxml>=4.8.0
- - matplotlib>=3.6.1
+ - matplotlib>=3.6.1, <3.8
- numba>=0.55.2
- numexpr>=2.8.0
- odfpy>=1.4.1
diff --git a/ci/deps/circle-310-arm64.yaml b/ci/deps/circle-310-arm64.yaml
index a945dbfe2ca20..c893beced8fb4 100644
--- a/ci/deps/circle-310-arm64.yaml
+++ b/ci/deps/circle-310-arm64.yaml
@@ -34,7 +34,7 @@ dependencies:
- gcsfs>=2022.05.0
- jinja2>=3.1.2
- lxml>=4.8.0
- - matplotlib>=3.6.1
+ - matplotlib>=3.6.1, <3.8
# test_numba_vs_cython segfaults with numba 0.57
- numba>=0.55.2, <0.57.0
- numexpr>=2.8.0
diff --git a/environment.yml b/environment.yml
index 447abd22e29b4..6f80e9af58643 100644
--- a/environment.yml
+++ b/environment.yml
@@ -36,7 +36,7 @@ dependencies:
- ipython
- jinja2>=3.1.2
- lxml>=4.8.0
- - matplotlib>=3.6.1
+ - matplotlib>=3.6.1, <3.8
- numba>=0.55.2
- numexpr>=2.8.0
- openpyxl>=3.0.10
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 15a8633d09039..01e537f7ea28f 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -25,7 +25,7 @@ gcsfs>=2022.05.0
ipython
jinja2>=3.1.2
lxml>=4.8.0
-matplotlib>=3.6.1
+matplotlib>=3.6.1, <3.8
numba>=0.55.2
numexpr>=2.8.0
openpyxl>=3.0.10
| Backport PR #55210: CI: Pin matplotlib < 3.8 | https://api.github.com/repos/pandas-dev/pandas/pulls/55216 | 2023-09-20T10:53:25Z | 2023-09-20T13:07:42Z | 2023-09-20T13:07:42Z | 2023-09-20T13:07:43Z |
DOC: Add release date for 2.1.1 | diff --git a/doc/source/whatsnew/v2.1.1.rst b/doc/source/whatsnew/v2.1.1.rst
index 0dc113bd9ab7f..42a7029b1fa77 100644
--- a/doc/source/whatsnew/v2.1.1.rst
+++ b/doc/source/whatsnew/v2.1.1.rst
@@ -1,6 +1,6 @@
.. _whatsnew_211:
-What's new in 2.1.1 (September XX, 2023)
+What's new in 2.1.1 (September 20, 2023)
----------------------------------------
These are the changes in pandas 2.1.1. See :ref:`release` for a full changelog
| - [ ] 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 [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] 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/55211 | 2023-09-20T02:59:09Z | 2023-09-20T11:57:17Z | 2023-09-20T11:57:17Z | 2023-09-20T11:57:18Z |
ENH: implement EA.size | diff --git a/pandas/core/arrays/base.py b/pandas/core/arrays/base.py
index b5da6d4c11616..6aa303dd04703 100644
--- a/pandas/core/arrays/base.py
+++ b/pandas/core/arrays/base.py
@@ -407,6 +407,13 @@ def shape(self) -> Tuple[int, ...]:
"""
return (len(self),)
+ @property
+ def size(self) -> int:
+ """
+ The number of elements in the array.
+ """
+ return np.prod(self.shape)
+
@property
def ndim(self) -> int:
"""
diff --git a/pandas/tests/extension/base/interface.py b/pandas/tests/extension/base/interface.py
index cdea96334be2a..95fb3d7439b56 100644
--- a/pandas/tests/extension/base/interface.py
+++ b/pandas/tests/extension/base/interface.py
@@ -19,6 +19,9 @@ class BaseInterfaceTests(BaseExtensionTests):
def test_len(self, data):
assert len(data) == 100
+ def test_size(self, data):
+ assert data.size == 100
+
def test_ndim(self, data):
assert data.ndim == 1
| I was surprised this didnt already exist. | https://api.github.com/repos/pandas-dev/pandas/pulls/32644 | 2020-03-11T23:59:44Z | 2020-03-14T03:17:18Z | 2020-03-14T03:17:18Z | 2020-03-14T17:00:56Z |
CLN: trim unnecessary checks | diff --git a/pandas/core/indexes/datetimelike.py b/pandas/core/indexes/datetimelike.py
index 894e1d95a17bc..7889829516f7f 100644
--- a/pandas/core/indexes/datetimelike.py
+++ b/pandas/core/indexes/datetimelike.py
@@ -22,7 +22,6 @@
is_list_like,
is_period_dtype,
is_scalar,
- needs_i8_conversion,
)
from pandas.core.dtypes.concat import concat_compat
from pandas.core.dtypes.generic import ABCIndex, ABCIndexClass, ABCSeries
@@ -484,7 +483,7 @@ def where(self, cond, other=None):
if is_categorical_dtype(other):
# e.g. we have a Categorical holding self.dtype
- if needs_i8_conversion(other.categories):
+ if is_dtype_equal(other.categories.dtype, self.dtype):
other = other._internal_get_values()
if not is_dtype_equal(self.dtype, other.dtype):
diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index 024d3b205df77..87892a804dac3 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -2284,11 +2284,7 @@ def to_native_types(
return np.atleast_2d(result)
def should_store(self, value) -> bool:
- return (
- issubclass(value.dtype.type, np.datetime64)
- and not is_datetime64tz_dtype(value)
- and not is_extension_array_dtype(value)
- )
+ return is_datetime64_dtype(value.dtype)
def set(self, locs, values):
"""
@@ -2536,9 +2532,7 @@ def fillna(self, value, **kwargs):
return super().fillna(value, **kwargs)
def should_store(self, value) -> bool:
- return issubclass(
- value.dtype.type, np.timedelta64
- ) and not is_extension_array_dtype(value)
+ return is_timedelta64_dtype(value.dtype)
def to_native_types(self, slicer=None, na_rep=None, quoting=None, **kwargs):
""" convert to our native types format, slicing if desired """
| https://api.github.com/repos/pandas-dev/pandas/pulls/32643 | 2020-03-11T23:57:12Z | 2020-03-12T02:38:18Z | 2020-03-12T02:38:18Z | 2020-03-12T02:41:44Z | |
fix infer_freq raises section | diff --git a/pandas/tseries/frequencies.py b/pandas/tseries/frequencies.py
index 4af516af8a28e..2477ff29fbfd5 100644
--- a/pandas/tseries/frequencies.py
+++ b/pandas/tseries/frequencies.py
@@ -249,9 +249,14 @@ def infer_freq(index, warn: bool = True) -> Optional[str]:
Returns
-------
str or None
- None if no discernible frequency
- TypeError if the index is not datetime-like
- ValueError if there are less than three values.
+ None if no discernible frequency.
+
+ Raises
+ ------
+ TypeError
+ If the index is not datetime-like.
+ ValueError
+ If there are fewer than three values.
"""
import pandas as pd
| Fixes the raises section of the `infer_freq` doc string. Seems small enough to skip a whats new entry.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/32642 | 2020-03-11T23:39:42Z | 2020-03-12T23:19:23Z | 2020-03-12T23:19:23Z | 2020-03-13T09:43:43Z |
PERF: copy cached attributes on extension index shallow_copy | diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst
index c473500c205d8..52919f966f284 100644
--- a/doc/source/whatsnew/v1.1.0.rst
+++ b/doc/source/whatsnew/v1.1.0.rst
@@ -89,9 +89,9 @@ Backwards incompatible API changes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- :meth:`DataFrame.swaplevels` now raises a ``TypeError`` if the axis is not a :class:`MultiIndex`.
Previously a ``AttributeError`` was raised (:issue:`31126`)
-- :meth:`DataFrameGroupby.mean` and :meth:`SeriesGroupby.mean` (and similarly for :meth:`~DataFrameGroupby.median`, :meth:`~DataFrameGroupby.std`` and :meth:`~DataFrameGroupby.var``)
+- :meth:`DataFrameGroupby.mean` and :meth:`SeriesGroupby.mean` (and similarly for :meth:`~DataFrameGroupby.median`, :meth:`~DataFrameGroupby.std` and :meth:`~DataFrameGroupby.var`)
now raise a ``TypeError`` if a not-accepted keyword argument is passed into it.
- Previously a ``UnsupportedFunctionCall`` was raised (``AssertionError`` if ``min_count`` passed into :meth:`~DataFrameGroupby.median``) (:issue:`31485`)
+ Previously a ``UnsupportedFunctionCall`` was raised (``AssertionError`` if ``min_count`` passed into :meth:`~DataFrameGroupby.median`) (:issue:`31485`)
- :meth:`DataFrame.at` and :meth:`Series.at` will raise a ``TypeError`` instead of a ``ValueError`` if an incompatible key is passed, and ``KeyError`` if a missing key is passed, matching the behavior of ``.loc[]`` (:issue:`31722`)
- Passing an integer dtype other than ``int64`` to ``np.array(period_index, dtype=...)`` will now raise ``TypeError`` instead of incorrectly using ``int64`` (:issue:`32255`)
-
@@ -188,9 +188,9 @@ Performance improvements
- Performance improvement in :class:`Timedelta` constructor (:issue:`30543`)
- Performance improvement in :class:`Timestamp` constructor (:issue:`30543`)
- Performance improvement in flex arithmetic ops between :class:`DataFrame` and :class:`Series` with ``axis=0`` (:issue:`31296`)
-- The internal :meth:`Index._shallow_copy` now copies cached attributes over to the new index,
- avoiding creating these again on the new index. This can speed up many operations
- that depend on creating copies of existing indexes (:issue:`28584`)
+- The internal index method :meth:`~Index._shallow_copy` now copies cached attributes over to the new index,
+ avoiding creating these again on the new index. This can speed up many operations that depend on creating copies of
+ existing indexes (:issue:`28584`, :issue:`32640`)
.. ---------------------------------------------------------------------------
diff --git a/pandas/core/indexes/category.py b/pandas/core/indexes/category.py
index 5997843f7ac6d..6388f2007cb12 100644
--- a/pandas/core/indexes/category.py
+++ b/pandas/core/indexes/category.py
@@ -233,6 +233,7 @@ def _simple_new(cls, values: Categorical, name: Label = None):
result._data = values
result.name = name
+ result._cache = {}
result._reset_identity()
result._no_setting_name = False
@@ -242,14 +243,9 @@ def _simple_new(cls, values: Categorical, name: Label = None):
@Appender(Index._shallow_copy.__doc__)
def _shallow_copy(self, values=None, name: Label = no_default):
- name = self.name if name is no_default else name
-
- if values is None:
- values = self.values
-
- cat = Categorical(values, dtype=self.dtype)
-
- return type(self)._simple_new(cat, name=name)
+ if values is not None:
+ values = Categorical(values, dtype=self.dtype)
+ return super()._shallow_copy(values=values, name=name)
def _is_dtype_compat(self, other) -> bool:
"""
diff --git a/pandas/core/indexes/datetimelike.py b/pandas/core/indexes/datetimelike.py
index 894e1d95a17bc..a5902916991ca 100644
--- a/pandas/core/indexes/datetimelike.py
+++ b/pandas/core/indexes/datetimelike.py
@@ -618,6 +618,7 @@ def _set_freq(self, freq):
def _shallow_copy(self, values=None, name: Label = lib.no_default):
name = self.name if name is lib.no_default else name
+ cache = self._cache.copy() if values is None else {}
if values is None:
values = self._data
@@ -636,7 +637,9 @@ def _shallow_copy(self, values=None, name: Label = lib.no_default):
del attributes["freq"]
attributes["name"] = name
- return type(self)._simple_new(values, **attributes)
+ result = self._simple_new(values, **attributes)
+ result._cache = cache
+ return result
# --------------------------------------------------------------------
# Set Operation Methods
diff --git a/pandas/core/indexes/datetimes.py b/pandas/core/indexes/datetimes.py
index 185ad8e4c365a..c8035a9de432b 100644
--- a/pandas/core/indexes/datetimes.py
+++ b/pandas/core/indexes/datetimes.py
@@ -268,6 +268,7 @@ def _simple_new(cls, values, name=None, freq=None, tz=None, dtype=None):
result = object.__new__(cls)
result._data = dtarr
result.name = name
+ result._cache = {}
result._no_setting_name = False
# For groupby perf. See note in indexes/base about _index_data
result._index_data = dtarr._data
diff --git a/pandas/core/indexes/interval.py b/pandas/core/indexes/interval.py
index efdaf5a331f5f..80d58de6f1437 100644
--- a/pandas/core/indexes/interval.py
+++ b/pandas/core/indexes/interval.py
@@ -243,6 +243,7 @@ def _simple_new(cls, array: IntervalArray, name: Label = None):
result = IntervalMixin.__new__(cls)
result._data = array
result.name = name
+ result._cache = {}
result._no_setting_name = False
result._reset_identity()
return result
@@ -332,12 +333,15 @@ def from_tuples(
# --------------------------------------------------------------------
@Appender(Index._shallow_copy.__doc__)
- def _shallow_copy(self, values=None, **kwargs):
+ def _shallow_copy(self, values=None, name: Label = lib.no_default):
+ name = self.name if name is lib.no_default else name
+ cache = self._cache.copy() if values is None else {}
if values is None:
values = self._data
- attributes = self._get_attributes_dict()
- attributes.update(kwargs)
- return self._simple_new(values, **attributes)
+
+ result = self._simple_new(values, name=name)
+ result._cache = cache
+ return result
@cache_readonly
def _isnan(self):
diff --git a/pandas/core/indexes/period.py b/pandas/core/indexes/period.py
index 9ab80c0b6145c..8ae792d3f63b5 100644
--- a/pandas/core/indexes/period.py
+++ b/pandas/core/indexes/period.py
@@ -233,6 +233,7 @@ def _simple_new(cls, values: PeriodArray, name: Label = None):
# For groupby perf. See note in indexes/base about _index_data
result._index_data = values._data
result.name = name
+ result._cache = {}
result._reset_identity()
return result
diff --git a/pandas/core/indexes/timedeltas.py b/pandas/core/indexes/timedeltas.py
index 5e4a8e83bd95b..a6d9d4dfc330b 100644
--- a/pandas/core/indexes/timedeltas.py
+++ b/pandas/core/indexes/timedeltas.py
@@ -180,6 +180,7 @@ def _simple_new(cls, values, name=None, freq=None, dtype=_TD_DTYPE):
result = object.__new__(cls)
result._data = values
result._name = name
+ result._cache = {}
# For groupby perf. See note in indexes/base about _index_data
result._index_data = values._data
| Follow-up to #32568.
Copies ``._cache`` also when copying using ``.shallow_copy`` for:
* CategoricalIndex
* DatetimeIndex
* PeriodIndex
* DateTimeIndex
* IntervalIndex
After this PR, only ``MultiIndex._shallow_copy`` is missing this optimization. ``MultiIndex._shallow_copy`` is a bit special and might require a refactor so I'd like to take that in a seperate PR.
Example performance improvement:
```pythn
>>> idx = pd.CategoricalIndex(np.arange(100_000))
>>> %timeit idx.get_loc(99_999)
4.46 Β΅s Β± 62.6 ns per loop # master and this PR
>>> %timeit idx._shallow_copy().get_loc(99_999)
4.19 ms Β± 117 Β΅s per loop # master
8.58 Β΅s Β± 254 ns per loop # this PR
``` | https://api.github.com/repos/pandas-dev/pandas/pulls/32640 | 2020-03-11T23:29:15Z | 2020-03-12T10:29:49Z | 2020-03-12T10:29:49Z | 2020-03-12T10:29:55Z |
DEPR: Categorical.to_dense | diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst
index 4e7bd5a2032a7..06e387d0e5073 100644
--- a/doc/source/whatsnew/v1.1.0.rst
+++ b/doc/source/whatsnew/v1.1.0.rst
@@ -207,6 +207,7 @@ Deprecations
- :meth:`DataFrame.mean` and :meth:`DataFrame.median` with ``numeric_only=None`` will include datetime64 and datetime64tz columns in a future version (:issue:`29941`)
- Setting values with ``.loc`` using a positional slice is deprecated and will raise in a future version. Use ``.loc`` with labels or ``.iloc`` with positions instead (:issue:`31840`)
- :meth:`DataFrame.to_dict` has deprecated accepting short names for ``orient`` in future versions (:issue:`32515`)
+- :meth:`Categorical.to_dense` is deprecated and will be removed in a future version, use ``np.asarray(cat)`` instead (:issue:`32639`)
.. ---------------------------------------------------------------------------
diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py
index 56ace48010108..8284a89a29b52 100644
--- a/pandas/core/arrays/categorical.py
+++ b/pandas/core/arrays/categorical.py
@@ -1675,6 +1675,12 @@ def to_dense(self):
-------
dense : array
"""
+ warn(
+ "Categorical.to_dense is deprecated and will be removed in "
+ "a future version. Use np.asarray(cat) instead.",
+ FutureWarning,
+ stacklevel=2,
+ )
return np.asarray(self)
def fillna(self, value=None, method=None, limit=None):
diff --git a/pandas/tests/arrays/categorical/test_api.py b/pandas/tests/arrays/categorical/test_api.py
index f49f70f5acf77..b99e172674f66 100644
--- a/pandas/tests/arrays/categorical/test_api.py
+++ b/pandas/tests/arrays/categorical/test_api.py
@@ -247,7 +247,7 @@ def test_set_categories(self):
tm.assert_index_equal(c.categories, Index([1, 2, 3, 4]))
exp = np.array([1, 2, 3, 4, 1], dtype=np.int64)
- tm.assert_numpy_array_equal(c.to_dense(), exp)
+ tm.assert_numpy_array_equal(np.asarray(c), exp)
# all "pointers" to '4' must be changed from 3 to 0,...
c = c.set_categories([4, 3, 2, 1])
@@ -260,7 +260,7 @@ def test_set_categories(self):
# output is the same
exp = np.array([1, 2, 3, 4, 1], dtype=np.int64)
- tm.assert_numpy_array_equal(c.to_dense(), exp)
+ tm.assert_numpy_array_equal(np.asarray(c), exp)
assert c.min() == 4
assert c.max() == 1
@@ -268,13 +268,19 @@ def test_set_categories(self):
c2 = c.set_categories([4, 3, 2, 1], ordered=False)
assert not c2.ordered
- tm.assert_numpy_array_equal(c.to_dense(), c2.to_dense())
+ tm.assert_numpy_array_equal(np.asarray(c), np.asarray(c2))
# set_categories should pass thru the ordering
c2 = c.set_ordered(False).set_categories([4, 3, 2, 1])
assert not c2.ordered
- tm.assert_numpy_array_equal(c.to_dense(), c2.to_dense())
+ tm.assert_numpy_array_equal(np.asarray(c), np.asarray(c2))
+
+ def test_to_dense_deprecated(self):
+ cat = Categorical(["a", "b", "c", "a"], ordered=True)
+
+ with tm.assert_produces_warning(FutureWarning):
+ cat.to_dense()
@pytest.mark.parametrize(
"values, categories, new_categories",
| - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Ideally I'd want to keep the method and change the behavior to (an improved variant of) _internal_get_values, but so it goes. | https://api.github.com/repos/pandas-dev/pandas/pulls/32639 | 2020-03-11T23:23:37Z | 2020-03-15T00:36:15Z | 2020-03-15T00:36:15Z | 2020-03-15T01:27:12Z |
Backport PR #32633: TYP: Remove _ensure_type | diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index 0a1619750de28..d00a418af3ddb 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -28,6 +28,7 @@ Fixed regressions
- Fixed regression in the repr of an object-dtype :class:`Index` with bools and missing values (:issue:`32146`)
- Fixed regression in :meth:`read_csv` in which the ``encoding`` option was not recognized with certain file-like objects (:issue:`31819`)
- Fixed regression in :meth:`DataFrame.reindex` and :meth:`Series.reindex` when reindexing with (tz-aware) index and ``method=nearest`` (:issue:`26683`)
+- Fixed regression in :meth:`DataFrame.reindex_like` on a :class:`DataFrame` subclass raised an ``AssertionError`` (:issue:`31925`)
.. ---------------------------------------------------------------------------
diff --git a/pandas/core/base.py b/pandas/core/base.py
index 5acf1a28bb4b6..a46b3256a9d48 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -8,7 +8,6 @@
import numpy as np
import pandas._libs.lib as lib
-from pandas._typing import T
from pandas.compat import PYPY
from pandas.compat.numpy import function as nv
from pandas.errors import AbstractMethodError
@@ -85,14 +84,6 @@ def __sizeof__(self):
# object's 'sizeof'
return super().__sizeof__()
- def _ensure_type(self: T, obj) -> T:
- """Ensure that an object has same type as self.
-
- Used by type checkers.
- """
- assert isinstance(obj, type(self)), type(obj)
- return obj
-
class NoNewAttributesMixin:
"""Mixin which prevents adding new attributes.
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index b680234cb0afd..819d341d2d5b4 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -3853,7 +3853,7 @@ def reindex(self, *args, **kwargs) -> "DataFrame":
# Pop these, since the values are in `kwargs` under different names
kwargs.pop("axis", None)
kwargs.pop("labels", None)
- return self._ensure_type(super().reindex(**kwargs))
+ return super().reindex(**kwargs)
def drop(
self,
@@ -4174,8 +4174,8 @@ def replace(
@Appender(_shared_docs["shift"] % _shared_doc_kwargs)
def shift(self, periods=1, freq=None, axis=0, fill_value=None) -> "DataFrame":
- return self._ensure_type(
- super().shift(periods=periods, freq=freq, axis=axis, fill_value=fill_value)
+ return super().shift(
+ periods=periods, freq=freq, axis=axis, fill_value=fill_value
)
def set_index(
@@ -8410,14 +8410,12 @@ def isin(self, values) -> "DataFrame":
from pandas.core.reshape.concat import concat
values = collections.defaultdict(list, values)
- return self._ensure_type(
- concat(
- (
- self.iloc[:, [i]].isin(values[col])
- for i, col in enumerate(self.columns)
- ),
- axis=1,
- )
+ return concat(
+ (
+ self.iloc[:, [i]].isin(values[col])
+ for i, col in enumerate(self.columns)
+ ),
+ axis=1,
)
elif isinstance(values, Series):
if not values.index.is_unique:
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index c8a6c7c760498..3e86e986eae80 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -8536,9 +8536,9 @@ def _align_frame(
)
if method is not None:
- left = self._ensure_type(
- left.fillna(method=method, axis=fill_axis, limit=limit)
- )
+ _left = left.fillna(method=method, axis=fill_axis, limit=limit)
+ assert _left is not None # needed for mypy
+ left = _left
right = right.fillna(method=method, axis=fill_axis, limit=limit)
# if DatetimeIndex have different tz, convert to UTC
@@ -10079,9 +10079,9 @@ def pct_change(
if fill_method is None:
data = self
else:
- data = self._ensure_type(
- self.fillna(method=fill_method, axis=axis, limit=limit)
- )
+ _data = self.fillna(method=fill_method, axis=axis, limit=limit)
+ assert _data is not None # needed for mypy
+ data = _data
rs = data.div(data.shift(periods=periods, freq=freq, axis=axis, **kwargs)) - 1
if freq is not None:
diff --git a/pandas/core/reshape/pivot.py b/pandas/core/reshape/pivot.py
index b443ba142369c..d91bf3e250f4a 100644
--- a/pandas/core/reshape/pivot.py
+++ b/pandas/core/reshape/pivot.py
@@ -148,7 +148,9 @@ def pivot_table(
table = table.sort_index(axis=1)
if fill_value is not None:
- table = table._ensure_type(table.fillna(fill_value, downcast="infer"))
+ _table = table.fillna(fill_value, downcast="infer")
+ assert _table is not None # needed for mypy
+ table = _table
if margins:
if dropna:
diff --git a/pandas/io/formats/format.py b/pandas/io/formats/format.py
index 6adf69a922000..9578bb9f85fda 100644
--- a/pandas/io/formats/format.py
+++ b/pandas/io/formats/format.py
@@ -281,9 +281,7 @@ def _chk_truncate(self) -> None:
series = series.iloc[:max_rows]
else:
row_num = max_rows // 2
- series = series._ensure_type(
- concat((series.iloc[:row_num], series.iloc[-row_num:]))
- )
+ series = concat((series.iloc[:row_num], series.iloc[-row_num:]))
self.tr_row_num = row_num
else:
self.tr_row_num = None
diff --git a/pandas/tests/frame/indexing/test_indexing.py b/pandas/tests/frame/indexing/test_indexing.py
index 4418dee66c092..db0c05040a22e 100644
--- a/pandas/tests/frame/indexing/test_indexing.py
+++ b/pandas/tests/frame/indexing/test_indexing.py
@@ -1596,6 +1596,17 @@ def test_reindex_methods(self, method, expected_values):
actual = df[::-1].reindex(target, method=switched_method)
tm.assert_frame_equal(expected, actual)
+ def test_reindex_subclass(self):
+ # https://github.com/pandas-dev/pandas/issues/31925
+ class MyDataFrame(DataFrame):
+ pass
+
+ expected = DataFrame()
+ df = MyDataFrame()
+ result = df.reindex_like(expected)
+
+ tm.assert_frame_equal(result, expected)
+
def test_reindex_methods_nearest_special(self):
df = pd.DataFrame({"x": list(range(5))})
target = np.array([-0.1, 0.9, 1.1, 1.5])
| https://github.com/pandas-dev/pandas/pull/32633 | https://api.github.com/repos/pandas-dev/pandas/pulls/32637 | 2020-03-11T21:56:16Z | 2020-03-11T23:25:00Z | 2020-03-11T23:25:00Z | 2020-03-12T02:33:57Z |
TST: tighten check_categorical=False tests | diff --git a/pandas/_testing.py b/pandas/_testing.py
index 136dfbd40276d..dff15c66750ac 100644
--- a/pandas/_testing.py
+++ b/pandas/_testing.py
@@ -824,10 +824,14 @@ def assert_categorical_equal(
left.codes, right.codes, check_dtype=check_dtype, obj=f"{obj}.codes",
)
else:
+ try:
+ lc = left.categories.sort_values()
+ rc = right.categories.sort_values()
+ except TypeError:
+ # e.g. '<' not supported between instances of 'int' and 'str'
+ lc, rc = left.categories, right.categories
assert_index_equal(
- left.categories.sort_values(),
- right.categories.sort_values(),
- obj=f"{obj}.categories",
+ lc, rc, obj=f"{obj}.categories",
)
assert_index_equal(
left.categories.take(left.codes),
diff --git a/pandas/tests/arrays/categorical/test_replace.py b/pandas/tests/arrays/categorical/test_replace.py
index 52530123bd52f..b9ac3ce9a37ae 100644
--- a/pandas/tests/arrays/categorical/test_replace.py
+++ b/pandas/tests/arrays/categorical/test_replace.py
@@ -1,3 +1,4 @@
+import numpy as np
import pytest
import pandas as pd
@@ -5,44 +6,46 @@
@pytest.mark.parametrize(
- "to_replace,value,expected,check_types,check_categorical",
+ "to_replace,value,expected,flip_categories",
[
# one-to-one
- (1, 2, [2, 2, 3], True, True),
- (1, 4, [4, 2, 3], True, True),
- (4, 1, [1, 2, 3], True, True),
- (5, 6, [1, 2, 3], True, True),
+ (1, 2, [2, 2, 3], False),
+ (1, 4, [4, 2, 3], False),
+ (4, 1, [1, 2, 3], False),
+ (5, 6, [1, 2, 3], False),
# many-to-one
- ([1], 2, [2, 2, 3], True, True),
- ([1, 2], 3, [3, 3, 3], True, True),
- ([1, 2], 4, [4, 4, 3], True, True),
- ((1, 2, 4), 5, [5, 5, 3], True, True),
- ((5, 6), 2, [1, 2, 3], True, True),
+ ([1], 2, [2, 2, 3], False),
+ ([1, 2], 3, [3, 3, 3], False),
+ ([1, 2], 4, [4, 4, 3], False),
+ ((1, 2, 4), 5, [5, 5, 3], False),
+ ((5, 6), 2, [1, 2, 3], False),
# many-to-many, handled outside of Categorical and results in separate dtype
- ([1], [2], [2, 2, 3], False, False),
- ([1, 4], [5, 2], [5, 2, 3], False, False),
+ ([1], [2], [2, 2, 3], True),
+ ([1, 4], [5, 2], [5, 2, 3], True),
# check_categorical sorts categories, which crashes on mixed dtypes
- (3, "4", [1, 2, "4"], True, False),
- ([1, 2, "3"], "5", ["5", "5", 3], True, False),
+ (3, "4", [1, 2, "4"], False),
+ ([1, 2, "3"], "5", ["5", "5", 3], True),
],
)
-def test_replace(to_replace, value, expected, check_types, check_categorical):
+def test_replace(to_replace, value, expected, flip_categories):
# GH 31720
+ stays_categorical = not isinstance(value, list)
+
s = pd.Series([1, 2, 3], dtype="category")
result = s.replace(to_replace, value)
expected = pd.Series(expected, dtype="category")
s.replace(to_replace, value, inplace=True)
+
+ if flip_categories:
+ expected = expected.cat.set_categories(expected.cat.categories[::-1])
+
+ if not stays_categorical:
+ # the replace call loses categorical dtype
+ expected = pd.Series(np.asarray(expected))
+
tm.assert_series_equal(
- expected,
- result,
- check_dtype=check_types,
- check_categorical=check_categorical,
- check_category_order=False,
+ expected, result, check_category_order=False,
)
tm.assert_series_equal(
- expected,
- s,
- check_dtype=check_types,
- check_categorical=check_categorical,
- check_category_order=False,
+ expected, s, check_category_order=False,
)
diff --git a/pandas/tests/generic/test_frame.py b/pandas/tests/generic/test_frame.py
index 8fe49b2ec2299..631f484cfc22a 100644
--- a/pandas/tests/generic/test_frame.py
+++ b/pandas/tests/generic/test_frame.py
@@ -273,17 +273,13 @@ def test_to_xarray_index_types(self, index):
assert isinstance(result, Dataset)
# idempotency
- # categoricals are not preserved
# datetimes w/tz are preserved
# column names are lost
expected = df.copy()
expected["f"] = expected["f"].astype(object)
expected.columns.name = None
tm.assert_frame_equal(
- result.to_dataframe(),
- expected,
- check_index_type=False,
- check_categorical=False,
+ result.to_dataframe(), expected,
)
@td.skip_if_no("xarray", min_version="0.7.0")
diff --git a/pandas/tests/io/pytables/test_store.py b/pandas/tests/io/pytables/test_store.py
index 34f6a73812c97..2702d378fd153 100644
--- a/pandas/tests/io/pytables/test_store.py
+++ b/pandas/tests/io/pytables/test_store.py
@@ -13,8 +13,6 @@
from pandas.compat import is_platform_little_endian, is_platform_windows
import pandas.util._test_decorators as td
-from pandas.core.dtypes.common import is_categorical_dtype
-
import pandas as pd
from pandas import (
Categorical,
@@ -1057,18 +1055,7 @@ def test_latin_encoding(self, setup_path, dtype, val):
s_nan = ser.replace(nan_rep, np.nan)
- if is_categorical_dtype(s_nan):
- assert is_categorical_dtype(retr)
- tm.assert_series_equal(
- s_nan, retr, check_dtype=False, check_categorical=False
- )
- else:
- tm.assert_series_equal(s_nan, retr)
-
- # FIXME: don't leave commented-out
- # fails:
- # for x in examples:
- # roundtrip(s, nan_rep=b'\xf8\xfc')
+ tm.assert_series_equal(s_nan, retr)
def test_append_some_nans(self, setup_path):
diff --git a/pandas/tests/io/test_stata.py b/pandas/tests/io/test_stata.py
index 3efac9cd605a8..eaa92fa53d799 100644
--- a/pandas/tests/io/test_stata.py
+++ b/pandas/tests/io/test_stata.py
@@ -1026,7 +1026,14 @@ def test_categorical_with_stata_missing_values(self, version):
original.to_stata(path, version=version)
written_and_read_again = self.read_dta(path)
res = written_and_read_again.set_index("index")
- tm.assert_frame_equal(res, original, check_categorical=False)
+
+ expected = original.copy()
+ for col in expected:
+ cat = expected[col]._values
+ new_cats = cat.remove_unused_categories().categories
+ cat = cat.set_categories(new_cats, ordered=True)
+ expected[col] = cat
+ tm.assert_frame_equal(res, expected)
@pytest.mark.parametrize("file", ["dta19_115", "dta19_117"])
def test_categorical_order(self, file):
@@ -1044,7 +1051,9 @@ def test_categorical_order(self, file):
cols = []
for is_cat, col, labels, codes in expected:
if is_cat:
- cols.append((col, pd.Categorical.from_codes(codes, labels)))
+ cols.append(
+ (col, pd.Categorical.from_codes(codes, labels, ordered=True))
+ )
else:
cols.append((col, pd.Series(labels, dtype=np.float32)))
expected = DataFrame.from_dict(dict(cols))
@@ -1052,7 +1061,7 @@ def test_categorical_order(self, file):
# Read with and with out categoricals, ensure order is identical
file = getattr(self, file)
parsed = read_stata(file)
- tm.assert_frame_equal(expected, parsed, check_categorical=False)
+ tm.assert_frame_equal(expected, parsed)
# Check identity of codes
for col in expected:
@@ -1137,18 +1146,30 @@ def test_read_chunks_117(
chunk = itr.read(chunksize)
except StopIteration:
break
- from_frame = parsed.iloc[pos : pos + chunksize, :]
+ from_frame = parsed.iloc[pos : pos + chunksize, :].copy()
+ from_frame = self._convert_categorical(from_frame)
tm.assert_frame_equal(
- from_frame,
- chunk,
- check_dtype=False,
- check_datetimelike_compat=True,
- check_categorical=False,
+ from_frame, chunk, check_dtype=False, check_datetimelike_compat=True,
)
pos += chunksize
itr.close()
+ @staticmethod
+ def _convert_categorical(from_frame: DataFrame) -> DataFrame:
+ """
+ Emulate the categorical casting behavior we expect from roundtripping.
+ """
+ for col in from_frame:
+ ser = from_frame[col]
+ if is_categorical_dtype(ser.dtype):
+ cat = ser._values.remove_unused_categories()
+ if cat.categories.dtype == object:
+ categories = pd.Index(cat.categories._values)
+ cat = cat.set_categories(categories)
+ from_frame[col] = cat
+ return from_frame
+
def test_iterator(self):
fname = self.dta3_117
@@ -1223,13 +1244,10 @@ def test_read_chunks_115(
chunk = itr.read(chunksize)
except StopIteration:
break
- from_frame = parsed.iloc[pos : pos + chunksize, :]
+ from_frame = parsed.iloc[pos : pos + chunksize, :].copy()
+ from_frame = self._convert_categorical(from_frame)
tm.assert_frame_equal(
- from_frame,
- chunk,
- check_dtype=False,
- check_datetimelike_compat=True,
- check_categorical=False,
+ from_frame, chunk, check_dtype=False, check_datetimelike_compat=True,
)
pos += chunksize
diff --git a/pandas/tests/reshape/merge/test_merge.py b/pandas/tests/reshape/merge/test_merge.py
index d80e2e7afceef..51e6f80df657d 100644
--- a/pandas/tests/reshape/merge/test_merge.py
+++ b/pandas/tests/reshape/merge/test_merge.py
@@ -2077,8 +2077,7 @@ def test_merge_equal_cat_dtypes(cat_dtype, reverse):
}
).set_index("foo")
- # Categorical is unordered, so don't check ordering.
- tm.assert_frame_equal(result, expected, check_categorical=False)
+ tm.assert_frame_equal(result, expected)
def test_merge_equal_cat_dtypes2():
@@ -2100,8 +2099,7 @@ def test_merge_equal_cat_dtypes2():
{"left": [1, 2], "right": [3, 2], "foo": Series(["a", "b"]).astype(cat_dtype)}
).set_index("foo")
- # Categorical is unordered, so don't check ordering.
- tm.assert_frame_equal(result, expected, check_categorical=False)
+ tm.assert_frame_equal(result, expected)
def test_merge_on_cat_and_ext_array():
diff --git a/pandas/tests/series/test_dtypes.py b/pandas/tests/series/test_dtypes.py
index 80a024eda7848..31f17be2fac7b 100644
--- a/pandas/tests/series/test_dtypes.py
+++ b/pandas/tests/series/test_dtypes.py
@@ -296,18 +296,18 @@ def cmp(a, b):
# array conversion
tm.assert_almost_equal(np.array(s), np.array(s.values))
- # valid conversion
- for valid in [
- lambda x: x.astype("category"),
- lambda x: x.astype(CategoricalDtype()),
- lambda x: x.astype("object").astype("category"),
- lambda x: x.astype("object").astype(CategoricalDtype()),
- ]:
-
- result = valid(s)
- # compare series values
- # internal .categories can't be compared because it is sorted
- tm.assert_series_equal(result, s, check_categorical=False)
+ tm.assert_series_equal(s.astype("category"), s)
+ tm.assert_series_equal(s.astype(CategoricalDtype()), s)
+
+ roundtrip_expected = s.cat.set_categories(
+ s.cat.categories.sort_values()
+ ).cat.remove_unused_categories()
+ tm.assert_series_equal(
+ s.astype("object").astype("category"), roundtrip_expected
+ )
+ tm.assert_series_equal(
+ s.astype("object").astype(CategoricalDtype()), roundtrip_expected
+ )
# invalid conversion (these are NOT a dtype)
msg = (
| This removes all check_categorical=False usages except for a) those in tests/util and b) a skipped json test test_latin_encoding (cc @WillAyd is that likely to be enabled in the foreseeable future?) | https://api.github.com/repos/pandas-dev/pandas/pulls/32636 | 2020-03-11T20:20:36Z | 2020-03-12T02:42:47Z | 2020-03-12T02:42:47Z | 2020-03-12T07:11:47Z |
Backport PR #32561 on branch 1.0.x (Ensure valid Block mutation in SeriesBinGrouper.) | diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index 768e08519543c..0a1619750de28 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -20,6 +20,7 @@ Fixed regressions
- Fixed regression in ``groupby(..).rolling(..).apply()`` (``RollingGroupby``) where the ``raw`` parameter was ignored (:issue:`31754`)
- Fixed regression in :meth:`rolling(..).corr() <pandas.core.window.rolling.Rolling.corr>` when using a time offset (:issue:`31789`)
- Fixed regression in :meth:`groupby(..).nunique() <pandas.core.groupby.DataFrameGroupBy.nunique>` which was modifying the original values if ``NaN`` values were present (:issue:`31950`)
+- Fixed regression in ``DataFrame.groupby`` raising a ``ValueError`` from an internal operation (:issue:`31802`)
- Fixed regression where :func:`read_pickle` raised a ``UnicodeDecodeError`` when reading a py27 pickle with :class:`MultiIndex` column (:issue:`31988`).
- Fixed regression in :class:`DataFrame` arithmetic operations with mis-matched columns (:issue:`31623`)
- Fixed regression in :meth:`groupby(..).agg() <pandas.core.groupby.GroupBy.agg>` calling a user-provided function an extra time on an empty input (:issue:`31760`)
diff --git a/pandas/_libs/reduction.pyx b/pandas/_libs/reduction.pyx
index 43d253f632f0f..2e0e41528d80f 100644
--- a/pandas/_libs/reduction.pyx
+++ b/pandas/_libs/reduction.pyx
@@ -177,6 +177,8 @@ cdef class _BaseGrouper:
object.__setattr__(cached_ityp, '_index_data', islider.buf)
cached_ityp._engine.clear_mapping()
object.__setattr__(cached_typ._data._block, 'values', vslider.buf)
+ object.__setattr__(cached_typ._data._block, 'mgr_locs',
+ slice(len(vslider.buf)))
object.__setattr__(cached_typ, '_index', cached_ityp)
object.__setattr__(cached_typ, 'name', self.name)
diff --git a/pandas/tests/groupby/test_bin_groupby.py b/pandas/tests/groupby/test_bin_groupby.py
index ad71f73e80e64..15ce28e0e12bd 100644
--- a/pandas/tests/groupby/test_bin_groupby.py
+++ b/pandas/tests/groupby/test_bin_groupby.py
@@ -5,6 +5,7 @@
from pandas.core.dtypes.common import ensure_int64
+import pandas as pd
from pandas import Index, Series, isna
import pandas._testing as tm
@@ -51,6 +52,30 @@ def test_series_bin_grouper():
tm.assert_almost_equal(counts, exp_counts)
+def assert_block_lengths(x):
+ assert len(x) == len(x._data.blocks[0].mgr_locs)
+ return 0
+
+
+def cumsum_max(x):
+ x.cumsum().max()
+ return 0
+
+
+@pytest.mark.parametrize("func", [cumsum_max, assert_block_lengths])
+def test_mgr_locs_updated(func):
+ # https://github.com/pandas-dev/pandas/issues/31802
+ # Some operations may require creating new blocks, which requires
+ # valid mgr_locs
+ df = pd.DataFrame({"A": ["a", "a", "a"], "B": ["a", "b", "b"], "C": [1, 1, 1]})
+ result = df.groupby(["A", "B"]).agg(func)
+ expected = pd.DataFrame(
+ {"C": [0, 0]},
+ index=pd.MultiIndex.from_product([["a"], ["a", "b"]], names=["A", "B"]),
+ )
+ tm.assert_frame_equal(result, expected)
+
+
@pytest.mark.parametrize(
"binner,closed,expected",
[
| Backport PR #32561: Ensure valid Block mutation in SeriesBinGrouper. | https://api.github.com/repos/pandas-dev/pandas/pulls/32635 | 2020-03-11T18:35:00Z | 2020-03-11T19:32:13Z | 2020-03-11T19:32:13Z | 2020-03-11T19:32:13Z |
TYP: Remove _ensure_type | diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index 123dfa07f4331..3d5e77b0350e6 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -27,6 +27,7 @@ Fixed regressions
- Fixed regression in the repr of an object-dtype :class:`Index` with bools and missing values (:issue:`32146`)
- Fixed regression in :meth:`read_csv` in which the ``encoding`` option was not recognized with certain file-like objects (:issue:`31819`)
- Fixed regression in :meth:`DataFrame.reindex` and :meth:`Series.reindex` when reindexing with (tz-aware) index and ``method=nearest`` (:issue:`26683`)
+- Fixed regression in :meth:`DataFrame.reindex_like` on a :class:`DataFrame` subclass raised an ``AssertionError`` (:issue:`31925`)
.. ---------------------------------------------------------------------------
diff --git a/pandas/core/base.py b/pandas/core/base.py
index 478b83f538b7d..40ff0640a5bc4 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -9,7 +9,6 @@
import numpy as np
import pandas._libs.lib as lib
-from pandas._typing import T
from pandas.compat import PYPY
from pandas.compat.numpy import function as nv
from pandas.errors import AbstractMethodError
@@ -87,15 +86,6 @@ def __sizeof__(self):
# no memory_usage attribute, so fall back to object's 'sizeof'
return super().__sizeof__()
- def _ensure_type(self: T, obj) -> T:
- """
- Ensure that an object has same type as self.
-
- Used by type checkers.
- """
- assert isinstance(obj, type(self)), type(obj)
- return obj
-
class NoNewAttributesMixin:
"""
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 30abcafb56ffb..d391f1be2c49f 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -3637,7 +3637,7 @@ def reindex(self, *args, **kwargs) -> "DataFrame":
# Pop these, since the values are in `kwargs` under different names
kwargs.pop("axis", None)
kwargs.pop("labels", None)
- return self._ensure_type(super().reindex(**kwargs))
+ return super().reindex(**kwargs)
def drop(
self,
@@ -3955,8 +3955,8 @@ def replace(
@Appender(_shared_docs["shift"] % _shared_doc_kwargs)
def shift(self, periods=1, freq=None, axis=0, fill_value=None) -> "DataFrame":
- return self._ensure_type(
- super().shift(periods=periods, freq=freq, axis=axis, fill_value=fill_value)
+ return super().shift(
+ periods=periods, freq=freq, axis=axis, fill_value=fill_value
)
def set_index(
@@ -8409,14 +8409,12 @@ def isin(self, values) -> "DataFrame":
from pandas.core.reshape.concat import concat
values = collections.defaultdict(list, values)
- return self._ensure_type(
- concat(
- (
- self.iloc[:, [i]].isin(values[col])
- for i, col in enumerate(self.columns)
- ),
- axis=1,
- )
+ return concat(
+ (
+ self.iloc[:, [i]].isin(values[col])
+ for i, col in enumerate(self.columns)
+ ),
+ axis=1,
)
elif isinstance(values, Series):
if not values.index.is_unique:
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index f53135174741e..6f743d7388574 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -8442,9 +8442,9 @@ def _align_frame(
)
if method is not None:
- left = self._ensure_type(
- left.fillna(method=method, axis=fill_axis, limit=limit)
- )
+ _left = left.fillna(method=method, axis=fill_axis, limit=limit)
+ assert _left is not None # needed for mypy
+ left = _left
right = right.fillna(method=method, axis=fill_axis, limit=limit)
# if DatetimeIndex have different tz, convert to UTC
@@ -9977,9 +9977,9 @@ def pct_change(
if fill_method is None:
data = self
else:
- data = self._ensure_type(
- self.fillna(method=fill_method, axis=axis, limit=limit)
- )
+ _data = self.fillna(method=fill_method, axis=axis, limit=limit)
+ assert _data is not None # needed for mypy
+ data = _data
rs = data.div(data.shift(periods=periods, freq=freq, axis=axis, **kwargs)) - 1
if freq is not None:
diff --git a/pandas/core/reshape/pivot.py b/pandas/core/reshape/pivot.py
index 61aa34f724307..a8801d8ab3f6e 100644
--- a/pandas/core/reshape/pivot.py
+++ b/pandas/core/reshape/pivot.py
@@ -150,7 +150,9 @@ def pivot_table(
table = table.sort_index(axis=1)
if fill_value is not None:
- table = table._ensure_type(table.fillna(fill_value, downcast="infer"))
+ _table = table.fillna(fill_value, downcast="infer")
+ assert _table is not None # needed for mypy
+ table = _table
if margins:
if dropna:
diff --git a/pandas/io/formats/format.py b/pandas/io/formats/format.py
index c879eaeda64e0..f011293273c5b 100644
--- a/pandas/io/formats/format.py
+++ b/pandas/io/formats/format.py
@@ -283,9 +283,7 @@ def _chk_truncate(self) -> None:
series = series.iloc[:max_rows]
else:
row_num = max_rows // 2
- series = series._ensure_type(
- concat((series.iloc[:row_num], series.iloc[-row_num:]))
- )
+ series = concat((series.iloc[:row_num], series.iloc[-row_num:]))
self.tr_row_num = row_num
else:
self.tr_row_num = None
diff --git a/pandas/tests/frame/indexing/test_indexing.py b/pandas/tests/frame/indexing/test_indexing.py
index ade17860a99b7..7892030a6727e 100644
--- a/pandas/tests/frame/indexing/test_indexing.py
+++ b/pandas/tests/frame/indexing/test_indexing.py
@@ -1605,6 +1605,17 @@ def test_reindex_methods(self, method, expected_values):
actual = df[::-1].reindex(target, method=switched_method)
tm.assert_frame_equal(expected, actual)
+ def test_reindex_subclass(self):
+ # https://github.com/pandas-dev/pandas/issues/31925
+ class MyDataFrame(DataFrame):
+ pass
+
+ expected = DataFrame()
+ df = MyDataFrame()
+ result = df.reindex_like(expected)
+
+ tm.assert_frame_equal(result, expected)
+
def test_reindex_methods_nearest_special(self):
df = pd.DataFrame({"x": list(range(5))})
target = np.array([-0.1, 0.9, 1.1, 1.5])
| - [ ] closes #31925
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/32633 | 2020-03-11T17:22:50Z | 2020-03-11T21:51:41Z | 2020-03-11T21:51:40Z | 2020-03-12T11:21:36Z |
TST: revert parts of #32571 | diff --git a/pandas/tests/frame/test_analytics.py b/pandas/tests/frame/test_analytics.py
index 82f8a102f9c64..3964e790c7c12 100644
--- a/pandas/tests/frame/test_analytics.py
+++ b/pandas/tests/frame/test_analytics.py
@@ -32,6 +32,7 @@ def assert_stat_op_calc(
has_skipna=True,
check_dtype=True,
check_dates=False,
+ check_less_precise=False,
skipna_alternative=None,
):
"""
@@ -53,6 +54,9 @@ def assert_stat_op_calc(
"alternative(frame)" should be checked.
check_dates : bool, default false
Whether opname should be tested on a Datetime Series
+ check_less_precise : bool, default False
+ Whether results should only be compared approximately;
+ passed on to tm.assert_series_equal
skipna_alternative : function, default None
NaN-safe version of alternative
"""
@@ -80,11 +84,17 @@ def wrapper(x):
result0 = f(axis=0, skipna=False)
result1 = f(axis=1, skipna=False)
tm.assert_series_equal(
- result0, frame.apply(wrapper), check_dtype=check_dtype,
+ result0,
+ frame.apply(wrapper),
+ check_dtype=check_dtype,
+ check_less_precise=check_less_precise,
)
# HACK: win32
tm.assert_series_equal(
- result1, frame.apply(wrapper, axis=1), check_dtype=False,
+ result1,
+ frame.apply(wrapper, axis=1),
+ check_dtype=False,
+ check_less_precise=check_less_precise,
)
else:
skipna_wrapper = alternative
@@ -92,12 +102,17 @@ def wrapper(x):
result0 = f(axis=0)
result1 = f(axis=1)
tm.assert_series_equal(
- result0, frame.apply(skipna_wrapper), check_dtype=check_dtype,
+ result0,
+ frame.apply(skipna_wrapper),
+ check_dtype=check_dtype,
+ check_less_precise=check_less_precise,
)
if opname in ["sum", "prod"]:
expected = frame.apply(skipna_wrapper, axis=1)
- tm.assert_series_equal(result1, expected, check_dtype=False)
+ tm.assert_series_equal(
+ result1, expected, check_dtype=False, check_less_precise=check_less_precise
+ )
# check dtypes
if check_dtype:
@@ -316,9 +331,15 @@ def kurt(x):
check_dates=True,
)
+ # GH#32571 check_less_precise is needed on apparently-random
+ # py37-npdev builds and OSX-PY36-min_version builds
# mixed types (with upcasting happening)
assert_stat_op_calc(
- "sum", np.sum, mixed_float_frame.astype("float32"), check_dtype=False,
+ "sum",
+ np.sum,
+ mixed_float_frame.astype("float32"),
+ check_dtype=False,
+ check_less_precise=True,
)
assert_stat_op_calc(
diff --git a/pandas/tests/io/test_sql.py b/pandas/tests/io/test_sql.py
index 86502a67e1869..bf0ed4fe25346 100644
--- a/pandas/tests/io/test_sql.py
+++ b/pandas/tests/io/test_sql.py
@@ -2372,7 +2372,7 @@ def test_write_row_by_row(self):
result = sql.read_sql("select * from test", con=self.conn)
result.index = frame.index
- tm.assert_frame_equal(result, frame)
+ tm.assert_frame_equal(result, frame, check_less_precise=True)
def test_execute(self):
frame = tm.makeTimeDataFrame()
@@ -2632,7 +2632,9 @@ def test_write_row_by_row(self):
result = sql.read_sql("select * from test", con=self.conn)
result.index = frame.index
- tm.assert_frame_equal(result, frame)
+ tm.assert_frame_equal(result, frame, check_less_precise=True)
+ # GH#32571 result comes back rounded to 6 digits in some builds;
+ # no obvious pattern
def test_chunksize_read_type(self):
frame = tm.makeTimeDataFrame()
| Should fix the broken CI builds. | https://api.github.com/repos/pandas-dev/pandas/pulls/32630 | 2020-03-11T15:21:48Z | 2020-03-11T16:40:50Z | 2020-03-11T16:40:50Z | 2020-03-11T16:40:59Z |
Backport PR #31524 on branch 1.0.x (BUG: non-iterable value in meta raise error in json_normalize) | diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index d7499bba55c97..768e08519543c 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -78,6 +78,7 @@ Bug fixes
**I/O**
- Using ``pd.NA`` with :meth:`DataFrame.to_json` now correctly outputs a null value instead of an empty object (:issue:`31615`)
+- Bug in :meth:`pandas.json_normalize` when value in meta path is not iterable (:issue:`31507`)
- Fixed pickling of ``pandas.NA``. Previously a new object was returned, which broke computations relying on ``NA`` being a singleton (:issue:`31847`)
- Fixed bug in parquet roundtrip with nullable unsigned integer dtypes (:issue:`31896`).
diff --git a/pandas/io/json/_normalize.py b/pandas/io/json/_normalize.py
index c0596c984575a..ea1f2405eec7f 100644
--- a/pandas/io/json/_normalize.py
+++ b/pandas/io/json/_normalize.py
@@ -8,6 +8,7 @@
import numpy as np
from pandas._libs.writers import convert_json_to_lines
+from pandas._typing import Scalar
from pandas.util._decorators import deprecate
import pandas as pd
@@ -230,14 +231,28 @@ def _json_normalize(
Returns normalized data with columns prefixed with the given string.
"""
- def _pull_field(js: Dict[str, Any], spec: Union[List, str]) -> Iterable:
+ def _pull_field(
+ js: Dict[str, Any], spec: Union[List, str]
+ ) -> Union[Scalar, Iterable]:
+ """Internal function to pull field"""
result = js # type: ignore
if isinstance(spec, list):
for field in spec:
result = result[field]
else:
result = result[spec]
+ return result
+
+ def _pull_records(js: Dict[str, Any], spec: Union[List, str]) -> Iterable:
+ """
+ Interal function to pull field for records, and similar to
+ _pull_field, but require to return Iterable. And will raise error
+ if has non iterable value.
+ """
+ result = _pull_field(js, spec)
+ # GH 31507 GH 30145, if result is not Iterable, raise TypeError if not
+ # null, otherwise return an empty list
if not isinstance(result, Iterable):
if pd.isnull(result):
result = [] # type: ignore
@@ -246,7 +261,6 @@ def _pull_field(js: Dict[str, Any], spec: Union[List, str]) -> Iterable:
f"{js} has non iterable value {result} for path {spec}. "
"Must be iterable or null."
)
-
return result
if isinstance(data, list) and not data:
@@ -296,7 +310,7 @@ def _recursive_extract(data, path, seen_meta, level=0):
_recursive_extract(obj[path[0]], path[1:], seen_meta, level=level + 1)
else:
for obj in data:
- recs = _pull_field(obj, path[0])
+ recs = _pull_records(obj, path[0])
recs = [
nested_to_record(r, sep=sep, max_level=max_level)
if isinstance(r, dict)
diff --git a/pandas/tests/io/json/test_normalize.py b/pandas/tests/io/json/test_normalize.py
index efb95a0cb2a42..898ede370f6e0 100644
--- a/pandas/tests/io/json/test_normalize.py
+++ b/pandas/tests/io/json/test_normalize.py
@@ -486,6 +486,16 @@ def test_non_interable_record_path_errors(self):
with pytest.raises(TypeError, match=msg):
json_normalize([test_input], record_path=[test_path])
+ def test_meta_non_iterable(self):
+ # GH 31507
+ data = """[{"id": 99, "data": [{"one": 1, "two": 2}]}]"""
+
+ result = json_normalize(json.loads(data), record_path=["data"], meta=["id"])
+ expected = DataFrame(
+ {"one": [1], "two": [2], "id": np.array([99], dtype=object)}
+ )
+ tm.assert_frame_equal(result, expected)
+
class TestNestedToRecord:
def test_flat_stays_flat(self):
| Backport PR #31524: BUG: non-iterable value in meta raise error in json_normalize | https://api.github.com/repos/pandas-dev/pandas/pulls/32629 | 2020-03-11T15:03:18Z | 2020-03-11T18:11:17Z | 2020-03-11T18:11:17Z | 2020-03-11T18:11:17Z |
DOC: fix formatting / links of API refs in 1.0.2 whatsnew (#32620) | diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index ca693ff177e3b..d7499bba55c97 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -17,16 +17,17 @@ Fixed regressions
- Fixed regression in :meth:`DataFrame.to_excel` when ``columns`` kwarg is passed (:issue:`31677`)
- Fixed regression in :meth:`Series.align` when ``other`` is a DataFrame and ``method`` is not None (:issue:`31785`)
-- Fixed regression in :meth:`pandas.core.groupby.RollingGroupby.apply` where the ``raw`` parameter was ignored (:issue:`31754`)
-- Fixed regression in :meth:`pandas.core.window.Rolling.corr` when using a time offset (:issue:`31789`)
-- Fixed regression in :meth:`pandas.core.groupby.DataFrameGroupBy.nunique` which was modifying the original values if ``NaN`` values were present (:issue:`31950`)
+- Fixed regression in ``groupby(..).rolling(..).apply()`` (``RollingGroupby``) where the ``raw`` parameter was ignored (:issue:`31754`)
+- Fixed regression in :meth:`rolling(..).corr() <pandas.core.window.rolling.Rolling.corr>` when using a time offset (:issue:`31789`)
+- Fixed regression in :meth:`groupby(..).nunique() <pandas.core.groupby.DataFrameGroupBy.nunique>` which was modifying the original values if ``NaN`` values were present (:issue:`31950`)
- Fixed regression where :func:`read_pickle` raised a ``UnicodeDecodeError`` when reading a py27 pickle with :class:`MultiIndex` column (:issue:`31988`).
- Fixed regression in :class:`DataFrame` arithmetic operations with mis-matched columns (:issue:`31623`)
-- Fixed regression in :meth:`pandas.core.groupby.GroupBy.agg` calling a user-provided function an extra time on an empty input (:issue:`31760`)
-- Joining on :class:`DatetimeIndex` or :class:`TimedeltaIndex` will preserve ``freq`` in simple cases (:issue:`32166`)
-- Fixed bug in the repr of an object-dtype :class:`Index` with bools and missing values (:issue:`32146`)
+- Fixed regression in :meth:`groupby(..).agg() <pandas.core.groupby.GroupBy.agg>` calling a user-provided function an extra time on an empty input (:issue:`31760`)
+- Fixed regression in joining on :class:`DatetimeIndex` or :class:`TimedeltaIndex` to preserve ``freq`` in simple cases (:issue:`32166`)
+- Fixed regression in the repr of an object-dtype :class:`Index` with bools and missing values (:issue:`32146`)
- Fixed regression in :meth:`read_csv` in which the ``encoding`` option was not recognized with certain file-like objects (:issue:`31819`)
--
+- Fixed regression in :meth:`DataFrame.reindex` and :meth:`Series.reindex` when reindexing with (tz-aware) index and ``method=nearest`` (:issue:`26683`)
+
.. ---------------------------------------------------------------------------
@@ -64,7 +65,7 @@ Bug fixes
**Datetimelike**
-- Bug in :meth:`DataFrame.reindex` and :meth:`Series.reindex` when reindexing with a tz-aware index (:issue:`26683`)
+- Bug in :meth:`Series.astype` not copying for tz-naive and tz-aware datetime64 dtype (:issue:`32490`)
- Bug where :func:`to_datetime` would raise when passed ``pd.NA`` (:issue:`32213`)
- Improved error message when subtracting two :class:`Timestamp` that result in an out-of-bounds :class:`Timedelta` (:issue:`31774`)
@@ -82,7 +83,9 @@ Bug fixes
**Experimental dtypes**
-- Fix bug in :meth:`DataFrame.convert_dtypes` for columns that were already using the ``"string"`` dtype (:issue:`31731`).
+- Fixed bug in :meth:`DataFrame.convert_dtypes` for columns that were already using the ``"string"`` dtype (:issue:`31731`).
+- Fixed bug in :meth:`DataFrame.convert_dtypes` for series with mix of integers and strings (:issue:`32117`)
+- Fixed bug in :meth:`DataFrame.convert_dtypes` where ``BooleanDtype`` columns were converted to ``Int64`` (:issue:`32287`)
- Fixed bug in setting values using a slice indexer with string dtype (:issue:`31772`)
- Fixed bug where :meth:`pandas.core.groupby.GroupBy.first` and :meth:`pandas.core.groupby.GroupBy.last` would raise a ``TypeError`` when groups contained ``pd.NA`` in a column of object dtype (:issue:`32123`)
- Fix bug in :meth:`Series.convert_dtypes` for series with mix of integers and strings (:issue:`32117`)
| https://github.com/pandas-dev/pandas/pull/32620 | https://api.github.com/repos/pandas-dev/pandas/pulls/32626 | 2020-03-11T13:56:09Z | 2020-03-11T14:48:58Z | 2020-03-11T14:48:58Z | 2020-03-11T14:49:06Z |
DOC: fix formatting / links of API refs in 1.0.2 whatsnew | diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index d99ce468d402f..10191542b6d41 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -17,16 +17,17 @@ Fixed regressions
- Fixed regression in :meth:`DataFrame.to_excel` when ``columns`` kwarg is passed (:issue:`31677`)
- Fixed regression in :meth:`Series.align` when ``other`` is a DataFrame and ``method`` is not None (:issue:`31785`)
-- Fixed regression in :meth:`pandas.core.groupby.RollingGroupby.apply` where the ``raw`` parameter was ignored (:issue:`31754`)
-- Fixed regression in :meth:`pandas.core.window.Rolling.corr` when using a time offset (:issue:`31789`)
-- Fixed regression in :meth:`pandas.core.groupby.DataFrameGroupBy.nunique` which was modifying the original values if ``NaN`` values were present (:issue:`31950`)
+- Fixed regression in ``groupby(..).rolling(..).apply()`` (``RollingGroupby``) where the ``raw`` parameter was ignored (:issue:`31754`)
+- Fixed regression in :meth:`rolling(..).corr() <pandas.core.window.rolling.Rolling.corr>` when using a time offset (:issue:`31789`)
+- Fixed regression in :meth:`groupby(..).nunique() <pandas.core.groupby.DataFrameGroupBy.nunique>` which was modifying the original values if ``NaN`` values were present (:issue:`31950`)
- Fixed regression where :func:`read_pickle` raised a ``UnicodeDecodeError`` when reading a py27 pickle with :class:`MultiIndex` column (:issue:`31988`).
- Fixed regression in :class:`DataFrame` arithmetic operations with mis-matched columns (:issue:`31623`)
-- Fixed regression in :meth:`pandas.core.groupby.GroupBy.agg` calling a user-provided function an extra time on an empty input (:issue:`31760`)
-- Joining on :class:`DatetimeIndex` or :class:`TimedeltaIndex` will preserve ``freq`` in simple cases (:issue:`32166`)
-- Fixed bug in the repr of an object-dtype :class:`Index` with bools and missing values (:issue:`32146`)
+- Fixed regression in :meth:`groupby(..).agg() <pandas.core.groupby.GroupBy.agg>` calling a user-provided function an extra time on an empty input (:issue:`31760`)
+- Fixed regression in joining on :class:`DatetimeIndex` or :class:`TimedeltaIndex` to preserve ``freq`` in simple cases (:issue:`32166`)
+- Fixed regression in the repr of an object-dtype :class:`Index` with bools and missing values (:issue:`32146`)
- Fixed regression in :meth:`read_csv` in which the ``encoding`` option was not recognized with certain file-like objects (:issue:`31819`)
--
+- Fixed regression in :meth:`DataFrame.reindex` and :meth:`Series.reindex` when reindexing with (tz-aware) index and ``method=nearest`` (:issue:`26683`)
+
.. ---------------------------------------------------------------------------
@@ -64,7 +65,6 @@ Bug fixes
**Datetimelike**
-- Bug in :meth:`DataFrame.reindex` and :meth:`Series.reindex` when reindexing with a tz-aware index (:issue:`26683`)
- Bug in :meth:`Series.astype` not copying for tz-naive and tz-aware datetime64 dtype (:issue:`32490`)
- Bug where :func:`to_datetime` would raise when passed ``pd.NA`` (:issue:`32213`)
- Improved error message when subtracting two :class:`Timestamp` that result in an out-of-bounds :class:`Timedelta` (:issue:`31774`)
@@ -83,11 +83,11 @@ Bug fixes
**Experimental dtypes**
-- Fix bug in :meth:`DataFrame.convert_dtypes` for columns that were already using the ``"string"`` dtype (:issue:`31731`).
+- Fixed bug in :meth:`DataFrame.convert_dtypes` for columns that were already using the ``"string"`` dtype (:issue:`31731`).
+- Fixed bug in :meth:`DataFrame.convert_dtypes` for series with mix of integers and strings (:issue:`32117`)
+- Fixed bug in :meth:`DataFrame.convert_dtypes` where ``BooleanDtype`` columns were converted to ``Int64`` (:issue:`32287`)
- Fixed bug in setting values using a slice indexer with string dtype (:issue:`31772`)
- Fixed bug where :meth:`pandas.core.groupby.GroupBy.first` and :meth:`pandas.core.groupby.GroupBy.last` would raise a ``TypeError`` when groups contained ``pd.NA`` in a column of object dtype (:issue:`32123`)
-- Fix bug in :meth:`Series.convert_dtypes` for series with mix of integers and strings (:issue:`32117`)
-- Fixed bug in :meth:`DataFrame.convert_dtypes`, where ``BooleanDtype`` columns were converted to ``Int64`` (:issue:`32287`)
**Strings**
| cc @TomAugspurger (did a few other clean-ups directly as well) | https://api.github.com/repos/pandas-dev/pandas/pulls/32620 | 2020-03-11T12:36:46Z | 2020-03-11T13:45:30Z | 2020-03-11T13:45:30Z | 2020-03-11T14:00:18Z |
DOC: Clarify pivot_table fill_value description | diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 30abcafb56ffb..e0887c870279b 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -5903,7 +5903,8 @@ def pivot(self, index=None, columns=None, values=None) -> "DataFrame":
If dict is passed, the key is column to aggregate and value
is function or list of functions.
fill_value : scalar, default None
- Value to replace missing values with.
+ Value to replace missing values with (in the resulting pivot table,
+ after aggregation).
margins : bool, default False
Add all row / columns (e.g. for subtotal / grand totals).
dropna : bool, default True
| - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
---
Clarify that `fill_value` gets used after performing the aggregation (i.e. it fills values in the resulting pivot table).
(Depending on the aggfunc and df, the result will be different if performing the filling before/after aggregation).
---
### Actually, why not remove it, requiring the user to more explicitly do `df.pivot_table(...).fillna(...)`? | https://api.github.com/repos/pandas-dev/pandas/pulls/32618 | 2020-03-11T10:14:22Z | 2020-03-11T23:25:48Z | 2020-03-11T23:25:48Z | 2020-03-11T23:25:54Z |
CLN: remove Block.array_dtype, SingleBlockManager.array_dtype | diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index 024d3b205df77..bb5b543538220 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -11,7 +11,6 @@
import pandas._libs.internals as libinternals
from pandas._libs.tslibs import Timedelta, conversion
from pandas._libs.tslibs.timezones import tz_compare
-from pandas._typing import DtypeObj
from pandas.util._validators import validate_bool_kwarg
from pandas.core.dtypes.cast import (
@@ -256,14 +255,6 @@ def mgr_locs(self, new_mgr_locs):
self._mgr_locs = new_mgr_locs
- @property
- def array_dtype(self) -> DtypeObj:
- """
- the dtype to return if I want to construct this block as an
- array
- """
- return self.dtype
-
def make_block(self, values, placement=None) -> "Block":
"""
Create a new block, with type inference propagate any values that are
@@ -2922,14 +2913,6 @@ def __init__(self, values, placement, ndim=None):
def _holder(self):
return Categorical
- @property
- def array_dtype(self):
- """
- the dtype to return if I want to construct this block as an
- array
- """
- return np.object_
-
def to_dense(self):
# Categorical.get_values returns a DatetimeIndex for datetime
# categories, so we can't simply use `np.asarray(self.values)` like
diff --git a/pandas/core/internals/managers.py b/pandas/core/internals/managers.py
index 0bba8de6682ea..469521426b13a 100644
--- a/pandas/core/internals/managers.py
+++ b/pandas/core/internals/managers.py
@@ -193,8 +193,10 @@ def make_empty(self: T, axes=None) -> T:
# preserve dtype if possible
if self.ndim == 1:
assert isinstance(self, SingleBlockManager) # for mypy
- arr = np.array([], dtype=self.array_dtype)
- blocks = [make_block(arr, placement=slice(0, 0), ndim=1)]
+ blk = self.blocks[0]
+ arr = blk.values[:0]
+ nb = blk.make_block_same_class(arr, placement=slice(0, 0), ndim=1)
+ blocks = [nb]
else:
blocks = []
return type(self).from_blocks(blocks, axes)
@@ -1571,10 +1573,6 @@ def index(self) -> Index:
def dtype(self) -> DtypeObj:
return self._block.dtype
- @property
- def array_dtype(self) -> DtypeObj:
- return self._block.array_dtype
-
def get_dtype_counts(self):
return {self.dtype.name: 1}
| We can get a more accurately-dtyped empty array instead. | https://api.github.com/repos/pandas-dev/pandas/pulls/32612 | 2020-03-11T04:35:40Z | 2020-03-14T03:30:13Z | 2020-03-14T03:30:13Z | 2020-03-14T16:27:26Z |
REF: implement _get_engine_target | diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index a5f133fb10d10..929fcafff36af 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -567,10 +567,10 @@ def _cleanup(self):
def _engine(self):
# property, for now, slow to look up
- # to avoid a reference cycle, bind `_ndarray_values` to a local variable, so
+ # to avoid a reference cycle, bind `target_values` to a local variable, so
# `self` is not passed into the lambda.
- _ndarray_values = self._ndarray_values
- return self._engine_type(lambda: _ndarray_values, len(self))
+ target_values = self._get_engine_target()
+ return self._engine_type(lambda: target_values, len(self))
# --------------------------------------------------------------------
# Array-Like Methods
@@ -2972,7 +2972,7 @@ def get_indexer(
"backfill or nearest reindexing"
)
- indexer = self._engine.get_indexer(target._ndarray_values)
+ indexer = self._engine.get_indexer(target._get_engine_target())
return ensure_platform_int(indexer)
@@ -2986,19 +2986,20 @@ def _convert_tolerance(self, tolerance, target):
def _get_fill_indexer(
self, target: "Index", method: str_t, limit=None, tolerance=None
) -> np.ndarray:
+
+ target_values = target._get_engine_target()
+
if self.is_monotonic_increasing and target.is_monotonic_increasing:
engine_method = (
self._engine.get_pad_indexer
if method == "pad"
else self._engine.get_backfill_indexer
)
- indexer = engine_method(target._ndarray_values, limit)
+ indexer = engine_method(target_values, limit)
else:
indexer = self._get_fill_indexer_searchsorted(target, method, limit)
if tolerance is not None:
- indexer = self._filter_indexer_tolerance(
- target._ndarray_values, indexer, tolerance
- )
+ indexer = self._filter_indexer_tolerance(target_values, indexer, tolerance)
return indexer
def _get_fill_indexer_searchsorted(
@@ -3911,6 +3912,12 @@ def _internal_get_values(self) -> np.ndarray:
"""
return self.values
+ def _get_engine_target(self) -> np.ndarray:
+ """
+ Get the ndarray that we can pass to the IndexEngine constructor.
+ """
+ return self._values
+
@Appender(IndexOpsMixin.memory_usage.__doc__)
def memory_usage(self, deep: bool = False) -> int:
result = super().memory_usage(deep=deep)
@@ -4653,7 +4660,7 @@ def get_indexer_non_unique(self, target):
elif self.is_all_dates and target.is_all_dates: # GH 30399
tgt_values = target.asi8
else:
- tgt_values = target._ndarray_values
+ tgt_values = target._get_engine_target()
indexer, missing = self._engine.get_indexer_non_unique(tgt_values)
return ensure_platform_int(indexer), missing
diff --git a/pandas/core/indexes/extension.py b/pandas/core/indexes/extension.py
index 7b11df15f69fb..4984fc27516ff 100644
--- a/pandas/core/indexes/extension.py
+++ b/pandas/core/indexes/extension.py
@@ -231,6 +231,9 @@ def __array__(self, dtype=None) -> np.ndarray:
def _ndarray_values(self) -> np.ndarray:
return self._data._ndarray_values
+ def _get_engine_target(self) -> np.ndarray:
+ return self._data._values_for_argsort()
+
@Appender(Index.dropna.__doc__)
def dropna(self, how="any"):
if how not in ("any", "all"):
| Follow-up to #32467. | https://api.github.com/repos/pandas-dev/pandas/pulls/32611 | 2020-03-11T03:01:59Z | 2020-03-12T04:30:03Z | 2020-03-12T04:30:03Z | 2020-03-12T15:30:46Z |
CLN: Clean frame/test_constructors.py | diff --git a/pandas/tests/frame/test_constructors.py b/pandas/tests/frame/test_constructors.py
index 071d2409f1be2..4407f204e0a0c 100644
--- a/pandas/tests/frame/test_constructors.py
+++ b/pandas/tests/frame/test_constructors.py
@@ -47,15 +47,15 @@
class TestDataFrameConstructors:
def test_series_with_name_not_matching_column(self):
# GH#9232
- x = pd.Series(range(5), name=1)
- y = pd.Series(range(5), name=0)
+ x = Series(range(5), name=1)
+ y = Series(range(5), name=0)
- result = pd.DataFrame(x, columns=[0])
- expected = pd.DataFrame([], columns=[0])
+ result = DataFrame(x, columns=[0])
+ expected = DataFrame([], columns=[0])
tm.assert_frame_equal(result, expected)
- result = pd.DataFrame(y, columns=[1])
- expected = pd.DataFrame([], columns=[1])
+ result = DataFrame(y, columns=[1])
+ expected = DataFrame([], columns=[1])
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
@@ -126,7 +126,7 @@ def test_constructor_cast_failure(self):
def test_constructor_dtype_copy(self):
orig_df = DataFrame({"col1": [1.0], "col2": [2.0], "col3": [3.0]})
- new_df = pd.DataFrame(orig_df, dtype=float, copy=True)
+ new_df = DataFrame(orig_df, dtype=float, copy=True)
new_df["col1"] = 200.0
assert orig_df["col1"][0] == 1.0
@@ -220,10 +220,10 @@ def test_constructor_rec(self, float_frame):
index = float_frame.index
df = DataFrame(rec)
- tm.assert_index_equal(df.columns, pd.Index(rec.dtype.names))
+ tm.assert_index_equal(df.columns, Index(rec.dtype.names))
df2 = DataFrame(rec, index=index)
- tm.assert_index_equal(df2.columns, pd.Index(rec.dtype.names))
+ tm.assert_index_equal(df2.columns, Index(rec.dtype.names))
tm.assert_index_equal(df2.index, index)
rng = np.arange(len(rec))[::-1]
@@ -298,7 +298,7 @@ def test_constructor_dict(self):
tm.assert_series_equal(frame["col1"], datetime_series.rename("col1"))
- exp = pd.Series(
+ exp = Series(
np.concatenate([[np.nan] * 5, datetime_series_short.values]),
index=datetime_series.index,
name="col2",
@@ -325,7 +325,7 @@ def test_constructor_dict(self):
# Length-one dict micro-optimization
frame = DataFrame({"A": {"1": 1, "2": 2}})
- tm.assert_index_equal(frame.index, pd.Index(["1", "2"]))
+ tm.assert_index_equal(frame.index, Index(["1", "2"]))
# empty dict plus index
idx = Index([0, 1, 2])
@@ -418,8 +418,8 @@ def test_constructor_dict_order_insertion(self):
def test_constructor_dict_nan_key_and_columns(self):
# GH 16894
- result = pd.DataFrame({np.nan: [1, 2], 2: [2, 3]}, columns=[np.nan, 2])
- expected = pd.DataFrame([[1, 2], [2, 3]], columns=[np.nan, 2])
+ result = DataFrame({np.nan: [1, 2], 2: [2, 3]}, columns=[np.nan, 2])
+ expected = DataFrame([[1, 2], [2, 3]], columns=[np.nan, 2])
tm.assert_frame_equal(result, expected)
def test_constructor_multi_index(self):
@@ -428,29 +428,29 @@ def test_constructor_multi_index(self):
tuples = [(2, 3), (3, 3), (3, 3)]
mi = MultiIndex.from_tuples(tuples)
df = DataFrame(index=mi, columns=mi)
- assert pd.isna(df).values.ravel().all()
+ assert isna(df).values.ravel().all()
tuples = [(3, 3), (2, 3), (3, 3)]
mi = MultiIndex.from_tuples(tuples)
df = DataFrame(index=mi, columns=mi)
- assert pd.isna(df).values.ravel().all()
+ assert isna(df).values.ravel().all()
def test_constructor_2d_index(self):
# GH 25416
# handling of 2d index in construction
- df = pd.DataFrame([[1]], columns=[[1]], index=[1, 2])
- expected = pd.DataFrame(
+ df = DataFrame([[1]], columns=[[1]], index=[1, 2])
+ expected = DataFrame(
[1, 1],
index=pd.Int64Index([1, 2], dtype="int64"),
- columns=pd.MultiIndex(levels=[[1]], codes=[[0]]),
+ columns=MultiIndex(levels=[[1]], codes=[[0]]),
)
tm.assert_frame_equal(df, expected)
- df = pd.DataFrame([[1]], columns=[[1]], index=[[1, 2]])
- expected = pd.DataFrame(
+ df = DataFrame([[1]], columns=[[1]], index=[[1, 2]])
+ expected = DataFrame(
[1, 1],
- index=pd.MultiIndex(levels=[[1, 2]], codes=[[0, 1]]),
- columns=pd.MultiIndex(levels=[[1]], codes=[[0]]),
+ index=MultiIndex(levels=[[1, 2]], codes=[[0, 1]]),
+ columns=MultiIndex(levels=[[1]], codes=[[0]]),
)
tm.assert_frame_equal(df, expected)
@@ -471,7 +471,7 @@ def test_constructor_error_msgs(self):
DataFrame(
np.arange(12).reshape((4, 3)),
columns=["foo", "bar", "baz"],
- index=pd.date_range("2000-01-01", periods=3),
+ index=date_range("2000-01-01", periods=3),
)
arr = np.array([[4, 5, 6]])
@@ -713,14 +713,12 @@ def test_constructor_period(self):
# PeriodIndex
a = pd.PeriodIndex(["2012-01", "NaT", "2012-04"], freq="M")
b = pd.PeriodIndex(["2012-02-01", "2012-03-01", "NaT"], freq="D")
- df = pd.DataFrame({"a": a, "b": b})
+ df = DataFrame({"a": a, "b": b})
assert df["a"].dtype == a.dtype
assert df["b"].dtype == b.dtype
# list of periods
- df = pd.DataFrame(
- {"a": a.astype(object).tolist(), "b": b.astype(object).tolist()}
- )
+ df = DataFrame({"a": a.astype(object).tolist(), "b": b.astype(object).tolist()})
assert df["a"].dtype == a.dtype
assert df["b"].dtype == b.dtype
@@ -882,8 +880,8 @@ def test_constructor_maskedarray_nonfloat(self):
def test_constructor_maskedarray_hardened(self):
# Check numpy masked arrays with hard masks -- from GH24574
mat_hard = ma.masked_all((2, 2), dtype=float).harden_mask()
- result = pd.DataFrame(mat_hard, columns=["A", "B"], index=[1, 2])
- expected = pd.DataFrame(
+ result = DataFrame(mat_hard, columns=["A", "B"], index=[1, 2])
+ expected = DataFrame(
{"A": [np.nan, np.nan], "B": [np.nan, np.nan]},
columns=["A", "B"],
index=[1, 2],
@@ -892,8 +890,8 @@ def test_constructor_maskedarray_hardened(self):
tm.assert_frame_equal(result, expected)
# Check case where mask is hard but no data are masked
mat_hard = ma.ones((2, 2), dtype=float).harden_mask()
- result = pd.DataFrame(mat_hard, columns=["A", "B"], index=[1, 2])
- expected = pd.DataFrame(
+ result = DataFrame(mat_hard, columns=["A", "B"], index=[1, 2])
+ expected = DataFrame(
{"A": [1.0, 1.0], "B": [1.0, 1.0]},
columns=["A", "B"],
index=[1, 2],
@@ -907,8 +905,8 @@ def test_constructor_maskedrecarray_dtype(self):
np.ma.zeros(5, dtype=[("date", "<f8"), ("price", "<f8")]), mask=[False] * 5
)
data = data.view(mrecords.mrecarray)
- result = pd.DataFrame(data, dtype=int)
- expected = pd.DataFrame(np.zeros((5, 2), dtype=int), columns=["date", "price"])
+ result = DataFrame(data, dtype=int)
+ expected = DataFrame(np.zeros((5, 2), dtype=int), columns=["date", "price"])
tm.assert_frame_equal(result, expected)
def test_constructor_mrecarray(self):
@@ -1271,9 +1269,9 @@ def test_constructor_list_of_series(self):
tm.assert_frame_equal(result, expected)
def test_constructor_list_of_series_aligned_index(self):
- series = [pd.Series(i, index=["b", "a", "c"], name=str(i)) for i in range(3)]
- result = pd.DataFrame(series)
- expected = pd.DataFrame(
+ series = [Series(i, index=["b", "a", "c"], name=str(i)) for i in range(3)]
+ result = DataFrame(series)
+ expected = DataFrame(
{"b": [0, 1, 2], "a": [0, 1, 2], "c": [0, 1, 2]},
columns=["b", "a", "c"],
index=["0", "1", "2"],
@@ -1500,12 +1498,12 @@ def test_constructor_Series_named_and_columns(self):
s1 = Series(range(5), name=1)
# matching name and column gives standard frame
- tm.assert_frame_equal(pd.DataFrame(s0, columns=[0]), s0.to_frame())
- tm.assert_frame_equal(pd.DataFrame(s1, columns=[1]), s1.to_frame())
+ tm.assert_frame_equal(DataFrame(s0, columns=[0]), s0.to_frame())
+ tm.assert_frame_equal(DataFrame(s1, columns=[1]), s1.to_frame())
# non-matching produces empty frame
- assert pd.DataFrame(s0, columns=[1]).empty
- assert pd.DataFrame(s1, columns=[0]).empty
+ assert DataFrame(s0, columns=[1]).empty
+ assert DataFrame(s1, columns=[0]).empty
def test_constructor_Series_differently_indexed(self):
# name
@@ -1984,7 +1982,7 @@ def test_from_records_to_records(self):
# TODO(wesm): unused
frame = DataFrame.from_records(arr) # noqa
- index = pd.Index(np.arange(len(arr))[::-1])
+ index = Index(np.arange(len(arr))[::-1])
indexed_frame = DataFrame.from_records(arr, index=index)
tm.assert_index_equal(indexed_frame.index, index)
@@ -2283,7 +2281,7 @@ def test_from_records_sequencelike(self):
# empty case
result = DataFrame.from_records([], columns=["foo", "bar", "baz"])
assert len(result) == 0
- tm.assert_index_equal(result.columns, pd.Index(["foo", "bar", "baz"]))
+ tm.assert_index_equal(result.columns, Index(["foo", "bar", "baz"]))
result = DataFrame.from_records([])
assert len(result) == 0
@@ -2442,20 +2440,20 @@ def test_datetime_date_tuple_columns_from_dict(self):
v = date.today()
tup = v, v
result = DataFrame({tup: Series(range(3), index=range(3))}, columns=[tup])
- expected = DataFrame([0, 1, 2], columns=pd.Index(pd.Series([tup])))
+ expected = DataFrame([0, 1, 2], columns=Index(Series([tup])))
tm.assert_frame_equal(result, expected)
def test_construct_with_two_categoricalindex_series(self):
# GH 14600
- s1 = pd.Series(
+ s1 = Series(
[39, 6, 4], index=pd.CategoricalIndex(["female", "male", "unknown"])
)
- s2 = pd.Series(
+ s2 = Series(
[2, 152, 2, 242, 150],
index=pd.CategoricalIndex(["f", "female", "m", "male", "unknown"]),
)
- result = pd.DataFrame([s1, s2])
- expected = pd.DataFrame(
+ result = DataFrame([s1, s2])
+ expected = DataFrame(
np.array(
[[np.nan, 39.0, np.nan, 6.0, 4.0], [2.0, 152.0, 2.0, 242.0, 150.0]]
),
@@ -2554,19 +2552,19 @@ def test_nested_dict_construction(self):
"Nevada": {2001: 2.4, 2002: 2.9},
"Ohio": {2000: 1.5, 2001: 1.7, 2002: 3.6},
}
- result = pd.DataFrame(pop, index=[2001, 2002, 2003], columns=columns)
- expected = pd.DataFrame(
+ result = DataFrame(pop, index=[2001, 2002, 2003], columns=columns)
+ expected = DataFrame(
[(2.4, 1.7), (2.9, 3.6), (np.nan, np.nan)],
columns=columns,
- index=pd.Index([2001, 2002, 2003]),
+ index=Index([2001, 2002, 2003]),
)
tm.assert_frame_equal(result, expected)
def test_from_tzaware_object_array(self):
# GH#26825 2D object array of tzaware timestamps should not raise
- dti = pd.date_range("2016-04-05 04:30", periods=3, tz="UTC")
+ dti = date_range("2016-04-05 04:30", periods=3, tz="UTC")
data = dti._data.astype(object).reshape(1, -1)
- df = pd.DataFrame(data)
+ df = DataFrame(data)
assert df.shape == (1, 3)
assert (df.dtypes == dti.dtype).all()
assert (df == dti).all().all()
@@ -2605,7 +2603,7 @@ def test_from_tzaware_mixed_object_array(self):
def test_from_2d_ndarray_with_dtype(self):
# GH#12513
array_dim2 = np.arange(10).reshape((5, 2))
- df = pd.DataFrame(array_dim2, dtype="datetime64[ns, UTC]")
+ df = DataFrame(array_dim2, dtype="datetime64[ns, UTC]")
- expected = pd.DataFrame(array_dim2).astype("datetime64[ns, UTC]")
+ expected = DataFrame(array_dim2).astype("datetime64[ns, UTC]")
tm.assert_frame_equal(df, expected)
| I think these classes are all imported directly so don't need to reference the pandas namespace | https://api.github.com/repos/pandas-dev/pandas/pulls/32610 | 2020-03-11T02:41:46Z | 2020-03-14T19:41:00Z | 2020-03-14T19:41:00Z | 2020-03-14T19:44:07Z |
Backport PR #32577 on branch 1.0.x (REG: Restore read_csv function for some file-likes) | diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index 9841df0507138..8db47000480ed 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -25,6 +25,7 @@ Fixed regressions
- Fixed regression in :meth:`pandas.core.groupby.GroupBy.agg` calling a user-provided function an extra time on an empty input (:issue:`31760`)
- Joining on :class:`DatetimeIndex` or :class:`TimedeltaIndex` will preserve ``freq`` in simple cases (:issue:`32166`)
- Fixed bug in the repr of an object-dtype :class:`Index` with bools and missing values (:issue:`32146`)
+- Fixed regression in :meth:`read_csv` in which the ``encoding`` option was not recognized with certain file-like objects (:issue:`31819`)
-
.. ---------------------------------------------------------------------------
diff --git a/pandas/_libs/parsers.pyx b/pandas/_libs/parsers.pyx
index 3077f73a8d1a4..ef887444e516d 100644
--- a/pandas/_libs/parsers.pyx
+++ b/pandas/_libs/parsers.pyx
@@ -638,7 +638,8 @@ cdef class TextReader:
raise ValueError(f'Unrecognized compression type: '
f'{self.compression}')
- if self.encoding and isinstance(source, (io.BufferedIOBase, io.RawIOBase)):
+ if (self.encoding and hasattr(source, "read") and
+ not hasattr(source, "encoding")):
source = io.TextIOWrapper(
source, self.encoding.decode('utf-8'), newline='')
diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py
index cb108362f4dc7..3ddfb71fd78e8 100755
--- a/pandas/io/parsers.py
+++ b/pandas/io/parsers.py
@@ -5,7 +5,7 @@
from collections import abc, defaultdict
import csv
import datetime
-from io import BufferedIOBase, RawIOBase, StringIO, TextIOWrapper
+from io import StringIO, TextIOWrapper
import re
import sys
from textwrap import fill
@@ -1876,7 +1876,7 @@ def __init__(self, src, **kwds):
# Handle the file object with universal line mode enabled.
# We will handle the newline character ourselves later on.
- if isinstance(src, (BufferedIOBase, RawIOBase)):
+ if hasattr(src, "read") and not hasattr(src, "encoding"):
src = TextIOWrapper(src, encoding=encoding, newline="")
kwds["encoding"] = "utf-8"
diff --git a/pandas/tests/io/parser/test_encoding.py b/pandas/tests/io/parser/test_encoding.py
index 620f837935718..e38b7fab6a603 100644
--- a/pandas/tests/io/parser/test_encoding.py
+++ b/pandas/tests/io/parser/test_encoding.py
@@ -175,3 +175,25 @@ def test_encoding_temp_file(all_parsers, utf_value, encoding_fmt, pass_encoding)
result = parser.read_csv(f, encoding=encoding if pass_encoding else None)
tm.assert_frame_equal(result, expected)
+
+
+def test_encoding_named_temp_file(all_parsers):
+ # see gh-31819
+ parser = all_parsers
+ encoding = "shift-jis"
+
+ if parser.engine == "python":
+ pytest.skip("NamedTemporaryFile does not work with Python engine")
+
+ title = "γ¦γγ¨"
+ data = "γγ"
+
+ expected = DataFrame({title: [data]})
+
+ with tempfile.NamedTemporaryFile() as f:
+ f.write(f"{title}\n{data}".encode(encoding))
+
+ f.seek(0)
+
+ result = parser.read_csv(f, encoding=encoding)
+ tm.assert_frame_equal(result, expected)
| Backport PR #32577: REG: Restore read_csv function for some file-likes | https://api.github.com/repos/pandas-dev/pandas/pulls/32609 | 2020-03-11T02:22:12Z | 2020-03-11T04:08:25Z | 2020-03-11T04:08:25Z | 2020-03-11T04:08:25Z |
TST: fixturize skipna in test_nanops | diff --git a/pandas/tests/test_nanops.py b/pandas/tests/test_nanops.py
index f7e652eb78e2d..852e1ce489893 100644
--- a/pandas/tests/test_nanops.py
+++ b/pandas/tests/test_nanops.py
@@ -19,6 +19,14 @@
has_c16 = hasattr(np, "complex128")
+@pytest.fixture(params=[True, False])
+def skipna(request):
+ """
+ Fixture to pass skipna to nanops functions.
+ """
+ return request.param
+
+
class TestnanopsDataFrame:
def setup_method(self, method):
np.random.seed(11235)
@@ -89,28 +97,14 @@ def teardown_method(self, method):
def check_results(self, targ, res, axis, check_dtype=True):
res = getattr(res, "asm8", res)
- res = getattr(res, "values", res)
-
- # timedeltas are a beast here
- def _coerce_tds(targ, res):
- if hasattr(targ, "dtype") and targ.dtype == "m8[ns]":
- if len(targ) == 1:
- targ = targ[0].item()
- res = res.item()
- else:
- targ = targ.view("i8")
- return targ, res
- try:
- if (
- axis != 0
- and hasattr(targ, "shape")
- and targ.ndim
- and targ.shape != res.shape
- ):
- res = np.split(res, [targ.shape[0]], axis=0)[0]
- except (ValueError, IndexError):
- targ, res = _coerce_tds(targ, res)
+ if (
+ axis != 0
+ and hasattr(targ, "shape")
+ and targ.ndim
+ and targ.shape != res.shape
+ ):
+ res = np.split(res, [targ.shape[0]], axis=0)[0]
try:
tm.assert_almost_equal(targ, res, check_dtype=check_dtype)
@@ -118,9 +112,7 @@ def _coerce_tds(targ, res):
# handle timedelta dtypes
if hasattr(targ, "dtype") and targ.dtype == "m8[ns]":
- targ, res = _coerce_tds(targ, res)
- tm.assert_almost_equal(targ, res, check_dtype=check_dtype)
- return
+ raise
# There are sometimes rounding errors with
# complex and object dtypes.
@@ -149,29 +141,29 @@ def check_fun_data(
targfunc,
testarval,
targarval,
+ skipna,
check_dtype=True,
empty_targfunc=None,
**kwargs,
):
for axis in list(range(targarval.ndim)) + [None]:
- for skipna in [False, True]:
- targartempval = targarval if skipna else testarval
- if skipna and empty_targfunc and isna(targartempval).all():
- targ = empty_targfunc(targartempval, axis=axis, **kwargs)
- else:
- targ = targfunc(targartempval, axis=axis, **kwargs)
+ targartempval = targarval if skipna else testarval
+ if skipna and empty_targfunc and isna(targartempval).all():
+ targ = empty_targfunc(targartempval, axis=axis, **kwargs)
+ else:
+ targ = targfunc(targartempval, axis=axis, **kwargs)
- res = testfunc(testarval, axis=axis, skipna=skipna, **kwargs)
+ res = testfunc(testarval, axis=axis, skipna=skipna, **kwargs)
+ self.check_results(targ, res, axis, check_dtype=check_dtype)
+ if skipna:
+ res = testfunc(testarval, axis=axis, **kwargs)
+ self.check_results(targ, res, axis, check_dtype=check_dtype)
+ if axis is None:
+ res = testfunc(testarval, skipna=skipna, **kwargs)
+ self.check_results(targ, res, axis, check_dtype=check_dtype)
+ if skipna and axis is None:
+ res = testfunc(testarval, **kwargs)
self.check_results(targ, res, axis, check_dtype=check_dtype)
- if skipna:
- res = testfunc(testarval, axis=axis, **kwargs)
- self.check_results(targ, res, axis, check_dtype=check_dtype)
- if axis is None:
- res = testfunc(testarval, skipna=skipna, **kwargs)
- self.check_results(targ, res, axis, check_dtype=check_dtype)
- if skipna and axis is None:
- res = testfunc(testarval, **kwargs)
- self.check_results(targ, res, axis, check_dtype=check_dtype)
if testarval.ndim <= 1:
return
@@ -184,12 +176,15 @@ def check_fun_data(
targfunc,
testarval2,
targarval2,
+ skipna=skipna,
check_dtype=check_dtype,
empty_targfunc=empty_targfunc,
**kwargs,
)
- def check_fun(self, testfunc, targfunc, testar, empty_targfunc=None, **kwargs):
+ def check_fun(
+ self, testfunc, targfunc, testar, skipna, empty_targfunc=None, **kwargs
+ ):
targar = testar
if testar.endswith("_nan") and hasattr(self, testar[:-4]):
@@ -202,6 +197,7 @@ def check_fun(self, testfunc, targfunc, testar, empty_targfunc=None, **kwargs):
targfunc,
testarval,
targarval,
+ skipna=skipna,
empty_targfunc=empty_targfunc,
**kwargs,
)
@@ -210,6 +206,7 @@ def check_funs(
self,
testfunc,
targfunc,
+ skipna,
allow_complex=True,
allow_all_nan=True,
allow_date=True,
@@ -217,10 +214,10 @@ def check_funs(
allow_obj=True,
**kwargs,
):
- self.check_fun(testfunc, targfunc, "arr_float", **kwargs)
- self.check_fun(testfunc, targfunc, "arr_float_nan", **kwargs)
- self.check_fun(testfunc, targfunc, "arr_int", **kwargs)
- self.check_fun(testfunc, targfunc, "arr_bool", **kwargs)
+ self.check_fun(testfunc, targfunc, "arr_float", skipna, **kwargs)
+ self.check_fun(testfunc, targfunc, "arr_float_nan", skipna, **kwargs)
+ self.check_fun(testfunc, targfunc, "arr_int", skipna, **kwargs)
+ self.check_fun(testfunc, targfunc, "arr_bool", skipna, **kwargs)
objs = [
self.arr_float.astype("O"),
self.arr_int.astype("O"),
@@ -228,18 +225,18 @@ def check_funs(
]
if allow_all_nan:
- self.check_fun(testfunc, targfunc, "arr_nan", **kwargs)
+ self.check_fun(testfunc, targfunc, "arr_nan", skipna, **kwargs)
if allow_complex:
- self.check_fun(testfunc, targfunc, "arr_complex", **kwargs)
- self.check_fun(testfunc, targfunc, "arr_complex_nan", **kwargs)
+ self.check_fun(testfunc, targfunc, "arr_complex", skipna, **kwargs)
+ self.check_fun(testfunc, targfunc, "arr_complex_nan", skipna, **kwargs)
if allow_all_nan:
- self.check_fun(testfunc, targfunc, "arr_nan_nanj", **kwargs)
+ self.check_fun(testfunc, targfunc, "arr_nan_nanj", skipna, **kwargs)
objs += [self.arr_complex.astype("O")]
if allow_date:
targfunc(self.arr_date)
- self.check_fun(testfunc, targfunc, "arr_date", **kwargs)
+ self.check_fun(testfunc, targfunc, "arr_date", skipna, **kwargs)
objs += [self.arr_date.astype("O")]
if allow_tdelta:
@@ -248,7 +245,7 @@ def check_funs(
except TypeError:
pass
else:
- self.check_fun(testfunc, targfunc, "arr_tdelta", **kwargs)
+ self.check_fun(testfunc, targfunc, "arr_tdelta", skipna, **kwargs)
objs += [self.arr_tdelta.astype("O")]
if allow_obj:
@@ -260,7 +257,7 @@ def check_funs(
targfunc = partial(
self._badobj_wrap, func=targfunc, allow_complex=allow_complex
)
- self.check_fun(testfunc, targfunc, "arr_obj", **kwargs)
+ self.check_fun(testfunc, targfunc, "arr_obj", skipna, **kwargs)
def _badobj_wrap(self, value, func, allow_complex=True, **kwargs):
if value.dtype.kind == "O":
@@ -273,28 +270,22 @@ def _badobj_wrap(self, value, func, allow_complex=True, **kwargs):
@pytest.mark.parametrize(
"nan_op,np_op", [(nanops.nanany, np.any), (nanops.nanall, np.all)]
)
- def test_nan_funcs(self, nan_op, np_op):
- # TODO: allow tdelta, doesn't break tests
- self.check_funs(
- nan_op, np_op, allow_all_nan=False, allow_date=False, allow_tdelta=False
- )
+ def test_nan_funcs(self, nan_op, np_op, skipna):
+ self.check_funs(nan_op, np_op, skipna, allow_all_nan=False, allow_date=False)
- def test_nansum(self):
+ def test_nansum(self, skipna):
self.check_funs(
nanops.nansum,
np.sum,
+ skipna,
allow_date=False,
check_dtype=False,
empty_targfunc=np.nansum,
)
- def test_nanmean(self):
+ def test_nanmean(self, skipna):
self.check_funs(
- nanops.nanmean,
- np.mean,
- allow_complex=False, # TODO: allow this, doesn't break test
- allow_obj=False,
- allow_date=False,
+ nanops.nanmean, np.mean, skipna, allow_obj=False, allow_date=False,
)
def test_nanmean_overflow(self):
@@ -336,22 +327,24 @@ def test_returned_dtype(self, dtype):
else:
assert result.dtype == dtype
- def test_nanmedian(self):
+ def test_nanmedian(self, skipna):
with warnings.catch_warnings(record=True):
warnings.simplefilter("ignore", RuntimeWarning)
self.check_funs(
nanops.nanmedian,
np.median,
+ skipna,
allow_complex=False,
allow_date=False,
allow_obj="convert",
)
@pytest.mark.parametrize("ddof", range(3))
- def test_nanvar(self, ddof):
+ def test_nanvar(self, ddof, skipna):
self.check_funs(
nanops.nanvar,
np.var,
+ skipna,
allow_complex=False,
allow_date=False,
allow_obj="convert",
@@ -359,10 +352,11 @@ def test_nanvar(self, ddof):
)
@pytest.mark.parametrize("ddof", range(3))
- def test_nanstd(self, ddof):
+ def test_nanstd(self, ddof, skipna):
self.check_funs(
nanops.nanstd,
np.std,
+ skipna,
allow_complex=False,
allow_date=False,
allow_obj="convert",
@@ -371,13 +365,14 @@ def test_nanstd(self, ddof):
@td.skip_if_no_scipy
@pytest.mark.parametrize("ddof", range(3))
- def test_nansem(self, ddof):
+ def test_nansem(self, ddof, skipna):
from scipy.stats import sem
with np.errstate(invalid="ignore"):
self.check_funs(
nanops.nansem,
sem,
+ skipna,
allow_complex=False,
allow_date=False,
allow_tdelta=False,
@@ -388,10 +383,10 @@ def test_nansem(self, ddof):
@pytest.mark.parametrize(
"nan_op,np_op", [(nanops.nanmin, np.min), (nanops.nanmax, np.max)]
)
- def test_nanops_with_warnings(self, nan_op, np_op):
+ def test_nanops_with_warnings(self, nan_op, np_op, skipna):
with warnings.catch_warnings(record=True):
warnings.simplefilter("ignore", RuntimeWarning)
- self.check_funs(nan_op, np_op, allow_obj=False)
+ self.check_funs(nan_op, np_op, skipna, allow_obj=False)
def _argminmax_wrap(self, value, axis=None, func=None):
res = func(value, axis)
@@ -408,17 +403,17 @@ def _argminmax_wrap(self, value, axis=None, func=None):
res = -1
return res
- def test_nanargmax(self):
+ def test_nanargmax(self, skipna):
with warnings.catch_warnings(record=True):
warnings.simplefilter("ignore", RuntimeWarning)
func = partial(self._argminmax_wrap, func=np.argmax)
- self.check_funs(nanops.nanargmax, func, allow_obj=False)
+ self.check_funs(nanops.nanargmax, func, skipna, allow_obj=False)
- def test_nanargmin(self):
+ def test_nanargmin(self, skipna):
with warnings.catch_warnings(record=True):
warnings.simplefilter("ignore", RuntimeWarning)
func = partial(self._argminmax_wrap, func=np.argmin)
- self.check_funs(nanops.nanargmin, func, allow_obj=False)
+ self.check_funs(nanops.nanargmin, func, skipna, allow_obj=False)
def _skew_kurt_wrap(self, values, axis=None, func=None):
if not isinstance(values.dtype.type, np.floating):
@@ -433,7 +428,7 @@ def _skew_kurt_wrap(self, values, axis=None, func=None):
return result
@td.skip_if_no_scipy
- def test_nanskew(self):
+ def test_nanskew(self, skipna):
from scipy.stats import skew
func = partial(self._skew_kurt_wrap, func=skew)
@@ -441,13 +436,14 @@ def test_nanskew(self):
self.check_funs(
nanops.nanskew,
func,
+ skipna,
allow_complex=False,
allow_date=False,
allow_tdelta=False,
)
@td.skip_if_no_scipy
- def test_nankurt(self):
+ def test_nankurt(self, skipna):
from scipy.stats import kurtosis
func1 = partial(kurtosis, fisher=True)
@@ -456,15 +452,17 @@ def test_nankurt(self):
self.check_funs(
nanops.nankurt,
func,
+ skipna,
allow_complex=False,
allow_date=False,
allow_tdelta=False,
)
- def test_nanprod(self):
+ def test_nanprod(self, skipna):
self.check_funs(
nanops.nanprod,
np.prod,
+ skipna,
allow_date=False,
allow_tdelta=False,
empty_targfunc=np.nanprod,
| https://api.github.com/repos/pandas-dev/pandas/pulls/32607 | 2020-03-11T01:56:21Z | 2020-03-11T02:55:24Z | 2020-03-11T02:55:24Z | 2020-03-11T03:09:37Z | |
Backport PR #32386 on branch 1.0.x (BUG: Fix rolling functions with variable windows on decreasing index) | diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index 9841df0507138..3e4dfd4e75a66 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -89,6 +89,10 @@ Bug fixes
- Using ``pd.NA`` with :meth:`Series.str.repeat` now correctly outputs a null value instead of raising error for vector inputs (:issue:`31632`)
+**Rolling**
+
+- Fixed rolling operations with variable window (defined by time duration) on decreasing time index (:issue:`32385`).
+
.. ---------------------------------------------------------------------------
.. _whatsnew_102.contributors:
diff --git a/pandas/_libs/window/aggregations.pyx b/pandas/_libs/window/aggregations.pyx
index 0348843abc129..495b436030120 100644
--- a/pandas/_libs/window/aggregations.pyx
+++ b/pandas/_libs/window/aggregations.pyx
@@ -1008,7 +1008,7 @@ def roll_max_variable(ndarray[float64_t] values, ndarray[int64_t] start,
def roll_min_fixed(ndarray[float64_t] values, ndarray[int64_t] start,
ndarray[int64_t] end, int64_t minp, int64_t win):
"""
- Moving max of 1d array of any numeric type along axis=0 ignoring NaNs.
+ Moving min of 1d array of any numeric type along axis=0 ignoring NaNs.
Parameters
----------
@@ -1025,7 +1025,7 @@ def roll_min_fixed(ndarray[float64_t] values, ndarray[int64_t] start,
def roll_min_variable(ndarray[float64_t] values, ndarray[int64_t] start,
ndarray[int64_t] end, int64_t minp):
"""
- Moving max of 1d array of any numeric type along axis=0 ignoring NaNs.
+ Moving min of 1d array of any numeric type along axis=0 ignoring NaNs.
Parameters
----------
diff --git a/pandas/_libs/window/indexers.pyx b/pandas/_libs/window/indexers.pyx
index 2d01d1964c043..8a1e7feb57ace 100644
--- a/pandas/_libs/window/indexers.pyx
+++ b/pandas/_libs/window/indexers.pyx
@@ -44,6 +44,7 @@ def calculate_variable_window_bounds(
cdef:
bint left_closed = False
bint right_closed = False
+ int index_growth_sign = 1
ndarray[int64_t, ndim=1] start, end
int64_t start_bound, end_bound
Py_ssize_t i, j
@@ -58,6 +59,9 @@ def calculate_variable_window_bounds(
if closed in ['left', 'both']:
left_closed = True
+ if index[num_values - 1] < index[0]:
+ index_growth_sign = -1
+
start = np.empty(num_values, dtype='int64')
start.fill(-1)
end = np.empty(num_values, dtype='int64')
@@ -78,7 +82,7 @@ def calculate_variable_window_bounds(
# end is end of slice interval (not including)
for i in range(1, num_values):
end_bound = index[i]
- start_bound = index[i] - window_size
+ start_bound = index[i] - index_growth_sign * window_size
# left endpoint is closed
if left_closed:
@@ -88,13 +92,13 @@ def calculate_variable_window_bounds(
# within the constraint
start[i] = i
for j in range(start[i - 1], i):
- if index[j] > start_bound:
+ if (index[j] - start_bound) * index_growth_sign > 0:
start[i] = j
break
# end bound is previous end
# or current index
- if index[end[i - 1]] <= end_bound:
+ if (index[end[i - 1]] - end_bound) * index_growth_sign <= 0:
end[i] = i + 1
else:
end[i] = end[i - 1]
diff --git a/pandas/tests/window/test_timeseries_window.py b/pandas/tests/window/test_timeseries_window.py
index 5f5e10b5dd497..0c5289cd78fed 100644
--- a/pandas/tests/window/test_timeseries_window.py
+++ b/pandas/tests/window/test_timeseries_window.py
@@ -709,20 +709,25 @@ def test_rolling_cov_offset(self):
tm.assert_series_equal(result, expected2)
def test_rolling_on_decreasing_index(self):
- # GH-19248
+ # GH-19248, GH-32385
index = [
- Timestamp("20190101 09:00:00"),
- Timestamp("20190101 09:00:02"),
- Timestamp("20190101 09:00:03"),
- Timestamp("20190101 09:00:05"),
- Timestamp("20190101 09:00:06"),
+ Timestamp("20190101 09:00:30"),
+ Timestamp("20190101 09:00:27"),
+ Timestamp("20190101 09:00:20"),
+ Timestamp("20190101 09:00:18"),
+ Timestamp("20190101 09:00:10"),
]
- df = DataFrame({"column": [3, 4, 4, 2, 1]}, index=reversed(index))
- result = df.rolling("2s").min()
- expected = DataFrame(
- {"column": [3.0, 3.0, 3.0, 2.0, 1.0]}, index=reversed(index)
- )
+ df = DataFrame({"column": [3, 4, 4, 5, 6]}, index=index)
+ result = df.rolling("5s").min()
+ expected = DataFrame({"column": [3.0, 3.0, 4.0, 4.0, 6.0]}, index=index)
+ tm.assert_frame_equal(result, expected)
+
+ def test_rolling_on_empty(self):
+ # GH-32385
+ df = DataFrame({"column": []}, index=[])
+ result = df.rolling("5s").min()
+ expected = DataFrame({"column": []}, index=[])
tm.assert_frame_equal(result, expected)
def test_rolling_on_multi_index_level(self):
| Backport PR #32386: BUG: Fix rolling functions with variable windows on decreasing index | https://api.github.com/repos/pandas-dev/pandas/pulls/32606 | 2020-03-11T01:53:52Z | 2020-03-11T02:57:58Z | 2020-03-11T02:57:58Z | 2020-03-11T02:57:58Z |
Backport PR #32499 on branch 1.0.x (Better error message for OOB result) | diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index 9841df0507138..88ee732ded071 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -65,6 +65,7 @@ Bug fixes
- Bug in :meth:`DataFrame.reindex` and :meth:`Series.reindex` when reindexing with a tz-aware index (:issue:`26683`)
- Bug where :func:`to_datetime` would raise when passed ``pd.NA`` (:issue:`32213`)
+- Improved error message when subtracting two :class:`Timestamp` that result in an out-of-bounds :class:`Timedelta` (:issue:`31774`)
**Categorical**
diff --git a/pandas/_libs/tslibs/c_timestamp.pyx b/pandas/_libs/tslibs/c_timestamp.pyx
index ed1df5f4fa595..62a039d15ef6f 100644
--- a/pandas/_libs/tslibs/c_timestamp.pyx
+++ b/pandas/_libs/tslibs/c_timestamp.pyx
@@ -286,6 +286,10 @@ cdef class _Timestamp(datetime):
# coerce if necessary if we are a Timestamp-like
if (PyDateTime_Check(self)
and (PyDateTime_Check(other) or is_datetime64_object(other))):
+ # both_timestamps is to determine whether Timedelta(self - other)
+ # should raise the OOB error, or fall back returning a timedelta.
+ both_timestamps = (isinstance(other, _Timestamp) and
+ isinstance(self, _Timestamp))
if isinstance(self, _Timestamp):
other = type(self)(other)
else:
@@ -301,7 +305,14 @@ cdef class _Timestamp(datetime):
from pandas._libs.tslibs.timedeltas import Timedelta
try:
return Timedelta(self.value - other.value)
- except (OverflowError, OutOfBoundsDatetime):
+ except (OverflowError, OutOfBoundsDatetime) as err:
+ if isinstance(other, _Timestamp):
+ if both_timestamps:
+ raise OutOfBoundsDatetime(
+ "Result is too large for pandas.Timedelta. Convert inputs "
+ "to datetime.datetime with 'Timestamp.to_pydatetime()' "
+ "before subtracting."
+ ) from err
pass
elif is_datetime64_object(self):
# GH#28286 cython semantics for __rsub__, `other` is actually
diff --git a/pandas/tests/scalar/timestamp/test_arithmetic.py b/pandas/tests/scalar/timestamp/test_arithmetic.py
index 1cab007c20a0e..ccd7bf721430a 100644
--- a/pandas/tests/scalar/timestamp/test_arithmetic.py
+++ b/pandas/tests/scalar/timestamp/test_arithmetic.py
@@ -3,6 +3,8 @@
import numpy as np
import pytest
+from pandas.errors import OutOfBoundsDatetime
+
from pandas import Timedelta, Timestamp
from pandas.tseries import offsets
@@ -60,6 +62,18 @@ def test_overflow_offset_raises(self):
with pytest.raises(OverflowError, match=msg):
stamp - offset_overflow
+ def test_overflow_timestamp_raises(self):
+ # https://github.com/pandas-dev/pandas/issues/31774
+ msg = "Result is too large"
+ a = Timestamp("2101-01-01 00:00:00")
+ b = Timestamp("1688-01-01 00:00:00")
+
+ with pytest.raises(OutOfBoundsDatetime, match=msg):
+ a - b
+
+ # but we're OK for timestamp and datetime.datetime
+ assert (a - b.to_pydatetime()) == (a.to_pydatetime() - b)
+
def test_delta_preserve_nanos(self):
val = Timestamp(1337299200000000123)
result = val + timedelta(1)
| Backport PR #32499: Better error message for OOB result | https://api.github.com/repos/pandas-dev/pandas/pulls/32605 | 2020-03-11T01:46:05Z | 2020-03-11T02:33:53Z | 2020-03-11T02:33:53Z | 2020-03-11T02:33:53Z |
Avoid bare pytest.raises in dtypes/cast/test_upcast.py | diff --git a/pandas/tests/dtypes/cast/test_upcast.py b/pandas/tests/dtypes/cast/test_upcast.py
index bb7a7d059c7ee..f9227a4e78a79 100644
--- a/pandas/tests/dtypes/cast/test_upcast.py
+++ b/pandas/tests/dtypes/cast/test_upcast.py
@@ -12,7 +12,7 @@ def test_upcast_error(result):
# GH23823 require result arg to be ndarray
mask = np.array([False, True, False])
other = np.array([61, 62, 63])
- with pytest.raises(ValueError):
+ with pytest.raises(ValueError, match="The result input must be a ndarray"):
result, _ = maybe_upcast_putmask(result, mask, other)
| * [x] ref #30999
* [x] tests added / passed
* [x] passes `black pandas`
* [x] passes `git diff origin/master -u -- "*.py" | flake8 --diff` | https://api.github.com/repos/pandas-dev/pandas/pulls/32603 | 2020-03-11T00:49:57Z | 2020-03-11T01:50:43Z | 2020-03-11T01:50:43Z | 2020-03-21T00:41:45Z |
DOC: Fix link to monthly meeting calendar | diff --git a/doc/source/development/meeting.rst b/doc/source/development/meeting.rst
index 1d19408692cda..803f1b7002de0 100644
--- a/doc/source/development/meeting.rst
+++ b/doc/source/development/meeting.rst
@@ -25,7 +25,7 @@ This calendar shows all the developer meetings.
You can subscribe to this calendar with the following links:
* `iCal <https://calendar.google.com/calendar/ical/pgbn14p6poja8a1cf2dv2jhrmg%40group.calendar.google.com/public/basic.ics>`__
-* `Google calendar <https://calendar.google.com/calendar/embed?src=pgbn14p6poja8a1cf2dv2jhrmg%40group.calendar.google.com>`__
+* `Google calendar <https://calendar.google.com/calendar/r?cid=pgbn14p6poja8a1cf2dv2jhrmg@group.calendar.google.com>`__
Additionally, we'll sometimes have one-off meetings on specific topics.
These will be published on the same calendar.
| The link we currently have for Google calendar is to embed, and let you see the calendar, but not subscribe to it. Fixing it here. | https://api.github.com/repos/pandas-dev/pandas/pulls/32602 | 2020-03-10T23:20:13Z | 2020-03-11T03:10:38Z | 2020-03-11T03:10:38Z | 2020-03-11T03:10:38Z |
BUG: Fix file descriptor leak | diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst
index 4e7bd5a2032a7..31010c98712ad 100644
--- a/doc/source/whatsnew/v1.1.0.rst
+++ b/doc/source/whatsnew/v1.1.0.rst
@@ -337,6 +337,7 @@ I/O
- Bug in :meth:`read_csv` was raising `TypeError` when `sep=None` was used in combination with `comment` keyword (:issue:`31396`)
- Bug in :class:`HDFStore` that caused it to set to ``int64`` the dtype of a ``datetime64`` column when reading a DataFrame in Python 3 from fixed format written in Python 2 (:issue:`31750`)
- Bug in :meth:`read_excel` where a UTF-8 string with a high surrogate would cause a segmentation violation (:issue:`23809`)
+- Bug in :meth:`read_csv` was causing a file descriptor leak on an empty file (:issue:`31488`)
Plotting
diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py
index 50b5db0274aa5..52783b3a9e134 100755
--- a/pandas/io/parsers.py
+++ b/pandas/io/parsers.py
@@ -2273,11 +2273,15 @@ def __init__(self, f, **kwds):
# Get columns in two steps: infer from data, then
# infer column indices from self.usecols if it is specified.
self._col_indices = None
- (
- self.columns,
- self.num_original_columns,
- self.unnamed_cols,
- ) = self._infer_columns()
+ try:
+ (
+ self.columns,
+ self.num_original_columns,
+ self.unnamed_cols,
+ ) = self._infer_columns()
+ except (TypeError, ValueError):
+ self.close()
+ raise
# Now self.columns has the set of columns that we will process.
# The original set is stored in self.original_columns.
diff --git a/pandas/tests/io/parser/test_common.py b/pandas/tests/io/parser/test_common.py
index b3aa1aa14a509..33460262a4430 100644
--- a/pandas/tests/io/parser/test_common.py
+++ b/pandas/tests/io/parser/test_common.py
@@ -15,6 +15,7 @@
from pandas._libs.tslib import Timestamp
from pandas.errors import DtypeWarning, EmptyDataError, ParserError
+import pandas.util._test_decorators as td
from pandas import DataFrame, Index, MultiIndex, Series, compat, concat
import pandas._testing as tm
@@ -2079,3 +2080,16 @@ def test_integer_precision(all_parsers):
result = parser.read_csv(StringIO(s), header=None)[4]
expected = Series([4321583677327450765, 4321113141090630389], name=4)
tm.assert_series_equal(result, expected)
+
+
+def test_file_descriptor_leak(all_parsers):
+ # GH 31488
+
+ parser = all_parsers
+ with tm.ensure_clean() as path:
+
+ def test():
+ with pytest.raises(EmptyDataError, match="No columns to parse from file"):
+ parser.read_csv(path)
+
+ td.check_file_leaks(test)()
| - [x] closes #31488
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/32598 | 2020-03-10T21:47:38Z | 2020-03-15T00:37:42Z | 2020-03-15T00:37:42Z | 2020-03-15T07:14:03Z |
REF: implement nanops.na_accum_func | diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index f53135174741e..427b1bfc28ba5 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -30,7 +30,7 @@
from pandas._config import config
-from pandas._libs import Timestamp, iNaT, lib
+from pandas._libs import Timestamp, lib
from pandas._typing import (
Axis,
FilePathOrBuffer,
@@ -10102,8 +10102,6 @@ def mad(self, axis=None, skipna=None, level=None):
desc="minimum",
accum_func=np.minimum.accumulate,
accum_func_name="min",
- mask_a=np.inf,
- mask_b=np.nan,
examples=_cummin_examples,
)
cls.cumsum = _make_cum_function(
@@ -10115,8 +10113,6 @@ def mad(self, axis=None, skipna=None, level=None):
desc="sum",
accum_func=np.cumsum,
accum_func_name="sum",
- mask_a=0.0,
- mask_b=np.nan,
examples=_cumsum_examples,
)
cls.cumprod = _make_cum_function(
@@ -10128,8 +10124,6 @@ def mad(self, axis=None, skipna=None, level=None):
desc="product",
accum_func=np.cumprod,
accum_func_name="prod",
- mask_a=1.0,
- mask_b=np.nan,
examples=_cumprod_examples,
)
cls.cummax = _make_cum_function(
@@ -10141,8 +10135,6 @@ def mad(self, axis=None, skipna=None, level=None):
desc="maximum",
accum_func=np.maximum.accumulate,
accum_func_name="max",
- mask_a=-np.inf,
- mask_b=np.nan,
examples=_cummax_examples,
)
@@ -11182,8 +11174,6 @@ def _make_cum_function(
desc: str,
accum_func: Callable,
accum_func_name: str,
- mask_a: float,
- mask_b: float,
examples: str,
) -> Callable:
@Substitution(
@@ -11205,61 +11195,15 @@ def cum_func(self, axis=None, skipna=True, *args, **kwargs):
if axis == 1:
return cum_func(self.T, axis=0, skipna=skipna, *args, **kwargs).T
- def na_accum_func(blk_values):
- # We will be applying this function to block values
- if blk_values.dtype.kind in ["m", "M"]:
- # GH#30460, GH#29058
- # numpy 1.18 started sorting NaTs at the end instead of beginning,
- # so we need to work around to maintain backwards-consistency.
- orig_dtype = blk_values.dtype
-
- # We need to define mask before masking NaTs
- mask = isna(blk_values)
-
- if accum_func == np.minimum.accumulate:
- # Note: the accum_func comparison fails as an "is" comparison
- y = blk_values.view("i8")
- y[mask] = np.iinfo(np.int64).max
- changed = True
- else:
- y = blk_values
- changed = False
-
- result = accum_func(y.view("i8"), axis)
- if skipna:
- np.putmask(result, mask, iNaT)
- elif accum_func == np.minimum.accumulate:
- # Restore NaTs that we masked previously
- nz = (~np.asarray(mask)).nonzero()[0]
- if len(nz):
- # everything up to the first non-na entry stays NaT
- result[: nz[0]] = iNaT
-
- if changed:
- # restore NaT elements
- y[mask] = iNaT # TODO: could try/finally for this?
-
- if isinstance(blk_values, np.ndarray):
- result = result.view(orig_dtype)
- else:
- # DatetimeArray
- result = type(blk_values)._from_sequence(result, dtype=orig_dtype)
-
- elif skipna and not issubclass(
- blk_values.dtype.type, (np.integer, np.bool_)
- ):
- vals = blk_values.copy().T
- mask = isna(vals)
- np.putmask(vals, mask, mask_a)
- result = accum_func(vals, axis)
- np.putmask(result, mask, mask_b)
- else:
- result = accum_func(blk_values.T, axis)
+ def block_accum_func(blk_values):
+ values = blk_values.T if hasattr(blk_values, "T") else blk_values
- # transpose back for ndarray, not for EA
- return result.T if hasattr(result, "T") else result
+ result = nanops.na_accum_func(values, accum_func, skipna=skipna)
+
+ result = result.T if hasattr(result, "T") else result
+ return result
- result = self._data.apply(na_accum_func)
+ result = self._data.apply(block_accum_func)
d = self._construct_axes_dict()
d["copy"] = False
diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
index 269843abb15ee..a5e70bd279d21 100644
--- a/pandas/core/nanops.py
+++ b/pandas/core/nanops.py
@@ -8,7 +8,7 @@
from pandas._config import get_option
from pandas._libs import NaT, Period, Timedelta, Timestamp, iNaT, lib
-from pandas._typing import Dtype, Scalar
+from pandas._typing import ArrayLike, Dtype, Scalar
from pandas.compat._optional import import_optional_dependency
from pandas.core.dtypes.cast import _int64_max, maybe_upcast_putmask
@@ -1500,3 +1500,75 @@ def nanpercentile(
return result
else:
return np.percentile(values, q, axis=axis, interpolation=interpolation)
+
+
+def na_accum_func(values: ArrayLike, accum_func, skipna: bool) -> ArrayLike:
+ """
+ Cumulative function with skipna support.
+
+ Parameters
+ ----------
+ values : np.ndarray or ExtensionArray
+ accum_func : {np.cumprod, np.maximum.accumulate, np.cumsum, np.minumum.accumulate}
+ skipna : bool
+
+ Returns
+ -------
+ np.ndarray or ExtensionArray
+ """
+ mask_a, mask_b = {
+ np.cumprod: (1.0, np.nan),
+ np.maximum.accumulate: (-np.inf, np.nan),
+ np.cumsum: (0.0, np.nan),
+ np.minimum.accumulate: (np.inf, np.nan),
+ }[accum_func]
+
+ # We will be applying this function to block values
+ if values.dtype.kind in ["m", "M"]:
+ # GH#30460, GH#29058
+ # numpy 1.18 started sorting NaTs at the end instead of beginning,
+ # so we need to work around to maintain backwards-consistency.
+ orig_dtype = values.dtype
+
+ # We need to define mask before masking NaTs
+ mask = isna(values)
+
+ if accum_func == np.minimum.accumulate:
+ # Note: the accum_func comparison fails as an "is" comparison
+ y = values.view("i8")
+ y[mask] = np.iinfo(np.int64).max
+ changed = True
+ else:
+ y = values
+ changed = False
+
+ result = accum_func(y.view("i8"), axis=0)
+ if skipna:
+ result[mask] = iNaT
+ elif accum_func == np.minimum.accumulate:
+ # Restore NaTs that we masked previously
+ nz = (~np.asarray(mask)).nonzero()[0]
+ if len(nz):
+ # everything up to the first non-na entry stays NaT
+ result[: nz[0]] = iNaT
+
+ if changed:
+ # restore NaT elements
+ y[mask] = iNaT # TODO: could try/finally for this?
+
+ if isinstance(values, np.ndarray):
+ result = result.view(orig_dtype)
+ else:
+ # DatetimeArray
+ result = type(values)._from_sequence(result, dtype=orig_dtype)
+
+ elif skipna and not issubclass(values.dtype.type, (np.integer, np.bool_)):
+ vals = values.copy()
+ mask = isna(vals)
+ vals[mask] = mask_a
+ result = accum_func(vals, axis=0)
+ result[mask] = mask_b
+ else:
+ result = accum_func(values, axis=0)
+
+ return result
| this is a follow-up that was requested a few months ago | https://api.github.com/repos/pandas-dev/pandas/pulls/32597 | 2020-03-10T21:41:06Z | 2020-03-14T03:32:24Z | 2020-03-14T03:32:24Z | 2020-03-14T17:03:00Z |
Backport PR #32592 on branch 1.0.x (DOC: cleanup 1.0.2 whatsnew) | diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index 462f243f14494..9841df0507138 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -1,7 +1,7 @@
.. _whatsnew_102:
-What's new in 1.0.2 (February ??, 2020)
----------------------------------------
+What's new in 1.0.2 (March 11, 2020)
+------------------------------------
These are the changes in pandas 1.0.2. See :ref:`release` for a full changelog
including other versions of pandas.
@@ -18,13 +18,13 @@ Fixed regressions
- Fixed regression in :meth:`DataFrame.to_excel` when ``columns`` kwarg is passed (:issue:`31677`)
- Fixed regression in :meth:`Series.align` when ``other`` is a DataFrame and ``method`` is not None (:issue:`31785`)
- Fixed regression in :meth:`pandas.core.groupby.RollingGroupby.apply` where the ``raw`` parameter was ignored (:issue:`31754`)
-- Fixed regression in :meth:`rolling(..).corr() <pandas.core.window.Rolling.corr>` when using a time offset (:issue:`31789`)
-- Fixed regression in :meth:`DataFrameGroupBy.nunique` which was modifying the original values if ``NaN`` values were present (:issue:`31950`)
+- Fixed regression in :meth:`pandas.core.window.Rolling.corr` when using a time offset (:issue:`31789`)
+- Fixed regression in :meth:`pandas.core.groupby.DataFrameGroupBy.nunique` which was modifying the original values if ``NaN`` values were present (:issue:`31950`)
- Fixed regression where :func:`read_pickle` raised a ``UnicodeDecodeError`` when reading a py27 pickle with :class:`MultiIndex` column (:issue:`31988`).
- Fixed regression in :class:`DataFrame` arithmetic operations with mis-matched columns (:issue:`31623`)
-- Fixed regression in :meth:`GroupBy.agg` calling a user-provided function an extra time on an empty input (:issue:`31760`)
+- Fixed regression in :meth:`pandas.core.groupby.GroupBy.agg` calling a user-provided function an extra time on an empty input (:issue:`31760`)
- Joining on :class:`DatetimeIndex` or :class:`TimedeltaIndex` will preserve ``freq`` in simple cases (:issue:`32166`)
-- Fixed bug in the repr of an object-dtype ``Index`` with bools and missing values (:issue:`32146`)
+- Fixed bug in the repr of an object-dtype :class:`Index` with bools and missing values (:issue:`32146`)
-
.. ---------------------------------------------------------------------------
@@ -82,7 +82,7 @@ Bug fixes
- Fix bug in :meth:`DataFrame.convert_dtypes` for columns that were already using the ``"string"`` dtype (:issue:`31731`).
- Fixed bug in setting values using a slice indexer with string dtype (:issue:`31772`)
-- Fixed bug where :meth:`GroupBy.first` and :meth:`GroupBy.last` would raise a ``TypeError`` when groups contained ``pd.NA`` in a column of object dtype (:issue:`32123`)
+- Fixed bug where :meth:`pandas.core.groupby.GroupBy.first` and :meth:`pandas.core.groupby.GroupBy.last` would raise a ``TypeError`` when groups contained ``pd.NA`` in a column of object dtype (:issue:`32123`)
- Fix bug in :meth:`Series.convert_dtypes` for series with mix of integers and strings (:issue:`32117`)
**Strings**
| Backport PR #32592: DOC: cleanup 1.0.2 whatsnew | https://api.github.com/repos/pandas-dev/pandas/pulls/32596 | 2020-03-10T20:46:56Z | 2020-03-10T21:52:46Z | 2020-03-10T21:52:46Z | 2020-03-10T21:52:46Z |
BUG: Don't multiply sets during construction | diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst
index 48eff0543ad4d..e9e87ec202ef5 100644
--- a/doc/source/whatsnew/v1.1.0.rst
+++ b/doc/source/whatsnew/v1.1.0.rst
@@ -241,7 +241,7 @@ Conversion
^^^^^^^^^^
- Bug in :class:`Series` construction from NumPy array with big-endian ``datetime64`` dtype (:issue:`29684`)
- Bug in :class:`Timedelta` construction with large nanoseconds keyword value (:issue:`32402`)
--
+- Bug in :class:`DataFrame` construction where sets would be duplicated rather than raising (:issue:`32582`)
Strings
^^^^^^^
diff --git a/pandas/core/construction.py b/pandas/core/construction.py
index e2d8fba8d4148..c9754ff588896 100644
--- a/pandas/core/construction.py
+++ b/pandas/core/construction.py
@@ -5,6 +5,7 @@
These should not depend on core.internals.
"""
+from collections import abc
from typing import TYPE_CHECKING, Any, Optional, Sequence, Union, cast
import numpy as np
@@ -446,6 +447,8 @@ def sanitize_array(
# GH#16804
arr = np.arange(data.start, data.stop, data.step, dtype="int64")
subarr = _try_cast(arr, dtype, copy, raise_cast_failure)
+ elif isinstance(data, abc.Set):
+ raise TypeError("Set type is unordered")
else:
subarr = _try_cast(data, dtype, copy, raise_cast_failure)
diff --git a/pandas/tests/frame/test_constructors.py b/pandas/tests/frame/test_constructors.py
index d938c0f6f1066..924952ad334c4 100644
--- a/pandas/tests/frame/test_constructors.py
+++ b/pandas/tests/frame/test_constructors.py
@@ -2604,3 +2604,9 @@ def test_from_2d_ndarray_with_dtype(self):
expected = DataFrame(array_dim2).astype("datetime64[ns, UTC]")
tm.assert_frame_equal(df, expected)
+
+ def test_construction_from_set_raises(self):
+ # https://github.com/pandas-dev/pandas/issues/32582
+ msg = "Set type is unordered"
+ with pytest.raises(TypeError, match=msg):
+ pd.DataFrame({"a": {1, 2, 3}})
| - [x] closes #32582
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/32594 | 2020-03-10T19:25:56Z | 2020-03-15T00:41:34Z | 2020-03-15T00:41:34Z | 2020-03-15T00:42:47Z |
DOC: cleanup 1.0.2 whatsnew | diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index 462f243f14494..9841df0507138 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -1,7 +1,7 @@
.. _whatsnew_102:
-What's new in 1.0.2 (February ??, 2020)
----------------------------------------
+What's new in 1.0.2 (March 11, 2020)
+------------------------------------
These are the changes in pandas 1.0.2. See :ref:`release` for a full changelog
including other versions of pandas.
@@ -18,13 +18,13 @@ Fixed regressions
- Fixed regression in :meth:`DataFrame.to_excel` when ``columns`` kwarg is passed (:issue:`31677`)
- Fixed regression in :meth:`Series.align` when ``other`` is a DataFrame and ``method`` is not None (:issue:`31785`)
- Fixed regression in :meth:`pandas.core.groupby.RollingGroupby.apply` where the ``raw`` parameter was ignored (:issue:`31754`)
-- Fixed regression in :meth:`rolling(..).corr() <pandas.core.window.Rolling.corr>` when using a time offset (:issue:`31789`)
-- Fixed regression in :meth:`DataFrameGroupBy.nunique` which was modifying the original values if ``NaN`` values were present (:issue:`31950`)
+- Fixed regression in :meth:`pandas.core.window.Rolling.corr` when using a time offset (:issue:`31789`)
+- Fixed regression in :meth:`pandas.core.groupby.DataFrameGroupBy.nunique` which was modifying the original values if ``NaN`` values were present (:issue:`31950`)
- Fixed regression where :func:`read_pickle` raised a ``UnicodeDecodeError`` when reading a py27 pickle with :class:`MultiIndex` column (:issue:`31988`).
- Fixed regression in :class:`DataFrame` arithmetic operations with mis-matched columns (:issue:`31623`)
-- Fixed regression in :meth:`GroupBy.agg` calling a user-provided function an extra time on an empty input (:issue:`31760`)
+- Fixed regression in :meth:`pandas.core.groupby.GroupBy.agg` calling a user-provided function an extra time on an empty input (:issue:`31760`)
- Joining on :class:`DatetimeIndex` or :class:`TimedeltaIndex` will preserve ``freq`` in simple cases (:issue:`32166`)
-- Fixed bug in the repr of an object-dtype ``Index`` with bools and missing values (:issue:`32146`)
+- Fixed bug in the repr of an object-dtype :class:`Index` with bools and missing values (:issue:`32146`)
-
.. ---------------------------------------------------------------------------
@@ -82,7 +82,7 @@ Bug fixes
- Fix bug in :meth:`DataFrame.convert_dtypes` for columns that were already using the ``"string"`` dtype (:issue:`31731`).
- Fixed bug in setting values using a slice indexer with string dtype (:issue:`31772`)
-- Fixed bug where :meth:`GroupBy.first` and :meth:`GroupBy.last` would raise a ``TypeError`` when groups contained ``pd.NA`` in a column of object dtype (:issue:`32123`)
+- Fixed bug where :meth:`pandas.core.groupby.GroupBy.first` and :meth:`pandas.core.groupby.GroupBy.last` would raise a ``TypeError`` when groups contained ``pd.NA`` in a column of object dtype (:issue:`32123`)
- Fix bug in :meth:`Series.convert_dtypes` for series with mix of integers and strings (:issue:`32117`)
**Strings**
| https://api.github.com/repos/pandas-dev/pandas/pulls/32592 | 2020-03-10T19:17:48Z | 2020-03-10T20:46:43Z | 2020-03-10T20:46:43Z | 2020-03-11T12:20:39Z | |
REG: dt64 shift with integer fill_value | diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index ee4b265ce3ed9..e6d65b1f828cb 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -29,6 +29,7 @@ Fixed regressions
- Fixed regression in :meth:`read_csv` in which the ``encoding`` option was not recognized with certain file-like objects (:issue:`31819`)
- Fixed regression in :meth:`DataFrame.reindex` and :meth:`Series.reindex` when reindexing with (tz-aware) index and ``method=nearest`` (:issue:`26683`)
- Fixed regression in :meth:`DataFrame.reindex_like` on a :class:`DataFrame` subclass raised an ``AssertionError`` (:issue:`31925`)
+- Fixed regression in :meth:`Series.shift` with ``datetime64`` dtype when passing an integer ``fill_value`` (:issue:`32591`)
.. ---------------------------------------------------------------------------
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index 8c870c6255200..105d9581b1a25 100644
--- a/pandas/core/arrays/datetimelike.py
+++ b/pandas/core/arrays/datetimelike.py
@@ -745,6 +745,57 @@ def _from_factorized(cls, values, original):
def _values_for_argsort(self):
return self._data
+ @Appender(ExtensionArray.shift.__doc__)
+ def shift(self, periods=1, fill_value=None, axis=0):
+ if not self.size or periods == 0:
+ return self.copy()
+
+ if is_valid_nat_for_dtype(fill_value, self.dtype):
+ fill_value = NaT
+ elif not isinstance(fill_value, self._recognized_scalars):
+ # only warn if we're not going to raise
+ if self._scalar_type is Period and lib.is_integer(fill_value):
+ # kludge for #31971 since Period(integer) tries to cast to str
+ new_fill = Period._from_ordinal(fill_value, freq=self.freq)
+ else:
+ new_fill = self._scalar_type(fill_value)
+
+ # stacklevel here is chosen to be correct when called from
+ # DataFrame.shift or Series.shift
+ warnings.warn(
+ f"Passing {type(fill_value)} to shift is deprecated and "
+ "will raise in a future version, pass "
+ f"{self._scalar_type.__name__} instead.",
+ FutureWarning,
+ stacklevel=7,
+ )
+ fill_value = new_fill
+
+ fill_value = self._unbox_scalar(fill_value)
+
+ new_values = self._data
+
+ # make sure array sent to np.roll is c_contiguous
+ f_ordered = new_values.flags.f_contiguous
+ if f_ordered:
+ new_values = new_values.T
+ axis = new_values.ndim - axis - 1
+
+ new_values = np.roll(new_values, periods, axis=axis)
+
+ axis_indexer = [slice(None)] * self.ndim
+ if periods > 0:
+ axis_indexer[axis] = slice(None, periods)
+ else:
+ axis_indexer[axis] = slice(periods, None)
+ new_values[tuple(axis_indexer)] = fill_value
+
+ # restore original order
+ if f_ordered:
+ new_values = new_values.T
+
+ return type(self)._simple_new(new_values, dtype=self.dtype)
+
# ------------------------------------------------------------------
# Additional array methods
# These are not part of the EA API, but we implement them because
diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index 024d3b205df77..3f5c27bf5269c 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -1917,10 +1917,7 @@ def diff(self, n: int, axis: int = 1) -> List["Block"]:
return super().diff(n, axis)
def shift(
- self,
- periods: int,
- axis: libinternals.BlockPlacement = 0,
- fill_value: Any = None,
+ self, periods: int, axis: int = 0, fill_value: Any = None,
) -> List["ExtensionBlock"]:
"""
Shift the block by `periods`.
@@ -2173,7 +2170,7 @@ def internal_values(self):
def iget(self, key):
# GH#31649 we need to wrap scalars in Timestamp/Timedelta
- # TODO: this can be removed if we ever have 2D EA
+ # TODO(EA2D): this can be removed if we ever have 2D EA
result = super().iget(key)
if isinstance(result, np.datetime64):
result = Timestamp(result)
@@ -2181,6 +2178,12 @@ def iget(self, key):
result = Timedelta(result)
return result
+ def shift(self, periods, axis=0, fill_value=None):
+ # TODO(EA2D) this is unnecessary if these blocks are backed by 2D EAs
+ values = self.array_values()
+ new_values = values.shift(periods, fill_value=fill_value, axis=axis)
+ return self.make_block_same_class(new_values)
+
class DatetimeBlock(DatetimeLikeBlockMixin, Block):
__slots__ = ()
diff --git a/pandas/tests/arrays/test_datetimelike.py b/pandas/tests/arrays/test_datetimelike.py
index f99ee542d543c..b8a70752330c5 100644
--- a/pandas/tests/arrays/test_datetimelike.py
+++ b/pandas/tests/arrays/test_datetimelike.py
@@ -240,6 +240,23 @@ def test_inplace_arithmetic(self):
arr -= pd.Timedelta(days=1)
tm.assert_equal(arr, expected)
+ def test_shift_fill_int_deprecated(self):
+ # GH#31971
+ data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9
+ arr = self.array_cls(data, freq="D")
+
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ result = arr.shift(1, fill_value=1)
+
+ expected = arr.copy()
+ if self.array_cls is PeriodArray:
+ fill_val = PeriodArray._scalar_type._from_ordinal(1, freq=arr.freq)
+ else:
+ fill_val = arr._scalar_type(1)
+ expected[0] = fill_val
+ expected[1:] = arr[:-1]
+ tm.assert_equal(result, expected)
+
class TestDatetimeArray(SharedTests):
index_cls = pd.DatetimeIndex
diff --git a/pandas/tests/frame/methods/test_shift.py b/pandas/tests/frame/methods/test_shift.py
index cfb17de892b1c..f6c89172bbf86 100644
--- a/pandas/tests/frame/methods/test_shift.py
+++ b/pandas/tests/frame/methods/test_shift.py
@@ -185,3 +185,26 @@ def test_tshift(self, datetime_frame):
msg = "Freq was not given and was not set in the index"
with pytest.raises(ValueError, match=msg):
no_freq.tshift()
+
+ def test_shift_dt64values_int_fill_deprecated(self):
+ # GH#31971
+ ser = pd.Series([pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-02")])
+ df = ser.to_frame()
+
+ with tm.assert_produces_warning(FutureWarning):
+ result = df.shift(1, fill_value=0)
+
+ expected = pd.Series([pd.Timestamp(0), ser[0]]).to_frame()
+ tm.assert_frame_equal(result, expected)
+
+ # axis = 1
+ df2 = pd.DataFrame({"A": ser, "B": ser})
+ df2._consolidate_inplace()
+
+ with tm.assert_produces_warning(FutureWarning):
+ result = df2.shift(1, axis=1, fill_value=0)
+
+ expected = pd.DataFrame(
+ {"A": [pd.Timestamp(0), pd.Timestamp(0)], "B": df2["A"]}
+ )
+ tm.assert_frame_equal(result, expected)
diff --git a/pandas/tests/series/methods/test_shift.py b/pandas/tests/series/methods/test_shift.py
index 8256e2f33b936..e8d7f5958d0a1 100644
--- a/pandas/tests/series/methods/test_shift.py
+++ b/pandas/tests/series/methods/test_shift.py
@@ -263,3 +263,13 @@ def test_shift_categorical(self):
tm.assert_index_equal(s.values.categories, sp1.values.categories)
tm.assert_index_equal(s.values.categories, sn2.values.categories)
+
+ def test_shift_dt64values_int_fill_deprecated(self):
+ # GH#31971
+ ser = pd.Series([pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-02")])
+
+ with tm.assert_produces_warning(FutureWarning):
+ result = ser.shift(1, fill_value=0)
+
+ expected = pd.Series([pd.Timestamp(0), ser[0]])
+ tm.assert_series_equal(result, expected)
| - [x] closes #31971
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/32591 | 2020-03-10T18:59:28Z | 2020-03-12T02:26:13Z | 2020-03-12T02:26:13Z | 2020-03-12T17:02:55Z |
Backport PR #31684 on branch 1.0.x (BUG: string methods with NA) | diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index 808e6ae709ce9..35358d8303175 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -84,6 +84,10 @@ Bug fixes
- Fixed bug where :meth:`GroupBy.first` and :meth:`GroupBy.last` would raise a ``TypeError`` when groups contained ``pd.NA`` in a column of object dtype (:issue:`32123`)
- Fix bug in :meth:`Series.convert_dtypes` for series with mix of integers and strings (:issue:`32117`)
+**Strings**
+
+- Using ``pd.NA`` with :meth:`Series.str.repeat` now correctly outputs a null value instead of raising error for vector inputs (:issue:`31632`)
+
.. ---------------------------------------------------------------------------
.. _whatsnew_102.contributors:
diff --git a/pandas/core/strings.py b/pandas/core/strings.py
index 18c7504f2c2f8..9ef066d55689f 100644
--- a/pandas/core/strings.py
+++ b/pandas/core/strings.py
@@ -778,6 +778,8 @@ def scalar_rep(x):
else:
def rep(x, r):
+ if x is libmissing.NA:
+ return x
try:
return bytes.__mul__(x, r)
except TypeError:
diff --git a/pandas/tests/test_strings.py b/pandas/tests/test_strings.py
index 8171072686443..76683d2c35854 100644
--- a/pandas/tests/test_strings.py
+++ b/pandas/tests/test_strings.py
@@ -1157,6 +1157,18 @@ def test_repeat(self):
assert isinstance(rs, Series)
tm.assert_series_equal(rs, xp)
+ def test_repeat_with_null(self):
+ # GH: 31632
+ values = Series(["a", None], dtype="string")
+ result = values.str.repeat([3, 4])
+ exp = Series(["aaa", None], dtype="string")
+ tm.assert_series_equal(result, exp)
+
+ values = Series(["a", "b"], dtype="string")
+ result = values.str.repeat([3, None])
+ exp = Series(["aaa", None], dtype="string")
+ tm.assert_series_equal(result, exp)
+
def test_match(self):
# New match behavior introduced in 0.13
values = Series(["fooBAD__barBAD", np.nan, "foo"])
| Backport PR #31684: BUG: string methods with NA | https://api.github.com/repos/pandas-dev/pandas/pulls/32578 | 2020-03-10T14:09:22Z | 2020-03-10T16:21:21Z | 2020-03-10T16:21:21Z | 2020-03-10T16:21:21Z |
REG: Restore read_csv function for some file-likes | diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index 9841df0507138..8db47000480ed 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -25,6 +25,7 @@ Fixed regressions
- Fixed regression in :meth:`pandas.core.groupby.GroupBy.agg` calling a user-provided function an extra time on an empty input (:issue:`31760`)
- Joining on :class:`DatetimeIndex` or :class:`TimedeltaIndex` will preserve ``freq`` in simple cases (:issue:`32166`)
- Fixed bug in the repr of an object-dtype :class:`Index` with bools and missing values (:issue:`32146`)
+- Fixed regression in :meth:`read_csv` in which the ``encoding`` option was not recognized with certain file-like objects (:issue:`31819`)
-
.. ---------------------------------------------------------------------------
diff --git a/pandas/_libs/parsers.pyx b/pandas/_libs/parsers.pyx
index 2fd227694800c..3a42a64046abd 100644
--- a/pandas/_libs/parsers.pyx
+++ b/pandas/_libs/parsers.pyx
@@ -638,7 +638,8 @@ cdef class TextReader:
raise ValueError(f'Unrecognized compression type: '
f'{self.compression}')
- if self.encoding and isinstance(source, (io.BufferedIOBase, io.RawIOBase)):
+ if (self.encoding and hasattr(source, "read") and
+ not hasattr(source, "encoding")):
source = io.TextIOWrapper(
source, self.encoding.decode('utf-8'), newline='')
diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py
index bc2fb9f0f41bc..50b5db0274aa5 100755
--- a/pandas/io/parsers.py
+++ b/pandas/io/parsers.py
@@ -5,7 +5,7 @@
from collections import abc, defaultdict
import csv
import datetime
-from io import BufferedIOBase, RawIOBase, StringIO, TextIOWrapper
+from io import StringIO, TextIOWrapper
import re
import sys
from textwrap import fill
@@ -1870,7 +1870,7 @@ def __init__(self, src, **kwds):
# Handle the file object with universal line mode enabled.
# We will handle the newline character ourselves later on.
- if isinstance(src, (BufferedIOBase, RawIOBase)):
+ if hasattr(src, "read") and not hasattr(src, "encoding"):
src = TextIOWrapper(src, encoding=encoding, newline="")
kwds["encoding"] = "utf-8"
diff --git a/pandas/tests/io/parser/test_encoding.py b/pandas/tests/io/parser/test_encoding.py
index 3661e4e056db2..13b74cf29f857 100644
--- a/pandas/tests/io/parser/test_encoding.py
+++ b/pandas/tests/io/parser/test_encoding.py
@@ -5,6 +5,7 @@
from io import BytesIO
import os
+import tempfile
import numpy as np
import pytest
@@ -174,3 +175,25 @@ def test_encoding_temp_file(all_parsers, utf_value, encoding_fmt, pass_encoding)
result = parser.read_csv(f, encoding=encoding if pass_encoding else None)
tm.assert_frame_equal(result, expected)
+
+
+def test_encoding_named_temp_file(all_parsers):
+ # see gh-31819
+ parser = all_parsers
+ encoding = "shift-jis"
+
+ if parser.engine == "python":
+ pytest.skip("NamedTemporaryFile does not work with Python engine")
+
+ title = "γ¦γγ¨"
+ data = "γγ"
+
+ expected = DataFrame({title: [data]})
+
+ with tempfile.NamedTemporaryFile() as f:
+ f.write(f"{title}\n{data}".encode(encoding))
+
+ f.seek(0)
+
+ result = parser.read_csv(f, encoding=encoding)
+ tm.assert_frame_equal(result, expected)
| Restore `read_csv` function for some file-likes
Restores behavior down to the fact that the Python engine cannot handle NamedTemporaryFile.
Closes https://github.com/pandas-dev/pandas/issues/31819
Credit to @sasanquaneuf for [originating idea](https://github.com/pandas-dev/pandas/issues/31819#issuecomment-584529415). | https://api.github.com/repos/pandas-dev/pandas/pulls/32577 | 2020-03-10T10:22:21Z | 2020-03-11T02:22:02Z | 2020-03-11T02:22:02Z | 2020-03-11T02:22:06Z |
TST: Add tests for duplicated and drop_duplicates | diff --git a/pandas/tests/indexes/categorical/test_category.py b/pandas/tests/indexes/categorical/test_category.py
index 543edc6b66ff2..83fe21fd20bfe 100644
--- a/pandas/tests/indexes/categorical/test_category.py
+++ b/pandas/tests/indexes/categorical/test_category.py
@@ -292,16 +292,81 @@ def test_is_monotonic(self, data, non_lexsorted_data):
assert c.is_monotonic_decreasing is False
def test_has_duplicates(self):
-
idx = CategoricalIndex([0, 0, 0], name="foo")
assert idx.is_unique is False
assert idx.has_duplicates is True
- def test_drop_duplicates(self):
+ idx = CategoricalIndex([0, 1], categories=[2, 3], name="foo")
+ assert idx.is_unique is False
+ assert idx.has_duplicates is True
- idx = CategoricalIndex([0, 0, 0], name="foo")
- expected = CategoricalIndex([0], name="foo")
- tm.assert_index_equal(idx.drop_duplicates(), expected)
+ idx = CategoricalIndex([0, 1, 2, 3], categories=[1, 2, 3], name="foo")
+ assert idx.is_unique is True
+ assert idx.has_duplicates is False
+
+ @pytest.mark.parametrize(
+ "data, categories, expected",
+ [
+ (
+ [1, 1, 1],
+ [1, 2, 3],
+ {
+ "first": np.array([False, True, True]),
+ "last": np.array([True, True, False]),
+ False: np.array([True, True, True]),
+ },
+ ),
+ (
+ [1, 1, 1],
+ list("abc"),
+ {
+ "first": np.array([False, True, True]),
+ "last": np.array([True, True, False]),
+ False: np.array([True, True, True]),
+ },
+ ),
+ (
+ [2, "a", "b"],
+ list("abc"),
+ {
+ "first": np.zeros(shape=(3), dtype=np.bool),
+ "last": np.zeros(shape=(3), dtype=np.bool),
+ False: np.zeros(shape=(3), dtype=np.bool),
+ },
+ ),
+ (
+ list("abb"),
+ list("abc"),
+ {
+ "first": np.array([False, False, True]),
+ "last": np.array([False, True, False]),
+ False: np.array([False, True, True]),
+ },
+ ),
+ ],
+ )
+ def test_drop_duplicates(self, data, categories, expected):
+
+ idx = CategoricalIndex(data, categories=categories, name="foo")
+ for keep, e in expected.items():
+ tm.assert_numpy_array_equal(idx.duplicated(keep=keep), e)
+ e = idx[~e]
+ result = idx.drop_duplicates(keep=keep)
+ tm.assert_index_equal(result, e)
+
+ @pytest.mark.parametrize(
+ "data, categories, expected_data, expected_categories",
+ [
+ ([1, 1, 1], [1, 2, 3], [1], [1]),
+ ([1, 1, 1], list("abc"), [np.nan], []),
+ ([1, 2, "a"], [1, 2, 3], [1, 2, np.nan], [1, 2]),
+ ([2, "a", "b"], list("abc"), [np.nan, "a", "b"], ["a", "b"]),
+ ],
+ )
+ def test_unique(self, data, categories, expected_data, expected_categories):
+
+ idx = CategoricalIndex(data, categories=categories)
+ expected = CategoricalIndex(expected_data, categories=expected_categories)
tm.assert_index_equal(idx.unique(), expected)
def test_repr_roundtrip(self):
diff --git a/pandas/tests/indexes/conftest.py b/pandas/tests/indexes/conftest.py
index a9fb228073ab4..fb17e1df6341b 100644
--- a/pandas/tests/indexes/conftest.py
+++ b/pandas/tests/indexes/conftest.py
@@ -16,3 +16,12 @@ def sort(request):
in in the Index setops methods.
"""
return request.param
+
+
+@pytest.fixture(params=["D", "3D", "-3D", "H", "2H", "-2H", "T", "2T", "S", "-3S"])
+def freq_sample(request):
+ """
+ Valid values for 'freq' parameter used to create date_range and
+ timedelta_range..
+ """
+ return request.param
diff --git a/pandas/tests/indexes/datetimes/test_ops.py b/pandas/tests/indexes/datetimes/test_ops.py
index cbf6b7b63bd50..c55b0481c1041 100644
--- a/pandas/tests/indexes/datetimes/test_ops.py
+++ b/pandas/tests/indexes/datetimes/test_ops.py
@@ -264,9 +264,9 @@ def test_order_without_freq(self, index_dates, expected_dates, tz_naive_fixture)
tm.assert_numpy_array_equal(indexer, exp, check_dtype=False)
assert ordered.freq is None
- def test_drop_duplicates_metadata(self):
+ def test_drop_duplicates_metadata(self, freq_sample):
# GH 10115
- idx = pd.date_range("2011-01-01", "2011-01-31", freq="D", name="idx")
+ idx = pd.date_range("2011-01-01", freq=freq_sample, periods=10, name="idx")
result = idx.drop_duplicates()
tm.assert_index_equal(idx, result)
assert idx.freq == result.freq
@@ -277,57 +277,38 @@ def test_drop_duplicates_metadata(self):
tm.assert_index_equal(idx, result)
assert result.freq is None
- def test_drop_duplicates(self):
+ @pytest.mark.parametrize(
+ "keep, expected, index",
+ [
+ ("first", np.concatenate(([False] * 10, [True] * 5)), np.arange(0, 10)),
+ ("last", np.concatenate(([True] * 5, [False] * 10)), np.arange(5, 15)),
+ (
+ False,
+ np.concatenate(([True] * 5, [False] * 5, [True] * 5)),
+ np.arange(5, 10),
+ ),
+ ],
+ )
+ def test_drop_duplicates(self, freq_sample, keep, expected, index):
# to check Index/Series compat
- base = pd.date_range("2011-01-01", "2011-01-31", freq="D", name="idx")
- idx = base.append(base[:5])
+ idx = pd.date_range("2011-01-01", freq=freq_sample, periods=10, name="idx")
+ idx = idx.append(idx[:5])
- res = idx.drop_duplicates()
- tm.assert_index_equal(res, base)
- res = Series(idx).drop_duplicates()
- tm.assert_series_equal(res, Series(base))
+ tm.assert_numpy_array_equal(idx.duplicated(keep=keep), expected)
+ expected = idx[~expected]
- res = idx.drop_duplicates(keep="last")
- exp = base[5:].append(base[:5])
- tm.assert_index_equal(res, exp)
- res = Series(idx).drop_duplicates(keep="last")
- tm.assert_series_equal(res, Series(exp, index=np.arange(5, 36)))
+ result = idx.drop_duplicates(keep=keep)
+ tm.assert_index_equal(result, expected)
- res = idx.drop_duplicates(keep=False)
- tm.assert_index_equal(res, base[5:])
- res = Series(idx).drop_duplicates(keep=False)
- tm.assert_series_equal(res, Series(base[5:], index=np.arange(5, 31)))
+ result = Series(idx).drop_duplicates(keep=keep)
+ tm.assert_series_equal(result, Series(expected, index=index))
- @pytest.mark.parametrize(
- "freq",
- [
- "A",
- "2A",
- "-2A",
- "Q",
- "-1Q",
- "M",
- "-1M",
- "D",
- "3D",
- "-3D",
- "W",
- "-1W",
- "H",
- "2H",
- "-2H",
- "T",
- "2T",
- "S",
- "-3S",
- ],
- )
- def test_infer_freq(self, freq):
+ def test_infer_freq(self, freq_sample):
# GH 11018
- idx = pd.date_range("2011-01-01 09:00:00", freq=freq, periods=10)
+ idx = pd.date_range("2011-01-01 09:00:00", freq=freq_sample, periods=10)
result = pd.DatetimeIndex(idx.asi8, freq="infer")
tm.assert_index_equal(idx, result)
- assert result.freq == freq
+ assert result.freq == freq_sample
def test_nat(self, tz_naive_fixture):
tz = tz_naive_fixture
diff --git a/pandas/tests/indexes/period/test_ops.py b/pandas/tests/indexes/period/test_ops.py
index 196946e696c8d..fc44226f9d72f 100644
--- a/pandas/tests/indexes/period/test_ops.py
+++ b/pandas/tests/indexes/period/test_ops.py
@@ -81,9 +81,10 @@ def test_value_counts_unique(self):
tm.assert_index_equal(idx.unique(), exp_idx)
- def test_drop_duplicates_metadata(self):
+ @pytest.mark.parametrize("freq", ["D", "3D", "H", "2H", "T", "2T", "S", "3S"])
+ def test_drop_duplicates_metadata(self, freq):
# GH 10115
- idx = pd.period_range("2011-01-01", "2011-01-31", freq="D", name="idx")
+ idx = pd.period_range("2011-01-01", periods=10, freq=freq, name="idx")
result = idx.drop_duplicates()
tm.assert_index_equal(idx, result)
assert idx.freq == result.freq
@@ -93,26 +94,32 @@ def test_drop_duplicates_metadata(self):
tm.assert_index_equal(idx, result)
assert idx.freq == result.freq
- def test_drop_duplicates(self):
+ @pytest.mark.parametrize("freq", ["D", "3D", "H", "2H", "T", "2T", "S", "3S"])
+ @pytest.mark.parametrize(
+ "keep, expected, index",
+ [
+ ("first", np.concatenate(([False] * 10, [True] * 5)), np.arange(0, 10)),
+ ("last", np.concatenate(([True] * 5, [False] * 10)), np.arange(5, 15)),
+ (
+ False,
+ np.concatenate(([True] * 5, [False] * 5, [True] * 5)),
+ np.arange(5, 10),
+ ),
+ ],
+ )
+ def test_drop_duplicates(self, freq, keep, expected, index):
# to check Index/Series compat
- base = pd.period_range("2011-01-01", "2011-01-31", freq="D", name="idx")
- idx = base.append(base[:5])
-
- res = idx.drop_duplicates()
- tm.assert_index_equal(res, base)
- res = Series(idx).drop_duplicates()
- tm.assert_series_equal(res, Series(base))
-
- res = idx.drop_duplicates(keep="last")
- exp = base[5:].append(base[:5])
- tm.assert_index_equal(res, exp)
- res = Series(idx).drop_duplicates(keep="last")
- tm.assert_series_equal(res, Series(exp, index=np.arange(5, 36)))
-
- res = idx.drop_duplicates(keep=False)
- tm.assert_index_equal(res, base[5:])
- res = Series(idx).drop_duplicates(keep=False)
- tm.assert_series_equal(res, Series(base[5:], index=np.arange(5, 31)))
+ idx = pd.period_range("2011-01-01", periods=10, freq=freq, name="idx")
+ idx = idx.append(idx[:5])
+
+ tm.assert_numpy_array_equal(idx.duplicated(keep=keep), expected)
+ expected = idx[~expected]
+
+ result = idx.drop_duplicates(keep=keep)
+ tm.assert_index_equal(result, expected)
+
+ result = Series(idx).drop_duplicates(keep=keep)
+ tm.assert_series_equal(result, Series(expected, index=index))
def test_order_compat(self):
def _check_freq(index, expected_index):
diff --git a/pandas/tests/indexes/timedeltas/test_ops.py b/pandas/tests/indexes/timedeltas/test_ops.py
index 4af5df6e2cc55..aa1bf997fc66b 100644
--- a/pandas/tests/indexes/timedeltas/test_ops.py
+++ b/pandas/tests/indexes/timedeltas/test_ops.py
@@ -134,9 +134,9 @@ def test_order(self):
tm.assert_numpy_array_equal(indexer, exp, check_dtype=False)
assert ordered.freq is None
- def test_drop_duplicates_metadata(self):
+ def test_drop_duplicates_metadata(self, freq_sample):
# GH 10115
- idx = pd.timedelta_range("1 day", "31 day", freq="D", name="idx")
+ idx = pd.timedelta_range("1 day", periods=10, freq=freq_sample, name="idx")
result = idx.drop_duplicates()
tm.assert_index_equal(idx, result)
assert idx.freq == result.freq
@@ -147,36 +147,38 @@ def test_drop_duplicates_metadata(self):
tm.assert_index_equal(idx, result)
assert result.freq is None
- def test_drop_duplicates(self):
+ @pytest.mark.parametrize(
+ "keep, expected, index",
+ [
+ ("first", np.concatenate(([False] * 10, [True] * 5)), np.arange(0, 10)),
+ ("last", np.concatenate(([True] * 5, [False] * 10)), np.arange(5, 15)),
+ (
+ False,
+ np.concatenate(([True] * 5, [False] * 5, [True] * 5)),
+ np.arange(5, 10),
+ ),
+ ],
+ )
+ def test_drop_duplicates(self, freq_sample, keep, expected, index):
# to check Index/Series compat
- base = pd.timedelta_range("1 day", "31 day", freq="D", name="idx")
- idx = base.append(base[:5])
+ idx = pd.timedelta_range("1 day", periods=10, freq=freq_sample, name="idx")
+ idx = idx.append(idx[:5])
- res = idx.drop_duplicates()
- tm.assert_index_equal(res, base)
- res = Series(idx).drop_duplicates()
- tm.assert_series_equal(res, Series(base))
+ tm.assert_numpy_array_equal(idx.duplicated(keep=keep), expected)
+ expected = idx[~expected]
- res = idx.drop_duplicates(keep="last")
- exp = base[5:].append(base[:5])
- tm.assert_index_equal(res, exp)
- res = Series(idx).drop_duplicates(keep="last")
- tm.assert_series_equal(res, Series(exp, index=np.arange(5, 36)))
+ result = idx.drop_duplicates(keep=keep)
+ tm.assert_index_equal(result, expected)
- res = idx.drop_duplicates(keep=False)
- tm.assert_index_equal(res, base[5:])
- res = Series(idx).drop_duplicates(keep=False)
- tm.assert_series_equal(res, Series(base[5:], index=np.arange(5, 31)))
+ result = Series(idx).drop_duplicates(keep=keep)
+ tm.assert_series_equal(result, Series(expected, index=index))
- @pytest.mark.parametrize(
- "freq", ["D", "3D", "-3D", "H", "2H", "-2H", "T", "2T", "S", "-3S"]
- )
- def test_infer_freq(self, freq):
+ def test_infer_freq(self, freq_sample):
# GH#11018
- idx = pd.timedelta_range("1", freq=freq, periods=10)
+ idx = pd.timedelta_range("1", freq=freq_sample, periods=10)
result = pd.TimedeltaIndex(idx.asi8, freq="infer")
tm.assert_index_equal(idx, result)
- assert result.freq == freq
+ assert result.freq == freq_sample
def test_repeat(self):
index = pd.timedelta_range("1 days", periods=2, freq="D")
| - [x] refers to #15752
- [x] tests added / passed
- [x] tests duplicated and drop_duplicates for period, categorical, datetimes, timedelta | https://api.github.com/repos/pandas-dev/pandas/pulls/32575 | 2020-03-10T09:35:01Z | 2020-04-06T23:23:00Z | 2020-04-06T23:23:00Z | 2020-04-16T17:15:04Z |
Backport PR #31875 on branch 1.0.x (DOC: add redirects from Rolling to rolling.Rolling) | diff --git a/doc/redirects.csv b/doc/redirects.csv
index ef93955c14fe6..3669ff4b7cc0b 100644
--- a/doc/redirects.csv
+++ b/doc/redirects.csv
@@ -49,7 +49,25 @@ internals,development/internals
# api moved function
reference/api/pandas.io.json.json_normalize,pandas.json_normalize
-# api rename
+# rename due to refactors
+reference/api/pandas.core.window.Rolling,pandas.core.window.rolling.Rolling
+reference/api/pandas.core.window.Rolling.aggregate,pandas.core.window.rolling.Rolling.aggregate
+reference/api/pandas.core.window.Rolling.apply,pandas.core.window.rolling.Rolling.apply
+reference/api/pandas.core.window.Rolling.corr,pandas.core.window.rolling.Rolling.corr
+reference/api/pandas.core.window.Rolling.count,pandas.core.window.rolling.Rolling.count
+reference/api/pandas.core.window.Rolling.cov,pandas.core.window.rolling.Rolling.cov
+reference/api/pandas.core.window.Rolling.kurt,pandas.core.window.rolling.Rolling.kurt
+reference/api/pandas.core.window.Rolling.max,pandas.core.window.rolling.Rolling.max
+reference/api/pandas.core.window.Rolling.mean,pandas.core.window.rolling.Rolling.mean
+reference/api/pandas.core.window.Rolling.median,pandas.core.window.rolling.Rolling.median
+reference/api/pandas.core.window.Rolling.min,pandas.core.window.rolling.Rolling.min
+reference/api/pandas.core.window.Rolling.quantile,pandas.core.window.rolling.Rolling.quantile
+reference/api/pandas.core.window.Rolling.skew,pandas.core.window.rolling.Rolling.skew
+reference/api/pandas.core.window.Rolling.std,pandas.core.window.rolling.Rolling.std
+reference/api/pandas.core.window.Rolling.sum,pandas.core.window.rolling.Rolling.sum
+reference/api/pandas.core.window.Rolling.var,pandas.core.window.rolling.Rolling.var
+
+# api url change (generated -> reference/api rename)
api,reference/index
generated/pandas.api.extensions.ExtensionArray.argsort,../reference/api/pandas.api.extensions.ExtensionArray.argsort
generated/pandas.api.extensions.ExtensionArray.astype,../reference/api/pandas.api.extensions.ExtensionArray.astype
| Backport PR #31875: DOC: add redirects from Rolling to rolling.Rolling | https://api.github.com/repos/pandas-dev/pandas/pulls/32574 | 2020-03-10T08:35:58Z | 2020-03-10T10:15:57Z | 2020-03-10T10:15:57Z | 2020-03-10T10:15:57Z |
Backport PR #32564 on branch 1.0.x (DOC: Add missing question mark icon) | diff --git a/doc/source/_static/question_mark_noback.svg b/doc/source/_static/question_mark_noback.svg
new file mode 100644
index 0000000000000..3abb4b806d20a
--- /dev/null
+++ b/doc/source/_static/question_mark_noback.svg
@@ -0,0 +1,72 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<!-- Created with Inkscape (http://www.inkscape.org/) -->
+
+<svg
+ xmlns:dc="http://purl.org/dc/elements/1.1/"
+ xmlns:cc="http://creativecommons.org/ns#"
+ xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
+ xmlns:svg="http://www.w3.org/2000/svg"
+ xmlns="http://www.w3.org/2000/svg"
+ xmlns:sodipodi="http://sodipodi.sourceforge.net/DTD/sodipodi-0.dtd"
+ xmlns:inkscape="http://www.inkscape.org/namespaces/inkscape"
+ width="6.1681423mm"
+ height="6.1681423mm"
+ viewBox="0 0 6.1681423 6.1681423"
+ version="1.1"
+ id="svg856"
+ inkscape:version="0.92.4 (f8dce91, 2019-08-02)"
+ sodipodi:docname="question_mark_noback.svg">
+ <defs
+ id="defs850" />
+ <sodipodi:namedview
+ id="base"
+ pagecolor="#ffffff"
+ bordercolor="#666666"
+ borderopacity="1.0"
+ inkscape:pageopacity="0.0"
+ inkscape:pageshadow="2"
+ inkscape:zoom="11.2"
+ inkscape:cx="4.3447038"
+ inkscape:cy="5.995975"
+ inkscape:document-units="mm"
+ inkscape:current-layer="layer1"
+ showgrid="false"
+ fit-margin-top="0"
+ fit-margin-left="0"
+ fit-margin-right="0"
+ fit-margin-bottom="0"
+ inkscape:window-width="1600"
+ inkscape:window-height="876"
+ inkscape:window-x="0"
+ inkscape:window-y="0"
+ inkscape:window-maximized="1" />
+ <metadata
+ id="metadata853">
+ <rdf:RDF>
+ <cc:Work
+ rdf:about="">
+ <dc:format>image/svg+xml</dc:format>
+ <dc:type
+ rdf:resource="http://purl.org/dc/dcmitype/StillImage" />
+ <dc:title></dc:title>
+ </cc:Work>
+ </rdf:RDF>
+ </metadata>
+ <g
+ inkscape:label="Layer 1"
+ inkscape:groupmode="layer"
+ id="layer1"
+ transform="translate(-71.755217,-98.124272)">
+ <text
+ xml:space="preserve"
+ style="font-style:normal;font-weight:normal;font-size:1.1868099px;line-height:1.25;font-family:sans-serif;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;stroke-width:0.26458335"
+ x="73.403717"
+ y="103.38733"
+ id="text1405"><tspan
+ sodipodi:role="line"
+ id="tspan1403"
+ x="73.403717"
+ y="103.38733"
+ style="font-style:normal;font-variant:normal;font-weight:bold;font-stretch:normal;font-size:5.6966877px;font-family:'Noto Sans Mono CJK HK';-inkscape-font-specification:'Noto Sans Mono CJK HK';fill:#ffffff;fill-opacity:1;stroke-width:0.26458335">?</tspan></text>
+ </g>
+</svg>
| Backport PR #32564: DOC: Add missing question mark icon | https://api.github.com/repos/pandas-dev/pandas/pulls/32573 | 2020-03-10T07:19:02Z | 2020-03-10T07:54:41Z | 2020-03-10T07:54:41Z | 2020-03-10T07:54:42Z |
TST: stricter tests, avoid check_categorical=False, check_less_precise | diff --git a/pandas/tests/frame/test_analytics.py b/pandas/tests/frame/test_analytics.py
index 07e30d41c216d..f4a10abea9757 100644
--- a/pandas/tests/frame/test_analytics.py
+++ b/pandas/tests/frame/test_analytics.py
@@ -32,7 +32,6 @@ def assert_stat_op_calc(
has_skipna=True,
check_dtype=True,
check_dates=False,
- check_less_precise=False,
skipna_alternative=None,
):
"""
@@ -54,9 +53,6 @@ def assert_stat_op_calc(
"alternative(frame)" should be checked.
check_dates : bool, default false
Whether opname should be tested on a Datetime Series
- check_less_precise : bool, default False
- Whether results should only be compared approximately;
- passed on to tm.assert_series_equal
skipna_alternative : function, default None
NaN-safe version of alternative
"""
@@ -84,17 +80,11 @@ def wrapper(x):
result0 = f(axis=0, skipna=False)
result1 = f(axis=1, skipna=False)
tm.assert_series_equal(
- result0,
- frame.apply(wrapper),
- check_dtype=check_dtype,
- check_less_precise=check_less_precise,
+ result0, frame.apply(wrapper), check_dtype=check_dtype,
)
# HACK: win32
tm.assert_series_equal(
- result1,
- frame.apply(wrapper, axis=1),
- check_dtype=False,
- check_less_precise=check_less_precise,
+ result1, frame.apply(wrapper, axis=1), check_dtype=False,
)
else:
skipna_wrapper = alternative
@@ -102,17 +92,12 @@ def wrapper(x):
result0 = f(axis=0)
result1 = f(axis=1)
tm.assert_series_equal(
- result0,
- frame.apply(skipna_wrapper),
- check_dtype=check_dtype,
- check_less_precise=check_less_precise,
+ result0, frame.apply(skipna_wrapper), check_dtype=check_dtype,
)
if opname in ["sum", "prod"]:
expected = frame.apply(skipna_wrapper, axis=1)
- tm.assert_series_equal(
- result1, expected, check_dtype=False, check_less_precise=check_less_precise
- )
+ tm.assert_series_equal(result1, expected, check_dtype=False)
# check dtypes
if check_dtype:
@@ -333,11 +318,7 @@ def kurt(x):
# mixed types (with upcasting happening)
assert_stat_op_calc(
- "sum",
- np.sum,
- mixed_float_frame.astype("float32"),
- check_dtype=False,
- check_less_precise=True,
+ "sum", np.sum, mixed_float_frame.astype("float32"), check_dtype=False,
)
assert_stat_op_calc(
diff --git a/pandas/tests/frame/test_to_csv.py b/pandas/tests/frame/test_to_csv.py
index cec2bd4b634c1..a49da7a5ec2fc 100644
--- a/pandas/tests/frame/test_to_csv.py
+++ b/pandas/tests/frame/test_to_csv.py
@@ -250,9 +250,7 @@ def make_dtnat_arr(n, nnat=None):
df.to_csv(pth, chunksize=chunksize)
recons = self.read_csv(pth)._convert(datetime=True, coerce=True)
- tm.assert_frame_equal(
- df, recons, check_names=False, check_less_precise=True
- )
+ tm.assert_frame_equal(df, recons, check_names=False)
@pytest.mark.slow
def test_to_csv_moar(self):
@@ -354,9 +352,7 @@ def _to_uni(x):
recons.columns = np.array(recons.columns, dtype=c_dtype)
df.columns = np.array(df.columns, dtype=c_dtype)
- tm.assert_frame_equal(
- df, recons, check_names=False, check_less_precise=True
- )
+ tm.assert_frame_equal(df, recons, check_names=False)
N = 100
chunksize = 1000
diff --git a/pandas/tests/generic/test_series.py b/pandas/tests/generic/test_series.py
index f119eb422a276..388bb8e3f636d 100644
--- a/pandas/tests/generic/test_series.py
+++ b/pandas/tests/generic/test_series.py
@@ -237,9 +237,7 @@ def test_to_xarray_index_types(self, index):
assert isinstance(result, DataArray)
# idempotency
- tm.assert_series_equal(
- result.to_series(), s, check_index_type=False, check_categorical=True
- )
+ tm.assert_series_equal(result.to_series(), s, check_index_type=False)
@td.skip_if_no("xarray", min_version="0.7.0")
def test_to_xarray(self):
diff --git a/pandas/tests/groupby/test_function.py b/pandas/tests/groupby/test_function.py
index 83080aa98648f..03278e69fe94a 100644
--- a/pandas/tests/groupby/test_function.py
+++ b/pandas/tests/groupby/test_function.py
@@ -661,7 +661,7 @@ def test_nlargest_mi_grouper():
]
expected = Series(exp_values, index=exp_idx)
- tm.assert_series_equal(result, expected, check_exact=False, check_less_precise=True)
+ tm.assert_series_equal(result, expected, check_exact=False)
def test_nsmallest():
diff --git a/pandas/tests/io/excel/test_writers.py b/pandas/tests/io/excel/test_writers.py
index 506d223dbedb4..59899673cfc31 100644
--- a/pandas/tests/io/excel/test_writers.py
+++ b/pandas/tests/io/excel/test_writers.py
@@ -564,7 +564,7 @@ def test_roundtrip_indexlabels(self, merge_cells, frame, path):
reader = ExcelFile(path)
recons = pd.read_excel(reader, "test1", index_col=[0, 1])
- tm.assert_frame_equal(df, recons, check_less_precise=True)
+ tm.assert_frame_equal(df, recons)
def test_excel_roundtrip_indexname(self, merge_cells, path):
df = DataFrame(np.random.randn(10, 4))
diff --git a/pandas/tests/io/test_sql.py b/pandas/tests/io/test_sql.py
index fc3876eee9d66..86502a67e1869 100644
--- a/pandas/tests/io/test_sql.py
+++ b/pandas/tests/io/test_sql.py
@@ -2372,7 +2372,7 @@ def test_write_row_by_row(self):
result = sql.read_sql("select * from test", con=self.conn)
result.index = frame.index
- tm.assert_frame_equal(result, frame, check_less_precise=True)
+ tm.assert_frame_equal(result, frame)
def test_execute(self):
frame = tm.makeTimeDataFrame()
@@ -2632,7 +2632,7 @@ def test_write_row_by_row(self):
result = sql.read_sql("select * from test", con=self.conn)
result.index = frame.index
- tm.assert_frame_equal(result, frame, check_less_precise=True)
+ tm.assert_frame_equal(result, frame)
def test_chunksize_read_type(self):
frame = tm.makeTimeDataFrame()
diff --git a/pandas/tests/io/test_stata.py b/pandas/tests/io/test_stata.py
index b65efac2bd527..3efac9cd605a8 100644
--- a/pandas/tests/io/test_stata.py
+++ b/pandas/tests/io/test_stata.py
@@ -254,12 +254,21 @@ def test_read_dta4(self, file):
)
# these are all categoricals
- expected = pd.concat(
- [expected[col].astype("category") for col in expected], axis=1
- )
+ for col in expected:
+ orig = expected[col].copy()
+
+ categories = np.asarray(expected["fully_labeled"][orig.notna()])
+ if col == "incompletely_labeled":
+ categories = orig
+
+ cat = orig.astype("category")._values
+ cat = cat.set_categories(categories, ordered=True)
+ cat.categories.rename(None, inplace=True)
+
+ expected[col] = cat
# stata doesn't save .category metadata
- tm.assert_frame_equal(parsed, expected, check_categorical=False)
+ tm.assert_frame_equal(parsed, expected)
# File containing strls
def test_read_dta12(self):
@@ -952,19 +961,27 @@ def test_categorical_writing(self, version):
original = pd.concat(
[original[col].astype("category") for col in original], axis=1
)
+ expected.index.name = "index"
expected["incompletely_labeled"] = expected["incompletely_labeled"].apply(str)
expected["unlabeled"] = expected["unlabeled"].apply(str)
- expected = pd.concat(
- [expected[col].astype("category") for col in expected], axis=1
- )
- expected.index.name = "index"
+ for col in expected:
+ orig = expected[col].copy()
+
+ cat = orig.astype("category")._values
+ cat = cat.as_ordered()
+ if col == "unlabeled":
+ cat = cat.set_categories(orig, ordered=True)
+
+ cat.categories.rename(None, inplace=True)
+
+ expected[col] = cat
with tm.ensure_clean() as path:
original.to_stata(path, version=version)
written_and_read_again = self.read_dta(path)
res = written_and_read_again.set_index("index")
- tm.assert_frame_equal(res, expected, check_categorical=False)
+ tm.assert_frame_equal(res, expected)
def test_categorical_warnings_and_errors(self):
# Warning for non-string labels
@@ -1056,9 +1073,11 @@ def test_categorical_sorting(self, file):
parsed.index = np.arange(parsed.shape[0])
codes = [-1, -1, 0, 1, 1, 1, 2, 2, 3, 4]
categories = ["Poor", "Fair", "Good", "Very good", "Excellent"]
- cat = pd.Categorical.from_codes(codes=codes, categories=categories)
+ cat = pd.Categorical.from_codes(
+ codes=codes, categories=categories, ordered=True
+ )
expected = pd.Series(cat, name="srh")
- tm.assert_series_equal(expected, parsed["srh"], check_categorical=False)
+ tm.assert_series_equal(expected, parsed["srh"])
@pytest.mark.parametrize("file", ["dta19_115", "dta19_117"])
def test_categorical_ordering(self, file):
diff --git a/pandas/tests/series/test_constructors.py b/pandas/tests/series/test_constructors.py
index 1a794f8656abe..46ac430a13394 100644
--- a/pandas/tests/series/test_constructors.py
+++ b/pandas/tests/series/test_constructors.py
@@ -393,7 +393,7 @@ def test_constructor_categorical_dtype(self):
expected = Series(
["a", "a"], index=[0, 1], dtype=CategoricalDtype(["a", "b"], ordered=True)
)
- tm.assert_series_equal(result, expected, check_categorical=True)
+ tm.assert_series_equal(result, expected)
def test_constructor_categorical_string(self):
# GH 26336: the string 'category' maintains existing CategoricalDtype
| Won't be too surprised if 32bit builds need some troubleshooting. | https://api.github.com/repos/pandas-dev/pandas/pulls/32571 | 2020-03-10T02:30:06Z | 2020-03-11T02:27:15Z | 2020-03-11T02:27:15Z | 2020-03-11T02:30:00Z |
CLN: avoid _internal_get_values in pandas._testing | diff --git a/pandas/_testing.py b/pandas/_testing.py
index 5e94ac3b3d108..1048e0d8b6dc6 100644
--- a/pandas/_testing.py
+++ b/pandas/_testing.py
@@ -1038,7 +1038,8 @@ def assert_extension_array_equal(
if hasattr(left, "asi8") and type(right) == type(left):
# Avoid slow object-dtype comparisons
- assert_numpy_array_equal(left.asi8, right.asi8)
+ # np.asarray for case where we have a np.MaskedArray
+ assert_numpy_array_equal(np.asarray(left.asi8), np.asarray(right.asi8))
return
left_na = np.asarray(left.isna())
@@ -1176,20 +1177,23 @@ def assert_series_equal(
raise AssertionError(msg)
elif is_interval_dtype(left.dtype) or is_interval_dtype(right.dtype):
assert_interval_array_equal(left.array, right.array)
- elif is_datetime64tz_dtype(left.dtype):
- # .values is an ndarray, but ._values is the ExtensionArray.
+ elif is_categorical_dtype(left.dtype) or is_categorical_dtype(right.dtype):
+ _testing.assert_almost_equal(
+ left._values,
+ right._values,
+ check_less_precise=check_less_precise,
+ check_dtype=check_dtype,
+ obj=str(obj),
+ )
+ elif is_extension_array_dtype(left.dtype) or is_extension_array_dtype(right.dtype):
+ assert_extension_array_equal(left._values, right._values)
+ elif needs_i8_conversion(left.dtype) or needs_i8_conversion(right.dtype):
+ # DatetimeArray or TimedeltaArray
assert_extension_array_equal(left._values, right._values)
- elif (
- is_extension_array_dtype(left)
- and not is_categorical_dtype(left)
- and is_extension_array_dtype(right)
- and not is_categorical_dtype(right)
- ):
- assert_extension_array_equal(left.array, right.array)
else:
_testing.assert_almost_equal(
- left._internal_get_values(),
- right._internal_get_values(),
+ left._values,
+ right._values,
check_less_precise=check_less_precise,
check_dtype=check_dtype,
obj=str(obj),
| 7 more usages left after this | https://api.github.com/repos/pandas-dev/pandas/pulls/32570 | 2020-03-10T02:26:43Z | 2020-03-11T02:19:30Z | 2020-03-11T02:19:30Z | 2020-03-13T17:28:11Z |
PERF: copy cached attributes on index shallow_copy | diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst
index e745bf3f5feed..f20c3df027fba 100644
--- a/doc/source/whatsnew/v1.1.0.rst
+++ b/doc/source/whatsnew/v1.1.0.rst
@@ -187,7 +187,9 @@ Performance improvements
- Performance improvement in :class:`Timedelta` constructor (:issue:`30543`)
- Performance improvement in :class:`Timestamp` constructor (:issue:`30543`)
- Performance improvement in flex arithmetic ops between :class:`DataFrame` and :class:`Series` with ``axis=0`` (:issue:`31296`)
--
+- The internal :meth:`Index._shallow_copy` now copies cached attributes over to the new index,
+ avoiding creating these again on the new index. This can speed up many operations
+ that depend on creating copies of existing indexes (:issue:`28584`)
.. ---------------------------------------------------------------------------
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 3eab757311ccb..17a1827fda027 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -1,7 +1,7 @@
from datetime import datetime
import operator
from textwrap import dedent
-from typing import TYPE_CHECKING, Any, FrozenSet, Hashable, Union
+from typing import TYPE_CHECKING, Any, Dict, FrozenSet, Hashable, Union
import warnings
import numpy as np
@@ -250,6 +250,7 @@ def _outer_indexer(self, left, right):
_typ = "index"
_data: Union[ExtensionArray, np.ndarray]
+ _cache: Dict[str, Any]
_id = None
_name: Label = None
# MultiIndex.levels previously allowed setting the index name. We
@@ -468,6 +469,7 @@ def _simple_new(cls, values, name: Label = None):
# we actually set this value too.
result._index_data = values
result._name = name
+ result._cache = {}
return result._reset_identity()
@@ -498,11 +500,13 @@ def _shallow_copy(self, values=None, name: Label = no_default):
name : Label, defaults to self.name
"""
name = self.name if name is no_default else name
-
+ cache = self._cache.copy() if values is None else {}
if values is None:
values = self.values
- return self._simple_new(values, name=name)
+ result = self._simple_new(values, name=name)
+ result._cache = cache
+ return result
def _shallow_copy_with_infer(self, values, **kwargs):
"""
diff --git a/pandas/core/indexes/numeric.py b/pandas/core/indexes/numeric.py
index 6c250ccd09a51..2d1d69772c100 100644
--- a/pandas/core/indexes/numeric.py
+++ b/pandas/core/indexes/numeric.py
@@ -104,15 +104,11 @@ def _maybe_cast_slice_bound(self, label, side, kind):
@Appender(Index._shallow_copy.__doc__)
def _shallow_copy(self, values=None, name: Label = lib.no_default):
- name = name if name is not lib.no_default else self.name
-
if values is not None and not self._can_hold_na and values.dtype.kind == "f":
+ name = self.name if name is lib.no_default else name
# Ensure we are not returning an Int64Index with float data:
return Float64Index._simple_new(values, name=name)
-
- if values is None:
- values = self.values
- return type(self)._simple_new(values, name=name)
+ return super()._shallow_copy(values=values, name=name)
def _convert_for_op(self, value):
"""
| - [x] closes #28584
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
The performance of the example in #28584 is:
```python
>>> idx = pd.Index(np.arange(100_000))
>>> %timeit idx.get_loc(99_999)
1.17 Β΅s Β± 80.6 ns per loop # master and this PR
>>> %timeit idx._shallow_copy().get_loc(99_999)
3.57 ms Β± 286 ns per loop # master
3.67 Β΅s Β± 286 ns per loop # this PR
```
The issue is still on the extension indexes, e.g. ``CategoricalIndex._shallow_copy``. I'd like to take them afterwards. | https://api.github.com/repos/pandas-dev/pandas/pulls/32568 | 2020-03-09T22:33:40Z | 2020-03-11T03:06:03Z | 2020-03-11T03:06:03Z | 2020-03-11T09:02:49Z |
TST: add test.indexes.common.Base.create_index and annotate .create_index | diff --git a/pandas/tests/indexes/common.py b/pandas/tests/indexes/common.py
index 69451501fd7bd..e5af0d9c03979 100644
--- a/pandas/tests/indexes/common.py
+++ b/pandas/tests/indexes/common.py
@@ -35,6 +35,9 @@ class Base:
_holder: Optional[Type[Index]] = None
_compat_props = ["shape", "ndim", "size", "nbytes"]
+ def create_index(self) -> Index:
+ raise NotImplementedError("Method not implemented")
+
def test_pickle_compat_construction(self):
# need an object to create with
msg = (
diff --git a/pandas/tests/indexes/datetimes/test_datetimelike.py b/pandas/tests/indexes/datetimes/test_datetimelike.py
index da1bd6f091d1a..e4785e5f80256 100644
--- a/pandas/tests/indexes/datetimes/test_datetimelike.py
+++ b/pandas/tests/indexes/datetimes/test_datetimelike.py
@@ -17,7 +17,7 @@ class TestDatetimeIndex(DatetimeLike):
def indices(self, request):
return request.param
- def create_index(self):
+ def create_index(self) -> DatetimeIndex:
return date_range("20130101", periods=5)
def test_shift(self):
diff --git a/pandas/tests/indexes/period/test_period.py b/pandas/tests/indexes/period/test_period.py
index b4c223be0f6a5..03f0be3f368cb 100644
--- a/pandas/tests/indexes/period/test_period.py
+++ b/pandas/tests/indexes/period/test_period.py
@@ -35,7 +35,7 @@ class TestPeriodIndex(DatetimeLike):
def indices(self, request):
return request.param
- def create_index(self):
+ def create_index(self) -> PeriodIndex:
return period_range("20130101", periods=5, freq="D")
def test_pickle_compat_construction(self):
diff --git a/pandas/tests/indexes/ranges/test_range.py b/pandas/tests/indexes/ranges/test_range.py
index c1cc23039eeaf..61ac937f5fda0 100644
--- a/pandas/tests/indexes/ranges/test_range.py
+++ b/pandas/tests/indexes/ranges/test_range.py
@@ -30,7 +30,7 @@ class TestRangeIndex(Numeric):
def indices(self, request):
return request.param
- def create_index(self):
+ def create_index(self) -> RangeIndex:
return RangeIndex(start=0, stop=20, step=2)
def test_can_hold_identifiers(self):
diff --git a/pandas/tests/indexes/test_base.py b/pandas/tests/indexes/test_base.py
index 9e0cef375fea3..2981f56e58ecc 100644
--- a/pandas/tests/indexes/test_base.py
+++ b/pandas/tests/indexes/test_base.py
@@ -59,7 +59,7 @@ def index(self, request):
# copy to avoid mutation, e.g. setting .name
return indices_dict[key].copy()
- def create_index(self):
+ def create_index(self) -> Index:
return Index(list("abcde"))
def test_can_hold_identifiers(self):
@@ -2277,7 +2277,7 @@ class TestMixedIntIndex(Base):
def indices(self, request):
return Index(request.param)
- def create_index(self):
+ def create_index(self) -> Index:
return Index([0, "a", 1, "b", 2, "c"])
def test_argsort(self):
diff --git a/pandas/tests/indexes/test_numeric.py b/pandas/tests/indexes/test_numeric.py
index 10d57d8616cf3..23877c2c7607a 100644
--- a/pandas/tests/indexes/test_numeric.py
+++ b/pandas/tests/indexes/test_numeric.py
@@ -118,7 +118,7 @@ def mixed_index(self):
def float_index(self):
return Float64Index([0.0, 2.5, 5.0, 7.5, 10.0])
- def create_index(self):
+ def create_index(self) -> Float64Index:
return Float64Index(np.arange(5, dtype="float64"))
def test_repr_roundtrip(self, indices):
@@ -663,7 +663,7 @@ class TestInt64Index(NumericInt):
def indices(self, request):
return Int64Index(request.param)
- def create_index(self):
+ def create_index(self) -> Int64Index:
# return Int64Index(np.arange(5, dtype="int64"))
return Int64Index(range(0, 20, 2))
@@ -801,7 +801,7 @@ def index_large(self):
large = [2 ** 63, 2 ** 63 + 10, 2 ** 63 + 15, 2 ** 63 + 20, 2 ** 63 + 25]
return UInt64Index(large)
- def create_index(self):
+ def create_index(self) -> UInt64Index:
# compat with shared Int64/Float64 tests; use index_large for UInt64 only tests
return UInt64Index(np.arange(5, dtype="uint64"))
diff --git a/pandas/tests/indexes/timedeltas/test_timedelta.py b/pandas/tests/indexes/timedeltas/test_timedelta.py
index d4a94f8693081..971203d6fc720 100644
--- a/pandas/tests/indexes/timedeltas/test_timedelta.py
+++ b/pandas/tests/indexes/timedeltas/test_timedelta.py
@@ -28,7 +28,7 @@ class TestTimedeltaIndex(DatetimeLike):
def indices(self):
return tm.makeTimedeltaIndex(10)
- def create_index(self):
+ def create_index(self) -> TimedeltaIndex:
return pd.to_timedelta(range(5), unit="d") + pd.offsets.Hour(1)
def test_numeric_compat(self):
| Makes ``test.indexes.common.Base`` and ``create_index`` a bit easier to work with. | https://api.github.com/repos/pandas-dev/pandas/pulls/32567 | 2020-03-09T22:26:59Z | 2020-03-10T11:09:21Z | 2020-03-10T11:09:20Z | 2020-03-10T11:09:31Z |
BUG: Fix segfault in csv tokenizer | diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst
index 21e59805fa143..36cfb4a904139 100644
--- a/doc/source/whatsnew/v1.1.0.rst
+++ b/doc/source/whatsnew/v1.1.0.rst
@@ -340,6 +340,7 @@ I/O
- Bug in :class:`HDFStore` that caused it to set to ``int64`` the dtype of a ``datetime64`` column when reading a DataFrame in Python 3 from fixed format written in Python 2 (:issue:`31750`)
- Bug in :meth:`read_excel` where a UTF-8 string with a high surrogate would cause a segmentation violation (:issue:`23809`)
- Bug in :meth:`read_csv` was causing a file descriptor leak on an empty file (:issue:`31488`)
+- Bug in :meth:`read_csv` was causing a segfault when there were blank lines between the header and data rows (:issue:`28071`)
Plotting
diff --git a/pandas/_libs/src/parser/tokenizer.c b/pandas/_libs/src/parser/tokenizer.c
index 2188ff6b0d464..7ba1a6cd398c9 100644
--- a/pandas/_libs/src/parser/tokenizer.c
+++ b/pandas/_libs/src/parser/tokenizer.c
@@ -1189,8 +1189,14 @@ int parser_consume_rows(parser_t *self, size_t nrows) {
/* cannot guarantee that nrows + 1 has been observed */
word_deletions = self->line_start[nrows - 1] + self->line_fields[nrows - 1];
- char_count = (self->word_starts[word_deletions - 1] +
- strlen(self->words[word_deletions - 1]) + 1);
+ if (word_deletions >= 1) {
+ char_count = (self->word_starts[word_deletions - 1] +
+ strlen(self->words[word_deletions - 1]) + 1);
+ } else {
+ /* if word_deletions == 0 (i.e. this case) then char_count must
+ * be 0 too, as no data needs to be skipped */
+ char_count = 0;
+ }
TRACE(("parser_consume_rows: Deleting %d words, %d chars\n", word_deletions,
char_count));
diff --git a/pandas/tests/io/parser/test_common.py b/pandas/tests/io/parser/test_common.py
index 33460262a4430..0f3a5be76ae60 100644
--- a/pandas/tests/io/parser/test_common.py
+++ b/pandas/tests/io/parser/test_common.py
@@ -2093,3 +2093,16 @@ def test():
parser.read_csv(path)
td.check_file_leaks(test)()
+
+
+@pytest.mark.parametrize("nrows", range(1, 6))
+def test_blank_lines_between_header_and_data_rows(all_parsers, nrows):
+ # GH 28071
+ ref = DataFrame(
+ [[np.nan, np.nan], [np.nan, np.nan], [1, 2], [np.nan, np.nan], [3, 4]],
+ columns=list("ab"),
+ )
+ csv = "\nheader\n\na,b\n\n\n1,2\n\n3,4"
+ parser = all_parsers
+ df = parser.read_csv(StringIO(csv), header=3, nrows=nrows, skip_blank_lines=False)
+ tm.assert_frame_equal(df, ref[:nrows])
| - [x] closes #28071
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/32566 | 2020-03-09T22:07:06Z | 2020-03-16T02:21:30Z | 2020-03-16T02:21:29Z | 2020-03-16T20:08:32Z |
TST: Fix bare pytest raises in generic/test_frame.py | diff --git a/pandas/tests/generic/test_frame.py b/pandas/tests/generic/test_frame.py
index dca65152e82db..8fe49b2ec2299 100644
--- a/pandas/tests/generic/test_frame.py
+++ b/pandas/tests/generic/test_frame.py
@@ -72,9 +72,10 @@ def test_nonzero_single_element(self):
assert not df.bool()
df = DataFrame([[False, False]])
- with pytest.raises(ValueError):
+ msg = "The truth value of a DataFrame is ambiguous"
+ with pytest.raises(ValueError, match=msg):
df.bool()
- with pytest.raises(ValueError):
+ with pytest.raises(ValueError, match=msg):
bool(df)
def test_get_numeric_data_preserve_dtype(self):
@@ -189,30 +190,31 @@ class TestDataFrame2:
def test_validate_bool_args(self, value):
df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
- with pytest.raises(ValueError):
+ msg = 'For argument "inplace" expected type bool, received type'
+ with pytest.raises(ValueError, match=msg):
super(DataFrame, df).rename_axis(
mapper={"a": "x", "b": "y"}, axis=1, inplace=value
)
- with pytest.raises(ValueError):
+ with pytest.raises(ValueError, match=msg):
super(DataFrame, df).drop("a", axis=1, inplace=value)
- with pytest.raises(ValueError):
+ with pytest.raises(ValueError, match=msg):
super(DataFrame, df)._consolidate(inplace=value)
- with pytest.raises(ValueError):
+ with pytest.raises(ValueError, match=msg):
super(DataFrame, df).fillna(value=0, inplace=value)
- with pytest.raises(ValueError):
+ with pytest.raises(ValueError, match=msg):
super(DataFrame, df).replace(to_replace=1, value=7, inplace=value)
- with pytest.raises(ValueError):
+ with pytest.raises(ValueError, match=msg):
super(DataFrame, df).interpolate(inplace=value)
- with pytest.raises(ValueError):
+ with pytest.raises(ValueError, match=msg):
super(DataFrame, df)._where(cond=df.a > 2, inplace=value)
- with pytest.raises(ValueError):
+ with pytest.raises(ValueError, match=msg):
super(DataFrame, df).mask(cond=df.a > 2, inplace=value)
def test_unexpected_keyword(self):
@@ -222,16 +224,17 @@ def test_unexpected_keyword(self):
ts = df["joe"].copy()
ts[2] = np.nan
- with pytest.raises(TypeError, match="unexpected keyword"):
+ msg = "unexpected keyword"
+ with pytest.raises(TypeError, match=msg):
df.drop("joe", axis=1, in_place=True)
- with pytest.raises(TypeError, match="unexpected keyword"):
+ with pytest.raises(TypeError, match=msg):
df.reindex([1, 0], inplace=True)
- with pytest.raises(TypeError, match="unexpected keyword"):
+ with pytest.raises(TypeError, match=msg):
ca.fillna(0, inplace=True)
- with pytest.raises(TypeError, match="unexpected keyword"):
+ with pytest.raises(TypeError, match=msg):
ts.fillna(0, in_place=True)
| - [x] refers https://github.com/pandas-dev/pandas/issues/30999
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Adds match arguments to pytest.raises calls in pandas/tests/generic/test_frame.py
| https://api.github.com/repos/pandas-dev/pandas/pulls/32565 | 2020-03-09T20:34:14Z | 2020-03-10T10:37:29Z | 2020-03-10T10:37:29Z | 2020-03-10T10:37:41Z |
DOC: Add missing question mark icon | diff --git a/doc/source/_static/question_mark_noback.svg b/doc/source/_static/question_mark_noback.svg
new file mode 100644
index 0000000000000..3abb4b806d20a
--- /dev/null
+++ b/doc/source/_static/question_mark_noback.svg
@@ -0,0 +1,72 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<!-- Created with Inkscape (http://www.inkscape.org/) -->
+
+<svg
+ xmlns:dc="http://purl.org/dc/elements/1.1/"
+ xmlns:cc="http://creativecommons.org/ns#"
+ xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
+ xmlns:svg="http://www.w3.org/2000/svg"
+ xmlns="http://www.w3.org/2000/svg"
+ xmlns:sodipodi="http://sodipodi.sourceforge.net/DTD/sodipodi-0.dtd"
+ xmlns:inkscape="http://www.inkscape.org/namespaces/inkscape"
+ width="6.1681423mm"
+ height="6.1681423mm"
+ viewBox="0 0 6.1681423 6.1681423"
+ version="1.1"
+ id="svg856"
+ inkscape:version="0.92.4 (f8dce91, 2019-08-02)"
+ sodipodi:docname="question_mark_noback.svg">
+ <defs
+ id="defs850" />
+ <sodipodi:namedview
+ id="base"
+ pagecolor="#ffffff"
+ bordercolor="#666666"
+ borderopacity="1.0"
+ inkscape:pageopacity="0.0"
+ inkscape:pageshadow="2"
+ inkscape:zoom="11.2"
+ inkscape:cx="4.3447038"
+ inkscape:cy="5.995975"
+ inkscape:document-units="mm"
+ inkscape:current-layer="layer1"
+ showgrid="false"
+ fit-margin-top="0"
+ fit-margin-left="0"
+ fit-margin-right="0"
+ fit-margin-bottom="0"
+ inkscape:window-width="1600"
+ inkscape:window-height="876"
+ inkscape:window-x="0"
+ inkscape:window-y="0"
+ inkscape:window-maximized="1" />
+ <metadata
+ id="metadata853">
+ <rdf:RDF>
+ <cc:Work
+ rdf:about="">
+ <dc:format>image/svg+xml</dc:format>
+ <dc:type
+ rdf:resource="http://purl.org/dc/dcmitype/StillImage" />
+ <dc:title></dc:title>
+ </cc:Work>
+ </rdf:RDF>
+ </metadata>
+ <g
+ inkscape:label="Layer 1"
+ inkscape:groupmode="layer"
+ id="layer1"
+ transform="translate(-71.755217,-98.124272)">
+ <text
+ xml:space="preserve"
+ style="font-style:normal;font-weight:normal;font-size:1.1868099px;line-height:1.25;font-family:sans-serif;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;stroke-width:0.26458335"
+ x="73.403717"
+ y="103.38733"
+ id="text1405"><tspan
+ sodipodi:role="line"
+ id="tspan1403"
+ x="73.403717"
+ y="103.38733"
+ style="font-style:normal;font-variant:normal;font-weight:bold;font-stretch:normal;font-size:5.6966877px;font-family:'Noto Sans Mono CJK HK';-inkscape-font-specification:'Noto Sans Mono CJK HK';fill:#ffffff;fill-opacity:1;stroke-width:0.26458335">?</tspan></text>
+ </g>
+</svg>
| - [x] closes #32469
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I added the question mark to the `_static` folder. When rebuilding the documentation pages locally using the latest version of the `pydata-bootstrap-sphinx-theme`, the `div.highlight` seems to be styled correctly (`background: white`), removing the _strange green around the code block_.
| https://api.github.com/repos/pandas-dev/pandas/pulls/32564 | 2020-03-09T20:29:51Z | 2020-03-10T07:18:29Z | 2020-03-10T07:18:29Z | 2020-03-10T07:18:29Z |
Fix warning in io/excel/test_openpyxl | diff --git a/pandas/tests/io/excel/test_openpyxl.py b/pandas/tests/io/excel/test_openpyxl.py
index 10ed192062d9c..60c943d95e510 100644
--- a/pandas/tests/io/excel/test_openpyxl.py
+++ b/pandas/tests/io/excel/test_openpyxl.py
@@ -114,7 +114,7 @@ def test_to_excel_with_openpyxl_engine(ext, tmpdir):
df2 = DataFrame({"B": np.linspace(1, 20, 10)})
df = pd.concat([df1, df2], axis=1)
styled = df.style.applymap(
- lambda val: "color: %s" % "red" if val < 0 else "black"
+ lambda val: "color: %s" % ("red" if val < 0 else "black")
).highlight_max()
filename = tmpdir / "styled.xlsx"
| as requested in PR #32544 | https://api.github.com/repos/pandas-dev/pandas/pulls/32563 | 2020-03-09T20:28:59Z | 2020-03-10T11:17:12Z | 2020-03-10T11:17:12Z | 2020-03-11T15:47:52Z |
Ensure valid Block mutation in SeriesBinGrouper. | diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index 123dfa07f4331..bc234bbd31662 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -20,6 +20,7 @@ Fixed regressions
- Fixed regression in ``groupby(..).rolling(..).apply()`` (``RollingGroupby``) where the ``raw`` parameter was ignored (:issue:`31754`)
- Fixed regression in :meth:`rolling(..).corr() <pandas.core.window.rolling.Rolling.corr>` when using a time offset (:issue:`31789`)
- Fixed regression in :meth:`groupby(..).nunique() <pandas.core.groupby.DataFrameGroupBy.nunique>` which was modifying the original values if ``NaN`` values were present (:issue:`31950`)
+- Fixed regression in ``DataFrame.groupby`` raising a ``ValueError`` from an internal operation (:issue:`31802`)
- Fixed regression where :func:`read_pickle` raised a ``UnicodeDecodeError`` when reading a py27 pickle with :class:`MultiIndex` column (:issue:`31988`).
- Fixed regression in :class:`DataFrame` arithmetic operations with mis-matched columns (:issue:`31623`)
- Fixed regression in :meth:`groupby(..).agg() <pandas.core.groupby.GroupBy.agg>` calling a user-provided function an extra time on an empty input (:issue:`31760`)
diff --git a/pandas/_libs/reduction.pyx b/pandas/_libs/reduction.pyx
index b27072aa66708..29a5a73ef08d0 100644
--- a/pandas/_libs/reduction.pyx
+++ b/pandas/_libs/reduction.pyx
@@ -177,6 +177,8 @@ cdef class _BaseGrouper:
object.__setattr__(cached_ityp, '_index_data', islider.buf)
cached_ityp._engine.clear_mapping()
object.__setattr__(cached_typ._data._block, 'values', vslider.buf)
+ object.__setattr__(cached_typ._data._block, 'mgr_locs',
+ slice(len(vslider.buf)))
object.__setattr__(cached_typ, '_index', cached_ityp)
object.__setattr__(cached_typ, 'name', self.name)
diff --git a/pandas/tests/groupby/test_bin_groupby.py b/pandas/tests/groupby/test_bin_groupby.py
index ff74d374e5e3f..152086c241a52 100644
--- a/pandas/tests/groupby/test_bin_groupby.py
+++ b/pandas/tests/groupby/test_bin_groupby.py
@@ -5,6 +5,7 @@
from pandas.core.dtypes.common import ensure_int64
+import pandas as pd
from pandas import Index, Series, isna
import pandas._testing as tm
@@ -51,6 +52,30 @@ def test_series_bin_grouper():
tm.assert_almost_equal(counts, exp_counts)
+def assert_block_lengths(x):
+ assert len(x) == len(x._data.blocks[0].mgr_locs)
+ return 0
+
+
+def cumsum_max(x):
+ x.cumsum().max()
+ return 0
+
+
+@pytest.mark.parametrize("func", [cumsum_max, assert_block_lengths])
+def test_mgr_locs_updated(func):
+ # https://github.com/pandas-dev/pandas/issues/31802
+ # Some operations may require creating new blocks, which requires
+ # valid mgr_locs
+ df = pd.DataFrame({"A": ["a", "a", "a"], "B": ["a", "b", "b"], "C": [1, 1, 1]})
+ result = df.groupby(["A", "B"]).agg(func)
+ expected = pd.DataFrame(
+ {"C": [0, 0]},
+ index=pd.MultiIndex.from_product([["a"], ["a", "b"]], names=["A", "B"]),
+ )
+ tm.assert_frame_equal(result, expected)
+
+
@pytest.mark.parametrize(
"binner,closed,expected",
[
| Closes https://github.com/pandas-dev/pandas/issues/31802
~This "fixes" #31802 by expanding the number of cases where we swallow an
exception in libreduction. Currently, we're creating an invalid Series
in SeriesBinGrouper where the `.mgr_locs` doesn't match the values. See
https://github.com/pandas-dev/pandas/issues/31802#issuecomment-595954511
for more.~
~For now, we simply catch more cases that fall back to Python. I've gone
with a minimal change which addresses only issues hitting this exact
exception. We might want to go broader, but that's not clear.~
cc @jbrockmendel & @WillAyd | https://api.github.com/repos/pandas-dev/pandas/pulls/32561 | 2020-03-09T19:16:11Z | 2020-03-11T18:34:48Z | 2020-03-11T18:34:48Z | 2020-03-11T18:34:49Z |
TST: Add extra test for pandas.to_numeric() for issue #32394 | diff --git a/pandas/tests/tools/test_to_numeric.py b/pandas/tests/tools/test_to_numeric.py
index 19385e797467c..e0dfeac4ab475 100644
--- a/pandas/tests/tools/test_to_numeric.py
+++ b/pandas/tests/tools/test_to_numeric.py
@@ -627,3 +627,13 @@ def test_non_coerce_uint64_conflict(errors, exp):
else:
result = to_numeric(ser, errors=errors)
tm.assert_series_equal(result, ser)
+
+
+def test_failure_to_convert_uint64_string_to_NaN():
+ # GH 32394
+ result = to_numeric("uint64", errors="coerce")
+ assert np.isnan(result)
+
+ ser = Series([32, 64, np.nan])
+ result = to_numeric(pd.Series(["32", "64", "uint64"]), errors="coerce")
+ tm.assert_series_equal(result, ser)
| - [x] closes #32394
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry (covered by PR #32541)
| https://api.github.com/repos/pandas-dev/pandas/pulls/32560 | 2020-03-09T19:01:15Z | 2020-03-15T00:38:58Z | 2020-03-15T00:38:58Z | 2020-03-15T07:16:17Z |
BUG: pivot_table losing tz | diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst
index 85fe33b7c83e8..fe9715313f0db 100644
--- a/doc/source/whatsnew/v1.1.0.rst
+++ b/doc/source/whatsnew/v1.1.0.rst
@@ -367,6 +367,7 @@ Reshaping
- :meth:`Series.append` will now raise a ``TypeError`` when passed a DataFrame or a sequence containing Dataframe (:issue:`31413`)
- :meth:`DataFrame.replace` and :meth:`Series.replace` will raise a ``TypeError`` if ``to_replace`` is not an expected type. Previously the ``replace`` would fail silently (:issue:`18634`)
- Bug in :meth:`DataFrame.apply` where callback was called with :class:`Series` parameter even though ``raw=True`` requested. (:issue:`32423`)
+- Bug in :meth:`DataFrame.pivot_table` losing timezone information when creating a :class:`MultiIndex` level from a column with timezone-aware dtype (:issue:`32558`)
Sparse
diff --git a/pandas/core/indexes/multi.py b/pandas/core/indexes/multi.py
index 122097f4478d7..5bffc4ec552af 100644
--- a/pandas/core/indexes/multi.py
+++ b/pandas/core/indexes/multi.py
@@ -565,6 +565,7 @@ def from_product(cls, iterables, sortorder=None, names=lib.no_default):
if names is lib.no_default:
names = [getattr(it, "name", None) for it in iterables]
+ # codes are all ndarrays, so cartesian_product is lossless
codes = cartesian_product(codes)
return MultiIndex(levels, codes, sortorder=sortorder, names=names)
diff --git a/pandas/core/reshape/util.py b/pandas/core/reshape/util.py
index d8652c9b4fac9..7abb14303f8cc 100644
--- a/pandas/core/reshape/util.py
+++ b/pandas/core/reshape/util.py
@@ -2,8 +2,6 @@
from pandas.core.dtypes.common import is_list_like
-import pandas.core.common as com
-
def cartesian_product(X):
"""
@@ -51,9 +49,20 @@ def cartesian_product(X):
# if any factor is empty, the cartesian product is empty
b = np.zeros_like(cumprodX)
- return [
- np.tile(
- np.repeat(np.asarray(com.values_from_object(x)), b[i]), np.product(a[i])
- )
- for i, x in enumerate(X)
- ]
+ return [_tile_compat(np.repeat(x, b[i]), np.product(a[i])) for i, x in enumerate(X)]
+
+
+def _tile_compat(arr, num: int):
+ """
+ Index compat for np.tile.
+
+ Notes
+ -----
+ Does not support multi-dimensional `num`.
+ """
+ if isinstance(arr, np.ndarray):
+ return np.tile(arr, num)
+
+ # Otherwise we have an Index
+ taker = np.tile(np.arange(len(arr)), num)
+ return arr.take(taker)
diff --git a/pandas/tests/reshape/test_pivot.py b/pandas/tests/reshape/test_pivot.py
index 75c3c565e9d58..cdb1a73abc431 100644
--- a/pandas/tests/reshape/test_pivot.py
+++ b/pandas/tests/reshape/test_pivot.py
@@ -1026,6 +1026,14 @@ def test_pivot_table_multiindex_only(self, cols):
tm.assert_frame_equal(result, expected)
+ def test_pivot_table_retains_tz(self):
+ dti = date_range("2016-01-01", periods=3, tz="Europe/Amsterdam")
+ df = DataFrame({"A": np.random.randn(3), "B": np.random.randn(3), "C": dti})
+ result = df.pivot_table(index=["B", "C"], dropna=False)
+
+ # check tz retention
+ assert result.index.levels[1].equals(dti)
+
def test_pivot_integer_columns(self):
# caused by upstream bug in unstack
diff --git a/pandas/tests/reshape/test_util.py b/pandas/tests/reshape/test_util.py
index cd518dda4edbf..9d074b5ade425 100644
--- a/pandas/tests/reshape/test_util.py
+++ b/pandas/tests/reshape/test_util.py
@@ -25,6 +25,22 @@ def test_datetimeindex(self):
tm.assert_index_equal(result1, expected1)
tm.assert_index_equal(result2, expected2)
+ def test_tzaware_retained(self):
+ x = date_range("2000-01-01", periods=2, tz="US/Pacific")
+ y = np.array([3, 4])
+ result1, result2 = cartesian_product([x, y])
+
+ expected = x.repeat(2)
+ tm.assert_index_equal(result1, expected)
+
+ def test_tzaware_retained_categorical(self):
+ x = date_range("2000-01-01", periods=2, tz="US/Pacific").astype("category")
+ y = np.array([3, 4])
+ result1, result2 = cartesian_product([x, y])
+
+ expected = x.repeat(2)
+ tm.assert_index_equal(result1, expected)
+
def test_empty(self):
# product of empty factors
X = [[], [0, 1], []]
| - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This gets rid of the last values_from_object usage (pending other PRs)
`_tile_compat` likely makes more sense as an Index method. I kept it here as a proof of concept because I think we actually need it in the EA interface too. Being able to broadcast a length=1 EA to a length=N EA will be necessary for some of the arithmetic perf going on. | https://api.github.com/repos/pandas-dev/pandas/pulls/32558 | 2020-03-09T18:46:52Z | 2020-03-14T23:28:54Z | 2020-03-14T23:28:54Z | 2020-03-14T23:31:12Z |
CLN: values_from_object in computation.pytables | diff --git a/pandas/core/computation/pytables.py b/pandas/core/computation/pytables.py
index 653d014775386..15d9987310f18 100644
--- a/pandas/core/computation/pytables.py
+++ b/pandas/core/computation/pytables.py
@@ -17,6 +17,7 @@
from pandas.core.computation.common import _ensure_decoded
from pandas.core.computation.expr import BaseExprVisitor
from pandas.core.computation.ops import UndefinedVariableError, is_term
+from pandas.core.construction import extract_array
from pandas.io.formats.printing import pprint_thing, pprint_thing_encoded
@@ -202,7 +203,7 @@ def stringify(value):
v = Timedelta(v, unit="s").value
return TermValue(int(v), v, kind)
elif meta == "category":
- metadata = com.values_from_object(self.metadata)
+ metadata = extract_array(self.metadata, extract_numpy=True)
result = metadata.searchsorted(v, side="left")
# result returns 0 if v is first element or if v is not in metadata
| By my count there is just one more `values_from_object` call to go. | https://api.github.com/repos/pandas-dev/pandas/pulls/32557 | 2020-03-09T18:30:47Z | 2020-03-11T02:33:14Z | 2020-03-11T02:33:14Z | 2020-03-11T02:34:56Z |
CLN: remove Categorical.put | diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py
index 92859479ec73f..ba4c2e168e0c4 100644
--- a/pandas/core/arrays/categorical.py
+++ b/pandas/core/arrays/categorical.py
@@ -1409,12 +1409,6 @@ def notna(self):
notnull = notna
- def put(self, *args, **kwargs):
- """
- Replace specific elements in the Categorical with given values.
- """
- raise NotImplementedError(("'put' is not yet implemented for Categorical"))
-
def dropna(self):
"""
Return the Categorical without null values.
| https://api.github.com/repos/pandas-dev/pandas/pulls/32554 | 2020-03-09T17:16:17Z | 2020-03-09T21:27:57Z | 2020-03-09T21:27:57Z | 2020-03-09T21:28:30Z | |
Backport PR #32242 on branch 1.0.x (BUG: Fixed bug, where pandas._libs.lib.maybe_convert_objects function improperly handled arrays with bools and NaNs) | diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index 808e6ae709ce9..eec471f989037 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -24,6 +24,7 @@ Fixed regressions
- Fixed regression in :class:`DataFrame` arithmetic operations with mis-matched columns (:issue:`31623`)
- Fixed regression in :meth:`GroupBy.agg` calling a user-provided function an extra time on an empty input (:issue:`31760`)
- Joining on :class:`DatetimeIndex` or :class:`TimedeltaIndex` will preserve ``freq`` in simple cases (:issue:`32166`)
+- Fixed bug in the repr of an object-dtype ``Index`` with bools and missing values (:issue:`32146`)
-
.. ---------------------------------------------------------------------------
diff --git a/pandas/_libs/lib.pyx b/pandas/_libs/lib.pyx
index 3f98a479bc587..87d9394be555d 100644
--- a/pandas/_libs/lib.pyx
+++ b/pandas/_libs/lib.pyx
@@ -2235,7 +2235,7 @@ def maybe_convert_objects(ndarray[object] objects, bint try_float=0,
return uints
else:
return ints
- elif seen.is_bool:
+ elif seen.is_bool and not seen.nan_:
return bools.view(np.bool_)
return objects
diff --git a/pandas/tests/dtypes/test_inference.py b/pandas/tests/dtypes/test_inference.py
index 63c484f5ea68f..b148fc57f0bb6 100644
--- a/pandas/tests/dtypes/test_inference.py
+++ b/pandas/tests/dtypes/test_inference.py
@@ -568,6 +568,13 @@ def test_maybe_convert_objects_nullable_integer(self, exp):
tm.assert_extension_array_equal(result, exp)
+ def test_maybe_convert_objects_bool_nan(self):
+ # GH32146
+ ind = pd.Index([True, False, np.nan], dtype=object)
+ exp = np.array([True, False, np.nan], dtype=object)
+ out = lib.maybe_convert_objects(ind.values, safe=1)
+ tm.assert_numpy_array_equal(out, exp)
+
def test_mixed_dtypes_remain_object_array(self):
# GH14956
array = np.array([datetime(2015, 1, 1, tzinfo=pytz.utc), 1], dtype=object)
diff --git a/pandas/tests/indexes/test_base.py b/pandas/tests/indexes/test_base.py
index eec1ea68b55e2..7b2a73287cb38 100644
--- a/pandas/tests/indexes/test_base.py
+++ b/pandas/tests/indexes/test_base.py
@@ -2697,6 +2697,17 @@ def test_intersect_str_dates(self):
expected = Index([], dtype=object)
tm.assert_index_equal(result, expected)
+ def test_index_repr_bool_nan(self):
+ # GH32146
+ arr = Index([True, False, np.nan], dtype=object)
+ exp1 = arr.format()
+ out1 = ["True", "False", "NaN"]
+ assert out1 == exp1
+
+ exp2 = repr(arr)
+ out2 = "Index([True, False, nan], dtype='object')"
+ assert out2 == exp2
+
class TestIndexUtils:
@pytest.mark.parametrize(
diff --git a/pandas/tests/series/methods/test_value_counts.py b/pandas/tests/series/methods/test_value_counts.py
index fdb35befeb0c2..f97362ce9c2a9 100644
--- a/pandas/tests/series/methods/test_value_counts.py
+++ b/pandas/tests/series/methods/test_value_counts.py
@@ -1,4 +1,5 @@
import numpy as np
+import pytest
import pandas as pd
from pandas import Categorical, CategoricalIndex, Series
@@ -177,3 +178,28 @@ def test_value_counts_categorical_with_nan(self):
exp = Series([2, 1, 3], index=CategoricalIndex(["a", "b", np.nan]))
res = ser.value_counts(dropna=False, sort=False)
tm.assert_series_equal(res, exp)
+
+ @pytest.mark.parametrize(
+ "ser, dropna, exp",
+ [
+ (
+ pd.Series([False, True, True, pd.NA]),
+ False,
+ pd.Series([2, 1, 1], index=[True, False, pd.NA]),
+ ),
+ (
+ pd.Series([False, True, True, pd.NA]),
+ True,
+ pd.Series([2, 1], index=[True, False]),
+ ),
+ (
+ pd.Series(range(3), index=[True, False, np.nan]).index,
+ False,
+ pd.Series([1, 1, 1], index=[True, False, pd.NA]),
+ ),
+ ],
+ )
+ def test_value_counts_bool_with_nan(self, ser, dropna, exp):
+ # GH32146
+ out = ser.value_counts(dropna=dropna)
+ tm.assert_series_equal(out, exp)
| Backport PR #32242: BUG: Fixed bug, where pandas._libs.lib.maybe_convert_objects function improperly handled arrays with bools and NaNs | https://api.github.com/repos/pandas-dev/pandas/pulls/32552 | 2020-03-09T15:42:45Z | 2020-03-10T16:22:32Z | 2020-03-10T16:22:31Z | 2020-03-10T16:22:32Z |
DOC: Fix EX01 in pandas.DataFrame.idxmax | diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index cd5d81bc70dd9..60fc69e8222d6 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -8029,6 +8029,35 @@ def idxmax(self, axis=0, skipna=True) -> Series:
Notes
-----
This method is the DataFrame version of ``ndarray.argmax``.
+
+ Examples
+ --------
+ Consider a dataset containing food consumption in Argentina.
+
+ >>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],
+ ... 'co2_emissions': [37.2, 19.66, 1712]},
+ ... index=['Pork', 'Wheat Products', 'Beef'])
+
+ >>> df
+ consumption co2_emissions
+ Pork 10.51 37.20
+ Wheat Products 103.11 19.66
+ Beef 55.48 1712.00
+
+ By default, it returns the index for the maximum value in each column.
+
+ >>> df.idxmax()
+ consumption Wheat Products
+ co2_emissions Beef
+ dtype: object
+
+ To return the index for the maximum value in each row, use ``axis="columns"``.
+
+ >>> df.idxmax(axis="columns")
+ Pork co2_emissions
+ Wheat Products consumption
+ Beef co2_emissions
+ dtype: object
"""
axis = self._get_axis_number(axis)
indices = nanops.nanargmax(self.values, axis=axis, skipna=skipna)
| - [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Related to #27977.
```
################################################################################
################################## Validation ##################################
################################################################################ | https://api.github.com/repos/pandas-dev/pandas/pulls/32551 | 2020-03-09T05:53:51Z | 2020-03-11T01:35:50Z | 2020-03-11T01:35:50Z | 2020-03-11T01:35:56Z |
DOC: Added docstring for Series.name and corrected docstring guide | diff --git a/doc/source/development/contributing_docstring.rst b/doc/source/development/contributing_docstring.rst
index 1c99b341f6c5a..18c2769d7ca4b 100644
--- a/doc/source/development/contributing_docstring.rst
+++ b/doc/source/development/contributing_docstring.rst
@@ -17,7 +17,7 @@ Also, it is a common practice to generate online (html) documentation
automatically from docstrings. `Sphinx <https://www.sphinx-doc.org>`_ serves
this purpose.
-Next example gives an idea on how a docstring looks like:
+The next example gives an idea of what a docstring looks like:
.. code-block:: python
@@ -26,8 +26,8 @@ Next example gives an idea on how a docstring looks like:
Add up two integer numbers.
This function simply wraps the `+` operator, and does not
- do anything interesting, except for illustrating what is
- the docstring of a very simple function.
+ do anything interesting, except for illustrating what
+ the docstring of a very simple function looks like.
Parameters
----------
@@ -56,14 +56,14 @@ Next example gives an idea on how a docstring looks like:
"""
return num1 + num2
-Some standards exist about docstrings, so they are easier to read, and they can
-be exported to other formats such as html or pdf.
+Some standards regarding docstrings exist, which make them easier to read, and allow them
+be easily exported to other formats such as html or pdf.
The first conventions every Python docstring should follow are defined in
`PEP-257 <https://www.python.org/dev/peps/pep-0257/>`_.
-As PEP-257 is quite open, and some other standards exist on top of it. In the
-case of pandas, the numpy docstring convention is followed. The conventions is
+As PEP-257 is quite broad, other more specific standards also exist. In the
+case of pandas, the numpy docstring convention is followed. These conventions are
explained in this document:
* `numpydoc docstring guide <https://numpydoc.readthedocs.io/en/latest/format.html>`_
@@ -83,8 +83,8 @@ about reStructuredText can be found in:
Pandas has some helpers for sharing docstrings between related classes, see
:ref:`docstring.sharing`.
-The rest of this document will summarize all the above guides, and will
-provide additional convention specific to the pandas project.
+The rest of this document will summarize all the above guidelines, and will
+provide additional conventions specific to the pandas project.
.. _docstring.tutorial:
@@ -101,9 +101,9 @@ left before or after the docstring. The text starts in the next line after the
opening quotes. The closing quotes have their own line
(meaning that they are not at the end of the last sentence).
-In rare occasions reST styles like bold text or italics will be used in
+On rare occasions reST styles like bold text or italics will be used in
docstrings, but is it common to have inline code, which is presented between
-backticks. It is considered inline code:
+backticks. The following are considered inline code:
* The name of a parameter
* Python code, a module, function, built-in, type, literal... (e.g. ``os``,
@@ -235,8 +235,8 @@ The extended summary provides details on what the function does. It should not
go into the details of the parameters, or discuss implementation notes, which
go in other sections.
-A blank line is left between the short summary and the extended summary. And
-every paragraph in the extended summary is finished by a dot.
+A blank line is left between the short summary and the extended summary.
+Every paragraph in the extended summary ends with a dot.
The extended summary should provide details on why the function is useful and
their use cases, if it is not too generic.
@@ -542,19 +542,19 @@ first (not an alias like ``np``). If the function is in a module which is not
the main one, like ``scipy.sparse``, list the full module (e.g.
``scipy.sparse.coo_matrix``).
-This section, as the previous, also has a header, "See Also" (note the capital
-S and A). Also followed by the line with hyphens, and preceded by a blank line.
+This section has a header, "See Also" (note the capital
+S and A), followed by the line with hyphens and preceded by a blank line.
After the header, we will add a line for each related method or function,
followed by a space, a colon, another space, and a short description that
-illustrated what this method or function does, why is it relevant in this
-context, and what are the key differences between the documented function and
-the one referencing. The description must also finish with a dot.
+illustrates what this method or function does, why is it relevant in this
+context, and what the key differences are between the documented function and
+the one being referenced. The description must also end with a dot.
-Note that in "Returns" and "Yields", the description is located in the
-following line than the type. But in this section it is located in the same
-line, with a colon in between. If the description does not fit in the same
-line, it can continue in the next ones, but it has to be indented in them.
+Note that in "Returns" and "Yields", the description is located on the line
+after the type. In this section, however, it is located on the same
+line, with a colon in between. If the description does not fit on the same
+line, it can continue onto other lines which must be further indented.
For example:
@@ -587,7 +587,7 @@ Section 6: Notes
~~~~~~~~~~~~~~~~
This is an optional section used for notes about the implementation of the
-algorithm. Or to document technical aspects of the function behavior.
+algorithm, or to document technical aspects of the function behavior.
Feel free to skip it, unless you are familiar with the implementation of the
algorithm, or you discover some counter-intuitive behavior while writing the
@@ -600,15 +600,15 @@ This section follows the same format as the extended summary section.
Section 7: Examples
~~~~~~~~~~~~~~~~~~~
-This is one of the most important sections of a docstring, even if it is
-placed in the last position. As often, people understand concepts better
-with examples, than with accurate explanations.
+This is one of the most important sections of a docstring, despite being
+placed in the last position, as often people understand concepts better
+by example than through accurate explanations.
Examples in docstrings, besides illustrating the usage of the function or
-method, must be valid Python code, that in a deterministic way returns the
-presented output, and that can be copied and run by users.
+method, must be valid Python code, that returns the given output in a
+deterministic way, and that can be copied and run by users.
-They are presented as a session in the Python terminal. `>>>` is used to
+Examples are presented as a session in the Python terminal. `>>>` is used to
present code. `...` is used for code continuing from the previous line.
Output is presented immediately after the last line of code generating the
output (no blank lines in between). Comments describing the examples can
@@ -636,7 +636,7 @@ A simple example could be:
Return the first elements of the Series.
This function is mainly useful to preview the values of the
- Series without displaying the whole of it.
+ Series without displaying all of it.
Parameters
----------
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 568e99622dd29..ae7251db10fe7 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -435,6 +435,52 @@ def dtypes(self) -> DtypeObj:
@property
def name(self) -> Label:
+ """
+ Return the name of the Series.
+
+ The name of a Series becomes its index or column name if it is used
+ to form a DataFrame. It is also used whenever displaying the Series
+ using the interpreter.
+
+ Returns
+ -------
+ label (hashable object)
+ The name of the Series, also the column name if part of a DataFrame.
+
+ See Also
+ --------
+ Series.rename : Sets the Series name when given a scalar input.
+ Index.name : Corresponding Index property.
+
+ Examples
+ --------
+ The Series name can be set initially when calling the constructor.
+
+ >>> s = pd.Series([1, 2, 3], dtype=np.int64, name='Numbers')
+ >>> s
+ 0 1
+ 1 2
+ 2 3
+ Name: Numbers, dtype: int64
+ >>> s.name = "Integers"
+ >>> s
+ 0 1
+ 1 2
+ 2 3
+ Name: Integers, dtype: int64
+
+ The name of a Series within a DataFrame is its column name.
+
+ >>> df = pd.DataFrame([[1, 2], [3, 4], [5, 6]],
+ ... columns=["Odd Numbers", "Even Numbers"])
+ >>> df
+ Odd Numbers Even Numbers
+ 0 1 2
+ 1 3 4
+ 2 5 6
+ >>> df["Even Numbers"].name
+ 'Even Numbers'
+ """
return self._name
@name.setter
| The main contribution is to add a docstring with examples for the "name" property of the Series object. Also corrected some typos and grammatical points in the "pandas docstring guide".
The type hint gives the type as "Label" and I could not find any other reference to a custom type defined in pandas._typing which was explicitly mentioned in the docs (instead they specify "str" or "int" or somesuch), so I chose "Label (int, str or other hashable object)".
Further I chose "whenever displaying the Series in the interpreter" as clearer alternative to "when invoking the __repr__ method" or a similar precise statement. | https://api.github.com/repos/pandas-dev/pandas/pulls/32549 | 2020-03-08T21:54:37Z | 2020-03-17T00:07:36Z | 2020-03-17T00:07:36Z | 2020-03-17T00:07:43Z |
BUG: Add extra check for failing UTF-8 conversion | diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst
index d644a995a4876..5d962ff04464e 100644
--- a/doc/source/whatsnew/v1.1.0.rst
+++ b/doc/source/whatsnew/v1.1.0.rst
@@ -302,6 +302,7 @@ I/O
timestamps with ``version="2.0"`` (:issue:`31652`).
- Bug in :meth:`read_csv` was raising `TypeError` when `sep=None` was used in combination with `comment` keyword (:issue:`31396`)
- Bug in :class:`HDFStore` that caused it to set to ``int64`` the dtype of a ``datetime64`` column when reading a DataFrame in Python 3 from fixed format written in Python 2 (:issue:`31750`)
+- Bug in :meth:`read_excel` where a UTF-8 string with a high surrogate would cause a segmentation violation (:issue:`23809`)
Plotting
diff --git a/pandas/_libs/src/parse_helper.h b/pandas/_libs/src/parse_helper.h
index 7fbe7a04d5b22..2ada0a4bd173d 100644
--- a/pandas/_libs/src/parse_helper.h
+++ b/pandas/_libs/src/parse_helper.h
@@ -34,6 +34,9 @@ int floatify(PyObject *str, double *result, int *maybe_int) {
data = PyBytes_AS_STRING(str);
} else if (PyUnicode_Check(str)) {
tmp = PyUnicode_AsUTF8String(str);
+ if (tmp == NULL) {
+ return -1;
+ }
data = PyBytes_AS_STRING(tmp);
} else {
PyErr_SetString(PyExc_TypeError, "Invalid object type");
diff --git a/pandas/tests/io/data/excel/high_surrogate.xlsx b/pandas/tests/io/data/excel/high_surrogate.xlsx
new file mode 100644
index 0000000000000..1e29b6bee6586
Binary files /dev/null and b/pandas/tests/io/data/excel/high_surrogate.xlsx differ
diff --git a/pandas/tests/io/excel/test_readers.py b/pandas/tests/io/excel/test_readers.py
index a59b409809eed..cbc043820e35e 100644
--- a/pandas/tests/io/excel/test_readers.py
+++ b/pandas/tests/io/excel/test_readers.py
@@ -1044,3 +1044,11 @@ def test_excel_read_binary(self, engine, read_ext):
actual = pd.read_excel(data, engine=engine)
tm.assert_frame_equal(expected, actual)
+
+ def test_excel_high_surrogate(self, engine):
+ # GH 23809
+ expected = pd.DataFrame(["\udc88"], columns=["Column1"])
+
+ # should not produce a segmentation violation
+ actual = pd.read_excel("high_surrogate.xlsx")
+ tm.assert_frame_equal(expected, actual)
| - [x] closes #23809
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/32548 | 2020-03-08T21:35:41Z | 2020-03-12T00:54:07Z | 2020-03-12T00:54:07Z | 2020-03-12T21:36:19Z |
CLN: remove unnecessary values_from_objects in groupby.ops | diff --git a/pandas/core/groupby/ops.py b/pandas/core/groupby/ops.py
index 7259268ac3f2b..577c874c9cbbe 100644
--- a/pandas/core/groupby/ops.py
+++ b/pandas/core/groupby/ops.py
@@ -217,7 +217,7 @@ def indices(self):
return self.groupings[0].indices
else:
codes_list = [ping.codes for ping in self.groupings]
- keys = [com.values_from_object(ping.group_index) for ping in self.groupings]
+ keys = [ping.group_index for ping in self.groupings]
return get_indexer_dict(codes_list, keys)
@property
| 2 values_from_object calls left to go... | https://api.github.com/repos/pandas-dev/pandas/pulls/32547 | 2020-03-08T21:19:39Z | 2020-03-11T02:32:41Z | 2020-03-11T02:32:41Z | 2020-03-11T02:49:54Z |
BUG: Dataframe.groupby aggregations with categorical columns lead to incorrect results. | diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst
index 92f7c0f6b59a3..0aa5538c92482 100644
--- a/doc/source/whatsnew/v1.1.0.rst
+++ b/doc/source/whatsnew/v1.1.0.rst
@@ -976,6 +976,7 @@ Groupby/resample/rolling
- Bug in :meth:`GroupBy.apply` raises ``ValueError`` when the ``by`` axis is not sorted and has duplicates and the applied ``func`` does not mutate passed in objects (:issue:`30667`)
- Bug in :meth:`DataFrameGroupby.transform` produces incorrect result with transformation functions (:issue:`30918`)
+- Bug in :meth:`Groupby.transform` was returning the wrong result when grouping by multiple keys of which some were categorical and others not (:issue:`32494`)
- Bug in :meth:`GroupBy.count` causes segmentation fault when grouped-by column contains NaNs (:issue:`32841`)
- Bug in :meth:`DataFrame.groupby` and :meth:`Series.groupby` produces inconsistent type when aggregating Boolean series (:issue:`32894`)
- Bug in :meth:`DataFrameGroupBy.sum` and :meth:`SeriesGroupBy.sum` where a large negative number would be returned when the number of non-null values was below ``min_count`` for nullable integer dtypes (:issue:`32861`)
diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 5894066dd33c8..db5df9818b0b0 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -546,6 +546,7 @@ def _transform_fast(self, result, func_nm: str) -> Series:
builtin/cythonizable functions
"""
ids, _, ngroup = self.grouper.group_info
+ result = result.reindex(self.grouper.result_index, copy=False)
cast = self._transform_should_cast(func_nm)
out = algorithms.take_1d(result._values, ids)
if cast:
@@ -1496,6 +1497,7 @@ def _transform_fast(self, result: DataFrame, func_nm: str) -> DataFrame:
# for each col, reshape to to size of original frame
# by take operation
ids, _, ngroup = self.grouper.group_info
+ result = result.reindex(self.grouper.result_index, copy=False)
output = []
for i, _ in enumerate(result.columns):
res = algorithms.take_1d(result.iloc[:, i].values, ids)
diff --git a/pandas/tests/groupby/transform/test_transform.py b/pandas/tests/groupby/transform/test_transform.py
index e7bc3801a08a7..fd4ee2a81ebd8 100644
--- a/pandas/tests/groupby/transform/test_transform.py
+++ b/pandas/tests/groupby/transform/test_transform.py
@@ -1205,3 +1205,36 @@ def test_transform_lambda_indexing():
),
)
tm.assert_frame_equal(result, expected)
+
+
+def test_categorical_and_not_categorical_key(observed):
+ # Checks that groupby-transform, when grouping by both a categorical
+ # and a non-categorical key, doesn't try to expand the output to include
+ # non-observed categories but instead matches the input shape.
+ # GH 32494
+ df_with_categorical = pd.DataFrame(
+ {
+ "A": pd.Categorical(["a", "b", "a"], categories=["a", "b", "c"]),
+ "B": [1, 2, 3],
+ "C": ["a", "b", "a"],
+ }
+ )
+ df_without_categorical = pd.DataFrame(
+ {"A": ["a", "b", "a"], "B": [1, 2, 3], "C": ["a", "b", "a"]}
+ )
+
+ # DataFrame case
+ result = df_with_categorical.groupby(["A", "C"], observed=observed).transform("sum")
+ expected = df_without_categorical.groupby(["A", "C"]).transform("sum")
+ tm.assert_frame_equal(result, expected)
+ expected_explicit = pd.DataFrame({"B": [4, 2, 4]})
+ tm.assert_frame_equal(result, expected_explicit)
+
+ # Series case
+ result = df_with_categorical.groupby(["A", "C"], observed=observed)["B"].transform(
+ "sum"
+ )
+ expected = df_without_categorical.groupby(["A", "C"])["B"].transform("sum")
+ tm.assert_series_equal(result, expected)
+ expected_explicit = pd.Series([4, 2, 4], name="B")
+ tm.assert_series_equal(result, expected_explicit)
| - [x] closes #32494
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/32546 | 2020-03-08T19:51:00Z | 2020-06-14T15:02:05Z | 2020-06-14T15:02:05Z | 2020-06-14T16:31:04Z |
CLN: to_dense->np.asarray | diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py
index 40a169d03f39c..92859479ec73f 100644
--- a/pandas/core/arrays/categorical.py
+++ b/pandas/core/arrays/categorical.py
@@ -1728,7 +1728,8 @@ def fillna(self, value=None, method=None, limit=None):
# pad / bfill
if method is not None:
- values = self.to_dense().reshape(-1, len(self))
+ # TODO: dispatch when self.categories is EA-dtype
+ values = np.asarray(self).reshape(-1, len(self))
values = interpolate_2d(values, method, 0, None, value).astype(
self.categories.dtype
)[0]
| This is the only non-test use of Categorical.to_dense, which is slightly different from _internal_get_values (for SparseArray the two methods are identical), and so liable to cause confusion. | https://api.github.com/repos/pandas-dev/pandas/pulls/32545 | 2020-03-08T19:41:28Z | 2020-03-09T13:47:45Z | 2020-03-09T13:47:45Z | 2020-03-09T16:38:51Z |
BUG: pd.ExcelFile closes stream on destruction | diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index e6d65b1f828cb..052de47b570da 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -28,6 +28,7 @@ Fixed regressions
- Fixed regression in the repr of an object-dtype :class:`Index` with bools and missing values (:issue:`32146`)
- Fixed regression in :meth:`read_csv` in which the ``encoding`` option was not recognized with certain file-like objects (:issue:`31819`)
- Fixed regression in :meth:`DataFrame.reindex` and :meth:`Series.reindex` when reindexing with (tz-aware) index and ``method=nearest`` (:issue:`26683`)
+- Fixed regression in :class:`ExcelFile` where the stream passed into the function was closed by the destructor. (:issue:`31467`)
- Fixed regression in :meth:`DataFrame.reindex_like` on a :class:`DataFrame` subclass raised an ``AssertionError`` (:issue:`31925`)
- Fixed regression in :meth:`Series.shift` with ``datetime64`` dtype when passing an integer ``fill_value`` (:issue:`32591`)
diff --git a/pandas/io/excel/_base.py b/pandas/io/excel/_base.py
index f98d9501f1f73..d1139f640cef4 100644
--- a/pandas/io/excel/_base.py
+++ b/pandas/io/excel/_base.py
@@ -366,6 +366,9 @@ def _workbook_class(self):
def load_workbook(self, filepath_or_buffer):
pass
+ def close(self):
+ pass
+
@property
@abc.abstractmethod
def sheet_names(self):
@@ -895,14 +898,7 @@ def sheet_names(self):
def close(self):
"""close io if necessary"""
- if self.engine == "openpyxl":
- # https://stackoverflow.com/questions/31416842/
- # openpyxl-does-not-close-excel-workbook-in-read-only-mode
- wb = self.book
- wb._archive.close()
-
- if hasattr(self.io, "close"):
- self.io.close()
+ self._reader.close()
def __enter__(self):
return self
diff --git a/pandas/io/excel/_openpyxl.py b/pandas/io/excel/_openpyxl.py
index a96c0f814e2d8..0696d82e51f34 100644
--- a/pandas/io/excel/_openpyxl.py
+++ b/pandas/io/excel/_openpyxl.py
@@ -492,6 +492,11 @@ def load_workbook(self, filepath_or_buffer: FilePathOrBuffer):
filepath_or_buffer, read_only=True, data_only=True, keep_links=False
)
+ def close(self):
+ # https://stackoverflow.com/questions/31416842/
+ # openpyxl-does-not-close-excel-workbook-in-read-only-mode
+ self.book.close()
+
@property
def sheet_names(self) -> List[str]:
return self.book.sheetnames
diff --git a/pandas/tests/io/excel/test_readers.py b/pandas/tests/io/excel/test_readers.py
index cbc043820e35e..8732d4063d74c 100644
--- a/pandas/tests/io/excel/test_readers.py
+++ b/pandas/tests/io/excel/test_readers.py
@@ -629,6 +629,17 @@ def test_read_from_py_localpath(self, read_ext):
tm.assert_frame_equal(expected, actual)
+ @td.check_file_leaks
+ def test_close_from_py_localpath(self, read_ext):
+
+ # GH31467
+ str_path = os.path.join("test1" + read_ext)
+ with open(str_path, "rb") as f:
+ x = pd.read_excel(f, "Sheet1", index_col=0)
+ del x
+ # should not throw an exception because the passed file was closed
+ f.read()
+
def test_reader_seconds(self, read_ext):
if pd.read_excel.keywords["engine"] == "pyxlsb":
pytest.xfail("Sheets containing datetimes not supported by pyxlsb")
@@ -1020,10 +1031,10 @@ def test_excel_read_buffer(self, engine, read_ext):
tm.assert_frame_equal(expected, actual)
def test_reader_closes_file(self, engine, read_ext):
- f = open("test1" + read_ext, "rb")
- with pd.ExcelFile(f) as xlsx:
- # parses okay
- pd.read_excel(xlsx, "Sheet1", index_col=0, engine=engine)
+ with open("test1" + read_ext, "rb") as f:
+ with pd.ExcelFile(f) as xlsx:
+ # parses okay
+ pd.read_excel(xlsx, "Sheet1", index_col=0, engine=engine)
assert f.closed
| - [x] closes #31467
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/32544 | 2020-03-08T19:14:23Z | 2020-03-12T12:18:21Z | 2020-03-12T12:18:21Z | 2020-03-12T21:35:56Z |
Fix failure to convert string "uint64" to NaN | diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst
index d644a995a4876..e745bf3f5feed 100644
--- a/doc/source/whatsnew/v1.1.0.rst
+++ b/doc/source/whatsnew/v1.1.0.rst
@@ -231,7 +231,7 @@ Timezones
Numeric
^^^^^^^
- Bug in :meth:`DataFrame.floordiv` with ``axis=0`` not treating division-by-zero like :meth:`Series.floordiv` (:issue:`31271`)
--
+- Bug in :meth:`to_numeric` with string argument ``"uint64"`` and ``errors="coerce"`` silently fails (:issue:`32394`)
-
Conversion
diff --git a/pandas/_libs/lib.pyx b/pandas/_libs/lib.pyx
index 61d6a660a0357..feccb447c5112 100644
--- a/pandas/_libs/lib.pyx
+++ b/pandas/_libs/lib.pyx
@@ -2024,8 +2024,6 @@ def maybe_convert_numeric(ndarray[object] values, set na_values,
except (TypeError, ValueError) as err:
if not seen.coerce_numeric:
raise type(err)(f"{err} at position {i}")
- elif "uint64" in str(err): # Exception from check functions.
- raise
seen.saw_null()
floats[i] = NaN
diff --git a/pandas/tests/dtypes/test_inference.py b/pandas/tests/dtypes/test_inference.py
index 48ae1f67297af..b01747ef010c1 100644
--- a/pandas/tests/dtypes/test_inference.py
+++ b/pandas/tests/dtypes/test_inference.py
@@ -507,6 +507,13 @@ def test_convert_numeric_int64_uint64(self, case, coerce):
result = lib.maybe_convert_numeric(case, set(), coerce_numeric=coerce)
tm.assert_almost_equal(result, expected)
+ def test_convert_numeric_string_uint64(self):
+ # GH32394
+ result = lib.maybe_convert_numeric(
+ np.array(["uint64"], dtype=object), set(), coerce_numeric=True
+ )
+ assert np.isnan(result)
+
@pytest.mark.parametrize("value", [-(2 ** 63) - 1, 2 ** 64])
def test_convert_int_overflow(self, value):
# see gh-18584
| Including regression test
- [x] closes #32394
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/32541 | 2020-03-08T13:44:42Z | 2020-03-09T03:29:30Z | 2020-03-09T03:29:29Z | 2020-03-11T15:48:23Z |
DOC: Remove absolute urls from the docs | diff --git a/doc/source/getting_started/comparison/comparison_with_sql.rst b/doc/source/getting_started/comparison/comparison_with_sql.rst
index 6a03c06de3699..e3c8f8f5ccbcd 100644
--- a/doc/source/getting_started/comparison/comparison_with_sql.rst
+++ b/doc/source/getting_started/comparison/comparison_with_sql.rst
@@ -75,7 +75,7 @@ Filtering in SQL is done via a WHERE clause.
LIMIT 5;
DataFrames can be filtered in multiple ways; the most intuitive of which is using
-`boolean indexing <https://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexing>`_.
+:ref:`boolean indexing <indexing.boolean>`
.. ipython:: python
diff --git a/doc/source/user_guide/cookbook.rst b/doc/source/user_guide/cookbook.rst
index 4afdb14e5c39e..e51b5c9097951 100644
--- a/doc/source/user_guide/cookbook.rst
+++ b/doc/source/user_guide/cookbook.rst
@@ -794,8 +794,7 @@ The :ref:`Resample <timeseries.resampling>` docs.
`Time grouping with some missing values
<https://stackoverflow.com/questions/33637312/pandas-grouper-by-frequency-with-completeness-requirement>`__
-`Valid frequency arguments to Grouper
-<https://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__
+Valid frequency arguments to Grouper :ref:`Timeseries <timeseries.offset_aliases>`
`Grouping using a MultiIndex
<https://stackoverflow.com/questions/41483763/pandas-timegrouper-on-multiindex>`__
| - [x] closes #32529
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/32539 | 2020-03-08T05:37:10Z | 2020-03-12T07:15:30Z | 2020-03-12T07:15:30Z | 2020-03-12T08:03:38Z |
ENH: IntegerArray.astype(dt64) | diff --git a/pandas/_libs/tslib.pyx b/pandas/_libs/tslib.pyx
index b78b623bfa187..94e757624c136 100644
--- a/pandas/_libs/tslib.pyx
+++ b/pandas/_libs/tslib.pyx
@@ -14,7 +14,7 @@ PyDateTime_IMPORT
cimport numpy as cnp
-from numpy cimport float64_t, int64_t, ndarray
+from numpy cimport float64_t, int64_t, ndarray, uint8_t
import numpy as np
cnp.import_array()
@@ -351,7 +351,6 @@ def format_array_from_datetime(
def array_with_unit_to_datetime(
ndarray values,
- ndarray mask,
object unit,
str errors='coerce'
):
@@ -373,8 +372,6 @@ def array_with_unit_to_datetime(
----------
values : ndarray of object
Date-like objects to convert.
- mask : boolean ndarray
- Not-a-time mask for non-nullable integer types conversion, can be None.
unit : object
Time unit to use during conversion.
errors : str, default 'raise'
@@ -395,6 +392,7 @@ def array_with_unit_to_datetime(
bint need_to_iterate = True
ndarray[int64_t] iresult
ndarray[object] oresult
+ ndarray mask
object tz = None
assert is_ignore or is_coerce or is_raise
@@ -404,9 +402,6 @@ def array_with_unit_to_datetime(
result = values.astype('M8[ns]')
else:
result, tz = array_to_datetime(values.astype(object), errors=errors)
- if mask is not None:
- iresult = result.view('i8')
- iresult[mask] = NPY_NAT
return result, tz
m = cast_from_unit(None, unit)
@@ -419,9 +414,8 @@ def array_with_unit_to_datetime(
if values.dtype.kind == "i":
# Note: this condition makes the casting="same_kind" redundant
iresult = values.astype('i8', casting='same_kind', copy=False)
- # If no mask, fill mask by comparing to NPY_NAT constant
- if mask is None:
- mask = iresult == NPY_NAT
+ # fill by comparing to NPY_NAT constant
+ mask = iresult == NPY_NAT
iresult[mask] = 0
fvalues = iresult.astype('f8') * m
need_to_iterate = False
diff --git a/pandas/core/arrays/integer.py b/pandas/core/arrays/integer.py
index e2b66b1a006e4..fb33840ad757c 100644
--- a/pandas/core/arrays/integer.py
+++ b/pandas/core/arrays/integer.py
@@ -13,6 +13,7 @@
from pandas.core.dtypes.cast import astype_nansafe
from pandas.core.dtypes.common import (
is_bool_dtype,
+ is_datetime64_dtype,
is_float,
is_float_dtype,
is_integer,
@@ -469,6 +470,8 @@ def astype(self, dtype, copy: bool = True) -> ArrayLike:
if is_float_dtype(dtype):
# In astype, we consider dtype=float to also mean na_value=np.nan
kwargs = dict(na_value=np.nan)
+ elif is_datetime64_dtype(dtype):
+ kwargs = dict(na_value=np.datetime64("NaT"))
else:
kwargs = {}
diff --git a/pandas/core/tools/datetimes.py b/pandas/core/tools/datetimes.py
index 5580146b37d25..c32b4d81c0988 100644
--- a/pandas/core/tools/datetimes.py
+++ b/pandas/core/tools/datetimes.py
@@ -323,15 +323,13 @@ def _convert_listlike_datetimes(
# GH 30050 pass an ndarray to tslib.array_with_unit_to_datetime
# because it expects an ndarray argument
if isinstance(arg, IntegerArray):
- # Explicitly pass NaT mask to array_with_unit_to_datetime
- mask = arg.isna()
- arg = arg._ndarray_values
+ result = arg.astype(f"datetime64[{unit}]")
+ tz_parsed = None
else:
- mask = None
- result, tz_parsed = tslib.array_with_unit_to_datetime(
- arg, mask, unit, errors=errors
- )
+ result, tz_parsed = tslib.array_with_unit_to_datetime(
+ arg, unit, errors=errors
+ )
if errors == "ignore":
from pandas import Index
diff --git a/pandas/tests/arrays/test_array.py b/pandas/tests/arrays/test_array.py
index f42b16cf18f20..ad6e6e4a98057 100644
--- a/pandas/tests/arrays/test_array.py
+++ b/pandas/tests/arrays/test_array.py
@@ -222,6 +222,8 @@ def test_array_copy():
# integer
([1, 2], IntegerArray._from_sequence([1, 2])),
([1, None], IntegerArray._from_sequence([1, None])),
+ ([1, pd.NA], IntegerArray._from_sequence([1, pd.NA])),
+ ([1, np.nan], IntegerArray._from_sequence([1, np.nan])),
# string
(["a", "b"], StringArray._from_sequence(["a", "b"])),
(["a", None], StringArray._from_sequence(["a", None])),
diff --git a/pandas/tests/arrays/test_integer.py b/pandas/tests/arrays/test_integer.py
index 0a5a2362bd290..70a029bd74bda 100644
--- a/pandas/tests/arrays/test_integer.py
+++ b/pandas/tests/arrays/test_integer.py
@@ -633,6 +633,15 @@ def test_astype_specific_casting(self, dtype):
expected = pd.Series([1, 2, 3, None], dtype=dtype)
tm.assert_series_equal(result, expected)
+ def test_astype_dt64(self):
+ # GH#32435
+ arr = pd.array([1, 2, 3, pd.NA]) * 10 ** 9
+
+ result = arr.astype("datetime64[ns]")
+
+ expected = np.array([1, 2, 3, "NaT"], dtype="M8[s]").astype("M8[ns]")
+ tm.assert_numpy_array_equal(result, expected)
+
def test_construct_cast_invalid(self, dtype):
msg = "cannot safely"
diff --git a/pandas/tests/frame/test_dtypes.py b/pandas/tests/frame/test_dtypes.py
index 713d8f3ceeedb..d1a7917bd127b 100644
--- a/pandas/tests/frame/test_dtypes.py
+++ b/pandas/tests/frame/test_dtypes.py
@@ -505,7 +505,7 @@ def test_df_where_change_dtype(self):
@pytest.mark.parametrize("dtype", ["M8", "m8"])
@pytest.mark.parametrize("unit", ["ns", "us", "ms", "s", "h", "m", "D"])
- def test_astype_from_datetimelike_to_objectt(self, dtype, unit):
+ def test_astype_from_datetimelike_to_object(self, dtype, unit):
# tests astype to object dtype
# gh-19223 / gh-12425
dtype = f"{dtype}[{unit}]"
| - [x] closes #32435
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Let's us de-kludge to_datetime code, getting rid of another _ndarray_values usage. | https://api.github.com/repos/pandas-dev/pandas/pulls/32538 | 2020-03-08T03:09:38Z | 2020-03-14T03:37:22Z | 2020-03-14T03:37:22Z | 2020-04-05T17:46:02Z |
CLN: avoid values_from_object in reshape.merge | diff --git a/pandas/core/reshape/merge.py b/pandas/core/reshape/merge.py
index e75dced21f488..aeec2a43f39bf 100644
--- a/pandas/core/reshape/merge.py
+++ b/pandas/core/reshape/merge.py
@@ -45,6 +45,7 @@
import pandas.core.algorithms as algos
from pandas.core.arrays.categorical import _recode_for_categories
import pandas.core.common as com
+from pandas.core.construction import extract_array
from pandas.core.frame import _merge_doc
from pandas.core.internals import concatenate_block_managers
from pandas.core.sorting import is_int64_overflow_possible
@@ -1820,9 +1821,14 @@ def _right_outer_join(x, y, max_groups):
def _factorize_keys(lk, rk, sort=True):
# Some pre-processing for non-ndarray lk / rk
- if is_datetime64tz_dtype(lk) and is_datetime64tz_dtype(rk):
- lk = getattr(lk, "_values", lk)._data
- rk = getattr(rk, "_values", rk)._data
+ lk = extract_array(lk, extract_numpy=True)
+ rk = extract_array(rk, extract_numpy=True)
+
+ if is_datetime64tz_dtype(lk.dtype) and is_datetime64tz_dtype(rk.dtype):
+ # Extract the ndarray (UTC-localized) values
+ # Note: we dont need the dtypes to match, as these can still be compared
+ lk, _ = lk._values_for_factorize()
+ rk, _ = rk._values_for_factorize()
elif (
is_categorical_dtype(lk) and is_categorical_dtype(rk) and lk.is_dtype_equal(rk)
@@ -1837,11 +1843,7 @@ def _factorize_keys(lk, rk, sort=True):
lk = ensure_int64(lk.codes)
rk = ensure_int64(rk)
- elif (
- is_extension_array_dtype(lk.dtype)
- and is_extension_array_dtype(rk.dtype)
- and lk.dtype == rk.dtype
- ):
+ elif is_extension_array_dtype(lk.dtype) and is_dtype_equal(lk.dtype, rk.dtype):
lk, _ = lk._values_for_factorize()
rk, _ = rk._values_for_factorize()
@@ -1849,15 +1851,15 @@ def _factorize_keys(lk, rk, sort=True):
# GH#23917 TODO: needs tests for case where lk is integer-dtype
# and rk is datetime-dtype
klass = libhashtable.Int64Factorizer
- lk = ensure_int64(com.values_from_object(lk))
- rk = ensure_int64(com.values_from_object(rk))
- elif issubclass(lk.dtype.type, (np.timedelta64, np.datetime64)) and issubclass(
- rk.dtype.type, (np.timedelta64, np.datetime64)
- ):
+ lk = ensure_int64(np.asarray(lk))
+ rk = ensure_int64(np.asarray(rk))
+
+ elif needs_i8_conversion(lk.dtype) and is_dtype_equal(lk.dtype, rk.dtype):
# GH#23917 TODO: Needs tests for non-matching dtypes
klass = libhashtable.Int64Factorizer
- lk = ensure_int64(com.values_from_object(lk))
- rk = ensure_int64(com.values_from_object(rk))
+ lk = ensure_int64(np.asarray(lk, dtype=np.int64))
+ rk = ensure_int64(np.asarray(rk, dtype=np.int64))
+
else:
klass = libhashtable.Factorizer
lk = ensure_object(lk)
| https://api.github.com/repos/pandas-dev/pandas/pulls/32537 | 2020-03-08T03:03:41Z | 2020-03-12T04:44:25Z | 2020-03-12T04:44:24Z | 2020-03-12T15:23:44Z | |
TST: separate out pd.crosstab tests from test_pivot | diff --git a/pandas/tests/reshape/test_crosstab.py b/pandas/tests/reshape/test_crosstab.py
new file mode 100644
index 0000000000000..8795af2e11122
--- /dev/null
+++ b/pandas/tests/reshape/test_crosstab.py
@@ -0,0 +1,700 @@
+import numpy as np
+import pytest
+
+from pandas import CategoricalIndex, DataFrame, Index, MultiIndex, Series, crosstab
+import pandas._testing as tm
+
+
+class TestCrosstab:
+ def setup_method(self, method):
+ df = DataFrame(
+ {
+ "A": [
+ "foo",
+ "foo",
+ "foo",
+ "foo",
+ "bar",
+ "bar",
+ "bar",
+ "bar",
+ "foo",
+ "foo",
+ "foo",
+ ],
+ "B": [
+ "one",
+ "one",
+ "one",
+ "two",
+ "one",
+ "one",
+ "one",
+ "two",
+ "two",
+ "two",
+ "one",
+ ],
+ "C": [
+ "dull",
+ "dull",
+ "shiny",
+ "dull",
+ "dull",
+ "shiny",
+ "shiny",
+ "dull",
+ "shiny",
+ "shiny",
+ "shiny",
+ ],
+ "D": np.random.randn(11),
+ "E": np.random.randn(11),
+ "F": np.random.randn(11),
+ }
+ )
+
+ self.df = df.append(df, ignore_index=True)
+
+ def test_crosstab_single(self):
+ df = self.df
+ result = crosstab(df["A"], df["C"])
+ expected = df.groupby(["A", "C"]).size().unstack()
+ tm.assert_frame_equal(result, expected.fillna(0).astype(np.int64))
+
+ def test_crosstab_multiple(self):
+ df = self.df
+
+ result = crosstab(df["A"], [df["B"], df["C"]])
+ expected = df.groupby(["A", "B", "C"]).size()
+ expected = expected.unstack("B").unstack("C").fillna(0).astype(np.int64)
+ tm.assert_frame_equal(result, expected)
+
+ result = crosstab([df["B"], df["C"]], df["A"])
+ expected = df.groupby(["B", "C", "A"]).size()
+ expected = expected.unstack("A").fillna(0).astype(np.int64)
+ tm.assert_frame_equal(result, expected)
+
+ def test_crosstab_ndarray(self):
+ a = np.random.randint(0, 5, size=100)
+ b = np.random.randint(0, 3, size=100)
+ c = np.random.randint(0, 10, size=100)
+
+ df = DataFrame({"a": a, "b": b, "c": c})
+
+ result = crosstab(a, [b, c], rownames=["a"], colnames=("b", "c"))
+ expected = crosstab(df["a"], [df["b"], df["c"]])
+ tm.assert_frame_equal(result, expected)
+
+ result = crosstab([b, c], a, colnames=["a"], rownames=("b", "c"))
+ expected = crosstab([df["b"], df["c"]], df["a"])
+ tm.assert_frame_equal(result, expected)
+
+ # assign arbitrary names
+ result = crosstab(self.df["A"].values, self.df["C"].values)
+ assert result.index.name == "row_0"
+ assert result.columns.name == "col_0"
+
+ def test_crosstab_non_aligned(self):
+ # GH 17005
+ a = Series([0, 1, 1], index=["a", "b", "c"])
+ b = Series([3, 4, 3, 4, 3], index=["a", "b", "c", "d", "f"])
+ c = np.array([3, 4, 3])
+
+ expected = DataFrame(
+ [[1, 0], [1, 1]],
+ index=Index([0, 1], name="row_0"),
+ columns=Index([3, 4], name="col_0"),
+ )
+
+ result = crosstab(a, b)
+ tm.assert_frame_equal(result, expected)
+
+ result = crosstab(a, c)
+ tm.assert_frame_equal(result, expected)
+
+ def test_crosstab_margins(self):
+ a = np.random.randint(0, 7, size=100)
+ b = np.random.randint(0, 3, size=100)
+ c = np.random.randint(0, 5, size=100)
+
+ df = DataFrame({"a": a, "b": b, "c": c})
+
+ result = crosstab(a, [b, c], rownames=["a"], colnames=("b", "c"), margins=True)
+
+ assert result.index.names == ("a",)
+ assert result.columns.names == ["b", "c"]
+
+ all_cols = result["All", ""]
+ exp_cols = df.groupby(["a"]).size().astype("i8")
+ # to keep index.name
+ exp_margin = Series([len(df)], index=Index(["All"], name="a"))
+ exp_cols = exp_cols.append(exp_margin)
+ exp_cols.name = ("All", "")
+
+ tm.assert_series_equal(all_cols, exp_cols)
+
+ all_rows = result.loc["All"]
+ exp_rows = df.groupby(["b", "c"]).size().astype("i8")
+ exp_rows = exp_rows.append(Series([len(df)], index=[("All", "")]))
+ exp_rows.name = "All"
+
+ exp_rows = exp_rows.reindex(all_rows.index)
+ exp_rows = exp_rows.fillna(0).astype(np.int64)
+ tm.assert_series_equal(all_rows, exp_rows)
+
+ def test_crosstab_margins_set_margin_name(self):
+ # GH 15972
+ a = np.random.randint(0, 7, size=100)
+ b = np.random.randint(0, 3, size=100)
+ c = np.random.randint(0, 5, size=100)
+
+ df = DataFrame({"a": a, "b": b, "c": c})
+
+ result = crosstab(
+ a,
+ [b, c],
+ rownames=["a"],
+ colnames=("b", "c"),
+ margins=True,
+ margins_name="TOTAL",
+ )
+
+ assert result.index.names == ("a",)
+ assert result.columns.names == ["b", "c"]
+
+ all_cols = result["TOTAL", ""]
+ exp_cols = df.groupby(["a"]).size().astype("i8")
+ # to keep index.name
+ exp_margin = Series([len(df)], index=Index(["TOTAL"], name="a"))
+ exp_cols = exp_cols.append(exp_margin)
+ exp_cols.name = ("TOTAL", "")
+
+ tm.assert_series_equal(all_cols, exp_cols)
+
+ all_rows = result.loc["TOTAL"]
+ exp_rows = df.groupby(["b", "c"]).size().astype("i8")
+ exp_rows = exp_rows.append(Series([len(df)], index=[("TOTAL", "")]))
+ exp_rows.name = "TOTAL"
+
+ exp_rows = exp_rows.reindex(all_rows.index)
+ exp_rows = exp_rows.fillna(0).astype(np.int64)
+ tm.assert_series_equal(all_rows, exp_rows)
+
+ msg = "margins_name argument must be a string"
+ for margins_name in [666, None, ["a", "b"]]:
+ with pytest.raises(ValueError, match=msg):
+ crosstab(
+ a,
+ [b, c],
+ rownames=["a"],
+ colnames=("b", "c"),
+ margins=True,
+ margins_name=margins_name,
+ )
+
+ def test_crosstab_pass_values(self):
+ a = np.random.randint(0, 7, size=100)
+ b = np.random.randint(0, 3, size=100)
+ c = np.random.randint(0, 5, size=100)
+ values = np.random.randn(100)
+
+ table = crosstab(
+ [a, b], c, values, aggfunc=np.sum, rownames=["foo", "bar"], colnames=["baz"]
+ )
+
+ df = DataFrame({"foo": a, "bar": b, "baz": c, "values": values})
+
+ expected = df.pivot_table(
+ "values", index=["foo", "bar"], columns="baz", aggfunc=np.sum
+ )
+ tm.assert_frame_equal(table, expected)
+
+ def test_crosstab_dropna(self):
+ # GH 3820
+ a = np.array(["foo", "foo", "foo", "bar", "bar", "foo", "foo"], dtype=object)
+ b = np.array(["one", "one", "two", "one", "two", "two", "two"], dtype=object)
+ c = np.array(
+ ["dull", "dull", "dull", "dull", "dull", "shiny", "shiny"], dtype=object
+ )
+ res = crosstab(a, [b, c], rownames=["a"], colnames=["b", "c"], dropna=False)
+ m = MultiIndex.from_tuples(
+ [("one", "dull"), ("one", "shiny"), ("two", "dull"), ("two", "shiny")],
+ names=["b", "c"],
+ )
+ tm.assert_index_equal(res.columns, m)
+
+ def test_crosstab_no_overlap(self):
+ # GS 10291
+
+ s1 = Series([1, 2, 3], index=[1, 2, 3])
+ s2 = Series([4, 5, 6], index=[4, 5, 6])
+
+ actual = crosstab(s1, s2)
+ expected = DataFrame()
+
+ tm.assert_frame_equal(actual, expected)
+
+ def test_margin_dropna(self):
+ # GH 12577
+ # pivot_table counts null into margin ('All')
+ # when margins=true and dropna=true
+
+ df = DataFrame({"a": [1, 2, 2, 2, 2, np.nan], "b": [3, 3, 4, 4, 4, 4]})
+ actual = crosstab(df.a, df.b, margins=True, dropna=True)
+ expected = DataFrame([[1, 0, 1], [1, 3, 4], [2, 3, 5]])
+ expected.index = Index([1.0, 2.0, "All"], name="a")
+ expected.columns = Index([3, 4, "All"], name="b")
+ tm.assert_frame_equal(actual, expected)
+
+ df = DataFrame(
+ {"a": [1, np.nan, np.nan, np.nan, 2, np.nan], "b": [3, np.nan, 4, 4, 4, 4]}
+ )
+ actual = crosstab(df.a, df.b, margins=True, dropna=True)
+ expected = DataFrame([[1, 0, 1], [0, 1, 1], [1, 1, 2]])
+ expected.index = Index([1.0, 2.0, "All"], name="a")
+ expected.columns = Index([3.0, 4.0, "All"], name="b")
+ tm.assert_frame_equal(actual, expected)
+
+ df = DataFrame(
+ {"a": [1, np.nan, np.nan, np.nan, np.nan, 2], "b": [3, 3, 4, 4, 4, 4]}
+ )
+ actual = crosstab(df.a, df.b, margins=True, dropna=True)
+ expected = DataFrame([[1, 0, 1], [0, 1, 1], [1, 1, 2]])
+ expected.index = Index([1.0, 2.0, "All"], name="a")
+ expected.columns = Index([3, 4, "All"], name="b")
+ tm.assert_frame_equal(actual, expected)
+
+ # GH 12642
+ # _add_margins raises KeyError: Level None not found
+ # when margins=True and dropna=False
+ df = DataFrame({"a": [1, 2, 2, 2, 2, np.nan], "b": [3, 3, 4, 4, 4, 4]})
+ actual = crosstab(df.a, df.b, margins=True, dropna=False)
+ expected = DataFrame([[1, 0, 1], [1, 3, 4], [2, 4, 6]])
+ expected.index = Index([1.0, 2.0, "All"], name="a")
+ expected.columns = Index([3, 4, "All"], name="b")
+ tm.assert_frame_equal(actual, expected)
+
+ df = DataFrame(
+ {"a": [1, np.nan, np.nan, np.nan, 2, np.nan], "b": [3, np.nan, 4, 4, 4, 4]}
+ )
+ actual = crosstab(df.a, df.b, margins=True, dropna=False)
+ expected = DataFrame([[1, 0, 1], [0, 1, 1], [1, 4, 6]])
+ expected.index = Index([1.0, 2.0, "All"], name="a")
+ expected.columns = Index([3.0, 4.0, "All"], name="b")
+ tm.assert_frame_equal(actual, expected)
+
+ a = np.array(["foo", "foo", "foo", "bar", "bar", "foo", "foo"], dtype=object)
+ b = np.array(["one", "one", "two", "one", "two", np.nan, "two"], dtype=object)
+ c = np.array(
+ ["dull", "dull", "dull", "dull", "dull", "shiny", "shiny"], dtype=object
+ )
+
+ actual = crosstab(
+ a, [b, c], rownames=["a"], colnames=["b", "c"], margins=True, dropna=False
+ )
+ m = MultiIndex.from_arrays(
+ [
+ ["one", "one", "two", "two", "All"],
+ ["dull", "shiny", "dull", "shiny", ""],
+ ],
+ names=["b", "c"],
+ )
+ expected = DataFrame(
+ [[1, 0, 1, 0, 2], [2, 0, 1, 1, 5], [3, 0, 2, 1, 7]], columns=m
+ )
+ expected.index = Index(["bar", "foo", "All"], name="a")
+ tm.assert_frame_equal(actual, expected)
+
+ actual = crosstab(
+ [a, b], c, rownames=["a", "b"], colnames=["c"], margins=True, dropna=False
+ )
+ m = MultiIndex.from_arrays(
+ [["bar", "bar", "foo", "foo", "All"], ["one", "two", "one", "two", ""]],
+ names=["a", "b"],
+ )
+ expected = DataFrame(
+ [[1, 0, 1], [1, 0, 1], [2, 0, 2], [1, 1, 2], [5, 2, 7]], index=m
+ )
+ expected.columns = Index(["dull", "shiny", "All"], name="c")
+ tm.assert_frame_equal(actual, expected)
+
+ actual = crosstab(
+ [a, b], c, rownames=["a", "b"], colnames=["c"], margins=True, dropna=True
+ )
+ m = MultiIndex.from_arrays(
+ [["bar", "bar", "foo", "foo", "All"], ["one", "two", "one", "two", ""]],
+ names=["a", "b"],
+ )
+ expected = DataFrame(
+ [[1, 0, 1], [1, 0, 1], [2, 0, 2], [1, 1, 2], [5, 1, 6]], index=m
+ )
+ expected.columns = Index(["dull", "shiny", "All"], name="c")
+ tm.assert_frame_equal(actual, expected)
+
+ def test_crosstab_normalize(self):
+ # Issue 12578
+ df = DataFrame(
+ {"a": [1, 2, 2, 2, 2], "b": [3, 3, 4, 4, 4], "c": [1, 1, np.nan, 1, 1]}
+ )
+
+ rindex = Index([1, 2], name="a")
+ cindex = Index([3, 4], name="b")
+ full_normal = DataFrame([[0.2, 0], [0.2, 0.6]], index=rindex, columns=cindex)
+ row_normal = DataFrame([[1.0, 0], [0.25, 0.75]], index=rindex, columns=cindex)
+ col_normal = DataFrame([[0.5, 0], [0.5, 1.0]], index=rindex, columns=cindex)
+
+ # Check all normalize args
+ tm.assert_frame_equal(crosstab(df.a, df.b, normalize="all"), full_normal)
+ tm.assert_frame_equal(crosstab(df.a, df.b, normalize=True), full_normal)
+ tm.assert_frame_equal(crosstab(df.a, df.b, normalize="index"), row_normal)
+ tm.assert_frame_equal(crosstab(df.a, df.b, normalize="columns"), col_normal)
+ tm.assert_frame_equal(
+ crosstab(df.a, df.b, normalize=1),
+ crosstab(df.a, df.b, normalize="columns"),
+ )
+ tm.assert_frame_equal(
+ crosstab(df.a, df.b, normalize=0), crosstab(df.a, df.b, normalize="index"),
+ )
+
+ row_normal_margins = DataFrame(
+ [[1.0, 0], [0.25, 0.75], [0.4, 0.6]],
+ index=Index([1, 2, "All"], name="a", dtype="object"),
+ columns=Index([3, 4], name="b", dtype="object"),
+ )
+ col_normal_margins = DataFrame(
+ [[0.5, 0, 0.2], [0.5, 1.0, 0.8]],
+ index=Index([1, 2], name="a", dtype="object"),
+ columns=Index([3, 4, "All"], name="b", dtype="object"),
+ )
+
+ all_normal_margins = DataFrame(
+ [[0.2, 0, 0.2], [0.2, 0.6, 0.8], [0.4, 0.6, 1]],
+ index=Index([1, 2, "All"], name="a", dtype="object"),
+ columns=Index([3, 4, "All"], name="b", dtype="object"),
+ )
+ tm.assert_frame_equal(
+ crosstab(df.a, df.b, normalize="index", margins=True), row_normal_margins
+ )
+ tm.assert_frame_equal(
+ crosstab(df.a, df.b, normalize="columns", margins=True), col_normal_margins,
+ )
+ tm.assert_frame_equal(
+ crosstab(df.a, df.b, normalize=True, margins=True), all_normal_margins
+ )
+
+ # Test arrays
+ crosstab(
+ [np.array([1, 1, 2, 2]), np.array([1, 2, 1, 2])], np.array([1, 2, 1, 2])
+ )
+
+ # Test with aggfunc
+ norm_counts = DataFrame(
+ [[0.25, 0, 0.25], [0.25, 0.5, 0.75], [0.5, 0.5, 1]],
+ index=Index([1, 2, "All"], name="a", dtype="object"),
+ columns=Index([3, 4, "All"], name="b"),
+ )
+ test_case = crosstab(
+ df.a, df.b, df.c, aggfunc="count", normalize="all", margins=True
+ )
+ tm.assert_frame_equal(test_case, norm_counts)
+
+ df = DataFrame(
+ {"a": [1, 2, 2, 2, 2], "b": [3, 3, 4, 4, 4], "c": [0, 4, np.nan, 3, 3]}
+ )
+
+ norm_sum = DataFrame(
+ [[0, 0, 0.0], [0.4, 0.6, 1], [0.4, 0.6, 1]],
+ index=Index([1, 2, "All"], name="a", dtype="object"),
+ columns=Index([3, 4, "All"], name="b", dtype="object"),
+ )
+ test_case = crosstab(
+ df.a, df.b, df.c, aggfunc=np.sum, normalize="all", margins=True
+ )
+ tm.assert_frame_equal(test_case, norm_sum)
+
+ def test_crosstab_with_empties(self):
+ # Check handling of empties
+ df = DataFrame(
+ {
+ "a": [1, 2, 2, 2, 2],
+ "b": [3, 3, 4, 4, 4],
+ "c": [np.nan, np.nan, np.nan, np.nan, np.nan],
+ }
+ )
+
+ empty = DataFrame(
+ [[0.0, 0.0], [0.0, 0.0]],
+ index=Index([1, 2], name="a", dtype="int64"),
+ columns=Index([3, 4], name="b"),
+ )
+
+ for i in [True, "index", "columns"]:
+ calculated = crosstab(df.a, df.b, values=df.c, aggfunc="count", normalize=i)
+ tm.assert_frame_equal(empty, calculated)
+
+ nans = DataFrame(
+ [[0.0, np.nan], [0.0, 0.0]],
+ index=Index([1, 2], name="a", dtype="int64"),
+ columns=Index([3, 4], name="b"),
+ )
+
+ calculated = crosstab(df.a, df.b, values=df.c, aggfunc="count", normalize=False)
+ tm.assert_frame_equal(nans, calculated)
+
+ def test_crosstab_errors(self):
+ # Issue 12578
+
+ df = DataFrame(
+ {"a": [1, 2, 2, 2, 2], "b": [3, 3, 4, 4, 4], "c": [1, 1, np.nan, 1, 1]}
+ )
+
+ error = "values cannot be used without an aggfunc."
+ with pytest.raises(ValueError, match=error):
+ crosstab(df.a, df.b, values=df.c)
+
+ error = "aggfunc cannot be used without values"
+ with pytest.raises(ValueError, match=error):
+ crosstab(df.a, df.b, aggfunc=np.mean)
+
+ error = "Not a valid normalize argument"
+ with pytest.raises(ValueError, match=error):
+ crosstab(df.a, df.b, normalize="42")
+
+ with pytest.raises(ValueError, match=error):
+ crosstab(df.a, df.b, normalize=42)
+
+ error = "Not a valid margins argument"
+ with pytest.raises(ValueError, match=error):
+ crosstab(df.a, df.b, normalize="all", margins=42)
+
+ def test_crosstab_with_categorial_columns(self):
+ # GH 8860
+ df = DataFrame(
+ {
+ "MAKE": ["Honda", "Acura", "Tesla", "Honda", "Honda", "Acura"],
+ "MODEL": ["Sedan", "Sedan", "Electric", "Pickup", "Sedan", "Sedan"],
+ }
+ )
+ categories = ["Sedan", "Electric", "Pickup"]
+ df["MODEL"] = df["MODEL"].astype("category").cat.set_categories(categories)
+ result = crosstab(df["MAKE"], df["MODEL"])
+
+ expected_index = Index(["Acura", "Honda", "Tesla"], name="MAKE")
+ expected_columns = CategoricalIndex(
+ categories, categories=categories, ordered=False, name="MODEL"
+ )
+ expected_data = [[2, 0, 0], [2, 0, 1], [0, 1, 0]]
+ expected = DataFrame(
+ expected_data, index=expected_index, columns=expected_columns
+ )
+ tm.assert_frame_equal(result, expected)
+
+ def test_crosstab_with_numpy_size(self):
+ # GH 4003
+ df = DataFrame(
+ {
+ "A": ["one", "one", "two", "three"] * 6,
+ "B": ["A", "B", "C"] * 8,
+ "C": ["foo", "foo", "foo", "bar", "bar", "bar"] * 4,
+ "D": np.random.randn(24),
+ "E": np.random.randn(24),
+ }
+ )
+ result = crosstab(
+ index=[df["A"], df["B"]],
+ columns=[df["C"]],
+ margins=True,
+ aggfunc=np.size,
+ values=df["D"],
+ )
+ expected_index = MultiIndex(
+ levels=[["All", "one", "three", "two"], ["", "A", "B", "C"]],
+ codes=[[1, 1, 1, 2, 2, 2, 3, 3, 3, 0], [1, 2, 3, 1, 2, 3, 1, 2, 3, 0]],
+ names=["A", "B"],
+ )
+ expected_column = Index(["bar", "foo", "All"], dtype="object", name="C")
+ expected_data = np.array(
+ [
+ [2.0, 2.0, 4.0],
+ [2.0, 2.0, 4.0],
+ [2.0, 2.0, 4.0],
+ [2.0, np.nan, 2.0],
+ [np.nan, 2.0, 2.0],
+ [2.0, np.nan, 2.0],
+ [np.nan, 2.0, 2.0],
+ [2.0, np.nan, 2.0],
+ [np.nan, 2.0, 2.0],
+ [12.0, 12.0, 24.0],
+ ]
+ )
+ expected = DataFrame(
+ expected_data, index=expected_index, columns=expected_column
+ )
+ tm.assert_frame_equal(result, expected)
+
+ def test_crosstab_dup_index_names(self):
+ # GH 13279
+ s = Series(range(3), name="foo")
+
+ result = crosstab(s, s)
+ expected_index = Index(range(3), name="foo")
+ expected = DataFrame(
+ np.eye(3, dtype=np.int64), index=expected_index, columns=expected_index
+ )
+ tm.assert_frame_equal(result, expected)
+
+ @pytest.mark.parametrize("names", [["a", ("b", "c")], [("a", "b"), "c"]])
+ def test_crosstab_tuple_name(self, names):
+ s1 = Series(range(3), name=names[0])
+ s2 = Series(range(1, 4), name=names[1])
+
+ mi = MultiIndex.from_arrays([range(3), range(1, 4)], names=names)
+ expected = Series(1, index=mi).unstack(1, fill_value=0)
+
+ result = crosstab(s1, s2)
+ tm.assert_frame_equal(result, expected)
+
+ def test_crosstab_both_tuple_names(self):
+ # GH 18321
+ s1 = Series(range(3), name=("a", "b"))
+ s2 = Series(range(3), name=("c", "d"))
+
+ expected = DataFrame(
+ np.eye(3, dtype="int64"),
+ index=Index(range(3), name=("a", "b")),
+ columns=Index(range(3), name=("c", "d")),
+ )
+ result = crosstab(s1, s2)
+ tm.assert_frame_equal(result, expected)
+
+ def test_crosstab_unsorted_order(self):
+ df = DataFrame({"b": [3, 1, 2], "a": [5, 4, 6]}, index=["C", "A", "B"])
+ result = crosstab(df.index, [df.b, df.a])
+ e_idx = Index(["A", "B", "C"], name="row_0")
+ e_columns = MultiIndex.from_tuples([(1, 4), (2, 6), (3, 5)], names=["b", "a"])
+ expected = DataFrame(
+ [[1, 0, 0], [0, 1, 0], [0, 0, 1]], index=e_idx, columns=e_columns
+ )
+ tm.assert_frame_equal(result, expected)
+
+ def test_crosstab_normalize_multiple_columns(self):
+ # GH 15150
+ df = DataFrame(
+ {
+ "A": ["one", "one", "two", "three"] * 6,
+ "B": ["A", "B", "C"] * 8,
+ "C": ["foo", "foo", "foo", "bar", "bar", "bar"] * 4,
+ "D": [0] * 24,
+ "E": [0] * 24,
+ }
+ )
+ result = crosstab(
+ [df.A, df.B],
+ df.C,
+ values=df.D,
+ aggfunc=np.sum,
+ normalize=True,
+ margins=True,
+ )
+ expected = DataFrame(
+ np.array([0] * 29 + [1], dtype=float).reshape(10, 3),
+ columns=Index(["bar", "foo", "All"], dtype="object", name="C"),
+ index=MultiIndex.from_tuples(
+ [
+ ("one", "A"),
+ ("one", "B"),
+ ("one", "C"),
+ ("three", "A"),
+ ("three", "B"),
+ ("three", "C"),
+ ("two", "A"),
+ ("two", "B"),
+ ("two", "C"),
+ ("All", ""),
+ ],
+ names=["A", "B"],
+ ),
+ )
+ tm.assert_frame_equal(result, expected)
+
+ def test_margin_normalize(self):
+ # GH 27500
+ df = DataFrame(
+ {
+ "A": ["foo", "foo", "foo", "foo", "foo", "bar", "bar", "bar", "bar"],
+ "B": ["one", "one", "one", "two", "two", "one", "one", "two", "two"],
+ "C": [
+ "small",
+ "large",
+ "large",
+ "small",
+ "small",
+ "large",
+ "small",
+ "small",
+ "large",
+ ],
+ "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
+ "E": [2, 4, 5, 5, 6, 6, 8, 9, 9],
+ }
+ )
+ # normalize on index
+ result = crosstab(
+ [df.A, df.B], df.C, margins=True, margins_name="Sub-Total", normalize=0
+ )
+ expected = DataFrame(
+ [[0.5, 0.5], [0.5, 0.5], [0.666667, 0.333333], [0, 1], [0.444444, 0.555556]]
+ )
+ expected.index = MultiIndex(
+ levels=[["Sub-Total", "bar", "foo"], ["", "one", "two"]],
+ codes=[[1, 1, 2, 2, 0], [1, 2, 1, 2, 0]],
+ names=["A", "B"],
+ )
+ expected.columns = Index(["large", "small"], dtype="object", name="C")
+ tm.assert_frame_equal(result, expected)
+
+ # normalize on columns
+ result = crosstab(
+ [df.A, df.B], df.C, margins=True, margins_name="Sub-Total", normalize=1
+ )
+ expected = DataFrame(
+ [
+ [0.25, 0.2, 0.222222],
+ [0.25, 0.2, 0.222222],
+ [0.5, 0.2, 0.333333],
+ [0, 0.4, 0.222222],
+ ]
+ )
+ expected.columns = Index(
+ ["large", "small", "Sub-Total"], dtype="object", name="C"
+ )
+ expected.index = MultiIndex(
+ levels=[["bar", "foo"], ["one", "two"]],
+ codes=[[0, 0, 1, 1], [0, 1, 0, 1]],
+ names=["A", "B"],
+ )
+ tm.assert_frame_equal(result, expected)
+
+ # normalize on both index and column
+ result = crosstab(
+ [df.A, df.B], df.C, margins=True, margins_name="Sub-Total", normalize=True
+ )
+ expected = DataFrame(
+ [
+ [0.111111, 0.111111, 0.222222],
+ [0.111111, 0.111111, 0.222222],
+ [0.222222, 0.111111, 0.333333],
+ [0.000000, 0.222222, 0.222222],
+ [0.444444, 0.555555, 1],
+ ]
+ )
+ expected.columns = Index(
+ ["large", "small", "Sub-Total"], dtype="object", name="C"
+ )
+ expected.index = MultiIndex(
+ levels=[["Sub-Total", "bar", "foo"], ["", "one", "two"]],
+ codes=[[1, 1, 2, 2, 0], [1, 2, 1, 2, 0]],
+ names=["A", "B"],
+ )
+ tm.assert_frame_equal(result, expected)
diff --git a/pandas/tests/reshape/test_pivot.py b/pandas/tests/reshape/test_pivot.py
index e09a2a7907177..75c3c565e9d58 100644
--- a/pandas/tests/reshape/test_pivot.py
+++ b/pandas/tests/reshape/test_pivot.py
@@ -17,7 +17,7 @@
)
import pandas._testing as tm
from pandas.api.types import CategoricalDtype as CDT
-from pandas.core.reshape.pivot import crosstab, pivot_table
+from pandas.core.reshape.pivot import pivot_table
@pytest.fixture(params=[True, False])
@@ -2064,708 +2064,3 @@ def agg(l):
)
with pytest.raises(KeyError, match="notpresent"):
foo.pivot_table("notpresent", "X", "Y", aggfunc=agg)
-
-
-class TestCrosstab:
- def setup_method(self, method):
- df = DataFrame(
- {
- "A": [
- "foo",
- "foo",
- "foo",
- "foo",
- "bar",
- "bar",
- "bar",
- "bar",
- "foo",
- "foo",
- "foo",
- ],
- "B": [
- "one",
- "one",
- "one",
- "two",
- "one",
- "one",
- "one",
- "two",
- "two",
- "two",
- "one",
- ],
- "C": [
- "dull",
- "dull",
- "shiny",
- "dull",
- "dull",
- "shiny",
- "shiny",
- "dull",
- "shiny",
- "shiny",
- "shiny",
- ],
- "D": np.random.randn(11),
- "E": np.random.randn(11),
- "F": np.random.randn(11),
- }
- )
-
- self.df = df.append(df, ignore_index=True)
-
- def test_crosstab_single(self):
- df = self.df
- result = crosstab(df["A"], df["C"])
- expected = df.groupby(["A", "C"]).size().unstack()
- tm.assert_frame_equal(result, expected.fillna(0).astype(np.int64))
-
- def test_crosstab_multiple(self):
- df = self.df
-
- result = crosstab(df["A"], [df["B"], df["C"]])
- expected = df.groupby(["A", "B", "C"]).size()
- expected = expected.unstack("B").unstack("C").fillna(0).astype(np.int64)
- tm.assert_frame_equal(result, expected)
-
- result = crosstab([df["B"], df["C"]], df["A"])
- expected = df.groupby(["B", "C", "A"]).size()
- expected = expected.unstack("A").fillna(0).astype(np.int64)
- tm.assert_frame_equal(result, expected)
-
- def test_crosstab_ndarray(self):
- a = np.random.randint(0, 5, size=100)
- b = np.random.randint(0, 3, size=100)
- c = np.random.randint(0, 10, size=100)
-
- df = DataFrame({"a": a, "b": b, "c": c})
-
- result = crosstab(a, [b, c], rownames=["a"], colnames=("b", "c"))
- expected = crosstab(df["a"], [df["b"], df["c"]])
- tm.assert_frame_equal(result, expected)
-
- result = crosstab([b, c], a, colnames=["a"], rownames=("b", "c"))
- expected = crosstab([df["b"], df["c"]], df["a"])
- tm.assert_frame_equal(result, expected)
-
- # assign arbitrary names
- result = crosstab(self.df["A"].values, self.df["C"].values)
- assert result.index.name == "row_0"
- assert result.columns.name == "col_0"
-
- def test_crosstab_non_aligned(self):
- # GH 17005
- a = pd.Series([0, 1, 1], index=["a", "b", "c"])
- b = pd.Series([3, 4, 3, 4, 3], index=["a", "b", "c", "d", "f"])
- c = np.array([3, 4, 3])
-
- expected = pd.DataFrame(
- [[1, 0], [1, 1]],
- index=Index([0, 1], name="row_0"),
- columns=Index([3, 4], name="col_0"),
- )
-
- result = crosstab(a, b)
- tm.assert_frame_equal(result, expected)
-
- result = crosstab(a, c)
- tm.assert_frame_equal(result, expected)
-
- def test_crosstab_margins(self):
- a = np.random.randint(0, 7, size=100)
- b = np.random.randint(0, 3, size=100)
- c = np.random.randint(0, 5, size=100)
-
- df = DataFrame({"a": a, "b": b, "c": c})
-
- result = crosstab(a, [b, c], rownames=["a"], colnames=("b", "c"), margins=True)
-
- assert result.index.names == ("a",)
- assert result.columns.names == ["b", "c"]
-
- all_cols = result["All", ""]
- exp_cols = df.groupby(["a"]).size().astype("i8")
- # to keep index.name
- exp_margin = Series([len(df)], index=Index(["All"], name="a"))
- exp_cols = exp_cols.append(exp_margin)
- exp_cols.name = ("All", "")
-
- tm.assert_series_equal(all_cols, exp_cols)
-
- all_rows = result.loc["All"]
- exp_rows = df.groupby(["b", "c"]).size().astype("i8")
- exp_rows = exp_rows.append(Series([len(df)], index=[("All", "")]))
- exp_rows.name = "All"
-
- exp_rows = exp_rows.reindex(all_rows.index)
- exp_rows = exp_rows.fillna(0).astype(np.int64)
- tm.assert_series_equal(all_rows, exp_rows)
-
- def test_crosstab_margins_set_margin_name(self):
- # GH 15972
- a = np.random.randint(0, 7, size=100)
- b = np.random.randint(0, 3, size=100)
- c = np.random.randint(0, 5, size=100)
-
- df = DataFrame({"a": a, "b": b, "c": c})
-
- result = crosstab(
- a,
- [b, c],
- rownames=["a"],
- colnames=("b", "c"),
- margins=True,
- margins_name="TOTAL",
- )
-
- assert result.index.names == ("a",)
- assert result.columns.names == ["b", "c"]
-
- all_cols = result["TOTAL", ""]
- exp_cols = df.groupby(["a"]).size().astype("i8")
- # to keep index.name
- exp_margin = Series([len(df)], index=Index(["TOTAL"], name="a"))
- exp_cols = exp_cols.append(exp_margin)
- exp_cols.name = ("TOTAL", "")
-
- tm.assert_series_equal(all_cols, exp_cols)
-
- all_rows = result.loc["TOTAL"]
- exp_rows = df.groupby(["b", "c"]).size().astype("i8")
- exp_rows = exp_rows.append(Series([len(df)], index=[("TOTAL", "")]))
- exp_rows.name = "TOTAL"
-
- exp_rows = exp_rows.reindex(all_rows.index)
- exp_rows = exp_rows.fillna(0).astype(np.int64)
- tm.assert_series_equal(all_rows, exp_rows)
-
- msg = "margins_name argument must be a string"
- for margins_name in [666, None, ["a", "b"]]:
- with pytest.raises(ValueError, match=msg):
- crosstab(
- a,
- [b, c],
- rownames=["a"],
- colnames=("b", "c"),
- margins=True,
- margins_name=margins_name,
- )
-
- def test_crosstab_pass_values(self):
- a = np.random.randint(0, 7, size=100)
- b = np.random.randint(0, 3, size=100)
- c = np.random.randint(0, 5, size=100)
- values = np.random.randn(100)
-
- table = crosstab(
- [a, b], c, values, aggfunc=np.sum, rownames=["foo", "bar"], colnames=["baz"]
- )
-
- df = DataFrame({"foo": a, "bar": b, "baz": c, "values": values})
-
- expected = df.pivot_table(
- "values", index=["foo", "bar"], columns="baz", aggfunc=np.sum
- )
- tm.assert_frame_equal(table, expected)
-
- def test_crosstab_dropna(self):
- # GH 3820
- a = np.array(["foo", "foo", "foo", "bar", "bar", "foo", "foo"], dtype=object)
- b = np.array(["one", "one", "two", "one", "two", "two", "two"], dtype=object)
- c = np.array(
- ["dull", "dull", "dull", "dull", "dull", "shiny", "shiny"], dtype=object
- )
- res = pd.crosstab(a, [b, c], rownames=["a"], colnames=["b", "c"], dropna=False)
- m = MultiIndex.from_tuples(
- [("one", "dull"), ("one", "shiny"), ("two", "dull"), ("two", "shiny")],
- names=["b", "c"],
- )
- tm.assert_index_equal(res.columns, m)
-
- def test_crosstab_no_overlap(self):
- # GS 10291
-
- s1 = pd.Series([1, 2, 3], index=[1, 2, 3])
- s2 = pd.Series([4, 5, 6], index=[4, 5, 6])
-
- actual = crosstab(s1, s2)
- expected = pd.DataFrame()
-
- tm.assert_frame_equal(actual, expected)
-
- def test_margin_dropna(self):
- # GH 12577
- # pivot_table counts null into margin ('All')
- # when margins=true and dropna=true
-
- df = pd.DataFrame({"a": [1, 2, 2, 2, 2, np.nan], "b": [3, 3, 4, 4, 4, 4]})
- actual = pd.crosstab(df.a, df.b, margins=True, dropna=True)
- expected = pd.DataFrame([[1, 0, 1], [1, 3, 4], [2, 3, 5]])
- expected.index = Index([1.0, 2.0, "All"], name="a")
- expected.columns = Index([3, 4, "All"], name="b")
- tm.assert_frame_equal(actual, expected)
-
- df = DataFrame(
- {"a": [1, np.nan, np.nan, np.nan, 2, np.nan], "b": [3, np.nan, 4, 4, 4, 4]}
- )
- actual = pd.crosstab(df.a, df.b, margins=True, dropna=True)
- expected = pd.DataFrame([[1, 0, 1], [0, 1, 1], [1, 1, 2]])
- expected.index = Index([1.0, 2.0, "All"], name="a")
- expected.columns = Index([3.0, 4.0, "All"], name="b")
- tm.assert_frame_equal(actual, expected)
-
- df = DataFrame(
- {"a": [1, np.nan, np.nan, np.nan, np.nan, 2], "b": [3, 3, 4, 4, 4, 4]}
- )
- actual = pd.crosstab(df.a, df.b, margins=True, dropna=True)
- expected = pd.DataFrame([[1, 0, 1], [0, 1, 1], [1, 1, 2]])
- expected.index = Index([1.0, 2.0, "All"], name="a")
- expected.columns = Index([3, 4, "All"], name="b")
- tm.assert_frame_equal(actual, expected)
-
- # GH 12642
- # _add_margins raises KeyError: Level None not found
- # when margins=True and dropna=False
- df = pd.DataFrame({"a": [1, 2, 2, 2, 2, np.nan], "b": [3, 3, 4, 4, 4, 4]})
- actual = pd.crosstab(df.a, df.b, margins=True, dropna=False)
- expected = pd.DataFrame([[1, 0, 1], [1, 3, 4], [2, 4, 6]])
- expected.index = Index([1.0, 2.0, "All"], name="a")
- expected.columns = Index([3, 4, "All"], name="b")
- tm.assert_frame_equal(actual, expected)
-
- df = DataFrame(
- {"a": [1, np.nan, np.nan, np.nan, 2, np.nan], "b": [3, np.nan, 4, 4, 4, 4]}
- )
- actual = pd.crosstab(df.a, df.b, margins=True, dropna=False)
- expected = pd.DataFrame([[1, 0, 1], [0, 1, 1], [1, 4, 6]])
- expected.index = Index([1.0, 2.0, "All"], name="a")
- expected.columns = Index([3.0, 4.0, "All"], name="b")
- tm.assert_frame_equal(actual, expected)
-
- a = np.array(["foo", "foo", "foo", "bar", "bar", "foo", "foo"], dtype=object)
- b = np.array(["one", "one", "two", "one", "two", np.nan, "two"], dtype=object)
- c = np.array(
- ["dull", "dull", "dull", "dull", "dull", "shiny", "shiny"], dtype=object
- )
-
- actual = pd.crosstab(
- a, [b, c], rownames=["a"], colnames=["b", "c"], margins=True, dropna=False
- )
- m = MultiIndex.from_arrays(
- [
- ["one", "one", "two", "two", "All"],
- ["dull", "shiny", "dull", "shiny", ""],
- ],
- names=["b", "c"],
- )
- expected = DataFrame(
- [[1, 0, 1, 0, 2], [2, 0, 1, 1, 5], [3, 0, 2, 1, 7]], columns=m
- )
- expected.index = Index(["bar", "foo", "All"], name="a")
- tm.assert_frame_equal(actual, expected)
-
- actual = pd.crosstab(
- [a, b], c, rownames=["a", "b"], colnames=["c"], margins=True, dropna=False
- )
- m = MultiIndex.from_arrays(
- [["bar", "bar", "foo", "foo", "All"], ["one", "two", "one", "two", ""]],
- names=["a", "b"],
- )
- expected = DataFrame(
- [[1, 0, 1], [1, 0, 1], [2, 0, 2], [1, 1, 2], [5, 2, 7]], index=m
- )
- expected.columns = Index(["dull", "shiny", "All"], name="c")
- tm.assert_frame_equal(actual, expected)
-
- actual = pd.crosstab(
- [a, b], c, rownames=["a", "b"], colnames=["c"], margins=True, dropna=True
- )
- m = MultiIndex.from_arrays(
- [["bar", "bar", "foo", "foo", "All"], ["one", "two", "one", "two", ""]],
- names=["a", "b"],
- )
- expected = DataFrame(
- [[1, 0, 1], [1, 0, 1], [2, 0, 2], [1, 1, 2], [5, 1, 6]], index=m
- )
- expected.columns = Index(["dull", "shiny", "All"], name="c")
- tm.assert_frame_equal(actual, expected)
-
- def test_crosstab_normalize(self):
- # Issue 12578
- df = pd.DataFrame(
- {"a": [1, 2, 2, 2, 2], "b": [3, 3, 4, 4, 4], "c": [1, 1, np.nan, 1, 1]}
- )
-
- rindex = pd.Index([1, 2], name="a")
- cindex = pd.Index([3, 4], name="b")
- full_normal = pd.DataFrame([[0.2, 0], [0.2, 0.6]], index=rindex, columns=cindex)
- row_normal = pd.DataFrame(
- [[1.0, 0], [0.25, 0.75]], index=rindex, columns=cindex
- )
- col_normal = pd.DataFrame([[0.5, 0], [0.5, 1.0]], index=rindex, columns=cindex)
-
- # Check all normalize args
- tm.assert_frame_equal(pd.crosstab(df.a, df.b, normalize="all"), full_normal)
- tm.assert_frame_equal(pd.crosstab(df.a, df.b, normalize=True), full_normal)
- tm.assert_frame_equal(pd.crosstab(df.a, df.b, normalize="index"), row_normal)
- tm.assert_frame_equal(pd.crosstab(df.a, df.b, normalize="columns"), col_normal)
- tm.assert_frame_equal(
- pd.crosstab(df.a, df.b, normalize=1),
- pd.crosstab(df.a, df.b, normalize="columns"),
- )
- tm.assert_frame_equal(
- pd.crosstab(df.a, df.b, normalize=0),
- pd.crosstab(df.a, df.b, normalize="index"),
- )
-
- row_normal_margins = pd.DataFrame(
- [[1.0, 0], [0.25, 0.75], [0.4, 0.6]],
- index=pd.Index([1, 2, "All"], name="a", dtype="object"),
- columns=pd.Index([3, 4], name="b", dtype="object"),
- )
- col_normal_margins = pd.DataFrame(
- [[0.5, 0, 0.2], [0.5, 1.0, 0.8]],
- index=pd.Index([1, 2], name="a", dtype="object"),
- columns=pd.Index([3, 4, "All"], name="b", dtype="object"),
- )
-
- all_normal_margins = pd.DataFrame(
- [[0.2, 0, 0.2], [0.2, 0.6, 0.8], [0.4, 0.6, 1]],
- index=pd.Index([1, 2, "All"], name="a", dtype="object"),
- columns=pd.Index([3, 4, "All"], name="b", dtype="object"),
- )
- tm.assert_frame_equal(
- pd.crosstab(df.a, df.b, normalize="index", margins=True), row_normal_margins
- )
- tm.assert_frame_equal(
- pd.crosstab(df.a, df.b, normalize="columns", margins=True),
- col_normal_margins,
- )
- tm.assert_frame_equal(
- pd.crosstab(df.a, df.b, normalize=True, margins=True), all_normal_margins
- )
-
- # Test arrays
- pd.crosstab(
- [np.array([1, 1, 2, 2]), np.array([1, 2, 1, 2])], np.array([1, 2, 1, 2])
- )
-
- # Test with aggfunc
- norm_counts = pd.DataFrame(
- [[0.25, 0, 0.25], [0.25, 0.5, 0.75], [0.5, 0.5, 1]],
- index=pd.Index([1, 2, "All"], name="a", dtype="object"),
- columns=pd.Index([3, 4, "All"], name="b"),
- )
- test_case = pd.crosstab(
- df.a, df.b, df.c, aggfunc="count", normalize="all", margins=True
- )
- tm.assert_frame_equal(test_case, norm_counts)
-
- df = pd.DataFrame(
- {"a": [1, 2, 2, 2, 2], "b": [3, 3, 4, 4, 4], "c": [0, 4, np.nan, 3, 3]}
- )
-
- norm_sum = pd.DataFrame(
- [[0, 0, 0.0], [0.4, 0.6, 1], [0.4, 0.6, 1]],
- index=pd.Index([1, 2, "All"], name="a", dtype="object"),
- columns=pd.Index([3, 4, "All"], name="b", dtype="object"),
- )
- test_case = pd.crosstab(
- df.a, df.b, df.c, aggfunc=np.sum, normalize="all", margins=True
- )
- tm.assert_frame_equal(test_case, norm_sum)
-
- def test_crosstab_with_empties(self):
- # Check handling of empties
- df = pd.DataFrame(
- {
- "a": [1, 2, 2, 2, 2],
- "b": [3, 3, 4, 4, 4],
- "c": [np.nan, np.nan, np.nan, np.nan, np.nan],
- }
- )
-
- empty = pd.DataFrame(
- [[0.0, 0.0], [0.0, 0.0]],
- index=pd.Index([1, 2], name="a", dtype="int64"),
- columns=pd.Index([3, 4], name="b"),
- )
-
- for i in [True, "index", "columns"]:
- calculated = pd.crosstab(
- df.a, df.b, values=df.c, aggfunc="count", normalize=i
- )
- tm.assert_frame_equal(empty, calculated)
-
- nans = pd.DataFrame(
- [[0.0, np.nan], [0.0, 0.0]],
- index=pd.Index([1, 2], name="a", dtype="int64"),
- columns=pd.Index([3, 4], name="b"),
- )
-
- calculated = pd.crosstab(
- df.a, df.b, values=df.c, aggfunc="count", normalize=False
- )
- tm.assert_frame_equal(nans, calculated)
-
- def test_crosstab_errors(self):
- # Issue 12578
-
- df = pd.DataFrame(
- {"a": [1, 2, 2, 2, 2], "b": [3, 3, 4, 4, 4], "c": [1, 1, np.nan, 1, 1]}
- )
-
- error = "values cannot be used without an aggfunc."
- with pytest.raises(ValueError, match=error):
- pd.crosstab(df.a, df.b, values=df.c)
-
- error = "aggfunc cannot be used without values"
- with pytest.raises(ValueError, match=error):
- pd.crosstab(df.a, df.b, aggfunc=np.mean)
-
- error = "Not a valid normalize argument"
- with pytest.raises(ValueError, match=error):
- pd.crosstab(df.a, df.b, normalize="42")
-
- with pytest.raises(ValueError, match=error):
- pd.crosstab(df.a, df.b, normalize=42)
-
- error = "Not a valid margins argument"
- with pytest.raises(ValueError, match=error):
- pd.crosstab(df.a, df.b, normalize="all", margins=42)
-
- def test_crosstab_with_categorial_columns(self):
- # GH 8860
- df = pd.DataFrame(
- {
- "MAKE": ["Honda", "Acura", "Tesla", "Honda", "Honda", "Acura"],
- "MODEL": ["Sedan", "Sedan", "Electric", "Pickup", "Sedan", "Sedan"],
- }
- )
- categories = ["Sedan", "Electric", "Pickup"]
- df["MODEL"] = df["MODEL"].astype("category").cat.set_categories(categories)
- result = pd.crosstab(df["MAKE"], df["MODEL"])
-
- expected_index = pd.Index(["Acura", "Honda", "Tesla"], name="MAKE")
- expected_columns = pd.CategoricalIndex(
- categories, categories=categories, ordered=False, name="MODEL"
- )
- expected_data = [[2, 0, 0], [2, 0, 1], [0, 1, 0]]
- expected = pd.DataFrame(
- expected_data, index=expected_index, columns=expected_columns
- )
- tm.assert_frame_equal(result, expected)
-
- def test_crosstab_with_numpy_size(self):
- # GH 4003
- df = pd.DataFrame(
- {
- "A": ["one", "one", "two", "three"] * 6,
- "B": ["A", "B", "C"] * 8,
- "C": ["foo", "foo", "foo", "bar", "bar", "bar"] * 4,
- "D": np.random.randn(24),
- "E": np.random.randn(24),
- }
- )
- result = pd.crosstab(
- index=[df["A"], df["B"]],
- columns=[df["C"]],
- margins=True,
- aggfunc=np.size,
- values=df["D"],
- )
- expected_index = pd.MultiIndex(
- levels=[["All", "one", "three", "two"], ["", "A", "B", "C"]],
- codes=[[1, 1, 1, 2, 2, 2, 3, 3, 3, 0], [1, 2, 3, 1, 2, 3, 1, 2, 3, 0]],
- names=["A", "B"],
- )
- expected_column = pd.Index(["bar", "foo", "All"], dtype="object", name="C")
- expected_data = np.array(
- [
- [2.0, 2.0, 4.0],
- [2.0, 2.0, 4.0],
- [2.0, 2.0, 4.0],
- [2.0, np.nan, 2.0],
- [np.nan, 2.0, 2.0],
- [2.0, np.nan, 2.0],
- [np.nan, 2.0, 2.0],
- [2.0, np.nan, 2.0],
- [np.nan, 2.0, 2.0],
- [12.0, 12.0, 24.0],
- ]
- )
- expected = pd.DataFrame(
- expected_data, index=expected_index, columns=expected_column
- )
- tm.assert_frame_equal(result, expected)
-
- def test_crosstab_dup_index_names(self):
- # GH 13279
- s = pd.Series(range(3), name="foo")
-
- result = pd.crosstab(s, s)
- expected_index = pd.Index(range(3), name="foo")
- expected = pd.DataFrame(
- np.eye(3, dtype=np.int64), index=expected_index, columns=expected_index
- )
- tm.assert_frame_equal(result, expected)
-
- @pytest.mark.parametrize("names", [["a", ("b", "c")], [("a", "b"), "c"]])
- def test_crosstab_tuple_name(self, names):
- s1 = pd.Series(range(3), name=names[0])
- s2 = pd.Series(range(1, 4), name=names[1])
-
- mi = pd.MultiIndex.from_arrays([range(3), range(1, 4)], names=names)
- expected = pd.Series(1, index=mi).unstack(1, fill_value=0)
-
- result = pd.crosstab(s1, s2)
- tm.assert_frame_equal(result, expected)
-
- def test_crosstab_both_tuple_names(self):
- # GH 18321
- s1 = pd.Series(range(3), name=("a", "b"))
- s2 = pd.Series(range(3), name=("c", "d"))
-
- expected = pd.DataFrame(
- np.eye(3, dtype="int64"),
- index=pd.Index(range(3), name=("a", "b")),
- columns=pd.Index(range(3), name=("c", "d")),
- )
- result = crosstab(s1, s2)
- tm.assert_frame_equal(result, expected)
-
- def test_crosstab_unsorted_order(self):
- df = pd.DataFrame({"b": [3, 1, 2], "a": [5, 4, 6]}, index=["C", "A", "B"])
- result = pd.crosstab(df.index, [df.b, df.a])
- e_idx = pd.Index(["A", "B", "C"], name="row_0")
- e_columns = pd.MultiIndex.from_tuples(
- [(1, 4), (2, 6), (3, 5)], names=["b", "a"]
- )
- expected = pd.DataFrame(
- [[1, 0, 0], [0, 1, 0], [0, 0, 1]], index=e_idx, columns=e_columns
- )
- tm.assert_frame_equal(result, expected)
-
- def test_crosstab_normalize_multiple_columns(self):
- # GH 15150
- df = pd.DataFrame(
- {
- "A": ["one", "one", "two", "three"] * 6,
- "B": ["A", "B", "C"] * 8,
- "C": ["foo", "foo", "foo", "bar", "bar", "bar"] * 4,
- "D": [0] * 24,
- "E": [0] * 24,
- }
- )
- result = pd.crosstab(
- [df.A, df.B],
- df.C,
- values=df.D,
- aggfunc=np.sum,
- normalize=True,
- margins=True,
- )
- expected = pd.DataFrame(
- np.array([0] * 29 + [1], dtype=float).reshape(10, 3),
- columns=Index(["bar", "foo", "All"], dtype="object", name="C"),
- index=MultiIndex.from_tuples(
- [
- ("one", "A"),
- ("one", "B"),
- ("one", "C"),
- ("three", "A"),
- ("three", "B"),
- ("three", "C"),
- ("two", "A"),
- ("two", "B"),
- ("two", "C"),
- ("All", ""),
- ],
- names=["A", "B"],
- ),
- )
- tm.assert_frame_equal(result, expected)
-
- def test_margin_normalize(self):
- # GH 27500
- df = pd.DataFrame(
- {
- "A": ["foo", "foo", "foo", "foo", "foo", "bar", "bar", "bar", "bar"],
- "B": ["one", "one", "one", "two", "two", "one", "one", "two", "two"],
- "C": [
- "small",
- "large",
- "large",
- "small",
- "small",
- "large",
- "small",
- "small",
- "large",
- ],
- "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
- "E": [2, 4, 5, 5, 6, 6, 8, 9, 9],
- }
- )
- # normalize on index
- result = pd.crosstab(
- [df.A, df.B], df.C, margins=True, margins_name="Sub-Total", normalize=0
- )
- expected = pd.DataFrame(
- [[0.5, 0.5], [0.5, 0.5], [0.666667, 0.333333], [0, 1], [0.444444, 0.555556]]
- )
- expected.index = MultiIndex(
- levels=[["Sub-Total", "bar", "foo"], ["", "one", "two"]],
- codes=[[1, 1, 2, 2, 0], [1, 2, 1, 2, 0]],
- names=["A", "B"],
- )
- expected.columns = Index(["large", "small"], dtype="object", name="C")
- tm.assert_frame_equal(result, expected)
-
- # normalize on columns
- result = pd.crosstab(
- [df.A, df.B], df.C, margins=True, margins_name="Sub-Total", normalize=1
- )
- expected = pd.DataFrame(
- [
- [0.25, 0.2, 0.222222],
- [0.25, 0.2, 0.222222],
- [0.5, 0.2, 0.333333],
- [0, 0.4, 0.222222],
- ]
- )
- expected.columns = Index(
- ["large", "small", "Sub-Total"], dtype="object", name="C"
- )
- expected.index = MultiIndex(
- levels=[["bar", "foo"], ["one", "two"]],
- codes=[[0, 0, 1, 1], [0, 1, 0, 1]],
- names=["A", "B"],
- )
- tm.assert_frame_equal(result, expected)
-
- # normalize on both index and column
- result = pd.crosstab(
- [df.A, df.B], df.C, margins=True, margins_name="Sub-Total", normalize=True
- )
- expected = pd.DataFrame(
- [
- [0.111111, 0.111111, 0.222222],
- [0.111111, 0.111111, 0.222222],
- [0.222222, 0.111111, 0.333333],
- [0.000000, 0.222222, 0.222222],
- [0.444444, 0.555555, 1],
- ]
- )
- expected.columns = Index(
- ["large", "small", "Sub-Total"], dtype="object", name="C"
- )
- expected.index = MultiIndex(
- levels=[["Sub-Total", "bar", "foo"], ["", "one", "two"]],
- codes=[[1, 1, 2, 2, 0], [1, 2, 1, 2, 0]],
- names=["A", "B"],
- )
- tm.assert_frame_equal(result, expected)
| https://api.github.com/repos/pandas-dev/pandas/pulls/32536 | 2020-03-08T02:57:28Z | 2020-03-09T20:17:16Z | 2020-03-09T20:17:15Z | 2020-03-09T20:18:35Z | |
BUG: retain tz in to_records | diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst
index 48eff0543ad4d..77da6fe035248 100644
--- a/doc/source/whatsnew/v1.1.0.rst
+++ b/doc/source/whatsnew/v1.1.0.rst
@@ -356,6 +356,7 @@ Other
instead of ``TypeError: Can only append a Series if ignore_index=True or if the Series has a name`` (:issue:`30871`)
- Set operations on an object-dtype :class:`Index` now always return object-dtype results (:issue:`31401`)
- Bug in :meth:`AbstractHolidayCalendar.holidays` when no rules were defined (:issue:`31415`)
+- Bug in :meth:`DataFrame.to_records` incorrectly losing timezone information in timezone-aware ``datetime64`` columns (:issue:`32535`)
.. ---------------------------------------------------------------------------
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index b0909e23b44c5..b76b986c526ef 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -1773,7 +1773,9 @@ def to_records(
else:
ix_vals = [self.index.values]
- arrays = ix_vals + [self[c]._internal_get_values() for c in self.columns]
+ arrays = ix_vals + [
+ np.asarray(self.iloc[:, i]) for i in range(len(self.columns))
+ ]
count = 0
index_names = list(self.index.names)
@@ -1788,7 +1790,7 @@ def to_records(
names = [str(name) for name in itertools.chain(index_names, self.columns)]
else:
- arrays = [self[c]._internal_get_values() for c in self.columns]
+ arrays = [np.asarray(self.iloc[:, i]) for i in range(len(self.columns))]
names = [str(c) for c in self.columns]
index_names = []
diff --git a/pandas/tests/frame/methods/test_to_records.py b/pandas/tests/frame/methods/test_to_records.py
index d0181f0309af1..34b323e55d8cd 100644
--- a/pandas/tests/frame/methods/test_to_records.py
+++ b/pandas/tests/frame/methods/test_to_records.py
@@ -3,7 +3,14 @@
import numpy as np
import pytest
-from pandas import CategoricalDtype, DataFrame, MultiIndex, Series, date_range
+from pandas import (
+ CategoricalDtype,
+ DataFrame,
+ MultiIndex,
+ Series,
+ Timestamp,
+ date_range,
+)
import pandas._testing as tm
@@ -18,6 +25,17 @@ def test_to_records_dt64(self):
result = df.to_records()["index"][0]
assert expected == result
+ def test_to_records_dt64tz_column(self):
+ # GH#32535 dont less tz in to_records
+ df = DataFrame({"A": date_range("2012-01-01", "2012-01-02", tz="US/Eastern")})
+
+ result = df.to_records()
+
+ assert result.dtype["A"] == object
+ val = result[0][1]
+ assert isinstance(val, Timestamp)
+ assert val == df.loc[0, "A"]
+
def test_to_records_with_multindex(self):
# GH#3189
index = [
| plus the initial motivation: get rid of two of our `_internal_get_values` calls (of which i count 16 left in master) | https://api.github.com/repos/pandas-dev/pandas/pulls/32535 | 2020-03-08T02:13:42Z | 2020-03-14T21:29:22Z | 2020-03-14T21:29:22Z | 2020-03-14T21:29:43Z |
CLN: remove unused in pd._testing | diff --git a/pandas/_testing.py b/pandas/_testing.py
index 33ec4e4886aa6..dcddb21cd1604 100644
--- a/pandas/_testing.py
+++ b/pandas/_testing.py
@@ -32,7 +32,6 @@
is_datetime64tz_dtype,
is_extension_array_dtype,
is_interval_dtype,
- is_list_like,
is_number,
is_period_dtype,
is_sequence,
@@ -417,10 +416,7 @@ def rands_array(nchars, size, dtype="O"):
.view((np.str_, nchars))
.reshape(size)
)
- if dtype is None:
- return retval
- else:
- return retval.astype(dtype)
+ return retval.astype(dtype)
def randu_array(nchars, size, dtype="O"):
@@ -432,10 +428,7 @@ def randu_array(nchars, size, dtype="O"):
.view((np.unicode_, nchars))
.reshape(size)
)
- if dtype is None:
- return retval
- else:
- return retval.astype(dtype)
+ return retval.astype(dtype)
def rands(nchars):
@@ -448,16 +441,6 @@ def rands(nchars):
return "".join(np.random.choice(RANDS_CHARS, nchars))
-def randu(nchars):
- """
- Generate one random unicode string.
-
- See `randu_array` if you want to create an array of random unicode strings.
-
- """
- return "".join(np.random.choice(RANDU_CHARS, nchars))
-
-
def close(fignum=None):
from matplotlib.pyplot import get_fignums, close as _close
@@ -724,10 +707,7 @@ def repr_class(x):
# return Index as it is to include values in the error message
return x
- try:
- return type(x).__name__
- except AttributeError:
- return repr(type(x))
+ return type(x).__name__
if exact == "equiv":
if type(left) != type(right):
@@ -2103,53 +2083,6 @@ def _gen_unique_rand(rng, _extra_size):
return i.tolist(), j.tolist()
-def makeMissingCustomDataframe(
- nrows,
- ncols,
- density=0.9,
- random_state=None,
- c_idx_names=True,
- r_idx_names=True,
- c_idx_nlevels=1,
- r_idx_nlevels=1,
- data_gen_f=None,
- c_ndupe_l=None,
- r_ndupe_l=None,
- dtype=None,
- c_idx_type=None,
- r_idx_type=None,
-):
- """
- Parameters
- ----------
- Density : float, optional
- Float in (0, 1) that gives the percentage of non-missing numbers in
- the DataFrame.
- random_state : {np.random.RandomState, int}, optional
- Random number generator or random seed.
-
- See makeCustomDataframe for descriptions of the rest of the parameters.
- """
- df = makeCustomDataframe(
- nrows,
- ncols,
- c_idx_names=c_idx_names,
- r_idx_names=r_idx_names,
- c_idx_nlevels=c_idx_nlevels,
- r_idx_nlevels=r_idx_nlevels,
- data_gen_f=data_gen_f,
- c_ndupe_l=c_ndupe_l,
- r_ndupe_l=r_ndupe_l,
- dtype=dtype,
- c_idx_type=c_idx_type,
- r_idx_type=r_idx_type,
- )
-
- i, j = _create_missing_idx(nrows, ncols, density, random_state)
- df.values[i, j] = np.nan
- return df
-
-
def makeMissingDataframe(density=0.9, random_state=None):
df = makeDataFrame()
i, j = _create_missing_idx(*df.shape, density=density, random_state=random_state)
@@ -2397,7 +2330,6 @@ def wrapper(*args, **kwargs):
def assert_produces_warning(
expected_warning=Warning,
filter_level="always",
- clear=None,
check_stacklevel=True,
raise_on_extra_warnings=True,
):
@@ -2427,12 +2359,6 @@ class for all warnings. To check that no warning is returned,
from each module
* "once" - print the warning the first time it is generated
- clear : str, default None
- If not ``None`` then remove any previously raised warnings from
- the ``__warningsregistry__`` to ensure that no warning messages are
- suppressed by this context manager. If ``None`` is specified,
- the ``__warningsregistry__`` keeps track of which warnings have been
- shown, and does not show them again.
check_stacklevel : bool, default True
If True, displays the line that called the function containing
the warning to show were the function is called. Otherwise, the
@@ -2465,19 +2391,6 @@ class for all warnings. To check that no warning is returned,
with warnings.catch_warnings(record=True) as w:
- if clear is not None:
- # make sure that we are clearing these warnings
- # if they have happened before
- # to guarantee that we will catch them
- if not is_list_like(clear):
- clear = [clear]
- for m in clear:
- try:
- m.__warningregistry__.clear()
- except AttributeError:
- # module may not have __warningregistry__
- pass
-
saw_warning = False
warnings.simplefilter(filter_level)
yield w
| https://api.github.com/repos/pandas-dev/pandas/pulls/32534 | 2020-03-07T23:52:18Z | 2020-03-10T11:24:34Z | 2020-03-10T11:24:34Z | 2020-03-10T15:03:19Z | |
CLN: remove unnecessary to_dense call | diff --git a/pandas/io/formats/format.py b/pandas/io/formats/format.py
index 2a528781f8c93..c879eaeda64e0 100644
--- a/pandas/io/formats/format.py
+++ b/pandas/io/formats/format.py
@@ -1352,8 +1352,6 @@ def format_values_with(float_format):
values = self.values
is_complex = is_complex_dtype(values)
mask = isna(values)
- if hasattr(values, "to_dense"): # sparse numpy ndarray
- values = values.to_dense()
values = np.array(values, dtype="object")
values[mask] = na_rep
imask = (~mask).ravel()
| This call is not reached in the tests, and if it were the following line would make it superfluous anyway.
I'm pretty sure the comment there is wrong too. | https://api.github.com/repos/pandas-dev/pandas/pulls/32533 | 2020-03-07T23:40:04Z | 2020-03-10T13:31:26Z | 2020-03-10T13:31:26Z | 2020-03-10T14:54:35Z |
TYP: Add type hint for DataFrame.T and certain array types | diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py
index ba4c2e168e0c4..da9eef995f193 100644
--- a/pandas/core/arrays/categorical.py
+++ b/pandas/core/arrays/categorical.py
@@ -1317,7 +1317,7 @@ def __setstate__(self, state):
setattr(self, k, v)
@property
- def T(self):
+ def T(self) -> "Categorical":
"""
Return transposed numpy array.
"""
diff --git a/pandas/core/arrays/sparse/array.py b/pandas/core/arrays/sparse/array.py
index 549606795f528..3b5e6b2d2bcd3 100644
--- a/pandas/core/arrays/sparse/array.py
+++ b/pandas/core/arrays/sparse/array.py
@@ -1296,14 +1296,14 @@ def mean(self, axis=0, *args, **kwargs):
nsparse = self.sp_index.ngaps
return (sp_sum + self.fill_value * nsparse) / (ct + nsparse)
- def transpose(self, *axes):
+ def transpose(self, *axes) -> "SparseArray":
"""
Returns the SparseArray.
"""
return self
@property
- def T(self):
+ def T(self) -> "SparseArray":
"""
Returns the SparseArray.
"""
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 60fc69e8222d6..aac012edf4222 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -2443,7 +2443,9 @@ def transpose(self, *args, copy: bool = False) -> "DataFrame":
return result.__finalize__(self)
- T = property(transpose)
+ @property
+ def T(self) -> "DataFrame":
+ return self.transpose()
# ----------------------------------------------------------------------
# Indexing Methods
| While updating a large pandas codebase with type coverage to 1.0, I noticed that `DataFrame.transpose()` is annotated to return `DataFrame` while `DataFrame.T` has no such hint. This PR also adds hints in a few places other places where they're trivial.
The definition of `pandas.core.base.IndexOpsMixIn.transpose` (and associated `.T`) currently do not have any type hints and fixing this seems more complicated, meaning `Index.T` and `Series.T` are not fixed by this PR.
Happy to amend to cover that case if anyone has suggestions, but I believe `DataFrame.T` to be the vast majority of usage.
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/32532 | 2020-03-07T23:31:02Z | 2020-03-11T03:19:11Z | 2020-03-11T03:19:11Z | 2020-03-11T03:19:15Z |
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